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Formula One races provide a wealth of data worth investigating. Although the time-varying data has a clear structure, it is pretty challenging to analyze it for further properties. Here the focus is on a visual classification for events, drivers, as well as time periods. As a first step, the Formula One data is visually encoded based on a line plot visual metaphor reflecting the dynamic lap times, and finally, a classification of the races based on the visual outcomes gained from these line plots is presented. The visualization tool is web-based and provides several interactively linked views on the data; however, it starts with a calendar-based overview representation. To illustrate the usefulness of the approach, the provided Formula One data from several years is visually explored while the races took place in different locations. The chapter discusses algorithmic, visual, and perceptual limitations that might occur during the visual classification of time-series data such as Formula One races.
Additive manufacturing (AM) is a promising manufacturing method for many industrial sectors. For this application, industrial requirements such as high production volumes and coordinated implementation must be taken into account. These tasks of the internal handling of production facilities are carried out by the Production Planning and Control (PPC) information system. A key factor in the planning and scheduling is the exact calculation of manufacturing times. For this purpose we investigate the use of Machine Learning (ML) for the prediction of manufacturing times of AM facilities.
Die Erfindung betrifft ein Verfahren zur extrinsischen Kalibrierung wenigstens eines bildgebenden Sensors, wonach eine Pose des wenigstens einen bildgebenden Sensors relativ zu dem Ursprung (U) eines dreidimensionalen Koordinatensystems einer Handhabungseinrichtung mittels einer Recheneinrichtung bestimmt wird, wobei bekannte dreidimensionale Koordinaten betreffend die Position wenigstens eines Gelenks der Handhabungseinrichtung durch die Recheneinrichtung berücksichtigt werden, und wobei zweidimensionale Koordinaten betreffend die Position des wenigstens einen Gelenks anhand von Rohdaten des wenigstens einen bildgebenden Sensors ermittelt werden, und wobei die Recheneinrichtung die Pose des wenigstens einen bildgebenden Sensors anhand der Korrespondenz zwischen den zweidimensionalen Koordinaten und den dreidimensionalen Koordinaten bestimmt.
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/.
Internet of Things (IoT) provides a strong platform for computer users to connect objects, devices, and people to the Internet for exchanging or sharing of information with each other. IoT is growing rapidly and is expected to adapt to disciplines such as manufacturing, agriculture, healthcare, and robotics. Furthermore, the new concept of IoT is proposed and shown, especially for robotics areas as Internet of Robotics Things (IoRT). IoRT is a mixed structure of diverse technologies such as cloud computing, artificial intelligence, and machine learning. However, to promote and realize IoRT, digitization and digital transformation should be proceeded and implemented in the robotics enterprise. In this paper, we propose and architecture framework for IoRT-based digital platforms an verify it using a planned case in a global robotics enterprise. The associated challenges and future research directions in this field are also presented.
Zero or plus energy office buildings must have very high building standards and require highly efficient energy supply systems due to space limitations for renewable installations. Conventional solar cooling systems use photovoltaic electricity or thermal energy to run either a compression cooling machine or an absorption-cooling machine in order to produce cooling energy during daytime, while they use electricity from the grid for the nightly cooling energy demand. With a hybrid photovoltaic-thermal collector, electricity as well as thermal energy can be produced at the same time. These collectors can produce also cooling energy at nighttime by longwave radiation exchange with the night sky and convection losses to the ambient air. Such a renewable trigeneration system offers new fields of applications. However, the technical, ecological and economical aspects of such systems are still largely unexplored.
In this work, the potential of a PVT system to heat and cool office buildings in three different climate zones is investigated. In the investigated system, PVT collectors act as a heat source and heat sink for a reversible heat pump. Due to the reduced electricity consumption (from the grid) for heat rejection, the overall efficiency and economics improve compared to a conventional solar cooling system using a reversible air-to-water heat pump as heat and cold source.
A parametric simulation study was carried out to evaluate the system design with different PVT surface areas and storage tank volumes to optimize the system for three different climate zones and for two different building standards. It is shown such systems are technically feasible today. With a maximum utilization of PV electricity for heating, ventilation, air conditioning and other electricity demand such as lighting and plug loads, high solar fractions and primary energy savings can be achieved.
Annual costs for such a system are comparable to conventional solar thermal and solar electrical cooling systems. Nevertheless, the economic feasibility strongly depends on country specific energy prices and energy policy. However, even in countries without compensation schemes for energy produced by renewables, this system can still be economically viable today. It could be shown, that a specific system dimensioning can be found at each of the investigated locations worldwide for a valuable economic and ecological operation of an office building with PVT technologies in different system designs.
