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A sleep study is a test used to diagnose sleep disorders and is usually done in sleep laboratories. The golden standard for evaluation of sleep is overnight polysomnography (PSG). Unfortunately, in-lab sleep studies are expensive and complex procedures. Furthermore, with a minimum of 22 wire attachments to the patient for sleep recording, this medical procedure is invasive and unfamiliar for the subjects. To solve this problem, low-cost home diagnostic systems, based on noninvasive recording methods requires further researches.
For this intention it is important to find suitable bio vital parameters for classifying sleep phases WAKE, REM, light sleep and deep sleep without any physical impairment at the same time. We decided to analyse body movement (BM), respiration rate (RR) and heart rate variability (HRV) from existing sleep recordings to develop an algorithm which is able to classify the sleep phases automatically. The preliminary results of this project show that BM, RR and HRV are suitable to identify WAKE, REM and NREM stage.
We present a compact battery charger topology for weight and cost sensitive applications with an average output current of 9A targeted for 36V batteries commonly found in electric bicycles. Instead of using a conventional boost converter with large DC-link capacitors, we accomplish PFC-functionality by shaping the charging current into a sin²-shape. In addition, a novel control scheme without input-current sensing is introduced. A-priori knowledge is used to implement a feed-forward control in combination with a closed-loop output current control to maintain the target current. The use of a full-bridge/half bridge LLC converter enables operation in a wide input-voltage range.
A fully featured prototype has been built with a peak output power of 1050W. An average output power of 400W was measured, resulting in a power density of 1.8 kW/dm³. At 9A charging current, a power factor of 0.96 was measured and the efficiency exceeds 93% on average with passive rectification.
The impact of pulse charging has been evaluated on a 400Wh battery which was charged with the proposed converter as well as CC-CV-charging for reference. Both charging schemes show similar battery surface temperatures.
Bis zum Jahr 2050 soll in Baden Württemberg mit dem Ziel „50-80-90“ der Energiebedarf um 50% reduziert werden, die erneuerbaren Energien sollen zu 80% an der Energieversorgung beteiligt sein und die Emissionen von Treibhausgasen um 90% sinken.Entsprechende Ziele sind für andere Regionen und Länder in ähnlicher Weise festgelegt.
Damit diese Ziele erreicht werden, muss bei der Gebäudewärmeversorgung ein konsequenter Umbau stattfinden. Hier spielt die Sektorenkopplung mit Hilfe von Wärmepumpen (WP) eine entscheidende Rolle. Zur Abschätzung des Potenzials sowie des Aufwandes für einen großflächigen Einsatz von Wärmepumpen ist es unmöglich, eine spezifische und angepasste Dimensionierung der Wärmepumpensysteme für jedes einzelne Gebäude durchzuführen. Stattdessen müssen auf Seiten der Bebauung Referenzgebäude definiert und auf Seiten der Wärmepumpensysteme mittlere Leistungsdaten der am Markt befindlichen Modelle verwendet werden. Während die Festlegung von Referenzgebäuden verschiedentlich in der Literatur zu finden ist, widmet sich der erste Teil der Veröffentlichung der Vorstellung von Korrelationsfunktionen für die thermische und elektrische Leistung sowie die Leistungszahl (COP) von Wärmepumpen, die auf Basis von Herstellerdaten in Abhängigkeit der Quellen- und Vorlauftemperatur ermittelt wurden.
Konkret wurden als Ausgangsbasis für die Korrelationsfunktionen Datenblätter verschiedener Sole- und Luft-Wasser Wärmepumpen (SWP, LWP) zusammengestellt und ausgewertet. Die Grundlage hierfür war die Liste „Wärmepumpen mit Prüfnachweis eines unabhängigen Prüfinstituts“ des Bundesamts für Wirtschaft und Ausfuhrkontrolle (BAFA).
Durch das Verbot der ozonschädigenden Fluor-Chlorkohlenwasserstoffen als Kältemittel und der heute überwiegend eingesetzten Fluor-Kohlenwasserstoffe, welche sich negativ auf den Treibhauseffekt auswirken, gewinnt das umweltfreundlichere CO2 (Kohlendioxid) in der Verwendung als Kältemittel an Bedeutung. Ausgangspunkt dieser Arbeit sind ein Prototyp einer reversiblen CO2 Wärmepumpe und ein Simulationsmodell derselbigen. Ziel dieser Arbeit ist es das Simulationsmodell, anhand von realen Messergebnissen des Prototyps, zu verifizieren. Durch die Berechnung von Vergleichsparametern, das Festlegen von Randbedingungen und geeigneten Messpunkten am Prototyp wird die Simulation optimiert. Abschließend folgt die Bewertung der Ergebnisse im Hinblick auf die Funktionalität der Wärmepumpe und deren Abbild in der Simulation.
Simulation eines dezentralen Regelungssystems zur netzdienlichen Erzeugung von grünem Wasserstoff
(2023)
Wasserstoff wird einen bedeutenden Beitrag zum Wandel von Industrie und Gesellschaft in eine klimaneutrale Zukunft leisten. Der Aufbau und die ökologisch und ökonomisch sinnvolle Nutzung einer Wasserstoffinfrastruktur sind hierbei die zentralen Herausforderungen. Ein notwendiger Baustein ist die effiziente Bereitstellung von grünem Strom und dem daraus produzierten grünen Wasserstoff. Der vorliegende Beitrag stellt ein dezentrales Regel- und Kommunikationssystem vor, mit dem Angebot und Nachfrage von grünem Strom und Wasserstoff in einem System aus dezentralen Akteuren in Einklang gebracht werden. In einer hierzu entwickelten Simulationsumgebung wird die Funktion und der Nutzen dieses dezentralen Ansatzes verdeutlicht.
Recognizing human actions is a core challenge for autonomous systems as they directly share the same space with humans. Systems must be able to recognize and assess human actions in real-time. To train the corresponding data-driven algorithms, a significant amount of annotated training data is required. We demonstrate a pipeline to detect humans, estimate their pose, track them over time and recognize their actions in real-time with standard monocular camera sensors. For action recognition, we transform noisy human pose estimates in an image like format we call Encoded Human Pose Image (EHPI). This encoded information can further be classified using standard methods from the computer vision community. With this simple procedure, we achieve competitive state-of-the-art performance in pose based action detection and can ensure real-time performance. In addition, we show a use case in the context of autonomous driving to demonstrate how such a system can be trained to recognize human actions using simulation data.
The current paper discusses the optimal choice of a filter time constant for filtering the steady state flux reference in an energy efficient control strategy for changing load torques. It is shown that by appropriately choosing the filter time constant as a fraction of the rotor time constant the instantaneous power losses after a load torque step can be significantly reduced compared to the standard case. The analysis for the appropriate choice of the filter time constant is based on a numerical study for three different induction motors with different rated powers.
Das Ziel dieser Arbeit ist, die Infrastruktur einer modernen Fahrzeug-zu Fahrzeug-Kommunikation auf ihre Sicherheit zu prüfen. Dazu werden die Sicherheitsstandards für die Funkkommunikation genauer beschrieben und anschließend mit möglichen Angriffsmodellen geprüft. Mit dem erläuterten Wissen der VANET Architektur werden verschiedene Angriffe verständlicher. Dadurch werden die Schwachstellen offengelegt und Gegenmaßnahmen an passenden Punkten in der Architektur verdeutlicht.
Die Arbeit stellt die Vision des Internet of Things (IoT) vor und betrachtet sowohl Möglichkeiten der Nutzung als auch Gefahrenpotentiale für die Sicherheit der Nutzer. Insbesondere wird hierbei der Anwendungsfall Smart Home näher betrachtet und am Beispiel ZigBee gravierende Schwächen dieser Geräte aufgezeigt.
Asymmetric read/write storage technologies such as Flash are becoming
a dominant trend in modern database systems. They introduce
hardware characteristics and properties which are fundamentally
different from those of traditional storage technologies such
as HDDs.
Multi-Versioning Database Management Systems (MV-DBMSs)
and Log-based Storage Managers (LbSMs) are concepts that can
effectively address the properties of these storage technologies but
are designed for the characteristics of legacy hardware. A critical
component of MV-DBMSs is the invalidation model: commonly,
transactional timestamps are assigned to the old and the new version,
resulting in two independent (physical) update operations.
Those entail multiple random writes as well as in-place updates,
sub-optimal for new storage technologies both in terms of performance
and endurance. Traditional page-append LbSM approaches
alleviate random writes and immediate in-place updates, hence reducing
the negative impact of Flash read/write asymmetry. Nevertheless,
they entail significant mapping overhead, leading to write
amplification.
In this work we present an approach called Snapshot Isolation
Append Storage Chains (SIAS-Chains) that employs a combination
of multi-versioning, append storage management in tuple granularity
and novel singly-linked (chain-like) version organization.
SIAS-Chains features: simplified buffer management, multi-version
indexing and introduces read/write optimizations to data placement
on modern storage media. SIAS-Chains algorithmically avoids
small in-place updates, caused by in-place invalidation and converts
them into appends. Every modification operation is executed
as an append and recently inserted tuple versions are co-located.
