Fuzzy clustering-based segmented attenuation correction in whole-body PET
Zaidi, H; Boudraa, A; Slosman, DO
2001-01-01
Segmented-based attenuation correction is now a widely accepted technique to reduce noise contribution of measured attenuation correction. In this paper, we present a new method for segmenting transmission images in positron emission tomography. This reduces the noise on the correction maps while still correcting for differing attenuation coefficients of specific tissues. Based on the Fuzzy C-Means (FCM) algorithm, the method segments the PET transmission images into a given number of clusters to extract specific areas of differing attenuation such as air, the lungs and soft tissue, preceded by a median filtering procedure. The reconstructed transmission image voxels are therefore segmented into populations of uniform attenuation based on the human anatomy. The clustering procedure starts with an over-specified number of clusters followed by a merging process to group clusters with similar properties and remove some undesired substructures using anatomical knowledge. The method is unsupervised, adaptive and a...
Information Clustering Based on Fuzzy Multisets.
Miyamoto, Sadaaki
2003-01-01
Proposes a fuzzy multiset model for information clustering with application to information retrieval on the World Wide Web. Highlights include search engines; term clustering; document clustering; algorithms for calculating cluster centers; theoretical properties concerning clustering algorithms; and examples to show how the algorithms work.…
Clustering based segmentation of text in complex color images
Institute of Scientific and Technical Information of China (English)
毛文革; 王洪滨; 张田文
2004-01-01
We propose a novel scheme based on clustering analysis in color space to solve text segmentation in complex color images. Text segmentation includes automatic clustering of color space and foreground image generation. Two methods are also proposed for automatic clustering: The first one is to determine the optimal number of clusters and the second one is the fuzzy competitively clustering method based on competitively learning techniques. Essential foreground images obtained from any of the color clusters are combined into foreground images. Further performance analysis reveals the advantages of the proposed methods.
Chen, Zhijia; Zhu, Yuanchang; Di, Yanqiang; Feng, Shaochong
2015-01-01
In IaaS (infrastructure as a service) cloud environment, users are provisioned with virtual machines (VMs). To allocate resources for users dynamically and effectively, accurate resource demands predicting is essential. For this purpose, this paper proposes a self-adaptive prediction method using ensemble model and subtractive-fuzzy clustering based fuzzy neural network (ESFCFNN). We analyze the characters of user preferences and demands. Then the architecture of the prediction model is constructed. We adopt some base predictors to compose the ensemble model. Then the structure and learning algorithm of fuzzy neural network is researched. To obtain the number of fuzzy rules and the initial value of the premise and consequent parameters, this paper proposes the fuzzy c-means combined with subtractive clustering algorithm, that is, the subtractive-fuzzy clustering. Finally, we adopt different criteria to evaluate the proposed method. The experiment results show that the method is accurate and effective in predicting the resource demands.
Chen, Zhijia; Zhu, Yuanchang; Di, Yanqiang; Feng, Shaochong
2015-01-01
In IaaS (infrastructure as a service) cloud environment, users are provisioned with virtual machines (VMs). To allocate resources for users dynamically and effectively, accurate resource demands predicting is essential. For this purpose, this paper proposes a self-adaptive prediction method using ensemble model and subtractive-fuzzy clustering based fuzzy neural network (ESFCFNN). We analyze the characters of user preferences and demands. Then the architecture of the prediction model is constructed. We adopt some base predictors to compose the ensemble model. Then the structure and learning algorithm of fuzzy neural network is researched. To obtain the number of fuzzy rules and the initial value of the premise and consequent parameters, this paper proposes the fuzzy c-means combined with subtractive clustering algorithm, that is, the subtractive-fuzzy clustering. Finally, we adopt different criteria to evaluate the proposed method. The experiment results show that the method is accurate and effective in predicting the resource demands. PMID:25691896
New Results in Fuzzy Clustering Based on the Concept of Indistinguishability Relation
1984-01-01
NEW RESULTS IN Fuzzy CLUSTERING BASED ON THE CONCEPT OF INDISTINGUISHABILITY RELATION KEYWORDS R . Lopez de Mantaras Facultat d ’Informatica...Universitat Politecnica de Barcelona Dulcet, 12. Barcelona-34. Spain. L. Valverde* Dept. de Matematiques i Estadistica Universitat Politecnica de... r -cluster that extend Ruspini’s definition (Ruspini, 1982). Our definition is based on the new concept of indis- tinguishability relation (Trillas
An Efficient Fuzzy Clustering-Based Approach for Intrusion Detection
Nguyen, Huu Hoa; Darmont, Jérôme
2011-01-01
The need to increase accuracy in detecting sophisticated cyber attacks poses a great challenge not only to the research community but also to corporations. So far, many approaches have been proposed to cope with this threat. Among them, data mining has brought on remarkable contributions to the intrusion detection problem. However, the generalization ability of data mining-based methods remains limited, and hence detecting sophisticated attacks remains a tough task. In this thread, we present a novel method based on both clustering and classification for developing an efficient intrusion detection system (IDS). The key idea is to take useful information exploited from fuzzy clustering into account for the process of building an IDS. To this aim, we first present cornerstones to construct additional cluster features for a training set. Then, we come up with an algorithm to generate an IDS based on such cluster features and the original input features. Finally, we experimentally prove that our method outperform...
Intuitionistic fuzzy segmentation of medical images.
Chaira, Tamalika
2010-06-01
This paper proposes a novel and probably the first method, using Attanassov intuitionistic fuzzy set theory to segment blood vessels and also the blood cells in pathological images. This type of segmentation is very important in detecting different types of human diseases, e.g., an increase in the number of vessels may lead to cancer in prostates, mammary, etc. The medical images are not properly illuminated, and segmentation in that case becomes very difficult. A novel image segmentation approach using intuitionistic fuzzy set theory and a new membership function is proposed using restricted equivalence function from automorphisms, for finding the membership values of the pixels of the image. An intuitionistic fuzzy image is constructed using Sugeno type intuitionistic fuzzy generator. Local thresholding is applied to threshold medical images. The results showed a much better performance on poor contrast medical images, where almost all the blood vessels and blood cells are visible properly. There are several fuzzy and intuitionistic fuzzy thresholding methods, but these methods are not related to the medical images. To make a comparison with the proposed method with other thresholding methods, the method is compared with six nonfuzzy, fuzzy, and intuitionistic fuzzy methods.
Outlier rejection fuzzy c-means (ORFCM) algorithm for image segmentation
segmentation, Outlier rejection fuzzy c-means (ORFCM)
2013-01-01
This paper presents a fuzzy clustering-based technique for image segmentation. Many attempts have been put into practice to increase the conventional fuzzy c-means (FCM) performance. In this paper, the sensitivity of the soft membership function of the FCM algorithm to the outlier is considered and the new exponent operator on the Euclidean distance is implemented in the membership function to improve the outlier rejection characteristics of the FCM. The comparative quantitative and qua...
FUZZY CLUSTERING BASED BAYESIAN FRAMEWORK TO PREDICT MENTAL HEALTH PROBLEMS AMONG CHILDREN
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M R Sumathi
2017-04-01
Full Text Available According to World Health Organization, 10-20% of children and adolescents all over the world are experiencing mental disorders. Correct diagnosis of mental disorders at an early stage improves the quality of life of children and avoids complicated problems. Various expert systems using artificial intelligence techniques have been developed for diagnosing mental disorders like Schizophrenia, Depression, Dementia, etc. This study focuses on predicting basic mental health problems of children, like Attention problem, Anxiety problem, Developmental delay, Attention Deficit Hyperactivity Disorder (ADHD, Pervasive Developmental Disorder(PDD, etc. using the machine learning techniques, Bayesian Networks and Fuzzy clustering. The focus of the article is on learning the Bayesian network structure using a novel Fuzzy Clustering Based Bayesian network structure learning framework. The performance of the proposed framework was compared with the other existing algorithms and the experimental results have shown that the proposed framework performs better than the earlier algorithms.
Supplier Segmentation using Fuzzy Linguistic Preference Relations and Fuzzy Clustering
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Pegah Sagheb Haghighi
2014-04-01
Full Text Available In an environment characterized by its competitiveness, managing and monitoring relationships with suppliers are of the essence. Supplier management includes supplier segmentation. Existing literature demonstrates that suppliers are mostly segmented by computing their aggregated scores, without taking each supplier’s criterion value into account. The principle aim of this paper is to propose a supplier segmentation method that compares each supplier’s criterion value with exactly the same criterion of other suppliers. The Fuzzy Linguistic Preference Relations (LinPreRa based Analytic Hierarchy Process (AHP is first used to find the weight of each criterion. Then, Fuzzy c-means algorithm is employed to cluster suppliers based on their membership degrees. The obtained results show that the proposed method enhances the quality of the previous findings.
Parallel fuzzy connected image segmentation on GPU
Zhuge, Ying; Cao, Yong; Udupa, Jayaram K.; Miller, Robert W.
2011-01-01
Purpose: Image segmentation techniques using fuzzy connectedness (FC) principles have shown their effectiveness in segmenting a variety of objects in several large applications. However, one challenge in these algorithms has been their excessive computational requirements when processing large image datasets. Nowadays, commodity graphics hardware provides a highly parallel computing environment. In this paper, the authors present a parallel fuzzy connected image segmentation algorithm implementation on NVIDIA’s compute unified device Architecture (cuda) platform for segmenting medical image data sets. Methods: In the FC algorithm, there are two major computational tasks: (i) computing the fuzzy affinity relations and (ii) computing the fuzzy connectedness relations. These two tasks are implemented as cuda kernels and executed on GPU. A dramatic improvement in speed for both tasks is achieved as a result. Results: Our experiments based on three data sets of small, medium, and large data size demonstrate the efficiency of the parallel algorithm, which achieves a speed-up factor of 24.4x, 18.1x, and 10.3x, correspondingly, for the three data sets on the NVIDIA Tesla C1060 over the implementation of the algorithm on CPU, and takes 0.25, 0.72, and 15.04 s, correspondingly, for the three data sets. Conclusions: The authors developed a parallel algorithm of the widely used fuzzy connected image segmentation method on the NVIDIA GPUs, which are far more cost- and speed-effective than both cluster of workstations and multiprocessing systems. A near-interactive speed of segmentation has been achieved, even for the large data set. PMID:21859037
Neuro-Fuzzy Phasing of Segmented Mirrors
Olivier, Philip D.
1999-01-01
A new phasing algorithm for segmented mirrors based on neuro-fuzzy techniques is described. A unique feature of this algorithm is the introduction of an observer bank. Its effectiveness is tested in a very simple model with remarkable success. The new algorithm requires much less computational effort than existing algorithms and therefore promises to be quite useful when implemented on more complex models.
Fuzzy entropy image segmentation based on particle Swarm optimization
Institute of Scientific and Technical Information of China (English)
Linyi Li; Deren Li
2008-01-01
Partide swaFnl optimization is a stochastic global optimization algorithm that is based on swarm intelligence.Because of its excellent performance,particle swarm optimization is introduced into fuzzy entropy image segmentation to select the optimal fuzzy parameter combination and fuzzy threshold adaptively.In this study,the particles in the swarm are constructed and the swarm search strategy is proposed to meet the needs of the segmentation application.Then fuzzy entropy image segmentation based on particle swarm opti-mization is implemented and the proposed method obtains satisfactory results in the segmentation experiments.Compared with the exhaustive search method,particle swarm optimization can give the salne optimal fuzzy parameter combination and fuzzy threshold while needing less search time in the segmentation experiments and also has good search stability in the repeated experiments.Therefore,fuzzy entropy image segmentation based on particle swarm optimization is an efficient and promising segmentation method.
AUTOMATIC MULTILEVEL IMAGE SEGMENTATION BASED ON FUZZY REASONING
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Liang Tang
2011-05-01
Full Text Available An automatic multilevel image segmentation method based on sup-star fuzzy reasoning (SSFR is presented. Using the well-known sup-star fuzzy reasoning technique, the proposed algorithm combines the global statistical information implied in the histogram with the local information represented by the fuzzy sets of gray-levels, and aggregates all the gray-levels into several classes characterized by the local maximum values of the histogram. The presented method has the merits of determining the number of the segmentation classes automatically, and avoiding to calculating thresholds of segmentation. Emulating and real image segmentation experiments demonstrate that the SSFR is effective.
Multi-Feature Segmentation and Cluster based Approach for Product Feature Categorization
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Bharat Singh
2016-03-01
Full Text Available At a recent time, the web has become a valuable source of online consumer review however as the number of reviews is growing in high speed. It is infeasible for user to read all reviews to make a valuable or satisfying decision because the same features, people can write it contrary words or phrases. To produce a useful summary of domain synonyms words and phrase, need to be a group into same feature group. We focus on feature-based opinion mining problem and this paper mainly studies feature based product categorization from the number of users - generated review available on the different website. First, a multi-feature segmentation method is proposed which segment multi-feature review sentences into the single feature unit. Second part of speech dictionary and context information is used to consider the irrelevant feature identification, sentiment words are used to identify the polarity of feature and finally an unsupervised clustering based product feature categorization method is proposed. Clustering is unsupervised machine learning approach that groups feature that have a high degree of similarity in a same cluster. The proposed approach provides satisfactory results and can achieve 100% average precision for clustering based product feature categorization task. This approach can be applicable to different product.
Chaira, Tamalika
2014-06-01
In this paper automatic leukocyte segmentation in pathological blood cell images is proposed using intuitionistic fuzzy and interval Type II fuzzy set theory. This is done to count different types of leukocytes for disease detection. Also, the segmentation should be accurate so that the shape of the leukocytes is preserved. So, intuitionistic fuzzy set and interval Type II fuzzy set that consider either more number of uncertainties or a different type of uncertainty as compared to fuzzy set theory are used in this work. As the images are considered fuzzy due to imprecise gray levels, advanced fuzzy set theories may be expected to give better result. A modified Cauchy distribution is used to find the membership function. In intuitionistic fuzzy method, non-membership values are obtained using Yager's intuitionistic fuzzy generator. Optimal threshold is obtained by minimizing intuitionistic fuzzy divergence. In interval type II fuzzy set, a new membership function is generated that takes into account the two levels in Type II fuzzy set using probabilistic T co norm. Optimal threshold is selected by minimizing a proposed Type II fuzzy divergence. Though fuzzy techniques were applied earlier but these methods failed to threshold multiple leukocytes in images. Experimental results show that both interval Type II fuzzy and intuitionistic fuzzy methods perform better than the existing non-fuzzy/fuzzy methods but interval Type II fuzzy thresholding method performs little bit better than intuitionistic fuzzy method. Segmented leukocytes in the proposed interval Type II fuzzy method are observed to be distinct and clear.
GPU accelerated fuzzy connected image segmentation by using CUDA.
Zhuge, Ying; Cao, Yong; Miller, Robert W
2009-01-01
Image segmentation techniques using fuzzy connectedness principles have shown their effectiveness in segmenting a variety of objects in several large applications in recent years. However, one problem of these algorithms has been their excessive computational requirements when processing large image datasets. Nowadays commodity graphics hardware provides high parallel computing power. In this paper, we present a parallel fuzzy connected image segmentation algorithm on Nvidia's Compute Unified Device Architecture (CUDA) platform for segmenting large medical image data sets. Our experiments based on three data sets with small, medium, and large data size demonstrate the efficiency of the parallel algorithm, which achieves a speed-up factor of 7.2x, 7.3x, and 14.4x, correspondingly, for the three data sets over the sequential implementation of fuzzy connected image segmentation algorithm on CPU.
A novel stepwise thresholding for fuzzy image segmentation
Institute of Scientific and Technical Information of China (English)
HE Xiao-hai; LUO Dai-sheng; WU Xiao-qiang; JIANG Li; TENG Qi-zhi; Tao De-yuan
2001-01-01
A novel stepwise thresholding method for fuzzy image segmentation is proposed. Unlike the published iterative or recursive thresholding mehtods, this method segments regions into sub-regions iteratively by increasing threshold value in a stepwise manner, based on a preset intensity homogeneity criteria. The method is particularly suited to segmentation of the laser scanning confocal microscopy (LSCM) images, computerised tomography (CT) images, magnetic resonance (MR) images, fingerprint images, etc. The method has been tested on some typical fuzzy image data sets. In this paper, the novel stepwise thresholding is first addressed. Next a new method of region labelling for region extraction is introduced.Then the design of intensity homogeneity segmentation criteria is presented. Some examples of the experiment results of fuzzy image segmentation by the method are given at the end.
Vehicles Recognition Using Fuzzy Descriptors of Image Segments
Płaczek, Bartłomiej
2011-01-01
In this paper a vision-based vehicles recognition method is presented. Proposed method uses fuzzy description of image segments for automatic recognition of vehicles recorded in image data. The description takes into account selected geometrical properties and shape coefficients determined for segments of reference image (vehicle model). The proposed method was implemented using reasoning system with fuzzy rules. A vehicles recognition algorithm was developed based on the fuzzy rules describing shape and arrangement of the image segments that correspond to visible parts of a vehicle. An extension of the algorithm with set of fuzzy rules defined for different reference images (and various vehicle shapes) enables vehicles classification in traffic scenes. The devised method is suitable for application in video sensors for road traffic control and surveillance systems.
Fuzzy Markov random fields versus chains for multispectral image segmentation.
Salzenstein, Fabien; Collet, Christophe
2006-11-01
This paper deals with a comparison of recent statistical models based on fuzzy Markov random fields and chains for multispectral image segmentation. The fuzzy scheme takes into account discrete and continuous classes which model the imprecision of the hidden data. In this framework, we assume the dependence between bands and we express the general model for the covariance matrix. A fuzzy Markov chain model is developed in an unsupervised way. This method is compared with the fuzzy Markovian field model previously proposed by one of the authors. The segmentation task is processed with Bayesian tools, such as the well-known MPM (Mode of Posterior Marginals) criterion. Our goal is to compare the robustness and rapidity for both methods (fuzzy Markov fields versus fuzzy Markov chains). Indeed, such fuzzy-based procedures seem to be a good answer, e.g., for astronomical observations when the patterns present diffuse structures. Moreover, these approaches allow us to process missing data in one or several spectral bands which correspond to specific situations in astronomy. To validate both models, we perform and compare the segmentation on synthetic images and raw multispectral astronomical data.
Directory of Open Access Journals (Sweden)
Endra Joelianto
2009-11-01
Full Text Available The well known PID controller has inherent limitations in fulfilling simultaneously the conflicting control design objectives. Parameters of the tuned PID controller should trade off the requirement of tracking set-point performances, disturbance rejection and stability robustness. Combination of hybrid reference control (HRC with PID controller results in the transient response performances can be independently achieved without deteriorating the disturbance rejection properties and the stability robustness requirement. This paper proposes a fuzzy based HRC where the membership functions of the fuzzy logic system are obtained by using a substractive clustering technique. The proposed method guarantees the transient response performances satisfaction while preserving the stability robustness of the closed loop system controlled by the PID controller with effective and systematic procedures in designing the fuzzy hybrid reference control system.
Fuzzy C-Means Clustering Based Phonetic Tied-Mixture HMM in Speech Recognition
Institute of Scientific and Technical Information of China (English)
XU Xiang-hua; ZHU Jie; GUO Qiang
2005-01-01
A fuzzy clustering analysis based phonetic tied-mixture HMM(FPTM) was presented to decrease parameter size and improve robustness of parameter training. FPTM was synthesized from state-tied HMMs by a modified fuzzy C-means clustering algorithm. Each Gaussian codebook of FPTM was built from Gaussian components within the same root node in phonetic decision tree. The experimental results on large vocabulary Mandarin speech recognition show that compared with conventional phonetic tied-mixture HMM and state-tied HMM with approximately the same number of Gaussian mixtures, FPTM achieves word error rate reductions by 4.84% and 13.02 % respectively. Combining the two schemes of mixing weights pruning and Gaussian centers fuzzy merging, a significantly parameter size reduction was achieved with little impact on recognition accuracy.
Unsupervised fuzzy segmentation of 3D magnetic resonance brain images
Velthuizen, Robert P.; Hall, Lawrence O.; Clarke, Laurence P.; Bensaid, Amine M.; Arrington, J. A.; Silbiger, Martin L.
1993-07-01
Unsupervised fuzzy methods are proposed for segmentation of 3D Magnetic Resonance images of the brain. Fuzzy c-means (FCM) has shown promising results for segmentation of single slices. FCM has been investigated for volume segmentations, both by combining results of single slices and by segmenting the full volume. Different strategies and initializations have been tried. In particular, two approaches have been used: (1) a method by which, iteratively, the furthest sample is split off to form a new cluster center, and (2) the traditional FCM in which the membership grade matrix is initialized in some way. Results have been compared with volume segmentations by k-means and with two supervised methods, k-nearest neighbors and region growing. Results of individual segmentations are presented as well as comparisons on the application of the different methods to a number of tumor patient data sets.
A Novel Multiresolution Fuzzy Segmentation Method on MR Image
Institute of Scientific and Technical Information of China (English)
ZHANG HongMei(张红梅); BIAN ZhengZhong(卞正中); YUAN ZeJian(袁泽剑); YE Min(叶敏); JI Feng(冀峰)
2003-01-01
Multiresolution-based magnetic resonance (MR) image segmentation has attractedattention for its ability to capture rich information across scales compared with the conventionalsegmentation methods. In this paper, a new scale-space-based segmentation model is presented,where both the intra-scale and inter-scale properties are considered and formulated as two fuzzyenergy functions. Meanwhile, a control parameter is introduced to adjust the contribution of thesimilarity character across scales and the clustering character within the scale. By minimizing thecombined inter/intra energy function, the multiresolution fuzzy segmentation algorithm is derived.Then the coarse to fine leading segmentation is performed automatically and iteratively on a set ofmultiresolution images. The validity of the proposed algorithm is demonstrated by the test imageand pathological MR images. Experiments show that by this approach the segmentation results,especially in the tumor area delineation, are more precise than those of the conventional fuzzy segmentation methods.
Understanding coastal change using shoreline trend analysis supported by cluster-based segmentation
Burningham, Helene; French, Jon
2017-04-01
Shoreline change analysis is a well defined and widely adopted approach for the examination of trends in coastal position over different timescales. Conventional shoreline change metrics are best suited to resolving progressive quasi-linear trends. However, coastal change is often highly non-linear and may exhibit complex behaviour including trend-reversals. This paper advocates a secondary level of investigation based on a cluster analysis to resolve a more complete range of coastal behaviours. Cluster-based segmentation of shoreline behaviour is demonstrated with reference to a regional-scale case study of the Suffolk coast, eastern UK. An exceptionally comprehensive suite of shoreline datasets covering the period 1881 to 2015 is used to examine both centennial- and intra-decadal scale change in shoreline position. Analysis of shoreline position changes at a 100 m alongshore interval along 74 km of coastline reveals a number of distinct behaviours. The suite of behaviours varies with the timescale of analysis. There is little evidence of regionally coherent shoreline change. Rather, the analyses reveal a complex interaction between met-ocean forcing, inherited geological and geomorphological controls, and evolving anthropogenic intervention that drives changing foci of erosion and deposition.
Image segmentation based on scaled fuzzy membership functions
DEFF Research Database (Denmark)
Jantzen, Jan; Ring,, P.; Christiansen, Pernille
1993-01-01
As a basis for an automated interpretation of magnetic resonance images, the authors propose a fuzzy segmentation method. The method uses five standard fuzzy membership functions: small, small medium, medium, large medium, and large. The method fits these membership functions to the modes...... of interest in the image histogram by means of a piecewise-linear transformation. A test example is given concerning a human head image, including a sensitivity analysis based on the fuzzy area measure. The method provides a rule-based interface to the physician...
Face Detection Based on Skin Color Segmentation Using Fuzzy Entropy
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Francisco A. Pujol
2017-01-01
Full Text Available Face detection is the first step of any automated face recognition system. One of the most popular approaches to detect faces in color images is using a skin color segmentation scheme, which in many cases needs a proper representation of color spaces to interpret image information. In this paper, we propose a fuzzy system for detecting skin in color images, so that each color tone is assumed to be a fuzzy set. The Red, Green, and Blue (RGB, the Hue, Saturation and Value (HSV, and the YCbCr (where Y is the luminance and Cb,Cr are the chroma components color systems are used for the development of our fuzzy design. Thus, a fuzzy three-partition entropy approach is used to calculate all of the parameters needed for the fuzzy systems, and then, a face detection method is also developed to validate the segmentation results. The results of the experiments show a correct skin detection rate between 94% and 96% for our fuzzy segmentation methods, with a false positive rate of about 0.5% in all cases. Furthermore, the average correct face detection rate is above 93%, and even when working with heterogeneous backgrounds and different light conditions, it achieves almost 88% correct detections. Thus, our method leads to accurate face detection results with low false positive and false negative rates.
Enhanced Image Segmentation Using Fuzzy Logic
Manpreet singh
2013-01-01
This research work proposed an improved edge detection techniques using fuzzy sets. The problem is to find edges in the image, as a first step in the process of scene reconstruction. Edges are scale-dependent and an edge may comprise other edges, but at a definite scale, an edge still has no width. This paper has presented different edge detection operators and their benefit when they merge with fuzzy logic theory. This paper has achieved the accuracy of edge detection up to 94.89 %. The prop...
Clustering-based Spam Image Filtering Considering Fuzziness of the Spam Image
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Master Prince
2016-12-01
Full Text Available If there are pros, corns are always there. As email becomes a part of individual’s need in our busy life with its benefits, it has negative aspect too by means of email spamming. Nowadays images with embedded text called image spamming have been used by the spammers as effective text spam filtering methods already been introduced. Tracking and stopping spam become challenge in the internet world because of versatility in the spam images. In this paper a novel model AFSIF (Autonomous Fuzzy Spam Image Filter has been introduced. The basic idea behind AFSIF is, an spam image can combine several basic features of different spam images, so feature fusion weight of the image has been generated, which keeps combined feature of spam images and user preference as well. Here user preference has not been applied separately; it is used to calculate the fusion weight in terms of predefined topics (rule table.
Uncovering and testing the fuzzy clusters based on lumped Markov chain in complex network.
Jing, Fan; Jianbin, Xie; Jinlong, Wang; Jinshuai, Qu
2013-01-01
Identifying clusters, namely groups of nodes with comparatively strong internal connectivity, is a fundamental task for deeply understanding the structure and function of a network. By means of a lumped Markov chain model of a random walker, we propose two novel ways of inferring the lumped markov transition matrix. Furthermore, some useful results are proposed based on the analysis of the properties of the lumped Markov process. To find the best partition of complex networks, a novel framework including two algorithms for network partition based on the optimal lumped Markovian dynamics is derived to solve this problem. The algorithms are constructed to minimize the objective function under this framework. It is demonstrated by the simulation experiments that our algorithms can efficiently determine the probabilities with which a node belongs to different clusters during the learning process and naturally supports the fuzzy partition. Moreover, they are successfully applied to real-world network, including the social interactions between members of a karate club.
AN IMPROVED FUZZY CLUSTERING ALGORITHM FOR MICROARRAY IMAGE SPOTS SEGMENTATION
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V.G. Biju
2015-11-01
Full Text Available An automatic cDNA microarray image processing using an improved fuzzy clustering algorithm is presented in this paper. The spot segmentation algorithm proposed uses the gridding technique developed by the authors earlier, for finding the co-ordinates of each spot in an image. Automatic cropping of spots from microarray image is done using these co-ordinates. The present paper proposes an improved fuzzy clustering algorithm Possibility fuzzy local information c means (PFLICM to segment the spot foreground (FG from background (BG. The PFLICM improves fuzzy local information c means (FLICM algorithm by incorporating typicality of a pixel along with gray level information and local spatial information. The performance of the algorithm is validated using a set of simulated cDNA microarray images added with different levels of AWGN noise. The strength of the algorithm is tested by computing the parameters such as the Segmentation matching factor (SMF, Probability of error (pe, Discrepancy distance (D and Normal mean square error (NMSE. SMF value obtained for PFLICM algorithm shows an improvement of 0.9 % and 0.7 % for high noise and low noise microarray images respectively compared to FLICM algorithm. The PFLICM algorithm is also applied on real microarray images and gene expression values are computed.
一种新的FART分类器%A New Cluster Based Fuzzy ART Model
Institute of Scientific and Technical Information of China (English)
雷洪利; 张殿治; 刘文华; 严盛文
2002-01-01
提出了一类基于贴近度理论的模糊ART神经网络模型,简称为CBFART(Closeness Based Fuzzy ART)模型.将模糊数学中的贴近度(Closeness)和择近原则(Closest Principle)概念与自适应共振理论(ART)相结合,形成了一种新的网络模型.该模型的学习以匹配-委托循环为特点,网络分类遵循择近原则.补码编码、匹配-委托和快速委托-慢速重编码方案相结合,保证了网络学习的收敛性和稳定性,并可以做到一次性学习,提高了学习速度.文中对高维样本进行分类仿真,给出了仿真结果,分析表明该模型具有良好的聚类特性,能够稳定地对高维样本进行分类.
A New Measure of Fuzzy Directed Divergence and Its Application in Image Segmentation
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P.K Bhatia
2013-03-01
Full Text Available An approach to develop new measures of fuzzy directed divergence is proposed here. A new measure of fuzzy directed divergence is proposed, and some mathematical properties of this measure are proved. The application of fuzzy directed divergence in image segmentation is explained. The proposed technique minimizes the fuzzy divergence or the separation between the actual and ideal thresholded image.
Fuzzy local Gaussian mixture model for brain MR image segmentation.
Ji, Zexuan; Xia, Yong; Sun, Quansen; Chen, Qiang; Xia, Deshen; Feng, David Dagan
2012-05-01
Accurate brain tissue segmentation from magnetic resonance (MR) images is an essential step in quantitative brain image analysis. However, due to the existence of noise and intensity inhomogeneity in brain MR images, many segmentation algorithms suffer from limited accuracy. In this paper, we assume that the local image data within each voxel's neighborhood satisfy the Gaussian mixture model (GMM), and thus propose the fuzzy local GMM (FLGMM) algorithm for automated brain MR image segmentation. This algorithm estimates the segmentation result that maximizes the posterior probability by minimizing an objective energy function, in which a truncated Gaussian kernel function is used to impose the spatial constraint and fuzzy memberships are employed to balance the contribution of each GMM. We compared our algorithm to state-of-the-art segmentation approaches in both synthetic and clinical data. Our results show that the proposed algorithm can largely overcome the difficulties raised by noise, low contrast, and bias field, and substantially improve the accuracy of brain MR image segmentation.
A Fuzzy View on k-Means Based Signal Quantization with Application in Iris Segmentation
Popescu-Bodorin, Nicolaie
2011-01-01
This paper shows that the k-means quantization of a signal can be interpreted both as a crisp indicator function and as a fuzzy membership assignment describing fuzzy clusters and fuzzy boundaries. Combined crisp and fuzzy indicator functions are defined here as natural generalizations of the ordinary crisp and fuzzy indicator functions, respectively. An application to iris segmentation is presented together with a demo program.
Yu, Hang; Xu, Luping; Feng, Dongzhu; He, Xiaochuan
2015-01-01
Synthetic aperture radar (SAR) image segmentation is investigated from feature extraction to algorithm design, which is characterized by two aspects: (1) multiple heterogeneous features are extracted to describe SAR images and the corresponding similarity measures are developed independently to avoid the mutual influences between different features in order to enhance the discriminability of the final similarity between objects. (2) A method called fuzzy clustering based on independent subspace iterative optimization (FCISIO) is proposed. FCISIO integrates multiple features into an objective function which is then iteratively optimized in each feature subspace to obtain final segmentation results. This strategy can protect the distribution structures of the data points in each feature subspace, which realizes an effective way to integrate multiple features of different properties. In order to improve the computation speed and the accuracy of feature description for FCISIO, we design a region merging algorithm before FCISIO which can use many kinds of information to quickly merge regions inside the true segments. Experiments on synthetic and real SAR images show that the proposed method is effective and robust and can obtain good segmentation results with a very short running time.
From MIP image to MRA segmentation using fuzzy set theory.
Vermandel, Maximilien; Betrouni, Nacim; Taschner, Christian; Vasseur, Christian; Rousseau, Jean
2007-04-01
The aim of this paper is to describe a semi-automatic method of segmentation in magnetic resonance angiography (MRA). This method, based on fuzzy set theory, uses the information (gray levels) contained in the maximum intensity projection (MIP) image to segment the 3D vascular structure from slices. Tests have been carried out on vascular phantom and on clinical MRA images. This 3D segmentation method has proved to be satisfactory for the detection of vascular structures even for very complex shapes. Finally, this MIP-based approach is semi-automatic and produces a robust segmentation thanks to the contrast-to-noise ratio and to the slice profile which are taken into account to determine the membership of a voxel to the vascular structure.
Automatic leukocyte nucleus segmentation by intuitionistic fuzzy divergence based thresholding.
Jati, Arindam; Singh, Garima; Mukherjee, Rashmi; Ghosh, Madhumala; Konar, Amit; Chakraborty, Chandan; Nagar, Atulya K
2014-03-01
The paper proposes a robust approach to automatic segmentation of leukocyte's nucleus from microscopic blood smear images under normal as well as noisy environment by employing a new exponential intuitionistic fuzzy divergence based thresholding technique. The algorithm minimizes the divergence between the actual image and the ideally thresholded image to search for the final threshold. A new divergence formula based on exponential intuitionistic fuzzy entropy has been proposed. Further, to increase its noise handling capacity, a neighborhood-based membership function for the image pixels has been designed. The proposed scheme has been applied on 110 normal and 54 leukemia (chronic myelogenous leukemia) affected blood samples. The nucleus segmentation results have been validated by three expert hematologists. The algorithm achieves an average segmentation accuracy of 98.52% in noise-free environment. It beats the competitor algorithms in terms of several other metrics. The proposed scheme with neighborhood based membership function outperforms the competitor algorithms in terms of segmentation accuracy under noisy environment. It achieves 93.90% and 94.93% accuracies for Speckle and Gaussian noises, respectively. The average area under the ROC curves comes out to be 0.9514 in noisy conditions, which proves the robustness of the proposed algorithm.
A Novel Pixon-Based Image Segmentation Process Using Fuzzy Filtering and Fuzzy C-mean Algorithm
DEFF Research Database (Denmark)
Nadernejad, Ehsan; Barari, Amin
2011-01-01
for image segmentation. The key idea is to create a pixon model by combining fuzzy filtering as a kernel function and a fuzzy c-means clustering algorithm for image segmentation. Use of fuzzy filters reduces noise and slightly smoothes the image. Use of the proposed pixon model prevented image over......Image segmentation, which is an important stage of many image processing algorithms, is the process of partitioning an image into nonintersecting regions, such that each region is homogeneous and the union of no two adjacent regions is homogeneous. This paper presents a novel pixon-based algorithm...
A New Approach to Lung Image Segmentation using Fuzzy Possibilistic C-Means Algorithm
Gomathi, M
2010-01-01
Image segmentation is a vital part of image processing. Segmentation has its application widespread in the field of medical images in order to diagnose curious diseases. The same medical images can be segmented manually. But the accuracy of image segmentation using the segmentation algorithms is more when compared with the manual segmentation. In the field of medical diagnosis an extensive diversity of imaging techniques is presently available, such as radiography, computed tomography (CT) and magnetic resonance imaging (MRI). Medical image segmentation is an essential step for most consequent image analysis tasks. Although the original FCM algorithm yields good results for segmenting noise free images, it fails to segment images corrupted by noise, outliers and other imaging artifact. This paper presents an image segmentation approach using Modified Fuzzy C-Means (FCM) algorithm and Fuzzy Possibilistic c-means algorithm (FPCM). This approach is a generalized version of standard Fuzzy CMeans Clustering (FCM) ...
Using Quadtree Algorithm for Improving Fuzzy C-means Method in Image Segmentation
Zahra Ghorbanzad; Farshid Babapour
2012-01-01
Image segmentation is an essential processing step for much image application and there are a large number of segmentation techniques. A new algorithm for image segmentation called Quad tree fuzzy c-means (QFCM) is presented I this work. The key idea in our approach is a Quad tree function combined with fuzzy c-means algorithm. In this article we also discuss the advantages and disadvantages of other image segmenting methods like: k-means, c-means, and blocked fuzzy c-means. Different experim...
Segmentation of electron tomographic data sets using fuzzy set theory principles.
Garduño, Edgar; Wong-Barnum, Mona; Volkmann, Niels; Ellisman, Mark H
2008-06-01
In electron tomography the reconstructed density function is typically corrupted by noise and artifacts. Under those conditions, separating the meaningful regions of the reconstructed density function is not trivial. Despite development efforts that specifically target electron tomography manual segmentation continues to be the preferred method. Based on previous good experiences using a segmentation based on fuzzy logic principles (fuzzy segmentation) where the reconstructed density functions also have low signal-to-noise ratio, we applied it to electron tomographic reconstructions. We demonstrate the usefulness of the fuzzy segmentation algorithm evaluating it within the limits of segmenting electron tomograms of selectively stained, plastic embedded spiny dendrites. The results produced by the fuzzy segmentation algorithm within the framework presented are encouraging.
A Novel Pixon-Based Image Segmentation Process Using Fuzzy Filtering and Fuzzy C-mean Algorithm
DEFF Research Database (Denmark)
Nadernejad, E; Barari, Amin
2011-01-01
for image segmentation. The key idea is to create a pixon model by combining fuzzy filtering as a kernel function and a fuzzy c-means clustering algorithm for image segmentation. Use of fuzzy filters reduces noise and slightly smoothes the image. Use of the proposed pixon model prevented image over......Image segmentation, which is an important stage of many image processing algorithms, is the process of partitioning an image into nonintersecting regions, such that each region is homogeneous and the union of no two adjacent regions is homogeneous. This paper presents a novel pixon-based algorithm......-segmentation and produced better experimental results than those obtained with other pixon-based algorithms....
Joint graph cut and relative fuzzy connectedness image segmentation algorithm.
Ciesielski, Krzysztof Chris; Miranda, Paulo A V; Falcão, Alexandre X; Udupa, Jayaram K
2013-12-01
We introduce an image segmentation algorithm, called GC(sum)(max), which combines, in novel manner, the strengths of two popular algorithms: Relative Fuzzy Connectedness (RFC) and (standard) Graph Cut (GC). We show, both theoretically and experimentally, that GC(sum)(max) preserves robustness of RFC with respect to the seed choice (thus, avoiding "shrinking problem" of GC), while keeping GC's stronger control over the problem of "leaking though poorly defined boundary segments." The analysis of GC(sum)(max) is greatly facilitated by our recent theoretical results that RFC can be described within the framework of Generalized GC (GGC) segmentation algorithms. In our implementation of GC(sum)(max) we use, as a subroutine, a version of RFC algorithm (based on Image Forest Transform) that runs (provably) in linear time with respect to the image size. This results in GC(sum)(max) running in a time close to linear. Experimental comparison of GC(sum)(max) to GC, an iterative version of RFC (IRFC), and power watershed (PW), based on a variety medical and non-medical images, indicates superior accuracy performance of GC(sum)(max) over these other methods, resulting in a rank ordering of GC(sum)(max)>PW∼IRFC>GC.
Fuzzy Control Hardware for Segmented Mirror Phasing Algorithm
Roth, Elizabeth
1999-01-01
This paper presents a possible implementation of a control model developed to phase a system of segmented mirrors, with a PAMELA configuration, using analog fuzzy hardware. Presently, the model is designed for piston control only, but with the foresight that the parameters of tip and tilt will be integrated eventually. The proposed controller uses analog circuits to exhibit a voltage-mode singleton fuzzifier, a mixed-mode inference engine, and a current-mode defuzzifier. The inference engine exhibits multiplication circuits that perform the algebraic product composition through the use of operational transconductance amplifiers rather than the typical min-max circuits. Additionally, the knowledge base, containing exemplar data gained a priori through simulation, interacts via a digital interface.
Li, Bing Nan; Chui, Chee Kong; Chang, Stephen; Ong, S H
2011-01-01
The performance of the level set segmentation is subject to appropriate initialization and optimal configuration of controlling parameters, which require substantial manual intervention. A new fuzzy level set algorithm is proposed in this paper to facilitate medical image segmentation. It is able to directly evolve from the initial segmentation by spatial fuzzy clustering. The controlling parameters of level set evolution are also estimated from the results of fuzzy clustering. Moreover the fuzzy level set algorithm is enhanced with locally regularized evolution. Such improvements facilitate level set manipulation and lead to more robust segmentation. Performance evaluation of the proposed algorithm was carried on medical images from different modalities. The results confirm its effectiveness for medical image segmentation.
Performance Evaluation of K-Mean and Fuzzy C-Mean Image Segmentation Based Clustering Classifier
Directory of Open Access Journals (Sweden)
Hind R.M Shaaban
2015-12-01
Full Text Available This paper presents Evaluation K-mean and Fuzzy c-mean image segmentation based Clustering classifier. It was followed by thresholding and level set segmentation stages to provide accurate region segment. The proposed stay can get the benefits of the K-means clustering. The performance and evaluation of the given image segmentation approach were evaluated by comparing K-mean and Fuzzy c-mean algorithms in case of accuracy, processing time, Clustering classifier, and Features and accurate performance results. The database consists of 40 images executed by K-mean and Fuzzy c-mean image segmentation based Clustering classifier. The experimental results confirm the effectiveness of the proposed Fuzzy c-mean image segmentation based Clustering classifier. The statistical significance Measures of mean values of Peak signal-to-noise ratio (PSNR and Mean Square Error (MSE and discrepancy are used for Performance Evaluation of K-mean and Fuzzy c-mean image segmentation. The algorithm’s higher accuracy can be found by the increasing number of classified clusters and with Fuzzy c-mean image segmentation.
Alternative Fuzzy Cluster Segmentation of Remote Sensing Images Based on Adaptive Genetic Algorithm
Institute of Scientific and Technical Information of China (English)
WANG Jing; TANG Jilong; LIU Jibin; REN Chunying; LIU Xiangnan; FENG Jiang
2009-01-01
Remote sensing image segmentation is the basis of image understanding and analysis. However, the precision and the speed of segmentation can not meet the need of image analysis, due to strong uncertainty and rich texture details of remote sensing images. We proposed a new segmentation method based on Adaptive Genetic Algorithm (AGA) and Alternative Fuzzy C-Means (AFCM). Segmentation thresholds were identified by AGA. Then the image was segmented by AFCM. The results indicate that the precision and the speed of segmentation have been greatly increased, and the accuracy of threshold selection is much higher compared with traditional Otsu and Fuzzy C-Means (FCM) segmentation methods. The segmentation results also show that multi-thresholds segmentation has been achieved by combining AGA with AFCM.
Interval TYPE-2 Fuzzy Based Neural Network for High Resolution Remote Sensing Image Segmentation
Wang, Chunyan; Xu, Aigong; Li, Chao; Zhao, Xuemei
2016-06-01
Recently, high resolution remote sensing image segmentation is a hot issue in image procesing procedures. However, it is a difficult task. The difficulties derive from the uncertainties of pixel segmentation and decision-making model. To this end, we take spatial relationship into consideration when constructing the interval type-2 fuzzy neural networks for high resolution remote sensing image segmentation. First, the proposed algorithm constructs a Gaussian model as a type-1 fuzzy model to describe the uncertainty contained in the image. Second, interval type-2 fuzzy model is obtained by blurring the mean and variance in type-1 model. The proposed interval type-2 model can strengthen the expression of uncertainty and simultaneously decrease the uncertainty in the decision model. Then the fuzzy membership function itself and its upper and lower fuzzy membership functions of the training samples are used as the input of neuron network which acts as the decision model in proposed algorithm. Finally, the relationship of neighbour pixels is taken into consideration and the fuzzy membership functions of the detected pixel and its neighbourhood are used to decide the class of each pixel to get the final segmentation result. The proposed algorithm, FCM and HMRF-FCM algorithm and an interval type-2 fuzzy neuron networks without spatial relationships are performed on synthetic and real high resolution remote sensing images. The qualitative and quantitative analyses demonstrate the efficient of the proposed algorithm, especially for homogeneous regions which contains a great difference in its gray level (for example forest).
Fat segmentation on chest CT images via fuzzy models
Tong, Yubing; Udupa, Jayaram K.; Wu, Caiyun; Pednekar, Gargi; Subramanian, Janani Rajan; Lederer, David J.; Christie, Jason; Torigian, Drew A.
2016-03-01
Quantification of fat throughout the body is vital for the study of many diseases. In the thorax, it is important for lung transplant candidates since obesity and being underweight are contraindications to lung transplantation given their associations with increased mortality. Common approaches for thoracic fat segmentation are all interactive in nature, requiring significant manual effort to draw the interfaces between fat and muscle with low efficiency and questionable repeatability. The goal of this paper is to explore a practical way for the segmentation of subcutaneous adipose tissue (SAT) and visceral adipose tissue (VAT) components of chest fat based on a recently developed body-wide automatic anatomy recognition (AAR) methodology. The AAR approach involves 3 main steps: building a fuzzy anatomy model of the body region involving all its major representative objects, recognizing objects in any given test image, and delineating the objects. We made several modifications to these steps to develop an effective solution to delineate SAT/VAT components of fat. Two new objects representing interfaces of SAT and VAT regions with other tissues, SatIn and VatIn are defined, rather than using directly the SAT and VAT components as objects for constructing the models. A hierarchical arrangement of these new and other reference objects is built to facilitate their recognition in the hierarchical order. Subsequently, accurate delineations of the SAT/VAT components are derived from these objects. Unenhanced CT images from 40 lung transplant candidates were utilized in experimentally evaluating this new strategy. Mean object location error achieved was about 2 voxels and delineation error in terms of false positive and false negative volume fractions were, respectively, 0.07 and 0.1 for SAT and 0.04 and 0.2 for VAT.
Evaluation of k-Means and fuzzy C-means segmentation on MR images of brain
S.Madhukumar; N. Santhiyakumari
2015-01-01
This paper does the qualitative comparison of Fuzzy C-means (FCM) and k-Means segmentation, with histogram guided initialization, on tumor edema complex MR images. The accuracy of any segmentation scheme depends on its ability to distinguish different tissue classes, separately. Hence, there is a serious pre-requisite to evaluate this ability before employing the segmentation scheme on medical images. This paper evaluates the ability of FCM and k-Means to segment Gray Matter (GM), White Matte...
Tracking fuzzy borders using geodesic curves with application to liver segmentation on planning CT.
Yuan, Yading; Chao, Ming; Sheu, Ren-Dih; Rosenzweig, Kenneth; Lo, Yeh-Chi
2015-07-01
This work aims to develop a robust and efficient method to track the fuzzy borders between liver and the abutted organs where automatic liver segmentation usually suffers, and to investigate its applications in automatic liver segmentation on noncontrast-enhanced planning computed tomography (CT) images. In order to track the fuzzy liver-chestwall and liver-heart borders where oversegmentation is often found, a starting point and an ending point were first identified on the coronal view images; the fuzzy border was then determined as a geodesic curve constructed by minimizing the gradient-weighted path length between these two points near the fuzzy border. The minimization of path length was numerically solved by fast-marching method. The resultant fuzzy borders were incorporated into the authors' automatic segmentation scheme, in which the liver was initially estimated by a patient-specific adaptive thresholding and then refined by a geodesic active contour model. By using planning CT images of 15 liver patients treated with stereotactic body radiation therapy, the liver contours extracted by the proposed computerized scheme were compared with those manually delineated by a radiation oncologist. The proposed automatic liver segmentation method yielded an average Dice similarity coefficient of 0.930 ± 0.015, whereas it was 0.912 ± 0.020 if the fuzzy border tracking was not used. The application of fuzzy border tracking was found to significantly improve the segmentation performance. The mean liver volume obtained by the proposed method was 1727 cm(3), whereas it was 1719 cm(3) for manual-outlined volumes. The computer-generated liver volumes achieved excellent agreement with manual-outlined volumes with correlation coefficient of 0.98. The proposed method was shown to provide accurate segmentation for liver in the planning CT images where contrast agent is not applied. The authors' results also clearly demonstrated that the application of tracking the fuzzy
Adaptive neuro-fuzzy inference system for breath phase detection and breath cycle segmentation.
Palaniappan, Rajkumar; Sundaraj, Kenneth; Sundaraj, Sebastian
2017-07-01
The monitoring of the respiratory rate is vital in several medical conditions, including sleep apnea because patients with sleep apnea exhibit an irregular respiratory rate compared with controls. Therefore, monitoring the respiratory rate by detecting the different breath phases is crucial. This study aimed to segment the breath cycles from pulmonary acoustic signals using the newly developed adaptive neuro-fuzzy inference system (ANFIS) based on breath phase detection and to subsequently evaluate the performance of the system. The normalised averaged power spectral density for each segment was fuzzified, and a set of fuzzy rules was formulated. The ANFIS was developed to detect the breath phases and subsequently perform breath cycle segmentation. To evaluate the performance of the proposed method, the root mean square error (RMSE) and correlation coefficient values were calculated and analysed, and the proposed method was then validated using data collected at KIMS Hospital and the RALE standard dataset. The analysis of the correlation coefficient of the neuro-fuzzy model, which was performed to evaluate its performance, revealed a correlation strength of r = 0.9925, and the RMSE for the neuro-fuzzy model was found to equal 0.0069. The proposed neuro-fuzzy model performs better than the fuzzy inference system (FIS) in detecting the breath phases and segmenting the breath cycles and requires less rules than FIS. Copyright © 2017 Elsevier B.V. All rights reserved.
Sun, Kaioqiong; Udupa, Jayaram K.; Odhner, Dewey; Tong, Yubing; Torigian, Drew A.
2014-03-01
This paper proposes a thoracic anatomy segmentation method based on hierarchical recognition and delineation guided by a built fuzzy model. Labeled binary samples for each organ are registered and aligned into a 3D fuzzy set representing the fuzzy shape model for the organ. The gray intensity distributions of the corresponding regions of the organ in the original image are recorded in the model. The hierarchical relation and mean location relation between different organs are also captured in the model. Following the hierarchical structure and location relation, the fuzzy shape model of different organs is registered to the given target image to achieve object recognition. A fuzzy connected delineation method is then used to obtain the final segmentation result of organs with seed points provided by recognition. The hierarchical structure and location relation integrated in the model provide the initial parameters for registration and make the recognition efficient and robust. The 3D fuzzy model combined with hierarchical affine registration ensures that accurate recognition can be obtained for both non-sparse and sparse organs. The results on real images are presented and shown to be better than a recently reported fuzzy model-based anatomy recognition strategy.