Vergleichende Analyse des YouTube- Auftritts von privat- und öffentlich-rechtlichen Sendegruppen
(2020)
Lange wurde das Internet als Antagonismus zum Fernsehen gesehen. Es wurde dementsprechend zur Zuschauerrück- bzw. -gewinnung genutzt, was sich allerdings als ineffizient erwies. Inzwischen haben die einzelnen Sendegruppen das Internet jedoch als mediale Erweiterung erkannt und genutzt. Durch diese späte Akzeptanz zeigen sich starke Unterschiede im Umfang und der Vorgehensweise hinsichtlich der Nutzung des Internets als zusätzliches Medium. Am besten lässt sich dies in einem Vergleich in Bezug auf die wichtigste videotechnische Social Media Plattform YouTube darstellen.
In diesem Vergleich sollen die einzelnen Sendegruppen hinsichtlich ihrer wahrgenommenen Vorteile, Nachteile und Attraktivität bezogen auf das Nutzerverhalten und die Nutzermeinung bewertet werden. Die zielgruppenorientierte Optimierung des YouTube-Auftrittes ist von außerordentlich hoher Bedeutung für die zukünftige Marktdurchdringung.
Going forward with the requirements of missions to the Moon and further into deep space, the European Space Agency is investigating new methods of astronaut training that can help accelerate learning, increase availability and reduce complexity and cost in comparison to currently used methods. To achieve this, technologies such as virtual reality may be utilized. In this paper, an investigation into the benefits of using virtual reality as a means for extravehicular activity training in comparison to conventional training methods, such as neutral buoyancy pools is given. To help determine the requirements and current uses of virtual reality for extravehicular activity training first hand tests of currently available software as well as expert interviews are utilized. With this knowledge a concept is developed that may be used to further advance training methods in virtual reality. The resulting concept is used as a basis for development of a prototype to showcase user interactions and locomotion in microgravity simulations.
Ein nicht unerheblicher Anteil der Autounfälle ist auf Müdigkeit am Steuer zurückzuführen. Um Unfälle aufgrund von Müdigkeit zu vermeiden, existieren schon einige Ansätze wie beispielsweise die Erkennung der Fahrweise. Im Rahmen des IOT-Labors des Masterstudiengangs Human Centered Computing der Hochschule Reutlingen sollen verschiedene Fahrassistenzsysteme entwickelt und getestet werden, um Unfälle aufgrund von Müdigkeit zu verhindern. Diese Arbeit beschäftigt sich mit der Müdigkeitserkennung über Computer Vision (CV) und das Elektrokardiogramm (EKG). Im Rahmen dieses Papers wird die Müdigkeitserkennung über CV am Steuer mittels den Open Source Bibliotheken OpenCV und Dlib und dem Embedded PC Nvidia Jetson Nano verwirklicht. Die Müdigkeit über EKG wird über den Herzschlag und die Herzfrequenzvariabilität erkannt. Ebenfalls wurde in dieser Arbeit eine Schnittstelle aus CV und EKG entwickelt, um aus den Python-Skripten der Müdigkeitserkennung über Computer Vision und der Müdigkeitserkennung über EKG die zur Erkennung wichtigen Daten zusammenzufassen. Diese werden anschließend zu einem gesamten Ergebnis ausgewertet.
In dieser Arbeit werden drei verschiedene Testumgebungen vorgestellt, welche in ein iteratives Vorgehen einfließen, um die Entwicklung von Augmented-Reality-Anwendungen zur Darstellung von autonomen Fahrfunktionen zu unterstützen.
Gestaltungsentwürfe und Softwareentwicklungen können in den Testumgebungen für unterschiedliche Zielsetzungen von Personenbefragungen vorgestellt und bewertet werden. Das entwicklungsbegleitende Testen ermöglicht eine frühzeitige Identifizierung von Änderungshinweisen, welche für einen gültigen Lösungsentwurf eingearbeitet werden können. Die entwickelten Testumgebungen sind ein verkleinertes Modell, ein Fahrsimulator und ein reales Fahrzeug. Eigenschaften, Funktionen und Aufbauten resultieren aus Erkenntnissen der Literatur und Erfahrungen aus ersten Entwicklungen. Diese und die Einsatzmöglichkeiten werden mit dieser Arbeit aufgezeigt.
In dieser Ausarbeitung wird auf Visualisierungsmöglichkeiten von neuronalen Netzen eingegangen. Ein neuronales Netz scheint zuerst nicht von außen einsehbar und ist somit für viele eine Blackbox. Häufig genutzte Python-Bibliotheken, zum Beispiel TensorFlow, werden vorgestellt und deren Stärken wie auch Schwächen präsentiert. Anhand dieser werden bereits bestehende Visualisierungen gezeigt und ihr derzeitiger Einsatz wird erläutert.
