scholarly journals Generalized Two-Dimensional Principal Component Analysis and Two Artificial Neural Networks to Detect Traveling Ionospheric Disturbances Using the Tsunami-Atmosphere-Ionosphere Coupling Mechanism

2021 ◽  
Author(s):  
Jyh-Woei Lin
Keyword(s):  
Neural Network ◽  
Principal Component Analysis ◽  
Ionospheric Disturbance ◽  
Principal Eigenvalue ◽  
Back Propagation ◽  
Principal Component ◽  
Component Analysis ◽  
Internal Gravity Waves ◽  
Coupling Mechanism ◽  
Two Dimensional

Abstract A weak tsunami was induced by the 2016 Mw = 7.8 Sumatra earthquake, which occurred at 12:49 on March 2, 2016 (UTC). The epicenter was at 5.060°S, 94.170°E at a depth of 10 km. At 15.02 on March 2 (UTC), the weak tsunami (amplitude: 0.11 m) arrived at the station located at 10.40°S, 105.67°E. The largest first principal eigenvalue derived using the bilateral projection-based two-dimensional principal component analysis (B2DPCA) indicated a spatial traveling ionospheric disturbance (TID), which was caused by internal gravity waves (IGWs), at 13:20 on March 2 (UTC). The largest second principal eigenvalue represented another TID expanding to the southwest. The two largest principal eigenvalues were associated with the TIDs, which were also determined using two back-propagation neural network (BPNN) models and two convolutional neural network (CNN) models, called the BPNN-B2DPCA and CNN-B2DPCA methods, respectively. These two methods yielded the same results as the B2DPCA. Therefore, the robustness and reliability of the B2DPCA were validated.

scholarly journals Power Transformer Differential Protection Based on Neural Network Principal Component Analysis, Harmonic Restraint and Park's Plots

2012 ◽  
Vol 2012 ◽  
pp. 1-9 ◽  
Author(s):  
Manoj Tripathy
Keyword(s):  
Neural Network ◽  
Principal Component Analysis ◽  
Power Transformer ◽  
Back Propagation ◽  
Principal Component ◽  
Component Analysis ◽  
Optimal Number ◽  
Differential Protection ◽  
Harmonic Restraint ◽  
Transformer Differential Protection

This paper describes a new approach for power transformer differential protection which is based on the wave-shape recognition technique. An algorithm based on neural network principal component analysis (NNPCA) with back-propagation learning is proposed for digital differential protection of power transformer. The principal component analysis is used to preprocess the data from power system in order to eliminate redundant information and enhance hidden pattern of differential current to discriminate between internal faults from inrush and overexcitation conditions. This algorithm has been developed by considering optimal number of neurons in hidden layer and optimal number of neurons at output layer. The proposed algorithm makes use of ratio of voltage to frequency and amplitude of differential current for transformer operating condition detection. This paper presents a comparative study of power transformer differential protection algorithms based on harmonic restraint method, NNPCA, feed forward back propagation neural network (FFBPNN), space vector analysis of the differential signal, and their time characteristic shapes in Park’s plane. The algorithms are compared as to their speed of response, computational burden, and the capability to distinguish between a magnetizing inrush and power transformer internal fault. The mathematical basis for each algorithm is briefly described. All the algorithms are evaluated using simulation performed with PSCAD/EMTDC and MATLAB.

Detection of Citrus HuangLongBing Based on Principal Component Analysis and Back Propagation Neural Network

2014 ◽  
Vol 43 (4) ◽  
pp. 430002 ◽  
Author(s):  
邓小玲 DENG Xiao-ling ◽  
孔晨 KONG Chen ◽  
吴伟斌 WU Wei-bin ◽  
梅慧兰 MEI Hui-lan ◽  
李震 LI Zhen ◽  
...  
Keyword(s):  
Neural Network ◽  
Principal Component Analysis ◽  
Back Propagation ◽  
Principal Component ◽  
Component Analysis ◽  
Back Propagation Neural Network ◽  
Citrus Huanglongbing

