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12th International Conference on Quality, Reliability, Risk, Maintenance, and Safety engineering, QR2MSE 2022

作者： Geng, Huapin Yuan, Feimeng Suo, Bin Science and Technology on Complex System Control and Intelligent Agent Cooperative Laboratory Beijing China Beijing Electro-mechanical Engineering Institute Beijing China School of Mathematics Southwest Jiaotong University Chengdu China School of Information Engineering Southwest University of Science and Technology Mianyang China

ISBN: (纸本)9781839538360

Aiming at the fusion problem of multi-source heterogeneous dynamic data sources, based on the subjective and objective comprehensive weighting idea, an efficient multi-source dynamic data fusion algorithm is proposed. First, the outlier detection method based on linear regression model is used to preprocess the multi-source dynamic time series data. Then, determine the weight of the dynamic data. The prior weight of the data source is determined by the AHP method, cyclic scoring method, etc., and the posterior weight is determined according to the similarity between the time series data. Based on the Euclidean distance, a similarity measure between different data sources and a similarity measure between different dynamic data samples are constructed respectively. The former is used for the fusion of dynamic data standard deviation expectations, and the latter is used for the fusion of mean expectations. Finally, the comprehensive weight of each dynamic time series of each data source is obtained, and the comprehensive weighted fusion method is used to obtain the expected value and standard deviation of the dynamic response. The analysis of the calculation example shows that the proposed algorithm has higher computational efficiency than the method that treats dynamic data as several discrete static data. © 2022 IEEE.

关键词： Data fusion

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Towards More Accurate Tiny Object Tracking: Benchmark and Algorithm

Towards More Accurate Tiny Object Tracking: Benchmark and Al...

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International Conference on Computer Supported Cooperative Work in Design

作者： Jizhe Yu Yu Liu Hongkui Wei Kaiping Xu DUT School of Software Technology & DUT-RU International School of Information Science and Engineering Dalian University of Technology Dalian China State Key Laboratory of Intelligent Manufacturing System Technology Beijing Institute of Electronic System Engineering Beijing China

ISBN: (数字)9798350349184

ISBN: (纸本)9798350349191

Despite the significant progress in tiny object tracking algorithms based on convolutional neural networks, the performance falls short of the ideal level due to dataset challenges like limited scale, limited match backdrop, low match popularity, and low-resolution(720p) images. The lack of specialized large-scale tiny object tracking datasets leads data-hungry tracking algorithms to rely on small and saturated datasets from TrackNet. This paper propels the development of tiny object tracking and contributes the first large-scale dataset, Badminton100K, for tiny ball tracking. We provide over 100K annotations covering over 200 videos from 12 professional match events. Our dataset covers high-resolution(1080p) match videos in broad and diverse contexts. Moreover, considering the fast-moving tiny balls in videos often leads to challenges such as blur, afterimage, overlap, and disappearance. As another contribution, we also propose a novel Multi-stage and Multi-scale Fusion Network (MMFNet), which can extract local high-resolution details in the shallow stages and capture global semantic information in the deep stages, addressing these challenging tasks through multiple stages multi-scale feature fusion, enabling accurate identification and localization of tiny balls. Extensive experiments show that our method achieves unprecedented state-of-the-art tracking performance on Badminton100K. The dataset and the algorithm code are available at https://***/Gi-gigi/MMFNet.

关键词： Location awareness Accuracy Image resolution Semantics Transfer learning Propulsion Feature extraction

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RESEARCH ON THE OVERALL DESIGN OF A HIGH-SPEED MORPHING UAV 33

RESEARCH ON THE OVERALL DESIGN OF A HIGH-SPEED MORPHING UAV

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33rd Congress of the International Council of the Aeronautical sciences, ICAS 2022

作者： Ding, Chao Ma, Tielin Fu, Jingcheng Wang, Xiangsheng School of Aeronautic Science and Engineering Beihang University Beijing100191 China Key Laboratory of Advanced Technology of Intelligent Unmanned Flight System Ministry of Industry and Information Technology Beijing100191 China Institute of Unmanned System Beihang University Beijing100191 China School of Transportation Science and Engineering Beihang University Beijing100191 China

