Refine
Document Type
- Conference proceeding (139)
- Book chapter (100)
- Journal article (84)
- Anthology (12)
- Book (11)
- Doctoral Thesis (1)
Is part of the Bibliography
- yes (347)
Institute
- Informatik (174)
- ESB Business School (70)
- Texoversum (50)
- Technik (26)
- Life Sciences (24)
- Zentrale Einrichtungen (3)
Publisher
- Springer (347) (remove)
Autonomous navigation is one of the main areas of research in mobile robots and intelligent connected vehicles. In this context, we are interested in presenting a general view on robotics, the progress of research, and advanced methods related to this field to improve autonomous robots’ localization. We seek to evaluate algorithms and techniques that give robots the ability to move safely and autonomously in a complex and dynamic environment. Under these constraints, we focused our work in the paper on a specific problem: to evaluate a simple, fast and light SLAM algorithm that can minimize localization errors. We presented and validated a FastSLAM 2.0 system combining scan matching and loop closure detection. To allow the robot to perceive the environment and detect objects, we have studied one of the best deep learning technique using convolutional neural networks (CNN). We validate our testing using the YOLOv3 algorithm.
Software and system development is complex and diverse, and a multitude of development approaches is used and combined with each other to address the manifold challenges companies face today. To study the current state of the practice and to build a sound understanding about the utility of different development approaches and their application to modern software system development, in 2016, we launched the HELENA initiative. This paper introduces the 2nd HELENA workshop and provides an overview of the current project state. In the workshop, six teams present initial findings from their regions, impulse talk are given, and further steps of the HELENA roadmap are discussed.
The digitization of factories will be a significant issue for the 2020s. New scenarios are emerging to increase the efficiency of production lines inside the factory, based on a new generation of robots’ collaborative functions. Manufacturers are moving towards data-driven ecosystems by leveraging product lifecycle data from connected goods. Energy-efficient communication schemes, as well as scalable data analytics, will support these various data collection scenarios. With augmented reality, new remote services are emerging that facilitate the efficient sharing of knowledge in the factory. Future communication solutions should generally ensure connectivity between the various production sites spread worldwide and new players in the value chain (e.g., suppliers, logistics) transparent, real-time, and secure. Industry 4.0 brings more intelligence and flexibility to production. Resulting in more lightweight equipment and, thus, offering better ergonomics. 5G will guarantee real-time transmissions with latencies of less than 1 ms. This will provide manufacturers with new possibilities to collect data and trigger actions automatically.
A 3D face modelling approach for pose-invariant face recognition in a human-robot environment
(2017)
Face analysis techniques have become a crucial component of human-machine interaction in the fields of assistive and humanoid robotics. However, the variations in head-pose that arise naturally in these environments are still a great challenge. In this paper, we present a real-time capable 3D face modelling framework for 2D in-the-wild images that is applicable for robotics. The fitting of the 3D Morphable Model is based exclusively on automatically detected landmarks. After fitting, the face can be corrected in pose and transformed back to a frontal 2D representation that is more suitable for face recognition. We conduct face recognition experiments with non-frontal images from the MUCT database and uncontrolled, in the wild images from the PaSC database, the most challenging face recognition database to date, showing an improved performance. Finally, we present our SCITOS G5 robot system, which incorporates our framework as a means of image pre-processing for face analysis.
The main aim of presented in this manuscript research is to compare the results of objective and subjective measurement of sleep quality for older adults (65+) in the home environment. A total amount of 73 nights was evaluated in this study. Placing under the mattress device was used to obtain objective measurement data, and a common question on perceived sleep quality was asked to collect the subjective sleep quality level. The achieved results confirm the correlation between objective and subjective measurement of sleep quality with the average standard deviation equal to 2 of 10 possible quality points.
A configuration-management-database driven approach for fabric-process specification and automation
(2014)
In this paper we describe an approach that integrates a Configuration- Management-Database into fabric-process specification and automation in order to consider different conditions regarding to cloud-services. By implementing our approach, the complexity of fabric processes gets reduced. We developed a prototype by using formal prototyping principles as research methods and integrated the Configuration-Management-Database Command into the Workflow- Management-System Activiti. We used this prototype to evaluate our approach. We implemented three different fabric-processes and show that by using our approach the complexity of these three fabric-processes gets reduced.
In recent years, artificial intelligence (AI) has increasingly become a relevant technology for many companies. While there are a number of studies that highlight challenges and success factors in the adoption of AI, there is a lack of guidance for firms on how to approach the topic in a holistic and strategic way. The aim of this study is therefore to develop a conceptual framework for corporate AI strategy. To address this aim, a systematic literature review of a wide spectrum of AI-related research is conducted, and the results are analyzed based on an inductive coding approach. An important conclusion is that companies should consider diverse aspects when formulating an AI strategy, ranging from technological questions to corporate culture and human resources. This study contributes to knowledge by proposing a novel, comprehensive framework to foster the understanding of crucial aspects that need to be considered when using the emerging technology of AI in a corporate context.
Purpose
Supporting the surgeon during surgery is one of the main goals of intelligent ORs. The OR-Pad project aims to optimize the information flow within the perioperative area. A shared information space should enable appropriate preparation and provision of relevant information at any time before, during, and after surgery.
Methods
Based on previous work on an interaction concept and system architecture for the sterile OR-Pad system, we designed a user interface for mobile and intraoperative (stationary) use, focusing on the most important functionalities like clear information provision to reduce information overload. The concepts were transferred into a high-fidelity prototype for demonstration purposes. The prototype was evaluated from different perspectives, including a usability study.
Results
The prototype’s central element is a timeline displaying all available case information chronologically, like radiological images, labor findings, or notes. This information space can be adapted for individual purposes (e.g., highlighting a tumor, filtering for own material). With the mobile and intraoperative mode of the system, relevant information can be added, preselected, viewed, and extended during the perioperative process. Overall, the evaluation showed good results and confirmed the vision of the information system.
Conclusion
The high-fidelity prototype of the information system OR-Pad focuses on supporting the surgeon via a timeline making all available case information accessible before, during, and after surgery. The information space can be personalized to enable targeted support. Further development is reasonable to optimize the approach and address missing or insufficient aspects, like the holding arm and sterility concept or new desired features.
A hybrid deep registration of MR scans to interventional ultrasound for neurosurgical guidance
(2021)
Despite the recent advances in image-guided neurosurgery, reliable and accurate estimation of the brain shift still remains one of the key challenges. In this paper, we propose an automated multimodal deformable registration method using hybrid learning-based and classical approaches to improve neurosurgical procedures. Initially, the moving and fixed images are aligned using classical affine transformation (MINC toolkit), and then the result is provided to the convolutional neural network, which predicts the deformation field using backpropagation. Subsequently, the moving image is transformed using the resultant deformation into a moved image. Our model was evaluated on two publicly available datasets: the retrospective evaluation of cerebral tumors (RESECT) and brain images of tumors for evaluation (BITE). The mean target registration errors have been reduced from 5.35 ± 4.29 to 0.99 ± 0.22 mm in the RESECT and from 4.18 ± 1.91 to 1.68 ± 0.65 mm in the BITE. Experimental results showed that our method improved the state-of-the-art in terms of both accuracy and runtime speed (170 ms on average). Hence, the proposed method provides a fast runtime for 3D MRI to intra-operative US pair in a GPU-based implementation, which shows a promise for its applicability in assisting the neurosurgical procedures compensating for brain shift.
While several service-based maintainability metrics have been proposed in the scientific literature, reliable approaches to automatically collect these metrics are lacking. Since static analysis is complicated for decentralized and technologically diverse microservice-based systems, we propose a dynamic approach to calculate such metrics from runtime data via distributed tracing. The approach focuses on simplicity, extensibility, and broad applicability. As a first prototype, we implemented a Java application with a Zipkin integrator, 23 different metrics, and five export formats. We demonstrated the feasibility of the approach by analyzing the runtime data of an example microservice based system. During an exploratory study with six participants, 14 of the 18 services were invoked via the system’s web interface. For these services, all metrics were calculated correctly from the generated traces.
A lot of people need help in their daily life to wash, select and manage their clothing. The goal of this work is to design an assistant system (eKlarA) to support the user by giving recommendations to choose the clothing combinations, to find the clothing and to wash the clothing. The idea behind eKlarA is to generate a system that uses sensors to identify the clothing and their state in the clothing cycle. The clothing cycle consists of the stations: closets, laundry basket and washing machine in one or several places. The system uses the information about the clothing, weather and calendar to support the user in the different steps of the clothing cycle. The first prototype of this system has been developed and tested. The test results are presented in this work.
Sleep quality and in general, behavior in bed can be detected using a sleep state analysis. These results can help a subject to regulate sleep and recognize different sleeping disorders. In this work, a sensor grid for pressure and movement detection supporting sleep phase analysis is proposed. In comparison to the leading standard measuring system, which is Polysomnography (PSG), the system proposed in this project is a non invasive sleep monitoring device. For continuous analysis or home use, the PSG or wearable actigraphy devices tends to be uncomfortable. Besides this fact, they are also very expensive. The system represented in this work classifies respiration and body movement with only one type of sensor and also in a non invasive way. The sensor used is a pressure sensor. This sensor is low cost and can be used for commercial proposes. The system was tested by carrying out an experiment that recorded the sleep process of a subject. These recordings showed the potential for classification of breathing rate and body movements. Although previous researches show the use of pressure sensors in recognizing posture and breathing, they have been mostly used by positioning the sensors between the mattress and bedsheet. This project however, shows an innovative way to position the sensors under the mattress.
Context: Many companies are facing an increasingly dynamic and uncertain market environment, making traditional product roadmapping practices no longer sufficiently applicable. As a result, many companies need to adapt their product roadmapping practices for continuing to operate successfully in today’s dynamic market environment. However, transforming product roadmapping practices is a difficult process for organizations. Existing literature offers little help on how to accomplish such a process.
Objective: The objective of this paper is to present a product roadmap transformation approach for organizations to help them identify appropriate improvement actions for their roadmapping practices using an analysis of their current practices.
Method: Based on an existing assessment procedure for evaluating product roadmapping practices, the first version of a product roadmap transformation approach was developed in workshops with company experts. The approach was then given to eleven practitioners and their perceptions of the approach were gathered through interviews.
