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arXiv 2021年

作者： Wang, Junjie Zhang, Qichao Zhao, Dongbin The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China College of Artificial Intelligence University of Chinese Academy of Sciences Beijing China

The development of autonomous driving has attracted extensive attention in recent years, and it is essential to evaluate the performance of autonomous driving. However, testing on the road is expensive and inefficient. Virtual testing is the primary way to validate and verify self-driving cars, and the basis of virtual testing is to build simulation scenarios. In this paper, we propose a training, testing, and evaluation pipeline for the lane-changing task from the perspective of deep reinforcement learning. First, we design lane change scenarios for training and testing, where the test scenarios include stochastic and deterministic parts. Then, we deploy a set of benchmarks consisting of learning and non-learning approaches. We train several state-of-the-art deep reinforcement learning methods in the designed training scenarios and provide the benchmark metrics evaluation results of the trained models in the test scenarios. The designed lane-changing scenarios and benchmarks are both opened to provide a consistent experimental environment for the lane-changing task. Copyright © 2021, The Authors. All rights reserved.

关键词： Deep learning

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Adaptive Takagi-Sugeno Fuzzy Model for Pneumatic Artificial Muscles

Adaptive Takagi-Sugeno Fuzzy Model for Pneumatic Artificial ...

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International Workshop on Advanced Computational Intelligence (IWACI)

作者： Xiuze Xia Long Cheng School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China

Pneumatic artificial muscle (PAM) usually exhibits strong hysteresis nonlinearity and time-varying features that bring PAM modeling and control difficulties. In this paper, an adaptive Takagi-Sugeno (T-S) fuzzy model is established based on nonlinear auto-regression moving average with exogenous input (NARMAX) structure to describe PAM's characteristics. Experiments show that compared with other phenomenology-based models, the presented model has lower predictive error and better adaptability. Finally, a model predictive controller is designed and validated to verify the adaptive T-S fuzzy model's practicability.

关键词： Adaptation models Computational modeling Force Predictive models Muscles Jitter Takagi-Sugeno model

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Exploring Painting Synthesis with Diffusion Models

Exploring Painting Synthesis with Diffusion Models

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Digital Twins and Parallel Intelligence (DTPI), IEEE International Conference on

作者： Da Yi Chao Guo Tianxiang Bai The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China

As a significant composition of art, fine art painting is becoming a research hotspot in machine learning community. With unique aesthetic value, paintings have quite different representations from natural images, making them irreplaceable. Meanwhile, the lack of training data is common in painting-related machine learning tasks. Therefore, the synthesis of fine art painting is meaningful and challenging work. There are two main types of generative models for image synthesis: generative adversarial networks (GANs) and likelihood-based models. GAN-based models can obtain high-quality samples but usually sacrifice diversity and training stability. Diffusion models are a class of likelihood-based models and have recently been shown to achieve state-of-the-art quality on the image synthesis tasks. In this paper, we explore generating fine art paintings by using diffusion models. We carried out the experiments on the partial impression paintings from the Wikiart dataset. The results demonstrate that the diffusion model can generate high-quality samples, and it is easy to train to cover more target distribution than the GAN-based methods.

关键词： Training Art Image synthesis Digital twin Training data Machine learning Generative adversarial networks

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A Convolutional Neural Network With Multi-scale Kernel and Feature Fusion for sEMG-based Gesture Recognition

A Convolutional Neural Network With Multi-scale Kernel and F...

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IEEE International Conference on Robotics and Biomimetics

作者： Lijun Han Yongxiang Zou Long Cheng State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781665405362

The sEMG-based gesture recognition has great potential in human-computer interaction. However, the current approaches are far from optimal. In this paper, a novel convolutional neural network which combines the multi-scale kernal and feature fusion (MKFF-CNN) is proposed. This model could extract multi-scale features and make full use of these feature maps. One dataset called “gForce dataset” is recorded in this work, comprising 13 able-bodied participants. To verify the effectiveness of the proposed model, evaluations on both the gForce dataset and the Ninapro DB6 are conducted. The experimental results demonstrate that, MKFF-CNN achieves an accuracy of 97.65% on the gForce dataset, which is 1.39% higher than that of the subnetwork without early fusion, and at least 6.54% higher than that of the subnetwork with single-scale convolution kernel. On the Ninapro DB6, MKFF-CNN achieves 98.52% accuracy, which is 1.3% higher than the state-of-the-art works, showing the superiority of the proposed model.