Durch einen Vergleich soll ersichtlich werden, welche Bibliothek am meisten Daten während des Trainings liefert, damit diese Informationen weiter verarbeitet werden. Diese Daten sollen so visualisiert werden, dass sie bei der Entwicklung eines neuronalen Netzes unterstützend sind. Ziel ist es, auf die Möglichkeiten einzugehen, welche geboten werden können. Durch eine Vereinfachung des Debuggings neuronaler Netze sollen weiterführende Entwicklungen in diese Richtung unterstützt werden.
Detecting semantic similarities between sentences is still a challenge today due to the ambiguity of natural languages. In this work, we propose a simple approach to identifying semantically similar questions by combining the strengths of word embeddings and Convolutional Neural Networks (CNNs). In addition, we demonstrate how the cosine similarity metric can be used to effectively compare feature vectors. Our network is trained on the Quora dataset, which contains over 400k question pairs. We experiment with different embedding approaches such as Word2Vec, Fasttext, and Doc2Vec and investigate the effects these approaches have on model performance. Our model achieves competitive results on the Quora dataset and complements the well-established evidence that CNNs can be utilized for paraphrase recognition tasks.
Die Entwicklung eines Medizinproduktes benötigt in der Regel mehrere Jahre. Gesetzliche Vorgaben, wie zum Beispiel das Medizinprodukte Durchführungsgesetz, bestimmen, welche Schritte während der Entwicklung durchgeführt werden müssen. Deren Einhaltung muss in der technischen Dokumentation nachgewiesen werden. Die darin enthaltenen technischen Dokumente entstehen im Verlauf der Entwicklung. Diese bauen aufeinander auf und verweisen sich gegenseitig. Dadurch entstehen heterogene und unübersichtliche Strukturen. Eine Lösung für dieses Problem bietet Traceability. Traceability sorgt dafür, dass die Anforderungen an das Medizinprodukt mit Dokumenten, wie dem Anforderungskatalog, Lastenheft oder der Spezifikation verknüpft werden können. Somit ist jederzeit nachvollziehbar, welche Anforderungen mit welchem Test, welchen Änderungen oder welchen Ergebnissen zusammenhängen. Ein wichtiger Prozess bei der Entwicklung von Medizinprodukten ist zudem das Usability Engineering, wodurch die Sicherheit eines Medizinprodukts sichergestellt und Risiken bei der Anwendung minimiert werden sollen. In diesem Prozess entstehen viele Artefakte, wie zum Beispiel Usability-Berichte. Um den Überblick über alle Usability-Daten behalten zu können, können diese mithilfe von Traceability verknüpft werden. In diesem Artikel wird herausgestellt, welche Voraussetzungen für das Usability Engineering in der Medizintechnik an Traceability gestellt
werden.
Requirements Engineering (RE) umfasst sämtliche systematische Schritte zur Entwicklung eines Systems, um die Bedürfnisse der Nutzer und Vorgaben, die an dieses gestellt werden, zu erfüllen. Das RE eines ausgewählten Herstellers für klinische Informationssysteme (KIS) wurde untersucht und es stellt sich als intransparent als auch teilweise unzureichend dar. Das Ausmaß des Einsatzes von systematischen Vorgehensweisen und Methoden zum RE wurden beim ausgewählten KIS-Hersteller analysiert. Die Analyse zeigt, dass RE weit verbreitet ist, aber differenziert betrieben wird.
Das Ziel dieser Arbeit ist es, den Stand der Technik des RE für die KIS Entwicklung zu ermitteln. Es werden wichtige Faktoren des RE für die Entwicklung von KIS beschrieben. Die Ergebnisse dieser Arbeit werden als erster Schritt für die Optimierung des RE des ausgewählten KIS-Herstellers dienen.
Medizinprodukte sind Gegenstände, Stoffe oder Software mit medizinischer Zweckbestimmung für die Anwendung am Menschen. Diese werden von Medizinprodukteherstellern entwickelt und auf den Markt eingeführt. Da die falsche Anwendung von Medizinprodukten bei Menschen zu Verletzbarkeit des menschlichen Körpers führen kann, ist eine angemessene Qualität der Medizinprodukte zu gewährtleisten. Um die Sicherstellung der Qualität einzuhalten, sind Medizinproduktehersteller verpflichtet, sich an die Medizinprodukteverordnung (MDR) zu halten. Für risikoreiche Produkte ist ergänzend die Nutzung eines Qualitätsmanagementsystems (QMS) verpflichtend. Dieses steuert die Struktur, Verantwortlichkeiten, Verfahren und Prozesse des Unternehmens, die für die Medizinprodukteentwicklung notwendig sind. In Zeiten der Digitalisierung werden Softwarelösungen eingesetzt, um die zeitaufwendigen Dokumentations- und Administrationstätigkeiten im QMS zu reduzieren und die Prozesse zu optimieren. Mit der Einführung einer Software wird ein QMS in der Praxis auch als elektronisches QMS (eQMS) bezeichnet. Weiterhin muss das gesamte QMS mit den Regularien konform sein. Deshalb ist das Ziel dieser Arbeit, mithilfe der regulatorischen Anforderungen herauszuarbeiten, welche Vorgaben bei der Einführung eines eQMS zu beachten sind und wie diese erfüllt werden können. Diese Arbeit bezieht sich auf die regulatorsichen Vorgaben aus der MDR und der ISO 13485. Die Norm beinhaltet Anforderungen an ein QMS von Medizinprodukten.
