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6th International Conference on Biomedical and Health Informatics, ICBHI 2024

作者： Tsai, Shu-Hui Chao, Chih-Chiang Lin, Yue-Der Kuo, Chin-Jung Dong, Shu-Ping Liu, Chun-Yuan Ko, Hsin-Yi Wang, Jenn-Hann Liu, Ching Hsiu Service Systems Technology Center Industrial Technology Research Institute Hsin-Chu Taiwan Center for Measurement Standards Industrial Technology Research Institute Hsin-Chu Taiwan Department of Automatic Control Engineering Feng-Chia University Taichung Taiwan Central Division Industrial Technology Research Institute Hsin-Chu Taiwan Mackay Memorial Hospital Hsin-Chu Taiwan

ISBN: (纸本)9783031863226

The staggering global prevalence of depression extends to Taiwan, where approximately 8.9% of the population exhibits symptoms of depression, affecting around 2 million individuals, with 1.25 million classified as severe cases. Depression exerts profound impacts on both socioeconomic and health aspects, ranking as one of the leading causes of disability and closely linked with dementia. Over 70% of suicides involve individuals with depression, and those with depression face significantly higher suicide risks compared to the general population. Of particular concern is the higher-than-expected prevalence of depression, which, if left untreated, may escalate suicide risks. Research indicates that individuals with depression display unstable and imbalanced brainwave patterns, characterized by a lack of normal baseline brainwaves and elevated slow waves. While the commonly used repeated transcranial magnetic stimulation (rTMS) therapy can modulate brainwaves, the treatment is costly and often associated with high relapse rates, imposing a significant burden on patients. Recent studies reveal that potential depression sufferers commonly experience brainwave dysregulation and neuro-immune disruption, emphasizing the need for personalized emotional, brainwave, and autonomic nervous system regulation, along with community support. This study aims to integrate elements such as "Brainwave Entrainment with Schumann Resonance and Autonomic Nervous System Feedback Device", "Light Therapy Device", and "Garden Train-Style Anxiety-Relief Environment" to provide simulated natural environment brainwave and autonomic nervous system regulation, as well as light therapy. Additionally, it involves designing a safe "Garden Train" virtual reality environment to offer a secure and comforting anxiety-relief space for individuals at risk or suffering from depression, as well as caregivers. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Electrotherapeutics

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Data-Driven Reachability Analysis for Piecewise Affine System

arXiv

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arXiv 2025年

作者： Xie, Peng Betz, Johannes Raimondo, Davide M. Alanwar, Amr TUM School of Computation Information and Technology Department of Computer Engineering Technical University of Munich Heilbronn74076 Germany TUM School of Engineering and Design Department of Autonomous Vehicles Systems Technical University of Munich Garching85748 Germany School of Automatic Control Department of Engineering and Architecture University of Trieste Via Valerio 10 Trieste34127 Italy

Hybrid systems play a crucial role in modeling real-world applications where discrete and continuous dynamics interact, including autonomous vehicles, power systems, and traffic networks. Safety verification for these systems requires determining whether system states can enter unsafe regions under given initial conditions and uncertainties—a question directly addressed by reachability analysis. However, hybrid systems present unique difficulties because their state space is divided into multiple regions with distinct dynamic models, causing traditional data-driven methods to produce inadequate over-approximations of reachable sets at region boundaries where dynamics change abruptly. This paper introduces a novel approach using hybrid zonotopes for data-driven reachability analysis of piecewise affine systems. Our method addresses the boundary transition problem by developing computational algorithms that calculate the family of set models guaranteed to contain the true system trajectories. Additionally, we extend and evaluate three methods for set-based estimation that account for input-output data with measurement noise. © 2025, CC BY.

关键词： Convergence of numerical methods

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Sensor Fault Estimation and Ftc Design for Fractional Linear systems Using the Conformable Derivative

Sensor Fault Estimation and Ftc Design for Fractional Linear...

