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2021 China Automation Congress, CAC 2021

作者： Zhang, Tian Zhang, XiangYin Shuang, Xia Faculty of Information Technology Beijing University of Technology Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing100124 China 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 Beijing100124 China

ISBN: (纸本)9781665426473

In the real world, neighboring individuals with communication links determine the state and the weight of different individuals in the group. There are closer relations between the status of neighboring individuals in a collective system. To imitate this situation, a strategic model of collective system coordination and control was proposed by imitating the decision-making mechanism of pigeon flocks. Each individual changes its situation based on the weighting and distance to the neighboring individuals in the collective system. This paper mainly studies the problem of formation control inspired by the decision-making mechanism of pigeon flocks under obstacle environments. By designing the control law of fixed relative position and speed consistency and integrating it with the decision-making mechanism of pigeon flocks, formation control of the unmanned aerial vehicle (UAV) swarm is realized. A judging mechanism is proposed to determine whether it is necessary for UAVs to avoid obstacles. The simulation results show that the UAVs can generate, maintain and reconstruct the desired formation in a real-time manner under consideration of avoiding obstacles. © 2021 IEEE

关键词： Unmanned aerial vehicles (UAV)

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GACL-Net:Hybrid Deep Learning Framework for Accurate Motor Imagery Classification in Stroke Rehabilitation

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Computers, Materials & Continua 2025年第4期83卷 517-536页

作者： Chayut Bunterngchit Laith H.Baniata Mohammad H.Baniata Ashraf ALDabbas Mohannad A.Khair Thanaphon Chearanai Sangwoo Kang State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of AutomationChinese Academy of SciencesBeijing100190China School of Computing Gachon UniversitySeongnam13120Republic of Korea Computer Science Department Faculty of Information TechnologyThe World Islamic Sciences and Education UniversityAmman11947Jordan Intelligent Systems Department Faculty of Artificial IntelligenceAl-Balqa Applied UniversityAl-Salt19117Jordan IT Infrastructure Department Qatrana Cement CompanyAmman11821Jordan Division of Industrial and Logistics Engineering Technology Faculty of Engineering and TechnologyKing Mongkut’s University of Technology North BangkokRayong CampusRayong21120Thailand

Stroke is a leading cause of death and disability worldwide,significantly impairing motor and cognitive *** rehabilitation is often hindered by the heterogeneity of stroke lesions,variability in recovery patterns,and the complexity of electroencephalography(EEG)signals,which are often contaminated by *** classification of motor imagery(MI)tasks,involving the mental simulation of movements,is crucial for assessing rehabilitation strategies but is challenged by overlapping neural signatures and patient-specific *** address these challenges,this study introduces a graph-attentive convolutional long short-term memory(LSTM)network(GACL-Net),a novel hybrid deep learning model designed to improve MI classification accuracy and ***-Net incorporates multi-scale convolutional blocks for spatial feature extraction,attention fusion layers for adaptive feature prioritization,graph convolutional layers to model inter-channel dependencies,and bidi-rectional LSTM layers with attention to capture temporal *** on an open-source EEG dataset of 50 acute stroke patients performing left and right MI tasks,GACL-Net achieved 99.52%classification accuracy and 97.43%generalization accuracy under leave-one-subject-out cross-validation,outperforming existing state-of-the-art ***,its real-time processing capability,with prediction times of 33–56 ms on a T4 GPU,underscores its clinical potential for real-time neurofeedback and adaptive *** findings highlight the model’s potential for clinical applications in assessing rehabilitation effectiveness and optimizing therapy plans through precise MI classification.

关键词： Motor imagery EEG stroke rehabilitation deep learning brain-computer interface

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Security and privacy with opacity-based state observation for finite state machine

Security and privacy with opacity-based state observation fo...

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作者： Zhang, Zhipeng Xia, Chengyi Chen, Shengyong Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology Tianjin University of Technology Tianjin China Engineering Research Center of Learning-Based Intelligent System Ministry of Education Tianjin University of Technology Tianjin China

Topics on security and privacy are very fundamental and important in the Internet of Things (IoT). In this paper, we explore the impact of state observation on the opacity of IoT, which is modeled by Moore-type finite state machine. Firstly, to characterize the intrusion estimator under the event-state observation, a multi-linear algebraic expression is proposed through the Boolean semi-tensor product (STP), and on the basis of the novel representation, the validation criterion is given by some matrix manipulations. Subsequently, considering that the input information may not be measured due to the physical shielding of the system against external intruders, we continue to discuss the opacity with state observation by a permutation structure matrix. Meanwhile, the verification of opacity with event-state observation is generalized to the one with only state observation. Finally, we offer an example to illustrate the effectiveness of the newly proposed method. Taken together, the current results are helpful to provide a novel theoretical approach for investigating the privacy-preserving problem in the area of IoT and complex cyber-physical networks. © 2021 Chinese Automatic Control Society and John Wiley & Sons Australia, Ltd.

