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检索条件"机构=School of Measurement and Control Technology and Communication Engineering"

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engineering Vehicle Recognition and Multi-Object Tracking System based on YOLOv5 and DeepSort Algorithm 23

Engineering Vehicle Recognition and Multi-Object Tracking Sy...

引用

Proceedings of the 2023 5th International Conference on Video, Signal and Image Processing

作者： Yanbo Wu Xintao Liang Quan Cui Qingyan Wang Heilongjiang Government Affairs Big Data Center China School of Measurement-Control and Communication Engineering Harbin University of Science and Technology China School of Electronics and Information Engineering Harbin Institute of Technology China

ISBN: (纸本)9798400709272

This study explores traditional object detection algorithms and deep neural network-based engineering vehicle detection algorithms. We applied preprocessing algorithms such as image denoising, enhancement, and edge detection to process the images and constructed our own trainable dataset through labeling. Next, we used the YOLOv5 algorithm, which has high detection accuracy and real-time capability, for engineering vehicle detection. To address the issues of missed targets and predicted bounding box misalignment, we introduced the DeepSORT algorithm for target prediction and tracking. This algorithm utilizes Kalman filtering for estimation and updates and employs the Hungarian algorithm to associate data between consecutive frames, thereby achieving engineering vehicle tracking. To tackle the problem of frequent identity switching due to camera motion and non-uniform vehicle movements, we adopted the modified GIoU to calculate the intersection over union between trajectories and detected target bounding boxes, reducing identity jumps during the tracking process. Finally, we designed an engineering vehicle detection and tracking system and applied this algorithm to practical production.

关键词： DeepSORT algorithm YOLOv5 algorithm engineering vehicle detection multi-object tracking real-time detection

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ITD Sample Entropy and Probabilistic Neural Network Bearing Fault Diagnosis Model

ITD Sample Entropy and Probabilistic Neural Network Bearing ...

引用

UNIfied Conference of International Workshop on Defence Applications of Multi-Agent Systems, DAMAS 2023, International Conference on Maintenance engineering, IncoME-V 2023, International conference on the Efficiency and Performance engineering Network, TEPEN 2023

作者： Wang, Nan Wang, Hongjun Wang, Ze Wang, Liang Zhang, Zhuangzhuang School of Mechanical and Electrical Engineering Beijing Information Science and Technology University Beijing100192 China Key Laboratory of Modern Measurement and Control Technology BISTU Beijing100192 China High-End Equipment Intelligent Perception and Control Beijing International Science and Technology Cooperation Base BISTU Beijing100192 China

ISBN: (纸本)9783031494123

A new fault diagnosis method based on ITD sample entropy and probabilistic neural network is proposed. Firstly, the vibration signal of the equipment is decomposed using intrinsic time-scale decomposition (ITD) to obtain several proper rotation components (PRC) and a trend item that spreads from high frequency to low frequency. Secondly, the components that have a high degree of correlation with the original signal are selected as the research object, and their sample entropy is used as the feature vector. Finally, the feature vector is used to train the probabilistic neural network, and fault identification is performed using the probabilistic neural network. The proposed method is verified using experimental data to accurately identify the type and extent of equipment failure. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Failure analysis

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Aero-Engine Remaining Useful Life Prediction Using A Multi-Dimensional Convolutional Feature Fusion Framework

Aero-Engine Remaining Useful Life Prediction Using A Multi-D...

引用

2023 CAA Symposium on Fault Detection, Supervision and Safety for Technical Processes, SAFEPROCESS 2023

作者： Wang, Liangcheng Wang, Yang Zhang, Hong Wang, Yanwei Zheng, Ying Huazhong University of Science and Technology The Belt and Road Joint Laboratory on Measurement and Control Technology School of Artificial Intelligence and Automation Wuhan430074 China Wuhan Institute of Technology School of Mechanical & Electrical Engineering Hubei Wuhan430073 China

ISBN: (纸本)9798350337754

Accurate prediction of aero-engine remaining useful life (RUL) is essential for providing reliable maintenance or alarm decisions. The extraction of degraded features has a great impact on the accuracy of RUL prediction. This paper proposes a feature fusion framework that relies on a multi-dimensional convolutional neural network (MD-CNN). First, the data of each working condition is normalized separately to extract degradation features more effectively. Subsequently, the temporal features are extracted using the one-dimensional convolutional neural network (1D-CNN), while the spatial local features are captured through the utilization of the two-dimensional convolutional neural network (2D-CNN). Finally, the long short-term memory network (LSTM) is utilized as the prediction model to establish the mapping relationship from fusion features to RUL. The result shows that the performance in predicting using the proposed method surpasses that of the existing methods. © 2023 IEEE.

