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17th International Conference on Brain Informatics, BI 2024

作者： Zuo, Guoyu Xiao, Erjun Zhang, Tielin Beijing Key Laboratory of Computing Intelligence and Intelligent Systems Beijing University of Technology Beijing100124 China Center for Excellence in Brain Science and Intelligence Technology University of Chinese Academy of Sciences Shanghai200031 China

ISBN: (纸本)9789819632930

Accurate detection of mental state is vital for sleep assessment and anesthesia monitoring. However, challenges exist, including 1) limited data sources caused by few modalities, few channels, biased sample distribution, or large individual differences;2) multiscale dynamic feature of mental state signals, containing complex and dynamic fluctuations. In this paper, we proposed a multiscale temporal convolution and attention improved neural network (MTA-NN) to handle these two challenges, including: a preprocessing method using a clustering-first and label-generation approach for data augmentation involves filtering the production samples to mitigate the impact of noise on model performance;multiscale temporal-convolution module for detecting different scales of temporal and frequency features;an attention module for decoding key features in different spatial and temporal channels, and for setting the importance of historical dependencies;a full-connection layer is designed to recognize different sleep stage. The proposed MTA-NN is then verified in three typical sleep-state recognition tasks, including SleepEDF-20, SleepEDF-78, and ISRUC3. Compared to some state-of-the-art algorithms, the proposed MTA-NN achieves higher performance. Further ablation experiments show the necessary and importance of these designed sub modules in MTA-NN. We consider the same algorithm can also be extended to further similar mental state recognition tasks such as anesthesia monitoring. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Anesthesiology

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An online self-organizing radial basis function neural network based on Gaussian Membership

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Applied intelligence 2025年第6期55卷 1-17页

作者： Jia, Lijie Li, Wenjing Qiao, Junfei Zhang, Xinliang School of Electrical Engineering and Automation Henan International Joint Laboratory of Direct Drive and Control of Intelligent Equipment Henan Polytechnic University Jiaozuo454003 China Faculty of Information Technology Beijing University of Technology No. 100 Pingleyuan Chaoyang District Beijing100124 China Beijing Key Laboratory of Computational Intelligence and Intelligent System No. 100 Pingleyuan Chaoyang District Beijing100124 China

Radial basis function neural network (RBFNN) is one of the most popular neural networks, and an appropriate selection of its structure and learning algorithms is crucial for its performance. Aiming to alleviate the sensitivity of the RBFNN to its parameters and improve the overall performance of the network, this study proposes a Gaussian Membership-based online self-organizing RBF neural network (GM-OSRBFNN). First, the Gaussian Membership is introduced to enhance network insensitivity to network parameters and used as a similarity metric to indicate the similarity between the sample to a hidden neuron and that between hidden neurons. Second, the similarity metric is used to design the neuron addition and merging rules to achieve a self-organizing network structure, and error constraints are introduced to the neuron addition rule;also, the noisy neuron deletion rule is defined to make the network structure more compact. In addition, an online fixed mini-batch gradient algorithm is used for online learning of network parameters, which can guarantee fast and stable convergence of the network. Finally, the proposed GM-OSRBFNN is tested on common nonlinear system modeling problems to verify its effectiveness. The experimental results show that compared to the existing models, the GM-OSRBFNN can achieve competitive prediction performance with a more compact network structure, faster convergence speed, and, more importantly, better insensitivity to network parameters. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Radial basis function networks

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Multimodal Physiological Signal Analysis Using Attention-Based Feature and Model Fusion 17th

Multimodal Physiological Signal Analysis Using Attention-Bas...

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17th International Conference on Brain Informatics, BI 2024

作者： Liu, Wei Jia, Kebin Sun, Zhonghua Feng, Jinchao Zhao, Xiaohu Faculty of Information Technology Beijing University of Technology Beijing100124 China Beijing Laboratory of Advanced Information Networks Beijing100124 China Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing University of Technology Beijing100124 China

ISBN: (纸本)9789819632930

Disease analysis using multimodal physiological signals is currently a hot research area. Aiming at the problem that current multimodal feature fusion approaches neglect the correlation between different modalities, this paper proposes a hybrid multimodal fusion model based on the attention mechanism for depression recognition. The model selects two modalities, EEG and speech signals, for feature fusion. In the feature extraction stage of EEG signals, the correlation between features is analyzed using Spearman’s rank correlation coefficient to achieve dimensionality reduction. In the feature-level modal fusion stage, the attention mechanism is applied to automatically learn the contribution of different features and the complementarity between different modalities. In the model fusion layer, a multi-layer long and short-term memory network (ML-LSTM) is adopted, which makes full use of the complementarity between the features of EEG signals and audio signals. Finally, the outputs of feature-level fusion and model-level fusion are weighted and combined to obtain the final prediction results. Experimental results on the MODMA dataset show that the accuracy of the proposed multimodal fusion model for depression recognition reached 88.54%. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Electroencephalography

