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6th International Conference on System Reliability and Safety engineering, SRSE 2024

作者： Xiang, Jiaxin Sun, Yongquan Wang, Hao Chen, Hongke Harbin University of Science and Technology School of Measurement and Control Technology and Communication Engineering Harbin China China Institute of Marine Technology & Economy Beijing China Xiamen University Tan Kah Kee College School of Mechanical and Electrical Engineering & Automation Zhangzhou China

ISBN: (纸本)9798350356083

Limited reliability data during the development of cruise ship automation systems often leads to subjective weight allocation. This paper proposes an reliability allocation method based on the Bayesian best-worst method and analytic hierarchy process to address this challenge. By considering the system reliability indices, influencing factors, and units need to be allocated, a hierarchical model is established for reliability allocation based on the analytic hierarchy process. The Bayesian best-worst method compares elements in each layer, generating best and worst matrices based on decision-makers. These matrices determine optimal weights for each layer's elements. The analytic hierarchy process determines relative importance weights among the system reliability index, influencing factors, and units. Consequently, the derived optimal weights are converted into allocation factors, which enable the allocation of reliability for cruise ship automation systems. This fusion-based approach presents a novel and effective method for optimizing reliability allocation during the development stage of complex systems. © 2024 IEEE.

关键词： Decision making

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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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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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Frequency Domain Separation of DAS Multi-Source Aliased Signals

Frequency Domain Separation of DAS Multi-Source Aliased Sign...

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2023 Asia communications and Photonics Conference/2023 International Photonics and Optoelectronics Meetings, ACP/POEM 2023

作者： Gu, Huaxin Liu, Shuaiqi Yu, Feihong Xu, Deyu Chen, Xingwei Shao, Liyang Wu, Yu Zhang, Haifeng College of Innovation and Entrepreneurship Southern University of Science and Technology Shenzhen China Southern University of Science and Technology Department of Electrical and Electronic Engineering Shenzhen China School of Information and Communication Engineering University of Electronic Science and Technology Chengdu China Research Institute of Tsinghua University Advanced Measurement and Control and Equipment R&d Center Chengdu China

ISBN: (纸本)9798350312614

In this paper, fast independent vector analysis (FastIVA) based on convolutional aliasing model is proposed to separate the aliased signals collected by distributed acoustic sensing (DAS) system, and the time-frequency entropy is used to judge the separation performance. The results show that the time-frequency entropy interval of the separated signals and the source signals correspond to each other, which means that the aliased signals are separated. This method is used to improve the accuracy of DAS system signal detection and recognition in complex real environment, and reduce the number of false alarm events. © 2023 IEEE.

关键词： convolutional aliasing model DAS FastIVA time-frequency entropy

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A Review of Machine Tool Motorized Spindle Variable Pressure Preload Technology Research

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Recent Patents on engineering 2025年第5期19卷

作者： Dai, Ye Deng, Hao Pan, Gaofeng Chen, Xinda Key Laboratory of Advanced Manufacturing and Intelligent Technology Ministry of Education School of Mechanical and Power Engineering Harbin University of Science and Technology Harbin150080 China School of College of Measurement and Control Technology and Communication Engineering Harbin University of Science and Technology Harbin150080 China

High-precision spindles are a vital study topic because of the growing demand for high-speed, high-precision, reliable, and long-lasting machine tools. Bearing preload is the primary means of improving the accuracy of the spindle rotor system. This patent study aims to summarize the research results, systematically analyze and evaluate the machine tool motorized spindle variable pressure preload technology, and provide a scientific basis for the practical application of the motor spindle variable pressure preload technology. This study is an in-depth analysis of the basic principles of the electric spindle preloading technology, the study of its influence mechanism on the thermal deformation, stiffness, slewing error, and life of the spindle system, and a discussion of the current shortcomings in the field of spindle preloading to provide a reference for the subsequent optimization. In the spindle system, the performance of rolling bearings and the design of bearing preload have an important impact on the spindle's accuracy and stability. The traditional preload method has yet to meet the requirements of modern high-speed machine tools. Real-time controllable preload has become the new direction of development, but the current preload technology still has certain limitations and needs to be further improved. In the future, the spindle bearing variable pressure preload technology will be more efficient and intelligent. © 2025 Bentham Science Publishers.

