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Environmental model extension for lane change prediction with neural networks 1

19th Stuttgart International Symposium on Automotive and Engine Technology, 2019

作者： Krüger, Martin Novo, Anne Stockem Nattermann, Till Mohamed, Manoj Bertram, Torsten ZF Group Automated Driving and Integral Cognitive Safety Düsseldorf40547 Germany TU Dortmund University Institute of Control Theory and Systems Engineering Dortmund44227 Germany

ISBN: (数字)9783658259396

ISBN: (纸本)9783658259389

Driven by the desire of improving traffic safety, traffic efficiency and a better utilization of the time people spend in traffic, the development proceeds from Advanced Driver Assistance systems (ADAS) towards fully automated systems. ADAS systems are categorized as level 2 systems according to the Society of Automotive Engineers (SAE) [1] definition, on a classification scheme from level 0 to 5 where level 5 is a fully automated system. With increasing automation level the complexity rises, due to the piecewise shift of responsibility towards the automated driving system.

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Non-intrusive reduced-order modeling for dynamical systems with spatially localized features

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Computer Methods in Applied Mechanics and engineering 2025年 444卷

作者： Leonidas Gkimisis Nicole Aretz Marco Tezzele Thomas Richter Peter Benner Karen E. Willcox Computational Methods in Systems and Control Theory (CSC) Max Planck Institute for Dynamics of Complex Technical Systems Sandtorstraße 1 39106 Magdeburg Germany Oden Institute for Computational Engineering and Sciences 201 E 24th St 78712 TX United States Department of Mathematics Emory University 400 Dowman Drive 30322 Atlanta United States Institute for Analysis and Numerics Otto-von-Guericke-Universität Magdeburg Universitätsplatz 2 39106 Magdeburg Germany

This work presents a non-intrusive reduced-order modeling framework for dynamical systems with spatially localized features characterized by slow singular value decay. The proposed approach builds upon two existing methodologies for reduced and full-order non-intrusive modeling, namely Operator Inference (OpInf) and sparse Full-Order Model (sFOM) inference. We decompose the domain into two complementary subdomains that exhibit fast and slow singular value decay. The dynamics of the subdomain exhibiting slow singular value decay are learned with sFOM while the dynamics with intrinsically low dimensionality on the complementary subdomain are learned with OpInf. The resulting, coupled OpInf-sFOM formulation leverages the computational efficiency of OpInf and the high resolution of sFOM, and thus enables fast non-intrusive predictions for conditions beyond those sampled in the training data set. A novel regularization technique with a closed-form solution based on the Gershgorin disk theorem is introduced to promote stable sFOM and OpInf models. We also provide a data-driven indicator for subdomain selection and ensure solution smoothness over the interface via a post-processing interpolation step. We evaluate the efficiency of the approach in terms of offline and online speedup through a quantitative, parametric computational cost analysis. We demonstrate the coupled OpInf-sFOM formulation for two test cases: a one-dimensional Burgers’ model for which accurate predictions beyond the span of the training snapshots are presented, and a two-dimensional parametric model for the Pine Island Glacier ice thickness dynamics, for which the OpInf-sFOM model achieves an average prediction error on the order of 1% with an online speedup factor of approximately 8 × compared to the numerical simulation.

关键词： Non-intrusive model reduction Data-driven modeling Operator inference Sparse full-order model inference

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Learning References with Gaussian Processes in Model Predictive control applied to Robot Assisted Surgery

Learning References with Gaussian Processes in Model Predict...

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European control Conference (ECC)

作者： Janine Matschek Tim Gonschorek Magnus Hanses Norbert Elkmann Frank Ortmeier Rolf Findeisen Laboratory for Systems Theory and Automatic Control Otto-von-Guericke Univ. Magdeburg Chair of Software Engineering Otto-von-Guericke Univ. Magdeburg Fraunhofer-Institute for Factory Operation and Automation Magdeburg

ISBN: (数字)9783907144022

ISBN: (纸本)9781728188133

One of the key benefits of model predictive control is the capability of controlling a system proactively in the sense of taking the future system evolution into account. However, often external disturbances or references are not a priori known, which renders the predictive controllers shortsighted or uninformed. Adaptive and learning based prediction models can provide suitable predictions to the controller and therefor can be applied to overcome this issue. We propose to learn references for model predictive controllers via Gaussian processes. To illustrate the approach, we consider robot assisted surgery, where a robotic manipulator must follow a learned reference position based on optical tracking measurements.