In Zusammenarbeit mit dem Medizinproduktehersteller ulrich medical wird eine User Experience und Usability Studie an der Software der im Moment eingesetzten Kontrastmittelinjektoren durchgeführt. Das Unternehmen möchte eine neue Variante eines Kontrastmittelinjektors entwickeln, der als Basis eine verbesserte Version dieser Softwares enthält. Benutzerstudien können mit den unterschiedlichsten Methoden durchgeführt werden. Das geeignete Vorgehen muss definiert und die Testpersonen in Bezug zur eingesetzten Methode ermittelt werden. Bei Medizinprodukten muss zusätzlich auf strikte Auflagen in Normen und Gesetzen geachtet werden. Die Grundlage zur Methodenauswahl bildet eine Recherche zu Usability und User Experience Vorgaben für Medizinprodukte. Die Studie wird anhand quantitativer Daten eines Usability Tests im Labor, Fragebögen zur User Experience und qualitativen Post Test- Interviews evaluiert. In erster Linie dient diese Studie der Ermittlung von möglichen Verbesserungen, welche in der darauf folgenden Masterthesis vertieft und umgesetzt werden.
The field of breath analysis has developed to be of growing interest in medical diagnosis and patient monitoring. The main advantages are that it’s noninvasive, painless and repeatable in flexible cycles. Even though breath analysis is being researched for a couple of decades there are still many unanswered questions. Human breath contains volatile organic compounds which are emitted from inside the body. Some of these compounds can be assigned to specific sources, such as inflammation or cancer, but also to non health related origins. This paper gives an overview of breath analysis for the purpose of disease diagnosis and health monitoring. Therefore, literature regarding breath analysis in the medical field has been analyzed, from its early stages to the present. As a result, this paper gives an outline of the topic of breath analysis.
Haptisches Feedback ist nach zahlreichen Studien ein wichtiger Bestandteil in der medizinischen Robotik. Die meisten Systeme befinden sich jedoch noch im Forschungsstatus und verfolgen unterschiedliche Ansätze. In der Teleoperation wird mit sensorlosen und Sensor-Systemen geforscht. Sensoren bieten, im Gegensatz zu den Encodern in sensorlosen Systemen, genaue Messungen, sind allerdings teuer in der Anschaffung, schwer zu desinfizieren und müssen in OP-Besteck integriert werden. In Hands-On Systemen fühlt der Operateur im Gegensatz zu Teleoperationssystemen direkt die auftretenden Kräfte bei der Benutzung. Der Roboter bietet in diesen Systemen nur die benötigte Stabilität und Genauigkeit, gesteuert werden sie direkt durch den Menschen. Dagegen werden in Teleoperationssystemen gezielte Controller eingesetzt. Hier hat sich der für den OP entwickelte sigma.7 durchgesetzt. Gegenüber der für die Allgemeinheit entwickelten Konkurrenz bietet er haptisches Feedback in allen nötigen Freiheitsgraden und eine entsprechende Kraftrückkoppelung.
In der Kryochirurgie wird Kälte verwendet, um tumoröses Gewebe abzutöten. Dazu werden Kryosonden in den Tumor gestochen und stark abgekühlt. Hierbei gibt es verschiedene Herausforderungen, welchen computergestützt begegnet werden kann. Diese Arbeit gibt die Ergebnisse einer Literaturrecherche zu den Herausforderungen wieder. Die vorgestellten Arbeiten beschäftigten sich mit der Simulation des im Tumor entstehenden Eisballs, dem korrekten Positionieren der Kryosonden im Tumor, dem Überwachen des Eingriffs sowie dem Entwickeln von Simulationen für Trainingszwecke. Dabei zeigt sich, dass der Einsatz von computergestützten Lösungen die Kryochirurgie für Operateur und Patient verbessern kann.
