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On Iterative Proportional Updating: Limitations and Improvements for General Population Synthesis

IEEE TRANSACTIONS ON CYBERNETICS 2022年第3期52卷 1726-1735页

作者： Ye, Peijun Tian, Bin Lv, Yisheng Li, Qijie Wang, Fei-Yue Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Beijing Univ Technol Sch Artificial Intelligence & Automat Beijing 100124 Peoples R China Qingdao Acad Intelligent Ind Parallel Intelligence Res Ctr Qingdao 266109 Peoples R China Macau Univ Sci & Technol Inst Syst Engn Taipa Macao Peoples R China

Population synthesis is the foundation of the agent-based social simulation. Current approaches mostly consider basic population and households, rather than other social organizations. This article starts with a theoretical analysis of the iterative proportional updating (IPU) algorithm, a representative method in this field, and then gives an extension to consider more social organization types. The IPU method, for the first time, proves to be unable to converge to an optimal population distribution that simultaneously satisfies the constraints from individual and household levels. It is further improved to a bilevel optimization, which can solve such a problem and include more than one type of social organization. Numerical simulations, as well as population synthesis using actual Chinese nationwide census data, support our theoretical conclusions and indicate that our proposed bilevel optimization can both synthesize more social organization types and get more accurate results.

关键词： Sociology Statistics Optimization Organizations Automation Iterative algorithms Agent-based simulation bilevel optimization iterative proportional updating (IPU) population synthesis

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Perception and Planning of Intelligent Vehicles Based on BEV in Extreme Off-Road Scenarios

IEEE TRANSACTIONS ON INTELLIGENT VEHICLES

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IEEE TRANSACTIONS ON INTELLIGENT VEHICLES 2024年第4期9卷 4568-4572页

作者： Fan, Jingjing Fan, Lili Ni, Qinghua Wang, Junhao Liu, Yi Li, Ren Wang, Yutong Wang, Sanjin Jiangsu Ind Innovat Ctr Intelligent Equipment Co Liyang 213300 Peoples R China North China Univ Technol Sch Elect & Control Engn Beijing 100144 Peoples R China Beijing Inst Technol Sch Informat & Elect Beijing 100081 Peoples R China Macau Univ Sci & Technol Fac Innovat Engn Macau 999078 Peoples R China Dalian Minzu Univ Sch Sci Dalian 116600 Peoples R China Beijing Inst Technol Sch Mech Engn Beijing 100081 Peoples R China Anhui Ankai Automobile Co Ltd Hefei 230000 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China China North Ind North Automat Control Technol Re North Automat Control Technol Inst Taiyuan 030006 Peoples R China

In extreme off-road scenarios, autonomous driving technology holds strategic significance for enhancing emergency rescue capabilities, reducing labor intensity, and mitigating safety risks. Challenges such as adverse conditions, complex terrains, unstable satellite signals, and lack of roads pose serious safety challenges for autonomous driving. This perspective first delves into a Bird's Eye View (BEV)-based perception-planning framework, aiming to enhance the adaptability of intelligent vehicles to their environment. Subsequently, this perspective further discusses key issues such as Cyber-Physical-Social systems (CPSS), foundation models, and technologies like Sora for off-road scenario generation, vehicle cognitive enhancement, and autonomous decision-making. Ultimately, the discussed framework is poised to endow intelligent vehicles with the capability to perform challenging tasks in complex off-road scenarios, realizing a more efficient, safer, and sustainable transportation system, thereby providing better support for the low-altitude economy.

关键词： Feature extraction Radar Roads Trajectory Millimeter wave communication Point cloud compression Image color analysis Cyber-Physical-Social systems (CPSS) emergency management intelligent vehicles low-altitude economy off-road scenarios

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An ACP-Based Parallel Approach for Color Image Encryption Using Redundant Blocks

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IEEE TRANSACTIONS ON CYBERNETICS 2022年第12期52卷 13181-13196页

作者： Zheng, Wenbo Yan, Lan Gou, Chao Wang, Fei-Yue Chinese Acad Sci State Key Lab Management & Control Complex Syst Inst Automat Beijing 100190 Peoples R China Xi An Jiao Tong Univ Sch Software Engn Xian 710049 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100190 Peoples R China Sun Yat Sen Univ Sch Intelligent Syst Engn Guangzhou 510275 Peoples R China