关键词： Predictive models Gaussian processes Robots Predictive control Task analysis control systems Tracking

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Unified resilience model using deep learning for assessing power system performance

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Heliyon 2025年第4期11卷 e42802页

作者： Artemchuk, Volodymyr Garbuz, Iurii Jamil Alsayaydeh, Jamil Abedalrahim Shkarupylo, Vadym Oliinyk, Andrii Bin Yusof, Mohd Faizal Herawan, Safarudin Gazali Department of Mathematical and Econometric Modelling G.E. Pukhov Institute for Modelling in Energy Engineering of the NAS of Ukraine Kyiv Ukraine Department of Environmental Protection Technologies and Radiation Safety Center for Information-analytical and Technical Support of Nuclear Power Facilities Monitoring of the NAS of Ukraine Kyiv Ukraine Department of Іnformation Systems in Economics Kyiv National Economic University Named After Vadym Hetman Kyiv Ukraine Department of Computerized Control System National Aviation University Kyiv Ukraine Department of Engineering Technology Fakulti Teknologi Dan Kejuruteraan Elektronik Dan Komputer (FTKEK) Universiti Teknikal Malaysia Melaka (UTeM) Melaka 76100 Malaysia Department of Computer Systems Networks and Cybersecurity of the National University of Life and Environmental Sciences of Ukraine Kyiv Ukraine Department of Software Tools Faculty of Computer Science and Technology Zaporizhzhia Polytechnic National University Zaporizhzhia Ukraine Research Section Faculty of Resilience Rabdan Academy Abu Dhabi United Arab Emirates Industrial Engineering Department Faculty of Engineering Bina Nusantara University Jakarta 11480 Indonesia

Energy resilience in renewable energy sources dissemination components such as batteries and inverters is crucial for achieving high operational fidelity. Resilience factors play a vital role in determining the performance of power systems, regardless of their operating environment and interruptions. This article introduces a Unified Resilience Model (URM) using Deep Learning (DL) to enhance power system performance. The proposed model analyzes environmental factors impacting the resilience of batteries and energy storage devices. This deep learning approach trains performance-impacting factors using previously known low resilience drain data. The learning output is utilized to augment various strengthening factors, thereby improving resilience. Drain mitigation and performance improvements are combined for direct impact verification. This process validates the model's fidelity in enhancing power system performance, with a specific focus on the impact of weather factors. © 2025 The Authors

关键词： Deep learning Energy resilience Fidelity Weather impact

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Extremum seeking algorithms based on non-commutative maps 11

Extremum seeking algorithms based on non-commutative maps

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11th IFAC Symposium on Nonlinear control systems, NOLCOS 2019

作者： Feiling, Jan Labar, Christophe Grushkovskaya, Victoria Garone, Emanuele Kinnaert, Michel Ebenbauer, Christian Institute for Systems Theory and Automatic Control University of Stuttgart Germany Dr. Ing. h.c. F. Porsche AG Stuttgart Germany Department of Control Engineering and System Analysis Université libre de Bruxelles Belgium Institute for Mathematics Julius-Maximilians-University Würzburg Germany

In this work, discrete-time extremum seeking algorithms for unconstrained optimization problems are developed. A general class of non-commutative maps and one- and two point function evaluation polices are presented to approximate a gradient-descent algorithm, suitable for extremum seeking problems. Moreover, adaptive step size rules are discussed to achieve faster convergence and vanishing steady state oscillations. © 2019, IFAC (International Federation of Automatic control) Hosting by Elsevier Ltd. All rights reserved.

