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2nd IEEE International Conference on Electronic technology, Communication and Information, ICETCI 2022

作者： Yan, Hongliang Long, Hulin Zhang, Jianan School of Electronic Engineering Xi'an Shiyou University Shaanxi Key Laboratory of Oil and Gas Well Measurement and Control Technology Xi'an China

ISBN: (数字)9781728181158

ISBN: (纸本)9781728181158

For the problem of large torque ripple of the traditional permanent magnet synchronous motor (PMSM) model to predict the torque ripple of the control system, an improved DSVM finite set model prediction current control (FCS-MPC) strategy of PMSM is proposed. The use of DSVM technology to increase the optional voltage vector effectively reduces the error between the reference voltage vector and the prediction voltage vector, thereby reducing current and torque ripple. To avoid enumeration the voltage vector and calculation online of duty cycle, this strategy optimizes the voltage vector table in two steps, and then directly finds the corresponding duty cycle table, so that the voltage vectors in the prediction process are reduced from 38 to 14. At the same time, the duty cycle table is used to fix the switching frequency by linear combination of fixed voltage vectors. The new control strategy reduces current and torque ripple and reduces the computational burden added by the DSVM method. The Simulink simulation is verified, and the simulation results verify the effectiveness of the proposed control strategy. © 2022 IEEE.

关键词： Model predictive control

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Hyperspectral Image Change Detection Based on Simam Multi-Scale Joint Features

Hyperspectral Image Change Detection Based on Simam Multi-Sc...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Xinling Li Qingyan Wang Junping Zhang Yujing Wang School of Measurement-Control and Communication Engineering Harbin University of Science and Technology Harbin China School of Electronics and Information Engineering Harbin Institute of Technology Harbin China

ISBN: (数字)9798350360325

ISBN: (纸本)9798350360332

The remote sensing satellite system has steadily developed, and the availability of massive high-quality satellite remote sensing data has rapidly improved. The dynamic monitoring of land cover changes using hyperspectral data has received great attention. The existing change detection methods usually use the spatial correlation or spatial-spectral correlation of hyperspectral images, lacking an overall consideration of the three-dimensional temporal-spatial-spectral joint features, resulting in suboptimal change detection results. Given the considerations above, this paper proposes a SimAM multi-scale joint feature extraction network for hyperspectral image change detection. The proposed method first adopts SimAM multi-scale joint features network to consider the hyperspectral image as a whole, and extracts multi-scale temporal-spatial-spectral joint features from the network. Then, uses multi-scale feature weighted fusion module to weight different scale features after simple fusion, highlighting the change regions. Finally, adopts the batch-balance measurement module to measure the similarity of the bi-temporal fusion features and output the change detection result map. Experiments show that on two public datasets, the proposed method can achieve a good change detection effect.

关键词： Satellites Correlation Fuses Land surface Euclidean distance Feature extraction Monitoring

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Hyperspectral Image Classification Method Based On Node Similarity Feature Fusion

Hyperspectral Image Classification Method Based On Node Simi...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Jiameng Wang Qingyan Wang Junping Zhang Yujing Wang School of Measurement-Control and Communication Engineering Harbin University of Science and Technology Harbin China School of Electronics and Information Engineering Harbin Institute of Technology Harbin China

ISBN: (数字)9798350360325

ISBN: (纸本)9798350360332

There is abundant spectral and spatial information in Hyperspectral images (HSI). However, there exists a limitation of not using spatial information sufficiently in HSI classification. Besides, there is the limitation of mononuclear in feature extraction, resulting in insufficient feature extraction and insufficient utilization of data information. In view of these problems, a node similarity semi-supervised classification method of multiscale feature is proposed to break the limitation of mononuclear and achieve full extraction of spatial information. First, to extract pixel-level features, a three-dimensional (3-D) multiscale convolutional neural network (CNN) is used. Second, based on node similarity superpixel graph U-Net (NSGUNet) is proposed to extract superpixel-level features. Finally, the above two features are weighted fusing, the fused features are classified by sparse graph regularization. Experiments on three datasets illustrate that the proposed method is effective.

关键词： Accuracy Geoscience and remote sensing Feature extraction Convolutional neural networks Data mining Hyperspectral imaging Image classification

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Cross-Domain Few-Shot Learning With Spectral-Spatial Split-Attention For Hyperspectral Image Classification

Cross-Domain Few-Shot Learning With Spectral-Spatial Split-A...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Peng Luo Qingyan Wang Junping Zhang Shouqiang Kang School of Measurement-Control and Communication Engineering Harbin University of Science and Technology Harbin China School of Electronics and Information Engineering Harbin Institute of Technology Harbin China

ISBN: (数字)9798350360325

ISBN: (纸本)9798350360332

Hyperspectral image classification (HSIC) is a pivotal technology in hyperspectral remote sensing, playing a widespread role in remote sensing applications. However, the limited number of labeled samples has always made hyperspectral image classification difficult. In response to this issue, researchers have delved into cross-domain classification studies. Moreover, significant progress on cross-domain HSIC has been made in recent years. Nevertheless, existing methods exhibit shortcomings, including inadequate exploitation of spectral and spatial information and a slow training speed, rendering them unsuitable for downstream application tasks. To address these challenges, this paper introduces a model of cross-domain few-shot learning with spectral-spatial split attention(S 3 A-CFSL). Channel attention and split attention are presented to emphasize effective spectral and spatial information for HSIC adaptively. Additionally, the ResNet variant, called ResNeSt, is employed to expedite the training speed of the model. Experimental results demonstrate notable enhancements in the proposed method's classification accuracy and model training speed across two public datasets.

