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第41届中国控制会议

作者： Junlin Zhang Kaoru Hirota Yaping Dai Sijie Yin School of Automation Beijing Institute of Technology State Key Laboratory of Intelligent Control and Decision of Complex Systems

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

The inter-class similarity of facial expressions is one of the key challenges in Facial Expression Recognition(FER).In this manuscript,a Latent Facial Action Units Network(LAUNet) is proposed for the problem of inter-class similarity of facial expressions in *** proposed method recognizes subtle differences between facial expressions by learning Latent Facial Action Units Features(LAUFs).Specifically,LAUNet is composed of two parts:the Latent Facial Action Units Features Extraction Network(LEN) and the Latent Facial Action Units Selection Network(LSN).Firstly,LEN extracts LAUFs from the feature map of the backbone using the spatial attention ***,taking advantage of the channel attention mechanism,LSN captures the latent relationships between features from LAUFs to select effective features for *** are performed on the dataset after removing the invalid data of non-face images from the original FER2013 *** with some previous state-of-the-art methods,LAUNet achieves the highest accuracy rate of 71.31%.Depending on the backbone,LAUNet can improve the accuracy by up to 5.46% compared to the original architecture of the backbone.

关键词： Facial Expression Recognition Attention Mechanism Computer Vision

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An Adversarial Video Moment Retrieval Algorithm 41

An Adversarial Video Moment Retrieval Algorithm

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第41届中国控制会议

作者： Mohan Jia Zhongjian Dai Yaping Dai Zhiyang Jia School of Automation Beijing Institute of Technology State Key Laboratory of Intelligent Control and Decision of Complex Systems

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

In one-stage methods for video moment retrieval,the common representations indirectly supervised by boundary prediction fail to fully preserve the inherent characteristic of the video and query,which limits the retrieval *** solve this problem,an Adversarial Video Moment Retrieval(AVMR) algorithm is proposed to learn the common representations with modality invariance and cross-modal *** is implemented through the process of adversarial learning between a feature projector and a modality *** feature projector tries to generate a modality-invariant common representation and to confuse the modality *** modality classifier tries to discriminate between different modalities based on the generated representation by the feature *** triplet constraints are further imposed on the feature projector to preserve the underlying cross-modal semantic structure of *** experimental results show that AVMR surpasses the baseline Attentive Cross-modal Relevance Matching(ACRM) by 1.10% and 1.73% in the "mIoU" metric on two public datasets Charades-STA and TACoS,respectively.

关键词： Video Moment Retrieval Cross-modal Retrieval Deep learning Adversarial Learning

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Multi-Human Pose Estimation by Deep Learning-Based Sequential Approach for Human keypoint Position and Human Body Detection

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Journal of Shanghai Jiaotong University (Science) 2023年 1-11页

作者： Tahir, Rizwan Cai, Yunze Department of Automation Shanghai Jiao Tong University Shanghai200240 China Key Laboratory of System Control and Information Processing of Ministry of Education Shanghai Jiao Tong University Shanghai200240 China Key Laboratory of Marine Intelligent Equipment and System of Ministry of Education Shanghai Jiao Tong University Shanghai200240 China

Recent multimedia and computer vision research has focused on analyzing human behavior and activity using images. Skeleton estimation, known as pose estimation, has received a significant attention. For human pose estimation, deep learning approaches primarily emphasize on the keypoint features. Conversely, in the case of occluded or incomplete poses, the keypoint feature is insufficiently substantial, especially when there are multiple humans in a single frame. Other features, such as the body border and visibility conditions, can contribute to pose estimation in addition to the keypoint feature. Our model framework integrates multiple features, namely the human body mask features, which can serve as a constraint to keypoint location estimation, the body keypoint features, and the keypoint visibility via mask region-based convolutional neural network (Mask-RCNN). A sequential multi-feature learning setup is formed to share multi-features across the structure, whereas, in the Mask-RCNN, the only feature that could be shared through the system is the region of interest feature. By two-way up-scaling with the shared weight process to produce the mask, we have addressed the problems of improper segmentation, small intrusion, and object loss when Mask-RCNN is used, for instance, segmentation. Accuracy is indicated by the percentage of correct keypoint, and our model can identify 86.1% of the correct keypoints. © 2023, Shanghai Jiao Tong University.

关键词： Convolutional neural networks

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Enhanced Data Augmentation for Bearing Fault Diagnosis by Using a Spectral Flow Model

Enhanced Data Augmentation for Bearing Fault Diagnosis by Us...

