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Theoretical foundation, effectiveness, and design artefact for machine learning service repositories
(2022)
Machine learning (ML) has played an important role in research in recent years. For companies that want to use ML, finding the algorithms and models that fit for their business is tedious. A review of the available literature on this problem indicates only a few research papers. Given this gap, the aim of this paper is to design an effective and easy-to-use ML service repository. The corresponding research is based on a multi-vocal literature analysis combined with design science research, addressing three research questions: (1) How is current white and gray literature on ML services structured with respect to repositories? (2) Which features are relevant for an effective ML service repository? (3) How is a prototype for an effective ML service repository conceptualized? Findings are relevant for the explanation of user acceptance of ML repositories. This is essential for corporate practice in order to create and use ML repositories effectively.
Machine learning (ML) techniques are rapidly evolving, both in academia and practice. However, enterprises show different maturity levels in successfully implementing ML techniques. Thus, we review the state of adoption of ML in enterprises. We find that ML technologies are being increasingly adopted in enterprises, but that small and medium-size enterprises (SME) are struggling with the introduction in comparison to larger enterprises. In order to identify enablers and success factors we conduct a qualitative empirical study with 18 companies in different industries. The results show that especially SME fail to apply ML technologies due to insufficient ML knowhow. However, partners and appropriate tools can compensate this lack of resources. We discuss approaches to bridge the gap for SME.
Long-term stability of membranes in membrane distillation operation is a problem nowadays which prevents the industrial breakthrough of this separation process. Fouling or slow pore wetting are the basic reasons for this.
Membrane distillation membranes were made by NIPS process rendering the membrane asymmetrically to achieve low permeation resistance and pores which can be over coated with polyelectrolyte polymers thus leading to thermopervaporation membranes. Those membranes prohibit pore wetting and may strongly reduce resorption of organic substances on for membrane distillation typically used hydrophobic surfaces thus leading to longterm operation stability in dewatering including stable membrane cleaning.
Asymmetric PVDF membranes have been coated with cation exchange polyelectrolyte leading to a very thin, defect-free layer which has a high permeation rate for water due to the domain structure of phase-separated hydrophilic and hydrophobic three-dimensional structures.
Motivation
In order to enable context-aware behavior of surgical assistance systems, the acquisition of various information about the current intraoperative situation is crucial. To achieve this, the complex task of situation recognition can be delegated to a specialized system. Consequently, a standardized interface is required for the seamless transfer of the recognized contextual information to the assistance systems, enabling them to adapt accordingly.
Methods
Our group analyzed four medical interface standards to determine their suitability for exchanging intraoperative contextual information. The assessment was based on a harmonized data and service model derived from the requirements of expected context-aware use cases. The Digital Imaging and Communications in Medicine (DICOM) and IEEE 11073 for Service-oriented Device Connectivity (SDC) were identified as the most appropriate standards.
Results
We specified how DICOM Unified Procedure Steps (UPS), can be used to effectively communicate contextual information. We proposed the inclusion of attributes to formalize different granularity levels of the surgical workflow.
Conclusions
DICOM UPS SOP classes can be used for the exchange of intraoperative contextual information between a situation recognition system and surgical assistance systems. This can pave the way for vendor-independent context awareness in the OR, leading to targeted assistance of the surgical team and an improvement of the surgical workflow.
The strong demand to transform the textile and fashion industry towards sustainability requires continuous implementation of the Education for Sustainable Development (ESD) mission statement in education and industry. To achieve this goal, the European research project "Fashion DIET - Sustainable Fashion Curriculum at Textile Universities in Europe. Development, Implementation and Evaluation of a Teaching Module for Educators", co-funded by the Erasmus+ programme of the European Union (2020-1-DE01-KA203-005657), aims to create an ESD module for university lecturers and research-based teaching and learning materials delivered through an e-learning portal. First, an online questionnaire was rolled out to assess university faculty attitudes toward and needs for ESD content and methods. The feedback questionnaire enabled the selection of the most relevant data for the elaboration of an action and research-oriented professional development module for ESD in textile education, which will be accessible through an information & e-learning portal. The e-learning portal can be used as a web-based tool to apply and evaluate the project outcomes, e.g. the further education module and the teaching and learning materials for educators, such as manuals, broadcasts and the provision of interactive and physical materials. It thus ensures that the teaching materials can be used sustainably in the classroom. It also provides country-specific data for the fashion and textile industry and its market, taking into account the different perspectives of universities and schools. In any case, the portal represents (1) the web-based platform to support the dissemination of ESD as a guiding principle and (2) a central contact point for the target group to obtain relevant information on ESD. Fashion DIET explores the use of e-learning to improve teaching and learning on ESD, by training educators and empowering them as multipliers for a sustainable textile and fashion industry. At a higher level, the European project strengthens the quality and relevance of learning provision in education towards the latest developments in textile research and innovation in terms of a more sustainable fashion.