关键词： Optimization

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Constrained Extremum Seeking: a Modified-Barrier Function Approach ⁎

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IFAC-PapersOnLine 2019年第16期52卷 694-699页

作者： Christophe Labar Emanuele Garone Michel Kinnaert Christian Ebenbauer Department of Control Engineering and System Analysis Université libre de Bruxelles 1050 Brussels Belgium Institute for Systems Theory and Automatic Control University of Stuttgart 70569 Stuttgart Germany

In this paper, we address the problem of steering the input of a convex function to a value that minimizes the function under a convex constraint. We consider the case where the constraint cannot be violated of more than a user-defined value during the whole transient phase. The mathematical expression of both the cost function and the constraint are assumed to be unknown. The only information available are the on-line values of the cost and the constraint. To tackle this problem, an optimization law, based on a modified-barrier function, and involving the gradient of both the cost function and the constraint, is firstly designed. The Lie bracket formalism is then exploited to approximate this law, by combining time-periodic signals with the on-line measurements of both the cost and the constraint. The stability property of the resulting constrained extremum seeking system is proved, and its effectiveness is shown in simulation.

关键词： Constrained Optimization Extremum Seeking Singular Perturbations Data-based control Modified Barrier-Function

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Incorporating Human Driving Data into Simulations and Trajectory Predictions 1

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4. Internationale ATZ-Fachtagung Automatisiertes Fahren, 2018

作者： Schmidt, Manuel Manna, Carlo Nattermann, Till Glander, Karl-Heinz Bertram, Torsten Institute of Control Theory and Systems Engineering TU Dortmund University Dortmund Germany ZF Group – TRW Automotive GmbH Automated Driving and Integral Cognitive Safety Düsseldorf Germany

ISBN: (数字)9783658237516

ISBN: (纸本)9783658237509

The development of algorithms for automated driving is a very challenging task. Recent progress in machine learning suggests that many algorithms will have a hybrid structure composed of deterministic or optimization and learning based elements. To train and validate such algorithms, realistic simulations are required. They need to be interaction based, incorporate intelligent surrounding traffic and the other traffic participants behavior has to be probabilistic. Current simulation environments for automotive systems often focus on vehicle dynamics. There are also microscopic traffic simulations that on the other hand don’t take vehicle dynamics into account. The few simulation software products that combine both elements still have at least one major problem. That is because lane change trajectories disregard human driving dynamics during such maneuvers. Consequently, machine learning algorithms developed and trained in simulations hardly generalize to non-synthetic data and therefore to real-world applications. © Springer Fachmedien Wiesbaden GmbH, ein Teil von Springer Nature 2019.

关键词： Adversarial machine learning

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Compositional Synthesis of Symbolic Models for Infinite Networks ⁎

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IFAC-PapersOnLine 2020年第2期53卷 1868-1873页

作者： Abdalla Swikir Navid Noroozi Majid Zamani Department of Electrical and Computer Engineering Technical University of Munich Germany Laboratory for Systems Theory and Automatic Control Otto-von-Guericke University Magdeburg Germany Computer Science Department University of Colorado BoulderUSA. Institute of Informatics Ludwig Maximilian University Munich Germany

In this paper, we provide a compositional method for the construction of symbolic models (a.k.a. finite abstractions) for infinite networks of discrete-time control systems. The concrete infinite network and its symbolic model are related by a so-called alternating simulation function which allows one to quantify the mismatch between the output behavior of the infinite interconnection of concrete subsystems and that of their symbolic models. We show that such an alternating simulation function can be obtained compositionally by assuming some small-gain type conditions and composing so-called local alternating simulation functions constructed for subsystems. Assuming certain stability property of concrete subsystems, we also provide a technique to synthesize their symbolic models together with their corresponding local alternating simulation functions. Finally, we apply our results to a traffic network divided into infinitely many cells.

关键词： Compositionality interconnected systems small-gain condition infinite networks symbolic models

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Chemical constituents,biological functions and pharmacological effects for comprehensive utilization of Eucommia ulmoides Oliver

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Food Science and Human Wellness 2019年第2期8卷 177-188页

作者： Yi-Fan Xing Dong He Yi Wang Wen Zeng Chong Zhang Yuan Lu Nan Su Yan-Hua Kong Xin-Hui Xing Key Laboratory for Industrial Biocatalysis Ministry of EducationInstitute of Biochemical EngineeringDepartment of Chemical EngineeringRoom 601Yingshi BuildingBeijing 100084China Centre for Synthetic and Systems Biology Tsinghua UniversityBeijing 100084China Animal Disease Control and Prevention Centre South Section of Hangu RoadLingbao CityHenan 472500China

Eucommia ulmoides Oliver is a native plant and valuable tonic Chinese medicine in China with a long history,great economic value and comprehensive development ***,the comprehensive utilization rate of *** *** still very low,only bark has been used as medicine and other parts of Eucommia ulmoides *** be fully utilized,even the leaves have been well utilized in food products in Japan in the past *** order to improve the comprehensive utilization efficiency of *** Oliv.,in this review,we summarized the varieties and contents of main active compounds,biological functions and pharmacological effects in different parts of *** *** findings suggest that other parts of *** *** replace the bark of *** *** some extent besides of their respective *** unique and extensive physiological functions between different parts of *** *** that the comprehensive utilization of *** *** a wide space to develop,which is also an effective way to protect *** *** and improve its the utilization rate.

