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Transfer Learning in Motor Imagery Brain Computer Interface: A Review

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Journal of Shanghai Jiaotong university(Science) 2024年第1期29卷 37-59页

作者：李明爱许冬芹 Faculty of Information technology Beijing University of TechnologyBeijing 100124China Beijing Key Laboratory of Computational intelligence and Intelligent System Beijing 100124China Engineering Research center of digital Community Ministry of EducationBeijing 100124China

Transfer learning,as a new machine learning methodology,may solve problems in related but different domains by using existing knowledge,and it is often applied to transfer training data from another domain for model training in the case of insuficient training *** recent years,an increasing number of researchers who engage in brain-computer interface(BCI),have focused on using transfer learning to make most of the available electroencephalogram data from different subjects,effectively reducing the cost of expensive data acquisition and labeling as well as greatly improving the learning performance of the *** paper surveys the development of transfer learning and reviews the transfer learning approaches in *** addition,according to the"what to transfer"question in transfer learning,this review is organized into three contexts:instance-based transfer learning,parameter-based transfer learning,and feature-based transfer ***,the current transfer learning applications in BCI research are summarized in terms of the transfer learning methods,datasets,evaluation performance,*** the end of the paper,the questions to be solved in future research are put forward,laying the foundation for the popularization and in-depth research of transfer learning in BCI.

关键词： transfer learning brain-computer interface(BCI) electroencephalogram machine learning

Dynamic motion of quadrupedal robots on challenging terrain:a kinodynamic optimization approach

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Frontiers of Mechanical Engineering 2024年第3期19卷 23-40页

作者： Qi LI Lei DING Xin LUO Engineering Research center of Hubei Province for Clothing Information Wuhan 430200China School of Computer Science and Artificial intelligence Wuhan Textile UniversityWuhan 430200China State Key Laboratory of digital Manufacturing Equipment and technology Huazhong University of Science and TechnologyWuhan 430074China

The dynamic motion of quadrupedal robots on challenging terrain generally requires elaborate spatial–temporal kinodynamic motion planning and accurate control at higher refresh rate in comparison with regular ***,conventional quadrupedal robots usually generate relatively coarse planning and employ motion replanning or reactive strategies to handle terrain *** resultant complex and computation-intensive controller may lead to nonoptimal motions or the breaking of locomotion *** this paper,a kinodynamic optimization approach is *** generate long-horizon optimal predictions of the kinematic and dynamic behavior of the quadruped robot on challenging terrain,we formulate motion planning as an optimization problem;jointly treat the foot’s locations,contact forces,and torso motions as decision variables;combine smooth motion and minimal energy consumption as the objective function;and explicitly represent feasible foothold region and friction constraints based on terrain *** track the generated motions accurately and stably,we employ a whole-body controller to compute reference position and velocity commands,which are fed forward to joint controllers of the robot’s *** verify the effectiveness of the developed approach through simulation and on a physical quadruped robot *** show that the quadruped robot can successfully traverse a 30°slope and 43% of nominal leg length high step while maintaining the rhythm of dynamic trot gait.

关键词： quadrupedal robot kinodynamic planning nonlinear optimization challenging terrain whole-body control

Understanding adversarial attacks on observations in deep reinforcement learning

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Science China(Information Sciences) 2024年第5期67卷 69-83页

作者： You QIAOBEN Chengyang YING Xinning ZHOU Hang SU Jun ZHU Bo ZHANG Department of Computer Science and technology Beijing National Research Center for Information Science and Technology Tsinghua-Bosch Joint Center for Machine Learning Institute for Artificial Intelligence Tsinghua University Peng Cheng Laboratory

Deep reinforcement learning models are vulnerable to adversarial attacks that can decrease the cumulative expected reward of a victim by manipulating its observations. Despite the efficiency of previous optimization-based methods for generating adversarial noise in supervised learning, such methods might not achieve the lowest cumulative reward since they do not generally explore the environmental ***, a framework is provided to better understand the existing methods by reformulating the problem of adversarial attacks on reinforcement learning in the function space. The reformulation approach adopted herein generates an optimal adversary in the function space of targeted attacks, repelling them via a generic two-stage framework. In the first stage, a deceptive policy is trained by hacking the environment and discovering a set of trajectories routing to the lowest reward or the worst-case performance. Next, the adversary misleads the victim to imitate the deceptive policy by perturbing the observations. Compared to existing approaches, it is theoretically shown that our adversary is strong under an appropriate noise level. Extensive experiments demonstrate the superiority of the proposed method in terms of efficiency and effectiveness, achieving state-of-the-art performance in both Atari and MuJoCo environments.