Due to the consequential impact of technological breakdowns, companies have to be prepared to deal with breakdowns or even better prevent them. In today's information technology, several methods and tools exist to downscale this concern. Therefore, this paper deals with the initial determination of a resilient enterprise architecture supporting predictive maintenance in the information technology domain and furthermore, concerns several mechanisms on how to reactively and proactively secure the state of resiliency on several abstraction levels. The objective of this paper is to give an overview on existing mechanisms for resiliency and to describe the foundation of an optimized approach, combining infrastructure and process mining techniques.
Gamification is one of the recognized methods of motivating people in various life processes, and it has spread to many spheres of life, including healthcare. This article proposes a system design for long-term care patients using the method mentioned. The proposed system aims to increase patient engagement in the treatment and rehabilitation process via gamification. Literature research on available and earlier proposed systems was conducted to develop a suited system design. The primary target group includes bedridden patients and a sedentary lifestyle (predominantly lying in bed). One of the main criteria for selecting a suitable option was its contactless realization for the mentioned target groups in long-term care cases. As a result, we developed the system design for hardware and software that could prevent bedsores and other health problems from occurring because of low activity. The proposed design can be tested in hospitals, nursing homes, and rehabilitation centers.
Lithographical hotspot (LH) detection using deep learning (DL) has received much attention in the recent years. It happens mainly due to the facts the DL approach leads to a better accuracy over the traditional, state-of-the-art programming approaches. The purpose of ths study is to compare existing data augmentation (DA) techniques for the integrated circuit (IC) mask data using DL methods. DA is a method which refers to the process of creating new samples similar to the training set, thereby helping to reduce the gap between classes as well as improving the performance of the DL system. Experimental results suggest that the DA methods increase overall DL models performance for the hotspot detection tasks.
The rapid development and growth of knowledge has resulted in a rich stream of literature on various topics. Information systems (IS) research is becoming increasingly extensive, complex, and heterogeneous. Therefore, a proper understanding and timely analysis of the existing body of knowledge are important to identify emerging topics and research gaps. Despite the advances of information technology in the context of big data, machine learning, and text mining, the implementation of systematic literature reviews (SLRs) is in most cases still a purely manual task. This might lead to serious shortcomings of SLRs in terms of quality and time. The outlined approach in this paper supports the process of SLRs with machine learning techniques. For this purpose, we develop a framework with embedded steps of text mining, cluster analysis, and network analysis to analyze and structure a large amount of research literature. Although the framework is presented using IS research as an example, it is not limited to the IS field but can also be applied to other research areas.
Public transport maps are typically designed in a way to support route finding tasks for passengers while they also provide an overview about stations, metro lines, and city-specific attractions. Most of those maps are designed as a static representation, maybe placed in a metro station or printed in a travel guide. In this paper we describe a dynamic, interactive public transport map visualization enhanced by additional views for the dynamic passenger data on different levels of temporal granularity. Moreover, we also allow extra statistical information in form of density plots, calendar-based visualizations, and line graphs. All this information is linked to the contextual metro map to give a viewer insights into the relations between time points and typical routes taken by the passengers. We illustrate the usefulness of our interactive visualization by applying it to the railway system of Hamburg in Germany while also taking into account the extra passenger data. As another indication for the usefulness of the interactively enhanced metro maps we conducted a user experiment with 20 participants.
