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ABCP: Automatic Blockwise and Channelwise Network Pruning via Joint Search

IEEE TRANSACTIONS ON COGNITIVE AND DEVELOPMENTAL systems 2023年第3期15卷 1560-1573页

作者： Li, Jiaqi Li, Haoran Chen, Yaran Ding, Zixiang Li, Nannan Ma, Mingjun Duan, Zicheng Zhao, Dongbin Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Beijing Inst Technol Sch Mech Engn Beijing 100081 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 101408 Peoples R China

Currently, an increasing number of model pruning methods are proposed to resolve the contradictions between the computer powers required by the deep learning models and the resource-constrained devices. However, for simple tasks like robotic detection, most of the traditional rule-based network pruning methods cannot reach a sufficient compression ratio with low accuracy loss and are time consuming as well as laborious. In this article, we propose automatic blockwise and channel-wise network pruning (ABCP) to jointly search the blockwise and channelwise pruning action for robotic detection by deep reinforcement learning. A joint sample algorithm is proposed to simultaneously generate the pruning choice of each residual block and the channel pruning ratio of each convolutional layer from the discrete and continuous search space, respectively. The best pruning action taking both the accuracy and the complexity of the model into account is obtained finally. Compared with the traditional rule-based pruning method, this pipeline saves human labor and achieves a higher compression ratio with lower accuracy loss. Tested on the mobile robot detection data set, the pruned YOLOv3 model saves 99.5% floating-point operations, reduces 99.5% parameters, and achieves 37.3x speed up with only 2.8% mean of average precision (mAP) loss. On the sim2real detection data set for robotic detection task, the pruned YOLOv3 model achieves 9.6% better mAP than the baseline model, showing better robustness performance.

关键词： Joint search model compression pruning reinforcement learning

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Automated Silicon-Substrate Ultra-Microtome for Automating the Collection of Brain Sections in Array Tomography

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IEEE/CAA Journal of Automatica Sinica 2021年第2期8卷 389-401页

作者： Long Cheng Weizhou Liu Chao Zhou Yongxiang Zou Zeng-Guang Hou the State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190 the School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100049China

Understanding the structure and working principle of brain neural networks requires three-dimensional reconstruction of brain tissue samples using array tomography *** order to improve the reconstruction performance,the sequence of brain sections should be collected with silicon wafers for subsequent electron microscopic ***,the current collection of brain sections based on silicon substrate involve mainly manual collection,which requires the involvement of automation techniques to increase collection *** paper presents the design of an automatic collection device for brain ***,a novel mechanism based on circular silicon substrates is proposed for collection of brain sections;second,an automatic collection system based on microscopic object detection and feedback control strategy is *** results verify the function of the proposed collection *** objects(brain section,left baffle,right baffle)can be detected from microscopic images by the proposed detection *** efficiency can be further improved with position feedback of brain sections *** has been experimentally verified that the proposed device can well fulfill the task of automatic collection of brain *** the help of the proposed automatic collection device,human operators can be partially liberated from the tedious manual collection process and collection efficiency can be improved.

关键词： Array tomography automatic collection system brain sections microscopic object detection serial section

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Traffic Forecasting via Dilated Temporal Convolution With Peak-Sensitive Loss

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IEEE INTELLIGENT TRANSPORTATION systems MAGAZINE 2023年第1期15卷 48-57页

作者： Guo, Ge Yuan, Wei Liu, Jinyuan Lv, Yisheng Liu, Wei Northeastern Univ State Key Lab Synthet Automat Proc Ind Shenyang 110819 Peoples R China Northeastern Univ Coll Informat Sci & Engn Shenyang 110819 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Northeastern Univ Res Acad Shenyang Peoples R China Neusoft Corp Intelligent Vis Lab Shenyang 110819 Peoples R China

Deep learning-based traffic forecasting methods can capture intricate spatiotemporal features in traffic data and environmental factors. However, they have unsatisfactory performance around the minority peaks and are inefficient for modeling wide-range spatial correlations. This article gives a peak-aware deep learning architecture for traffic forecasting by involving a cost-sensitive loss function called peak-sensitive loss. This method can improve the performance since different costs are employed on the prevalent metrics such as mean-square loss and square of mean absolute percentage loss. A spatiotemporal convolutional architecture based on a dilated convolutional network (DCN) and a temporal convolutional network (TCN) is constructed that models the spatial features (both wide and short range) by the DCN and learns the time characteristics by the TCN. The effectiveness of the model is demonstrated with real-world data sets.