Results: The result of the study is a transformation approach consisting of a process describing what steps are necessary to adapt the currently applied product roadmapping practice to a dynamic and uncertain market environment. It also includes recommendations on how to select areas for improvement and two empirically based mapping tables. The interviews with the practitioners revealed that the product roadmap transformation approach was perceived as comprehensible, useful, and applicable. Nevertheless, we identified potential for improvements, such as a clearer presentation of some processes and the need for more improvement options in the mapping tables. In addition, minor usability issues were identified.
Die weiterhin hohen Schulden in einigen Staaten der Europäischen Wirtschafts- und Währungsunion lassen nach wie vor staatliche Insolvenzen befürchten. Um die entstandenen Probleme zu bewältigen, aber auch damit eine solche Situation erst gar nicht eintritt, hält der Autor eine staatliche Insovenzordnung – mit Bail-out durch die anderen Mitgliedstaaten nur in Notfällen – für erforderlich. Er schlägt einen staatlichen Abwicklungsmechanismus für überschuldete Euro-Länder vor, der auf einem Konzept des Sachverständigenrates für Wirtschaft von 2016 beruht.
Glioblastoma WHO IV belongs to a group of brain tumors that are still incurable. A promising treatment approach applies photodynamic therapy (PDT) with hypericin as a photosensitizer. To generate a comprehensive understanding of the photosensitizer-tumor interactions, the first part of our study is focused on investigating the distribution and penetration behavior of hypericin in glioma cell spheroids by fluorescence microscopy. In the second part, fluorescence lifetime imaging microscopy (FLIM) was used to correlate fluorescence lifetime (FLT) changes of hypericin to environmental effects inside the spheroids. In this context, 3D tumor spheroids are an excellent model system since they consider 3D cell–cell interactions and the extracellular matrix is similar to tumors in vivo. Our analytical approach considers hypericin as probe molecule for FLIM and as photosensitizer for PDT at the same time, making it possible to directly draw conclusions of the state and location of the drug in a biological system. The knowledge of both state and location of hypericin makes a fundamental understanding of the impact of hypericin PDT in brain tumors possible. Following different incubation conditions, the hypericin distribution in peripheral and central cryosections of the spheroids were analyzed. Both fluorescence microscopy and FLIM revealed a hypericin gradient towards the spheroid core for short incubation periods or small concentrations. On the other hand, a homogeneous hypericin distribution is observed for long incubation times and high concentrations. Especially, the observed FLT change is crucial for the PDT efficiency, since the triplet yield, and hence the O2 activation, is directly proportional to the FLT. Based on the FLT increase inside spheroids, an incubation time 30 min is required to achieve most suitable conditions for an effective PDT.
The investigation of stress requires to distinguish between stress caused by physical activity and stress that is caused by psychosocial factors. The behaviour of the heart in response to stress and physical activity is very similar in case the set of monitored parameters is reduced to one. Currently, the differentiation remains difficult and methods which only use the heart rate are not able to differentiate between stress and physical activity, without using additional sensor data input. The approach focusses on methods which generate signals providing characteristics that are useful for detecting stress, physical activity, no activity and relaxation.
The basic idea behind a wearable robotic grasp assistancesystem is to support people that suffer from severe motor impairments in daily activities. Such a system needs to act mostly autonomously and according to the user’s intent. Vision-based hand pose estimation could be an integral part of a larger control and assistance framework. In this paper we evaluate the performance of egocentric monocular hand pose estimation for a robot-controlled hand exoskeleton in a simulation. For hand pose estimation we adopt a Convolutional Neural Network (CNN). We train and evaluate this network with computer graphics, created by our own data generator. In order to guide further design decisions we focus in our experiments on two egocentric camera viewpoints tested on synthetic data with the help of a 3D-scanned hand model, with and without an exoskeleton attached to it.We observe that hand pose estimation with a wrist-mounted camera performs more accurate than with a head-mounted camera in the context of our simulation. Further, a grasp assistance system attached to the hand alters visual appearance and can improve hand pose estimation. Our experiment provides useful insights for the integration of sensors into a context sensitive analysis framework for intelligent assistance.
The Internet of Things, enterprise social networks, adaptive case management, mobility systems, analytics for big data, and cloud services environments are emerging to support smart connected products and services and the digital transformation. Biological metaphors of living and adaptable ecosystems provide the logical foundation for self-optimizing and resilient run-time environments for intelligent business services and related distributed information systems with service-oriented enterprise architectures. We are investigating mechanisms for flexible adaptation and evolution for the next digital enterprise architecture systems in the context of the digital transformation. Our aim is to support flexibility and agile transformation for both business and related enterprise systems through adaptation and dynamical evolution of digital enterprise architectures. The present research paper investigates digital transformations of business and IT and integrates fundamental mappings between adaptable digital enterprise architectures and service-oriented information systems. We are putting a spotlight with the example domain – Internet of Things.
Due to digitalization, constant technological progress and ever shorter product life cycles, enterprises are currently facing major challenges. In order to succeed in the market, business models have to be adapted more often and more quickly to changing market conditions than they used to be. Fast adaptability, also called agility, is a decisive competitive factor in today’s world. Because of the ever-growing IT part of products and the fact that they are manufactured using IT, changing the business model has a major impact on the enterprise architecture (EA). However, developing EAs is a very complex task, because many stakeholders with conflicting interests are involved in the decision-making process. Therefore, a lot of collaboration is required. To support organizations in developing their EA, this article introduces a novel integrative method that systematically integrates stakeholder interests into decision-making activities. By using the method, collaboration between stakeholders involved is improved by identifying points of contact between them. Furthermore, standardized activities make decision-making more transparent and comparable without limiting creativity.
The purpose of this paper is to give an overview about the links between fashion businesses and film from a fashion business perspective. It focuses on the idea that digitalization brought much more film use for the fashion industry and that this development has just begun and not ended. This change finally also has an intense impact on the fashion industry, as fashion companies nowadays are content producers with films, too. The resulting closer connection with viewers via social media exposes fashion companies, gives on the other hand new influence potential to the fashion system. An in-depth future research about the fashion and film system is therefore required to develop answers for the current situation. This article should be interpreted more as a personal viewpoint of the author to this topic rather than a research paper based on the usual methodological criteria.
The purpose of this paper is to give an overview about the links between the fashion and music industry. It focuses on the idea that digitalization has broken the rules of the traditional music industry value chain. This touches both the production and the consumption side of music. This change finally also has an intense impact on the fashion industry, as the music industry has been big supplier of fashion trends itself. The absence of this supplier plus the changes within the fashion industry itself by the fast-fashion development are considered as a reason for more competition and therefore price pressure. An in-depth future research about the fashion and music system is therefore required to develop answers for the current situation. This article should be interpreted more as a personal viewpoint of the author to this topic rather than a research paper based on the usual methodological criteria.
Fragestellung: Das klinische Standardverfahren und Referenz der Schlafmessung und der Klassifizierung der einzelnen Schlafstadien ist die Polysomnographie (PSG). Alternative Ansätze zu diesem aufwändigen Verfahren könnten einige Vorteile bieten, wenn die Messungen auf eine komfortablere Weise durchgeführt werden. Das Hauptziel dieser Forschung Studie ist es, einen Algorithmus für die automatische Klassifizierung von Schlafstadien zu entwickeln, der ausschließlich Bewegungs- und Atmungssignale verwendet [1].
Patienten und Methoden: Nach der Analyse der aktuellen Forschungsarbeiten haben wir multinomiale logistische Regression als Grundlage für den Ansatz gewählt [2]. Um die Genauigkeit der Auswertung zu erhöhen, wurden vier Features entwickelt, die aus Bewegungs- und Atemsignalen abgeleitet wurden. Für die Auswertung wurden die nächtlichen Aufzeichnungen von 35 Personen verwendet, die von der Charité-Universitätsmedizin Berlin zur Verfügung gestellt wurden. Das Durchschnittsalter der Teilnehmer betrug 38,6 +/– 14,5 Jahre und der BMI lag bei durchschnittlich 24,4 +/– 4,9 kg/m2. Da der Algorithmus mit drei Stadien arbeitet, wurden die Stadien N1, N2 und N3 zum NREM-Stadium zusammengeführt. Der verfügbare Datensatz wurde strikt aufgeteilt: in einen Trainingsdatensatz von etwa 100 h und in einen Testdatensatz mit etwa 160 h nächtlicher Aufzeichnungen. Beide Datensätze wiesen ein ähnliches Verhältnis zwischen Männern und Frauen auf, und der durchschnittliche BMI wies keine signifikante Abweichung auf.
Ergebnisse: Der Algorithmus wurde implementiert und lieferte erfolgreiche Ergebnisse: die Genauigkeit der Erkennung von Wach-/NREM-/REM-Phasen liegt bei 73 %, mit einem Cohen’s Kappa von 0,44 für die analysierten 19.324 Schlafepochen von jeweils 30 s. Die beobachtete gewisse Überschätzung der NREM-Phase lässt sich teilweise durch ihre Prävalenz in einem typischen Schlafmuster erklären. Selbst die Verwendung eines ausbalancierten Trainingsdatensatzes konnte dieses Problem nicht vollständig lösen.
Schlussfolgerungen: Die erreichten Ergebnisse haben die Tauglichkeit des Ansatzes prinzipiell bestätigt. Dieser hat den Vorteil, dass nur Bewegungs- und Atemsignale verwendet werden, die mit weniger Aufwand und komfortabler für Benutzer aufgezeichnet werden können als z. B. Herz- oder EEG-Signale. Daher stellt das neue System eine deutliche Verbesserung im Vergleich zu bestehenden Ansätzen dar. Die Zusammenführung der beschriebenen algorithmischen Software mit dem in [1] beschriebenen Hardwaresystem zur Messung von Atem- und Körperbewegungssignalen zu einem autonomen, berührungslosen System zur kontinuierlichen Schlafüberwachung ist eine mögliche Richtung zukünftiger Arbeiten.
Today, digitalization is firmly anchored in society and business. It is also recognized to have significant impact on the retailing sector. The in-store display of moving images has so far, however, gained little attention by researchers. The aim of this research is to provide a first estimation on the current state of moving images distribution in stationary retail stores. A store check was the basis for analysis and evaluation. In sum, 152 stores were analyzed in Stuttgart, Germany. Out of 152 observed stores, 62 stores showed 177 moving images. Detailed analyses about content, mood, color and the actors of motion pictures showed that all aspects are very well harmonized with the target group of the store. The chapter provides a basic estimation of the in-store diffusion of moving images. Thereby, avenues for further research are opened up.