Improved Fuzzy C-Means Algorithm for MR Brain Image Segmentation
Directory of Open Access Journals (Sweden)
P.Vasuda,
2010-08-01
Full Text Available Segmentation is an important aspect of medical image processing, where Clustering approach is widely used in biomedical applications particularly for brain tumor detection in abnormal Magnetic Resonance Images (MRI. Fuzzy clustering using Fuzzy C- Means (FCM algorithm proved to be superior over the other clustering approaches in terms of segmentation efficiency. But the major drawback of the FCM algorithm is the huge computational time required for convergence. Theeffectiveness of the FCM algorithm in terms of computational rate is improved by modifying the cluster center and membership value updation criterion. In this paper, convergence rate is compared between the conventional FCM and the Improved FCM.
Directory of Open Access Journals (Sweden)
Savita Agrawal
2015-11-01
Full Text Available In the last decades, image segmentation has proved its applicability in various areas like satellite image processing, medical image processing and many more. In the present scenario the researchers tries to develop hybrid image segmentation techniques to generates efficient segmentation. Due to the development of the parallel programming, the lattice Boltzmann method (LBM has attracted much attention as a fast alternative approach for solving partial differential equations. In this project work, first designed an energy functional based on the fuzzy c-means objective function which incorporates the bias field that accounts for the intensity in homogeneity of the real-world image. Using the gradient descent method, corresponding level set equations are obtained from which we deduce a fuzzy external force for the LBM solver based on the model by Zhao. The method is speedy, robust for denoise, and does not dependent on the position of the initial contour, effective in the presence of intensity in homogeneity, highly parallelizable and can detect objects with or without edges. For the implementation of segmentation techniques defined for gray images, most of the time researchers determines single channel segments of the images and superimposes the single channel segment information on color images. This idea leads to provide color image segmentation using single channel segments of multi channel images. Though this method is widely adopted but doesn’t provide complete true segmentation of multichannel ie color images because a color image contains three different channels for Red, green and blue components. Hence segmenting a color image, by having only single channel segments information, will definitely loose important segment regions of color images. To overcome this problem this paper work starts with the development of Enhanced Level Set Segmentation for single channel Images Using Fuzzy Clustering and Lattice Boltzmann Method. For the
Directory of Open Access Journals (Sweden)
Savita Agrawal
2014-05-01
Full Text Available In the last decades, image segmentation has proved its applicability in various areas like satellite image processing, medical image processing and many more. In the present scenario the researchers tries to develop hybrid image segmentation techniques to generates efficient segmentation. Due to the development of the parallel programming, the lattice Boltzmann met hod (LBM has attracted much attention as a fast alternative approach for solving partial differential equations. In this project work, first designed an energy functional based on the fuzzy c-means objective function which incorporates the bias field that accounts for the intensity in homogeneity of the real-world image. Using the gradient descent method, corresponding level set equations are obtained from which we deduce a fuzzy external force for the LBM solver based on the model by Zhao. The method is speedy, robust for denoise, and does not dependent on the position of the initial contour, effective in the presence of intensity in homogeneity, highly parallelizable and can detect objects with or without edges. For the implementation of segmentation techniques defined for gr ay images, most of the time researchers determines single channel segments of the images and superimposes the single channel segment information on color images. This idea leads to provide color image segmentation using single channel segments of multi chann el images. Though this method is widely adopted but doesn’t provide complete true segmentation of multichannel ie color images because a color image contains three different channels for Red, green and blue components. Hence segmenting a color image, b y having only single channel segments information, will definitely loose important segment regions of color images. To overcome this problem this paper work starts with the development of Enhanced Level Set Segmentation for single channel Images Using Fuzzy Clustering and Lattice Boltzmann Method. For the
Neighborhood Supported Model Level Fuzzy Aggregation for Moving Object Segmentation.
Chiranjeevi, Pojala; Sengupta, Somnath
2014-02-01
We propose a new algorithm for moving object detection in the presence of challenging dynamic background conditions. We use a set of fuzzy aggregated multifeature similarity measures applied on multiple models corresponding to multimodal backgrounds. The algorithm is enriched with a neighborhood-supported model initialization strategy for faster convergence. A model level fuzzy aggregation measure driven background model maintenance ensures more robustness. Similarity functions are evaluated between the corresponding elements of the current feature vector and the model feature vectors. Concepts from Sugeno and Choquet integrals are incorporated in our algorithm to compute fuzzy similarities from the ordered similarity function values for each model. Model updating and the foreground/background classification decision is based on the set of fuzzy integrals. Our proposed algorithm is shown to outperform other multi-model background subtraction algorithms. The proposed approach completely avoids explicit offline training to initialize background model and can be initialized with moving objects also. The feature space uses a combination of intensity and statistical texture features for better object localization and robustness. Our qualitative and quantitative studies illustrate the mitigation of varieties of challenging situations by our approach.
STEENKAMP, JBEM; WEDEL, M
1993-01-01
This article describes a new technique for benefit segmentation, fuzzy clusterwise regression analysis (FCR). It combines clustering with prediction and is based on multiattribute models of consumer behavior. FCR is especially useful when the number of observations per subject is small, when the rel
Othman, Khairulnizam; Ahmad, Afandi
2016-11-01
In this research we explore the application of normalize denoted new techniques in advance fast c-mean in to the problem of finding the segment of different breast tissue regions in mammograms. The goal of the segmentation algorithm is to see if new denotes fuzzy c- mean algorithm could separate different densities for the different breast patterns. The new density segmentation is applied with multi-selection of seeds label to provide the hard constraint, whereas the seeds labels are selected based on user defined. New denotes fuzzy c- mean have been explored on images of various imaging modalities but not on huge format digital mammograms just yet. Therefore, this project is mainly focused on using normalize denoted new techniques employed in fuzzy c-mean to perform segmentation to increase visibility of different breast densities in mammography images. Segmentation of the mammogram into different mammographic densities is useful for risk assessment and quantitative evaluation of density changes. Our proposed methodology for the segmentation of mammograms on the basis of their region into different densities based categories has been tested on MIAS database and Trueta Database.
Halder, Amiya
2012-01-01
This paper proposes a Genetic Algorithm based segmentation method that can automatically segment gray-scale images. The proposed method mainly consists of spatial unsupervised grayscale image segmentation that divides an image into regions. The aim of this algorithm is to produce precise segmentation of images using intensity information along with neighborhood relationships. In this paper, Fuzzy Hopfield Neural Network (FHNN) clustering helps in generating the population of Genetic algorithm which there by automatically segments the image. This technique is a powerful method for image segmentation and works for both single and multiple-feature data with spatial information. Validity index has been utilized for introducing a robust technique for finding the optimum number of components in an image. Experimental results shown that the algorithm generates good quality segmented image.
New two-dimensional fuzzy C-means clustering algorithm for image segmentation
Institute of Scientific and Technical Information of China (English)
无
2008-01-01
To solve the problem of poor anti-noise performance of the traditional fuzzy C-means (FCM) algorithm in image segmentation,a novel two-dimensional FCM clustering algorithm for image segmentation was proposed.In this method,the image segmentation was converted into an optimization problem.The fitness function containing neighbor information was set up based on the gray information and the neighbor relations between the pixcls described by the improved two-dimensional histogram.By making use of the global searching ability of the predator-prey particle swarm optimization,the optimal cluster center could be obtained by iterative optimization,and the image segmentation could be accomplished.The simulation results show that the segmentation accuracy ratio of the proposed method is above 99%.The proposed algorithm has strong anti-noise capability,high clustering accuracy and good segment effect,indicating that it is an effective algorithm for image segmentation.
A HYBRID FIREFLY ALGORITHM WITH FUZZY-C MEAN ALGORITHM FOR MRI BRAIN SEGMENTATION
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Mutasem K. Alsmadi
2014-01-01
Full Text Available Image processing is one of the essential tasks to extract suspicious region and robust features from the Magnetic Resonance Imaging (MRI. A numbers of the segmentation algorithms were developed in order to satisfy and increasing the accuracy of brain tumor detection. In the medical image processing brain image segmentation is considered as a complex and challenging part. Fuzzy c-means is unsupervised method that has been implemented for clustering of the MRI and different purposes such as recognition of the pattern of interest and image segmentation. However; fuzzy c-means algorithm still suffers many drawbacks, such as low convergence rate, getting stuck in the local minima and vulnerable to initialization sensitivity. Firefly algorithm is a new population-based optimization method that has been used successfully for solving many complex problems. This paper proposed a new dynamic and intelligent clustering method for brain tumor segmentation using the hybridization of Firefly Algorithm (FA with Fuzzy C-Means algorithm (FCM. In order to automatically segment MRI brain images and improve the capability of the FCM to automatically elicit the proper number and location of cluster centres and the number of pixels in each cluster in the abnormal (multiple sclerosis lesions MRI images. The experimental results proved the effectiveness of the proposed FAFCM in enhancing the performance of the traditional FCM clustering. Moreover; the superiority of the FAFCM with other state-of-the-art segmentation methods is shown qualitatively and quantitatively. Conclusion: A novel efficient and reliable clustering algorithm presented in this work, which is called FAFCM based on the hybridization of the firefly algorithm with fuzzy c-mean clustering algorithm. Automatically; the hybridized algorithm has the capability to cluster and segment MRI brain images.
Taghribi, Abolfazl; Sharifian, Saeed
2017-09-19
Precise segmentation of magnetic resonance image (MRI) seems challenging because of the complex structure of the brain, non-uniform field in images, and noise. As a result, decision-making is associated with uncertainty. Fuzzy based approaches have been developed to overcome this problem, though most of them use fuzzy type 1 method, and sometimes contain a pre-processing step. This paper "modified type 2 fuzzy system" (MT2FS) declares a state-of-the-art method to segment MRI images using interval fuzzy type-2. Furthermore, Genetic algorithm has been employed to specify the best values for mean and variance of upper and lower membership functions. This strategy will determine discrimination boundaries for different brain tissues to be less independent from the training set. Finally, the result of fuzzy rules is extracted by using Dempster-Shafer rule combination method. Simulation results demonstrate a satisfactory output on both simulated and real MRI images in comparison with previously conducted research works without the need for a pre-processing stage.
Fast interactive segmentation algorithm of image sequences based on relative fuzzy connectedness
Institute of Scientific and Technical Information of China (English)
Tian Chunna; Gao Xinbo
2005-01-01
A fast interactive segmentation algorithm of image-sequences based on relative fuzzy connectedness is presented. In comparison with the original algorithm, the proposed one, with the same accuracy, accelerates the segmentation speed by three times for single image. Meanwhile, this fast segmentation algorithm is extended from single object to multiple objects and from single-image to image-sequences. Thus the segmentation of multiple objects from complex background and batch segmentation of image-sequences can be achieved. In addition, a post-processing scheme is incorporated in this algorithm, which extracts smooth edge with one-pixel-width for each segmented object. The experimental results illustrate that the proposed algorithm can obtain the object regions of interest from medical image or image-sequences as well as man-made images quickly and reliably with only a little interaction.
A Survey Paper on Fuzzy Image Segmentation Techniques
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Ms. R. Saranya Pon Selvi
2014-03-01
Full Text Available The image segmentation plays an important role in the day-to-day life. The new technologies are emerging in the field of Image processing, especially in the domain of segmentation.Segmentation is considered as one of the main steps in image processing. It divides a digital image into multiple regions in order to analyze them. It is also used to distinguish different objects in the image. Several image segmentation techniques have been developed by the researchers in order to make images smooth and easy to evaluate. This paper presents a brief outline on some of the most commonly used segmentation techniques like thresholding, Region based, Model based, Edge detection..etc. mentioning its advantages as well as the drawbacks. Some of the techniques are suitable for noisy images.
Affinity functions: recognizing essential parameters in fuzzy connectedness based image segmentation
Ciesielski, Krzysztof C.; Udupa, Jayaram K.
2009-02-01
Fuzzy connectedness (FC) constitutes an important class of image segmentation schemas. Although affinity functions represent the core aspect (main variability parameter) of FC algorithms, they have not been studied systematically in the literature. In this paper, we present a thorough study to fill this gap. Our analysis is based on the notion of equivalent affinities: if any two equivalent affinities are used in the same FC schema to produce two versions of the algorithm, then these algorithms are equivalent in the sense that they lead to identical segmentations. We give a complete characterization of the affinity equivalence and show that many natural definitions of affinity functions and their parameters used in the literature are redundant in the sense that different definitions and values of such parameters lead to equivalent affinities. We also show that two main affinity types - homogeneity based and object feature based - are equivalent, respectively, to the difference quotient of the intensity function and Rosenfeld's degree of connectivity. In addition, we demonstrate that any segmentation obtained via relative fuzzy connectedness (RFC) algorithm can be viewed as segmentation obtained via absolute fuzzy connectedness (AFC) algorithm with an automatic and adaptive threshold detection. We finish with an analysis of possible ways of combining different component affinities that result in non equivalent affinities.
Malleable Fuzzy Local Median C Means Algorithm for Effective Biomedical Image Segmentation
Rajendran, Arunkumar; Balakrishnan, Nagaraj; Varatharaj, Mithya
2016-12-01
The traditional way of clustering plays an effective role in the field of segmentation which was developed to be more effective and also in the recent development the extraction of contextual information can be processed with ease. This paper presents a modified Fuzzy C-Means (FCM) algorithm that provides the better segmentation in the contour grayscale regions of the biomedical images where effective cluster is needed. Malleable Fuzzy Local Median C-Means (M-FLMCM) is the proposed algorithm, proposed to overcome the disadvantage of the traditional FCM method in which the convergence time requirement is more, lack of ability to remove the noise, and the inability to cluster the contour region such as images. M-FLMCM shows promising results in the experiment with real-world biomedical images. The experiment results, with 96 % accuracy compared to the other algorithms.
Jagadeesan, R; S.N. Sivanandam
2013-01-01
In image processing, it is difficult to detect the abnormalities in brain especially in MRI brain images. Also the tumor segmentation from MRI image data is an important; however it is time consumingwhile carried out by medical specialists. A lot of methods have been proposed to solve MR images problems, quite difficult to develop an automated recognition system which could process on a large information of patient and provide a correct estimation. Hence enhanced k-means and fuzzy c-means wit...
Mookiah, Muthu Rama Krishnan; Acharya, U Rajendra; Chua, Chua Kuang; Min, Lim Choo; Ng, E Y K; Mushrif, Milind M; Laude, Augustinus
2013-01-01
The human eye is one of the most sophisticated organs, with perfectly interrelated retina, pupil, iris cornea, lens, and optic nerve. Automatic retinal image analysis is emerging as an important screening tool for early detection of eye diseases. Uncontrolled diabetic retinopathy (DR) and glaucoma may lead to blindness. The identification of retinal anatomical regions is a prerequisite for the computer-aided diagnosis of several retinal diseases. The manual examination of optic disk (OD) is a standard procedure used for detecting different stages of DR and glaucoma. In this article, a novel automated, reliable, and efficient OD localization and segmentation method using digital fundus images is proposed. General-purpose edge detection algorithms often fail to segment the OD due to fuzzy boundaries, inconsistent image contrast, or missing edge features. This article proposes a novel and probably the first method using the Attanassov intuitionistic fuzzy histon (A-IFSH)-based segmentation to detect OD in retinal fundus images. OD pixel intensity and column-wise neighborhood operation are employed to locate and isolate the OD. The method has been evaluated on 100 images comprising 30 normal, 39 glaucomatous, and 31 DR images. Our proposed method has yielded precision of 0.93, recall of 0.91, F-score of 0.92, and mean segmentation accuracy of 93.4%. We have also compared the performance of our proposed method with the Otsu and gradient vector flow (GVF) snake methods. Overall, our result shows the superiority of proposed fuzzy segmentation technique over other two segmentation methods.
Regional SAR Image Segmentation Based on Fuzzy Clustering with Gamma Mixture Model
Li, X. L.; Zhao, Q. H.; Li, Y.
2017-09-01
Most of stochastic based fuzzy clustering algorithms are pixel-based, which can not effectively overcome the inherent speckle noise in SAR images. In order to deal with the problem, a regional SAR image segmentation algorithm based on fuzzy clustering with Gamma mixture model is proposed in this paper. First, initialize some generating points randomly on the image, the image domain is divided into many sub-regions using Voronoi tessellation technique. Each sub-region is regarded as a homogeneous area in which the pixels share the same cluster label. Then, assume the probability of the pixel to be a Gamma mixture model with the parameters respecting to the cluster which the pixel belongs to. The negative logarithm of the probability represents the dissimilarity measure between the pixel and the cluster. The regional dissimilarity measure of one sub-region is defined as the sum of the measures of pixels in the region. Furthermore, the Markov Random Field (MRF) model is extended from pixels level to Voronoi sub-regions, and then the regional objective function is established under the framework of fuzzy clustering. The optimal segmentation results can be obtained by the solution of model parameters and generating points. Finally, the effectiveness of the proposed algorithm can be proved by the qualitative and quantitative analysis from the segmentation results of the simulated and real SAR images.
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Yachun Pang
2012-01-01
Full Text Available This paper presents a novel two-step approach that incorporates fuzzy c-means (FCMs clustering and gradient vector flow (GVF snake algorithm for lesions contour segmentation on breast magnetic resonance imaging (BMRI. Manual delineation of the lesions by expert MR radiologists was taken as a reference standard in evaluating the computerized segmentation approach. The proposed algorithm was also compared with the FCMs clustering based method. With a database of 60 mass-like lesions (22 benign and 38 malignant cases, the proposed method demonstrated sufficiently good segmentation performance. The morphological and texture features were extracted and used to classify the benign and malignant lesions based on the proposed computerized segmentation contour and radiologists’ delineation, respectively. Features extracted by the computerized characterization method were employed to differentiate the lesions with an area under the receiver-operating characteristic curve (AUC of 0.968, in comparison with an AUC of 0.914 based on the features extracted from radiologists’ delineation. The proposed method in current study can assist radiologists to delineate and characterize BMRI lesion, such as quantifying morphological and texture features and improving the objectivity and efficiency of BMRI interpretation with a certain clinical value.
Segmentation of pomegranate MR images using spatial fuzzy c-means (SFCM) algorithm
Moradi, Ghobad; Shamsi, Mousa; Sedaaghi, M. H.; Alsharif, M. R.
2011-10-01
Segmentation is one of the fundamental issues of image processing and machine vision. It plays a prominent role in a variety of image processing applications. In this paper, one of the most important applications of image processing in MRI segmentation of pomegranate is explored. Pomegranate is a fruit with pharmacological properties such as being anti-viral and anti-cancer. Having a high quality product in hand would be critical factor in its marketing. The internal quality of the product is comprehensively important in the sorting process. The determination of qualitative features cannot be manually made. Therefore, the segmentation of the internal structures of the fruit needs to be performed as accurately as possible in presence of noise. Fuzzy c-means (FCM) algorithm is noise-sensitive and pixels with noise are classified inversely. As a solution, in this paper, the spatial FCM algorithm in pomegranate MR images' segmentation is proposed. The algorithm is performed with setting the spatial neighborhood information in FCM and modification of fuzzy membership function for each class. The segmentation algorithm results on the original and the corrupted Pomegranate MR images by Gaussian, Salt Pepper and Speckle noises show that the SFCM algorithm operates much more significantly than FCM algorithm. Also, after diverse steps of qualitative and quantitative analysis, we have concluded that the SFCM algorithm with 5×5 window size is better than the other windows.
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Jzau-sheng Lin
2013-02-01
Full Text Available In this study, segmentation of medical images using a fuzzy artificial bee colony algorithm with a cooling schedule is created. In this study, we embedded fuzzy inference strategy into the artificial bee colony system to construct a segmentation system named Fuzzy Artificial Bee Colony System (FABCS. A conventional FCM algorithm did not utilize the spatial information in the image. We set a local circular area with a variable radius by using a cooling schedule for each bee to search suitable cluster centers with the FCM algorithm in an image. The cluster centers can be calculated by each bee with the membership states in the FABCS and then updated iteratively for all bees in order to find near-global solution in MR image segmentation. The proposed FABCS found the cluster centers with local spatial information instead of global pixels’ intensities. In the simulation and real medical-image segmentation results, the proposed FABCS network can reserve the segmentation performance.
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R.Jagadeesan
2013-07-01
Full Text Available In image processing, it is difficult to detect the abnormalities in brain especially in MRI brain images. Also the tumor segmentation from MRI image data is an important; however it is time consumingwhile carried out by medical specialists. A lot of methods have been proposed to solve MR images problems, quite difficult to develop an automated recognition system which could process on a large information of patient and provide a correct estimation. Hence enhanced k-means and fuzzy c-means with firefly algorithm for a segmentation of brain magnetic resonance images were developed. Thisalgorithm is based on maximum measure of the distance function which is found for cluster center detection process using the Mahalanobis concept. Particularly the firefly algorithm is implemented tooptimize the Fuzzy C-means membership function for better accuracy segmentation process. At the same time the convergence criteria is fixed for the efficient clustering method. The Firefly algorithmparameters are set fixed and they do not adjust by the time. As well Firefly algorithm does not memorize any history of better situation for each firefly and this reasons they travel in any case of it, and they miss their situations. So there is a need of better algorithm that could provide even better solution than the firefly algorithm. To attain this requirement as a proposed work the Artificial Fish Swarm Algorithm to optimize the fuzzy membership function. During surveying of the previous literature, it has been found out that no work has been done in segmentation of brain tumor using AFSA based clustering. In AFSA, artificial fishes for next movement act completely independent from past and next movement is justrelated to current position of artificial fish and its other companions which lead to select best initial centers for the MRI brain tumor segmentation. Experimental results show that presented method has an acceptable performance than the previous method.
Shi, Jun; Xiao, Zhiheng; Zhou, Shichong
2010-07-01
Image segmentation is very important in the field of image processing. The pulse coupled neural network (PCNN) has been efficiently applied to image processing, especially for image segmentation. In this study, a simplified PCNN (S-PCNN) model is proposed, the fuzzy mutual information (FMI) is improved as optimization criterion for S-PCNN, and then the S-PCNN and improved FMI (IFMI) based segmentation algorithm is proposed and applied for the segmentation of breast tumor in ultrasound image. To validate the proposed algorithm, a comparative experiment is implemented to segment breast images not only by our proposed algorithm, but also by the improved C-V algorithm, the max-entropy-based PCNN algorithm, the MI-based PCNN algorithm, and the IFMI-based PCNN algorithm. The results show that the breast lesions are well segmented by the proposed algorithm without image preprocessing, with the mean Hausdorff of distance of 5.631+/-0.822, mean average minimum Euclidean distance of 0.554+/-0.049, mean Tanimoto coefficient of 0.961+/-0.019, and mean misclassified error of 0.038+/-0.004. These values of evaluation indices are better than those of other segmentation algorithms. The results indicate that the proposed algorithm has excellent segmentation accuracy and strong robustness against noise, and it has the potential for breast ultrasound computer-aided diagnosis (CAD).
Automatic lung tumor segmentation on PET/CT images using fuzzy Markov random field model.
Guo, Yu; Feng, Yuanming; Sun, Jian; Zhang, Ning; Lin, Wang; Sa, Yu; Wang, Ping
2014-01-01
The combination of positron emission tomography (PET) and CT images provides complementary functional and anatomical information of human tissues and it has been used for better tumor volume definition of lung cancer. This paper proposed a robust method for automatic lung tumor segmentation on PET/CT images. The new method is based on fuzzy Markov random field (MRF) model. The combination of PET and CT image information is achieved by using a proper joint posterior probability distribution of observed features in the fuzzy MRF model which performs better than the commonly used Gaussian joint distribution. In this study, the PET and CT simulation images of 7 non-small cell lung cancer (NSCLC) patients were used to evaluate the proposed method. Tumor segmentations with the proposed method and manual method by an experienced radiation oncologist on the fused images were performed, respectively. Segmentation results obtained with the two methods were similar and Dice's similarity coefficient (DSC) was 0.85 ± 0.013. It has been shown that effective and automatic segmentations can be achieved with this method for lung tumors which locate near other organs with similar intensities in PET and CT images, such as when the tumors extend into chest wall or mediastinum.
Automatic Lung Tumor Segmentation on PET/CT Images Using Fuzzy Markov Random Field Model
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Yu Guo
2014-01-01
Full Text Available The combination of positron emission tomography (PET and CT images provides complementary functional and anatomical information of human tissues and it has been used for better tumor volume definition of lung cancer. This paper proposed a robust method for automatic lung tumor segmentation on PET/CT images. The new method is based on fuzzy Markov random field (MRF model. The combination of PET and CT image information is achieved by using a proper joint posterior probability distribution of observed features in the fuzzy MRF model which performs better than the commonly used Gaussian joint distribution. In this study, the PET and CT simulation images of 7 non-small cell lung cancer (NSCLC patients were used to evaluate the proposed method. Tumor segmentations with the proposed method and manual method by an experienced radiation oncologist on the fused images were performed, respectively. Segmentation results obtained with the two methods were similar and Dice’s similarity coefficient (DSC was 0.85 ± 0.013. It has been shown that effective and automatic segmentations can be achieved with this method for lung tumors which locate near other organs with similar intensities in PET and CT images, such as when the tumors extend into chest wall or mediastinum.
Optimized Fuzzy Logic Based Segmentation for Abnormal MRI Brain Images Analysis
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Indah Soesanti
2011-09-01
Full Text Available In this paper an optimized fuzzy logic based segmentation for abnormal MRI brain images analysis is presented. A conventional fuzzy c-means (FCM technique does not use the spatial information in the image. In this research, we use a FCM algorithm that incorporates spatial information into the membership function for clustering. The FCM algorithm that incorporates spatial information into the membership function is used for clustering, while a conventional FCM algorithm does not fully utilize the spatial information in the image.The advantage of the technique is less sensitive to noise than the others. Originality of this research is focused in application of the technique on a normal and a glioma MRI brain images, and analysis of the area of abnormal mass from segmented images. The results show that the method effectively segmented MRI brain images, and the segmented normal and glioma MRI brain images can be analyzed for diagnosis purpose. The area of abnormal mass is identified from 7.15 to 19.41 cm2.
A Modified Fuzzy C-Means Algorithm for Brain MR Image Segmentation and Bias Field Correction
Institute of Scientific and Technical Information of China (English)
Wen-Qian Deng; Xue-Mei Li; Xifeng Gao; Cai-Ming Zhang
2016-01-01
In quantitative brain image analysis, accurate brain tissue segmentation from brain magnetic resonance image (MRI) is a critical step. It is considered to be the most important and diﬃcult issue in the field of medical image processing. The quality of MR images is influenced by partial volume effect, noise, and intensity inhomogeneity, which render the segmentation task extremely challenging. We present a novel fuzzy c-means algorithm (RCLFCM) for segmentation and bias field correction of brain MR images. We employ a new gray-difference coeﬃcient and design a new impact factor to measure the effect of neighbor pixels, so that the robustness of anti-noise can be enhanced. Moreover, we redefine the objective function of FCM (fuzzy c-means) by adding the bias field estimation model to overcome the intensity inhomogeneity in the image and segment the brain MR images simultaneously. We also construct a new spatial function by combining pixel gray value dissimilarity with its membership, and make full use of the space information between pixels to update the membership. Compared with other state-of-the-art approaches by using similarity accuracy on synthetic MR images with different levels of noise and intensity inhomogeneity, the proposed algorithm generates the results with high accuracy and robustness to noise.
Solid oxide fuel cell anode image segmentation based on a novel quantum-inspired fuzzy clustering
Fu, Xiaowei; Xiang, Yuhan; Chen, Li; Xu, Xin; Li, Xi
2015-12-01
High quality microstructure modeling can optimize the design of fuel cells. For three-phase accurate identification of Solid Oxide Fuel Cell (SOFC) microstructure, this paper proposes a novel image segmentation method on YSZ/Ni anode Optical Microscopic (OM) images. According to Quantum Signal Processing (QSP), the proposed approach exploits a quantum-inspired adaptive fuzziness factor to adaptively estimate the energy function in the fuzzy system based on Markov Random Filed (MRF). Before defuzzification, a quantum-inspired probability distribution based on distance and gray correction is proposed, which can adaptively adjust the inaccurate probability estimation of uncertain points caused by noises and edge points. In this study, the proposed method improves accuracy and effectiveness of three-phase identification on the micro-investigation. It provides firm foundation to investigate the microstructural evolution and its related properties.
A pixel-based color image segmentation using support vector machine and fuzzy C-means.
Wang, Xiang-Yang; Zhang, Xian-Jin; Yang, Hong-Ying; Bu, Juan
2012-09-01
Image segmentation is an important tool in image processing and can serve as an efficient front end to sophisticated algorithms and thereby simplify subsequent processing. In this paper, we present a pixel-based color image segmentation using Support Vector Machine (SVM) and Fuzzy C-Means (FCM). Firstly, the pixel-level color feature and texture feature of the image, which is used as input of the SVM model (classifier), are extracted via the local spatial similarity measure model and Steerable filter. Then, the SVM model (classifier) is trained by using FCM with the extracted pixel-level features. Finally, the color image is segmented with the trained SVM model (classifier). This image segmentation can not only take full advantage of the local information of the color image but also the ability of the SVM classifier. Experimental evidence shows that the proposed method has a very effective computational behavior and effectiveness, and decreases the time and increases the quality of color image segmentation in comparison with the state-of-the-art segmentation methods recently proposed in the literature.
Blood Cell Segmentation Based on Improved Pulse Coupled Neural Network and Fuzzy Entropy
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Zhanbo Liu
2016-12-01
Full Text Available In the field of biomedical image processing, because of the low intensity and brightness of the cell image, and the complex structure of the cell image, the segmentation of cell images is very difficult. A large number of studies have shown that the Pulse Coupled Neural Networks (PCNN is suitable for image segmentation. However, the traditional PCNN must set a large number of parameters in image segmentation, and the optimal number of iterations cannot be automatically determined. In this paper, a new improved PCNN model is proposed. The work of improved PCNN includes the acceptance portion of the PCNN model being simplified and the connection portion of PCNN being improved. In addition, the maximum fuzzy entropy is used as the criterion to determine the optimal number of iterations. Experimental results on blood cell image segmentation show that this proposed method can automatically determine the number of loop iterations and automatically select the best threshold. It also has the characteristics of fast convergence, high accuracy and good segmentation effect in blood cell image segmentation processing.
Tong, Yubing; Udupa, Jayaram K.; Odhner, Dewey; Wu, Caiyun; Zhao, Yue; McDonough, Joseph M.; Capraro, Anthony; Torigian, Drew A.; Campbell, Robert M.
2017-03-01
Lung delineation via dynamic 4D thoracic magnetic resonance imaging (MRI) is necessary for quantitative image analysis for studying pediatric respiratory diseases such as thoracic insufficiency syndrome (TIS). This task is very challenging because of the often-extreme malformations of the thorax in TIS, lack of signal from bone and connective tissues resulting in inadequate image quality, abnormal thoracic dynamics, and the inability of the patients to cooperate with the protocol needed to get good quality images. We propose an interactive fuzzy connectedness approach as a potential practical solution to this difficult problem. Manual segmentation is too labor intensive especially due to the 4D nature of the data and can lead to low repeatability of the segmentation results. Registration-based approaches are somewhat inefficient and may produce inaccurate results due to accumulated registration errors and inadequate boundary information. The proposed approach works in a manner resembling the Iterative Livewire tool but uses iterative relative fuzzy connectedness (IRFC) as the delineation engine. Seeds needed by IRFC are set manually and are propagated from slice-to-slice, decreasing the needed human labor, and then a fuzzy connectedness map is automatically calculated almost instantaneously. If the segmentation is acceptable, the user selects "next" slice. Otherwise, the seeds are refined and the process continues. Although human interaction is needed, an advantage of the method is the high level of efficient user-control on the process and non-necessity to refine the results. Dynamic MRI sequences from 5 pediatric TIS patients involving 39 3D spatial volumes are used to evaluate the proposed approach. The method is compared to two other IRFC strategies with a higher level of automation. The proposed method yields an overall true positive and false positive volume fraction of 0.91 and 0.03, respectively, and Hausdorff boundary distance of 2 mm.
Color-texture based unsupervised segmentation using JSEG with fuzzy connectedness
Institute of Scientific and Technical Information of China (English)
Zheng Yuanjie; Yang Jie; Zhou Yue; Wang Yuzhong
2006-01-01
Color quantization is bound to lose spatial information of color distribution. If too much necessary spatial distribution information of color is lost in JSEG, it is difficult or even impossible for JSEG to segment image correctly. Enlightened from segmentation based on fuzzy theories, soft class-map is constracted to solve that problem. The definitions of values and other related ones are adjusted according to the soft class-map. With more detailed values obtained from soft class map, more color distribution information is preserved. Experiments on a synthetic image and many other color images illustrate that JSEG with soft class-map can solve efficiently the problem that in a region there may exist color gradual variation in a smooth transition. It is a more robust method especially for images which haven' t been heavily blurred near boundaries of underlying regions.
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Yao-Tien Chen
2017-01-01
Full Text Available Segmentation of brain tissues is an important but inherently challenging task in that different brain tissues have similar grayscale values and the intensity of a brain tissue may be confused with that of another one. The paper accordingly develops an ICKFCM method based on kernelized fuzzy c-means clustering with ICA analysis for extracting regions of interest in MRI brain images. The proposed method first removes the skull region using a skull stripping algorithm. Through ICA, three independent components are then extracted from multimodal medical images containing T1-weighted, T2-weighted, and PD-weighted MRI images. As MRI signals can be regarded as a combination of the signals from brain matters, ICA can be used for contrast enhancement of MRI images. Finally, the three independent components are utilized as inputs by KFCM algorithm to extract different brain tissues. Relying on the decomposition of a multivariate signal into independent non-Gaussian components and using a more appropriate kernel-induced distance for fuzzy clustering, the proposed method is capable of achieving greater reliability in both theory and practice than other segmentation approaches. According to the experiment results, the proposed method is capable of accurately extracting the complicated shapes of brain tissues and still remaining robust against various types of noises.
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Ghazaal Sheikhi
2013-10-01
Full Text Available Syllable, as a sub-word unit, nowadays plays an active role in the field of speech processing and recognition research according to its robust relation to human speech production and cognition. Automatic syllable boundaries detection is an important step forward in the areas of speech prosody, natural speech synthesis and speech recognition. In this paper, a novel method in automatic syllabification of Farsi continuous speech based on acoustic structure is proposed. Our previous studies, showed the proficiency of energy contour fuzzy smoothing method, compared with other prominent works in this area. This paper suggests that the conventional methodology-used in speech enhancement based on wavelet coefficient thresholding would improve syllable segmentation by decreasing insertion error. This process declines the energy in high energy consonants which are responsible for extra peaks in short term energy contour. Experimental results showed that utilizing proposed method along with fuzzy smoothing would diminish insertion error about 8% with no reasonable effect on other efficiency criteria. More than 94% of syllables are automatically segmented using presented technique with less than 50ms error.
Hybrid PET/MRI co-segmentation based on joint fuzzy connectedness and graph cut.
Sbei, Arafet; ElBedoui, Khaoula; Barhoumi, Walid; Maksud, Philippe; Maktouf, Chokri
2017-10-01
Tumor segmentation from hybrid PET/MRI scans may be highly beneficial in radiotherapy treatment planning. Indeed, it gives for both modalities the suitable information that could make the delineation of tumors more accurate than using each one apart. We aim in this work to propose a co-segmentation method that deals with several challenges, notably the lack of one-to-one correspondence between tumors of the two modalities and the boundaries' smoothing. The proposed method is designed to surpass these limits, we propose a segmentation method based on the GCsum(max) technique. The method takes the advantage of Iterative Relative Fuzzy Connectedness (IRFC) on seeds initialization, and the standard min-cut/max-flow technique for the boundary smoothing. Seed initialization was accurately performed thanks to high uptake regions on PET. Besides, a visibility weighting scheme was adapted to achieve the task of co-segmentation using the IRFC algorithm. Then, given the co-segmented regions, we introduce a morphological-based technique that provides object seeds to standard Graph Cut (GC) allowing it to avoid the shrinking problem. Finally, for each modality, the segmentation task is formulated as an energy minimization problem which is resolved by a min-cut/max-flow technique. The overlap ratio (denoted DSC) between our segmentation results and the ground-truth for PET images is 92.63 ± 1.03, while the DSC for MRI images is 90.61 ± 3.70. The proposed method was tested on different types of diseases and it outperformed the state-of-the-art methods. We show its superiority in terms of assymetric relation between PET and MRI and tumors heterogeneity. Copyright © 2017 Elsevier B.V. All rights reserved.
Jati, A; Singh, G; Koley, S; Konar, A; Ray, A K; Chakraborty, C
2015-03-01
Medical image segmentation demands higher segmentation accuracy especially when the images are affected by noise. This paper proposes a novel technique to segment medical images efficiently using an intuitionistic fuzzy divergence-based thresholding. A neighbourhood-based membership function is defined here. The intuitionistic fuzzy divergence-based image thresholding technique using the neighbourhood-based membership functions yield lesser degradation of segmentation performance in noisy environment. Its ability in handling noisy images has been validated. The algorithm is independent of any parameter selection. Moreover, it provides robustness to both additive and multiplicative noise. The proposed scheme has been applied on three types of medical image datasets in order to establish its novelty and generality. The performance of the proposed algorithm has been compared with other standard algorithms viz. Otsu's method, fuzzy C-means clustering, and fuzzy divergence-based thresholding with respect to (1) noise-free images and (2) ground truth images labelled by experts/clinicians. Experiments show that the proposed methodology is effective, more accurate and efficient for segmenting noisy images.
Evaluation of k-Means and fuzzy C-means segmentation on MR images of brain
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S. Madhukumar
2015-06-01
Full Text Available This paper does the qualitative comparison of Fuzzy C-means (FCM and k-Means segmentation, with histogram guided initialization, on tumor edema complex MR images. The accuracy of any segmentation scheme depends on its ability to distinguish different tissue classes, separately. Hence, there is a serious pre-requisite to evaluate this ability before employing the segmentation scheme on medical images. This paper evaluates the ability of FCM and k-Means to segment Gray Matter (GM, White Matter (WM, Cerebro-Spinal Fluid (CSF, Necrotic Focus of Glioblastoma Multiforme (GBM and the perifocal vasogenic edema from pre-processed T1 contrast axial plane MR images of tumor edema complex. The experiment reveals that FCM identifies the vasogenic edema and the white matter as a single tissue class and similarly gray matter and necrotic focus, also. k-Means is able to characterize these regions comparatively better than FCM. FCM identifies only three tissue classes whereas; k-Means identifies all the six classes. The experimental evaluation of k-Means and FCM, with histogram guided initialization is performed in Matlab®.
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Temitope Mapayi
2015-09-01
Full Text Available As the use of robotic-assisted surgery systems continue to increase, highly accurate and timely efficient automatic vasculature detection techniques for large and thin vessels in the retinal images are needed. Vascular segmentation has however been challenging due to uneven illumination in retinal images. The use of efficient pre-processing techniques as well as good segmentation techniques are highly needed to produce good vessel segmentation results. This paper presents an investigatory study on the combination of phase congruence with fuzzy c-means and the combination of phase congruence with gray level co-occurrence (GLCM matrix sum entropy for the segmentation of retinal vessels. Fuzzy C-Means combined with phase congruence yields a higher accuracy rate but a longer running time while compared to GLCM sum entropy combined with phase congruence. While compared with the widely previously used techniques on DRIVE and STARE databases, the techniques investigated yield high average accuracy rates.
Fuzzy hidden Markov chains segmentation for volume determination and quantitation in PET
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Hatt, M [INSERM U650, Laboratoire du Traitement de l' Information Medicale (LaTIM), CHU Morvan, Bat 2bis (I3S), 5 avenue Foch, Brest, 29609 (France); Lamare, F [INSERM U650, Laboratoire du Traitement de l' Information Medicale (LaTIM), CHU Morvan, Bat 2bis (I3S), 5 avenue Foch, Brest, 29609, (France); Boussion, N [INSERM U650, Laboratoire du Traitement de l' Information Medicale (LaTIM), CHU Morvan, Bat 2bis (I3S), 5 avenue Foch, Brest, 29609 (France); Turzo, A [INSERM U650, Laboratoire du Traitement de l' Information Medicale (LaTIM), CHU Morvan, Bat 2bis (I3S), 5 avenue Foch, Brest, 29609 (France); Collet, C [Ecole Nationale Superieure de Physique de Strasbourg (ENSPS), ULP, Strasbourg, F-67000 (France); Salzenstein, F [Institut d' Electronique du Solide et des Systemes (InESS), ULP, Strasbourg, F-67000 (France); Roux, C [INSERM U650, Laboratoire du Traitement de l' Information Medicale (LaTIM), CHU Morvan, Bat 2bis (I3S), 5 avenue Foch, Brest, 29609 (France); Jarritt, P [Medical Physics Agency, Royal Victoria Hospital, Belfast (United Kingdom); Carson, K [Medical Physics Agency, Royal Victoria Hospital, Belfast (United Kingdom); Rest, C Cheze-Le [INSERM U650, Laboratoire du Traitement de l' Information Medicale (LaTIM), CHU Morvan, Bat 2bis (I3S), 5 avenue Foch, Brest, 29609 (France); Visvikis, D [INSERM U650, Laboratoire du Traitement de l' Information Medicale (LaTIM), CHU Morvan, Bat 2bis (I3S), 5 avenue Foch, Brest, 29609 (France)
2007-07-21
Accurate volume of interest (VOI) estimation in PET is crucial in different oncology applications such as response to therapy evaluation and radiotherapy treatment planning. The objective of our study was to evaluate the performance of the proposed algorithm for automatic lesion volume delineation; namely the fuzzy hidden Markov chains (FHMC), with that of current state of the art in clinical practice threshold based techniques. As the classical hidden Markov chain (HMC) algorithm, FHMC takes into account noise, voxel intensity and spatial correlation, in order to classify a voxel as background or functional VOI. However the novelty of the fuzzy model consists of the inclusion of an estimation of imprecision, which should subsequently lead to a better modelling of the 'fuzzy' nature of the object of interest boundaries in emission tomography data. The performance of the algorithms has been assessed on both simulated and acquired datasets of the IEC phantom, covering a large range of spherical lesion sizes (from 10 to 37 mm), contrast ratios (4:1 and 8:1) and image noise levels. Both lesion activity recovery and VOI determination tasks were assessed in reconstructed images using two different voxel sizes (8 mm{sup 3} and 64 mm{sup 3}). In order to account for both the functional volume location and its size, the concept of % classification errors was introduced in the evaluation of volume segmentation using the simulated datasets. Results reveal that FHMC performs substantially better than the threshold based methodology for functional volume determination or activity concentration recovery considering a contrast ratio of 4:1 and lesion sizes of <28 mm. Furthermore differences between classification and volume estimation errors evaluated were smaller for the segmented volumes provided by the FHMC algorithm. Finally, the performance of the automatic algorithms was less susceptible to image noise levels in comparison to the threshold based techniques. The
Segmentation and Labelling of Human Spine MR Images Using Fuzzy Clustering
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Jiyo.S.Athertya
2016-04-01
Full Text Available Computerized medical image segmentation is a challe nging area because of poor resolution and weak contrast. The predominantly used conventio nal clustering techniques and the thresholding methods suffer from limitations owing to their heavy dependence on user interactions. Uncertainties prevalent in an image c annot be captured by these techniques. The performance further deteriorates when the images ar e corrupted by noise, outliers and other artifacts. The objective of this paper is to develo p an effective robust fuzzy C- means clustering for segmenting vertebral body from magnetic resonan ce images. The motivation for this work is that spine appearance, shape and geometry measureme nts are necessary for abnormality detection and thus proper localisation and labellin g will enhance the diagnostic output of a physician. The method is compared with Otsu thresho lding and K-means clustering to illustrate the robustness. The reference standard for validation was the annot ated images from the radiologist, and the Dice coefficient and Hausdorff distance measures were used to evaluate the segmentation.
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Chuin-Mu Wang
2014-01-01
Full Text Available After long-term clinical trials, MRI has been proven to be used in humans harmlessly, and it is popularly used in medical diagnosis. Although MR is highly sensitive, it provides abundant organization information. Therefore, how to transform the multi-spectral images which is easier to be used for doctor’s clinical diagnosis. In this thesis, the fuzzy bidirectional edge detection method is used to solve conventional SRG problem of growing order in the initial seed stages. In order to overcome the problems of the different regions, although it is the same Euclidean distance for region growing and merging process stages, we present the peak detection method to improve them. The standard deviation target generation process (SDTGP is applied to guarantee the regions merging process does not cause over- or undersegmentation. Experimental results reveal that FISRG segments a multispectral MR image much more effectively than FAST and K-means.
Fuzzy-Based Segmentation for Variable Font-Sized Text Extraction from Images/Videos
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Samabia Tehsin
2014-01-01
Full Text Available Textual information embedded in multimedia can provide a vital tool for indexing and retrieval. A lot of work is done in the field of text localization and detection because of its very fundamental importance. One of the biggest challenges of text detection is to deal with variation in font sizes and image resolution. This problem gets elevated due to the undersegmentation or oversegmentation of the regions in an image. The paper addresses this problem by proposing a solution using novel fuzzy-based method. This paper advocates postprocessing segmentation method that can solve the problem of variation in text sizes and image resolution. The methodology is tested on ICDAR 2011 Robust Reading Challenge dataset which amply proves the strength of the recommended method.
An improved parallel fuzzy connected image segmentation method based on CUDA.
Wang, Liansheng; Li, Dong; Huang, Shaohui
2016-05-12
Fuzzy connectedness method (FC) is an effective method for extracting fuzzy objects from medical images. However, when FC is applied to large medical image datasets, its running time will be greatly expensive. Therefore, a parallel CUDA version of FC (CUDA-kFOE) was proposed by Ying et al. to accelerate the original FC. Unfortunately, CUDA-kFOE does not consider the edges between GPU blocks, which causes miscalculation of edge points. In this paper, an improved algorithm is proposed by adding a correction step on the edge points. The improved algorithm can greatly enhance the calculation accuracy. In the improved method, an iterative manner is applied. In the first iteration, the affinity computation strategy is changed and a look up table is employed for memory reduction. In the second iteration, the error voxels because of asynchronism are updated again. Three different CT sequences of hepatic vascular with different sizes were used in the experiments with three different seeds. NVIDIA Tesla C2075 is used to evaluate our improved method over these three data sets. Experimental results show that the improved algorithm can achieve a faster segmentation compared to the CPU version and higher accuracy than CUDA-kFOE. The calculation results were consistent with the CPU version, which demonstrates that it corrects the edge point calculation error of the original CUDA-kFOE. The proposed method has a comparable time cost and has less errors compared to the original CUDA-kFOE as demonstrated in the experimental results. In the future, we will focus on automatic acquisition method and automatic processing.
Directory of Open Access Journals (Sweden)
E.A. Zanaty
2012-03-01
Full Text Available In this paper, we determine the suitable validity criterion of kernelized fuzzy C-means and kernelized fuzzy C-means with spatial constraints for automatic segmentation of magnetic resonance imaging (MRI. For that; the original Euclidean distance in the FCM is replaced by a Gaussian radial basis function classifier (GRBF and the corresponding algorithms of FCM methods are derived. The derived algorithms are called as the kernelized fuzzy C-means (KFCM and kernelized fuzzy C-means with spatial constraints (SKFCM. These methods are implemented on eighteen indexes as validation to determine whether indexes are capable to acquire the optimal clusters number. The performance of segmentation is estimated by applying these methods independently on several datasets to prove which method can give good results and with which indexes. Our test spans various indexes covering the classical and the rather more recent indexes that have enjoyed noticeable success in that field. These indexes are evaluated and compared by applying them on various test images, including synthetic images corrupted with noise of varying levels, and simulated volumetric MRI datasets. Comparative analysis is also presented to show whether the validity index indicates the optimal clustering for our datasets.
Medical Image Segmentation using the HSI color space and Fuzzy Mathematical Morphology
Gasparri, J. P.; Bouchet, A.; Abras, G.; Ballarin, V.; Pastore, J. I.
2011-12-01
Diabetic retinopathy is the most common cause of blindness among the active population in developed countries. An early ophthalmologic examination followed by proper treatment can prevent blindness. The purpose of this work is develop an automated method for segmentation the vasculature in retinal images in order to assist the expert in the evolution of a specific treatment or in the diagnosis of a potential pathology. Since the HSI space has the ability to separate the intensity of the intrinsic color information, its use is recommended for the digital processing images when they are affected by lighting changes, characteristic of the images under study. By the application of color filters, is achieved artificially change the tone of blood vessels, to better distinguish them from the bottom. This technique, combined with the application of fuzzy mathematical morphology tools as the Top-Hat transformation, creates images of the retina, where vascular branches are markedly enhanced over the original. These images provide the visualization of blood vessels by the specialist.