In order to evaluate the performance of different stapes prosthesis types, a coupled finite element (FE) model of human ear was developed. First, the middle-ear FE model was developed and validated using the middle-ear transfer function measurements available in literature including pathological cases. Then, the inner-ear FE model was developed and validated using tonotopy, impedance, and level of cochlea amplification curves from literature. Both models are based on pre-existing research with some improvements and were combined into one coupled FE model. The stapes in the coupled FE ear model was replaced with a model of a stapes prosthesis to create a reconstructed ear model that can be used to estimate how different types of protheses perform relative to each other as well as to the natural ear. This will help in designing of new innovative types of stapes prostheses or any other type of middle-ear prostheses as well as to improve the ones that are already available on the market.
Urgent action is needed to keep the chance of limiting global warming to 1.5°C or even 2.0°C. Current outlooks by IPCC, and many other organisations forecast that this will be impossible at current pace of emission 'reductions' – Germany has already hit 1.5° warming this year. Across 2019, particularly during the UN New York Climate summit, numerous organisations declared their ambition to become net carbon neutral. Amongst these were investors and companies, including quite a number of German ones.
We apply a mixed methods approach, utilising data gathered from approx. 900 companies after Climate Week in context of the Energy Efficiency Index of German Industry (EEI), along with media research focusing on decarbonisation plans announced and initiatives pledging climate action.
With this, we analyse how German companies in the manufacturing sectors react to rising societal pressure and emerging policies, particularly what measures they have taken or plan to implement to reduce the footprint of their company, their products and their supply chain. In this, we particularly analyse whether and in what way energy- and resource consumption, as well as carbon emissions are considered in the development and lifecycle of goods manufactured. This is of huge relevance as these goods determine the future footprint of buildings, vehicles and industry.
Regarding the supply chain, current articles indicate that small and medium-sized enterprises (SME) are particularly challenged by increasing demands from their large corporate clients and an alleged lack of preparedness to be able to take and afford prompt decarbonisation action themselves (Buchenau et. al. 2019). Notably the automotive industry recently announced new models that will be 100% carbon neutral all the way through (ibid). We thus analyse if and how factors such as company size, energy intensity and sector affiliation influence a company’s plan to fully decarbonize. Ownership structure and corporate culture, it appears, significantly impact on the degree of decarbonisation action underway.
IGBT modules with anti-parallel FWDs are widely used in inductive load switching power applications, such as motor drive applications. Nowadays there is a continuous effort to increase the efficiency of such systems by decreasing their switching losses. This paper addresses the problems arising in the turn-on process of an IGBT working in hard-switching conditions. A method is proposed which achieves – contrary to most other approaches – a high switching speed and, at the same time, a low peak reverse-recovery current. This is done by applying an improved gate current waveform that is briefly lowered during the turn-on process. The proposed method achieves low switching losses. Its effectiveness is demonstrated by experimental results with IGBT modules for 600V and 1200V.
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.
Power line communications (PLC) reuse the existing power-grid infrastructure for the transmission of data signals. As power line the communication technology does not require a dedicated network setup, it can be used to connect a multitude of sensors and Internet of Things (IoT) devices. Those IoT devices could be deployed in homes, streets, or industrial environments for sensing and to control related applications. The key challenge faced by future IoT-oriented narrowband PLC networks is to provide a high quality of service (QoS). In fact, the power line channel has been traditionally considered too hostile. Combined with the fact that spectrum is a scarce resource and interference from other users, this requirement calls for means to increase spectral efficiency radically and to improve link reliability. However, the research activities carried out in the last decade have shown that it is a suitable technology for a large number of applications. Motivated by the relevant impact of PLC on IoT, this paper proposed a cooperative spectrum allocation in IoT-oriented narrowband PLC networks using an iterative water-filling algorithm.
