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Efficient Brain Decoding Based on Adaptive EEG Channel Selection and Transformation

IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE 2022年第6期6卷 1314-1323页

作者： Wang, Jiaxing Shi, Lei Wang, Weiqun Hou, Zeng-Guang Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Meituan Inc Beijing 100102 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China CAS Ctr Excellence Brain Sci & Intelligence Tech Beijing 100190 Peoples R China Macau Univ Sci & Technol Inst Syst Engn MUST CASIA Joint Lab Intelligence Sci & Technol Macau Peoples R China

Electroencephalography (EEG) based brain-computer interface (BCI) has a wide range of applications in neuro-rehabilitation and motor assistance. However, brain activities, acquired from a large number of EEG channels, are highly inter-correlated or irrelevant to the brain decoding task, thus reducing the decoding efficiency and accuracy. How to adaptively select the optimal channel number depend on different trials remains a big challenge. To solve this problem, an efficient end-to-end brain decoding model named AdaEEGNet, is proposed in this study. It can reduce the computational cost by adaptively controlling the number of input channels and improve the classification accuracy by reducing over-fitting. Specifically, a lightweight policy module is designed to analyze which channel is needed for decoding current EEG trial. Due to the channel selection process is indifferentiable, we propose to use the Gumbel-Estimator to back-propagate the gradient to train the whole framework. Additionally, a weight coefficient is designed to make a trade-off between brain decoding accuracy and efficiency. To validate the proposed AdaEEGNet feasibility in improving decoding efficiency and accuracy, extensive experiments were conducted on BCI competition IV dataset. The results show that our methods can improve the decoding accuracy by 2% with only 65% computational cost significantly compared with the baseline method.

关键词： Electroencephalography Decoding Brain modeling Computational modeling Optimized production technology Feature extraction Data models Channel selection channel transformation brain decoding computational cost classification accuracy

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Supervised assisted deep reinforcement learning for emergency voltage control of power systems

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NEUROCOMPUTING 2022年 475卷 69-79页

作者： Li, Xiaoshuang Wang, Xiao Zheng, Xinhu Dai, Yuxin Yu, Zhihong Zhang, Jun Jason Bu, Guangquan Wang, Fei-Yue 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 Qingdao Acad Intelligent Ind Parallel Intelligence Res Ctr Qingdao 266109 Peoples R China Univ Minnesota Dept Elect & Comp Engn Minneapolis MN 55455 USA Wuhan Univ Sch Elect Engn & Automat Wuhan 430072 Peoples R China China Elect Power Res Inst State Key Lab Power Grid Safety & Energy Conserva Beijing 100192 Peoples R China

The increasing complexity of power systems makes existing deep reinforcement learning-based emergency voltage control methods face challenges in learning speed and data utilization efficiency. Meanwhile, the accumulated data containing expert experience and domain knowledge has not been fully utilized to improve the performance of the deep reinforcement learning methods. To address the above issues, a novel hybrid emergency voltage control method that combines expert experience and machine intelligence is proposed in this paper. Specifically, the expert experience in the off-line demonstration is extracted through a behavioral cloning model and the deep reinforcement learning method is applied to discover and learn new knowledge autonomously. A special supervised expert loss is designed to utilize the pre-trained behavioral cloning model to assist the self-learning process. The demonstration is dynamically updated during the training process such that the behavioral cloning model and the deep reinforcement learning model can facilitate each other continuously. Experiments are conducted on the open-source RLGC platform to validate the performance and the experimental results show that the proposed method can effectively improve the learning speed and the applicability of the model to different test situations. (c) 2021 Elsevier B.V. All rights reserved.