Kumar, Rajesh; Srivastava, Subodh; Srivastava, Rajeev
2017-07-01
For cancer detection from microscopic biopsy images, image segmentation step used for segmentation of cells and nuclei play an important role. Accuracy of segmentation approach dominate the final results. Also the microscopic biopsy images have intrinsic Poisson noise and if it is present in the image the segmentation results may not be accurate. The objective is to propose an efficient fuzzy c-means based segmentation approach which can also handle the noise present in the image during the segmentation process itself i.e. noise removal and segmentation is combined in one step. To address the above issues, in this paper a fourth order partial differential equation (FPDE) based nonlinear filter adapted to Poisson noise with fuzzy c-means segmentation method is proposed. This approach is capable of effectively handling the segmentation problem of blocky artifacts while achieving good tradeoff between Poisson noise removals and edge preservation of the microscopic biopsy images during segmentation process for cancer detection from cells. The proposed approach is tested on breast cancer microscopic biopsy data set with region of interest (ROI) segmented ground truth images. The microscopic biopsy data set contains 31 benign and 27 malignant images of size 896 × 768. The region of interest selected ground truth of all 58 images are also available for this data set. Finally, the result obtained from proposed approach is compared with the results of popular segmentation algorithms; fuzzy c-means, color k-means, texture based segmentation, and total variation fuzzy c-means approaches. The experimental results shows that proposed approach is providing better results in terms of various performance measures such as Jaccard coefficient, dice index, Tanimoto coefficient, area under curve, accuracy, true positive rate, true negative rate, false positive rate, false negative rate, random index, global consistency error, and variance of information as compared to other
Meena Prakash, R; Shantha Selva Kumari, R
2017-01-01
The Fuzzy C Means (FCM) and Expectation Maximization (EM) algorithms are the most prevalent methods for automatic segmentation of MR brain images into three classes Gray Matter (GM), White Matter (WM) and Cerebrospinal Fluid (CSF). The major difficulties associated with these conventional methods for MR brain image segmentation are the Intensity Non-uniformity (INU) and noise. In this paper, EM and FCM with spatial information and bias correction are proposed to overcome these effects. The spatial information is incorporated by convolving the posterior probability during E-Step of the EM algorithm with mean filter. Also, a method of pixel re-labeling is included to improve the segmentation accuracy. The proposed method is validated by extensive experiments on both simulated and real brain images from standard database. Quantitative and qualitative results depict that the method is superior to the conventional methods by around 25% and over the state-of-the art method by 8%.
A new method based on Dempster-Shafer theory and fuzzy c-means for brain MRI segmentation
Liu, Jie; Lu, Xi; Li, Yunpeng; Chen, Xiaowu; Deng, Yong
2015-10-01
In this paper, a new method is proposed to decrease sensitiveness to motion noise and uncertainty in magnetic resonance imaging (MRI) segmentation especially when only one brain image is available. The method is approached with considering spatial neighborhood information by fusing the information of pixels with their neighbors with Dempster-Shafer (DS) theory. The basic probability assignment (BPA) of each single hypothesis is obtained from the membership function of applying fuzzy c-means (FCM) clustering to the gray levels of the MRI. Then multiple hypotheses are generated according to the single hypothesis. Then we update the objective pixel’s BPA by fusing the BPA of the objective pixel and those of its neighbors to get the final result. Some examples in MRI segmentation are demonstrated at the end of the paper, in which our method is compared with some previous methods. The results show that the proposed method is more effective than other methods in motion-blurred MRI segmentation.
DEFF Research Database (Denmark)
Nadernejad, Ehsan; Sharifzadeh, Sara
2013-01-01
In this paper, a new pixon-based method is presented for image segmentation. In the proposed algorithm, bilateral filtering is used as a kernel function to form a pixonal image. Using this filter reduces the noise and smoothes the image slightly. By using this pixon-based method, the image over s...... the hierarchical clustering method (Fuzzy C-means algorithm). The experimental results show that the proposed pixon-based approach has a reduced computational load and a better accuracy compared to the other existing pixon-based image segmentation techniques.......In this paper, a new pixon-based method is presented for image segmentation. In the proposed algorithm, bilateral filtering is used as a kernel function to form a pixonal image. Using this filter reduces the noise and smoothes the image slightly. By using this pixon-based method, the image over...
Directory of Open Access Journals (Sweden)
Liao Chun-Chih
2011-08-01
Full Text Available Abstract Background In recent years, magnetic resonance imaging (MRI has become important in brain tumor diagnosis. Using this modality, physicians can locate specific pathologies by analyzing differences in tissue character presented in different types of MR images. This paper uses an algorithm integrating fuzzy-c-mean (FCM and region growing techniques for automated tumor image segmentation from patients with menigioma. Only non-contrasted T1 and T2 -weighted MR images are included in the analysis. The study's aims are to correctly locate tumors in the images, and to detect those situated in the midline position of the brain. Methods The study used non-contrasted T1- and T2-weighted MR images from 29 patients with menigioma. After FCM clustering, 32 groups of images from each patient group were put through the region-growing procedure for pixels aggregation. Later, using knowledge-based information, the system selected tumor-containing images from these groups and merged them into one tumor image. An alternative semi-supervised method was added at this stage for comparison with the automatic method. Finally, the tumor image was optimized by a morphology operator. Results from automatic segmentation were compared to the "ground truth" (GT on a pixel level. Overall data were then evaluated using a quantified system. Results The quantified parameters, including the "percent match" (PM and "correlation ratio" (CR, suggested a high match between GT and the present study's system, as well as a fair level of correspondence. The results were compatible with those from other related studies. The system successfully detected all of the tumors situated at the midline of brain. Six cases failed in the automatic group. One also failed in the semi-supervised alternative. The remaining five cases presented noticeable edema inside the brain. In the 23 successful cases, the PM and CR values in the two groups were highly related. Conclusions Results indicated
Rastgarpour, Maryam; Shanbehzadeh, Jamshid; Soltanian-Zadeh, Hamid
2014-08-01
medical images are more affected by intensity inhomogeneity rather than noise and outliers. This has a great impact on the efficiency of region-based image segmentation methods, because they rely on homogeneity of intensities in the regions of interest. Meanwhile, initialization and configuration of controlling parameters affect the performance of level set segmentation. To address these problems, this paper proposes a new hybrid method that integrates a local region-based level set method with a variation of fuzzy clustering. Specifically it takes an information fusion approach based on a coarse-to-fine framework that seamlessly fuses local spatial information and gray level information with the information of the local region-based level set method. Also, the controlling parameters of level set are directly computed from fuzzy clustering result. This approach has valuable benefits such as automation, no need to prior knowledge about the region of interest (ROI), robustness on intensity inhomogeneity, automatic adjustment of controlling parameters, insensitivity to initialization, and satisfactory accuracy. So, the contribution of this paper is to provide these advantages together which have not been proposed yet for inhomogeneous medical images. Proposed method was tested on several medical images from different modalities for performance evaluation. Experimental results approve its effectiveness in segmenting medical images in comparison with similar methods.
Elazab, Ahmed; Wang, Changmiao; Jia, Fucang; Wu, Jianhuang; Li, Guanglin; Hu, Qingmao
2015-01-01
An adaptively regularized kernel-based fuzzy C-means clustering framework is proposed for segmentation of brain magnetic resonance images. The framework can be in the form of three algorithms for the local average grayscale being replaced by the grayscale of the average filter, median filter, and devised weighted images, respectively. The algorithms employ the heterogeneity of grayscales in the neighborhood and exploit this measure for local contextual information and replace the standard Euclidean distance with Gaussian radial basis kernel functions. The main advantages are adaptiveness to local context, enhanced robustness to preserve image details, independence of clustering parameters, and decreased computational costs. The algorithms have been validated against both synthetic and clinical magnetic resonance images with different types and levels of noises and compared with 6 recent soft clustering algorithms. Experimental results show that the proposed algorithms are superior in preserving image details and segmentation accuracy while maintaining a low computational complexity.
Directory of Open Access Journals (Sweden)
Ahmed Elazab
2015-01-01
Full Text Available An adaptively regularized kernel-based fuzzy C-means clustering framework is proposed for segmentation of brain magnetic resonance images. The framework can be in the form of three algorithms for the local average grayscale being replaced by the grayscale of the average filter, median filter, and devised weighted images, respectively. The algorithms employ the heterogeneity of grayscales in the neighborhood and exploit this measure for local contextual information and replace the standard Euclidean distance with Gaussian radial basis kernel functions. The main advantages are adaptiveness to local context, enhanced robustness to preserve image details, independence of clustering parameters, and decreased computational costs. The algorithms have been validated against both synthetic and clinical magnetic resonance images with different types and levels of noises and compared with 6 recent soft clustering algorithms. Experimental results show that the proposed algorithms are superior in preserving image details and segmentation accuracy while maintaining a low computational complexity.
模糊逻辑和谱聚类的字符图像分割%Spectral clustering based text image segmentation using fuzzy logic
Institute of Scientific and Technical Information of China (English)
吴锐; 尹芳; 唐降龙; 黄剑华
2010-01-01
为了从复杂背景中有效分离出字符图像,提出了一种基于模糊逻辑的谱聚类字符图像分割方法.利用最大信息熵准则获得模糊函数的参数,将原始图像模糊化;在模糊后的图像上建立像素闻的相似矩阵,文本图像的纹理、灰度及像素间的距离是定义相似函数的依据,计算相似矩阵最小特征值对应的特征向量,并对其聚类划分;利用分类后的特征向量对相似矩阵进行划分,进而实现原图像的分割.实验结果表明:本文方法优于一般的阈值化分割方法,能够有效处理背景复杂的自然场景文本图像.
Multivariate image segmentation with cluster size insensitive Fuzzy C-means
Noordam, J.C.; Broek, van den W.H.A.M.; Buydens, L.M.C.
2002-01-01
This paper describes a technique to overcome the sensitivity of fuzzy C-means clustering for unequal cluster sizes in multivariate images. As FCM tends to balance the number of points in each cluster, cluster centres of smaller clusters are drawn to larger adjacent clusters. In order to overcome
Iris segmentation using an edge detector based on fuzzy sets theory and cellular learning automata.
Ghanizadeh, Afshin; Abarghouei, Amir Atapour; Sinaie, Saman; Saad, Puteh; Shamsuddin, Siti Mariyam
2011-07-01
Iris-based biometric systems identify individuals based on the characteristics of their iris, since they are proven to remain unique for a long time. An iris recognition system includes four phases, the most important of which is preprocessing in which the iris segmentation is performed. The accuracy of an iris biometric system critically depends on the segmentation system. In this paper, an iris segmentation system using edge detection techniques and Hough transforms is presented. The newly proposed edge detection system enhances the performance of the segmentation in a way that it performs much more efficiently than the other conventional iris segmentation methods.
Mohammadi Nasrabadi, Ali; Hosseinpour, Mohammad Hossein; Ebrahimnejad, Sadoullah
2013-05-01
In competitive markets, market segmentation is a critical point of business, and it can be used as a generic strategy. In each segment, strategies lead companies to their targets; thus, segment selection and the application of the appropriate strategies over time are very important to achieve successful business. This paper aims to model a strategy-aligned fuzzy approach to market segment evaluation and selection. A modular decision support system (DSS) is developed to select an optimum segment with its appropriate strategies. The suggested DSS has two main modules. The first one is SPACE matrix which indicates the risk of each segment. Also, it determines the long-term strategies. The second module finds the most preferred segment-strategies over time. Dynamic network process is applied to prioritize segment-strategies according to five competitive force factors. There is vagueness in pairwise comparisons, and this vagueness has been modeled using fuzzy concepts. To clarify, an example is illustrated by a case study in Iran's coffee market. The results show that success possibility of segments could be different, and choosing the best ones could help companies to be sure in developing their business. Moreover, changing the priority of strategies over time indicates the importance of long-term planning. This fact has been supported by a case study on strategic priority difference in short- and long-term consideration.
Hybrid of Fuzzy Logic and Random Walker Method for Medical Image Segmentation
Jasdeep Kaur; Manish Mahajan
2015-01-01
The procedure of partitioning an image into various segments to reform an image into somewhat that is more significant and easier to analyze, defined as image segmentation. In real world applications, noisy images exits and there could be some measurement errors too. These factors affect the quality of segmentation, which is of major concern in medical fields where decisions about patients’ treatment are based on information extracted from radiological images. Several algorithms and technique...
Hybrid of Fuzzy Logic and Random Walker Method for Medical Image Segmentation
National Research Council Canada - National Science Library
Jasdeep Kaur; Manish Mahajan
2015-01-01
...’ treatment are based on information extracted from radiological images. Several algorithms and techniques have developed for image segmentation and have their own advantages and disadvantages...
Molinari, Filippo; Gaetano, Laura; Balestra, Gabriella; Suri, Jasjit S
2010-01-01
The automated segmentation of the carotid artery wall from ultrasound images is required for an accurate measurement of the artery intima-media thickness. Segmentation accuracy of automated techniques is usually limited by noise and artifacts. In 2005, the authors developed a methodology called CULEX whose performance was noise sensitive. The final stage of CULEX segmentation was fuzzy clustering of the pixels, to detect the lumen-intima (LI) and media-adventitia (MA) carotid wall interfaces. In this paper, we show the effect of a fuzzy Mamdani-type pre-classifier used to improve the segmentation performance. Thanks to the Mamdami fuzzy pre-classifier, we optimized the de-fuzzyfication threshold, increasing the LI and MA performance by 62% and 3.5%, respectively. The obtained segmentation errors (55.6 ± 69.4 microm for LI and 34.4 ± 24.4 microm for MA), validated against human tracings and on a 200-images dataset containing a mixture of healthy and plaque vessels.
Directory of Open Access Journals (Sweden)
Ashraf Afifi
2012-07-01
Full Text Available In this paper we present a hybrid approach based on combining fuzzy k-means clustering, seed region growing, and sensitivity and specificity algorithms to measure gray (GM and white matter (WM tissue. The proposed algorithm uses intensity and anatomic information for segmenting of MRIs into different tissue classes, especially GM and WM. It starts by partitioning the image into different clusters using fuzzy k-means clustering. The centers of these clusters are the input to the region growing (SRG method for creating the closed regions. The outputs of SRG technique are fed to sensitivity and specificity algorithm to merge the similar regions in one segment. The proposed algorithm is applied to challenging applications: gray matter/white matter segmentation in magnetic resonance image (MRI datasets. The experimental results show that the proposed technique produces accurate and stable results.
Evaluation of image segmentation based on type-2 fuzzy sets%图像分割质量评价的二型模糊集方法
Institute of Scientific and Technical Information of China (English)
邓廷权; 焦颖颖
2011-01-01
Evaluation for quality of image segmentation is an essential stage and has been extensively studied in image analysis and computer vision.In view of advantages of type-2 fuzzy sets in describing inaccuracy of objects, criteria for image segmentation evaluation are characterized by using type-2 fuzzy sets.Two kinds of fuzzy metric of type-2 fuzzy sets are introduced and a model is established to evaluate quality of image segmentation.Simulated experiments show the validity and practicability of the proposed model.%图像分割质量的评价是图像分割技术和算法研究的重要环节,在图像分析和计算机视觉中有着重要应用.依据二型模糊集在不精确性描述方面的独特优势,提出一种图像分割评判指标的二型模糊集表示方法,引入两种二型模糊集的模糊性度量作为图像分割质量的评判标准,构建图像分割质量评价模型.模拟实验验证了该模型的有效性和实用性.
FUZZY CLUSTERING ALGORITHMS FOR WEB PAGES AND CUSTOMER SEGMENTS%Web页面和客户群体的模糊聚类算法
Institute of Scientific and Technical Information of China (English)
宋擒豹; 沈钧毅
2001-01-01
Web log mining is broadly used in E-commerce and personalizationof the Web. In this paper, the fuzzy clustering algorithms for Web pages and customers is presented. First, the fuzzy sets of Web page and customer are setup separately according to the hitting information of customers. Second, the fuzzy similarity matrices ave constructed on the basis of the fuzzy sets and the Max-Min similarity measure scheme. Finally, Web page clusters and tustomer segments are abstracted directly from the corresponding fuzzy similarity matrix. Experiments show the effectiveness of the algorithm.%web日志挖掘在电子商务和个性化web等方面有着广泛的应用.文章介绍了一种web页面和客户群体的模糊聚类算法.在该算法中，首先根据客户对Web站点的浏览情况分别建立Web页面和客户的模糊集，在此基础上根据Max—Min模糊相似性度量规则构造相应的模糊相似矩阵，然后根据模糊相似矩阵直接进行聚类.实验结果表明该算法是有效的.
Institute of Scientific and Technical Information of China (English)
陈又星
2011-01-01
The article does internal risk identification and quantitative measurement of industrial cluster based on the problem of fuzzy uncertainty. Then the article takes a particular industry cluster as an example to illustrate the model's validity,reliability and practicality. It has a more important theoretical and practical value because the research provides a new thinking for business and government to do decision - making of industry.%基于问题的模糊不确定性,运用模糊AHP模型时产业集群内部风险进行识别和定量测度,并以某一具体产业集群为例,以说明该模型的有效性、可靠性和实用性为企业和政府进行产业决策提供一种新思路和新方法.
Lucieer, A.; Stein, A.; Fisher, P.
2005-01-01
In this study, a segmentation procedure is proposed, based on grey¿level and multivariate texture to extract spatial objects from an image scene. Object uncertainty was quantified to identify transitions zones of objects with indeterminate boundaries. The Local Binary Pattern (LBP) operator, modelli
A new fuzzy level set method for SAR image segmentation%基于模糊水平集的SAR图像分割方法
Institute of Scientific and Technical Information of China (English)
毛万峰; 张红; 张波; 王超
2013-01-01
We present a new method which integrates fuzzy c-means cluttering and region-based level set evolution for SAR image segmentation. Benefited by spatial fuzzy clustering, the initial level set segmentation approximates the component of interest. The controlling parameters are also estimated on the basis of the results of the spatial fuzzy clustering. The proposed method was evaluated on synthetic and real SAR images, and the results show that the new method is more robust, fast, and accurate in segmentation and does not need manual intervention.%提出一种SAR图像分割方法,即整合了模糊C均值聚类和基于区域水平集演化的分割方法.该方法通过模糊聚类的结果计算水平集演化的初始化条件及控制参数,从而克服了水平集演化依赖于初始化条件和控制参数且需要较多人工干预的缺陷,增强了方法的鲁棒性.模拟图像及真实SAR图像的实验表明,该方法在不需要人工干预的情况下,能够快速、准确地分割出感兴趣区域.
SEGMENTATION OF MAGNETIC RESONANCE BRAIN TUMOR USING INTEGRATED FUZZY K-MEANS CLUSTERING
P.Pedda Sadhu Naik; T.Venu Gopal
2016-01-01
Segmentation is a process of partitioning the image into several objects. It plays a vital role in many fields such as satellite, remote sensing, object identification, face tracking and most importantly in medical field. In radiology, magnetic resonance imaging (MRI) is used to investigate the human body processes and functions of organisms. In hospitals, this technique has been using widely for medical diagnosis, to find the disease stage and follow-up without exposure to ionizi...
Yu, Xuelian; Chen, Qian; Gu, Guohua; Qian, Weixian; Xu, Mengxi
2014-11-01
The integration between polarization and intensity images possessing complementary and discriminative information has emerged as a new and important research area. On the basis of the consideration that the resulting image has different clarity and layering requirement for the target and background, we propose a novel fusion method based on non-subsampled Contourlet transform (NSCT) and fuzzy C-means (FCM) segmentation for IR polarization and light intensity images. First, the polarization characteristic image is derived from fusion of the degree of polarization (DOP) and the angle of polarization (AOP) images using local standard variation and abrupt change degree (ACD) combined criteria. Then, the polarization characteristic image is segmented with FCM algorithm. Meanwhile, the two source images are respectively decomposed by NSCT. The regional energy-weighted and similarity measure are adopted to combine the low-frequency sub-band coefficients of the object. The high-frequency sub-band coefficients of the object boundaries are integrated through the maximum selection rule. In addition, the high-frequency sub-band coefficients of internal objects are integrated by utilizing local variation, matching measure and region feature weighting. The weighted average and maximum rules are employed independently in fusing the low-frequency and high-frequency components of the background. Finally, an inverse NSCT operation is accomplished and the final fused image is obtained. The experimental results illustrate that the proposed IR polarization image fusion algorithm can yield an improved performance in terms of the contrast between artificial target and cluttered background and a more detailed representation of the depicted scene.
Parallel Implementation of Bias Field Correction Fuzzy C-Means Algorithm for Image Segmentation
Directory of Open Access Journals (Sweden)
Nouredine AITALI
2016-03-01
Full Text Available Image segmentation in the medical field is one of the most important phases to diseases diagnosis. The bias field estimation algorithm is the most interesting techniques to correct the in-homogeneity intensity artifact on the image. However, the use of such technique requires a powerful processing and quite expensive for big size as medical images. Hence the idea of parallelism becomes increasingly required. Several researchers have followed this path mainly in the bioinformatics field where they have suggested different algorithms implementations. In this paper, a novel Single Instruction Multiple Data (SIMD architecture for bias field estimation and image segmentation algorithm is proposed. In order to accelerate compute-intensive portions of the sequential implementation, we have implemented this algorithm on three different graphics processing units (GPU cards named GT740m, GTX760 and GTX580 respectively, using Compute Unified Device Architecture (CUDA software programming tool. Numerical obtained results for the computation speed up, allowed us to conclude on the suitable GPU architecture for this kind of applications and closest ones.
Directory of Open Access Journals (Sweden)
Maryam Rastgarpour
2014-01-01
Full Text Available Researchers recently apply an integrative approach to automate medical image segmentation for benefiting available methods and eliminating their disadvantages. Intensity inhomogeneity is a challenging and open problem in this area, which has received less attention by this approach. It has considerable effects on segmentation accuracy. This paper proposes a new kernel-based fuzzy level set algorithm by an integrative approach to deal with this problem. It can directly evolve from the initial level set obtained by Gaussian Kernel-Based Fuzzy C-Means (GKFCM. The controlling parameters of level set evolution are also estimated from the results of GKFCM. Moreover the proposed algorithm is enhanced with locally regularized evolution based on an image model that describes the composition of real-world images, in which intensity inhomogeneity is assumed as a component of an image. Such improvements make level set manipulation easier and lead to more robust segmentation in intensity inhomogeneity. The proposed algorithm has valuable benefits including automation, invariant of intensity inhomogeneity, and high accuracy. Performance evaluation of the proposed algorithm was carried on medical images from different modalities. The results confirm its effectiveness for medical image segmentation.
Rastgarpour, Maryam; Shanbehzadeh, Jamshid
2014-01-01
Researchers recently apply an integrative approach to automate medical image segmentation for benefiting available methods and eliminating their disadvantages. Intensity inhomogeneity is a challenging and open problem in this area, which has received less attention by this approach. It has considerable effects on segmentation accuracy. This paper proposes a new kernel-based fuzzy level set algorithm by an integrative approach to deal with this problem. It can directly evolve from the initial level set obtained by Gaussian Kernel-Based Fuzzy C-Means (GKFCM). The controlling parameters of level set evolution are also estimated from the results of GKFCM. Moreover the proposed algorithm is enhanced with locally regularized evolution based on an image model that describes the composition of real-world images, in which intensity inhomogeneity is assumed as a component of an image. Such improvements make level set manipulation easier and lead to more robust segmentation in intensity inhomogeneity. The proposed algorithm has valuable benefits including automation, invariant of intensity inhomogeneity, and high accuracy. Performance evaluation of the proposed algorithm was carried on medical images from different modalities. The results confirm its effectiveness for medical image segmentation.
Ghosh, Madhumala; Chakraborty, Chandan; Konar, Amit; Ray, Ajoy K
2014-02-01
This paper introduces a hedge operator based fuzzy divergence measure and its application in segmentation of leukocytes in case of chronic myelogenous leukemia using light microscopic images of peripheral blood smears. The concept of modified discrimination measure is applied to develop the measure of divergence based on Shannon exponential entropy and Yager's measure of entropy. These two measures of divergence are compared with the existing literatures and validated by ground truth images. Finally, it is found that hedge operator based divergence measure using Yager's entropy achieves better segmentation accuracy i.e., 98.29% for normal and 98.15% for chronic myelogenous leukocytes. Furthermore, Jaccard index has been performed to compare the segmented image with ground truth ones where it is found that that the proposed scheme leads to higher Jaccard index (0.39 for normal, 0.24 for chronic myelogenous leukemia).
Genetic fuzzy clustering algorithm for point cloud data segmentation%应用遗传模糊聚类实现点云数据区域分割
Institute of Scientific and Technical Information of China (English)
李海伦; 黎荣; 丁国富; 葛源坤
2012-01-01
为了准确地实现点云数据的区域分割,将基于遗传算法的模糊聚类算法应用于逆向工程中的点云数据区域分割中.首先估算出法矢量、高斯曲率和平均曲率,并与坐标一起组成八维特征向量,用加权距离代替欧氏距离,然后通过遗传算法获得全局最优解的近似解；最后将近似解作为模糊聚类的初始解进行迭代,实现点云数据的区域分割,从而避免传统FCM算法的局部性和对初始解的敏感性,减少了迭代次数.以汽车钣金件为例,证明了应用遗传模糊聚类实现点云数据区域分割的有效性,并验证了该方法能快速、准确地实现点云数据的区域分割.%In order to realize point cloud data segmentation accurately, this paper applied genetic fuzzy clustering algorithm to the point cloud data segmentation in reverse engineering. First, it estimated the normal vector, Gaussian curvature and mean curvature, together with the coordinates of the eight-dimensional feature vector component, using weight distance replaced the Euclidean distance. Through the genetic algorithm, it obtained the approximate solution of the global optimal solution. Finally it used the approximate solution as the initial solutions of fuzzy clustering iteration achieved the point cloud data region segmentation , therefore, avoided the locality and sensitiveness of the initial condition of fuzzy clustering algorithm, at the same time, it reduced the number of iterations. Taking car sheet metal for an example proves the validation of genetic fuzzy clustering algorithm applied to the point cloud data segmentation. And point cloud data can be segmented fast and accurately by this algorithm.
Fuzzy Boundary and Fuzzy Semiboundary
Athar, M.; Ahmad, B.
2008-01-01
We present several properties of fuzzy boundary and fuzzy semiboundary which have been supported by examples. Properties of fuzzy semi-interior, fuzzy semiclosure, fuzzy boundary, and fuzzy semiboundary have been obtained in product-related spaces. We give necessary conditions for fuzzy continuous (resp., fuzzy semicontinuous, fuzzy irresolute) functions. Moreover, fuzzy continuous (resp., fuzzy semicontinuous, fuzzy irresolute) functions have been characterized via fuzzy-derived (resp., fuzz...
Mekhmoukh, Abdenour; Mokrani, Karim
2015-11-01
In this paper, a new image segmentation method based on Particle Swarm Optimization (PSO) and outlier rejection combined with level set is proposed. A traditional approach to the segmentation of Magnetic Resonance (MR) images is the Fuzzy C-Means (FCM) clustering algorithm. The membership function of this conventional algorithm is sensitive to the outlier and does not integrate the spatial information in the image. The algorithm is very sensitive to noise and in-homogeneities in the image, moreover, it depends on cluster centers initialization. To improve the outlier rejection and to reduce the noise sensitivity of conventional FCM clustering algorithm, a novel extended FCM algorithm for image segmentation is presented. In general, in the FCM algorithm the initial cluster centers are chosen randomly, with the help of PSO algorithm the clusters centers are chosen optimally. Our algorithm takes also into consideration the spatial neighborhood information. These a priori are used in the cost function to be optimized. For MR images, the resulting fuzzy clustering is used to set the initial level set contour. The results confirm the effectiveness of the proposed algorithm.
Directory of Open Access Journals (Sweden)
Katayoon Ahmadi
2016-01-01
Full Text Available Automatic segmentation of medical CT scan images is one of the most challenging fields in digital image processing. The goal of this paper is to discuss the automatic segmentation of CT scan images to detect and separate vessels in the liver. The segmentation of liver vessels is very important in the liver surgery planning and identifying the structure of vessels and their relationship to tumors. Fuzzy C-means (FCM method has already been proposed for segmentation of liver vessels. Due to classical optimization process, this method suffers lack of sensitivity to the initial values of class centers and segmentation of local minima. In this article, a method based on FCM in conjunction with genetic algorithms (GA is applied for segmentation of liver’s blood vessels. This method was simulated and validated using 20 CT scan images of the liver. The results showed that the accuracy, sensitivity, specificity, and CPU time of new method in comparison with FCM algorithm reaching up to 91%, 83.62, 94.11%, and 27.17 were achieved, respectively. Moreover, selection of optimal and robust parameters in the initial step led to rapid convergence of the proposed method. The outcome of this research assists medical teams in estimating disease progress and selecting proper treatments.
Institute of Scientific and Technical Information of China (English)
韦艳玲
2011-01-01
信用风险是贷款业务中的关键问题.对贷款农户的信用风险进行预测分析是搞好小额农贷业务的关键环节.笔者利用模糊聚类对农户进行软分类,并应用F检验找出合理分类,建立了小额农贷信用风险预测模型.实验表明使用该方法能取得较好的效果,具有良好的应用前景.%Credit risk is a key issue in the bank loans, therefore, the prediction of credit risk of offering loans to farmers is very important in the business of micro agricultural loans. The author adopts the fuzzy clustering to conduct a soft classification of farmers, searches for a reasonable classification by using F test and constructs a model for predictitg the credit risk of micro agricultural loans. The experiment results indicate that the method achieves good results and has bright application prospects.
Cluster Based Text Classification Model
DEFF Research Database (Denmark)
2011-01-01
We propose a cluster based classification model for suspicious email detection and other text classification tasks. The text classification tasks comprise many training examples that require a complex classification model. Using clusters for classification makes the model simpler and increases th...... datasets. Our model also outperforms A Decision Cluster Classification (ADCC) and the Decision Cluster Forest Classification (DCFC) models on the Reuters-21578 dataset....
Balla-Arabé, Souleymane; Gao, Xinbo; Wang, Bin
2013-06-01
In the last decades, due to the development of the parallel programming, the lattice Boltzmann method (LBM) has attracted much attention as a fast alternative approach for solving partial differential equations. In this paper, we first designed an energy functional based on the fuzzy c-means objective function which incorporates the bias field that accounts for the intensity inhomogeneity of the real-world image. Using the gradient descent method, we obtained the corresponding level set equation from which we deduce a fuzzy external force for the LBM solver based on the model by Zhao. The method is fast, robust against noise, independent to the position of the initial contour, effective in the presence of intensity inhomogeneity, highly parallelizable and can detect objects with or without edges. Experiments on medical and real-world images demonstrate the performance of the proposed method in terms of speed and efficiency.
Energy Technology Data Exchange (ETDEWEB)
Wu, Shandong; Weinstein, Susan P.; Conant, Emily F.; Kontos, Despina, E-mail: despina.kontos@uphs.upenn.edu [Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania 19104 (United States)
2013-12-15
Purpose: Breast magnetic resonance imaging (MRI) plays an important role in the clinical management of breast cancer. Studies suggest that the relative amount of fibroglandular (i.e., dense) tissue in the breast as quantified in MR images can be predictive of the risk for developing breast cancer, especially for high-risk women. Automated segmentation of the fibroglandular tissue and volumetric density estimation in breast MRI could therefore be useful for breast cancer risk assessment. Methods: In this work the authors develop and validate a fully automated segmentation algorithm, namely, an atlas-aided fuzzy C-means (FCM-Atlas) method, to estimate the volumetric amount of fibroglandular tissue in breast MRI. The FCM-Atlas is a 2D segmentation method working on a slice-by-slice basis. FCM clustering is first applied to the intensity space of each 2D MR slice to produce an initial voxelwise likelihood map of fibroglandular tissue. Then a prior learned fibroglandular tissue likelihood atlas is incorporated to refine the initial FCM likelihood map to achieve enhanced segmentation, from which the absolute volume of the fibroglandular tissue (|FGT|) and the relative amount (i.e., percentage) of the |FGT| relative to the whole breast volume (FGT%) are computed. The authors' method is evaluated by a representative dataset of 60 3D bilateral breast MRI scans (120 breasts) that span the full breast density range of the American College of Radiology Breast Imaging Reporting and Data System. The automated segmentation is compared to manual segmentation obtained by two experienced breast imaging radiologists. Segmentation performance is assessed by linear regression, Pearson's correlation coefficients, Student's pairedt-test, and Dice's similarity coefficients (DSC). Results: The inter-reader correlation is 0.97 for FGT% and 0.95 for |FGT|. When compared to the average of the two readers’ manual segmentation, the proposed FCM-Atlas method achieves a
GÜNER, Erdal
2007-01-01
Abstract. In this paper, .rstly some fundamental concepts are included re- lating to fuzzy topological spaces. Secondly, the fuzzy connected set is intro- duced. Finally, de.ning fuzzy contractible space, it is shown that X is a fuzzy contractible space if and only if X is fuzzy homotopic equivalent with a fuzzy single-point space.
Azar, Fariba Beiramzadeh Azar
2013-01-01
ABSTRACT: As biomedical image analysis has been improved over the last decades, the widespread advancement of detection/estimation approaches has aided the rapid development of new technologies for monitoring and diagnosis, as well as, treatment of patients. Image segmentation plays a substantial role as part of the preprocessing in various biomedical applications. The segmentation technique is widely used by the radiologists to interpret the input medical image into meaningful data to be use...
Fuzzy Clustering Method for Web User Based on Pages Classification
Institute of Scientific and Technical Information of China (English)
ZHAN Li-qiang; LIU Da-xin
2004-01-01
A new method for Web users fuzzy clustering based on analysis of user interest characteristic is proposed in this article.The method first defines page fuzzy categories according to the links on the index page of the site, then computes fuzzy degree of cross page through aggregating on data of Web log.After that, by using fuzzy comprehensive evaluation method, the method constructs user interest vectors according to page viewing times and frequency of hits, and derives the fuzzy similarity matrix from the interest vectors for the Web users.Finally, it gets the clustering result through the fuzzy clustering method.The experimental results show the effectiveness of the method.
Institute of Scientific and Technical Information of China (English)
王士龙; 徐玉如; 庞永杰
2011-01-01
The S/N of an underwater image is low and has a fuzzy edge. Ifusing traditional methods to process it directly, the result is not satisfying. Though the traditional fuzzy C-means algorithm can sometimes divide the image into object and background, its time-consuming computation is often an obstacle. The mission of the vision system of an autonomous underwater vehicle (AUV) is to rapidly and exactly deal with the information about the object in a complex environment for the AUV to use the obtained result to execute the next task. So,by using the statistical characteristics of the gray image histogram, a fast and effective fuzzy C-means underwater image segmentation algorithm was presented. With the weighted histogram modifying the fuzzy membership, the above algorithm can not only cut down on a large amount of data processing and storage during the computation process compared with the traditional algorithm, so as to speed up the efficiency of the segmentation, but also improve the quality of underwater image segmentation. Finally, particle swarm optimization (PSO) described by the sine function was introduced to the algorithm mentioned above. It made up for the shortcomings that the FCM algorithm can not get the global optimal solution. Thus, on the one hand,it considers the global impact and achieves the local optimal solution, and on the other hand, further greatly increases the computing speed. Experimental results indicate that the novel algorithm can reach a better segmentation quality and the processing time of each image is reduced. They enhance efficiency and satisfy the requirements of a highly effective, real-time AUV.
Image Segmentation with Fuzzy C-means Clustering Based on Image Patch%基于图像片的模糊C均值聚类图像分割
Institute of Scientific and Technical Information of China (English)
顾建伟
2011-01-01
本文提出了一种全新的基于图像片的模糊C均值聚类的图像分割方法.将图像片的思想引入聚类分割中,提出IPFCM方法,用局部的图像片来代替聚类分割中的像素点,从而增大不同类别之间的差异,并对隶属度更新函数进行改造使隶属度函数分布具有单峰值性.实验结果表明,本文方法具有较强的抗噪性和较高的分割精度,图像的隶属度函数与理想隶属度函数十分接近.同时无需过多控制参数,具有较强的可靠性和适应性.另一方面,本文将聚类中心的每一个成员扩展为一个向量,并给出了向量聚类中心的更新公式,为日后将多种图像特征加入FCM对图像进行分割提供了充分的理论基础.
The Research on Strawberry Image Segmentation based on Fuzzy Clustering Analysis%基于模糊聚类分析的草莓图像分割方法研究
Institute of Scientific and Technical Information of China (English)
张红旗; 刘宇; 李海军
2014-01-01
In order to improve the segmentation effect of fruit image in the vision system for strawberry picking machine, the author makes analysis of the theory of common -means clustering segmentation method. In view of the characteristics of strawberry fruit image, fuzzy -means clustering algorithm was introduced into the segmentation algorithm, greatly improving the image segmentation effect of strawberry fruits.%为改善草莓采摘机器视觉系统中果实图像的分割效果，对普通均值聚类的分割方法理论进行分析，针对草莓果实图像的特点将模糊-均值聚类算法引入分割算法，大大改善草莓果实图像的分割效果。
Energy Technology Data Exchange (ETDEWEB)
Ozekes, Serhat; Osman, Onur; Ucan, N. [Istanbul Commerce University, Ragip Gumuspala Cad. No: 84 34378 Eminonu, Istanbul (Turkmenistan)
2008-02-15
The purpose of this study was to develop a new method for automated lung nodule detection in serial section CT images with using the characteristics of the 3D appearance of the nodules that distinguish themselves from the vessels. Lung nodules were detected in four steps. First, to reduce the number of region of interests (ROIs) and the computation time, the lung regions of the CTs were segmented using Genetic Cellular Neural Networks (G-CNN). Then, for each lung region, ROIs were specified with using the 8 directional search; +1 or -1 values were assigned to each voxel. The 3D ROI image was obtained by combining all the 2-Dimensional (2D) ROI images. A 3D template was created to find the nodule-like structures on the 3D ROI image. Convolution of the 3D ROI image with the proposed template strengthens the shapes that are similar to those of the template and it weakens the other ones. Finally, fuzzy rule based thresholding was applied and the ROI's were found. To test the system's efficiency, we used 16 cases with a total of 425 slices, which were taken from the Lung Image Database Consortium (LIDC) dataset. The computer aided diagnosis (CAD) system achieved 100% sensitivity with 13.375 FPs per case when the nodule thickness was greater than or equal to 5.625 mm. Our results indicate that the detection performance of our algorithm is satisfactory, and this may well improve the performance of computer aided detection of lung nodules.
Erwin; Saparudin; Fachrurrozi, Muhammad
2017-04-01
Ogan Komering Ilir (OKI) is located at the eastern of South Sumatra Province, 2030‧-4015‧ latitude and 104020‧-106000‧ longitude. Digital image of land was captured from Landsat 8 satellite path 124/row 062. Landsat 8 is new generation satellite which has two sensors, Operation Land Manager (OLI) and Thermal Infra-Red Sensor (TIRS). In pre-processing step, there are a geometric correction, radiometric correction, and cropping of the digital images which resulting coordinated geography. Classification uses maximum likelihood estimator algorithm. In segmentation process and classification, grey value spread is into evenly after applying histogram technique. The results of entropy value are7.42 which is the highest of result image classification, then the smallest entropy value in the result of correction mapping are 6.39. The three of them prove that they have enough high entropy value. Then the result of peatlands classification is given overall accuracy value = = 94.0012% and overall kappa value = 0.9230 so the result of classification can be considered to be right.
Fuzzy Set Field and Fuzzy Metric
Gebru Gebray; B. Krishna Reddy
2014-01-01
The notation of fuzzy set field is introduced. A fuzzy metric is redefined on fuzzy set field and on arbitrary fuzzy set in a field. The metric redefined is between fuzzy points and constitutes both fuzziness and crisp property of vector. In addition, a fuzzy magnitude of a fuzzy point in a field is defined.
SEQUENTIAL CLUSTERING-BASED EVENT DETECTION FOR NONINTRUSIVE LOAD MONITORING
Directory of Open Access Journals (Sweden)
Karim Said Barsim
2016-01-01
Full Text Available The problem of change-point detection has been well studied and adopted in many signal processing applications. In such applications, the informative segments of the signal are the stationary ones before and after the change-point. However, for some novel signal processing and machine learning applications such as Non-Intrusive Load Monitoring (NILM, the information contained in the non-stationary transient intervals is of equal or even more importance to the recognition process. In this paper, we introduce a novel clustering-based sequential detection of abrupt changes in an aggregate electricity consumption profile with accurate decomposition of the input signal into stationary and non-stationary segments. We also introduce various event models in the context of clustering analysis. The proposed algorithm is applied to building-level energy profiles with promising results for the residential BLUED power dataset.
Institute of Scientific and Technical Information of China (English)
王孟磊
2014-01-01
文章采用模糊聚类算法，根据顾客的月消费水平隶属度进行百货企业的市场细分，提出基于ABC控制法的顾客目标市场定位，为百货企业提供决策依据。%This paper uses fuzzy clustering algorithm for market segmentation of general merchandise enterprise based on customer's monthly consumption level and proposes thecustomer target market location based on ABC Control Act to provide the basis for decision-making of general merchandise enterprise.
FUZZY ECCENTRICITY AND GROSS ERROR IDENTIFICATION
Institute of Scientific and Technical Information of China (English)
无
2006-01-01
The dominant and recessive effect made by exceptional interferer is analyzed in measurement system based on responsive character, and the gross error model of fuzzy clustering based on fuzzy relation and fuzzy equipollence relation is built. The concept and calculate formula of fuzzy eccentricity are defined to deduce the evaluation rule and function of gross error, on the base of them, a fuzzy clustering method of separating and discriminating the gross error is found. Utilized in the dynamic circular division measurement system, the method can identify and eliminate gross error in measured data, and reduce measured data dispersity. Experimental results indicate that the use of the method and model enables repetitive precision of the system to improve 80% higher than the foregoing system, to reach 3.5 s, and angle measurement error is less than 7 s.
模糊分段光滑图像分割模型及其快速算法%Fuzzy piecewise smooth image segmentation model and a fast algorithm
Institute of Scientific and Technical Information of China (English)
赵在新; 成礼智
2011-01-01
灰度分布不均图像是图像分割中一个难点,为此提出一种模糊分段光滑(FPS)图像分割模型.借鉴分段光滑Mumford-Shah(MS)模型与模糊聚类思想,新模型通过两个定义在图像域的光滑函数描述区域特征,并利用模糊隶属度函数代替MS模型中的特征函数.同时,边界检测算子的引入能够有效保护图像中的边界信息.数值求解采用分裂Bregman方法与Gauss-Seidel迭代相结合的快速算法.对合成图像以及真实图像分割实验表明,本文算法能够有效分割灰度分布不均图像,同时具有较高的计算效率.%A fuzzy piecewise smooth (FPS) model is proposed aiming at the intensity- inhomogeneous image segmentation. Motivated by piecewise smooth Munford-Shah (MS) model and fuzzy clustering,two smooth functions were used to represent the region characteristics respectively and a fuzzy membership function was adopted to replace the hard membership function of MS model. An edge detection operator was also incorporated into the minimization ernergy function. The new energy is convex for the membership function,and the final segmentation does not depend on the initial contour. For numerical computation, a fast algorithm based on split Bregman method and Gauss-Seidel iteration was employed. Experimental results for synthetic and real images show desirable performance of the proposed method.
2010-05-01
the world of logic than friction in mechanics. — Charles Sanders Peirce 1 Rational deterrence theory rests on the foundation that...4 Kosko, Fuzzy Thinking, 4-17. 5 Daniel McNeill and Paul Freiberger, Fuzzy Logic: The Revolutionary Computer Technology That Is Changing Our...1 McNeill and Freiberger, Fuzzy Logic, 174. 2 Yarger, Little Book on Big Strategy, 16. 3 Mukaidono, Fuzzy Logic for
Cluster-based adaptive metric classification
Giotis, Ioannis; Petkov, Nicolai
2012-01-01
Introducing adaptive metric has been shown to improve the results of distance-based classification algorithms. Existing methods are often computationally intensive, either in the training or in the classification phase. We present a novel algorithm that we call Cluster-Based Adaptive Metric (CLAM) c
Cluster-based adaptive metric classification
Giotis, Ioannis; Petkov, Nicolai
2012-01-01
Introducing adaptive metric has been shown to improve the results of distance-based classification algorithms. Existing methods are often computationally intensive, either in the training or in the classification phase. We present a novel algorithm that we call Cluster-Based Adaptive Metric (CLAM)
Directory of Open Access Journals (Sweden)
Guoying Liu
2015-01-01
Full Text Available This paper presents a variation of the fuzzy local information c-means clustering (FLICM algorithm that provides color texture image clustering. The proposed algorithm incorporates region-level spatial, spectral, and structural information in a novel fuzzy way. The new algorithm, called RFLICM, combines FLICM and region-level Markov random field model (RMRF together to make use of large scale interactions between image patches instead of pixels. RFLICM can overcome the weakness of FLICM when dealing with textured images and at the same time enhances the clustering performance. The major characteristic of RFLICM is the use of a region-level fuzzy factor, aiming to guarantee texture homogeneity and preserve region boundaries. Experiments performed on synthetic and remote sensing images show that RFLICM is effective in providing accuracy to color texture images.
改进的模糊聚类遥感影像分割%Improved Fuzzy Clustering Image Segmentation Algorithm for Remote Sensing Images
Institute of Scientific and Technical Information of China (English)
高国勇
2015-01-01
聚类是数据挖掘的重要分支之一，引入模糊理论的模糊聚类分析为显示数据提供了模糊处理能力，在许多领域被广泛应用。本文应用考虑邻域关系的约束模糊C均值（ Fuzzy C-Means with Constrains ， FCM＿S）算法，将邻域像素引入到目标函数中，进而有效地利用邻域像素信息，提高分割精度。本文应用FCM＿S算法对模拟彩色纹理图像进行分割，计算其混淆矩阵，定性定量地与FCM算法进行对比分析，证明了该算法的鲁棒性。%Clustering is an important branch of data mining , fuzzy theory of fuzzy clustering analysis provides a fuzzy display data pro-cessing capability , is widely used in many fields .In the paper , considering constraints neighborhood relations Fuzzy C -means ( Fuzz-y C-means with Constrains , FCM_S) algorithm will be introduced to the neighboring pixels objective function , thus the effective use of field-pixel information to improve classification accuracy .In this paper , FCM_S algorithm to simulate color texture image classifi-cation, calculate the confusion matrix , qualitative and quantitative comparison with FCM algorithm analysis to prove the robustness of algorithm.
Fuzzy Cores and Fuzzy Balancedness
van Gulick, G.; Norde, H.W.
2011-01-01
We study the relation between the fuzzy core and balancedness for fuzzy games. For regular games, this relation has been studied by Bondareva (1963) and Shapley (1967). First, we gain insight in this relation when we analyse situations where the fuzzy game is continuous. Our main result shows that a
Fuzzy Cores and Fuzzy Balancedness
van Gulick, G.; Norde, H.W.
2011-01-01
We study the relation between the fuzzy core and balancedness for fuzzy games. For regular games, this relation has been studied by Bondareva (1963) and Shapley (1967). First, we gain insight in this relation when we analyse situations where the fuzzy game is continuous. Our main result shows that
Fuzzy Ideals and Fuzzy Distributive Lattices%Fuzzy Ideals and Fuzzy Distributive Lattices*
Institute of Scientific and Technical Information of China (English)
S.H.Dhanani; Y. S. Pawar
2011-01-01
Our main objective is to study properties of a fuzzy ideals (fuzzy dual ideals). A study of special types of fuzzy ideals (fuzzy dual ideals) is also furnished. Some properties of a fuzzy ideals (fuzzy dual ideals) are furnished. Properties of a fuzzy lattice homomorphism are discussed. Fuzzy ideal lattice of a fuzzy lattice is defined and discussed. Some results in fuzzy distributive lattice are proved.
On Fuzzy Simplex and Fuzzy Convex Hull
Institute of Scientific and Technical Information of China (English)
Dong QIU; Wei Quan ZHANG
2011-01-01
In this paper,we discuss fuzzy simplex and fuzzy convex hull,and give several representation theorems for fuzzy simplex and fuzzy convex hull.In addition,by giving a new characterization theorem of fuzzy convex hull,we improve some known results about fuzzy convex hull.
The Fuzzy Set by Fuzzy Interval
Dr.Pranita Goswami
2011-01-01
Fuzzy set by Fuzzy interval is atriangular fuzzy number lying between the two specified limits. The limits to be not greater than 2 and less than -2 by fuzzy interval have been discussed in this paper. Through fuzzy interval we arrived at exactness which is a fuzzymeasure and fuzzy integral
On the Splitting Algorithm Based on Multi-target Model for Image Segmentation
National Research Council Canada - National Science Library
Yuezhongyi Sun
2014-01-01
.... The model uses a sparse regularization method to maintain the boundaries of segmented regions, to overcome the disadvantages of segmentation fuzzy boundaries resulting from total variation regularization...
Automated leukocyte recognition using fuzzy divergence.
Ghosh, Madhumala; Das, Devkumar; Chakraborty, Chandan; Ray, Ajoy K
2010-10-01
This paper aims at introducing an automated approach to leukocyte recognition using fuzzy divergence and modified thresholding techniques. The recognition is done through the segmentation of nuclei where Gamma, Gaussian and Cauchy type of fuzzy membership functions are studied for the image pixels. It is in fact found that Cauchy leads better segmentation as compared to others. In addition, image thresholding is modified for better recognition. Results are studied and discussed.
Institute of Scientific and Technical Information of China (English)
张虎重; 康志伟
2011-01-01
Variational level set and fuzzy clustering both have the characteristic that extracts the target objects by minimizing the objective function in image segmentation. This paper presents a method which integrates with the advantages of two methods. The new method establishes a variational level set energy function model by using the subordinate property of the membership function in fuzzy clustering. The extraction of target object is implemented by Partial Difference Equation (PDE) derived from minimizing the energy functional. The results show that the rationality and effectiveness of the proposed model are demonstrated by the good segmentation quality and efficiency.%针对在图像分割中变分水平集与模糊聚类都具有通过最小化目标函数来提取目标物体这一特征,提出了汲取两种方法优势、实现融合的新方法.该方法利用模糊聚类中隶属度函数的从属性质,建立了新的变分水平集能量函数模型,通过极小化能量泛函,获得了水平集函数演化的偏微分方程,实现了目标物体的提取.实验结果表明,该方法具有良好的分割质量与分割效率,验证了新模型的合理性与有效性.
Combinational reasoning of quantitative fuzzy topological relations for simple fuzzy regions.