A premise guaranteeing the successful interdisciplinary teamwork in product design is a mutual understanding of both professional and academic communities of the different design expertise and the role they play in the process. It appears that the open compound word industrial design is open to interpretation in European education. This ambiguity had a negative impact on the labour policies of some European countries, which have labelled some professions with incorrect names. Therefore, this terminological inconsistency urges for clarification within the design community. This work analyses the term industrial design, it presents historical developments in European industrial design education, in particular in Germany and in the Netherlands, and discusses how the education to the industrial design profession was positioned towards product development. This paper suggests that the causes for the observed lack of clarity about the meaning of the term industrial design are of an etymological and disciplinary kind. In order to act as a bridge between the professional and academic communities, universities should create the premises for interdisciplinary collaboration between designers and engineers through standardized communication, ultimately contributing for a sustainable future in both design and engineering education.
Diese Arbeit liefert einen Konzeptentwurf, der die Integration verschiedener Systeme mit prozessrelevanten klinischen Diensten gewährleistet. Chirurgische Abläufe werden in Form von Prozessen modelliert. Die Wahl der Notation und die Art der Modellierung dieser Prozesse spielt in der heutigen Forschung in diesem Gebiet eine zentrale Rolle. Sind diese Prozesse modelliert, besteht die Möglichkeit, diese in einer Workflow-Engine automatisiert auszuführen. Im Rahmen der Entwicklung eines Workflow-Managment-Systems stellt sich die Frage, wie die Anbindung dieser Workflow-Engine mit anderen Systemen erfolgen soll. In der Arbeit werden Schnittstellen abstrakt in der Web Services Description Language (WSDL) definiert. Darum werden automatisiert Artefakte erzeugt. Auf der Grundlage dieser Artefakte erfolgt die Integration der Systeme. Die Workflow-Engine kommunizieren über SOAP-Nachrichten (Simple Object Access Protocol) mit den entsprechenden Systemen. Dieser Ansatz wurde mithilfe eines Prototyps validiert und umgesetzt.
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.
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.
For collision and obstacle avoidance as well as trajectory planning, robots usually generate and use a simple 2D costmap without any semantic information about the detected obstacles. Thus a robot’s path planning will simply adhere to an arbitrarily large safety margin around obstacles. A more optimal approach is to adjust this safety margin according to the class of an obstacle. For class prediction, an image processing convolutional neural network can be trained. One of the problems in the development and training of any neural network is the creation of a training dataset. The first part of this work describes methods and free open source software, allowing a fast generation of annotated datasets. Our pipeline can be applied to various objects and environment settings and is extremely easy to use to anyone for synthesising training data from 3D source data. We create a fully synthetic industrial environment dataset with 10 k physically-based rendered images and annotations. Our da taset and sources are publicly available at https://github.com/LJMP/synthetic-industrial-dataset. Subsequently, we train a convolutional neural network with our dataset for costmap safety class prediction. We analyse different class combinations and show that learning the safety classes end-to-end directly with a small dataset, instead of using a class lookup table, improves the quantity and precision of the predictions.
OpenAPI, WADL, RAML, and API Blueprint are popular formats for documenting Web APIs. Although these formats are in general both human and machine-readable, only the part of the format describing the syntax of a Web API is machine-understandable. Descriptions, which explain the meaning and purpose of Web API elements, are embedded as natural language text snippets into documents and target human readers but not machines. To enable machines to read and process these state-of-practice Web API documentation, we propose a Transformer model that solves the generic task of identifying a Web API element within a syntax structure that matches a natural language query. For our first prototype, we focus on the Web API integration task of matching output with input parameters and fined-tuned a pre-trained CodeBERT model to the downstream task of question answering with samples from 2,321 OpenAPI documentation. We formulate the original question answering problem as a multiple choice task: given a semantic natural language description of an output parameter (question) and the syntax of the input schema (paragraph), the model chooses the input parameter (answer) in the schema that best matches the description. The paper describes the data preparation, tokenization, and fine-tuning process as well as discusses possible applications of our model as part of a recommender system. Furthermore, we evaluate the generalizability and the robustness of our fine-tuned model, with the result that it achieves an accuracy of 81.46% correctly chosen parameters.
The planning and control of intralogistics systems in line with versatile production systems of smart factories requires new approaches and methods to cope with changing requirements within future factories. The planning of intralogistics can no longer follow a static, sequential approach as in the past since the planning assumptions are going to change in a high frequency. Reasons for these constant changes are amongst others external turbulences like rapidly changing market conditions, decreasing batch sizes down to customer-specific products with a batch size of one and on the other hand internal turbulences (like production and logistic resource breakdowns) affecting the production system. This paper gives an insight into research approaches and results how capabilities of intelligent logistical objects (intelligent bins, autonomous transport systems etc.) can be used to achieve a self-organized, cost and performance optimized intralogistics system with autonomously controlled process execution within versatile production environments. A first consistent method has been developed which has been validated and implemented within a scenario at the pilot factory Werk150 at the ESB Business School (Reutlingen University). Based on the incoming production orders, the method of the Extended Profitability Appraisal (EPA) covering the work system value to define the most effective work system for order fulfilment is applied. To derive the appropriate intralogistics processes, an autonomous control method involving principles of decentralized and target-oriented decision-making (e.g. intelligent bins are interacting with autonomously controlled transport systems to fulfil material orders of assembly workstations) has been developed and applied to achieve a target-optimized process execution. The results of the first stage research using predefined material sources and sinks described in this paper is going to set the basis for the further development of a self-organized and autonomously controlled method for intralogistics systems considering dynamic source and sink relations. By allowing dynamic shifts of production orders in the sense of dynamic source and sink relations the cost and performance aims of the intralogistics system can be directly aligned with the aims of the entire versatile production system in the sense of self-organized and autonomously controlled systems.
Im präventiven Krisenmanagement geht es um die frühzeitige Erkennung von möglichen, unvorhersehbaren Ereignissen. Hierzu zählen beispielhaft Busunfälle, einstürzende Gebäude und ähnliche Großschadensereignisse. Krisen treten meist unerwartet auf und neigen oftmals aufgrund der knapp bemessenen Handlungszeit zu Fehlentscheidungen. Um dies zu verhindern, dient das präventive Krisenmanagement dazu, sämtliche auftretende Ereignisse mittels einer Simulation zuvor durchzuspielen, um im Falle einer reellen Krise die notwendigen Schritte bestmöglich einzuleiten. Um Simulationen für das Krisenmanagement zu präzisieren und die Ergebnisse effektiv und vereinfacht zu veranschaulichen, ist es notwendig, eine Vorauswahl an vorhandenen Szenarien für Vergleiche heraussuchen zu können. Diese Arbeit entstand im Rahmen des FP-7 EU Projekts CRISMA (Crisis Management) [1] und dient zur Evaluation eines Konzepts zur Vorauswahl geeigneter Szenarien, welche in früheren Simulationen entstanden.
In the present paper we demonstrate the novel technique to apply the recently proposed approach of In-Place Appends – overwrites on Flash without a prior erase operation. IPA can be applied selectively: only to DB-objects that have frequent and relatively small updates. To do so we couple IPA to the concept of NoFTL regions, allowing the DBA to place update-intensive DB-objects into special IPA-enabled regions. The decision about region configuration can be (semi-)automated by an advisor analyzing DB-log files in the background.
We showcase a Shore-MT based prototype of the above approach, operating on real Flash hardware. During the demonstration we allow the users to interact with the system and gain hands-on experience under different demonstration scenarios.
Die zunehmende Durchdringung von cyber-physischen Systemen und deren Vernetzung zu cyberphysischen Produktionssystemen (CPPS) führt zu fundamentalen Veränderungen von zukünftigen Montage-, Fertigungs- und Logistiksystemen, welche innovative Methoden zur Planung, Steuerung und Kontrolle von wandlungsfähigen Produktionssystemen erfordern. Zukünftige logistische Systeme werden dabei den Anforderungen einer hochfrequenten Veränderung und Re-Konfiguration ausgelöst durch wandlungsfähige Produktionssysteme für individualisierte Produkte und kleinen Losgrößen unterliegen. Der Einsatz dezentraler Steuerungssysteme, bei denen die komplexen Planungs-, Steuerungs- und Kontrollprozesse auf zahlreiche Knoten und Entitäten des entstehenden Steuerungssystems verteilt werden, bietet ein großes Potential, den Anforderungen in cyber-physischen Logistiksystemen gerecht zu werden. Eine zentrale Herausforderung ist dabei die echtzeitfähige Steuerung und Re-Konfiguration von sogenannten hybriden Logistiksystemen, welche u.a. durch die Kollaboration von Mensch und Maschine, der Kombination verschiedenartiger Fördermittel sowie verschiedenartiger Steuerungsarchitekturen geprägt sind und darüber hinaus auf hybriden Entscheidungsfindungsprozessen beruhen, welche die Fähigkeiten von Menschen und (cyber-physischen) Systemen synergetisch nutzen.
