Kernal factor analysis （KFA） with vafimax was proposed by using Mercer kernel function which can map the data in the original space to a high-dimensional feature space, and was compared with the kernel principle com...Kernal factor analysis （KFA） with vafimax was proposed by using Mercer kernel function which can map the data in the original space to a high-dimensional feature space, and was compared with the kernel principle component analysis （KPCA）. The results show that the best error rate in handwritten digit recognition by kernel factor analysis with vadmax （4.2%） was superior to KPCA （4.4%）. The KFA with varimax could more accurately image handwritten digit recognition.展开更多
Multivariate statistical process monitoring methods are often used in chemical process fault diagnosis.In this article,(I)the cycle temporal algorithm(CTA)combined with the dynamic kernel principal component analysis(...Multivariate statistical process monitoring methods are often used in chemical process fault diagnosis.In this article,(I)the cycle temporal algorithm(CTA)combined with the dynamic kernel principal component analysis(DKPCA)and the multiway dynamic kernel principal component analysis(MDKPCA)fault detection algorithms are proposed,which are used for continuous and batch process fault detections,respectively.In addition,(II)a fault variable identification model based on reconstructed-based contribution(RBC)model that paves the way for determining the cause of the fault are proposed.The proposed fault diagnosis model was applied to Tennessee Eastman(TE)process and penicillin fermentation process for fault diagnosis.And compare with other fault diagnosis methods.The results show that the proposed method has better detection effects than other methods.Finally,the reconstruction-based contribution(RBC)model method is used to accurately locate the root cause of the fault and determine the fault path.展开更多
The eigenface method that uses principal component analysis(PCA) has been the standard and popular method used in face recognition.This paper presents a PCA-memetic algorithm(PCA-MA) approach for feature selection.PCA...The eigenface method that uses principal component analysis(PCA) has been the standard and popular method used in face recognition.This paper presents a PCA-memetic algorithm(PCA-MA) approach for feature selection.PCA has been extended by MAs where the former was used for feature extraction/dimensionality reduction and the latter exploited for feature selection.Simulations were performed over ORL and YaleB face databases using Euclidean norm as the classifier.It was found that as far as the recognition rate is concerned,PCA-MA completely outperforms the eigenface method.We compared the performance of PCA extended with genetic algorithm(PCA-GA) with our proposed PCA-MA method.The results also clearly established the supremacy of the PCA-MA method over the PCA-GA method.We further extended linear discriminant analysis(LDA) and kernel principal component analysis(KPCA) approaches with the MA and observed significant improvement in recognition rate with fewer features.This paper also compares the performance of PCA-MA,LDA-MA and KPCA-MA approaches.展开更多
In order to enhance the accuracy and efficiency of coal and gas outburst prediction,a novel approach combining Kernel Principal Component Analysis(KPCA)with an Improved Whale Optimization Algorithm(IWOA)optimized extr...In order to enhance the accuracy and efficiency of coal and gas outburst prediction,a novel approach combining Kernel Principal Component Analysis(KPCA)with an Improved Whale Optimization Algorithm(IWOA)optimized extreme learning machine(ELM)is proposed for precise forecasting of coal and gas outburst disasters in mines.Firstly,based on the influencing factors of coal and gas outburst disasters,nine coupling indexes are selected,including gas pressure,geological structure,initial velocity of gas emission,and coal structure type.The correlation between each index was analyzed using the Pearson correlation coefficient matrix in SPSS 27,followed by extraction of the principal components of the original data through Kernel Principal Component Analysis(KPCA).The Whale Optimization Algorithm(WOA)was enhanced by incorporating adaptive weight,variable helix position update,and optimal neighborhood disturbance to augment its performance.The improved Whale Optimization Algorithm(IWOA)is subsequently employed to optimize the weight Φ of the Extreme Learning Machine(ELM)input layer and the threshold g of the hidden layer,thereby enhancing its predictive accuracy and mitigating the issue of"over-fitting"associated with ELM to some extent.The principal components extracted by KPCA were utilized as input,while the outburst risk grade served as output.Subsequently,a comparative analysis was conducted between these results and those obtained from WOA-SVC,PSO-BPNN,and SSA-RF models.The IWOA-ELM model accurately predicts the risk grade of coal and gas outburst disasters,with results consistent with actual situations.Compared to other models tested,the model's performance showed an increase in Ac by 0.2,0.3,and 0.2 respectively;P increased by 0.15,0.2167,and 0.1333 respectively;R increased by 0.25,0.3,and 0.2333 respectively;F1-Score increased by 0.2031,0.2607,and 0.1864 respectively;Kappa coefficient k increased by 0.3226,0.4762 and 0.3175,respectively.The practicality and stability of the IWOAELM model were verified through its application in a coal mine in Shanxi Province where the predicted values exactly matched the actual values.This indicates that this model is more suitable for predicting coal and gas outburst disaster risks.展开更多
基金The National Defence Foundation of China (No.NEWL51435Qt220401)
文摘Kernal factor analysis （KFA） with vafimax was proposed by using Mercer kernel function which can map the data in the original space to a high-dimensional feature space, and was compared with the kernel principle component analysis （KPCA）. The results show that the best error rate in handwritten digit recognition by kernel factor analysis with vadmax （4.2%） was superior to KPCA （4.4%）. The KFA with varimax could more accurately image handwritten digit recognition.