An autonomous vehicle is a robotic vehicle with decision and action capability capable of performing assigned tasks without or with minimal human intervention. Autonomous cars have been in development for many years. The Society of Automotive Engineers (SAE International) published in 2014 a classification in five levels of driving automation, with level 0 corresponding to completely manual driving, and level 5 to an ideal dream where the vehicle would be able to navigate entirely autonomously for all missions and in all environments. This work addressed the navigation of an autonomous vehicle in general. We focus on one of the most complex scenarios of the road network and crossing of road intersections. In this paper, the critical features of autonomous intelligent vehicles are reviewed. Furthermore, the associated problems are presented, and the most advanced solutions are derived. This article aims to allow a novice in this field to understand the different facets of localization and perception problems for autonomous vehicles.
Rotating machinery occupies a predominant place in many industrial applications. However, rotating machines are often encountered with severe vibration problems. The measurement of these machines’ vibrations signal is of particular importance since it plays a crucial role in predictive maintenance. When the vibrations are too high, they often cause fatigue failure. They announce an unexpected stop or break and, consequently, a significant loss of productivity or an attack on the personnel’s safety. Therefore, fault identification at early stages will significantly enhance the machine’s health and significantly reduce maintenance costs. Although considerable efforts have been made to master the field of machine diagnostics, the usual signal processing methods still present several drawbacks. This paper examines the rotating machinery condition monitoring in the time and frequency domains. It also provides a framework for the diagnosis process based on machine learning by analyzing the vibratory signals.
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.
In this work, a web-based software architecture and framework for management and diagnosis of large amounts of medical data in an ophthalmologic reading center is proposed. Data management for multi-center studies requires merging of standing data and repeatedly gathered clinical evidence such as vital signs and raw data. If ophthalmologic questions are involved the data acquisition is often provided by non-medical staff at the point of care or a study center, whereas the medical finding is mostly provided by an ophthalmologist in a specialized reading center. The study data such as participants, cohorts and measured values are administrated at a single data center for the entire study. Since a specialized reading center maintains several studies, the medical staff must learn the different data administration for the different data center. With respect to the increasing number and sizes of clinical studies, two aspects must be considered. At first, an efficient software framework is required to support the data management, processing and diagnosis by medical experts at the reading center. In the second place, this software needs a standardized user-interface that has not to be trained/taylore /adapted for each new study. Furthermore different aspects of quality and security controls have to be included. Therefore, the objective of this work is to establish a multi purpose ophthalmologic reading center, which can be connected to different data centers via configurable data interfaces in order to treat various topics simultaneously.
Personalized remote healthcare monitoring is in continuous development due to the technology improvements of sensors and wearable electronic systems. A state of the art of research works on wearable sensors for healthcare applications is presented in this work. Furthermore, a state of the art of wearable devices, chest and wrist band and smartwatches available on the market for health and sport monitoring is presented in this paper. Many activity trackers are commercially available. The prices are continuously reducing and the performances are improving, but commercial devices do not provide raw data and are therefore not useful for research purposes.
The impact of stress of every human being has become a serious problem. Reported impact on persons are a higher rate or health disorders like heart problems, obesity, asthma, diabetes, depressions and many others. An individual in a stressful situation has to deal with altered cognition as well as an affected decision making skill and problem solving. This could lead to a higher risk for accidents in dynamic environments such as automotive. Different papers faced the estimation as well as prediction of drivers’ stress level during driving. Another important question is not only the stress level of the driver himself, but also the influence on and of a group of other drivers in the near area. This paper proposes a system, which determines a group of drivers in a near area as clusters and it derives or computes the individual stress level. This information will be analyzed to generate a stress map, which represents a graphical view about road section with a higher stress influence. Aggregated data can be used to generate navigation routes with a lower stress influence as well as recommend driving behavior to decrease stress influenced driving as well as improve road safety.
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.
Urban platforms are essential for smart and sustainable city planning and operation. Today they are mostly designed to handle and connect large urban data sets from very different domains. Modelling and optimisation functionalities are usually not part of the cities software infrastructure. However, they are considered crucial for transformation scenario development and optimised smart city operation. The work discusses software architecture concepts for such urban platforms and presents case study results on the building sector modelling, including urban data analysis and visualisation. Results from a case study in New York are presented to demonstrate the implementation status.
