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arXiv 2024年

作者： Li, Yan Ma, Chenyu Gao, Rong Wu, Youxi Li, Jinyan Wang, Wenjian Wu, Xindong School of Economics and Management Hebei University of Technology Tianjin300400 China School of Artificial Intelligence Hebei University of Technology Tianjin300400 China School of Computer Science and Control Engineering Shenzhen University of Advanced Technology Shenzhen518055 China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China School of Computer and Information Technology Shanxi University Taiyuan237016 China Key Laboratory of Knowledge Engineering with Big Data The Ministry of Education of China Hefei University of Technology Hefei230009 China

Order-preserving pattern (OPP) mining is a type of sequential pattern mining method in which a group of ranks of time series is used to represent an OPP. This approach can discover frequent trends in time series. Existing OPP mining algorithms consider data points at different time to be equally important;however, newer data usually have a more significant impact, while older data have a weaker impact. We therefore introduce the forgetting mechanism into OPP mining to reduce the importance of older data. This paper explores the mining of OPPs with forgetting mechanism (OPF) and proposes an algorithm called OPF-Miner that can discover frequent OPFs. OPF-Miner performs two tasks, candidate pattern generation and support calculation. In candidate pattern generation, OPF-Miner employs a maximal support priority strategy and a group pattern fusion strategy to avoid redundant pattern fusions. For support calculation, we propose an algorithm called support calculation with forgetting mechanism, which uses prefix and suffix pattern pruning strategies to avoid redundant support calculations. The experiments are conducted on nine datasets and 12 alternative algorithms. The results verify that OPF-Miner is superior to other competitive algorithms. More importantly, OPF-Miner yields good clustering performance for time series, since the forgetting mechanism is employed. Copyright © 2024, The Authors. All rights reserved.

关键词： Miners

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Social Force Embedded Mixed Graph Convolutional Network for Multi-class Trajectory Prediction

arXiv

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arXiv 2024年

作者： Du, Quancheng Wang, Xiao Yin, Shouguo Li, Lingxi Ning, Huansheng The School of Computer and Communication Engineering University of Science and Technology Beijing Beijing100083 China The School of Artificial Intelligence Anhui University Hefei230031 China The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China The School of Electrical and Computer Engineering Indiana University Purdue University IndianapolisIN46202 United States

Accurate prediction of agent motion trajectories is crucial for autonomous driving, contributing to the reduction of collision risks in human-vehicle interactions and ensuring ample response time for other traffic participants. Current research predominantly focuses on traditional deep learning methods, including convolutional neural networks (CNNs) and recurrent neural networks (RNNs). These methods leverage relative distances to forecast the motion trajectories of a single class of agents. However, in complex traffic scenarios, the motion patterns of various types of traffic participants exhibit inherent randomness and uncertainty. Relying solely on relative distances may not adequately capture the nuanced interaction patterns between different classes of road users. In this paper, we propose a novel multi-class trajectory prediction method named the social force embedded mixed graph convolutional network (SFEM-GCN). The primary goal is to extract social interactions among agents more accurately. SFEM-GCN comprises three graph topologies: the semantic graph (SG), position graph (PG), and velocity graph (VG). These graphs encode various of social force relationships among different classes of agents in complex scenes. Specifically, SG utilizes one-hot encoding of agent-class information to guide the construction of graph adjacency matrices based on semantic information. PG and VG create adjacency matrices to capture motion interaction relationships between different classes agents. These graph structures are then integrated into a mixed graph, where learning is conducted using a spatio-temporal graph convolutional neural network (ST-GCNN). To further enhance prediction performance, we adopt temporal convolutional networks (TCNs) to generate the predicted trajectory with fewer parameters. Experimental results on publicly available datasets demonstrate that SFEM-GCN surpasses state-of-the-art methods in terms of accuracy and robustness. Copyright © 2024, The Authors.

关键词： Forecasting

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Inequality-To-Inequality Zhang Equivalency(ZE) for Integrals of 0,1,2,3,...,Times Illustrating Pascal Triangle

Inequality-To-Inequality Zhang Equivalency(ZE) for Integrals...