Lernfabriken, wie die ESB Logistik-Lernfabrik an der ESB Business School (Hochschule Reutlingen), bieten dabei weitreichende Möglichkeiten, diese innovativen Methoden, Systeme und technischen Lösungen in einer industrienahen und risikofreien Fabrikumgebung zu entwickeln sowie in die Ausbildung von Studierenden und Weiterbildung von Teilnehmern aus der Industrie zu transferieren. Um die Forschung, Lehre sowie Aus- und Weiterbildung im Bereich zukünftiger Montage-, Fertigungs- und Logistiksysteme auszuweiten, wird das bestehende Produktionssystem der ESB Logistik-Lernfabrik im Rahmen verschiedenster Forschungs- und Studentenprojekte schrittweise in ein dezentral gesteuertes cyber-physisches Produktionssystem, basierend auf einer ereignisorientierten, cloud-basierten und dezentralen Steuerungsarchitektur, überführt.
Segmentierung von Polypen in Koloskopie-Bilddaten : eine Potentialanalyse von Deep-Learning-Methoden
(2018)
Kolorektale Karzinome haben eine hohe Sterblichkeitsrate, wenn sie spät entdeckt werden. Eine frühzeitige Entfernung von bösartigen Polypen im Magen-Darm-Trakt, die deren Vorstufen bilden, bietet jedoch hohe Überlebenschancen. Bei Darmspiegelungen werden gerade kleine Polypen aber recht häufig übersehen. Zuverlässige bildverarbeitende Systeme, die Polypen in einem Koloskopie-Frame nicht nur detektieren, sondern pixelgenau segmentieren, könnten Ärzten bei Darmkrebs-Screenings helfen. Diese Arbeit analysiert den aktuellen Stand der Segmentierung von Polypen im Gastrointestinaltrakt. Weiterführend wird untersucht, inwiefern die in letzter Zeit sehr erfolgreichen Methoden des Deep Learning hier Vorteile bieten.
Die Segmentierung und das Tracking von minimal-invasiven robotergeführten Instrumenten ist ein wesentlicher Bestandteil für verschiedene computer assistierte Eingriffe. Allerdings treten in der minimal-invasiven Chirurgie, die das Anwendungsfeld für den hier beschriebenen Ansatz darstellt, häufig Schwierigkeiten durch Reflexionen, Schatten oder visuelle Verdeckungen durch Rauch und Organe auf und erschweren die Segmentierung und das Tracking der Instrumente.
Dieser Beitrag stellt einen Deep Learning Ansatz für ein markerloses Tracking von minimal-invasiven Instrumenten vor und wird sowohl auf simulierten als auch realen Daten getestet. Es wird ein simulierter als auch realer Datensatz mit Ground Truth Kennzeichnung für die binäre Segmentierung von Instrument und Hintergrund erstellt. Für den simulierten Datensatz werden Bilder aus einem simulierten Instrument und realem Hintergrund zusammengesetzt. Im Falle des realen Datensatzes spricht man von der Zusammensetzung der Bilder aus einem realen Instrument und Hintergrund. Insgesamt wird auf den simulierten Daten eine Pixelgenauigkeit von 94.70 Prozent und auf den realen Daten eine Pixelgenauigkeit von 87.30 Prozent erreicht.
Motivation: Aim of this project is the automatic classification of total hip endoprosthesis (THEP) components in 2D Xray images. Revision surgeries of total hip arthroplasty (THA) are common procedures in orthopedics and trauma surgery. Currently, around 400.000 procedures per year are performed in the United States (US) alone. To achieve the best possible result, preoperative planning is crucial. Especially if parts of the current THEP system are to be retained.
Methods: First, a ground truth based on 76 X-ray images was created: We used an image processing pipeline consisting of a segmentation step performed by a convolutional neural network and a classification step performed by a support vector machine (SVM). In total, 11 classes (5 pans and 6 shafts) shall be classified.
Results: The ground truth generated was of good quality even though the initial segmentation was performed by technicians. The best segmentation results were achieved using a U-net architecture. For classification, SVM architectures performed much better than additional neural networks.
Conclusions: The overall image processing pipeline performed well, but the ground truth needs to be extended to include a broader variability of implant types and more examples per training class.
The digital transformation of the automotive industry has a significant impact on how development processes need to be organized in future. Dynamic market and technological environments require capabilities to react on changes and to learn fast. Agile methods are a promising approach to address these needs but they are not tailored to the specific characteristics of the automotive domain like product line development. Although, there have been efforts to apply agile methods in the automotive domain for many years, significant and widespread adoptions have not yet taken place. The goal of this literature review is to gain an overview and a better understanding of agile methods for embedded software development in the automotive domain, especially with respect to product line development. A mapping study was conducted to analyze the relation between agile software development, embedded software development in the automotive domain and software product line development. Three research questions were defined and 68 papers were evaluated. The study shows that agile and product line development approaches tailored for the automotive domain are not yet fully explored in the literature. Especially, literature on the combination of agile and product line development is rare. Most of the examined combinations are customizations of generic approaches or approaches stemming from other domains. Although, only few approaches for combining agile and software product line development in the automotive domain were found, these findings were valuable for identifying research gaps and provide insights into how existing approaches can be combined, extended and tailored to suit the characteristics of the automotive domain.
The need for creating digitally enhanced products, services, and experiences as well as the emergence of new or modified business models has a significant impact on the automotive domain. Innovative solutions and new topics such as Smart Mobility or Connectivity require current automotive development processes to undergo major changes. They need to be redesigned in a way that it is possible to learn and adapt continuously at a fast pace. Agile methods are promising approaches to address these new challenges. However, agile methods are not tailored to the specific characteristics of the automotive domain such as software product line (SPLs) development. Although, there have been efforts to apply agile methods in the automotive domain, widespread adoptions have not yet taken place.
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.
Scenario-based analysis is a comprehensive technique to evaluate software quality and can provide more detailed insights than e.g. maintainability metrics. Since such methods typically require significant manual effort, we designed a lightweight scenario-based evolvability evaluation method. To increase efficiency and to limit assumptions, the method exclusively targets service- and microservice-based systems. Additionally, we implemented web-based tool support for each step. Method and tool were also evaluated with a survey (N=40) that focused on change effort estimation techniques and hands-on interviews (N=7) that focused on usability. Based on the evaluation results, we improved method and tool support further. To increase reuse and transparency, the web-based application as well as all survey and interview artifacts are publicly available on GitHub. In its current state, the tool-supported method is ready for first industry case studies.
Governments and public institutions increasingly embrace digital opportunities to involve citizens in public issues and decision making. While public participation is generally seen as an important and promising venture, the design of the participation processes and the utilized digital infrastructure poses challenges, especially to the public sector. Instead of limiting conceptual guidance and exchange to one domain, we therefore develop a taxonomy for digital involvement projects that unites the domains of e-participation, citizen science and crowd-X. Embedded in a design science research approach, we follow an iterative design process to elaborate the key characteristics of a digital involvement project based on the participation process, its individuals and digital infrastructure. Through evaluating the artifact in a focus group with domain practitioners, we find support for the usefulness of our taxonomy and its ability to provide guidance and a basis for discussion of digital involvement projects across domains.
When forecasting sales figures, not only the sales history but also the future price of a product will influence the sales quantity. At first sight, multivariate time series seem to be the appropriate model for this task. Nontheless, in real life history is not always repeatable, i.e. in the case of sales history there is only one price for a product at a given time. This complicates the design of a multivariate time series. However, for some seasonal or perishable products the price is rather a function of the expiration date than of the sales history. This additional information can help to design a more accurate and causal time series model. The proposed solution uses an univariate time series model but takes the price of a product as a parameter that influences systematically the prediction. The price influence is computed based on historical sales data using correlation analysis and adjustable price ranges to identify products with comparable history. Compared to other techniques this novel approach is easy to compute and allows to preset the price parameter for predictions and simulations. Tests with data from the Data Mining Cup 2012 demonstrate better results than established sophisticated time series methods.
Forecasting demand is challenging. Various products exhibit different demand patterns. While demand may be constant and regular for one product, it may be sporadic for another, as well as when demand occurs, it may fluctuate significantly. Forecasting errors are costly and result in obsolete inventory or unsatisfied demand. Methods from statistics, machine learning, and deep learning have been used to predict such demand patterns. Nevertheless, it is not clear for what demand pattern, which algorithm would achieve the best forecast. Therefore, even today a large number of models are used to forecast on a test period. The model with the best result on the test period is used for the actual forecast. This approach is computationally and time intensive and, in most cases, uneconomical. In our paper we show the possibility to use a machine learning classification algorithm, which predicts the best possible model based on the characteristics of a time series. The approach was developed and evaluated on a dataset from a B2B-technical-retailer. The machine learning classification algorithm achieves a mean ROC-AUC of 89%, which emphasizes the skill of the model.