关键词： Eucommia ulmoides Oliver Comprehensive utilization Chemical constituents Biological functions Pharmacological effects

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Traceable random numbers from a nonlocal quantum advantage

arXiv

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arXiv 2024年

作者： Kavuri, Gautam A. Palfree, Jasper Reddy, Dileep V. Zhang, Yanbao Bienfang, Joshua C. Mazurek, Michael D. Alhejji, Mohammad A. Siddiqui, Aliza U. Cavanagh, Joseph M. Dalal, Aagam Abellán, Carlos Amaya, Waldimar Mitchell, Morgan W. Stange, Katherine E. Beale, Paul D. Brandão, Luís T.A.N. Booth, Harold Peralta, René Nam, Sae Woo Mirin, Richard P. Stevens, Martin J. Knill, Emanuel Shalm, Lynden K. Department of Physics University of Colorado BoulderCO80309 United States The National Institute of Standards and Technology BoulderCO80305 United States Quantum Information Science Section Computational Sciences and Engineering Division Oak Ridge National Laboratory Oak RidgeTN37831 United States Joint Quantum Institute National Institute of Standards and Technology University of Maryland 100 Bureau Drive GaithersburgMD20899 United States Center for Quantum Information and Control University of New Mexico AlbuquerqueNM87131 United States Department of Electrical Computer and Energy Engineering University of Colorado BoulderCO80309 United States Physical Measurement Laboratory National Institute of Standards and Technology GaithersburgMD20899 United States Quside Technologies S.L. Castelldefels Barcelona Spain ICFO-Institut de Ciencies Fotoniques The Barcelona Institute of Science and Technology Castelldefels Barcelona08860 Spain ICREA - Institució Catalana de Recerca i Estudis Avançats Barcelona08010 Spain Department of Mathematics University of Colorado BoulderCO80309 United States GaithersburgMD20899 United States Information Technology Laboratory National Institute of Standards and Technology GaithersburgMD20899 United States Physical Measurement Laboratory National Institute of Standards and Technology BoulderCO80305 United States Center for Theory of Quantum Matter University of Colorado BoulderCO80305 United States Applied and Computational Mathematics Division National Institute of Standards and Technology BoulderCO80305 United States Quantum Engineering Initiative Department of Electrical Computer and Energy Engineering University of Colorado BoulderCO80309 United States

The unpredictability of random numbers is fundamental to both digital security [1, 2] and applications that fairly distribute resources [3, 4]. However, existing random number generators have limitations—the generation processes cannot be fully traced, audited, and certified to be unpredictable. The algorithmic steps used in pseudorandom number generators [5] are auditable, but they cannot guarantee that their outputs were a priori unpredictable given knowledge of the initial seed. Device-independent quantum random number generators [6–9] can ensure that the source of randomness was unknown beforehand, but the steps used to extract the randomness are vulnerable to tampering. Here, for the first time, we demonstrate a fully traceable random number generation protocol based on device-independent techniques. Our protocol extracts randomness from unpredictable non-local quantum correlations, and uses distributed intertwined hash chains to cryptographically trace and verify the extraction process. This protocol is at the heart of a public traceable and certifiable quantum randomness beacon that we have launched [10]. Over the first 40 days of operation, we completed the protocol 7434 out of 7454 attempts—a success rate of 99.7%. Each time the protocol succeeded, the beacon emitted a pulse of 512 bits of traceable randomness. The bits are certified to be uniform with error times actual success probability bounded by 2−64. The generation of certifiable and traceable randomness represents one of the first public services that operates with an entanglement-derived advantage over comparable classical approaches. Copyright © 2024, The Authors. All rights reserved.

关键词： Random number generation

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