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arXiv 2021年

作者： Xiong, Mingliang Liu, Qingwen Wang, Gang Giannakis, Georgios B. Zhang, Sihai Zhu, Jinkang Huang, Chuan College of Electronics and Information Engineering Tongji University Shanghai201804 China State Key Lab of Intelligent Control and Decision of Complex Systems School of Automation Beijing Institute of Technology Beijing100081 China Department of Electrical and Computer Engineering University of Minnesota MinneapolisMN55455 United States Key Lab of Wireless-Optical Communications University of Science and Technology of China Hefei Anhui230026 China School of Science and Engineering Chinese University of Hong Kong Shenzhen Guangdong518172 Hong Kong

Resonant beam communications (RBCom) is capable of providing wide bandwidth when using light as the carrier. Besides, the RBCom system possesses the characteristics of mobility, high signal-to-noise ratio (SNR), and multiplexing. Nevertheless, the channel of the RBCom system is distinct from other light communication technologies due to the echo interference issue. In this paper, we reveal the mechanism of the echo interference and propose the method to eliminate the interference. Moreover, we present an exemplary design based on frequency shifting and optical filtering, along with its mathematic model and performance analysis. The numerical evaluation shows that the channel capacity is greater than 15 bit/s/Hz. Copyright © 2021, The Authors. All rights reserved.

关键词： Internet of things

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Visual-Gyroscope-Wheel Odometry With Ground Plane Constraint for Indoor Robots in Dynamic Environment

IEEE SENSORS LETTERS

引用

IEEE SENSORS LETTERS 2021年第3期5卷 1页

作者： Ouyang, Ming Cao, Zhiqiang Guan, Peiyu Li, Zhonghui Zhou, Chao 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 Beijing Engo Technol Co Ltd Beijing 101318 Peoples R China Peking Univ Dept Mech & Engn Sci Coll Engn BIC ESAT Beijing 100871 Peoples R China

Localization is an essential prerequisite for autonomous robots to execute tasks. In this letter, a multisensor fused odometry is proposed for the robot applied to dynamic indoor environments. The gyroscope and wheel encoder information is integrated into visual odometry, where the robot pose is parameterized with 3-D position and 3-D Euler angle to relieve the estimation drift during planar motion and conveniently fuse wheel encoder measurements. According to the results of semantic segmentation, the dynamic elements are removed. On this basis, ground plane constraint is exerted on the optimization procedure. As a result, the estimation accuracy is improved. The experimental results on the OpenLORIS-scene dataset demonstrate the effectiveness of the proposed odometry.

关键词： Sensor applications dynamic environment indoor robot semantics visual-gyroscope-wheel odometry

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On controllability of Neuronal Networks With Constraints on the Average of control Gains

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IEEE TRANSACTIONS ON CYBERNETICS 2014年第12期44卷 2670-2681页

作者： Tang, Yang Wang, Zidong Gao, Huijun Qiao, Hong Kurths, Juergen Potsdam Inst Climate Impact Res D-14473 Potsdam Germany Humboldt Univ Inst Phys D-12489 Berlin Germany Brunel Univ Dept Comp Sci Uxbridge UB8 3PH Middx England King Abdulaziz Univ Fac Engn Jeddah 21589 Saudi Arabia Harbin Inst Technol Res Inst Intelligent Control & Syst Harbin 150080 Peoples R China King Abdulaziz Univ Jeddah 21589 Saudi Arabia Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Univ Aberdeen Inst Complex Syst & Math Biol Aberdeen AB24 3UE Scotland

control gains play an important role in the control of a natural or a technical system since they reflect how much resource is required to optimize a certain control objective. This paper is concerned with the controllability of neuronal networks with constraints on the average value of the control gains injected in driver nodes, which are in accordance with engineering and biological backgrounds. In order to deal with the constraints on control gains, the controllability problem is transformed into a constrained optimization problem (COP). The introduction of the constraints on the control gains unavoidably leads to substantial difficulty in finding feasible as well as refining solutions. As such, a modified dynamic hybrid framework (MDyHF) is developed to solve this COP, based on an adaptive differential evolution and the concept of Pareto dominance. By comparing with statistical methods and several recently reported constrained optimization evolutionary algorithms (COEAs), we show that our proposed MDyHF is competitive and promising in studying the controllability of neuronal networks. Based on the MDyHF, we proceed to show the controlling regions under different levels of constraints. It is revealed that we should allocate the control gains economically when strong constraints are considered. In addition, it is found that as the constraints become more restrictive, the driver nodes are more likely to be selected from the nodes with a large degree. The results and methods presented in this paper will provide useful insights into developing new techniques to control a realistic complex network efficiently.

