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IEEE International Conference on Consumer Electronics (ICCE)

作者： Wen-Hui Chen Yu-Hung Huang Yu-Chen Lin Graduate Institute of Automation Technology National Taipei University of Technology Taipei Taiwan Department of Automatic Control Engineering Feng Chia University Taichung Taiwan

ISBN: (数字)9798331521165

ISBN: (纸本)9798331521172

Traffic accidents pose significant social and economic challenges, making effective accident prediction systems essential. Traditional machine learning methods often struggle to capture the complex, nonlinear patterns in visual accident data. Recent advancements suggest that deep learning offers a promising solution to this challenge. These vision-based systems rely on object detection as a foundational step, identifying key entities in traffic scenes captured by cameras to improve prediction accuracy. Despite its importance, the impact of object detection accuracy on overall accident prediction effectiveness remains underexamined. This study investigates this relationship through cross-dataset analysis. Experimental results show that object detection accuracy directly impacts accident prediction performance, with training dataset choice playing a critical role. Enhancing model generalization is essential to stabilizing accident prediction systems.

关键词： Training Economics Visualization Accuracy Transportation Object detection Predictive models Safety Object recognition Accidents

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Adaptive Attitude control for Spinning Projectiles with Time-Varying Aerodynamic Uncertainties

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IEEE Transactions on Aerospace and Electronic systems 2025年

作者： Shi, Zhongjiao Liu, Zhijie Han, Feng Wang, Xinchun Beijing Institute of Technology School of Aerospace Engineering Beijing100081 China University of Science and Technology Beijing School of Intelligence Science and Technology Beijing100083 China Beijing Aerospace Automatic Control Institute Beijing100070 China

Most gun-launched guided projectiles adopt a spinning airframe to simplify the control structure and improve stability. However, the cross-coupling effects of inertia, aerodynamics, and control caused by spinning increase the difficulty of the autopilot design. This paper proposes an adaptive attitude autopilot design method for a class of spinning projectiles with time-varying aerodynamic uncertainties. First, a fully actuated attitude dynamic model of the pitch/yaw channel for a spinning projectile is established, in which the aerodynamic force/moment are regarded as time-varying uncertainties. Second, an adaptive attitude autopilot is proposed by combining fully actuated system theory and adaptive control theory to restore the linear characteristics of the closed-loop system. Then, a parameter-safe adaptive law is presented using control barrier functions to ensure the boundedness of estimation parameters and improve the robustness of the closed-loop system. Finally, comparative numerical simulations are performed to demonstrate that the proposed attitude control method can guarantee the boundedness of both the tracking error and estimation error. © 2025 IEEE.

关键词： Attitude control

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Modeling and Simulation of Pollution Removal Efficiency in Activated Sludge Wastewater Treatment Processes

Modeling and Simulation of Pollution Removal Efficiency in A...

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

作者： Sameh Chattaoui Raja Jarray Soufiene Bouallègue Research Laboratory in Automatic Control (LARA) National Engineering School of Tunis National Higher Engineering School of Tunis Tunis Tunisia Research Laboratory in Automatic Control (LARA) National Engineering School of Tunis High Institute of Industrial Systems of Gabès Gabès Tunisia

ISBN: (数字)9798331542726

ISBN: (纸本)9798331542733

Water pollution is a growing challenge for the environment and ecosystems, necessitating management of Wastewater Treatment Plants (WWTPs). Modeling the dynamical behaviors of these complex systems significantly affects the control and prediction performance. This study compares the performance of three widely used Activated Sludge Models (ASM1, ASM2, and ASM3) in removing key pollutants, mainly including Chemical Oxygen Demand (COD), Biochemical Oxygen Demand (BOD5), Total Suspended Solids (TSS), Total Nitrogen (TN), and Hydrogen potential (pH). Each model of the activated sludge process incorporates unique representation of biological and chemical processes like nitrification, denitrification, and phosphorus removal. The results demonstrate significant differences in the models' stability, sensitivity, and effectiveness under varied operational conditions. The ASM3 benchmark consistently performs well in extreme scenarios, ASM1 shows strong stability, and ASM2 balances efficiency and reliability. These findings provide actionable insights for optimizing WWTP's operations, guiding model selection based on specific treatment objectives.

关键词： Analytical models Oxygen Sensitivity Purification Biological system modeling Predictive models Stability analysis Wastewater treatment Sludge treatment Context modeling

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DSAC-T Based Resource Allocation Strategy for Delay Minimization in RIS-Aided MEC Networks

DSAC-T Based Resource Allocation Strategy for Delay Minimiza...

