Integration of Virtualized Environments in PDM Systems for Embedded Software Product Development

AbstractThe number of products with embedded software increases across all application areas continuously. Thus, the complexity between the hardware and software is steadily increasing. This leads to an increment of software defects. Therefore, new approaches are needed to ensure the product quality. In the context of PLM, virtualization can support crucial stages of the product development and test automation by providing virtual environments. This paper shows an architectural approach, and how to perform an integration of virtualization software in PDM systems

Sensitivity-Based Optimization of Unsupervised Drift Detection for Categorical Data Streams

Real-world data streams are rarely characterized by stationary data distributions. Instead, the phenomenon commonly termed as concept drift, threatens the performance of estimators conducting inference on such data. Our contribution builds on the unsupervised concept drift detector CDCStream, which is specialized on processing categorical data directly. We propose a cooldown mechanism aiming at reducing its excessive sensitivity in order to curb false-alarm detections. Using practical classification and regression problems, we evaluate the impact of the mechanism on estimation performance and highlight the transferability of our mechanism on other detection methods. Additionally, we provide an intuitive means for tuning the sensitivity of drift detectors. While only marginally improving the unaltered form of the detector on publicly available benchmark data, our mechanism does so consistently in almost all configurations. In contrast, within the context of another real-world scenario, almost none of the tested drift-detection-based approaches could outperform a baseline approach. However, potentially false-alarm detections are reduced drastically in all scenarios. With this resulting in a cutback in signals for refitting estimators, while maintaining a better or at least comparable performance to vanilla CDCStream, compute infrastructure utilization could be economized further

Effiziente Produktion und Wartung durch die Industrie 4.0: Anwendung

Die „Industrie 4.0“ steht für eine neue Stufe der Organisation und Steuerung der gesamten Wertschöpfungskette über den Produktlebenszyklus, der sich an den zunehmend individualisierten Kundenwünschen hinweg orientiert und von der Idee, dem Auftrag über die Entwicklung und Fertigung, die Auslieferung eines Produkts an den Endkunden bis hin zum Recycling, einschließlich der damit verbundenen Dienstleistungen erstreckt. Hierfür ist die Basis die Verfügbarkeit aller relevanten Informationen in Echtzeit durch Vernetzung aller an der Wertschöpfung beteiligten Instanzen sowie die Fähigkeit, aus den Daten den zu jedem Zeitpunkt optimalen Wertschöpfungsfluss abzuleiten (Ovtcharova 2016). Weiterhin lässt sich die Industrie 4.0 als durchgängige Vernetzung der Produktion und des gesamten Produktlebenszyklus mit Hilfe von Internettechnologien verstehen. Dabei sind Kunden- und Maschinendaten vernetzt (vgl. Abbildung 1). Weiterhin tauschen die intelligenten Maschinen und Systeme Informationen untereinander in Echtzeit aus. Somit verschmilzt die Produktion mit der Informations- und Kommunikationstechnik und wird von Werkstücken sowie Maschinen selbstständig, flexibel, effizient und ressourcenschonend gesteuert. Weiterhin werden die ausgetauschten Informationen und entstandenen Daten ständig geprüft, sodass es zu keiner Zeit zu einem Stillstand oder Engpass in der Produktion kommen kann. Durch die Anwendung von Industrie 4.0 entsteht ein großer Nutzen, der das Unternehmen im globalen Wettbewerb stark macht. [... aus Punkt 1