关键词： Human computer interaction Convolution Conferences Biomimetics Biological system modeling Gesture recognition Feature extraction

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Accident Prediction in Mesoscopic View: A CPSS-Based Social Transportation Approach

Accident Prediction in Mesoscopic View: A CPSS-Based Social ...

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Digital Twins and Parallel Intelligence (DTPI), IEEE International Conference on

作者： Chen Sun Ruichen Tan Jie Deng Rui Zhou Long Chen Fei-Yue Wang Dongpu Cao University of Waterloo Waterloo Canada Waytous Inc. Qingdao China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China

In modern society, traffic accidents are becoming an essential social safety issue that cannot be ignored. Along with the convenience of the high-speed development of modernization, the prosperity of vehicles has also led to severe traffic accidents, which cause massive loss of lives and properties every year. Accident analysis and prediction based on big data have become increasingly important in social transportation. However, the traditional data analysis and prediction methods only focused on either macroscope(transportation) or microscope(vehicle), and the relationships between the two layers are missing. This paper proposes a novel analysis framework by introducing the mesoscopic factors to bridge the transportation and vehicle operation domain under the parallel driving setting. The feature aggregation and mesoscopic factors extraction are included in the proposed prediction strategy to improve the performance of various commonly used decision tree-based classifiers. Methods are compared under the most recent 5-year accident database, which stores over three million records. Insights are discussed based on the future application of connected autonomous vehicles and intelligent road infrastructures.

关键词： Databases Roads Microscopy Digital twin Transportation Prediction methods Feature extraction

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A New Constrained Cost Value Iteration for Optimal control of Discrete-Time Nonlinear systems

A New Constrained Cost Value Iteration for Optimal Control o...

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Chinese automation Congress (CAC)

作者： Tao Li Qinglai Wei The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781665426480

In this paper, a new constrained cost value iteration (CCVI) adaptive dynamic programming (ADP) algorithm is developed to solve optimal control problems with constrained cost function. The CCVI algorithm is initialized by a feasible control law, and the cost function of this initial feasible control constitutes the constraint condition in the policy update step. The convergence and optimality of the CCVI are analyzed. Furthermore, the feasibility of any iterative control law and the on-line evolving control law are proven. It is emphasized that the feasibility of any iterative control law and the on-line evolving control law guarantee that the CCVI algorithm can be used in situations where the cost function constraint is extremely important. Finally, a simulation example is given to verify the effect of the developed algorithm.

关键词： Costs automation Heuristic algorithms Simulation Optimal control Cost function Iterative algorithms

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Blind Spectrum Sensing Based on the Statistical Covariance Matrix and K-Median Clustering Algorithm 6th

Blind Spectrum Sensing Based on the Statistical Covariance M...

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6th International Conference on Artificial Intelligence and Security, ICAIS 2020

作者： Zhuang, Jiawei Wang, Yonghua Wan, Pin Zhang, Shunchao Zhang, Yongwei Li, Yi School of Automation Guangdong University of Technology Guangzhou510006 China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China Hubei Key Laboratory of Intelligent Wireless Communications South-Central University for Nationalities Wuhan430074 China

ISBN: (纸本)9783030578831

Spectrum sensing is a fundamental function for cognitive radio systems, which can improve spectrum utilization. In this article, a blind spectrum sensing method based on the sample covariance matrix and K-median clustering algorithm is proposed to further improve the sensing performance. Specifically, to obtain a two-dimensional signal feature vector, the received signal matrix is rebuilt into two sub-matrices by the decomposition and reorganization (DAR) method. Moreover, two statistical covariance matrices are constructed by the sub-matrices, respectively. The ratios between the sum of some elements from the sample covariance matrices and the sum of diagonal elements from those matrices are used as a signal feature vector. It is demonstrated that the new signal feature vector and the feature vector based on the improved covariance absolute value method are equivalent. Furthermore, K-median clustering algorithm is trained by signal feature vectors to obtain a classifier. Indeed, this classifier can directly be used to detect whether the PU signal is absent or not. Simulation results report that the proposed algorithm has better sensing performance than some popular sensing algorithms based on random matrix theory or information geometry. © 2020, Springer Nature Switzerland AG.

