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arXiv 2024年

作者： Huang, Jingwei Wang, Chuansheng Huang, Jiayan Fan, Haoyi Grau, Antoni Zhang, Fuquan The College of Computer and Big Data Fuzhou University Fuzhou China The Department of Automatic Control Polytechnic University of Catalonia Barcelona Spain The New Engineering Industry College Putian University Putian China School of Computer and Artificial Intelligence Zhengzhou University China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Minjiang University Fuzhou China College of Computer and Control Engineering Minjiang University Fuzhou China Key Laboratory of Sichuan Province Sichuan Conservatory of Music Chengdu China Fuzhou Technology Innovation Center of Intelligent Manufacturing information System Minjiang University Fuzhou China Fujian Polytechnic Normal University Fujian Province University Fuzhou China

Driver drowsiness electroencephalography (EEG) signal monitoring can timely alert drivers of their drowsiness status, thereby reducing the probability of traffic accidents. Graph convolutional networks (GCNs) have shown significant advancements in processing the non-stationary, time-varying, and non-Euclidean nature of EEG signals. However, the existing single-channel EEG adjacency graph construction process lacks interpretability, which hinders the ability of GCNs to effectively extract adjacency graph features, thus affecting the performance of drowsiness monitoring. To address this issue, we propose an edge-end lightweight dual graph convolutional network (LDGCN). Specifically, we are the first to incorporate neurophysiological knowledge to design a Baseline Drowsiness Status Adjacency Graph (BDSAG), which characterizes driver drowsiness status. Additionally, to express more features within limited EEG data, we introduce the Augmented Graph-level Module (AGM). This module captures global and local information at the graph level, ensuring that BDSAG features remain intact while enhancing effective feature expression capability. Furthermore, to deploy our method on the fourth-generation Raspberry Pi, we utilize Adaptive Pruning Optimization (APO) on both channels and neurons, reducing inference latency by almost half. Experiments on benchmark datasets demonstrate that LDGCN offers the best trade-off between monitoring performance and hardware resource utilization compared to existing state-of-the-art algorithms. All our source code can be found at https://***/BryantDom/Driver-Drowsiness-Monitoring. Copyright © 2024, The Authors. All rights reserved.

关键词： Electroencephalography

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Green Routing Game: Strategic Logistical Planning using Mixed Fleets of ICEVs and EVs

Green Routing Game: Strategic Logistical Planning using Mixe...

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IEEE Conference on Decision and control

作者： Hampei Sasahara György Dán Saurabh Amin Henrik Sandberg Department of Systems and Control Engineering School of Engineering Tokyo Institute of Technology Tokyo Japan Division of Network and Systems Engineering School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden Laboratory for Information and Decision Systems Massachusetts Institute of Technology Cambridge MA USA Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden

ISBN: (数字)9781665467612

ISBN: (纸本)9781665467629

This paper introduces a “green” routing game between multiple logistic operators (players), each owning a mixed fleet of internal combustion engine vehicle (ICEV) and electric vehicle (EV) trucks. Each player faces the cost of delayed delivery (due to charging requirements of EVs) and a pollution cost levied on the ICEVs. This cost structure models: 1) limited battery capacity of EVs and their charging requirement; 2) shared nature of charging facilities; 3) pollution cost levied by regulatory agency on the use of ICEVs. We characterize Nash equilibria of this game and derive a condition for its uniqueness. We also use the gradient projection method to compute this equilibrium in a distributed manner. Our equilibrium analysis is useful to analyze the trade-off faced by players in incurring higher delay due to congestion at charging locations when the share of EVs increases versus a higher pollution cost when the share of ICEVs increases. A numerical example suggests that to increase marginal pollution cost can reduce inefficiency of equilibria.

关键词： Costs Pollution Green products Internal combustion engines Games Routing Planning

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Operator-Adapted Evolutionary Large-Scale Multiobjective Optimization for Voltage Transformer Ratio Error Estimation 11th

Operator-Adapted Evolutionary Large-Scale Multiobjective Opt...

