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Tolerance-based multigranulation rough sets in incomplete systems

Frontiers of computer Science 2014年第5期8卷 753-762页

作者： Zaiyue ZHANG Xibei YANG School of Computer Science and Engineering Jiangsu University of Science and Technology Zhenjiang 212003 China Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information (Nanjing University of Science and Technology) Ministry of Education Nanjing 210094 China Artificial Intelligence Key Laboratory of Sichuan Province Zigong 643000 China

Presently, the notion of multigranulation has been brought to our attention. In this paper, the multigranulation technique is introduced into incomplete information systems. Both tolerance relations and maximal consistent blocks are used to construct multigranulation rough sets. Not only are the basic properties about these models studied, but also the relationships between different multigranulation rough sets are explored. It is shown that by using maximal consistent blocks, the greater lower approximation and the same upper approximation as from tolerance relations can be obtained. Such a result is consistent with that of a single-granulation framework.

关键词： incomplete information system maximal con-sistent block multigranulation rough sets tolerance relation

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Brain tumor segmentation from multispectral MRIs using sparse representation classification and markov random field regularization

International Journal of Signal Processing, Image Processing...

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International Journal of Signal processing, Image processing and Pattern Recognition 2015年第9期8卷 229-238页

作者： Zhan, Tianming Gu, Shenghua Feng, Can Zhan, Yongzhao Wang, Jin School of Computer Science & communications Engineering Jiangsu University Zhenjiang China Jiangsu Key Laboratory of Big Data Analysis Technology Nanjing University of Information Science and Technology Nanjing China Center of software China North Industries Group Corporation Co. Ltd Nanjing China

Automatic brain tumor segmentation from multispectral magnetic resonance imaging (MRI) data is an important but a challenging task because of the high diversity in the appearance of tumor tissues among different patients and in many cases similarity with the normal tissues. In this paper, we propose a fully automatic technique for brain tumor segmentation from multispectral human brain MRIs. We first use the intensities of different patches in multispectral MRIs to represent the features of both normal and abnormal tissues and generate a dictionary for following tissue classification. Then, the sparse representation classification (SRC) is applied to classify the brain tumor and normal brain tissue in the whole image. At last, the Markov random field (MRF) regularization introduces spatial constraints to the SRC to take into account the pair-wise homogeneity in terms of classification labels and multispectral voxel intensities. Our method was evaluated on 20 multi-modality patient datasets with competitive segmentation results. ©, 2015 SERSC.

关键词： Magnetic resonance imaging

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The green project of data management for industrial integration

International Journal of Database Theory and Application

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International Journal of Database Theory and Application 2015年第6期8卷 333-342页

作者： Tian, Yiming Zhu, keyu Pan, Ruoyu Liang, Changyong School of Computer and Information Hefei University of Technology Anhui Transport Interconnection Management Center Hefei China Institute of Computer Network Systems Hefei University of Technology Key Laboratory of Process Optimization and Intelligent Decision-making Ministry of Education Hefei China Key Technologies and Equipment of IOT of Highway Traffic Anhui Engineering Technology Research Center Hefei China

The green project fulfills the requests of industrial management and application by sharing data rather than isolating. The data center with energy-efficient equipment is correspondingly effective in industrial service, management and construction of information. This study suggests a green data center in the two-level framework as a basic factor in industrial integration with potent procedures. In the case of transportation, it processed validating data to resolve logistics and emergency operations. © 2015 SERSC.

关键词： information management

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MRG-DBSCAN: An improved DBSCAN clustering method based on map reduce and grid

International Journal of Database Theory and Application

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International Journal of Database Theory and Application 2015年第2期8卷 119-128页

作者： Ma, Li Gu, Lei Li, Bo Qiao, Shouyi Wang, Jin Jiangsu Engineering Center of Network Monitoring Nanjing University of Information Science and Technology Nanjing China School of Computer and Software Nanjing University of Information Science and Technology Nanjing China Key Laboratory of Meteorological Disaster Ministry of Education Nanjing University of Information Science and Technology Nanjing China CMA Research Centre for Strategic Development Beijing China

