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SSRN 2022年

作者： Yang, Xiaoteng Wu, Zhenqiang Zhang, WenBo Javaid, Shumaila School of Computer Science Shaanxi Normal University Xi’an710119 China Key Laboratory of Modern Teaching Technology Ministry of Education Xi’an710062 China Department of Control Science and Engineering College of Electronics and Information Engineering Tongji University Shanghai201804 China Frontiers Science Center for Intelligent Autonomous Systems Shanghai201210 China

The evolution of hierarchies in complex systems is an exciting phenomenon since hierarchies exist in many social and biological networks. So far, many approaches have been proposed, however, they are either based on the locality of the nodes or the cluster relationships between networks. These approaches often use traditional time-series network modeling methods that focus only on local static relationships between different nodes but ignore the dynamic interactions between real network nodes. Local static and dynamic global relationships should be considered to understand better the generation and maintenance of hierarchical phenomena in networks. To address this problem, we propose a long-term, flexible hierarchical evolution model for explaining the hierarchical phenomena arising in networks. Our model can integrate both local static and dynamic global information in the network and use time and higher-order inter-node interactions to integrate static and dynamic relationships. New interactions in the network are formed based on preference information between nodes, while old links fade out over time. In addition, we derive critical points for the hierarchical evolution of the system over long periods. Importantly, our model allows the comparison of network rank formation across different data as a way to perform rank evolution studies. © 2022, The Authors. All rights reserved.

关键词： Time series

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An Efficient Spatial-Temporal Trajectory Planner for Autonomous Vehicles in Unstructured Environments

arXiv

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arXiv 2022年

作者： Han, Zhichao Wu, Yuwei Li, Tong Zhang, Lu Pei, Liuao Xu, Long Li, Chengyang Ma, Changjia Xu, Chao Shen, Shaojie Gao, Fei State Key Laboratory of Industrial Control Technology Zhejiang University Hangzhou310027 China Huzhou Institute of Zhejiang University Huzhou313000 China Department of Electrical and Systems Engineering University of Pennsylvania PhiladelphiaPA19104 United States Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Hong Kong

As a core part of autonomous driving systems, motion planning has received extensive attention from academia and industry. However, real-time trajectory planning capable of spatial-temporal joint optimization is challenged by nonholonomic dynamics, particularly in the presence of unstructured environments and dynamic obstacles. To bridge the gap, we propose a real-time trajectory optimization method that can generate a high-quality whole-body trajectory under arbitrary environmental constraints. By leveraging the differential flatness property of car-like robots, we simplify the trajectory representation and analytically formulate the planning problem while maintaining the feasibility of the nonholonomic dynamics. Moreover, we achieve efficient obstacle avoidance with a safe driving corridor for unmodelled obstacles and signed distance approximations for dynamic moving objects. We present comprehensive benchmarks with State-of-the-Art methods, demonstrating the significance of the proposed method in terms of efficiency and trajectory quality. Real-world experiments verify the practicality of our algorithm. We will release our codes for the research community.1 Copyright © 2022, The Authors. All rights reserved.

关键词： Autonomous vehicles

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A Zeroth-Order Momentum Method for Risk-Averse Online Convex Games

A Zeroth-Order Momentum Method for Risk-Averse Online Convex...

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IEEE Conference on Decision and control

作者： Zifan Wang Yi Shen Zachary I. Bell Scott Nivison Michael M. Zavlanos Karl H. Johansson Division of Decision and Control Systems School of Electrical Enginnering and Computer Science KTH Royal Institute of Technology Digital Futures Stockholm Sweden Department of Mechanical Engineering and Materials Science Duke University Durham NC USA Air Force Research Laboratory Eglin AFB FL USA

ISBN: (数字)9781665467612

ISBN: (纸本)9781665467629

We consider risk-averse learning in repeated unknown games where the goal of the agents is to minimize their individual risk of incurring significantly high cost. Specifically, the agents use the conditional value at risk (CVaR) as a risk measure and rely on bandit feedback in the form of the cost values of the selected actions at every episode to estimate their CVaR values and update their actions. A major challenge in using bandit feedback to estimate CVaR is that the agents can only access their own cost values, which, however, depend on the actions of all agents. To address this challenge, we propose a new risk-averse learning algorithm with momentum that utilizes the full historical information on the cost values. We show that this algorithm achieves sub-linear regret and matches the best known algorithms in the literature. We provide numerical experiments for a Cournot game that show that our method outperforms existing methods.

