Parametric Fault Diagnosis of Analog Circuits Based on a Semi-Supervised Algorithm
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Wei Zhang | Dongfang Zhou | Hui Tian | Ling Wang | Hao Zhang | Wei Zhang | Hao-Yu Zhang | Ling Wang | Dongfang Zhou | H. Tian
[1] Yuan Zhijie,et al. Diagnostics Method for Analog Circuits Based on Improved KECA and Minimum Variance ELM , 2017 .
[2] Yuehai Wang,et al. Wavelet-Based Feature Extraction in Fault Diagnosis for Biquad High-Pass Filter Circuit , 2016 .
[3] Wei He,et al. A Novel Approach for Diagnosis of Analog Circuit Fault by Using GMKL-SVM and PSO , 2016, J. Electron. Test..
[4] Arthur K. Kordon,et al. Fault diagnosis based on Fisher discriminant analysis and support vector machines , 2004, Comput. Chem. Eng..
[5] Avrim Blum,et al. The Bottleneck , 2021, Monopsony Capitalism.
[6] Cardona Alzate,et al. Predicción y selección de variables con bosques aleatorios en presencia de variables correlacionadas , 2020 .
[7] Wan-Chi Siu,et al. A fast and low memory image coding algorithm based on lifting wavelet transform and modified SPIHT , 2008, Signal Process. Image Commun..
[8] Dongsheng Zhao,et al. A novel binary bat algorithm with chaos and Doppler effect in echoes for analog fault diagnosis , 2016, Analog Integrated Circuits and Signal Processing.
[9] Shambhu Upadhyaya,et al. Linear circuit fault diagnosis using neuromorphic analyzers , 1997 .
[10] Yong Yin,et al. A Gaussian-based kernel Fisher discriminant analysis for electronic nose data and applications in spirit and vinegar classification , 2016, Journal of Food Measurement and Characterization.
[11] Fuqiang Zhou,et al. A new decision tree approach of support vector machine for analog circuit fault diagnosis , 2016 .
[12] Ting Long,et al. SELECCIÓN DE CARACTERÍSTICAS BASADAS EN ENTROPÍA CONDICIONAL PARA LA DETECCIÓN DE AVERÍAS EN CIRCUITOS ANALÓGICOS , 2016 .
[13] Rong Wang,et al. Robust Locality Preserving Projections With Cosine-Based Dissimilarity for Linear Dimensionality Reduction , 2017, IEEE Access.
[14] Xueming Qian,et al. Complex SAR Image Compression Based on Directional Lifting Wavelet Transform With High Clustering Capability , 2013, IEEE Transactions on Geoscience and Remote Sensing.
[15] Yigang He,et al. A new switched current circuit fault diagnosis approach based on pseudorandom test and preprocess by using entropy and Haar wavelet transform , 2017 .
[16] Junyou Shi,et al. Analog circuit fault diagnosis based on Quantum Clustering based Multi-valued Quantum Fuzzification Decision Tree (QC-MQFDT) , 2016 .
[17] Shinichi Nakajima,et al. Semi-supervised local Fisher discriminant analysis for dimensionality reduction , 2009, Machine Learning.
[18] Qiuqi Ruan,et al. Facial expression recognition using sparse local Fisher discriminant analysis , 2016, Neurocomputing.
[19] Elias G. Strangas,et al. On the Accuracy of Fault Detection and Separation in Permanent Magnet Synchronous Machines Using MCSA/MVSA and LDA , 2016, IEEE Transactions on Energy Conversion.
[20] Samia Boukir,et al. Exploring issues of training data imbalance and mislabelling on random forest performance for large area land cover classification using the ensemble margin , 2015 .
[21] Masashi Sugiyama,et al. Dimensionality Reduction of Multimodal Labeled Data by Local Fisher Discriminant Analysis , 2007, J. Mach. Learn. Res..
[22] Shiming Xiang,et al. Semisupervised Hyperspectral Image Classification via Discriminant Analysis and Robust Regression , 2016, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.
[23] Mariana Belgiu,et al. Random forest in remote sensing: A review of applications and future directions , 2016 .
[24] Dean Zhao,et al. An optimized classification algorithm by BP neural network based on PLS and HCA , 2014, Applied Intelligence.
[25] 李志刚,et al. Fault detection in analog and mixed-signal circuits by using Hilbert-Huang transform and coherence analysis , 2015 .
[26] Jing-Yu Yang,et al. Semi-supervised linear discriminant analysis for dimension reduction and classification , 2016, Pattern Recognit..
[27] Xuan Xie,et al. Analog Circuits Soft Fault Diagnosis Using Rényi’s Entropy , 2015, J. Electron. Test..
[28] A. P. Misra,et al. Audio signal encryption using chaotic Hénon map and lifting wavelet transforms , 2017, ArXiv.
[29] Zihan Zhou,et al. Label Information Guided Graph Construction for Semi-Supervised Learning , 2017, IEEE Transactions on Image Processing.
[30] Hong Liu,et al. Analog circuit fault diagnosis using lifting wavelet transform and SVM: Analog circuit fault diagnosis using lifting wavelet transform and SVM , 2010 .
[31] Chuan Liu,et al. Spectral identification of melon seeds variety based on k-nearest neighbor and Fisher discriminant analysis , 2017, Applied Optics and Photonics China.
[32] Weijia Cui,et al. Statistical property feature extraction based on FRFT for fault diagnosis of analog circuits , 2016 .
[33] Diego Cabrera,et al. Gearbox fault diagnosis based on deep random forest fusion of acoustic and vibratory signals , 2016 .
[34] Wei He,et al. Analog Circuit Fault Diagnosis via Joint Cross-Wavelet Singular Entropy and Parametric t-SNE , 2018, Entropy.
[35] Chi-Man Vong,et al. Capturing High-Discriminative Fault Features for Electronics-Rich Analog System via Deep Learning , 2017, IEEE Transactions on Industrial Informatics.
[36] Ramón Díaz-Uriarte,et al. Gene selection and classification of microarray data using random forest , 2006, BMC Bioinformatics.
[37] Saurabh Prasad,et al. Active and Semisupervised Learning With Morphological Component Analysis for Hyperspectral Image Classification , 2017, IEEE Geoscience and Remote Sensing Letters.
[38] Li Chen,et al. Fault detection in analog and mixed-signal circuits by using Hilbert-Huang transform and coherence analysis , 2015, Microelectron. J..
[39] Wenxin Yu,et al. The Faults Diagnostic Analysis for Analog Circuit Based on FA-TM-ELM , 2016, J. Electron. Test..
[40] Zhao Jian-hu. One of semi-supervised classification algorithm named Co-S3OM based on cooperative training , 2013 .
[41] Yuanyuan Jiang,et al. Fault diagnosis of analog circuit based on a second map SVDD , 2015 .
[42] Orestes Llanes-Santiago,et al. Principal components selection for dimensionality reduction using discriminant information applied to fault diagnosis , 2015 .