Data analysis is becoming increasingly important to pursue organizational goals, especially in the context of Industry 4.0, where a wide variety of data is available. Here numerous challenges arise, especially when using unstructured data. However, this subject has not been focused by research so far. This research paper addresses this gap, which is interesting for science and practice as well. In a study three major challenges of using unstructured data has been identified: analytical know-how, data issues, variety. Additionally, measures how to improve the analysis of unstructured data in the industry 4.0 context are described. Therefore, the paper provides empirical insights about challenges and potential measures when analyzing unstructured data. The findings are presented in a framework, too. Hence, next steps of the research project and future research points become apparent.
In this paper a method for the generation of gSPM with ontology-based generalization was presented. The resulting gSPM was modeled with BPMN/BPMNsix in an efficient way and could be executed with BPMN workflow engines. In the next step the implementation of resource concepts, anatomical structures, and transition probabilities for workflow execution will be realized.
To illustrate the power and the pitfalls of Bionic Optimization, we will show some examples spanning classes of applications and employing various strategies. These applications cover a broad range of engineering tasks. Nevertheless, there is no guarantee that our experiences and our examples will be sufficient to deal with all questions and issues in a comprehensive way. As general rule it might be stated, that for each class of problems, novices should begin with a learning phase. So, in this introductory phase, we use simple and quick examples, e.g., using small FE-models, linear load cases, short time intervals and simple material models. Here beginners within the Bionic Optimization community can learn which parameter combinations to use. In Sect. 3.3 we discuss strategies for optimization study acceleration. Making use of these parameters as starting points is one way to set the specific ranges, e.g., number of parents and kids, crossing, mutation radii and, numbers of generations. On the other hand, these trial runs will doubtless indicate that Bionic Optimization needs large numbers of individual designs, and considerable time and computing power. We recommend investing enough time preparing each task in order to avoid the frustration should large jobs fail after long calculation times.
Application to CAE systems
(2016)
Due to the broad acceptance of CAD-systems based on 3D solids, the geometric data of all common CAE (Computer-Aided Engineering) software, at least in mechanical engineering, are based on these solids. We use solid models, where the space filled by material is defined in a simple and easily useable way. Solid models allow for the development of automated meshers that transform solid volumes into finite elements. Even after some unacceptable initial trials, users are able to generate meshes of non-trivial geometries within minutes to hours, instead of days or weeks. Once meshing had no longer been the cost limiting factor of finite element studies, numerical simulation became a tool for smaller industries as well.
Due to the broad acceptance of CAD-systems based on 3D solids , the geometric data of all common CAE (Computer-Aided Engineering) software, at least in mechanical engineering, are based on these solids. We use solid models , where the space filled by material is defined in a simple and easily useable way. Solid models allow for the development of automated meshers that transform solid volumes into finite elements. Even after some unacceptable initial trials, users are able to generate meshes of non-trivial geometries within minutes to hours, instead of days or weeks. Once meshing had no longer been the cost limiting factor of finite element studies, numerical simulation became a tool for smaller industries as well.
In the early days of automated meshing development, there were discussions over the use of tetragonal (Fig. 4.1) or hexagonal based meshes. But, after a short period of time, it became evident, that there were and will always be many problems using automated meshers to generate hexagonal elements . So today nearly all automated 3D-meshing systems use tetragonal elements .
Enterprises and societies currently face essential challenges, and digital transformation can contribute to their resolution. Enterprise architecture (EA) is useful for promoting digital transformation in global companies and information societies covering ecosystem partners. The advancement of new business models can be promoted with digital platforms and architectures for Industry 4.0 and Society 5.0. Therefore, products from the sector of healthcare, manufacturing and energy, etc. can increase in value. The adaptive integrated digital architecture framework (AIDAF) for Industry 4.0 and the design thinking approach is expected to promote and implement the digital platforms and digital products for healthcare, manufacturing and energy communities more efficiently. In this paper, we propose various cases of digital transformation where digital platforms and products are designed and evaluated for digital IT, digital manufacturing and digital healthcare with Industry 4.0 and Society 5.0. The vision of AIDAF applications to perform digital transformation in global companies is explained and referenced, extended toward the digitalized ecosystems such as Society 5.0 and Industry 4.0.
Enterprises and societies currently face crucial challenges, while Industry 4.0 becomes important in the global manufacturing industry all the more. Industry 4.0 offers a range of opportunities for companies to increase the flexibility and efficiency of production processes. The development of new business models can be promoted with digital platforms and architectures for Industry 4.0. Therefore, products from the healthcare sector can increase in value. The adaptive integrated digital architecture framework (AIDAF) for Industry 4.0 is expected to promote and implement the digital platforms and robotics for healthcare and medical communities efficiently. In this paper, we propose that various digital platforms and robotics are designed and evaluated for digital healthcare as for manufacturing industry with Industry 4.0. We argue that the design of an open healthcare platform “Open Healthcare Platform 2030 - OHP2030” for medical product design and robotics can be developed with AIDAF. The vision of AIDAF applications to enable Industry 4.0 in the OHP2030 research initiative is explained and referenced, extended in the context of Society 5.0.
Military organizations have special features like following different organizational laws in times of peace and war and their specific embeddedness in society and politics. Especially the latter aspect has made the military an important object of study since the beginnings of modern sociology. In the wake of establishing specific sociological accounts, military sociology has been developed, dedicated to the different facets of the military. This research is based on different theoretical perspectives, but has hardly embraced the frameworks from economics and sociology of conventions (EC/SC) so far. The aim of the chapter is to explore and demonstrate the potentials of this approach. In a first step, the state of the art of military sociology research is outlined, and potential avenues for analyzing military forces based on EC/SC are identified. It is argued that especially the connection to organizational theory (military as organization) and civil-military relations, including leadership and professionalism, offer starting points. After introducing existing studies addressing military-related topics with reference to EC/SC, relevant concepts and approaches of convention theory that prove to be particularly enriching for military research are discussed. An outlook on possible further fields and topics of research is given to concretize how an inclusion of the perspective of EC/SC could look like.
The evolution of Services Oriented Architectures (SOA) presents many challenges due to their complex, dynamic and heterogeneous nature. We describe how SOA design principles can facilitate SOA evolvability and examine several approaches to support SOA evolution. SOA evolution approaches can be classified based on the level of granularity they address, namely, service code level, service interaction level and model level. We also discuss emerging trends, such as microservices and knowledge-based support, which can enhance the evolution of future SOA systems.
Enterprises are currently transforming their strategy, processes, and their information systems to extend their degree of digitalization. The potential of the Internet and related digital technologies, like Internet of Things, services computing, cloud computing, artificial intelligence, big data with analytics, mobile systems, collaboration networks, and cyber physical systems both drives and enables new business designs. Digitalization deeply disrupts existing businesses, technologies and economies and fosters the architecture of digital environments with many rather small and distributed structures. This has a strong impact for new value producing opportunities and architecting digital services and products guiding their design through exploiting a Service-Dominant Logic. The main result of the book chapter extends methods for integral digital strategies with value-oriented models for digital products and services which are defined in the framework of a multi-perspective digital enterprise architecture reference model.
Our paper gives first answers on a fundamental question: how can the design of architectures of intelligent digital systems and services be accomplished methodologically? Intelligent systems and services are the goals of many current digitalization efforts today and part of massive digital transformation efforts based on digital technologies. Digital systems and services are the foundation of digital platforms and ecosystems. Digtalization disrupts existing businesses, technologies, and economies and promotes the architecture of open environments. This has a strong impact on new value-added opportunities and the development of intelligent digital systems and services. Digital technologies such as artificial intelligence, the Internet of Things, services computing, cloud computing, big data with analytics, mobile systems, and social enterprise networks systems are important enablers of digitalization. The current publication presents our research on the architecture of intelligent digital ecosystems and products and services influenced by the service-dominant logic. We present original methodological extensions and a new reference model for digital architectures with an integral service and value perspective to model intelligent systems and services that effectively align digital strategies and architectures with artificial intelligence as main elements to support intelligent digitalization.
The current advancement of Artificial Intelligence (AI) combined with other digitalization efforts significantly impacts service ecosystems. Artificial intelligence has a substantial impact on new opportunities for the co-creation of value and the development of intelligent service ecosystems. Motivated by experiences and observations from digitalization projects, this paper presents new methodological perspectives and experiences from academia and practice on architecting intelligent service ecosystems and explores the impact of artificial intelligence through real cases supporting an ongoing validation. Digital enterprise architecture models serve as an integral representation of business, information, and technological perspectives of intelligent service-based enterprise systems to support management and development. This paper focuses on architectural models for intelligent service ecosystems, showing the fundamental business mechanism of AI-based value co-creation, the corresponding digital architecture, and management models. The focus of this paper presents the key architectural model perspectives for the development of intelligent service ecosystems.
Presently, many companies are transforming their strategy and product base, as well as their culture, processes and information systems to become more digital or to approach for a digital leadership. In the last years new business opportunities appeared using the potential of the Internet and related digital technologies, like Internet of Things, services computing, cloud computing, edge and fog computing, social networks, big data with analytics, mobile systems, collaboration networks, and cyber physical systems. Digitization fosters the development of IT environments with many rather small and distributed structures, like the Internet of Things, Microservices, or other micro-granular elements. This has a strong impact for architecting digital services and products. The change from a closed-world modeling perspective to more flexible open-world composition and evolution of micro-granular system architectures defines the moving context for adaptable systems. We are focusing on a continuous bottom-up integration of micro-granular architectures for a huge amount of dynamically growing systems and services, as part of a new digital enterprise architecture for service dominant digital products.
This chapter presents an introduction to the emerging trends for architecting the digital transformation having a strong focus on digital products, intelligent services, and related systems together with methods, models and architectures. The primary aim of this book is to highlight some of the most recent research results in the field. We are providing a focused set of brief descriptions of the chapters included in the book.
This research-oriented book presents key contributions on architecting the digital transformation. It includes the following main sections covering 20 chapters: · Digital Transformation · Digital Business · Digital Architecture · Decision Support · Digital Applications Focusing on digital architectures for smart digital products and services, it is a valuable resource for researchers, doctoral students, postgraduates, graduates, undergraduates, academics and practitioners interested in digital transformation.
