@article{63720, abstract = {{The aging behavior of closed-cell polyurethane (PUR) foam, a material widely used in household refrigeration, is studied by examining the variation of cell gas composition and thermal conductivity over time. Aging is primarily driven by gas permeation, wherein the initially present cell gases carbon dioxide and cyclopentane are progressively replaced by nitrogen and oxygen from the ambient, resulting in an increased thermal conductivity and reduced insulation performance. The cell gas composition is measured over 1400 days employing gas chromatography, and the thermal conductivity of the foam is measured over 190 days. Morphological foam characteristics, such as average cell diameter, are determined via scanning electron microscopy and barrier measurements are performed to estimate the effective diffusion coefficient of oxygen. To simulate the aging process, one-dimensional and three-dimensional models are developed for both diffusive mass transfer as well as heat transfer. The present model for the thermal conductivity explicitly accounts for condensation effects, i.e. partial condensation of cyclopentane and carbon dioxide occurring at around 12°C, which significantly influences the insulation behavior of the foam. Sensitivity analyses indicate that an initial cell gas pressure of approximately 0.7 bar yields results that closely coincide with the experimental measurements, where the three-dimensional model demonstrates better accuracy. These measurements and simulations provide valuable insights for evaluating and predicting the long-term degradation of the insulation performance of PUR foams.}}, author = {{Schumacher, Daniel and Guevara-Carrion, Gabriela and Kasper, Tina and Paul, Andreas and Elsner, Andreas and Peters, Bettina and Wollny, Wenke and Bluemel, Marcus and Hoelscher, Heike and Brzoska-Steinhaus, Nicola and Heil, Klaus and Schleelein, Lukas and Becker, Wolfgang and Gries, Ulrich and Vrabec, Jadran}}, issn = {{1359-4311}}, journal = {{Applied Thermal Engineering}}, keywords = {{Polyurethane, Foam, Gas permeation, Diffusion models, Thermal conductivity, Condensation, Gas chromatography, Scanning electron microscopy}}, publisher = {{Elsevier BV}}, title = {{{Aging of polyurethane foam: Experimental analysis and modeling of cell gas composition and thermal conductivity}}}, doi = {{10.1016/j.applthermaleng.2026.129850}}, volume = {{289}}, year = {{2026}}, } @inproceedings{65606, abstract = {{Sound capture by microphone arrays opens the possibility to exploit spatial, in addition to spectral, information for diarization and signal enhancement, two important tasks in meeting transcription. However, there is no one-to-one mapping of positions in space to speakers if speakers move. Here, we address this by proposing a novel joint spatial and spectral mixture model, whose two submodels are loosely coupled by modeling the relationship between speaker and position index probabilistically. Thus, spatial and spectral information can be jointly exploited, while at the same time allowing for speakers speaking from different positions. Experiments on the LibriCSS data set with simulated speaker position changes show great improvements over tightly coupled subsystems.}}, author = {{Meise, Adrian Tobias and Cord-Landwehr, Tobias and Boeddeker, Christoph and Delcroix, Marc and Nakatani, Tomohiro and Haeb-Umbach, Reinhold}}, booktitle = {{ICASSP 2026 - 2026 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)}}, keywords = {{mixture models, meeting processing, diarization, source separation}}, location = {{Barcelona}}, publisher = {{IEEE}}, title = {{{Loose Coupling of Spectral and Spatial Models for Multi-Channel Diarization and Enhancement of Meetings in Dynamic Environments}}}, doi = {{10.1109/icassp55912.2026.11463540}}, year = {{2026}}, } @article{62643, author = {{Schwabe, Tobias and Kress, Christian and Kruse, Stephan and Weizel, Maxim and Rhee, Hanjo and Scheytt, J. Christoph}}, journal = {{Journal of Lightwave Technology}}, keywords = {{Integrated circuit modeling, Capacitance, Silicon, Modulation, Adaptation models, Semiconductor device modeling, Bandwidth, Data communication, electrooptical transmitter, equalization, free-carrier-plasma dispersion effect, modelling, optical modulator, phase shifter, silicon photonics}}, number = {{1}}, pages = {{255--270}}, title = {{{Forward-Biased Silicon Phase