摘要 :
Partial Discharge (PD) pattern recognition is one of the most important steps of PD based condition monitoring of high voltage cables, which is challenging as some types of the PD induced by cable defects are with high similarity....
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Partial Discharge (PD) pattern recognition is one of the most important steps of PD based condition monitoring of high voltage cables, which is challenging as some types of the PD induced by cable defects are with high similarity. In recently years, deep learning based pattern recognition methods have achieved impressive pattern recognition accuracy on speech recognition and image recognition, which is one of the most potential techniques applicable for PD pattern recognition. The Stacked Denoising Autoencoder (SDAE) based deep learning method for PD pattern recognition of different insulation defects of high voltage cables is presented in the paper. Firstly, five types of artificial insulation defects of ethylene-propylene-rubber cables are manufactured in the laboratory, based on which PD testing in the high voltage lab is carried out to produce 5 types of PD signals, 500 samples for each defect types. PD feature extraction is carried out to generate 34 kinds of PD features, which are the input parameters of the PD pattern recognition methods. Secondly, the principle and network architecture of SDAE method and the flowchart of SDAE based PD pattern recognition are presented in details. Thirdly, the SDAE method is evaluated with the experimental data, 5 different types of PD signals, which achieves a recognition accuracy of 92.19%. Finally, the proposed method is compared with the traditional pattern recognition methods, Support Vector Machine (SVM) and Back Propagation Neural Network (BPNN). The results show that the pattern recognition accuracy of the proposed method is improved by 5.33% and 6.09% compared with the SVM method and the BPNN method respectively, which is applicable for pattern recognition of PD signals with high similarity.
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摘要 :
Partial Discharge (PD) pattern recognition is one of the most important steps of PD based condition monitoring of high voltage cables, which is challenging as some types of the PD induced by cable defects are with high similarity....
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Partial Discharge (PD) pattern recognition is one of the most important steps of PD based condition monitoring of high voltage cables, which is challenging as some types of the PD induced by cable defects are with high similarity. In recently years, deep learning based pattern recognition methods have achieved impressive pattern recognition accuracy on speech recognition and image recognition, which is one of the most potential techniques applicable for PD pattern recognition. The Stacked Denoising Autoencoder (SDAE) based deep learning method for PD pattern recognition of different insulation defects of high voltage cables is presented in the paper. Firstly, five types of artificial insulation defects of ethylene-propylene-rubber cables are manufactured in the laboratory, based on which PD testing in the high voltage lab is carried out to produce 5 types of PD signals, 500 samples for each defect types. PD feature extraction is carried out to generate 34 kinds of PD features, which are the input parameters of the PD pattern recognition methods. Secondly, the principle and network architecture of SDAE method and the flowchart of SDAE based PD pattern recognition are presented in details. Thirdly, the SDAE method is evaluated with the experimental data, 5 different types of PD signals, which achieves a recognition accuracy of 92.19%. Finally, the proposed method is compared with the traditional pattern recognition methods, Support Vector Machine (SVM) and Back Propagation Neural Network (BPNN). The results show that the pattern recognition accuracy of the proposed method is improved by 5.33% and 6.09% compared with the SVM method and the BPNN method respectively, which is applicable for pattern recognition of PD signals with high similarity.
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摘要 :
Using perchloric acid, Nd_2O_3, Er_2O_3 and L-isoleucine (Ile) as raw materials, two crystalline compounds of rare earth isoleucine chlorate, [Nd_2(Ile)_4(H_2O)_8](ClO_4)_6 and [Er_2(Ile)_4(H_2O)_8](ClO_4)_6, were synthesized. The...
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Using perchloric acid, Nd_2O_3, Er_2O_3 and L-isoleucine (Ile) as raw materials, two crystalline compounds of rare earth isoleucine chlorate, [Nd_2(Ile)_4(H_2O)_8](ClO_4)_6 and [Er_2(Ile)_4(H_2O)_8](ClO_4)_6, were synthesized. Their compositions were determined after chemical analysis, IR spectrum study, thermogravimetric (TG) and differential thermal analysis (DTA). By using the RD496-2000 microcalorimeter, the standard enthalpies of formation of the synthesized compounds were obtained according to a pre-designed thermal cycle. Four sets of TG data with different heating rates (2.5 K·min~(-1), 5 K·min~(-1), 10 K·min~(-1) and 15 K·min~(-1)) were obtained. After the exploration of the kinetic data in the non-isothermal thermal decomposition process, the activation energy E, the logarithm of the pre-exponential factor ln A, the reaction order n, and the dynamical equation of the water loss step were derived. Ozawa method was also used to test the obtained dynamical data.
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摘要 :
Using perchloric acid, Nd_2O_3, Er_2O_3 and L-isoleucine (Ile) as raw materials, two crystalline compounds of rare earth isoleucine chlorate, [Nd_2(Ile)_4(H_2O)_8](ClO_4)_6 and [Er_2(Ile)_4(H_2O)_8](ClO_4)_6, were synthesized. The...
