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Asynchronous Threshold ECDSA With Batch Processing

IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL systemS 2024年第1期11卷 566-575页

作者： Zhang, Hongxin Xie, Guanghuan Zou, Xin Zhang, Chi Li, Zhuo Qin, Rui Xiong, Gang Wang, Fei-Yue Zhejiang Univ State Key Lab CAD & CG Hangzhou 310058 Peoples R China State St Technol Zhejiang Ltd Global Technol Serv Dept Hangzhou 310000 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China 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 Macau Univ Sci & Technol Macao Inst Syst Engn Macau 999078 Peoples R China

Threshold Elliptic Curve Digital Signature Algorithm (ECDSA) has attracted a lot of attention due to the wide applications of ECDSA in crypto asset. Although several variants of threshold signature protocols can provide functions, such as key generation and signing, they suffer from two shortfalls. First, these schemes only discuss a single signature computation task in a synchronous algorithm context, which is difficult to adapt to real crypto-asset applications, such as custody. Second, these schemes are computing intensive and not scalable, hence can hardly support large-scale processing operations in real life even after traditional optimization, such as multithreading, is applied. In this article, we propose an innovative computation method called asynchronous threshold ECDSA with batch processing, based on the interactive threshold signature protocols. The method provides a reliable solution for critical operational scenarios, such as threshold signing and distributed key generation (DKG) in crypto-asset custody, and can be a future reference in secure data distribution mechanisms. The performance and scalability of our methods are validated through a benchmark testing.

关键词： Protocols Peer-to-peer computing Batch production systems Digital signatures Blockchains Electronic mail Urban areas Blockchain crypto-asset custody secure multiparty computation (MPC) threshold signature

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Integration of Train Regulation and Speed Profile Optimization Based on Feature Learning and Hybrid Search Algorithm

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IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL systemS 2024年第2期11卷 2535-2544页

作者： Zhou, Min Hou, Zhuopu Wu, Xingtang Dong, Hairong Wang, Fei-Yue Beijing Jiaotong Univ State Key Lab Adv Rail Autonomous Operat Beijing 100044 Peoples R China Beihang Univ Sch Automat Sci & Elect Engn Beijing 100191 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China

The independent hierarchy of train dispatching command and train operation control in the existing urban rail transit systems restricts the improvement of operational efficiency and emergency handling capability. This article focuses on integrating train regulation and speed profile optimization by utilizing a feature learning and hybrid search algorithm. Specifically, a genetic algorithm (GA) is used to optimize the train speed profile for a fixed interval running time, and then, the generated labeled sample data are used to train a convolutional neural network (CNN) to learn and extract the features of the optimal speed profile. The nonlinear mapping relationship between input and output variables in trajectory optimization is characterized by a well-trained CNN to reduce the computation time of the optimal speed profile during train regulation. The input variables comprise line conditions and interval running times, while the output variables include the corresponding energy consumption and operating condition switching points of the optimal speed profile. An integrated model of train regulation and operation control is developed with the objective of minimizing total train delay time and energy consumption. To ensure convergence and global search capability, we design a hybrid search algorithm-based train regulation algorithm. Simulation experiments are conducted using data from the Beijing Yizhuang line to validate the effectiveness of the proposed model and algorithms. The experimental results demonstrate that the proposed method can provide an optimal scheme for train regulation and speed profiles.

关键词： Convolutional neural network (CNN) hybrid search algorithm integration speed profile train control train regulation

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AGGREGATION AND ADJUSTMENT MECHANISMS FOR DISASTER RELIEF TASK ALLOCATION WITH UNEVEN DISTRIBUTION

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JOURNAL OF INDUSTRIAL AND management OPTIMIZATION 2023年第3期19卷 1734-1754页

作者： Ma, Xinwei Wang, Danli Zheng, Nan Zhang, Song Univ Chinese Acad Sci Sch Artificial Intelligence Beijing Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing Peoples R China