Shifter Modeling for Electronic-Photonic Co-Simulation and Validation in a 250 nm EPIC BiCMOS Technology}}}, doi = {{10.1109/JLT.2024.3450949}}, volume = {{43}}, year = {{2025}}, } @article{63498, author = {{Kirchgässner, Wilhelm and Förster, Nikolas and Piepenbrock, Till and Schweins, Oliver and Wallscheid, Oliver}}, journal = {{IEEE Transactions on Power Electronics}}, keywords = {{Mathematical models, Estimation, Data models, Convolutional neural networks, Accuracy, Magnetic hysteresis, Magnetic cores, Temperature measurement, Magnetic domains, Temperature distribution, Convolutional neural network (CNN), machine learning (ML), magnetics}}, number = {{2}}, pages = {{3326--3335}}, title = {{{HARDCORE: H-Field and Power Loss Estimation for Arbitrary Waveforms With Residual, Dilated Convolutional Neural Networks in Ferrite Cores}}}, doi = {{10.1109/TPEL.2024.3488174}}, volume = {{40}}, year = {{2025}}, } @article{58076, abstract = {{This paper presents the concept of Information Circularity Assistance, which provides decision support in the early stages of product creation for Circular Economy. Engineers in strategic product planning need to proactively predict the quantity, quality, and timing of secondary materials and returned components. For example, products with high recycled content will only be economically sustainable if the material is actually available in the future product life. Our assumption is that Information Circularity Assistance enables decision makers to incorporate insights from extreme data – high-volume, high-velocity, heterogeneous and distributed data from the product life – into product creation through intelligent Digital Twins. Artificial Intelligence can help to derive sustainable actions in favor of circular products by processing extreme data and enriching it with expert knowledge. The research contributes in three key dimensions. First, a comprehensive literature review is conducted. This review covers concepts of intelligence in Scenario-Technique for strategic product planning, Digital Twin-based analysis of extreme data and relevant technologies from Data Science and Artificial Intelligence. In all areas, the state of the art and emerging trends are identified. Secondly, the study identifies information needs along the steps of the Scenario-Technique and information offerings based on Digital Twins. The concept of Information Circularity Assistance results from the coupling of these demands and offerings, extending the Scenario-Technique beyond traditional expert-based methods. Third, we extend existing Digital Twin methods used in circularity and discuss the deployment of Data Science and Artificial Intelligence algorithms within the product creation process. Our approach uses extreme data to provide a strategic advantage in optimizing product life cycle planning, which is illustrated by two sample applications. The aim is to provide Information Circularity Assistance that will support experienced product planners, developers, and decision makers in the future.}}, author = {{Gräßler, Iris and Weyrich, Michael and Pottebaum, Jens and Kamm, Simon}}, issn = {{0178-2312}}, journal = {{at - Automatisierungstechnik}}, keywords = {{Scenario-Technique, Artificial Intelligence, Digital Twin, Large Language Models}}, number = {{1}}, pages = {{3--21}}, publisher = {{Walter de Gruyter GmbH}}, title = {{{Information Circularity Assistance based on extreme data}}}, doi = {{10.1515/auto-2024-0039}}, volume = {{73}}, year = {{2025}}, } @inproceedings{61057, abstract = {{Verification and Validation (V&V) are essential processes in engineering Cyber-Physical Systems. However, the role of V&V engineers is often not given sufficient attention. Based on a systematic literature analysis and practical observations, a four-step method for Test-oriented Resilient Requirements Engineering (ToRRE) is developed. The steps are planning V&V, executing V&V activities, documenting V&V activities and analyzing results of V&V activities. Applying ToRRE ensures continuous information flow and traceability. Engineers are enabled to analyze requirements using engineering artifacts connected through Model-Based Systems Engineering. Adopting methods for Model-Based Effect Chain analysis to evaluated test cases and test scenarios, conclusions on requirements engineering and change management are enabled. The method is evaluated in an EU research project.