Dynamic Process of Quality Abnormal Pattern Recognition Based on PCA-SVM

2013 ◽  
Vol 860-863 ◽  
pp. 2686-2689
Author(s):  
Yu Min Liu ◽  
Shuai Zhang
Keyword(s):  
Pattern Recognition ◽  
Dynamic Process ◽  
Recognition Accuracy ◽  
Principal Component ◽  
Svm Classifier ◽  
Feature Selection Technique ◽  
Automatic Production ◽  
Practical Applications ◽  
Online Quality Control ◽  
Abnormal Pattern

Quality abnormal pattern recognition for dynamic process is the key problem to achieve the online quality control and diagnose of automatic production. In the practical applications, there are some existing problems such as computational complexity and low recognition accuracy. A recognition method for quality abnormal pattern of dynamic process with PCA-SVM was proposed. This paper proposes a feature selection technique that employs a principal component analysis, to avoid this information loss. Then, the extracted features were treated as input vector for SVM classifier, following a particle swarm optimization algorithm is proposed to improve the generalization performance of the recognizer. Simulation results show that the proposed algorithm has very high recognition accuracy and high generalization ability. It is significant for quality monitoring and diagnosis in manufacture dynamic process.

A MSVM Quality Pattern Recognition Model for Dynamic Process

2013 ◽  
Vol 433-435 ◽  
pp. 555-561
Author(s):  
Yu Min Liu ◽  
Hao Fei Zhou ◽  
Shuai Zhang
Keyword(s):  
Pattern Recognition ◽  
Dynamic Process ◽  
Simulation Experiment ◽  
Recognition Accuracy ◽  
Kernel Functions ◽  
Automatic Production ◽  
Recognition Model ◽  
Online Quality Control ◽  
Recognition Quality ◽  
Abnormal Pattern

Quality abnormal pattern recognition for dynamic process is the key problem to achieve the online quality control and diagnose of automatic production. Firstly, this paper analyzed the quality patterns of dynamic process. Secondly, we established recognition model of quality recognition in dynamic process using MSVM and compared the SVM recognition accuracy of different kernel functions for different quality patterns. Simulation experiment indicates that different SVM classifiers should choose specified kernel functions to recognition quality patterns. At last, we established MSVM recognition model of quality pattern in dynamic process using multi-kernel function according to the experiment results.

Improved combination of RPCA and MEL for sparse representation-based face recognition

2014 ◽  
Vol 12 (03) ◽  
pp. 1450031 ◽  
Author(s):  
Rokan Khaji ◽  
Hong Li ◽  
Hongfeng Li ◽  
Rabiu Haruna ◽  
Ramadhan Abdo Musleh Alsaidi
Keyword(s):  
Pattern Recognition ◽  
Face Recognition ◽  
Sparse Representation ◽  
Principal Component ◽  
Second Phase ◽  
Robust Principal Component Analysis ◽  
Practical Applications ◽  
Face Images ◽  
Two Phases ◽  
Improved Technique

Face recognition (FR) is an important and challenging task in pattern recognition and has many important practical applications. This paper presents an improved technique for Face Recognition, which consists of two phases where in each phase; a technique is employed effectively that is used extensively in computer vision and pattern recognition. Initially, the Robust Principal Component Analysis (RPCA) is used specifically in the first phase, which is employed to reduce dimensionality and to extract abstract features of faces. The framework of the second phase is sparse representation based classification (SRC) and introduced metaface learning (MFL) of face images. Experiments for face recognition have been performed on ORL and AR face database. It is shown that the proposed method can perform much best than other methods. And with the proposed method, we can obtain a best understanding of data.

scholarly journals Intrusion Detection System using One Class SVM with and without Feature Selection in Wormhole Attack Detection

2019 ◽  
Vol 9 (2S4) ◽  
pp. 629-638
Keyword(s):  
Principal Component Analysis ◽  
Feature Selection ◽  
Ad Hoc ◽  
Detection System ◽  
Principal Component ◽  
Component Analysis ◽  
Svm Classifier ◽  
Feature Selection Technique ◽  
Selection Technique ◽  
Wormhole Attack