Liu, Bo; Li, Dajun; Xia, Yuanping; Ruan, Jian; Xu, Lili; Wu, Huanyi
2015-01-01
In recent years, formalization and reasoning of topological relations have become a hot topic as a means to generate knowledge about the relations between spatial objects at the conceptual and geometrical levels. These mechanisms have been widely used in spatial data query, spatial data mining, evaluation of equivalence and similarity in a spatial scene, as well as for consistency assessment of the topological relations of multi-resolution spatial databases. The concept of computational fuzzy topological space is applied to simple fuzzy regions to efficiently and more accurately solve fuzzy topological relations. Thus, extending the existing research and improving upon the previous work, this paper presents a new method to describe fuzzy topological relations between simple spatial regions in Geographic Information Sciences (GIS) and Artificial Intelligence (AI). Firstly, we propose a new definition for simple fuzzy line segments and simple fuzzy regions based on the computational fuzzy topology. And then, based on the new definitions, we also propose a new combinational reasoning method to compute the topological relations between simple fuzzy regions, moreover, this study has discovered that there are (1) 23 different topological relations between a simple crisp region and a simple fuzzy region; (2) 152 different topological relations between two simple fuzzy regions. In the end, we have discussed some examples to demonstrate the validity of the new method, through comparisons with existing fuzzy models, we showed that the proposed method can compute more than the existing models, as it is more expressive than the existing fuzzy models.
Combinational reasoning of quantitative fuzzy topological relations for simple fuzzy regions.
Directory of Open Access Journals (Sweden)
Bo Liu
Full Text Available In recent years, formalization and reasoning of topological relations have become a hot topic as a means to generate knowledge about the relations between spatial objects at the conceptual and geometrical levels. These mechanisms have been widely used in spatial data query, spatial data mining, evaluation of equivalence and similarity in a spatial scene, as well as for consistency assessment of the topological relations of multi-resolution spatial databases. The concept of computational fuzzy topological space is applied to simple fuzzy regions to efficiently and more accurately solve fuzzy topological relations. Thus, extending the existing research and improving upon the previous work, this paper presents a new method to describe fuzzy topological relations between simple spatial regions in Geographic Information Sciences (GIS and Artificial Intelligence (AI. Firstly, we propose a new definition for simple fuzzy line segments and simple fuzzy regions based on the computational fuzzy topology. And then, based on the new definitions, we also propose a new combinational reasoning method to compute the topological relations between simple fuzzy regions, moreover, this study has discovered that there are (1 23 different topological relations between a simple crisp region and a simple fuzzy region; (2 152 different topological relations between two simple fuzzy regions. In the end, we have discussed some examples to demonstrate the validity of the new method, through comparisons with existing fuzzy models, we showed that the proposed method can compute more than the existing models, as it is more expressive than the existing fuzzy models.
Fuzzy Riesz subspaces, fuzzy ideals, fuzzy bands and fuzzy band projections
Hong Liang
2015-01-01
Fuzzy ordered linear spaces, Riesz spaces, fuzzy Archimedean spaces and $\\sigma$-complete fuzzy Riesz spaces were defined and studied in several works. Following the efforts along this line, we define fuzzy Riesz subspaces, fuzzy ideals, fuzzy bands and fuzzy band projections and establish their fundamental properties.
Statistical Images Segmentation
Directory of Open Access Journals (Sweden)
Corina Curilă
2008-05-01
Full Text Available This paper deals with fuzzy statistical imagesegmentation. We introduce a new hierarchicalMarkovian fuzzy hidden field model, which extends to thefuzzy case the classical Pérez and Heitz hard model. Twofuzzy statistical segmentation methods related with themodel proposed are defined in this paper and we show viasimulations that they are competitive with, in some casesthan, the classical Maximum Posterior Mode (MPMbased methods. Furthermore, they are faster, which willshould facilitate extensions to more than two hard classesin future work. In addition, the model proposed isapplicable to the multiscale segmentation andmultiresolution images fusion problems.
Directory of Open Access Journals (Sweden)
Abdul Hameed Q. A. Al-Tai
2011-01-01
Full Text Available The aim of this paper is to introduce and study the fuzzy neighborhood, the limit fuzzy number, the convergent fuzzy sequence, the bounded fuzzy sequence, and the Cauchy fuzzy sequence on the base which is adopted by Abdul Hameed (every real number r is replaced by a fuzzy number r¯ (either triangular fuzzy number or singleton fuzzy set (fuzzy point. And then, we will consider that some results respect effect of the upper sequence on the convergent fuzzy sequence, the bounded fuzzy sequence, and the Cauchy fuzzy sequence.
Cluster based Intrusion Detection System for Manets
Directory of Open Access Journals (Sweden)
Nisha Dang
2012-07-01
Full Text Available Manets are the ad hoc networks that are build on demand or instantly when some mobile nodes come in the mobility range of each other and decide to cooperate for data transfer and communication. Therefore there is no defined topology for Manets. They communicate in dynamic topology which continuously changes as nodes are not stable. Due to this lack of infrastructure and distributed nature they are more vulnerable for attacks and provide a good scope to malicious users to become part of the network. To prevent the security of mobile ad hoc networks many security measures are designed such as encryption algorithms, firewalls etc. But still there is some scope of malicious actions. So, Intrusion detection systems are proposed to detect any intruder in the network and its malicious activities. Cluster based intrusion detection system is also designed to restrict the intruders activities in clusters of mobile nodes. In clusters each node run some intrusion detection code to detect local as well as global intrusion. In this paper we have taken insight of intrusion detection systems and different attacks on Manet security. Then we proposed how overhead involved in cluster based intrusion detection system can be reduced.
Zadeh, Lofti A.
1988-01-01
The author presents a condensed exposition of some basic ideas underlying fuzzy logic and describes some representative applications. The discussion covers basic principles; meaning representation and inference; basic rules of inference; and the linguistic variable and its application to fuzzy control.
DEFF Research Database (Denmark)
Anker, Thomas Boysen; Kappel, Klemens; Eadie, Douglas
2012-01-01
This article clarifies the commonplace assumption that brands make promises by developing definitions of brand promise delivery. Distinguishing between clear and fuzzy brand promises, we develop definitions of what it is for a brand to deliver on fuzzy functional, symbolic, and experiential...
Energy Technology Data Exchange (ETDEWEB)
Khateri, Parisa; Rad, Hamidreza Saligheh [Department of Medical Physics and Biomedical Engineering, Tehran University of Medical Sciences, Tehran (Iran, Islamic Republic of); Research Center for Molecular and Cellular Imaging, Tehran University of Medical Sciences, Tehran (Iran, Islamic Republic of); Jafari, Amir Homayoun [Department of Medical Physics and Biomedical Engineering, Tehran University of Medical Sciences, Tehran (Iran, Islamic Republic of); Ay, Mohammad Reza, E-mail: mohammadreza_ay@tums.ac.ir [Department of Medical Physics and Biomedical Engineering, Tehran University of Medical Sciences, Tehran (Iran, Islamic Republic of); Research Center for Molecular and Cellular Imaging, Tehran University of Medical Sciences, Tehran (Iran, Islamic Republic of)
2014-01-11
Quantitative PET image reconstruction requires an accurate map of attenuation coefficients of the tissue under investigation at 511 keV (μ-map), and in order to correct the emission data for attenuation. The use of MRI-based attenuation correction (MRAC) has recently received lots of attention in the scientific literature. One of the major difficulties facing MRAC has been observed in the areas where bone and air collide, e.g. ethmoidal sinuses in the head area. Bone is intrinsically not detectable by conventional MRI, making it difficult to distinguish air from bone. Therefore, development of more versatile MR sequences to label the bone structure, e.g. ultra-short echo-time (UTE) sequences, certainly plays a significant role in novel methodological developments. However, long acquisition time and complexity of UTE sequences limit its clinical applications. To overcome this problem, we developed a novel combination of Short-TE (ShTE) pulse sequence to detect bone signal with a 2-point Dixon technique for water–fat discrimination, along with a robust image segmentation method based on fuzzy clustering C-means (FCM) to segment the head area into four classes of air, bone, soft tissue and adipose tissue. The imaging protocol was set on a clinical 3 T Tim Trio and also 1.5 T Avanto (Siemens Medical Solution, Erlangen, Germany) employing a triple echo time pulse sequence in the head area. The acquisition parameters were as follows: TE1/TE2/TE3=0.98/4.925/6.155 ms, TR=8 ms, FA=25 on the 3 T system, and TE1/TE2/TE3=1.1/2.38/4.76 ms, TR=16 ms, FA=18 for the 1.5 T system. The second and third echo-times belonged to the Dixon decomposition to distinguish soft and adipose tissues. To quantify accuracy, sensitivity and specificity of the bone segmentation algorithm, resulting classes of MR-based segmented bone were compared with the manual segmented one by our expert neuro-radiologist. Results for both 3 T and 1.5 T systems show that bone segmentation applied in several
Khateri, Parisa; Rad, Hamidreza Saligheh; Jafari, Amir Homayoun; Ay, Mohammad Reza
2014-01-01
Quantitative PET image reconstruction requires an accurate map of attenuation coefficients of the tissue under investigation at 511 keV (μ-map), and in order to correct the emission data for attenuation. The use of MRI-based attenuation correction (MRAC) has recently received lots of attention in the scientific literature. One of the major difficulties facing MRAC has been observed in the areas where bone and air collide, e.g. ethmoidal sinuses in the head area. Bone is intrinsically not detectable by conventional MRI, making it difficult to distinguish air from bone. Therefore, development of more versatile MR sequences to label the bone structure, e.g. ultra-short echo-time (UTE) sequences, certainly plays a significant role in novel methodological developments. However, long acquisition time and complexity of UTE sequences limit its clinical applications. To overcome this problem, we developed a novel combination of Short-TE (ShTE) pulse sequence to detect bone signal with a 2-point Dixon technique for water-fat discrimination, along with a robust image segmentation method based on fuzzy clustering C-means (FCM) to segment the head area into four classes of air, bone, soft tissue and adipose tissue. The imaging protocol was set on a clinical 3 T Tim Trio and also 1.5 T Avanto (Siemens Medical Solution, Erlangen, Germany) employing a triple echo time pulse sequence in the head area. The acquisition parameters were as follows: TE1/TE2/TE3=0.98/4.925/6.155 ms, TR=8 ms, FA=25 on the 3 T system, and TE1/TE2/TE3=1.1/2.38/4.76 ms, TR=16 ms, FA=18 for the 1.5 T system. The second and third echo-times belonged to the Dixon decomposition to distinguish soft and adipose tissues. To quantify accuracy, sensitivity and specificity of the bone segmentation algorithm, resulting classes of MR-based segmented bone were compared with the manual segmented one by our expert neuro-radiologist. Results for both 3 T and 1.5 T systems show that bone segmentation applied in several
Cosmological Constraints with Clustering-Based Redshifts
Kovetz, Ely D; Rahman, Mubdi
2016-01-01
We demonstrate that observations lacking reliable redshift information, such as photometric and radio continuum surveys, can produce robust measurements of cosmological parameters when empowered by clustering-based redshift estimation. This method infers the redshift distribution based on the spatial clustering of sources, using cross-correlation with a reference dataset with known redshifts. Applying this method to the existing SDSS photometric galaxies, and projecting to future radio continuum surveys, we show that sources can be efficiently divided into several redshift bins, increasing their ability to constrain cosmological parameters. We forecast constraints on the dark-energy equation-of-state and on local non-gaussianity parameters. We explore several pertinent issues, including the tradeoff between including more sources versus minimizing the overlap between bins, the shot-noise limitations on binning, and the predicted performance of the method at high redshifts. Remarkably, we find that, once this ...
A Cluster- Based Secure Active Network Environment
Institute of Scientific and Technical Information of China (English)
CHEN Xiao-lin; ZHOU Jing-yang; DAI Han; LU Sang-lu; CHEN Gui-hai
2005-01-01
We introduce a cluster-based secure active network environment (CSANE) which separates the processing of IP packets from that of active packets in active routers. In this environment, the active code authorized or trusted by privileged users is executed in the secure execution environment (EE) of the active router, while others are executed in the secure EE of the nodes in the distributed shared memory (DSM) cluster. With the supports of a multi-process Java virtual machine and KeyNote, untrusted active packets are controlled to securely consume resource. The DSM consistency management makes that active packets can be parallelly processed in the DSM cluster as if they were processed one by one in ANTS (Active Network Transport System). We demonstrate that CSANE has good security and scalability, but imposing little changes on traditional routers.
Cluster-based control of nonlinear dynamics
Kaiser, Eurika; Spohn, Andreas; Cattafesta, Louis N; Morzynski, Marek
2016-01-01
The ability to manipulate and control fluid flows is of great importance in many scientific and engineering applications. Here, a cluster-based control framework is proposed to determine optimal control laws with respect to a cost function for unsteady flows. The proposed methodology frames high-dimensional, nonlinear dynamics into low-dimensional, probabilistic, linear dynamics which considerably simplifies the optimal control problem while preserving nonlinear actuation mechanisms. The data-driven approach builds upon a state space discretization using a clustering algorithm which groups kinematically similar flow states into a low number of clusters. The temporal evolution of the probability distribution on this set of clusters is then described by a Markov model. The Markov model can be used as predictor for the ergodic probability distribution for a particular control law. This probability distribution approximates the long-term behavior of the original system on which basis the optimal control law is de...
Cluster-based exposure variation analysis.
Samani, Afshin; Mathiassen, Svend Erik; Madeleine, Pascal
2013-04-04
Static posture, repetitive movements and lack of physical variation are known risk factors for work-related musculoskeletal disorders, and thus needs to be properly assessed in occupational studies. The aims of this study were (i) to investigate the effectiveness of a conventional exposure variation analysis (EVA) in discriminating exposure time lines and (ii) to compare it with a new cluster-based method for analysis of exposure variation. For this purpose, we simulated a repeated cyclic exposure varying within each cycle between "low" and "high" exposure levels in a "near" or "far" range, and with "low" or "high" velocities (exposure change rates). The duration of each cycle was also manipulated by selecting a "small" or "large" standard deviation of the cycle time. Theses parameters reflected three dimensions of exposure variation, i.e. range, frequency and temporal similarity.Each simulation trace included two realizations of 100 concatenated cycles with either low (ρ = 0.1), medium (ρ = 0.5) or high (ρ = 0.9) correlation between the realizations. These traces were analyzed by conventional EVA, and a novel cluster-based EVA (C-EVA). Principal component analysis (PCA) was applied on the marginal distributions of 1) the EVA of each of the realizations (univariate approach), 2) a combination of the EVA of both realizations (multivariate approach) and 3) C-EVA. The least number of principal components describing more than 90% of variability in each case was selected and the projection of marginal distributions along the selected principal component was calculated. A linear classifier was then applied to these projections to discriminate between the simulated exposure patterns, and the accuracy of classified realizations was determined. C-EVA classified exposures more correctly than univariate and multivariate EVA approaches; classification accuracy was 49%, 47% and 52% for EVA (univariate and multivariate), and C-EVA, respectively (p analysis are the advantages
Cluster-Based Context-Aware Routing Protocol for Mobile Environments
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Ahmed. A. A. Gad-ElRab
2015-01-01
Full Text Available Mobile environment has many issues due to mobility, energy limitations and status changing over time. Routing method is an important issue and has a significant impact in mobile networks, whereas selecting the optimum routing path will reduce the wasting in network resources, reduce network overhead and increase network reliability and lifetime. To decide which path will achieve the networks objectives, we need to construct a new routing algorithm that uses context attributes of a mobile device such as available bandwidth, residual energy, connection number and mobility value. In this paper, we propose a new mobile nodes ranking scheme based on the combination of two multi-criteria decision making approaches, the analytic hierarchy process (AHP and the technique for order performance by similarity to ideal solution (TOPSIS in Fuzzy environments. The Fuzzy AHP is used to analyze the structure of the clusterhead selection problem and to determine weights of the criteria, while the Fuzzy TOPSIS method is used to obtain the final mobile node ranking value. By basing on node ranking, we propose a new cluster based routing algorithm select the optimal clusterheads and the best routing path. Our simulation results show that the proposed method increases the network accuracy and lifetime and reduces network overhead.
Encoding spatial images: A fuzzy set theory approach
Sztandera, Leszek M.
1992-01-01
As the use of fuzzy set theory continues to grow, there is an increased need for methodologies and formalisms to manipulate obtained fuzzy subsets. Concepts involving relative position of fuzzy patterns are acknowledged as being of high importance in many areas. In this paper, we present an approach based on the concept of dominance in fuzzy set theory for modelling relative positions among fuzzy subsets of a plane. In particular, we define the following spatial relations: to the left (right), in front of, behind, above, below, near, far from, and touching. This concept has been implemented to define spatial relationships among fuzzy subsets of the image plane. Spatial relationships based on fuzzy set theory, coupled with a fuzzy segmentation, should therefore yield realistic results in scene understanding.
Image Segmentation Based on Support Vector Machine
Institute of Scientific and Technical Information of China (English)
XU Hai-xiang; ZHU Guang-xi; TIAN Jin-wen; ZHANG Xiang; PENG Fu-yuan
2005-01-01
Image segmentation is a necessary step in image analysis. Support vector machine (SVM) approach is proposed to segment images and its segmentation performance is evaluated.Experimental results show that: the effects of kernel function and model parameters on the segmentation performance are significant; SVM approach is less sensitive to noise in image segmentation; The segmentation performance of SVM approach is better than that of back-propagation multi-layer perceptron (BP-MLP) approach and fuzzy c-means (FCM) approach.
Fuzzy Clustering Using the Convex Hull as Geometrical Model
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Luca Liparulo
2015-01-01
Full Text Available A new approach to fuzzy clustering is proposed in this paper. It aims to relax some constraints imposed by known algorithms using a generalized geometrical model for clusters that is based on the convex hull computation. A method is also proposed in order to determine suitable membership functions and hence to represent fuzzy clusters based on the adopted geometrical model. The convex hull is not only used at the end of clustering analysis for the geometric data interpretation but also used during the fuzzy data partitioning within an online sequential procedure in order to calculate the membership function. Consequently, a pure fuzzy clustering algorithm is obtained where clusters are fitted to the data distribution by means of the fuzzy membership of patterns to each cluster. The numerical results reported in the paper show the validity and the efficacy of the proposed approach with respect to other well-known clustering algorithms.
Clustering Based Approximation in Facial Image Retrieval
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R.Pitchaiah
2016-11-01
Full Text Available The web search tool returns a great many pictures positioned by the essential words separated from the encompassing content. Existing article acknowledgment systems to prepare characterization models from human-named preparing pictures or endeavor to deduce the connection/probabilities in the middle of pictures and commented magic words. Albeit proficient in supporting in mining comparatively looking facial picture results utilizing feebly named ones, the learning phase of above bunch based close estimations is shortened with idleness elements for ongoing usage which is fundamentally highlighted in our showings. So we propose to utilize shading based division driven auto face location methodology combined with an adjusted Clustering Based Approximation (CBA plan to decrease the dormancy but then holding same proficiency amid questioning. The specialized phases of our proposed drew closer is highlighted in the accompanying stream diagram. Every phase of the above specialized procedure guarantees the question results at tremendously lessened handling time in this way making our method much achievable for ongoing usage
COOPERATIVE CLUSTERING BASED ON GRID AND DENSITY
Institute of Scientific and Technical Information of China (English)
HU Ruifei; YIN Guofu; TAN Ying; CAI Peng
2006-01-01
Based on the analysis of features of the grid-based clustering method-clustering in quest(CLIQUE) and density-based clustering method-density-based spatial clustering of applications with noise (DBSCAN), a new clustering algorithm named cooperative clustering based on grid and density(CLGRID) is presented. The new algorithm adopts an equivalent rule of regional inquiry and density unit identification. The central region of one class is calculated by the grid-based method and the margin region by a density-based method. By clustering in two phases and using only a small number of seed objects in representative units to expand the cluster, the frequency of region query can be decreased, and consequently the cost of time is reduced. The new algorithm retains positive features of both grid-based and density-based methods and avoids the difficulty of parameter searching. It can discover clusters of arbitrary shape with high efficiency and is not sensitive to noise. The application of CLGRID on test data sets demonstrates its validity and higher efficiency, which contrast with traditional DBSCAN with R* tree.
Defuzzification Strategies for Fuzzy Classifications of Remote Sensing Data
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Peter Hofmann
2016-06-01
Full Text Available The classes in fuzzy classification schemes are defined as fuzzy sets, partitioning the feature space through fuzzy rules, defined by fuzzy membership functions. Applying fuzzy classification schemes in remote sensing allows each pixel or segment to be an incomplete member of more than one class simultaneously, i.e., one that does not fully meet all of the classification criteria for any one of the classes and is member of more than one class simultaneously. This can lead to fuzzy, ambiguous and uncertain class assignation, which is unacceptable for many applications, indicating the need for a reliable defuzzification method. Defuzzification in remote sensing has to date, been performed by “crisp-assigning” each fuzzy-classified pixel or segment to the class for which it best fulfills the fuzzy classification rules, regardless of its classification fuzziness, uncertainty or ambiguity (maximum method. The defuzzification of an uncertain or ambiguous fuzzy classification leads to a more or less reliable crisp classification. In this paper the most common parameters for expressing classification uncertainty, fuzziness and ambiguity are analysed and discussed in terms of their ability to express the reliability of a crisp classification. This is done by means of a typical practical example from Object Based Image Analysis (OBIA.
Nguyen, Hung T
2005-01-01
THE CONCEPT OF FUZZINESS Examples Mathematical modeling Some operations on fuzzy sets Fuzziness as uncertainty Exercises SOME ALGEBRA OF FUZZY SETS Boolean algebras and lattices Equivalence relations and partitions Composing mappings Isomorphisms and homomorphisms Alpha-cuts Images of alpha-level sets Exercises FUZZY QUANTITIES Fuzzy quantities Fuzzy numbers Fuzzy intervals Exercises LOGICAL ASPECTS OF FUZZY SETS Classical two-valued logic A three-valued logic Fuzzy logic Fuzzy and Lukasiewi
Microscopic Halftone Image Segmentation
Institute of Scientific and Technical Information of China (English)
WANG Yong-gang; YANG Jie; DING Yong-sheng
2004-01-01
Microscopic halftone image recognition and analysis can provide quantitative evidence for printing quality control and fault diagnosis of printing devices, while halftone image segmentation is one of the significant steps during the procedure. Automatic segmentation on microscopic dots by the aid of the Fuzzy C-Means (FCM) method that takes account of the fuzziness of halftone image and utilizes its color information adequately is realized. Then some examples show the technique effective and simple with better performance of noise immunity than some usual methods. In addition, the segmentation results obtained by the FCM in different color spaces are compared, which indicates that the method using the FCM in the f1f2f3 color space is superior to the rest.
Fuzzy Set Approximations in Fuzzy Formal Contexts
Institute of Scientific and Technical Information of China (English)
Mingwen Shao; Shiqing Fan
2006-01-01
In this paper, a kind of multi-level formal concept is introduced. Based on the proposed multi-level formal concept, we present a pair of rough fuzzy set approximations within fuzzy formal contexts. By the proposed rough fuzzy set approximations, we can approximate a fuzzy set according to different precision level. We discuss the properties of the proposed approximation operators in detail.
Uncertainties in segmentation and their visualisation
Lucieer, Arko
2004-01-01
This thesis focuses on uncertainties in remotely sensed image segmentation and their visualisation. The first part describes a visualisation tool, allowing interaction with the parameters of a fuzzy classification algorithm by visually adjusting fuzzy membership functions of classes in a 3D feature
Ozekes, Serhat; Osman, Onur; UCAN, Osman N.
2008-01-01
Objective The purpose of this study was to develop a new method for automated lung nodule detection in serial section CT images with using the characteristics of the 3D appearance of the nodules that distinguish themselves from the vessels. Materials and Methods Lung nodules were detected in four steps. First, to reduce the number of region of interests (ROIs) and the computation time, the lung regions of the CTs were segmented using Genetic Cellular Neural Networks (G-CNN). Then, for each lu...
DEFF Research Database (Denmark)
Berks, G.; Keyserlingk, Diedrich Graf von; Jantzen, Jan
2000-01-01
A symptom is a condition indicating the presence of a disease, especially, when regarded as an aid in diagnosis.Symptoms are the smallest units indicating the existence of a disease. A syndrome on the other hand is an aggregate, set or cluster of concurrent symptoms which together indicate...... and clustering are the basic concerns in medicine. Classification depends on definitions of the classes and their required degree of participant of the elements in the cases' symptoms. In medicine imprecise conditions are the rule and therefore fuzzy methods are much more suitable than crisp ones. Fuzzy c......-mean clustering is an easy and well improved tool, which has been applied in many medical fields. We used c-mean fuzzy clustering after feature extraction from an aphasia database. Factor analysis was applied on a correlation matrix of 26 symptoms of language disorders and led to five factors. The factors...
Energy Technology Data Exchange (ETDEWEB)
Mackey, Lester [Department of Statistics, Stanford University,Stanford, CA 94305 (United States); Nachman, Benjamin [Department of Physics, Stanford University,Stanford, CA 94305 (United States); SLAC National Accelerator Laboratory, Stanford University,2575 Sand Hill Rd, Menlo Park, CA 94025 (United States); Schwartzman, Ariel [SLAC National Accelerator Laboratory, Stanford University,2575 Sand Hill Rd, Menlo Park, CA 94025 (United States); Stansbury, Conrad [Department of Physics, Stanford University,Stanford, CA 94305 (United States)
2016-06-01
Collimated streams of particles produced in high energy physics experiments are organized using clustering algorithms to form jets. To construct jets, the experimental collaborations based at the Large Hadron Collider (LHC) primarily use agglomerative hierarchical clustering schemes known as sequential recombination. We propose a new class of algorithms for clustering jets that use infrared and collinear safe mixture models. These new algorithms, known as fuzzy jets, are clustered using maximum likelihood techniques and can dynamically determine various properties of jets like their size. We show that the fuzzy jet size adds additional information to conventional jet tagging variables in boosted topologies. Furthermore, we study the impact of pileup and show that with some slight modifications to the algorithm, fuzzy jets can be stable up to high pileup interaction multiplicities.
Dr.Pranita Goswami
2011-01-01
The Partial Fuzzy Set is a portion of the Fuzzy Set which is again a Fuzzy Set. In the Partial Fuzzy Set the baseline is shifted from 0 to 1 to any of its α cuts . In this paper we have fuzzified a portion of the Fuzzy Set by transformation
Properties of fuzzy hyperplanes
Institute of Scientific and Technical Information of China (English)
ZHANG Zhong; LI Chuandong; WU Deyin
2004-01-01
Some properties of closed fuzzy matroid and those of its hyperplanes are investigated. A fuzzy hyperplane property,which extends the analog of a crisp matroid from crisp set systems to fuzzy set systems, is proved.
Close Clustering Based Automated Color Image Annotation
Garg, Ankit; Asawa, Krishna
2010-01-01
Most image-search approaches today are based on the text based tags associated with the images which are mostly human generated and are subject to various kinds of errors. The results of a query to the image database thus can often be misleading and may not satisfy the requirements of the user. In this work we propose our approach to automate this tagging process of images, where image results generated can be fine filtered based on a probabilistic tagging mechanism. We implement a tool which helps to automate the tagging process by maintaining a training database, wherein the system is trained to identify certain set of input images, the results generated from which are used to create a probabilistic tagging mechanism. Given a certain set of segments in an image it calculates the probability of presence of particular keywords. This probability table is further used to generate the candidate tags for input images.
Intuitionistic Fuzzy Cycles and Intuitionistic Fuzzy Trees
Alshehri, N. O.
2014-01-01
Connectivity has an important role in neural networks, computer network, and clustering. In the design of a network, it is important to analyze connections by the levels. The structural properties of intuitionistic fuzzy graphs provide a tool that allows for the solution of operations research problems. In this paper, we introduce various types of intuitionistic fuzzy bridges, intuitionistic fuzzy cut vertices, intuitionistic fuzzy cycles, and intuitionistic fuzzy trees in intuitionistic fuzzy graphs and investigate some of their interesting properties. Most of these various types are defined in terms of levels. We also describe comparison of these types. PMID:24701155
Fuzzy Clustering Validity for Spatial Data%空间数据模糊聚类的有效性
Institute of Scientific and Technical Information of China (English)
胡春春; 孟令奎; 史文中
2008-01-01
The validity measurement of fuzzy clustering is a key problem. If clustering is formed, it needs a kind of machine to verify its validity. To make mining more accountable, comprehensible and with a usable spatial pattern, it is necessary to first detect whether the data set has a clustered structure or not before clustering. This paper discusses a detection method for clustered patterns and a fuzzy clustering algorithm, and studies the validity function of the result produced by fuzzy clustering based on two aspects, which reflect the uncertainty of classification during fuzzy partition and spatial location features of spatial data, and proposes a new validity function of fuzzy clustering for spatial data. The experimental result indicates that the new validity function can accurately measure the validity of the results of fuzzy clustering. Especially, for the result of fuzzy clustering of spatial data, it is robust and its classification result is better when compared to other indices.
Some Additions to the Fuzzy Convergent and Fuzzy Bounded Sequence Spaces of Fuzzy Numbers
Şengönül, M.; Z. Zararsız
2011-01-01
Some properties of the fuzzy convergence and fuzzy boundedness of a sequence of fuzzy numbers were studied in Choi (1996). In this paper, we have consider, some important problems on these spaces and shown that these spaces are fuzzy complete module spaces. Also, the fuzzy α-, fuzzy β-, and fuzzy γ-duals of the fuzzy module spaces of fuzzy numbers have been computeded, and some matrix transformations are given.
Chen, Guanrong
2005-01-01
Introduction to Fuzzy Systems provides students with a self-contained introduction that requires no preliminary knowledge of fuzzy mathematics and fuzzy control systems theory. Simplified and readily accessible, it encourages both classroom and self-directed learners to build a solid foundation in fuzzy systems. After introducing the subject, the authors move directly into presenting real-world applications of fuzzy logic, revealing its practical flavor. This practicality is then followed by basic fuzzy systems theory. The book also offers a tutorial on fuzzy control theory, based mainly on th
Fuzziness in Chang's fuzzy topological spaces
1999-01-01
It is known that fuzziness within the concept of openness of a fuzzy set in a Chang's fuzzy topological space (fts) is absent. In this paper we introduce a gradation of openness for the open sets of a Chang jts (X, $\\mathcal{T}$) by means of a map $\\sigma\\;:\\; I^{x}\\longrightarrow I\\left(I=\\left[0,1\\right]\\right)$, which is at the same time a fuzzy topology on X in Shostak 's sense. Then, we will be able to avoid the fuzzy point concept, and to introduce an adeguate theory f...
Representation Theorems for Fuzzy Random Sets and Fuzzy Stochastic Processes
Institute of Scientific and Technical Information of China (English)
无
1999-01-01
The fuzzy static and dynamic random phenomena in an abstract separable Banach space is discussed in this paper. The representation theorems for fuzzy set-valued random sets, fuzzy random elements and fuzzy set-valued stochastic processes are obtained.
Kosko, Bart
1991-01-01
Mappings between fuzzy cubes are discussed. This level of abstraction provides a surprising and fruitful alternative to the propositional and predicate-calculas reasoning techniques used in expert systems. It allows one to reason with sets instead of propositions. Discussed here are fuzzy and neural function estimators, neural vs. fuzzy representation of structured knowledge, fuzzy vector-matrix multiplication, and fuzzy associative memory (FAM) system architecture.
Directory of Open Access Journals (Sweden)
S. Nazmul
2014-03-01
Full Text Available Notions of Lowen type fuzzy soft topological space are introduced and some of their properties are established in the present paper. Besides this, a combined structure of a fuzzy soft topological space and a fuzzy soft group, which is termed here as fuzzy soft topological group is introduced. Homomorphic images and preimages are also examined. Finally, some definitions and results on fuzzy soft set are studied.
Fuzzy Logic Connectivity in Semiconductor Defect Clustering
Energy Technology Data Exchange (ETDEWEB)
Gleason, S.S.; Kamowski, T.P.; Tobin, K.W.
1999-01-24
In joining defects on semiconductor wafer maps into clusters, it is common for defects caused by different sources to overlap. Simple morphological image processing tends to either join too many unrelated defects together or not enough together. Expert semiconductor fabrication engineers have demonstrated that they can easily group clusters of defects from a common manufacturing problem source into a single signature. Capturing this thought process is ideally suited for fuzzy logic. A system of rules was developed to join disconnected clusters based on properties such as elongation, orientation, and distance. The clusters are evaluated on a pair-wise basis using the fuzzy rules and are joined or not joined based on a defuzzification and threshold. The system continuously re-evaluates the clusters under consideration as their fuzzy memberships change with each joining action. The fuzzy membership functions for each pair-wise feature, the techniques used to measure the features, and methods for improving the speed of the system are all developed. Examples of the process are shown using real-world semiconductor wafer maps obtained from chip manufacturers. The algorithm is utilized in the Spatial Signature Analyzer (SSA) software, a joint development project between Oak Ridge National Lab (ORNL) and SEMATECH.
Fuzzy Logic Connectivity in Semiconductor Defect Clustering
Energy Technology Data Exchange (ETDEWEB)
Gleason, S.S.; Kamowski, T.P.; Tobin, K.W.
1999-01-24
In joining defects on semiconductor wafer maps into clusters, it is common for defects caused by different sources to overlap. Simple morphological image processing tends to either join too many unrelated defects together or not enough together. Expert semiconductor fabrication engineers have demonstrated that they can easily group clusters of defects from a common manufacturing problem source into a single signature. Capturing this thought process is ideally suited for fuzzy logic. A system of rules was developed to join disconnected clusters based on properties such as elongation, orientation, and distance. The clusters are evaluated on a pair-wise basis using the fuzzy rules and are joined or not joined based on a defuzzification and threshold. The system continuously re-evaluates the clusters under consideration as their fuzzy memberships change with each joining action. The fuzzy membership functions for each pair-wise feature, the techniques used to measure the features, and methods for improving the speed of the system are all developed. Examples of the process are shown using real-world semiconductor wafer maps obtained from chip manufacturers. The algorithm is utilized in the Spatial Signature Analyzer (SSA) software, a joint development project between Oak Ridge National Lab (ORNL) and SEMATECH.
Intuitionistic supra fuzzy topological spaces
Energy Technology Data Exchange (ETDEWEB)
Abbas, S.E. E-mail: sabbas73@yahoo.com
2004-09-01
In this paper, We introduce an intuitionistic supra fuzzy closure space and investigate the relationship between intuitionistic supra fuzzy topological spaces and intuitionistic supra fuzzy closure spaces. Moreover, we can obtain intuitionistic supra fuzzy topological space induced by an intuitionistic fuzzy bitopological space. We study the relationship between intuitionistic supra fuzzy closure space and the intuitionistic supra fuzzy topological space induced by an intuitionistic fuzzy bitopological space.
Carlsson, Christer; Fullér, Robert
2004-01-01
Fuzzy Logic in Management demonstrates that difficult problems and changes in the management environment can be more easily handled by bringing fuzzy logic into the practice of management. This explicit theme is developed through the book as follows: Chapter 1, "Management and Intelligent Support Technologies", is a short survey of management leadership and what can be gained from support technologies. Chapter 2, "Fuzzy Sets and Fuzzy Logic", provides a short introduction to fuzzy sets, fuzzy relations, the extension principle, fuzzy implications and linguistic variables. Chapter 3, "Group Decision Support Systems", deals with group decision making, and discusses methods for supporting the consensus reaching processes. Chapter 4, "Fuzzy Real Options for Strategic Planning", summarizes research where the fuzzy real options theory was implemented as a series of models. These models were thoroughly tested on a number of real life investments, and validated in 2001. Chapter 5, "Soft Computing Methods for Reducing...
Xu, Zeshui
2014-01-01
This book provides the readers with a thorough and systematic introduction to hesitant fuzzy theory. It presents the most recent research results and advanced methods in the field. These includes: hesitant fuzzy aggregation techniques, hesitant fuzzy preference relations, hesitant fuzzy measures, hesitant fuzzy clustering algorithms and hesitant fuzzy multi-attribute decision making methods. Since its introduction by Torra and Narukawa in 2009, hesitant fuzzy sets have become more and more popular and have been used for a wide range of applications, from decision-making problems to cluster analysis, from medical diagnosis to personnel appraisal and information retrieval. This book offers a comprehensive report on the state-of-the-art in hesitant fuzzy sets theory and applications, aiming at becoming a reference guide for both researchers and practitioners in the area of fuzzy mathematics and other applied research fields (e.g. operations research, information science, management science and engineering) chara...
Train repathing in emergencies based on fuzzy linear programming.
Meng, Xuelei; Cui, Bingmou
2014-01-01
Train pathing is a typical problem which is to assign the train trips on the sets of rail segments, such as rail tracks and links. This paper focuses on the train pathing problem, determining the paths of the train trips in emergencies. We analyze the influencing factors of train pathing, such as transferring cost, running cost, and social adverse effect cost. With the overall consideration of the segment and station capability constraints, we build the fuzzy linear programming model to solve the train pathing problem. We design the fuzzy membership function to describe the fuzzy coefficients. Furthermore, the contraction-expansion factors are introduced to contract or expand the value ranges of the fuzzy coefficients, coping with the uncertainty of the value range of the fuzzy coefficients. We propose a method based on triangular fuzzy coefficient and transfer the train pathing (fuzzy linear programming model) to a determinate linear model to solve the fuzzy linear programming problem. An emergency is supposed based on the real data of the Beijing-Shanghai Railway. The model in this paper was solved and the computation results prove the availability of the model and efficiency of the algorithm.
A fuzzy-clustering analysis based phonetic tied-mixture HMM
Institute of Scientific and Technical Information of China (English)
XU Xianghua; ZHU Jie; GUO Qiang
2005-01-01
To efficiently decrease the size of parameters and improve the robustness of parameters training, a fuzzy clustering based phonetic tied-mixture model, FPTM, is presented.The Gaussian codebook of FPTM is synthesized from Gaussian components belonging to the same root node in phonetic decision tree. Fuzzy clustering method is further used for FPTM covariance sharing. Experimental results show that compared with the conventional PTM with approximately the same parameters size, FPTM decrease the size of Gaussian weights by 77.59% and increases word accuracy by 7.92%, which proves Gaussian fuzzy clustering is efficient. Compared with FPTM, covariance-shared FPTM decreases word error rate by 1.14% , which proves the combined fuzzy clustering for both Gaussian and covariance is superior to Gaussian fuzzy clustering alone.
Dermoscopic Image Segmentation using Machine Learning Algorithm
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L. P. Suresh
2011-01-01
Full Text Available Problem statement: Malignant melanoma is the most frequent type of skin cancer. Its incidence has been rapidly increasing over the last few decades. Medical image segmentation is the most essential and crucial process in order to facilitate the characterization and visualization of the structure of interest in medical images. Approach: This study explains the task of segmenting skin lesions in Dermoscopy images based on intelligent systems such as Fuzzy and Neural Networks clustering techniques for the early diagnosis of Malignant Melanoma. The various intelligent system based clustering techniques used are Fuzzy C Means Algorithm (FCM, Possibilistic C Means Algorithm (PCM, Hierarchical C Means Algorithm (HCM; C-mean based Fuzzy Hopfield Neural Network, Adaline Neural Network and Regression Neural Network. Results: The segmented images are compared with the ground truth image using various parameters such as False Positive Error (FPE, False Negative Error (FNE Coefficient of similarity, spatial overlap and their performance is evaluated. Conclusion: The experimental results show that the Hierarchical C Means algorithm( Fuzzy provides better segmentation than other (Fuzzy C Means, Possibilistic C Means, Adaline Neural Network, FHNN and GRNN clustering algorithms. Thus Hierarchical C Means approach can handle uncertainties that exist in the data efficiently and useful for the lesion segmentation in a computer aided diagnosis system to assist the clinical diagnosis of dermatologists.
A Fuzzy Approach to Classify Learning Disability
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Pooja Manghirmalani
2012-05-01
Full Text Available The endeavor of this work is to support the special education community in their quest to be with the mainstream. The initial segment of the paper gives an exhaustive study of the different mechanisms of diagnosing learning disability. After diagnosis of learning disability the further classification of learning disability that is dyslexia, dysgraphia or dyscalculia are fuzzy. Hence the paper proposes a model based on Fuzzy Expert System which enables the classification of learning disability into its various types. This expert system facilitates in simulating conditions which are otherwise imprecisely defined.
Transformation and entropy for fuzzy rough sets
Institute of Scientific and Technical Information of China (English)
无
2008-01-01
A new method for translating a fuzzy rough set to a fuzzy set is introduced and the fuzzy approximation of a fuzzy rough set is given.The properties of the fuzzy approximation of a fuzzy rough set are studied and a fuzzy entropy measure for fuzzy rough sets is proposed.This measure is consistent with similar considerations for ordinary fuzzy sets and is the result of the fuzzy approximation of fuzzy rough sets.
heuristically improved bayesian segmentation of brain mr images
African Journals Online (AJOL)
Gaussian Bayes classifier is used in proposed method and the expectation maximization (EM) is the ... probability distribution functions of gray values in each tissue are used to segment them .... Fuzzy Measures and Weighted Co-. Occurrence ...
Institute of Scientific and Technical Information of China (English)
刘叙华; 邓安生
1994-01-01
A new approach of operator fuzzy logic, Boolean operator fuzzy logic (BOFL) based on Boolean algebra, is presented. The resolution principle is also introduced into BOFL. BOFL is a natural generalization of classical logic and can be applied to the qualitative description of fuzzy knowledge.
DEFF Research Database (Denmark)
Rodríguez, J. Tinguaro; Franco de los Ríos, Camilo; Gómez, Daniel
2015-01-01
In this paper we want to stress the relevance of paired fuzzy sets, as already proposed in previous works of the authors, as a family of fuzzy sets that offers a unifying view for different models based upon the opposition of two fuzzy sets, simply allowing the existence of different types...
Fuzzy Linguistic Topological Spaces
Kandasamy, W B Vasantha; Amal, K
2012-01-01
This book has five chapters. Chapter one is introductory in nature. Fuzzy linguistic spaces are introduced in chapter two. Fuzzy linguistic vector spaces are introduced in chapter three. Chapter four introduces fuzzy linguistic models. The final chapter suggests over 100 problems and some of them are at research level.
Howard, Ayanna
2005-01-01
The Fuzzy Logic Engine is a software package that enables users to embed fuzzy-logic modules into their application programs. Fuzzy logic is useful as a means of formulating human expert knowledge and translating it into software to solve problems. Fuzzy logic provides flexibility for modeling relationships between input and output information and is distinguished by its robustness with respect to noise and variations in system parameters. In addition, linguistic fuzzy sets and conditional statements allow systems to make decisions based on imprecise and incomplete information. The user of the Fuzzy Logic Engine need not be an expert in fuzzy logic: it suffices to have a basic understanding of how linguistic rules can be applied to the user's problem. The Fuzzy Logic Engine is divided into two modules: (1) a graphical-interface software tool for creating linguistic fuzzy sets and conditional statements and (2) a fuzzy-logic software library for embedding fuzzy processing capability into current application programs. The graphical- interface tool was developed using the Tcl/Tk programming language. The fuzzy-logic software library was written in the C programming language.
Some weakly mappings on intuitionistic fuzzy topological spaces
Zhen-Guo Xu; Fu-Gui Shi
2008-01-01
In this paper, we shall introduce concepts of fuzzy semiopen set, fuzzy semiclosed set, fuzzy semiinterior, fuzzy semiclosure on intuitionistic fuzzy topological space and fuzzy open (fuzzy closed) mapping, fuzzy irresolute mapping, fuzzy irresolute open (closed) mapping, fuzzy semicontinuous mapping and fuzzy semiopen (semiclosed) mapping between two intuitionistic fuzzy topological spaces. Moreover, we shall discuss their some properties.
An Improved FCM Medical Image Segmentation Algorithm Based on MMTD
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Ningning Zhou
2014-01-01
Full Text Available Image segmentation plays an important role in medical image processing. Fuzzy c-means (FCM is one of the popular clustering algorithms for medical image segmentation. But FCM is highly vulnerable to noise due to not considering the spatial information in image segmentation. This paper introduces medium mathematics system which is employed to process fuzzy information for image segmentation. It establishes the medium similarity measure based on the measure of medium truth degree (MMTD and uses the correlation of the pixel and its neighbors to define the medium membership function. An improved FCM medical image segmentation algorithm based on MMTD which takes some spatial features into account is proposed in this paper. The experimental results show that the proposed algorithm is more antinoise than the standard FCM, with more certainty and less fuzziness. This will lead to its practicable and effective applications in medical image segmentation.
Clustering-based redshift estimation: application to VIPERS/CFHTLS
Scottez, V; Granett, B R; Moutard, T; Kilbinger, M; Scodeggio, M; Garilli, B; Bolzonella, M; de la Torre, S; Guzzo, L; Abbas, U; Adami, C; Arnouts, S; Bottini, D; Branchini, E; Cappi, A; Cucciati, O; Davidzon, I; Fritz, A; Franzetti, P; Iovino, A; Krywult, J; Brun, V Le; Fèvre, O Le; Maccagni, D; Małek, K; Marulli, F; Polletta, M; Pollo, A; Tasca, L A M; Tojeiro, R; Vergani, D; Zanichelli, A; Bel, J; Coupon, J; De Lucia, G; Ilbert, O; McCracken, H J; Moscardini, L
2016-01-01
We explore the accuracy of the clustering-based redshift estimation proposed by M\\'enard et al. (2013) when applied to VIPERS and CFHTLS real data. This method enables us to reconstruct redshift distributions from measurement of the angular clus- tering of objects using a set of secure spectroscopic redshifts. We use state of the art spectroscopic measurements with iAB 0.5 which allows us to test the accuracy of the clustering-based red- shift distributions. We show that this method enables us to reproduce the true mean color-redshift relation when both populations have the same magnitude limit. We also show that this technique allows the inference of redshift distributions for a population fainter than the one of reference and we give an estimate of the color-redshift mapping in this case. This last point is of great interest for future large redshift surveys which suffer from the need of a complete faint spectroscopic sample.
Intelligent Hybrid Cluster Based Classification Algorithm for Social Network Analysis
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S. Muthurajkumar
2014-05-01
Full Text Available In this paper, we propose an hybrid clustering based classification algorithm based on mean approach to effectively classify to mine the ordered sequences (paths from weblog data in order to perform social network analysis. In the system proposed in this work for social pattern analysis, the sequences of human activities are typically analyzed by switching behaviors, which are likely to produce overlapping clusters. In this proposed system, a robust Modified Boosting algorithm is proposed to hybrid clustering based classification for clustering the data. This work is useful to provide connection between the aggregated features from the network data and traditional indices used in social network analysis. Experimental results show that the proposed algorithm improves the decision results from data clustering when combined with the proposed classification algorithm and hence it is proved that of provides better classification accuracy when tested with Weblog dataset. In addition, this algorithm improves the predictive performance especially for multiclass datasets which can increases the accuracy.
Countable Fuzzy Topological Space and Countable Fuzzy Topological Vector Space
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Apu Kumar Saha
2015-06-01
Full Text Available This paper deals with countable fuzzy topological spaces, a generalization of the notion of fuzzy topological spaces. A collection of fuzzy sets F on a universe X forms a countable fuzzy topology if in the definition of a fuzzy topology, the condition of arbitrary supremum is relaxed to countable supremum. In this generalized fuzzy structure, the continuity of fuzzy functions and some other related properties are studied. Also the class of countable fuzzy topological vector spaces as a generalization of the class of fuzzy topological vector spaces has been introduced and investigated.
Fast Affinity Propagation Clustering based on Machine Learning
Shailendra Kumar Shrivastava; J. L. Rana; DR.R.C.JAIN
2013-01-01
Affinity propagation (AP) was recently introduced as an un-supervised learning algorithm for exemplar based clustering. In this paper a novel Fast Affinity Propagation clustering Approach based on Machine Learning (FAPML) has been proposed. FAPML tries to put data points into clusters based on the history of the data points belonging to clusters in early stages. In FAPML we introduce affinity learning constant and dispersion constant which supervise the clustering process. FAPML also enforces...
Cluster-based localization and tracking in ubiquitous computing systems
Martínez-de Dios, José Ramiro; Torres-González, Arturo; Ollero, Anibal
2017-01-01
Localization and tracking are key functionalities in ubiquitous computing systems and techniques. In recent years a very high variety of approaches, sensors and techniques for indoor and GPS-denied environments have been developed. This book briefly summarizes the current state of the art in localization and tracking in ubiquitous computing systems focusing on cluster-based schemes. Additionally, existing techniques for measurement integration, node inclusion/exclusion and cluster head selection are also described in this book.
Mathematics of Fuzzy Sets and Fuzzy Logic
Bede, Barnabas
2013-01-01
This book presents a mathematically-based introduction into the fascinating topic of Fuzzy Sets and Fuzzy Logic and might be used as textbook at both undergraduate and graduate levels and also as reference guide for mathematician, scientists or engineers who would like to get an insight into Fuzzy Logic. Fuzzy Sets have been introduced by Lotfi Zadeh in 1965 and since then, they have been used in many applications. As a consequence, there is a vast literature on the practical applications of fuzzy sets, while theory has a more modest coverage. The main purpose of the present book is to reduce this gap by providing a theoretical introduction into Fuzzy Sets based on Mathematical Analysis and Approximation Theory. Well-known applications, as for example fuzzy control, are also discussed in this book and placed on new ground, a theoretical foundation. Moreover, a few advanced chapters and several new results are included. These comprise, among others, a new systematic and constructive approach for fuzzy infer...
How we pass from fuzzy $po$-semigroups to fuzzy $po$-$\\Gamma$-semigroups
Kehayopulu, Niovi
2014-01-01
The results on fuzzy ordered semigroups (or on fuzzy semigroups) can be transferred to fuzzy ordered gamma (or to fuzzy gamma) semigroups. We show the way we pass from fuzzy ordered semigroups to fuzzy ordered gamma semigroups.
Directory of Open Access Journals (Sweden)
Orlov A. I.