关键词： Machine tools

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Fault Classification Method of Information System Based on Cascaded Convolutional Neural Network Model 5

Fault Classification Method of Information System Based on C...

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5th IEEE International Conference on Information Systems and Computer Aided Education, ICISCAE 2022

作者： Ou, Ruiting Chen, Shuo Qiao, Lin Liu, Ziang Cheng, Xu Qiao, Yongfeng State Grid Liaoning Information and Communication Company Shenyang China College of Computer Science and Engineering Northeastern University Shenyang China College of Economics and Management Shenyang Agricultural University Shenyang China Dandong Dongfang Measurement & Control Technology Co. Ltd Dandong China

ISBN: (数字)9781665481229

ISBN: (纸本)9781665481229

Artificial intelligence plays an important role in data anomaly detection, which can significantly improve the speed and accuracy of fault diagnosis and detection in power information system. With its powerful fitting ability, deep learning begins to emerge in various fields, especially in the field of da-ta anomaly detection, which can also use deep learning methods. Due to various types and similar features of power data sets, fault classification cannot be carried out accurately. At present, frequent detection of text information needs to be performed manually. To solve this problem, this paper proposes a fault classification method for information system based on cascaded convolutional neural network model. BERT deep learning network is used for model training, and a cascaded classification network is constructed. Multiple BERT models are called hierarchically to achieve the fault detection effect of hierarchical classification. The method presented in this paper improves the accuracy of fault prediction and classification, which also provides fast, accurate and reliable decision information basis for improving fault diagnosis. © 2022 IEEE.

关键词： Failure analysis

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Cooperative control for Low-Voltage Ride-Through Based Improved Active Crowbar of Dfigm

SSRN

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SSRN 2024年

作者： Zhang, Yu Li, Kai Gao, Hanying Kang, Shouqiang Sun, Yutian Wang, Peng College of Electrical and Electronic Engineering Harbin University of Science and Technology Harbin China College of Measurement and Control Technology and Communication Engineering Harbin University of Science and Technology Harbin China Harbin Large Motor Research Institute Harbin China College of Electrical and Electronic Engineering Nanyang Technological University Singapore

To solve the low voltage fault problem of doubly-fed induction generator-motor (DFIGM), this paper proposes a cooperative control strategy based on improved current tracking control (ICTC) and improved active crowbar topology. The strategy adopts ICTC in the case of slight dip fault. Under the constraints of rotor voltage and current, the tracking coefficient is optimized by particle swarm optimization (PSO) algorithm. The improved active crowbar is operated when severe dip fault occurs, which decreases the negative sequence component, reduces the total harmonic distortion (THD) of the point of common coupling (PCC) current. Taking a 10 MW DFIGM as a model, the magnetic flux variation characteristics are analyzed, and the cooperative control method in this paper is compared with the inductance emulating control (IEC) and the traditional crowbar in the case of symmetrical voltage dip fault and interphase voltage dip fault. The simulation experimental results verify the effectiveness and applicability of the proposed method. © 2024, The Authors. All rights reserved.

关键词： Asynchronous generators

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Spectrum Sensing for Cognitive Radio Based on Feature Extraction and Deep Learning 11

Spectrum Sensing for Cognitive Radio Based on Feature Extrac...

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2022 11th International Conference on Information and Electronics engineering, ICIEE 2022

作者： Geng, Yue Huang, Jingyi Yang, Jianxin Zhang, Sen School of Electrical and Electronic Engineering Nanyang Technological University Singapore639798 Singapore College of Physics and Optoelectronic Engineering Shenzhen University Shenzhen518061 China School of Mechanical Engineering & Automation Beihang University Beijing100191 China School of Measurement-Control and Communication Engineering Harbin University of Science and Technology Harbin150080 China

In cognitive radio, spectrum sensing is used to determine whether the primary user is using the spectrum based on the signal received on a specific frequency band, thereby determining whether the secondary user can use the spectrum. The main problem faced by spectrum sensing is how to identify the existence of the primary signal under the condition of low signal-to-noise ratio (SNR). Compared with traditional technologies, deep learning methods can identify the features of input data more efficiently and accurately. Based on convolutional neural network (CNN), This paper regard spectrum sensing as a binary classification problem. In the method we proposed, different features of received are extracted, and a dataset of feature matrices obtained under different SNRs is constructed for the training of the CNN network. Experiment results show that under the condition of low signal-to-noise ratio, the performance of our method is improved compared with the traditional method, and the combination of different features can improve the sensing accuracy. © Published under licence by IOP Publishing Ltd.