关键词： Costs Estimation Games Numerical simulation Standards

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Bioformers: Embedding Transformers for Ultra-Low Power sEMG-based Gesture Recognition

arXiv

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arXiv 2022年

作者： Burrello, Alessio Morghet, Francesco Bianco Scherer, Moritz Benatti, Simone Benini, Luca Macii, Enrico Poncino, Massimo Pagliari, Daniele Jahier DEI Università di Bologna Bologna Italy Department of Control and Computer Engineering Politecnico di Torino Turin Italy Integrated Systems Laboratory ETH Zurich Switzerland Department of Sciences and Methods for Engineering University of Modena and Reggio Emilia Italy Interuniversity Department of Regional and Urban Studies and Planning Politecnico di Torino Turin Italy

Human-machine interaction is gaining traction in rehabilitation tasks, such as controlling prosthetic hands or robotic arms. Gesture recognition exploiting surface electromyographic (sEMG) signals is one of the most promising approaches, given that sEMG signal acquisition is non-invasive and is directly related to muscle contraction. However, the analysis of these signals still presents many challenges since similar gestures result in similar muscle contractions. Thus the resulting signal shapes are almost identical, leading to low classification accuracy. To tackle this challenge, complex neural networks are employed, which require large memory footprints, consume relatively high energy and limit the maximum battery life of devices used for classification. This work addresses this problem with the introduction of the Bioformers. This new family of ultra-small attention-based architectures approaches state-of-the-art performance while reducing the number of parameters and operations of 4.9×. Additionally, by introducing a new inter-subjects pre-training, we improve the accuracy of our best Bioformer by 3.39%, matching state-of-the-art accuracy without any additional inference cost. Deploying our best performing Bioformer on a Parallel, Ultra-Low Power (PULP) microcontroller unit (MCU), the GreenWaves GAP8, we achieve an inference latency and energy of 2.72 ms and 0.14 mJ, respectively, 8.0× lower than the previous state-of-the-art neural network, while occupying just 94.2 kB of memory. © 2022, CC BY-SA.

关键词： Embedded systems

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Digital Map Generation Workflow Demonstrated on ZalaZONE Automotive Proving Ground Elements

Digital Map Generation Workflow Demonstrated on ZalaZONE Aut...

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IEEE Symposium on Network Operations and Management

作者： Árpád Somogyi Tamás Tettamanti Pál Varga Zsolt Szalay Dániel Baranyai Tamás Lovas Dept. of Photogrammetry and Geoinformatics Fac. of Civil Engineering Budapest University of Technology and Economics Budapest Hungary Systems and Control Laboratory Institute for Computer Science and Control of the Eötvös Loránd Research Network Budapest Hungary Dept. of Telecommunications and Media Informatics Fac. of Electrical Engineering and Informatics Budapest University of Technology and Economics Budapest Hungary Dept. of Automotive Technologies Fac. of Transportation Eng. and Vehicle Eng. Budapest University of Technology and Economics Budapest Hungary

One major goal of digital twin technology applied in the Architecture, engineering, and Construction (AEC) Industry is the mapping of roads and road environments with their associated information. Such digital twins can support cost-effective testing, simulations, and the production of mapping backgrounds for automated vehicles and generally for Cooperative, Connected and Automated Mobility (CCAM) developments. The efficient workflow that leads from data acquisition to finished products depends on a number of requirements and knowledge sets. The main contribution of the paper is a general methodology of creating HD-map from point cloud and image data set. Accordingly, the most widely used survey techniques are presented and the requirements for the objects to be derived are summarized. The paper then outlines a general implementation procedure as an expert knowledge through a real-life example, where the results obtained for the ZalaZONE Automotive Proving Ground are demonstrated.

