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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 Macau 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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Analyzing the Evolutionary Characteristics of the Cluster of COVID-19 under Anti-contagion Policies 18

Analyzing the Evolutionary Characteristics of the Cluster of...

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18th IEEE International Conference on Intelligence and Security Informatics, ISI 2020

作者： Miao, Pu Tian, Hu Zhang, Xingwei He, Saike Zheng, Xiaolong Dash Wu, Desheng Zeng, Daniel Chinese Academy of Sciences The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Beijing100190 China School of Artificial Intelligence Chinese Academy of Sciences Beijing100190 China University of Chinese Academy of Sciences Beijing100190 China Stockholm Business School Stockholm University StockholmSE-106 91 Sweden

ISBN: (纸本)9781728188003

With the rampaging of Coronavirus disease 2019 (COVID-19) across the world, analyzing the dynamic characteristics and understanding the evolutionary patterns of clusters are becoming even more crucial for people and policymakers to make timely responses for avoiding injury caused by COVID-19. To solve the scarcity of the fine-grained spatiotemporal data, we construct a novel dataset about the spread of patients during the resurgent period of the COVID-19 epidemic at the Xinfadi Market in Beijing. Leveraging our self-build dataset, we analyze the evolutionary characteristics of the cluster of COVID-19 under anti-contagion policies and obtained some remarkable evolution patterns. These findings can provide significant insights for policymakers and researchers to understand the evolutionary characteristics regarding the cluster of COVID-19 and deploy effective anti-contagion policies. © 2020 IEEE.

关键词： COVID-19

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Design of A Real-time Three-electrode Cylindrical Conductivity Sensor

Design of A Real-time Three-electrode Cylindrical Conductivi...

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Chinese Automation Congress (CAC)

作者： GuangYao Xu En Li School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China The State Key Labrary of Management and Control for Complex System Institute of Automation Chinese Academy of Science Beijing China

ISBN: (数字)9781728176871

ISBN: (纸本)9781728176888

In this paper, we designed a real-time three-electrode cylindrical conductivity sensor for special working conditions. Compared with traditional conductivity sensors, the three-electrode design balances the relationship between measurement accuracy and structural complexity. The cylindrical electrode core can adapt to some complex working environments that traditional intrusive electrodes cannot. The CoOS real-time operating system adopted by the measurement software has the characteristics of a small kernel and low requirement for computing resources. It can well meet the demand for online measurement of conductivity sensor. The amplitude and frequency of the excitation signal can be adjusted by the measurement software, avoiding the invariance of the signal by hardware adjustment. Since the error is greatly affected by temperature, the sensor can effectively reduce the error by compensating for temperature. All electronic components of the sensor are designed with low power consumption, which can extend the working time while ensuring normal operation.

关键词： Conductivity Temperature measurement Electrodes Conductivity measurement Electrical resistance measurement Software measurement Software

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A New Self-Learning Optimization management Scheme for Smart Grid systems

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The International Journal of Intelligent control and systems 2024年第2期29卷 66-70页

作者： Qinglai Wei Zehua Liao School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100049China State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China Institute of Systems Engineering Macao University of Science and TechnologyMacao 999078China

This paper presents a new energy optimization management scheme for the smart grid system(SGS),which considers not only the inclusion of renewable energy in energy management but also the feedback sale of redundant renewable energy to the grid,so as to obtain better optimization management results and avoid energy waste.A new performance index function is constructed,which aims to save users’electricity costs and protect energy storage *** construct a periodic iterative self-learning method based on the characteristics that the relevant data of SGS are approximately *** proposed self-learning optimization method is based on adaptive dynamic programming(ADP).The initial iterative control law is obtained by pre-training,and the designed method can be realized by neural *** experiments are carried out by using the relevant data of SGS for four *** experimental results show that the new energy management scheme can help users save a lot of electricity costs,and is more effective than some common methods.

