On reducible Heegaard splittings

Sun, DongQi; Lei, Fengchun; Li, FengLing

doi:10.1007/s11425-012-4548-y

Cited by 2 publications

(4 citation statements)

References 3 publications

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“…(4) By applying the relative entropy distance, the changes in output voltages of specific frequency responses under nominal values were examined with the changes in the parameters of some key electronic elements. (5) In the curve of relative entropy distance, the first 100 time indices were selected as training samples while the 101st to 150th time indices were taken as testing samples to carry out fault prediction. (6) By taking the RMSE of predicted and tested values as the objective function, the parameters of GBDT were optimized to determine the best predicted result.…”

Section: Example Analysismentioning

confidence: 99%

“…The easily measured voltages of some circuits are generally taken as extracted features. For example, in the literature [5][6][7][8][9][10][11], the voltage signals in the amplitude-frequency response curve were uniformly extracted as eigenvalues. Owing to the large dataset, the voltage amplitudes of frequency response corresponding to typical frequencies were selected as eigenvalues [7,12,13].…”

Section: Introductionmentioning

confidence: 99%

“…Owing to the large dataset, the voltage amplitudes of frequency response corresponding to typical frequencies were selected as eigenvalues [7,12,13]. In the literature [5], the first five voltage features output from amplitude-frequency characteristic curves were preferentially selected based on a minimum redundancy maximum relevance (mRMR) principle and were considered as the optimal fault features.…”

Section: Introductionmentioning

confidence: 99%

“…In the literature [8][9][10], the eigenvalues were extracted by applying a wavelet packet transform and the health of elements was measured based on Pearson's correlation coefficient and Euclidean distance. Afterwards, the RUL of electronic elements was predicted by carrying out regression analyses using support vector machines [5][6][7][8][9][10][11][12][13] and particle filters [14] based on FI. Gradient boosting decision tree (GBDT) can be applied to both regression and classification, with the advantages of completeness, robustness, and a good explanation therewith.…”

Section: Introductionmentioning

confidence: 99%

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Application of Relative Entropy and Gradient Boosting Decision Tree to Fault Prognosis in Electronic Circuits

Zhou

Zhang

et al. 2018

Symmetry

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Fault prognosis of electronic circuits is the premise of guaranteeing normal operation of a system and carrying out on-condition maintenance. In this work, the remaining useful life (RUL) of electronic elements was estimated by selecting fault features based on variance, measuring fault severity based on relative entropy distance, and conducting fault prognosis based on the gradient boosting decision tree (GBDT) model. At first, the corresponding voltages of amplitude-frequency response, under conditions of changing full-band element parameters, were extracted, and then the frequency bands with large change amplitude were further selected based on variance. Afterwards, using relative entropy distance, the degradation of element parameters was measured, and then the RUL of electronic elements was diagnosed through regression analysis by GBDT. By comparing the data with those arising from the use of other distance-measuring methods, the relative entropy distance shows a larger change range and less apt to suffer interference from noise, which is favorable to subsequent regression prediction. The regression analysis through GBDT is easy to understand and conveniently applied in engineering practice. The application of the method proposed in the study in two examples of electronic circuits indicates that the prediction accuracy of the method for RUL of electronic elements is higher than that of the other distance-measuring methods, and its application in engineering practice is convenient.

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Section: Example Analysismentioning

confidence: 99%