Public concerns on image encryption grow significantly as the development and application of edge computing and the Internet of Things intensified recently. However, most existing image cryptosystems are not sophisticated enough to resist the two major attack strategies available currently, that is: 1) differential attacks and 2) chosen-plaintext attacks, which are famous for their destructive power, especially their capability of exploiting cryptosystems' features to recover the secret key. In this article, we propose an artificial image, computational experiment, and parallel execution (ACP)-based color image encryption approach using redundant blocks. First, a redundant blocks strategy with redundant spaces is proposed to prevent differential attacks and accelerate operating speed while guaranteeing the security of image cryptosystems. Second, real-world chaotic data (e.g., stock data) are obtained to generate artificial images and conduct computational experiments. Furthermore, artificial images are encrypted via real-world chaos, while the original images are encrypted via simulated chaos (such as Chen's hyperchaos). Finally, we design the process of parallel execution for image encryption and use DNA XOR to merge two groups of encrypted subimages to fuse the effect of the chaotic characteristics in both the real world and the simulation. The final encrypted image is realized through the recovery of redundant blocks. The ACP mechanism of color image encryption achieves the goal of improving the sophistication of chaos-based cryptosystems and resists both the differential and chosen-plaintext attacks. Experimental results and security analysis show that our approach provides not only excellent encryption but also security sufficient to prevent known attacks.

关键词： Encryption DNA Color Resists Computational modeling Image coding complex systems Artificial image and parallel execution (ACP) method color image encryption computational experiment image division block parallel computing

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Sora for Hierarchical Parallel Motion Planner: A Safe End-to-End Method Against OOD Events

IEEE TRANSACTIONS ON INTELLIGENT VEHICLES

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IEEE TRANSACTIONS ON INTELLIGENT VEHICLES 2024年第4期9卷 4573-4576页

作者： Teng, Siyu Yan, Ran Zhang, Xiaotong Li, Yuchen Wang, Xingxia Wang, Yutong Tian, Yonglin Yu, Hui Li, Lingxi Chen, Long Wang, Fei-Yue Nanyang Technol Univ Singapore 639798 Singapore Hong Kong Baptist Univ Hong Kong Peoples R China Chinese Acad Sci State Key Lab Management & Control Complex Syst Beijing 100045 Peoples R China Hong Kong Baptist Univ Hong Kong Peoples R China WAYTOUS Inc Beijing 100083 Peoples R China Univ Portsmouth Sch Creat Technol Portsmouth PO1 2UP England Purdue Univ Elmore Family Sch Elect & Comp Engn W Lafayette IN 47907 USA WAYTOUS Inc Beijing 100083 Peoples R China

End-to-end motion planners have shown great potential for enabling fully autonomous driving. However, when facing out-of-distribution (OOD) events, these planners might not guarantee the optimal prediction of control commands. To better enhance safety, an end-to-end method that benefits robust and general policy learning from potential OOD events is urgently desirable. In this perspective, Sore4PMP, a hierarchical parallel motion planner, is presented as a suitable solution. Based on raw perception data and descriptive prompts, Sore4PMP can first leverage the advanced generative capabilities of Sora to generate virtual OOD events, and then integrate these events into the decision-making process, thereby enhancing the robustness and generalization of autonomous vehicles (AVs) in emergency scenarios. With a comprehensive outlook, this perspective aims to provide a potential direction for the development of foundation models coupled with autonomous driving and finally promote the safety, efficiency, reliability, and sustainability of AVs.

关键词： Planning Feature extraction Robustness Vectors Visualization Task analysis Cognition Sora end-to-end planning safety motion planning interpretable imitation learning

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Observer-based event and self-triggered adaptive output feedback control of robotic manipulators

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INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR control 2022年第16期32卷 8842-8873页

作者： Gao, Jie He, Wei Qiao, Hong Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Chinese Acad Sci Inst Automat Beijing Key Lab Res & Applicat Robot Intelligence Beijing Peoples R China Univ Chinese Acad Sci Inst Automat Beijing Peoples R China Univ Sci & Technol Beijing Sch Automat & Elect Engn Beijing Peoples R China Chinese Acad Sci CAS Ctr Excellence Brain Sci & Intelligence Techn Shanghai Peoples R China