In recent years, the rise of the digital transformation received significant importance in Business-to-Business (B2B) research. Social media applications provide executives with a raft of new options. Consequently, interfaces to social media platforms have also been integrated into B2B salesforce applications, although very little is as yet known about their usage and general impact on B2B sales performance. This paper evaluates 1) the conceptualization of social media usage in a dyadic B2B relationship; 2) the effects of a more differentiated usage construct on customer satisfaction; 3) antecedents of social media usage on multiple levels; and 4) the effectiveness of social media usage for different types of customers. The framework presented here is tested cross-industry against data collected from dyadic buyer seller relationships in the IT service industry. The results elucidate the preconditions and the impact of social media usage strategies in B2B sales relations.
Methods based exclusively on heart rate hardly allow to differentiate between physical activity, stress, relaxation, and rest, that is why an additional sensor like activity/movement sensor added for detection and classification. The response of the heart to physical activity, stress, relaxation, and no activity can be very similar. In this study, we can observe the influence of induced stress and analyze which metrics could be considered for its detection. The changes in the Root Mean Square of the Successive Differences provide us with information about physiological changes. A set of measurements collecting the RR intervals was taken. The intervals are used as a parameter to distinguish four different stages. Parameters like skin conductivity or skin temperature were not used because the main aim is to maintain a minimum number of sensors and devices and thereby to increase the wearability in the future.
Energy efficient electric control of drives is more and more important for electric mobility and manufacturing industries. Online dynamic optimization of induction machines is challenging due to the computational complexity involved and the variable power losses during dynamic operation of induction machines. This paper proposes a simple technique for sub-optimal online loss optimization using rotor flux linkage templates for energy efficient dynamic operation of induction machines. Such a rotor flux linkage template is given by a rotor flux linkage trajectory which is optimal for a specific scenario. This template is calculated in an offline optimization process. For a specific scenario during real time operation the rotor flux linkage is calculated by appropriately scaling the given template.
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.
As production workspaces become more mobile and dynamic it becomes increasingly important to reliably monitor the overall state of the environment. Therein manipulators or other robotic systems likely have to be able to act autonomously together with humans and other systems within a joint workspace. Such interactions require that all components in non-stationary environments are able to perceive the state relative to each other. As vision-sensors provide a rich source of information to accomplish this, we present RoPose, a convolutional neural network (CNN) based approach, to estimate the two dimensional joint configuration of a simulated industrial manipulator from a camera image. This pose information can further be used by a novel targetless calibration setup to estimate the pose of the camera relative to the manipulator’s space. We present a pipeline to automatically generate synthetic training data and conclude with a discussion of the potential usage of the same pipeline to acquire real image datasets of physically existent robots.
RoPose-Real: real world dataset acquisition for data-driven industrial robot arm pose estimation
(2019)
It is necessary to employ smart sensory systems in dynamic and mobile workspaces where industrial robots are mounted on mobile platforms. Such systems should be aware of flexible and non-stationary workspaces and able to react autonomously to changing situations. Building upon our previously presented RoPose-system, which employs a convolutional neural network architecture that has been trained on pure synthetic data to estimate the kinematic chain of an industrial robot arm system, we now present RoPose-Real. RoPose-Real extends the prior system with a comfortable and targetless extrinsic calibration tool, to allow for the production of automatically annotated datasets for real robot systems. Furthermore, we use the novel datasets to train the estimation network with real world data. The extracted pose information is used to automatically estimate the observing sensor pose relative to the robot system. Finally we evaluate the performance of the presented subsystems in a real world robotic scenario.
Die Digitalisierung und Nachhaltigkeit werden die Erwartungen und Anforderungen an die Controller dauerhaft und umfassend verändern. Die Lehre hat für den Rollenwandel eine hohe Relevanz. Eine auf die veränderten Anforderungen abgestimmte Ausbildung bietet den Unternehmen die Möglichkeit, Controller mit diesen veränderte Rollenprofilen für ihre Organisation zu gewinnen. Für die Absolventen mit dem Berufswunsch Controlling sichert das veränderte Rollenprofil ihre langfristige Arbeitsmarktfähigkeit. Für den Rollenwandel selbst kann diese als Treiber verstanden werden.
Trotz der Bedeutung der Lehre für den Rollenwandel gibt es dazu bislang wenige Forschungsergebnisse zur konkreten Abbildung der Rollen in der Lehre. Es stellt sich daher die Frage, wie Hochschulen in ihren Studiengängen die Rollen grundsätzlich abbilden und mit welcher Intensität sowie Kombinationen die Rollen gelehrt werden. Diese Forschungsfrage wird anhand einer Analyse von controllingspezifischen Masterstudiengängen und deren Modulhandbücher evaluiert und diskutiert.
Im Ergebnis stellt sich der Rollenwandel in der Controllinglehre sehr heterogen dar. Es dominiert die Vermittlung der klassische Controllerrolle gefolgt von der Business Partner Rolle. Lehrinhalte bezogen auf die Rollen des digitalen Controllers oder Risikocontrollers sind schwach ausgeprägt. Für die Übernahme einer Controllerrolle im Nachhaltigkeitsmanagement existiert kaum ein Lehrangebot. Diese Ergebnisse sollen zum Diskurs über den Rollenwandel und die Gestaltung der Lehre im Controlling beitragen.
In this paper, we present a new approach for achieving robust performance of data structures making it easier to reuse the same design for different hardware generations but also for different workloads. To achieve robust performance, the main idea is to strictly separate the data structure design from the actual strategies to execute access operations and adjust the actual execution strategies by means of so-called configurations instead of hard-wiring the execution strategy into the data structure. In our evaluation we demonstrate the benefits of this configuration approach for individual data structures as well as complex OLTP workloads.
Significant advances have been achieved in mobile robot localization and mapping in dynamic environments, however these are mostly incapable of dealing with the physical properties of automotive radar sensors. In this paper we present an accurate and robust solution to this problem, by introducing a memory efficient cluster map representation. Our approach is validated by experiments that took place on a public parking space with pedestrians, moving cars, as well as different parking configurations to provide a challenging dynamic environment. The results prove its ability to reproducibly localize our vehicle within an error margin of below 1% with respect to ground truth using only point based radar targets. A decay process enables our map representation to support local updates.
The automation of work by means of disruptive technologies such as Artificial Intelligence (AI) and Robotic Process Automation (RPA) is currently intensely discussed in business practice and academia. Recent studies indicate that many tasks manually conducted by humans today will not in the future. In a similar vein, it is expected that new roles will emerge. The aim of this study is to analyze prospective employment opportunities in the context of RPA in order to foster our understanding of the pivotal qualifications, expertise and skills necessary to find an occupation in a completely changing world of work. This study is based on an explorative, content analysis of 119 job advertisements related to RPA in Germany. The data was collected from major German online job platforms, qualitatively coded, and subsequently analyzed quantitatively. The research indicates that there indeed are employment opportunities, especially in the consulting sector. The positions require different technological expertise such as specific programming languages and knowledge in statistics. The results of this study provide guidance for organizations and individuals on reskilling requirements for future employment. As many of the positions require profound IT expertise, the generally accepted perspective that existing employees affected by automation can be retrained to work in the emerging positions has to be seen extremely critical. This paper contributes to the body of knowledge by providing a novel perspective on the ongoing discussion of employment opportunities, and reskilling demands of the existing workforce in the context of recent technological developments and automation.
In this paper we present our work in progress on revisiting traditional DBMS mechanisms to manage space on native Flash and how it is administered by the DBA. Our observations and initial results show that: the standard logical database structures can be used for physical organization of data on native Flash; at the same time higher DBMS performance is achieved without incurring extra DBA overhead. Initial experimental evaluation indicates a 20% increase in transactional throughput under TPC-C, by performing intelligent data placement on Flash, less erase operations and thus better Flash longevity.
Since its first publication in 2015, the learning factory morphology has been frequently used to design new learning factories and to classify existing ones. The structuring supports the concretization of ideas and promotes exchange between stakeholders.
However, since the implementation of the first learning factories, the learning factory concept has constantly evolved.
Therefore, in the Working Group "Learning Factory Design" of the International Association of Learning Factories, the existing morphology has been revised and extended based on an analysis of the trends observed in the evolution of learning factory concepts. On the one hand, new design elements were complemented to the previous seven design dimensions, and on the other hand, new design dimensions were added. The revised version of the morphology thus provides even more targeted support in the design of new learning factories in the future.
Recently, blockchain-based tokens have earned an important role in fields such as the art market or online gaming. First approaches exist, which adopt the potentials of blockchain tokens in supply chain management to increase transparency, visibility, automation, and disintermediation of supply chains. In context, the tokenization of assets in supply chains refers to the practice of creating virtual representations of physical assets on the blockchain. Solutions in supply chain management based on the tokenization of assets vary in terms of application objectives, token types, asset characteristics, as well as the complexities of supply chain events to be mapped on the blockchain. Currently, however, no review exists that summarizes the characteristics of blockchain-based tokens and their scope of applications. This paper provides a clear terminological distinction of existing blockchain token types and therefore distinguishes between fungible tokens, non-fungible tokens, smart non-fungible tokens, and dynamic smart non-fungible tokens. Subsequently, the token types are classified regarding their traceability, modifiability, and authorization to evaluate suitability for mapping assets in supply chains. Given the potential of blockchain in supply chain management, the results of the review serve as a foundation for a practical guide supporting the selection process of suitable token types for industrial applications.