DBSCAN is a density-based clustering algorithm. This algorithm clusters data of high density. The traditional DBSCAN clustering algorithm in finding the core object, will use this object as the center core, extends outwards continuously. At this point, the core objects growing, unprocessed objects are retained in memory, which will occupy a lot of memory and I/O overhead, algorithm efficiency is not high. In order to ensure the high efficiency of DBSCAN clustering algorithm, and reduce its memory footprint. In this paper, the original DBSCAN algorithm was improved, and the G-DBSCAN algorithm is proposed. G-DBSCAN algorithm reduces the number of query object as a starting point. Put the data into the grid, with the center point of the data in the grid to replace all the grid points as the algorithm input. The query object will be drastically reduced, thus improving the efficiency of the algorithm, reduces the memory footprint. In order to make the G-DBSCAN algorithm can adapt to large data processing, we will parallelize the G-DBSCAN algorithm, and combining it with Map Reduce framework. The results prove that G-DBSCAN and MRG-DBSCAN algorithm are feasible and effective. © 2015 SERSC.

关键词： Cluster analysis

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Constructions of generalized bent boolean functions on odd number of variables

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International Journal of Hybrid information technology 2015年第5期8卷 91-96页

作者： Zhao, Yong-Bin Zhang, Feng-Rong Hu, Yu-Pu State Key Laboratory of Integrated Service Networks Xidian University Xi’an710071 China School of Information Science and Technology Shijiazhuang Tiedao University Shijiazhuang050043 China School of Computer Science and Technology China University of Mining and Technology XuzhouJiangsu221116 China

In this paper, we investigate the constructions of generalized bent Boolean functions defined on with values in Z4. We first present a construction of generalized bent Boolean functions defined on with values in Z4. The main technique is to utilize bent functions to derive generalized bent functions on odd number of variables. In addition, by using Boolean permutations, we provide a specific method to construct generalized bent functions on odd number of variables. © 2015 SERSC.

关键词： Boolean functions

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Image reconstruction of concrete based on Filtered Backprojection method using ultrasonic time of flight data

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computer Modelling and New Technologies 2014年第6期18卷 72-78页

作者： Fan, Honghui Zhu, Hongjin Han, Qingbang School of Computer Engineering Jiangsu University of Technology Changzhou Jiangsu China Sichuan Province Key Laboratory of Bridge non-destructive testing and engineering computing Zigong Sichuan China Key Laboratory of Cloud Computing Intelligent Information Processing of Changzhou City Changzhou Jiangsu China College of IOT Engineering Hohai University Jiangsu China

This research aims to recognize the defect of concrete materials using an ultrasonic computed tomography imaging technique. Filtered Backprojection method was used to reconstruct concrete images in this paper. Ultrasonic time of flight data was measured to reconstruct computer tomography images. 306 data paths were obtained in total by manual scanning for one computer tomography image. We examined the effect of the interpolation data as the density of time of flight data has a considerable effect on image quality. The feasibility of concrete reconstruction system and time of flight data interpolation were examined in detail using numerical and concrete phantoms.

关键词： Interpolation

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Study on Breast Mass Segmentation in Mammograms

Study on Breast Mass Segmentation in Mammograms

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International Conference on computer, information and Application (CIA)

作者： Shenghua Gu Yao Ji Yunjie Chen Jin Wang Jeong-Uk Kim Jiangsu Key Laboratory of Big Data Analysis Technology Nanjing University of Information Science and Technology Nanjing P. R. China College of Computer & Software Nanjing University of Information Science & Technology Nanjing P. R. China Department of Energy Grid Sangmyung University Seoul Korea

ISBN: (纸本)9781467375726

Breast cancer is regarded as one of the most frequent mortality causes among women. It is very important to create a system to diagnose suspicious masses in mammograms for early breast cancer detection. In this paper, we propose an automatic breast mass segmentation method based on patch merging method and generalized hierarchical Fuzzy C Means (GHFCM). The patch merging method is used to obtain the adaptive region of interest (ROI), while the GHFCM method which is able to overcome the drawbacks of effect of image noise and Euclidean distance FCM which is sensitive to outliers is used to obtain the precisely mass segmentation results. The new method is evaluated over Mini MIAS dataset. The segmentation performance from experimentations demonstrates that our method outperforms the other compared methods.