}}, author = {{Gräßler, Iris and Ebel, Marcel}}, booktitle = {{Proceedings of the Design Society}}, issn = {{2732-527X}}, keywords = {{systems engineering (SE), product modelling/models, design methods, verification & validation, test cases & test scenarios}}, location = {{Dallas, Texas, USA}}, pages = {{3031--3040}}, publisher = {{Cambridge University Press (CUP)}}, title = {{{Test-oriented Resilient Requirements Engineering (ToRRE): extending model-based effect chain analysis to verification objectives}}}, doi = {{10.1017/pds.2025.10317}}, volume = {{5}}, year = {{2025}}, } @article{57892, abstract = {{The present paper discusses the extent to which Large Language Models (LLMs) may affect the scientific enterprise, reinforcing or mitigating existing structural inequalities expressed by the Matthew Effect and introducing a “bot delusion” in academia. In a theory-led thought experiment, we first focus on the academic publication and citation system and develop three scenarios of the anticipated consequences of using LLMs: reproducing content and status quo (Scenario 1), enabling content coherence evaluation (Scenario 2) and content evaluation (Scenario 3). Second, we discuss the interaction between the use of LLMs and academic (counter)norms for citation selection and their impact on the publication and citation system. Finally, we introduce communal counter-norms to capture academics’ loyal citation behavior and develop three future scenarios that academia may face when LLMs are widely used in the research process, namely status quo future of science, mixed-access future, and open science future.}}, author = {{Wieczorek, Oliver and Steinhardt, Isabel and Schmidt, Rebecca and Mauermeister, Sylvi and Schneijderberg, Christian}}, issn = {{0016-3287}}, journal = {{Futures}}, keywords = {{Large Language Models, Matthew Effect, Academic Publishing and Citation Systems, Scientific Norms, Thought Experiment}}, publisher = {{Elsevier BV}}, title = {{{The Bot Delusion. Large language models and anticipated consequences for academics’ publication and citation behavior}}}, doi = {{10.1016/j.futures.2024.103537}}, volume = {{166}}, year = {{2024}}, } @inproceedings{56983, abstract = {{Detecting the veracity of a statement automatically is a challenge the world is grappling with due to the vast amount of data spread across the web. Verifying a given claim typically entails validating it within the framework of supporting evidence like a retrieved piece of text. Classifying the stance of the text with respect to the claim is called stance classification. Despite advancements in automated fact-checking, most systems still rely on a substantial quantity of labeled training data, which can be costly. In this work, we avoid the costly training or fine-tuning of models by reusing pre-trained large language models together with few-shot in-context learning. Since we do not train any model, our approach ExPrompt is lightweight, demands fewer resources than other stance classification methods and can serve as a modern baseline for future developments. At the same time, our evaluation shows that our approach is able to outperform former state-of-the-art stance classification approaches regarding accuracy by at least 2 percent. Our scripts and data used in this paper are available at https://github.com/dice-group/ExPrompt.}}, author = {{Qudus, Umair and Röder, Michael and Vollmers, Daniel and Ngonga Ngomo, Axel-Cyrille}}, booktitle = {{Proceedings of the 33rd ACM International Conference on Information and Knowledge Management}}, isbn = {{79-8-4007-0436-9/24/10}}, keywords = {{Stance Classification, Few-shot in-context learning, Pre-trained large language models}}, location = {{Boise, ID, USA}}, pages = {{3994 -- 3999}}, publisher = {{ACM}}, title = {{{ExPrompt: Augmenting Prompts Using Examples as Modern Baseline for Stance Classification}}}, doi = {{10.1145/3627673.3679923}}, volume = {{9}}, year = {{2024}}, } @article{37613, abstract = {{Chemical phenomena are only observable on a macroscopic level, whereas they are explained by entities on a non-visible level. Students often demonstrate limited ability to link these different levels. Augmented reality (AR) offers the possibility to increase contiguity by embedding virtual models into hands-on experiments. Therefore, this paper presents a pre- and post-test study investigating how learning