关键词： Smart grid system energy management renewable energy self-learning adaptive dynamic programming

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Residual Squeeze-and-Excitation Network with Multi-scale Spatial Pyramid Module for Fast Robotic Grasping Detection

Residual Squeeze-and-Excitation Network with Multi-scale Spa...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Hu Cao Guang Chen Zhijun Li Jianjie Lin Alois Knoll Chair of Robotics Artificial Intelligence and Real-Time Systems Technische Universität München München Germany State Key Laboratory of Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Tongji University Shanghai China University of Science and Technology of China China

This paper proposes an efficient, fully convolutional neural network to generate robotic grasps by using 300×300 depth images as input. Specifically, a residual squeeze-and-excitation network (RSEN) is introduced for deep feature extraction. Following the RSEN block, a multi-scale spatial pyramid module (MSSPM) is developed to obtain multi-scale contextual information. The outputs of each RSEN block and MSSPM are combined as inputs for hierarchical feature fusion. Then, the fused global features are upsampled to perform pixel-wise learning for grasping pose estimation. The experimental results on Cornell and Jacquard grasping datasets indicate that the proposed method has a fast inference speed of 5ms while achieving high grasp detection accuracy of 96.4% and 94.8% on Cornell and Jacquard, respectively, which strikes a balance between accuracy and running speed. Our method also gets a 90% physical grasp success rate with a UR5 robot arm.

关键词： Automation Conferences Pose estimation Grasping Manipulators Feature extraction Inference algorithms

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Formation control with obstacle avoidance of second-order multi-agent systems under directed communication topology

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Science China(Information Sciences) 2019年第9期62卷 144-157页

作者： Guoxing WEN C.L.Philip CHEN Hui DOU Hongli YANG Chunfang LIU College of Science Binzhou University Department of Computer and Information Science Faculty of Science and Technology University of Macau College of Navigation Dalian Maritime University State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of Sciences Mathematics and Systems Science College Shandong University of Science and Technology

This paper addresses the obstacle avoidance problem of formation control for the multi-agent systems modeled by double integrator dynamics under a directed interconnection topology. The control task is finished by a leader-follower formation scheme combined with an artificial potential field(APF) *** leader-follower scheme is carried out by taking the desired trajectory with the desired velocity as virtual leader, while the APF method is carried out by dealing with the obstacles as the high potential points. When the obstacle avoidance tasks are finished, the artificial potential forces degrade the formation performance, so their undesired effects are treated as disturbances, which is analyzed by the robust H∞performance. Based on Lyapunov stability theory, it is proved that the proposed formation approach can realize the control objective. The result is also extended to the switching multi-agent formation. The effectiveness of the proposed formation scheme is further confirmed by simulation studies.

关键词： formation control obstacle avoidance artificial potential field method H_∞ performance second-order multi-agent system directed topology

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Three-Dimensional Path Following control of an Underactuated Robotic Dolphin Using Deep Reinforcement Learning

Three-Dimensional Path Following Control of an Underactuated...

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IEEE International Conference on Real-time Computing and Robotics (RCAR)

作者： Jincun Liu Zhenna Liu Zhengxing Wu Junzhi Yu State Key Laboratory for Turbulence and Complex Systems BIC-ESAT College of Engineering Peking University Beijing China Shandong Labor Vocational and Technical College Jinan China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China

ISBN: (数字)9781728172934

ISBN: (纸本)9781728172941

In this paper, a novel improved deep reinforcement learning (DRL) strategy is utilized to solve the three-dimensional path-following control problem for an underactuated robotic dolphin in order to meet the complex and intractable hydrodynamic model. Firstly, a brief overview of the developed robotic dolphin consisting of two-degree-of-freedom paired pectoral fins and the slider-crank-based flapping two-joint tail is introduced. After that, path-following controller includes a novel lookahead-based guidance law, decoupling motion strategy, and a deep deterministic policy gradient algorithm based on the the prior knowledge is proposed. The novel lookahead based 3-D LOS guidance law is employed to transform the 3-D waypoints to the desired heading and pitch angles with its simplicity, intuitiveness, and small computational footprint. Finally, the effectiveness of the proposed controller is demonstrated after numerous simulations. It will offer a rich vein of insight for the real-time task execution in bioinspired underwater robots.