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Multi-Scale Time Series Segmentation Network Based on Eddy Current Testing for Detecting Surface Metal Defects

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IEEE/CAA Journal of Automatica Sinica 2025年第3期12卷 528-538页

作者： Xiaorui Li Xiaojuan Ban Haoran Qiao Zhaolin Yuan Hong-Ning Dai Chao Yao Yu Guo Mohammad S.Obaidat George Q.Huang the School of Intelligence Science and Technology University of Science and Technology Beijing the Beijing Advanced Innovation Center for Materials Genome Engineering the Key Laboratory of Intelligent Bionic Unmanned Systems and the Institute of Materials Intelligent Technology Liaoning Academy of Materials IEEE the Department of Computer Science Hong Kong Baptist University the School of Computer and Communication Engineering Key Laboratory of Advanced Materials and Devices for Post-Moore Chips Ministry of Education University of Science and Technology Beijing the Beijing Advanced Innovation Center for Materials Genome Engineering University of Science and Technology Beijing the School of Computer and Communication Engineering University of Science and Technology Beijing the King Abdullah Ⅱ School of Information Technology The University of Jordan the Department of Computational Intelligence the School of Computing SRM University the School of Engineering The Amity University The Hong Kong Polytechnic University

In high-risk industrial environments like nuclear power plants, precise defect identification and localization are essential for maintaining production stability and safety. However, the complexity of such a harsh environment leads to significant variations in the shape and size of the defects. To address this challenge, we propose the multivariate time series segmentation network(MSSN), which adopts a multiscale convolutional network with multi-stage and depth-separable convolutions for efficient feature extraction through variable-length templates. To tackle the classification difficulty caused by structural signal variance, MSSN employs logarithmic normalization to adjust instance distributions. Furthermore, it integrates classification with smoothing loss functions to accurately identify defect segments amid similar structural and defect signal subsequences. Our algorithm evaluated on both the Mackey-Glass dataset and industrial dataset achieves over 95% localization and demonstrates the capture capability on the synthetic dataset. In a nuclear plant's heat transfer tube dataset, it captures 90% of defect instances with75% middle localization F1 score.

关键词： Eddy current testing nondestructive testing semantic segmentation time series analysis

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RL-LVIS: a robust, lightweight LiDAR–visual–inertial state estimator and mapping for mobile robots

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Advanced Robotics 2025年第8期39卷 424-440页

作者： Li, Xiuzhi Du, Bowen School of Information Science and Technology Beijing University of Technology Beijing China Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing China

In recent years, the application of autonomous outdoor robots has faced challenges in improving the computational efficiency and robustness of underlying algorithms due to the limitations of size and weight, making it difficult to use high-quality sensors and powerful processors. This paper introduces a robust, lightweight, and high-precision framework tailored for outdoor mobile robots to achieve robust and accurate state estimation. We unify LiDAR, visual, and inertial measurements within the framework of Iterated-Error-State Kalman Filter (IESKF), by constructing multi-state-constrained visual observations and calculating visual reprojection errors, rather than using computationally expensive graph optimization methods. In order to reduce the computational load of the system, we maintain the Inertial Measurement Unit (IMU) states within a sliding window by state cloning and updating the finite-sized state dimension. Due to its simplicity, we derived the formulas for the proposed framework on differential manifolds. Leveraging data from these three sensors, our system maintains comparable accuracy to optimization algorithms in three large-scale scenes while ensuring normal operation in scenarios where visual or LiDAR measurements degrade. Our experiments validate that the proposed system has low computational load, making it suitable for operation on embedded platforms with limited computational resources. © 2025 Informa UK Limited, trading as Taylor & Francis Group and The Robotics Society of Japan.

关键词： Mobile robots

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Assessment for pigmentation and vascularity of hypertrophic scar based on residual network 17

Assessment for pigmentation and vascularity of hypertrophic ...