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Properly Positioning Coal Power in Decarbonizing the Global Power systems

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engineering 2025年

作者： Jinze Li Fang Fang Yuanye Chen Songyuan Yu Jizhen Liu Yanjun Du Ming Du Yi Zhang Qinghua Wang Yuguang Niu School of Control and Computer Engineering North China Electric Power University Beijing 102206 China National Key Laboratory of New Energy Power Systems Beijing 102206 China Beijing Huairou Laboratory Beijing 101499 China

Coal-based electricity generation stands as the largest single source of global carbon dioxide emissions. The United Nations Climate Change Conference 28 (COP28) reached a historic consensus by all parties to transition away from fossil energy. On a global scale, a tripling of renewable energy capacity and a doubling of energy efficiency improvements by 2030, while gradually phasing out unabated coal power. As we move toward a decarbonized power system in a just, orderly, and equitable manner, coal power can enhance the integration of renewable energy into the electricity system by providing flexible services such as rapid ramping, cycling, and reliable capacity backup, ensuring the secure and stable operation of the system. Herein, we contribute to clarifying the role of coal power flexibility in the different phases of renewable energy development. By analyzing the metrics and costs of coal flexibility, we find that flexibility retrofitting can improve the regulatory capacity of the power system, reduce carbon emissions, and provide economic benefits for operators. The research findings emphasize that coal flexibility can positively contribute to decarbonization through policy support and technological innovation.

关键词： Coal power flexibility Energy transition Flexibility option Flexibility economic

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Feasible Regions Identification based on Historical Solutions for Constrained Optimization Problems

Feasible Regions Identification based on Historical Solution...

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Congress on Evolutionary Computation

作者： Mengli Shan Changhe Li Mai Peng Xiaobo Liu Michalis Mavrovouniotis Shengxiang Yang School of Automation China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China School of Artificial Intelligence Anhui University of Science & Technology Hefei China State Key Laboratory of Digital Intelligent Technology for Unmanned Coal Mining Anhui University of Science & Technology Huainan China ERATOSTHENES Centre of Excellence Limassol Cyprus Department of Civil Engineering and Geomatics Cyprus University of Technology Limassol Cyprus School of Computer Science and Informatics De Montfort University Leicester United Kingdom

ISBN: (数字)9798331534318

ISBN: (纸本)9798331534325

The presence of constraints often leads to the formation of narrow and fragmented feasible regions within the search region, presenting significant challenges for optimization problem-solving. This paper introduces a novel approach, Feasible Regions Identification based on Historical Solutions (FRIHS), designed to address these challenges. FRIHS leverages previously evaluated solutions to partition the search region into ε-feasible and ε-infeasible regions. Additionally, by analyzing the correlations among constraints, they are reformulated as auxiliary objectives, effectively transforming the constrained optimization problem into a constrained multi-objective optimization problem. The method employs the classical evolutionary algorithm Differential Evolution and the multi-objective method NSGA-III to search the most promising feasible regions. The effectiveness of FRIHS is evaluated through a comparative analysis with five advanced constraint-handling algorithms across a benchmark test suite. Experimental results indicate that the proposed approach demonstrates competitive performance on the test problems.

关键词： Correlation Evolutionary computation Benchmark testing Search problems Rendering (computer graphics) Problem-solving Optimization Faces

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$H_{\infty }$ PID control for Discrete-Time Fuzzy systems With Infinite-Distributed Delays Under Round-Robin Communication Protocol

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IEEE Transactions on Fuzzy systems 2021年第6期30卷 1875-1888页

作者： Yezheng Wang Zidong Wang Lei Zou Hongli Dong College of Electrical Engineering and Automation Shandong University of Science and Technology Qingdao China Department of Computer Science Brunel University London Uxbridge U.K. Institute of Complex Systems and Advanced Control and the Heilongjiang Provincial Key Laboratory of Networking and Intelligent Control Northeast Petroleum University Daqing China

This article is concerned with the $H_{\infty }$ proportional–integral–derivative (PID) control problem for class of discrete-time Takagi–Sugeno fuzzy systems subject to infinite-distributed time delays and round-robin (RR) protocol scheduling effects. The information exchange between the sensors and the controller is conducted through a shared communication network. For the purpose of alleviating possible data collision, the well-known RR communication protocol is deployed to schedule the data transmissions. To stabilize the target system with guaranteed $H_{\infty }$ performance index, a novel yet easy-to-implement fuzzy PID controller is developed whose integral term is calculated based on the past measurements defined in a limited time window with hope to improve computational efficiency and reduce accumulation error. Based on the Lyapunov stability theory and the convex optimization technique, sufficient conditions are derived to ensure the exponential stability as well as the $H_{\infty }$ disturbance attenuation/rejection capacity of the underlying system. Furthermore, by utilizing the cone complementarity linearization algorithm, the nonconvex controller design problem is transformed into an iterative optimization one that facilitates the controller implementation. Finally, simulation examples are given to show the effectiveness and correctness of the developed control method.

