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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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Geometry Problem Solving Based on Counter-Factual Evolutionary Reasoning

Geometry Problem Solving Based on Counter-Factual Evolutiona...

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IEEE International Conference on automation Science and Engineering (CASE)

作者： Bing Song Gang Xiong Zhen Shen Fenghua Zhu Yisheng Lv Peijun Ye 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 The Beijing Engineering Research Center of Intelligent Systems and Technology Institute of Automation Chinese Academy of Sciences China The Guangdong Engineering Research Center of 3D Printing and Intelligent Manufacturing The Cloud Computing Center Chinese Academy of Sciences Dongguan China Intelligent Manufacturing Center Qingdao Academy of Intelligent Industries Qingdao China

As a representative topic in natural language processing and automated theorem proving, geometry problem solving requires an abstract problem understanding and symbolic reasoning. A major challenge here is to find a feasible reasoning sequence that is consistent with given axioms and the theorems already proved. Most recent methods have exploited neural network-based techniques to automatically discover eligible solving steps. Such a kind of methods, however, is greatly impacted by the expert solutions for training. To improve the accuracy, this paper proposes a new method called counterfactual evolutionary reasoning, which uses a generative adversarial network to generate initial reasoning sequences and then introduces counterfactual reasoning to explore potential solutions. By directly exploring theorem candidates rather than the neural network selection, the new method can sufficiently extend the searching space to get a more appropriate reasoning step. Through comparative experiments on the recent proposed Geometry3k, the largest geometry problem solving dataset, our method generally achieves a higher accuracy than most previous methods, bringing an overall improvement about 4.4% compared with the transformer models.

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Automatically Discovering Novel Visual Categories with Self-supervised Prototype Learning

arXiv

引用

arXiv 2022年

作者： Zhang, Lu Qi, Lu Yang, Xu Qiao, Hong Yang, Ming-Hsuan Liu, Zhiyong State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences China The School of Artificial Intelligence University of Chinese Academy of Sciences China Center for Excellence in Brain Science and Intelligence Technology Chinese Academy of Sciences China University of California Merced United States

This paper tackles the problem of novel category discovery (NCD), which aims to discriminate unknown categories in large-scale image collections. The NCD task is challenging due to the closeness to the real world scenarios, where we have only encountered some partial classes and images. Unlike other works on the NCD, we leverage the prototypes to emphasize the importance of category discrimination and alleviate the issue with missing annotations of novel classes. Concretely, we propose a novel adaptive prototype learning method consisting of two main stages: prototypical representation learning and prototypical self-training. In the first stage, we obtain a robust feature extractor, which could serve for all images with base and novel categories. This ability of instance and category discrimination of the feature extractor is boosted by self-supervised learning and adaptive prototypes. In the second stage, we utilize the prototypes again to rectify offline pseudo labels and train a final parametric classifier for category clustering. We conduct extensive experiments on four benchmark datasets, and demonstrate the effectiveness and robustness of the proposed method with the state-of-the-art performance. The source code and trained models will be made available at this github site. Copyright © 2022, The Authors. All rights reserved.

关键词： Image recognition

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Development of GPU Enhanced AI Education Platforms for K-12 Schools

Development of GPU Enhanced AI Education Platforms for K-12 ...

引用

2021 China automation Congress, CAC 2021

作者： Gong, Xiaoyan Wen, Yifan Liu, Xiwei Zhao, Hongxia Jing, Sifeng Li, Qiang Gao, Kai Luo, Jilong The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China Institute of Smart Education Systems Qingdao Academy of Intelligent Industries Qingdao 266000 China The 21st Century International School Beijing100142 China Beijing NO. 2 Middle School Beijing100087 China Beijing University of Chemical Technology Beijing100029 China

ISBN: (纸本)9781665426473

Recently, with continuous implementation of education reform, AI (Artificial Intelligence) has been included in K-12 (Kindergarten to 12th grade) curricula and AI education is in full swing in China. However, standard, cost-efficient AI education platforms for K-12 schools are still not available. So in this paper, based on Jetson Nano, we developed a GPU-enhanced AI teaching platform including AI learning computers for students to develop Al-related algorithms and applications, AI computer clusters for whole classes, and modular auto-driving cars to run and test algorithms and applications. © 2021 IEEE

关键词： Graphics processing unit

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Muscle-Synergy-Based Planning and Neural-Adaptive control for a Prosthetic Arm

IEEE Transactions on Artificial Intelligence

引用

IEEE Transactions on Artificial Intelligence 2021年第5期2卷 424-436页

作者： Li, Guoxin Li, Zhijun Li, Junjun Liu, Yueyue Qiao, Hong The Department of Automation University of Science and Technology of China Hefei230026 China The Institute of Artificial Intelligence Hefei Comprehensive National Science Center Hefei230026 China The School of Automation Science and Engineering South China University of Technology Guangzhou511442 China The State Key Laboratory of Management and Control for Complex System Institute of Automation Chinese Academy of Science Beijing100190 China The School of Artificial Intelligence University of Chinese Academy of Sciences Beijing100049 China The Center for Excellence in Brain Science and Intelligence Technology Institute of Neuroscience Chinese Academy of Science Shanghai200031 China