关键词： Deep reinforcement learning Behavioral cloning Dynamic demonstration Emergency control

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An Ultra-Stretchable and Highly Sensitive Photoelectric Effect-Based Strain Sensor: Implementation and Applications

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IEEE SENSORS JOURNAL 2021年第4期21卷 4365-4376页

作者： Li, Zhengwei Cheng, Long Song, Qingkun 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 Harbin Univ Sci & Technol Sch Automat Harbin 150080 Peoples R China

High performance strain sensors have recently attracted considerable attention because of their applications in human motion detection, soft robotics, and human-machine interface. However, it remains some challenges in achieving satisfactory features (e.g., high sensitivity, high stretchable, linearity, and easy fabrication) for soft strain sensors. Inspired by the working principle of the eye's adaptation to different light luminance, this paper presents an ultra-stretchable, highly sensitive and high linear strain sensor based on the photoelectric effect. The sensor has a simple structure which is made of light-emitting diode (LED), photosensitive element, silicone rod, and sensor shell. When the silicone rod is stretched, the light's shielding area becomes larger, causing the increase of light luminous received by the photosensitive element. Hence, the sensitivity of the sensor can be tuned by changing luminance of LED and the amplification factor. Experiments demonstrate that the sensor has a high sensitivity (the maximum gauge factor was 1050), wide strain-sensing range (the max sensing range was up to 800%), high linearity range (from 100% to 300%), good repeatability (decreased by 5.48% over 5000 cycles at the wide tensile strain of 0 - 300%), low drift, and high signal-to-noise ratio (over 60dB). The proposed strain sensor has been applied in the bending detection of the wrist and finger, the recording of the human moving patterns, and the human's pulse counting. The sensor can be further fabricated into a data glove for detecting the human hand movement.

关键词： Capacitive sensors Optical sensors Sensitivity Robot sensing systems Strain Light emitting diodes Strain sensor soft and stretchable sensor photoelectric effect wrist pulse detection hand-motion monitoring

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A Rapid Spiking Neural Network Approach With an Application on Hand Gesture Recognition

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IEEE TRANSACTIONS ON COGNITIVE AND DEVELOPMENTAL systems 2021年第1期13卷 151-161页

作者： Cheng, Long Liu, Yang Hou, Zeng-Guang Tan, Min Du, Dajun Fei, Minrui Chinese Acad Sci Inst Automat State Key Lab Control & Management Complex Syst Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China Shanghai Univ Sch Mechatron Engn & Automat Shanghai 200072 Peoples R China

The spiking neural network (SNN) is considered to be the third generation of neural networks featured by its low power consumption and high computing capability, which has great application potential in robotics. However, the present SNN has two limitations: 1) the neuron's spike firing time is calculated based on the iterative approach, which dramatically slows down the calculation rate of the SNN and 2) the existing learning algorithm is more suitable for the single-layer structure, which can hardly train the network with "deep structure." To this end, this paper proposes a novel spike firing time search algorithm that can narrow the search interval. In addition, a pretrained subnet SNN is designed, which makes the SNN have more hidden layers. This setting of the SNN can effectively improve its performance in pattern recognition tasks. Furthermore, by using the surface electromyography signal (sEMG), the proposed SNN is used to recognize the hand gestures. The experimental results show that: 1) the spike firing time search algorithm can significantly increase the forward propagation rate of the SNN and 2) the proposed SNN can reach a satisfactory recognition accuracy ratio 97.4%, which is 0.9% higher than that of the fully connected SNN.

关键词： Forward propagation hand gesture recognition spiking neural networks (SNNs) surface electromyography

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Broad Learning System Based on Maximum Correntropy Criterion

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING systems 2021年第7期32卷 3083-3097页

作者： Zheng, Yunfei Chen, Badong Wang, Shiyuan Wang, Weiqun Xi An Jiao Tong Univ Inst Artificial Intelligence & Robot Xian 710049 Peoples R China Southwest Univ Coll Elect & Informat Engn Chongqing 400715 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China