Anhaltend erlebt die Künstliche Intelligenz (KI) eine Renaissance in vielen Branchen. Der Trend, komplexe Zusammenhänge in Daten zu erfassen und zu nutzen, hält an. Hierbei ist jedoch der Grundgedanke des Maschinellen Lernens basierend auf empirischen Daten nicht neu. Es bleibt nach wie vor die Herausforderung, erst ein oft auch interdisziplinäres Verständnis von komplexen Zusammenhängen für verschiedenste Anwendungs-Domänen zu gewinnen, um zum Beispiel KI sinnvoll zum Einsatz zu bringen. Als Besucher der Konferenz erwarten Sie Beiträge aus den unterschiedlichsten Bereichen. Hierzu gehören zum Beispiel Müdigkeitserkennungssysteme im Automobil, ein Tastsinn auch für Roboter, aber auch neue Ansätze zur Erzeugung und Nutzung von Virtuellen Realitäten für die Erprobung des autonomen Fahrens bis hin zur Simulation von Außenboardeinsätzen in der Raumfahrt.
On the design of an urban data and modeling platform and its application to urban district analyses
(2020)
An integrated urban platform is the essential software infrastructure for smart, sustainable and resilitent city planning, operation and maintenance. Today such platforms are mostly designed to handle and analyze large and heterogeneous urban data sets from very different domains. Modeling and optimization functionalities are usually not part of the software concepts. However, such functionalities are considered crucial by the authors to develop transformation scenarios and to optimized smart city operation. An urban platform needs to handle multiple scales in the time and spatial domain, ranging from long term population and land use change to hourly or sub-hourly matching of renewable energy supply and urban energy demand.
In Folge der gegenwärtigen Digitalisierung in der produzierenden Industrie werden Anwendungen oder Services mit potentiell positiven Auswirkungen auf Faktoren wie Effektivität und Arbeitsqualität entwickelt. Ein geeigneter Ansatz zur Stärkung motivierender Aspekte im Arbeitskontext kann Gamification darstellen. In dieser Arbeit ist die initiale Konzeption und Evaluation eines Gamification-Ansatzes für Anwender eines KI-Service zur Maschinenoptimierung dargestellt und möglichen Anforderungen an ein Konzept zur Motivationssteigerung extrahiert.
In dieser Ausarbeitung wird eine zeitliche Vorhersage von Erdbeben getroffen. Hierfür werden mit einem Datensatz aus Labor-Erdbeben Convolutional Neural Networks (CNN) trainiert. Die trainierten Netzwerke geben Vorhersagen, indem sie einen Input an seismischen Daten klassifizieren. Durch das Klassifizieren kann das CNN die zeitliche Entfernung zum nächsten Erdbeben vorhersagen. Es werden hierfür zwei Ansätze miteinander verglichen. Beim ersten Ansatz werden die Originaldaten in ein CNN gegeben. Beim zweiten Ansatz wird vor dem CNN eine Vorverarbeitung der Daten mit den Mel Frequency Cepstral Coefficients (MFCC) durchgeführt. Es zeigt sich, dass mit beiden Ansätzen eine gute Klassifikation möglich ist. Die Kombination aus MFCC und CNN liefert die besseren quantitativen Ergebnisse. Hierbei konnte eine Genauigkeit von 65 % erreicht werden.
Semi-automated image data labelling using AprilTags as a pre-processing step for machine learning
(2019)
Data labelling is a pre-processing step to prepare data for machine learning. There are many ways to collect and prepare this data, but these are usually associated with a greater effort. This paper presents an approach to semi-automated image data labelling using AprilTags. The AprilTags attached to the object, which contain a unique ID, make it possible to link the object surfaces to a particular class. This approach will be implemented and used to label data of a stackable box.
The data is evaluated by training a You Only Look Once (YOLO) net, with a subsequent evaluation of the detection results. These results show that the semi-automatically collected and labelled data can certainly be used for machine learning. However, if concise features of an object surface are covered by the AprilTag, there is a risk that the concerned class will not be recognized. It can be assumed that the labelled data can not only be used for YOLO, but also for other machine learning approaches.
Bereits zum elften Mal findet nun die Studierendenkonferenz Informatics Inside statt. Als Teil des Masterstudiengangs Human-Centered Computing organisieren Masterstudierende selbständig eine vollumfängliche wissenschaftliche Konferenz.
Die Informatik ist nach wie vor ständigem Wandel unterworfen. Unsere Studierenden tragen diesem Wandel bei, indem sie in ihrer wissenschaftllichen Vertiefung aktuelle Problemstellungen durch innovative Konzepte lösen. Inzwischen ist die Informatik aber auch nicht immer sofort sichtbar. Das merken wir immer dann, wenn etwas nicht wie vorgesehen funktioniert. Das diesjährige Motto der Informatics Inside ist experience (IT), verdeckt als Funktionsaufruf:).
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.
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.