关键词： Image segmentation Mammography Merging Breast cancer computers

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Sub-pixel change detection for urban land-cover analysis via multi-temporal remote sensing images

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Geo-Spatial information Science 2014年第1期17卷 26-38页

作者： Peijun DU Sicong LIU Pei LIU Kun TAN Liang CHENG Key Laboratory for Satellite Mapping Technology and Application of National Administration of Surveying Mapping and GeoinformationNanjing UniversityNanjing CityJiangsu ProvinceP.R.China Department of Information Engineering and Computer Science University of TrentoTrentoItaly Jiangsu Provincial Key Laboratory for Resources and Environment Information Engineering China University of Mining and TechnologyXuzhouJiangsu ProvinceP.R.China

Conventional change detection approaches are mainly based on per-pixel processing,which ignore the sub-pixel spectral variation resulted from spectral *** for medium-resolution remote sensing images used in urban landcover change monitoring,land use/cover components within a single pixel are usually complicated and heterogeneous due to the limitation of the spatial ***,traditional hard detection methods based on pure pixel assumption may lead to a high level of omission and commission errors inevitably,degrading the overall accuracy of change *** order to address this issue and find a possible way to exploit the spectral variation in a sub-pixel level,a novel change detection scheme is designed based on the spectral mixture analysis and decision-level *** spectral mixture model is selected for spectral unmixing,and change detection is implemented in a sub-pixel level by investigating the inner-pixel subtle changes and combining multiple composition *** proposed method is tested on multi-temporal Landsat Thematic Mapper and China–Brazil Earth Resources Satellite remote sensing images for the land-cover change detection over urban *** effectiveness of the proposed approach is confirmed in terms of several accuracy indices in contrast with two pixel-based change detection methods(*** vector analysis and principal component analysis-based method).In particular,the proposed sub-pixel change detection approach not only provides the binary change information,but also obtains the characterization about change direction and intensity,which greatly extends the semantic meaning of the detected change targets.

关键词： change detection sub-pixel level processing multi-temporal images spectral mixture model back propagation neural network remote sensing

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An Improved DV-Hop Positioning Algorithm for Wireless Sensor Network

An Improved DV-Hop Positioning Algorithm for Wireless Sensor...

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2015 IEEE International Conference on Progress in Informatics and Computing(PIC 2015)

作者： Jiuhu Zheng Huanyan Qian Lie Wang School of Computer Science and Engineering Nanjing University of Science and Technology School of Information Technology Jiangsu Institute of Commerce Key Lab of Broadband Wireless Communication and Sensor Network Technology ( Nanjing University of Posts and Telecommunications) Ministry of Education National Mobile Communications esearch Laboratory Southeast University

In the view of the DV-Hop positioning algorithm which relies on the distance vector and beacon nodes, several aspects are improved: the average hop distance and hop count are corrected;a method to detect the abnormal distribution of beacon nodes is used to avoid the influence of the abnormal distribution on the positioning performance. The proposed algorithm is simple and effective. Simulation results show that compared with the traditional DV-Hop algorithm, the performance of the proposed algorithm is greatly *** performance is even even superior to that of the algorithm mentioned in literature [10]

关键词： Wireless sensor network DV-Hop algorithm node positioning positioning accuracy

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Pathological Brain Detection by Wavelet-Energy and Fuzzy Support Vector Machine

Pathological Brain Detection by Wavelet-Energy and Fuzzy Sup...

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International Symposium on Computational Intelligence and Design, ISCID

作者： Shuihua Wang Yi Chen Xing-Xing Zhou Jianfei Yang Ling Wei Ping Sun Yudong Zhang Jiangsu Key Laboratory of 3D Printing Equipment and Manufacturing Nanjing Jiangsu China School of Computer Science and Technology Nanjing Normal University Nanjing Jiangsu China School of Electronic Information & Electrical Engineering Shanghai Jiaotong University Shanghai China Department of Electrical Engineering CUNY New York NY USA

ISBN: (纸本)9781467395885

It is important to early detect pathological brains. Traditional methods used plain support vector machine (SVM) that is vulnerable to noises and outliers. In this study, we presented a hybrid method that combined wavelet-energy (WE) and fuzzy support vector machine (FSVM). The results over a 5x5-fold cross validation showed that the proposed "WE + FSVM" produced accuracy of 93.78%, higher than "WE + KSVM" of 91.78%, "DWT + PCA + RBF-NN" of 91.33%, "WE + BP-NN" of 86.67%, and "DWT + PCA + BP-NN" of 86.22%. Therefore, this study offered a new means to solve the problem with excellent performance.

关键词： Discrete wavelet transforms Support vector machines Pathology Principal component analysis Feature extraction Diseases Artificial neural networks

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