Revenue management information systems are very important in the hospitality sector. Revenue decisions can be better prepared based on different information from different information systems and decision strategies. There is a lack of research about the usage of such systems in small and medium-sized hotels and architectural configurations. Our paper empirically shows the current development of revenue information systems. Furthermore, we define future developments and requirements to improve such systems and the architectural base.
Current data-intensive systems suffer from scalability as they transfer massive amounts of data to the host DBMS to process it there. Novel near-data processing (NDP) DBMS architectures and smart storage can provably reduce the impact of raw data movement. However, transferring the result-set of an NDP operation may increase the data movement, and thus, the performance overhead. In this paper, we introduce a set of in-situ NDP result-set management techniques, such as spilling, materialization, and reuse. Our evaluation indicates a performance improvement of 1.13 × to 400 ×.
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.
Medical applications are becoming increasingly important in the current development of health care and therefore a crucial part of the medical industry. The work focuses on the analysis of requirements and the challenges arisen from designing mobile medical applications in relation to the user interface. The paper describes the current status in the development of mobile medical apps and illustrates the development of e-health market. The author will explain the requirements and will illustrate the hurdles and problems. He refers to the German market which is similar to the European and compares that with the market in the USA.
Global, competitive markets which are characterised by mass customisation and rapidly changing customer requirements force major changes in production styles and the configuration of manufacturing systems. As a result, factories may need to be regularly adapted and optimised to meet short-term requirements. One way to optimise the production process is the adaptation of the plant layout to the current or expected order situation. To determine whether a layout change is reasonable, a model of the current layout is needed. It is used to perform simulations and in the case of a layout change it serves as a basis for the reconfiguration process. To aid the selection of possible measurement systems, a requirements analysis was done to identify the important parameters for the creation of a digital shadow of a plant layout. Based on these parameters, a method is proposed for defining limit values and specifying exclusion criteria. The paper thus contributes to the development and application of systems that enable an automatic synchronisation of the real layout with the digital layout.
This study is about estimating the reproducibility of finding palpation points of three different anatomical landmarks in the human body (Xiphoid Process and the 2 Hip Crests) to support a navigated ultrasound application. On 6 test subjects with different body mass index the three palpation points were located five times by two examiners. The deviation from the target position was calculated and correlated to the fat thickness above each palpation point. The reproducibility of the measurements had a mean error of ≈13.5 mm +- 4 mm, which seems to be sufficient for the desired application field.
Perforations of the tympanic membrane (TM) can occur as a result of injury or inflammation of the middle ear. These perforations can lead to conductive hearing loss (HL), where in some cases the magnitude of HL exceeds that attributable to the observed TM perforation alone. We aim with this study to better understand the effects of location and size of TM perforations on the sound transmitting properties of the middle ear.
The middle ear transfer function (METF) of six human temporal bones (TB; freshly frozen specimen of body donors) were compared before and after perforation of the TM at different locations (anterior or posterior lower quadrant) and of different sizes (1mm, ¼ of the TM, ½ of the TM, and full ablation). The
METF were correlated with a Finite Element (FE) model of the middle ear, in which similar alterations were simulated.
The measured and simulated FE model METFs exhibited frequency and perforation size dependent amplitude losses at all locations and severities. In direct comparison, posterior TM perforations affected the transmission properties to a larger degree than perforations of the anterior quadrant. This could possibly be caused by an asymmetry of the TM, where the malleus-incus complex rotates and results in larger deflections in the posterior TM half than in the anterior TM half. The FE model of the TM with a sealed cavity suggest that small perforations result in a decrease of TM rigidity and thus to an increase in oscillation amplitude of the TM, mostly above 1 kHz.
The location and size of TM perforations influence the METF in a reproducible way. Correlating our data with the FE model could help to better understand the pathologic mechanisms of middle-ear diseases. If small TM perforations with uncharacteristically significant HL are observed in daily clinical practice, additional middle ear pathologies should be considered. Further investigations on the loss of TM pretension due to perforations may be informative.
In this paper, an approach is introduced how reinforcement learning can be used to achieve interoperability between heterogeneous Internet of Things (IoT) components. More specifically, we model an HTTP REST service as a Markov Decision Process and adapt Q-Learning to the properties of REST so that an agent in the role of an HTTP REST client can learn the semantics of the service and, especially an optimal sequence of service calls to achieve an application specific goal. With our approach, we want to open up and facilitate a discussion in the community, as we see the key for achieving interoperability in IoT by the utilization of artificial intelligence techniques.
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.
Die sogenannte Systemsimulation, bei der mehrere physikalische Domänen gemeinsam simuliert werden, erlaubt die Analyse komplexer und damit realitätsnaher Systeme und spielt eine zunehmend größere Rolle bei der Auslegung von Komponenten. Enthält das System Teile, die durch Feldgrößen aus unterschiedlichen physikalischen Domänen beschrieben werden müssen, kann man Co-Simulationen einsetzen, die allerdings zeitaufwändig sind. Für die Auslegung des Systems ist es dagegen notwendig, Systemsimulationen schnell durchzuführen zu können. Hierfür können für ausgewählte Bauteile oder Domänen schnellere reduzierte Ersatzmodelle (ROM) eingesetzt werden. In dieser Arbeit stellen wir ein reduziertes Modell für elektromechanische Bauteile mit Berücksichtigung von Wirbelströmen vor. Wirbelstromeffekte hängen nicht nur vom aktuellen Zustand, sondern auch von der Geschichte der elektromagnetischen Domäne ab. Das vorgestellte Ersatzmodell basiert auf Daten, die mit einer Reihe von stationären Feldsimulationen vorab erzeugt werden. Für die Modellierung der geschichtsabhängigen Wirbelstromeffekte wird ein Konvolutionsansatz (Faltungsansatz) verwendet. Vergleiche mit entsprechenden Co-Simulationen in ANSYS Maxwell und Simplorer zeigen am Beispiel eines Hubankers, dass das Ersatzmodell in der Lage ist, die wesentlichen Eigenschaften des Bauteils physikalisch korrekt abzubilden.
Der Ressourcenverbrauch in unserer Industrie- und Konsumgesellschaft steigt stetig, wobei die zur Verfügung stehenden Rohstoffe gleichzeitig knapper werden. Gleichzeitig fallen jährlich in Deutschland Millionen von Tonnen an Faserverbunden und Textilprodukten als "Abfall" an. Immer mehr Firmen, aus dem Verbundwerkstoffbereich über Textilproduzenten bis hin zu großen Modeketten, propagieren ein "Recycling" ihrer Produkte unter dem Zeichen der Nachhaltigkeit. Was geschieht mit den Textilien, die wir im Altkleidercontainer "entsorgen"? Und was wird aus Produktionsabfällen und was aus Faserverbundwerkstoffen nach ihrer Nutzungsphase? Sind die Recycling- und Nachhaltigkeitskampagnen der Industrie ein sinnvoller Beitrag zur Rohstoffsicherheit oder handelt es sich um reine Marketingstrategien unter dem Motto "Greenwashing"? Wie steht es um die Wiederverwertung der Textilien und der Faserrohstoffe, welche technischen Voraussetzungen sind dafür notwendig und was ist der Begriff "Nachhaltigkeit" überhaupt wert? Eine kritische Übersicht über die Thematik.
At DBKDA 2019, we demonstrated that StrongDBMS with simple but rigorous optimistic algorithms, provides better performance in situations of high concurrency than major commercial database management systems (DBMS). The demonstration was convincing but the reasons for its success were not fully analysed. There is a brief account of the results below. In this short contribution, we wish to discuss the reasons for the results. The analysis leads to a strong criticism of all DBMS algorithms based on locking, and based on these results, it is not fanciful to suggest that it is time to re-engineer existing DBMS.
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.
This document presents an algorithm for a nonobtrusive recognition of Sleep/Wake states using signals derived from ECG, respiration, and body movement captured while lying in a bed. As a core mathematical base of system data analytics, multinomial logistic regression techniques were chosen. Derived parameters of the three signals are used as the input for the proposed method. The overall achieved accuracy rate is 84% for Wake/Sleep stages, with Cohen’s kappa value 0.46. The presented algorithm should support experts in analyzing sleep quality in more detail. The results confirm the potential of this method and disclose several ways for its improvement.
We present a fully automatic approach to real-time 3D face reconstruction from monocular in-the-wild videos. We use a 3D morphable face model to obtain a semi-dense shape and combine it with a fast median-based super-resolution technique to obtain a high-fidelity textured 3D face model. Our system does not need prior training and is designed to work in uncontrolled scenarios.
