IFAC-PapersOnLine最新文献

{"title":"Disaggregating Train Delays into Primary and Secondary Components using Gated Graph Convolutional Networks⁎","authors":"Maximilian Viehauser ,&nbsp;Martin Bicher ,&nbsp;Matthias Rößler ,&nbsp;Niki Popper","doi":"10.1016/j.ifacol.2025.03.075","DOIUrl":"10.1016/j.ifacol.2025.03.075","url":null,"abstract":"<div><div>This study presents a novel approach for disaggregating aggregated train delays into primary and secondary components using Gated Graph Convolutional Networks (GatedGCNs). We develop a graph-based representation of railway traffic that captures complex spatiotemporal relationships and long-range dependencies. Our method is applied to synthetic delay data generated from an agent-based simulation model of the Austrian railway network. We evaluate the model on classification and regression tasks, demonstrating high accuracy in distinguishing between primary and secondary delays. The classification task achieves 96% accuracy and 0.99 AUC, while the regression task attains an R-squared value of 0.86. These results significantly outperform a naive baseline model. The findings suggest that GatedGCN is a promising method for delay disaggregation and has potential for broader applications in capturing delay propagation processes. However, while the results on synthetic data demonstrate strong performance, further validation on real-world data is essential to confirm its practical applicability.</div></div>","PeriodicalId":37894,"journal":{"name":"IFAC-PapersOnLine","volume":"59 1","pages":"Pages 439-444"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143704140","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Using the Envelope of the Electroencephalogram as a Model for Gaussianity during Sleep and Anesthesia","authors":"Julian Ostertag ,&nbsp;Tobias Kraft-Blank ,&nbsp;Gerhard Schneider ,&nbsp;Matthias Kreuzer ,&nbsp;Juliana Zimmermann","doi":"10.1016/j.ifacol.2025.03.051","DOIUrl":"10.1016/j.ifacol.2025.03.051","url":null,"abstract":"<div><div>Despite the significant differences between sleeping and being under anesthesia, i.e., a physiological process vs. a pharmacologically induced state, they share notable similarities. This is particularly evident when examining the electroencephalogram (EEG), where the spectral content of both states reveals marked increased power within delta (1 - 4 Hz) and alpha (8 - 13 Hz) frequency ranges. To further explore this, a novel analytical framework called the coefficient of variation of the envelope (CVE) was utilized to assess the alpha and delta EEG envelopes during sleep and general anesthesia. This measure is sensitive to different underlying neural dynamics by linking signal morphology and signal energy, specifically through examining deviations from Gaussianity as a marker of synchronicity. Stable episodes were extracted from patients under general anesthesia and controls in non-REM sleep stage 2 and 3. After filtering the EEGs to isolate the delta and alpha bands, the EEG data was segmented into 24-second intervals with a 50% overlap. In addition to the envelope’s energy, CVEs were calculated using the Hilbert transformation. Cutoff values for Gaussianity were derived from simulated EEG signals. CVE values outside the 99% confidence intervals (CI) of the simulated data are considered to indicate either rhythmic (CV E &lt; lowerCI) or pulsatile (CV E &gt; upperCI) activity. The findings revealed differences in CVEs across both delta and alpha-band filtered EEG. Specifically, during sleep, CVEs derived from the delta band were more frequently classified as pulsatile and fell less often within the gaussian range, compared to those observed during general anesthesia. Similar distinctions were observed for alpha-band oscillations. Although the spectral content related to delta and alpha power may appear similar, the morphology of the underlying neural oscillations differs. These differences are critical points that differentiate anesthesia from sleep.</div></div>","PeriodicalId":37894,"journal":{"name":"IFAC-PapersOnLine","volume":"59 1","pages":"Pages 295-300"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143704258","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Towards time adaptive observations for model order reduction in data assimilation","authors":"C. Gräßle ,&nbsp;J. Marquardt","doi":"10.1016/j.ifacol.2025.03.059","DOIUrl":"10.1016/j.ifacol.2025.03.059","url":null,"abstract":"<div><div>In this work, we focus on two aspects of 4D-var data assimilation (DA) governed by parabolic partial differential equations (PDEs). First, we are interested on how to set up adaptive time grids for DA problems and in what extend DA benefits from it. Second, we study the application of model order reduction (MOR) for DA problems. Since solving DA problems requires to solve the involved PDEs repeatedly, the use of MOR techniques is an obvious approach. We apply the methods Proper Orthogonal Decomposition (POD) and Dynamic Mode Decomposition (DMD) and investigate whether the previously introduced adaptive time grid facilitates the MOR with respect to accuracy and efficiency.