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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Lisong Ma 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

With the development of hyperspectral sensors, there is an increasing amount of accessible hyperspectral data, and the classification task for land cover categories has gained significant attention. Existing classification methods typically extract features from either the pixel or superpixel perspective. However, using a single-scale feature extraction approach fails to simultaneously consider both local and global features of land cover, leading to suboptimal classification results. To address this issue, this paper proposes a parallel graph attention network model based on pixel and superpixel feature fusion (SSPGAT) for hyperspectral image classification, which leverages the fusion of pixel-level and superpixel-level features. The proposed approach first employs spectral convolutional layers to reduce the redundant spectral dimension. Then, it utilizes graph attention network (GAT) to extract local and global features of land cover separately from the pixel and superpixel perspectives. Finally, a fully connected network is employed to classify the fused features from both branches. Experimental results on two different datasets demonstrate the effectiveness of the proposed approach.

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Remaining Life Prediction Method of Rolling Bearing Based on TD3 Optimization Model Updating Strategy

Remaining Life Prediction Method of Rolling Bearing Based on...

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International Conference on Electronic measurement and Instruments, ICEMI

作者： Yujing Wang Guangpeng Jiao Shouqiang Kang Jinbao Xie Yulin Sun School of Measurement-Control and Communication Engineering Harbin University of Science and Technology Harbin China School of Physics and Electronic Engineering Hainan Normal University Haikou China

To address the problems of model degradation and low prediction accuracy in the of update procedure the rolling bearing life prediction model, the updating strategy of twin delayed deep deterministic policy gradient(TD3) is suggested to optimize the prediction model, and it applied to rolling bearing remaining useful life prediction. Continuous wavelet transform is used to build a dataset by converting the rolling bearing vibration data into two-dimensional time-frequency spectrum diagram. Deep reinforcement learning is introduced to design the elements of state, action, reward value and loss function, which are employed to training of the prediction model, this model is composed convolutional neural network (CNN) and bidirectional long short-term memory. The delayed update and soft update techniques of the TD3 algorithm are used to reduce the update frequency of the CNN feature network. A new loss function is proposed to establish a new model updating strategy for solving the prediction model degradation problem. The experimental study using the PHM2012 dataset and the finding indicate that, in comparison to the compared methodologies, the suggested prediction method with model optimization strategy can reduce the root mean square error and mean square error by more than 23.8% and 20.8% respectively, reducing the prediction error by at least 18.6% and improve the scores by more than 0.213.

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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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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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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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Fault Diagnosis Method of Rolling Bearings Under Different Working Conditions Based on Federated Feature Transfer Learning

Fault Diagnosis Method of Rolling Bearings Under Different W...

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Sensing, measurement & Data Analytics in the era of Artificial Intelligence (ICSMD), International Conference on

作者： Shouqiang Kang Jiawei Yang Yulin Sun Yujing Wang Qingyan Wang V.I. Mikulovich School of Measurement-Control and Communication Engineering Harbin University of Science and Technology Harbin China Belarusian State University Minsk Belarus

A rolling bearing fault diagnosis method based on the federated feature transfer learning is proposed for the low accuracy of the diagnosis model in the presence of large differences in data distribution under different working conditions, difficulty in obtaining labeled data and non-sharing of data among different users. This method performs wavelet transformation on the time domain vibration data of rolling bearings to obtain a time-frequency diagram. The priori labeled public data and the multi-user island private data are regarded as the source domain and the target domain. The multi-representation feature extraction structure is introduced to improve the original residual network. Based on an improved residual network and multi-representation features in the source domain and the target domain, every local model and a federated global model are constructed. Through verification of bearing data, the proposed method can establish an effective fault diagnosis model with high fault diagnosis accuracy. It can integrate the knowledge of isolated island data without sharing data among multiple users.

关键词： Fault diagnosis Employee welfare Wavelet transforms Adaptation models Wavelet domain Transfer learning Rolling bearings

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Temperature Response Analysis of Intelligent OPPC Cable Core Based on Finite Element Simulation 22

Temperature Response Analysis of Intelligent OPPC Cable Core...

