One of the notable drivers of the fourth industrial revolution is the collection of vast amounts of data along the entire lifecycle of a product. The analysis of product lifecycle data in conjunction with product hypo...
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One of the notable drivers of the fourth industrial revolution is the collection of vast amounts of data along the entire lifecycle of a product. The analysis of product lifecycle data in conjunction with product hypotheses leads to promising potentials in strategic product planning. In this thesis paper, we postulate the need for data-driven product generation and retrofit planning as an interdisciplinary field of research. We define and analyze the key concepts and derive requirements in a structured way. Based on an exhaustive research of existing approaches, we structure open research questions and propose a roadmap in order to shape future research efforts.
Cyber-physical systems (CPS) are networked, intelligent technical systems that interact with the physical and digital world alike. Companies now increasingly face the challenge of rapidly and consistently exploiting t...
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Mobile applications are today ubiquitous, and everybody uses them on a daily basis. This applies also to security-critical mobile applications such as online banking apps. In today's architectures, these mobile ap...
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ISBN:
(数字)9781728189154
ISBN:
(纸本)9781728189161
Mobile applications are today ubiquitous, and everybody uses them on a daily basis. This applies also to security-critical mobile applications such as online banking apps. In today's architectures, these mobile applications are usually fed from the same source as mobile applications on smart phones, i.e. web services. This makes security testing of web services inevitable. Furthermore, regulation increases and requires stronger security mechanisms as with the strong customer authentication from the Revised European Payment Services Directive (PSD2). Automated security testing is a way to cope with the increasing requirements on assuring the security of such web services and their implemented security controls whilst dealing with decreasing resources for such efforts. In this paper, we present our experiences from a case study provided by Kuveyt Türk Bank performed within the ITEA-3 project TESTOMAT where we introduced automated security testing in terms of fuzzing to complement manual security testing.
Optical frequency combs are revolutionizing modern time and frequency metrology. In the past years, their range of applications has increased substantially, driven by their miniaturization through microresonator-based...
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Optical frequency combs are revolutionizing modern time and frequency metrology. In the past years, their range of applications has increased substantially, driven by their miniaturization through microresonator-based solutions. The combs in such devices are typically generated using the third-order χ(3) nonlinearity of the resonator material. An alternative approach is making use of second-order χ(2) nonlinearities. While the idea of generating combs this way has been around for almost two decades, so far only few demonstrations are known, based either on bulky bow-tie cavities or on relatively low-Q waveguide resonators. Here, we present the first such comb that is based on a millimeter-sized microresonator made of lithium niobate, that allows for cascaded second-order nonlinearities. This proof-of-concept device comes already with pump powers as low as 2 mW, generating repetition-rate-locked combs around 1064 and 532 nm. From the nonlinear dynamics point of view, the observed combs correspond to Turing roll patterns.
We investigate the threshold of χ(2) frequency comb generation in lithium niobate whispering gallery microresonators theoretically and experimentally. When generating a frequency comb via second-harmonic generation, ...
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Advanced production planning and scheduling approaches increasingly rely on simulation-based optimization methods. This entails the problem of a high computational effort due to complex models, resulting in limitation...
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Advanced production planning and scheduling approaches increasingly rely on simulation-based optimization methods. This entails the problem of a high computational effort due to complex models, resulting in limitations for the practical application of otherwise powerful methods. While machine-learning methods offer a potential for performance improvement, approaches for real-life applications with a high complexity are still lacking. This paper explores the potential for machine learning, especially artificial neural networks, used as surrogate models, to improve the performance of a recently developed planning method for real life production planning applications. The simulation considered in this paper is a complex hybrid discrete-continuous model, enabling the method to pursue energy efficiency simultaneously with economic goals, in a complex multi-criteria goal system. The artificial neural network is trained via offline learning and is meant to provide a computationally cheap evaluation of intermediate planning solutions, compiled by an optimization algorithm during an optimization run. The approach is developed and evaluated in a case-study on the food industry, indicating a basic feasibility of the approach but also pointing out necessary future challenges to be solved towards practical applicability.
Alkaline methanol oxidation is an electrochemical process, perspective for the design of efficient high energy density fuel cells. The process involves a large number of elementary reactions, forming a complex reactio...
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Digitalization of the economy requires enterprises from all industries to revisit their current business models and prepare their organizations for the digital age. One task is the (re-)design of hybrid and digital pr...
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In this paper, the considerations and simulations made for the design and characterization of a high-Q resonator for wireless identification at 90 GHz are presented with the perspective to extend them to the low THz r...
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