ISBN: (纸本)9781713871163

Due to its broad application prospects in the military and civilian fields, air-launch high-speed aircraft is an important direction for the development of high-speed aircraft in the future. However, air-launch supersonic vehicles face the characteristics of limited volume, small lift-to-drag ratio, difficult trimming, and poor handling characteristics. A supersonic vehicle with morphing wing can flexibly change its aerodynamic shape according to different flight environments, which can improve the weakness of supersonic flight. This paper takes the air-launch high-speed aircraft with the outer wing folded as the research object and carries out the overall design research according to the proposed task requirements. The tailless aerodynamic layout combined with the strake wing and the multi-section swept wing was proposed. Aiming at the difficult problem of longitudinal moment trimming, a combined analysis method of pressure center position and pressure map is proposed. The research results show that the aircraft designed in this study has good aerodynamic performance in different speed domains, and it can maintain the longitudinal moment trim at the cruise angle of attack(AOA). © 2022 ICAS. All Rights Reserved.

关键词： Angle of attack

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Preliminary study of online real-time control system for lower extremity exoskeletons based on EEG and sEMG fusion

Preliminary study of online real-time control system for low...

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2022 IEEE International Conference on robotics and Biomimetics, ROBIO 2022

作者： Li, Wenju Shao, Keyong Zhu, Chi Ma, Yue Cao, Wujing Yin, Meng Yang, Lijun Luo, Mingxiang Wu, Xinyu Northeast Petroleum University School of Electrical Engineering Information Daqing China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Guangdong Provincial Key Lab of Robotics and Intelligent System Guangdong Shenzhen518055 China Maebashi Institute of Technology Department of Systems Life Engineering Maebashi3710132 Japan

ISBN: (纸本)9781665481090

Motion imaging (MI) is widely used in exoskele-ton robot control direction. However, online real-time con-trol of exoskeletons based on biological signals is difficult to achieve. Because the anti-interference of biological signals in real life is poor and affected by individual differences, the control of auxiliary equipment by the realization of human-machine interface is still facing great challenges. In this paper, a human-machine interface (HMI) based on hybrid biological signals is designed and online real-time control of lower extremity exoskeletons is realized. Based on a comprehensive analysis of human walking, the HMI uses the electromyography signals (upper extremities) and EEG signals generated during real exercise as input signals. Different analysis methods are designed for different modal signals. We extracted four features of sEMG signals to distinguish between different movement processes in the human body. For EEG signals, the use of Independent Component Analysis (ICA) increases the robustness of the system. Event-related desynchronization/synchronization (ERS/ERD) and power spectral densities are reused for analysis from the perspective of the interaction of multiple information in the channel, time and frequency domains. The human-machine interface integrates EEG and EMG multimodal biological signals, and the average recognition accuracy of different subjects has reached 93.7%. In order to increase the recognition accuracy of practical applications and reduce the differences between different individuals, a window voting mechanism is designed. Healthy subjects were tested, and the recognition accuracy of the lower limb exoskeleton online real-time assistance system reached 99%, with high accuracy and reliability. © 2022 IEEE.

关键词： Real time control

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GNN-Assisted BiG-AMP: Joint Channel Estimation and Data Detection for Massive MIMO Receiver

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IEEE Transactions on Wireless Communications 2025年第6期24卷 4631-4646页

作者： Liu, Zishen Wu, Nan He, Dongxuan Yuan, Weijie Li, Yonghui Quek, Tony Q. S. School of Information and Electronics Beijing Institute of Technology Beijing100081 China School of System Design and Intelligent Manufacturing Shenzhen Key Laboratory of Robotics and Computer Vision Southern University of Science and Technology Shenzhen518055 China School of Electrical and Information Engineering The University of Sydney SydneyNSW2008 Australia Singapore University of Technology and Design 487372 Singapore Department of Electronic Engineering Kyung Hee University Yongin17104 Korea Republic of

