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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Jinting Wu Yujia Zhang Xiaoguang Zhao The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781665429825

Excessive stress will have a negative impact on people’s physical and mental health, especially for some special occupations. Because stressful stimuli can trigger a variety of physiological responses, analyzing physiological signals collected by wearable devices has become an important way to evaluate the stress state in recent years. However, the number of available subjects of a target group may be small, and collecting a large amount of data when the target group changes is costly and time-consuming. To solve this problem, we propose a stress detection framework for a small target group which uses adversarial transfer learning method to learn shared knowledge about stress between different groups. In order to verify the performance of the framework, we establish a dataset consisting of 264 ordinary college students and 32 police school students, aiming to evaluate the acute stress state of police school students under video stimuli for psychological training in the future. Comprehensive experiments show that our algorithm has achieved a significant improvement in the target group compared with the baseline methods.

关键词： Training Protocols Law enforcement Wearable computers Transfer learning Signal processing algorithms Feature extraction

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Consensus control of Leader-following Multi-agent System in Partial Directed Topology 2

Consensus Control of Leader-following Multi-agent System in ...

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2nd International Conference on Industrial Artificial Intelligence, IAI 2020

作者： Yan, Chunsheng Zheng, Yongjian Huang, Chuan Xue, Simeng Sun, Hongwei Wang, Xin Wei, Qinglai LiaoNing Electric Power Development Stock CO. LTD Shenyang China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China

ISBN: (数字)9781728182162

ISBN: (纸本)9781728182162

In this paper, the consensus control of linear multi-agent system in partial directed topology is discussed. The key innovation of this paper is that we propose a novel consensus control by using partial directed topology with the external stochastic disturbance. Since the noise inference is unavoidable in real life, the external disturbance produced by the Gauss white noise through Brown motion is taken into account when the states of the leader and followers are described. To settle this kind of problem, a distributed controller based on Riccati inequalities with an adaptive law is designed for leader-following multi-agent systems. We give proof through Lyapunov function and get the result which is the same as the theory. Finally, a simulation example is given to support the Theorems. © 2020 IEEE.

关键词： Multi agent systems

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The Quality Evaluation of Academic Papers in Carbon Capture, Utilization, and Storage

The Quality Evaluation of Academic Papers in Carbon Capture,...

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Machine Learning, control, and Robotics (MLCR), International Conference on

作者： Hangyue Zhang Jie Bai School of Computer Science and Technology Sun Yat-sen University Guangzhou P. R. China City State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing P. R. China

ISBN: (纸本)9781665454605

Carbon capture, utilization, and storage (CCUS) technology is a research hotspot worldwide owing to global climate change and warm gas control. CCUS papers from the last twenty years, derived from the Web of Science database, were analyzed to investigate the impact and citation of academic papers using machine learning methods. Firstly, nine author characteristics, keywords, and three other categories were extracted from the literature. Then, the natural logarithm distribution of the annual average number of citations was obtained, and five evaluation grades were decided based on the citations. Next, the keyword evolution of papers in the CCUS field is analyzed, and a model on the keyword evolution trend index is proposed. Data integration and imbalance processing were conducted after random sampling. A citation prediction model was proposed to evaluate the quality of CCUS academic papers and was verified on the recent five years' CCUS papers by comparing it with nine other mainstream machine learning models. The results indicate that the random forest model exhibits the best performance, with an accuracy rate reaching 88.7%; an ablation experiment was also conducted using the same algorithm. The results prove the effectiveness of our proposed method, shedding light on the quality of academic papers and technology development in CCUS.