关键词： complex networks controllability evolutionary algorithms multiagent systems neural networks synchronization/consensus

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PSSE Redux: Convex relaxation, decentralized, robust, and dynamic approaches

arXiv

引用

arXiv 2017年

作者： Kekatos, Vassilis Wang, Gang Zhu, Hao Giannakis, Georgios B. Bradley Department of Electrical and Computer Engineering Virginia Tech BlacksburgVA24061 United States Department of Electrical and Computer Engineering University of Minnesota MinneapolisMN55455 United States State Key Lab of Intelligent Control and Decision of Complex Systems Beijing Institute of Technology Beijing100081 China Department of Electrical and Computer Engineering University of Illinois UrbanaIL61801 United States

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Traffic Congestion Prediction using Decision Tree, Logistic Regression and Neural Networks

引用

IFAC-PapersOnLine 2020年第5期53卷 512-517页

作者： Tariku Sinshaw Tamir Gang Xiong Zhishuai Li Hao Tao Zhen Shen Bin Hu Heruye Mulugeta Menkir State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing 100190 China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing 100049 China Guangdong Engineering Research Center of 3D Printing and Intelligent Manufacturing Cloud Computing Center Chinese Academy of Sciences Dongguan 523808 China China Ship Development and Design Center Wuhan 430064 China Beijing Engineering Research Center of Intelligent Systems and Technology Institute of Automation Chinese Academy of Sciences Beijing 100190 China Debremarkos University Institute of Technology Debremarkos 269 Ethiopia

Traffic congestion is a serious problem around the world and to a great extent influences urban communities in various manners including increased stress levels, delayed deliveries, fuel wastage, and monetary losses. Therefore, an accurate congestion prediction algorithm to limit these misfortunes is fundamental. This paper presents a comparative study of traffic congestion prediction systems including decision tree, logistic regression, and neural networks. Five days of traffic information (1,231,200 samples) are utilized to drive the prediction model. The TensorFlow and the Clementine machine learning platforms are used for data preprocessing, training, and testing of the model. The confusion matrix clears that decision tree has better prediction performance and leads the other two methods with accuracy (97%), macro-average precision (95%), macro-average recall (96%), and macro-average F1_score (96%) in the python programming environment. Moreover, performance of the three prediction models is verified in Clementine environment and decision tree outperforms all other models with an accuracy of 97.65%.

关键词： Confusion matrix Data preprocessing Python TensorFlow Clementine environment ITS

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Urban Trip Generation Forecasting Based on Gradient Boosting Algorithm

Urban Trip Generation Forecasting Based on Gradient Boosting...

引用

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

作者： Zhishuai Li Gang Xiong Yu Zhang Meng Zheng Xisong Dong Yisheng Lv 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 Beijing Engineering Research Center of Intelligent Systems and Technology Institute of Automation Chinese Academy of Sciences Beijing China Guangdong Engineering Research Center of 3D Printing and Intelligent Manufacturing Cloud Computing Center Chinese Academy of Sciences Dongguan China Beijing Municipal Institute of City Planning and Design Beijing China

The four-step transportation model plays an important role in urban planning. The quality of the first phase, i.e. trip generation, determines the performance of the global course. The majority of trip generation forecasting models highly rely on mathematical derivation and have many predictor variables during the prediction, which leads to low accuracy of results and requires laboriously hand-crafted design of input vectors. This paper is the first to introduce the gradient boosting decision tree (GBDT) algorithm for trip generation prediction, and harmonizes such a powerful machine learning method with traditional urban planning requirements to achieve better prediction performance. Unlike the commonly used linear regression method, GBDT can automatically perform feature selection and model the non-linear relationships between input and output variables. Experimental results on real-world residential travel census in Beijing prove that the GBDT model significantly outperforms the baseline and can forecast the trip generation more accurately.

关键词： Machine learning algorithms Urban planning Transportation Predictive models Prediction algorithms Feature extraction Boosting

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Geometry Problem Solving Based on Counter-Factual Evolutionary Reasoning

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

引用

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.

关键词：

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Muscle Synergy Analysis Based on NMF for Lower Limb Motor Function Assessment

Muscle Synergy Analysis Based on NMF for Lower Limb Motor Fu...

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IEEE International Conference on Robotics and Biomimetics

作者： Kexin Xiang Weiqun Wang Zeng-Guang Hou Chutian Zhang Jiaxing Wang Weiguo Shi Yuze Jiao Tianyu Lin 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 School of Artifificial Intelligence University of Chinese Academy of Sciences Beijing China CASIA-MUST Joint Laboratory of Intelligence Science and Technology Institute of Systems Engineering Macau University of Science and Technology Macao Macau Institute of Systems Engineering Macau University of Science and Technology Macau China CASIA-MUST Joint Laboratory of Intelligence Science and Technology Institute of Systems Engineering Macau University of Science and Technology China