关键词： Correlation Spatiotemporal phenomena Forecasting Data models Deep learning Convolution Predictive models

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Parallel Driving with Big Models and Foundation Intelligence in Cyber-hysical-ocial Spaces

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Research 2024年第2期2024卷 1-17页

作者： Xiao Wang Jun Huang Yonglin Tian Chen Sun Lie Yang Shanhe Lou Chen Lv Changyin Sun Fei-Yue Wang School of Artificial Intelligence Anhui UniversityHefeiChina Macao University of Science and Technology MacaoChina State Key Laboratory for Management and Control of Complex Systems Institute of AutomationChinese Academy of SciencesBeijingChina MVSLab Department of Mechanical and Mechatronics EngineeringUniversity of Waterloo200 University Ave WestWaterlooON N2L3G1Canada School of Mechanical and Aerospace Engineering Nanyang Technological UniversitySingaporeSingapore

Recent years have witnessed numerous technical breakthroughs in connected and autonomous vehicles (CAVs). On the one hand, these breakthroughs have significantly advanced the development of intelligent transportation systems (ITSs);on the other hand, these new traffic participants introduce more complex and uncertain elements to ITSs from the social space. Digital twins (DTs) provide real-time, data-driven, precise modeling for constructing the digital mapping of physical-world ITSs. Meanwhile, the metaverse integrates emerging technologies such as virtual reality/mixed reality, artificial intelligence, and DTs to model and explore how to realize improved sustainability, increased efficiency, and enhanced safety. More recently, as a leading effort toward general artificial intelligence, the concept of foundation model was proposed and has achieved significant success, showing great potential to lay the cornerstone for diverse artificial intelligence applications across different domains. In this article, we explore the big models embodied foundation intelligence for parallel driving in cyber-physical-social spaces, which integrate metaverse and DTs to construct a parallel training space for CAVs, and present a comprehensive elucidation of the crucial characteristics and operational mechanisms. Beyond providing the infrastructure and foundation intelligence of big models for parallel driving, this article also discusses future trends and potential research directions, and the ?S?goals of parallel driving.

关键词： breakthrough integrate corners

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DeSci Based on Web3 and DAO: A Comprehensive Overview and Reference Model

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IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL systems 2022年第5期9卷 1563-1573页

作者： Ding, Wenwen Hou, Jiachen Li, Juanjuan Guo, Chao Qin, Jirong Kozma, Robert Wang, Fei-Yue Macau Univ Sci & Technol Macao Inst Syst Engn Macau 999078 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Chinese Acad Sci State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China North Automat Control Technol Inst Taiyuan 030006 Shanxi Peoples R China Chinese Inst Command & Control CICC Beijing 100089 Peoples R China Univ Memphis Dept Comp Sci & Math Memphis TN 38152 USA

Decentralized science (DeSci) is a hot topic emerging with the development of Web3 or Web3.0 and decentralized autonomous organizations (DAOs) and operations. DeSci fundamentally differs from the centralized science (CeSci) and Open Science (OS) movement built in the centralized way with centralized protocols. It changes the basic structure and legacy norms of current scientific systems via reshaping the cooperation mode, value system, and incentive mechanism. As such, it can provide a viable path for solving bottleneck problems in the development of science, such as oligarchy, silos, and so on, and make science more fair, free, responsible, and sensitive. However, DeSci itself still faces many challenges, including scaling, balancing the quality of participants, system suboptimal loops, lack of accountability mechanism, and so on. Taking these into consideration, this article presents a systematic introduction of DeSci, proposes a novel reference model with a six-layer architecture, addresses the potential applications, and also outlines the key research directions in this emerging field. This article is committed to providing helpful guidance and reference for future research efforts on DeSci.