The energy turnaround, digitalization and decreasing revenues forces enterprises in the energy domain to develop new business models. Business models for renewable energy are compound on different logic than business models for larger scale power plants. Following a design science research approach, we examined the business models of three enterprises in the energy domain in a first step. We identified that these business models result in complex ecosystems with multiple actors and difficult relationships between them. One cause is the fast changing and complicated state regulation in Germany. In order to solve the problem, we captured together with the partners of the enterprises the requirements in a second phase. Further we developed the prototype Business Model Configurator (BMConfig) based on the e3Value Ontology on the metamodelling platform ADOxx. We demonstrate the feasibility of our approach in business model of energy efficiency service based on smart meter data.
In many cases continuous monitoring of vital signals is required and low intrusiveness is an important requirement. Incorporating monitoring systems in the hospital or home bed could have benefits for patients and caregivers. The objective of this work is the definition of a measurement protocol and the creation of a data set of measurements using commercial and low-cost prototypes devices to estimate heart rate and breathing rate. The experimental data will be used to compare results achieved by the devices and to develop algorithms for feature extraction of vital signals.
The recovery of our body and brain from fatigue directly depends on the quality of sleep, which can be determined from the results of a sleep study. The classification of sleep stages is the first step of this study and includes the measurement of vital data and their further processing. The non-invasive sleep analysis system is based on a hardware sensor network of 24 pressure sensors providing sleep phase detection. The pressure sensors are connected to an energy-efficient microcontroller via a system-wide bus. A significant difference between this system and other approaches is the innovative way in which the sensors are placed under the mattress. This feature facilitates the continuous use of the system without any noticeable influence on the sleeping person. The system was tested by conducting experiments that recorded the sleep of various healthy young people. Results indicate the potential to capture respiratory rate and body movement.
In recent decades, it can be observed that a steady increase in the volume of tourism is a stable trend. To offer travel opportunities to all groups, it is also necessary to prepare offers for people in need of long-term care or people with disabilities. One of the ways to improve accessibility could be digital technologies, which could help in planning as well as in carrying out trips. In the work presented, a study of barriers was first conducted, which led to selecting technologies for a test setup after analysis. The main focus was on a mobile app with travel information and 360° tours. The evaluation results showed that both technologies could increase accessibility, but some essential aspects (such as usability, completeness, relevance, etc.) need to be considered when implementing them.
Intermittent time series forecasting is a challenging task which still needs particular attention of researchers. The more unregularly events occur, the more difficult is it to predict them. With Croston’s approach in 1972 (1.Nr. 3:289–303), intermittence and demand of a time series were investigated the first time separately. He proposes an exponential smoothing in his attempt to generate a forecast which corresponds to the demand per period in average. Although this algorithm produces good results in the field of stock control, it does not capture the typical characteristics of intermittent time series within the final prediction. In this paper, we investigate a time series’ intermittence and demand individually, forecast the upcoming demand value and inter-demand interval length using recent machine learning algorithms, such as long-short-term-memories and light-gradient-boosting machines, and reassemble both information to generate a prediction which preserves the characteristics of an intermittent time series. We compare the results against Croston’s approach, as well as recent forecast procedures where no split is performed.
Ein wichtiges Qualifikationsziel von heutigen Wirtschaftsingenieurstudien-programmen ist, Studierende dazu zu befähigen, vernetzt, ganzheitlich, interkulturell und interdisziplinär zu denken und zu handeln. Die Lehr- und Lerninnovation Quest 3C fördert durch ein integratives Blended-Learning Format sowohl die Vermittlung von grundlegendem Fachwissen als auch von berufsqualifizierenden Schlüsselkompetenzen.
In recent years, the cloud has become an attractive execution environment for parallel applications, which introduces novel opportunities for versatile optimizations. Particularly promising in this context is the elasticity characteristic of cloud environments. While elasticity is well established for client-server applications, it is a fundamentally new concept for parallel applications. However, existing elasticity mechanisms for client-server applications can be applied to parallel applications only to a limited extent. Efficient exploitation of elasticity for parallel applications requires novel mechanisms that take into account the particular runtime characteristics and resource requirements of this application type. To tackle this issue, we propose an elasticity description language. This language facilitates users to define elasticity policies, which specify the elasticity behavior at both cloud infrastructure level and application level. Elasticity at the application level is supported by an adequate programming and execution model, as well as abstractions that comply with the dynamic availability of resources. We present the underlying concepts and mechanisms, as well as the architecture and a prototypical implementation. Furthermore, we illustrate the capabilities of our approach through real-world scenarios.