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17th International Conference on Photonics and Imaging in Biology and Medicine, PIBM 2024

作者： Fu, Ruixin Li, Zhe Tian, Peng Wang, Chong Tu, Feng Lu, Ming Bai, Jiangtao Feng, Jinchao Jia, Kebin Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing University of Technology Beijing100124 China Beijing Laboratory of Advanced Information Networks Beijing100124 China Burn and plastic surgery Beijing Jishuitan Hospital Capital Medical University Beijing100035 China Department of Medical Engineering Beijing Jishuitan Hospital Capital Medical University Beijing100096 China

ISBN: (数字)9781510688070

ISBN: (纸本)9781510688063

Hypertrophic scars are a type of pathological scar that can cause discomfort, pain, and itching. The assessment of hypertrophic scars depends largely on the clinician's long-term experience. Computer-aided assessment can greatly improve the efficiency and accuracy of scar assessment. In this paper, we propose a deep neural network-based assessment method for the degree of scar pigmentation and vascularity using real scar images. Firstly, a large amount of hypertrophic scar images were collected to produce a dataset including patients of all ages and the distribution of lesions in different parts of the body. These images were then scored on pigmentation and vascularity using the Vancouver Scar Scale (VSS). Then, the residual network model was proposed to evaluate the degree of scar pigmentation and vascularity according to the hypertrophic scar dataset. The degree of the pigmentation and vascularity were assessed respectively by two residual network models. The experimental results demonstrated that our method provides a reliable tool for the application of a scar diagnosis system. © 2025 SPIE.

关键词： Diagnosis

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Accurate and Real-time Hierarchical Ensemble Network for Activity Classification in Construction Worker

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IEEE Journal of Biomedical and Health Informatics 2025年 PP卷 PP页

作者： Zuo, Guoyu Wu, Qifei Gao, Wenbin Li, Cheng Sun, Liangkun Yu, Shuangyue Beijing University of Technology School of Information Science and Technology Beijing100124 China Beijing Key Laboratory of Computing Intelligence and Intelligent Systems Beijing100124 China

Accurate and real-time locomotion classification is crucial for exoskeletons to assist construction workers in completing multiple tasks. However, state-of-the-art algorithms for classifying multiple activities face multifaceted challenges in both accuracy and real-time capability. In addition, advanced studies typically provide a single solution based on certain sensor combinations, which may have an indirect impact on different assistive devices (e.g., an algorithm using feet IMUs is not suited for bilateral portable hip exoskeletons or unilateral knee exoskeletons), limiting its practicality and applicability in diverse applications. To fill these two gaps, first, we developed a novel hierarchical ensemble network framework that can accurately and real-time classify 11 typical lower limb activities of construction workers. Second, building upon this hierarchical ensemble network framework, we developed 6 configurations wearing IMU sensors on different body segments, which are potentially used for different wearable devices. Experimental results with leave-one-out cross-validation obtained from 10 able-bodied subjects validated the effectiveness of the proposed algorithm. Compared to the baseline ANN-based algorithm, our algorithm under 6 configurations on average was able to improve accuracy, precision, recall, and F1-score by 4.97%, 3.40%, 4.97%, and 5.31%, respectively, and reduce the number of parameters and inference time by 71.86% and 47.85%, respectively. This study showcases multiple solutions with different wearable sensor configurations, offering high accuracy and strong real-time performance for classifying multiple activities, which can be deployed to controllers for multiple types of assistive devices targeting construction workers. © 2013 IEEE.

关键词： Exoskeleton (Robotics)

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Binary-Encoding-Based Quantized Kalman Filter: An Approximate MMSE Approach

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IEEE Transactions on Automatic Control 2025年第5期70卷 3181-3196页

作者： Liu, Qinyuan Nie, Yao Wang, Zidong Dong, Hongli Jiang, Changjun Tongji University School of Computer Science and Technology Shanghai201804 China Tongji University Key Laboratory of Embedded System and Service Computing Ministry of Education Shanghai201804 China Tongji University Shanghai Artificial Intelligence Laboratory Shanghai201804 China Brunel University London Department of Computer Science UxbridgeUB8 3PH United Kingdom Northeast Petroleum University Artificial Intelligence Energy Research Institute Daqing163318 China Northeast Petroleum University Heilongjiang Provincial Key Laboratory of Networking and Intelligent Control Daqing163318 China