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.
his book highlights new trends and challenges in intelligent systems, which play an important part in the digital transformation of many areas of science and practice. It includes papers offering a deeper understanding of the human-centred perspective on artificial intelligence, of intelligent value co-creation, ethics, value-oriented digital models, transparency, and intelligent digital architectures and engineering to support digital services and intelligent systems, the transformation of structures in digital businesses and intelligent systems based on human practices, as well as the study of interaction and the co-adaptation of humans and systems. All papers were originally presented at the International KES Conference on Human Centred Intelligent Systems 2020 (KES HCIS 2020), held on June 17–19, 2020, in Split, Croatia.
Serverless computing is an emerging cloud computing paradigm with the goal of freeing developers from resource management issues. As of today, serverless computing platforms are mainly used to process computations triggered by events or user requests that can be executed independently of each other. These workloads benefit from on-demand and elastic compute resources as well as per-function billing. However, it is still an open research question to which extent parallel applications, which comprise most often complex coordination and communication patterns, can benefit from serverless computing.
In this paper, we introduce serverless skeletons for parallel cloud programming to free developers from both parallelism and resource management issues. In particular, we investigate on the well known and widely used farm skeleton, which supports the implementation of a wide range of applications. To evaluate our concepts, we present a prototypical development and runtime framework and implement two applications based on our framework: Numerical integration and hyperparameter optimization - a commonly applied technique in machine learning. We report on performance measurements for both applications and discuss
the usefulness of our approach.
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.
Business process models provide a considerable number of benefits for enterprises and organizations, but the creation of such models is costly and time-consuming, which slows down the organizational adoption of business process modeling. Social paradigms pave new ways for business process modeling by integrating stakeholders and leveraging knowledge sources. However, empirical research about the impact of social paradigms on costs of business process modeling is sparse. A better understanding of their impact could help to reduce the cost of business process modeling and improve decision-making on BPM activities. The paper constributes to this field by reporting about an empirical investigation via survey research on the perceived influence of different cost factors among experts. Our results indicate that different cost components, as well as the use of social paradigms, influence cost.
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.
This book contains the proceedings of the KES International conferences on Innovation in Medicine and Healthcare (KES-InMed-19) and Intelligent Interactive Multimedia Systems and Services (KES-IIMSS-19), held on 17–19 June 2019 and co-located in St. Julians, on the island of Malta, as part of the KES Smart Digital Futures 2019 multi theme conference.
The major areas covered by KES-InMed-19 include: Digital IT Architecture in Healthcare; Advanced ICT for Medical and Healthcare; Biomedical Engineering, Trends, Research and Technologies and Healthcare Support System. The major areas covered by KES-IIMSS-19 were: Interactive Technologies; Artificial Intelligence and Data Analytics; Intelligent Services and Architectures and Applications.
This book is of use to researchers in these vibrant areas, managers, industrialists and anyone wishing to gain an overview of the latest research in these fields.
The rise of digital technologies has become an important driver for change in multiple industries. Therefore, firms need to develop digital capabilities to manage the transformation process successfully. Prior research assumes that the development of a specific set of digital capabilities leads to higher digital maturity. However, a measurement framework for digital maturity does not exist in scholarly work. Therefore, this paper develops a conceptualization and measuremnent model for digital maturity.
Autism spectrum disorders (ASD) affect a large number of children both in the Russian Federation and in Germany. Early diagnosis is key for these children, because the sooner parents notice such disorders in a child and the rehabilitation and treatment program starts, the higher the likelihood of his social adaptation. The difficulties in raising such a child lie in the complexity of his learning outside of children's groups and the complexity of his medical care. In this regard, the development of digital applications that facilitate medical care and education of such children at home is important and relevant. The purpose of the project is to improve the availability and quality of healthcare and social adaptation at home of children with ASD through the use of digital technologies.
Continuous refactoring is necessary to maintain source code quality and to cope with technical debt. Since manual refactoring is inefficient and error prone, various solutions for automated refactoring have been proposed in the past. However, empirical studies have shown that these solutions are not widely accepted by software developers and most refactorings are still performed manually. For example, developers reported that refactoring tools should support functionality for reviewing changes. They also criticized that introducing such tools would require substantial effort for configuration and integration into the current development environment.
In this paper, we present our work towards the Refactoring-Bot, an autonomous bot that integrates into the team like a human developer via the existing version control platform. The bot automatically performs refactorings to resolve code smells and presents the changes to a developer for asynchronous review via pull requests. This way, developers are not interrupted in their workflow and can review the changes at any time with familiar tools. Proposed refactorings can then be integrated into the code base via the push of a button. We elaborate on our vision, discuss design decisions, describe the current state of development, and give an outlook on planned development and research activities.
The Third International Conference on Data Analytics (DATA ANALYTICS 2014), held on August 24 - 28, 2014 - Rome, Italy, continued the inaugural event on fundamentals in supporting data analytics, special mechanisms and features of applying principles of data analytics, application oriented analytics, and target-area analytics.