As an effective and efficient discriminative learning method, broad learning system (BLS) has received increasing attention due to its outstanding performance in various regression and classification problems. However, the standard BLS is derived under the minimum mean square error (MMSE) criterion, which is, of course, not always a good choice due to its sensitivity to outliers. To enhance the robustness of BLS, we propose in this work to adopt the maximum correntropy criterion (MCC) to train the output weights, obtaining a correntropy-based BLS (C-BLS). Due to the inherent superiorities of MCC, the proposed C-BLS is expected to achieve excellent robustness to outliers while maintaining the original performance of the standard BLS in the Gaussian or noise-free environment. In addition, three alternative incremental learning algorithms, derived from a weighted regularized least-squares solution rather than pseudoinverse formula, for C-BLS are developed. With the incremental learning algorithms, the system can be updated quickly without the entire retraining process from the beginning when some new samples arrive or the network deems to be expanded. Experiments on various regression and classification data sets are reported to demonstrate the desirable performance of the new methods.

关键词： Learning systems Robustness Standards Optimization Training Perturbation methods Mean square error methods Broad learning system (BLS) incremental learning algorithms maximum correntropy criterion (MCC) regression and classification

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Visual fatigue assessment based on multi-task learning 31

Visual fatigue assessment based on multi-task learning

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31st Stereoscopic Displays and Applications Conference, SD and A 2020

作者： Wang, Danli Wang, Xueyu Song, Yaguang Xing, Qian Zheng, Nan State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China

In recent years, with the rapid development of stereoscopic display technology, its applications have become increasingly popular in many fields, and, meanwhile, the number of audiences is also growing. The problem of visual fatigue is becoming more and more prominent. Visual fatigue is mainly caused by vergence-accommodation conflicts. An evaluation experiment was conducted, and the electroencephalogram (EEG) data of the subjects were collected when they were watching stereoscopic content, and then the stereoscopic fatigue state of the subjects during the viewing process was analyzed. As deep learning is proved to be an effective end-to-end learning method and multi-task learning can alleviate the problem of lacking annotated data, the authors establish a user visual fatigue assessment model based on EEG by using multi-task learning, which can effectively obtain the user's visual fatigue status, so as to make the comfort designs to avoid the harm caused by user's visual fatigue. © Society for Imaging Science and Technology 2019

关键词： Electroencephalography

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Optimal Feedback control of Pedestrian Flow in Heterogeneous Corridors

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IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING 2021年第3期18卷 1097-1108页

作者： Zhu, Yuanheng Zhao, Dongbin He, Haibo 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 Univ Rhode Isl Dept Elect Comp & Biomed Engn Kingston RI 02881 USA

Maintaining the orderliness and efficiency of pedestrian flow through an architectural area is critical for the evacuation process. Especially, clogs and jams are easily triggered in width-changing areas. In this article, we consider pedestrian movement in heterogeneous corridors and design an optimal feedback control to regulate pedestrian flow. Flow characteristics are first studied based on microscopic social-force simulations. A Gaussian process describes the relationship between flow variables with the observation data. The macroscopic model for flow in heterogeneous corridors is developed. To avoid jams, discharges among these corridors are balanced with the narrowest corridor as the primary concern. At the equilibrium, a continuous-time nonlinear control system is formulated, and the adaptive dynamic programming learns the optimal feedback controller. Policy iteration (PI) and neural networks are combined together, and the convergence of neural-network-based PI is demonstrated by analyzing its equivalence to the Gauss-Newton method. Batch normalization is introduced to stabilize the learning process. Simulated experiments demonstrate that the control design can effectively regulate pedestrian flow for both macroscopic and microscopic models. Note to Practitioners-The development of video-processing techniques provides a powerful tool to detect human behavior in real time. In crowd events, the pedestrian movement must be regulated;otherwise, it is easy to fall into the faster-is-slower effect. It is especially important for evacuation routes with different widths. In this article, the optimal feedback control is studied to regulate pedestrian flow in heterogeneous corridors. It takes flow densities as state and produces commands that are composed of entrance influx and free-flow velocities. These commands can be executed with the support of speakers, displays, or the recently developed interactive robots. To avoid congestion, discharges of different corridors