关键词： Internet of things

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Multi-objective Order Dispatching for Electronic Products Recycling Process

Multi-objective Order Dispatching for Electronic Products Re...

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第33届中国过程控制会议

作者： Mei Yang Shengli Du Honggui Han Junfei Qiao School of Artificial Intelligence and Automation Beijing University of Technology Beijing Key Laboratory of Computational Intelligence and Intelligent System

This paper is concerned with the online order dispatching problem(OODP) of the recycling process of electronic products,whose aim is to allocate orders to vehicles properly while satisfying several practical *** this paper,the customer satisfaction degree is first proposed as an objective of OODP,and then the considered OODP becomes a multi-objective OODP by optimizing the system cost and customer satisfaction degree *** taking the customer satisfaction into the objective design,a new model for the OODP of electronic products is *** order to tackle with the OODP with multiple optimization objectives,the Non-dominated sorting genetic algorithm Ⅱ(NSGA-Ⅱ) is *** experiment executed in this paper verifies the validity of multi-objective model in this paper and efficiency of the used algorithm NSGA-Ⅱ.

关键词： Electronic products recycling Order dispatching problem Multi-objective optimizing NSGA-Ⅱ

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Deep User and Item Inter-matching Network for CTR Prediction 28th

Deep User and Item Inter-matching Network for CTR Predicti...

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28th International Conference on Database systems for Advanced Applications, DASFAA 2023

作者： Yuan, Zhiyang Xiao, Yingyuan Yang, Peilin Hao, Qingbo Wang, Hongya Engineering Research Center of Learning-Based Intelligent System Ministry of Education Tianjin University of Technology Tianjin300384 China Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology Tianjin University of Technology Tianjin300384 China College of Computer Science and Engineering Donghua University Shanghai China

ISBN: (纸本)9783031306716

CTR prediction plays an important role in increasing company revenue and user experience, and many efforts start with historical behavior to uncover user interest. There are two main problems with previous works: (1) When most previous works mined interests from users’ historical behaviors, they only focus on implicit or explicit interests. (2) When most previous works mined user interests through the relationship between target users and similar users, the representation of target users was not rich and accurate enough, resulting in less accurate item recommendations. Therefore the Deep User and Item Inter-Matching Network (DUIIN) is proposed in this paper to solve the above problems. First, we design Item-to-Item Network (IIN), using two different sub-networks Evolving Interest Network (EIN) and Feature Interaction Network (FIN) to mine the hidden interests and explicit interests shown by users’ historical behaviors, respectively. Then the User-to-User Network (UUN) is designed to mine user interests through the relationship between target users and similar users after representing the target users more accurately and richly. The experimental results show that the DUIIN model proposed in this paper performs better than other state-of-the-art models. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Forecasting

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End-to-end Visual Navigation with Intrinsic Motivation in 3D Maze-like Environments

End-to-end Visual Navigation with Intrinsic Motivation in 3D...

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2021 China Automation Congress, CAC 2021

作者： Li, Peng Ruan, Xiao-Gang Zhu, Xiao-Qing Faculty of Information Technology Beijing University of Technology Beijing100124 China Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing100124 China

ISBN: (纸本)9781665426473

Learning to navigate in visually rich environment with sparse reward is a long-standing problem in developing AI agent. In this paper, inspired by spontaneous exploration behaviour in animals, we proposed an end-to-end visual navigation method which use intrinsic reward to motivate explore-exploit strategy. Firstly, in order to directly obtain control policy from pixel-level input and implement end-to-end training, we take deep reinforcement learning as basic navigation framework. Secondly, we allow the agent to create reward for itself, and the intrinsic reward is related to episode memory and include two parts: (1) the frequency of agent reach a state is recorded, and these counts are then used to compute bonus according to the classic count-based method;(2) we also take temporal distance as a basis for distributing bonus, and the magnitude of it is determined by environment steps between current observation and those in memory. Finally, such intrinsic reward is summed up with the real task reward, thus making agent is able to use the combined reward to learn control policy. We test our approach in 3D maze-like environment in DMlab, and validate its navigation performance through different reward scenarios. The experiment results show our approach can get effective goal-driven behaviour from raw sensory input and outperform the baselines across all tasks. Furthermore, the agent equipped with our intrinsic motivation appear to explore faster in no reward environment. © 2021 IEEE

关键词： Reinforcement learning

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Monitoring of Wastewater Treatment Process Based on Slow Feature Analysis Variational Autoencoder 10

Monitoring of Wastewater Treatment Process Based on Slow Fea...

