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Permanent magnet synchronous motor demagnetization fault diagnosis based on PCA-ISSA-PNN

SCIENTIFIC REPORTS 2024年第1期14卷 1-15页

作者： Yu, Yinquan Li, Yang Zeng, Dequan Hu, Yiming Yang, Jinwen East China Jiaotong Univ Inst Precis Machining & Intelligent Equipment Mfg Key Lab Conveyance & Equipment Minist Educ Nanchang 330013 Peoples R China East China Jiaotong Univ Sch Mechatron & Vehicle Engn Nanchang 330013 Peoples R China

Aiming at the demagnetization fault problem of the permanent magnet synchronous motor (PMSM), a demagnetization fault diagnosis method based on the combination of the principal component analysis (PCA) algorithm, the improved sparrow search algorithm (ISSA), and the probabilistic neural network (PNN) algorithm is proposed. First, the principal components of phase currents are extracted using PCA. Second, ISSA is used to optimize the smoothing coefficients of the PNN algorithm, and the optimized PNN algorithm is combined with PCA to obtain the PCA-ISSA-PNN fault diagnosis model. Finally, the established fault diagnosis model was tested using the current data collected from the experiments and compared with the fault diagnosis indexes and optimization performance of the conventional PNN, PCA-PNN, PCA-GA (genetic algorithm)-PNN, PCA-DA (dragonfly algorithm)-PNN, PCA-GTO (artificial gorilla troop optimizer)-PNN, PCA-AHA-PNN, and PCA-SSA-PNN. The test results show that the fault diagnosis accuracy of PCA-ISSA-PNN reaches 95.83%, and the fault diagnosis indexes are significantly higher than those of PNN, PCA-PNN, PCA-GA-PNN, and PCA-DA-PNN;its optimization performance is also significantly better than that of PCA-GTO-PNN, PCA-AHA-PNN, and PCA-SSA-PNN, which verifies the accuracy and efficiency of the proposed method.

关键词： Permanent magnet synchronous motor principal component analysis algorithm Improved sparrow search algorithm Probabilistic neural network

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EXPERIMENTAL STUDY ON INFRARED PHASE-LOCKED THERMAL IMAGING INSPECTION OF CARBON FIBER REINFORCED POLYMER LAMINATES

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THERMAL SCIENCE 2022年第2期26卷 1105-1111页

作者： Tang, Qing-Ju Ji, Juan Fan, Wei-Ming Ran, Ling An, Si-Jie Zhang, Tao Bu, Chi-Wu Heilongjiang Univ Sci & Technol Sch Mech Engn Harbin Peoples R China Harbin Univ Commerce Sch Light Ind Harbin Peoples R China

Aiming at the debonding defect of carbon fiber reinforced polymer laminates, an infrared phase-locked thermal imaging inspection system was established, and the influence of different defect diameter and depth parameters on the test was analyzed. The principal component analysis algorithm and Karhunen-Loeve Transform algorithm are used to process the image sequence, and the signal-tonoise ratio is calculated. It is concluded that principal component analysis algorithm can improve the image quality more. Gray enhancement and sharpening filter are used to improve the image clarity, thus accurately segmenting the defect features, and realize a clear and intuitive visual image.

关键词： carbon fiber reinforced polymer laminate infrared thermal imaging principal component analysis algorithm

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Path loss modelling based on path profile in urban propagation environments

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IET COMMUNICATIONS 2022年第6期16卷 685-694页

作者： Juang, Rong-Terng Feng Chia Univ Dept Elect Engn Taichung Taiwan

This paper presents a path loss model based on path profile in urban propagation environments for 5G systems. Although deep learning approaches are indeed powerful in tasks involving prediction or classification, they often lack transparency and suffer from high computational complexity. The proposed model combines the log-distance path loss model for line-of-sight propagation scenarios and a machine-learning-based model for non-line-of-sight (NLOS) cases. This paper uses the principal component analysis algorithm to extract relevant features out of some selected attributes of the path profile for NLOS cases. Then, the path loss model can be constructed based on the approach of polynomial regression. Simulation results show that the proposed model outperforms the conventional models when operating in the 3.5 GHz frequency band. The standard deviation of prediction error was reduced by about 22.2-37.2% dB when compared to the conventional models. Furthermore, the prediction performance was also evaluated in a non-standalone 5G New Radio network in the urban environment of Taipei city. The real-world measurements show that the standard deviation of prediction error can be reduced by 3.33-6.13 dB when compared to the conventional models.