Context: The current situation and future scenarios of the automotive domain require a new strategy to develop high quality software in a fast pace. In the automotive domain, it is assumed that a combination of agile development practices and software product lines is beneficial, in order to be capable to handle high frequency of improvements. This assumption is based on the understanding that agile methods introduce more flexibility in short development intervals. Software product lines help to manage the high amount of variants and to improve quality by reuse of software for long term development.
Goal: This study derives a better understanding of the expected benefits for a combination. Furthermore, it identifies the automotive specific challenges that prevent the adoption of agile methods within the software product line.
Method: Survey based on 16 semi structured interviews from the automotive domain, an internal workshop with 40 participants and a discussion round on ESE congress 2016. The results are analyzed by means of thematic coding.
Real Time Charging (RTC) applications that reside in the telecommunications domain have the need for extremely fast database transactions. Today´s providers rely mostly on in-memory databases for this kind of information processing. A flexible and modular benchmark suite specifically designed for this domain provides a valuable framework to test the performance of different DB candidates. Besides a data and a load generator, the suite also includes decoupled database connectors and use case components for convenient customization and extension. Such easily produced test results can be used as guidance for choosing a subset of candidates for further tuning/testing and finally evaluating the database most suited to the chosen use cases. This is why our benchmark suite can be of value for choosing databases for RTC use cases.
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.
Quest 3C : an integrative simulation game used to encourage cross-disciplinary thinking and action
(2014)
Interdisciplinary, complex problem-solving and the necessity to communicate effectively in global Teams characterise today’s rapidly changing Business environment. Employers consistently stress the need for business engineering graduates to demonstrate technical expertise, methodological competences and diverse soft skills. The "silo effect" in higher education has partially created a gap between what industry wants and what academia provides. Here we examine how interdisciplinary team teaching and shared ICT might be more effective in bringing higher education teaching in sync with industry and its demands.
In this work we investigate the behavior of MIS- and Schottky-gate AlGaN/GaN HEMTs under high-power pulsestress. A special setup capable of applying pulses of constant power is used to evaluate the electro-thermal response in different operating points. For both types of devices, the time to failure was found to decrease with increasing drain-source voltage. Overall, the Schottky-gate device displays a higher pulse robustness. The pulse withstand time of the MIS-gate device is limited by the occurrence of a thermal instability at approximately 240°C while the Schottky-gate device displays a rapid increase of the gate leakage current prior to failure. The mechanism responsible for this gate current is further investigated by static and transient temperature measurements and yielded activation energies of 0.6 eV and 0.84 eV.
Die minimal-invasive Chirurgie (MIC) entwickelt sich durch den Einsatz von medizinischen Robotern wie dem da Vinci System von Intuitive Surgical stetig weiter. Hierdurch kann eine bessere oder gleichwertige Operation bei deutlich geringerer körperlicher Belastung des Operateurs erreicht werden. Dabei entstehen jedoch neue Problemstellungen wie beispielsweise Kollision zwischen Roboterarmen und die benötigte Zeit zum Einrichten einer geeigneten Roboterkonfiguration. Daher ist eine effiziente Vorbereitung und Planung der Interventionen erforderlich. Diese Arbeit präsentiert einen Ansatz für eine verbesserte Planung mit Augmented Reality (AR) und einer Robotik Simulationssoftware (RS). Die Robotik Simulation dient zur Berechnung einer Roboterkonfiguration unter Vorgabe der Port-Positionen. Augmented Reality wird verwendet, um die berechneten Pose in der realen Umgebung zu visualisieren und somit leichter in den Operationssaal zu übertragen.
Enterprise architecture management (EAM) is a holistic approach to tackle the complex Business and IT architecture. The transformation of an organization’s EA towards a strategy-oriented system is a continuous task. Many stakeholders have to elaborate on various parts of the EA to reach the best decisions to shape the EA towards an optimized support of the organizations’ capabilities. Since the real world is too complex, analyzing techniques are needed to detect optimization potentials and to get all information needed about an issue. In practice visualizations are commonly used to analyze EAs. However these visualizations are mostly static and do not provide analyses. In this article we combine analyzing techniques from literature and interactive visualizations to support stakeholders in EA decision-making.
Context: The software-intensive business is characterized by increasing market dynamics, rapid technological changes, and fast-changing customer behaviors. Organizations face the challenge of moving away from traditional roadmap formats to an outcome-oriented approach that focuses on delivering value to the customer and the business. An important starting point and a prerequisite for creating such outcome-oriented roadmaps is the development of a product vision to which internal and external stakeholders can be aligned. However, the process of creating a product vision is little researched and understood.
Objective: The goal of this paper is to identify lessons-learned from product vision workshops, which were conducted to develop outcome-oriented product roadmaps.
Method: We conducted a multiple-case study consisting of two different product vision workshops in two different corporate contexts.
Results: Our results show that conducting product vision workshops helps to create a common understanding among all stakeholders about the future direction of the products. In addition, we identified key organizational aspects that contribute to the success of product vision workshops, including the participation of employees from functionally different departments.
Matthias Varga von Kibéd und Insa Sparrer unterscheiden zwischen drei verschiedenen Aufstellungsmethoden (Sparrer und Varga von Kibéd, o. D.): Der spezifisch (konkreten), der virtuellen und der prototypischen Aufstellung. Bei spezifischen Aufstellungen wird ein konkretes Anliegen eines Klienten betrachtet. Im Gegensatz dazu, werden bei virtuellen Aufstellungen eine Übungsumgebung geschaffen. In dieser können Aufstellungstechniken und Interventionsmethoden geübt werden. Bei prototypischen Strukturaufstellungen werden Themen zusammengefasst, die mehrere Teilnehmer im Seminar berühren bzw. in deren Alltag immer wieder auftreten können. Dieses Thema wird wie eine spezifische Aufstellung bearbeitet jedoch ohne ein konkretes vorliegendes Anliegen. Beispiele für prototypische Strukturaufstellungen kommen aus vielen Bereichen z.B. dem Führungsalltag, Teamentwicklung, Konfliktmanagement, Gesprächsführung, Zeit‐ und Selbstmanagement.
Die Entwicklung neuer Produkte findet nicht nur abteilungs-, sondern zunehmend organisationsübergreifend statt. Kooperationen in der Produktentstehung gewinnen folglich vermehrt an Bedeutung, was neue Anforderungen an den Produktentstehungsprozess (PEP) und die Zusammenarbeit in diesem schafft. Mit diesen Herausforderungen sieht sich auch der Forschungscampus ARENA2036 konfrontiert. Die vorliegende Arbeit beschäftigt sich mit der Entwicklung eines für die interdisziplinäre, interorganisationale Zusammenarbeit geeigneten PEP-Modells. Dieses wird auf Basis von theoretischen Grundlagen, Experteninterviews und unter Berücksichtigung der praktischen Gegebenheiten in ARENA2036 modelliert. Der finale PEP untergliedert sich in einen übergeordneten Prozess, in den die individuellen PEPs der ARENA2036-Partner untergeordnet sind. Durch diese Struktur können die heterogenen PEPs der Partner vereint und die notwendige forscherische Freiheit und Flexibilität gewährleistet werden. Weiterhin wird der PEP durch geeignete Konzepte und Methoden der kooperativen Zusammenarbeit flankiert.
The increase in product variance and shorter product lifecycles result in higher production ramp-up frequencies and promote the usage of mixed-model lines. The ramp-up is considered a critical step in the product life cycle and in the automotive industry phases of the ramp-up are often executed on separated production lines (pilot lines) or factories (pilot plants) to verify processes and to qualify employees without affecting the production of other products in the mixed-model line. The required financial funds for planning and maintaining dedicated pilot lines prevent small and medium-sized enterprises (SMEs) from the application. Hence, SMEs require different tools for piloting and training during the production ramp-up. Learning islands on which employees can be trained through induced and autonomous learning propose a solution. In this work, a concept for the development and application which contains the required organization, activities, and materials is developed through expert interviews. The results of a case study application with a medium-sized automotive manufacturer show that learning islands are a viable tool for employee qualification and process verification during the ramp-up of mixed-model lines.
Product roadmaps in the new mobility domain: state of the practice and industrial experiences
(2021)
Context: The New Mobility industry is a young market that includes high market dynamics and is therefore associated with a high degree of uncertainty. Traditional product roadmapping approaches such a detailed planning of features over a long-time horizon typically fail in such environments. For this reason, companies that are active in the field of New Mobility are faced with the challenge of keeping their product roadmaps reliable for stakeholders while at the same time being able to react flexibly to changing market requirements.
Objective: The goal of this paper is to identify the state of practice regarding product roadmapping of New Mobility companies. In addition, the related challenges within the product roadmapping process as well as the success factors to overcome these challenges will be highlighted.
Method: We conducted semi-structured expert interviews with 8 experts (7 German company and one Finnish company) from the field of New Mobility and performed a content analysis.