关键词： Convolution

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Parallel Graph Attention Network Model Based on Pixel and Superpixel Feature Fusion for Hyperspectral Image Classification

Parallel Graph Attention Network Model Based on Pixel and Su...

引用

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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Reconfigurable Feed Network For Multi-Polar Antennas

Reconfigurable Feed Network For Multi-Polar Antennas

引用

2023 International Applied Computational Electromagnetics Society Symposium, ACES-China 2023

作者： Shi, Fengyuan Chen, Juan Xu, Wenjing Yang, Jianfeng Tan, Jing School of Information and Communication Engineering Xi'an Jiaotong University Xi'an China School of Information and Control Engineering Xi'an University of Architecture and Technology Xi'an China Shanghai Aerospace Electronic Technology Institute Department of Antenna Shanghai China

ISBN: (纸本)9781733509657

A reconfigurable feed network for multi-polar antennas is proposed in this article. It uses PIN diodes to make reconfigurable structure and change the output. The network can produce four kinds of output signal with only one input port. It has four output ports with specific phase delay, which uses differential-feed to suppress cross polarization. The simulated results show that the operating frequency range of the feed network is about 2.5-3.5 GHz with good matching and isolation performance. The variable polarization and wide band make it be a good choice for late-model multi-polar antennas with both linear and circular polarization. © 2023 Applied Computational Electromagnetics Society (ACES).

关键词： Antenna feeders

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Deep Reinforcement Learning Vehicle Visual Navigation Based on Attention Mechanism and LSTM 16th

Deep Reinforcement Learning Vehicle Visual Navigation Based ...

引用

16th International Conference on Modelling, Identification and control, ICMIC 2024

作者： Yu, Zhentao Zhang, Guojun Li, Ruoting Liu, Peng School of Instrument and Electronics North University of China Taiyuan030051 China The State Key Laboratory of Dynamic Measurement Technology North University of China Taiyuan030051 China School of Electrical and Control Engineering North University of China Taiyuan030051 China

ISBN: (纸本)9789819617760

This paper studies the visual navigation of vehicles in unknown environments. Due to the lack of global information, the accuracy of graphical navigation method will be decline. We propose a double LSTM attention navigation model to improve vehicle navigation performance. The model utilizes a dual-channel LSTM network for processing time series data and introduces an attention mechanism into the asynchronous dominant actor critic algorithm to mitigate the reward sparsity problem. Through comparative with various models on the AI2-THOR platform, the enhanced model improves the cumulative rewards and success rates of visual navigation. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Deep reinforcement learning

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Optimal Scheduling Model of Community Integrated Energy System Based on Cooperative Game Theory

Optimal Scheduling Model of Community Integrated Energy Syst...

引用

2023 Panda Forum on Power and Energy, PandaFPE 2023

作者： Du, Jialin Zhang, Sen Luo, Shihua Yuan, Yiping Hu, Weihao School of Mechanical and Electrical Engineering Sichuan Provincial Key Lab of Power System Wide Area Measurement and Control University of Electronic Science and Technology of China Chengdu China

ISBN: (纸本)9798350321173

Currently, a large number of community integrated energy systems operate independently and lack coordination and cooperation with each other. On the whole, the complementary coupling potential between energy production and consumption has not been fully explored, which is not conducive to the efficient utilization of clean energy. To solve the above problem, this paper proposes an optimal scheduling model of community integrated energy system considering integrated demand response and carbon tax. Moreover, based on cooperative game theory, this paper adopts the Sharpley value to achieve fair and reasonable distribution of cooperation profits. The case study results indicate that the energy sharing model proposed in this article can achieve better economic benefits. © 2023 IEEE.

关键词： Game theory

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Wrapper-based feature selection for general data set: the quantum sand cat swarm optimization

Wrapper-based feature selection for general data set: the qu...