关键词： deep learning reinforcement learning adversarial robustness adversarial attack

Weakly Supervised Network with Scribble-Supervised and Edge-Mask for Road Extraction from High-Resolution Remote Sensing Images

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Computers, Materials & Continua 2024年第4期79卷 549-562页

作者： Supeng Yu Fen Huang Chengcheng Fan College of Artificial intelligence Nanjing Agricultural UniversityNanjing210095China Innovation Academy for Microsatellites of CAS Shanghai201210China Shanghai Engineering center for Microsatellites Shanghai201210China Key Laboratory of Satellite digital technology Shanghai201210China

Significant advancements have been achieved in road surface extraction based on high-resolution remote sensingimage processing. Most current methods rely on fully supervised learning, which necessitates enormous humaneffort to label the image. Within this field, other research endeavors utilize weakly supervised methods. Theseapproaches aim to reduce the expenses associated with annotation by leveraging sparsely annotated data, such asscribbles. This paper presents a novel technique called a weakly supervised network using scribble-supervised andedge-mask (WSSE-net). This network is a three-branch network architecture, whereby each branch is equippedwith a distinct decoder module dedicated to road extraction tasks. One of the branches is dedicated to generatingedge masks using edge detection algorithms and optimizing road edge details. The other two branches supervise themodel’s training by employing scribble labels and spreading scribble information throughout the image. To addressthe historical flaw that created pseudo-labels that are not updated with network training, we use mixup to blendprediction results dynamically and continually update new pseudo-labels to steer network training. Our solutiondemonstrates efficient operation by simultaneously considering both edge-mask aid and dynamic pseudo-labelsupport. The studies are conducted on three separate road datasets, which consist primarily of high-resolutionremote-sensing satellite photos and drone images. The experimental findings suggest that our methodologyperforms better than advanced scribble-supervised approaches and specific traditional fully supervised methods.

关键词： Semantic segmentation road extraction weakly supervised learning scribble supervision remote sensing image

Joint Sample Position Based Noise Filtering and Mean Shift Clustering for Imbalanced Classification Learning

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Tsinghua Science and technology 2024年第1期29卷 216-231页

作者： Lilong Duan Wei Xue Jun Huang Xiao Zheng School of Computer Science and technology Anhui University of TechnologyMaanshan 243032China Institute of Artificial intelligence Hefei Comprehensive National Science CenterHefei 230088China

The problem of imbalanced data classification learning has received much *** classification algorithms are susceptible to data skew to favor majority samples and ignore minority *** weighted minority oversampling technique(MWMOTE)is an effective approach to solve this problem,however,it may suffer from the shortcomings of inadequate noise filtering and synthesizing the same samples as the original minority *** this end,we propose an improved MWMOTE method named joint sample position based noise filtering and mean shift clustering(SPMSC)to solve these ***,in order to effectively eliminate the effect of noisy samples,SPMsC uses a new noise filtering mechanism to determine whether a minority sample is noisy or not based on its position and distribution relative to the majority *** that MWMOTE may generate duplicate samples,we then employ the mean shift algorithm to cluster minority samples to reduce synthetic replicate ***,data cleaning is performed on the processed data to further eliminate class *** on extensive benchmark datasets demonstrate the effectiveness of SPMsC compared with other sampling methods.

关键词： imbalanced data classification oversampling noise filtering clustering

Sparse convolutional model with semantic expression for waste electrical appliances recognition

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Science China Technological Sciences 2024年第9期67卷 2881-2893页

作者： HAN HongGui LIU YiMing LI FangYu DU YongPing Faculty of Information technology Beijing University of Technology Beijing Key Laboratory of Computational intelligence and Intelligent System Engineering Research center of digital Community Ministry of Education Beijing Artificial intelligence Institute and Beijing Laboratory for Intelligent Environmental Protection

Deep neural networks play an important role in the recognition of waste electrical appliances. However, deep neural network components still lack reliability in decision-making features. To address this problem, a sparse convolutional model with semantic expression(SCMSE) is proposed. First, a low-rank sparse semantic expression component, combining the benefits of residual networks and sparse representation, is adapted to enhance sparse feature extraction and semantic expression. Second, a reliable network architecture is obtained by iterating the optimal sparse solution, enhancing semantic expression. Finally, the results of visualization experiments on the waste electrical appliances dataset demonstrate that the proposed SCMSE can obtain excellent semantic performance.

