Sharif Digital Repository

In this paper, we address the problem of constrained clustering along with active selection of clustering constraints in a unified framework. To this aim, we extend the improved possibilistic c-Means algorithm (IPCM) with multiple kernels learning setting under supervision of side information. By incorporating multiple kernels, the limitation of improved possibilistic c-means to spherical clusters is addressed by mapping non-linear separable data to appropriate feature space. The proposed method is immune to inefficient kernels or irrelevant features by automatically adjusting the weight of kernels. Moreover, extending IPCM to incorporate constraints, its strong robustness and fast... 

In this paper, we propose a fuzzification method for clusters produced from a clustering process, based on Active Learning Method (ALM). ALM is a soft computing methodology which is based on a hypothesis claiming that human brain interprets information in pattern-like images. The proposed fuzzification method is applicable to all non-fuzzy clustering algorithms as a post process. The most outstanding advantage of this method is the ability to determine the membership degrees of each data to all clusters based on the density and shape of the clusters. It is worth mentioning that for existing fuzzy clustering algorithms such as FCM the membership degree is usually determined as a function of... 

Until now, many algorithms have been introduced for finding arbitrary shaped clusters, but none of these algorithms is able to identify all sorts of cluster shapes and structures that are encountered in practice. Furthermore, the time complexity of the existing algorithms is usually high and applying them on large dataseis is time-consuming. In this paper, a novel fast clustering algorithm is proposed. This algorithm distinguishes clusters of different shapes using a twostage clustering approach. In the first stage, the data points are grouped into a relatively large number of fuzzy ellipsoidal sub-clusters. Then, connections between sub-clusters are established according to the Bhatiacharya... 

The main objective of this paper is to design a fast,non-expensive and practical controller for towercranes. The controllers are designed to transfer theloads from point to point, as fast as possible, and at thesame time the load swing is kept small during thetransfer process. Moreover, variations of the systemparameters such as cable length are taken intoconsideration. It is shown how the state feedbackcontrollers along with the gain scheduling could beused for a time varying linear model with varyingparameters. For this reason, linear controllers aredesigned for a number of operating points, where theirparameters are interpolated for conditions in between.In this way, a globally nonlinear... 

This study proposes a spiking neuro-fuzzy clustering system based on a novel spike encoding scheme and a compatible learning algorithm. In this system, we utilize an analog to binary encoding scheme that properly maps the concept of "distance" in multi-dimensional analog spaces to the concept of "dissimilarity " of binary bits in the equivalent binary spaces. When this scheme is combined with a novel binary to spike encoding scheme and a proper learning algorithm is applied, a powerful clustering algorithm is produced. This algorithm creates flexible fuzzy clusters in its analog input space and modifies their shapes to different convex shapes during the learning process. This system has... 

Clustering-based image segmentation is a well-known multispectral image segmentation method. However, as it inherently does not account for the spatial relation among image pixels, it often results in inhomogeneous segmented regions. The recently proposed membership-connectedness (MC)-based segmentation method considers the local and global spatial relations besides the fuzzy clustering stage to improve segmentation accuracy. However, the inherent spatial and intraclass redundancies in multispectral images might decrease the accuracy and efficiency of the method. This letter addresses these two problems and proposes a segmentation method that is based on the MC method, watershed transform,... 

Reconstructing haplotype in MEC (Minimum Error Correction) model is an important clustering problem which focuses on inferring two haplotypes from SNP fragments (Single Nucleotide Polymorphism) containing gaps and errors. Mutated form of human genome is responsible for genetic diseases which mostly occur in SNP sites. In this paper, a fuzzy clustering approach is performed for haplotype reconstruction or haplotype assembly from a given sample Single Nucleotide Polymorphism (SNP). In the best previous approach based on reconstruction rate (Wang 2007[2]), all SNP-fragments are considered with equal values. In our proposed method the value of the fragments are based on the degree of membership... 