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11th International Conference on Evolutionary Multi-Criterion Optimization, EMO 2021

作者： Huang, Changwu Li, Lianghao He, Cheng Cheng, Ran Yao, Xin Guangdong Provincial Key Laboratory of Brain-Inspired Intelligent Computation Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China Key Laboratory of Image Information Processing and Intelligent Control of Education Ministry of China School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China

ISBN: (纸本)9783030720612

Large-scale multiobjective optimization problems (LSMOPs) exist widely in real-world applications, and they are challenging for existing evolutionary algorithms due to their massive volume of search space. Despite that a number of large-scale multiobjective evolutionary algorithms (LSMOEAs) have been proposed in recent years, their effectiveness in solving LSMOPs remains unsatisfactory. One main reason is that most existing LSMOEAs may fail to balance convergence enhancement and diversity maintenance, especially for solving real-world problems. To address this issue, we propose to use a hybridized LSMOEA with adaptive operator selection (AOS) to handle real-world LSMOPs. Specifically, the proposed hybridized LSMOEA with AOS (AOS-LSMOEA) includes multiple different offspring generation and environmental selection strategies extracted from some existing LSMOEAs. Then it uses the AOS to adaptively determine the application rates of different offspring generation and environmental selection operators in an online manner. The proposed approach is capable of taking advantage of existing LSMOEAs, and the AOS enables the algorithm to choose suitable operators for solving different LSMOPs. In this study, the proposed algorithm is expected to solve the voltage transformer ratio error estimation (TREE) problems effectively. Experimental results show that AOS-LSMOEA achieves significant performance improvement due to the hybridization of different operators and the adoption of AOS method. © 2021, Springer Nature Switzerland AG.

关键词： Multiobjective optimization

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Terminal Sliding Modes-Based PLLs for Three-Phase Grid-Connected Inverters

Terminal Sliding Modes-Based PLLs for Three-Phase Grid-Conne...

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Annual Conference of Industrial Electronics Society

作者： Pooyan Alinaghi Hosseinabadi Mostefa Kermadi Hemanshu Pota Saad Mekhilef Ali Soltani Sharif Abadi The School of Engineering and Information Technology The University of New South Wales Canberra ACT Australia The Power Electronics and Renewable Energy Research Laboratory (PEARL) University of Malaya Kuala Lumpur Malaysia The School of Science Computing and Engineering Technologies Swinburne University of Technology Hawthorn VIC Australia Faculty of Mechatronics Institute of Automatic Control and Robotics Warsaw University of Technology Warsaw Poland

This paper proposes and experimentally implements new phase-locked loop algorithms using non-adaptive and adaptive terminal sliding modes for the three-phase grid-connected inverters. The proposed algorithms aim to extract the grid voltage phase and frequency and provide them for the inverter controller. The proposed sliding mode-based PLL algorithms provide faster, more robust, and more accurate detection of the grid voltage information compared to the conventional PI-PLL. This leads to better current controller performance and subsequently, improves the quality of the injected currents into the grid. The finite-time stability notion is incorporated with robust sliding modes to ensure a fast convergence rate. An adaptive law is proposed to determine a proper value for the switching gain which leads to a better overall performance of the PLL compared with a non-adaptive one. The Lyapunov stability theory is used to obtain the stability proofs of the closed-loop systems under both designed sliding modes-based PLLs. The proposed PLL algorithms are validated by simulation and experimental results for the three-phase grid-connected inverter under normal and various abnormal grid conditions.

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Dynamic Optimization of Electric Vehicle Charging for Sustainable Power Grids using Sine Hunter-Prey Optimization with GRUs

Dynamic Optimization of Electric Vehicle Charging for Sustai...

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International Conference on Electrical Power and Energy Systems (ICEPES)

作者： Mohd Khizar Syed Prakash Chandra Behera P Joel Josephson Shamim Ahmad Khan Timur Kholmukhamedov Ponni Valavan M Department of Physics Kohinoor college Maharashtra India Department of Science St. Claret College Bangalore India Department of ECE Malla Reddy Engineering College Medchal Malkajgiri Telangana India Glocal School of Science & Technology Glocal University Uttar Pradesh India Automatic Control and Computer Engineering Department Turin Polytechnic University Tashkent Uzbekistan Department of AI & DS K.Ramakrishnan College of Engineering Trichy Tamil Nadu India

ISBN: (数字)9798350390728

ISBN: (纸本)9798350390735

The integration of electric vehicles (EVs) into power grids presents a significant challenge for sustainability due to their dynamic charging demands and the need to accommodate renewable energy sources. Existing optimization methods often fail to adequately address the complexity of this problem. In this study, propose a novel approach that combines Sine Hunter-Prey Optimization (SHPO) with Gated Recurrent Units (GRUs) to dynamically optimize EV charging for sustainable power grid operation. Traditional optimization methods struggle to capture the nonlinear and time-varying nature of EV charging demands and renewable energy availability. To overcome this limitation, SHPO's ability to efficiently explore complex solution spaces and GRUs' capability to model temporal dependencies in sequential data. This proposed method optimizes EV charging schedules by considering grid stability, renewable energy utilization, and cost minimization. Validating the approach using historical data on EV charging patterns, renewable energy generation, and grid conditions. Performance metrics include grid stability indices, renewable energy integration rates, and cost savings compared to baseline strategies. Experimental results demonstrate that the method outperforms traditional optimization techniques, leading to more sustainable and resilient power grid operation with 92% stability. Overall, this study contributes to advancing the modern methods in dynamic optimization of EV charging, paving the way for more sustainable transportation and energy systems.

