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computer Vision, Image and Deep Learning (CVIDL), International Conference on

作者： Xiuen Wu Tao Wang Yuanzheng Cai Lingyu Liang George Papageorgiou Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Minjiang University Fuzhou China College of Computer and Data Science Fuzhou University Fuzhou China School of Electronic and Information Engineering South China University of Technology Guangzhou China Department of Computer Science and Engineering European University Cyprus Nicosia Cyprus

ISBN: (数字)9798350373820

ISBN: (纸本)9798350373837

Pedestrian trajectory prediction plays a pivotal role in ensuring the safety and efficiency of various applications, including autonomous vehicles and traffic management systems. This paper proposes a novel method for pedestrian trajectory prediction, called multi-stage goal-driven network (MGNet). Diverging from prior approaches relying on stepwise recursive prediction and the singular forecasting of a long-term goal, MGNet directs trajectory generation by forecasting intermediate stage goals, thereby reducing prediction errors. The network comprises three main components: a conditional variational autoencoder (CVAE), an attention module, and a multi-stage goal evaluator. Trajectories are encoded using conditional variational autoencoders to acquire knowledge about the approximate distribution of pedestrians’ future trajectories, and combined with an attention mechanism to capture the temporal dependency between trajectory sequences. The pivotal module is the multi-stage goal evaluator, which utilizes the encoded feature vectors to predict intermediate goals, effectively minimizing cumulative errors in the recursive inference process. The effectiveness of MGNet is demonstrated through comprehensive experiments on the JAAD and PIE datasets. Comparative evaluations against state-of-the-art algorithms reveal significant performance improvements achieved by our proposed method.

关键词： Deep learning Pedestrians Computational modeling Predictive models Prediction algorithms Vectors Inference algorithms

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Do We Run Large-scale Multi-Robot Systems on the Edge? More Evidence for Two-Phase Performance in System Size Scaling

Do We Run Large-scale Multi-Robot Systems on the Edge? More ...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Jonas Kuckling Robin Luckey Viktor Avrutin Andrew Vardy Andreagiovanni Reina Heiko Hamann Department of Computer and Information Science University of Konstanz Konstanz Germany Centre for the Advanced Study of Collective Behaviour University of Konstanz Germany Institute of Computer Engineering University of Lübeck Lübeck Germany Institute for Systems Theory and Automatic Control University of Stuttgart Stuttgart Germany Department of Computer Science and the Department of Electrical and Computer Engineering Memorial University of Newfoundland St. John’s Canada IRIDIA Université Libre de Bruxelles Brussels Belgium

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

With increasing numbers of mobile robots arriving in real-world applications, more robots coexist in the same space, interact, and possibly collaborate. Methods to provide such systems with system size scalability are known, for example, from swarm robotics. Example strategies are self-organizing behavior, a strict decentralized approach, and limiting the robot-robot communication. Despite applying such strategies, any multi-robot system breaks above a certain critical system size (i.e., number of robots) as too many robots share a resource (e.g., space, communication channel). We provide additional evidence based on simulations, that at these critical system sizes, the system performance separates into two phases: nearly optimal and minimal performance. We speculate that in real-world applications that are configured for optimal system size, the supposedly high-performing system may actually live on borrowed time as it is on a transient to breakdown. We provide two modeling options (based on queueing theory and a population model) that may help to support this reasoning.

关键词： Limiting System performance Space communications Scalability Sociology Swarm robotics Mobile robots

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Impact of Customer Order Change Dimensions on Order Management

Impact of Customer Order Change Dimensions on Order Manageme...

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2022 IEEE International Conference on Industrial engineering and engineering Management, IEEM 2022

作者： Fries, Christian Szaller, Adam Bauernhansl, Thomas Schuh, Gunther Fraunhofer Institute for Manufacturing Engineering and Automation Ipa Stuttgart70569 Germany University of Stuttgart Institute of Industrial Manufacturing and Management Iff Stuttgart70569 Germany Institute for Computer Science and Control Budapest1111 Hungary Bme Department of Manufacturing Science and Engineering Budapest1111 Hungary of Rwth Aachen University Aachen Germany

ISBN: (数字)9781665486873

ISBN: (纸本)9781665486873

Today, manufacturing companies are surrounded by constantly changing external influences. These entail the risk of inefficiencies in the operational process, especially when existing plans have to be adapted. A common trigger of such modifications are customer order changes. They cause unplanned costs, which endanger the profitability of the company. However, the estimation of these additional costs is complex and therefore missing in today's operation. This paper therefore defines customer order change types and determines their effects based on the Aachener PPS-Model. The individual processing areas of a customer order are described and mathematically formulated. Agent-based simulation is used to evaluate the critical change dimensions. Simulation experiments showed that the additional costs generated due to customer order changes decrease if the average relative date and quantity change are reduced. If both change scopes are reduced equally, in the case of reducing quantity, the additional costs are a slightly higher than in the case of reducing date change. © 2022 IEEE.