The use of unmanned aerial vehicles (UAVs) to perform disaster relief task is becoming more and more popular. Generally, UAVs for rescuing are required to be heterogeneous, since the types of rescue missions are diverse. There are many researches on the task allocation of heterogeneous UAVs, in which they setup tasks to be randomly distributed when simulation. However, in most cases the distribution of tasks is regular rather than random. Therefore, we setup a set of special scenarios to simulate real disaster relief tasks, where most tasks concentrate in a few small areas and others are distributed randomly. LAL algorithm is an appropriate alternative to deal with assignment problems in scenarios where tasks are completely randomly distributed, but with a bit poor performance in the scenarios above mentioned. In order to obtain better results, this paper proposes three algorithms based on LAL algorithm in such scenarios. They are the Aggregation-LAL (A-LAL) using aggregation mechanism, Dynamic Adjustment-LAL (DA-LAL) using dynamic adjustment mechanism and Aggregation and Adjustment (AA-LAL) *** the experiments in the NetLogo simulation environment, the proposed algorithms are evaluated. The result shows improvements, which are average 5% obtained by AA-LAL, of task allocation in our special scenarios compared with existing algorithms.

关键词： Task allocation Swarm intelligence Heterogeneous UAVs Threshold-based method Disaster relief

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One Size Fits All: A Unified Traffic Predictor for Capturing the Essential Spatial-Temporal Dependency

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING systemS 2024年第8期35卷 11317-11331页

作者： Luo, Guiyang Zhang, Hui Yuan, Quan Li, Jinglin Wang, Wendong Wang, Fei-Yue Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China Beijing Jiaotong Univ Sch Comp & Informat Technol Beijing 100044 Peoples R China Xidian Univ State Key Lab Integrated Serv Networks Xian 710071 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China

Traffic prediction is a keystone for building smart cities in the new era and has found wide applications in traffic scheduling and management, environment policy making, public safety, and so on. Instead of creating a traffic predictor for each city, this article focuses on designing a unified network model that could be directly applied for traffic prediction in any city, by learning the essential spatial-temporal dependencies, i.e., the mutual relationship between traffic and the corresponding fine-grained road network. To achieve this goal, this article proposes a joint knowledge-and data-driven mechanism that novelly divides dependencies into three kinds of correlations, i.e., road segment, intra-intersection, and inter-intersection correlation, which capture the microcosmic, middle, and macroscopic dependencies between traffic and the road network, respectively. Specifically, we first construct traffic datasets that could cover all road segments from real-world trajectory datasets, which makes it possible to model the whole road network as a graph, with the help of fine-grained road topology. Then, we propose meta road segment learner, connection-aware spatial-temporal graph convolutional network (GCN), and multiscale residual networks for capturing the microcosmic, middle, and macroscopic dependencies, respectively. Our experiments on three real-world datasets demonstrate that our proposed method could: 1) achieve better prediction accuracy compared with several approaches and 2) capture the mutual relationship between traffic and the fine-grained road network since our model trained only using data from the source city achieves good performance when it is directly applied for traffic prediction in the target city, without any fine-tuning. The codes will be made publicly available.

关键词： Graph convolutional network (GCN) spatial-temporal dependencies (STDs) spatial-temporal GCN traffic prediction

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Improving performance of robots using human-inspired approaches: a survey

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Science China(Information Sciences) 2022年第12期65卷 5-35页

作者： Hong QIAO Shanlin ZHONG Ziyu CHEN Hongze WANG State Key Laboratory of Management and Control for Complex System Institute of Automation Chinese Academy of Sciences School of Artifcial Intelligence University of Chinese Academy of Sciences CAS Center for Excellence in Brain Science and Intelligence Technology

Realizing high performance of ordinary robots is one of the core problems in robotic research. Improving the performance of ordinary robots usually relies on the collaborative development of multiple research fields, resulting in high costs and difficulty to complete some high-precision tasks. As a comparison,humans can realize extraordinary overall performance under the condition of limited computational-energy consumption and low absolute precision in sensing and controlling each body unit. Therefore, developing human-inspired robotic systems and algorithms is a promising avenue to improve the performance of robotic systems. In this review, the cutting-edge research work on human-inspired intelligent robots in decisionmaking, cognition, motion control, and system design is summarized from behavior-and neural-inspired aspects. This review aims to provide a significant insight into human-inspired intelligent robots, which may be beneficial for promoting the integration of neuroscience, machinery, and control, so as to develop a new generation of robotic systems.