</div><div>In order to construct an adaptive time grid, we interpret the DA problem in the context of optimal control and use a reformulation of the optimality conditions. Following Gong et al. (2012), we transferred their idea of deriving a-posteriori error estimates to the 4D-var problem in Graßle and Marquardt (2024). In this work, we extend our previous results where we derived an error estimate for the adjoint state by additionally considering an estimate for the state. The resulting time grid is used for MOR, which has already been done for distributed control problems in order to identify suitable snapshot locations, see Alla et al. (2016, 2018). We conclude with a numerical example.</div></div>","PeriodicalId":37894,"journal":{"name":"IFAC-PapersOnLine","volume":"59 1","pages":"Pages 343-348"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143704266","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Impact of topography and combustion functions on fire front propagation in an advection-diffusion-reaction model for wildfires","authors":"Luca Nieding ,&nbsp;Cordula Reisch ,&nbsp;Dirk Langemann ,&nbsp;Adrián Navas-Montilla","doi":"10.1016/j.ifacol.2025.03.019","DOIUrl":"10.1016/j.ifacol.2025.03.019","url":null,"abstract":"<div><div>Given the recent increase in wildfires, developing a better understanding of their dynamics is crucial. For this purpose, the advection-diffusion-reaction model has been widely used to study wildfire dynamics. In this study, we introduce the previously unconsidered influence of topography through an additional advective term. Furthermore, we propose a linear term for the combustion function, comparing it with the commonly used Arrhenius law to offer a simpler model for further analysis. Our findings on the model’s dynamics are supported by numerical simulations showing the differences of model extensions and approximations.</div></div>","PeriodicalId":37894,"journal":{"name":"IFAC-PapersOnLine","volume":"59 1","pages":"Pages 103-108"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143704308","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Control-Oriented Gray-Box Modeling for Thermoset Injection Molding⁎","authors":"Jens Ahlers ,&nbsp;Christopher Schulte ,&nbsp;Moritz Mascher ,&nbsp;Christoph Zimmermann ,&nbsp;Heike Vallery ,&nbsp;Christian Hopmann ,&nbsp;Sebastian Stemmler","doi":"10.1016/j.ifacol.2025.03.004","DOIUrl":"10.1016/j.ifacol.2025.03.004","url":null,"abstract":"<div><div>Cavity pressure control can enhance the repeatability of injection molding processes. While extensive research has focused on thermoplastic cavity pressure control, there is a notable gap in models and control strategies for thermoset injection molding. This study aims to develop a model structure for thermoset injection molding suitable for integration into a model-based control scheme. The modeling approach is intended to be as generalizable as possible and sufficiently flexible to adapt to various process conditions. At the same time, it should be easy to parameterize or to train. To address this challenge, we first derive a first-principles process model. In the second step, we integrate a feed-forward artificial neural network into this model, which learns parameters and source terms from past injection molding cycles, resulting in a gray-box model. The neural network outputs replace the initial model parameters with functions of system inputs, states, and time. We validate both models against experimental data from a thermoset injection molding machine using a fat-plate mold geometry and a phenolic resin compound. We identify limitations of the proposed approach and suggest potential solutions.</div></div>","PeriodicalId":37894,"journal":{"name":"IFAC-PapersOnLine","volume":"59 1","pages":"Pages 13-18"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143704317","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Modelling of an underground electric vehicle cycle for battery sizing analysis using Modelica","authors":"Juan J. Pérez-Sánchez ,&nbsp;Alfonso Urquía","doi":"10.1016/j.ifacol.2025.03.007","DOIUrl":"10.1016/j.ifacol.2025.03.007","url":null,"abstract":"<div><div>A Modelica library for battery sizing of underground mining electric vehicles is presented. The library named <em>UGMiningBEV</em>, facilitates modelling underground mining electric vehicles with different design parameters and architectures, under varying working scenarios. <em>UGMiningBEV</em> includes models of vehicle transmission, auxiliary driving-related systems (steering and braking), mixer drum, lighting, HVAC (heating, ventilation and air conditioning), cooling, battery and battery charging. It also includes validation examples of relevant components and the model of an electric concrete mixer for underground mining and its typical working cycle. The technical contribution of the library lies in the variety of subsystems that can be integrated, combined with a working cycle that allows the simulation of vehicle mission profiles based on distance travelled or time elapsed. The <em>UGMiningBEV</em> library has been developed using Dymola 2021x and is freely available under the Modelica License 2 terms.