In this paper, we develop a graph neural network (GNN)-assisted bilinear inference approach to enhance the receiver performance of the MIMO system through message passing-based joint channel estimation and data detection (JCD). Specifically, based on the bilinear generalized approximate message passing (BiG-AMP) framework and conditional correlation of signal, we propose a GNN-assisted BiG-AMP (GNN-BiGAMP) approach, which integrates a GNN module into the data-detection-loop to compensate the inaccurate marginal likelihood approximation. By leveraging the coupling between the channel and received symbols, a bilinear GNN-assisted BiG-AMP (BiGNN-BiGAMP) JCD receiver is further proposed. This method incorporates two GNNs with similar graph representation into the bilinear posterior estimation loops, which not only compensates for approximation errors but also alleviates performance loss due to premature variance convergence, thereby enhancing the receiver performance significantly. To fully exploit the supervised information from channel estimation and data detection, we propose a multitask learning based training scheme, which coordinates GNNs with different tasks in two loops. Simulation results show that our proposed GNN-assisted JCD receivers significantly outperform other JCD counterparts in terms of both channel estimation and data detection. © 2025 IEEE. All rights reserved.

关键词： Graph neural networks

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HOI-Based Method for Detecting Cell Phone Behavior in Industrial Safety Production Environments

HOI-Based Method for Detecting Cell Phone Behavior in Indust...

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Computer science, Electronic information engineering and intelligent Control Technology (CEI), IEEE International Conference on

作者： Zijian Nong Shaofei Shi Chi Jiang Ke Zhang Yin Zhang Shenzhen Institute for Advanced Study University of Electronic Science and Technology of China Shenzhen China School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu China Guangdong Intelligent Robotics Institute Dongguan China

ISBN: (数字)9798350378542

ISBN: (纸本)9798350378559

To address the practical problems of algorithm inference timeliness and the complex environment in industrial settings, The presented dissertation introduces a comprehensive human-object interaction recognition technique, leveraging the attention-based architecture of the Transformer model. By sampling key features at different scales from the industrial complex environment and using Transformer's attention mechanism to aggregate global information, the algorithm achieves accurate detection of targets in complex environments while eliminating post-processing processes such as non-maximal value suppression to achieve real-time detection. Moreover, a deformable attention module is used to optimize small target detection and the multi-headed attention mechanism of Transformer, which adaptively adjusts the position and shape of the feature extraction window, reducing the computational effort of the attention computation process and accelerating the model training time. The investigation outcomes reveal that the introduced HOI-centric technique for identifying mobile phone infractions within industrial safety contexts exhibits superior accuracy in detection and notable enhancements in computational efficiency when benchmarked against other sophisticated algorithms in the expanded mobile phone category within the HICO-DET dataset.

关键词： Training Attention mechanisms Accuracy Annotations Shape Transformers Feature extraction Mobile handsets Inference algorithms Safety

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Advanced Affine Optimal Tracking Control Through Online Value Iteration and Its Stability Proof 41

Advanced Affine Optimal Tracking Control Through Online Valu...

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第41届中国控制会议

作者： Junlong Wu Ding Wang Mingming Ha Mingming Zhao Jin Ren Faculty of Information Technology Beijing University of Technology Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing Laboratory of Smart Environmental Protection Beijing Institute of Artificial Intelligence School of Automation and Electrical Engineering University of Science and Technology Beijing

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

In this paper,to tackle the optimal tracking control problem for discrete-time systems,an advanced online value iteration(VI) algorithm is ***,the derivation of traditional VI for the optimal tracking control problem is ***,the stability condition of traditional VI is established,in order to evaluate the current iterative tracking control ***,based on the concept of attraction domain,the improved tracking control policies under online VI can be obtained by judging the location of the current tracking ***,we prove the stability of the online VI algorithm for the present tracking control problem.A simulation example is displayed to verify the effectiveness of the developed algorithm.

关键词： Adaptive dynamic programming attraction domain online value iteration optimal tracking control stability proof

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A Robust SLAM For Highly Dynamic Environments

A Robust SLAM For Highly Dynamic Environments

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IEEE Conference on Industrial Electronics and Applications (ICIEA)

作者： Yuansong Cai Shiqian Wu Institute of Robotics and Intelligent Systems School of Information Science and Engineering Wuhan University of Science and Technology Wuhan China

ISBN: (纸本)9781665409858

Simultaneous Localization and Mapping(SLAM) has broad applications such as driverless cars and indoor service robots. The SLAM techniques usually assume that environments are static, and it is difficult to obtain good accuracy in highly dynamic scenes. In this paper, we adopt the semantic segmentation method to filter out dynamic points according to prior knowledge. To avoid tracking failure due to insufficient feature points, epipolar geometry is used to retain potential dynamic points as much as possible. The proposed method is implemented based on the classical SLAM framework ORBSLAM2 and carried out experiments on the TUM RGBD datasets. Compared with ORBSLAM2, the localization accuracy of this algorithm is improved by 95% in highly dynamic environments.