关键词： Microscopy Feedback control Mathematical model Data models Dynamic programming Psychology Computational modeling Adaptive dynamic programming (ADP) heterogeneous corridors macroscopic pedestrian dynamics optimal feedback control pedestrian flow

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Hydrodynamic Modeling and Parameter Identification of a Bionic Underwater Vehicle:RobDact

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Cyborg and Bionic systems 2022年第1期2022卷 164-176页

作者： Qiyuan Cao Rui Wang Tiandong Zhang Yu Wang Shuo Wang State Key Laboratory of Management and Control for Complex Systems Institute of A utomationChinese Academy of SciencesBeijingChina School of Artific ial Itelligence University of Chinese Academy of Sciences BeijingChina Center for Excellence in Brain Science and Itelligence Technology Chinese Academy of SciencesShanghaiChina

In this paper,the hydrodynamic modeling and parameter identifcation of the RobDact,a bionic underwater vehide inspired byDactylopteridae,are carried out based on computational fluid dynamics(CFD)and force measurement ***,thePaper briely describes the RobDact,then establishes the kinematis model and rigid body dynamics model of the RobDactaccording to the hydrodynamic force and moment *** CFD simulations,the hydrodynamic force of theRobDact at diferent speeds is obtained,and then,the hydrodynamic model parameters are ***,themeasurement platform is developed to obtain the relationship between the thrust generated by the RobDact and the inputfluctuation parameters,Finally,combining the rigid body dynamics model and the fin'thrust mapping model,thehydrodynamic model of the RobDact at diferent motion states is constructed.

关键词： dynamics(CFD) equations hydrodynamic

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Social Radars for Social Vision of Intelligent Vehicles: A New Direction for Vehicle Research and Development

IEEE TRANSACTIONS ON INTELLIGENT VEHICLES

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IEEE TRANSACTIONS ON INTELLIGENT VEHICLES 2024年第3期9卷 4244-4248页

作者： Fan, Lili Wang, Xingxia Yang, Jing Liu, Yuhang Lv, Chen Yu, Hui Ma, Jiaqi Wang, Fei-Yue Beijing Inst Technol Sch Informat & Elect Beijing 100081 Peoples R China Jiangsu Ind Innovat Ctr Intelligent Equipment Co L Beijing 100081 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Multimodal Artificial Intelligence S Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China Nanyang Technol Univ Sch Mech & Aerosp Engn Nanyang 639798 Singapore Univ Portsmouth Sch Creat Technol Portsmouth 639798 England Univ Calif Los Angeles Dept Civil & Environm Engn Los Angeles CA 90024 USA Chinese Acad Sci State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Macau Univ Sci & Technol Fac Innovat Engn Taipa 999078 Macau Peoples R China

The low-altitude economy is playing a crucial role in promoting economic development, strengthening social security, and serving international security, thus becoming an increasingly vital engine for global development. As an essential technological backbone and application carrier of the low-altitude economy, intelligent vehicles are not only active on land but also increasingly needed to actively participate in the air and water. The integration of social radars and social vision will enable intelligent vehicles to perceive complex scenarios and task demands from a human perspective, providing more efficient and safer services for the low-altitude economy. This presents an exciting prospect for the application of social radars and social vision, as well as their integration with intelligent vehicles for envisaged service scenarios in the low-altitude economy.

关键词： Intelligent vehicles Radar Social networking (online) Psychology Fans Real-time systems Radar imaging Social radars social vision intelligent vehicles low-altitude economic

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Benchmarking Lane-changing Decision-making for Deep Reinforcement Learning 21

Benchmarking Lane-changing Decision-making for Deep Reinforc...

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Proceedings of the 7th International Conference on Robotics and Artificial Intelligence

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

ISBN: (纸本)9781450385855

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 task1 .

关键词： Autonomous driving Reinforcement learning Lane change scenarios

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