and cognitive load are influenced by AR during hands-on experiments. Three comparison groups (AR, animation and filmstrip), with a total of N = 104 German secondary school students, conducted and explained two hands-on experiments. Whereas the AR group was allowed to use an AR app showing virtual models of the processes on the submicroscopic level during the experiments, the two other groups were provided with the same dynamic or static models after experimenting. Results indicate no significant learning gain for the AR group in contrast to the two other groups. The perceived intrinsic cognitive load was higher for the AR group in both experiments as well as the extraneous load in the second experiment. It can be concluded that AR could not unleash its theoretically derived potential in the present study.}}, author = {{Peeters, Hendrik and Habig, Sebastian and Fechner, Sabine}}, issn = {{2414-4088}}, journal = {{Multimodal Technologies and Interaction}}, keywords = {{augmented reality, chemistry education, models, experiment, cognitive load}}, number = {{2}}, publisher = {{MDPI AG}}, title = {{{Does augmented reality help to understand chemical phenomena during hands-on experiments?–Implications for cognitive load and learning}}}, doi = {{10.3390/mti7020009}}, volume = {{7}}, year = {{2023}}, } @inproceedings{52865, abstract = {{This paper addresses new challenges of detecting campaigns in social media, which emerged with the rise of Large Language Models (LLMs). LLMs particularly challenge algorithms focused on the temporal analysis of topical clusters. Simple similarity measures can no longer capture and map campaigns that were previously broadly similar in content. Herein, we analyze whether the classification of messages over time can be profitably used to rediscover poorly detectable campaigns at the content level. Thus, we evaluate classical classifiers and a new method based on siamese neural networks. Our results show that campaigns can be detected despite the limited reliability of the classifiers as long as they are based on a large amount of simultaneously spread artificial content.}}, author = {{Grimme, Britta and Pohl, Janina and Winkelmann, Hendrik and Stampe, Lucas and Grimme, Christian}}, booktitle = {{Disinformation in Open Online Media: 5th Multidisciplinary International Symposium, MISDOOM 2023, Amsterdam, The Netherlands, November 21–22, 2023, Proceedings}}, isbn = {{978-3-031-47895-6}}, keywords = {{Social Media, Campaign Detection, Large Language Models, Siamese Neural Networks}}, pages = {{72–87}}, publisher = {{Springer-Verlag}}, title = {{{Lost in Transformation: Rediscovering LLM-Generated Campaigns in Social Media}}}, doi = {{10.1007/978-3-031-47896-3_6}}, year = {{2023}}, } @article{35992, abstract = {{In this paper new semiparametric generalized autoregressive conditional heteroscedasticity (GARCH) models with long memory are introduced. A multiplicative decomposition of the volatility into a conditional component and an unconditional component is assumed. The estimation of the latter is carried out by means of a data-driven local polynomial smoother. According to the revised recommendations by the Basel Committee on Banking Supervision to measure market risk in the banks’ trading books, these new semiparametric GARCH models are applied to obtain rolling one-step ahead forecasts for the value-at-risk and expected shortfall (ES) for market risk assets. Standard regulatory traffic-light tests and a newly introduced traffic-light test for the ES are carried out for all models. In addition, model performance is assessed via a recently introduced model selection criterion. The practical relevance of our proposal is demonstrated by a comparative study. Our results indicate that semiparametric long-memory GARCH models are a meaningful substitute for their conventional, parametric counterparts. }}, author = {{Letmathe, Sebastian and Feng, Yuanhua and Uhde, André}}, journal = {{Journal of Risk}}, keywords = {{long memory, generalized autoregressive conditional heteroscedasticity (GARCH) models, value-at-risk (VaR), expected shortfall (ES), traffic-light test, backtesting}}, number = {{2}}, title = {{{Semiparametric GARCH models with long memory applied to Value at Risk and Expected Shortfall}}}, volume = {{25}}, year = {{2022}}, } @article{29317, abstract = {{In this paper new semiparametric GARCH models with long