Immersive Kansei Engineering : A New Method and its Potentials

Product development becomes more and more complex. Products obtain more and more functions and at the same time they must be still attractive for the customers to ensure a successful product launch. To predict their acceptance and to gain knowledge on how to design attractive products new methods are developed in the field of the emotional design. Such a method is the Kansei Engineering, which collects the customers hidden subjective needs and their translation into concrete products. We present and validate a new form of the Kansei Engineering method for emotional assessment by the customers during the product development, based on an interactive product experience in Virtual Reality. The major novelty of our kind of method is the use of immersive representations which focuses on both, the product itself and its environmental context, too. Customers experience these virtual representations quite dynamically and with this freely describe their emotional influence on them. We come to the conclusion that more reliable emotional customer feedback can be acquired through the implementation of the proposed context paradigm shift. The fusion of product and environmental context enables the simultaneous role of the customer as a subject (actor) and an object (observer) in the virtual world, thus promoting reliable emotional reactions. Despite of some disadvantages, we propose Immersive Kansei Engineering as a reliable method for emotional product assessment by the customer

Key-Performance-Analyse von Methoden des Anforderungsmanagements. (KIT Scientific Reports ; 7620)

Der vorliegende Bericht beinhaltet eine Beschreibung und anschließende Bewertung der wichtigsten Methoden des Anforderungsmanagements. Zunächst erfolgt eine Einordnung und Beschreibung der Methoden in die drei Phasen des Anforderungsprozesses. Anschließend werden die vorgestellten Methoden mithilfe einer Key- Performance-Analyse anhand von zuvor definierten Schlüsselfaktoren bewertet, um eine Aussagefähigkeit über den sinnvollen und möglichst optimalen Einsatz der Methoden abzuleiten

Dual-stage attention-based long-short-term memory neural networks for energy demand prediction

Forecasting energy demand of residential buildings plays an important role in the operation of smart cities, as it forms the basis for decision-making in the planning and operation of urban energy systems. Deep learning algorithms are commonly used to reliably predict potential energy usage since they can overcome the issue of dependency on long-distance data in energy forecasting relative to the standard regression model. However, there are still two problems to be solved for energy forecasting, including the encoding of categorical characteristics and adaptive extraction of the most relevant characteristics for the use in predictions. To address the problems, we proposed a sequential forecasting model for medium- and long-term energy demand forecasting based on an embedding mechanism and a two-stage attention-based long-term memory neural network. An empirical study was conducted on three years of daily electricity consumption data from the residential buildings of the Pudong district of Shanghai to evaluate the model. The results show that the model can effectively extract the key features that are highly correlated with energy consumption dynamics by employing long-term dependencies in time series. In addition, the hybrid model outperforms others in terms of long-term forecasting capability. This paper also discusses future research directions and the possibilities for applying deep learning techniques in the energy sector

Collaborative Work Enabled by Immersive Environments

Digital transformation facilitates new methods for remote collaboration while shaping a new understanding of working together. In this chapter, we consider global collaboration in the context of digital transformation, discuss the role of Collaborative Virtual Environments (CVEs) within the transformation process, present an overview of the state of CVEs and go into more detail on significant challenges in CVEs by providing recent approaches from research

Prediction of cybersickness in virtual environments using topological data analysis and machine learning

Recent significant progress in Virtual Reality (VR) applications and environments raised several challenges. They proved to have side effects on specific users, thus reducing the usability of the VR technology in some critical domains, such as flight and car simulators. One of the common side effects is cybersickness. Some significant commonly reported symptoms are nausea, oculomotor discomfort, and disorientation. To mitigate these symptoms and consequently improve the usability of VR systems, it is necessary to predict the incidence of cybersickness. This paper proposes a machine learning approach to VR’s cybersickness prediction based on physiological and subjective data. We investigated combinations of topological data analysis with a range of classifier algorithms and assessed classification performance. The highest performance of Topological Data Analysis (TDA) based methods was achieved in combination with SVMs with Gaussian RBF kernel, indicating that Gaussian RBF kernels provide embeddings of physiological time series data into spaces that are rich enough to capture the essential geometric features of this type of data. Comparing several combinations with feature descriptors for physiological time series, the performance of the TDA + SVM combination is in the top group, statistically being on par or outperforming more complex and less interpretable methods. Our results show that heart rate does not seem to correlate with cybersickness