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IEEE Conference on Wireless Communications and Networking

作者： Tianyu Pang Fuhao Liu Xinpei Chen Jiansong Miao Junsheng Mu Zaodi Song School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing China North Automatic Control Technology Institute Taiyuan China

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

With the explosive growth of user data in 6G networks, existing infrastructures face significant scalability and latency challenges. Mobile Edge Computing (MEC) partially alleviates these issues by deploying computational resources closer to users, but still struggles to fully meet the growing demands. Re-configurable Intelligent Surfaces (RIS) enhance communication performance by improving channel quality. However, optimizing resource allocation in RIS-aided MEC systems remains a critical challenge due to the complexity of real-time optimization of multiple parameters. Although Deep Reinforcement Learning (DRL) methods like Deep Deterministic Policy Gradient (DDPG) have been applied, they often suffer from Q-value overestimation and instability, resulting in suboptimal performance in dynamic environments. This paper focuses on a single-cell RIS-aided MEC network where multiple user devices offload computational tasks to an edge server. We propose an optimized resource allocation scheme using an improved Distributed Soft Actor-Critic (DSAC-T) algorithm. This approach jointly optimizes power control, computation offloading, edge computing resource allocation, and RIS phase shifts, aiming to minimize total offloading delay to ensure real-time service requirements. Simulation results demonstrate that DSAC-T outperforms multiple baseline methods (e.g., DDPG and SAC) in reducing offloading delay by 46.39% and 16.70%, respectively, while significantly enhancing system stability and convergence speed.

关键词： Heuristic algorithms Simulation Power control Stability analysis Real-time systems Delays Resource management Servers Optimization Convergence

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Sequential Binary Hypothesis Testing with Competing Agents under Information Asymmetry

arXiv

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arXiv 2025年

作者： Raghavan, Aneesh Niazi, M. Umar B. Johansson, Karl H. Division of Decision and Control Systems KTH Royal Institute of Technology StockholmSE-100 44 Sweden

This paper concerns sequential hypothesis testing in competitive multi-agent systems where agents exchange potentially manipulated information. Specifically, a two-agent scenario is studied where each agent aims to correctly infer the true state of nature while optimizing decision speed and accuracy. At each iteration, agents collect private observations, update their beliefs, and share (possibly corrupted) belief signals with their counterparts before deciding whether to stop and declare a state, or continue gathering more information. The analysis yields three main results: (1) when agents share information strategically, the optimal signaling policy involves equal-probability randomization between truthful and inverted beliefs;(2) agents maximize performance by relying solely on their own observations for belief updating while using received information only to anticipate their counterpart’s stopping decision;and (3) the agent reaching their confidence threshold first cause the other agent to achieve a higher conditional probability of error. Numerical simulations further demonstrate that agents with higher KL divergence in their conditional distributions gain competitive advantage. Furthermore, our results establish that information sharing—despite strategic manipulation—reduces overall system stopping time compared to non-interactive scenarios, which highlights the inherent value of communication even in this competitive setup. © 2025, CC BY.

关键词： Information dissemination

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An Analysis of Safety Guarantees in Multi-Task Bayesian Optimization

arXiv

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arXiv 2025年

作者： Lübsen, Jannis O. Eichler, Annika Institute of Control Systems Hamburg University of Technology Hamburg Germany Deutsches-Elektronen Synchrotron Hamburg Germany

In many practical scenarios of black box optimization, the objective function is subject to constraints that must be satisfied to avoid undesirable outcomes. Such constraints are typically unknown and must be learned during optimization. Safe Bayesian optimization aims to find the global optimum while ensuring that the constraints are satisfied with high probability. However, it is often sample-inefficient due to the small initial feasible set, which requires expansion by evaluating the objective or constraint functions, limiting its applicability to low-dimensional or inexpensive problems. To enhance sample efficiency, additional information from cheap simulations can be leveraged, albeit at the cost of safeness guarantees. This paper introduces a novel safe multi-task Bayesian optimization algorithm that integrates multiple tasks while maintaining high-probability safety. We derive robust uniform error bounds for the multi-task case and demonstrate the effectiveness of the approach on benchmark functions and a control problem. Our results show a significant improvement in sample efficiency, making the proposed method well-suited for expensive-to-evaluate functions. © 2025, CC BY-NC-ND.

关键词： Optimization algorithms

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Dynamic modeling and simulation of blade-casing system with rubbing considering time-varying stiffness and mass of casing

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Applied Mathematics and Mechanics(English Edition) 2025年第5期46卷 849-868页

作者： Hui MA Hong GUAN Lin QU Xumin GUO Qinqin MU Yao ZENG Yanyan CHEN School of Mechanical Engineering and Automation Northeastern UniversityShenyang 110819China Key Laboratory of Vibration and Control of Aero-Propulsion Systems Ministry of Education of ChinaNortheastern UniversityShenyang 110819China Sunny Optical Technology(Group)Co.Ltd. Ningbo 315400Zhejiang ProvinceChina Aircraft Strength Institute of China Xi’an 710065China AECC Sichuan Gas Turbine Research Institute Chengdu 610500China

As a common fault of the aero-engine,the blade-casing rubbing(BCR)has the potential to cause catastrophic *** this paper,to investigate the dynamic responses and wear characteristics of the system,the laminated shell element is used to establish the finite element model(FEM)of a flexibly coated casing *** the shell element,the blade is modeled,and the surface stress of the blade is *** stress-solving method of the blade is validated through comparisons with the measured time-domain waveform of the ***,a dynamic model of a blade-flexibly coated casing system with rubbing is proposed,accounting for the time-varying mass and stiffness of the casing caused by coating *** effects of the proposed flexible casing model are compared with those of a rigid casing model,and the stress changes induced by rubbing are *** results show that the natural characteristics of the coated casing decrease due to the coating *** flexibly coated casing model is found to be more suitable for studying casing ***,the stress changes caused by rubbing are slight,and the change in the stress maximum is approximately 5%under the influence of the abrasive coating.