Results: Overall the results of the study showed that the participating companies are aware of the requirements that the New Mobility sector entails. Therefore, they exhibit a high level of maturity in terms of product roadmapping. Nevertheless, some aspects were revealed that pose specific challenges for the participating companies. One major challenge, for example, is that New Mobility in terms of public clients is often a tender business with non-negotiable product requirements. Thus, the product roadmap can be significantly influenced from the outside. As factors for a successful product roadmapping mainly soft factors such as trust between all people involved in the product development process and transparency throughout the entire roadmapping process were mentioned.
Context: Currently, most companies apply approaches for product roadmapping that are based on the assumption that the future is highly predicable. However, nowadays companies are facing the challenge of increasing market dynamics, rapidly evolving technologies, and shifting user expectations. Together with the adaption of lean and agile practices it makes it increasingly difficult to plan and predict upfront which products, services or features will satisfy the needs of the customers. Therefore, they are struggling with their ability to provide product roadmaps that fit into dynamic and uncertain market environments and that can be used together with lean and agile software development practices.
Objective: To gain a better understanding of modern product roadmapping processes, this paper aims to identify suitable processes for the creation and evolution of product roadmaps in dynamic and uncertain market environments.
Method: We performed a Grey Literature Review (GLR) according to the guidelines from Garousi et al.
Results: 32 approaches to product roadmapping were identified. Typical characteristics of these processes are the strong connection between the product roadmap and the product vision, an emphasis on stakeholder alignment, the definition of business and customer goals as part of the roadmapping process, a high degree of flexibility with respect to reaching these goals, and the inclusion of validation activities in the roadmapping process. An overall goal of nearly all approaches is to avoid waste by early reducing development and business risks. From the list of the 32 approaches found, four representative roadmapping processes are described in detail.
Context: Companies in highly dynamic markets increasingly struggle with their ability to plan product development and to create reliable roadmaps. A main reason is the decreasing lack of predictability of markets, technologies, and customer behaviors. New approaches for product roadmapping seem to be necessary in order to cope with today's highly dynamic conditions. Little research is available with respect to such new approaches. Objective: In order to better understand the state of the art and to identify research gaps, this article presents a review of the scientific literature with respect to product roadmapping. Method: We performed a systematic literature review (SLR) with respect to identify papers in the field of computer science. Results: After filtering, the search resulted in a set of 23 relevant papers. The identified papers focus on different aspects such as roadmap types, processes for creating and updating roadmaps, problems and challenges with roadmapping, approaches to visualize roadmaps, generic frameworks and specific aspects such as the combination of roadmaps with business modeling. Overall, the scientific literature covers many important aspects of roadmapping but does provide only little knowledge on how to create product roadmaps under highly dynamic conditions. Research gaps address, for instance, the inclusion of goals or outcomes into product roadmaps, the alignment of a roadmap with a product vision, and the inclusion of product discovery activities in product roadmaps. In addition, the transformation from traditional roadmapping processes to new ways of roadmapping is not sufficiently addressed in the scientific literature.
Product roadmaps are an important tool in product development. They provide direction, enable consistent development in relation to a product vision and support communication with relevant stakeholders. There are many different formats for product roadmaps, but they are often based on the assumption that the future is highly predictable. However, especially software-intensive businesses are faced with increasing market dynamics, rapidly evolving technologies and changing user expectations. As a result, many organizations are wondering what roadmap format is appropriate for them and what components it should have to deal with an unpredictable future. Objectives: To gain a better understanding of the formats of product roadmaps and their components, this paper aims to identify suitable formats for the development and handling of product roadmaps in dynamic and uncertain markets. Method: We performed a grey literature review (GLR) according to the guidelines from Garousi. Results: A Google search identified 426 articles, 25 of which were included in this study. First, various components of the roadmap were identified, especially the product vision, themes, goals, outcomes and outputs. In addition, various product roadmap formats were discovered, such as feature-based, goal-oriented, outcome-driven and a theme-based roadmap. The roadmap components were then assigned to the various product roadmap formats. This overview aims at providing initial decision support for companies to select a suitable product roadmap format and adapt it to their own needs.
Context: A product roadmap is an important tool in product development. It sets the strategic direction in which the product is to be developed to achieve the company’s vision. However, for product roadmaps to be successful, it is essential that all stakeholders agree with the company’s vision and objectives and are aligned and committed to a common product plan.
Objective: In order to gain a better understanding of product roadmap alignment, this paper aims at identifying measures, activities and techniques in order to align the different stakeholders around the product roadmap.
Method: We conducted a grey literature review according the guidelines to Garousi et al.
Results: Several approaches to gain alignment were identified such as defining and communicating clear objectives based on the product vision, conducting cross-functional workshops, shuttle diplomacy, and mission briefing. In addition, our review identified the “Behavioural Change Stairway Model” that suggests five steps to gain alignment by building empathy and a trustful relationship.
In recent years companies have faced challenges by high market dynamics, rapidly evolving technologies and shifting user expectations. Together with the adaption of lean and agile practices, it is increasingly difficult to predict upfront which products, features or services will satisfy the needs of the customers and the organization. Currently, many new products fail to produce a significant financial return. One reason is that companies are not doing enough product discovery activities. Product discovery aims at tackling the various risks before the implementation of a product starts. The academic literature only provides little guidance for conducting product discovery in practice. Objective: In order to gain a better understanding of product discovery activities in practice, this paper aims at identifying motivations, approaches, challenges, risks, and pitfalls of product discovery reported in the grey literature. Method: We performed a grey literature review (GLR) according to the guidelines to Garousi et al. Results: The study shows that the main motivation for conducting product discovery activities is to reduce the uncertainty to a level that makes it possible to start building a solution that provides value for the customers and the business. Several product discovery approaches are reported in the grey literature which include different phases such as alignment, problem exploration, ideation, and validation. Main challenges are, among others, the lack of clarity of the problem to be solved, the prescription of concrete solutions through management or experts, and the lack of cross-functional collaboration.
Digital technologies are moving into physical products. Smart cars, connected lightbulbs and data-generating tennis rackets are examples of previously “pure” physical products that turned into “digitized products”. Digitizing products offers many use cases for consumers that will hopefully persuade them to buy these products. Yet, as revenues from selling digitized products will remain small in the near future, digitized product manufacturers have to look for other sources of benefits. Producer-side use cases describe how manufacturers can benefit internally from the digitized products they produce. Our article identifies three categories of such use cases: product-, service-, and process-related ones.
A shift in attitudes to purchasing departments can be perceived. No longer is the chief goal solely to reduce costs; the procurement function is assuming strategic relevance in the business model, leveraging the supplier as a foundation for innovation. The knowledge accumulated by suppliers is accessed over the journey of long-term partnerships to streamline business practice. Businesses are finding themselves in increasingly competitive environments, and thus need to address inefficiencies in supplier management. “Procurement 4.0” is a concept used in discussing digitalisation in business processes, referring to the process of supplier relationship management and optimisation. This model and its application to supplier relationship management will be the focus of this article. Realising the efficiencies to be obtained in buyer-supplier relationships through “Procurement 4.0” will be explored, primarily through an emphasis on digitalisation of the relationship between the procurement department and supplier.
Proceedings of the International Workshop on Mobile Networks for Biometric Data Analysis (mBiDA)
(2014)
Prevention and treatment of common and widesprea (chronic) diseases is a challenge in any modern Society and vitally important for health maintenance in aging societies. Capturing biometric data is a cornerstone for any analysis and Treatment strategy. Latest advances in sensor technology allow accurate data measurement in a non-intrusive way. In many cases, it is necessary to provide online monitoring and real-time data capturing to support patients´ prevention plans or to allow medical professionals to access the current status. Different communication standards are required to push sensor data and to store and analyze them on different (mobile) platforms. The objective of the workshop is to show new and innovative approaches dedicated to biometric data capture and analysis in a non-intrusive way maintaining mobility. Examples can be found in human centered ambient intelligence attributed with sensors or even in methodologies applied in automotive real-time conform mobile system design. The workshop´s main challenge is to focus on approaches promoting non-intrusiveness, reliable prediction algorithms and high user-acceptance. The workshop will provide overview presentations, Young researcher poster tracks, doctoral tracks and classical peer-review full paper tracks. Especially, would like to encourage students and young researchers to participate and to contribute to the workshop. Scientific contributions to the event are peer-reviewed by a suited program committee.
Preface of IDEA 2015
(2016)
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.
Due to decreased mobility or families living apart, older adults are especially vulnerable to the issue of social isolation. Literature suggests that technology can help to prevent this isolation. The present work addresses an approach to participate in society by sharing knowledge that is cherished. We propose the cooking recipe exchange application PrecRec for older adults to make them feel precious and valued. PrecRec has been developed and evaluated in an iterative process with eleven older adults. The results show that a broad perspective has to be taken into account when designing such systems.