关键词： Location awareness Tracking loops Simultaneous localization and mapping Service robots Heuristic algorithms Semantic segmentation Semantics

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High-Speed Trajectory Tracking Control for Quadrotors via Deep Reinforcement Learning

High-Speed Trajectory Tracking Control for Quadrotors via De...

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Youth Academic Annual Conference of Chinese Association of Automation (YAC)

作者： Hean Hua Hang Zhong Hui Zhang Yongchun Fang Yaonan Wang College of Electrical and Information Engineering Hunan University Changsha China National Engineering Research Center for Robot Visual Perception and Control Technology Changsha China School of Robotics Hunan University Changsha China Institute of Robotics and Automatic Information System College of Artificial Intelligence Nankai University Tianjin Key Laboratory of Intelligent Robotics Nankai University Tianjin China

ISBN: (数字)9798350379228

ISBN: (纸本)9798350390780

This paper presents a learning-based high-speed trajectory tracking control strategy for quadrotors, which achieves efficient learning and strong reliability by the collaboration of deep reinforcement learning (RL) and self-tuning mechanism. Different from existing methods, the proposed strategy is designed to explore optimal control performance by taking advantage of model-based self-tuning mechanism and deep reinforcement learning. Specifically, the self-tuning guided deep RL scheme is put forward for quadrotors, with superior learning efficiency and strong adaptability. Firstly, a novel self-tuning mechanism is constructed and some auxiliary variables are introduced to enhance the tracking performance. Then, based on the model-driven self-tuning design, the deep RL is proposed to achieve model-guided learning, where the tuning actions are adopted in the evaluation process during training, aiming at removing the bad explorations by the carefully designed parallel evaluation. Finally, the convergence is analyzed based on the proposed learning framework, which indicates the efficient cooperation of exploration and self-tuning mechanism. To verify the effectiveness of the proposed controller, the guided training and hardware experiments are implemented to show efficient cooperation and satisfactory high-speed trajectory tracking control of the proposed method.

关键词： Training Adaptation models Trajectory tracking Heuristic algorithms Optimal control Deep reinforcement learning Hardware

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HairDiffusion: Vivid Multi-Colored Hair Editing via Latent Diffusion

arXiv

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arXiv 2024年

作者： Zeng, Yu Zhang, Yang Liu, Jiachen Shen, Linlin Deng, Kaijun He, Weizhao Wang, Jinbao Computer Vision Institute School of Computer Science & Software Engineering Shenzhen University China Shenzhen Institute of Artificial Intelligence and Robotics for Society China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China Guangdong Provincial Key Laboratory of Intelligent Information Processing China

Hair editing is a critical image synthesis task that aims to edit hair color and hairstyle using text descriptions or reference images, while preserving irrelevant attributes (e.g., identity, background, cloth). Many existing methods are based on StyleGAN to address this task. However, due to the limited spatial distribution of StyleGAN, it struggles with multiple hair color editing and facial preservation. Considering the advancements in diffusion models, we utilize Latent Diffusion Models (LDMs) for hairstyle editing. Our approach introduces Multi-stage Hairstyle Blend (MHB), effectively separating control of hair color and hairstyle in diffusion latent space. Additionally, we train a warping module to align the hair color with the target region. To further enhance multi-color hairstyle editing, we fine-tuned a CLIP model using a multi-color hairstyle dataset. Our method not only tackles the complexity of multi-color hairstyles but also addresses the challenge of preserving original colors during diffusion editing. Extensive experiments showcase the superiority of our method in editing multi-color hairstyles while preserving facial attributes given textual descriptions and reference images. © 2024, CC BY.

关键词： Color image processing

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