ISBN: (纸本)9781665481106

Accurate rehabilitation assessments are essential for designing effective rehabilitation methods and helping patients recover better. It's well known that commonly used scale assessment methods for neurorehabilitation suffer from the issue of subjectivity, thus investigation of objective assessment methods is very necessary. Muscle synergy analysis can be uesd to assess limb motor functions from the perspective of neuromuscular control. In this paper, a method for evaluation of human lower limb motor functions based on muscle synergy analysis is presented. Muscle synergy modules are designed using surface electromyography (sEMG) signals of the subjects' lower limbs by non-negative matrix factorizations (NMF). By comparing the cosine similarities of these synergy modules, it can be seen that muscle synergies of healthy subjects and patients are significantly different, while they are similar among healthy subjects. Therefore, a reference synergy module (RSM) is designed by averaging the muscle synergy modules for healthy subjects, and the similarities can be calculated by comparing the synergy modules for healthy subjects or patients with the RSM. In the experiment carried out in this study, average similarities of the three synergy modules for healthy subjects are respectively 0.97166, 0.87368 and 0.84932, and on the other hand, the average similarities for the three synergy modules for patients are respectively 0.59979, 0.56426 and 0.69042. Therefore, the similarities for healthy subjects are much higher than those for SCI patients, which denotes that the similarity between an individual synergy module and the RSM can be used as an objective assessing index for evaluating patients' motor function.

关键词： Neuromuscular Biomimetics Stability criteria Muscles Electromyography Indexes Robots

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Impacts of Export Restrictions on the Global Personal Protective Equipment Trade Network During COVID-19

引用

ADVANCED THEORY AND SIMULATIONS 2022年第4期5卷 2100352页

作者： Ye, Yang Zhang, Qingpeng Cao, Zhidong Chen, Frank Youhua Yan, Houmin Stanley, H. Eugene Zeng, Daniel Dajun City Univ Hong Kong Sch Data Sci Hong Kong Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100864 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China Shenzhen Artificial Intelligence & Data Sci Inst Shenzhen Peoples R China City Univ Hong Kong Coll Business Dept Management Sci Hong Kong Peoples R China Boston Univ Dept Phys Boston MA 02215 USA

The COVID-19 pandemic has caused a dramatic surge in demand for personal protective equipment (PPE) worldwide. Many countries have imposed export restrictions on PPE to ensure the sufficient domestic supply. The surging demand and export restrictions cause shortage contagions on the global PPE trade network. Here, an integrated network model is developed, which integrates a metapopulation model and a threshold model, to investigate the shortage contagion patterns. The metapopulation model captures disease contagion across countries. The threshold model captures the shortage contagion on the global PPE trade network. Due to the Pareto distribution in global exports, the shortage contagion pattern is mainly determined by the export restriction policies of the top exporters. Export restrictions exacerbate the shortages of PPE and cause the shortage contagion to transmit even faster than the disease contagion. To some extent, export restrictions can provide benefits for self-sufficient countries, at the sacrifice of immediate economic shocks at not-self-sufficient countries. With export restrictions, a large amount of PPE is hoarded instead of being distributed to where it is most needed, particularly at the early stage. Cooperation between countries plays an essential role in preventing global shortages of PPE regardless of the production level. Except for promoting global cooperation, governments and international organizations should take actions to reduce supply chain barriers and work together to increase global PPE production.

关键词： coronavirus disease 2019 export restrictions network personal protective equipment severe acute respiratory syndrome coronavirus 2

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BNAS: An efficient neural architecture search approach using broad scalable architecture

arXiv

引用

arXiv 2020年

作者： Ding, Zixiang Chen, Yaran Li, Nannan Zhao, Dongbin Sun, Zhiquan Philip Chen, C.L. State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China University of Chinese Academy of Sciences Beijing100049 China School of Automation and Electric Engineering University of Science and Technology Beijing Beijing100083 China School of Computer Science & Engineering South China University of Technology Guangzhou Guangdong510006 China College of Navigation Dalian Maritime University Dalian116026 China

Efficient Neural Architecture Search (ENAS) achieves novel efficiency for learning architecture with high-performance via parameter sharing and reinforcement learning. In the phase of architecture search, ENAS employs deep scalable architecture as search space whose training process consumes most of search cost. Moreover, time consuming of model training is proportional to the depth of deep scalable architecture. Through experiments using ENAS on CIFAR-10, we find that layer reduction of scalable architecture is an effective way to accelerate the search process of ENAS but suffers from prohibitive performance drop in the phase of architecture estimation. In this paper, we propose Broad Neural Architecture Search (BNAS) where we elaborately design broad scalable architecture dubbed Broad Convolutional Neural Network (BCNN) to solve the above issue. On one hand, the proposed broad scalable architecture has fast training speed due to its shallow topology. Moreover, we also adopt reinforcement learning and parameter sharing used in ENAS as the optimization strategy of BNAS. Hence, the proposed approach can achieve higher search efficiency. On the other hand, the broad scalable architecture extracts multi-scale features and enhancement representations, and feeds them into global average pooling layer to yield more reasonable and comprehensive representations. Therefore, the performance of broad scalable architecture can be promised. In particular, we also develop two variants for BNAS who modify the topology of BCNN. In order to verify the effectiveness of BNAS, several experiments are performed and experimental results show that 1) BNAS delivers 0.19 days which is 2.37x less expensive than ENAS who ranks the best in reinforcement learning-based NAS approaches, 2) compared with small-size (0.5 millions parameters) and medium-size (1.1 millions parameters) models, the architecture learned by BNAS obtains state-of-the-art performance (3.58% and 3.24% test error) on CIFAR-1

关键词： Topology

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