memory are in- troduced. The estimation of the nonparametric scale function is carried out by an adapted version of the SEMIFAR algorithm (Beran et al., 2002). Recurring on the revised recommendations by the Basel Committee to measure market risk in the banks' trading books (Basel Committee on Banking Supervision, 2013), the semi- parametric GARCH models are applied to obtain rolling one-step ahead forecasts for the Value at Risk (VaR) and Expected Shortfall (ES) for market risk assets. In addition, standard regulatory traffic light tests (Basel Committee on Banking Supervision, 1996) and a newly introduced traffic light test for the ES are carried out for all models. The practical relevance of our proposal is demonstrated by a comparative study. Our results indicate that semiparametric long memory GARCH models are an attractive alternative to their conventional, parametric counterparts.}}, author = {{Letmathe, Sebastian and Feng, Yuanhua and Uhde, André}}, journal = {{Journal of Risk}}, keywords = {{Semiparametric, long memory, GARCH models, forecasting, Value at Risk, Expected Shortfall, traffic light test, Basel Committee on Banking Supervision}}, title = {{{Semiparametric GARCH models with long memory applied to Value at Risk and Expected Shortfall}}}, doi = {{10.21314/JOR.2022.044}}, year = {{2022}}, } @inproceedings{21727, abstract = {{Platform-based business models underlie the success of many of today’s largest, fastest-growing, and most disruptive companies. Despite the success of prominent examples, such as Uber and Airbnb, creating a profitable platform ecosystem presents a key challenge for many companies across all industries. Although research provides knowledge about platforms’ different value drivers (e.g., network effects), companies that seek to transform their current business model into a platform-based one lack an artifact to reduce knowledge boundaries, collaborate effectively, and cope with the complexities and dynamics of platform ecosystems. We address this challenge by developing two artifacts and combining research from variability modeling, business model dependencies, and system dynamics. This paper presents a design science research approach to develop the platform ecosystem modeling language and the platform ecosystem development tool that support researcher and practitioner by visualizing and simulating platform ecosystems. }}, author = {{Vorbohle, Christian and Gottschalk, Sebastian}}, booktitle = {{Proceedings of the 29th European Conference on Information Systems (ECIS)}}, keywords = {{Platform Ecosystems, Platform Ecosystem Modeling Language, Platform Ecosystem Development Tool, Business Models, Design Science}}, location = {{Virtual Conference/Workshop}}, publisher = {{AIS}}, title = {{{Towards Visualizing and Simulating Business Models in Dynamic Platform Ecosystems }}}, year = {{2021}}, } @inbook{25528, abstract = {{Developing effective business models is a complex process for a company where several tasks (e.g., conduct customer interviews) need to be accomplished, and decisions (e.g., advertisement as a revenue stream) must be made. Here, domain experts can guide the choices of tasks and decisions with their knowledge. Nevertheless, this knowledge needs to match the situation of the company (e.g., financial resources) and the application domain of the product/service (e.g., mobile app) to reduce the risk of developing ineffective business models with low market penetration. This is not covered by one-size-fits-all development methods without tailoring before the enaction. Therefore, we conduct a design science study to create a situation-specific development approach for business models. Based on situational method engineering and our previous work in storing knowledge of methods and models in distinct repositories, this paper shows the situation-specific composition and enaction of business model development methods. First, the method engineer composes the development method out of both repositories based on the situational context. Second, the business developer enacts the method and develops the business model. We implement the approach in a tool and evaluate it with a industrial case study on mobile apps.