关键词： Analytical models Systematics Protocols Decentralized autonomous organization Computational modeling Education Computer architecture CPSS DAO decentralized autonomous operations decentralized autonomous organizations decentralized funding decentralized science DeSci metaverses parallel DeSci parallel intelligence Web3

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Analyzing Bitcoin transaction fees using a queueing game model

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ELECTRONIC COMMERCE RESEARCH 2022年第1期22卷 135-155页

作者： Li, Juanjuan Yuan, Yong Wang, Fei-Yue Beijing Inst Technol Sch Automat Beijing 100081 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China

In the Bitcoin system, large numbers of miners invest massive computing resources in the blockchain mining process in pursuit of Bitcoin rewards, which are comprised of a fixed amount of system-generated new block reward and a variable amount of user-submitted transaction fees. Here, transaction fees serve as the important tuner for the Bitcoin system to define the priorities in users' transaction confirmation. In this paper, we aim to study the priority rule for queueing transactions based on their associated fees, and in turn users' strategies in formulating their fees in the transaction confirmation game. We first establish a full-information game-theoretical model to study users' equilibrium fee decisions;and then discuss three types of Nash equilibria, under which no, all and some users submit transaction fees. Moreover, we conduct empirical studies and design computational experiments to validate our theoretical analysis. The experimental results show that (1) users' fee decisions will be significantly affected by their waiting time;(2) the reduced time costs, instead of transaction values, are the basis for users to evaluate their revenues;(3) longer waiting time and higher unit time cost drive users to submit transaction fees in pursuit of desired priorities;(4) with the required transaction fee increasing, the proportion of fee-submitting users decreases slowly at first followed by a sharp decline, and over-high required fees will make the transaction confirmation game end up with no users submitting fees.

关键词： Bitcoin Blockchain Transaction fees Transaction confirmation queueing game

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Federated Vehicular Transformers and Their Federations: Privacy-Preserving Computing and Cooperation for Autonomous Driving

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IEEE TRANSACTIONS ON INTELLIGENT VEHICLES 2022年第3期7卷 456-465页

作者： Tian, Yonglin Wang, Jiangong Wang, Yutong Zhao, Chen Yao, Fei Wang, Xiao Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China North Automat Control Technol Inst Taiyuan 030006 Peoples R China Qingdao Acad Intelligent Ind Qingdao 266109 Peoples R China

Cooperative computing is promising to enhance the performance and safety of autonomous vehicles benefiting from the increase in the amount, diversity as well as scope of data resources. However, effective and privacy-preserving utilization of multi-modal and multi-source data remains an open challenge during the construction of cooperative mechanisms. Recently, Transformers have demonstrated their potential in the unified representation of multi-modal features, which provides a new perspective for effective representation and fusion of diverse inputs of intelligent vehicles. Federated learning proposes a distributed learning scheme and is hopeful to achieve privacy-secure sharing of data resources among different vehicles. Towards privacy-preserving computing and cooperation in autonomous driving, this paper reviews recent progress of Transformers, federated learning as well as cooperative perception, and proposes a hierarchical structure of Transformers for intelligent vehicles which is comprised of Vehicular Transformers, Federated Vehicular Transformers and the Federation of Vehicular Transformers to exploit their potential in privacy-preserving collaboration.