Dieses forschungsorientierte Buch enthält wichtige Beiträge zur Gestaltung der digitalen Transformation. Es umfasst die folgenden Hauptabschnitte in 20 Kapiteln:
- Digitale Transformation
- Digitales Geschäft
- Digitale Architektur
- Entscheidungshilfe
- Digitale Anwendungen
Es konzentriert sich auf digitale Architekturen für intelligente digitale Produkte und Dienstleistungen und ist eine wertvolle Ressource für Forscher, Doktoranden, Postgraduierte, Absolventen, Studenten, Akademiker und Praktiker, die sich für die digitale Transformation interessieren.
Unternehmen sind derzeit dabei, ihre Strategie, ihre Prozesse und ihre Informationssysteme zu verändern, um ihren Digitalisierungsgrad zu erhöhen. Das Potenzial des Internets und verwandter digitaler Technologien wie Internet der Dinge, Services Computing, Cloud Computing, künstliche Intelligenz, Big Data mit Analysen, mobile Systeme, Kollaborationsnetzwerke und cyber-physikalische Systeme treibt neue Geschäftsmodelle an und ermöglicht sie. Die Digitalisierung führt zu einer tiefgreifenden Umwälzung bestehender Unternehmen, Technologien und Volkswirtschaften und fördert die Architektur digitaler Umgebungen mit vielen eher kleinen und verteilten Strukturen. Dies hat starke Auswirkungen auf neue Wertschöpfungsmöglichkeiten und die Gestaltung digitaler Dienste und Produkte, die durch die Nutzung einer service-dominanten Logik gesteuert werden. Das Hauptergebnis des Buchkapitels erweitert Methoden für integrale digitale Strategien um wertorientierte Modelle für digitale Produkte und Dienstleistungen, die im Rahmen eines multiperspektivischen digitalen Unternehmensarchitektur-Referenzmodells definiert werden.
Artefaktkorrektur und verfeinerte Metriken für ein EEG-basiertes System zur Müdigkeitserkennung
(2019)
Fragestellung: Müdigkeit ist ein oft unterschätztes, aber dennoch großes Problem im Straßenverkehr. Von rund 2,5 Mio. Verkehrsunfällen 2015 in Deutschland, waren 2898 Unfälle, mit insgesamt 59 Toten (~1,7 % der Todesfälle), auf Übermüdung zurückzuführen. Schätzungen gehen von einer Dunkelziffer von bis zu 20 % aus. In einer ersten eigenen Studie wurde überprüft, ob ein mobiles EEG in einem Fahrsimulator Müdigkeitszustände zuverlässig erkennen kann. Die Erkennungsrate lag lediglich bei 61 %. Ziel dieser Arbeit ist, das verwendete Messsystem zu verbessern. Dazu wird die Genauigkeit durch eine Artefaktkorrektur und mit Hilfe von verfeinerten Qualitätsmetriken erhöht. Eine erkannte Übermüdung wird dem Fahrer dann in angemessener Weise angezeigt, so dass er entsprechend reagieren kann.
Patienten und Methoden: Die Independent Component Analysis (ICA) ist ein multivariates Verfahren, um mehrere Zufallsvariablen zu analysieren. Für die Entscheidung, ob ein Fahrer gerade müde oder wach ist, wird der erstellte Merkmalsvektor für jede Sequenz mit ICA klassifiziert. Dafür wird ein trainierter Machine-Learning-Algorithmus eingesetzt, der in der Lage ist, auch unbekannte Datensätze in Klassen einzuteilen. Um die benötigten Frequenzwerte zu erhalten, wurde für jeden EEG-Kanal eine Fourier Transformation durchgeführt. Der erstellte Merkmalsvektor wird im nächsten Schritt durch ein Künstliches Neuronales Netz klassifiziert. Für das Training werden vorab erstellte Merkmalsvektoren mit den Klassen „Wach“ und „Müde“ versehen. Diese Daten werden zufällig gemischt und im Verhältnis 2:1 in eine Trainings- und Testmenge geteilt. Das Experiment wurde mit acht Personen mit jeweils zweimal 45 min Testfahrt durchgeführt.
Ergebnisse: Der komplette Datensatz besteht aus 150.000 Signalwerten, welche zu ca. 7000 Sequenzen zusammengefasst werden. Durch die Anwendung der Qualitätsmetrik bleiben 4370 Sequenzen für das Training übrig. Bei invaliden Sequenzen aufgrund von EEG-Artefakten gibt es deutliche Unterschiede. Im „Wach“ Zustand werden dreimal so viele Sequenzen verworfen als im „Müde“ Zustand. Insgesamt werden bei wachen Probanden im Schnitt ca. 50 % der Sequenzen verworfen, bei Müden lediglich 25 %. Im Durchschnitt erreicht das System eine Erkennungsrate von 73 % für beide Zustände. Vergleicht man nun das Verhältnis von „Wach“ und „Müde“ und lässt „Leichte Müdigkeit“ außen vor, liegen die Ergebnisse bei über 90 %.
Schlussfolgerungen: Die Ergebnisse zeigen, dass die Aufmerksamkeit während des Experiments abnimmt bzw. die Müdigkeit zunimmt. Dies verdeutlichen zum einen subjektive und objektive Beobachtungen von Müdigkeitsanzeichen. Zum anderen lassen sich messbare und klassifizierbare Unterschiede im EEG Signal nachweisen. Die als Merkmale eingesetzten Theta-Wellen zeigten eine niedrigere Amplitude gegen Ende des Experiments. Die Erweiterung der binären Klassifizierung führt zu einer weiteren Stabilisierung der Ergebnisse. Artefaktkorrektur und Qualitätsmetriken steigern die Güte der Daten weiter. Die entwickelte Anwendung zur Müdigkeitserkennung ermittelt messbare Zeichen von Müdigkeit und kann eine gute Entscheidung über die Fahrtauglichkeit treffen.
Artificial Intelligence (AI) in der Markenführung: Künstliche Neuronale Netze zur Markenimagemessung
(2023)
Da Künstliche Neuronale Netze die Modellierung nichtlinearer und vielschichtiger Beziehungen ermöglichen, befasst sich dieser Beitrag mit deren Einsatzmöglichkeiten für die methodisch anspruchsvolle Analyse und Messung des Markenimages. Zur Veranschaulichung des konzeptionellen Ansatzes wird am empirischen Beispiel des Sportartikelherstellers adidas ein mehrschichtiges Künstliches Neuronales Netz zwischen den Bewertungen spezifischer Markenattribute und der Gesamtbewertung der Marke erzeugt. Auf der Grundlage einer Analyse der Verbindungsgewichte des Künstliches Neuronales Netzes wird die Bedeutung verschiedener Markenattribute für die Markenbewertung gemessen, wodurch sich konkrete Implikationen für die Praxis der Markenführung ableiten lassen.
Assistant platforms
(2023)
Many assistant systems have evolved toward assistant platforms. These platforms combine a range of resources from various actors via a declarative and generative interface. Among the examples are voice-oriented assistant platforms like Alexa and Siri, as well as text-oriented assistant platforms like ChatGPT and Bard. They have emerged as valuable tools for handling tasks without requiring deeper domain expertise and have received large attention with the present advances in generative artificial intelligence. In view of their growing popularity, this Fundamental outlines the key characteristics and capabilities that define assistant platforms. The former comprise a multi-platform architecture, a declarative interface, and a multi-platform ecosystem, while the latter include capabilities for composition, integration, prediction, and generativity. Based on this framework, a research agenda is proposed along the capabilities and affordances for assistant platforms.
Automatic classification of rotating machinery defects using Machine Learning (ML) algorithms
(2020)
Electric machines and motors have been the subject of enormous development. New concepts in design and control allow expanding their applications in different fields. The vast amount of data have been collected almost in any domain of interest. They can be static; that is to say, they represent real-world processes at a fixed point of time. Vibration analysis and vibration monitoring, including how to detect and monitor anomalies in vibration data are widely used techniques for predictive maintenance in high-speed rotating machines. However, accurately identifying the presence of a bearing fault can be challenging in practice, especially when the failure is still at its incipient stage, and the signal-to-noise ratio of the monitored signal is small. The main objective of this work is to design a system that will analyze the vibration signals of a rotating machine, based on recorded data from sensors, in the time/frequency domain. As a consequence of such substantial interest, there has been a dramatic increase of interest in applying Machine Learning (ML) algorithms to this task. An ML system will be used to classify and detect abnormal behavior and recognize the different levels of machine operation modes. The proposed solution can be deployed as predictive maintenance for Industry 4.0.
Back to the future: origins and directions of the “Agile Manifesto” – views of the originators
(2018)
In 2001, seventeen professionals set up the manifesto for agile software development. They wanted to define values and basic principles for better software development. On top of brought into focus, the manifesto has been widely adopted by developers, in software-developing organizations and outside the world of IT. Agile principles and their implementation in practice have paved the way for radical new and innovative ways of software and product development. In parallel, the understanding of the manifesto’s underlying principles evolved over time. This, in turn, may affect current and future applications of agile principles. This article presents results from a survey and an interview study in collaboration with the original contributors of the manifesto for agile software development. Furthermore, it comprises the results from a workshop with one of the original authors. This publication focuses on the origins of the manifesto, the contributors’ views from today’s perspective, and their outlook on future directions. We evaluated 11 responses from the survey and 14 interviews to understand the viewpoint of the contributors. They emphasize that agile methods need to be carefully selected and agile should not be seen as a silver bullet. They underline the importance of considering the variety of different practices and methods that had an influence on the manifesto. Furthermore, they mention that people should question their current understanding of "agile" and recommend reconsidering the core ideas of the manifesto.
Eine realistische Risikoeinschätzung ist Basis von verantwortungsvollen Unternehmensentscheidungen. Doch wie lassen sich Risiken richtig einschätzen? Verschiedene Instrumente des Risiko-Managements erlauben es, Risiken systematisch zu identifizieren, zu quantifizieren, zu bewerten und zu dokumentieren.
Excellence in IT is a key enabler for the digital transformation of enterprises. To realize the vision of digital enterprises it is necessary to cope with changing business requirements and to align business and IT. In order to evaluate the contribution of enterprise architecture management to these goals, our paper explores the impact of various factors to the perceived benefit of EAM in enterprises. Based on literature, we build an empirical research model. It is tested with empirical data of European EAM experts using a structural equation modelling approach. It is shown that changing business requirements, IT business alignment, the complexity of information technology infrastructure as well as enterprise architecture knowledge of information technology employees are crucial impact factors to the perceived benefit of EAM in enterprises.