The demonstration project Virtual Power Plant Neckar-Alb is constructing a Virtual Power Plant (VPP) demonstration site at the Reutlingen University campus. The VPP demonstrator integrates a heterogeneous set of distributed energy resources (DERs) which are connected to control the infrastructure and an energy management system. This paper describes the components and the architecture of the demonstrator and presents strategies for demonstration of multiple optimization and control systems with different control paradigms.
This paper introduces a highly scalable heteromodular origami art technique for constructing 3D framework structures using elementary struts and connectors folded from uncut sheets of standard A4 office paper. The presented technique, named ZEBRA, allows the design of meter-scale architectural objects, such as truss bridges and towers, which are capable of bearing substantial mechanical loads. Moving parts, ranging from simple levers to complete multi-bar linkages, can be integrated into static frameworks using a set of kinematic extensions. An overview is given of how the ZEBRA system can be used to teach university students various theoretical and practical aspects of the engineering sciences in an entertaining and hands-on way.
Processing of recycled carbon fibers by wet-laid technology has many advantages. The fibers are implemented in water - no dusk formation. Further benefits are the light density of the fabric and the implementation of other layers (multilayer). First results of recyled carbon fibers from composite parts are showing good performances.
Companies are continuously changing their strategy, processes, and information systems to benefit from the digital transformation. Controlling the digital architecture and governance is the fundamental goal. Enterprise Governance, Risk and Compliance (GRC) systems are vital for managing digital risks threatening in modern enterprises from many different angles. The most significant constituent to GRC systems is the definition of controls that is implemented on different layers of a digital Enterprise Architecture (EA). As part of the compliant aspect of GRC, the effectiveness of these controls is assessed and reported to relevant management bodies within the enterprise. In this paper, we present a metamodel which links controls to the affected elements of a digital EA and supplies a way of expressing associated assessment techniques and results. We complement a metamodel with an expository instantiation of a control compliance cockpit in an international insurance enterprise.
In this paper we claim that a competitive analysis with new players entering a market requires a specific and systems-based analysis. System dynamics provides such an approach. We infer from our study that established premium automobile manufacturers could have identified a possible threat by a newcomer like Tesla earlier with using system dynamics. In particular, we postulate that a feedback view supports decision makers to better understand the significance of competitive information and perceive information faster and more reliably.
One of the challenges in condition monitoring systems is the residual life time prediction. This prediction is done based on statistical methods, based on physical knowledge about the considered process or a combination of these approaches. Physical knowledge of the system is a result of long-term experience of process operators. However, it can be gained as well by analyzing appropriately designed process models. The additional benefit of such models is that particular effects and their impact on the process behavior can be analyzed in detail and without plant operation in a shorter time. The current contribution developed in the framework of the research project Model Based Hierarchic Condition Monitoring presents such models for condition monitoring of roller chains. First, already existing high order dynamic models given by nonlinear differential equations of such chains are extended to incorporate effects that occur due to a deterioration of the chain condition. Then, a simple model is developed and compared to the high order model. Based on the two models the change in the process behavior due to a deterioration of the roller chain condition is analyzed to illustrate that these models can be used in future research in the above mentioned research project to better predict the residual life time of the considered roller chains.
Despite strong political efforts across Europe, small and medium- sized enterprises (SMEs) seem to neglect adopting effective measures for energy efficiency. Adopting a cultural perspective and based on a study among industrial SMEs in Southern Germany, we investigate what drives decisions for energy efficiency in SMEs and how energy management contributes to closing the energy efficiency gap. The study follows a mixed-methods approach and combines eleven ethnographic case studies and a quantitative survey among 500 manufacturing SMEs in Southern Germany.
The main contribution of the paper is to offer a perspective on energy efficiency in SMEs beyond the diffusion of energyefficient technology. By contrast, our results strongly suggest that the diffusion of energy efficiency in industrial companies should not be solely reduced to decisions for technical measures. We shed light on how energy efficiency is established and the importance of energy management in SMEs.
