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Fault Diagnosis Method of Rolling Bearings under Different Working Conditions Based on Federated Feature Transfer Learning 3

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

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3rd International Conference on Sensing, measurement and Data Analytics in the Era of Artificial Intelligence, ICSMD 2022

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

ISBN: (纸本)9781665492812

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. © 2022 IEEE.

关键词： Different working conditions domain adaptation fault diagnosis feature transfer learning federated learning

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Analysis and Simulation of Interference Effects on CSK Modulation Systems 15

Analysis and Simulation of Interference Effects on CSK Modul...

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15th International Congress on Image and Signal Processing, BioMedical engineering and Informatics, CISP-BMEI 2022

作者： Guan, Lele Xu, Zhan Tian, Lu Shi, Chenrui School of Information and Communication Engineering Beijing Information Science and Technology University Key Laboratory of Modern Measurement and Control Technology Ministry of Education Beijing China

ISBN: (数字)9781665488877

ISBN: (纸本)9781665488877

As the space environment of information transmission becomes more and more complex, the accuracy of communication becomes a new challenge. In order to study the anti-jamming performance of code shift keying (CSK), this paper mainly simulates the performance of CSK and binary phase shift keying (BPSK) under 5 signal interferences, and discusses it according to the bit error rates (BER) obtained by simulation. In the face of different forms of signal interference, CSK has better anti-jamming performance than BPSK. Under various forms of signal interference, CSK has the strongest anti-interference ability to multi-tone interference, and its anti-interference ability to pulse interference, 50% narrowband interference, single-tone interference and channel noise decreases successively. © 2022 IEEE.

关键词： Binary phase shift keying

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Research on the Wirtinger Flow algorithm based on quadratic distribution initial value 1

Research on the Wirtinger Flow algorithm based on quadratic ...

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1st Advanced Imaging and Information Processing Conference, AIIP 2023

作者： Xie, Zhenfei Yu, Xuelian Wang, Zhengxian Li, Heng Heilongjiang Provincial Key Laboratory of Quantum Control School of Measurement and Communication Engineering Harbin University of Science and Technology Harbin150080 China Shenzhen Engineering Laboratory of Aerospace Detection and Imaging College of Electronic and Information Engineering Harbin Institute of Technology Shenzhen518055 China

ISBN: (纸本)9781510671904

Phase retrieval algorithms, such as the Wirtinger Flow (WF) algorithm, are widely used in various fields. As a non-convex optimization algorithm for phase retrieval, WF is commonly employed in the reconstruction of holograms in holographic image projection. These types of algorithms typically involve two stages: an initialization stage and an iterative optimization stage. In the initialization stage, an initial value is provided, and a spectral method is used to calculate an approximate solution as the initial guess. The iterative optimization stage then utilizes the Wirtinger gradient to iteratively compute and converge the initial guess to a nearby real solution, thereby obtaining the global optimal solution. However, due to the random nature of the initial values, the computed results often exhibit significant instability. To address this issue, this paper proposes an approach based on a quadratic distribution for improving the stability of the results. In the initialization stage, the initial value is set as the quadratic distribution initial value. Then, the spectral method is applied again to calculate the initial guess. Since the quadratic distribution initial value is artificially assigned, it enhances the stability of the computed results. To validate this method, the paper applies the quadratic distribution initial value to both the initialization stage of the WF algorithm and the Truncated Amplitude Flow (TAF) algorithm. A comparison is made between the results obtained using random initial value and those obtained using the quadratic distribution initial values. The results demonstrate that compared to random initial values, the quadratic distribution initial values can achieve faster and equally accurate computation results with higher stability. Finally, this method is applied to simulation experiments of in-line digital holography, and the reconstruction results from the experiments further confirm the effectiveness of our approach. © 2023 SPIE.

关键词： Convex optimization

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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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Combined Classification of Hyperspectral and LiDAR Data based on Dual-Channel Cross-Transformer 23

Combined Classification of Hyperspectral and LiDAR Data base...

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

作者： Binbin Zhou Qingyan Wang Junping Zhang Yujing Wang 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

In the face of complex scenes, single-modal dominant classification tasks encounter limitations in performance due to insufficient information. On the other hand, joint classification of multimodal remote sensing data faces challenges such as data sample differences and lack of correlation in physical features between modalities, which can impact classification performance. To fully integrate the heterogeneous information of multimodal data and improve classification performance, we propose a dual-channel cross-transformer feature fusion extraction network. The framework leverages self-attention mechanisms to aggregate features within each modality, and the feature fusion module based on cross-modal attention fully considers the complementary information between modalities. Classification tasks are performed using the fused spatial-spectral features obtained from the joint representation of modalities. Extensive experiments conducted on the Houston and MUUFL datasets demonstrate the effectiveness of the proposed model compared to existing methods.