After the initiator of the ESB Logistics Learning Factory, Prof. Vera Hummel had made experience in developing and implementing a concept for a Learning Factory for Advanced Industrial Engineering (aIE) at the University of Stuttgart, Institute IFF between 2005 and 2008, she was appointed as a full professor at ESB Business School, a faculty of Reutlingen University in March 2010. Lacking a realistic, hands on learning and teaching environment of industrial scale for its industrial engineering students, first ideas for a Learning Factory that would strongly focus on all aspects of production logistics were drafted in 2012. Already back then, a strong integration of virtual and physical factory was desired: While the Learning Factory itself would be physical, the neighboring partners along the supply chain, such as suppliers or distribution warehouses, could be added in a fully virtual way. Considering implementation of the ESB Logistics Learning Factory a strategic initiative of the university, initial funding was provided by the faculty ESB Business School itself. Following its own creed, to provide future-oriented training for the region, also primarily local suppliers and manufacturers were selected as equipment providers to the new Learning Factory. During the initialization phase, 2014, a total of three researchers and nine students worked approximately four months to set up a first assembly line, storage racks, AGVs, or pick-by-light systems in conjunction with the underlying didactical concept. Since then, several hundred of students have participated in trainings and lectures held in the ESB Logistics Learning Factory, several research projects were carried out, and multiple high-level politicians and industry executives have been touring the shop floor. Also, more than EUR 2 million in research and infrastructure funds could be secured for expansion and upgrade — allowing the ESB Logistics Learning Factory today to represent many core aspects of an Industrie 4.0 production environment.
Silicon neurons represent different levels of biological details and accuracies as a trade-off between complexity and power consumption. With respect to this trade-off and high similarity to neuron behaviour models, relaxation-type oscillator circuits often yield a good compromise to emulate neurons. In this chapter, two exemplified relaxation-type silicon neurons are presented that emulate neural behaviour with energy consumption under the scale of nJ/spike. The first proposed fully CMOS relaxation SiN is based on mathematical Izhikevich model and can mimic a broad range of physiologically observable spike patterns. The results of kinds of biologically plausible output patterns and coupling process of two SiNs are presented in 0.35 μm CMOS technology. The second type is a novel ultra-low-frequency hybrid CMOS-memristive SiN based on relaxation oscillators and analog memristive devices. The hybrid SiN directly emulates neuron behaviour in the range of physiological spiking frequencies (less than 100 Hz). The relaxation oscillator is implemented and fabricated in 0.13 μm CMOS technology. An autonomous neuronal synchronization process is demonstrated with two relaxation oscillators coupled by an analog memristive device in the measurement to emulate the synchronous behaviour between spiking neurons.
Today fiber reinforced plastics (FRP) are well established in manifold technical applications, because they provide advantages such as low weight, high stiffness, high strength and chemical resistance. The broad range of production methods starts from cost effective mass production up to the manufacturing of ultra-lightweight composite parts.
Biological materials are also usually composite materials: Higher plants or bones of higher animals are hierarchically organized and are composed of only a few materials such as lignin, cellulose, apatite and collagen. The large variety and the mechanical properties of natural tissues results primarily from an optimized fiber lay-up to adapt to the mechanical requirements of the respective “installation circumstances”.
Advanced lightweight technical solutions need strong materials and structurally optimized structures. In many industries, the structural optimization by an appropriate fiber lay-up has become an important method to save more weight. Corresponding software tools help to optimize topology/shape (e.g. Mattheck: CAO/SKO, Co. Altair: Optistruct), mainly using finite element analyzing technology.
The combination of strong lightweight materials, optimized topology and sophisticated fiber lay-up is also present in many bio-mineralized planktonic shells — for instance diatoms and radiolaria—but also in glass sponges.
Following it is shown, how the high weight-related mechanical properties of plankton are biomimetically transferred into ultra-lightweight technical structures.
The book provides suggestions on how to start using bionic optimization methods, including pseudo-code examples of each of the important approaches and outlines of how to improve them. The most efficient methods for accelerating the studies are discussed. These include the selection of size and generations of a study’s parameters, modification of these driving parameters, switching to gradient methods when approaching local maxima, and the use of parallel working hardware.
Bionic optimization means finding the best solution to a problem using methods found in nature. As evolutionary strategies and particle swarm optimization seem to be the most important methods for structural optimization, we primarily focus on them. Other methods such as neural nets or ant colonies are more suited to control or process studies, so their basic ideas are outlined in order to motivate readers to start using them.
A set of sample applications shows how bionic optimization works in practice. From academic studies on simple frames made of rods to earthquake-resistant buildings, readers follow the lessons learned, difficulties encountered and effective strategies for overcoming them. For the problem of tuned mass dampers, which play an important role in dynamic control, changing the goal and restrictions paves the way for multi-objective-optimization. As most structural designers today use commercial software such as FE-Codes or CAE systems with integrated simulation modules, ways of integrating bionic optimization into these software packages are outlined and examples of typical systems and typical optimization approaches are presented.
The closing section focuses on an overview and outlook on reliable and robust as well as on multi-objective optimization, including discussions of current and upcoming research topics in the field concerning a unified theory for handling stochastic design processes.
In this chapter we introduce methods to improve mechanical designs by bionic methods. In most cases we assume that a general idea of the part or system is given by a set of data or parameters. Our task is to modify these free parameters so that a given goal or objective is optimized without violation of any of the existing restrictions.
In the period from the 1950s to 2013, the American Food and Drug Administration (FDA) approved 1346 new molecular entities (NMEs) or new biologics entities (NBEs). On average, the approval rate was 20 NMEs per year. In the past 40 years, the number of new drugs launched into the market increased slightly from 15 NMEs in the 1970s to 25–30 NMEs since the 1990s. The highest number of new drugs approved by FDA was in 1996 and 1997, which might be related to the enactment of the Prescription Drug User Fee Act (PDUFA) in 1993.
An ongoing challenge in our days is to lower the impact on the quality of life caused by dysfunctionality through individual support. With the background of an aging society and continuous increases in costs for care, a holistic solution is needed. This solution must integrate individual needs and preferences, locally available possibilities, regional conditions, professional and informal caregivers and provide the flexibility to implement future requirements. The proposed model is a result of a common initiative to overcome the major obstacles and to center a solution on individual needs caused by dysfunctionality.
The purpose of this paper is to investigate the use of sustainable closed-loop supply chain of the fashion brand Filippa K. Information on green fashion has been gathered and a case study approach on the fashion retailer
Filippa K conducted. Results show a switch in knowledge content between a fast fashion supply chain and a sustainable supply chain. Also there is an evolution in sustainability as companies, retailers, and manufactures suffer under pressure from the customers, governments, and the media. Sustainable fashion brands like Filippa K are interested in sharing precise knowledge on variety of aspects linked to the sustainable closed-loop supply chain. This research paper has been limited by less information and unexplored topics in the theme green fashion. This led to the personal critical disputation with the brand Filippa K.
Since there is no denying that transparency is increasingly central to corporate sustainability, the purpose of this paper is a case study on a company’s attempt to be fully transparent, hence, picking up the existent scholarly conversation about uncompromising supply chain transparency. Literature so far was found to be fairly limited, but, following a trend, has been rising in numbers over recent years. Addressing these shortcomings in the methodology, an in-depth literature review about the multiple dimensions of supply chain transparency has been performed and links within supply networks stressed. On this basis, a case study by exemplary illustrating the fashion label Honestby has been drafted and the effort to become the world’s first 100 % transparent company further examined. Findings are discussed whether more supply chain transparency is desirable in any case, obstacles listed and an outlook for this kind of business model has been drawn. The research is clearly limited by the amount of scholarly literature concerning Honestby in particular. Out of this reason, magazines and journal entries are used as reference as well. Only with the extension of the topic itself to supply chain transparency and the literature review beforehand, the paper gained its necessary academic standard. Concerning implications, it needs to be mentioned that even though Honest by demonstrates to be fully transparent, it was not possible to find any public information about the degree of supplier relationship. In particular, concerning the applied control mechanisms used to exert influence and to balance out the power gradient between company and suppliers.
Case study: EMP
(2018)
The purpose of this research paper is to investigate the business model of the retailer EMP. The in-depth literature review develops the relevance of merchandising for the rock and heavy metal scene and the relevance of EMP within that market. Literature about existing approaches of multi-channelling has been reviewed. Based on this theoretical framework, a case study of EMP has been drafted. Findings are discussed, focusing on the performance of EMP as a multi-channel and lifestyle retailer and additionally provide valuable managerial implications for fashion retailers. Implications for further research address lifestyle retailers to contribute to the findings or validate them with different examples. The research is clearly limited by the amount of scholar literature concerning EMP in particular. Hence, magazines, journals and information provided by the company serve as reference. Even though EMP provided some information, gathering any information about how EMP manages multi channelling operationally was not possible.
This paper is purposed to examine the impact of grunge music on fashion and to explain how grunge music is reflected in grunge style. The research methodology applied is a case study on grunge music and grunge style. Key findings suggest that different elements of grunge music had a great impact on the evolution of grunge style: Mentality and philosophy of the movement, musical style and sound as well as lyrical concerns are incorporated by grunge style. Commercial exploitation of grunge partly led to its downfall. Moreover, the original spirit of the movement is not commonly shared by all sub-genres’ respective contemporary styles. Musicians had great impact on the evolution of grunge style and unintentional rose to style icons. The research is limited by the amount of academic literature concerning the connection between grunge music and grunge style. Therefore, journal entries and blogs are used as reference as well.
Case study: Marillion
(2018)
The purpose of this paper is to highlight the use of crowdfunding,
demonstrated by a case study about the rock band Marillion. The research
methodology applied is a literature review examining academic references. On this basis, a case study by exemplary illustrating the rock band Marillion and how they invented crowdfunding has been drafted. Findings suggest that the crowdfunding concept is no new phenomenon, since the rock band Marillion has investigated the business model. Recently, the funding method is applied to the fashion industry; hence it is efficient and engaging to finance projects by that specific business model. A limitation of this paper is that the topic of crowdfunding is new to the fashion business and needs further research and tests until they are practicable to interpret. Results show that there is a high potential for using crowdfunding in fashion by reaching a long-term change in this industry.