2016-05-01
Full Text Available Fuzzy sets are the special form of objects of nonnumeric nature. Therefore, in the processing of the sample, the elements of which are fuzzy sets, a variety of methods for the analysis of statistical data of any nature can be used - the calculation of the average, non-parametric density estimators, construction of diagnostic rules, etc. We have told about the development of our work on the theory of fuzziness (1975 - 2015. In the first of our work on fuzzy sets (1975, the theory of random sets is regarded as a generalization of the theory of fuzzy sets. In non-fiction series "Mathematics. Cybernetics" (publishing house "Knowledge" in 1980 the first book by a Soviet author fuzzy sets is published - our brochure "Optimization problems and fuzzy variables". This book is essentially a "squeeze" our research of 70-ies, ie, the research on the theory of stability and in particular on the statistics of objects of non-numeric nature, with a bias in the methodology. The book includes the main results of the fuzzy theory and its note to the random set theory, as well as new results (first publication! of statistics of fuzzy sets. On the basis of further experience, you can expect that the theory of fuzzy sets will be more actively applied in organizational and economic modeling of industry management processes. We discuss the concept of the average value of a fuzzy set. We have considered a number of statements of problems of testing statistical hypotheses on fuzzy sets. We have also proposed and justified some algorithms for restore relationships between fuzzy variables; we have given the representation of various variants of fuzzy cluster analysis of data and variables and described some methods of collection and description of fuzzy data
On generalized fuzzy strongly semiclosed sets in fuzzy topological spaces
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Oya Bedre Ozbakir
2002-01-01
semiclosed, generalized fuzzy almost-strongly semiclosed, generalized fuzzy strongly closed, and generalized fuzzy almost-strongly closed sets. In the light of these definitions, we also define some generalizations of fuzzy continuous functions and discuss the relations between these new classes of functions and other fuzzy continuous functions.
Seminal Quality Prediction Using Clustering-Based Decision Forests
Directory of Open Access Journals (Sweden)
Hong Wang
2014-08-01
Full Text Available Prediction of seminal quality with statistical learning tools is an emerging methodology in decision support systems in biomedical engineering and is very useful in early diagnosis of seminal patients and selection of semen donors candidates. However, as is common in medical diagnosis, seminal quality prediction faces the class imbalance problem. In this paper, we propose a novel supervised ensemble learning approach, namely Clustering-Based Decision Forests, to tackle unbalanced class learning problem in seminal quality prediction. Experiment results on real fertility diagnosis dataset have shown that Clustering-Based Decision Forests outperforms decision tree, Support Vector Machines, random forests, multilayer perceptron neural networks and logistic regression by a noticeable margin. Clustering-Based Decision Forests can also be used to evaluate variables’ importance and the top five important factors that may affect semen concentration obtained in this study are age, serious trauma, sitting time, the season when the semen sample is produced, and high fevers in the last year. The findings could be helpful in explaining seminal concentration problems in infertile males or pre-screening semen donor candidates.
Kwong, C K; Fung, K Y; Jiang, Huimin; Chan, K Y; Siu, Kin Wai Michael
2013-01-01
Affective design is an important aspect of product development to achieve a competitive edge in the marketplace. A neural-fuzzy network approach has been attempted recently to model customer satisfaction for affective design and it has been proved to be an effective one to deal with the fuzziness and non-linearity of the modeling as well as generate explicit customer satisfaction models. However, such an approach to modeling customer satisfaction has two limitations. First, it is not suitable for the modeling problems which involve a large number of inputs. Second, it cannot adapt to new data sets, given that its structure is fixed once it has been developed. In this paper, a modified dynamic evolving neural-fuzzy approach is proposed to address the above mentioned limitations. A case study on the affective design of mobile phones was conducted to illustrate the effectiveness of the proposed methodology. Validation tests were conducted and the test results indicated that: (1) the conventional Adaptive Neuro-Fuzzy Inference System (ANFIS) failed to run due to a large number of inputs; (2) the proposed dynamic neural-fuzzy model outperforms the subtractive clustering-based ANFIS model and fuzzy c-means clustering-based ANFIS model in terms of their modeling accuracy and computational effort.
EXTENSION OF THE PROJECTION THEOREM ON HILBERT SPACE TO FUZZY HILBERT SPACE OVER FUZZY NUMBER SPACE
K. P. DEEPA; Dr.S.Chenthur Pandian
2012-01-01
In this paper, we extend the projection theorem on Hilbert space to its fuzzy version over fuzzy number space embedded with fuzzy number mapping. To prove this we discuss the concepts of fuzzy Hilbert space over fuzzy number space with fuzzy number mapping. The fuzzy orthogonality, fuzzy orthonormality, fuzzy complemented subset property etc. of fuzzy Hilbert space over fuzzy number space using fuzzy number mapping also been discussed.
Mahanta, J.; P. K. Das
2012-01-01
A new class of fuzzy closed sets, namely fuzzy weakly closed set in a fuzzy topological space is introduced and it is established that this class of fuzzy closed sets lies between fuzzy closed sets and fuzzy generalized closed sets. Alongwith the study of fundamental results of such closed sets, we define and characterize fuzzy weakly compact space and fuzzy weakly closed space.
Compactness in intuitionistic fuzzy topological spaces
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S. E. Abbas
2005-02-01
Full Text Available We introduce fuzzy almost continuous mapping, fuzzy weakly continuous mapping, fuzzy compactness, fuzzy almost compactness, and fuzzy near compactness in intuitionistic fuzzy topological space in view of the definition of Ã…Â ostak, and study some of their properties. Also, we investigate the behavior of fuzzy compactness under several types of fuzzy continuous mappings.
B Gibilisco, Michael; E Albert, Karen; N Mordeson, John; J Wierman, Mark; D Clark, Terry
2014-01-01
This book offers a comprehensive analysis of the social choice literature and shows, by applying fuzzy sets, how the use of fuzzy preferences, rather than that of strict ones, may affect the social choice theorems. To do this, the book explores the presupposition of rationality within the fuzzy framework and shows that the two conditions for rationality, completeness and transitivity, do exist with fuzzy preferences. Specifically, this book examines: the conditions under which a maximal set exists; the Arrow’s theorem; the Gibbard-Satterthwaite theorem; and the median voter theorem. After showing that a non-empty maximal set does exists for fuzzy preference relations, this book goes on to demonstrating the existence of a fuzzy aggregation rule satisfying all five Arrowian conditions, including non-dictatorship. While the Gibbard-Satterthwaite theorem only considers individual fuzzy preferences, this work shows that both individuals and groups can choose alternatives to various degrees, resulting in a so...
Special functions in Fuzzy Analysis
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Angel Garrido
2006-01-01
Full Text Available In the treatment of Fuzzy Logic an useful tool appears: the membership function, with the information about the degree of completion of a condition which defines the respective Fuzzy Set or Fuzzy Relation. With their introduction, it is possible to prove some results on the foundations of Fuzzy Logic and open new ways in Fuzzy Analysis.
Vector-valued fuzzy multifunctions
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Ismat Beg
2001-01-01
Full Text Available Some of the properties of vector-valued fuzzy multifunctions are studied. The notion of sum fuzzy multifunction, convex hull fuzzy multifunction, close convex hull fuzzy multifunction, and upper demicontinuous are given, and some of the properties of these fuzzy multifunctions are investigated.
Approximate Reasoning with Fuzzy Booleans
Broek, van den P.M.; Noppen, J.A.R.
2004-01-01
This paper introduces, in analogy to the concept of fuzzy numbers, the concept of fuzzy booleans, and examines approximate reasoning with the compositional rule of inference using fuzzy booleans. It is shown that each set of fuzzy rules is equivalent to a set of fuzzy rules with singleton crisp ante
Fuzzy Sets and Mathematical Education.
Alsina, C.; Trillas, E.
1991-01-01
Presents the concept of "Fuzzy Sets" and gives some ideas for its potential interest in mathematics education. Defines what a Fuzzy Set is, describes why we need to teach fuzziness, gives some examples of fuzzy questions, and offers some examples of activities related to fuzzy sets. (MDH)
Quality Improvement of Liver Ultrasound Images Using Fuzzy Techniques
Bayani, Azadeh; Langarizadeh, Mostafa; Radmard, Amir Reza; Nejad, Ahmadreza Farzaneh
2016-01-01
Background: Liver ultrasound images are so common and are applied so often to diagnose diffuse liver diseases like fatty liver. However, the low quality of such images makes it difficult to analyze them and diagnose diseases. The purpose of this study, therefore, is to improve the contrast and quality of liver ultrasound images. Methods: In this study, a number of image contrast enhancement algorithms which are based on fuzzy logic were applied to liver ultrasound images - in which the view of kidney is observable - using Matlab2013b to improve the image contrast and quality which has a fuzzy definition; just like image contrast improvement algorithms using a fuzzy intensification operator, contrast improvement algorithms applying fuzzy image histogram hyperbolization, and contrast improvement algorithms by fuzzy IF-THEN rules. Results: With the measurement of Mean Squared Error and Peak Signal to Noise Ratio obtained from different images, fuzzy methods provided better results, and their implementation - compared with histogram equalization method - led both to the improvement of contrast and visual quality of images and to the improvement of liver segmentation algorithms results in images. Conclusion: Comparison of the four algorithms revealed the power of fuzzy logic in improving image contrast compared with traditional image processing algorithms. Moreover, contrast improvement algorithm based on a fuzzy intensification operator was selected as the strongest algorithm considering the measured indicators. This method can also be used in future studies on other ultrasound images for quality improvement and other image processing and analysis applications. PMID:28077898
Quality Improvement of Liver Ultrasound Images Using Fuzzy Techniques.
Bayani, Azadeh; Langarizadeh, Mostafa; Radmard, Amir Reza; Nejad, Ahmadreza Farzaneh
2016-12-01
Liver ultrasound images are so common and are applied so often to diagnose diffuse liver diseases like fatty liver. However, the low quality of such images makes it difficult to analyze them and diagnose diseases. The purpose of this study, therefore, is to improve the contrast and quality of liver ultrasound images. In this study, a number of image contrast enhancement algorithms which are based on fuzzy logic were applied to liver ultrasound images - in which the view of kidney is observable - using Matlab2013b to improve the image contrast and quality which has a fuzzy definition; just like image contrast improvement algorithms using a fuzzy intensification operator, contrast improvement algorithms applying fuzzy image histogram hyperbolization, and contrast improvement algorithms by fuzzy IF-THEN rules. With the measurement of Mean Squared Error and Peak Signal to Noise Ratio obtained from different images, fuzzy methods provided better results, and their implementation - compared with histogram equalization method - led both to the improvement of contrast and visual quality of images and to the improvement of liver segmentation algorithms results in images. Comparison of the four algorithms revealed the power of fuzzy logic in improving image contrast compared with traditional image processing algorithms. Moreover, contrast improvement algorithm based on a fuzzy intensification operator was selected as the strongest algorithm considering the measured indicators. This method can also be used in future studies on other ultrasound images for quality improvement and other image processing and analysis applications.
Digital Repository Service at National Institute of Oceanography (India)
Chakraborty, B.; Menezes, A.A.A.; Dandapath, S.; Fernandes, W.A.; Karisiddaiah, S.M.; Haris, K.; Gokul, G.S.
(involving pockmarks and faulted structures) subjected to strong bottom currents and seasonal upwelling. Index Terms ─ Multi-beam backscatter, Seafloor classification and characterizations, Self-Organizing map (SOM), Fuzzy C- means (FCM), Power spectral..., ANN techniques were proposed for hydro-acoustic data classification [10]. The SOM exercises unsupervised competitive learning on the unknown dataset (input) onto coarser clusters i.e., primary classifications [11]. For real time survey applications...
On fuzzy almost continuous convergence in fuzzy function spaces
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A.I. Aggour
2013-10-01
Full Text Available In this paper, we study the fuzzy almost continuous convergence of fuzzy nets on the set FAC(X, Y of all fuzzy almost continuous functions of a fuzzy topological space X into another Y. Also, we introduce the notions of fuzzy splitting and fuzzy jointly continuous topologies on the set FAC(X, Y and study some of its basic properties.
A New Type Fuzzy Module over Fuzzy Rings
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Ece Yetkin
2014-01-01
Full Text Available A new kind of fuzzy module over a fuzzy ring is introduced by generalizing Yuan and Lee’s definition of the fuzzy group and Aktaş and Çağman’s definition of fuzzy ring. The concepts of fuzzy submodule, and fuzzy module homomorphism are studied and some of their basic properties are presented analogous of ordinary module theory.
Decision making with fuzzy probability assessments and fuzzy payoff
Institute of Scientific and Technical Information of China (English)
Song Yexin; Yin Di; Chen Mianyun
2005-01-01
A novel method for decision making with fuzzy probability assessments and fuzzy payoff is presented. The consistency of the fuzzy probability assessment is considered. A fuzzy aggregate algorithm is used to indicate the fuzzy expected payoff of alternatives. The level sets of each fuzzy expected payoff are then obtained by solving linear programming models. Based on a defuzzification function associated with the level sets of fuzzy number and a numerical integration formula (Newton-Cotes formula), an effective approach to rank the fuzzy expected payoff of alternatives is also developed to determine the best alternative. Finally, a numerical example is provided to illustrate the proposed method.
Dhane, Dhiraj Manohar; Krishna, Vishal; Achar, Arun; Bar, Chittaranjan; Sanyal, Kunal; Chakraborty, Chandan
2016-09-01
Chronic lower extremity wound is a complicated disease condition of localized injury to skin and its tissues which have plagued many elders worldwide. The ulcer assessment and management is expensive and is burden on health establishment. Currently accurate wound evaluation remains a tedious task as it rely on visual inspection. This paper propose a new method for wound-area detection, using images digitally captured by a hand-held, optical camera. The strategy proposed involves spectral approach for clustering, based on the affinity matrix. The spectral clustering (SC) involves construction of similarity matrix of Laplacian based on Ng-Jorden-Weiss algorithm. Starting with a quadratic method, wound photographs were pre-processed for color homogenization. The first-order statistics filter was then applied to extract spurious regions. The filter was selected based on the performance, evaluated on four quality metrics. Then, the spectral method was used on the filtered images for effective segmentation. The segmented regions were post-processed using morphological operators. The performance of spectral segmentation was confirmed by ground-truth pictures labeled by dermatologists. The SC results were additionally compared with the results of k-means and Fuzzy C-Means (FCM) clustering algorithms. The SC approach on a set of 105 images, effectively delineated targeted wound beds yielding a segmentation accuracy of 86.73 %, positive predictive values of 91.80 %, and a sensitivity of 89.54 %. This approach shows the robustness of tool for ulcer perimeter measurement and healing progression. The article elucidates its potential to be incorporated in patient facing medical systems targeting a rapid clinical assistance.
Shape Prediction Linear Algorithm Using Fuzzy
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Navjot Kaur
2012-10-01
Full Text Available The goal of the proposed method is to develop shape prediction algorithm using fuzzy that is computationally fast and invariant. To predict the overlapping and joined shapes accurately, a method of shape prediction based on erosion and over segmentation is used to estimate values for dependent variables from previously unseen predictor values based on the variation in an underlying learning data set.
Generation of fuzzy mathematical morphologies
2001-01-01
Fuzzy Mathematical Morphology aims to extend the binary morphological operators to grey-level images. In order to define the basic morphological operations fuzzy erosion, dilation, opening and closing, we introduce a general method based upon fuzzy implication and inclusion grade operators, including as particular case, other ones existing in related literature In the definition of fuzzy erosion and dilation we use several fuzzy implications (Annexe A, Table of fuzzy implic...
Introduction to Fuzzy Set Theory
Kosko, Bart
1990-01-01
An introduction to fuzzy set theory is described. Topics covered include: neural networks and fuzzy systems; the dynamical systems approach to machine intelligence; intelligent behavior as adaptive model-free estimation; fuzziness versus probability; fuzzy sets; the entropy-subsethood theorem; adaptive fuzzy systems for backing up a truck-and-trailer; product-space clustering with differential competitive learning; and adaptive fuzzy system for target tracking.
Research and Implementation of Unsupervised Clustering-Based Intrusion Detection
Institute of Scientific and Technical Information of China (English)
Luo Min; Zhang Huan-guo; Wang Li-na
2003-01-01
An unsupervised clustering-based intrusion de tection algorithm is discussed in this paper. The basic idea of the algorithm is to produce the cluster by comparing the distances of unlabeled training data sets. With the classified data instances, anomaly data clusters can be easily identified by normal cluster ratio and the identified cluster can be used in real data detection. The benefit of the algorithm is that it doesnt need labeled training data sets. The experiment concludes that this approach can detect unknown intrusions efficiently in the real network connections via using the data sets of KDD99.
Research and Implementation of Unsupervised Clustering-Based Intrusion Detection
Institute of Scientific and Technical Information of China (English)
LuoMin; ZhangHuan-guo; WangLi-na
2003-01-01
An unsupervised clustering-based intrusion detection algorithm is discussed in this paper. The basic idea of the algorithm is to produce the cluster by comparing the distances of unlabeled training data sets. With the classified data instances, anomaly data clusters can be easily identified by normal duster ratio and the identified cluster can be used in real data detection. The benefit of the algorithm is that it doesn't need labeled training data sets. The experiment coneludes that this approach can detect unknown intrusions efficiently in the real network connections via using the data sets of KDD99.
Cluster-based global firms' use of local capabilities
DEFF Research Database (Denmark)
Andersen, Poul Houman; Bøllingtoft, Anne
2011-01-01
knowledge base as a mediating variable, the purpose of this paper is to examine how globalization affected the studied firms’ use of local cluster-based knowledge, integration of local and global knowledge, and networking capabilities. Design/methodology/approach – Qualitative case studies of nine firms...... knowledge were highly active in local knowledge use, whereas CBFs characterized by a more implicit knowledge base did not use localized knowledge. Research limitations/implications – The study is exploratory and covers three clusters in one small and open developed economy. Further corroboration through...... takes a micro-oriented perspective and focus on clusters in Denmark, a small and mature economy...
The fuzzy space construction kit
Sykora, Andreas
2016-01-01
Fuzzy spaces like the fuzzy sphere or the fuzzy torus have received remarkable attention, since they appeared as objects in string theory. Although there are higher dimensional examples, the most known and most studied fuzzy spaces are realized as matrix algebras defined by three Hermitian matrices, which may be seen as fuzzy membrane or fuzzy surface. We give a mapping between directed graphs and matrix algebras defined by three Hermitian matrices and show that the matrix algebras of known two-dimensional fuzzy spaces are associated with unbranched graphs. By including branchings into the graphs we find matrix algebras that represent fuzzy spaces associated with surfaces having genus 2 and higher.
Fuzzy Model for Trust Evaluation
Institute of Scientific and Technical Information of China (English)
Zhang Shibin; He Dake
2006-01-01
Based on fuzzy set theory, a fuzzy trust model is established by using membership function to describe the fuzziness of trust. The trust vectors of subjective trust are obtained based on a mathematical model of fuzzy synthetic evaluation. Considering the complicated and changeable relationships between various subjects, the multi-level mathematical model of fuzzy synthetic evaluation is introduced. An example of a two-level fuzzy synthetic evaluation model confirms the feasibility of the multi-level fuzzy synthesis evaluation model. The proposed fuzzy model for trust evaluation may provide a promising method for research of trust model in open networks.
Lei, Qian
2017-01-01
This book offers a comprehensive and systematic review of the latest research findings in the area of intuitionistic fuzzy calculus. After introducing the intuitionistic fuzzy numbers’ operational laws and their geometrical and algebraic properties, the book defines the concept of intuitionistic fuzzy functions and presents the research on the derivative, differential, indefinite integral and definite integral of intuitionistic fuzzy functions. It also discusses some of the methods that have been successfully used to deal with continuous intuitionistic fuzzy information or data, which are different from the previous aggregation operators focusing on discrete information or data. Mainly intended for engineers and researchers in the fields of fuzzy mathematics, operations research, information science and management science, this book is also a valuable textbook for postgraduate and advanced undergraduate students alike.
DEFF Research Database (Denmark)
Jantzen, Jan
as any PID controller. In the nonlinear domain, the stability of four standard control surfaces is analysed by means of describing functions and Nyquist plots. The self-organizing controller (SOC) is shown to be a model reference adaptive controller. There is a possibility that a nonlinear fuzzy PID......The objective of this textbook is to acquire an understanding of the behaviour of fuzzy logic controllers. Under certain conditions a fuzzy controller is equivalent to a proportional-integral-derivative (PID) controller. Using that equivalence as a link, the book applies analysis methods from...... linear and nonlinear control theory. In the linear domain, PID tuning methods and stability analyses are transferred to linear fuzzy controllers. The Nyquist plot shows the robustness of different settings of the fuzzy gain parameters. As a result, a fuzzy controller is guaranteed to perform as well...
DEFF Research Database (Denmark)
Jantzen, Jan
linear and nonlinear control theory. In the linear domain, PID tuning methods and stability analyses are transferred to linear fuzzy controllers. The Nyquist plot shows the robustness of different settings of the fuzzy gain parameters. As a result, a fuzzy controller is guaranteed to perform as well......The objective of this textbook is to acquire an understanding of the behaviour of fuzzy logic controllers. Under certain conditions a fuzzy controller is equivalent to a proportional-integral-derivative (PID) controller. Using that equivalence as a link, the book applies analysis methods from...... as any PID controller. In the nonlinear domain, the stability of four standard control surfaces is analysed by means of describing functions and Nyquist plots. The self-organizing controller (SOC) is shown to be a model reference adaptive controller. There is a possibility that a nonlinear fuzzy PID...
Consumer preference models: fuzzy theory approach
Turksen, I. B.; Wilson, I. A.
1993-12-01
Consumer preference models are widely used in new product design, marketing management, pricing and market segmentation. The purpose of this article is to develop and test a fuzzy set preference model which can represent linguistic variables in individual-level models implemented in parallel with existing conjoint models. The potential improvements in market share prediction and predictive validity can substantially improve management decisions about what to make (product design), for whom to make it (market segmentation) and how much to make (market share prediction).
Metamathematics of fuzzy logic
Hájek, Petr
1998-01-01
This book presents a systematic treatment of deductive aspects and structures of fuzzy logic understood as many valued logic sui generis. Some important systems of real-valued propositional and predicate calculus are defined and investigated. The aim is to show that fuzzy logic as a logic of imprecise (vague) propositions does have well-developed formal foundations and that most things usually named `fuzzy inference' can be naturally understood as logical deduction.
DEFF Research Database (Denmark)
Dotoli, M.; Jantzen, Jan
1999-01-01
The tutorial concerns automatic control of an inverted pendulum, especially rule based control by means of fuzzy logic. A ball balancer, implemented in a software simulator in Matlab, is used as a practical case study. The objectives of the tutorial are to teach the basics of fuzzy control......, and to show how to apply fuzzy logic in automatic control. The tutorial is distance learning, where students interact one-to-one with the teacher using e-mail....
DEFF Research Database (Denmark)
Dotoli, M.; Jantzen, Jan
1999-01-01
The tutorial concerns automatic control of an inverted pendulum, especially rule based control by means of fuzzy logic. A ball balancer, implemented in a software simulator in Matlab, is used as a practical case study. The objectives of the tutorial are to teach the basics of fuzzy control, and t......, and to show how to apply fuzzy logic in automatic control. The tutorial is distance learning, where students interact one-to-one with the teacher using e-mail....
DEFF Research Database (Denmark)
Jantzen, Jan
1998-01-01
Design of a fuzzy controller requires more design decisions than usual, for example regarding rule base, inference engine, defuzzification, and data pre- and post processing. This tutorial paper identifies and describes the design choices related to single-loop fuzzy control, based...... on an international standard which is underway. The paper contains also a design approach, which uses a PID controller as a starting point. A design engineer can view the paper as an introduction to fuzzy controller design....
Reliability analysis of cluster-based ad-hoc networks
Energy Technology Data Exchange (ETDEWEB)
Cook, Jason L. [Quality Engineering and System Assurance, Armament Research Development Engineering Center, Picatinny Arsenal, NJ (United States); Ramirez-Marquez, Jose Emmanuel [School of Systems and Enterprises, Stevens Institute of Technology, Castle Point on Hudson, Hoboken, NJ 07030 (United States)], E-mail: Jose.Ramirez-Marquez@stevens.edu
2008-10-15
The mobile ad-hoc wireless network (MAWN) is a new and emerging network scheme that is being employed in a variety of applications. The MAWN varies from traditional networks because it is a self-forming and dynamic network. The MAWN is free of infrastructure and, as such, only the mobile nodes comprise the network. Pairs of nodes communicate either directly or through other nodes. To do so, each node acts, in turn, as a source, destination, and relay of messages. The virtue of a MAWN is the flexibility this provides; however, the challenge for reliability analyses is also brought about by this unique feature. The variability and volatility of the MAWN configuration makes typical reliability methods (e.g. reliability block diagram) inappropriate because no single structure or configuration represents all manifestations of a MAWN. For this reason, new methods are being developed to analyze the reliability of this new networking technology. New published methods adapt to this feature by treating the configuration probabilistically or by inclusion of embedded mobility models. This paper joins both methods together and expands upon these works by modifying the problem formulation to address the reliability analysis of a cluster-based MAWN. The cluster-based MAWN is deployed in applications with constraints on networking resources such as bandwidth and energy. This paper presents the problem's formulation, a discussion of applicable reliability metrics for the MAWN, and illustration of a Monte Carlo simulation method through the analysis of several example networks.
Clustering-based redshift estimation: application to VIPERS/CFHTLS
Scottez, V.; Mellier, Y.; Granett, B. R.; Moutard, T.; Kilbinger, M.; Scodeggio, M.; Garilli, B.; Bolzonella, M.; de la Torre, S.; Guzzo, L.; Abbas, U.; Adami, C.; Arnouts, S.; Bottini, D.; Branchini, E.; Cappi, A.; Cucciati, O.; Davidzon, I.; Fritz, A.; Franzetti, P.; Iovino, A.; Krywult, J.; Le Brun, V.; Le Fèvre, O.; Maccagni, D.; Małek, K.; Marulli, F.; Polletta, M.; Pollo, A.; Tasca, L. A. M.; Tojeiro, R.; Vergani, D.; Zanichelli, A.; Bel, J.; Coupon, J.; De Lucia, G.; Ilbert, O.; McCracken, H. J.; Moscardini, L.
2016-10-01
We explore the accuracy of the clustering-based redshift estimation proposed by Ménard et al. when applied to VIMOS Public Extragalactic Redshift Survey (VIPERS) and Canada-France-Hawaii Telescope Legacy Survey (CFHTLS) real data. This method enables us to reconstruct redshift distributions from measurement of the angular clustering of objects using a set of secure spectroscopic redshifts. We use state-of-the-art spectroscopic measurements with iAB 0.5 which allows us to test the accuracy of the clustering-based redshift distributions. We show that this method enables us to reproduce the true mean colour-redshift relation when both populations have the same magnitude limit. We also show that this technique allows the inference of redshift distributions for a population fainter than the reference and we give an estimate of the colour-redshift mapping in this case. This last point is of great interest for future large-redshift surveys which require a complete faint spectroscopic sample.
High Dimensional Data Clustering Using Fast Cluster Based Feature Selection
Directory of Open Access Journals (Sweden)
Karthikeyan.P
2014-03-01
Full Text Available Feature selection involves identifying a subset of the most useful features that produces compatible results as the original entire set of features. A feature selection algorithm may be evaluated from both the efficiency and effectiveness points of view. While the efficiency concerns the time required to find a subset of features, the effectiveness is related to the quality of the subset of features. Based on these criteria, a fast clustering-based feature selection algorithm (FAST is proposed and experimentally evaluated in this paper. The FAST algorithm works in two steps. In the first step, features are divided into clusters by using graph-theoretic clustering methods. In the second step, the most representative feature that is strongly related to target classes is selected from each cluster to form a subset of features. Features in different clusters are relatively independent; the clustering-based strategy of FAST has a high probability of producing a subset of useful and independent features. To ensure the efficiency of FAST, we adopt the efficient minimum-spanning tree (MST using the Kruskal‟s Algorithm clustering method. The efficiency and effectiveness of the FAST algorithm are evaluated through an empirical study. Index Terms—
Institute of Scientific and Technical Information of China (English)
吴一全; 曹鹏祥; 王凯; 朱丽
2015-01-01
针对传统核模糊C均值聚类(KernelFuzzyC-Means,KFCM)畜肉图像分割方法对噪声适应能力不强的问题,提出基于广义核函数或混合核函数的模糊局部信息C均值聚奚(Fuzzy Local Information C-Means,FLICM)畜肉图像分割方法(KFLICM UG 方法和KFLICM_MG方法).首先利用广义核函数或混合核函数可以有效兼顾学习能力和泛化能力的优势,将图像的每一个像素映射到高维的特征空间,扩大像素有用特征的类间差异,使像素在高维特征空间中拥有更优的线性可聚性;然后结合像素的局部空间和灰度信息,确定其模糊隶属度,在高维的特征空间中依据图像特征对像素进行模糊局部信息C均值聚类,最终实现畜肉图像的分割.大量的实验结果表明,相比现有的模糊C均值(Fuzzy C-Means,FCM)分割方法、KFCM分割方法和FLICM分割方法,本文提出的KFLICM UG方法和KFLICM MG方法可以获得更好的分割效果,更低的分割错误率,且具有更强的噪声适应能力和鲁棒性.
T Atanassov, Krassimir
2017-01-01
The book offers a comprehensive survey of intuitionistic fuzzy logics. By reporting on both the author’s research and others’ findings, it provides readers with a complete overview of the field and highlights key issues and open problems, thus suggesting new research directions. Starting with an introduction to the basic elements of intuitionistic fuzzy propositional calculus, it then provides a guide to the use of intuitionistic fuzzy operators and quantifiers, and lastly presents state-of-the-art applications of intuitionistic fuzzy sets. The book is a valuable reference resource for graduate students and researchers alike.
Institute of Scientific and Technical Information of China (English)
无
2005-01-01
The typical BDI (belief desire intention) model of agent is not efficiently computable and the strict logic expression is not easily applicable to the AUV (autonomous underwater vehicle) domain with uncertainties. In this paper, an AUV fuzzy neural BDI model is proposed. The model is a fuzzy neural network composed of five layers: input ( beliefs and desires) , fuzzification, commitment, fuzzy intention, and defuzzification layer. In the model, the fuzzy commitment rules and neural network are combined to form intentions from beliefs and desires. The model is demonstrated by solving PEG (pursuit-evasion game), and the simulation result is satisfactory.
RANDOM VARIABLE WITH FUZZY PROBABILITY
Institute of Scientific and Technical Information of China (English)
吕恩琳; 钟佑明
2003-01-01
Mathematic description about the second kind fuzzy random variable namely the random variable with crisp event-fuzzy probability was studied. Based on the interval probability and using the fuzzy resolution theorem, the feasible condition about a probability fuzzy number set was given, go a step further the definition arid characters of random variable with fuzzy probability ( RVFP ) and the fuzzy distribution function and fuzzy probability distribution sequence of the RVFP were put forward. The fuzzy probability resolution theorem with the closing operation of fuzzy probability was given and proved. The definition and characters of mathematical expectation and variance of the RVFP were studied also. All mathematic description about the RVFP has the closing operation for fuzzy probability, as a result, the foundation of perfecting fuzzy probability operation method is laid.
Clustering-based limb identification for pressure ulcer risk assessment.
Baran Pouyan, M; Nourani, M; Pompeo, M
2015-01-01
Bedridden patients have a high risk of developing pressure ulcers. Risk assessment for pressure ulceration is critical for preventive care. For a reliable assessment, we need to identify and track the limbs continuously and accurately. In this paper, we propose a method to identify body limbs using a pressure mat. Three prevalent sleep postures (supine, left and right postures) are considered. Then, predefined number of limbs (body parts) are identified by applying Fuzzy C-Means (FCM) clustering on key attributes. We collected data from 10 adult subjects and achieved average accuracy of 93.2% for 10 limbs in supine and 7 limbs in left/right postures.
Document Clustering Based on Semi-Supervised Term Clustering
Directory of Open Access Journals (Sweden)
Hamid Mahmoodi
2012-05-01
Full Text Available The study is conducted to propose a multi-step feature (term selection process and in semi-supervised fashion, provide initial centers for term clusters. Then utilize the fuzzy c-means (FCM clustering algorithm for clustering terms. Finally assign each of documents to closest associated term clusters. While most text clustering algorithms directly use documents for clustering, we propose to first group the terms using FCM algorithm and then cluster documents based on terms clusters. We evaluate effectiveness of our technique on several standard text collections and compare our results with the some classical text clustering algorithms.
Relations Among Some Fuzzy Entropy Formulae
Institute of Scientific and Technical Information of China (English)
卿铭
2004-01-01
Fuzzy entropy has been widely used to analyze and design fuzzy systems, and many fuzzy entropy formulae have been proposed. For further in-deepth analysis of fuzzy entropy, the axioms and some important formulae of fuzzy entropy are introduced. Some equivalence results among these fuzzy entropy formulae are proved, and it is shown that fuzzy entropy is a special distance measurement.
Results on fuzzy soft topological spaces
Mahanta, J
2012-01-01
B. Tanay et. al. introduced and studied fuzzy soft topological spaces. Here we introduce fuzzy soft point and study the concept of neighborhood of a fuzzy soft point in a fuzzy soft topological space. We also study fuzzy soft closure and fuzzy soft interior. Separation axioms and connectedness are introduced and investigated for fuzzy soft topological spaces.
Segmentation of respiratory signals by evidence theory.
Belghith, Akram; Collet, Christophe
2009-01-01
This paper presents an evidential segmentation scheme of respiratory signals for the detection of the wheezing sounds. The segmentation is based on the modeling of the data by evidence theory which is well suited to represent such uncertain and imprecise data. In this paper, we particularly focus on the modelization of the data imprecision using the fuzzy theory. The modelization result is then used to define the mass function. The effectiveness of the method is demonstrated on synthetic and real signals.
Some properties of fuzzy soft proximity spaces.
Demir, İzzettin; Özbakır, Oya Bedre
2015-01-01
We study the fuzzy soft proximity spaces in Katsaras's sense. First, we show how a fuzzy soft topology is derived from a fuzzy soft proximity. Also, we define the notion of fuzzy soft δ-neighborhood in the fuzzy soft proximity space which offers an alternative approach to the study of fuzzy soft proximity spaces. Later, we obtain the initial fuzzy soft proximity determined by a family of fuzzy soft proximities. Finally, we investigate relationship between fuzzy soft proximities and proximities.
Some Properties of Fuzzy Soft Proximity Spaces
Demir, İzzettin; Özbakır, Oya Bedre
2015-01-01
We study the fuzzy soft proximity spaces in Katsaras's sense. First, we show how a fuzzy soft topology is derived from a fuzzy soft proximity. Also, we define the notion of fuzzy soft δ-neighborhood in the fuzzy soft proximity space which offers an alternative approach to the study of fuzzy soft proximity spaces. Later, we obtain the initial fuzzy soft proximity determined by a family of fuzzy soft proximities. Finally, we investigate relationship between fuzzy soft proximities and proximities. PMID:25793224
Brain tumor segmentation based on a hybrid clustering technique
Directory of Open Access Journals (Sweden)
Eman Abdel-Maksoud
2015-03-01
This paper presents an efficient image segmentation approach using K-means clustering technique integrated with Fuzzy C-means algorithm. It is followed by thresholding and level set segmentation stages to provide an accurate brain tumor detection. The proposed technique can get benefits of the K-means clustering for image segmentation in the aspects of minimal computation time. In addition, it can get advantages of the Fuzzy C-means in the aspects of accuracy. The performance of the proposed image segmentation approach was evaluated by comparing it with some state of the art segmentation algorithms in case of accuracy, processing time, and performance. The accuracy was evaluated by comparing the results with the ground truth of each processed image. The experimental results clarify the effectiveness of our proposed approach to deal with a higher number of segmentation problems via improving the segmentation quality and accuracy in minimal execution time.
On Intuitionistic Fuzzy Filters of Intuitionistic Fuzzy Coframes
Directory of Open Access Journals (Sweden)
Rajesh K. Thumbakara
2013-01-01
Full Text Available Frame theory is the study of topology based on its open set lattice, and it was studied extensively by various authors. In this paper, we study quotients of intuitionistic fuzzy filters of an intuitionistic fuzzy coframe. The quotients of intuitionistic fuzzy filters are shown to be filters of the given intuitionistic fuzzy coframe. It is shown that the collection of all intuitionistic fuzzy filters of a coframe and the collection of all intutionistic fuzzy quotient filters of an intuitionistic fuzzy filter are coframes.
Fast Affinity Propagation Clustering based on Machine Learning
Directory of Open Access Journals (Sweden)
Shailendra Kumar Shrivastava
2013-01-01
Full Text Available Affinity propagation (AP was recently introduced as an un-supervised learning algorithm for exemplar based clustering. In this paper a novel Fast Affinity Propagation clustering Approach based on Machine Learning (FAPML has been proposed. FAPML tries to put data points into clusters based on the history of the data points belonging to clusters in early stages. In FAPML we introduce affinity learning constant and dispersion constant which supervise the clustering process. FAPML also enforces the exemplar consistency and one of 'N constraints. Experiments conducted on many data sets such as Olivetti faces, Mushroom, Documents summarization, Thyroid, Yeast, Wine quality Red, Balance etc. show that FAPML is up to 54 % faster than the original AP with better Net Similarity.
Cluster-Based Distributed Algorithms for Very Large Linear Equations
Institute of Scientific and Technical Information of China (English)
无
2006-01-01
In many applications such as computational fluid dynamics and weather prediction, as well as image processing and state of Markov chain etc., the grade of matrix n is often very large, and any serial algorithm cannot solve the problems. A distributed cluster-based solution for very large linear equations is discussed, it includes the definitions of notations, partition of matrix, communication mechanism, and a master-slaver algorithm etc., the computing cost is O(n3/N), the memory cost is O(n2/N), the I/O cost is O(n2/N), and the communication cost is O(Nn), here, N is the number of computing nodes or processes. Some tests show that the solution could solve the double type of matrix under 106×106 effectively.
Cluster-based distributed face tracking in camera networks.
Yoder, Josiah; Medeiros, Henry; Park, Johnny; Kak, Avinash C
2010-10-01
In this paper, we present a distributed multicamera face tracking system suitable for large wired camera networks. Unlike previous multicamera face tracking systems, our system does not require a central server to coordinate the entire tracking effort. Instead, an efficient camera clustering protocol is used to dynamically form groups of cameras for in-network tracking of individual faces. The clustering protocol includes cluster propagation mechanisms that allow the computational load of face tracking to be transferred to different cameras as the target objects move. Furthermore, the dynamic election of cluster leaders provides robustness against system failures. Our experimental results show that our cluster-based distributed face tracker is capable of accurately tracking multiple faces in real-time. The overall performance of the distributed system is comparable to that of a centralized face tracker, while presenting the advantages of scalability and robustness.
Weighted Clustering Based Preemptive Scheduling For Real Time System
Directory of Open Access Journals (Sweden)
H.S Behera
2012-05-01
Full Text Available In this paper a new improved clustering based scheduling algorithm for a single processor environment is proposed. In the proposed method, processes are organized into non-overlapping clusters.For each process the variance from the median, is calculated and compared with the variance from the means of other clusters. Each process is assigned to the cluster associated with the closest median. The new median of each cluster is calculated and the procedure is repeated until the medians are fixed. Weight is assigned to each cluster using the externally assigned priorities and the burst time. The cluster with highest weight is executed first and jobs are scheduled using the Round Robin algorithm with calculated dynamic time slice.. The experimental study of the proposed scheduling algorithm shows that the high priority jobs can be executed first to meet the deadlines and also prevents starvation of processes at the same time which is crucial in a real time system.
Performance Improvement of Cache Management In Cluster Based MANET
Directory of Open Access Journals (Sweden)
Abdulaziz Zam
2013-08-01
Full Text Available Caching is one of the most effective techniques used to improve the data access performance in wireless networks. Accessing data from a remote server imposes high latency and power consumption through forwarding nodes that guide the requests to the server and send data back to the clients. In addition, accessing data may be unreliable or even impossible due to erroneous wireless links and frequently disconnections. Due to the nature of MANET and its high frequent topology changes, and also small cache size and constrained power supply in mobile nodes, the management of the cache would be a challenge. To maintain the MANET’s stability and scalability, clustering is considered as an effective approach. In this paper an efficient cache management method is proposed for the Cluster Based Mobile Ad-hoc NETwork (C-B-MANET. The performance of the method is evaluated in terms of packet delivery ratio, latency and overhead metrics.
Properties of Bipolar Fuzzy Hypergraphs
M. Akram; Dudek, W. A.; Sarwar, S.
2013-01-01
In this article, we apply the concept of bipolar fuzzy sets to hypergraphs and investigate some properties of bipolar fuzzy hypergraphs. We introduce the notion of $A-$ tempered bipolar fuzzy hypergraphs and present some of their properties. We also present application examples of bipolar fuzzy hypergraphs.
Fuzzy Markov chains: uncertain probabilities
2002-01-01
We consider finite Markov chains where there are uncertainties in some of the transition probabilities. These uncertainties are modeled by fuzzy numbers. Using a restricted fuzzy matrix multiplication we investigate the properties of regular, and absorbing, fuzzy Markov chains and show that the basic properties of these classical Markov chains generalize to fuzzy Markov chains.
Achieving of Fuzzy Automata for Processing Fuzzy Logic
Institute of Scientific and Technical Information of China (English)
SHU Lan; WU Qing-e
2005-01-01
At present, there has been an increasing interest in neuron-fuzzy systems, the combinations of artificial neural networks with fuzzy logic. In this paper, a definition of fuzzy finite state automata (FFA) is introduced and fuzzy knowledge equivalence representations between neural networks, fuzzy systems and models of automata are discussed. Once the network has been trained, we develop a method to extract a representation of the FFA encoded in the recurrent neural network that recognizes the training rules.
Entropy of Fuzzy Partitions and Entropy of Fuzzy Dynamical Systems
Directory of Open Access Journals (Sweden)
Dagmar Markechová
2016-01-01
Full Text Available In the paper we define three kinds of entropy of a fuzzy dynamical system using different entropies of fuzzy partitions. It is shown that different definitions of the entropy of fuzzy partitions lead to different notions of entropies of fuzzy dynamical systems. The relationships between these entropies are studied and connections with the classical case are mentioned as well. Finally, an analogy of the Kolmogorov–Sinai Theorem on generators is proved for fuzzy dynamical systems.
DEFF Research Database (Denmark)
Jantzen, Jan
1998-01-01
A logic based on the two truth values True and False is sometimes inadequate when describing human reasoning. Fuzzy logic uses the whole interval between 0 (False) and 1 (True) to describe human reasoning. As a result, fuzzy logic is being applied in rule based automatic controllers, and this paper...
Extended Fuzzy Clustering Algorithms
U. Kaymak (Uzay); M. Setnes
2000-01-01
textabstractFuzzy clustering is a widely applied method for obtaining fuzzy models from data. It has been applied successfully in various fields including finance and marketing. Despite the successful applications, there are a number of issues that must be dealt with in practical applications of fuz
Statistical Methods for Fuzzy Data
Viertl, Reinhard
2011-01-01
Statistical data are not always precise numbers, or vectors, or categories. Real data are frequently what is called fuzzy. Examples where this fuzziness is obvious are quality of life data, environmental, biological, medical, sociological and economics data. Also the results of measurements can be best described by using fuzzy numbers and fuzzy vectors respectively. Statistical analysis methods have to be adapted for the analysis of fuzzy data. In this book, the foundations of the description of fuzzy data are explained, including methods on how to obtain the characterizing function of fuzzy m
Axiomatic of Fuzzy Complex Numbers
Angel Garrido
2012-01-01
Fuzzy numbers are fuzzy subsets of the set of real numbers satisfying some additional conditions. Fuzzy numbers allow us to model very difficult uncertainties in a very easy way. Arithmetic operations on fuzzy numbers have also been developed, and are based mainly on the crucial Extension Principle. When operating with fuzzy numbers, the results of our calculations strongly depend on the shape of the membership functions of these numbers. Logically, less regular membership functions may lead ...
MODELING FUZZY GEOGRAPHIC OBJECTS WITHIN FUZZY FIELDS
Institute of Scientific and Technical Information of China (English)
无
2001-01-01
To improve the current GIS functions in describing geographic objects w ith fuzziness,this paper begins with a discussion on the distance measure of sp atial objects based on the theory of sets and an introduction of dilation and er osion operators.Under the assumption that changes of attributes in a geographic region are gradual,the analytic expressions for the fuzzy objects of points,l ines and areas,and the description of their formal structures are presented.Th e analytic model of geographic objects by means of fuzzy fields is developed.We have shown that the 9-intersection model proposed by Egenhofer and Franzosa (19 91) is a special case of the model presented in the paper.
Super Fuzzy Matrices and Super Fuzzy Models for Social Scientists
Kandasamy, W B Vasantha; Amal, K
2008-01-01
This book introduces the concept of fuzzy super matrices and operations on them. This book will be highly useful to social scientists who wish to work with multi-expert models. Super fuzzy models using Fuzzy Cognitive Maps, Fuzzy Relational Maps, Bidirectional Associative Memories and Fuzzy Associative Memories are defined here. The authors introduce 13 multi-expert models using the notion of fuzzy supermatrices. These models are described with illustrative examples. This book has three chapters. In the first chaper, the basic concepts about super matrices and fuzzy super matrices are recalled. Chapter two introduces the notion of fuzzy super matrices adn their properties. The final chapter introduces many super fuzzy multi expert models.
IMPROVED HYBRID SEGMENTATION OF BRAIN MRI TISSUE AND TUMOR USING STATISTICAL FEATURES
Directory of Open Access Journals (Sweden)
S. Allin Christe
2010-08-01
Full Text Available Medical image segmentation is the most essential and crucial process in order to facilitate the characterization and visualization of the structure of interest in medical images. Relevant application in neuroradiology is the segmentation of MRI data sets of the human brain into the structure classes gray matter, white matter and cerebrospinal fluid (CSF and tumor. In this paper, brain image segmentation algorithms such as Fuzzy C means (FCM segmentation and Kohonen means(K means segmentation were implemented. In addition to this, new hybrid segmentation technique, namely, Fuzzy Kohonen means of image segmentation based on statistical feature clustering is proposed and implemented along with standard pixel value clustering method. The clustered segmented tissue images are compared with the Ground truth and its performance metric is also found. It is found that the feature based hybrid segmentation gives improved performance metric and improved classification accuracy rather than pixel based segmentation.
Fuzzy Dot Structure of BG-algebras
Directory of Open Access Journals (Sweden)
Tapan Senapati
2014-09-01
Full Text Available In this paper, the notions of fuzzy dot subalgebras is introduced together with fuzzy normal dot subalgebras and fuzzy dot ideals of BG-algebras. The homomorphic image and inverse image are investigated in fuzzy dot subalgebras and fuzzy dot ideals of BG-algebras. Also, the notion of fuzzy relations on the family of fuzzy dot subalgebras and fuzzy dot ideals of BG-algebras are introduced with some related properties.
Structural Holes in Directed Fuzzy Social Networks
Renjie Hu; Guangyu Zhang
2014-01-01
The structural holes have been a key issue in fuzzy social network analysis. For undirected fuzzy social networks where edges are just present or absent undirected fuzzy relation and have no more information attached, many structural holes measures have been presented, such as key fuzzy structural holes, general fuzzy structural holes, strong fuzzy structural holes, and weak fuzzy structural holes. There has been a growing need to design structural holes measures for directed fuzzy social net...
MOTION MODELLINGUSINGCONCEPTS OF FUZZY ARTIFICIAL POTENTIAL FIELDS
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O. Motlagh
2010-12-01
Full Text Available Artificial potential fields (APF are well established for reactive navigation of mobile robots. This paper describes a fast and robust fuzzy-APF on an ActivMedia AmigoBot. Obstacle-related information is fuzzified by using sensory fusion, which results in a shorter runtime. In addition, the membership functions of obstacle direction and range have been merged into one function, obtaining a smaller block of rules. The system is tested in virtual environments with non-concave obstacles. Then, the paper describes a new approach to motion modelling where the motion of intelligent travellers is modelled by consecutive path segments. In previous work, the authors described a reliable motion modelling technique using causal inference of fuzzy cognitive maps (FCM which has been efficiently modified for the purpose of this contribution. Results and analysis are given to demonstrate the efficiency and accuracy of the proposed motion modelling algorithm.
Grosse, Harald; Grosse, Harald; Reiter, Gert
1998-01-01
We introduce the fuzzy supersphere as sequence of finite-dimensional, noncommutative $Z_{2}$-graded algebras tending in a suitable limit to a dense subalgebra of the $Z_{2}$-graded algebra of ${\\cal H}^{\\infty}$-functions on the $(2| 2)$-dimensional supersphere. Noncommutative analogues of the body map (to the (fuzzy) sphere) and the super-deRham complex are introduced. In particular we reproduce the equality of the super-deRham cohomology of the supersphere and the ordinary deRham cohomology of its body on the "fuzzy level".
A fuzzy disaggregation technique
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Alessandro Polli
2013-05-01
Full Text Available The aim of this paper is to analyze a problem of time series disaggregation in presence of broad information lack. In this framework it is not possible to follow standard methodologies, like those stemming from the Chow and Lin algorithm and based on probabilistic assumptions. In general terms, when information sets are limited, instead of referring to probabilistic measures it could be more appropriate to adopt an uncertainty measure satisfying only some general properties, like the fuzzy one. After a synthetic survey about fuzzy aggregation operators, we introduce a fuzzy disaggregation technique, based on Choquet capacity theory and characterized by De Finetti coherence.
An Adaptive Fuzzy System in Large Scale Mobile Ad Hoc Networks
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B.G.Obulla Reddy
2012-04-01
Full Text Available Multicast protocols in MANETs must consider control overhead for maintenance, energy efficiency of nodes and routing trees managements to frequent changes of network topology. Now-adaysMulticast protocols extended with Cluster based approach. Cluster based multicast tree formation is still research issues. The tree reconstruction of cluster-based multicast routing protocol will take place if any link of the trees has malfunction or the nodes move out of the link, therefore, its robust performance is unsatisfactory. The mobility of nodes will always increase the communication delay because of re-clustering and cluster head selections. For this issue we proposed the new scheme Adaptive Fuzzy System (AFS, its fuzzy based clustering and predicting the next cluster head (CH based their location updates with clustered group. A new location management scheme is proposed to handle the mobility of cluster members, based on a hybrid strategy that includes location updating and location prediction. In a clustered zone predicts movement of members and CH based on Kalman filtering of previously received updates and based on location updates CH will selected. Here location managements will leads to reduce cluster head selections. We used ns2 for our AFS.We present simulation results that demonstrate a significant reduce the communication delay over the traditional cluster based MANETs deployments.