关键词： Training Adaptation models Accuracy Feature extraction Rendering (computer graphics) Data mining Few shot learning

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Joint Classification Of Hyperspectral And LiDAR Data Based On Heterogeneous Attention Feature Fusion Network

Joint Classification Of Hyperspectral And LiDAR Data Based O...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Zhaoyang Wang Qingyan Wang Junping Zhang Xintao Liang School of Measurement-Control and Communication Engineering Harbin University of Science and Technology Harbin China School of Electronics and Information Engineering Harbin Institute of Technology Harbin China

ISBN: (数字)9798350360325

ISBN: (纸本)9798350360332

The fusion of hyperspectral image (HSI) and LiDAR data for classification has gained widespread attention. However, the current fusion methods still have limitations on the use of heterogeneous data information and the interaction of heterogeneous data features. Therefore, we propose a classification method based on heterogeneous attention feature fusion network (HAFF-Net). Firstly, The convolutional neural network is employed to capture local spatial features from multi-scale inputs of heterogeneous data. Secondly, the heterogeneous feature attention module is designed to deeply fuse the extracted local spatial features and promote the interaction of heterogeneous data. Next, a transformer encoder is utilized to extract global spectral features. Finally, the two extracted features are classified by decision fusion using different classifiers. The proposed approach exhibits effectiveness through experimental results on two datasets.

关键词： Laser radar Fuses Convolution Soft sensors Geoscience and remote sensing Feature extraction Transformers

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Robust Federated Learning Algorithm Based on Chaotic Encryption and Dual-Server Detection

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IEEE Internet of Things Journal 2025年

作者： Kang, Shouqiang Wu, Yuxuan Wang, Yujing Wang, Qingyan Liang, Xintao Harbin University of Science and Technology School of Measurement and Control Technology and Communication Engineering Harbin150080 China

Federated learning allows users to collaboratively train models, but studies have shown that local model parameters may leak users’ privacy. Furthermore, the global model in federated learning could be compromised by Byzantine attacks. To address these issues, a robust federated learning algorithm based on chaotic encryption and dual-server detection (CDRFL) is proposed. First, CDRFL introduces a lightweight chaotic encryption method, where ciphertexts have a unique property of mutual cancellation, allowing the plaintext global model parameters to be derived without decryption. Moreover, CDRFL allows users to encrypt with different keys, further reducing the risk of privacy leakage. Next, a dual-server detection scheme is designed to defend against Byzantine attacks while preserving user privacy. Lastly, a reputation score mechanism with a forgetting function is proposed to fairly distinguish Byzantine participants. Experimental results demonstrate that, compared with popular robust aggregation schemes, CDRFL improves testing accuracy by 3.26% to 9.06%. When compared to existing chaotic encryption federated learning schemes, CDRFL enhances the ability to defend against Byzantine attacks and reduces time and communication overhead by 73.63% and 95.40%, respectively. © 2014 IEEE.

关键词： Federated learning

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FAILURE DATA ANALYSIS FOR TRACKED VEHICLES UNDER DIFFERENT ENVIRONMENTS USING PRINCIPAL COMPONENT ANALYSIS 14

FAILURE DATA ANALYSIS FOR TRACKED VEHICLES UNDER DIFFERENT E...

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14th International Conference on Quality, Reliability, Risk, Maintenance, and Safety engineering, QR2MSE 2024

作者： Hu, Junming Vehicle Measurement Control and Safety Key Laboratory of Sichuan Province Xihua University Chengdu China Provincial Engineering Research Center for New Energy Vehicle Intelligent Control and Simulation Test Technology of Sichuan Xihua University Chengdu China School of Automobile and Transportation Xihua University Chengdu China

ISBN: (纸本)9781837242672

Enhancing vehicle reliability is of paramount importance during the research and development phase, with reliability growth testing playing a crucial role in this process. Traditional reliability growth testing often treats vehicle failures under varying environmental conditions with uniform significance, overlooking the specific impacts these conditions may have on failure occurrence. This paper aims to illuminate the influence of environmental factors on vehicle failures. To explore the relationship between vehicle failures and the contexts in which they occur, Principal Component Analysis (PCA) was employed. The findings indicate that a diversified testing approach, which assigns different levels of importance to various testing scenarios, is recommended. This approach is likely to yield a more nuanced understanding of vehicle performance and foster a more effective strategy for enhancing reliability. © The Institution of engineering & technology 2024.