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IEEE SSD International Multi-Conference on systems, Signals and Devices

作者： Ali Omar M. Alsharif A. Jmal O. Naifar Ausama H. Ahmed H. Gassara A. Ben Makhlouf Control and Energy Management laboratory (CEM lab) Sfax University Engineering National School Tunisia Department of Electrical & Computer Engineering Faculty of Engineering Elmergib University Alkhums Libya Control and Energy Management laboratory (CEM lab) Monastir University Tunisia Laboratory of Sciences and Techniques of Automatic Control and Computer Engineering National School of Engineering of Sfax University of Sfax Tunisia Department of Mathematics Faculty of Sciences of Sfax Sfax Tunisia

ISBN: (数字)9798331542726

ISBN: (纸本)9798331542733

In this paper, Fault Tolerant control (FTC) for fractional-order systems (FOS) has not received as much attention in the literature as it has for standard integer-order systems. This work aims to bridge that gap. We propose a practical approach to fault estimation and fault-tolerant control (FTC) specifically designed for linear (FOS) with sensor failures. Our method leverages fractional exponential stability within the Lyapunov framework and employs the Conformable formulation of fractional derivatives. Notably, the primary contribution of this paper is the first application of the Conformable fractional derivative (CFD) in the context of (FTC). To accurately estimate states and faults, the proposed approach utilizes an observer coupled with a customized adaptation law. To validate our theoretical contributions, we present a detailed simulation of a numerical case study.

关键词： Linear systems Bridges Stability Fault tolerant systems Fault tolerant control Observers control theory Standards Lyapunov methods

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FROST: Fusion and Multimodal 3D Reconstruction of Icy Surfaces for Robotic Exploration

FROST: Fusion and Multimodal 3D Reconstruction of Icy Surfac...

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Computational Intelligence on engineering/Cyber Physical systems Companion (CIES Companion), IEEE Symposium on

作者： Huy Xuan Pham Erdal Kayacan Department of Electrical and Computer Engineering Artificial Intelligence in Robotics Laboratory (Air Lab) Aarhus University Aarhus C Denmark Automatic Control Group (RAT) Paderborn University Paderborn Germany

ISBN: (数字)9798331508494

ISBN: (纸本)9798331508500

Three-dimensional (3D) reconstruction serves as a cornerstone in various robotic applications, playing critical roles in scene understanding and navigation. Traditionally, LiDAR has been instrumental in generating precise point clouds of the environment, providing essential data for these applications. However, the efficacy of LiDAR sensors is significantly hindered in challenging conditions, such as the presence of water or icy surfaces. The complex interplay between laser beams and icy or non-ideal surfaces can result in signal degradation, distortion, or even complete signal loss, adversely affecting the accuracy and reliability of the 3D reconstruction process. The reflective and refractive properties of ice, along with its variable surface conditions, present challenges that traditional LiDAR sensors struggle to address. This paper proposes a diverse dataset to facilitate a multimodal approach for reconstructing icy surfaces using various sensors. A preliminary study based on this dataset demonstrates the feasibility of combining the geometric surface obtained from consecutive LiDAR scans with reconstructed meshes from visual cameras. The integration of distinct data sources has the potential to improve the robustness of reconstruction algorithms in diverse scenarios. The dataset can be downloaded at https://***/records/13862009.

关键词： Point cloud compression Surface reconstruction Laser radar Three-dimensional displays Navigation Ice Robustness Laser beams Image reconstruction Surface treatment

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Symbiotic control of Uncertain Dynamical systems: Harnessing Synergy Between Fixed-Gain control and Adaptive Learning Architectures

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IEEE Transactions on automatic control 2025年

作者： Yucelen, Tansel Sarsilmaz, Selahattin Burak Yildirim, Emre University of South Florida Department of Mechanical Engineering Laboratory for Autonomy Control Information and Systems TampaFL33620 United States Utah State University Department of Electrical and Computer Engineering LoganUT84322 United States

