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Prediction of Claims in Export Credit Finance: A Comparison of Four Machine Learning Techniques
(2020)
This study evaluates four machine learning (ML) techniques (Decision Trees (DT), Random Forests (RF), Neural Networks (NN) and Probabilistic Neural Networks (PNN)) on their ability to accurately predict export credit insurance claims. Additionally, we compare the performance of the ML techniques against a simple benchmark (BM) heuristic. The analysis is based on the utilisation of a dataset provided by the Berne Union, which is the most comprehensive collection of export credit insurance data and has been used in only two scientific studies so far. All ML techniques performed relatively well in predicting whether or not claims would be incurred, and, with limitations, in predicting the order of magnitude of the claims. No satisfactory results were achieved predicting actual claim ratios. RF performed significantly better than DT, NN and PNN against all prediction tasks, and most reliably carried their validation performance forward to test performance.
A disturbed synchronization of the ventricular contraction can cause a highly developed systolic heart failure in affected patients with reduction of the left ventricular ejection fraction, which can often be explained by a diseased left bundle branch block (LBBB). If medication remains unresponsive, the concerned patients will be treated with a cardiac resynchronization therapy (CRT) system. The aim of this study was to integrate His-bundle pacing into the Offenburg heart rhythm model in order to visualize the electrical pacing field generated by His-Bundle-Pacing. Modelling and electrical field simulation activities were performed with the software CST (Computer Simulation Technology) from Dessault Systèms. CRT with biventricular pacing is to be achieved by an apical right ventricular electrode and an additional left ventricular electrode, which is floated into the coronary vein sinus. The non-responder rate of the CRT therapy is about one third of the CRT patients. His- Bundle-Pacing represents a physiological alternative to conventional cardiac pacing and cardiac resynchronization. An electrode implanted in the His-bundle emits a stronger electrical pacing field than the electrical pacing field of conventional cardiac pacemakers. The pacing of the Hisbundle was performed by the Medtronic Select Secure 3830 electrode with pacing voltage amplitudes of 3 V, 2 V and 1,5 V in combination with a pacing pulse duration of 1 ms. Compared to conventional pacemaker pacing, His-bundle pacing is capable of bridging LBBB conduction disorders in the left ventricle. The His-bundle pacing electrical field is able to spread via the physiological pathway in the right and left ventricles for CRT with a narrow QRS-complex in the surface ECG.
The TriRhenaTech alliance presents a collection of accepted papers of the cancelled tri-national 'Upper-Rhine Artificial Intelligence Symposium' planned for 13th May 2020 in Karlsruhe. The TriRhenaTech alliance is a network of universities in the Upper-Rhine Trinational Metropolitan Region comprising of the German universities of applied sciences in Furtwangen, Kaiserslautern, Karlsruhe, and Offenburg, the Baden-Wuerttemberg Cooperative State University Loerrach, the French university network Alsace Tech (comprised of 14 'grandes écoles' in the fields of engineering, architecture and management) and the University of Applied Sciences and Arts Northwestern Switzerland. The alliance's common goal is to reinforce the transfer of knowledge, research, and technology, as well as the cross-border mobility of students.
For the standard ISO 16842 cruciform test specimen, stresses obtained from the gauge area are far below the ultimate tensile strength due to high stress concentrations at the slit ends which lead to premature failure. Objective: To introduce a new cruciform specimen design which has been optimized with respect to the determination of yield surfaces. Methods: The proposed design differs from the ISO standard by an additional thinning of the gauge area and wider slits in the arms to avoid stress singularities. Compared to other cruciform test piece designs found in the literature, the stress distribution is still homogeneous and there is no need to reduce the size of the gauge area, thanks to the specimen’s well-balanced proportions. Results: Biaxial tensile tests have been conducted with aluminium 5754 alloy samples of different thicknesses. For the standard cruciform test piece, the maximum strain achieved at the gauge area is only 25% of the fracture strain. The optimized cruciform test piece can attain about 66% of the fracture strain before breaking. Conclusions: The optimized specimen design enables the measurement of yield surfaces at higher stress levels. In case of other materials such as elastomers, the slit length has be to adjusted accordingly.
OVVL (the Open Weakness and Vulnerability Modeller) is a tool and methodology to support threat modeling in the early stages of the secure software development lifecycle. We provide an overview of OVVL (https://ovvl.org), its data model and browser-based UI. We equally provide a discussion of initial experiments on how identified threats in the design phase can be aligned with later activities in the software lifecycle (issue management and security testing).
Threat Modelling is an accepted technique to identify general threats as early as possible in the software development lifecycle. Previous work of ours did present an open-source framework and web-based tool (OVVL) for automating threat analysis on software architectures using STRIDE. However, one open problem is that available threat catalogues are either too general or proprietary with respect to a certain domain (e.g. .Net). Another problem is that a threat analyst should not only be presented (repeatedly) with a list of all possible threats, but already with some automated support for prioritizing these. This paper presents an approach to dynamically generate individual threat catalogues on basis of the established CWE as well as related CVE databases. Roughly 60% of this threat catalogue generation can be done by identifying and matching certain key values. To map the remaining 40% of our data (~50.000 CVE entries) we train a text classification model by using the already mapped 60% of our dataset to perform a supervised machine-learning based text classification. The generated entire dataset allows us to identify possible threats for each individual architectural element and automatically provide an initial prioritization. Our dataset as well as a supporting Jupyter notebook are openly available.
While prospect of tracking mobile devices' users is widely discussed all over European countries to counteract COVID-19 propagation, we propose a Bloom filter based construction providing users' location privacy and preventing mass surveillance.
We apply a solution based on Bloom filters data structure that allows a third party, a government agency, to perform some privacy-preserving set relations on a mobile telco's access logfile.
By computing set relations, the government agency, given the knowledge of two identified persons, has an instrument that provides a (possible) infection chain from the initial to the final infected user no matter at which location on a worldwide scale they are.
The benefit of our approach is that intermediate possible infected users can be identified and subsequently contacted by the agency. With such approach, we state that solely identities of possible infected users will be revealed and location privacy of others will be preserved. To this extent, it meets General Data Protection Regulation (GDPR)requirements in this area.
Power systems are increasingly built from distributed generation units and smart consumers that are able to react to grid conditions. Managing this large number of decentralized electricity sources and flexible loads represent a very huge optimization problem. Both from the regulatory and the computational perspective, no one central coordinator can optimize this overall system. Decentralized control mechanisms can, however, distribute the optimization task through price signals or market-based mechanisms. This chapter presents the concepts that enable a decentralized control of demand and supply while enhancing overall efficiency of the electricity system. It highlights both technological and business challenges that result from the realization of these concepts, and presents the state-of-the-art in the respective domains.