ChuZhou Vocational Technology College

Chuzhou, China

ChuZhou Vocational Technology College

Chuzhou, China

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Wu X.H.,Jiangsu University | Qiu S.W.,Jiangsu University | Li X.,Jiangsu University | Wu B.,ChuZhou Vocational Technology College | And 2 more authors.
Applied Mechanics and Materials | Year: 2014

Pork storage time is relevant to its freshness which influences pork quality. To achieve the rapid and effective discrimination of pork storage time, near infrared spectroscopy was used to collect the near infrared reflectance (NIR) spectra of pork in different storage time. The high-dimensional NIR spectra was firstly compressed by principal component analysis (PCA) and then classified by fuzzy learning vector quantization (FLVQ). PCA plus FLVQ is a completely unsupervised learning algorithm which finds hidden patterns in unlabeled data. Experimental results showed that PCA plus FLVQ could classify pork NIR spectra effectively. © (2014) Trans Tech Publications, Switzerland.


Wu X.,Jiangsu University | Wu B.,ChuZhou Vocational Technology College | Sun J.,Jiangsu University | Li M.,Leshan Normal University
International Journal of Food Engineering | Year: 2015

Discrimination of apple varieties plays an important role in apple post-harvest commercial processing. A fast allied fuzzy c-means (FAFCM) clustering algorithm was proposed to classify the apple varieties using near-infrared reflectance (NIR) spectroscopy technology and orthogonal linear discriminant analysis (OLDA) which was used as feature extraction and dimensionality reduction method. Our classification method: the high-dimensional NIR data were reduced to three-dimensional data by OLDA at first, and the FAFCM clustering algorithm was implemented to classify the reduced data. Furthermore, the principal component analysis (PCA) and linear discriminant analysis (LDA) combined with k-nearest neighbor classifier (KNNC), fuzzy c-means (FCM) clustering and unsupervised possibilistic clustering algorithm (UPCA), formed the other four classification methods to classify apple samples in comparison with our proposed method. The experimental results showed that FAFCM achieved the best performance of classification. © 2015 by De Gruyter.


Wu X.H.,Jiangsu University | Cai T.X.,Jiangsu University | Wu B.,ChuZhou Vocational Technology College | Sun J.,Jiangsu University
Advanced Materials Research | Year: 2013

Near infrared reflectance (NIR) spectroscopy has been used to obtain NIR spectra of two varieties of apple samples. The dimensionality of NIR spectra was reduced by principal component analysis (PCA), and discriminant information was extracted by linear discriminant analysis (LDA). Last, a hybrid possibilistic clustering algorithm (HPCA) was utilized as classifier to discriminate the apple samples of different varieties. HPCA integrates possibilistic clustering algorithm (PCA) and improved possibilistic c-means (IPCM) clustering algorithm, and produces not only the membership values but also typicality values by simple computation of the sample co-variance. Experimental results showed that HPCA, as an unsupervised learning algorithm, could quickly and easily discriminate the apple varieties. © (2013) Trans Tech Publications, Switzerland.


Wu X.H.,Jiangsu University | Wan X.X.,Jiangsu University | Wu B.,ChuZhou Vocational Technology College | Wu F.,Jiangsu University
Advanced Materials Research | Year: 2013

Classification of apple is an important link in postharvest commercialization processing. To realize the non-destructive, rapid and effective discrimination of apple fruits, the near infrared reflectance spectra of four varieties of apples were collected using near infrared spectroscopy, reduced by principal component analysis (PCA) and used to extract the discriminant information by linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), fuzzy discriminant analysis (FDA) and Foley-Sammon discriminant analysis. Finally k-nearest neighbor finished the classification. The classification results showed that FDA could extract the discriminant information of NIR spectra more effectively, and achieved the highest classification accuracy. © (2013) Trans Tech Publications, Switzerland.


Liew C.Z.,Charles Laboratory | Shaw R.,Charles Laboratory | Li L.,Chuzhou Vocational Technology College | Yang Y.,Chuzhou Vocational Technology College
Proceedings - 2014 International Conference on Medical Biometrics, ICMB 2014 | Year: 2014

This paper discusses the information security issue on biometric data. A brief survey is given at first with the discussion on the significance of biometric application. A novel encrypting strategy combined with Bernoulli-Logistic chaotic cipher system is proposed to improve the performance on cryptographic text with consideration both of volatility and correlation. Experimental results show that the proposed approach for encryption provides an efficient and more secure performance. © 2014 IEEE.