关键词： sparse convolutional model deep neural network semantic expression visualization computer vision

Security and Privacy in Solar Insecticidal Lamps Internet of Things:Requirements and Challenges

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IEEE/CAA Journal of Automatica Sinica 2024年第1期11卷 58-73页

作者： Qingsong Zhao Lei Shu Kailiang Li Mohamed Amine Ferrag Ximeng Liu Yanbin Li the College of Artificial intelligence Nanjing Agricultural UniversityNanjing 210031 the State Key Laboratory for Novel Software technology Nanjing UniversityNanjing 210023 the State Key Laboratory of Cryptology Beijing 100878China IEEE the School of Engineering University of LincolnLincoln LN67TSUK the Artificial intelligence and digital Science Research center Technology Innovation InstituteMasdar City 9639UAE the College of Computer and Data Science Fuzhou UniversityFuzhou 350108China

Solar insecticidal lamps(SIL) can effectively control pests and reduce the use of pesticides. Combining SIL and Internet of Things(IoT) has formed a new type of agricultural IoT,known as SIL-IoT, which can improve the effectiveness of migratory phototropic pest control. However, since the SIL is connected to the Internet, it is vulnerable to various security *** issues can lead to serious consequences, such as tampering with the parameters of SIL, illegally starting and stopping SIL,etc. In this paper, we describe the overall security requirements of SIL-IoT and present an extensive survey of security and privacy solutions for SIL-IoT. We investigate the background and logical architecture of SIL-IoT, discuss SIL-IoT security scenarios, and analyze potential attacks. Starting from the security requirements of SIL-IoT we divide them into six categories, namely privacy, authentication, confidentiality, access control, availability,and integrity. Next, we describe the SIL-IoT privacy and security solutions, as well as the blockchain-based solutions. Based on the current survey, we finally discuss the challenges and future research directions of SIL-IoT.

关键词： Challenges Internet of Things(IoT) privacy and security security requirements solar insecticidal lamps(SIL)

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Vector field path following for a micro flapping-wing robot

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Science China(Information Sciences) 2024年第6期67卷 442-444页

作者： Haifeng HUANG Yingte LIU Tao NIU Chang WANG Yao ZOU Wei HE School of intelligence Science and technology University of Science and Technology Beijing Institute of Artificial intelligence University of Science and Technology Beijing Key Laboratory of Intelligent Bionic Unmanned Systems Ministry of EducationUniversity of Science and Technology Beijing

Flapping-wing robots mimic the flight patterns of natural flying creatures,granting them natural *** hold promising applications in the military reconnaissance,urban surveillance,and various other fields[1–3].However,the current limitations in size often restrict the suitability of existing bird-like flapping-wing robots,

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Ensemble successor representations for task generalization in offline-to-online reinforcement learning

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Science China(Information Sciences) 2024年第7期67卷 240-255页

作者： Changhong WANG Xudong YU Chenjia BAI Qiaosheng ZHANG Zhen WANG Space Control and Inertial technology Research center Harbin Institute of Technology Shanghai Artificial intelligence Laboratory Shenzhen Research Institute of Northwestern Polytechnical University School of Cybersecurity Northwestern Polytechnical University

In reinforcement learning(RL), training a policy from scratch with online experiences can be inefficient because of the difficulties in exploration. Recently, offline RL provides a promising solution by giving an initialized offline policy, which can be refined through online interactions. However, existing approaches primarily perform offline and online learning in the same task, without considering the task generalization problem in offline-to-online adaptation. In real-world applications, it is common that we only have an offline dataset from a specific task while aiming for fast online-adaptation for several tasks. To address this problem, our work builds upon the investigation of successor representations for task generalization in online RL and extends the framework to incorporate offline-to-online learning. We demonstrate that the conventional paradigm using successor features cannot effectively utilize offline data and improve the performance for the new task by online fine-tuning. To mitigate this, we introduce a novel methodology that leverages offline data to acquire an ensemble of successor representations and subsequently constructs ensemble Q functions. This approach enables robust representation learning from datasets with different coverage and facilitates fast adaption of Q functions towards new tasks during the online fine-tuning *** empirical evaluations provide compelling evidence showcasing the superior performance of our method in generalizing to diverse or even unseen tasks.

关键词： offline reinforcement learning online fine-tuning task generalization successor representations ensembles

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