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Pan Y.,Jiangsu Engineering R and nter for Information Fusion Software | Deng Z.,Jiangsu Engineering R and nter for Information Fusion Software | Deng Z.,Jiangnan University | Deng Z.,Soochow University of China | And 3 more authors.
Information Technology Journal | Year: 2012

A key technique for protein analysis is the geometric alignment of two-dimensional polyacrylamide gel electrophoresis (2-D PAGE), i.e., 2-D PAGE image registration. In this study, the adaptability in elastic image registration was emphasized. According to the characteristics of 2-D gel image registration, a fuzzy-inference-rule based flexible model (FIM-FM) is proposed to model the complex transformation between 2-D gel image pairs. By introducing the concept of motion estimation, the parameter learning rules of the proposed model are derived for registration. The experiments show that the proposed algorithm is highly effective for registration of 2-D gel images and is competitive to the existing state-of-the-art algorithms. © 2012 Asian Network for Scientific Information.


Deng Z.,Jiangnan University | Deng Z.,Soochow University of China | Shitong Wang,Jiangnan University | Shitong Wang,Soochow University of China | And 2 more authors.
Proceedings - 2011 8th International Conference on Fuzzy Systems and Knowledge Discovery, FSKD 2011 | Year: 2011

A key technique for protein analysis is the geometric alignment of two-dimensional polyacrylamide gel electrophoresis (2-D PAGE), i.e. 2-D PAGE image registration. In this study, according to the characteristics of 2-D gel image registration, a fuzzy-inference-rule based flexible model (FIM-FM) is proposed to model the complex geometric transformation and intensity variation between 2-D gel image pairs. Then, by introducing the concept of motion estimation and the corresponding sum-of-squared-difference (SSD) objective function, the parameter learning rules of the proposed model are derived for registration. Accordingly, an adaptive FIR-FM-based automatic elastic image registration algorithm is presented for 2-D gel image registration here. It is distinguished by its strong ability in approximating complex nonlinear transformation inherited from fuzzy inference and its efficiency and adaptability in obtaining precise model parameters through effective parameter learning rules. Our experiments show that the proposed algorithm is highly effective for registration of 2-D gel images. © 2011 IEEE.


Pan Y.,Jiangsu Engineering R and nter for Information Fusion Software | Wang J.,Jiangsu Engineering R and nter for Information Fusion Software | Wang J.,Jiangnan University | Deng Z.,Jiangnan University
Journal of Information and Computational Science | Year: 2013

While the Euclidean distance metric is commonly utilized in most soft subspace clustering algorithms, some robust distance metrics can also be considered for soft subspace clustering. In this study, a novel soft subspace clustering algorithm called Alternative Soft Subspace Clustering (ASSC) is proposed by incorporating the alternative distance metric into the framework of entropy weighting subspace clustering. The properties of ASSC are investigated and a theoretical analysis on robustness is established accordingly. Experiments on UCI data are conducted and the experimental results show the effectiveness of the proposed algorithm. © 2013 by Binary Information Press.


Pan Y.,Jiangyin Polytechnic College | Pan Y.,Jiangsu Engineering R and nter for Information Fusion Software | Bao F.,Jiangyin Polytechnic College | Bao F.,Jiangsu Engineering R and nter for Information Fusion Software | And 2 more authors.
Advanced Materials Research | Year: 2011

By extracting five kernel principal components of fabric FAST (Fabric Assurance by Simple Testing) low mechanical data, this paper proposed a supervised fuzzy clustering radial basis function neural network to construct fabric sewability prediction system. Our experimental results demonstrate that the proposed system could efficiently be used as an objective seam pucker evaluation system with high accuracy and is robust for various structures and mechanical properties of middle-thickness woolen fabric. © (2011) Trans Tech Publications, Switzerland.


Yonghui P.,Jiangyin Polytechnic College | Yonghui P.,Jiangsu Engineering R and nter for Information Fusion Software | Rui F.,Jiangyin Polytechnic College
Journal of Computers | Year: 2012

Using wavelet transform to handle auto-mobile image with complex background for license localization, then preprocess license characters on vehicle licenses, and extracting the textural features of license characters in wavelet space, this paper proposed a novel algorithm for vehicle license localization and character recognition which is based on adaptive wavelet neural networks. Firstly, it uses the wavelet transform to preprocess color vehicle image into index image which undergoes wavelet transform to obtain wavelet feature coefficients. Secondly, license position could be located through morphological operation. Thirdly, it extracts the features of localized license characters in wavelet space which is presented to the wavelet neural network as inputs. At last, an adaptive wavelet neural network based on wavelet transform is constructed to recognize license characters. Experimental results demonstrate that the proposed approach could efficiently be used as a vehicle license characters recognition system with high convergence, which is robust for license-size, licensecolor and background complexity. © 2012 ACADEMY PUBLISHER.


Tu L.,Jiangsu Engineering R and nter for Information Fusion Software | Tu L.,Jiangyin Polytechnic College
Advances in Intelligent and Soft Computing | Year: 2012

This paper presents a multiple data streams clustering algorithm CA-cluster based on Kendall correlation analysis. CA-cluster instantaneously adjusts the number of clusters and detects the development of the data stream. Since the high velocity and the large number of the data stream, it is not feasible to retain the raw data to calculate the correlation coefficient. We propose a compression mechanism based on AU statistics to support the only once-scanned algorithm to calculate the Kendall correlation coefficient. Experimental results show that our algorithms are more superior to other methods in the aspect of clustering quality, speed and scalability. © 2012 Springer-Verlag GmbH.


Tu L.,Jiangsu Engineering R and nter for Information Fusion Software | Tu L.,Jiangyin Polytechnic College | Cui P.,Jiangsu Engineering R and nter for Information Fusion Software | Cui P.,Jiangyin Polytechnic College
WIT Transactions on Engineering Sciences | Year: 2014

Most existing clustering algorithms on uncertain data stream cannot discover the arbitrary shapes since they are based on k-means. To address this issue, this paper proposes a density grid-based clustering algorithm (DG-UStream) over uncertain data stream. DG-UStream uses the grid structure to store the summary information of data tuples in the stream which could be easily updated periodically. Clusters are formed by merging the adjacent grids. Furthermore, an efficient technique is developed to detect and delete the isolated grids which could greatly reduce the time and space costs. The experimental results show that DG-UStream has superior clustering performance in terms of clustering quality and time efficiency. © 2013 WIT Press.

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