The Research of Fuzzy Weighted Twin Support Vector Regression in the MBR Simulation Prediction

2014 ◽  
Vol 556-562 ◽  
pp. 3648-3653 ◽  
Author(s):  
Chan Juan Ji ◽  
Chun Qing Li ◽  
Tao Wang
Keyword(s):  
Support Vector Regression ◽  
Membrane Fouling ◽  
Principal Component ◽  
Support Vector ◽  
Support Vector Data Description ◽  
Vector Data ◽  
Negative Effects ◽  
Twin Support Vector Regression ◽  
Output Layer ◽  
Input Layer

This paper using the way of Support Vector Data Description (SVDD) and considering the tightness between the Membrane Bio-Reactor (MBR) samples, applies the Fuzzy Weighted Twin Support Vector Regression (FTSVR) to the MBR simulation prediction research. Firstly,adopt the principal component analysis (PCA) on membrane fouling factors to achieve dimension reduction and de-correlation, then put the PCA output layer as the input layer of FTSVR, flux as the output layer, eventually, the MBR Membrane Fouling Prediction Model is built. This method considers the different effects on the regression hyperplane of different MBR samples,and effectively eliminates the negative effects due to error even outliers in the process of MBR data measurement.

Adjustment mode decision based on support vector data description and evidence theory for assembly lines

2018 ◽  
Vol 118 (8) ◽  
pp. 1711-1726 ◽  
Author(s):  
Youlong Lv ◽  
Wei Qin ◽  
Jungang Yang ◽  
Jie Zhang
Keyword(s):  
Assembly Line ◽  
Historical Data ◽  
Classification Model ◽  
Support Vector ◽  
Support Vector Data Description ◽  
Vector Data ◽  
Assembly Lines ◽  
Content Type ◽  
Input Layer ◽  
Multi Classification

PurposeThree adjustment modes are alternatives for mixed-model assembly lines (MMALs) to improve their production plans according to constantly changing customer requirements. The purpose of this paper is to deal with the decision-making problem between these modes by proposing a novel multi-classification method. This method recommends appropriate adjustment modes for the assembly lines faced with different customer orders through machine learning from historical data.Design/methodology/approachThe decision-making method uses the classification model composed of an input layer, two intermediate layers and an output layer. The input layer describes the assembly line in a knowledge-intensive manner by presenting the impact degrees of production parameters on line performances. The first intermediate layer provides the support vector data description (SVDD) of each adjustment mode through historical data training. The second intermediate layer employs the Dempster–Shafer (D–S) theory to combine the posterior classification possibilities generated from different SVDDs. The output layer gives the adjustment mode with the maximum posterior possibility as the classification result according to Bayesian decision theory.FindingsThe proposed method achieves higher classification accuracies than the support vector machine methods and the traditional SVDD method in the numerical test consisting of data sets from the machine-learning repository and the case study of a diesel engine assembly line.Practical implicationsThis research recommends appropriate adjustment modes for MMALs in response to customer demand changes. According to the suggested adjustment mode, the managers can improve the line performance more effectively by using the well-designed optimization methods for a specific scope.Originality/valueThe adjustment mode decision belongs to the multi-classification problem featured with limited historical data. Although traditional SVDD methods can solve these problems by providing the posterior possibility of each classification result, they might have poor classification accuracies owing to the conflicts and uncertainties of these possibilities. This paper develops a novel classification model that integrates the SVDD method with the D–S theory. By handling the conflicts and uncertainties appropriately, this model achieves higher classification accuracies than traditional methods.

scholarly journals An Integration Method Using Kernel Principal Component Analysis and Cascade Support Vector Data Description for Pipeline Leak Detection with Multiple Operating Modes

Processes ◽  
2019 ◽  
Vol 7 (10) ◽  
pp. 648 ◽  
Author(s):  
Mengfei Zhou ◽  
Qiang Zhang ◽  
Yunwen Liu ◽  
Xiaofang Sun ◽  
Yijun Cai ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Principal Component ◽  
Leak Detection ◽  
Component Analysis ◽  
Kernel Principal Component Analysis ◽  
Support Vector ◽  
Support Vector Data Description ◽  
Vector Data ◽  
Data Description ◽  
Operating Modes

Pipelines are one of the most efficient and economical methods of transporting fluids, such as oil, natural gas, and water. However, pipelines are often subject to leakage due to pipe corrosion, pipe aging, pipe weld defects, or damage by a third-party, resulting in huge economic losses and environmental degradation. Therefore, effective pipeline leak detection methods are important research issues to ensure pipeline integrity management and accident prevention. The conventional methods for pipeline leak detection generally need to extract the features of leak signal to establish a leak detection model. However, it is difficult to obtain actual leakage signal data samples in most applications. In addition, the operating modes of pipeline fluid transportation process often have frequent changes, such as regulating valves and pump operation. Aiming at these issues, this paper proposes a hybrid intelligent method that integrates kernel principal component analysis (KPCA) and cascade support vector data description (Cas-SVDD) for pipeline leak detection with multiple operating modes, using data samples that are leak-free during pipeline operation. Firstly, the local mean decomposition method is used to denoise and reconstruct the measured signal to obtain the feature variables. Then, the feature dimension is reduced and the nonlinear principal component is extracted by the KPCA algorithm. Secondly, the K-means clustering algorithm is used to identify multiple operating modes and then obtain multiple support vector data description models to obtain the decision boundaries of the corresponding hyperspheres. Finally, pipeline leak is detected based on the Cas-SVDD method. The experimental results show that the proposed method can effectively detect small leaks and improve leak detection accuracy.