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2024 Prognostics and system Health Management Conference, PHM 2024

作者： Gong, Xiaoyun Hao, Mengxuan Li, Chuan Du, Wenliao Huang, Yixiang Intelligent Monitoring and Control Henan International Joint Laboratory of Complex Mechanical Equipment Zhengzhou University of Light Industry Zhengzhou China Research Center for System Health Maintenance Chongqing Technology and Business University Chongqing China Shanghai Jiao Tong University The State Key Laboratory of Mechanical System and Vibration Shanghai China

ISBN: (纸本)9798350360585

While existing flow-based models have the potential for data enhancement and intelligent fault diagnosis of bearings, these methods are mainly based on the time domain to learn features. In general, time-domain and frequency-domain features are important for recognizing faults. Especially, the spectral features in the frequency domain can reveal highly distinct patterns associated with fault types. Therefore, learning spectral feature helps to obtain comprehensive fault information and achieve high fault diagnosis accuracy. In this work, a spectral flow model framework is developed to learn and generate realistic data from mechanical sensor signals. The performance of fault diagnosis under unbalanced data is improved by learning the information contained in the spectral features, which are utilized to generate insufficient fault data. Experimental results show that the present method can generate a large number of samples with higher similarity and better diversity on the bearing dataset. The generated samples may be used to expand the limited dataset and significantly improve the accuracy of the bearing fault diagnosis task. © 2024 IEEE.

关键词： Frequency domain analysis

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Multi-dimensional Visualization Method of SCD File Information of intelligent Substation Based on Knowledge Graph 6

Multi-dimensional Visualization Method of SCD File Informati...

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6th International Conference on Energy, Electrical and Power Engineering, CEEPE 2023

作者： Wang, Feng Li, Yiquan Tan, Qian Wu, Ziliang Suo, Jianglei Gu, Qianshuo Power Dispatching Control Center Guangdong Power Guangzhou China Shangdong University Key Laboratory of Power System Intelligent Dispatch and Control Jinan China

ISBN: (纸本)9798350348279

The structure of intelligent substation SCD file is complex, and there are many layers of information. Different functions have corresponding information basis, and often need to be associated across multiple information nodes, resulting in poor visualization. This paper presents a multi-dimensional visualization method of SCD file information of smart substation based on knowledge graph. Firstly, the file structure and function structure of SCD text expression are analyzed. Then the SCD file node is extended and the seven-step method is adopted to construct the intelligent substation ontology, which is realized by Protégé. The knowledge graph of substation is constructed by using ontology and relationship between SCD nodes. The graph expression of SCD is completed, and the visualization of file structure information is realized. The information path of functions such as the correlation between primary and secondary devices and the correlation between virtual and real circuits is mined. The information nodes in the path are searched in the SCD knowledge graph to realize the visualization of functional structure information. The above work was done through Neoj4, Python. The simulation results show that knowledge graph can decouple SCD information and realize multi-dimensional visualization of information with a high degree of visualization, which can provide guidance for operation and maintenance and information retrieval. © 2023 IEEE.

关键词： Visualization

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Simulation and optimization of inchworm-like pneumatic crawling actuator based on ABAQUS software 7

Simulation and optimization of inchworm-like pneumatic crawl...

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7th International Conference on Robotics, control and Automation Engineering, RCAE 2024

作者： Mu, Junqi Zong, Xiaofeng Wei, Yiyang Hou, Xuping China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education School of Automation Wuhan430074 China

ISBN: (纸本)9798350355642

The actuator, serving as the fundamental power source of a soft robot, functions as its central component. The field of soft robotics has garnered increasing research attention since its inception. Pneumatic crawling robots, being an essential branch within the realm of soft robotics, possess notable characteristics such as rapid response time, cost-effectiveness, and human-computer interaction safety. Consequently, they hold significant research value. Among various types of crawling robots that have garnered considerable interest from researchers is the inchworm crawling robot. This paper aims to address the design and development aspects pertaining to inchworm actuators. By utilizing ABAQUS simulation software and taking displacement as the reference parameter, an analysis was conducted to investigate the impact of various geometric parameters on actuator displacement. Additionally, optimization of these geometric parameters was performed. The values of aspect ratio, length-to-width ratio, vertex angle and gap depth suitable for inchworm-like actuator are obtained. These findings are presented for the first time in the field of crawling soft robots and provide a theoretical basis for the development of pneumatic inchworm-like actuators. © 2024 IEEE.

关键词： Aspect ratio

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A Weighting Factors Autotuning Strategy for Model Predictive Torque control of Permanent Magnet Synchronous Motor Based on Adaptive Multi-Objective Black Hole Algorithm 42

A Weighting Factors Autotuning Strategy for Model Predictive...

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42nd Chinese control Conference, CCC 2023

作者： Zhou, Zixiang Wu, Tao School of Automation China University of Geosciences Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China System Integration and Industrial Robotics Laboratory Wuhan430074 China

ISBN: (纸本)9789887581543

The rational design of weighting factors in the cost function for finite control set model predictive torque control (FCS-MPTC) has been a matter of great interest in power electronics and electrical drives. In order to solve this problem, a weighting factors autotuning strategy for FCS-MPTC of permanent magnet synchronous motor (PMSM) based on the adaptive multi-objective black hole algorithm (AMOBH) is proposed. In this paper, the design process of the FCS-MPTC algorithm is first analyzed in detail. Then, an AMOBH algorithm that can take into account both population convergence and population diversity is introduced, and based on this algorithm, the design problem of the weighting factors is successfully transformed into a multi-objective optimization problem by means of reconstructing the cost function and designing the motor operation information collected in real time as the objective functions of the multi-objective optimization algorithm. Simulation results show that the proposed method can find a set of weighting factor combinations suitable for different working condition requirements, and these weighting factors can effectively improve the operation performance of the PMSM system. © 2023 Technical Committee on control Theory, Chinese Association of Automation.