关键词： prediction error regression analysis wireless channels deep learning (artificial intelligence) relevant feature extraction computational complexity path loss modelling urban propagation environments urban environment nonline-of-sight cases path profile line-of-sight propagation scenarios log-distance path loss model nonstandalone 5G New Radio network radiowave propagation 5G mobile communication Taipei city deep learning approaches polynomial regression machine-learning-based model principal component analysis principal component analysis algorithm NLOS cases telecommunication computing

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A PCA-EEMD-CNN-Attention-GRU-Encoder-Decoder Accurate Prediction Model for Key Parameters of Seawater Quality in Zhanjiang Bay

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MATERIALS 2022年第15期15卷 5200-5200页

作者： Xie, Zaimi Li, Zhenhua Mo, Chunmei Wang, Ji Guangdong Ocean Univ Sch Math & Comp Sci Zhanjiang 524088 Peoples R China Guangdong Engn Technol Res Ctr Intelligent Ocean Zhanjiang 524088 Peoples R China Guangdong Ocean Univ Coll Elect & Informat Engn Zhanjiang 524088 Peoples R China

In order to effectively solve the problem of low accuracy of seawater water quality prediction, an optimized water quality parameter prediction model is constructed in this paper. The model first screened the key factors of water quality data with the principal component analysis (PCA) algorithm, then realized the de-noising of the key factors of water quality data with an ensemble empirical mode decomposition (EEMD) algorithm, and the data were input into the two-dimensional convolutional neural network (2D-CNN) module to extract features, which were used for training and learning by attention, gated recurrent unit, and an encoder-decoder (attention-GRU-encoder-decoder, attention-GED) integrated module. The trained prediction model was used to predict the content of key parameters of water quality. In this paper, the water quality data of six typical online monitoring stations from 2017 to 2021 were used to verify the proposed model. The experimental results show that, based on short-term series prediction, the root mean square error (RMSE), mean absolute percentage error (MAPE), and decision coefficient (R-2) were 0.246, 0.307, and 97.80%, respectively. Based on the long-term series prediction, RMSE, MAPE, and R-2 were 0.878, 0.594, and 92.23%, respectively, which were all better than the prediction model based on an enhanced clustering algorithm and adam with a radial basis function neural network (ECA-Adam-RBFNN), a prediction model based on a softplus extreme learning machine method with partial least squares and particle swarm optimization (PSO-SELM-PLS), and a wavelet transform-depth Bi-S-SRU (Bi-directional Stacked Simple Recurrent Unit) prediction model. The PCA-EEMD-CNN-attention-GED prediction model not only has high prediction accuracy but can also provide a decision-making basis for the water quality control and management of aquaculture in the waters around Zhanjiang Bay.

关键词： seawater water quality prediction attention principal component analysis algorithm ensemble empirical modal decomposition algorithm sea area near Zhanjiang Bay

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Air quality analysis of Sichuan province based on complex network and CSP algorithm

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INTERNATIONAL JOURNAL OF MODERN PHYSICS C 2022年第1期33卷 2250007-2250007页

作者： Huang, Xiao Li Hu, Si Yu Chen, Jing Xian Feng, Wan Qi Xihua Univ Coll Elect & Elect Informat Chengdu 610039 Peoples R China Fribourg Univ Dept Phys CH-1700 Fribourg Switzerland

The air quality is directly related to people's lives. This paper selects air quality data of Sichuan Province as the research object, and explores the inherent characteristics of air quality from the perspective of complex network theory. First, based on the complexity of network topology and nodes, a community detection algorithm which combines the clustering idea with principal component analysis (PCA) algorithm and self-organization competitive neural network (SOM) is designed (CSP). Compared with the classic community detection algorithm, the result proves that the CSP algorithm can accurately dig out a better community structure. Second, based on the strong correlation distance and strong correlation coefficient of the air quality network, the Sichuan Air Quality Complex Network (SCCN) was constructed. The SCCN is divided into five communities using the CSP algorithm. Combining the characteristics of each community and the Hurst coefficient, it is found that the air quality inside the community has long-term memory. Finally, based on the idea of time-dependent cross-correlation, this paper analyzes the cross-correlation of AQI time series of different stations in each community, constructs a directed air quality cross-correlation network combined with complex network theory, and locates the important pollution sources in each region of Sichuan Province according to the topological structure of the network. The work of this paper can provide the corresponding theoretical support and guidance for the current environmental pollution control.