Our study shows that energy efficiency is well established in the investigated SMEs. At the same time, establishment cannot be explained by company size or energy demand. By contrast, the contextual environment of the company and the individual leadership of the company appear to have a more substantial influence. The embedding of energy efficiency in corporate strategy, a broad spectrum of different practices, the involvement of the employees, actions for raising awareness in everyday work life, and distributing attention by organizational measures constitute the driving forces in establishing energy efficiency, and these drivers can be subsumed under the label of energy management.
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 hearing contact lens® (HCL) is a new type of hearing aid devices. One of its main components is a piezo-electric actuator. In order to evaluate and maximize the HCL’s performance, a model of the HCL coupled to the middle ear was developed using finite element approach. The model was validated step by step starting with the HCL only. To validate the HCL model, vibrational measurements on the HCL were performed using a Laser-Doppler-Vibrometer (LDV). Then, a silicone cap was placed onto the HCL to provide an interface between the HCL and the tympanic membrane of the middle-ear model and additional LDV measurements on temporal bones were performed to validate the coupled model. The coupled model was used to evaluate the equivalent sound pressure of the HCL. Moreover, a deeper insight was gained into the contact between the HCL and tympanic membrane and its effects on the HCL performance. The model can be used to investigate the sensitivity of geometrical and material parameters with respect to performance measures of the HCL and evaluate the feedback behavior.
The limited interfaces of today's IC design environments for editing PCell parameters hinder a solid advancement towards more complex analog PCell modules. This paper presents Hierarchical Instance Parameter Editing (HIPE), a highly flexible concept for the customization of PCell sub-instances. Introducing a new type of parameter, HIPE facilitates the dynamic creation of multi-level editing forms reflecting the actual contents of a PCell instance. This approach greatly improves a PCell's ease-of-use, substantially simplifies PCell development, and allows for a hierarchical execution of parameter validation callbacks. Our HIPE implementation has been integrated into a professional PCell development tool and represents a key enabling technology for upcoming generations of high-level hierarchical PCells.
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 Society 5.0 can contribute to a supersmart society, especially for manufacturing and healthcare, and Industry 4.0 becomes important in the global manufacturing industry. Smart energy digital platforms are architected to manage energy supply efficiently. Furthermore, the above digital platforms are expected to collect various kinds of data and analyze Big Data for the trends in the sharing economy in ecosystems. The adaptive integrated digital architecture framework (AIDAF) for Design Thinking Approach with Risk Management is expected to make an alignment with digital IT strategy. In this paper, we propose that various energy management systems and related digital platforms are designed and implemented in an alignment to digital IT strategy for sharing economy toward Society 5.0, with the AIDAF framework for Design Thinking Approach with Risk Management. The vision of AIDAF applications to enable sharing economy and digital platforms is explained and extended in the context of Society 5.0. In addition, challenges and future activities for this area are discussed that cover the directions of smart energy for Society 5.0.
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.
What might the attendee be able to do after being in your session?
Our work shows how to connect intra-operative devices via IEEE 11073 Service-oriented Device Connectivity (SDC).
Description of the Problem or Gap
Standardized device communication is essential for interoperability, availability of device data, and therefore for the intelligent operating room (OR) and arising solutions. The SDC standard was developed to make information from medical devices available in a uniform manner and enable interoperability. Existing devices are rarely SDC-capable and need interfaces to be interoperable via SDC.
Methods: What did you do to address the problem or gap?
We conceived an SDC-based architecture consisting of a service provider and service consumer. In our concept, the service provider is connected to the medical device and capable to translate the proprietary protocol of the device into SDC and vice versa. The service consumer is used to request or send information via the SDC protocol to the service provider and can function as a uniform bidirectional interface (e.g. for displaying or controlling). This concept was exemplarily demonstrated with the patient monitor MX800 of Philips to retrieve the device data (e.g. vital parameters) via SDC and partly for the operating light marLED X of KLS Martin Group.
Results: What was the outcome(s) of what you did to address the problem or gap?