Einpaarige Verkabelungssysteme gewinnen aufgrund des Internets der Dinge und aufgrund deren Einsatz im Automobil- bzw. Industriebereich zunehmend an Bedeutung. Im Rahmen dieser Arbeit wird gezeigt, wie bei der Übertragung über ein einpaariges Kabel die Bitrate bei gleichbleibender Übertragungsbandbreite durch Nutzung des Phantomkreises erhöht werden kann. Als Übertragungsverfahren kommt jeweils eine 16-stufige Pulsamplitudenmodulation zum Einsatz. Die Ergebnisse werden durch Augendiagramme qualitativ und durch die Messung der Symbolfehler bzw. der Bitfehler quantitativ untersucht.
Potentials of smart contracts-based disintermediation in additive manufacturing supply chains
(2019)
We investigate which potentials are created by using smart contracts for disintermediation in supply chains for additive manufacturing. Using a qualitative, critical realist research approach, we analyzed three case studies with companies active in additive manufactures. Based on interviews with experts from these companies, we could identify eight key requirements for disintermediation and associate four potentials of smart contracts-based disintermediation.
An enormous amount of data in the context of business processes is stored as images. They contain valuable information for business process management. Up to now this data had to be integrated manually into the business process. By advances of capturing it is possible to extract information from an increasing number of images. Therefore, we systematically investigate the potentials of Image Mining for business process management by a literature research and an in-depth analysis of the business process lifecycle. As a first step to evaluate our research, we developed a prototype for recovering process model information from drawings using Rapidminer.
Die additive Fertigung hat sich in den vergangenen Jahren wesentlich weiter entwickelt. Dabei wurde die Prozesstechnologie, Anlagen und die Werkstoffe optimiert. Für die industrielle Anwendung auch bei größeren Stückzahlen in der flexiblen Fertigung fehlen noch automatisierte Lösungen für die gesamte Prozesskette. In diesem Beitrag werden Werkzeuge und Technologie für die Reinigung interner Strukturelemente dargestellt.
Die additive Fertigung hat sich in den vergangenen Jahren wesentlich weiterentwickelt. Dabei wurde die Prozesstechnologie, Anlagen und die Werkstoffe optimiert. Für die industrielle Anwendung auch bei größeren Stückzahlen in der flexiblen Fertigung fehlen noch automatisierte Lösungen für die gesamte Prozesskette. In diesem Beitrag werden Werkzeuge und Technologie für die Reinigung interner Strukturelemente dargestellt.
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.
Human pose estimation (HPE) is integral to scene understanding in numerous safety-critical domains involving human-machine interaction, such as autonomous driving or semi-automated work environments. Avoiding costly mistakes is synonymous with anticipating failure in model predictions, which necessitates meta-judgments on the accuracy of the applied models. Here, we propose a straightforward human pose regression framework to examine the behavior of two established methods for simultaneous aleatoric and epistemic uncertainty estimation: maximum a-posteriori (MAP) estimation with Monte-Carlo variational inference and deep evidential regression (DER). First, we evaluate both approaches on the quality of their predicted variances and whether these truly capture the expected model error. The initial assessment indicates that both methods exhibit the overconfidence issue common in deep probabilistic models. This observation motivates our implementation of an additional recalibration step to extract reliable confidence intervals. We then take a closer look at deep evidential regression, which, to our knowledge, is applied comprehensively for the first time to the HPE problem. Experimental results indicate that DER behaves as expected in challenging and adverse conditions commonly occurring in HPE and that the predicted uncertainties match their purported aleatoric and epistemic sources. Notably, DER achieves smooth uncertainty estimates without the need for a costly sampling step, making it an attractive candidate for uncertainty estimation on resource-limited platforms.
Milk-run systems are becoming more and more popular when it comes to in-plant material supply. Planning and dimensioning such a system poses challenges, which are difficult to overcome, especially in scenarios characterized by a large number of hard constraints and by well-established processes. This paper is set to ease the task of the planner by presenting an innovative flexible method for the planning and dimensioning of in-plant milk-run systems in high constrained scenarios. After an overview on tugger train systems and existing planning methods, an extensive description of the new method will be given. The new method proposed will be critically analyzed and discussed before suggesting forthcoming research.
Planning of available resources considering ergonomics under deterministic highly variable demand
(2020)
In this paper, a method for hybrid short- to long-term planning of available resources for operations is presented, which is based on a known or deterministically forecasted but highly variable demand. The method considers quantitative measures such as the performance and the availability of resources, ergonomically relevant KPI and ultimately process costs in order to serve as a pragmatic planning tool for operations managers in SMEs. Specifically, the method enables exploiting the ergonomic advantages of available flexible automation technology (e.g. AGVs or picking robots), while assuring that these do not represent a capacity bottleneck. After presenting the method along with the necessary assumptions, mainly concerning the availability of data for the calculations, we report a case study that quantifies the impact of throughput variability on the selection of different process alternatives, where different teams of resources are used.
The performance and scalability of modern data-intensive systems are limited by massive data movement of growing datasets across the whole memory hierarchy to the CPUs. Such traditional processor-centric DBMS architectures are bandwidth- and latency-bound. Processing-in-Memory (PIM) designs seek to overcome these limitations by integrating memory and processing functionality on the same chip. PIM targets near- or in-memory data processing, leveraging the greater in-situ parallelism and bandwidth.
In this paper, we introduce pimDB and provide an initial comparison of processor-centric and PIM-DBMS approaches under different aspects, such as scalability and parallelism, cache-awareness, or PIM-specific compute/bandwidth tradeoffs. The evaluation is performed end-to-end on a real PIM hardware system from UPMEM.
Um den Übergang von der Schule zur Hochschule zu erleichtern, brauchen Studierende technischer Fächer häufig eine Auffrischung ihrer Kenntnisse in Mathematik und Physik. Ein Online-Lernsystem für Physik kann Studierende bei der Beschäftigung mit physikalischen Inhalten unterstützen. Zudem kann ein Physik-Wissenstest Lücken im individuellen Wissensstand aufzeigen und zum Lernen der fehlenden Themen motivieren. Die Arbeitsgruppe "eLearning in der Physik" der Hochschulföderation Süd-West (HfSW) bestehend aus den baden-württembergischen Hochschulen Aalen, Esslingen, Heilbronn, Mannheim und Reutlingen hat einen Aufgabenpool von über 200 Physikaufgaben für Erstsemester erarbeitet. Sie stehen den Studierenden mit Lösungen in Lernmanagementsystemen zum Selbststudium und jetzt auch im "Zentralen Open Educational Resources Repositorium der Hochschulen in Baden-Württemberg" (ZOERR) zur Verfügung. In diesem Beitrag wird über den Einsatz der Online-Übungsaufgaben in 2020/2021 berichtet, über die Ergebnisse der Wissenstests und über die in der Corona-Zeit neu eingerichteten eTutorien.
Assistive environments are entering our homes faster than ever. However, there are still various barriers to be broken. One of the crucial points is a personalization of offered services and integration of assistive technologies in common objects and therefore in a regular daily routine. Recognition of sleep patterns for the preliminary sleep study is one of the Health services that could be performed in an undisturbing way. This article proposes the hardware system for the measurement of bio-vital signals necessary for initial sleep study in a nonobtrusive way. The first results confirm the potential of measurement of breathing and movement signals with the proposed system.
Sleep is an important aspect in life of every human being. The average sleep duration for an adult is approximately 7 h per day. Sleep is necessary to regenerate physical and psychological state of a human. A bad sleep quality has a major impact on the health status and can lead to different diseases. In this paper an approach will be presented, which uses a long-term monitoring of vital data gathered by a body sensor during the day and the night supported by mobile application connected to an analyzing system, to estimate sleep quality of its user as well as give recommendations to improve it in real-time. Actimetry and historical data will be used to improve the individual recommendations, based on common techniques used in the area of machine learning and big data analysis.
The loss contribution of a 2.3kW synchronous GaN-HEMT boost converter for an input voltage of 250V and an output voltage of 500V was analyzed. A simulation model which consists of two parts is introduced. First, a physics-based model is used to determine the switching losses. Then, a system simulation is applied to calculate the losses of the specific elements. This approach allows a fast and accurate system evaluation as required for further system optimization.
In this work, a hard- and a zero-voltage turn-on switching converter are compared. Measurements were performed to verify the simulation model, showing a good agreement. A peak efficiency of 99% was achieved for an output power of 1.4kW. Even with an output power above 400W, it was possible to obtain a system efficiency exceeding 98 %.
We present the results of an extensive characterization of the performance and stability of a third-order continuous-time delta-sigma modulator with active coefficient error compensation. Using our previously published coefficient tuning technique, process variation induced R-C time-constant (TC) errors in the forward signal path can be compensated indirectly using continuously tunable DACs in the feedback path. To validate our technique experimentally with a range of real TC variations, we designed a modulator with discretely configurable integration capacitor arrays in a 0.35-μm CMOS process. We configured the capacitors of the fabricated device for a range of total TC variations from -28.4 % to +19.3 % and measured the signal-to-noise ratio (SNR) as a function of the input amplitude before and after compensating the variations electrically using the feedback DACs. The results show that our tuning technique is capable of restoring the desired nominal modulator performance over the entire parameter variation range, including the system’s nominal maximum stable amplitude (MSA).