Wu X.,Jiangsu University | Wu B.,ChuZhou Vocational Technology College | Sun J.,Jiangsu University | Li M.,Leshan Normal University | Du H.,Jiangsu University
International Journal of Food Properties | Year: 2016

Near infrared spectra of apples contain the most useful information of the soluble solids content and firmness of apples. A new feature extraction method, called sorting discriminant analysis, was proposed to use a sorting method based on principal component analysis and linear discriminant analysis to extract the features of near infrared spectra. The objective of this research was to make use of feature extraction methods, such as principal component analysis, linear discriminant analysis, discriminant partial least squares, and sorting discriminant analysis to extract information from near infrared spectra of the "Huaniu" apples and the "Fuji" apples. After feature extraction, the nearest neighbor classifier was used to classify the apples, and the classification results were compared to study that which feature extraction method performed best. The experimental results showed principal component analysis + linear discriminant analysis and sorting discriminant analysis could extract discriminant information from near infrared spectra of apples better than principal component analysis and discriminant partial least squares, and sorting discriminant analysis was the best one. Sorting discriminant analysis can not only compress the highdimensional near infrared spectra to the low-dimensional data but also project near infrared spectra to a new feature space where the data can be classified easily and effectively, and sorting discriminant analysis is superior to principal component analysis + linear discriminant analysis in most cases. Copyright © Taylor & Francis Group, LLC.


Li L.,ChuZhou Vocational Technology College
2011 International Conference on Electronics, Communications and Control, ICECC 2011 - Proceedings | Year: 2011

Sensor network is a hot topic in resent and the applications for it are many and varied. Even it had been researched for long time, but there is always some new idea and new challenge put into which bring its holly green vigorous development in different period of researching. In this paper, based on preliminary study, basic concept, issue of sensor network and its research is given, based on analysis, several related idea and theory will also been discussed. A trial exploring utilization based on gray dynamic theory were discussed later and with its practice, the platform such as Tiny Os and some hardware issue for research and application has been introduced and discussed; therefore take a glance into the future about the work which is worth to do aiming at the challenge it faced now and might in tomorrow. © 2011 IEEE.


Wu X.,Jiangsu University | Fu H.,Jiangsu University | Wu B.,ChuZhou Vocational Technology College | Zhao J.,Jiangsu University
Journal of Information and Computational Science | Year: 2010

Fuzzy learning vector quantization (FLVQ) is a well-known fuzzy clustering network. However, FLVQ is sensitive to noises or outliers. In this paper, two novel fuzzy learning vector quantization algorithms, called possibilistic fuzzy learning vector quantization (PFLVQ), are proposed to overcome the noise sensitivity problem of FLVQ. PFLVQ integrates possibilistic fuzzy c-means (PFCM) clustering model into fuzzy learning vector quantization. Different from FLVQ that only offers membership, PFLVQ can produce both membership and typicality values simultaneously. PFLVQ can deal with noisy data better than FLVQ and does not generate coincident clusters that occur in possiblistic c-means (PCM). The experimental results show the better performance of PFLVQ. Copyright © 2010 Binary Information Press.


Wu X.,Jiangsu University | Sun J.,Jiangsu University | Wu B.,ChuZhou Vocational Technology College | Zhao J.,Jiangsu University
Journal of Information and Computational Science | Year: 2010

Fuzzy entropy (FE) clustering is sensitive to noises, and possibilistic c-means (PCM) clustering is very sensitive to initializations and sometimes generates coincident clusters. A novel hybrid fuzzy entropy clustering model is proposed by combining FE model and PCM model. The proposed model is called allied fuzzy entropy (AFE) clustering model. It is claimed that AFE model is an extension of FE model. Different from PCM model that produces possibilities and FE model that produces memberships, AFE model generates both possibilities and memberships simultaneously. Furthermore, AFE overcomes the noise sensitivity problem of FE and the coincident clusters problem of PCM. The experimental results show that AFE compares favorably with PCM and FE. Copyright © 2010 Binary Information Press.


Wu X.,Jiangsu University | Wu B.,Chuzhou Vocational Technology College | Sun J.,Jiangsu University | Fu H.,Jiangsu University
Journal of Information and Computational Science | Year: 2010

Recently, an interesting possibilistic clustering algorithm (PCA) was proposed. The possibilistic c-means (PCM) clustering must run fuzzy c-means (FCM) clustering to calculate the parameters but PCA need not do that. However, we found PCA is very sensitive to initializations and sometimes generates coincident clusters. A novel unsupervised possibilistic fuzzy clustering (UPFC) is proposed to overcome this shortcoming. UPFC is an extension of PCA model by combining FCM and PCA. Different from PCA that produces possibilities and FCM which produces memberships, UPFC generates both possibilities and memberships simultaneously. Furthermore, UPFC overcomes the noise sensitivity problem of FCM and the coincident clusters problem of PCA. Experiments show the effectiveness of our proposed algorithm. Copyright ©. 2010 Binary Information Press.

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