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第34届中国控制与决策会议

作者： Yunong Zhang Wuyi Yang Zhenyu Li School of Computer Science and Engineering Sun Yat-sen University Guangdong Key Laboratory of Modern Control Technology School of Artificial Intelligence Sun Yat-sen University

By applying equivalency,it is easier to solve original problems at different aspects and *** equivalency(ZE) has been originally proposed for equalities(i.e.,equations),which means that one can solve original equations at different ***,in order to satisfy the requirements of practical applications,inequality ZE has been proposed,which supplements and develops the concept of *** this study,the concept of inequality ZE is *** order-n inequality ZE is proposed and proved by mathematical *** applying inequality ZE,the differentorder ZE formulas of 0,1,2,3,…,times integral inequalities are *** the process of applying inequality ZE,we find that the coefficients of inequality-to-inequality ZE formulas are consistent with the rows of Pascal ***,some theoretics are proposed to reveal the *** the help of Pascal triangle,we can quickly construct any order inequality ZE formula to solve various problems.

关键词： Zhang equivalency(ZE) inequality to inequality Pascal triangle mathematical induction

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Convergence Rate Analysis of Gaussian Belief Propagation for Markov Networks

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IEEE/CAA Journal of automatica Sinica 2020年第3期7卷 668-673页

作者： Zhaorong Zhang Minyue Fu School of Electrical Engineering and Computer Science The University of Newcastle.University DriveNSW 2308Australia IEEE School of Automation Guangdong University of Technologyand Guangdong Key Laboratory of Intelligent Decision and Cooperative ControlGuangzhou 510006China

Gaussian belief propagation algorithm(GaBP) is one of the most important distributed algorithms in signal processing and statistical learning involving Markov networks. It is well known that the algorithm correctly computes marginal density functions from a high dimensional joint density function over a Markov network in a finite number of iterations when the underlying Gaussian graph is acyclic. It is also known more recently that the algorithm produces correct marginal means asymptotically for cyclic Gaussian graphs under the condition of walk summability(or generalised diagonal dominance). This paper extends this convergence result further by showing that the convergence is exponential under the generalised diagonal dominance condition,and provides a simple bound for the convergence rate. Our results are derived by combining the known walk summability approach for asymptotic convergence analysis with the control systems approach for stability analysis.

关键词： Belief propagation distributed algorithm distributed estimation Gaussian belief propagation Markov networks

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Two Enhanced-rate Power Allocation Strategies for Active IRS-assisted Wireless Network

arXiv

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arXiv 2023年

作者： Cheng, Qiankun Dong, Rongen Cai, Wenlong Liu, Ruiqi Shu, Feng Wang, Jiangzhou The School of Information and Communication Engineering Hainan University Haikou570228 China The School of Information and Communication Engineering Collaborative Innovation Center of Information Technology Hainan University Haikou570228 China The School of Electronic and Optical Engineering Nanjing University of Science and Technology Nanjing210094 China National Key Laboratory of Science and Technology on Aerospace Intelligence Control Beijing Aerospace Automatic Control Institute Beijing China The Wireless and Computing Research Institute ZTE Corporation Beijing100029 China The School of Engineering University of Kent CanterburyCT2 7NT United Kingdom

Due to its ability of overcoming the impact of double-fading effect, active intelligent reflecting surface (IRS) has attracted a lot of attention. Unlike passive IRS, active IRS should be supplied by power, thus adjusting power between base station (BS) and IRS having a direct impact on the system rate performance. In this paper, the active IRS-aided network under a total power constraint is modeled with an ability of adjusting power between BS and IRS. Given the transmit beamforming at BS and reflecting beamforming at IRS, the SNR expression is derived to be a function of power allocation (PA) factor, and the optimization of maximizing the SNR is given. Subsequently, two high-performance PA strategies, enhanced multiple random initialization Newton's (EMRIN) and Taylor polynomial approximation (TPA), are proposed. The former is to improve the rate performance of classic Netwon's method to avoid involving a local optimal point by using multiple random initializations. To reduce its high computational complexity, the latter provides a closed-form solution by making use of the first-order Taylor polynomial approximation to the original SNR function. Actually, using TPA, the original optimization problem is transformed into a problem of finding a root for a third-order polynomial. Simulation results are as follows: the first-order TPA of SNR fit its exact expression well, the proposed two PA methods performs much better than fixed PA in accordance with rate, and appoaches exhaustive search as the number of IRS reflecting elements goes to large-scale. Copyright © 2023, The Authors. All rights reserved.

关键词： Polynomial approximation

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Hierarchical Clustering-Based State Grouping Reinforcement Learning for Switching Decision of Autonomous Vehicles

Hierarchical Clustering-Based State Grouping Reinforcement L...