An Ad-hoc network is a kind of wireless construction from one to another computer, without having Wi-Fi access point or Router. However, the Ad hoc approach offers marginal security and decreases the data transfer rate. Consequently, it helps the attacker to connect with the ad-hoc network without any trouble. Therefore, a robust and reliable intrusion detection system (IDS) is a necessity of today’s information security domain. These IDS systems play a vital role in monitoring the threats encountered in a network by detecting the change in the normal profile due to attacks. Recently, to detect attacks the IDS are being equipped with machine learning algorithms to attain better accuracy and fast detection speed. Most of the IDS use different network features. However, enormous number of features makes the detection and prevention complicated. The IDS presented in this paper employs random forest and principal component analysis to minimize the number of features for network IDS for wireless ad hoc networks. The one class SVM has been used for detection of worm hole attack with and without feature selection. The performances of these approaches are compared with various existing techniques with false positive rate (FPR), accuracy and detection rate. Here, the accuracy improves and false positive rate reduces when intrusion is detected with feature selection technique. This paper discusses the performance of the one class SVM classifier in the wireless adhoc network IDS with random forest feature selection and principal component analysis feature selection techniques and one class SVM classifier without feature selection technique in the detection of wormhole attack. And the performance of one class SVM IDS is better in the detection of wormhole attack while it is implemented with principal component analysis feature selection technique.

scholarly journals Distinction between Early Keratoconus with Normal Vision and Normal Cornea based on Pattern Recognition Analysis

2017 ◽  
Vol 6 (2) ◽  
pp. 58-66
Author(s):  
Alfredo Vega-Estrada ◽  
Jorge L Alio ◽  
Pablo Sanz ◽  
María J Prieto ◽  
Antonio Cardona ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Pattern Recognition ◽  
Discriminant Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Corneal Topography ◽  
Normal Vision ◽  
Total Variability ◽  
Abnormal Pattern ◽  
Pattern Recognition Analysis

ABSTRACT Aim To find the profile that differentiates most normal corneas from early keratoconus with normal vision. Materials and methods Multicentric, comparative study including a total of 995 eyes and divided into two groups: 625 eyes suffering from early keratoconus but with normal vision [spectacle corrected distance visual acuity (CDVA) of 0.9 decimal or better] and 370 normal control eyes with same normal vision level. To ascertain the main differences that would allow the identification of the keratoconic eyes from normals, a pattern recognition analysis was performed combining two statistical methods: Principal component analysis (PCA) and discriminant analysis. Visual and refractive parameters, corneal topography, aberrometry, and PCA were evaluated in both groups. Results The application of the PCA with Varimax rotation offered a total of five factors which explains the 85.51% of the total variability. Discriminant analysis indicated that factors 1 and 3 were at the greatest discriminating capacity. From a total of 318 cases, the newly identified abnormal pattern profile allowed the recognition of 275, which presents a sensitivity and specificity of 71.6 and 97.3% respectively. Conclusion In eyes with normal CDVA, those factors related to the nonorthogonal shape irregularity of the cornea and the refractive power are the ones that showed more discriminating capabilities between normal and early keratoconic eyes. Clinical significance Principal component analysis allows to correctly discriminate between normal and mild keratoconus patients; additionally, this method is not restricted to a particular corneal topography technology and is available to any normally equipped ophthalmology office. How to cite this article Alio JL, Vega-Estrada A, Sanz P, Prieto MJ, Cardona A, Maldonado M, Gutierrez R, Barraquer RI, Sádaba LM. Distinction between Early Keratoconus with Normal Vision and Normal Cornea Based on Pattern Recognition Analysis. Int J Kerat Ect Cor Dis 2017;6(2):58-66.

scholarly journals Research on an SVM Coupling Algorithm of Image Recognition

2016 ◽  
Author(s):  
Ai-Min Yang ◽  
Yang Han ◽  
Jin-Ze Li ◽  
Yu-Hang Pan ◽  
Lei-Jie Shen ◽  
...  
Keyword(s):  
Face Recognition ◽  
Recognition Accuracy ◽  
Algorithm Design ◽  
Principal Component ◽  
Facial Feature ◽  
Face Image ◽  
Support Vector ◽  
Svm Classifier ◽  
Face Image Processing ◽  
Coupling Algorithm