Both fixed-gain control and adaptive learning architectures aim to mitigate the effects of uncertainties. In particular, fixed-gain control offers more predictable closed-loop system behavior but requires the knowledge of uncertainty bounds. In contrast, while adaptive learning does not necessarily require such knowledge, it often results in less predictable closed-loop system behavior compared to fixed-gain control. To this end, this paper presents a novel symbiotic control framework that offers the strengths of fixed-gain control and adaptive learning architectures. Specifically, this framework synergistically integrates these architectures to mitigate the effects of uncertainties in a more predictable manner as compared to adaptive learning alone and it does not require any knowledge on such uncertainties. Both parametric and nonparametric uncertainties are considered, where we utilize neural networks to approximate the unknown uncertainty basis for the latter case. Counterintuitively, the proposed framework has the ability to achieve a desired level of closed-loop system behavior even with an insufficient number of neurons (e.g., when the neural network approximation error is large) or in the face of injudiciously selected adaptive learning parameters (e.g., high leakage term parameters). © 1963-2012 IEEE.

关键词： Closed loop control systems

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Editorial: Biomedical signals and artificial intelligence towards smart robots control strategies

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Frontiers in Neurorobotics 2025年 19卷 1570870页

作者： Seddik, Hassene Fourati, Hassen Ben Jabeur, Chiraz Khraief, Nahla Department of Computer and Embedded Systems Higher National Engineering School of Tunis Tunis Tunisia Department of Automatic Control Université Grenoble Alpes Auvergne-Rhone-Alpes Saint Martin d'Héres France Ariana Tunisia Tunis Tunisia

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Binary Stochastic Flip Optimization for Training Binary Neural Networks

Binary Stochastic Flip Optimization for Training Binary Neur...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Tatsukichi Shibuya Nakamasa Inoue Rei Kawakami Ikuro Sato Department of Computer Science Institute of Science Tokyo Tokyo Japan Department of Systems and Control Engineering Institute of Science Tokyo Tokyo Japan Denso IT Laboratory Tokyo Japan

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

For deploying deep neural networks on edge devices with limited resources, binary neural networks (BNNs) have attracted significant attention, due to their computational and memory efficiency. However, once a neural network is binarized, finetuning it on edge devices becomes challenging because most conventional training algorithms for BNNs are designed for use on centralized servers and require storing real-valued parameters during training. To address this limitation, this paper introduces binary stochastic flip optimization (BinSFO), a novel training algorithm for BNNs. BinSFO employs a parameter update rule based on Boolean operations, eliminating the need to store real-valued parameters and thereby reducing memory requirements and computational overhead. In experiments, we demonstrated the effectiveness and memory efficiency of BinSFO in fine-tuning scenarios on six image classification datasets. BinSFO performed comparably to conventional training algorithms with a 70.7% smaller memory requirement. Code is released at https://***/TatsukichiShibuya/ICASSP2025_BinSFO

关键词： Training Speech coding Memory management Signal processing algorithms Signal processing Classification algorithms Servers Speech processing Optimization Image classification

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System Identification Beyond the Nyquist Frequency: A Kernel-Regularized Approach

arXiv

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arXiv 2025年

作者： van Haren, Max Smith, Roy S. Oomen, Tom Department of Mechanical Engineering Control Systems Technology Section Eindhoven University of Technology Groene Loper 5 Eindhoven5612 AE Netherlands Automatic Control Laboratory ETH Zürich Zürich8092 Switzerland Delft Center for Systems and Control Delft University of Technology Mekelweg 2 Delft2628 CN Netherlands

Models that contain intersample behavior are important for control design of systems with slow-rate outputs. The aim of this paper is to develop a system identification technique for fast-rate models of systems where only slow-rate output measurements are available, e.g., vision-in-the-loop systems. In this paper, the intersample response is estimated by identifying fast-rate models through least-squares criteria, and the limitations of these models are determined. In addition, a method is developed that surpasses these limitations and is capable of estimating unique fast-rate models of arbitrary order by regularizing the least-squares estimate. The developed method utilizes fast-rate inputs and slow-rate outputs and identifies fast-rate models accurately in a single identification experiment. Finally, both simulation and experimental validation on a prototype wafer stage demonstrate the effectiveness of the framework. © 2025, CC BY-NC-ND.