The problem of fuzzy data association for target tracking in a cluttered environment is discussed in this paper. In data association filters based on fuzzy clustering, the association probabilities of tracking filters are reconstructed by utilizing the fuzzy membership degree of the measurement belonging to the target. Clearly in these filters, the fuzzy clustering method has an important role; better approach causes better precision in target tracking. Recently, by using the information theory, the maximum entropy fuzzy data association filter (MEF-DAF), as a fast and efficient algorithm, is introduced in literature. In this paper, by modification of a fuzzy clustering objective function,... 

Developing a computational model for studying tribological performance is essential for computing accurate life cycle of various materials. Caused by the existence of complicated and nonlinear interactions between material surfaces, exact modeling of wear behavior is very difficult. Artificial intelligence (AI) can be used in distinguishing similar patterns in experimental data and predictive modeling of a certain material’s wear behavior. In this paper, artificial neural networks (ANNs) approach, adaptive neural-based fuzzy inference system (ANFIS) technique, and fuzzy clustering method (FCM) are used to develop a simple, accurate, and applicable model for predicting the wear behavior of... 

Variability and uncertainty of wind and photovoltaic (PV) generations greatly influence technical and financial aspects of power systems. This paper examines the potential impacts of large-scale wind and PV farms on reliability level of composite generation and transmission systems. At first, reliability models of renewable-based units are developed. In these models, both component failure rates and uncertainty nature of renewable resources are taken into account. Using the proposed technique, the multi-state analytical models of a wind farm placed in Manjil and a PV farm placed in Jask both in Iran are extracted. Then, reliability studies of high renewable-energies penetrated power system... 

Variability and uncertainty of wind and photovoltaic (PV) generations greatly influence technical and financial aspects of power systems. This paper examines the potential impacts of large-scale wind and PV farms on reliability level of composite generation and transmission systems. At first, reliability models of renewable-based units are developed. In these models, both component failure rates and uncertainty nature of renewable resources are taken into account. Using the proposed technique, the multi-state analytical models of a wind farm placed in Manjil and a PV farm placed in Jask both in Iran are extracted. Then, reliability studies of high renewable-energies penetrated power system... 

In this paper, we propose a novel density-based fuzzy clustering algorithm based on Active Learning Method (ALM), which is a methodology of soft computing inspired by some hypotheses claiming that human brain interprets information in pattern-like images rather than numerical quantities. The proposed clustering algorithm, Fuzzy Unsupervised Active Learning Method (FUALM), is performed in two main phases. First, each data point spreads in the feature space just like an ink drop that spreads on a sheet of paper. As a result of this process, densely connected ink patterns are formed that represent clusters. In the second phase, a fuzzifying process is applied in order to summarize the effects... 

In this paper, we propose a novel density-based fuzzy clustering algorithm based on Active Learning Method (ALM), which is a methodology of soft computing inspired by some hypotheses claiming that human brain interprets information in pattern-like images rather than numerical quantities. The proposed clustering algorithm, Fuzzy Unsupervised Active Learning Method (FUALM), is performed in two main phases. First, each data point spreads in the feature space just like an ink drop that spreads on a sheet of paper. As a result of this process, densely connected ink patterns are formed that represent clusters. In the second phase, a fuzzifying process is applied in order to summarize the effects... 

Reciprocal interaction and facial expression are some of the most interesting topics in the fields of social and cognitive robotics. On the other hand, children with autism show a particular interest toward robots, and facial expression recognition can improve these children’s social interaction abilities in real life. In this research, a robotic platform has been developed for reciprocal interaction consisting of two main phases, namely as Non-structured and Structured interaction modes. In the Non-structured interaction mode, a vision system recognizes the facial expressions of the user through a fuzzy clustering method. The interaction decision-making unit is combined with a fuzzy finite... 

Using word base n-gram language models in continuous speech recognition systems is so prevalent. For using this type of language models, we should extract them from large corpora. Since Persian corpora are not rich, therefore the extracted language models are not credible. For this reason, most researchers extract class n-grams instead of finding word n-grams. In this research a new idea for fuzzy word clustering is represented that each word can be assigned to more that one class. The Fuzzy c-mean algorithm is used for our clustering method and we have examined its various parameters of it. Finally, this algorithm was applied on 20000 most frequent Persian words extracted from "Persian Text...