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Single-Valued Neutrosophic Minimum Spanning Tree and Its clustering Method

JOURNAL OF INTELLIGENT SYSTEMS 2014年第3期23卷 311-324页

作者： Ye, Jun Shaoxing Univ Dept Elect & Informat Engn 508 Huancheng West Rd Shaoxing 312000 Zhejiang Peoples R China

clustering plays an important role in data mining, pattern recognition, and machine learning. Then, single-valued neutrosophic sets (SVNSs) are a useful means to describe and handle indeterminate and inconsistent information, which fuzzy sets and intuitionistic fuzzy sets cannot describe and deal with. To cluster the data represented by single-value neutrosophic information, the article proposes a single-valued neutrosophic minimum spanning tree (SVNMST) clustering algorithm. Firstly, we defined a generalized distance measure between SVNSs. Then, we present an SVNMST clustering algorithm for clustering single-value neutrosophic data based on the generalized distance measure of SVNSs. Finally, two illustrative examples are given to demonstrate the application and effectiveness of the developed approach.

关键词： Neutrosophic set single-valued neutrosophic set minimum spanning tree clustering algorithm generalized distance measure

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Improvement of Features Extraction Process and Classification of Cervical Cancer for the NeuralPap System

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Procedia Computer Science 2015年 60卷 750-759页

作者： Siti Noraini Sulaiman Nor Ashidi Mat-Isa Nor Hayati Othman Fadzil Ahmad Faculty of Electrical Engineering Universiti Teknologi MARA Penang Campus 13500 Permatang Pauh Penang MALAYSIA. Imaging and Intelligent System Research Team (ISRT) School of Electrical & Electronics Engineering Universiti Sains Malaysia Engineering Campus 14300 Nibong Tebal Penang MALAYSIA. Clinical Research Platform School of Medical Sciences Universiti Sains Malaysia 16150 Kubang Kerian Kelantan MALAYSIA

Cervical cancer has caused many deaths each year. Screening tests, such as Pap smear test used for the detection of the precancerous stage are able to avoid the occurrence of cervical cancer. However, the Pap smear test has several disadvantages such as less effective slides preparation and human error. Therefore, a computer-aided diagnosis system is introduced as a solution to the problem. One of the diagnostic systems that has been built is NeuralPap. However, the NeuralPap performance is limited by several constraints. This research proposed several new image processing algorithms to reduce these constraints. The Adaptive Fuzzy-k-Means (AFKM) clustering algorithm is proposed to replace the Moving k-Means (MKM) to segment Pap smear images into the nucleus, cytoplasm and background regions. Next, the feature extraction algorithm based on pseudo colouring called the Pseudo Colour Feature Extraction (PCFE) manual and Semi-Automatic PCFE are designed to replace the Region Growing Based Feature Extraction (RGBFE) which uses monochromatic images. This research is a step forward compared with the NeuralPap system by proposing the feature extraction algorithm for overlapping cells by combining the concept of colour space with Semi-Automatic PCFE algorithm. In addition, this research has also suggested the AFKM algorithm as a new centre positioning algorithm for the Radial Basis Function (RBF) and Hybrid RBF (HRBF) networks replacing the MKM algorithm. The entire proposed algorithm has been proven to produce better performance than the corresponding algorithm used in the NeuralPap. In addition, the combination of all algorithms has managed to increase the accuracy of the classification of cervical cancer to 76.35%, compared with 73.40% which is obtained from the previous NeuralPap system.

关键词： Diagnosis System Neural Network Region Growing Feature Extraction Cervical Cancer clustering algorithm

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Implementation of gray-level clustering algorithm for image segmentation

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Procedia Computer Science 2010年 2卷 314-320页

作者： A.R. Kavitha C. Chellamuthu Jerusalem College of Engineering velachery main road Chennai.600100 India R.M.K College of Engineering karaipettai Thiruvallur dist India

The watershed algorithm is an important technique for image segmentation which converts the gray-level image to a segmented image. We propose a watershed algorithm based on the mean value, the standard deviation of the histogram and the PSNR within a sub-interval, a novel recursive algorithm for deriving clustered images that is also used to increase the quality of an image. The clustered images using the watershed algorithm produce close results to that of the original image.