关键词： dynamic modeling flexibly coated casing rubbing coating wear nonlinear vibration

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Proposal for a Job Management Tool for Deep Learning in a Mixed Environment of Heterogeneous GPUs

Proposal for a Job Management Tool for Deep Learning in a Mi...

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Consumer technology (ICCT-Pacific), International Conference on

作者： Nobuyuki Ishii Aoi Kondo Hiroaki Koga Dept. Control and Information Systems Engineering National Institute of Technology Kurume College Fukuoka Japan

ISBN: (数字)9798331504120

ISBN: (纸本)9798331504137

In recent years, the use of GPUs for computation has become commonplace in deep learning. It is not uncommon for deep learning labs to procure GPU servers for computation without standardising specifications such as GPU architecture and VRAM capacity, resulting in a heterogeneous GPU server environment. Container technology is also widely used as a method of isolating software execution environments. Among them, Docker is the de facto standard for container platforms. In this paper, we have developed a management system that can allocate deep learning training jobs to GPU servers (workers) using container technology to absorb differences in execution environments in heterogeneous GPU server environments. In experiments, we confirmed that the proposed system can estimate the completion time of training for each worker and can detect Out-Of-Memory (OOM) of GPUs in advance. We were able to confirm in advance the “inference time” and the “possibility of OOM”, which are difficult to predict when performing deep learning. We have succeeded in obtaining the “predicted value of learning time” and “possibility of OOM” that engineers need to know in advance, and have shown that the proposed system is effective in deep learning tests.

关键词： Deep learning Training Graphics processing units Containers Software Servers Resource management Proposals Standards Testing

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Verification of the Effect of Label Modification on Neural Network Learning

Verification of the Effect of Label Modification on Neural N...

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Consumer technology (ICCT-Pacific), International Conference on

作者： Tsubasa Oka Hiroaki Koga Dept. Control and Information Systems Engineering National Institute of Technology Kurume College Fukuoka Japan

ISBN: (数字)9798331504120

ISBN: (纸本)9798331504137

This study focuses on the problem of subjective labeling that arises in the application of machine learning with neural networks to the medical field. The first problem is that of subjectivity, which arises because the labeling of medical data relies on expert knowledge and the process can be subjective. The second problem is the reduced information content of labels caused by discrete and quantitative labeling. If the intermediate values between the discrete values also have evaluative meaning, the detailed information is lost due to the discrete numerical labeling. We focus on such instability in labels and propose an approach to label modification using values inferred during neural network training. Specifically, we consider labels that are discrete, quantitative numbers as continuous numbers, compare the inferred value with the label value at each step in the learning phase, and compare the label value with the inferred value at each step in the learning phase. The update method is to give the label a sign-responsive variation. The goal is to modify each label using the value inferred by the inference made during learning in this way. As an experiment, we conducted an experiment on label modification under pseudo-conditions to verify its effectiveness. The results showed that when the amount of label modification was controlled by a constant, the generalization performance and the nature of label modification differed depending on the constant. In particular, generalization performance was improved when the constant of the label modification amount was determined so that the label value was separated from the inferred value at each step.

关键词： Training Neural networks Machine learning Labeling

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Predictive Synthesis of control Barrier Functions and its Application to Time-Varying Constraints

arXiv

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arXiv 2025年

作者： Wiltz, Adrian Dimarogonas, Dimos V. Division of Decision and Control Systems KTH Royal Institute of Technology StockholmSE-100 44 Sweden

This paper presents a systematic method for synthesizing a control Barrier Function (CBF) that encodes predictive information into a CBF. Unlike other methods, the synthesized CBF can account for changes and time-variations in the constraints even when constructed for time-invariant constraints. This avoids recomputing the CBF when the constraint specifications change. The method provides an explicit characterization of the extended class Ke function α that determines the dynamic properties of the CBF, and α can even be explicitly chosen as a design parameter in the controller synthesis. The resulting CBF further accounts for input constraints, and its values can be determined at any point without having to compute the CBF over the entire domain. The synthesis method is based on a finite horizon optimal control problem inspired by Hamilton-Jacobi reachability analysis and does not rely on a nominal control law. The synthesized CBF is time-invariant if the constraints are. The method poses mild assumptions on the controllability of the dynamic system and assumes the knowledge of at least a subset of some control invariant set. The paper provides a detailed analysis of the properties of the synthesized CBF, including its application to time-varying constraints. A simulation study applies the proposed approach to various dynamic systems in the presence of time-varying constraints. The paper is accompanied by an online available parallelized implementation of the proposed synthesis method. Copyright © 2025, The Authors. All rights reserved.

关键词： Invariance

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