Neighborhood preserving regression embedding based data regression and its applications on soft sensor modeling

Miao, Aimin; Li, Peng; Ye, Lingjian

doi:10.1016/j.chemolab.2015.07.012

Cited by 20 publications

(7 citation statements)

References 42 publications

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“…(2) Get an adaptive bandwidth by the diffusion method based on [41]; (3) Estimate the probability density of variables using (42), and calculate the control limit C α by (43).…”

Section: B Control Limitsmentioning

confidence: 99%

“…(1) Get the three-way training dataset (I×J×K) of reference batches within in-control; (2) Transform the three-way dataset into functional matrix X(t) via functional data analysis (FDA) using (13); (3) Calculate the kernel matrix K by (34), and centralize the kernel matrix asK using (21); (4) Determine the hyper-parameter values of latent variables P and kernel parameters S via the generalized cross-validation approach; (5) Solve the generalized eigenvector problem in (32) to obtain the coefficient vector α; (6) Transform the training dataset into T 2 and SPE statistics based on (36) and (41), and calculate the control limits of fault detection according to (42).…”

Section: For the Offline Modelling Stagementioning

confidence: 99%

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Nonlinear Fault Detection of Batch Processes Using Functional Local Kernel Principal Component Analysis

Zhang

2020

IEEE Access

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In order to guarantee and improve the product quality, the data-driven fault detection technique has been widely used in industry. For three-way datasets of batch process in industry process (i.e., batch × variable × time), a novel method named functional local kernel principal component analysis (FLKPCA) is proposed. Since the variables' trajectories often show functional nature and can be considered as smooth functions rather than just vectors. Firstly, the variables' trajectory is expressed as the combination of smooth basis functions using functional data analysis (FDA), which means that the datasets of batches process would be transformed from the three-ways array into two-ways function matrix. Then, kernel locality preserving projections (LKPCA) is used to perform dimensionality reduction on two-way function matrix directly. Different from kernel principal component analysis (KPCA). LKPCA aims at preserving the both local and global structure of the data in a new optimization objective. Consequently, FLKPCA could more effectively seek the potential information that hidden in the three-ways datasets. Lastly, the effectiveness of the proposed approach is illustrated by the benchmark of fed-batch penicillin fermentation process and the hot strip rolling process. INDEX TERMS Kernel functional local principal component analysis, process monitoring, fault detection, function data analysis.

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“…(2) Get an adaptive bandwidth by the diffusion method based on [41]; (3) Estimate the probability density of variables using (42), and calculate the control limit C α by (43).…”

Section: B Control Limitsmentioning

confidence: 99%

Section: For the Offline Modelling Stagementioning

confidence: 99%

Nonlinear Fault Detection of Batch Processes Using Functional Local Kernel Principal Component Analysis

Zhang

2020

IEEE Access

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“…With improvements in SAR image resolution, detailed information of the image is obvious, and texture features of the building area are more abundant and applied to the information extraction of a high-resolution SAR image. Zhao, GAO, and Kuang [4] used the variation function to calculate the texture features of SAR images and applied the facial recognition and facial clustering, image indexing, and image classification [32][33][34][35][36][37][38][39][40][41][42][43][44][45][46]. Bao et al presented the supervised NPE for feature extraction, using a class label to define the new distance to find the k nearest neighbors [43].…”

Section: Introductionmentioning

confidence: 99%

Research on an Urban Building Area Extraction Method with High-Resolution PolSAR Imaging Based on Adaptive Neighborhood Selection Neighborhoods for Preserving Embedding