关键词： PD control Protocols PI control control systems Symmetric matrices Communication networks Optimization

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Parallel Planning:A New Motion Planning Framework for Autonomous Driving

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IEEE/CAA Journal of Automatica Sinica 2019年第1期6卷 236-246页

作者： Long Chen Xuemin Hu Wei Tian Hong Wang Dongpu Cao Fei-Yue Wang IEEE School of Data and Computer Science Sun Yat-sen University the School of Computer Science and Information Engineering Hubei University Institute of Measurement and Control Systems Karlsruhe Institute of Technology Department of Mechanical and Mechatronics Engineering University of Waterloo the State Key Laboratory of Management and Control for Complex Systems.Institute of Automation Chinese Academy of Sciences the Research Center for Military Computational Experiments and Parallel Systems Technology National University of Defense Technology

Motion planning is one of the most significant technologies for autonomous driving. To make motion planning models able to learn from the environment and to deal with emergency situations, a new motion planning framework called as"parallel planning" is proposed in this paper. In order to generate sufficient and various training samples, artificial traffic scenes are firstly constructed based on the knowledge from the reality.A deep planning model which combines a convolutional neural network(CNN) with the Long Short-Term Memory module(LSTM) is developed to make planning decisions in an end-toend mode. This model can learn from both real and artificial traffic scenes and imitate the driving style of human ***, a parallel deep reinforcement learning approach is also presented to improve the robustness of planning model and reduce the error rate. To handle emergency situations, a hybrid generative model including a variational auto-encoder(VAE) and a generative adversarial network(GAN) is utilized to learn from virtual emergencies generated in artificial traffic scenes. While an autonomous vehicle is moving, the hybrid generative model generates multiple video clips in parallel, which correspond to different potential emergency scenarios. Simultaneously, the deep planning model makes planning decisions for both virtual and current real scenes. The final planning decision is determined by analysis of real observations. Leveraging the parallel planning approach, the planner is able to make rational decisions without heavy calculation burden when an emergency occurs.

关键词： Autonomous driving artificial traffic scene deep learning emergencies motion planning parallel planning

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The Multi-Blockchain Based Data Trading Markets with Novel Pricing Mechanisms

SSRN

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SSRN 2022年

作者： Li, Juanjuan Li, Junqing Wang, Xiao Qin, Rui Yuan, Yong Wang, Fei-Yue The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China College of Computer Science and Engineering Shandong University of Science and Technology Qingdao266590 China Qingdao Academy of Intelligent Industries Qingdao266109 China School of Mathematics Renmin University of China Beijing100872 China Macao Institute of Systems Engineering Macau University of Science and Technology 999078 China

Aiming to deal with the challenge of data security and data pricing in data trading markets, we propose the novel multi-blockchain-based framework and design the related pricing mechanisms. Specifically, data recording and trading are conducted separately in the Data Blockchain (DChain) and the Value Blockchain (VChain). Based on these blockchains, two-layer data trading markets are established to manage the initial data trading in the primary market and the data resale in the secondary market. Pricing mechanisms are proposed to protect the formulated data trading markets against strategic trading behaviors and balance the payoffs of both suppliers and users. In the regular data trading on VChain-S2D, two auction models are used to deal with users' strategic bidding in terms of the demand scale, where the incentive-compatible Vickrey-Clarke-Groves (VCG) model is dedicated to the low-demand trading scenario, while the nearly incentive-compatible Monopolistic Price (MP) model is for the high-demand trading scenario. In the temporary data trading on VChain-D2S, a reverse auction mechanism named Two-Stage Obscure Selection (TSOS) is designed to regulate both suppliers' quoting and users' valuation strategies. Moreover, experiments are carried out to validate our theoretical analysis. Our work is expected to enhance the data security and improve the trading efficiency. © 2022, The Authors. All rights reserved.

关键词： Blockchain

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