关键词： Watershed algorithm Image processing clustering algorithm Image segmentation Histogram

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车辆自组织网络MAC层协议的研究

车辆自组织网络MAC层协议的研究

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作者：杨琼东南大学

学位级别：博士

Vehicular Ad Hoc Network (VANET) is an application of Mobile Ad Hoc (MANET) for road traffic. VANET has the characteristics of high moving speed, frequent changing topology, and different node densities. In this dissertation, according to the characteristics of VANET, it is aimed to improve the performance of MAC layer and provide reliable and real-time communications between vehicles. Modeling and analysis of periodic broadcast, modeling and analysis of broadcast in control channel, dynamic contention window scheme, MAC delay based clustering algorithm and cluster based time division multiple access are studied in *** main research contributions are as follows.(1) Modeling of periodic broadcast and dynamic contention window scheme in VANETA 1-D Markov model is proposed to analyze the performance of periodic broadcast in VANET. In this model, a new idle state is introduced under non-saturated condition when there is no message to send in the buffer of a node. The freezing of backoff time counter in backoff process is also considered in this model and a discrete time D/M/l queue is established to model the buffer of each node. Theoretical analysis show when vehicle density increases the performance of periodic broadcast decreases accordingly. Dynamic contention window scheme is proposed according to the changing of node density. Simulation results show the collision probability of DCW scheme is lower than that of fixed-contention window broadcast in IEEE 802.11 p. Simulation results also verify the accuracy of the Markov model.(2) Modeling of priority access to control channel in VANETAccording to the characteristics of messages with different priorities accessing to control channel, discrete time queue D/M/1 and M/M/1 are proposed to model periodic messages and emergent messages, respectively. Priority analysis is added to this model which is based on the previous 1-D Markov model. Packet collision probability, access delay of periodic message and emergent me

关键词： Vehicular Ad HOC Network medium access control safety-related application modeling clustering algorithm

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An efficient channel clustering and flow rate allocation algorithm for non-uniform microfluidic cooling of 3D integrated circuits

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INTEGRATION-THE VLSI JOURNAL 2013年第1期46卷 57-68页

作者： Qian, Hanhua Chang, Chip-Hong Yu, Hao Nanyang Technol Univ Div Circuits & Syst Sch Elect & Elect Engn Singapore 639669 Singapore

Heat removal problem has been a bane of three dimensional integrated circuits (3DICs). Comparing with other passive cooling techniques, microfluidic cooling appears to be an ideal cooling solution due to its high thermal conductivity and scalability. Without regarding to the fact of non-uniform power distribution of integrated circuits, existing microfluidic cooling with uniform cooling effort incurs large thermal gradient and wastes pump power. This can be avoided by the customized non-uniform cooling scheme proposed in this paper. The microfluidic channels are divided into clusters of relatively homogeneous power distribution and an appropriate flow rate setting is applied to each cluster based on the total flow rate and the maximum allowable temperature of the 3DIC. This paper proposes an efficient clustering algorithm to guide the division of microchannels into clusters and the allocation of cooling resources to each cluster in order to achieve an effective microfluidic cooling with minimal total flow rate. A compact steady state thermal simulator has been developed and verified. Supported by this fast and accurate thermal model, the proposed cooling method and clustering algorithm have been applied to a 3D multi-core testbench for simulation. Compared to the uniform flow rate cooling, the maximum temperature and thermal gradient were reduced under the same total flow rate settings. On the other hand, for a specific peak temperature constraint, up to 21.8% saving in total flow rate with moderate thermal gradients is achieved by the proposed clustered microfluidic cooling. (C) 2011 Elsevier B.V. All rights reserved.

关键词： 3DIC Microfluidic cooling clustering algorithm Channel cluster Clustered cooling effort Thermal model

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JLMC: A clustering Method based on Jordan-Form of Laplacian-Matrix

JLMC: A Clustering Method based on Jordan-Form of Laplacian-...

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33rd IEEE International Performance, Computing, and Communications Conference (IPCCC)

作者： Niu, Jianwei Fan, Jinyang Stojmenovic, Ivan Beihang Univ Sch Comp Sci & Engn State Key Lab Virtual Real Technol & Syst Beijing 100191 Peoples R China Univ Ottawa Elect Engn & Comp Sci Ottawa ON K1N 6N5 Canada

ISBN: (纸本)9781479975754

Among the current clustering algorithms of complex networks, Laplacian-based spectral clustering algorithms have the advantage of rigorous mathematical basis and high accuracy. However, their applications are limited due to their dependence on prior knowledge, such as the number of clusters. For most of application scenarios, it is hard to obtain the number of clusters beforehand. To address this problem, we propose a novel clustering algorithm-Jordan-Form of Laplacian-Matrix based clustering algorithm (JLMC). In JLMC, we propose a model to calculate the number (eta) of clusters in a complex network based on the Jordan-Form of its corresponding Laplacian matrix. JLMC clusters the network into eta clusters by using our proposed modularity density function (P function). We conduct extensive experiments over real and synthetic data, and the experimental results reveal that JLMC can accurately obtain the number of clusters in a complex network, and outperforms Fast-Newman algorithm and Girvan-Newman algorithm in terms of clustering accuracy and time complexity.