关键词： Cost functions

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Rnbformer: A High-Performance Roadside Noise Barriers Recognition Algorithm 24

Rnbformer: A High-Performance Roadside Noise Barriers Recogn...

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3rd Asia Conference on Algorithms, Computing and Machine Learning, CACML 2024

作者： Zhang, Zirui Wu, Jiaheng Huang, Heqiang Chen, Junzhou Hu, Hongyu Zhang, Ronghui Guangdong Provincial Key Laboratory of Intelligent Transportation System School of Intelligent Systems Engineering Sun Yat-Sen Uniyersity Guangdong Guangzhou510275 China State Key Laboratory of Automotive Simulation and Control Jilin University Changchun Jilin Changchun130022 China

ISBN: (纸本)9798400716416

Traffic noise pollution is a significant source of urban pollution. Roadside noise barriers (RNBs) serve as a primary solution to urban traffic noise pollution, but achieving precise positioning of RNBs requires on-site inspections and manual labeling, which is time-consuming and labor-intensive. Deploying an RNB recognition algorithm on inspection vehicles equipped with cameras and positioning devices is an efficient approach to establishing a high-precision RNB positioning database. We propose a high-accuracy RNB recognition algorithm, RNBFormer. In the model design, we integrate both convolutional and transformer structures to enhance the model's global feature extraction capability while optimizing the representation of local features. Additionally, we incorporate a channel attention mechanism to provide more discriminative features at the channel level. We trained and tested our model on a publicly available dataset, achieving a classification accuracy of 98.93%. © 2024 ACM.

关键词： Convolution

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Temperature Rising Model of Tubular Permanent Magnet Linear Synchronous Motor Based on Transfer Learning-Deep Neural Network 42

Temperature Rising Model of Tubular Permanent Magnet Linear ...

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42nd Chinese control Conference, CCC 2023

作者： Wang, YiFei Wu, Tao School of Automation China University of Geosciences Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China System Integration and Industrial Robotics Laboratory Wuhan430074 China

ISBN: (纸本)9789887581543

High temperature rise of permanent magnet linear synchronous motor can lead to irreversible demagnetization of the motor permanent magnet, which can negatively affect the motor performance. To address this problem, a thermal modeling analysis method based on Transfer learning-Deep neural network (TL-DNN) was proposed in this paper. Its specific implementation steps include (1) corresponding to different heat source inputs, the equivalent thermal circuit method and the finite element analysis was adopted based on the structure and main parameters of PMLSM including overall average temperature rise, coil temperature rise and permanent magnet temperature rise from the data sets of the motor;(2) TL-DNN was used to fit the functional relationship between the input source features and the output targets based on the characteristics of the sample data sets. In order to verify the accuracy of the prediction model with small sample data sets, this paper divided the proportion of the data set and compared the results with other classic nonparametric models (random forest, support vector machine, and deep neural network). The results show that the TL-DNN model outperforms other machine learning models and has better robustness and generalization ability when the training datasets are insufficient, and achieves an organic combination of physical field path model and data-driven model, which provides a feasible solution for PMLSM modeling in the case of small samples. © 2023 Technical Committee on control Theory, Chinese Association of Automation.

关键词： Finite element method

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A Data Storage Method of intelligent Substation Based on Physical Model

A Data Storage Method of Intelligent Substation Based on Phy...

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2023 International Conference on Power Energy systems and Applications, ICoPESA 2023

作者： Liu, Shidan Tan, Qian Suo, Jianglei Chen, Xiaowa Jiao, Shaolin Wang, Feng Li, Yiquan Gu, Qianshuo Power Dispatching Control Center Guangdong Power Guangzhou China Shangdong University Key Laboratory of Power System Intelligent Dispatch and Control Jinan China

ISBN: (纸本)9798350345605

In view of the sharp increase in the amount of data in intelligent substations at present, the gap between the data transmission mode and the data storage mode is large, resulting in the problem of low data storage efficiency. This paper presents a data storage method of intelligent substation based on physical model. Firstly, this paper analyzes the data transmission mode of IEC 61850, introduces the object-oriented modeling technology of IEC 61850, the relationship between various configuration files, and the tree structure of the transmitted data. Then, this paper introduces the concept, structure and characteristics of the physical model. Build a physical model of the device based on the CID (Configured IED Description) file of the device, and form a device self-describing JSON file. After then, according to the characteristics of each service in the physical model, this paper uses different types of databases to store the data under this service. Finally, this paper develops and applies the method, and completes the back-to-back data storage model of a substation equipment. The experimental results show that the functional requirements and performance indicators of the developed substation data storage method can achieve the desired goals and effectively improve the efficiency of data storage. © 2023 IEEE.

关键词： Storage efficiency

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