</div></div>","PeriodicalId":37894,"journal":{"name":"IFAC-PapersOnLine","volume":"59 1","pages":"Pages 31-36"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143704405","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Interpolatory model reduction of dynamical systems with root mean squared error","authors":"Sean Reiter ,&nbsp;Steffen W.R. Werner","doi":"10.1016/j.ifacol.2025.03.066","DOIUrl":"10.1016/j.ifacol.2025.03.066","url":null,"abstract":"<div><div>The root mean squared error is an important measure used in a variety of applications like structural dynamics and acoustics to model averaged deviations from standard behavior. For large-scale systems, simulations of this quantity quickly become computationally prohibitive. Model order reduction techniques resolve this issue via the construction of surrogate models that emulate the root mean squared error measure using an intermediate linear system. However, classical approaches require a large number of system outputs, which is disadvantageous in the design of reduced-order models. In this work, we consider directly the root mean squared error as the quantity of interest using the concept of quadratic-output models and propose several new model reduction techniques for the construction of appropriate surrogates. Numerical tests are performed on a model of a plate with tuned vibration absorbers.</div></div>","PeriodicalId":37894,"journal":{"name":"IFAC-PapersOnLine","volume":"59 1","pages":"Pages 385-390"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143704131","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"2D Sheet Temperature Estimation for Multi-Stage Press Hardening in a Progressive Die⁎","authors":"Henry Baumann ,&nbsp;Jakob Nazarenus ,&nbsp;Juri Martschin ,&nbsp;Erman Tekkaya ,&nbsp;Thomas Meurer","doi":"10.1016/j.ifacol.2025.03.024","DOIUrl":"10.1016/j.ifacol.2025.03.024","url":null,"abstract":"<div><div>During a multi-stage press hardening process, where a metal sheet undergoes rapid austenitization, tempering, and forming, the spatial-temporal temperature development is a driving factor and its control enables it to reach desired product properties. A two-dimensional model extension is proposed, which enables a precise and computationally efficient temperature modeling along the whole metal sheet. Thereupon, a Kalman filter with a time-varying stage dependent output matrix is used to estimate the spatial-temporal temperature development of the sheet based on measurements from a thermal imaging camera. Both the proposed modeling approach and the estimation scheme are validated experimentally.</div></div>","PeriodicalId":37894,"journal":{"name":"IFAC-PapersOnLine","volume":"59 1","pages":"Pages 133-138"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143704142","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Model-based fault simulation and detection for gauge-sensorized strain wave gears","authors":"Julian Kißkalt ,&nbsp;Andreas Michalka ,&nbsp;Christoph Strohmeyer ,&nbsp;Maik Horn ,&nbsp;Knut Graichen","doi":"10.1016/j.ifacol.2025.03.047","DOIUrl":"10.1016/j.ifacol.2025.03.047","url":null,"abstract":"<div><div>Strain wave gears (SWG) are important parts to drive robots. Yet, they are susceptible to wear and degradation carrying high risk for malfunction of the robot. Hence, the detection of faults in SWGs is of great interest for the robot’s safe operation. In this paper, the capability of a simulation chain is shown to generate faulty SWGs’ signals of strain gauge sensors mounted on the back of their flex spline with qualitatively similar behavior to real-world measurement signals. Furthermore, the simulated sensor signals are used for a data-driven detection of faults in measurement data showing the practicality of this approach for real-world applications.</div></div>","PeriodicalId":37894,"journal":{"name":"IFAC-PapersOnLine","volume":"59 1","pages":"Pages 271-276"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143704254","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Online adaptive surrogates for the value function of high-dimensional nonlinear optimal control problems⁎","authors":"Tobias Ehring ,&nbsp;Bernard Haasdonk","doi":"10.1016/j.ifacol.2025.03.057","DOIUrl":"10.1016/j.ifacol.2025.03.057","url":null,"abstract":"<div><div>We introduce a strategy that generates an adaptive surrogate of the value function of high-dimensional nonlinear optimal control problems. It exploits the relevant operating domain online on which the resulting surrogate satisfies the Hamilton–Jacobi–Bellman (HJB) equation up to a given threshold. The approximate value function is based on Hermite kernel regression, where the data stems from open-loop control of reduced-order optimal control problems. As a measure of accuracy, the full-order HJB residual, known as the Bellman error, is used to determine whether the current Hermite kernel surrogate is sufficient or further training is required. In addition, the reduced-order model can also be improved using the full-order data if the same HJB-based error indicator suggests that the current reduced system is not accurate enough. Numerical experiments support the effectiveness of the new scheme.</div></div>","PeriodicalId":37894,"journal":{"name":"IFAC-PapersOnLine","volume":"59 1","pages":"Pages 331-336"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143704264","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}