关键词： Transformers Autonomous vehicles Collaborative work Point cloud compression Trajectory Computational modeling Vehicle dynamics Cooperative autonomous driving Federated Vehicular Transformers Federation of Vehicular Transformers Vehicular Transformers

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AUTOSIM:Automated Urban Traffic Operation Simulation via Meta-Learning

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IEEE/CAA Journal of Automatica Sinica 2023年第9期10卷 1871-1881页

作者： Yuanqi Qin Wen Hua Junchen Jin Jun Ge Xingyuan Dai Lingxi Li Xiao Wang Fei-Yue Wang Zhejiang Lab Hangzhou 310030China ECARX Technology Co. Ltd.Shanghai 201811China IEEE Zhejiang Supcon Information Company Ltd. Hangzhou 310052China the State Key Laboratory for Management and Control of Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China Indiana University-Purdue University Indianapolis(IUPUI) IndianapolisIndiana 46202 USA School of Artificial Intelligence Anhui UniversityHefei 230039China

Online traffic simulation that feeds from online information to simulate vehicle movement in real-time has recently seen substantial advancement in the development of intelligent transportation systems and urban traffic *** has been a challenging problem due to three aspects:1)The diversity of traffic patterns due to heterogeneous layouts of urban intersections;2)The nature of complex spatiotemporal correlations;3)The requirement of dynamically adjusting the parameters of traffic models in a real-time *** cater to these challenges,this paper proposes an online traffic simulation framework called automated urban traffic operation simulation via meta-learning(AUTOSIM).In particular,simulation models with various intersection layouts are automatically generated using an open-source simulation tool based on static traffic geometry *** a meta-learning technique,AUTOSIM enables an automated learning process for dynamic model settings of traffic scenarios featured with different spatiotemporal ***,AUTOSIM is capable of adapting traffic model parameters according to dynamic traffic information in real-time by using a *** computational experiments,we demonstrate the effectiveness of the meta-learningbased framework that is capable of providing reliable supports to real-time traffic simulation and dynamic traffic operations.

关键词： Conditional generative adversarial network signalized urban networks short-term link speed prediction

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Testing Intelligence: Accelerating the Verification and Validation of Intelligent Vehicles

IEEE TRANSACTIONS ON INTELLIGENT VEHICLES

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IEEE TRANSACTIONS ON INTELLIGENT VEHICLES 2023年第2期8卷 1003-1016页

作者： Wang, Fei-Yue Hu, Jia Lai, Jintao Chinese Acad Sci State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Tongji Univ ZhongTe Distinguished Chair Cooperat Automat Key Lab Rd & Traff Engn Minist Educ Shanghai 201804 Peoples R China Tongji Univ Key Lab Rd & Traff Engn Minist Educ Shanghai 201804 Peoples R China

form only. Presents a summary of articles presented in this issue of the publication.

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Simultaneously Calibration of Multi Hand-Eye Robot System Based on Graph

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IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS 2023年第5期71卷 5010-5020页

作者： Zhou, Zishun Ma, Liping Liu, Xilong Cao, Zhiqiang Yu, Junzhi Chinese Acad Sci Inst Automat Beijing 100190 Peoples R China Australian Natl Univ Coll Engn Comp & Cybernet Canberra ACT 2601 Australia Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China Peking Univ Dept Adv Mfg & Robot Coll Engn State Key Lab Turbulence & Complex SystBIC ESAT Beijing 100871 Peoples R China

Precise calibration is the basis for the vision-guided robot system to achieve high-precision operations. systems with multieyes (cameras) and multihands (robots) are particularly sensitive to calibration errors. Most existing methods focus on the calibration of a single unit of the whole system, such as poses between hand and eye, or between two hands. These methods can be used to determine the pose between each unit, but the serialized incremental calibration strategy cannot avoid the error accumulation problem in a large-scale system. Instead of focusing on a single unit, this article models the multieye and multihand system calibration problem as a graph and proposes a method based on the minimum spanning tree and graph optimization. This method can automatically plan the serialized optimal calibration strategy in accordance with the system settings to get coarse calibration results initially. Then, with these initial values, the closed-loop constraints are introduced to carry out global optimization with different sensor's accuracy considered. As a general calibration method, it can be applied to different multirobot systems. Simulation experiments demonstrate the performance of the proposed algorithm under different noises and various hand-eye configurations. In addition, experiments on real robot systems are presented to further verify the proposed method.

关键词： Calibration error correction graph theory multirobot systems robot vision systems.

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