An Adaptive Fuzzy Clustering and Location Management in Mobile Ad Hoc Networks
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Obulla Reddy
2012-11-01
Full Text Available In the typical Ad Hoc networks application, the network hosts usually perform the given task according to groups, e.g. the command and control over staff and accruement in military affairs, traffic management, etc. Therefore, it is very significant for the study of multicast routing protocols of the Ad Hoc networks. Multicast protocols in MANETs must consider control overhead for maintenance, energy efficiency of nodes and routing trees managements to frequent changes of network topology. Now-a days Multicast protocols extended with Cluster based approach. Cluster based multicast tree formation is still research issues. The mobility of nodes will always increase the communication delay because of re-clustering and cluster head selections. For this issue we evaluate Adaptive Fuzzy System (AFS to multicast communication in mobile ad hoc networks (MANETs. To evaluate the performance of AFS, we simulate the fuzzy clustering in a variety of mobile network topologies in NS-2 and compare it with Cluster-based On Demand Multicast Routing Protocol (CODMRP and Cluster-based routing protocol (CBRP. Our simulation result shows the effectiveness and efficiency of AFMR: high packet delivery ratio is achieved while the delay and overhead are the lowest.
A Novel Weak Fuzzy Solution for Fuzzy Linear System
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Soheil Salahshour
2016-03-01
Full Text Available This article proposes a novel weak fuzzy solution for the fuzzy linear system. As a matter of fact, we define the right-hand side column of the fuzzy linear system as a piecewise fuzzy function to overcome the related shortcoming, which exists in the previous findings. The strong point of this proposal is that the weak fuzzy solution is always a fuzzy number vector. Two complex and non-complex linear systems under uncertainty are tested to validate the effectiveness and correctness of the presented method.
Fuzzy Modeling for Uncertainty Nonlinear Systems with Fuzzy Equations
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Raheleh Jafari
2017-01-01
Full Text Available The uncertain nonlinear systems can be modeled with fuzzy equations by incorporating the fuzzy set theory. In this paper, the fuzzy equations are applied as the models for the uncertain nonlinear systems. The nonlinear modeling process is to find the coefficients of the fuzzy equations. We use the neural networks to approximate the coefficients of the fuzzy equations. The approximation theory for crisp models is extended into the fuzzy equation model. The upper bounds of the modeling errors are estimated. Numerical experiments along with comparisons demonstrate the excellent behavior of the proposed method.
Axiomatic of Fuzzy Complex Numbers
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Angel Garrido
2012-04-01
Full Text Available Fuzzy numbers are fuzzy subsets of the set of real numbers satisfying some additional conditions. Fuzzy numbers allow us to model very difficult uncertainties in a very easy way. Arithmetic operations on fuzzy numbers have also been developed, and are based mainly on the crucial Extension Principle. When operating with fuzzy numbers, the results of our calculations strongly depend on the shape of the membership functions of these numbers. Logically, less regular membership functions may lead to very complicated calculi. Moreover, fuzzy numbers with a simpler shape of membership functions often have more intuitive and more natural interpretations. But not only must we apply the concept and the use of fuzzy sets, and its particular case of fuzzy number, but also the new and interesting mathematical construct designed by Fuzzy Complex Numbers, which is much more than a correlate of Complex Numbers in Mathematical Analysis. The selected perspective attempts here that of advancing through axiomatic descriptions.
PC-Cluster based Storage System Architecture for Cloud Storage
Yee, Tin Tin
2011-01-01
Design and architecture of cloud storage system plays a vital role in cloud computing infrastructure in order to improve the storage capacity as well as cost effectiveness. Usually cloud storage system provides users to efficient storage space with elasticity feature. One of the challenges of cloud storage system is difficult to balance the providing huge elastic capacity of storage and investment of expensive cost for it. In order to solve this issue in the cloud storage infrastructure, low cost PC cluster based storage server is configured to be activated for large amount of data to provide cloud users. Moreover, one of the contributions of this system is proposed an analytical model using M/M/1 queuing network model, which is modeled on intended architecture to provide better response time, utilization of storage as well as pending time when the system is running. According to the analytical result on experimental testing, the storage can be utilized more than 90% of storage space. In this paper, two parts...
An improved unsupervised clustering-based intrusion detection method
Hai, Yong J.; Wu, Yu; Wang, Guo Y.
2005-03-01
Practical Intrusion Detection Systems (IDSs) based on data mining are facing two key problems, discovering intrusion knowledge from real-time network data, and automatically updating them when new intrusions appear. Most data mining algorithms work on labeled data. In order to set up basic data set for mining, huge volumes of network data need to be collected and labeled manually. In fact, it is rather difficult and impractical to label intrusions, which has been a big restrict for current IDSs and has led to limited ability of identifying all kinds of intrusion types. An improved unsupervised clustering-based intrusion model working on unlabeled training data is introduced. In this model, center of a cluster is defined and used as substitution of this cluster. Then all cluster centers are adopted to detect intrusions. Testing on data sets of KDDCUP"99, experimental results demonstrate that our method has good performance in detection rate. Furthermore, the incremental-learning method is adopted to detect those unknown-type intrusions and it decreases false positive rate.
A Secure Cluster-Based Multipath Routing Protocol for WMSNs
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Jamal N. Al-Karaki
2011-04-01
Full Text Available The new characteristics of Wireless Multimedia Sensor Network (WMSN and its design issues brought by handling different traffic classes of multimedia content (video streams, audio, and still images as well as scalar data over the network, make the proposed routing protocols for typical WSNs not directly applicable for WMSNs. Handling real-time multimedia data requires both energy efficiency and QoS assurance in order to ensure efficient utility of different capabilities of sensor resources and correct delivery of collected information. In this paper, we propose a Secure Cluster-based Multipath Routing protocol for WMSNs, SCMR, to satisfy the requirements of delivering different data types and support high data rate multimedia traffic. SCMR exploits the hierarchical structure of powerful cluster heads and the optimized multiple paths to support timeliness and reliable high data rate multimedia communication with minimum energy dissipation. Also, we present a light-weight distributed security mechanism of key management in order to secure the communication between sensor nodes and protect the network against different types of attacks. Performance evaluation from simulation results demonstrates a significant performance improvement comparing with existing protocols (which do not even provide any kind of security feature in terms of average end-to-end delay, network throughput, packet delivery ratio, and energy consumption.
Energy Aware Cluster Based Routing Scheme For Wireless Sensor Network
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Roy Sohini
2015-09-01
Full Text Available Wireless Sensor Network (WSN has emerged as an important supplement to the modern wireless communication systems due to its wide range of applications. The recent researches are facing the various challenges of the sensor network more gracefully. However, energy efficiency has still remained a matter of concern for the researches. Meeting the countless security needs, timely data delivery and taking a quick action, efficient route selection and multi-path routing etc. can only be achieved at the cost of energy. Hierarchical routing is more useful in this regard. The proposed algorithm Energy Aware Cluster Based Routing Scheme (EACBRS aims at conserving energy with the help of hierarchical routing by calculating the optimum number of cluster heads for the network, selecting energy-efficient route to the sink and by offering congestion control. Simulation results prove that EACBRS performs better than existing hierarchical routing algorithms like Distributed Energy-Efficient Clustering (DEEC algorithm for heterogeneous wireless sensor networks and Energy Efficient Heterogeneous Clustered scheme for Wireless Sensor Network (EEHC.
Cluster based parallel database management system for data intensive computing
Institute of Scientific and Technical Information of China (English)
Jianzhong LI; Wei ZHANG
2009-01-01
This paper describes a computer-cluster based parallel database management system (DBMS), InfiniteDB, developed by the authors. InfiniteDB aims at efficiently sup-port data intensive computing in response to the rapid grow-ing in database size and the need of high performance ana-lyzing of massive databases. It can be efficiently executed in the computing system composed by thousands of computers such as cloud computing system. It supports the parallelisms of intra-query, inter-query, intra-operation, inter-operation and pipelining. It provides effective strategies for managing massive databases including the multiple data declustering methods, the declustering-aware algorithms for relational operations and other database operations, and the adaptive query optimization method. It also provides the functions of parallel data warehousing and data mining, the coordinator-wrapper mechanism to support the integration of heteroge-neous information resources on the Internet, and the fault tol-erant and resilient infrastructures. It has been used in many applications and has proved quite effective for data intensive computing.
A secure cluster-based multipath routing protocol for WMSNs.
Almalkawi, Islam T; Zapata, Manel Guerrero; Al-Karaki, Jamal N
2011-01-01
The new characteristics of Wireless Multimedia Sensor Network (WMSN) and its design issues brought by handling different traffic classes of multimedia content (video streams, audio, and still images) as well as scalar data over the network, make the proposed routing protocols for typical WSNs not directly applicable for WMSNs. Handling real-time multimedia data requires both energy efficiency and QoS assurance in order to ensure efficient utility of different capabilities of sensor resources and correct delivery of collected information. In this paper, we propose a Secure Cluster-based Multipath Routing protocol for WMSNs, SCMR, to satisfy the requirements of delivering different data types and support high data rate multimedia traffic. SCMR exploits the hierarchical structure of powerful cluster heads and the optimized multiple paths to support timeliness and reliable high data rate multimedia communication with minimum energy dissipation. Also, we present a light-weight distributed security mechanism of key management in order to secure the communication between sensor nodes and protect the network against different types of attacks. Performance evaluation from simulation results demonstrates a significant performance improvement comparing with existing protocols (which do not even provide any kind of security feature) in terms of average end-to-end delay, network throughput, packet delivery ratio, and energy consumption.
Energy Efficient Cluster Based Scheduling Scheme for Wireless Sensor Networks.
Janani, E Srie Vidhya; Kumar, P Ganesh
2015-01-01
The energy utilization of sensor nodes in large scale wireless sensor network points out the crucial need for scalable and energy efficient clustering protocols. Since sensor nodes usually operate on batteries, the maximum utility of network is greatly dependent on ideal usage of energy leftover in these sensor nodes. In this paper, we propose an Energy Efficient Cluster Based Scheduling Scheme for wireless sensor networks that balances the sensor network lifetime and energy efficiency. In the first phase of our proposed scheme, cluster topology is discovered and cluster head is chosen based on remaining energy level. The cluster head monitors the network energy threshold value to identify the energy drain rate of all its cluster members. In the second phase, scheduling algorithm is presented to allocate time slots to cluster member data packets. Here congestion occurrence is totally avoided. In the third phase, energy consumption model is proposed to maintain maximum residual energy level across the network. Moreover, we also propose a new packet format which is given to all cluster member nodes. The simulation results prove that the proposed scheme greatly contributes to maximum network lifetime, high energy, reduced overhead, and maximum delivery ratio.
Utility-guided Clustering-based Transaction Data Anonymization
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Aris Gkoulalas-Divanis
2012-04-01
Full Text Available Transaction data about individuals are increasingly collected to support a plethora of applications, spanning from marketing to biomedical studies. Publishing these data is required by many organizations, but may result in privacy breaches, if an attacker exploits potentially identifying information to link individuals to their records in the published data. Algorithms that prevent this threat by transforming transaction data prior to their release have been proposed recently, but they may incur significant utility loss due to their inability to: (i accommodate a range of different privacy requirements that data owners often have, and (ii guarantee that the produced data will satisfy data owners’ utility requirements. To address this issue, we propose a novel clustering-based framework to anonymizing transaction data, which provides the basis for designing algorithms that better preserve data utility. Based on this framework, we develop two anonymization algorithms which explore a larger solution space than existing methods and can satisfy a wide range of privacy requirements. Additionally, the second algorithm allows the specification and enforcement of utility requirements, thereby ensuring that the anonymized data remain useful in intended tasks. Experiments with both benchmark and real medical datasets verify that our algorithms significantly outperform the current state-of-the-art algorithms in terms of data utility, while being comparable in terms of efficiency.
WORMHOLE ATTACK MITIGATION IN MANET: A CLUSTER BASED AVOIDANCE TECHNIQUE
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Subhashis Banerjee
2014-01-01
Full Text Available A Mobile Ad-Hoc Network (MANET is a self configuring, infrastructure less network of mobile devices connected by wireless links. Loopholes like wireless medium, lack of a fixed infrastructure, dynamic topology, rapid deployment practices, and the hostile environments in which they may be deployed, make MANET vulnerable to a wide range of security attacks and Wormhole attack is one of them. During this attack a malicious node captures packets from one location in the network, and tunnels them to another colluding malicious node at a distant point, which replays them locally. This paper presents a cluster based Wormhole attack avoidance technique. The concept of hierarchical clustering with a novel hierarchical 32- bit node addressing scheme is used for avoiding the attacking path during the route discovery phase of the DSR protocol, which is considered as the under lying routing protocol. Pinpointing the location of the wormhole nodes in the case of exposed attack is also given by using this method.
Homomorphic Properties of Fuzzy Rough Groups
Institute of Scientific and Technical Information of China (English)
QIN Ke-yun; ZHANG Xiao-hua
2012-01-01
This paper is devoted to the discussion of homomorphic properties of fuzzy rough groups.The fuzzy approximation space was generated by fuzzy normal subgroups and the fuzzy rough approximation operators were discussed in the frame of fuzzy rough set model.The basic properties of fuzzy rough approximation operators were obtained.
Some Results on Fuzzy Soft Topological Spaces
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Cigdem Gunduz (Aras
2013-01-01
Full Text Available We introduce some important properties of fuzzy soft topological spaces. Furthermore, fuzzy soft continuous mapping, fuzzy soft open and fuzzy soft closed mappings, and fuzzy soft homeomorphism for fuzzy soft topological spaces are given and structural characteristics are discussed and studied.
Fuzzy Rough Ring and Its Prop erties
Institute of Scientific and Technical Information of China (English)
REN Bi-jun; FU Yan-ling
2013-01-01
This paper is devoted to the theories of fuzzy rough ring and its properties. The fuzzy approximation space generated by fuzzy ideals and the fuzzy rough approximation operators were proposed in the frame of fuzzy rough set model. The basic properties of fuzzy rough approximation operators were analyzed and the consistency between approximation operators and the binary operation of ring was discussed.
COMPARISON OF DIFFERENT SEGMENTATION ALGORITHMS FOR DERMOSCOPIC IMAGES
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A.A. Haseena Thasneem
2015-05-01
Full Text Available This paper compares different algorithms for the segmentation of skin lesions in dermoscopic images. The basic segmentation algorithms compared are Thresholding techniques (Global and Adaptive, Region based techniques (K-means, Fuzzy C means, Expectation Maximization and Statistical Region Merging, Contour models (Active Contour Model and Chan - Vese Model and Spectral Clustering. Accuracy, sensitivity, specificity, Border error, Hammoude distance, Hausdorff distance, MSE, PSNR and elapsed time metrices were used to evaluate various segmentation techniques.
Some Weaker Forms of Fuzzy Faintly Open Mappings
Hakeem A. Othman
2015-01-01
This paper is devoted to introduce and investigate some weak forms of fuzzy open mappings, namely fuzzy faintly semi open (fuzzy faintly semi closed), fuzzy faintly preopen (fuzzy faintly preclosed), fuzzy faintly $\\alpha$-open (fuzzy faintly $\\alpha$-closed), fuzzy faintly semi preopen (fuzzy faintly semi preclosed) and fuzzy faintly $sp$- open (fuzzy faintly $sp$- closed) mappings and their fundamental properties are obtained. Moreover, their relationship with other types of fuzzy open (clo...
Fuzzy Sets, Fuzzy S-Open and S-Closed Mappings
Ahmad, B; Athar Kharal
2009-01-01
Several properties of fuzzy semiclosure and fuzzy semi-interior of fuzzy sets defined by Yalvac (1988), have been established and supported by counterexamples. We also study the characterizations and properties of fuzzy semi-open and fuzzy semi-closed sets. Moreover, we define fuzzy s-open and fuzzy s-closed mappings and give some interesting characterizations.
On the Splitting Algorithm Based on Multi-target Model for Image Segmentation
Yuezhongyi Sun
2014-01-01
Against to the different regions of membership functions indicated image in the traditional image segmentation variational model, resulting segmentation is not clear, de-noising effect is not obvious problems, this paper proposes multi-target model for image segmentation and the splitting algorithm. The model uses a sparse regularization method to maintain the boundaries of segmented regions, to overcome the disadvantages of segmentation fuzzy boundaries resulting from total variation regular...
Automated Brain Tumor Segmentation on MR Images Based on Neutrosophic Set Approach
Mohan J; Krishnaveni V; Yanhui Huo
2015-01-01
Brain tumor segmentation for MR images is a difficult and challenging task due to variation in type, size, location and shape of tumors. This paper presents an efficient and fully automatic brain tumor segmentation technique. This proposed technique includes non local preprocessing, fuzzy intensification to enhance the quality of the MR images, k - means clustering method for brain tumor segmentation.
Segmentation: Identification of consumer segments
DEFF Research Database (Denmark)
Høg, Esben
2005-01-01
It is very common to categorise people, especially in the advertising business. Also traditional marketing theory has taken in consumer segments as a favorite topic. Segmentation is closely related to the broader concept of classification. From a historical point of view, classification has its...... and analysed possible segments in the market. Results show that the statistical model used identified two segments - a segment of so-called "fish lovers" and another segment called "traditionalists". The "fish lovers" are very fond of eating fish and they actually prefer fish to other dishes...... origin in other sciences as for example biology, anthropology etc. From an economic point of view, it is called segmentation when specific scientific techniques are used to classify consumers to different characteristic groupings. What is the purpose of segmentation? For example, to be able to obtain...
Extended Fuzzy Logic Programs with Fuzzy Answer Set Semantics
Saad, Emad
This paper extends fuzzy logic programs [12, 24] to allow the explicit representation of classical negation as well as non-monotonic negation, by introducing the notion of extended fuzzy logic programs. We present the fuzzy answer set semantics for the extended fuzzy logic programs, which is based on the classical answer set semantics of classical extended logic programs [7]. We show that the proposed semantics is a natural extension to the classical answer set semantics of classical extended logic programs [7]. Furthermore, we define fixpoint semantics for extended fuzzy logic programs with and without non-monotonic negation, and study their relationship to the fuzzy answer set semantics. In addition, we show that the fuzzy answer set semantics is reduced to the stable fuzzy model semantics for normal fuzzy logic programs introduced in [42]. The importance of that is computational methods developed for normal fuzzy logic programs can be applied to the extended fuzzy logic programs. Moreover, we show that extended fuzzy logic programs can be intuitively used for representing and reasoning about actions in fuzzy environment.
Fuzziness and Relevance Theory
Institute of Scientific and Technical Information of China (English)
Grace Qiao Zhang
2005-01-01
This paper investigates how the phenomenon of fuzzy language, such as `many' in `Mary has many friends', can be explained by Relevance Theory. It is concluded that fuzzy language use conforms with optimal relevance in that it can achieve the greatest positive effect with the least processing effort. It is the communicators themselves who decide whether or not optimal relevance is achieved, rather than the language form (fuzzy or non-fuzzy) used. People can skillfully adjust the deployment of different language forms or choose appropriate interpretations to suit different situations and communication needs. However, there are two challenges to RT: a. to extend its theory from individual relevance to group relevance; b. to embrace cultural considerations (because when relevance principles and cultural protocols are in conflict, the latter tends to prevail).
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Renato César Scarparo
2002-01-01
Full Text Available En este trabajo se presentan y demuestran algunos resultados de D.T. Luc y C, Vargas referentes a multifunciones con dominio y blanco en espacios vectoriales topológicos de Hausdorff sobre R, como así mismo se explícita el concepto de multifunción fuzzy de acuerdo a Papageogiou, y se demuestran dos teorema de S. S. Chag, con respecto a las multifunciones fuzzy, proposiciones todas estas, que integran una línea de resultados necesarios para la demostración de desigualdades variacionales para multifunciones fuzzy, a su vez necesarias, para la extensión fuzzy de conocido teorema de Walras.
Bandemer, Hans
1992-01-01
Fuzzy data such as marks, scores, verbal evaluations, imprecise observations, experts' opinions and grey tone pictures, are quite common. In Fuzzy Data Analysis the authors collect their recent results providing the reader with ideas, approaches and methods for processing such data when looking for sub-structures in knowledge bases for an evaluation of functional relationship, e.g. in order to specify diagnostic or control systems. The modelling presented uses ideas from fuzzy set theory and the suggested methods solve problems usually tackled by data analysis if the data are real numbers. Fuzzy Data Analysis is self-contained and is addressed to mathematicians oriented towards applications and to practitioners in any field of application who have some background in mathematics and statistics.
Fuzzy stochastic multiobjective programming
Sakawa, Masatoshi; Katagiri, Hideki
2011-01-01
With a stress on interactive decision-making, this work breaks new ground by covering both the random nature of events related to environments, and the fuzziness of human judgements. The text runs from mathematical preliminaries to future research directions.
Malhas, Othman Qasim
1993-10-01
The concept of “abacus logic” has recently been developed by the author (Malhas, n.d.). In this paper the relation of abacus logic to the concept of fuzziness is explored. It is shown that if a certain “regularity” condition is met, concepts from fuzzy set theory arise naturally within abacus logics. In particular it is shown that every abacus logic then has a “pre-Zadeh orthocomplementation”. It is also shown that it is then possible to associate a fuzzy set with every proposition of abacus logic and that the collection of all such sets satisfies natural conditions expected in systems of fuzzy logic. Finally, the relevance to quantum mechanics is discussed.
Dialectic operator fuzzy logic
Institute of Scientific and Technical Information of China (English)
程晓春; 姜云飞; 刘叙华
1996-01-01
Dialectic operator fuzzy logic (DOFL) is presented which is relevant,paraconsistent and nonmonotonic.DOFL can vividly describe the belief revision in the cognitive process and can infer reasonably well while the knowledge is inconsistent,imprecise or incomplete.
Yildiz, Cemil; ABBAS, Fadhil
2011-01-01
The concepts of fuzzy regular-I-closed set and fuzzy semi-I-regular set in fuzzy ideal topological spaces are investigated and some of their properties are obtained. Key words: Topological, Spaces, Fuzzy, Regular, Sets
Fuzzy forecasting based on fuzzy-trend logical relationship groups.
Chen, Shyi-Ming; Wang, Nai-Yi
2010-10-01
In this paper, we present a new method to predict the Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX) based on fuzzy-trend logical relationship groups (FTLRGs). The proposed method divides fuzzy logical relationships into FTLRGs based on the trend of adjacent fuzzy sets appearing in the antecedents of fuzzy logical relationships. First, we apply an automatic clustering algorithm to cluster the historical data into intervals of different lengths. Then, we define fuzzy sets based on these intervals of different lengths. Then, the historical data are fuzzified into fuzzy sets to derive fuzzy logical relationships. Then, we divide the fuzzy logical relationships into FTLRGs for forecasting the TAIEX. Moreover, we also apply the proposed method to forecast the enrollments and the inventory demand, respectively. The experimental results show that the proposed method gets higher average forecasting accuracy rates than the existing methods.
Fuzzy variable linear programming with fuzzy technical coefficients
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Sanwar Uddin Ahmad
2012-11-01
Full Text Available Normal 0 false false false EN-US X-NONE X-NONE Fuzzy linear programming is an application of fuzzy set theory in linear decision making problems and most of these problems are related to linear programming with fuzzy variables. In this paper an approximate but convenient method for solving these problems with fuzzy non-negative technical coefficient and without using the ranking functions, is proposed. With the help of numerical examples, the method is illustrated.
Intuitionistic fuzzy alpha-continuity and intuitionistic fuzzy precontinuity
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Joung Kon Jeon
2005-01-01
Full Text Available A characterization of intuitionistic fuzzy α-open set is given, and conditions for an IFS to be an intuitionistic fuzzy α-open set are provided. Characterizations of intuitionistic fuzzy precontinuous (resp., α-continuous mappings are given.
一种基于模糊核聚类的脑部磁共振图像分割算法%An algorithm for MRI brain image segmentation based on fuzzy kernel clustering
Institute of Scientific and Technical Information of China (English)
相艳; 贺建峰; 易三莉; 徐家萍; 张娴文
2013-01-01
目的 针对普通模糊核聚类算法(kernel fuzzy c-means clustering algorithm,KFCM)存在的随机选择初始聚类中心的问题,本文提出一种根据直方图得到确定的初始聚类中心的模糊核聚类算法,以更快速地分割脑部磁共振图像.方法 首先利用区域生长法和形态学方法对原始脑部磁共振图像进行预处理,提取脑实质,然后计算出预处理图像的直方图,将直方图的4个峰值作为模糊核聚类的初始聚类中心,最后利用模糊核聚类算法对脑实质进行分割.结果 本文算法能有效地提取出脑组织中的白质(white matter,WM)、灰质(grey matter,GM)和脑脊髓液(cerebral spinal fluid,CSF).与普通模糊核聚类算法相比,该算法的目标函数能更快地达到平稳,从而缩短运行时间.结论 本文算法与随机选择聚类中心的模糊核聚类算法相比,可减少迭代次数,更快地得到分割结果.
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Kyung Ho Kim
2001-01-01
Full Text Available We consider the semigroup S¯ of the fuzzy points of a semigroup S, and discuss the relation between the fuzzy interior ideals and the subsets of S¯ in an (intra-regular semigroup S.
Shapley's value for fuzzy games
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Raúl Alvarado Sibaja
2009-02-01
Full Text Available This is the continuation of a previous article titled "Fuzzy Games", where I defined a new type of games based on the Multilinear extensions f, of characteristic functions and most of standard theorems for cooperative games also hold for this new type of games: The fuzzy games. Now we give some other properties and the extension of the definition of Shapley¨s Value for Fuzzy Games Keywords: game theory, fuzzy sets, multiattribute decisions.
Compactness theorems of fuzzy semantics
Institute of Scientific and Technical Information of China (English)
无
2000-01-01
The relationship among diverse fuzzy semantics vs. the corresponding logic consequence operators has been analyzed systematically. The results that compactness and logical compactness of fuzzy semantics are equivalent to compactness and continuity of the logic consequence operator induced by the semantics respectively have been proved under certain conditions. A general compactness theorem of fuzzy semantics have been established which says that every fuzzy semantics defined on a free algebra with members corresponding to continuous functions is compact.
Yildiz, Osman; Bal, Abdullah; Gulsecen, Sevinc
2015-01-01
The demand for distance education has been increasing at a rapid pace all around the world. This, in turn, places a special importance on the need for the development of more distance education systems. However, there is an alarming rise in the number of distance education students that drop out of the system without asking for any help. The…
Directory of Open Access Journals (Sweden)
Kharatti Lal
2015-12-01
Full Text Available This section define a level subring or level ideals obtain a set of necessary and sufficient condition for the equality of two ideals and characterizes field in terms of its fuzzy ideals. It also presents a procedure to construct a fuzzy subrings (fuzzy ideals from any given ascending chain of subring ideal. We prove that the lattice of fuzzy congruence of group G (respectively ring R is isomorphic to the lattice of fuzzy normal subgroup of G (respectively fuzzy ideals of R.In Yuan Boond Wu wangrning investigated the relationship between the fuzzy ideals and the fuzzy congruences on a distributive lattice and obtained that the lattice of fuzzy ideals is isomorphic to the lattice of fuzzy congruences on a generalized Boolean algebra. Fuzzy group theory can be used to describe, symmetries and permutation in nature and mathematics. The fuzzy group is one of the oldest branches of abstract algebra. For example group can be used is classify to all of the forms chemical crystal can take. Group can be used to count the number of non-equivalent objects and permutation or symmetries. For example, the number of different is switching functions of n, variable when permutation of the input are allowed. Beside crystallography and combinatory group have application of quantum mechanics.
Possibility Intuitionistic Fuzzy Soft Set
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Maruah Bashir
2012-01-01
Full Text Available Possibility intuitionistic fuzzy soft set and its operations are introduced, and a few of their properties are studied. An application of possibility intuitionistic fuzzy soft sets in decision making is investigated. A similarity measure of two possibility intuitionistic fuzzy soft sets has been discussed. An application of this similarity measure in medical diagnosis has been shown.
Fuzzy Soft Compact Topological Spaces
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Seema Mishra
2016-01-01
Full Text Available In this paper, we have studied compactness in fuzzy soft topological spaces which is a generalization of the corresponding concept by R. Lowen in the case of fuzzy topological spaces. Several basic desirable results have been established. In particular, we have proved the counterparts of Alexander’s subbase lemma and Tychonoff theorem for fuzzy soft topological spaces.
Two-Point Fuzzy Ostrowski Type Inequalities
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Muhammad Amer Latif
2013-08-01
Full Text Available Two-point fuzzy Ostrowski type inequalities are proved for fuzzy Hölder and fuzzy differentiable functions. The two-point fuzzy Ostrowski type inequality for M-lipshitzian mappings is also obtained. It is proved that only the two-point fuzzy Ostrowski type inequality for M-lipshitzian mappings is sharp and as a consequence generalize the two-point fuzzy Ostrowski type inequalities obtained for fuzzy differentiable functions.
Cluster-based Multihop Synchronization Scheme for Femtocell Network
Directory of Open Access Journals (Sweden)
Aisha H. Abdalla
2012-10-01
Full Text Available ABSTRACT: Femtocell technology has been drawing considerable attention as a cost-effective means of improving cellular coverage and capacity. It is connected to the core network through an IP backhaul and can only use timing protocols such as IEEE1588 or Network Time Protocol (NTP. Furthermore, the femtocell is installed indoor, and cannot use a GPS antenna for time synchronization. High-precision crystal oscillators can solve the timing problem, but they are often too expensive for consumer grade devices. Therefore, femtocell Base Station (fBS synchronization is one of the principle technical trends in femtocell deployment. Since fBSand macrocell Base Station (mBS network operates on the same frequency under a licensed spectrum, fBS network can interfere with the macrocell network. In addition, fBSs can also interfere with each other if multiple units are in close proximity. Furthermore, in a flat fBS structured network using IEEE 1588 synchronization algorithm and fBS-fBS synchronization scheme creates offset and frequency error which results inaccurate synchronization. In order to reduce offset and frequency error (skew, this paper proposed a cluster-based multihop synchronization scheme to achieve precise in fBS neighbor nodes. The proposed scheme is able to reduce the offset and skew significantly.ABSTRAK: Teknologi Femtocell telah menjadi tumpuan sebagai alat yang kos-efektif dalam memperbaiki liputan mudahalih dan kapasiti. Ia menghubungkan jaringan teras melalui IP backhaul dan hanya boleh menggunakan protokol masa seperti IEEE1588 atau Protokol Jaringan Masa (NTP. Seterusnya, femtocell dipasang di dalam, dan tidak boleh menggunakan antena GPS untuk sinkronisasi masa. Osilator Kristal yang tinggi kejituannya boleh menyelesaikan masalah masa, tetapi ianya mahal bagi gred peranti consumer. Oleh itu, sinkronisasi Stesen Asas femtocell (fBS adalah salah satu tren teknikal prinsip dalam deployment femtocell. Memandangkan fBS dan jaringan
Bifundamental Fuzzy 2-Sphere and Fuzzy Killing Spinors
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Horatiu Nastase
2010-07-01
Full Text Available We review our construction of a bifundamental version of the fuzzy 2-sphere and its relation to fuzzy Killing spinors, first obtained in the context of the ABJM membrane model. This is shown to be completely equivalent to the usual (adjoint fuzzy sphere. We discuss the mathematical details of the bifundamental fuzzy sphere and its field theory expansion in a model-independent way. We also examine how this new formulation affects the twisting of the fields, when comparing the field theory on the fuzzy sphere background with the compactification of the 'deconstructed' (higher dimensional field theory.
量子蚁群模糊聚类算法在图像分割中的应用%Image Segmentation Based on Quantum Ant Colony Fuzzy Clustering Algorithm
Institute of Scientific and Technical Information of China (English)
李积英; 党建武
2013-01-01
Fuzzy C-Means algorithm is dependent on the initial value, resulting in easy to fall into the disadvantage of the local optimum value. A combination of quantum ant colony algorithm and FCM clustering algorithm is put forward. Firstly, the original center and numbers of cluster of the image are determined by using global type, robustness and advantages of fast convergence of quantum ant colony algorithm. Secondly, the obtained results are taken as the initial parameters for FCM clustering algorithm, and then the medical image is divided by using FCM clustering algorithm. It is proved that the method has reduced the dependence of FCM clustering algorithm on initial parameters effectively, overcome the shortcomings of easy falling into the local minimum of both algorithms,and greatly improved dividing speed and accuracy, which is simulated by real experiment.% 针对模糊C-均值算法对初始值的依赖，容易陷入局部最优值的缺点，本文提出将量子蚁群算法与FCM聚类算法结合，首先利用量子蚁群算法的全局性和鲁棒性以及快速收敛的优点确定图像的初始聚类中心和聚类个数，再将所得结果作为FCM聚类算法的初始参数，然后用FCM聚类算法对医学图像进行分割。实验结果表明，该方法有效解决了FCM算法对初始参数的依赖，克服了FCM算法及蚁群算法容易陷入局部极值的的缺点，而且在分割速度和精度上得到了较大提高。
Multiple Fuzzy Classification Systems
Scherer, Rafał
2012-01-01
Fuzzy classiﬁers are important tools in exploratory data analysis, which is a vital set of methods used in various engineering, scientiﬁc and business applications. Fuzzy classiﬁers use fuzzy rules and do not require assumptions common to statistical classiﬁcation. Rough set theory is useful when data sets are incomplete. It deﬁnes a formal approximation of crisp sets by providing the lower and the upper approximation of the original set. Systems based on rough sets have natural ability to work on such data and incomplete vectors do not have to be preprocessed before classiﬁcation. To achieve better performance than existing machine learning systems, fuzzy classifiers and rough sets can be combined in ensembles. Such ensembles consist of a ﬁnite set of learning models, usually weak learners. The present book discusses the three aforementioned ﬁelds – fuzzy systems, rough sets and ensemble techniques. As the trained ensemble should represent a single hypothesis, a lot of attention is placed o...
Possibilistic Exponential Fuzzy Clustering
Institute of Scientific and Technical Information of China (English)
Kiatichai Treerattanapitak; Chuleerat Jaruskulchai
2013-01-01
Generally,abnormal points (noise and outliers) cause cluster analysis to produce low accuracy especially in fuzzy clustering.These data not only stay in clusters but also deviate the centroids from their true positions.Traditional fuzzy clustering like Fuzzy C-Means (FCM) always assigns data to all clusters which is not reasonable in some circumstances.By reformulating objective function in exponential equation,the algorithm aggressively selects data into the clusters.However noisy data and outliers cannot be properly handled by clustering process therefore they are forced to be included in a cluster because of a general probabilistic constraint that the sum of the membership degrees across all clusters is one.In order to improve this weakness,possibilistic approach relaxes this condition to improve membership assignment.Nevertheless,possibilistic clustering algorithms generally suffer from coincident clusters because their membership equations ignore the distance to other clusters.Although there are some possibilistic clustering approaches that do not generate coincident clusters,most of them require the right combination of multiple parameters for the algorithms to work.In this paper,we theoretically study Possibilistic Exponential Fuzzy Clustering (PXFCM) that integrates possibilistic approach with exponential fuzzy clustering.PXFCM has only one parameter and not only partitions the data but also filters noisy data or detects them as outliers.The comprehensive experiments show that PXFCM produces high accuracy in both clustering results and outlier detection without generating coincident problems.
COMPARISON OF SVM AND FUZZY CLASSIFIER FOR AN INDIAN SCRIPT
Directory of Open Access Journals (Sweden)
M. J. Baheti
2012-01-01
Full Text Available With the advent of technological era, conversion of scanned document (handwritten or printed into machine editable format has attracted many researchers. This paper deals with the problem of recognition of Gujarati handwritten numerals. Gujarati numeral recognition requires performing some specific steps as a part of preprocessing. For preprocessing digitization, segmentation, normalization and thinning are done with considering that the image have almost no noise. Further affine invariant moments based model is used for feature extraction and finally Support Vector Machine (SVM and Fuzzy classifiers are used for numeral classification. . The comparison of SVM and Fuzzy classifier is made and it can be seen that SVM procured better results as compared to Fuzzy Classifier.
Using Fuzzy Hybrid Features to Classify Strokes in Interactive Sketches
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Shuxia Wang
2013-01-01
Full Text Available A novel method is presented based on fuzzy hybrid-based features to classify strokes into 2D line drawings, and a human computer interactive system is developed for assisting designers in conceptual design stage. Fuzzy classifiers are built based on some geometric features and speed features. The prototype system can support rapid classification based on fuzzy classifiers, and the classified stroke is then fitted with a 2D geometry primitive which could be a line segment, polyline, circle, circular arc, ellipse, elliptical arc, hyperbola, and parabola. The human computer interaction can determine the ambiguous results and then revise the misrecognitions. The test results showed that the proposed method can support online freehand sketching based on conceptual design with no limitation on drawing sequence and direction while achieving a satisfactory interpretation rate.
A comparative analysis between fuzzy topsis and simplified fuzzy topsis
Ahmad, Sharifah Aniza Sayed; Mohamad, Daud
2017-08-01
Fuzzy Multiple Criteria Decision Making plays an important role in solving problems in decision making under fuzzy environment. Among the popular methods used is the fuzzy TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) where the solution is based on the shortest distance from its positive ideal solution and the farthest distance from its negative ideal solution. The fuzzy TOPSIS method was first introduced by Chen (2000). At present, there are several variants of fuzzy TOPSIS methods and each of them claimed to have its own advantages. In this paper, a comparative analysis is made between the classical fuzzy TOPSIS method proposed by Chen in 2000 and the simplified fuzzy TOPSIS proposed by Sodhi in 2012. The purpose of this study is to show the similarities and the differences between these two methods and also elaborate on their strengths and limitations as well. A comparison is also made by providing numerical examples of both methods.
A neural fuzzy controller learning by fuzzy error propagation
Nauck, Detlef; Kruse, Rudolf
1992-01-01
In this paper, we describe a procedure to integrate techniques for the adaptation of membership functions in a linguistic variable based fuzzy control environment by using neural network learning principles. This is an extension to our work. We solve this problem by defining a fuzzy error that is propagated back through the architecture of our fuzzy controller. According to this fuzzy error and the strength of its antecedent each fuzzy rule determines its amount of error. Depending on the current state of the controlled system and the control action derived from the conclusion, each rule tunes the membership functions of its antecedent and its conclusion. By this we get an unsupervised learning technique that enables a fuzzy controller to adapt to a control task by knowing just about the global state and the fuzzy error.
A Cluster-based Method to Map Urban Area from DMSP/OLS Nightlights
Energy Technology Data Exchange (ETDEWEB)
Zhou, Yuyu; Smith, Steven J.; Elvidge, Christopher; Zhao, Kaiguang; Thomson, Allison M.; Imhoff, Marc L.
2014-05-05
Accurate information of urban areas at regional and global scales is important for both the science and policy-making communities. The Defense Meteorological Satellite Program/Operational Linescan System (DMSP/OLS) nighttime stable light data (NTL) provide a potential way to map urban area and its dynamics economically and timely. In this study, we developed a cluster-based method to estimate the optimal thresholds and map urban extents from the DMSP/OLS NTL data in five major steps, including data preprocessing, urban cluster segmentation, logistic model development, threshold estimation, and urban extent delineation. Different from previous fixed threshold method with over- and under-estimation issues, in our method the optimal thresholds are estimated based on cluster size and overall nightlight magnitude in the cluster, and they vary with clusters. Two large countries of United States and China with different urbanization patterns were selected to map urban extents using the proposed method. The result indicates that the urbanized area occupies about 2% of total land area in the US ranging from lower than 0.5% to higher than 10% at the state level, and less than 1% in China, ranging from lower than 0.1% to about 5% at the province level with some municipalities as high as 10%. The derived thresholds and urban extents were evaluated using high-resolution land cover data at the cluster and regional levels. It was found that our method can map urban area in both countries efficiently and accurately. Compared to previous threshold techniques, our method reduces the over- and under-estimation issues, when mapping urban extent over a large area. More important, our method shows its potential to map global urban extents and temporal dynamics using the DMSP/OLS NTL data in a timely, cost-effective way.
-Fuzzy Ideals in Ordered Semigroups
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Asghar Khan
2009-01-01
Full Text Available We introduce the concept of 𝒩-fuzzy left (right ideals in ordered semigroups and characterize ordered semigroups in terms of 𝒩-fuzzy left (right ideals. We characterize left regular (right regular and left simple (right simple ordered semigroups in terms of 𝒩-fuzzy left (𝒩-fuzzy right ideals. The semilattice of left (right simple semigroups in terms of 𝒩-fuzzy left (right ideals is discussed.
Tuning of Fuzzy PID Controllers
DEFF Research Database (Denmark)
Jantzen, Jan
1998-01-01
Since fuzzy controllers are nonlinear, it is more difficult to set the controller gains compared to proportional-integral-derivative (PID) controllers. This research paper proposes a design procedure and a tuning procedure that carries tuning rules from the PID domain over to fuzzy single......-loop controllers. The idea is to start with a tuned, conventional PID controller, replace it with an equivalent linear fuzzy controller, make the fuzzy controller nonlinear, and eventually fine-tune the nonlinear fuzzy controller. This is relevant whenever a PID controller is possible or already implemented....
The foundations of fuzzy control
Lewis, Harold W
1997-01-01
Harold Lewis applied a cross-disciplinary approach in his highly accessible discussion of fuzzy control concepts. With the aid of fifty-seven illustrations, he thoroughly presents a unique mathematical formalism to explain the workings of the fuzzy inference engine and a novel test plant used in the research. Additionally, the text posits a new viewpoint on why fuzzy control is more popular in some countries than in others. A direct and original view of Japanese thinking on fuzzy control methods, based on the author's personal knowledge of - and association with - Japanese fuzzy research, is also included.
Fuzzy Multiresolution Neural Networks
Ying, Li; Qigang, Shang; Na, Lei
A fuzzy multi-resolution neural network (FMRANN) based on particle swarm algorithm is proposed to approximate arbitrary nonlinear function. The active function of the FMRANN consists of not only the wavelet functions, but also the scaling functions, whose translation parameters and dilation parameters are adjustable. A set of fuzzy rules are involved in the FMRANN. Each rule either corresponding to a subset consists of scaling functions, or corresponding to a sub-wavelet neural network consists of wavelets with same dilation parameters. Incorporating the time-frequency localization and multi-resolution properties of wavelets with the ability of self-learning of fuzzy neural network, the approximation ability of FMRANN can be remarkable improved. A particle swarm algorithm is adopted to learn the translation and dilation parameters of the wavelets and adjusting the shape of membership functions. Simulation examples are presented to validate the effectiveness of FMRANN.
Directory of Open Access Journals (Sweden)
Abbas Parchami
2016-09-01
Full Text Available Such as other statistical problems, we may confront with uncertain and fuzzy concepts in quality control. One particular case in process capability analysis is a situation in which specification limits are two fuzzy sets. In such a uncertain and vague environment, the produced product is not qualified with a two-valued Boolean view, but to some degree depending on the decision-maker strictness and the quality level of the produced product. This matter can be cause to a rational decision-making on the quality of the production line. First, a comprehensive approach is presented in this paper for modeling the fuzzy quality concept. Then, motivations and advantages of applying this flexible approach instead of using classical quality are mentioned.
A Semantic Connected Coherence Scheme for Efficient Image Segmentation
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S.Pannirselvam
2012-06-01
Full Text Available Image processing is a comprehensively research topic with an elongated history. Segmenting an image is the most challenging and difficult task in image processing and analysis. The principal intricacy met in image segmentation is the ability of techniques to discover semantic objects efficiently from an image without any prior knowledge. One recent work presented connected coherence tree algorithm (CCTA for image segmentation (with no prior knowledge which discovered regions of semantic coherence based on neighbor coherence segmentation criteria. It deployed an adaptive spatial scale and a suitable intensity-difference scale to extract several sets of coherent neighboring pixels and maximize the probability of single image content and minimize complex backgrounds. However CCTA segmented images either consists of small, lengthy and slender objects or rigorously ruined by noise, irregular lighting, occlusion, poor illumination, and shadow.In this paper, we present a Cluster based Semantic Coherent Tree (CBSCT scheme for image segmentation. CBSCT’s initial work is on the semantic connected coherence criteria for the image segregation. Semantic coherent regions are clustered based on Bayesian nearest neighbor search of neighborhood pixels. The segmentation regions are extracted from the images based on the cluster object purity obtained through semantic coherent regions. The clustered image regions are post processed with non linear noise filters. Performance metrics used in the evaluation of CBSCT are semantic coherent pixel size, number of cluster objects, and purity levels of the cluster, segmented coherent region intensity threshold, and quality of segmented images in terms of image clarity with PSNR.
Directory of Open Access Journals (Sweden)
Fu-Gui Shi
2010-01-01
Full Text Available The notion of (L,M-fuzzy σ-algebras is introduced in the lattice value fuzzy set theory. It is a generalization of Klement's fuzzy σ-algebras. In our definition of (L,M-fuzzy σ-algebras, each L-fuzzy subset can be regarded as an L-measurable set to some degree.
DEFF Research Database (Denmark)
Jantzen, Jan
1998-01-01
Control problems in the process industry are dominated by non-linear and time-varying behaviour, many inner loops, and much interaction between the control loops. Fuzzy controllers have in some cases nevertheless mimicked the control actions of a human operator. For high level control and supervi......Control problems in the process industry are dominated by non-linear and time-varying behaviour, many inner loops, and much interaction between the control loops. Fuzzy controllers have in some cases nevertheless mimicked the control actions of a human operator. For high level control...
Energy Technology Data Exchange (ETDEWEB)
Venkatesh, B.; George, M.K. [Multimedia University (Malaysia). Faculty of Engineering and Technology; Gooi, H.B. [Nanyang Technological University (Singapore). School of Electrical and Electronics Engineering
2004-09-01
A new optimal reactive power flow (ORPF) method is proposed which considers the inclusion of unified powerflow controllers (UPFC). The modelling and inclusion of UPFC in the solution of power flow equations is presented. The ORPF problem is formulated as a fuzzy optimisation problem considering the objectives of minimising system transmission loss and obtaining the best voltage profile. The fuzzy formulation of the ORPF problem is solved using an EP algorithm. The proposed method is applied on the 6-bus and 57-bus IEEE test systems and on a 191-bus Indian electric power system. The results demonstrate the applicability of the method. (author)
Alexanian, G G; Immirzi, G; Ydri, B
2001-01-01
Regularization of quantum field theories (QFT's) can be achieved by quantizing the underlying manifold (spacetime or spatial slice) thereby replacing it by a non-commutative matrix model or a ``fuzzy manifold''. Such discretization by quantization is remarkably successful in preserving symmetries and topological features, and altogether overcoming the fermion-doubling problem. In this paper, we report on our work on the ``fuzzification'' of the four-dimensional CP2 and its QFT's. CP2 is not spin, but spin${}_c$. Its Dirac operator has many unique features. They are explained and their fuzzy versions are described.
Syropoulos, Apostolos
2011-01-01
Dialectica categories are a very versatile categorical model of linear logic. These have been used to model many seemingly different things (e.g., Petri nets and Lambek's calculus). In this note, we expand our previous work on fuzzy petri nets to deal with fuzzy topological systems. One basic idea is to use as the dualizing object in the Dialectica categories construction, the unit real interval [0,1], which has all the properties of a {\\em lineale}. The second basic idea is to generalize Vickers's notion of a topological system.
Al-saggaf, Alawi A
2008-01-01
This paper attempt has been made to explain a fuzzy commitment scheme. In the conventional Commitment schemes, both committed string m and valid opening key are required to enable the sender to prove the commitment. However there could be many instances where the transmission involves noise or minor errors arising purely because of the factors over which neither the sender nor the receiver have any control. The fuzzy commitment scheme presented in this paper is to accept the opening key that is close to the original one in suitable distance metric, but not necessarily identical. The concept itself is illustrated with the help of simple situation.
IMPROVED HYBRID SEGMENTATION OF BRAIN MRI TISSUE AND TUMOR USING STATISTICAL FEATURES
S. Allin Christe; K. Malathy; A.Kandaswamy
2010-01-01
Medical image segmentation is the most essential and crucial process in order to facilitate the characterization and visualization of the structure of interest in medical images. Relevant application in neuroradiology is the segmentation of MRI data sets of the human brain into the structure classes gray matter, white matter and cerebrospinal fluid (CSF) and tumor. In this paper, brain image segmentation algorithms such as Fuzzy C means (FCM) segmentation and Kohonen means(K means) segmentati...
Metrics for evaluating 3D medical image segmentation: analysis, selection, and tool
Taha, Abdel Aziz; Hanbury, Allan
2015-01-01
Background Medical Image segmentation is an important image processing step. Comparing images to evaluate the quality of segmentation is an essential part of measuring progress in this research area. Some of the challenges in evaluating medical segmentation are: metric selection, the use in the literature of multiple definitions for certain metrics, inefficiency of the metric calculation implementations leading to difficulties with large volumes, and lack of support for fuzzy segmentation by ...
Jomaa, Diala
2009-01-01
In this thesis, a new algorithm has been proposed to segment the foreground of the fingerprint from the image under consideration. The algorithm uses three features, mean, variance and coherence. Based on these features, a rule system is built to help the algorithm to efficiently segment the image. In addition, the proposed algorithm combine split and merge with modified Otsu. Both enhancements techniques such as Gaussian filter and histogram equalization are applied to enhance and improve th...