关键词： FAILURE DATA ANALYSIS PRINCIPAL COMPONENT ANALYSIS RELIABILITY GROWTH TESTING TRACKED VEHICLE

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BFP Net: Balanced Feature Pyramid Network for Small Apple Detection in Complex Orchard Environment

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Plant Phenomics 2022年第1期4卷 75-93页

作者： Meili Sun Liancheng Xu Xiude Chen Ze Ji Yuanjie Zheng Weikuan Jia School of Information Science and Engineering Shandong Normal UniversityJinanChina Key Laboratory of Facility Agriculture Measurement and Control Technology and Equipment of Machinery Industry Zhenjiang 212013China National Engineering Research Center for Apple Shandong Agriculture UniversityTaian 271018China School of Engineering Cardiff UniversityCardiff CF243AAUK

Despite of significant achievements made in the detection of target fruits,small fruit detection remains a great challenge,especially for immature small green fruits with a few *** closeness of color between the fruit skin and the background greatly increases the difficulty of locating small target fruits in the natural orchard *** this paper,we propose a balanced feature pyramid network(BFP Net)for small apple detection.

关键词： Net pyramid locating

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Dynamic Programming Based Strategies of Future Crop Plant 24

Dynamic Programming Based Strategies of Future Crop Plant

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Proceedings of the 2024 5th International Conference on Big Data Economy and Information Management

作者： Beining Zhang Xueying Wang Yongkang Deng Kai Chen Qinglang Liu Measurement and Control Technology and Instruments School of Nuclear Technology and Automation Engineering Chengdu University of Technology Chengdu Sichuan China School of Nuclear Technology and Automation Engineering Chengdu University of Technology Chengdu Sichuan China

ISBN: (纸本)9798400711862

In this paper, we develop a decision model based on dynamic programming to create an optimal crop plan for 2024-2030 in rural northern China. Considering different marketing scenarios, uncertainties, and crop substitutions and complementarities, this paper solves the problem with three sub-problems. First, the optimal cropping plan for 2024-2030 is calculated based on the cropping scenario for 2023. It is assumes that expected sales, cropping costs, yields and marketing prices are stable. Secondly, a multi-objective planning model was developed. It aimed at profitability and stability and took into account uncertainties. Finally, the planting plan was further optimised and it considered crop substitutability and complementarity, and the relationship between expected sales and selling prices and planting costs. Through dynamic planning and Monte Carlo simulation, this paper takes into account uncertainty and crop relationships and it solves the optimal crop planting plan for the years 2024-2030 with a maximum profit of about $7.737 million. Therefore, this paper proposes a decision model based on dynamic programming and it takes into account various factors of the planting plan for the next seven years. An effective method is provided for solving the crop planting strategy problem in sustainable agricultural development.

关键词： Dynamic Programming

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The Research Focuses on Enhancing the RTDETR Model for Improved Detection of PCB Defects

The Research Focuses on Enhancing the RTDETR Model for Impro...

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Advanced Algorithms and control engineering (ICAACE), International Conference on

作者： Gang Wang Yaohui Pan Yu Jun School of Mechanical Engineering Jilin Communications Polytechnic Changchun China School of Information and Control Engineering Jilin Institute of Chemical Technology Jilin China

ISBN: (数字)9798331535087

ISBN: (纸本)9798331535094

In response to the low accuracy, slow speed, and compatibility issues commonly exist in current PCB circuit board defect detection methods, this paper proposes an improved circuit board defect detection model called RTDETR-CGRFPN based on RTDETR. This model is designed with a Rectangular Self-Calibration Module for spatial feature reconstruction and pyramid context extraction. Additionally, considering the limited background information of PCB circuit boards and the performance limitations of general object detection models, FuseBlock Multi and Dynamic Interpolation Fusion modules are added to the original model for multi-scale feature fusion through dynamic interpolation. Furthermore, dynamic interpolation and multi-feature fusion are utilized to further enhance target detection capabilities in complex backgrounds. Moreover, Pyramid Context Extraction module is also introduced to enhance the model's perception of context and improve its ability to detect small defects. Experimental results demonstrate that our proposed algorithm achieves an average accuracy rate of 98.1% with a recall rate of 99.1% on the test dataset. In comparison, the original RTDETR-r18 algorithm has an average accuracy rate of 95.0% with a recall rate of 90.9%. Clearly, our proposed algorithm outperforms in these two metrics significantly. Additionally, this model maintains relatively few parameters while ensuring performance improvement and computational efficiency simultaneously; thus enabling quick and accurate defect detection in PCB circuit boards potentially providing technical support for fields such as smart factories.

关键词： YOLO Training Interpolation Accuracy Computational modeling Printed circuits Feature extraction Integrated circuit modeling Defect detection Context modeling

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