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10th IEEE Data Driven Control and Learning systems Conference, DDCLS 2021

作者： Wang, Kai Chang, Peng Meng, Fanchao Faculty of Information Technology Beijing University of Technology Beijing100124 China Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing100124 China

ISBN: (纸本)9781665424233

The wastewater treatment process (WWTP) is a complex nonlinear, uncertain and dynamic physical and biochemical reaction process. The non-linearity, uncertainty and dynamicity of the WWTP increase the difficulty of extracting data features, and also make it difficult to monitor the faults in this process. Aiming at the problems of non-linearity, uncertainty and dynamicity, a slow feature variational autoencoder (SFAVAE) process monitoring model is proposed. With the dynamicity of WWTP data taken into account, the slow feature analysis algorithm (SFA) is used to extract the slowly changing dynamic features of wastewater data. The variational autoencoder can impose Gaussian distribution restrictions on its hidden layer features, so that it can simultaneously learn nonlinear and certain features that obey the Gaussian distribution to deal with the nonlinearity and uncertainty of data. Finally, the hidden layer space of the variational autoencoder model is used to construct hidden layer feature statistics Z2 to realize process monitoring. Compared with the principal component analysis (PCA), independent component analysis (ICA), kernel principal analysis (KPCA) and variational auto-encoder (VAE) models, the experimental results of the benchmark simulation model 1 (BSM1) model show that the SFAVAE model has higher effectiveness in process monitoring. © 2021 IEEE.

关键词： Process monitoring

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The Batch Process Fault Monitoring Using Adversarial Auto-encoder and K-Nearest Neighbor Rule 10

The Batch Process Fault Monitoring Using Adversarial Auto-en...

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10th IEEE Data Driven Control and Learning systems Conference, DDCLS 2021

作者： Li, Zeyu Chang, Peng Wang, Kai Wang, Pu Beijing University of Technology Faculty of Information Technology Beijing100124 China Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing100124 China

ISBN: (纸本)9781665424233

In the industrial batch process monitoring domain, the conventional multivariate monitoring methods may not always function well in monitoring faults that have both Non-Linear and Non-Gaussian properties. To enhance the monitoring capability, the adversarial auto-encoder (AAE) was introduced to increase the sensitivity to Non-Gaussian anomalies by projecting non-Gaussian information into a given Gaussian distribution feature space. At the same time, low-dimensional feature space can avoid the problem of 'Concentration of measure' and improve the ability to distinguish minor small abnormalities. Therefore, A novel statistic index was constructed in the feature space based on the k-nearest neighbor rule (KNN) to improve the ability of minor fault monitoring. The proposed model is compared with the traditional multivariate statistical process monitoring methods in numerical examples and penicillin fermentation platform, which proves that it has better monitoring ability for minor magnitude and non-Gaussian faults. © 2021 IEEE.

关键词： Gaussian distribution

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Feature selection method based on maximum information coefficient and its application in wastewater treatment processes

Feature selection method based on maximum information coeffi...

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2021 China Automation Congress, CAC 2021

作者： Wang, Congcong Chai, Wei Faculty of Information Technology Beijing University of Technology Beijing100124 China Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing100124 China

ISBN: (纸本)9781665426473

High-dimensional data are widely present in machine learning tasks, and various dimensionality reduction methods have emerged to improve model training speed and avoid dimensionality catastrophe. Filter-based feature selection methods are favored for their fast processing speed and lack of dependence on model structure. A filtered feature selection method that combines the maximum information coefficient (MIC) with the feature selection framework of redundancy coefficient gradual up (RCGU) is proposed, and the attention mechanism is introduced into it. The method is verified to be effective in eliminating irrelevant variables and reducing redundant variables through Friedman numerical experiment. Finally, the proposed method is successfully applied to the selection of auxiliary variables for the soft measurement model used in a wastewater treatment plant, and the variables selected by this method improve prediction performance of the model in comparison with other feature selection methods. © 2021 IEEE

关键词： Redundancy

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TransCoop: Cooperation of Transformers and CNNs for Camouflaged Object Segmentation

TransCoop: Cooperation of Transformers and CNNs for Camoufla...

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2022 IEEE International Conference on Multimedia and Expo, ICME 2022

作者： Wu, Fucai Li, Xuanya Zhang, Yuan Hu, Kai Xiangtan University Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education Xiangtan411105 China Baidu Inc. Beijing100085 China Xiangnan University Key Laboratory of Medical Imaging and Artifical Intelligence of Hunan Province Chenzhou423000 China

ISBN: (数字)9781665485630

ISBN: (纸本)9781665485630

Camouflaged object segmentation (COS) is a challenging task due to the existence of high intrinsic similarities between the object and background. To overcome this challenge, we pro-pose a new framework, called TransCoop, for COS through the cooperation of Transformers and convolutional neural net-works (CNNs). Specifically, Transformer is used to model global context and structural information for accurately positioning potential target objects. Meanwhile, a well-designed texture feature fusion module (TFFM) is used to fuse the features encoded by CNN with the shallow Transformer to fully mine the low-level features in the scene. Furthermore, we propose a noise removal module (NRM), which can eliminate the background noise of low-level features with the guidance of precise target location. Notably, our TFFM and NRM can effectively realize the interaction between Transformer and CNN features. Extensive experiments on four benchmark datasets demonstrate the superiority of our TransCoop against existing state-of-the-art methods. © 2022 IEEE.

关键词： Convolutional neural networks

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