关键词： clustering algorithm eigenvalue Laplacian-Matrix Jordan-Form

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Simulation Experiment on Path Planning in Incomplete Fuzzy System Based on Dominance Rough Set

Simulation Experiment on Path Planning in Incomplete Fuzzy S...

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2015 International Conference on Automation,Mechanical Control and Computational Engineering(AMCCE 2015)

作者： Sheng Pu School of Physical Education Mianyang Normal University

ISBN: (纸本)9781510803084

In order to enhance the processing capacity of the inconsistent ordered study information system and transform the rough set of different precision dominance relation, they can enhance the adaptability of inconsistent information by introducing the clustering algorithm, because rough set can effectively deal with imprecise, inconsistent and incomplete information, it can effectively get rid of dependence on a priori knowledge in the learning process, and has strong ability of independent learning. Through the simulation analysis, we found that the autonomous learning method has higher inconsistency ordered information system with the prominent advantages. In the number of different rough set, this method has different accuracy and can be very good to adapt to the change of the offensive and defensive line as well as automatic planning prevention path, which provide the theory reference for the study of basketball training autonomous learning.

关键词： Ordered information Autonomous learning clustering algorithm Rough set Adaptability

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Uncover protein complexes in *** network

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IEEE International Conference on Bioinformatics and Biomedicine

作者： Wei Liu Aiping Wu Department of Automation Shanghai Jiao Tong University Center for Systems Medicine Institute of Basic Medical Sciences Chinese Academy of Medical Sciences& Peking Union Medical College

ISBN: (纸本)9781467368001

Recent advances in proteomic technologies have enabled high-throughput binary data on protein-protein interactions of E. coli to be released into public domain, and many protein complexes have been identified by experimental methods. Although it has a long study history, a large-scale analysis of protein complex in binary PPI network of E. coli is still absent. We used a novel link clustering algorithm named ELPA to infer protein complexes and functional modules in E. coli PPI network. By mapping our results to 276 gold standard protein complexes and protein function annotations offered by EcoCyc, we found that 80.2% of predicted modules mapping well with one or more complexes, while 92.8% of predicted modules tally well with certain GO terms. Furthermore, we compare our results with MCL algorithm, and evaluated our results with several accuracy measures and biological relevance, the result shows that ELPA achieved an average 18.3% improvement over MCL based on the accuracy measures, which means our method will contributes to uncover the complexes of Ecoli.

关键词： Protein complex E. coli PPI network Functional modules clustering algorithm

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CFFL: Cluster Formation using Fuzzy Logic for Wireless Sensor Networks

CFFL: Cluster Formation using Fuzzy Logic for Wireless Senso...

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IEEE/ACS International Conference on Computer Systems and Applications

作者： Hassan EL ALAMI Abdellah NAJID STRS Lab. National Institute of Posts and Telecommunications - INPT Rabat - Morocco

ISBN: (纸本)9781509004799

In order to conserve energy in wireless sensor networks (WSNs), sensor nodes are partitioned into clusters. clustering algorithm provides an effective way to extend the network lifetime of WSNs. The operation of clustering algorithm is divided into cluster heads (CHs) selection phase and cluster formation phase. However, most of the previous researches have focused on CHs selection, and have not considered the cluster formation phase, which is important problem in WSNs and can drastically affect the network lifetime in WSNs. In this paper, cluster formation using fuzzy logic (CFFL) approach has been proposed to prolong network lifetime and reduce energy consumption in WSNs. this approach uses fuzzy logic in the formation cluster phase, two fuzzy parameters are used. These parameters are residual energy which is energy level of each CH and closeness to base station (BS) which is the distance between the CH and the BS. Simulation results show that the proposed approach consumes less energy and prolongs the network lifetime compared with Low Energy Adaptive clustering Hierarchy (LEACH) protocol.

关键词： cluster formation cluster head selection clustering algorithm fuzzy logic wireless sensor networks

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A Grouping based Prioritized Sensor data fusion algorithm for deciding the State of a Coal Mine

A Grouping based Prioritized Sensor data fusion Algorithm fo...

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International Conference on Advances in Computing, Communications and Informatics

作者： Anibroto Sarkar Indrajit Banerjee Department of Information Technology Indian Institute of Engineering Science & Technology

ISBN: (纸本)9781479987931

The main constraints of a wireless sensor network are limited battery power and short lifetime. One of the main reasons of energy consumption is the data transmission. Each node senses the data and sends them over to the base station. The sensor data fusion reduces the volume of message transmission and makes the network energy efficient. In this paper, we have presented a data fusion algorithm which minimizes the computation cost, communication cost and in the same way it reduces the consumption of energy. The algorithm derives the state of the network using the concept of priority of the sensors. The proposed algorithm gives a better false alarm rate than the existing data fusion algorithm used in the coal mine.

关键词： Data fusion algorithm Wireless Sensor network (WSN) clustering algorithm

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