This article investigates the event and self-triggered adaptive output feedback control of a manipulator suffering from limited knowledge of states and dynamics, to realize the trajectory tracking with less communication occupation. In this control scheme, the configuration of co-located observer and controller with discontinued output feedback is considered. To guarantee the convergence of observation and control errors with few events as much as possible, an adaptive event-triggered mechanism based on model estimation is constructed to compensate for the error accumulation produced by the intermittent open-loop. Based on the model state, adaptive backstepping method with network estimation is used for deriving the controller, to solve the control stability under uncertainty of system dynamics. Aiming at removing the "derivative explosion and singularity" of discontinuous virtual signal, a first-order filter is incorporated to get the smooth approximation of the virtual signal, and an additional self-adaption signal is designed for the filtering error compensation. In view of the state updating at event instants, a gradual updating method is designed such that the state jumping-induced chattering instability could be handled. With the above designed method, a dead-zone event-triggered condition with the relative threshold and variable tolerance boundary is built to avoid Zeno-behavior. Furthermore, an easy-implemented self-triggered mechanism is also constructed. Finally, the Lyapunov function is utilized to derive the setting principle for the stability of the system, and the simulation is given to show the validity of the proposed control method.

关键词： first-order filter impulsive dynamical system model-based event-triggered control neural network nonlinear uncertainty observer estimation robotic manipulator

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Improving Domain-Adaptive Person Re-Identification by Dual-Alignment Learning With Camera-Aware Image Generation

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IEEE TRANSACTIONS ON CIRCUITS AND systems FOR VIDEO TECHNOLOGY 2021年第11期31卷 4334-4346页

作者： Zhang, Chenyang Tang, Yongqiang Zhang, Zhizhong Li, Ding Yang, Xuebing Zhang, Wensheng 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 101408 Peoples R China Chinese Acad Sci Res Ctr Precis Sensing & Control Inst Automat Beijing 100190 Peoples R China

Domain adaptation in person re-identification (re-ID) has always been challenging, especially for the lack of supervision information on the target domain. Existing methods generally introduced extra supervision by adversarial learning techniques, then added all the augmented data in the training process to optimize the re-ID model. However, the direct utilization of all the generated data not only increases additional computational cost but also ignores the potential correlation between the origin and generated data. In this article, we propose a novel dual-alignment learning framework (DAL) with camera-aware image generation to efficiently and effectively tackle this issue. Specifically, we propose a camera transfer matching module to generate additional training images with different camera styles, and construct the matching pairs with each containing a origin image and one corresponding camera transferred image. To strengthen the correlation of images for each matching pair, we align the pseudo-labels via clustering algorithm to reduce the pseudo-labels distribution discrepancy between the origin and generated images. Besides, to avoid model degeneration affected by some inaccurate pseudo-labels on unlabelled data, we maximize the mutual information to align the image feature representations of matching pair. The DAL allows us to decrease the camera variance and enhance the discrimination ability of re-ID model. Extensive experiments on three large-scale benchmarks demonstrate the superiority of DAL over state-of-the-art methods.

关键词： Cameras Training Data models Correlation Clustering algorithms Adaptation models Prediction algorithms Person re-identification convolutional neural networks generative adversarial networks mutual information

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ReCUS: Reconvolution and Upsampling Network for Object Detection 7

ReCUS: Reconvolution and Upsampling Network for Object Detec...

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7th IEEE International Conference on Cloud Computing and Intelligence systems, CCIS 2021

作者： Li, Fudong Gao, Dongyang Yang, Yuequan Cao, Zhiqiang Wang, Wei Yangzhou225000 China Institute of Automation Chinese Academy of Sciences State Key Laboratory of Management and Control for Complex Systems Beijing100190 China Affiliated Hospital of Yangzhou University Yangzhou225012 China

ISBN: (纸本)9781665441490

Most of the mainstream object detection models such as RetinaNet, SSD, YOLO, and Faster RCNN hardly achieve a good balance between detection accuracy and speed. A major reason is rich deep feature semantic information of images is not fully exploited. To solve this problem, a novel deep convolutional network structure termed as reconvolution and upsampling network (ReCUS) is proposed. In the ReCUS, a modified path aggregation network(mPAN) is added after the backbone, which is beneficial to strengthen the foreground salient feature information and weaken background information. Moreover, two new spatial pyramid pooling (SPP) modules are embedded before output heads for multi-scale fusion of local and global features. The experiments show that the effectiveness of our proposed ReCUS. Furthermore, the better detectability of the ReCUS network is demonstrated for both small scale objects and large scale objects. © 2021 IEEE.