关键词： Complex network self-organizing competitive neural network principal component analysis algorithm air quality time-dependent cross-correlations

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Plant leaf classification using GIST texture features

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IET COMPUTER VISION 2019年第4期13卷 369-375页

作者： Kheirkhah, Fateme Mostajer Asghari, Habibollah ICT Res Inst ACECR Tehran Iran

The leaves of plants have rich information in recognition of plants. In general, agriculture experts accomplish information extraction from the leaves. Since the leaves contain useful features for recognising various types of plants, so these features can be extracted and applied by automatic image recognition algorithms to classify plant species. In this study, the authors investigate a novel approach for recognition of plant species using GIST texture features. Then, the principal and suitable features are selected by principal component analysis (PCA) algorithm. In the classification step, three different approaches such as Patternnet neural network, support vector machine, and K-nearest neighbour (KNN) algorithms were applied to the extracted features. For evaluation of the authors' approach, they applied their proposed algorithm on three famous datasets. In comparison to some widely used features, the results show that their approach outperforms the other methods in the case of the time and the accuracy. The best results were achieved by applying PCA algorithm to GIST feature vector and using the Cosine KNN classifier.

关键词： principal component analysis feature extraction image classification image texture biology computing feature selection information extraction automatic image recognition algorithms plant species GIST texture features principal component analysis algorithm plant leaf classification

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UWB-radar-based synchronous motion recognition using time-varying range-Doppler images

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IET RADAR SONAR AND NAVIGATION 2019年第12期13卷 2131-2139页

作者： Wang, Yichuan Zhou, Jinhai Tong, Jingping Wu, Xiangfei Zhejiang Univ Coll Informat Sci & Elect Engn Hangzhou 310027 Peoples R China Hangzhou Magent Intelligent Technol Co Ltd Hangzhou 310000 Peoples R China

Motion recognition has long been a research topic in the field of ambient assisted living. Radar has a good application value in indoor activity monitoring due to its privacy protection and non-intrusive. In this study, the authors use an ultra-wideband (UWB) radar with the centre frequency of 7.25 GHz to receive the reflection signal. Time-frequency analysis is performed on the radar signal to attain the time-varying range-Doppler images (TRDI) which represent changes in motion characteristics over time. They then use the principal component analysis (PCA) algorithm or a pre-trained convolutional autoencoder (CAE) to extract features from TRDI. Finally, gated recurrent unit is employed to dynamically model these features and classify different motions. This recognition process can be synchronised with the action flow without having to wait for the motion to complete before starting the classification. The experimental results show that it has an accuracy of 93.06% for the PCA-based method and 96.80% for the CAE-based method in recognising eight kinds of indoor motions, reaching or even exceeding the performance of the non-synchronous algorithms.

关键词： time-frequency analysis synchronisation image motion analysis ultra wideband radar object detection feature extraction Doppler radar patient monitoring principal component analysis UWB-radar-based synchronous motion recognition time-varying range-Doppler images ambient assisted living good application value indoor activity monitoring ultra-wideband radar centre frequency reflection signal time-frequency analysis radar signal TRDI motion characteristics principal component analysis algorithm pre-trained convolutional autoencoder classify different motions recognition process CAE-based method indoor motions nonsynchronous algorithms frequency 7 25 GHz

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Classification of healthcare data using hybridised fuzzy and convolutional neural network

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HEALTHCARE TECHNOLOGY LETTERS 2019年第3期6卷 59-63页

作者： Ramasamy, Balamurugan Hameed, Abdul Zubar M Kumarasamy Coll Engn Dept Mech Engn Karur 639113 TN India King Abdulaziz Univ Fac Engn Jeddah Saudi Arabia

Healthcare performs a key role in the health of humans in the world. While gathering a huge amount of medical data, the problems will appear on the classification of healthcare data. In this work, a fuzzy hybridised convolutional neural network (FCNN) model is stated to guess the class of healthcare data. This model collects the information from the data set and builds the decision table based on the collected features from data sets. The attributes that are unrelated are deleted by using principal component analysis algorithm. The decision of normal and cardiac disease is described by using FCNN classifier. Using the data sets from UCI (University of California Irvine) repository the estimation of the presented model is carried on. The performance of the authors' classification technique is measured by various metrics such as accuracy, F-measure, G-mean, precision, and recall. The experimental results while compared with some of the existing machine learning methods such as probabilistic neural network, support vector machine and neural network, show the higher performance of FCNN. This model presented in this work acts as a decision support pattern in healthcare for therapeutic specialists.