Fuzzy Modeled K-Cluster Quality Mining of Hidden Knowledge for Decision Support
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S. Parkash Kumar
2011-01-01
Full Text Available Problem statement: The work presented Fuzzy Modeled K-means Cluster Quality Mining of hidden knowledge for Decision Support. Based on the number of clusters, number of objects in each cluster and its cohesiveness, precision and recall values, the cluster quality metrics is measured. The fuzzy k-means is adapted approach by using heuristic method which iterates the cluster to form an efficient valid cluster. With the obtained data clusters, quality assessment is made by predictive mining using decision tree model. Validation criteria focus on the quality metrics of the institution features for cluster formation and handle efficiently the arbitrary shaped clusters. Approach: The proposed work presented a fuzzy k-means cluster algorithm in the formation of student, faculty and infrastructural clusters based on the performance, skill set and facilitation availability respectively. The knowledge hidden among the educational data set is extracted through Fuzzy k-means cluster an unsupervised learning depends on certain initiation values to define the subgroups present in the data set. Results: Based on the features of the dataset and input parameters cluster formation vary, which motivates the clarification of cluster validity. The results of quality indexed fuzzy k-means shows better cluster validation compared to that of traditional k-family algorithm. Conclusion: The experimental results of cluster validation scheme confirm the reliability of validity index showing that it performs better than other k-family clusters.
FCM Clustering Algorithms for Segmentation of Brain MR Images
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Yogita K. Dubey
2016-01-01
Full Text Available The study of brain disorders requires accurate tissue segmentation of magnetic resonance (MR brain images which is very important for detecting tumors, edema, and necrotic tissues. Segmentation of brain images, especially into three main tissue types: Cerebrospinal Fluid (CSF, Gray Matter (GM, and White Matter (WM, has important role in computer aided neurosurgery and diagnosis. Brain images mostly contain noise, intensity inhomogeneity, and weak boundaries. Therefore, accurate segmentation of brain images is still a challenging area of research. This paper presents a review of fuzzy c-means (FCM clustering algorithms for the segmentation of brain MR images. The review covers the detailed analysis of FCM based algorithms with intensity inhomogeneity correction and noise robustness. Different methods for the modification of standard fuzzy objective function with updating of membership and cluster centroid are also discussed.
DEFF Research Database (Denmark)
Franco de los Rios, Camilo Andres; Hougaard, Jens Leth; Nielsen, Kurt
for decision support and multidimensional interval analysis. First, the original approach is extended using fuzzy set theory which makes it possible to handle both non-interval and interval data. Second, we re-examine the ranking procedure based on semi-equivalence classes and suggest a new complementary...
FUZZY PREFERENCES IN CONFLICTS
Institute of Scientific and Technical Information of China (English)
Mubarak S. AL-MUTAIRI; Keith W. HIPEL; Mohamed S. KAMEL
2008-01-01
A systematic fuzzy approach is developed to model fuzziness and uncertainties in the preferences of decision makers involved in a conflict. This unique fuzzy preference formulation is used within the paradigm of the Graph Model for Conflict Resolution in which a given dispute is modeled in terms of decision makers, each decision maker's courses of actions or options, and each decision maker's preferences concerning the states or outcomes which could take place. In order to be able to determine the stability of each state for each decision maker and the possible equilibria or resolutions, a range of solution concepts describing potential human behavior under conflict are defined for use with fuzzy preferences. More specifically, strong and weak definitions of stability are provided for the solution concepts called Nash, general metarational, symmetric metarational, and sequential stability. To illustrate how these solution concepts can be conveniently used in practice, they are applied to a dispute over the contamination of an aquifer by a chemical company located in Elmira, Ontario, Canada.
Fuzzy efficiency without convexity
DEFF Research Database (Denmark)
Hougaard, Jens Leth; Balezentis, Tomas
2014-01-01
approach builds directly upon the definition of Farrell's indexes of technical efficiency used in crisp FDH. Therefore we do not require the use of fuzzy programming techniques but only utilize ranking probabilities of intervals as well as a related definition of dominance between pairs of intervals. We...
Energy Technology Data Exchange (ETDEWEB)
Batic, Davide, E-mail: dbatic@uniandes.edu.c [Departamento de Matematica, Universidad de los Andes, Cra 1E, No. 18A-10, Bogota, Colombia Department of Mathematics, University of West Indies, Kingston (Jamaica); Nicolini, Piero, E-mail: nicolini@th.physik.uni-frankfurt.d [Frankfurt Institute for Advanced Studies (FIAS), Institut fuer Theoretische Physik, Johann Wolfgang Goethe-Universitaet, Ruth-Moufang-Strasse 1, 60438 Frankfurt am Main (Germany)
2010-08-16
We study the stability of the noncommutative Schwarzschild black hole interior by analysing the propagation of a massless scalar field between the two horizons. We show that the spacetime fuzziness triggered by the field higher momenta can cure the classical exponential blue-shift divergence, suppressing the emergence of infinite energy density in a region nearby the Cauchy horizon.
Fuzzy knowledge management for the semantic web
Ma, Zongmin; Yan, Li; Cheng, Jingwei
2014-01-01
This book goes to great depth concerning the fast growing topic of technologies and approaches of fuzzy logic in the Semantic Web. The topics of this book include fuzzy description logics and fuzzy ontologies, queries of fuzzy description logics and fuzzy ontology knowledge bases, extraction of fuzzy description logics and ontologies from fuzzy data models, storage of fuzzy ontology knowledge bases in fuzzy databases, fuzzy Semantic Web ontology mapping, and fuzzy rules and their interchange in the Semantic Web. The book aims to provide a single record of current research in the fuzzy knowledge representation and reasoning for the Semantic Web. The objective of the book is to provide the state of the art information to researchers, practitioners and graduate students of the Web intelligence and at the same time serve the knowledge and data engineering professional faced with non-traditional applications that make the application of conventional approaches difficult or impossible.
Intuitionistic Fuzzy Graphs with Categorical Properties
Directory of Open Access Journals (Sweden)
Hossein Rashmanlou
2015-09-01
Full Text Available The main purpose of this paper is to show the rationality of some operations, defined or to be defined, on intuitionistic fuzzy graphs. Firstly, three kinds of new product operations (called direct product, lexicographic product, and strong product are defined in intuitionistic fuzzy graphs, and some important notions on intuitionistic fuzzy graphs are demonstrated by characterizing these notions and their level counterparts graphs such as intuitionistic fuzzy complete graph, cartesian product of intuitionistic fuzzy graphs, composition of intuitionistic fuzzy graphs, union of intuitionistic fuzzy graphs, and join of intuitionistic fuzzy graphs. As a result, a kind of representations of intuitionistic fuzzy graphs and intuitionistic fuzzy complete graphs are given. Next, categorical goodness of intuitionistic fuzzy graphs is illustrated by proving that the category of intuitionistic fuzzy graphs and homomorphisms between them is isomorphic-closed, complete, and co-complete.
Probability representations of fuzzy systems
Institute of Scientific and Technical Information of China (English)
LI Hongxing
2006-01-01
In this paper, the probability significance of fuzzy systems is revealed. It is pointed out that COG method, a defuzzification technique used commonly in fuzzy systems, is reasonable and is the optimal method in the sense of mean square. Based on different fuzzy implication operators, several typical probability distributions such as Zadeh distribution, Mamdani distribution, Lukasiewicz distribution, etc. are given. Those distributions act as "inner kernels" of fuzzy systems. Furthermore, by some properties of probability distributions of fuzzy systems, it is also demonstrated that CRI method, proposed by Zadeh, for constructing fuzzy systems is basically reasonable and effective. Besides, the special action of uniform probability distributions in fuzzy systems is characterized. Finally, the relationship between CRI method and triple I method is discussed. In the sense of construction of fuzzy systems, when restricting three fuzzy implication operators in triple I method to the same operator, CRI method and triple I method may be related in the following three basic ways: 1) Two methods are equivalent; 2) the latter is a degeneration of the former; 3) the latter is trivial whereas the former is not. When three fuzzy implication operators in triple I method are not restricted to the same operator, CRI method is a special case of triple I method; that is, triple I method is a more comprehensive algorithm. Since triple I method has a good logical foundation and comprises an idea of optimization of reasoning, triple I method will possess a beautiful vista of application.
Hierarchical type-2 fuzzy aggregation of fuzzy controllers
Cervantes, Leticia
2016-01-01
This book focuses on the fields of fuzzy logic, granular computing and also considering the control area. These areas can work together to solve various control problems, the idea is that this combination of areas would enable even more complex problem solving and better results. In this book we test the proposed method using two benchmark problems: the total flight control and the problem of water level control for a 3 tank system. When fuzzy logic is used it make it easy to performed the simulations, these fuzzy systems help to model the behavior of a real systems, using the fuzzy systems fuzzy rules are generated and with this can generate the behavior of any variable depending on the inputs and linguistic value. For this reason this work considers the proposed architecture using fuzzy systems and with this improve the behavior of the complex control problems.
Neuro-fuzzy system modeling based on automatic fuzzy clustering
Institute of Scientific and Technical Information of China (English)
Yuangang TANG; Fuchun SUN; Zengqi SUN
2005-01-01
A neuro-fuzzy system model based on automatic fuzzy clustering is proposed.A hybrid model identification algorithm is also developed to decide the model structure and model parameters.The algorithm mainly includes three parts:1) Automatic fuzzy C-means (AFCM),which is applied to generate fuzzy rules automatically,and then fix on the size of the neuro-fuzzy network,by which the complexity of system design is reducesd greatly at the price of the fitting capability;2) Recursive least square estimation (RLSE).It is used to update the parameters of Takagi-Sugeno model,which is employed to describe the behavior of the system;3) Gradient descent algorithm is also proposed for the fuzzy values according to the back propagation algorithm of neural network.Finally,modeling the dynamical equation of the two-link manipulator with the proposed approach is illustrated to validate the feasibility of the method.
Multiscale Opening of Conjoined Fuzzy Objects: Theory and Applications.
Saha, Punam K; Basu, Subhadip; Hoffman, Eric A
2016-10-01
Theoretical properties of a multi-scale opening (MSO) algorithm for two conjoined fuzzy objects are established, and its extension to separating two conjoined fuzzy objects with different intensity properties is introduced. Also, its applications to artery/vein (A/V) separation in pulmonary CT imaging and carotid vessel segmentation in CT angiograms (CTAs) of patients with intracranial aneurysms are presented. The new algorithm accounts for distinct intensity properties of individual conjoined objects by combining fuzzy distance transform (FDT), a morphologic feature, with fuzzy connectivity, a topologic feature. The algorithm iteratively opens the two conjoined objects starting at large scales and progressing toward finer scales. Results of application of the method in separating arteries and veins in a physical cast phantom of a pig lung are presented. Accuracy of the algorithm is quantitatively evaluated in terms of sensitivity and specificity on patients' CTA data sets and its performance is compared with existing methods. Reproducibility of the algorithm is examined in terms of volumetric agreement between two users' carotid vessel segmentation results. Experimental results using this algorithm on patients' CTA data demonstrate a high average accuracy of 96.3% with 95.1% sensitivity and 97.5% specificity and a high reproducibility of 94.2% average agreement between segmentation results from two mutually independent users. Approximately, twenty-five to thirty-five user-specified seeds/separators are needed for each CTA data through a custom designed graphical interface requiring an average of thirty minutes to complete carotid vascular segmentation in a patient's CTA data set.
Generalised Interval-Valued Fuzzy Soft Set
Shawkat Alkhazaleh; Abdul Razak Salleh
2012-01-01
We introduce the concept of generalised interval-valued fuzzy soft set and its operations and study some of their properties. We give applications of this theory in solving a decision making problem. We also introduce a similarity measure of two generalised interval-valued fuzzy soft sets and discuss its application in a medical diagnosis problem: fuzzy set; soft set; fuzzy soft set; generalised fuzzy soft set; generalised interval-valued fuzzy soft set; interval-valued fuzz...
On Intuitionistic Fuzzy Magnified Translation in Semigroups
Sardar, Sujit Kumar; Mandal, Manasi; Majumder, Samit Kumar
2011-01-01
The notion of intuitionistic fuzzy sets was introduced by Atanassov as a generalization of the notion of fuzzy sets. S.K Sardar and S.K. Majumder unified the idea of fuzzy translation and fuzzy multiplication of Vasantha Kandasamy to introduce the concept of fuzzy magnified translation in groups and semigroups. The purpose of this paper is to intuitionistically fuzzify(by using Atanassov's idea) the concept of fuzzy magnified translation in semigroups. Here among other results we obtain some ...
Lower and Upper Fuzzy Topological Subhypergroups
Institute of Scientific and Technical Information of China (English)
Irina CRISTEA; Jian Ming ZHAN
2013-01-01
This paper provides a new connection between algebraic hyperstructures and fuzzy sets.More specifically,using both properties of fuzzy topological spaces and those of fuzzy subhypergroups,we define the notions of lower (upper) fuzzy topological subhypergroups of a hypergroup endowed with a fuzzy topology.Some results concerning the image and the inverse image of a lower (upper) topological subhypergroup under a very good homomorphism of hypergroups (endowed with fuzzy topologies) are pointed out.
The squashed fuzzy sphere, fuzzy strings and the Landau problem
Andronache, Stefan
2015-01-01
We discuss the squashed fuzzy sphere, which is a projection of the fuzzy sphere onto the equatorial plane, and use it to illustrate the stringy aspects of noncommutative field theory. We elaborate explicitly how strings linking its two coincident sheets arise in terms of fuzzy spherical harmonics. In the large N limit, the matrix-model Laplacian is shown to correctly reproduce the semi-classical dynamics of these charged strings, as given by the Landau problem.
The squashed fuzzy sphere, fuzzy strings and the Landau problem
Andronache, Stefan; Steinacker, Harold C.
2015-07-01
We discuss the squashed fuzzy sphere, which is a projection of the fuzzy sphere onto the equatorial plane, and use it to illustrate the stringy aspects of noncommutative field theory. We elaborate explicitly how strings linking its two coincident sheets arise in terms of fuzzy spherical harmonics. In the large N limit, the matrix-model Laplacian is shown to correctly reproduce the semi-classical dynamics of these charged strings, as given by the Landau problem.
Genetic Algorithm Optimization for Determining Fuzzy Measures from Fuzzy Data
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Chen Li
2013-01-01
Full Text Available Fuzzy measures and fuzzy integrals have been successfully used in many real applications. How to determine fuzzy measures is a very difficult problem in these applications. Though there have existed some methodologies for solving this problem, such as genetic algorithms, gradient descent algorithms, neural networks, and particle swarm algorithm, it is hard to say which one is more appropriate and more feasible. Each method has its advantages. Most of the existed works can only deal with the data consisting of classic numbers which may arise limitations in practical applications. It is not reasonable to assume that all data are real data before we elicit them from practical data. Sometimes, fuzzy data may exist, such as in pharmacological, financial and sociological applications. Thus, we make an attempt to determine a more generalized type of general fuzzy measures from fuzzy data by means of genetic algorithms and Choquet integrals. In this paper, we make the first effort to define the σ-λ rules. Furthermore we define and characterize the Choquet integrals of interval-valued functions and fuzzy-number-valued functions based on σ-λ rules. In addition, we design a special genetic algorithm to determine a type of general fuzzy measures from fuzzy data.
GENERALIZED FUZZY FILTERS OF BL-ALGEBRAS
Institute of Scientific and Technical Information of China (English)
无
2007-01-01
The concept of quasi-coincidence of a fuzzy interval value with an interval valued fuzzy set is considered. In fact, this is a generalization of quasi-coincidence of a fuzzy point with a fuzzy set. By using this new idea, the notion of interval valued (∈, ∈∨q)-fuzzy filters in BL-algebras which is a generalization of fuzzy filters of BL-algebras, is defined, and related properties are investigated. In particular, the concept of a fuzzy subgroup with thresholds is extended to the concept of an interval valued fuzzy filter with thresholds in BL-algebras.
On the intuitionistic fuzzy topological spaces
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Saadati, Reza [Department of Mathematics, Azad University, Amol, P.O. Box 678 (Iran, Islamic Republic of)] e-mail: rsaadati@eml.cc; Park, Jin Han [Division of Mathematical Sciences, Pukyong National University, 599-1 Daeyeon, 3-Dong Nam-Gu, Pusan 608 737 (Korea, Republic of)] e-mail: jihpark@pknu.ac.kr
2006-01-01
In this paper, we define precompact set in intuitionistic fuzzy metric spaces and prove that any subset of an intuitionistic fuzzy metric space is compact if and only if it is precompact and complete. Also we define topologically complete intuitionistic fuzzy metrizable spaces and prove that any G{sub {delta}} set in a complete intuitionistic fuzzy metric spaces is a topologically complete intuitionistic fuzzy metrizable space and vice versa. Finally, we define intuitionistic fuzzy normed spaces and fuzzy boundedness for linear operators and so we prove that every finite dimensional intuitionistic fuzzy normed space is complete.
On the intuitionistic fuzzy topological spaces
Saadati, Reza; Park, Jin Han
2006-01-01
In this paper, we define precompact set in intuitionistic fuzzy metric spaces and prove that any subset of an intuitionistic fuzzy metric space is compact if and only if it is precompact and complete. Also we define topologically complete intuitionistic fuzzy metrizable spaces and prove that any $G_{\\delta }$ set in a complete intuitionistic fuzzy metric spaces is a topologically complete intuitionistic fuzzy metrizable space and vice versa. Finally, we define intuitionistic fuzzy normed spaces and fuzzy boundedness for linear operators and so we prove that every finite dimensional intuitionistic fuzzy normed space is complete.
Robust Segmentation of Voxel Shapes using Medial Surfaces
Reniers, Dennie; Telea, Alexandru
2008-01-01
We present a new patch-type segmentation method for 3D voxel shapes based on the medial surface, also called surface skeleton. The boundaries of the simplified fore- and background skeletons map one-to-one to increasingly fuzzy, soft convex, respectively concave, edges of the shape. Using this prope
Unsupervised segmentation of predefined shapes in multivariate images
Noordam, J.C.; Broek, van den W.H.A.M.; Buydens, L.M.C.
2003-01-01
Fuzzy C-means (FCM) is an unsupervised clustering technique that is often used for the unsupervised segmentation of multivariate images. In traditional FCM the clustering is based on spectral information only and the geometrical relationship between neighbouring pixels is not used in the clustering
Stochastic Optimal Estimation with Fuzzy Random Variables and Fuzzy Kalman Filtering
Institute of Scientific and Technical Information of China (English)
FENG Yu-hu
2005-01-01
By constructing a mean-square performance index in the case of fuzzy random variable, the optimal estimation theorem for unknown fuzzy state using the fuzzy observation data are given. The state and output of linear discrete-time dynamic fuzzy system with Gaussian noise are Gaussian fuzzy random variable sequences. An approach to fuzzy Kalman filtering is discussed. Fuzzy Kalman filtering contains two parts: a real-valued non-random recurrence equation and the standard Kalman filtering.
Fuzzy logic particle tracking velocimetry
Wernet, Mark P.
1993-01-01
Fuzzy logic has proven to be a simple and robust method for process control. Instead of requiring a complex model of the system, a user defined rule base is used to control the process. In this paper the principles of fuzzy logic control are applied to Particle Tracking Velocimetry (PTV). Two frames of digitally recorded, single exposure particle imagery are used as input. The fuzzy processor uses the local particle displacement information to determine the correct particle tracks. Fuzzy PTV is an improvement over traditional PTV techniques which typically require a sequence (greater than 2) of image frames for accurately tracking particles. The fuzzy processor executes in software on a PC without the use of specialized array or fuzzy logic processors. A pair of sample input images with roughly 300 particle images each, results in more than 200 velocity vectors in under 8 seconds of processing time.
Fuzzy pharmacology: theory and applications.
Sproule, Beth A; Naranjo, Claudio A; Türksen, I Burhan
2002-09-01
Fuzzy pharmacology is a term coined to represent the application of fuzzy logic and fuzzy set theory to pharmacological problems. Fuzzy logic is the science of reasoning, thinking and inference that recognizes and uses the real world phenomenon that everything is a matter of degree. It is an extension of binary logic that is able to deal with complex systems because it does not require crisp definitions and distinctions for the system components. In pharmacology, fuzzy modeling has been used for the mechanical control of drug delivery in surgical settings, and work has begun evaluating its use in other pharmacokinetic and pharmacodynamic applications. Fuzzy pharmacology is an emerging field that, based on these initial explorations, warrants further investigation.
Intuitionistic fuzzy aggregation and clustering
Xu, Zeshui
2012-01-01
This book offers a systematic introduction to the clustering algorithms for intuitionistic fuzzy values, the latest research results in intuitionistic fuzzy aggregation techniques, the extended results in interval-valued intuitionistic fuzzy environments, and their applications in multi-attribute decision making, such as supply chain management, military system performance evaluation, project management, venture capital, information system selection, building materials classification, and operational plan assessment, etc.
Phase structures in fuzzy geometries
Govindarajan, T R; Gupta, K S; Martin, X
2012-01-01
We study phase structures of quantum field theories in fuzzy geometries. Several examples of fuzzy geometries as well as QFT's on such geometries are considered. They are fuzzy spheres and beyond as well as noncommutative deformations of BTZ blackholes. Analysis is done analytically and through simulations. Several features like novel stripe phases as well as spontaneous symmetry breaking avoiding Colemen, Mermin, Wagner theorem are brought out. Also we establish that these phases are stable due to topological obstructions.
Heddam, Salim
2014-01-01
This article presents a comparison of two adaptive neuro-fuzzy inference systems (ANFIS)-based neuro-fuzzy models applied for modeling dissolved oxygen (DO) concentration. The two models are developed using experimental data collected from the bottom (USGS station no: 420615121533601) and top (USGS station no: 420615121533600) stations at Klamath River at site KRS12a nr Rock Quarry, Oregon, USA. The input variables used for the ANFIS models are water pH, temperature, specific conductance, and sensor depth. Two ANFIS-based neuro-fuzzy systems are presented. The two neuro-fuzzy systems are: (1) grid partition-based fuzzy inference system, named ANFIS_GRID, and (2) subtractive-clustering-based fuzzy inference system, named ANFIS_SUB. In both models, 60 % of the data set was randomly assigned to the training set, 20 % to the validation set, and 20 % to the test set. The ANFIS results are compared with multiple linear regression models. The system proposed in this paper shows a novelty approach with regard to the usage of ANFIS models for DO concentration modeling.
SEGMENTING RETAIL MARKETS ON STORE IMAGE USING A CONSUMER-BASED METHODOLOGY
STEENKAMP, JBEM; WEDEL, M
1991-01-01
Various approaches to segmenting retail markets based on store image are reviewed, including methods that have not yet been applied to retailing problems. It is argued that a recently developed segmentation technique, fuzzy clusterwise regression analysis (FCR), holds high potential for store-image
Unsupervised Retinal Vessel Segmentation Using Combined Filters.
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Wendeson S Oliveira
Full Text Available Image segmentation of retinal blood vessels is a process that can help to predict and diagnose cardiovascular related diseases, such as hypertension and diabetes, which are known to affect the retinal blood vessels' appearance. This work proposes an unsupervised method for the segmentation of retinal vessels images using a combined matched filter, Frangi's filter and Gabor Wavelet filter to enhance the images. The combination of these three filters in order to improve the segmentation is the main motivation of this work. We investigate two approaches to perform the filter combination: weighted mean and median ranking. Segmentation methods are tested after the vessel enhancement. Enhanced images with median ranking are segmented using a simple threshold criterion. Two segmentation procedures are applied when considering enhanced retinal images using the weighted mean approach. The first method is based on deformable models and the second uses fuzzy C-means for the image segmentation. The procedure is evaluated using two public image databases, Drive and Stare. The experimental results demonstrate that the proposed methods perform well for vessel segmentation in comparison with state-of-the-art methods.
Unsupervised Retinal Vessel Segmentation Using Combined Filters.
Oliveira, Wendeson S; Teixeira, Joyce Vitor; Ren, Tsang Ing; Cavalcanti, George D C; Sijbers, Jan
2016-01-01
Image segmentation of retinal blood vessels is a process that can help to predict and diagnose cardiovascular related diseases, such as hypertension and diabetes, which are known to affect the retinal blood vessels' appearance. This work proposes an unsupervised method for the segmentation of retinal vessels images using a combined matched filter, Frangi's filter and Gabor Wavelet filter to enhance the images. The combination of these three filters in order to improve the segmentation is the main motivation of this work. We investigate two approaches to perform the filter combination: weighted mean and median ranking. Segmentation methods are tested after the vessel enhancement. Enhanced images with median ranking are segmented using a simple threshold criterion. Two segmentation procedures are applied when considering enhanced retinal images using the weighted mean approach. The first method is based on deformable models and the second uses fuzzy C-means for the image segmentation. The procedure is evaluated using two public image databases, Drive and Stare. The experimental results demonstrate that the proposed methods perform well for vessel segmentation in comparison with state-of-the-art methods.
COMPATIBLE EXTENSIONS OF FUZZY RELATIONS
Institute of Scientific and Technical Information of China (English)
Irina GEORGESCU
2003-01-01
In 1930 Szpilrajn proved that any strict partial order can be embedded in a strict linear order.This theorem was later refined by Dushnik and Miller (1941), Hansson (1968), Suzumura (1976),Donaldson and Weymark (1998), Bossert (1999). Particularly Suzumura introduced the important concept of compatible extension of a (crisp) relation. These extension theorems have an important role in welfare economics. In particular Szpilrajn theorem is the main tool for proving a known theorem of Richter that establishes the equivalence between rational and congruous consumers. In 1999 Duggan proved a general extension theorem that contains all these results. In this paper we introduce the notion of compatible extension of a fuzzy relation and we prove an extension theorem for fuzzy relations. Our result generalizes to fuzzy set theory the main part of Duggan's theorem. As applications we obtain fuzzy versions of the theorems of Szpilrajn, Hansson and Suzumura. We also prove that an asymmetric and transitive fuzzy relation has a compatible extension that is total, asymmetric and transitive.Our results can be useful in the theory of fuzzy consumers. We can prove that any rational fuzzyconsumer is congruous, extending to a fuzzy context a part of Richter's theorem. To prove that acongruous fuzzy consumer is rational remains an open problem. A proof of this result can somehowuse a fuzzy version of Szpilrajn theorem.
Fuzzy-Contextual Contrast Enhancement.
Parihar, Anil; Verma, Om; Khanna, Chintan
2017-02-08
This paper presents contrast enhancement algorithms based on fuzzy contextual information of the images. We introduce fuzzy similarity index and fuzzy contrast factor to capture the neighborhood characteristics of a pixel. A new histogram, using fuzzy contrast factor of each pixel is developed, and termed as the fuzzy dissimilarity histogram (FDH). A cumulative distribution function (CDF) is formed with normalized values of FDH and used as a transfer function to obtain the contrast enhanced image. The algorithm gives good contrast enhancement and preserves the natural characteristic of the image. In order to develop a contextual intensity transfer function, we introduce a fuzzy membership function based on fuzzy similarity index and coefficient of variation of the image. The contextual intensity transfer function is designed using the fuzzy membership function to achieve final contrast enhanced image. The overall algorithm is referred as the fuzzy contextual contrast-enhancement (FCCE) algorithm. The proposed algorithms are compared with conventional and state-of-art contrast enhancement algorithms. The quantitative and visual assessment of the results is performed. The results of quantitative measures are statistically analyzed using t-test. The exhaustive experimentation and analysis show the proposed algorithm efficiently enhances contrast and yields in natural visual quality images.
A New View on Fuzzy Hypermodules
Institute of Scientific and Technical Information of China (English)
Jian Ming ZHAN; Bijan DAVVAZ; K. P. SHUM
2007-01-01
We describe the relationship between the fuzzy sets and the algebraic hyperstructures.In fact,this paper is a continuation of the ideas presented by Davvaz in (Fuzzy Sets Syst.,117: 477-484,2001) and Bhakat and Das in (Fuzzy Sets Syst.,80: 359-368,1996).The concept of the quasi-coincidence of a fuzzy interval value with an interval-valued fuzzy set is introduced and this is a naturalgeneralization of the quasi-coincidence of a fuzzy point in fuzzy sets.By using this new idea,the conceptof interval-valued (α,β)-fuzzy sub-hypermodules of a hypermodule is defined.This newly definedinterval-valued (α,β)-fuzzy sub-hypermodule is a generalization of the usual fuzzy sub-hypermodule.We shall study such fuzzy sub-hypermodules and consider the implication-based interval-valued fuzzysub-hypermodules of a hypermodule.
Directory of Open Access Journals (Sweden)
Mikael Collan
2015-01-01
Full Text Available This paper introduces new closeness coefficients for fuzzy similarity based TOPSIS. The new closeness coefficients are based on multidistance or fuzzy entropy, are able to take into consideration the level of similarity between analysed criteria, and can be used to account for the consistency or homogeneity of, for example, performance measuring criteria. The commonly known OWA operator is used in the aggregation process over the fuzzy similarity values. A range of orness values is considered in creating a fuzzy overall ranking for each object, after which the fuzzy rankings are ordered to find a final linear ranking. The presented method is numerically applied to a research and development project selection problem and the effect of using two new closeness coefficients based on multidistance and fuzzy entropy is numerically illustrated.
Fuzzy dot ideals and fuzzy dot H-ideals of BCH-algebras
Institute of Scientific and Technical Information of China (English)
PENG Jia-yin
2008-01-01
The notions of fuzzy dot ideals and fuzzy dot H-ideals in BCH-algebras are intro duced,several appropriate examples are provided,and their some properties are investigated.The relations among fuzzy ideal,fuzzy H-ideal,fuzzy dot ideal and fuzzy dot H-ideals in BCH algebras are discussed,several equivalent depictions of fuzzy dot ideal are obtained. How to deal with the homomorphic image and inverse image of fuzzy dot ideals (fuzzy dot H-ideals) are studied. The relations between a fuzzy dot ideal (fuzzy dot H-ideal) in BCH-algebras and a fuzzy dot ideal (fuzzy dot H-ideal) in the product algebra of BCH-algebras are given.
Fuzzy Perfect Mappings and Q-Compactness in Smooth Fuzzy Topological Spaces
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C. Kalaivani
2014-03-01
Full Text Available We point out that the product of two fuzzy closed sets of smooth fuzzy topological spaces need not be fuzzy closed with respect to the the existing notion of product smooth fuzzy topology. To get this property, we introduce a new suitable product smooth fuzzy topology. We investigate whether F1×F2 and (F,H are weakly smooth fuzzy continuity whenever F1, F2, F and H are weakly smooth fuzzy continuous. Using this new product smooth fuzzy topology, we define smooth fuzzy perfect mapping and prove that composition of two smooth fuzzy perfect mappings is smooth fuzzy perfect under some additional conditions. We also introduce two new notions of compactness called Q-compactness and Q-α-compactness; and discuss the compactness of the image of a Q-compact set (Q-α-compact set under a weakly smooth fuzzy continuous function ((α,β-weakly smooth fuzzy continuous function.
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Berenstein, David [Department of Applied Mathematics and Theoretical Physics,University of Cambridge, Wilberforce Road, Cambridge CB3 0WA (United Kingdom); Department of Physics, University of California Santa Barbara,Santa Barbara, California 93106 (United States); Dzienkowski, Eric; Lashof-Regas, Robin [Department of Physics, University of California Santa Barbara,Santa Barbara, California 93106 (United States)
2015-08-27
We construct various exact analytical solutions of the SO(3) BMN matrix model that correspond to rotating fuzzy spheres and rotating fuzzy tori. These are also solutions of Yang Mills theory compactified on a sphere times time and they are also translationally invariant solutions of the N=1{sup ∗} field theory with a non-trivial charge density. The solutions we construct have a ℤ{sub N} symmetry, where N is the rank of the matrices. After an appropriate ansatz, we reduce the problem to solving a set of polynomial equations in 2N real variables. These equations have a discrete set of solutions for each value of the angular momentum. We study the phase structure of the solutions for various values of N. Also the continuum limit where N→∞, where the problem reduces to finding periodic solutions of a set of coupled differential equations. We also study the topology change transition from the sphere to the torus.
Fuzzy controllers based on some fuzzy implication operators and their response functions
Institute of Scientific and Technical Information of China (English)
LI Hongxing; YOU Fei; PENG Jiayin
2004-01-01
The fuzzy controllers constructed by 23 fuzzy implication operators based on CRI algorithm and their response functions are discussed.The conclusions show that the fuzzy controllers constructed by 9 fuzzy implication operators are universal approximators to continuous functions and can be used in practical fuzzy control systems.And these 9 fuzzy implication operators except for Einstein operator intersection are all the adjoint pairs of some fuzzy implication operators.Besides, there are 3 other fuzzy controllers formed by fuzzy implication operators being regarded approximately as fitted functions.
Color Image Segmentation via Improved K-Means Algorithm
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Ajay Kumar
2016-03-01
Full Text Available Data clustering techniques are often used to segment the real world images. Unsupervised image segmentation algorithms that are based on the clustering suffer from random initialization. There is a need for efficient and effective image segmentation algorithm, which can be used in the computer vision, object recognition, image recognition, or compression. To address these problems, the authors present a density-based initialization scheme to segment the color images. In the kernel density based clustering technique, the data sample is mapped to a high-dimensional space for the effective data classification. The Gaussian kernel is used for the density estimation and for the mapping of sample image into a high- dimensional color space. The proposed initialization scheme for the k-means clustering algorithm can homogenously segment an image into the regions of interest with the capability of avoiding the dead centre and the trapped centre by local minima phenomena. The performance of the experimental result indicates that the proposed approach is more effective, compared to the other existing clustering-based image segmentation algorithms. In the proposed approach, the Berkeley image database has been used for the comparison analysis with the recent clustering-based image segmentation algorithms like k-means++, k-medoids and k-mode.
Fuzzy controlofanylonpolymerizationsemi-batchreactor
Wakabayashi, C; Embiruçu, Marcelo; Fontes, Cristiano; Kalid, Ricardo
2009-01-01
Acesso restrito: Texto completo. p. 537-553 Batch and semi-batch polymerization reactors with specified trajectories for certain process variables present challenging control problems. This work reports, results and procedures related to the application of PI (proportional and integral) fuzzy control in a semi-batch reactor for the production of nylon 6. Closed loop simulation results were based on a phenomenological model adjusted for a commercial reactor and they attest to the potential ...
Institute of Scientific and Technical Information of China (English)
曹立明
1990-01-01
By the similarity between the syllogism in logic and a path proposition in graph theory,a new concept,fuzzy reasoning graph G has been given in this paper. Transitive closure has been studied and used to do reasoning related to self-loop in G,and an algorithm has been designed to cope with reasoning in other cycles in G. Both approaches are applicable and efficient.
Pham, T. D.
2016-12-01
Recurrence plots display binary texture of time series from dynamical systems with single dots and line structures. Using fuzzy recurrence plots, recurrences of the phase-space states can be visualized as grayscale texture, which is more informative for pattern analysis. The proposed method replaces the crucial similarity threshold required by symmetrical recurrence plots with the number of cluster centers, where the estimate of the latter parameter is less critical than the estimate of the former.
Fuzzy 2-partition entropy threshold selection based on Big Bang–Big Crunch Optimization algorithm
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Baljit Singh Khehra
2015-03-01
Full Text Available The fuzzy 2-partition entropy approach has been widely used to select threshold value for image segmenting. This approach used two parameterized fuzzy membership functions to form a fuzzy 2-partition of the image. The optimal threshold is selected by searching an optimal combination of parameters of the membership functions such that the entropy of fuzzy 2-partition is maximized. In this paper, a new fuzzy 2-partition entropy thresholding approach based on the technology of the Big Bang–Big Crunch Optimization (BBBCO is proposed. The new proposed thresholding approach is called the BBBCO-based fuzzy 2-partition entropy thresholding algorithm. BBBCO is used to search an optimal combination of parameters of the membership functions for maximizing the entropy of fuzzy 2-partition. BBBCO is inspired by the theory of the evolution of the universe; namely the Big Bang and Big Crunch Theory. The proposed algorithm is tested on a number of standard test images. For comparison, three different algorithms included Genetic Algorithm (GA-based, Biogeography-based Optimization (BBO-based and recursive approaches are also implemented. From experimental results, it is observed that the performance of the proposed algorithm is more effective than GA-based, BBO-based and recursion-based approaches.
Development of Fuzzy Logic System to Predict the SAW Weldment Shape Profiles
Institute of Scientific and Technical Information of China (English)
H.K.Narang; M.M.Mahapatra; P.K.Jha; P.Biswas
2012-01-01
A fuzzy model was presented to predict the weldment shape profile of submerged arc welds (SAW)including the shape of heat affected zone (HAZ).The SAW bead-on-plates were welded by following a full factorial design matrix.The design marx consisted of three levels of input welding process parameters.The welds were cross-sectioned and etched,and the zones were measured.A mapping technique was used to measure the various segments of the weld zones.These mapped zones were used to build a fuzzy logic model.The membership functions of the fuzzy model were chosen for the accurate prediction of the weld zone.The fuzzy model was further tested for a set of test case data.The weld zone predicted by the fuzzy logic model was compared with the experimentally obtained shape profiles and close agreement between the two was noted.The mapping technique developed for the weld zones and the fuzzy logic model can be used for on-line control of the SAW process.From the SAW fuzzy logic model an estimation of the fusion and HAZ can also be developed.
Emergent fuzzy geometry and fuzzy physics in four dimensions
Ydri, Badis; Rouag, Ahlam; Ramda, Khaled
2017-03-01
A detailed Monte Carlo calculation of the phase diagram of bosonic mass-deformed IKKT Yang-Mills matrix models in three and six dimensions with quartic mass deformations is given. Background emergent fuzzy geometries in two and four dimensions are observed with a fluctuation given by a noncommutative U (1) gauge theory very weakly coupled to normal scalar fields. The geometry, which is determined dynamically, is given by the fuzzy spheres SN2 and SN2 × SN2 respectively. The three and six matrix models are effectively in the same universality class. For example, in two dimensions the geometry is completely stable, whereas in four dimensions the geometry is stable only in the limit M ⟶ ∞, where M is the mass of the normal fluctuations. The behaviors of the eigenvalue distribution in the two theories are also different. We also sketch how we can obtain a stable fuzzy four-sphere SN2 × SN2 in the large N limit for all values of M as well as models of topology change in which the transition between spheres of different dimensions is observed. The stable fuzzy spheres in two and four dimensions act precisely as regulators which is the original goal of fuzzy geometry and fuzzy physics. Fuzzy physics and fuzzy field theory on these spaces are briefly discussed.
Performance comparison of fuzzy and non-fuzzy classification methods
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B. Simhachalam
2016-07-01
Full Text Available In data clustering, partition based clustering algorithms are widely used clustering algorithms. Among various partition algorithms, fuzzy algorithms, Fuzzy c-Means (FCM, Gustafson–Kessel (GK and non-fuzzy algorithm, k-means (KM are most popular methods. k-means and Fuzzy c-Means use standard Euclidian distance measure and Gustafson–Kessel uses fuzzy covariance matrix in their distance metrics. In this work, a comparative study of these algorithms with different famous real world data sets, liver disorder and wine from the UCI repository is presented. The performance of the three algorithms is analyzed based on the clustering output criteria. The results were compared with the results obtained from the repository. The results showed that Gustafson–Kessel produces close results to Fuzzy c-Means. Further, the experimental results demonstrate that k-means outperforms the Fuzzy c-Means and Gustafson–Kessel algorithms. Thus the efficiency of k-means is better than that of Fuzzy c-Means and Gustafson–Kessel algorithms.
fuzzy control technique fuzzy control technique applied to modified ...
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eobe
ABSTRACT. In this paper, fuzzy control technique is applied to the modified mathematical model for malaria control presented ... be devised for rule-based systems that deals with continuous ... necessary to use fuzzy logic as it is not easy to follow a particular .... point movement and control is realized and designed. (e.g. α1 ...
Almost Fuzzy Compactness in L-fuzzy Top ological Spaces
Institute of Scientific and Technical Information of China (English)
Li Hong-yan; Cui Wei
2015-01-01
In this paper, the notion of almost fuzzy compactness is defined in L-fuzzy topological spaces by means of inequality, where L is a completely distributive DeMorgan algebra. Its properties are discussed and many characterizations of it are presented.
How to combine probabilistic and fuzzy uncertainties in fuzzy control
Nguyen, Hung T.; Kreinovich, Vladik YA.; Lea, Robert
1991-01-01
Fuzzy control is a methodology that translates natural-language rules, formulated by expert controllers, into the actual control strategy that can be implemented in an automated controller. In many cases, in addition to the experts' rules, additional statistical information about the system is known. It is explained how to use this additional information in fuzzy control methodology.
Fuzzy Reasoning Methods by Choosing Different Fuzzy Counters and Analysis of Effect
Institute of Scientific and Technical Information of China (English)
无
2001-01-01
Different fuzzy reasoning methods were gave by choosing different fuzzy counters. This article generally introduced the basic structure of fuzzy controller,and compared and analysised the reasoning effect of fuzzy reasoning methods and the effect of computer simulating control basicly on different fuzzy counters.
L-Fuzzy Semi-Preopen Operator in L-Fuzzy Topological Spaces
Ghareeb, A
2010-01-01
In this paper, we give the concept of L-fuzzy Semi-Preopen operator in L-fuzzy topological spaces, and use them to score L-fuzzy SP-cmpactnness in L-fuzzy topological spaces. We also study the relationship between L-fuzzy SP-compactness and SP-compactness in L-topological spaces.
Recognition of Handwritten Arabic words using a neuro-fuzzy network
Boukharouba, Abdelhak; Bennia, Abdelhak
2008-06-01
We present a new method for the recognition of handwritten Arabic words based on neuro-fuzzy hybrid network. As a first step, connected components (CCs) of black pixels are detected. Then the system determines which CCs are sub-words and which are stress marks. The stress marks are then isolated and identified separately and the sub-words are segmented into graphemes. Each grapheme is described by topological and statistical features. Fuzzy rules are extracted from training examples by a hybrid learning scheme comprised of two phases: rule generation phase from data using a fuzzy c-means, and rule parameter tuning phase using gradient descent learning. After learning, the network encodes in its topology the essential design parameters of a fuzzy inference system. The contribution of this technique is shown through the significant tests performed on a handwritten Arabic words database.
Segmentation and Tracking of Neural Stem Cell
Institute of Scientific and Technical Information of China (English)
TANG Chun-ming; ZHAO Chun-hui; Ewert Bengtsson
2005-01-01
In order to understand the development of stem cells into specialized mature cells it is necessary to study the growth of cells in culture. For this purpose it is very useful to have an efficient computerized cell tracking system. In this paper a prototype system for tracking neural stem cells in a sequence of images is described. In order to get reliable tracking results it is important to have good and robust segmentation of the cells. To achieve this we have implemented three levels of segmentation. The primary level, applied to all frames, is based on fuzzy threshold and watershed segmentation of a fuzzy gray weighted distance transformed image.The second level, applied to difficult frames where the first algorithm seems to have failed, is based on a fast geometric active contour model based on the level set algorithm. Finally, the automatic segmentation result on the crucial first frame can be interactively inspected and corrected. Visual inspection and correction can also be applied to other frames but this is generally not needed. For the tracking all cells are classified into inactive, active, dividing and clustered cells. Different algorithms are used to deal with the different cell categories. A special backtracking step is used to automatically correct for some common errors that appear in the initial forward tracking process.
Fuzzy MCDM Based on Fuzzy Relational Degree Analysis
Institute of Scientific and Technical Information of China (English)
无
2002-01-01
This paper presents a new fuzzy multiple criteria (both qualitative and quantitative) decision-making (MCDM) method based on fuzzy relational degree analysis. The concepts of fuzzy set theory are used to construct a weighted suitability decision matrix to evaluate the weighted suitability of different alternatives versus various criteria. The positive ideal solution and negative ideal solution are then obtained by using a method of ranking fuzzy numbers, and the fuzzy relational degrees of different alternatives versus positive ideal solution and negative ideal solution are calculated by using the proposed arithmetic. Finally, the relative relational degrees of various alternatives versus positive ideal solution are ranked to determine the best alternative. A numerical example is provided to illustrate the proposed method at the end of this paper.
Directory of Open Access Journals (Sweden)
Özlem Türkşen
2013-01-01
Full Text Available The solution set of a multi-response experiment is characterized by Pareto solution set. In this paper, the multi-response experiment is dealed in a fuzzy framework. The responses and model parameters are considered as triangular fuzzy numbers which indicate the uncertainty of the data set. Fuzzy least square approach and fuzzy modified NSGA-II (FNSGA-II are used for modeling and optimization, respectively. The obtained fuzzy Pareto solution set is grouped by using fuzzy relational clustering approach. Therefore, it could be easier to choose the alternative solutions to make better decision. A fuzzy response valued real data set is used as an application.
On Fuzzy Ideals of BL-Algebras
Directory of Open Access Journals (Sweden)
Biao Long Meng
2014-01-01
Full Text Available In this paper we investigate further properties of fuzzy ideals of a BL-algebra. The notions of fuzzy prime ideals, fuzzy irreducible ideals, and fuzzy Gödel ideals of a BL-algebra are introduced and their several properties are investigated. We give a procedure to generate a fuzzy ideal by a fuzzy set. We prove that every fuzzy irreducible ideal is a fuzzy prime ideal but a fuzzy prime ideal may not be a fuzzy irreducible ideal and prove that a fuzzy prime ideal ω is a fuzzy irreducible ideal if and only if ω0=1 and |Im(ω|=2. We give the Krull-Stone representation theorem of fuzzy ideals in BL-algebras. Furthermore, we prove that the lattice of all fuzzy ideals of a BL-algebra is a complete distributive lattice. Finally, it is proved that every fuzzy Boolean ideal is a fuzzy Gödel ideal, but the converse implication is not true.
Directory of Open Access Journals (Sweden)
Grzegorz Dymek
2015-01-01
set to be a fuzzy ideal are given. The relationships between ideals and fuzzy ideals of a BN-algebra are established. The homomorphic properties of fuzzy ideals of a BN-algebra are provided. Finally, characterizations of Noetherian BN-algebras and Artinian BN-algebras via fuzzy ideals are obtained.
On Fuzzy Ideals of BL-Algebras
Xin, Xiao Long
2014-01-01
In this paper we investigate further properties of fuzzy ideals of a BL-algebra. The notions of fuzzy prime ideals, fuzzy irreducible ideals, and fuzzy Gödel ideals of a BL-algebra are introduced and their several properties are investigated. We give a procedure to generate a fuzzy ideal by a fuzzy set. We prove that every fuzzy irreducible ideal is a fuzzy prime ideal but a fuzzy prime ideal may not be a fuzzy irreducible ideal and prove that a fuzzy prime ideal ω is a fuzzy irreducible ideal if and only if ω(0) = 1 and |Im(ω)| = 2. We give the Krull-Stone representation theorem of fuzzy ideals in BL-algebras. Furthermore, we prove that the lattice of all fuzzy ideals of a BL-algebra is a complete distributive lattice. Finally, it is proved that every fuzzy Boolean ideal is a fuzzy Gödel ideal, but the converse implication is not true. PMID:24892085
FUZZY ALGEBRA IN TRIANGULAR NORM SYSTEM
Institute of Scientific and Technical Information of China (English)
宋晓秋; 潘志
1994-01-01
Triangular norm is a powerful tool in the theory research and application development of fuzzy sets. In this paper, using the triangular norm, we introduce some concepts such as fuzzy algebra, fuzzy o algebra and fuzzy monotone class, and discuss the relations among them, obtaining the following main conclusions.
Set Theory and Arithmetic in Fuzzy Logic
Běhounek, L. (Libor); Haniková, Z. (Zuzana)
2015-01-01
This chapter offers a review of Petr Hájek’s contributions to first-order axiomatic theories in fuzzy logic (in particular, ZF-style fuzzy set theories, arithmetic with a fuzzy truth predicate, and fuzzy set theory with unrestricted comprehension schema). Generalizations of Hájek’s results in these areas to MTL as the background logic are presented and discussed.
AN ALGORITHM OF TEST FOR FUZZY CODES
Institute of Scientific and Technical Information of China (English)
MoZhiwen; PenJiayin
2001-01-01
Abstract. How to verify that a given fuzzy set A∈F(X ) is a fuzzy code? In this paper, an al-gorithm of test has been introduced and studied with the example of test. The measure notionfor a fuzzy code and a precise formulation of fuzzy codes and words have been discussed.
Fuzzy clustering with Minkowski distance
P.J.F. Groenen (Patrick); U. Kaymak (Uzay); J.M. van Rosmalen (Joost)
2006-01-01
textabstractDistances in the well known fuzzy c-means algorithm of Bezdek (1973) are measured by the squared Euclidean distance. Other distances have been used as well in fuzzy clustering. For example, Jajuga (1991) proposed to use the L_1-distance and Bobrowski and Bezdek (1991) also used the L_inf
Duality in Dynamic Fuzzy Systems
Yoshida, Yuji
1995-01-01
This paper shows the resolvent equation, the maximum principle and the co-balayage theorem for a dynamic fuzzy system. We define a dual system for the dynamic fuzzy system, and gives a duality for Snell's optimal stopping problem by the dual system.
Efficient adaptive fuzzy control scheme
Papp, Z.; Driessen, B.J.F.
1995-01-01
The paper presents an adaptive nonlinear (state-) feedback control structure, where the nonlinearities are implemented as smooth fuzzy mappings defined as rule sets. The fine tuning and adaption of the controller is realized by an indirect adaptive scheme, which modifies the parameters of the fuzzy
Egalitarianism in Convex Fuzzy Games
Brânzei, R.; Dimitrov, D.A.; Tijs, S.H.