In today’s education, healthcare, and manufacturing sectors, organizations and information societies are discussing new enhancements to corporate structure and process efficiency using digital platforms. These enhancements can be achieved using digital tools. Industry 5.0 and Society 5.0 give several potentials for businesses to enhance the adaptability and efficacy of their industrial processes, paving the door for developing new business models facilitated by digital platforms. Society 5.0 can contribute to a super-intelligent society that includes the healthcare industry. In the past decade, the Internet of Things, Big Data Analytics, Neural Networks, Deep Learning, and Artificial Intelligence (AI) have revolutionized our approach to various job sectors, from manufacturing and finance to consumer products. AI is developing quickly and efficiently. We have heard of the latest artificial intelligence chatbot, ChatGPT. OpenAI created this, which has taken the internet by storm. We tested the effectiveness of a considerable language model referred to as ChatGPT on four critical questions concerning “Society 5.0”, “Healthcare 5.0”, “Industry,” and “Future Education” from the perspectives of Age 5.0.
The connection of fashion and film seems symbiotic at first sight and they influence each other. There exist differences, including a different understanding of clothing by costume designers and fashion businesses. This article focuses on two successful movies „The Hunger Games“ and „The Great Gatsby“ in order to explore the role of film in fashion and vice versa. The findings suggest, that there are various collections in the fashion world, based on both movies. Therefore, movies indeed have an influence on the development of seasonal fashion. However, this connection is not natural, but rather artificially created by both industries. Through nowadays organized co-operation, the lines between costume designers and fashion designers get blurred. Furthermore, today fashion doesn’t trickle down to an audience naturally, but promoted using the film and its broad reach.
The amount of image data has been rising exponentially over the last decades due to numerous trends like social networks, smartphones, automotive, biology, medicine and robotics. Traditionally, file systems are used as storage. Although they are easy to use and can handle large data volumes, they are suboptimal for efficient sequential image processing due to the limitation of data organisation on single images. Database systems and especially column-stores support more stuctured storage and access methods on the raw data level for entiere series.
In this paper we propose definitions of various layouts for an efficient storage of raw image data and metadata in a column store. These schemes are designed to improve the runtime behaviour of image processing operations. We present a tool called column-store Image Processing Toolbox (cIPT) allowing to easily combine the data layouts and operations for different image processing scenarios.
The experimental evaluation of a classification task on a real world image dataset indicates a performance increase of up to 15x on a column store compared to a traditional row-store (PostgreSQL) while the space consumption is reduced 7x. With these results cIPT provides the basis for a future mature database feature.
Sustainability is a development that meets the needs of the present without compromising the ability of future generations to meet their own needs.
Business Model is a plan for the successful operation of a business, identifying sources of revenue, the intended customer base, products, and details of financing.
Circular economy is an approach of how a company creates, captures and delivers value, with a value creation logic designed to improve resource efficiency through contributing to extending the useful life of products and parts (e.g., through long-life design, repair and remanufacturing) and closing material loops.
New business concepts such as Enterprise 2.0 foster the use of social software in enterprises. Especially social production significantly increases the amount of data in the context of business processes. Unfortunately, these data are still an unearthed treasure in many enterprises. Due to advances in data processing such as Big Data, the exploitation of context data becomes feasible. To provide a foundation for the methodical exploitation of context data, this paper introduces a classification, based on two classes, intrinsic and extrinsic data.
Indoor localization systems are becoming more and more important with the digitalization of the industrial sector. Sensor data such as the current position of machines, transport vehicles, goods or tools represent an essential component of cyber physical production systems (CCPS). However, due to the high costs of these sensors, they are not widespread and are used mainly in special scenarios. However, especially optical indoor positioning systems (OIPS) based on cameras have certain advantages due to their technological specifications. In this paper, the application scenarios and requirements as well as their characteristics are presented and a classification approach of OIPS is introduced.
The purpose of this paper is to investigate how the practice of closed-loop production systems (CLPS) is implemented in the fashion industry. This paper offers a critical literature review to present a thorough understanding of the actual status of literature. Subsequently, the paper reveals that CLPS are of great importance. Generally, such systems include different activities that have to be integrated. Critical points are the product acquisition, the recovering process itself and the remarketing to the customer. A lack of reliable data concerning CLPS in the specific case of fashion industry can be identified. Important research fields could be marketing strategies, controlling the acquisition process, evolvement of return technologies and strategies, adaption of recovered products to the mass market, and the development of new technologies concerning recovering processes.
Co-design and endorsement
(2018)
The purpose of this paper is to determine the success factors regarding celebrities of the music business involved in fashion advertising. That famous people have the power to help brands and products to stand out among others is proven and popular. This paper is concentrating on successful musicians and their endorsements of fashion brands and examines the benefits for both, the brand and the artist. It investigates how consumer perceives brand and artist collaboration and what factors enhance the purchase intention and increase sales. This paper is structured in the following manner: The introduction presents the research question and sets the aim for the paper, followed by the analysis of the existing literature. The paper ends with conclusions, limitations and suggestions for further research.
The purpose of this paper is to evaluate consisting consumption patterns caused by fast fashion with a new appearing form of consumption and retaining potentials as an alternative as well as sustainable form of fast fashion consumption. This research is set up on a theoretical background of scientific literature including governmental as well as press releases in order to evaluate the status quo of consumption and answering the research question. A new consumption pattern as well as an appearing economy of sharing can be stated including potential aspects of raising businesses and sustainable alternative forms of fast fashion. The framework of the research is limited to the textile and fashion industry in industrialized countries focusing on consumption in the twenty first century.
While many maintainability metrics have been explicitly designed for service-based systems, tool-supported approaches to automatically collect these metrics are lacking. Especially in the context of microservices, decentralization and technological heterogeneity may pose challenges for static analysis. We therefore propose the modular and extensible RAMA approach (RESTful API Metric Analyzer) to calculate such metrics from machine-readable interface descriptions of RESTful services. We also provide prototypical tool support, the RAMA CLI, which currently parses the formats OpenAPI, RAML, and WADL and calculates 10 structural service-based metrics proposed in scientific literature. To make RAMA measurement results more actionable, we additionally designed a repeatable benchmark for quartile-based threshold ranges (green, yellow, orange, red). In an exemplary run, we derived thresholds for all RAMA CLI metrics from the interface descriptions of 1,737 publicly available RESTful APIs. Researchers and practitioners can use RAMA to evaluate the maintainability of RESTful services or to support the empirical evaluation of new service interface metrics.
The relevance of Robotic Process Automation (RPA) has increased over the last few years. Combining RPA with Artificial Intelligence (AI) can further enhance the business value of the technology. The aim of this research was to analyze applications, terminology, benefits, and challenges of combining the two technologies. A total of 60 articles were analyzed in a systematic literature review to evaluate the aforementioned areas. The results show that by adding AI, RPA applications can be used in more complex contexts, it is possible to minimize the human factor during the development process, and AI-based decision-making can be integrated into RPA routines. This paper also presents a current overview of the used terminology. Moreover, it shows that by integrating AI, some unseen challenges in RPA projects can emerge, but also a lot of new benefits will come along with it. Based on the outcome, it is concluded that the topic offers a lot of potential, but further research and development is required. The result of this study help researches to gain an overview of the state-of-the-art in combining RPA and AI.
The influence of turbidity on the Raman signal strengths of condensed matter is theoretically analyzed and measured with laboratory - scale equipment for remote sensing. The results show the quantitative dependence of back- and forward-scattered signals on the thickness and elastic-scattering properties of matter. In the extreme situation of thin, highly turbid layers, the measured Raman signal strengths exceed their transparent analogs by more than a factor of ten. The opposite behavior is found for thick layers of low turbidity, where the presence of a small amount of scatterers leads to a decrease of the measured signal. The wide range of turbidities appearing in nature is experimentally realized with stacked polymer layers and solid/liquid dispersions, and theoretically modeled by the equation of radiative transfer using the analytical diffusion approximation or random walk simulations.
The early detection of head and neck cancer is a prolonged challenging task. It requires a precise and accurate identification of tissue alterations as well as a distinct discrimination of cancerous from healthy tissue areas. A novel approach for this purpose uses microspectroscopic techniques with special focus on hyperspectral imaging (HSI) methods. Our proof-of-principle study presents the implementation and application of darkfield elastic light scattering spectroscopy (DF ELSS) as a non-destructive, high-resolution, and fast imaging modality to distinguish lingual healthy from altered tissue regions in a mouse model. The main aspect of our study deals with the comparison of two varying HSI detection principles, which are a point-by-point and line scanning imaging, and whether one might be more appropriate in differentiating several tissue types. Statistical models are formed by deploying a principal component analysis (PCA) with the Bayesian discriminant analysis (DA) on the elastic light scattering (ELS) spectra. Overall accuracy, sensitivity, and precision values of 98% are achieved for both models whereas the overall specificity results in 99%. An additional classification of model-unknown ELS spectra is performed. The predictions are verified with histopathological evaluations of identical HE-stained tissue areas to prove the model’s capability of tissue distinction. In the context of our proof-of-principle study, we assess the Pushbroom PCA-DA model to be more suitable for tissue type differentiations and thus tissue classification. In addition to the HE-examination in head and neck cancer diagnosis, the usage of HSI-based statistical models might be conceivable in a daily clinical routine.
Public transport causes in rural areas high costs per passenger and kilometer as the frequency of scheduled busses is low and therefore, many people avoid using public transport. With the trend of moving from urban regions to countryside individual traffic will further increase. To tackle issues of emissions, mobility for young and elderly people and provide economically meaningful public transport a new concept was elaborated in Germany. This consists of (partly) autonomous shuttle busses which are remote controlled. For implementation rural districts of Germany have worked together and set up a three-phase plan consisting of a project with public funding, a highly frequent used pilot region and industrial partners with the commitment and possibilities for necessary investments. The concept promises economical value with respect to installation, service and maintaining costs, it leads to lower barriers for public transport of young and elderly people and ultimately reduces emissions and congestions.
The blockchain technology represents a decentralized database that stores information securely in immutable data blocks. Regarding supply chain management, these characteristics offer potentials in increasing supply chain transparency, visibility, automation, and efficiency. In this context, first token-based mapping approaches exist to transfer certain manufacturing processes to the blockchain, such as the creation or assembly of parts as well as their transfer of ownership. However, the decentralized and immutable structure of blockchain technology also creates challenges when applying these token-based approaches to dynamic manufacturing processes. As a first step, this paper investigates existing mapping approaches and exemplifies weaknesses regarding their suitability for products with changeable configurations. Secondly, a concept is proposed to overcome these weaknesses by introducing logically coupled tokens embedded into a flexible smart contract structure. Finally, a concept for a token-based architecture is introduced to map manufacturing processes of products with changeable configurations.