基金financial support from the National Natural Science Foundation of China (21706220)
文摘Multivariate statistical process monitoring methods are often used in chemical process fault diagnosis.In this article,(I)the cycle temporal algorithm(CTA)combined with the dynamic kernel principal component analysis(DKPCA)and the multiway dynamic kernel principal component analysis(MDKPCA)fault detection algorithms are proposed,which are used for continuous and batch process fault detections,respectively.In addition,(II)a fault variable identification model based on reconstructed-based contribution(RBC)model that paves the way for determining the cause of the fault are proposed.The proposed fault diagnosis model was applied to Tennessee Eastman(TE)process and penicillin fermentation process for fault diagnosis.And compare with other fault diagnosis methods.The results show that the proposed method has better detection effects than other methods.Finally,the reconstruction-based contribution(RBC)model method is used to accurately locate the root cause of the fault and determine the fault path.
文摘The eigenface method that uses principal component analysis(PCA) has been the standard and popular method used in face recognition.This paper presents a PCA-memetic algorithm(PCA-MA) approach for feature selection.PCA has been extended by MAs where the former was used for feature extraction/dimensionality reduction and the latter exploited for feature selection.Simulations were performed over ORL and YaleB face databases using Euclidean norm as the classifier.It was found that as far as the recognition rate is concerned,PCA-MA completely outperforms the eigenface method.We compared the performance of PCA extended with genetic algorithm(PCA-GA) with our proposed PCA-MA method.The results also clearly established the supremacy of the PCA-MA method over the PCA-GA method.We further extended linear discriminant analysis(LDA) and kernel principal component analysis(KPCA) approaches with the MA and observed significant improvement in recognition rate with fewer features.This paper also compares the performance of PCA-MA,LDA-MA and KPCA-MA approaches.
基金supported by the National Natural Science Foun-dation of China (No.52174188)the Graduate Education Innovation Project of Shanxi Datong University of China (No.23CX49).
文摘In order to enhance the accuracy and efficiency of coal and gas outburst prediction,a novel approach combining Kernel Principal Component Analysis(KPCA)with an Improved Whale Optimization Algorithm(IWOA)optimized extreme learning machine(ELM)is proposed for precise forecasting of coal and gas outburst disasters in mines.Firstly,based on the influencing factors of coal and gas outburst disasters,nine coupling indexes are selected,including gas pressure,geological structure,initial velocity of gas emission,and coal structure type.The correlation between each index was analyzed using the Pearson correlation coefficient matrix in SPSS 27,followed by extraction of the principal components of the original data through Kernel Principal Component Analysis(KPCA).The Whale Optimization Algorithm(WOA)was enhanced by incorporating adaptive weight,variable helix position update,and optimal neighborhood disturbance to augment its performance.The improved Whale Optimization Algorithm(IWOA)is subsequently employed to optimize the weight Φ of the Extreme Learning Machine(ELM)input layer and the threshold g of the hidden layer,thereby enhancing its predictive accuracy and mitigating the issue of"over-fitting"associated with ELM to some extent.The principal components extracted by KPCA were utilized as input,while the outburst risk grade served as output.Subsequently,a comparative analysis was conducted between these results and those obtained from WOA-SVC,PSO-BPNN,and SSA-RF models.The IWOA-ELM model accurately predicts the risk grade of coal and gas outburst disasters,with results consistent with actual situations.Compared to other models tested,the model's performance showed an increase in Ac by 0.2,0.3,and 0.2 respectively;P increased by 0.15,0.2167,and 0.1333 respectively;R increased by 0.25,0.3,and 0.2333 respectively;F1-Score increased by 0.2031,0.2607,and 0.1864 respectively;Kappa coefficient k increased by 0.3226,0.4762 and 0.3175,respectively.The practicality and stability of the IWOAELM model were verified through its application in a coal mine in Shanxi Province where the predicted values exactly matched the actual values.This indicates that this model is more suitable for predicting coal and gas outburst disaster risks.