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Using perchloric acid, Nd_2O_3, Er_2O_3 and L-isoleucine (Ile) as raw materials, two crystalline compounds of rare earth isoleucine chlorate, [Nd_2(Ile)_4(H_2O)_8](ClO_4)_6 and [Er_2(Ile)_4(H_2O)_8](ClO_4)_6, were synthesized. Their compositions were determined after chemical analysis, IR spectrum study, thermogravimetric (TG) and differential thermal analysis (DTA). By using the RD496-2000 microcalorimeter, the standard enthalpies of formation of the synthesized compounds were obtained according to a pre-designed thermal cycle. Four sets of TG data with different heating rates (2.5 K·min~(-1), 5 K·min~(-1), 10 K·min~(-1) and 15 K·min~(-1)) were obtained. After the exploration of the kinetic data in the non-isothermal thermal decomposition process, the activation energy E, the logarithm of the pre-exponential factor ln A, the reaction order n, and the dynamical equation of the water loss step were derived. Ozawa method was also used to test the obtained dynamical data.
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摘要 :
Using perchloric acid, Nd_2O_3, Er_2O_3 and L-isoleucine (He) as raw materials, two crystalline compounds of rare earth isoleucine chlorate, [Nd_2(Ile)_4(H_2O)_8](C1O_4)_6 and [Er_2(Ile)_4(H_2O)_8](ClO_4)_6, were synthesized. Thei...
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Using perchloric acid, Nd_2O_3, Er_2O_3 and L-isoleucine (He) as raw materials, two crystalline compounds of rare earth isoleucine chlorate, [Nd_2(Ile)_4(H_2O)_8](C1O_4)_6 and [Er_2(Ile)_4(H_2O)_8](ClO_4)_6, were synthesized. Their compositions were determined after chemical analysis, IR spectrum study, thermogravimetric (TG) and differential thermal analysis (DTA). By using the RD496-2000 microcalorimeter, the standard enthalpies of formation of the synthesized compounds were obtained according to a pre-designed thermal cycle. Four sets of TG data with different heating rates (2.5 K-min~(-1), 5 K-min~(-1), 10 Kmin~(-1) and 15 K-min~(-1)) were obtained. After the exploration of the kinetic data in the non-isothermal thermal decomposition process, the activation energy E, the logarithm of the pre-exponential factor \nA, the reaction order n, and the dynamical equation of the water loss step were derived. Ozawa method was also used to test the obtained dynamical data.
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摘要 :
Using perchloric acid, Nd_2O_3, Er_2O_3 and L-isoleucine (Ile) as raw materials, two crystalline compounds of rare earth isoleucine chlorate, [Nd_2(Ile)_4(H_2O)_8](ClO_4)_6 and [Er_2(Ile)_4(H_2O)_8](ClO_4)_6, were synthesized. The...
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Using perchloric acid, Nd_2O_3, Er_2O_3 and L-isoleucine (Ile) as raw materials, two crystalline compounds of rare earth isoleucine chlorate, [Nd_2(Ile)_4(H_2O)_8](ClO_4)_6 and [Er_2(Ile)_4(H_2O)_8](ClO_4)_6, were synthesized. Their compositions were determined after chemical analysis, IR spectrum study, thermogravimetric (TG) and differential thermal analysis (DTA). By using the RD496-2000 microcalorimeter, the standard enthalpies of formation of the synthesized compounds were obtained according to a pre-designed thermal cycle. Four sets of TG data with different heating rates (2.5 K·min~(-1), 5 K·min~(-1), 10 K·min~(-1) and 15 K·min~(-1)) were obtained. After the exploration of the kinetic data in the non-isothermal thermal decomposition process, the activation energy E, the logarithm of the pre-exponential factor lnA, the reaction order n, and the dynamical equation of the water loss step were derived. Ozawa method was also used to test the obtained dynamical data.
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摘要 :
Unsupervised feature selection (UFS) as an effective method to reduce time complexity and storage burden has been widely applied to various machine learning tasks. The selected features should model data distribution, preserve dat...
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Unsupervised feature selection (UFS) as an effective method to reduce time complexity and storage burden has been widely applied to various machine learning tasks. The selected features should model data distribution, preserve data reconstruction and maintain manifold structure. However, most UFS methods don't consider these three factors simultaneously. Motivated by this, we propose a novel joint dictionary learning method, which handles these three key factors simultaneously. In joint dictionary learning, an intrinsic space shared by feature space and pseudo label space is introduced, which can model cluster structure and reveal data reconstruction. To ensure the sparseness of intrinsic space, the ℓ_1-norm regularization is imposed on the representation coefficients matrix. The joint learning of robust sparse regression model and spectral clustering can select features that maintain data distribution and manifold structure. An efficient algorithm is designed to solve the proposed optimization problem. Experimental results on various types of benchmark datasets validate the effectiveness of our method.
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摘要 :
Unsupervised feature selection (UFS) as an effective method to reduce time complexity and storage burden has been widely applied to various machine learning tasks. The selected features should model data distribution, preserve dat...
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Unsupervised feature selection (UFS) as an effective method to reduce time complexity and storage burden has been widely applied to various machine learning tasks. The selected features should model data distribution, preserve data reconstruction and maintain manifold structure. However, most UFS methods don't consider these three factors simultaneously. Motivated by this, we propose a novel joint dictionary learning method, which handles these three key factors simultaneously. In joint dictionary learning, an intrinsic space shared by feature space and pseudo label space is introduced, which can model cluster structure and reveal data reconstruction. To ensure the sparseness of intrinsic space, the ?_1-norm regularization is imposed on the representation coefficients matrix. The joint learning of robust sparse regression model and spectral clustering can select features that maintain data distribution and manifold structure. An efficient algorithm is designed to solve the proposed optimization problem. Experimental results on various types of benchmark datasets validate the effectiveness of our method.
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