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IEEE International Conference on Unmanned Systems (ICUS)

作者： Wenjie Ouyang Yiwen Jiao Yang Liu Yang Li Manjiang Hu Hongmao Qin College of Mechanical and Vehicle Engineering Hunan University Changsha China College of Computer Science and Electronic Engineering Hunan University Changsha China College of Electrical Mechanical and Computer Science Delft University of Technology Delft The Netherlands State Key Laboratory of Advanced Design and Manufacturing Technology for Vehicle College of Mechanical and Vehicle Engineering Hunan University Changsha China Wuxi Intelligent Control Research Institute of Hunan University Wuxi China

Reinforcement learning (RL) has gained wide attention, but its implementation in autonomous vehicles is still limited by insufficient sample efficiency and heavy training costs. The training efficiency of RL agents is influenced by the dimension of the state space, which can be partitioned to reduce the complexity of sampling and computation. This study proposes a hierarchical clustering-based state grouping reinforcement learning (HCSG-RL) method for the switching decision of autonomous vehicles. First, we partition the base state space into groups and generate a hierarchical tree of state space groups. Then, we train multiple sub-agents for each node in the hierarchical tree. Finally, we add these trained-well sub-model into master policy. This method allows us to fully explore all state spaces and improve the training efficiency of individual agents, which handles the “long-tail” issue and the curse of dimensionality issue. We conduct experiments in a simulation environment and results show that the proposed method has 16–72% reward improvement compared to the tree model in different road length.

关键词：

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A Multi-Player Potential Game Approach for Sensor Network Localization with Noisy Measurements

A Multi-Player Potential Game Approach for Sensor Network Lo...

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IEEE Conference on Decision and control

作者： Gehui Xu Guanpu Chen Baris Fidan Yiguang Hong Hongsheng Qi Thomas Parisini Karl H. Johansson Department of Electrical and Electronic Engineering Imperial College London London UK Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden Department of Mechanical and Mechatronics Engineering University of Waterloo Waterloo ON Canada Department of Control Science and Engineering Tongji University shanghai China Shanghai Research Institute for Intelligent Autonomous Systems Shanghai China Key Laboratory of Systems and Control Academy of Mathematics and Systems Science Beijing China School of Mathematical Sciences University of Chinese Academy of Sciences Beijing China Department of Electronic Systems Aalborg University Denmark Department of Engineering and Architecture University of Trieste Italy

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

Sensor network localization (SNL) is a challenging problem due to its inherent non-convexity and the effects of noise in inter-node ranging measurements and anchor node position. We formulate a non-convex SNL problem as a multiplayer non-convex potential game and investigate the existence and uniqueness of a Nash equilibrium (NE) in both the ideal setting without measurement noise and the practical setting with measurement noise. We first show that the NE exists and is unique in the noiseless case, and corresponds to the precise network localization. Then, we study the SNL for the case with errors affecting the anchor node position and the inter-node distance measurements. Specifically, we establish that in case these errors are sufficiently small, the NE exists and is unique. It is shown that the NE is an approximate solution to the SNL problem, and that the position errors can be quantified accordingly. Based on these findings, we apply the results to case studies involving only inter-node distance measurement errors and only anchor position information inaccuracies.

关键词： Location awareness Noise Games Position measurement Nash equilibrium Distance measurement Noise measurement

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Unexpected Trade-Offs of Fossil Fuel Reduction on PM2.5and O3Pollution Regulation Can Be Offset by Synergistic control of VOCs Source

ACS ES&T Air

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ACS ES&T Air 2024年第7期1卷 660-669页

作者： Guoliang Shi Haofei Yu Jie Li Zhang Zhang Feng Wang Junbo Huang Yue Li Yulong Yan Yinchang Feng Armistead G. Russell State Environmental Protection Key Laboratory of Urban Ambient Air Particulate Matter Pollution Prevention and Control Tianjin Key Laboratory of Urban Transport Emission Research College of Environmental Science and Engineering Nankai University Tianjin 300350 People’s Republic of China China Meteorological Administration-Nankai University (CMA-NKU) Cooperative Laboratory for Atmospheric Environment-Health Research College of Environmental Science and Engineering Nankai University Tianjin 300350 China Department of Civil Environmental and Construction Engineering University of Central Florida Orlando Florida 32816 United States School of Environmental Science and Safety Engineering Tianjin University of Technology Tianjin 300384 China College of Computer Science Nankai University Tianjin 300350 China School of Environment Beijing Jiaotong University Beijing 100044 China School of Civil and Environmental Engineering Georgia Institute of Technology Atlanta Georgia 30332-0512 United States