关键词： Decentralized systems

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A Globally Convergent Method for Computing B-stationary Points of Mathematical Programs with Equilibrium Constraints

arXiv

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arXiv 2025年

作者： Nurkanovic, Armin Leyffer, Sven Systems Control and Optimization Laboratory Department of Microsystems Engineering University of Freiburg Germany Mathematics and Computer Science Division Argonne National Laboratory United States

This paper introduces a method that globally converges to Bstationary points of mathematical programs with equilibrium constraints (MPECs) in a finite number of iterations. B-stationarity is necessary for optimality and means that no feasible first-order direction improves the objective. Given a feasible point of an MPEC, B-stationarity can be certified by solving a linear program with equilibrium constraints (LPEC) constructed at this point. The proposed method solves a sequence of LPECs, which either certify B-stationarity or provide an active-set estimate for the complementarity constraints, and nonlinear programs (NLPs) - referred to as branch NLPs (BNLPs) - obtained by fixing the active set in the MPEC. A BNLP is more regular than the MPEC, easier to solve, and with the correct active set, its solution coincides with the solution of the MPEC. The method has two phases: the first phase identifies a feasible BNLP or certifies local infeasibility, and the second phase solves a finite sequence of BNLPs until a B-stationary point of the MPECis found. The paper provides a detailed convergence analysis and discusses implementation details. In addition, extensive numerical experiments and an open-source software implementation are provided. The experiments demonstrate that the proposed method is more robust and faster than relaxation-based methods, while also providing a certificate of B-stationarity without requiring the usual too-restrictive *** Codes 90C30, 90C33, 49M37, 65K10, 90C11 © 2025, CC BY.

关键词： Nonlinear programming

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REGULARIZED HIGHER-ORDER TAYLOR APPROXIMATION METHODS FOR COMPOSITE NONLINEAR LEAST-SQUARES

arXiv

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arXiv 2025年

作者： Nabou, Yassine Necoara, Ion Automatic Control and Systems Engineering Department National University of Science and Technology Politehnica Bucharest Romania Gheorghe Mihoc-Caius Iacob Institute of Mathematical Statistics and Applied Mathematics The Romanian Academy Bucharest Romania

In this paper, we develop a regularized higher-order Taylor based method for solving composite (e.g., nonlinear least-squares) problems. At each iteration, we replace each smooth component of the objective function by a higher-order Taylor approximation with an appropriate regularization, leading to a regularized higher-order Taylor approximation (RHOTA) algorithm. We derive global convergence guarantees for RHOTA algorithm. In particular, we prove stationary point convergence guarantees for the iterates generated by RHOTA, and leveraging a Kurdyka-Lojasiewicz (KL) type property of the objective function, we derive improved rates depending on the KL parameter. When the Taylor approximation is of order 2, we present an efficient implementation of RHOTA algorithm, demonstrating that the resulting nonconvex subproblem can be effectively solved utilizing standard convex programming tools. Furthermore, we extend the scope of our investigation to include the behavior and efficacy of RHOTA algorithm in handling systems of nonlinear equations and optimization problems with nonlinear equality constraints deriving new rates under improved constraint qualifications conditions. Finally, we consider solving the phase retrieval problem with a higher-order proximal point algorithm, showcasing its rapid convergence rate for this particular application. Numerical simulations on phase retrieval and output feedback control problems also demonstrate the efficacy and performance of the proposed methods when compared to some state-of-the-art optimization methods and software. Copyright © 2025, The Authors. All rights reserved.

关键词： Feedback control

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