关键词： human-inspired intelligent robots brain-inspired intelligence decision making visual cognition musculoskeletal robots

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ADMM-based joint rescheduling method for high-speed railway timetabling and platforming in case of uncertain perturbation✩

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TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES 2023年第1期152卷

作者： Liu, Xuan Zhou, Min Dong, Hairong Wu, Xingtang Li, Yidong Wang, Fei-Yue Beijing Jiaotong Univ State Key Lab Rail Traff Control & Safety Beijing 100044 Peoples R China Beijing Jiaotong Univ Sch Comp & Informat Technol Beijing 100044 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China

The operation of high-speed trains is susceptible to perturbations such as strong winds and heavy snowfall, causing them to deviate from operating schedules. A joint mixed-integer linear programming (MILP) model based on the event-activity network is proposed to reschedule the train timetable and platform assignment jointly. Trains traveling in different directions are simultaneously considered in this model, and they can occupy the available reverse platforms in stations. Furthermore, to strengthen the robustness of the rescheduling scheme in case of uncertain perturbations, such as variable temporary speed restrictions caused by changing wind speed, the uncertainty is modeled using scenario-based chance constraints, which are then converted into deterministic constraints. Model predictive control (MPC) divides the entire time horizon into several stages. In each stage, the proposed model is decomposed according to directions. The constraints for trains traveling in different directions are dualized by the Alternating Direction Method of Multipliers (ADMM), which makes the sub-problems decoupled and solvable in parallel in each iteration of ADMM, promoting real-time performance. The real timetable and line data of Beijing-Shanghai HSR are utilized to investigate the effect of the proposed method. Compared to the commonly used strategies, it can significantly reduce delays and the number of affected events. The proposed method also shows relatively high robustness compared to the method in the case of predefined perturbations. It causes fewer conflicts and fewer needs for rescheduling when facing the changing perturbation and gets rescheduling results with higher quality.

关键词： Timetable rescheduling Platforming Uncertain perturbation Model predictive control Alternative direction method of multipliers

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Piecewise Compensation Model Predictive Governor Combined With Conditional Disturbance Negation for Underactuated AUV Tracking control

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IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS 2023年第6期70卷 6191-6200页

作者： Kong, Shihan Sun, Jinlin Wang, Jian Zhou, Ziye Shao, Jinyan Yu, Junzhi Peking Univ Coll Engn Dept Adv Mfg & Robot State Key Lab Turbulence & Complex Syst Beijing 100871 Peoples R China Jiangsu Univ Sch Elect & Informat Engn Zhenjiang 212013 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China

With respect to the accurate tracking control for autonomous underwater vehicles (AUVs), this article proposes a novel control architecture consisting of piecewise compensation model predictive governor and conditional disturbance negation-based active disturbance rejection controller. The designed piecewise compensation mechanism is implemented to eliminate the steady tracking errors resulting from the underactuation problem of AUVs, and the model predictive governor aims to generate reference tracking velocities satisfying the constraints. Concurrently, a finite-time extended state observer is designed to improve the convergence performance of the estimation errors. In addition, the concept of conditional disturbance negation is employed to endow AUVs with the capability of taking advantage of disturbances rather than only rejecting disturbances. Finally, comprehensive analyses of the simulations and the experiments demonstrate the impressive control performance, verifying the feasibility of potential applications on real AUVs.

关键词： Underwater vehicles Predictive models Kinetic theory Trajectory Force Convergence Observers Autonomous underwater vehicle (AUV) conditional disturbance negation (CDN) finite-time extended state observer (ESO) piecewise compensation tracking control

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Line Feature Based Visual Servo control with TDE for Underwater Vehicle Manipulator systems Target Grasping

Line Feature Based Visual Servo Control with TDE for Underwa...

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2024 IEEE International Conference on Unmanned systems, ICUS 2024

作者： Zhang, Yunfei Bian, Xinyu Sun, Yize Zhang, Zongyu Huang, Hai Yang, Xu National Key Laboratory of Autonomous Marine Vehicle Technology Harbin Engineering University Harbin China State Key Laboratory of Management and Control for Complex System Institute of Automation Chinese Academy of Sciences Beijing China

ISBN: (纸本)9798350384185

Underwater vehicle manipulator systems (UVMSs) is becoming a research hotspot in the field of marine intelligent equipment. Introducing visual feedback equipped with cameras in the loop to guide UVMS to complete the grasping has comprehensive advantages of response speed, work efficiency and intelligence. In this paper, mapping between the feature error and the motion of the UVMS generalized degrees of freedom is established to realize the suspended grasping under the coordinated motion of the vehicle body and the manipulator. Then, a UVMS motion control algorithm based on time delay estimation (TDE) is constructed for visual servo autonomous grasping process, the TDE of which is used to estimate and compensate the lumped interference in real time. The Lyapunov theory is applied to analyze its stability and its control performance is verified through simulation experiments. The simulation results shew the effectiveness of the proposed visual servo control with TED method based on line features. © 2024 IEEE.