scholarly journals Authenticity Detection of Black Rice by Near-Infrared Spectroscopy and Support Vector Data Description

2018 ◽  
Vol 2018 ◽  
pp. 1-8 ◽  
Author(s):  
Hui Chen ◽  
Chao Tan ◽  
Zan Lin
Keyword(s):  
Near Infrared ◽  
Nearest Neighbor ◽  
Principal Component ◽  
Support Vector ◽  
Support Vector Data Description ◽  
Vector Data ◽  
K Nearest Neighbor ◽  
Black Rice ◽  
Target Class ◽  
Data Description

Black rice is an important rice species in Southeast Asia. It is a common phenomenon to pass low-priced black rice off as high-priced ones for economic benefit, especially in some remote towns. There is increasing need for the development of fast, easy-to-use, and low-cost analytical methods for authenticity detection. The feasibility to utilize near-infrared (NIR) spectroscopy and support vector data description (SVDD) for such a goal is explored. Principal component analysis (PCA) is used for exploratory analysis and feature extraction. Another two data description methods, i.e., k-nearest neighbor data description (KNNDD) and GAUSS method, are used as the reference. A total of 142 samples from three brands were collected for spectral analysis. Each time, the samples of a brand serve as the target class whereas other samples serve as the outlier class. Based on both the first two principal components (PCs) and original variables, three types of data descriptions were constructed. On average, the optimized SVDD model achieves acceptable performance, i.e., a specificity of 100% and a sensitivity of 94.2% on the independent test set with tight boundary. It indicates that SVDD combined with NIR is feasible and effective for authenticity detection of black rice.

Helicopter main reduction planetary gear fault diagnosis method based on SVDD

2020 ◽  
Vol 64 (1-4) ◽  
pp. 137-145
Author(s):  
Yubin Xia ◽  
Dakai Liang ◽  
Guo Zheng ◽  
Jingling Wang ◽  
Jie Zeng
Keyword(s):  
Fault Diagnosis ◽  
Planetary Gear ◽  
Gaussian Kernel ◽  
Support Vector ◽  
Support Vector Data Description ◽  
Vector Data ◽  
Energy Characteristics ◽  
Gear Fault ◽  
Channel Information ◽  
Diagnosis Method

Aiming at the irregularity of the fault characteristics of the helicopter main reducer planetary gear, a fault diagnosis method based on support vector data description (SVDD) is proposed. The working condition of the helicopter is complex and changeable, and the fault characteristics of the planetary gear also show irregularity with the change of working conditions. It is impossible to diagnose the fault by the regularity of a single fault feature; so a method of SVDD based on Gaussian kernel function is used. By connecting the energy characteristics and fault characteristics of the helicopter main reducer running state signal and performing vector quantization, the planetary gear of the helicopter main reducer is characterized, and simultaneously couple the multi-channel information, which can accurately characterize the operational state of the planetary gear’s state.

MK-FSVM-SVDD: A Multiple Kernel-based Fuzzy SVM Model for Predicting DNA-binding Proteins via Support Vector Data Description

2020 ◽  
Vol 15 ◽  
Author(s):  
Yi Zou ◽  
Hongjie Wu ◽  
Xiaoyi Guo ◽  
Li Peng ◽  
Yijie Ding ◽  
...  
Keyword(s):  
Dna Binding ◽  
Binding Proteins ◽  
Detection Efficiency ◽  
Dna Binding Proteins ◽  
Support Vector ◽  
Support Vector Data Description ◽  
Vector Data ◽  
Data Description ◽  
Multiple Kernel ◽  
Svm Model

Background: Detecting DNA-binding proetins (DBPs) based on biological and chemical methods is time consuming and expensive. Objective: In recent years, the rise of computational biology methods based on Machine Learning (ML) has greatly improved the detection efficiency of DBPs. Method: In this study, Multiple Kernel-based Fuzzy SVM Model with Support Vector Data Description (MK-FSVM-SVDD) is proposed to predict DBPs. Firstly, sex features are extracted from protein sequence. Secondly, multiple kernels are constructed via these sequence feature. Than, multiple kernels are integrated by Centered Kernel Alignment-based Multiple Kernel Learning (CKA-MKL). Next, fuzzy membership scores of training samples are calculated with Support Vector Data Description (SVDD). FSVM is trained and employed to detect new DBPs. Results: Our model is test on several benchmark datasets. Compared with other methods, MK-FSVM-SVDD achieves best Matthew's Correlation Coefficient (MCC) on PDB186 (0.7250) and PDB2272 (0.5476). Conclusion: We can conclude that MK-FSVM-SVDD is more suitable than common SVM, as the classifier for DNA-binding proteins identification.

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