Performance Comparison between ANN and PCA Techniques for Road Signs Recognition

2013 ◽  
Vol 393 ◽  
pp. 611-616 ◽  
Author(s):  
Nursabillilah Mohd Ali ◽  
Yasir Mohd Mustafah ◽  
Nahrul Khair Alang Md Rashid
Keyword(s):  
Neural Network ◽  
Principal Component Analysis ◽  
Back Propagation ◽  
Principal Component ◽  
Component Analysis ◽  
Performance Comparison ◽  
Feed Forward Neural Network ◽  
Classification Rate ◽  
The Road ◽  
Road Signs

This study reports about a comparison in recognizing road signs between Neural Network and Principal Component Analysis (PCA). The road sign with circular, triangular, octagonal and diamond shapes have been used in this study. Two recognition systems to determine the classes of the road signs class were implemented which are based on Feed Forward Neural Network and Principal Component Analysis (PCA). The performance of the trained classifier using Scaled Conjugate Gradient (SCG) back propagation function in Neural Network and PCA technique were evaluated on our test datasets. The experiments show that the system using PCA has a higher accuracy as compared to Neural Network with a minimum of 94% classification rate of road signs.

scholarly journals Detection of Water Content in Transformer Oil Using Multi Frequency Ultrasonic with PCA-GA-BPNN

Energies ◽  
2019 ◽  
Vol 12 (7) ◽  
pp. 1379 ◽  
Author(s):  
Zhuang Yang ◽  
Qu Zhou ◽  
Xiaodong Wu ◽  
Zhongyong Zhao ◽  
Chao Tang ◽  
...  
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Principal Component Analysis ◽  
Water Content ◽  
Back Propagation ◽  
Principal Component ◽  
Component Analysis ◽  
Back Propagation Neural Network ◽  
Transformer Oil ◽  
Bpnn Model

The water content in oil is closely related to the deterioration performance of an insulation system, and accurate prediction of water content in oil is important for the stability and security level of power systems. A novel method of measuring water content in transformer oil using multi frequency ultrasonic with a back propagation neural network that was optimized by principal component analysis and genetic algorithm (PCA-GA-BPNN), is reported in this paper. 160 oil samples of different water content were investigated using the multi frequency ultrasonic detection technology. Then the multi frequency ultrasonic data were preprocessed using principal component analysis (PCA), which was implemented to obtain main principal components containing 95% of original information. After that, a genetic algorithm (GA) was incorporated to optimize the parameters for a back propagation neural network (BPNN), including the weight and threshold. Finally, the BPNN model with the optimized parameters was trained with a random 150 sets of pretreatment data, and the generalization ability of the model was tested with the remaining 10 sets. The mean squared error of the test sets was 8.65 × 10−5, with a correlation coefficient of 0.98. Results show that the developed PCA-GA-BPNN model is robust and enables accurate prediction of a water content in transformer oil using multi frequency ultrasonic technology.

scholarly journals Research on prediction model of thermal and moisture comfort of underwear based on principal component analysis and Genetic Algorithm–Back Propagation neural network

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Pengpeng Cheng ◽  
Daoling Chen ◽  
Jianping Wang
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Principal Component Analysis ◽  
Prediction Model ◽  
Bp Neural Network ◽  
Prediction Models ◽  
Back Propagation ◽  
Principal Component ◽  
Component Analysis ◽  
Back Propagation Neural Network

AbstractIn order to improve the efficiency and accuracy of thermal and moisture comfort prediction of underwear, a new prediction model is designed by using principal component analysis method to reduce the dimension of related variables and eliminate the multi-collinearity relationship between variables, and then inputting the converted variables into genetic algorithm (GA) and BP neural network. In order to avoid the problems of slow convergence speed and easy falling into local minimum of Back Propagation (BP) neural network, this paper adopted GA to optimize the weights and thresholds of BP neural network, and utilized MATLAB software to program, and established the prediction models of BP neural network and GA–BP neural network. To verify the superiority of the model, the predicted result of GA–BP, PCA–BP and BP are compared with GA–BP neural network. The results show that PCA could improve the accuracy and adaptability of GA–BP neural network for thermal and moisture comfort prediction. PCA–GA–BP model is obviously superior to GA–BP, PCA–BP, BP, SVM and K-means prediction models, which could accurately predict thermal and moisture comfort of underwear. The model has better accuracy prediction and simpler structure.

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