Cheng

Cui

et al. 2020

IJGI

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Feature extraction of an urban area is one of the most important directions of polarimetric synthetic aperture radar (PolSAR) applications. A high-resolution PolSAR image has the characteristics of high dimensions and nonlinearity. Therefore, to find intrinsic features for target recognition, a building area extraction method for PolSAR images based on the Adaptive Neighborhoods selection Neighborhood Preserving Embedding (ANSNPE) algorithm is proposed. First, 52 features are extracted by using the Gray level co-occurrence matrix (GLCM) and five polarization decomposition methods. The feature set is divided into 20 dimensions, 36 dimensions, and 52 dimensions. Next, the ANSNPE algorithm is applied to the training samples, and the projection matrix is obtained for the test image to extract the new features. Lastly, the Support Vector machine (SVM) classifier and post processing are used to extract the building area, and the accuracy is evaluated. Comparative experiments are conducted using Radarsat-2, and the results show that the ANSNPE algorithm could effectively extract the building area and that it had a better generalization ability; the projection matrix is obtained using the training data and could be directly applied to the new sample, and the building area extraction accuracy is above 80%. The combination of polarization and texture features provide a wealth of information that is more conducive to the extraction of building areas.unsupervised fuzzy mean classification method to extract the building area. Texture feature images and filter images obtained by wavelet transform, according to Zhu et al. [5], are used as an input layer of the BP (Back Propagation) neural network, and the texture classification of high-resolution SAR images is completed. However, in the field of radar remote sensing, a new synthetic aperture radar (SAR) satellite constellation has been successfully launched in recent years. As SAR data have shown explosive growth, the SAR big data era has been flourishing. High-resolution polarimetric synthetic aperture radar (referred to as PolSAR) images can obtain ground scene information from multiple dimensions, on the one hand to provide rich information for identification of features and on the other hand to increase the complexity of information extraction. In the context of big data, rapid and accurate automatic extraction technology is the future development trend and is of great significance in promoting the application of PolSAR.Feature extraction is the most critical and central part of information extraction on SAR images. Studying the feature extraction method for high-resolution PolSAR images, reducing the space dimension and removing redundant information are highly important for the fast and accurate automatic extraction of building areas. Traditional feature extraction methods based on the global linear structure hypothesis premise feature of a PolSAR image set are proposed. Although they can achieve the purpose of reducing dimension, the intrinsic structure of high-dimension...

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“…The online model will be obtained via updating the founded historical model with new samples according to the approximate linear dependence (ALD) condition. Finally, the simulation experiments will be carried out according to the actual operation data of the RMU, and the results of the ATNPE algorithm compared with the results of the time neighborhood preserving embedding (TNPE) algorithm (A. Miao, Ge, Song, & Zhou, ; A. M. Miao, Zhi‐Qiang, Song, Jiang, & Zhou, ), the traditional neighborhood preserving embedding (NPE) algorithm (Aimin, Peng, & Lingjian, ; Yuan, Ge, Ye, & Song, ), and the adaptive NPE (ANPE) algorithm to verify the effectiveness of the proposed method.…”

Section: Introductionmentioning

confidence: 99%

“…Miao, Ge, Song, & Zhou, 2013;A. M. Miao, Zhi-Qiang, Song, Jiang, & Zhou, 2014), the traditional neighborhood preserving embedding (NPE) algorithm (Aimin, Peng, & Lingjian, 2015;Yuan, Ge, Ye, & Song, 2016), and the adaptive NPE (ANPE) algorithm to verify the effectiveness of the proposed method.…”

mentioning

confidence: 99%

Cable joint fault detection for the ring main unit based on an adaptive TNPE algorithm

Tan

Tao

et al. 2019

WIREs Data Min & Knowl

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The ring main unit (RMU) is the main equipment of the electrical power distribution network. The deterioration process of the RMU cable joint is multivariable and nonstationary over time. Existing fault detection models for the RMU cable joint are single‐variable and static over time. A novel algorithm of adaptive time neighborhood preserving embedding (ATNPE) is proposed in this paper, a multivariable time series statistical algorithm with online learning ability to improve the robustness and generalization ability of cable joint fault detection. This method combines an approximate linear dependence (ALD) condition with the time neighborhood preserving embedding (TNPE). New independent samples were determined by calculating the ALD value between new samples and modeling samples. In addition, the new independent samples were used to update the TNPE model. The offline training module was used to update RMU fault detection based on the TNPE model by training new samples with historical offline data. The online updating module trained the historical model to ensure the correct rate of online detection. The experimental results indicated that the method has the ability to adapt to new samples and the squared prediction error statistic limit responded to changes in statistical characteristics of monitoring data. The method can effectively reduce the false alarm rate and has the same fault detection rate compared to the existing model. This article is categorized under: Technologies > Computational Intelligence Application Areas > Business and Industry Technologies > Machine Learning

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Neighborhood preserving regression embedding based data regression and its applications on soft sensor modeling

Cited by 20 publications

References 42 publications

Nonlinear Fault Detection of Batch Processes Using Functional Local Kernel Principal Component Analysis

Nonlinear Fault Detection of Batch Processes Using Functional Local Kernel Principal Component Analysis

Research on an Urban Building Area Extraction Method with High-Resolution PolSAR Imaging Based on Adaptive Neighborhood Selection Neighborhoods for Preserving Embedding

Cable joint fault detection for the ring main unit based on an adaptive TNPE algorithm

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