关键词： Cognitive radio

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Hyperspectral Image Cross Scene Classification Combined Domain Adaptation with XGBoost

Hyperspectral Image Cross Scene Classification Combined Doma...

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2024 International Conference on measurement, communication, and Virtual Reality, MCVR 2024

作者： Liu, Chengyang Wu, Haibin Li, Jiarong Liu, Xinyu Yan, Siqi Wang, Aili Heilongjiang Province Key Laboratory of Laser Spectroscopy Technology and Application College of Measurement and Control Technology and Communication Engineering Harbin University of Science and Technology Harbin150080 China School of Electronics and Information Engineering Harbin Institute of Technology Harbin150001 China

ISBN: (纸本)9781510691773

To address the issues of domain discrepancies and limited labeled samples in hyperspectral image classification of cross scene, this paper proposes an ensemble learning framework that integrates eXtreme Gradient Boosting (XGBoost) with confidence domain adaptation strategies to improve classification performance. First, the Houston dataset from different scenes is input separately into a deep embedding model. The hyperspectral images are embedded into a low-dimensional space, ensuring that similar spectra are represented more closely in this space, thereby capturing the intrinsic structure and distribution of the hyperspectral image dataset. Subsequently, these samples are mapped into a discriminator model, where domain adaptation techniques are employed to reduce differences in feature distributions across different scenes. Additionally, confidence-based learning is used to identify and remove noisy samples in the Houston dataset, thereby enhancing the alignment of feature distributions among various scenes. Finally, through ensemble learning, a weighted voting strategy is employed to combine multiple base classifiers into a strong classification model. This approach better captures the complex features within the data, thereby reducing the bias of individual classifiers and enhancing the overall generalization ability and robustness of the model. Experimental results on the Houston dataset demonstrate that the proposed method outperforms popular domain adaptation learning models and traditional methods in terms of classification accuracy and robustness, showcasing its potential and advantages in practical applications. This result confirms the effectiveness of combining XGBoost and confidence domain adaptation strategies in handling hyperspectral image classification. © 2025 SPIE.

关键词： Photointerpretation

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Reliability Allocation of Cruise Ship Automation System Based on BBWM-AHP

Reliability Allocation of Cruise Ship Automation System Base...

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System Reliability and Safety engineering (SRSE), International Conference on

作者： Jiaxin Xiang Yongquan Sun Hao Wang Hongke Chen School of Measurement and Control Technology and Communication Engineering Harbin University of Science and Technology Harbin China China Institute of Marine Technology & Economy Beijing China School of Mechanical and Electrical Engineering & Automation Xiamen University Tan Kah Kee College Zhangzhou China

ISBN: (数字)9798350356083

ISBN: (纸本)9798350356090

Limited reliability data during the development of cruise ship automation systems often leads to subjective weight allocation. This paper proposes an reliability allocation method based on the Bayesian best-worst method and analytic hierarchy process to address this challenge. By considering the system reliability indices, influencing factors, and units need to be allocated, a hierarchical model is established for reliability allocation based on the analytic hierarchy process. The Bayesian best-worst method compares elements in each layer, generating best and worst matrices based on decision-makers. These matrices determine optimal weights for each layer’s elements. The analytic hierarchy process determines relative importance weights among the system reliability index, influencing factors, and units. Consequently, the derived optimal weights are converted into allocation factors, which enable the allocation of reliability for cruise ship automation systems. This fusion-based approach presents a novel and effective method for optimizing reliability allocation during the development stage of complex systems.

关键词： Automation Analytic hierarchy process Reliability engineering Bayes methods Safety Reliability Resource management Indexes Marine vehicles Matrix converters

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