关键词： Renewable energy sources Costs Stability criteria Power system dynamics Optimization methods Power system stability Electric vehicle charging

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Attention Mechanism-Based Multilayer Pruned Graph Convolutional Network for Industrial Fault Diagnosis

SSRN

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SSRN 2024年

作者： Zhao, Qiang Chen, Libin Chen, Qiyue Han, Yinghua Sun, Jie College of Information Science and Engineering Northeastern University Shenyang China School of Control Engineering Northeastern University at Qinhuangdao Qinhuangdao China School of Computer and Communication Engineering Northeastern University at Qinhuangdao Qinhuangdao China The State Key Laboratory of Rolling and Automation Northeastern University United States Hebei Key Laboratory of Micro-Nano Precision Optical Sensing and Measurement Technology Qinhuangdao066004 China

Fault diagnosis in complex industrial processes has become a vital issue drawing the attention in the industrial field. With the increasing deployment of sensors in industrial process equipment, deep learning (DL) based approaches have become more feasible. However, most existing DL-based approaches have two main drawbacks. Firstly, there is complex coupling between variables in industrial processes. Most existing approaches have only one metric to build the network, which cannot fully explore the potential relationships between variables. Secondly, there are few studies on aggregating critical features of different variables and reducing the interference of irrelevant and redundant information. To overcome these challenges, this paper presents a new approach for industrial process troubleshooting, called the variational modal decomposition-multi-head graph pruning network (VMD-MHGPN). This approach draws inspiration from the attention mechanism and combines it with graph construction and pruning. Specifically, we use multiple attention mechanisms to learn the interactions between nodes. Previously, variational modal decomposition (VMD) is applied to extract fault features at different time scales and use them as subsequent graph nodes. To reduce the impact of irrelevant and redundant nodes on the fault feature representation, we introduce the graph pruning operation, which uses an attention mechanism to score nodes and discard relatively unimportant ones. The final representation of fault features is generated by computing the weights of different interaction graphs based on the attention mechanism and aggregating multiple interaction graphs according to the weights. The proposed approach for diagnosing faults in industrial processes proved efficient and superior based on experimental findings from a three-phase flow apparatus and a Tennessee Eastman process. © 2024, The Authors. All rights reserved.

关键词： Failure analysis

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Multiplicative Fault Detection and Isolation in Dynamic Systems Using Data-Driven K-Gap Metric based kNN Algorithm

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IFAC-PapersOnLine 2022年第6期55卷 169-174页

作者： Caroline Charlotte Zhu Linlin Li Steven X. Ding Institute of Automatic Control and Complex Systems (AKS) University of Duisburg-Essen Duisburg 47057 Germany Key Laboratory of Knowledge Automation for Industrial Processes of Ministry of Education School of Automation and Electrical Engineering University of Science and Technology Beijing 100083 Beijing China

In this paper, a fault detection and isolation scheme for multiplicative faults in dynamic systems based on data-driven K-Gap metric and k-nearest neighbour (kNN) classification is proposed. To detect multiplicative faults, the standard classification task of kNN is studied from the viewpoint of system analysis. To this end, the data-driven stable kernel representation based on input/output data is presented for feature extraction capturing the dynamic of linear time-invariant (LTI) systems. Data-driven K-Gap metric is used as an alternative tool for distance measure between two kernel subspaces in the kNN algorithm. A simulation example on the three-tank system (DTS200) demonstrates the successful detection and isolation of various multiplicative faults

关键词： Fault detection isolation multiplicative faults data-driven methods multiclass classification process monitoring K-Gap gap metrics

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Reinforcement Learning-Enhanced Dynamic Multi-Objective Evolutionary Algorithm for Cooperative Shovel Scheduling in Open-Pit Mining

Reinforcement Learning-Enhanced Dynamic Multi-Objective Evol...