Particulate matter (PM2.5) and ozone (O3) are typical air pollutants that require effective simultaneous control. Current PM2.5and O3co-mitigation strategies are primarily focused on the emission optimization of nitrogen oxides (NOx) and volatile organic compounds (VOCs); however, fundamental mechanisms underlying these co-mitigation efforts remain largely unanswered. Here, utilizing an atmospheric chemistry model combined with machine learning technique, we extend the scope of coordinated control efforts beyond just precursors by identifying and controlling common sources of PM2.5and O3, which are fossil fuel combustion sources including coal combustion, industrial emission, and vehicle exhausts. We conducted further sensitivity simulations and found that reducing fossil fuel combustion emissions significantly lowers PM2.5levels but has trade-off effects on O3control, which is attributable to the suppressed aerosol sink of hydroperoxyl radicals and the enhanced atmospheric oxidizing capacity. Furthermore, this unexpected enhancement of O3can be offset by synergistically controlling certain other specific VOCs sources. For example, during days with high levels of both PM2.5and O3pollution, a 40% reduction of VOCs sources at the corresponding level of coal combustion reduction is predicted to lead to an ∼13 ppb (∼14%) reduction in O3concentration. Our results provide the scientific evidence and theoretical framework for mitigating PM2.5and O3pollution through joint control of fossil fuel combustion sources and specific VOCs sources.

关键词： Environmental pollution Fossil fuels Particulate matter Redox reactions Volatile organic compounds

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A Multi-Conditional Proxy Broadcast Re-Encryption Scheme for Sensor Networks

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computers, Materials & Continua 2020年第12期65卷 2079-2090页

作者： Pang Li Lifeng Zhu Brij B.Gupta Sunil Kumar Jha Nanjing Vocational College of Information Technology Nanjing210044China State Key Laboratory of Bioelectronics Jiangsu Key Laboratory of Remote Measurement and ControlSchool of Instrument Science and EngineeringSoutheast UniversityNanjing210096China Department of Computer Engineering National Institute of TechnologyKurukshetra136119India Department of Computer Science and Information Engineering Asia UniversityTaichung41449Taiwan IT Fundamentals and Education Technologies Applications University of Information Technology and Management in RzeszowRzeszow Voivodeship100031Poland

In sensor networks,it is a challenge to ensure the security of data exchange between packet switching nodes holding different private *** order to solve this problem,the present study proposes a scheme called multi-conditional proxy broadcast re-encryption(MC-PBRE).The scheme consists of the following roles:the source node,proxy server,and the target *** the condition is met,the proxy can convert the encrypted data of the source node into data that the target node can directly *** allows the proxy server to convert the ciphertext of the source node to a new ciphertext of the target node in a different group,while the proxy server does not need to store the key or reveal the *** the same time,the proxy server cannot obtain any valuable information in the *** paper formalizes the concept of MC-PBRE and its security model,and proposes a MC-PBRE scheme of ciphertext ***,the scheme security has been proved in the random oracle.

关键词： Proxy re-encryption sensor network security broadcast re-encryption

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Full Point Encoding for Local Feature Aggregation in 3D Point Clouds

arXiv

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arXiv 2023年

作者： He, Yong Yu, Hongshan Yang, Zhengeng Liu, Xiaoyan Sun, Wei Mian, Ajmal National Engineering Laboratory for Robot Visual Perception and Control Technology College of Electrical and Information Engineering Hunan University Lushan South Rd. Yuelu Dist. Changsha410082 China Department of Computer Science The University of Western Australia WA6009 Australia

Point cloud processing methods exploit local point features and global context through aggregation which does not explicity model the internal correlations between local and global features. To address this problem, we propose full point encoding which is applicable to convolution and transformer architectures. Specifically, we propose Full Point Convolution (FPConv) and Full Point Transformer (FPTransformer) architectures. The key idea is to adaptively learn the weights from local and global geometric connections, where the connections are established through local and global correlation functions respectively. FPConv and FPTransformer simultaneously model the local and global geometric relationships as well as their internal correlations, demonstrating strong generalization ability and high performance. FPConv is incorporated in classical hierarchical network architectures to achieve local and global shape-aware learning. In FPTransformer, we introduce full point position encoding in self-attention, that hierarchically encodes each point position in the global and local receptive field. We also propose a shape aware downsampling block which takes into account the local shape and the global context. Experimental comparison to existing methods on benchmark datasets show the efficacy of FPConv and FPTransformer for semantic segmentation, object detection, classification, and normal estimation tasks. In particular, we achieve state-of-the-art semantic segmentation results of 76% mIoU on S3DIS 6-fold and 72.2% on S3DIS Area 5. Copyright © 2023, The Authors. All rights reserved.

关键词： Convolution

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