In this article, the Kalman filter design problem is investigated for linear discrete-time systems under binary encoding schemes. Under such a scheme, the local information is quantized into a bit string by the remote sensor based on a probabilistic quantizer, and then the bit string is transmitted via memoryless binary symmetric channels (BSCs). Due to the communication link noises, the bit flipping occurs in a random manner, and thus, the transmission of the bit string would suffer from specific bit-error rates. With the received bits, a recursive binary-encoding-based quantized Kalman filter is established in the approximate minimum mean-square error (MMSE) sense, which relies on the Gaussian approximation of the conditional probability density function at each iteration. Furthermore, the proposed estimator is shown to be of a Kalman-like type through performance analysis, which exhibits computational complexity comparable to the conventional Kalman filter. Subsequently, a posterior Cramér-Rao lower bound is derived for the proposed binary-encoding-based quantized Kalman filter. The effectiveness of the proposed estimator is demonstrated through numerical results. © 1963-2012 IEEE.

关键词： Mean square error

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Adaptive Fuzzy Cooperative Control Based on a Predefined Time Disturbance Observer for MASs

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IEEE Transactions on systems, Man, and Cybernetics: systems 2025年

作者： Han, Hong-Gui Hou, Han-Qian Sun, Hao-Yuan Qiao, Jun-Fei The School of Information Science and Technology the Beijing Key Laboratory of Computational Intelligence and Intelligent System The Engineering Research Center of Digital Community Ministry of Education The Beijing Artificial Intelligence Institute The Beijing Laboratory for Intelligent Environmental Protection Beijing University of Technology Beijing100124 China

External disturbances can render constrained multiagent systems (MASs) unstable and affect their convergence speed. To address this issue, the cooperative control strategy for MASs based on a predefined time (PT) disturbance observer (PTDOB) is proposed in this article. First, the original time-varying constrained states of MASs, which are influenced by external disturbances and have a coupled communication topology, are transformed into unconstrained states using the nonlinear transformed function (NTF). This transformation ensures that the limitations of various physical states for agents are not exceeded. Second, the transformed unconstrained states are used to construct the PTDOB for fast observation of external disturbances. Then, based on the proposed PTDOB and the backstepping control technique, an adaptive fuzzy PT cooperative controller is designed. This controller not only ensures the convergence of MASs within PT but also prevents the states from exceeding the time-varying bounds. Finally, the effectiveness of the proposed control strategy is demonstrated through its application to multiple single-link robotic arms as illustrative examples. © 2013 IEEE. All rights reserved.

关键词： Robotic arms

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DNN Task Assignment in UAV Networks: A Generative AI Enhanced Multi-Agent Reinforcement Learning Approach

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IEEE Internet of Things Journal 2025年第10期12卷 13340-13352页

作者： Tang, Xin Chen, Qian Weng, Wenjie Liao, Binhan Wang, Jiacheng Cao, Xianbin Li, Xiaohuan Guilin University of Electronic Technology Guangxi University Key Laboratory of Intelligent Networking and Scenario System School of Information and Communication Guilin541004 China Nanyang Technological University College of Computing and Data Science 639798 Singapore Guilin University of Electronic Technology School of Architecture and Transportation Engineering Guilin541004 China Nanning530001 China Beihang University School of Electronic and Information Engineering Key Laboratory of Advanced Technology of Near Space Information System Ministry of Industry and Information Technology of China Beijing100191 China

Unmanned Aerial Vehicles (UAVs) offer high mobility and flexible deployment capabilities, making them ideal for Internet of Things (IoT) applications. However, the substantial amount of data generated by various applications within the existing low-altitude network requires processing through Deep Neural Networks (DNN) on UAVs, which is challenging due to their limited computational resources. To address this issue, we propose a two-stage optimization method for flight path planning and task allocation based on a mother-child UAV swarm system. In the first stage, we employ a greedy algorithm to solve the path planning problem by considering the task size of the target area to be inspected and the shortest flight path as constraints. The goal is to minimize both the flight path of the UAV and the overall cost of the system. In the second stage, we introduce a novel DNN task assignment algorithm that combines Multi-Agent Deep Deterministic Policy Gradient (MADDPG) and Generative Diffusion Models (GDM), named GDM-MADDPG. This algorithm takes advantage of the reverse denoising process of GDM to replace the actor network in MADDPG. It enables UAVs to generate specific DNN task assignment actions based on agents' observations in a dynamic environment, thereby improving the efficiency of task assignment and overall system performance. The simulation results demonstrate that our algorithm outperforms the benchmarks in terms of path planning, Age of Information (AoI), task completion rate, and system utility, demonstrating its effectiveness. © 2014 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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