Platforms and their surrounding ecosystems are becoming increasingly important components of many companies' strategies. Artificial Intelligence, in particular, has created new opportunities to create and develop ecosystems around the platform. However, there is not yet a methodology to systematically develop these new opportunities for enterprise development strategy. Therefore, this paper aims to lay a foundation for the conceptualization of Artificial Intelligence-based service ecosystems exploiting a Service-Dominant Logic. The basis for conceptualization is the study of value creation and particularly effective network effects. This research investigates the fundamental idea of extending specific digital concepts considering the influence of Artificial Intelligence on the design of intelligent services, along with their architecture of digital platforms and ecosystems, to enable a smooth evolutionary path and adaptability for human-centric collaborative systems and services. The paper explores an extended digital enterprise conceptual model through a combined, iterative, and permanent task of co-creating value between humans and intelligent systems as part of a new idea of cognitively adapted intelligent services.
The use of Wireless Sensor and Actuator Networks (WSAN) as an enabling technology for Cyber-Physical Systems has increased significantly in recent past. The challenges that arise in different application areas of Cyber- Physical Systems, in general, and in WSAN in particular, are getting the attention of academia and industry both. Since reliability issues for message delivery in wireless communication are of critical importance for certain safety related applications, it is one of the areas that has received significant focus in the research community. Additionally, the diverse needs of different applications put different demands on the lower layers in the protocol stack, thus necessitating such mechanisms in place in the lower layers which enable them to dynamically adapt. Another major issue in the realization of networked wirelessly communicating cyber-physical systems, in general, and WSAN, in particular, is the lack of approaches that tackle the reliability, configurability and application awareness issues together. One could consider tackling these issues in isolation. However, the interplay between these issues create such challenges that make the application developers spend more time on meeting these challenges, and that too not in very optimal ways, than spending their time on solving the problems related to the application being developed. Starting from some fundamental concepts, general issues and problems in cyber-physical systems, this chapter discusses such issues like energy-efficiency, application and channel-awareness for networked wirelessly communicating cyber-physical systems. Additionally, the chapter describes a middleware approach called CEACH, which is an acronym for Configurable, Energy-efficient, Application- and Channel-aware Clustering based middleware service for cyber-physical systems. The state of-the art in the area of cyberphysical systems with a special focus on communication reliability, configurability, application- and channel-awareness is described in the chapter. The chapter also describes how these features have been considered in the CEACH approach. Important node level and network level characteristics and their significance vis-àvis the design of applications for cyber physical systems is also discussed. The issue of adaptively controlling the impact of these factors vis-à-vis the application demands and network conditions is also discussed. The chapter also includes a description of Fuzzy-CEACH which is an extension of CEACH middleware service and which uses fuzzy logic principles. The fuzzy descriptors used in different stages of Fuzzy-CEACH have also been described. The fuzzy inference engine used in the Fuzzy-CEACH cluster head election process is described in detail. The Rule-Bases used by fuzzy inference engine in different stages of Fuzzy-CEACH is also included to show an insightful description of the protocol. The chapter also discusses in detail the experimental results validating the authenticity of the presented concepts in the CEACH approach. The applicability of the CEACH middleware service in different application scenarios in the domain of cyberphysical systems is also discussed. The chapter concludes by shedding light on the Publish-Subscribe mechanisms in distributed event-based systems and showing how they can make use of the CEACH middleware to reliably communicate detected events to the event-consumers or the actuators if the WSAN is modeled as a distributed event-based system.
In recent years, the parallel computing community has shown increasing interest in leveraging cloud resources for executing parallel applications. Clouds exhibit several fundamental features of economic value, like on-demand resource provisioning and a pay-per-use model. Additionally, several cloud providers offer their resources with significant discounts; however, possessing limited availability. Such volatile resources are an auspicious opportunity to reduce the costs arising from computations, thus achieving higher cost efficiency. In this paper, we propose a cost model for quantifying the monetary costs of executing parallel applications in cloud environments, leveraging volatile resources. Using this cost model, one is able to determine a configuration of a cloud-based parallel system that minimizes the total costs of executing an application.
Context: Nowadays the market environment is characterized by high uncertainties due to high market dynamics, confronting companies with new challenges in creating and updating product roadmaps. Most companies are still using traditional approaches which typically fail in such environments. Therefore, companies are seeking opportunities for new product roadmapping approaches.
Objective: This paper presents good practices to support companies better understand what factors are required to conduct a successful product roadmapping in a dynamic and uncertain market environment.
Method: Based on a grey literature review, essential aspects for conducting product roadmapping in a dynamic and uncertain market environment were identified. Expert workshops were then held with two researchers and three practitioners to develop best practices and the proposed approach for an outcome-driven roadmap. These results were then given to another set of practitioners and their perceptions were gathered through interviews.
Results: The study results in the development of 9 good practices that provide practitioners with insights into what aspects are crucial for product roadmapping in a dynamic and uncertain market environment. Moreover, we propose an approach to product roadmapping that includes providing a flexible structure and focusing on delivering value to the customer and the business. To ensure the latter, this approach consists of the main items outcome hypothesis, validated outcomes, and discovered outputs.
The purpose of this paper is to find out how musicians are able to differentiate themselves from their competitors by using their style. Casting shows and the evolution of the contestants’ style during and after the show serve as a paradigm for creating differentiation by style. The method of research was diverse but largely drawn from research papers as well as online magazines and newspapers. Within the scope of the research, it was feasible to draw on a varied range of sources to answer the research question. In the course of this research paper, it was possible to define key factors for a musician to create differentiation by style in modern times. By examining the style of casting show contestants, it was explored to which extent they transform from rather normal people to pop stars. In reducing the detailed analysis of casting shows to three shows and contestants, only a broad overview was provided. The paper is of interest to those working for casting shows in order to develop those.
The purpose of this research is to explore current boundaries of the fashion industry’s second hand market and which solutions and approaches can be adopted from the used-car industry. The paper is based on the study of existing literature which deals with sustainability in combination with second hand markets in general and adaptable features of the used-car industry. Adaptable features are found using the business model canvas. The key finding of this study indicates that the fashion industry faces immense social and environmental challenges which can be partly solved by the development of the second hand market. Used-car industry can be seen as role model for fashion retail. In this study only aspects of used-car distribution are highlighted; therefore, characteristics of the recycling of used cars are not examined.
The purpose of this paper is to identify key success factors of Crowdfunding in the Music Business in order to discuss their applicability to the Fashion Industry. The research methodology applied is a literature review examining academic and non-academic references. Key research findings include four main success factors. First explains the innovative and adaptive nature of the music industry caused by historical evolution. Second strong commitment and connection to the fan base is identified as success factor. Third manageable effort for the realisation on a large scale reduces the risk of a failure. And, last success factor describes the successful implementation of campaign specific aspects. The discussion finally shows that three of four success factors can be adapted to the Fashion Business. Due to little scientific research in the field of Crowdfunding in the Music Business, the success factors are worked out independently, based on general literature. Accordingly, quantitative testing and further analysis is recommended.
Current fields of interest
(2016)
If we review the research done in the field of optimization, the following topics appear to be the focus of current development:
– Optimization under uncertainties, taking into account the inevitable scatter of parts, external effects and internal properties. Reliability and robustness both have to be taken into account when running optimizations, so the name Robust Design Optimization (RDO) came into use.
– Multi-Objective Optimization (MOO) handles situations in which different participants in the development process are developing in different directions. Typically we think of commercial and engineering aspects, but other constellations have to be looked at as well, such as comfort and performance or price and consumption.
– Process development of the entire design process, including optimization from early stages, might help avoid inefficient efforts. Here the management of virtual development has to be re-designed to fit into a coherent scheme.
...
There are many other fields where interesting progress is being made. We limit our discussion to the first three questions.
The scoring of sleep stages is one of the essential tasks in sleep analysis. Since a manual procedure requires considerable human and financial resources, and incorporates some subjectivity, an automated approach could result in several advantages. There have been many developments in this area, and in order to provide a comprehensive overview, it is essential to review relevant recent works and summarise the characteristics of the approaches, which is the main aim of this article. To achieve it, we examined articles published between 2018 and 2022 that dealt with the automated scoring of sleep stages. In the final selection for in-depth analysis, 125 articles were included after reviewing a total of 515 publications. The results revealed that automatic scoring demonstrates good quality (with Cohen's kappa up to over 0.80 and accuracy up to over 90%) in analysing EEG/EEG + EOG + EMG signals. At the same time, it should be noted that there has been no breakthrough in the quality of results using these signals in recent years. Systems involving other signals that could potentially be acquired more conveniently for the user (e.g. respiratory, cardiac or movement signals) remain more challenging in the implementation with a high level of reliability but have considerable innovation capability. In general, automatic sleep stage scoring has excellent potential to assist medical professionals while providing an objective assessment.
The purpose of this paper is to investigate how motion pictures are currently used for the product presentation of fashion articles in online shops in the German, American and British markets. This study shows that the use of moving images for the presentation of fashion articles in online shops is underutilized. With the amount of data that was manageable within the scope of this chapter, no valid generalizations can be made. All described results must be understood as an indication. In order to be able to use product presentation videos meaningfully, one should consider before exactly what is the purpose of these videos. Different goals require different means. However, retailer should obtain enough information in advance to assess whether they can afford the production and post processing of these videos.
Durch die Entwicklungen der vergangenen Jahre hin zu technisch komplexeren Maschinen und Anlagen steigt die Bedeutung der Instandhaltung als wesentlichem Schlüssel zur Sicherung der Verfügbarkeit von Maschinen und Anlagen. Wesentliche Ansatzpunkte zur Verbesserung sind hier die Verfügbarkeit von Informationen, voraussagende Instandhaltungsstrategien und eine verbesserte Informationsbereitstellung. Diese können auf technischer Ebene durch spezialisierte Cyberphysische Systeme realisiert werden. In diesem Beitrag wird ein Überblick über die wesentlichen Bausteine, aus smarten Komponenten, smarten Planungssystemen und smarten Benutzerschnittstellen gegeben, die für eine erfolgreiche Umsetzung notwendig sind.
In a networked world, companies depend on fast and smart decisions, especially when it comes to reacting to external change. With the wealth of data available today, smart decisions can increasingly be based on data analysis and be supported by IT systems that leverage AI. A global pandemic brings external change to an unprecedented level of unpredictability and severity of impact. Resilience therefore becomes an essential factor in most decisions when aiming at making and keeping them smart. In this chapter, we study the characteristics of resilient systems and test them with four use cases in a wide-ranging set of application areas. In all use cases, we highlight how AI can be used for data analysis to make smart decisions and contribute to the resilience of systems.