}}, author = {{Gottschalk, Sebastian and Yigitbas, Enes and Nowosad, Alexander and Engels, Gregor}}, booktitle = {{Product-focused Software Process Improvement}}, keywords = {{Business Model Development, Situational Method Engineering, Lean Development, Kanban Boards, Canvas Models}}, location = {{Turin}}, publisher = {{Springer}}, title = {{{Situation- and Domain-specific Composition and Enactment of Business Model Development Methods}}}, year = {{2021}}, } @inproceedings{9275, abstract = {{In the last years, store-oriented software ecosystems are gaining more and more attention from a business perspective. In these ecosystems, third-party developers upload extensions to a store which can be downloaded by end users. While the functional scope of such ecosystems is relatively similar, the underlying business models differ greatly in and between their different product domains (e.g. Mobile Phone, Smart TV). This variability, in turn, makes it challenging for store providers to find a business model that fits their own needs. To handle this variability, we introduce the Business Variability Model (BVM) for modeling business model decisions. The basis of these decisions is the analysis of 60 store-oriented software ecosystems in eight different product domains. We map their business model decisions to the Business Model Canvas, condense them to a variability model and discuss particular variants and their dependencies. Our work provides store providers a new approach for modeling business model decisions together with insights of existing business models. This, in turn, supports them in creating new and improving existing business models.}}, author = {{Gottschalk, Sebastian and Rittmeier, Florian and Engels, Gregor}}, booktitle = {{Business Modeling and Software Design}}, editor = {{Shishkov, Boris}}, keywords = {{Software Ecosystems, Business Models, Variabilities}}, location = {{Lisbon}}, pages = {{153--169}}, publisher = {{Springer International Publishing}}, title = {{{Business Models of Store-Oriented Software Ecosystems: A Variability Modeling Approach}}}, doi = {{10.1007/978-3-030-24854-3_10}}, year = {{2019}}, } @inproceedings{9974, abstract = {{The integrated modeling of behavior and reliability in system development delivers a model-based approach for reliability investigation by taking into account the dynamic system behavior as well as the system architecture at different phases of the development process. This approach features an automated synthesis of a reliability model out of a behavior model enabling for the closed loop modeling of degradation of the system and its (dynamic) behavior. The approach is integrated into the development process following Systems Engineering. It is based on standard models used in model-based development methodologies i.e. SysML or Matlab/Simulink. In addition to the theoretical description of the necessary steps the procedure is validated by an application example at two stages of the development process.}}, author = {{Hentze, Julian and Kaul, Thorben and Grässler, Iris and Sextro, Walter}}, booktitle = {{ICED17, 21st International conference on enginieering design}}, keywords = {{Design for X (DfX), Product modelling / models, Robust design, Systems Engineering (SE), Reliability}}, pages = {{385--394}}, title = {{{Integrated modeling og behavior and reliability in system development}}}, year = {{2017}}, } @article{4586, abstract = {{This study examines the loan-pricing behavior of German banks for a large variety of retail and corporate loan products. We find that a bank’s operational efficiency is priced in bank loan rates and alters interest-setting behavior. Specifically, we establish that a higher degree of operational efficiency leads to lower loan markups, which makes prices more competitive and smoothes the setting of interest rates. By employing state-of-the-art stochastic frontier efficiency measures to capture a bank’s operational efficiency, we take a look at the bank customers’ perspective and demonstrate the extent to which bor-rowers benefit from cost-efficient banking. }}, author = {{Schlueter, Tobias and Busch, Ramona and Sievers, Soenke and Hartmann-Wendels, Thomas}}, journal = {{Credit and Capital Markets--Kredit und Kapital}}, keywords = {{interest rate pass-through models, error correction models, bank efficiency, cost efficiency, stochastic frontier analysis}}, number = {{1}}, pages = {{93--125}}, title = {{{Loan Pricing: Do Borrowers Benefit from Cost-Efficient Banking?