关键词： Robots Dolphins Sports Oscillators Biological system modeling Turning Aerospace electronics

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A Self-identifying Checkerboard-like Pattern for Camera Calibration

A Self-identifying Checkerboard-like Pattern for Camera Cali...

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第三十九届中国控制会议

作者： Zhang Gao Mingzhu Zhu Junzhi Yu School of Artificial Intelligence University of Chinese Academy of Sciences State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of Sciences State Key Laboratory for Turbulence and Complex Systems Department of Mechanics and Engineering ScienceBIC-ESATCollege of EngineeringPeking University

This paper proposes a new type of planar pattern for camera calibration featuring its checkerboard-like structure and self-identity. White and black blocks are arranged by a novel method to ensure the uniqueness of each local pattern, and meanwhile maintain all checkerboard corners. The pattern is generated by combining identification units based on a random search strategy. Positions of local patterns can be acquired by searching the global pattern, achieving the benefit of self-identity. Because of the checkerboard-like structure, no burden is introduced in sampling compared to traditional calibrations. Experimental results show the effectiveness of the proposed calibration method. The conceived pattern is especially suitable for the calibration of multi-camera system with limited overlapping scope.

关键词： Camera calibration pattern self-identity checkerboard-like

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Coordinated operation of electricity and hydrogen blended gas networks via correlation-enhanced joint chance-constrained approach

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International Journal of Hydrogen Energy 2025年 149卷

作者： Sicheng Liu Bo Yang Lun Yang Xu Yang Xin Li Zhaojian Wang Kai Ma Xinping Guan State Key Laboratory of Submarine Geoscience School of Automation and Intelligent Sensing Shanghai Jiao Tong University Shanghai 200240 China Key Laboratory for System Control and Information Processing Ministry of Education of China Shanghai 200240 China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai 200240 China School of Automation Science and Engineering Xi’an Jiaotong University Xi’an 710049 China Key Lab of Industrial Computer Control Engineering of Hebei Province and the School of Electrical Engineering Yanshan University Qinhuangdao 066000 China

Blending green hydrogen into natural gas can effectively promote hydrogen consumption and operational flexibility of electricity-gas coupled networks. However, the volatility of renewable energy sources threatens the operation of green hydrogen production and blending, while existing system-level risk management methods either exhibit over-conservatism or are computationally inefficient when constraints are numerous. Therefore, this paper proposes a correlation-enhanced distributionally robust joint chance-constrained (DRJCC) method for the joint scheduling of electricity and hydrogen-enriched compressed natural gas (E-HCNG) networks, aiming to minimize operating cost while limiting joint violation risk under renewable energy uncertainty. Specifically, before DRJCC, an improved hydrogen fraction model is developed for hydrogen blending, refining the representation of fraction variations by incorporating the buffer effect of line packs. Then, for DRJCC solving, the commonly used Bonferroni approximations are overly conservative in allocating joint violation risks to individual chance constraints, especially when constraints are numerous. To mitigate this, by leveraging strongly violation correlations of operational constraints under affine policies, a grouping-based risk allocation strategy is proposed to reduce the over-conservatism. Theoretical derivations confirm the guarantee of joint violation risk via the proposed method. Additionally, customized transformations are developed to address the non-convexities introduced by variable hydrogen fractions. Case studies demonstrate the effectiveness and superiority of the proposed method.

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TLogic: A Tangible Programming Tool to Help Children Solve Problems 12th

TLogic: A Tangible Programming Tool to Help Children Solve P...

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12th International Conference on e-Learning and Games, Edutainment 2018

作者： Deng, Xiaozhou Wang, Danli Jin, Qiao The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China School of Computer and Control Engineering University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9783030237110

In this paper, we present TLogic, a tangible programming tool for children aged 6–8. The tool contains two parts: tangible programming blocks and visual tasks with different difficulty levels. Children could use tangible programming blocks to complete visual tasks shown on computer screen. To evaluate our tool, a user study was conducted with 10 children. Results of user study show that the tool could reduce children’s cognitive load while solving the tasks and children are more interested in challenging tasks than easy tasks in our tool. © 2019, Springer Nature Switzerland AG.

关键词： Computer programming

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