引用

Computer, Vision and Intelligent technology (ICCVIT), International Conference on

作者： Jin Yue Ying Guo Hongyuan Gao Dalian Institute of Measurement and Control Technology Dalian China College of Information and Communication Engineering Harbin Engineering University Harbin China

ISBN: (数字)9798331540043

ISBN: (纸本)9798331540050

The existing wrapper feature selection methods have problems such as the difficulty of balancing a relatively high classification accuracy rate with a low feature selection rate, and there are problems such as long time consumption. In response to these issues, this paper combines quantum computing theory and sand cat swarm optimization to design quantum sand cat swarm optimization and combined with K-nearest neighbor method for feature selection. The optimal solution of quantum sand cat swarm optimization is set as the selected optimal feature subset. Simulation experiments are executed on 6 UCI data sets to compare with 4 classical swarm intelligence algorithms. The simulation outcomes show that the wrapper feature selection on the ground of quantum sand cat swarm optimization achieves lower feature selection rate , faster time and higher classification accuracy than the comparison method. It is a high precision and practical wrapper feature selection method.

关键词： Quantum computing Accuracy High dimensional data Quantum mechanics Nearest neighbor methods Feature extraction Robustness Classification algorithms Particle swarm optimization Optimization

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Sparse Recovery Space Time Adaptive Processing Based on Log-Determinant 15

Sparse Recovery Space Time Adaptive Processing Based on Log-...

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15th International Conference on Signal Processing Systems, ICSPS 2023

作者： Chang, Yuqing Du, Xiaolin Jin, Biao Tian, Tuanwei Li, Jianbo School of Computer and Control Engineering Yantai University Yantai China Ocean College Jiangsu University of Science and Technology Zhenjiang China School of Physics and Electronics Henan University Kaifeng China School of Communication and Information Engineering Chongqing University of Posts and Telecommunications Chongqing China

ISBN: (纸本)9781510675056

Airborne radars usually face non-uniform clutter environments, and it is difficult to obtain enough independent and identical distributed (i.i.d.) training samples, which degrades the clutter suppression performance of space time adaptive processing (STAP). To address the problem, a log-determinant sparse rocovery based STAP (LSR-STAP) method is proposed in this paper. Through the sparse recovery theory of low-rank matrices and the prior knowledge of clutter covariance matrices, the corresponding problem is modeled using the log-determinant (LogDet) approximation of the rank function, and the solution of the resulting nonconvex optimization problem is derived in the framework of the symmetric alternating direction method of multipliers (S-ADMM). The simulation results show the superiority of the proposed method over similar algorithms. © 2024 SPIE.

关键词： Space time adaptive processing

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Receiver-Agnostic Radio Frequency Fingerprint Identification via Feature Disentanglement 24

Receiver-Agnostic Radio Frequency Fingerprint Identification...

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24th IEEE International Workshop on Signal Processing Advances in Wireless communications, SPAWC 2023

作者： Liu, Hongli Zhu, Shaoxian Yang, Liu Li, Qiang Lin, Jingran University of Electronic Science and Technology of China School of Information and Communication Engineering China Laboratory of Electromagnetic Space Cognition and Intelligent Control Beijing100089 China

ISBN: (纸本)9781665496261

The Radio Frequency Fingerprint Identification (RFFI) technique utilizes the subtle and unintentional modulation present in the transmitted RF waveform, caused by the non-ideal characteristics of the device, to uniquely identify an emitter. RFFI has recently gained significant attention, and deep learning (DL) approach has achieved superior performance. In this paper, we study the DL-based RFFI with an emphasis on the fingerprint contamination by receivers. Specifically, during reception, the receivers' fingerprints are inevitably introduced, and different receivers affect emitters' fingerprints differently, thereby degrading recognition performance when the RFFI recognition model is deployed at different receivers. To mitigate the receivers' effect, we propose a receiver-independent recognition model based on the idea of feature disentanglement. With the aid of some carefully designed model structure, loss functions and training strategy, the proposed model learns a feature space, in which the emitters' features are disentangled from the receivers', through multi-receiver's training data. When deploying the model on a new receiver, the emitter-dependent features are exploited to achieve receiver-independent identification. Experiments on two real-world datasets demonstrate that our method significantly outperforms the baseline method without considering fingerprint contamination. © 2023 IEEE.

关键词： Deep learning

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