}}}, doi = {{10.3790/ccm.49.1.93}}, volume = {{49}}, year = {{2016}}, } @inproceedings{11813, abstract = {{The parametric Bayesian Feature Enhancement (BFE) and a datadriven Denoising Autoencoder (DA) both bring performance gains in severe single-channel speech recognition conditions. The first can be adjusted to different conditions by an appropriate parameter setting, while the latter needs to be trained on conditions similar to the ones expected at decoding time, making it vulnerable to a mismatch between training and test conditions. We use a DNN backend and study reverberant ASR under three types of mismatch conditions: different room reverberation times, different speaker to microphone distances and the difference between artificially reverberated data and the recordings in a reverberant environment. We show that for these mismatch conditions BFE can provide the targets for a DA. This unsupervised adaptation provides a performance gain over the direct use of BFE and even enables to compensate for the mismatch of real and simulated reverberant data.}}, author = {{Heymann, Jahn and Haeb-Umbach, Reinhold and Golik, P. and Schlueter, R.}}, booktitle = {{Acoustics, Speech and Signal Processing (ICASSP), 2015 IEEE International Conference on}}, keywords = {{codecs, signal denoising, speech recognition, Bayesian feature enhancement, denoising autoencoder, reverberant ASR, single-channel speech recognition, speaker to microphone distances, unsupervised adaptation, Adaptation models, Noise reduction, Reverberation, Speech, Speech recognition, Training, deep neuronal networks, denoising autoencoder, feature enhancement, robust speech recognition}}, pages = {{5053--5057}}, title = {{{Unsupervised adaptation of a denoising autoencoder by Bayesian Feature Enhancement for reverberant asr under mismatch conditions}}}, doi = {{10.1109/ICASSP.2015.7178933}}, year = {{2015}}, } @inproceedings{17659, author = {{Polevoy, Gleb and Trajanovski, Stojan and de Weerdt, Mathijs M.}}, booktitle = {{Proceedings of the 2014 International Conference on Autonomous Agents and Multi-agent Systems}}, isbn = {{978-1-4503-2738-1}}, keywords = {{competition, equilibrium, market, models, shared effort games, simulation}}, pages = {{861--868}}, publisher = {{International Foundation for Autonomous Agents and Multiagent Systems}}, title = {{{Nash Equilibria in Shared Effort Games}}}, year = {{2014}}, } @inproceedings{9879, abstract = {{Application of prognostics and health management (PHM) in the field of Proton Exchange Membrane (PEM) fuel cells is emerging as an important tool in increasing the reliability and availability of these systems. Though a lot of work is currently being conducted to develop PHM systems for fuel cells, various challenges have been encountered including the self-healing effect after characterization as well as accelerated degradation due to dynamic loading, all which make RUL predictions a difficult task. In this study, a prognostic approach based on adaptive particle filter algorithm is proposed. The novelty of the proposed method lies in the introduction of a self-healing factor after each characterization and the adaption of the degradation model parameters to fit to the changing degradation trend. An ensemble of five different state models based on weighted mean is then developed. The results show that the method is effective in estimating the remaining useful life of PEM fuel cells, with majority of the predictions falling within 5\% error. The method was employed in the IEEE 2014 PHM Data Challenge and led to our team emerging the winner of the RUL category of the challenge.}}, author = {{Kimotho, James Kuria and Meyer, Tobias and Sextro, Walter}}, booktitle = {{Prognostics and Health Management (PHM), 2014 IEEE Conference on}}, keywords = {{ageing, particle filtering (numerical methods), proton exchange membrane fuel cells, remaining life assessment, PEM fuel cell prognostics, PHM, RUL predictions, accelerated degradation, adaptive particle filter algorithm, dynamic loading, model parameter adaptation, prognostics and health management, proton exchange membrane fuel cells, remaining useful life estimation, self-healing effect, Adaptation models, Data models, Degradation, Estimation, Fuel cells, Mathematical model, Prognostics and health management}}, pages = {{1--6}}, title = {{{PEM fuel cell prognostics using particle filter with model parameter adaptation}}}, doi = {{10.1109/ICPHM.2014.7036406}}, year = {{2014}}, }