关键词： Machine learning Climate change Random forests Modeling Energy storage

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Multi-View Spatio-Temporal Deep Graph Neural Network for Travel Demand Prediction

SSRN

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SSRN 2022年

作者： Zhao, Tianhong Huang, Zhengdong Tu, Wei Biljecki, Filip Chen, Long School of Architecture and Urban Planning Shenzhen University China Department of Architecture National University of Singapore Singapore Department of Real Estate National University of Singapore Singapore State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences China

Predicting travel demand is an essential task in urban mobility management. Previous methods use the spatio-temporal dependencies of travel demand to improve the prediction accuracy, but pay less attention to the cross-modal dependency among different transportation modes (bus, metro, taxi, etc.). We propose a multi-view spatio-temporal graph neural networks (MSTGNN) model to predict the travel demand. The development, bridging the gaps of the state of the art, emphasizes the ability to capture cross-view dependencies, derived from the interaction of multiple transportation modes. Specifically, a multi-view graph with the bus, metro, and taxi views is constructed according to spatial adjacency and transportation interaction adjacency. A multi-view attention temporal graph convolution module is designed to capture spatio-temporal and cross-view dependencies. Additionally, the cross-view features spatial consistency loss is presented to overcome the problem of uneven distribution of multi-view features. Taking the bus travel demand as an example, intensive experiments are conducted using real-world bus, metro, and taxi data in Shenzhen. The results indicate that the MAE of the MSTGNN model improves baseline methods by 9.13%. The contributions of different transportation modes and local/global cross-view dependencies are demonstrated in the ablation experiments. The attention mechanism in the MSTGNN model demonstrates certain interpretability of the interaction between different transportation modes. The dataset generated in this research has been released as open data. © 2022, The Authors. All rights reserved.

关键词： Smart cards

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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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Recent advancements in multimodal human-robot interaction

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FRONTIERS IN NEUROROBOTICS 2023年 17卷 1084000页

作者： Su, Hang Qi, Wen Chen, Jiahao Yang, Chenguang Sandoval, Juan Laribi, Med Amine Politecn Milan Dept Elect Informat & Bioengn Milan Italy South China Univ Technol Sch Future Technol Guangzhou Peoples R China Chinese Acad Sci Inst Automation State Key Lab Management & Control Complex Syst Beijing Peoples R China Univ West England Bristol Robot Lab Bristol England Univ Poitiers Pprime Inst Dept GMSC CNRSENSMA Poitiers France

Robotics have advanced significantly over the years, and human-robot interaction (HRI) is now playing an important role in delivering the best user experience, cutting down on laborious tasks, and raising public acceptance of robots. New HRI approaches are necessary to promote the evolution of robots, with a more natural and flexible interaction manner clearly the most crucial. As a newly emerging approach to HRI, multimodal HRI is a method for individuals to communicate with a robot using various modalities, including voice, image, text, eye movement, and touch, as well as bio-signals like EEG and ECG. It is a broad field closely related to cognitive science, ergonomics, multimedia technology, and virtual reality, with numerous applications springing up each year. However, little research has been done to summarize the current development and future trend of HRI. To this end, this paper systematically reviews the state of the art of multimodal HRI on its applications by summing up the latest research articles relevant to this field. Moreover, the research development in terms of the input signal and the output signal is also covered in this manuscript.

关键词： multi-modal signal processing multi-modal feedback multi-modal human-robot interaction physical human-robot interaction human-robot interaction

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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 ...

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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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Parameter estimation survey for multi-joint robot dynamic calibration case study

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Science China(Information Sciences) 2019年第10期62卷 102-116页

作者： Shaolin ZHANG Shuo WANG Fengshui JING Min TAN The State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of Sciences University of Chinese Academy of Sciences CAS Center for Excellence in Brain Science and Intelligence Technology

Accurate model parameters are the basis of robot dynamics. Many linear and nonlinear models have been proposed to calibrate the inertial parameters and friction parameters of multi-joint ***, methods of choosing a model and calculating its parameters still have few summaries. This paper reviews typical linear/nonlinear models and different calculation methods for robot dynamic *** simulations, the features of different methods are analyzed, including torque error, parameter error, model adaptability, solution time, and anti-interference ability of the calibration results. Finally, an experiment performed on a six-degree-of-freedom industrial manipulator is used as an example to illustrate how to select the model for a specified robot. These comparisons and experiments provide references for the parameter calibration of multi-joint robots.