关键词： diseases support vector machines learning (artificial intelligence) principal component analysis health care decision tables pattern classification medical computing fuzzy neural nets fuzzy hybridised convolutional neural network model probabilistic neural network medical data healthcare data classification decision table principal component analysis algorithm cardiac disease FCNN classifier model author classification technique machine learning methods support vector machine decision support pattern

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Feature Recognition of Face with Real-Time Variations Using Eigen Face Approach Methodology with PCA algorithm 2nd

Feature Recognition of Face with Real-Time Variations Using ...

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2nd International Conference on Intelligent Communication, Control and Devices (ICICCD)

作者： Dar, Ishfaq Gaffar Khan, Azzan Sharma, Manish Amity Univ Amity Sch Engn & Technol Noida 201303 UP India

ISBN: (纸本)9789811059032;9789811059025

Face is a composite multidimensional structure, and it needs great assessing methods for acknowledgment. Our approach regards confront acknowledgment as a two-dimensional acknowledgment issue. In this plan, confront acknowledgment is finished by foremost component analysis (PCA) (Biometric Technology Application Manual Volume One: Biometric Basics, National Biometric Security Project, 2008 [1, Turk and Pentland, "Face Recognition using Eigen-faces", IEEE Conference on Computer Vision and Pattern Recognition, 1991 2]). The primary point is to execute the model (framework) for a chosen confront and separate it from a substantial number of as of now put away faces with some ongoing varieties also. The Eigen-confront approach utilizes principal component analysis (PCA) calculation for the acknowledgment of pictures. It gives us efficient approach to discover the lower dimensional space. The face is characterized by Eigen-confronts which are Eigenvectors of the arrangement of confronts, which may not relate to general facial component, for example, lips, nose, and eyes. The framework (Velikiy Novgorod, "Pattern Recognition", the 6th international Conference on Image Analysion, October 21-26, 2002 [3]) performs by anticipating pre-removed face picture onto an arrangement of face space that speaks to noteworthy varieties among known face pictures. Face will be labeled as perceived or not perceived face in the wake of coordinating with the present database. In the event that the client is new to the face acknowledgment framework, then his/her information (format) will be put away in the database else coordinated against the information (layouts) (Turk and Pentland, Face Recognition using Eigen-faces, IEEE Conference on Computer Vision and Pattern Recognition, 1991 [4]) which is as of now put away in the database. The dimensionality lessening through PCA represents the littler face space than the preparation set of appearances and henceforth more prominent computational adap

关键词： Feature recognition Real-time variation principal component analysis algorithm Face Sociology Vectors Databases Covariance matrix

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An intrusion detection method for internet of things based on suppressed fuzzy clustering

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EURASIP JOURNAL ON WIRELESS COMMUNICATIONS AND NETWORKING 2018年第1期2018卷 113-113页

作者： Liu, Liqun Xu, Bing Zhang, Xiaoping Wu, Xianjun Guangdong Ocean Univ Sch Math & Comp Zhanjiang Peoples R China Guangdong Univ Petrochem Technol Sch Comp & Elect Informat Maoming Peoples R China Univ Sheffield Sch Math & Stat Sheffield S3 7RH S Yorkshire England Guangdong Univ Petrochem Technol Sch Comp Ctr Maoming Peoples R China

In order to improve the effectiveness of intrusion detection, an intrusion detection method of the Internet of Things (IoT) is proposed by suppressed fuzzy clustering (SFC) algorithm and principal component analysis (PCA) algorithm. In this method, the data are classified into high-risk data and low-risk data at first, which are detected by high frequency and low frequency, respectively. At the same time, the self-adjustment of the detection frequency is carried out according to the suppressed fuzzy clustering algorithm and the principal component analysis algorithm. Finally, the key factors influencing the algorithm are analyzed deeply by simulation experiment. The results shows that, compared to traditional method, this method has better adaptability.

关键词： Internet of things Intrusion detection Suppressed fuzzy clustering algorithm principal component analysis algorithm

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