关键词： Sales

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Improved Proximal Policy Optimization Algorithm for Multi-objective Disassembly Line Balancing Considering Hazardous Tasks

Improved Proximal Policy Optimization Algorithm for Multi-ob...

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IEEE International Conference on Networking, Sensing and control

作者： Zeyu Guo Xiwang Guo Jiacun Wang Shujin Qin Liang Qi Qi Kang Information and Control Engineering Liaoning Petrochemical University Fushun China Computer Science and Software Eng. Monmouth University Wes Long Branch New Jersey USA Economics and Management Shangqiu Normal University Shangqiu China Computer Science Department Shandong Technology University Qingdao China Electrical and Computer Engineering New Jersey Institute of Technology Newark USA

ISBN: (数字)9798350365221

ISBN: (纸本)9798350365238

As disassembly lines evolve, keeping workers safe from all potential dangers has become paramount. This work proposes an improved approach, Proximal Policy Optimization for Disassembly Line(PPO-DL), to address Multi-objective Dis-assembly Line Balancing Problem(MDLBP) that integrates task switching time and hazardous tasks. The MDLBP model aims to optimize disassembly net profit, balance workstation utilization, and mitigate penalties from hazardous tasks. The proposed PPO-DL modifies the action space of PPO to accommodate MD LBP characteristics, ensuring efficient learning by restricting actions to valid ranges through action masking. Comparative experiments with PPO, Q-learning, and A2C demonstrate PPO-DL's superiority in finding near-optimal solutions across various scenarios.

关键词： Q-learning Switches Fatigue control systems Workstations Sensors Optimization

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control of Nonlinear Systems Under Multiple Time-Varying Output Constraints: A Single Funnel Approach

Control of Nonlinear Systems Under Multiple Time-Varying Out...

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IEEE Conference on Decision and control

作者： Farhad Mehdifar Lars Lindemann Charalampos P. Bechlioulis Dimos V. Dimarogonas Division of Decision and Control Systems KTH Royal Institute of Technology Stockholm Sweden Thomas Lord Department of Computer Science University of Southern California Los Angeles CA USA Department of Electrical and Computer Engineering Division of Systems and Control University of Patras Patra Greece

This paper proposes a novel control framework for handling (potentially coupled) multiple time-varying output constraints for uncertain nonlinear systems. First, it is shown that the satisfaction of multiple output constraints boils down to ensuring the positiveness of a scalar variable (the signed distance from the time-varying output-constrained set's boundary). Next, a single funnel constraint is designed properly, whose satisfaction ensures convergence to and invariance of the time-varying output-constrained set. Then a robust and low-complexity funnel-based feedback controller is designed em-ploying the prescribed performance control method. Finally, a simulation example clarifies and verifies the proposed approach.

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Learning of Nash Equilibria in Risk-Averse Games

Learning of Nash Equilibria in Risk-Averse Games

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American control Conference (ACC)

作者： Zifan Wang Yi Shen Michael M. Zavlanos Karl H. Johansson Division of Decision and Control Systems School of Electrical Enginnering and Computer Science KTH Royal Institute of Technology and also with Digital Futures Stockholm Sweden Department of Mechanical Engineering and Materials Science Duke University Durham NC USA

ISBN: (数字)9798350382655

ISBN: (纸本)9798350382662

This paper considers risk-averse learning in convex games involving multiple agents that aim to minimize their individual risk of incurring significantly high costs. Specifically, the agents adopt the conditional value at risk (CVaR) as a risk measure with possibly different risk levels. To solve this problem, we propose a first-order risk-averse leaning algorithm, in which the CVaR gradient estimate depends on an estimate of the Value at Risk (VaR) value combined with the gradient of the stochastic cost function. Although estimation of the CVaR gradients using finitely many samples is generally biased, we show that the accumulated error of the CVaR gradient estimates is bounded with high probability. Moreover, assuming that the risk-averse game is strongly monotone, we show that the proposed algorithm converges to the risk-averse Nash equilibrium. We present numerical experiments on a Cournot game example to illustrate the performance of the proposed method.

关键词： Reactive power Costs Estimation Games Nash equilibrium Numerical simulation Cost function

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Remotely controllable Data Self-Destruction for Privacy-Preserving Sensitive Data Services

Remotely Controllable Data Self-Destruction for Privacy-Pres...

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IEEE International Conference on High Performance Computing and Communications (HPCC)

作者： Chengxin Zhou Xianbin Xue Khaled Riad Yan Zhu William Cheng-Chung Chu School of Computer and Communication Engineering University of Science and Technology Beijing Beijing China North Navigation Control Technology Co. Ltd. Beijing China College of Computer Science & Information Technology King Faisal University Saudi Arabia School of Computer & Communication Engineering University of Science and Technology Beijing Beijing China Department of Computer Science Tunghai University Taichung Taiwan

This paper address a comprehensive solution to address data privacy and security challenges in data science, providing secure self-destruction of sensitive data, confidentiality through encryption, and flexible control over message destruction. Based on outsourcing cloud data services, we introduce a cloud retrograde storage (CRS) system for privacy-preserving self-destruction of sensitive data. The CRS is based on a frequently colliding hash table and recycle pool, and supports remote time, count, and range control. Hereby, we provide a remotely controllable mechanism for the prompt destruction of inactive messages against digital forensics without reducing the availability of services. The method used in the CRS involves storing messages in a recycle pool, where they are automatically erased at the end of their validity period or after being viewed by all recipients. The system also incorporates encryption and decryption processes to ensure confidentiality. The result of performance evaluation shows that the CRS effectively handles the destruction of messages and provides secure data removal from storage pools. The experiments demonstrate the efficiency and practicality of the CRS for large-scale data services.