The patient monitor MX800 was connected to a Raspberry Pi (RPi) via LAN, on which the service provider is running. The python script on the RPi establishes a connection to the monitor and translates incoming and outgoing messages from the proprietary protocol to SDC and vice versa to/from the service consumer. The service consumer is running on a laptop and acts as a simulation for different kinds of systems that want to get vital parameters or other information from the patient monitor. The operating light marLED X was connected to an RPi via USB-to-RS232. A python script on the RPi establishes a connection to the light and makes it possible via proprietary commands to get information of the light (e.g. status) and to control it (e.g. toggle the light, increment the intensity). A translation to SDC is not integrated yet.
Discussion of Results
Our practical implementation shows that medical devices can be accessed via external connections to get device data and control the device via commands. The example SDC implementation of the patient monitor MX800 makes it possible to request its data via the standardized communication protocol SDC. This is also possible for the operating light marLED X if its proprietary protocol is analyzed to be translatable to/from SDC. This would allow to control the device from an external system, or automatically depending on the status of the ongoing procedure. The advantage is, that existing intra-operative devices can be extended by a service provider which is capable of translating the proprietary protocol of the device in SDC and vice versa. This enables interoperability and an intelligent OR that, for example, is aware of all devices, their status, and data and can use this information to optimally support the surgeons and their team (e.g. provision of information, automated documentation). This interoperability allows that future innovations merely need to understand the SDC protocol instead of all vendor-dependent communication protocols.
Conclusion
Standardized device communication is essential to reach interoperability, and therefore intelligent ORs. Our contribution addresses the possibility of subsequently making medical devices SDC-capable. This may eliminate the need of understanding all the different proprietary protocols when developing new innovative solutions for the OR.
Scheduled flexibility and individualization of knowledge transfer in foundations of computer science
(2017)
The opening of the German higher education system for new target groups involves a heterogeneous composition of students as never before and face up the universities to new challenges. Due to different educational biographies, the students don't show a homogeneous level of knowledge. Furthermore, their access to course content and their individual learning methods are very diverse. The existing lack of knowledge and the very unequal study speed have a significant influence on the learning behavior and learning motivation. During the first semesters, the dropout rate is appreciably higher. The reform project gives an overview of a didactic restructuring from a formerly conventional teaching and learning concept to a stronger combination of digital offers, combined with classical lectures in the basic modules of computer science. The teaching content is adjusted to the individual requirements and knowledge. Students with different previous knowledge get the possibility to increase their knowledge in different levels of abstraction. The aim of the reform project has to point out the possibilities, also the challenges of the digital process in higher education. At the same time the question has to be explored, how far does an accompanied and self-directed learning in own speed and in own individual depth of knowledge have a positive impact on the motivation and on the study success of a learner.
By 2019, Germany-based Kärcher, “the world’s leading provider of cleaning technology,” had turned its professional cleaning devices into IoT products. The data generated by these IoT-connected cleaning devices formed a key ingredient in the company’s ongoing strategic shift in its B2B business: Kärcher was transforming from a seller of cleaning devices to a provider of consulting services in order to help professional cleaning companies improve their cleaning processes. Based on interviews with seven IT- and non-IT executives, the case illustrates how the company learned to generate value from IoT products. And it demonstrates how a family-owned company transformed its organization in order to be able to more effectively develop and provide IoT products, while adding roles, developing technology platforms, and changing organizational structures and ways of working.
In 2017, Philips' goal was to use innovation to improve the lives of three billion people a year by 2025. To achieve that, the company was shifting from selling medical products in a transactional manner to providing integrated healthcare solutions based on digital health technology. Based on our interviews with 23 executives at Philips, the case examines the two directions of the transformation required by this shift: externally, Philips worked on transforming how healthcare was conducted. Healthcare professionals would have to change the way they worked and reimbursement schemes needed to change to incentivize payers, providers, and patients in vastly different ways. Internally, Philips needed to redesign how its employees worked. The company componentized its business, introduced digital platforms, and co-created integrated solutions with the various stakeholders of the healthcare industry. In other words: Philips was transforming itself in order the reinvent healthcare in the digital age.