关键词： Visual servoing

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Self-Supervised Monocular Depth Estimation With Geometric Prior and Pixel-Level Sensitivity

IEEE TRANSACTIONS ON INTELLIGENT VEHICLES

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IEEE TRANSACTIONS ON INTELLIGENT VEHICLES 2023年第3期8卷 2244-2256页

作者： Liu, Jierui Cao, Zhiqiang Liu, Xilong Wang, Shuo Yu, Junzhi 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 Coll Engn Dept Adv Mfg & Robot State Key Lab Turbulence & Complex Syst Beijing Peoples R China

Self-supervised monocular depth estimation has gained popularity due to its convenience of training network without dense ground truth depth annotation. Specifically, the multi-frame monocular depth estimation achieves promising results in virtue of temporal information. However, existing multi-frame solutions ignore the different impacts of pixels of input frame on depth estimation and the geometric information is still insufficiently explored. In this paper, a self-supervised monocular depth estimation framework with geometric prior and pixel-level sensitivity is proposed. Geometric constraint is involved through a geometric pose estimator with prior depth predictor and optical flow predictor. Further, an alternative learning strategy is designed to improve the learning of prior depth predictor by decoupling it with the ego-motion from the geometric pose estimator. On this basis, prior feature consistency regularization is introduced into the depth encoder. By taking the dense prior cost volume based on optical flow map and ego-motion as the supervising signal for feature consistency learning, the cost volume is obtained with more reasonable feature matching. To deal with the pixel-level difference of sensitivity in input frame, a sensitivity-adaptive depth decoder is built by flexibly adding a shorter path from cost volume to the final depth prediction. In this way, the back propagation of gradient to cost volume is adaptively adjusted, and an accurate depth map is decoded. The effectiveness of the proposed method is verified on public datasets.

关键词： Estimation Costs Training Sensitivity Cameras Optical flow Semantics Monocular depth estimation self-supervised learning prior feature consistency sensitivity adaptation

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A Novel DAO-Based Parallel Enterprise management Framework in Web3 Era

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IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL systemS 2024年第1期11卷 839-848页

作者： Wang, Ge Qin, Rui Li, Juanjuan Wang, Fei-Yue Gan, Yu Yan, Lihua Macau Univ Sci & Technol Macao Inst Syst Engn Macau 999078 Peoples R China Chinese Acad Sci State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Multimodal Artificial Intelligence S Beijing 100190 Peoples R China 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 Qingdao Huituo Waytous Intelligent Robot Co Ltd Qingdao 266114 Peoples R China

This article proposes a novel parallel management mode based on decentralized autonomous organizations (DAOs) for enterprises by utilizing the artificial systems, computational experiments, parallel execution (ACP) approach, parallel intelligence theory, and blockchain technologies, to realize the distributed management of an enterprise. The artificial enterprise DAO (EnDAO) corresponding to the actual enterprise is constructed, and they constitute a parallel system via virtual-real interaction and parallel execution. Through the non-fungible token (NFT)-based incentive mechanism, metaverse-based virtual learning and training, as well as DAO-based distributed management and decision-making, the management and control of the actual enterprise as well as its employees can be carried out. By virtue of the virtual-real interactions of three types of employees, as well as the virtual-real feedback of three closed loops in the parallel systems, DAO-based parallel management for enterprises can realize descriptive intelligence, predictive intelligence, and prescriptive intelligence. On this basis, this article takes the recruitment-oriented key performance indicator (KPI) management of a startup technology enterprise as the case to introduce the operation processes and illustrate the superiorities of the proposed DAO-based enterprise parallel management mode.

关键词： Blockchain decentralized autonomous organizations (DAOs) metaverse parallel intelligence parallel management

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