In this presentation the audience will be: (a) introduced to the aims and objectives of the DBTechNet initiative, (b) briefed on the DBTech EXT virtual laboratory workshops (VLW), i.e. the educational and training (E&T) content which is freely available over the internet and includes vendor-neutral hands-on laboratory training sessions on key database technology topics, and (c) informed on some of the practical problems encountered and the way they have been addressed. Last but not least, the audience will be invited to consider incorporating some or all of the DBTech EXT VLW content into their higher education (HE), vocational education and training (VET), and/or lifelong learning/training type course curricula. This will come at no cost and no commitment on behalf of the teacher/trainer; the latter is only expected to provide his/her feedback on the pedagogical value and the quality of the E&T content received/used.
The Internet of Things (IoT), enterprise social networks, adaptive case management, mobility systems, analytics for big data, and cloud services environments are emerging to support smart connected products and services and the digital transformation. Biological metaphors of living and adaptable ecosystems with service oriented enterprise architectures provide the foundation for self-optimizing and resilient run-time environments for intelligent business services and related distributed information systems. We are investigating mechanisms for flexible adaptation and evolution for the next digital enterprise architecture systems in the context of the digital transformation. Our aim is to support flexibility and agile transformation for both business and related enterprise systems through adaptation and dynamical evolution of digital enterprise architectures. The present research paper investigates mechanisms for decision case management in the context of multi-perspective explorations of enterprise services and Internet of Things architectures by extending original enterprise architecture reference models with state of art elements for architectural engineering for the digitization and architectural decision support.
Digitization of societies changes the way we live, work, learn, communicate, and collaborate. In the age of digital transformation IT environments with a large number of rather small structures like Internet of Things (IoT), microservices, or mobility systems are emerging to support flexible and agile digitized products and services. Adaptable ecosystems with service oriented enterprise architectures are the foundation for self-optimizing, resilient run-time environments and distributed information systems. The resulting business disruptions affect almost all new information processes and systems in the context of digitization. Our aim are more flexible and agile transformations of both business and information technology domains with more flexible enterprise information systems through adaptation and evolution of digital enterprise architectures. The present research paper investigates mechanisms for decision-controlled digitization architectures for Internet of Things and microservices by evolving enterprise architecture reference models and state of the art elements for architectural engineering for micro-granular systems.
Enterprises are presently transforming their strategy, culture, processes, and their information systems to become more digital. The digital transformation deeply disrupts existing enterprises and economies. Digitization fosters the development of IT systems with many rather small and distributed structures, like Internet of Things or mobile systems. Since years a lot of new business opportunities appeared using the potential of the Internet and related digital technologies, like Internet of Things, services computing, cloud computing, big data with analytics, mobile systems, collaboration networks, and cyber physical systems. This has a strong impact for architecting digital services and products. The change from a closed-world modeling perspective to more flexible open-world composition and evolution of system architectures defines the moving context for adaptable systems, which are essential to enable the digital transformation. In this paper, we are focusing on a decision-oriented architectural composition approach to support the transformation for digital services and products.
The Internet of Things (IoT) is coined by many different standards, protocols, and data formats that are often not compatible to each other. Thus, the integration of different heterogeneous (IoT) components into a uniform IoT setup can be a time-consuming manual task. This lacking interoperability between IoT components has been addressed with different approaches in the past. However, only very few of these approaches rely on Machine Learning techniques. In this work, we present a new way towards IoT interoperability based on Deep Reinforcement Learning (DRL). In detail, we demonstrate that DRL algorithms, which use network architectures inspired by Natural Language Processing (NLP), can be applied to learn to control an environment by merely taking raw JSON or XML structures, which reflect the current state of the environment, as input. Applied to IoT setups, where the current state of a component is often reflected by features embedded into JSON or XML structures and exchanged via messages, our NLP DRL approach eliminates the need for feature engineering and manually written code for pre-processing of data, feature extraction, and decision making.
Purpose: Gliomas are the most common and aggressive type of brain tumors due to their infiltrative nature and rapid progression. The process of distinguishing tumor boundaries from healthy cells is still a challenging task in the clinical routine. Fluid attenuated inversion recovery (FLAIR) MRI modality can provide the physician with information about tumor infiltration. Therefore, this paper proposes a new generic deep learning architecture, namely DeepSeg, for fully automated detection and segmentation of the brain lesion using FLAIR MRI data.
Methods: The developed DeepSeg is a modular decoupling framework. It consists of two connected core parts based on an encoding and decoding relationship. The encoder part is a convolutional neural network (CNN) responsible for spatial information extraction. The resulting semantic map is inserted into the decoder part to get the full-resolution probability map. Based on modified U-Net architecture, different CNN models such as residual neural network (ResNet), dense convolutional network (DenseNet), and NASNet have been utilized in this study.
Results: The proposed deep learning architectures have been successfully tested and evaluated on-line based on MRI datasets of brain tumor segmentation (BraTS 2019) challenge, including s336 cases as training data and 125 cases for validation data. The dice and Hausdorff distance scores of obtained segmentation results are about 0.81 to 0.84 and 9.8 to 19.7 correspondingly.
Conclusion: This study showed successful feasibility and comparative performance of applying different deep learning models in a new DeepSeg framework for automated brain tumor segmentation in FLAIR MR images. The proposed DeepSeg is open source and freely available at https://github.com/razeineldin/DeepSeg/.
Delphi Markets
(2023)
Delphi markets refer to approaches and implementations of integrating prediction markets and Delphi studies (Real-time Delphi). The combination of the two methods for producing forecasts can potentially compensate for each other´s weaknesses. For example, prediction markets can be used to select participants with expertise and also motivate long-term participation through their gamified approach and incentive mechanisms. In this paper, two potentials for prediction markets and four potentials for Delphi studies, which are made possible by integration, are derived theoretically. Subsequently, three different integration approaches are presented, on the basis of which the integration on user, market and Delphi question-level is exemplified and it is shown that, depending on the approach, not all potentials can be achieved. At the end, recommendations for the use of Delphi markets are derived, existing limitations for Delphi markets as well as future developments are pointed out.
There have been substantial research efforts for algorithms to improve continuous and automated assessment of various health-related questions in recent years. This paper addresses the deployment gap between those improving algorithms and their usability in care and mobile health applications. In practice, most algorithms require significant and founded technical knowledge to be deployed at home or support healthcare professionals. Therefore, the digital participation of persons in need of health care professionals lacks a usable interface to use the current technological advances. In this paper, we propose applying algorithms taken from research as web-based microservices following the common approach of a RESTful service to bridge the gap and make algorithms accessible to caregivers and patients without technical knowledge and extended hardware capabilities. We address implementation details, interpretation and realization of guidelines, and privacy concerns using our self-implemented example. Also, we address further usability guidelines and our approach to those.
Diese Studie untersucht den kurzfristigen Einfluss der Tagespflege auf die kindliche Entwicklung im Vergleich zur Betreuung in der Kita. Internationale Studien deuten darauf hin, dass der Besuch einer Tagespflege im Vergleich zur Kita eher negative Auswirkungen auf Kinder hat. Mithilfe der Neugeborenen-Kohorte des NEPS können wir evaluieren, ob dies auch im deutschen Kontext gilt. Wir nutzen zwei verschiedene methodische Ansätze, um den Effekt der Tagespflege zu schätzen. Unsere Ergebnisse zeigen, dass die Tagespflege für die Mehrzahl der untersuchten Entwicklungsindikatoren keinen statistisch signifikant schlechteren Einfluss auf die kindliche Entwicklung hat, außer im Bereich der Habituation.
Der Girlboss Mythos : die gesellschaftlichen und ökonomischen Perspektiven der Gender-Debatte
(2019)
Faktisch sind Frauen heute gleichberechtigt. Sie haben die gleichen Chancen, Rechte und Möglichkeiten wie Männer. Dennoch weisen maßgebliche Studien darauf hin, dass die Anzahl von Frauen auf allen Führungsebenen stagniert oder nur im Schneckentempo wächst. In der medialen Diskussion rund um das Thema Frauen im Management ist die Welt auf den ersten Blick in zwei Lager geteilt. Ein Lager stellt ernüchtert fest, dass Frauen selbst Schuld sind an ihrer Situation. Oft werden hier gerade erfolgreiche Frauen zitiert, die ihren Geschlechtsgenossinnen den nötigen Erfolgswillen oder die Opferbereitschaft absprechen. Das andere Lager scheint die Sachlage genau entgegengesetzt zu beurteilen. Überall gut ausgebildete, hochmotivierte Frauen, die an Glasdecken stoßen oder denen von der Gesellschaft im Allgemeinen und Männern im Besonderen die Türen versperrt werden. Dieses Buch trägt zu einer wissenschaftlich nüchternen Diskussion bei, um die aktuelle gesellschaftspolitische Situation differenzierter und abseits von abgegriffenen Dogmen zu betrachten.
Additive Manufacturing is increasingly used in the industrial sector as a result of continuous development. In the Production Planning and Control (PPC) system, AM enables an agile response in the area of detailed and process planning, especially for a large number of plants. For this purpose, a concept for a PPC system for AM is presented, which takes into account the requirements for integration into the operational enterprise software system. The technical applicability will be demonstrated by individual implemented sections. The presented solution approach promises a more efficient utilization of the plants and a more elastic use.
The proposed approach applies current unsupervised clustering approaches in a different dynamic manner. Instead of taking all the data as input and finding clusters among them, the given approach clusters Holter ECG data (longterm electrocardiography data from a holter monitor) on a given interval which enables a dynamic clustering approach (DCA). Therefore advanced clustering techniques based on the well known Dynamic TimeWarping algorithm are used. Having clusters e.g. on a daily basis, clusters can be compared by defining cluster shape properties. Doing this gives a measure for variation in unsupervised cluster shapes and may reveal unknown changes in healthiness. Embedding this approach into wearable devices offers advantages over the current techniques. On the one hand users get feedback if their ECG data characteristic changes unforeseeable over time which makes early detection possible. On the other hand cluster properties like biggest or smallest cluster may help a doctor in making diagnoses or observing several patients. Further, on found clusters known processing techniques like stress detection or arrhythmia classification may be applied.