关键词： Covariance matrix

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Toward parallel intelligence: an interdisciplinary solution for complex systems

arXiv

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arXiv 2023年

作者： Zhao, Yong Zhu, Zhengqiu Chen, Bin Qiu, Sihang Huang, Jincai Lu, Xin Yang, Weiyi Ai, Chuan Huang, Kuihua He, Cheng Jin, Yucheng Liu, Zhong Wang, Fei-Yue College of Systems Engineering National University of Defense Technology Changsha410073 China Hunan Institute of Advanced Technology Changsha410073 China Shanghai Institute of Infectious Disease and Biosecurity Shanghai200032 China Neuherberg Germany Department of Computer Science Hong Kong Baptist University 999077 Hong Kong State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China

The growing complexity of real-world systems necessitates interdisciplinary solutions to confront myriad challenges in modeling, analysis, management, and control. To meet these demands, the parallel systems method rooted in Artificial systems, Computational experiments, and Parallel execution (ACP) approach has been developed. The method cultivates a cycle, termed parallel intelligence, which iteratively creates data, acquires knowledge, and refines the actual system. Over the past two decades, the parallel systems method has continuously woven advanced knowledge and technologies from various disciplines, offering versatile interdisciplinary solutions for complex systems across diverse fields. This review explores the origins and fundamental concepts of the parallel systems method, showcasing its accomplishments as a diverse array of parallel technologies and applications, while also prognosticating potential challenges. We posit that this method will considerably augment sustainable development while enhancing interdisciplinary communication and cooperation. © 2023, CC BY-NC-ND.

关键词： Iterative methods

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An IoT Edge Computing System Architecture and its Application

An IoT Edge Computing System Architecture and its Applicatio...

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2020 IEEE International Conference on Networking, Sensing and control, ICNSC 2020

作者： Chen, Shichao Li, Qijie Zhang, Hua Zhu, Fenghua Xiong, Gang Tang, Ying Macau University of Science and Technology State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences China Beijing University of Technology State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China Beijing Aerospace Smart Manufacturing Technology Development Co. Ltd Platform Research and Development Department Beijing China State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China Engineering Research Center of Intelligent Systems and Technology Institute of Automation Cloud Computing Center Chinese Academy of Sciences Beijing China Rowan University Department of Electrical and Computer Engineering GlassboroNJ United States Institute of Smart Education Qingdao Academy of Intelligent Industries Qingdao China

ISBN: (纸本)9781728168531

As the number of devices connected to the Internet of things (IoT) surges, the amount of data explodes. Therefore it not only increases the bandwidth load of data transmission but also aggravates the computing and storage load of a cloud platform. At the same time, the traditional computing paradigm centered on cloud computing cannot meet the real-time requirements in many application scenarios. The emergence of edge computing can solve the problems of realtime data processing and network bandwidth occupation in the current IoT scene. In this paper, according to the characteristics of IoT, such as fragmented data, heterogeneous network, and limited energy consumption, the architecture of an IoT edge computing system is constructed to suit better an IoT scene. In addition, the application of edge computing key technologies such as virtualization, edge intelligence, computing offload, collaborative scheduling and micro-services in resource-constrained IoT scenarios is analyzed in detail. Finally, the functions and application of energy consumption monitoring and optimization to a central air-conditioning system are analyzed and summarized, which is a typical application of edge computing in the context of the IoT. © 2020 IEEE.

关键词： Edge computing

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Distributed Containment control of Markov Jump Multi-agent systems under Asynchronous Communications

Distributed Containment Control of Markov Jump Multi-agent S...

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第40届中国控制会议

作者： Chao Ma Yidao Ji Hang Fu Wei Wu School of Mechanical Engineering University of Science and Technology Beijing School of Automation and Electrical Engineering University of Science and Technology Beijing State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences

This paper studies the distributed containment control issue for general Markov jump multi-agent systems. More specifically, a novel asynchronous mode-dependent communication scheme is proposed, which is based on observed communication mode information. By means of mode-dependent Lyapunov method, sufficient containment conditions are derived and communication protocol gains are further designed by convex optimization. A simulation example is finally presented to validate the correctness of our developed control scheme.

关键词： Distributed containment Markov jump multi-agent systems asynchronous communications

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