关键词： Object detection

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Moving Target Shooting control Policy Based on Deep Reinforcement Learning

Moving Target Shooting Control Policy Based on Deep Reinforc...

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2021 International Conference on Information, Cybernetics, and Computational Social systems, ICCSS 2021

作者： Li, Boyu Jin, Tao Zhu, Yuanheng Li, Haoran Wu, Yingnian Zhao, Dongbin Institute of Automation Chinese Academy of Sciences State Key Laboratory of Management and Control for Complex Systems Beijing100190 China Beijing Information Science and Technology University School of Automation Beijing100192 China

ISBN: (纸本)9781665402453

Robots are playing a more and more important role in people's production and life, recently. However, robot control in dynamic environment is still a difficulty. With the great breakthrough of deep reinforcement learning in the field of video games, this method is also extended to the field of robots. Due to the gap between the simulation environment and the real environment, the deep reinforcement learning algorithm trained in the simulation environment is difficult to be applied to the real environment. Aiming at the gimbal control with two degrees of freedom (DOF), a pipline combining system identification and deep reinforcement learning is proposed. On the one hand, the shooting accuracy of the gimbal to moving objects is improved through deep reinforcement learning algorithm. On the other hand, the gap between simulation and reality is reduced through system identification. The method is verified in the RoboMaster University AI Challenge (RMUA) shooting system. The results show that the shooting accuracy is better than the classical control method. © 2021 IEEE.

关键词： Deep learning

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UCAS-hand: An underactuated powered hand exoskeleton for assisting grasping task

UCAS-hand: An underactuated powered hand exoskeleton for ass...

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2021 IEEE International Conference on Real-Time Computing and Robotics, RCAR 2021

作者： Li, Houcheng Cheng, Long Li, Zhengwei Li, Guotao Institute of Automation Chinese Academy of Sciences State Key Laboratory of Management and Control for Complex Systems Beijing100190 China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing100049 China

ISBN: (纸本)9781665436786

This paper presents a novel underactuated coupled adaptive hand exoskeleton, called UCAS-Hand, which is designed to assist users with weak muscle strength to complete the operation of daily living items. In mechanical design, the proposed UCAS-Hand considers the human-robot kinematic compatibility, grasping adaptability for different objects, portability with motors attached to the hand, force transfer efficiency with applying normal force to the finger phalanges, and passive backdrivability with low gear reduction ratio motor. The UCAS-Hand can realize finger underactuated motions such as flexion/extension and thumb underactuated motions such as flexion/extension, abduction/adduction, and thumb opposability. To minimize the backlashes of bevel gear transmission, a spherical four-bar mechanism and a spherical seven-bar mechanism are designed for the index finger and thumb mechanisms, respectively. Finally, the experiment is implemented to reveal the characteristics of the UCAS-Hand. © 2021 IEEE.

关键词： Machine design

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A Study on Decentralized Autonomous Organizations Based Intelligent Transportation System enabled by Blockchain and Smart Contract

A Study on Decentralized Autonomous Organizations Based Inte...

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2021 China Automation Congress, CAC 2021

作者： Hou, JiaChen Ding, WenWen Liang, Xiaolong Zhu, FengHua Yuan, Yong Wang, FeiYue Institute of Systems Engineering Macau University of Science and Technology China State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China School of Mathematics Renmin University of China Beijing China

ISBN: (纸本)9781665426473

With the rapid development of the transportation industry, Intelligent Transportation System (ITS) tries to adapt to industry changes through constructing new organizational forms, management methods, and incentive mechanisms. However, many new governance issues have arisen due to the transportation system's complexity, diversity, and uncertainty. DAO(Decentralized autonomous organization) enabled by blockchain and smart contracts are regarded as effective methods to solve the organizational governance problems for complex systems. Therefore, this article analyzes the main issues currently faced by ITS. Then, this paper proposed a 6-layer DAO structure framework for ITS and discussed its key research issues. Finally, a case study for the Macau transportation system is carried out, and further research directions are proposed. The DAO-based transportation system will represent the future trend of ITS development. This paper aims to motivate more discussion for this new direction and provide helpful guidance and reference for future research work. © 2021 IEEE

关键词： Smart contract

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