关键词： dynamic parameter calibration friction calibration robot dynamics industrial manipulator dynamic models

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Data cleaning and restoring method for vehicle battery big data platform

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APPLIED ENERGY 2022年 320卷

作者： Li, Shuangqi He, Hongwen Zhao, Pengfei Cheng, Shuang Beijing Inst Technol Sch Mech Engn Natl Engn Lab Elect Vehicles Beijing 100081 Peoples R China Univ Bath Dept Elect & Elect Engn Bath England Chinese Acad Sci Inst Automation State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China

Battery is one of the most important and costly devices in electric vehicles (EVs). Developing an efficient battery management method is of great significance to enhancing vehicle safety and economy. Recently developed bigdata and cloud platform computing technologies bring a bright perspective for efficient utilization and protection of vehicle batteries. However, a reliable data transmission network and a high-quality cloud battery dataset are indispensable to enable this *** paper makes the first effort to systematically solve data quality problems in cloud-based vehicle battery monitoring and management by developing a novel integrated battery data cleaning framework. In the first stage, the outlier samples are detected by analyzing the temporal features in the battery data time series. The outlier data in the dataset can be accurately detected to avoid their impacts on battery monitoring and management. Then, the abnormal samples, including the noise polluted data and missing value, are restored by a novel future fusion data restoring model. The real electric bus operation data collected by a cloud-based battery monitoring and management platform are used to verify the performance of the developed data cleaning method. More than 93.3% of outlier samples can be detected, and the data restoring error can be limited to 2.11%, which validates the effectiveness of the developed methods. The proposed data cleaning method provides an effective data quality assessment tool in cloud-based vehicle battery management, which can further boost the practical application of the vehicle big data platform and Internet of vehicle.

关键词： Big data Internet of vehicle Electric vehicles Data cleaning Battery management system Battery state estimation

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Data-driven resilient predictive control under denial-of-service

arXiv

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arXiv 2021年

作者： Liu, Wenjie Sun, Jian Wang, Gang Bullo, Francesco Chen, Jie State Key Lab of Intelligent Control and Decision of Complex Systems The School of Automation Beijing Institute of Technology Beijing100081 China State Key Lab of Intelligent Control and Decision of Complex Systems The School of Automation Beijing Institute of Technology Beijing100081 China The Beijing Institute of Technology Chongqing Innovation Center Chongqing401120 China Mechanical Engineering Department The Center of Control Dynamical Systems and Computation UC Santa Barbara CA93106-5070 United States Department of Control Science and Engineering Tongji University Shanghai201804 China State Key Lab of Intelligent Control and Decision of Complex Systems The School of Automation Beijing Institute of Technology Beijing100081 China

The study of resilient control of linear time-invariant (LTI) systems against denial-of-service (DoS) attacks is gaining popularity in emerging cyber-physical applications. In previous works, explicit system models are required to design a predictor-based resilient controller. These models can be either given a priori or obtained through a prior system identification step. Recent research efforts have focused on data-driven control based on pre-collected input-output trajectories (i.e., without explicit system models). In this paper, we take an initial step toward data-driven stabilization of stochastic LTI systems under DoS attacks, and develop a resilient model predictive control (MPC) scheme driven purely by data-dependent conditions. The proposed data-driven control method achieves the same level of resilience as the model-based control method. For example, local input-to-state stability (ISS) is achieved under mild assumptions on the noise and the DoS attacks. To recover global ISS, two modifications are further suggested at the price of reduced resilience against DoS attacks or increased computational complexity. Finally, a numerical example is given to validate the effectiveness of the proposed control method. © 2021, CC BY.

关键词： Model predictive control

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