Processing of terabytes to petabytes of data, or incorporating non-structural data and multistructured data sources and types require advanced analytics and data science mechanisms for both raw and partially-processed information. Despite considerable advancements on high performance, large storage, and high computation power, there are challenges in identifying, clustering, classifying, and interpreting of a large spectrum of information.
The Eleventh International Conference on Advances in Databases, Knowledge, and Data Applications (DBKDA 2019), held between June 02, 2019 to June 06, 2019 - Athens, Greece, continued a series of international events covering a large spectrum of topics related to advances in fundamentals on databases, evolution of relation between databases and other domains, data base technologies and content processing, as well as specifics in applications domains databases.
Advances in different technologies and domains related to databases triggered substantial improvements for content processing, information indexing, and data, process and knowledge mining. The push came from Web services, artificial intelligence, and agent technologies, as well as from the generalization of the XML adoption.
High-speed communications and computations, large storage capacities, and loadbalancing for distributed databases access allow new approaches for content processing with incomplete patterns, advanced ranking algorithms and advanced indexing methods.
Evolution on e-business, ehealth and telemedicine, bioinformatics, finance and marketing, geographical positioning systems put pressure on database communities to push the ‘de facto’ methods to support new requirements in terms of scalability, privacy, performance, indexing, and heterogeneity of both content and technology.
We welcomed academic, research and industry contributions. The conference had the followingtracks:
Knowledgeanddecisionbase
Databasestechnologies
Datamanagement
GraphSM: Large-scale Graph Analysis, Management and Applications
The Tenth International Conference on Advances in Databases, Knowledge, and Data Applications (DBKDA 2018), held between May 20 - 24, 2018 - Nice, France, continued a series of international events covering a large spectrum of topics related to advances in fundamentals on databases, evolution of relation between databases and other domains, data base technologies and content processing, as well as specifics in applications domains databases.
Advances in different technologies and domains related to databases triggered substantial improvements for content processing, information indexing, and data, process and knowledge mining. The push came from Web services, artificial intelligence, and agent technologies, as well as from the generalization of the XML adoption.
High-speed communications and computations, large storage capacities, and loadbalancing for distributed databases access allow new approaches for content processing with incomplete patterns, advanced ranking algorithms and advanced indexing methods.
Evolution on e-business, ehealth and telemedicine, bioinformatics, finance and marketing, geographical positioning systems put pressure on database communities to push the ‘de facto’ methods to support new requirements in terms of scalability, privacy, performance, indexing, and heterogeneity of both content and technology.
To remain competitive in a fast changing environment, many companies started to migrate their legacy applications towards a Microservices architecture. Such extensive migration processes require careful planning and consideration of implications and challenges likewise. In this regard, hands-on experiences from industry practice are still rare. To fill this gap in scientific literature, we contribute a qualitative study on intentions, strategies, and challenges in the context of migrations to Microservices. We investigated the migration process of 14 systems across different domains and sizes by conducting 16 in-depth interviews with software professionals from 10 companies. Along with a summary of the most important findings, we present a separate discussion of each case. As primary migration drivers, maintainability and scalability were identified. Due to the high complexity of their legacy systems, most companies preferred a rewrite using current technologies over splitting up existing code bases. This was often caused by the absence of a suitable decomposition approach. As such, finding the right service cut was a major technical challenge, next to building the necessary expertise with new technologies. Organizational challenges were especially related to large, traditional companies that simultaneously established agile processes. Initiating a mindset change and ensuring smooth collaboration between teams were crucial for them. Future research on the evolution of software systems can in particular profit from the individual cases presented.
While Microservices promise several beneficial characteristics for sustainable long-term software evolution, little empirical research covers what concrete activities industry applies for the evolvability assurance of Microservices and how technical debt is handled in such systems. Since insights into the current state of practice are very important for researchers, we performed a qualitative interview study to explore applied evolvability assurance processes, the usage of tools, metrics, and patterns, as well as participants’ reflections on the topic. In 17 semi-structured interviews, we discussed 14 different Microservice-based systems with software professionals from 10 companies and how the sustainable evolution of these systems was ensured. Interview transcripts were analyzed with a detailed coding system and the constant comparison method.
We found that especially systems for external customers relied on central governance for the assurance. Participants saw guidelines like architectural principles as important to ensure a base consistency for evolvability. Interviewees also valued manual activities like code review, even though automation and tool support was described as very important. Source code quality was the primary target for the usage of tools and metrics. Despite most reported issues being related to Architectural Technical Debt (ATD), our participants did not apply any architectural or service-oriented tools and metrics. While participants generally saw their Microservices as evolvable, service cutting and finding an appropriate service granularity with low coupling and high cohesion were reported as challenging. Future Microservices research in the areas of evolution and technical debt should take these findings and industry sentiments into account.