2002-01-01
In this paper the egalitarian solution for convex cooperative fuzzy games is introduced.The classical Dutta-Ray algorithm for finding the constrained egalitarian solution for convex crisp games is adjusted to provide the egalitarian solution of a convex fuzzy game.This adjusted algorithm is also a f
Representation of Fuzzy Symmetric Relations
1986-03-19
Std Z39-18 REPRESENTATION OF FUZZY SYMMETRIC RELATIONS L. Valverde Dept. de Matematiques i Estadistica Universitat Politecnica de Catalunya Avda...REPRESENTATION OF FUZZY SYMMETRIC RELATIONS L. "Valverde* Dept. de Matematiques i Estadistica Universitat Politecnica de Catalunya Avda. Diagonal, 649
Teaching Machines to Think Fuzzy
Technology Teacher, 2004
2004-01-01
Fuzzy logic programs for computers make them more human. Computers can then think through messy situations and make smart decisions. It makes computers able to control things the way people do. Fuzzy logic has been used to control subway trains, elevators, washing machines, microwave ovens, and cars. Pretty much all the human has to do is push one…
FINDCLUS : Fuzzy INdividual Differences CLUStering
Giordani, Paolo; Kiers, Henk A. L.
ADditive CLUStering (ADCLUS) is a tool for overlapping clustering of two-way proximity matrices (objects x objects). In Simple Additive Fuzzy Clustering (SAFC), a variant of ADCLUS is introduced providing a fuzzy partition of the objects, that is the objects belong to the clusters with the so-called
Institute of Scientific and Technical Information of China (English)
沈理
1997-01-01
A fuzzy logic control VLSI chip,F100,for industry process real-time control has been designed and fabricated with 0.8μm CMOS technology.The chip has the features of simplicity,felexibility and generality.This paper presents the Fuzzy control inrerence method of the chip,its VLSI implementation,and testing esign consideration.
Fuzzy linguistic model for interpolation
Energy Technology Data Exchange (ETDEWEB)
Abbasbandy, S. [Department of Mathematics, Science and Research Branch, Islamic Azad University, Tehran 14778 (Iran, Islamic Republic of); Department of Mathematics, Faculty of Science, Imam Khomeini International University, Qazvin 34194-288 (Iran, Islamic Republic of); Adabitabar Firozja, M. [Department of Mathematics, Science and Research Branch, Islamic Azad University, Tehran 14778 (Iran, Islamic Republic of)
2007-10-15
In this paper, a fuzzy method for interpolating of smooth curves was represented. We present a novel approach to interpolate real data by applying the universal approximation method. In proposed method, fuzzy linguistic model (FLM) applied as universal approximation for any nonlinear continuous function. Finally, we give some numerical examples and compare the proposed method with spline method.
A novel vehicle navigation map matching algorithm based on fuzzy logic and its application
Institute of Scientific and Technical Information of China (English)
TONG Xiao-hua; WU Song-chun; WU Shu-qing; LIU Da-jie
2005-01-01
A new real-time map matching algorithm based on fuzzy logic is proposed. 3 main factors affecting the reliability of map matching, including the distance between the vehicle location and the matching road segment, the angle between the vehicle direction and the road segment direction and the road connectivity are discussed. Fuzzy rules for the distance, angle and connectivity are presented to calculate the matching reliability. 2 indicators for estimating the matching reliability are then derived, one is the lower limit of the reliability, and the other is the limit error of the difference between the maximal value and the second-maximal value of the reliability. A real-time map-matching system based on fuzzy logic is therefore developed. Using the real data of global positioning system(GIS) based navigation and geographic information system(GPS) based road map, the method is verified and the results prove the effectiveness of the proposed method.
[Segmental neurofibromatosis].
Zulaica, A; Peteiro, C; Pereiro, M; Pereiro Ferreiros, M; Quintas, C; Toribio, J
1989-01-01
Four cases of segmental neurofibromatosis (SNF) are reported. It is a rare entity considered to be a localized variant of neurofibromatosis (NF)-Riccardi's type V. Two cases are male and two female. The lesions are located to the head in a patient and the other three cases in the trunk. No family history nor transmission to progeny were manifested. The rest of the organs are undamaged.
ON FUZZY h-IDEALS OF HEMIRINGS
Institute of Scientific and Technical Information of China (English)
Xueling MA; Jianming ZHAN
2007-01-01
The concept of quasi-coincidence of a fuzzy interval value in an interval valued fuzzy set is considered. In fact, this concept is a generalized concept of the quasi-coincidence of a fuzzy point in a fuzzy set. By using this new concept, the authors define the notion of interval valued (∈, ∈ Vq)-fuzzy h-ideals of hemirings and study their related properties. In addition, the authors also extend the concept of a fuzzy subgroup with thresholds to the concept of an interval valued fuzzy h-ideal with thresholds in hemirings.
A new fuzzy edge detection algorithm
Institute of Scientific and Technical Information of China (English)
SunWei; XiaLiangzheng
2003-01-01
Based upon the maximum entropy theorem of information theory, a novel fuzzy approach for edge detection is presented. Firsdy, a definition of fuzzy partition entropy is proposed after introducing the concepts of fuzzy probability and fuzzy partition. The relation of the probability partition and the fuzzy c-partition of the image gradient are used in the algorithm. Secondly, based on the conditional probabilities and the fuzzy partition, the optimal thresholding is searched adaptively through the maximum fuzzy entropy principle, and then the edge image is obtained. Lastly, an edge-enhancing procedure is executed on the edge image. The experimental results show that the proposed approach performs well.
Image matching navigation based on fuzzy information
Institute of Scientific and Technical Information of China (English)
田玉龙; 吴伟仁; 田金文; 柳健
2003-01-01
In conventional image matching methods, the image matching process is mostly based on image statistic information. One aspect neglected by all these methods is that there is much fuzzy information contained in these images. A new fuzzy matching algorithm based on fuzzy similarity for navigation is presented in this paper. Because the fuzzy theory is of the ability of making good description of the fuzzy information contained in images, the image matching method based on fuzzy similarity would look forward to producing good performance results. Experimental results using matching algorithm based on fuzzy information also demonstrate its reliability and practicability.
On Intuitionistic Fuzzy Magnified Translation in Semigroups
Sardar, Sujit Kumar; Majumder, Samit Kumar
2011-01-01
The notion of intuitionistic fuzzy sets was introduced by Atanassov as a generalization of the notion of fuzzy sets. S.K Sardar and S.K. Majumder unified the idea of fuzzy translation and fuzzy multiplication of Vasantha Kandasamy to introduce the concept of fuzzy magnified translation in groups and semigroups. The purpose of this paper is to intuitionistically fuzzify(by using Atanassov's idea) the concept of fuzzy magnified translation in semigroups. Here among other results we obtain some characterization theorems of regular, intra-regular, left(right) regular semigroups in terms of intuitionistic fuzzy magnified translation.
On the L-fuzzy topological spaces
Energy Technology Data Exchange (ETDEWEB)
Saadati, Reza [Islamic Azad University-Aiatollah Amoly Branch, Amol 678 (Iran, Islamic Republic of); Department of Mathematics and Computer Science, Amirkabir University of Technology, 424 Hafez Avenue, Tehran 15914 (Iran, Islamic Republic of)], E-mail: rsaadati@eml.cc
2008-09-15
As a natural generalization of fuzzy metric spaces due to George and Veeramani [George A, Veeramani P. On some result in fuzzy metric space. Fuzzy Sets Syst 1994;64:395-9], the present author defined the notion of L-fuzzy metric spaces. In this paper we prove some known results of metric spaces including Uniform continuity theorem and Ascoli-Arzela theorem for L-fuzzy metric spaces. We also prove that every L-fuzzy metric space has a countably locally finite basis and use this result to conclude that every L-fuzzy metric space is metrizable.
Concept Approximation between Fuzzy Ontologies
Institute of Scientific and Technical Information of China (English)
无
2006-01-01
Fuzzy ontologies are efficient tools to handle fuzzy and uncertain knowledge on the semantic web; but there are heterogeneity problems when gaining interoperability among different fuzzy ontologies. This paper uses concept approximation between fuzzy ontologies based on instances to solve the heterogeneity problems. It firstly proposes an instance selection technology based on instance clustering and weighting to unify the fuzzy interpretation of different ontologies and reduce the number of instances to increase the efficiency. Then the paper resolves the problem of computing the approximations of concepts into the problem of computing the least upper approximations of atom concepts. It optimizes the search strategies by extending atom concept sets and defining the least upper bounds of concepts to reduce the searching space of the problem. An efficient algorithm for searching the least upper bounds of concept is given.
Design of interpretable fuzzy systems
Cpałka, Krzysztof
2017-01-01
This book shows that the term “interpretability” goes far beyond the concept of readability of a fuzzy set and fuzzy rules. It focuses on novel and precise operators of aggregation, inference, and defuzzification leading to flexible Mamdani-type and logical-type systems that can achieve the required accuracy using a less complex rule base. The individual chapters describe various aspects of interpretability, including appropriate selection of the structure of a fuzzy system, focusing on improving the interpretability of fuzzy systems designed using both gradient-learning and evolutionary algorithms. It also demonstrates how to eliminate various system components, such as inputs, rules and fuzzy sets, whose reduction does not adversely affect system accuracy. It illustrates the performance of the developed algorithms and methods with commonly used benchmarks. The book provides valuable tools for possible applications in many fields including expert systems, automatic control and robotics.
On Intuitionistic Fuzzy Sets Theory
Atanassov, Krassimir T
2012-01-01
This book aims to be a comprehensive and accurate survey of state-of-art research on intuitionistic fuzzy sets theory and could be considered a continuation and extension of the author´s previous book on Intuitionistic Fuzzy Sets, published by Springer in 1999 (Atanassov, Krassimir T., Intuitionistic Fuzzy Sets, Studies in Fuzziness and soft computing, ISBN 978-3-7908-1228-2, 1999). Since the aforementioned book has appeared, the research activity of the author within the area of intuitionistic fuzzy sets has been expanding into many directions. The results of the author´s most recent work covering the past 12 years as well as the newest general ideas and open problems in this field have been therefore collected in this new book.
Modelling on fuzzy control systems
Institute of Scientific and Technical Information of China (English)
LI; Hongxing(李洪兴); WANG; Jiayin(王加银); MIAO; Zhihong(苗志宏)
2002-01-01
A kind of modelling method for fuzzy control systems is first proposed here, which is calledmodelling method based on fuzzy inference (MMFI). It should be regarded as the third modelling method thatis different from two well-known modelling methods, that is, the first modelling method, mechanism modellingmethod (MMM), and the second modelling method, system identification modelling method (SlMM). Thismethod can, based on the interpolation mechanism on fuzzy logic system, transfer a group of fuzzy inferencerules describing a practice system into a kind of nonlinear differential equation with variable coefficients, calledHX equations, so that the mathematical model of the system can be obtained. This means that we solve thedifficult problem of how to get a model represented as differential equations on a complicated or fuzzy controlsystem.
Fuzzy control in environmental engineering
Chmielowski, Wojciech Z
2016-01-01
This book is intended for engineers, technicians and people who plan to use fuzzy control in more or less developed and advanced control systems for manufacturing processes, or directly for executive equipment. Assuming that the reader possesses elementary knowledge regarding fuzzy sets and fuzzy control, by way of a reminder, the first parts of the book contain a reminder of the theoretical foundations as well as a description of the tools to be found in the Matlab/Simulink environment in the form of a toolbox. The major part of the book presents applications for fuzzy controllers in control systems for various manufacturing and engineering processes. It presents seven processes and problems which have been programmed using fuzzy controllers. The issues discussed concern the field of Environmental Engineering. Examples are the control of a flood wave passing through a hypothetical, and then the real Dobczyce reservoir in the Raba River, which is located in the upper Vistula River basin in Southern Poland, th...
Data fusion based on fuzzy measures
Institute of Scientific and Technical Information of China (English)
无
2007-01-01
Choquet integral based on fuzzy measure is a very popular data fusion approach. A major problem in applying the Choquet integral is how to determine a large number of fuzzy measures as the number of attributes increases. The λ-fuzzy measure proposed by Sugeno is a powerful method to resolve this problem. However, the modeling ability of the λ-fuzzy measure is too limited to satisfy actual requirements. In this paper, an extended λ-fuzzy measure is proposed using Shapley value index, and the limitation of the λ-fuzzy measure is significantly overcome under little additional computational loads. The extended fuzzy measure has stronger modeling power than the λ-fuzzy measure, straightforwardly representing interaction among attributes. We apply the extended fuzzy measure to an artificial data set and a real dataset in an iron-steel plant. The results verify the usefulness of the extended fuzzy measure compared with other main existing methods.
On Fuzzy Improper Integral and Its Application for Fuzzy Partial Differential Equations
ElHassan ElJaoui; Said Melliani
2016-01-01
We establish some important results about improper fuzzy Riemann integrals; we prove some properties of fuzzy Laplace transforms, which we apply for solving some fuzzy linear partial differential equations of first order, under generalized Hukuhara differentiability.
On Fuzzy Improper Integral and Its Application for Fuzzy Partial Differential Equations
Directory of Open Access Journals (Sweden)
ElHassan ElJaoui
2016-01-01
Full Text Available We establish some important results about improper fuzzy Riemann integrals; we prove some properties of fuzzy Laplace transforms, which we apply for solving some fuzzy linear partial differential equations of first order, under generalized Hukuhara differentiability.
Web Fuzzy Clustering and a Case Study
Institute of Scientific and Technical Information of China (English)
LIU Mao-fu; HE Jing; HE Yan-xiang; HU Hui-jun
2004-01-01
We combine the web usage mining and fuzzy clustering and give the concept of web fuzzy clustering, and then put forward the web fuzzy clustering processing model which is discussed in detail. Web fuzzy clustering can be used in the web users clustering and web pages clustering. In the end, a case study is given and the result has proved the feasibility of using web fuzzy clustering in web pages clustering.
Topology Correction of Segmented Medical Images using a Fast Marching Algorithm
2007-01-01
We present here a new method for correcting the topology of objects segmented from medical images. Whereas previous techniques alter a surface obtained from a binary segmentation of the object, our technique can be applied directly to the image intensities of a probabilistic or fuzzy segmentation, thereby propagating the topology for all isosurfaces of the object. From an analysis of topological changes and critical points in implicit surfaces, we derive a topology propagation algorithm that ...
Improvement of Fuzzy Image Contrast Enhancement Using Simulated Ergodic Fuzzy Markov Chains
Directory of Open Access Journals (Sweden)
Behrouz Fathi-Vajargah
2014-01-01
Full Text Available This paper presents a novel fuzzy enhancement technique using simulated ergodic fuzzy Markov chains for low contrast brain magnetic resonance imaging (MRI. The fuzzy image contrast enhancement is proposed by weighted fuzzy expected value. The membership values are then modified to enhance the image using ergodic fuzzy Markov chains. The qualitative performance of the proposed method is compared to another method in which ergodic fuzzy Markov chains are not considered. The proposed method produces better quality image.
A fuzzy-decision based approach for Composite event detection in wireless sensor networks.
Zhang, Shukui; Chen, Hao; Zhu, Qiaoming; Jia, Juncheng
2014-01-01
The event detection is one of the fundamental researches in wireless sensor networks (WSNs). Due to the consideration of various properties that reflect events status, the Composite event is more consistent with the objective world. Thus, the research of the Composite event becomes more realistic. In this paper, we analyze the characteristics of the Composite event; then we propose a criterion to determine the area of the Composite event and put forward a dominating set based network topology construction algorithm under random deployment. For the unreliability of partial data in detection process and fuzziness of the event definitions in nature, we propose a cluster-based two-dimensional τ-GAS algorithm and fuzzy-decision based composite event decision mechanism. In the case that the sensory data of most nodes are normal, the two-dimensional τ-GAS algorithm can filter the fault node data effectively and reduce the influence of erroneous data on the event determination. The Composite event judgment mechanism which is based on fuzzy-decision holds the superiority of the fuzzy-logic based algorithm; moreover, it does not need the support of a huge rule base and its computational complexity is small. Compared to CollECT algorithm and CDS algorithm, this algorithm improves the detection accuracy and reduces the traffic.
A Fuzzy-Decision Based Approach for Composite Event Detection in Wireless Sensor Networks
Directory of Open Access Journals (Sweden)
Shukui Zhang
2014-01-01
Full Text Available The event detection is one of the fundamental researches in wireless sensor networks (WSNs. Due to the consideration of various properties that reflect events status, the Composite event is more consistent with the objective world. Thus, the research of the Composite event becomes more realistic. In this paper, we analyze the characteristics of the Composite event; then we propose a criterion to determine the area of the Composite event and put forward a dominating set based network topology construction algorithm under random deployment. For the unreliability of partial data in detection process and fuzziness of the event definitions in nature, we propose a cluster-based two-dimensional τ-GAS algorithm and fuzzy-decision based composite event decision mechanism. In the case that the sensory data of most nodes are normal, the two-dimensional τ-GAS algorithm can filter the fault node data effectively and reduce the influence of erroneous data on the event determination. The Composite event judgment mechanism which is based on fuzzy-decision holds the superiority of the fuzzy-logic based algorithm; moreover, it does not need the support of a huge rule base and its computational complexity is small. Compared to CollECT algorithm and CDS algorithm, this algorithm improves the detection accuracy and reduces the traffic.
Fuzzy portfolio model with fuzzy-input return rates and fuzzy-output proportions
Tsaur, Ruey-Chyn
2015-02-01
In the finance market, a short-term investment strategy is usually applied in portfolio selection in order to reduce investment risk; however, the economy is uncertain and the investment period is short. Further, an investor has incomplete information for selecting a portfolio with crisp proportions for each chosen security. In this paper we present a new method of constructing fuzzy portfolio model for the parameters of fuzzy-input return rates and fuzzy-output proportions, based on possibilistic mean-standard deviation models. Furthermore, we consider both excess or shortage of investment in different economic periods by using fuzzy constraint for the sum of the fuzzy proportions, and we also refer to risks of securities investment and vagueness of incomplete information during the period of depression economics for the portfolio selection. Finally, we present a numerical example of a portfolio selection problem to illustrate the proposed model and a sensitivity analysis is realised based on the results.
Location optimization of multiple distribution centers under fuzzy environment
Institute of Scientific and Technical Information of China (English)
Yong WANG; Xiao-lei MA; Yin-hai WANG; Hai-jun MAO; Yong ZHANG
2012-01-01
Locating distribution centers optimally is a crucial and systematic task for decision-makers.Optimally located distribution centers can significantly improve the logistics system's efficiency and reduce its operational costs.However,it is not an easy task to optimize distribution center locations and previous studies focused primarily on location optimization of a single distribution center.With growing logistics demands,multiple distribution centers become necessary to meet customers' requirements,but few studies have tackled the multiple distribution center locations (MDCLs) problem.This paper presents a comprehensive algorithm to address the MDCLs problem.Fuzzy integration and clustering approach using the improved axiomatic fuzzy set (AFS) theory is developed for location clustering based on multiple hierarchical evaluation criteria.Then,technique for order preference by similarity to ideal solution (TOPSIS) is applied for evaluating and selecting the best candidate for each cluster.Sensitivity analysis is also conducted to assess the influence of each criterion in the location planning decision procedure.Results from a case study in Guiyang,China,reveals that the proposed approach developed in this study outperforms other similar algorithms for MDCLs selection.This new method may easily be extended to address location planning of other types of facilities,including hospitals,fire stations and schools.
Face Recognition using Segmental Euclidean Distance
Directory of Open Access Journals (Sweden)
Farrukh Sayeed
2011-09-01
Full Text Available In this paper an attempt has been made to detect the face using the combination of integral image along with the cascade structured classifier which is built using Adaboost learning algorithm. The detected faces are then passed through a filtering process for discarding the non face regions. They are individually split up into five segments consisting of forehead, eyes, nose, mouth and chin. Each segment is considered as a separate image and Eigenface also called principal component analysis (PCA features of each segment is computed. The faces having a slight pose are also aligned for proper segmentation. The test image is also segmented similarly and its PCA features are found. The segmental Euclidean distance classifier is used for matching the test image with the stored one. The success rate comes out to be 88 per cent on the CG(full database created from the databases of California Institute and Georgia Institute. However the performance of this approach on ORL(full database with the same features is only 70 per cent. For the sake of comparison, DCT(full and fuzzy features are tried on CG and ORL databases but using a well known classifier, support vector machine (SVM. Results of recognition rate with DCT features on SVM classifier are increased by 3 per cent over those due to PCA features and Euclidean distance classifier on the CG database. The results of recognition are improved to 96 per cent with fuzzy features on ORL database with SVM.Defence Science Journal, 2011, 61(5, pp.431-442, DOI:http://dx.doi.org/10.14429/dsj.61.1178
Colour application on mammography image segmentation
Embong, R.; Aziz, N. M. Nik Ab.; Karim, A. H. Abd; Ibrahim, M. R.
2017-09-01
The segmentation process is one of the most important steps in image processing and computer vision since it is vital in the initial stage of image analysis. Segmentation of medical images involves complex structures and it requires precise segmentation result which is necessary for clinical diagnosis such as the detection of tumour, oedema, and necrotic tissues. Since mammography images are grayscale, researchers are looking at the effect of colour in the segmentation process of medical images. Colour is known to play a significant role in the perception of object boundaries in non-medical colour images. Processing colour images require handling more data, hence providing a richer description of objects in the scene. Colour images contain ten percent (10%) additional edge information as compared to their grayscale counterparts. Nevertheless, edge detection in colour image is more challenging than grayscale image as colour space is considered as a vector space. In this study, we implemented red, green, yellow, and blue colour maps to grayscale mammography images with the purpose of testing the effect of colours on the segmentation of abnormality regions in the mammography images. We applied the segmentation process using the Fuzzy C-means algorithm and evaluated the percentage of average relative error of area for each colour type. The results showed that all segmentation with the colour map can be done successfully even for blurred and noisy images. Also the size of the area of the abnormality region is reduced when compare to the segmentation area without the colour map. The green colour map segmentation produced the smallest percentage of average relative error (10.009%) while yellow colour map segmentation gave the largest percentage of relative error (11.367%).
Rizvi, Meher; Nagy, Philip
2016-01-01
This paper presents and evaluates a teacher training approach called the cluster-based mentoring programme (CBMP) for the professional development of government primary school teachers in Pakistan. The study sought to find differences in the teaching practices between districts where the CBMP was used (intervention) and control districts where it…
Khelifi, Manel
2012-01-01
In this paper, we propose LMEEC, a cluster-based routing protocol with low energy consumption for wireless sensor networks. Our protocol is based on a strategy which aims to provide a more reasonable exploitation of the selected nodes (cluster-heads) energy. Simulation results show the effectiveness of LMEEC in decreasing the energy consumption, and in prolonging the network lifetime, compared to LEACH.
DEFF Research Database (Denmark)
Bonde, Anders; Aagaard, Morten; Hansen, Allan Grutt
This book is about using recent developments in the fields of data analytics and data visualization to frame new ways of identifying target groups in media communication. Based on a mixed-methods approach, the authors combine psychophysiological monitoring (galvanic skin response) with textual...... content analysis and audience segmentation in a single-source perspective. The aim is to explain and understand target groups in relation to, on the one hand, emotional response to commercials or other forms of audio-visual communication and, on the other hand, living preferences and personality traits...
A New Swarm Intelligence Approach for Clustering Based on Krill Herd with Elitism Strategy
Directory of Open Access Journals (Sweden)
Zhi-Yong Li
2015-10-01
Full Text Available As one of the most popular and well-recognized clustering methods, fuzzy C-means (FCM clustering algorithm is the basis of other fuzzy clustering analysis methods in theory and application respects. However, FCM algorithm is essentially a local search optimization algorithm. Therefore, sometimes, it may fail to find the global optimum. For the purpose of getting over the disadvantages of FCM algorithm, a new version of the krill herd (KH algorithm with elitism strategy, called KHE, is proposed to solve the clustering problem. Elitism tragedy has a strong ability of preventing the krill population from degrading. In addition, the well-selected parameters are used in the KHE method instead of originating from nature. Through an array of simulation experiments, the results show that the KHE is indeed a good choice for solving general benchmark problems and fuzzy clustering analyses.
Sanchez, Mauricio A; Castro, Juan R
2017-01-01
In this book, a series of granular algorithms are proposed. A nature inspired granular algorithm based on Newtonian gravitational forces is proposed. A series of methods for the formation of higher-type information granules represented by Interval Type-2 Fuzzy Sets are also shown, via multiple approaches, such as Coefficient of Variation, principle of justifiable granularity, uncertainty-based information concept, and numerical evidence based. And a fuzzy granular application comparison is given as to demonstrate the differences in how uncertainty affects the performance of fuzzy information granules.
Strong sum distance in fuzzy graphs.
Tom, Mini; Sunitha, Muraleedharan Shetty
2015-01-01
In this paper the idea of strong sum distance which is a metric, in a fuzzy graph is introduced. Based on this metric the concepts of eccentricity, radius, diameter, center and self centered fuzzy graphs are studied. Some properties of eccentric nodes, peripheral nodes and central nodes are obtained. A characterisation of self centered complete fuzzy graph is obtained and conditions under which a fuzzy cycle is self centered are established. We have proved that based on this metric, an eccentric node of a fuzzy tree G is a fuzzy end node of G and a node is an eccentric node of a fuzzy tree if and only if it is a peripheral node of G and the center of a fuzzy tree consists of either one or two neighboring nodes. The concepts of boundary nodes and interior nodes in a fuzzy graph based on strong sum distance are introduced. Some properties of boundary nodes, interior nodes and complete nodes are studied.
Implementation of Steiner point of fuzzy set.
Liang, Jiuzhen; Wang, Dejiang
2014-01-01
This paper deals with the implementation of Steiner point of fuzzy set. Some definitions and properties of Steiner point are investigated and extended to fuzzy set. This paper focuses on establishing efficient methods to compute Steiner point of fuzzy set. Two strategies of computing Steiner point of fuzzy set are proposed. One is called linear combination of Steiner points computed by a series of crisp α-cut sets of the fuzzy set. The other is an approximate method, which is trying to find the optimal α-cut set approaching the fuzzy set. Stability analysis of Steiner point of fuzzy set is also studied. Some experiments on image processing are given, in which the two methods are applied for implementing Steiner point of fuzzy image, and both strategies show their own advantages in computing Steiner point of fuzzy set.
Fuzzy Clustering Methods and their Application to Fuzzy Modeling
DEFF Research Database (Denmark)
Kroszynski, Uri; Zhou, Jianjun
1999-01-01
Fuzzy modeling techniques based upon the analysis of measured input/output data sets result in a set of rules that allow to predict system outputs from given inputs. Fuzzy clustering methods for system modeling and identification result in relatively small rule-bases, allowing fast, yet accurate...... prediction of outputs. This article presents an overview of some of the most popular clustering methods, namely Fuzzy Cluster-Means (FCM) and its generalizations to Fuzzy C-Lines and Elliptotypes. The algorithms for computing cluster centers and principal directions from a training data-set are described....... A method to obtain an optimized number of clusters is outlined. Based upon the cluster's characteristics, a behavioural model is formulated in terms of a rule-base and an inference engine. The article reviews several variants for the model formulation. Some limitations of the methods are listed...
UNDERSTANDING OF FUZZY OPTIMIZATION:THEORIES AND METHODS
Institute of Scientific and Technical Information of China (English)
TANG Jiafu; WANG Dingwei; Richard Y K FUNG; Kai-Leung Yung
2004-01-01
A brief summary on and comprehensive understanding of fuzzy optimizationis presentedThis summary is made on aspects of fuzzy modelling and fuzzy optimization,classification and formulation for the fuzzy optimization problems, models and methods.The importance of interpretation of the problem and formulation of the optimal solutionin fuzzy sense are emphasized in the summary of the fuzzy optimization.
Majorizational Choosing of SeveralDifferent Fuzzy Counter Operator
Institute of Scientific and Technical Information of China (English)
无
2000-01-01
Different fuzzy reasoning methods were made by choosing different fuzzy operater. This article generally introduced the basic structure of fuzzy controller ,and gave several different fuzzy controllers ,and compared and analyzed different fuzzy counters in theory and computer simulating control and realized majorizational choosing of several fuzzy counters.
Fuzzy random variables — I. definitions and theorems
Kwakernaak, H.
1978-01-01
Fuzziness is discussed in the context of multivalued logic, and a corresponding view of fuzzy sets is given. Fuzzy random variables are introduced as random variables whose values are not real but fuzzy numbers, and subsequently redefined as a particular kind of fuzzy set. Expectations of fuzzy
Fuzzy random variables — I. definitions and theorems
Kwakernaak, Huibert
1978-01-01
Fuzziness is discussed in the context of multivalued logic, and a corresponding view of fuzzy sets is given. Fuzzy random variables are introduced as random variables whose values are not real but fuzzy numbers, and subsequently redefined as a particular kind of fuzzy set. Expectations of fuzzy rand
A Bibliography on Fuzzy Automata, Grammars and Lanuages
Asveld, P.R.J.
1996-01-01
This bibliography contains references to papers on fuzzy formal languages, the generation of fuzzy languages by means of fuzzy grammars, the recognition of fuzzy languages by fuzzy automata and machines, as well as some applications of fuzzy set theory to syntactic pattern recognition, linguistics a
A Bibliography on Fuzzy Automata, Grammars and Lanuages
Asveld, Peter R.J.
1995-01-01
This bibliography contains references to papers on fuzzy formal languages, the generation of fuzzy languages by means of fuzzy grammars, the recognition of fuzzy languages by fuzzy automata and machines, as well as some applications of fuzzy set theory to syntactic pattern recognition, linguistics a
Fuzzy random variables — I. definitions and theorems
Kwakernaak, H.
1978-01-01
Fuzziness is discussed in the context of multivalued logic, and a corresponding view of fuzzy sets is given. Fuzzy random variables are introduced as random variables whose values are not real but fuzzy numbers, and subsequently redefined as a particular kind of fuzzy set. Expectations of fuzzy rand
Fuzzy Content-Based Retrieval in Image Databases.
Wu, Jian Kang; Narasimhalu, A. Desai
1998-01-01
Proposes a fuzzy-image database model and a concept of fuzzy space; describes fuzzy-query processing in fuzzy space and fuzzy indexing on complete fuzzy vectors; and uses an example image database, the computer-aided facial-image inference and retrieval system (CAFIIR), for explanation throughout. (Author/LRW)
Linear time distances between fuzzy sets with applications to pattern matching and classification.
Lindblad, Joakim; Sladoje, Nataša
2014-01-01
We present four novel point-to-set distances defined for fuzzy or gray-level image data, two based on integration over α-cuts and two based on the fuzzy distance transform. We explore their theoretical properties. Inserting the proposed point-to-set distances in existing definitions of set-to-set distances, among which are the Hausdorff distance and the sum of minimal distances, we define a number of distances between fuzzy sets. These set distances are directly applicable for comparing gray-level images or fuzzy segmented objects, but also for detecting patterns and matching parts of images. The distance measures integrate shape and intensity/membership of observed entities, providing a highly applicable tool for image processing and analysis. Performance evaluation of derived set distances in real image processing tasks is conducted and presented. It is shown that the considered distances have a number of appealing theoretical properties and exhibit very good performance in template matching and object classification for fuzzy segmented images as well as when applied directly on gray-level intensity images. Examples include recognition of hand written digits and identification of virus particles. The proposed set distances perform excellently on the MNIST digit classification task, achieving the best reported error rate for classification using only rigid body transformations and a kNN classifier.
13. workshop fuzzy systems. Proceedings; 13. Workshop Fuzzy Systeme. Beitraege
Energy Technology Data Exchange (ETDEWEB)
Mikut, R.; Reischl, M. (eds.)
2003-11-01
This volume contains the papers presented at the 13th workshop on fuzzy systems of TC 5.2.2 'Fuzzy Control' of the VDI/VDE-Gesellschaft fuer Mess- und Automatisierungstechnik (GMA) and the TG 'Fuzzy Systems and Soft Computing' of the Gesellschaft fuer Informatik (GI), which took place at Dortmund on November 19-21, 2003. New methods and applications of fuzzy logic, artificial neuronal nets and evolutionary algorithms were presented. The focus was on automation, e.g. in chemical engineering, energy engineering, motor car engineering, robotics and medical engineering. Other applications, e.g. data mining for technical and non-technical applications, were gone into as well. [German] Dieser Tagungsband enthaelt die Beitraege des 13. Workshops ''Fuzzy System'' des Fachausschusses 5.22 ''Fuzzy Control'' der VDI/VDE-Gesellschaft fuer Mess- und Automatisierungstechnik (GMA) und der Fachgruppe ''Fuzzy-Systeme und Soft-Computing'' der Gesellschaft fuer Informatik (GI), der vom 19.-21. November 2003 im Haus Bommerholz, Dortmund, stattfindet. Der jaehrliche Workshop unseres Fachausschusses bietet ein Forum zur Diskussion neuer methodischer Ansaetze und industrieller Anwendungen auf dem Gebiet der Fuzzy-Logik und in angrenzenden Gebieten wie Kuenstlichen Neuronalen Netzen und Evolutionaeren Algorithmen. Besondere Schwerpunkte sind automatisierungstechnische Anwendungen, z.B. in der Verfahrenstechnik, Energietechnik, Kfz-Technik, Robotik und Medizintechnik, aber auch Loesungen in anderen Problemgebieten (z.B. Data Mining fuer technische und nichttechnische Anwendungen) sind von Interesse. (orig.)
FuzzySTAR: Fuzzy set theory of axiomatic design review
Huang, GQ; Jiang, Z
2002-01-01
Product development involves multiple phases. Design review (DR) is an essential activity formally conducted to ensure a smooth transition from one phase to another. Such a formal DR is usually a multicriteria decision problem, involving multiple disciplines. This paper proposes a systematic framework for DR using fuzzy set theory. This fuzzy approach to DR is considered particularly relevant for several reasons. First, information available at early design phases is often incomplete and impr...
Segmented blockcopolymers with uniform amide segments
Husken, D.; Krijgsman, J.; Gaymans, R.J.
2004-01-01
Segmented blockcopolymers based on poly(tetramethylene oxide) (PTMO) soft segments and uniform crystallisable tetra-amide segments (TxTxT) are made via polycondensation. The PTMO soft segments, with a molecular weight of 1000 g/mol, are extended with terephthalic groups to a molecular weight of 6000
Directory of Open Access Journals (Sweden)
Gurumurthy Sasikumar
2016-01-01
Full Text Available The new direction of understand the signal that is created from the brain organization is one of the main chores in the brain signal processing. Amid all the neurological disorders the human brain epilepsy is measured as one of the extreme prevalent and then programmed artificial intelligence detection technique is an essential due to the crooked and unpredictable nature of happening of epileptic seizures. We proposed an Improved Fuzzy firefly algorithm, which would enhance the classification of the brain signal efficiently with minimum iteration. An important bunching technique created on fuzzy logic is the Fuzzy C means. Together in the feature domain with the spatial domain the features gained after multichannel EEG signals remained combined by means of fuzzy algorithms. And for better precision segmentation process the firefly algorithm is applied to optimize the Fuzzy C-means membership function. Simultaneously for the efficient clustering method the convergence criteria are set. On the whole the proposed technique yields more accurate results and that gives an edge over other techniques. This proposed algorithm result compared with other algorithms like fuzzy c means algorithm and PSO algorithm.
Rough Fuzzy Relation on Two Universal Sets
Directory of Open Access Journals (Sweden)
Xuan Thao Nguyen
2014-03-01
Full Text Available Fuzzy set theory was introduced by L.A. Zadeh in 1965. Immediately, it has many applications in practice and in building databases, one of which is the construction of a fuzzy relational database based on similar relationship. The study of cases of fuzzy relations in different environments will help us understand its applications. In this paper, the rough fuzzy relation on Cartesian product of two universe sets is defined, and then the algebraic properties of them, such as the max, min, and composition of two rough fuzzy relations are examined. Finally, reflexive, α-reflexive, symmetric and transitive rough fuzzy relations on two universe sets are also defined.
Institute of Scientific and Technical Information of China (English)
饶三平
2012-01-01
基于完备剩余格,本文在模糊完备格中,引入模糊Prime元概念.给出了模糊Prime元的等价刻画,证明了所有的模糊Prime元构成的模糊集是模糊完全分配格.%Based on complete residuated lattices, the concept of fuzzy Prime elements in fuzzy complete lattices is given, then the equivalent characterization of fuzzy Prime elements is obtained. We also prove that the fuzzy subsets of fuzzy Prime elements is a fuzzy completely distributice lattice.
FUZZY EPQ INVENTORY MODELS WITH BACKORDER
Institute of Scientific and Technical Information of China (English)
Xiaobin WANG; Wansheng TANG
2009-01-01
This paper considers the economic production quantity (EPQ) problem with backorder in which the setup cost, the holding cost and the backorder cost are characterized as fuzzy variables, respectively. Following expected value criterion and chance constrained criterion, a fuzzy expected value model (EVM) and a chance constrained programming (CCP) model are constructed. Then fuzzy simulations are employed to estimate the expected value of fuzzy variable and α-level minimal average cost. In order to solve the CCP model, a particle swarm optimization (PSO) algorithm based on the fuzzy simulation is designed. Finally, the effectiveness of PSO algorithm based on the fuzzy simulation is illustrated by a numerical example.
Adaptive Fuzzy Control for CVT Vehicle
Institute of Scientific and Technical Information of China (English)
无
2005-01-01
On the simple continuously variable transmission (CVT) driveline model, the design of adaptive fuzzy control system for CVT vehicle is presented. The adaptive fuzzy control system consists of a scaling factor self-tuning fuzzy-PI throttle controller, and a hybrid fuzzy-PID CVT ratio and brake controller. The presented adaptive fuzzy control strategy is vehicle model independent, which depends only on the instantaneous vehicle states, but does not depend on vehicle parameters. So it has good robustness against uncertain vehicle parameters and exogenous load disturbance. Simulation results show that the proposed adaptive fuzzy strategy has good adaptability and practicality value.
Type-2 fuzzy fractional derivatives
Mazandarani, Mehran; Najariyan, Marzieh
2014-07-01
In this paper, we introduce two definitions of the differentiability of type-2 fuzzy number-valued functions of fractional order. The definitions are in the sense of Riemann-Liouville and Caputo derivative of order β ɛ (0, 1), and based on type-2 Hukuhara difference and H2-differentiability. The existence and uniqueness of the solutions of type-2 fuzzy fractional differential equations (T2FFDEs) under Caputo type-2 fuzzy fractional derivative and the definition of Laplace transform of type-2 fuzzy number-valued functions are also given. Moreover, the approximate solution to T2FFDE by a Predictor-Evaluate-Corrector-Evaluate (PECE) method is presented. Finally, the approximate solutions of two examples of linear and nonlinear T2FFDEs are obtained using the PECE method, and some cases of T2FFDEs applications in some sciences are presented.
Intuitionistic fuzzy hierarchical clustering algorithms
Institute of Scientific and Technical Information of China (English)
Xu Zeshui
2009-01-01
Intuitionistic fuzzy set (IFS) is a set of 2-tuple arguments, each of which is characterized by a mem-bership degree and a nonmembership degree. The generalized form of IFS is interval-valued intuitionistic fuzzy set (IVIFS), whose components are intervals rather than exact numbers. IFSs and IVIFSs have been found to be very useful to describe vagueness and uncertainty. However, it seems that little attention has been focused on the clus-tering analysis of IFSs and IVIFSs. An intuitionistic fuzzy hierarchical algorithm is introduced for clustering IFSs, which is based on the traditional hierarchical clustering procedure, the intuitionistic fuzzy aggregation operator, and the basic distance measures between IFSs: the Hamming distance, normalized Hamming, weighted Hamming, the Euclidean distance, the normalized Euclidean distance, and the weighted Euclidean distance. Subsequently, the algorithm is extended for clustering IVIFSs. Finally the algorithm and its extended form are applied to the classifications of building materials and enterprises respectively.
Fuzzy indicators for customer retention
National Research Council Canada - National Science Library
Valenzuela-Fernández, Leslier; Nicolas, Carolina; Gil-Lafuente, Jaime; Merigó, José M
2016-01-01
.... Nevertheless, one cannot ignore the existence of a gap on how to measure this relationship. Following this idea, this study proposes six fuzzy key performance indicators that aims to measure customer retention and loyalty of the portfolio...
Directory of Open Access Journals (Sweden)
V. Lakshmana Gomathi Nayagam
2007-01-01
Full Text Available The notion of fuzzy filters was studied by Vicente and Aranguren (1988, Lowen (1979, and Ramakrishnan and Nayagam (2002. The notion of fuzzily compactness was introduced and studied by Ramakrishnan and Nayagam (2002. In this paper, an equivalent condition of fuzzily compactness is studied and a new notion of semi-Hausdorffness on fuzzy filters, which cannot be defined in crisp theory of filters, is introduced and studied.
FUZZY LOGIC IN LEGAL EDUCATION
Directory of Open Access Journals (Sweden)
Z. Gonul BALKIR
2011-04-01
Full Text Available The necessity of examination of every case within its peculiar conditions in social sciences requires different approaches complying with the spirit and nature of social sciences. Multiple realities require different and various perceptual interpretations. In modern world and social sciences, interpretation of perception of valued and multi-valued have been started to be understood by the principles of fuzziness and fuzzy logic. Having the verbally expressible degrees of truthness such as true, very true, rather true, etc. fuzzy logic provides the opportunity for the interpretation of especially complex and rather vague set of information by flexibility or equivalence of the variables’ of fuzzy limitations. The methods and principles of fuzzy logic can be benefited in examination of the methodological problems of law, especially in the applications of filling the legal loopholes arising from the ambiguities and interpretation problems in order to understand the legal rules in a more comprehensible and applicable way and the efficiency of legal implications. On the other hand, fuzzy logic can be used as a technical legal method in legal education and especially in legal case studies and legal practice applications in order to provide the perception of law as a value and the more comprehensive and more quality perception and interpretation of value of justice, which is the core value of law. In the perception of what happened as it has happened in legal relationships and formations, the understanding of social reality and sociological legal rules with multi valued sense perspective and the their applications in accordance with the fuzzy logic’s methods could create more equivalent and just results. It can be useful for the young lawyers and law students as a facilitating legal method especially in the materialization of the perception and interpretation of multi valued and variables. Using methods and principles of fuzzy logic in legal
2005-01-01
A new all-electronic Particle Image Velocimetry technique that can efficiently map high speed gas flows has been developed in-house at the NASA Lewis Research Center. Particle Image Velocimetry is an optical technique for measuring the instantaneous two component velocity field across a planar region of a seeded flow field. A pulsed laser light sheet is used to illuminate the seed particles entrained in the flow field at two instances in time. One or more charged coupled device (CCD) cameras can be used to record the instantaneous positions of particles. Using the time between light sheet pulses and determining either the individual particle displacements or the average displacement of particles over a small subregion of the recorded image enables the calculation of the fluid velocity. Fuzzy logic minimizes the required operator intervention in identifying particles and computing velocity. Using two cameras that have the same view of the illumination plane yields two single exposure image frames. Two competing techniques that yield unambiguous velocity vector direction information have been widely used for reducing the single-exposure, multiple image frame data: (1) cross-correlation and (2) particle tracking. Correlation techniques yield averaged velocity estimates over subregions of the flow, whereas particle tracking techniques give individual particle velocity estimates. For the correlation technique, the correlation peak corresponding to the average displacement of particles across the subregion must be identified. Noise on the images and particle dropout result in misidentification of the true correlation peak. The subsequent velocity vector maps contain spurious vectors where the displacement peaks have been improperly identified. Typically these spurious vectors are replaced by a weighted average of the neighboring vectors, thereby decreasing the independence of the measurements. In this work, fuzzy logic techniques are used to determine the true
A novel definition of L-fuzzy lattice based on fuzzy set.
Zhang, Jun-Fang
2013-01-01
The concept of L-fuzzy lattice is presented by means of an L-fuzzy partially ordered set. An L-fuzzy partially ordered set A is an L-fuzzy lattice if and only if one of A[a], A([a]), and A(a) is a lattice.
On The Transition Probabilities for the Fuzzy States of a Fuzzy Markov Chain
Directory of Open Access Journals (Sweden)
J.Earnest Lazarus Piriyakumar
2015-12-01
Full Text Available In this paper the theory of fuzzy logic is mixed with the theory of Markov systems and the abstraction of a Markov system with fuzzy states introduced. The notions such as fuzzy transient, fuzzy recurrent etc., were introduced. The results based on these notions are introduced.
Fuzzy rule-based seizure prediction based on correlation dimension changes in intracranial EEG.
Rabbi, Ahmed F; Aarabi, Ardalan; Fazel-Rezai, Reza
2010-01-01
In this paper, we present a method for epileptic seizure prediction from intracranial EEG recordings. We applied correlation dimension, a nonlinear dynamics based univariate characteristic measure for extracting features from EEG segments. Finally, we designed a fuzzy rule-based system for seizure prediction. The system is primarily designed based on expert's knowledge and reasoning. A spatial-temporal filtering method was used in accordance with the fuzzy rule-based inference system for issuing forecasting alarms. The system was evaluated on EEG data from 10 patients having 15 seizures.
A Gloss Composition and Context Clustering Based Distributed Word Sense Representation Model
Directory of Open Access Journals (Sweden)
Tao Chen
2015-08-01
Full Text Available In recent years, there has been an increasing interest in learning a distributed representation of word sense. Traditional context clustering based models usually require careful tuning of model parameters, and typically perform worse on infrequent word senses. This paper presents a novel approach which addresses these limitations by first initializing the word sense embeddings through learning sentence-level embeddings from WordNet glosses using a convolutional neural networks. The initialized word sense embeddings are used by a context clustering based model to generate the distributed representations of word senses. Our learned representations outperform the publicly available embeddings on half of the metrics in the word similarity task, 6 out of 13 sub tasks in the analogical reasoning task, and gives the best overall accuracy in the word sense effect classification task, which shows the effectiveness of our proposed distributed distribution learning model.
Supply chain management under fuzziness recent developments and techniques
Öztayşi, Başar
2014-01-01
Supply Chain Management Under Fuzziness presents recently developed fuzzy models and techniques for supply chain management. These include: fuzzy PROMETHEE, fuzzy AHP, fuzzy ANP, fuzzy VIKOR, fuzzy DEMATEL, fuzzy clustering, fuzzy linear programming, and fuzzy inference systems. The book covers both practical applications and new developments concerning these methods. This book offers an excellent resource for researchers and practitioners in supply chain management and logistics, and will provide them with new suggestions and directions for future research. Moreover, it will support graduate students in their university courses, such as specialized courses on supply chains and logistics, as well as related courses in the fields of industrial engineering, engineering management and business administration.
Segmentation of multiple sclerosis lesions in MR images: a review
Energy Technology Data Exchange (ETDEWEB)
Mortazavi, Daryoush; Kouzani, Abbas Z. [Deakin University, School of Engineering, Geelong, Victoria (Australia); Soltanian-Zadeh, Hamid [Henry Ford Health System, Image Analysis Laboratory, Radiology Department, Detroit, MI (United States); University of Tehran, Control and Intelligent Processing Center of Excellence (CIPCE), School of Electrical and Computer Engineering, Tehran (Iran, Islamic Republic of); School of Cognitive Sciences, Institute for Studies in Theoretical Physics and Mathematics (IPM), Tehran (Iran, Islamic Republic of)
2012-04-15
Multiple sclerosis (MS) is an inflammatory demyelinating disease that the parts of the nervous system through the lesions generated in the white matter of the brain. It brings about disabilities in different organs of the body such as eyes and muscles. Early detection of MS and estimation of its progression are critical for optimal treatment of the disease. For diagnosis and treatment evaluation of MS lesions, they may be detected and segmented in Magnetic Resonance Imaging (MRI) scans of the brain. However, due to the large amount of MRI data to be analyzed, manual segmentation of the lesions by clinical experts translates into a very cumbersome and time consuming task. In addition, manual segmentation is subjective and prone to human errors. Several groups have developed computerized methods to detect and segment MS lesions. These methods are not categorized and compared in the past. This paper reviews and compares various MS lesion segmentation methods proposed in recent years. It covers conventional methods like multilevel thresholding and region growing, as well as more recent Bayesian methods that require parameter estimation algorithms. It also covers parameter estimation methods like expectation maximization and adaptive mixture model which are among unsupervised techniques as well as kNN and Parzen window methods that are among supervised techniques. Integration of knowledge-based methods such as atlas-based approaches with Bayesian methods increases segmentation accuracy. In addition, employing intelligent classifiers like Fuzzy C-Means, Fuzzy Inference Systems, and Artificial Neural Networks reduces misclassified voxels. (orig.)
An Integrated Intrusion Detection Model of Cluster-Based Wireless Sensor Network
Xuemei Sun; Bo Yan; Xinzhong Zhang; Chuitian Rong
2015-01-01
Considering wireless sensor network characteristics, this paper combines anomaly and mis-use detection and proposes an integrated detection model of cluster-based wireless sensor network, aiming at enhancing detection rate and reducing false rate. Adaboost algorithm with hierarchical structures is used for anomaly detection of sensor nodes, cluster-head nodes and Sink nodes. Cultural-Algorithm and Artificial-Fish-Swarm-Algorithm optimized Back Propagation is applied to mis-use detection of Si...
Wiese, Lena
2014-01-01
One feature of cloud storage systems is data fragmentation (or sharding) so that data can be distributed over multiple servers and subqueries can be run in parallel on the fragments. On the other hand, flexible query answering can enable a database system to find related information for a user whose original query cannot be answered exactly. Query generalization is a way to implement flexible query answering on the syntax level. In this paper we study a clustering-based fragmentat...
Khelifi, Manel
2012-01-01
In this paper, we propose LMEEC, a cluster-based rout- ing protocol with low energy consumption for wireless sensor networks. Our protocol is based on a strategy which aims to provide a more equitable exploitation of the selected nodes (cluster-heads) energy. Simulation results show the effective- ness of LMEEC in decreasing the energy consumption, and in prolonging the network lifetime, compared to LEACH.
Directory of Open Access Journals (Sweden)
Mathieu L. Lepage
2015-05-01
Full Text Available The synthesis and photophysical properties of the first examples of iminosugar clusters based on a BODIPY or a pyrene core are reported. The tri- and tetravalent systems designed as molecular probes and synthesized by way of Cu(I-catalysed azide–alkyne cycloadditions are fluorescent analogues of potent pharmacological chaperones/correctors recently reported in the field of Gaucher disease and cystic fibrosis, two rare genetic diseases caused by protein misfolding.