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Parameter fault estimation in distributed heating/cooling systems 22

Parameter fault estimation in distributed heating/cooling sy...

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Proceedings of the 7th International Conference on Sustainable Information engineering and Technology

作者： Wijaya Kurniawan Katalin M. Hangos Lorinc Marton Faculty of Computer Science University of Brawijaya Indonesia) and Departement of Electrical Engineering and Information Systems University of Pannonia Hungary Departement of Electrical Engineering and Information Systems University of Pannonia Hungary and Institute for Computer Science and Control Hungary Department of Electrical Engineering Sapientia Hungarian University of Transylvania Romania)

ISBN: (纸本)9781450397117

In this paper, the problem of fault estimation and localization in the connecting dynamic elements of distributed heating and cooling systems are treated. The fault represents the physical parameter change related to the heat transfer between the system and the external environment. First, based on the bi-linear dynamic state space model of the system in the presence of a fault, structural observability analysis using Signed Directed Graph (SDG) has been performed to investigate the sensor placement problem. Then, a nonlinear observer with a parameter adaptation algorithm was proposed for fault estimation. The simulation results show that it can successfully detect and estimate the fault. Fault localization along the length of the element has also been attempted, but it has been found that the localization cannot be performed using practically changeable input variables. Frequency domain analysis is presented to discuss this phenomenon.

关键词： Nonlinear Observer Heating/cooling system Fault diagnosis Structural observability Parameter estimation

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Star-Searcher: A Complete and Efficient Aerial System for Autonomous Target Search in Complex Unknown Environments

arXiv

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arXiv 2024年

作者： Luo, Yiming Zhuang, Zixuan Pan, Neng Feng, Chen Shen, Shaojie Gao, Fei Cheng, Hui Zhou, Boyu Sun Yat-Sen University China State Key Laboratory of Industrail Control Technology Institute of Cyber-Systems and Control Zhejiang University Hangzhou China Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology Hong Kong

This paper tackles the challenge of autonomous target search using unmanned aerial vehicles (UAVs) in complex unknown environments. To fill the gap in systematic approaches for this task, we introduce Star-Searcher, an aerial system featuring specialized sensor suites, mapping, and planning modules to optimize searching. Path planning challenges due to increased inspection requirements are addressed through a hierarchical planner with a visibility-based viewpoint clustering method. This simplifies planning by breaking it into global and local sub-problems, ensuring efficient global and local path coverage in real-time. Furthermore, our global path planning employs a history-aware mechanism to reduce motion inconsistency from frequent map changes, significantly enhancing search efficiency. We conduct comparisons with state-of-the-art methods in both simulation and the real world, demonstrating shorter flight paths, reduced time, and higher target search completeness. Our approach will be open-sourced for community benefit. Copyright © 2024, The Authors. All rights reserved.

关键词： Stars

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Application of Deep Learning Method to Estimate Bottomhole Pressure Dynamics of Oil Wells 32

Application of Deep Learning Method to Estimate Bottomhole P...

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32nd IEEE International Symposium on Industrial Electronics, ISIE 2023

作者： Cheng, Haibo Li, Shichao Zeng, Peng Vyatkin, Valeriy State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China Luleå University of Technology Electrical and Space Engineering Department of Computer Science Luleå Sweden Aalto University Department of Electrical Engineering and Automation Helsinki Finland

ISBN: (纸本)9798350399714

Surrogate models, which have become an effective and popular method to close loop reservoir management problems, use a data-driven approach to predict dynamic injection and production wells parameters and optimize waterflooding development. In this study, a deep learning-based surrogate model method is proposed to estimate bottomhole pressure (BHP) of production wells in waterflooding reservoirs. Bidirectional long short-term memory (BiLSTM) network, as an efficient deep learning approach, is applied to BHP estimation using fluctuation data. Extended Fourier amplitude sensitivity test (EFAST) method is employed to analyse the influence of different input factors on BHP dynamics, and a reduced dataset is rebuilt to predict BHP parameter based on BiLSTM-EFAST algorithm. The estimation results are tested on a dataset from Volve oilfield in North Sea, and compared with other deep learning methods. The test results indicate that the proposed method can achieve higher prediction accuracy. A reduced dataset-based approach provides a new attempt to reduce model complexity and improve calculation speed for big data-driven surrogate model in oil and gas industry. © 2023 IEEE.

关键词： Long short-term memory

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