关键词： Hyperspectral image LiDAR data cross modality feature fusion transformer

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FPGA-Oriented Target Detection Accelerator Design

FPGA-Oriented Target Detection Accelerator Design

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Frontiers technology of Information and Computer (ICFTIC), IEEE International Conference on

作者： Weizhe Huang School of Measurement and Control Technology and Communication Engineering Harbin University of Science and Technology Harbin China

Target detection is widely applied in fields such as face recognition, autonomous driving, and industrial automation. However, when deploying target detection models based on convolutional neural networks on resource-constrained embedded platforms, issues like high computational complexity, long execution times, and significant on-chip memory usage arise. To address these challenges, this paper presents a target detection accelerator design based on Field Programmable Gate Array (FPGA). Firstly, the computational workload of convolutional neural networks is reduced by merging convolutional layers with batch normalization layers. Secondly, to mitigate on-chip storage space concerns, on-chip data fixed-point quantization is employed to decrease on-chip memory consumption. Subsequently, to tackle the issue of long execution times, a convolution calculation module based on the Winograd algorithm is designed, which reduces computation latency. Finally, a dual-buffer pipeline architecture is devised to optimize memory bandwidth. Experimental results demonstrate that the proposed solution accelerates YOLOv4-tiny on the ZYNQ-7020, achieving a performance of 21.35 GOP/s. The energy efficiency is 32.56 times higher than CPU and 9.76 times higher than GPU. Additionally, the convolution calculation module in this paper reduces inference time by 15% compared to the sliding window convolution calculation method.

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Multi-Object Detection and Classification in Construction Sites Based on YOLOv5 23

Multi-Object Detection and Classification in Construction Si...

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

作者： Xintao Liang Tianqi Jiang Qiang Fu Qingyan Wang 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

A thorough analysis of the framework structure of the YOLO algorithm is conducted, and based on the YOLOv5 algorithm, rapid detection and classification of extracted features are implemented. Addressing the issue of multi-object detection and classification in engineering sites, this study utilizes the YOLOv5 algorithm for object detection in engineering scenarios, constructs the ResNet50 network, and achieves training and recognition of categories for engineering vehicles, whether washed or unwashed. Additionally, the YOLOv5 algorithm is employed to detect whether construction personnel are wearing safety helmets and reflective clothing, enabling fast and accurate entity detection and classification at construction sites.

关键词： Deep Learning Object Detection ResNet50 YOLOv5

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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...

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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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MULTI-ROBOT PATH PLANNING AND COOPERATIVE OBSTACLE AVOIDANCE BASED ON MONTE-CARLO TREE SEARCH 10

MULTI-ROBOT PATH PLANNING AND COOPERATIVE OBSTACLE AVOIDANCE...

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10th International Symposium on Test Automation and Instrumentation, ISTAI 2024

作者： Dai, Juan Jing, Yanzhang Su, Zhong Zhu, Cui Beijing Key Laboratory of High Dynamic Navigation Technology Beijing Information Science & Technology University Beijing China Key Laboratory of Modern Measurement and Control Technology Ministry of Education Beijing China School of Automation Beijing Information Science & Technology University Beijing China School of Information and Communication Engineering Beijing Information Science & Technology University Beijing China

ISBN: (纸本)9781837242672

With the rapid development of artificial intelligence technologies, multi-robot cooperative processing tasks are becoming more and more common in various applications. However, traditional single-robot planning algorithms show obvious limitations in dealing with complex social interactions and dynamic environments in the real world. To address this challenge, this paper applies Monte Carlo Tree Search (MCTS) to multi-robot path planning and proposes a collaborative obstacle avoidance motion planning algorithm based on MCTS. Specifically, the algorithm prunes the Monte Carlo tree by excluding cases that do not meet the robot speed constraints and potential collisions, which significantly improves the computational speed and success rate of the algorithm, making it applicable to the multi-robot path conflict problem. In addition, we design a policy space that conforms to the robot's operation and a cost function that meets the requirements of robot path planning to plan an efficient cooperative path for each robot. Through a series of simulation experiments, we demonstrate the algorithm's path planning and obstacle avoidance performance in a variety of conflict scenarios. The results show that the method is highly cooperative and effective in complex environments. © The Institution of engineering & technology 2024.

关键词： Cost functions

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High-fidelity parametric amplification of Ince–Gaussian beams

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Chinese Optics Letters 2022年第11期20卷 105-109页

作者： Ding Yan Zhiyuan Zhong Tong Qi Hongying Chen Wei Gao Heilongjiang Provincial Key Laboratory of Quantum Control School of Measurement and Communication EngineeringHarbin University of Science and TechnologyHarbin 150080China

Ince–Gaussian (IG) beams, as eigenfunctions of the paraxial wave equation in elliptical coordinates, are attracting increasing interest owing to their propagation-invariant and full-field properties. Optical amplification via parametric interactions can further expand their application areas, yet it is rarely studied. In this work, we report on a high-fidelity parametric amplifier for IG beams. The nonlinear transformation of the spatial spectra of the signal and associated influences on the beam profiles of the amplified signal, under different pump structures, were theoretically and experimentally investigated. By using a perfect flattop beam as the pump, we show that the transverse structure of IG signals is well maintained,and the distortion induced by radial-mode degeneration is overcome during amplification. This proof-of-principle demonstration paves the way for a mode-independent and distortion-free amplifier of arbitrary structured light and has great significance in relevant areas, such as quantum optics, tunable infrared-laser generation, and image amplification.

关键词： optical parametric amplification Ince–Gaussian beam high fidelity perfect flattop beam

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