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22nd International Conference on Optical communications and Networks, ICOCN 2024

作者： Xie, Wenping Nie, Ming Liang, Yongchun Luo, Xiaoyu Han, Yu Li, Hongwei Tang, Xiaohui Electric Power Research Institute of Guangdong Power Grid Co. Ltd. Guangzhou510080 China School of Measurement and Communication Engineering Heilongjiang Provincial Key Laboratory of Quantum Control Harbin150006 China Harbin University of Science and Technology Harbin150001 China

ISBN: (纸本)9798350367652

The Optical Fiber Composite Overhead Conductor (OPPC) cable is a special type of power optical cable that combines overhead conductor and communication capabilities. It has advantages such as strong wind resistance, good corrosion resistance, high bandwidth transmission, low loss transmission, and high reliability. However, as the OPPC cable operates for a long time power transmission, its long-term working temperature has a significant impact on transmission performance and lifespan. Therefore, monitoring the temperature of the OPPC cable and internal fibers can improve the transmission capacity of the line, ensure good thermal stability, and provide important references for fault warning and maintenance. This study aims to explore the temperature characteristics of the OPPC cable through actual monitoring and analysis, providing guidance and decision-making basis for practical applications. © 2024 IEEE.

关键词： Corrosion resistance

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Quick Routing Response to Link Failure in Low-Earth Orbit Satellite Networks 8

Quick Routing Response to Link Failure in Low-Earth Orbit Sa...

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8th IEEE International Conference on Computer and communications, ICCC 2022

作者： Lu, Zhiping Zhi, Ruxin Ma, Weiguo School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing China Beijing China Key Laboratory of Information and Communication Systems Ministry of Information Industry Beijing Information Science and Technology University Beijing China Key Laboratory of Modern Measurement and Control Technology Ministry of Education Beijing Information Science and Technology University Beijing China

ISBN: (纸本)9781665450515

Low earth orbit satellite communication systems are attractive for provisioning global, high-speed and low latency Internet access services. In order to achieve global continuous multiple coverage, the number of satellites is tremendous. But Low earth orbit satellite networks are prone to instability due to unpredictable link failures and frequent topology changes. Therefore, in this paper, a novel routing schemes with quick response to link failures is first proposed. Based on Dijkstra algorithm, a scalable routing algorithm is proposed to minimize the end-to-end transmission delay. It takes advantage of the predictable constellation trajectory, and changes dynamically according to the changes of network topology. It provides the shortest routing path and an alternative path simultaneously. When a link failure is detected by one satellite, notification packets will be sent to its neighbors for adjustment to the alternative routing path. Notification packets also will be sent to the head node for the recalculation of routing table. Finally, extensive simulations have been conducted, and the results show that the proposed scheme is able to produce desired performance. © 2022 IEEE.

关键词： Routing algorithms

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Micro-vessel segmentation and tiny particle speed estimation

International Journal of Signal Processing, Image Processing...

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International Journal of Signal Processing, Image Processing and Pattern Recognition 2013年第4期6卷 441-449页

作者： Luo, Zhongming Liu, Zhuofu Li, Weijie Zhao, Dongyang Higher educational key lab for Measuring and Control Technology and Instrumentations of Heilongjiang School of Measurement-Control Technology and Communication Engineering Harbin University of Science and Technology Harbin China

On the basis of the flow feature in the micro blood vessels, a method to track the trace of cell movement was proposed in this paper. The algorithm employed the space domain enhancement, adaptive threshold segmentation techniques. For the sake of calculating blood flow, the active contour-based template matching method was applied to sequential images. The experimental results show that this method can accurately track movement of tiny particles and estimate its moving speed in the micro-blood vessels.

关键词： Image segmentation

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Development of quantitative loading monitoring device based on the ARM

Development of quantitative loading monitoring device based ...

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International Forum on Strategic Technology, IFOST

作者： Zhongming Luo Zhuofu Liu Chunying Song The higher educational key laboratory for Measuring & Control Technology and Instrumentations School of Measurement-Control Technology and Communication Engineering Harbin University of Science and Technology Harbin Heilongjiang China

The rapid development of the petrochemical industry and the increasing demand of energy require the loading process of liquid fuel more efficient, automatic and environmental friendly. The quantitative loading system includes computer control, instrument automation, network technology, and communication technique. The aim of crane tube loading system is to achieve automatic quantification of the monitor and control. ARM9-based automatic loading system is introduced in this paper, which can meet the requirement of large flow rapidly quantitative loading using quantitative loading control device. The device can improve the efficiency of oil production and economic benefits, reduce the labor intensity and has great significance in the aspect of market competitiveness.

关键词： Monitoring Loading Petrochemicals Biomass

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