The key links of face recognition are digital image preprocessing, facial feature extraction and pattern recognition, this article aimed at the current problem of slow speed and low recognition accuracy of face recognition , from the above three key links, on the basic of analyzing the therories of Fractional Differential Masks Operator (FDMO), Principal Component Analysis (PCA) and Support Vector Machine (SVM), design a kind of FDMO+PVA+SVM coupling algorithm that applies to face recognition to improve the speed and accuracy of it. To realize FDMO+PCA+SVM coupling algorithm, first, we should apply FDMO to face image processing binary marginalization, the purpose is getting face contour; Then, we apply PCA to get the feature of face image which is disposed by binary marainalization. At last, we can apply One-Against All of the SVM classifier and LibSVM software package to realize face recognition. Beside, the article with nine different coupling algorithm design four groups of experimental reaults on the ORL face database verified by comparative analysic FDMO+PCA+SVM coupling algorithm in the superiority of face recognition accuracy and speed.

The Heartbeat Classification Based on PCA

2012 ◽  
Vol 195-196 ◽  
pp. 402-406
Author(s):  
Xue Qin Chen ◽  
Rui Ping Wang
Keyword(s):  
Principal Component Analysis ◽  
Pattern Recognition ◽  
Recognition Task ◽  
Principal Component ◽  
True Positive Rate ◽  
True Positive ◽  
Heartbeat Classification ◽  
Positive Rate ◽  
Electrocardiogram Ecg ◽  
Pattern Recognition Task

Classify the electrocardiogram (ECG) into different pathophysiological categories is a complex pattern recognition task which has been tried in lots of methods. This paper will discuss a method of principal component analysis (PCA) in exacting the heartbeat features, and a new method of classification that is to calculate the error between the testing heartbeat and reconstructed heartbeat. Training and testing heartbeat is taken from the MIT-BIH Arrhythmia Database, in which 8 types of arrhythmia signals are selected in this paper. The true positive rate (TPR) is 83%.

Two-parameter KNN algorithm and its application in recognition of brand rice

2021 ◽  
pp. 1-7
Author(s):  
Zhu Siyu ◽  
He Chongnan ◽  
Song Mingjuan ◽  
Li Linna
Keyword(s):  
Near Infrared ◽  
Nearest Neighbor ◽  
Recognition Accuracy ◽  
Principal Component ◽  
Kernel Principal Component Analysis ◽  
K Nearest Neighbor ◽  
Fisher Discriminant Analysis ◽  
Pattern Class ◽  
Fisher Discriminant ◽  
Two Parameter

In response to the frequent counterfeiting of Wuchang rice in the market, an effective method to identify brand rice is proposed. Taking the near-infrared spectroscopy data of a total of 373 grains of rice from the four origins (Wuchang, Shangzhi, Yanshou, and Fangzheng) as the observations, kernel principal component analysis(KPCA) was employed to reduce the dimensionality, and Fisher discriminant analysis(FDA) and k-nearest neighbor algorithm (KNN) were used to identify brand rice respectively. The effects of the two recognition methods are very good, and that of KNN is relatively better. Howerver the shortcomings of KNN are obvious. For instance, it has only one test dimension and its test of samples is not delicate enough. In order to further improve the recognition accuracy, fuzzy k-nearest neighbor set is defined and fuzzy probability theory is employed to get a new recognition method –Two-Parameter KNN discrimination method. Compared with KNN algorithm, this method increases the examination dimension. It not only examines the proportion of the number of samples in each pattern class in the k-nearest neighbor set, but also examines the degree of similarity between the center of each pattern class and the sample to be identified. Therefore, the recognition process is more delicate and the recognition accuracy is higher. In the identification of brand rice, the discriminant accuracy of Two-Parameter KNN algorithm is significantly higher than that of FDA and that of KNN algorithm.

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