Upper limb loss has significant effects on the individual's quality of life. Artificial prosthetic limbs as an alternative to the lost limb are designed to allow amputees to regain motor function. Motion classification via extracted surface electromyogram (sEMG) signals is widely utilized to realize a friendly human-robot interface in the control of the prosthesis. However, limited classification of discrete motion patterns from sEMG prevents intuitive motor control. Thus, instead of using discrete patterns, decoding the human intention continuously from sEMG would significantly benefit the prosthesis control. In this article, we propose a muscle-synergy-based intention decoding and motion planning that can model a broad set of complex upper limb movements as a combination of motor primitives. A novel muscle activation-to-force mapping model is developed to detect muscular effort of the healthy side to drive the affected side. A neural-network-approximation-based controller is developed for the bionic neuroprosthetic arm to execute the movement. Operational experiments with prosthetic movement control were performed on four healthy participants and an upper limb amputee participant. Results of controlling prosthetic arm (0.94 ± 0.02 of R2 in the horizontal reaching tasks and 0.95 ± 0.01 in the vertical reaching tasks for the healthy subjects, 0.95 and 0.97 for the amputee) demonstrate that our control method could successfully capture human movement intention and effectively control the movement of prosthesis. Impact statement-Powered robotic prostheses have enabled individuals with limb amputations to regain motor function via motion intention decoding from extracting surface electromyogram signals. Most research works have provided human-robot interaction through motion classification obtained from pattern recognition using surface electromyogram signals and simple movement control. However, decoding the human intent continuously from surface electromyogram is con

关键词： Matrix algebra

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Fusionplanner: A Multi-Task Motion Planner for Mining Trucks Using Multisensor Fusion Method

SSRN

引用

SSRN 2023年

作者： Teng, Siyu Chen, Long Li, Luxi Li, Yuchen Hu, Xuein Li, Lingxi Ai, Yunfeng Department of Computer Science Hong Kong Baptist University Hong Kong The School of Artificial Intelligence Hubei University Wuhan China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences China Waytous Inc. Haidian Distinct Beijing China Indianapolis United States School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China

In recent years, significant achievements have been made in motion planning for intelligent vehicles. However, as a typical unstructured environment, open-pit mining attracts limited attention due to its complex operational conditions and adverse environmental factors. A comprehensive paradigm for unmanned transportation in open-pit mines is proposed in this research, including a simulation platform, a testing benchmark, and a trustworthy and robust motion planner. Firstly, we introduce the Parallel Mining Simulator (PMS), a high-fidelity simulator specifically designed for open-pit mining scenarios. PMS enables to management and control of open-pit mine transportation from both the single-truck control and multi-truck scheduling perspectives. Then, we develop a novel benchmark called MiningNav, which offers three validation approaches suitable for transportation roads. The purpose of MiningNav is to evaluate the trustworthiness and robustness of algorithms in open-pit mines. Finally, we propose the first multi-task motion planning algorithm, called FusionPlanner, for the mining truck by the multisensor fusion method to adapt both lateral and longitudinal control tasks for unmanned transportation. We validate the performance of our planner using MiningNav in PMS, and empirical results demonstrate a significant reduction in the number of collisions and takeovers of FusionPlanner, thereby improving overall trustworthiness and robustness. © 2023, The Authors. All rights reserved.

关键词： Motion planning

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Point Cloud Completion by Larger Receptive Fields for Intelligent Vehicles

Point Cloud Completion by Larger Receptive Fields for Intell...

引用

International Conference on Intelligent Transportation

作者： Yunqing Zhang Xiaotong Zhang Kunhua Liu Yisheng Lv Wei He Long Chen School of Computer Science and Engineering Sun Yat-Sen University Guangzhou China School of Mechanical and Automotive Engineering Qingdao University of Technology Qingdao China State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China School of Intelligence Science and Technology University of Science and Technology Beiing Beijing China Waytous Inc. Beijing China

Point cloud completion is a critical task for intelligent vehicles in the Intelligent Transportation systems (ITS). Due to the disordered nature of point clouds, current point-based point completion methods utilize max pooling operations and Convolutional Neural Networks (CNNs) with a convolution kernel size of 1 to extract point features. However, a network with a kernel size of 1 has a small receptive field and can only extract one point feature at a time; as a result, such a network struggles to precisely extract highly abstracted features. To address this issue, we propose a point cloud completion network with larger receptive fields, termed PLRF-Net, which is composed of two parts: a reconstruction network and a completion network. The reconstruction network focuses on repairing the structures of partial points and includes an offset-attention embedding module, a rearrangement module, several convolution layers with varying kernel sizes, a pooling and repeating layer, and transposed convolution layers with different kernel sizes. Meanwhile, the completion network strives to improve the precision of the completed point cloud and consists of several convolution layers, along with a downsampling and upsampling module. Experimental results indicate that our approach achieves superior quantitative and qualitative outcomes on both the Multiview Partial Point Cloud (MVP) and KITTI datasets.

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