PCA-Based Fast Search Method Using PCA-LBG-Based VQ Codebook for Codebook Search

Yang, Po-Yuan; Tsai, Jinn-Tsong; Chou, Jyh–Horng

doi:10.1109/access.2016.2548664

Cited by 14 publications

(3 citation statements)

References 24 publications

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“…, A (n) , and C (n) were calculated. smaller representative dataset from a larger one [28]. According to the description above, the process of model simplification is elaborated as follows.…”

Section: Data-driven Compartmental Modeling Methods (Dcmm)mentioning

confidence: 99%

“…The first several coordinate axes were employed to classify the measured data of EAF because the coordinate axes with small enough variances were unable to distinguish the data. That is, the PCA can extract a smaller representative dataset from a larger one [28]. According to the description above, the process of model simplification is elaborated as follows.…”

Section: Multi-mode Eaf Harmonic Modelmentioning

confidence: 99%

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Data-Driven Compartmental Modeling Method for Harmonic Analysis—A Study of the Electric Arc Furnace

Shao

Chen

2019

Energies

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The electric arc furnace (EAF) contributes to almost one-third of the global iron and steel industry, and its harmonic pollution has drawn attention. An accurate EAF harmonic model is essential to evaluate the harmonic pollution of EAF. In this paper, a data-driven compartmental modeling method (DCMM) is proposed for the multi-mode EAF harmonic model. The proposed DCMM considers the coupling relationship among different frequencies of harmonics to enhance the modeling accuracy, meanwhile, the dimensions of the harmonic dataset are reduced to improve computational efficiency. Furthermore, the proposed DCMM is applicable to establish a multi-mode EAF harmonic model by dividing the multi-mode EAF harmonic dataset into several clusters corresponding to the different modes of the EAF smelting process. The performance evaluation results show that the proposed DCMM is adaptive in terms of establishing the multi-mode model, even if the data volumes, number of clusters, and sample distribution change significantly. Finally, a case study of EAF harmonic data is conducted to establish a multi-mode EAF harmonic model, showing that the proposed DCMM is effective and accurate in EAF modeling.

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Section: Data-driven Compartmental Modeling Methods (Dcmm)mentioning

confidence: 99%

Section: Multi-mode Eaf Harmonic Modelmentioning

confidence: 99%

Data-Driven Compartmental Modeling Method for Harmonic Analysis—A Study of the Electric Arc Furnace

Shao

Chen

2019

Energies

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“…In the meanwhile, many detection methods were presented and applied in various fields to provide timely diagnosis. For instance, Yang et al (2016) proposed a fast search method based on principal component analysis (PCA) to search codewords using vector quantization codebooks, which is obtained by PCA with Linde-Buzo-Gray algorithms. Yin et al (2014b) constructed a fault detection scheme based on the proposed robust 1-class support vector machine (SVM), and the simulation example showed that the robust 1-class SVM was superior to the general 1-class SVM, especially when the training data set is corrupted by outliers.…”

Section: Introductionmentioning

confidence: 99%

A data-driven principal component analysis-support vector machine approach for breast cancer diagnosis: Comparison and application

Faisal

2019

Transactions of the Institute of Measurement and Control

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In recent years, with the development of artificial intelligence, data-driven methodologies have been widely studied in fault diagnosis and detection, since an increasing number of complexities of modern complex systems make the mechanism model information difficult to obtain. Especially in people’s health monitoring, it is very difficult to achieve the mechanism model. The existing challenges, such as huge amount of data, high data dimension, large noise interference, and so forth, make the applications of data-driven approaches more suitable. For the sake of solving the problems above, we present principal component analysis-support vector machine (PCA-SVM) method with different kernels to reduce data dimension, and two sets of breast-cancer data are utilized to verify the method. Additionally, support vector machine-recursive feature elimination (SVM-RFE), the original SVM with different kernels, PCA and modified PCA (MPCA) methods are also applied to diagnose malignant cancer in comparison with PCA-SVM. In experiments, PCA-SVM via radial basis function (RBF) kernel shows better performance than other methods, with the two breast cancer datasets obtained from the University of Wisconsin Hospital. Finally, PCA-SVM in this study uses only six principal components and obtains better accuracy (97.19%) than most of the previous studies.

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Vector Quantization Image Compression Algorithm Based on Bat Algorithm of Adaptive Separation Search

Guo

Huang

et al. 2021

Proceedings of the International Conference on Advanced Intelligent Systems and Informatics 2021

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PCA-Based Fast Search Method Using PCA-LBG-Based VQ Codebook for Codebook Search

Cited by 14 publications

References 24 publications

Data-Driven Compartmental Modeling Method for Harmonic Analysis—A Study of the Electric Arc Furnace

Data-Driven Compartmental Modeling Method for Harmonic Analysis—A Study of the Electric Arc Furnace

A data-driven principal component analysis-support vector machine approach for breast cancer diagnosis: Comparison and application

Vector Quantization Image Compression Algorithm Based on Bat Algorithm of Adaptive Separation Search

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