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Chinese Automation Congress (CAC)

作者： Zihao Zhang Changhe Li Shengxiang Yang Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration School of Automation China University of Geosciences Ministry of Education Wuhan China School of Artificial Intelligence Anhui University of Science & Technology Hefei China School of Computer Science and Informatics De Montfort University Leicester UK

ISBN: (数字)9798350368604

ISBN: (纸本)9798350368611

Aiming at the ore blending problem in the open-pit mining scenario, the optimization objective is to minimize the grade variance of the sliding window of the ore flow, and a cooperative shovel scheduling model for real-time ore blending is established. A reinforcement learning-enhanced dynamic multi- objective evolutionary algorithm is proposed, where a Q-learning operator selection mechanism is introduced to reuse the information from the previous environment for tracking the optimal solution in the dynamic environment. Experimental results show that the proposed method can effectively control the grade fluctuation of the ore flow and dynamically respond to external events, and can find a better solution in real time compared with the traditional operator selection algorithm.

关键词： Q-learning Ores Heuristic algorithms Process control Evolutionary computation Production Dynamic scheduling Real-time systems Windows Optimization

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Recognizing Human-object Interactions for a Home Assistive Robot

Recognizing Human-object Interactions for a Home Assistive R...

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Automation, control and Robotics engineering (CACRE), International Conference on

作者： Liangliang Wang Guanglei Huo Xinwei Chen Chengxi Huang School of Computer Science and Artificial Intelligence Wuhan University of Technology Wuhan China Fujian(Quanzhou)-HIT Research Institute of Engineering and Technology Quanzhou China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Minjiang University Fuzhou China

To enable robots to understand a specific assistive task during human-robot interactions under complex home scenes, at the center is the problem of human-object interaction (HOI) recognition. In particular, aiming at identifying efficiently, this paper proposes a graph convolutional network (GCN) by considering the similarity and proximity of human and object nodes, based on which the geometric positions and morphological relationships are explored. Also, a supplementary convolutional neural network (CNN) is fused to GCN to extract the global semantic features, which also ensures the recognition applicability for the cases where detectors fall into failure. Experiments on V-COCO demonstrate our approach, and results in real-world scenes on our assistive robot – Xiaozhi II verify its robustness.

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Distributed Online Convex Optimization with Time-Varying Constraints: Tighter Cumulative Constraint Violation Bounds under Slater's Condition

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IEEE Transactions on automatic control 2025年

作者： Yi, Xinlei Li, Xiuxian Yang, Tao Xie, Lihua Hong, Yiguang Chai, Tianyou Johansson, Karl H. National Key Laboratory of Autonomous Intelligent Unmanned Systems Shanghai Institute of Intelligent Science and Technology China Ministry of Education Frontiers Science Center for Intelligent Autonomous Systems China Massachusetts Institute of Technology Lab for Information & Decision Systems CambridgeMA02139 United States Northeastern University State Key Laboratory of Synthetical Automation for Process Industries Shenyang110819 China Nanyang Technological University School of Electrical and Electronic Engineering 50 Nanyang Avenue 639798 Singapore Kth Royal Institute of Technology Division of Decision and Control Systems School of Electrical Engineering and Computer Science Sweden Digital Futures Stockholm10044 Sweden

This paper considers distributed online convex optimization with time-varying constraints. In this setting, a network of agents makes decisions at each round, and then only a portion of the loss function and a coordinate block of the constraint function are privately revealed to each agent. The loss and constraint functions are convex and can vary arbitrarily across rounds. The agents collaborate to minimize static network regret and network cumulative constraint violation. A novel distributed online algorithm with a vanishing stepsize is proposed and it achieves an O(Tmax {c,1-c}) static network regret bound and an O(T1-c/2) network cumulative constraint violation bound, where T is the number of rounds and c∈ (0,1) is a user-defined trade-off parameter. When Slater's condition holds (i.e, there is a point that strictly satisfies the inequality constraints), the network cumulative constraint violation bound is reduced to O(T1-c). Moreover, if the loss functions are strongly convex, then static network regret bound is reduced to O(log (T)), and the network cumulative constraint violation bound is reduced to O(√log (T)T) and O(log (T)) without and with Slater's condition, respectively. To the best of our knowledge, this paper is the first to achieve tighter (network) cumulative constraint violation bounds for (distributed) online convex optimization with time-varying constraints under Slater's condition. Finally, the theoretical results are verified through numerical simulations. © 1963-2012 IEEE.

关键词： Convex optimization

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