Small and Medium Enterprises (SMEs) which play substantial role in the development of any economy have been on the rise in the recent periods. Consequently, these enterprises are faced with a myriad of challenges which could potentially be solved through adoption of technology. Nonetheless, it has been observed that the new technological uptake among SMEs remains limited with the majority of them opting to maintain the status quo with regards to technology awareness and innovation strategies.
In a literature review, this paper explores three major dynamics curtailing adoption of new technologies by SMEs in the manufacturing: Knowledge absorptive capacity and management factors, organisational structures as well as technological awareness. Firstly, with regards to knowledge absorptive capacity and management factors, this study shows how these factors drive innovation potentials in SMEs.
Secondly, with regards to technological awareness factors, this study documents how perceived usefulness, costs, network and infrastructure, education and skills, training and attitude as well as knowledge influence adoption of new technologies among SMEs in the world. Lastly, the study concludes by analysing how organisational structures drive innovation potentials of SMEs in the wake of swift and profound technological changes in the market.
The relevance of technology knowledge in digital transformation especially in small and mediumsized enterprises (SMEs) that are still largely dependent on physical human capital has become increasingly obvious. This is due to the rapid revolution in business environment coupled with increased living examples of firms disrupted by advancement in technological knowledge. Consequently, we find it progressively vital for SMEs to spot and mitigate both threats and take advantage of opportunities arising from digital transformation dynamism.
Our study aims at exploring the relevance of technology knowledge in SMEs for digital transformation to uncover the opportunities, roadmaps, and models that SMEs can take advantage of in the digital transformation and gain a competitive edge.
We conclude that irrespective relevance of technology knowledge for digital transformation coupled with its low costs and accessibility, SMEs are yet to realize the full potential of technological knowledge. This is mainly due to technologies appearing, changing and also vanishing so rapidly in the digital age, that gaining proper understanding without dedicated resources is utterly difficult for SMEs - making them less competitive as incumbent large firms in the market.
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 a time of upheaval and digitalization, new business models for companies play an important role. Decentralized power generation and energy efficiency indicators to achieve climate goals and to reduce global warming are currently forcing energy companies to develop new business models. In recent years, many methods of business model development have been introduced to create new business ideas. But what are the obstacles in implementing these business models in the energy sector to develop new business opportunities? And what challenges do companies face in this respect? To answer this question, a systematic literature review was conducted in this paper. As a result, eight categories were identified which summarise the main barriers for the implementation of new business models in the energy domain.
We introduce IPA-IDX – an approach to handle index modifications modern storage technologies (NVM, Flash) as physical in-place appends, using simplified physiological log records. IPA-IDX provides similar performance and longevity advantages for indexes as basic IPA [5] does for tables. The selective application of IPA-IDX and basic IPA to certain regions and objects, lowers the GC overhead by over 60%, while keeping the total space overhead to 2%. The combined effect of IPA and IPA-IDX increases performance by 28%.
Multi-dimensional patient data, such as time varying volume data, data of different imaging modalities, surface segmentations etc. are of growing importance in the clinical routine. For many use cases, it is of major importance to replicate a certain visualization of a data set created on one machine on a different computer using different software tools. Up until now, there exists no standardized methodology for this consistent presentation. We propose an extension of the Digital Imaging und Communications in Medicine (DICOM) called “Multi dimensional Presentation State” and outline scope and first results of the standardization process.
Workflow driven support systems in the peri-operative area have the potential to optimize clinical processes and to allow new situation-adaptive support systems. We started to develop a workflow management system supporting all involved actors in the operating theatre with the goal to synchronize the tasks of the different stakeholders by giving relevant information to the right team members. Using the OMG standards BPMN, CMMN and DMN gives us the opportunity to bring established methods from other industries into the medical field. The system shows each addressed actor their information in the right place at the right time to make sure every member can execute their task in time to ensure a smooth workflow. The system has the overall view of all tasks. Accordingly, a workflow management system including the Camunda BPM workflow engine to run the models, and a middleware to connect different systems to the workflow engine and some graphical user interfaces to show necessary information or to interact with the system are used. The complete pipeline is implemented with a RESTful web service. The system is designed to include different systems like hospital information system (HIS) via the RESTful web service very easily and without loss of data. The first prototype is implemented and will be expanded.
The goal of the presented project is to develop the concept of home e-health centers for barrier-free and cross-border telemedicine. AAL technologies are already present on the market but there is still a gap to close until they can be used for ordinary patient needs. The general idea needs to be accompanied by new services, which should be brought together in order to provide a full coverage of service for the users. Sleep and stress were chosen as predominant influence in the population. The executed scientific study of available home devices analyzing sleep has provided the necessary to select appropriate devices. The first choice for the project implementation is the device EMFIT QS+. This equipment provides a part of a complete system that a home telemedical hospital can provide at a level of precision and communication with internal and/or external health services.
