Sharif Digital Repository

Partial discharge (PD) activity can be destructive to the transformer insulation, and ultimately may result in total breakdown of the insulation. Partial discharge sources identification in a power transformer enables the operator to evaluate the transformer insulation condition during its lifetime. In order to identify the PD source; in the case of presence of multiple sources; the first step is to capture the PD signals and to extract their specific features. In this contribution, the frequency domain analysis, the time domain analysis and the wavelet transform are employed for feature extraction purpose. In practice, there might be plenty of features, and in each scenario, only some of... 

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... 

In this paper the Unsupervised Active Learning Method (UALM), a novel clustering method based on the Active Learning Method (ALM) is introduced. ALM is an adaptive recursive fuzzy learning algorithm inspired by some behavioral features of human brain functionality. UALM is a density-based clustering algorithm that relies on discovering densely connected components of data, where it can find clusters of arbitrary shapes. This approach is a noise-robust clustering method. The algorithm first blurs the data points as ink drop patterns, then summarizes the effects of all data points, and finally puts a threshold on the resulting pattern. It uses the connected-component algorithm for finding... 

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... 

Karyotyping, is the process of classification of human chromosomes within the microscopic images. This is a common task for diagnosing many genetic disorders and abnormalities. Automatic Karyotyping algorithms usually suffer the poor quality of the images due to the non rigid nature of the chromosomes which makes them to have unpredictable shapes and sizes in various images. One of the main problems that usually need operator's interaction is the identification and separation of the overlapping/touching chromosomes. This paper presents an effective algorithm for identification of any cluster of the overlapping/touching chromosomes together with the number of chromosomes in the cluster, which... 

Data stream clustering has attracted a huge attention in recent years. Many one-pass and evolving algorithms have been developed in this field but feature selection and its influence on clustering solution has not been addressed by these algorithms. In this paper we explain a feature-based clustering method for streaming data. Our method establishes a ranking between features based on their appropriateness in terms of clustering compactness and separateness. Then, it uses an automatic algorithm to identify unimportant features and remove them from feature set. These two steps take place continuously during lifetime of clustering task. © 2009 IEEE  

The k-means is the most popular clustering algorithm, but, as it needs too many distance computations, its speed is dramatically fall down against high-dimensional data. Although, there are some quite fast variants proposed in literature, but, there is still much room for improvement against high-dimensional large-scale datasets. What proposed here, k-means-g*, is based on a simple geometric concept. For four distinct points, if distance between all pairs except one pair are known, then, a lower bound can be determined for the unknown distance. Utilizing this technique in the assignment step of the k-means, many high-dimensional distance computations can be easily ignored, where small amount... 

This study presents a novel statistical volume element (SVE) for micromechanical modeling of the white matter structures, with histology-informed randomized distribution of axonal tracts within the extracellular matrix. The model was constructed based on the probability distribution functions obtained from the results of diffusion tensor imaging as well as the histological observations of scanning electron micrograph, at two structures of white matter susceptible to traumatic brain injury, i.e. corpus callosum and corona radiata. A simplistic representative volume element (RVE) with symmetrical arrangement of fully alligned axonal fibers was also created as a reference for comparison. A... 

A new approach, based on Adaptive-Network-based Fuzzy Inference System (ANFIS), is presented for the classification of arthrometric data of normal/ACL-ruptured knees, considering the insufficiency of existing criteria. An ANFIS classifier was developed and tested on a total of 4800 arthrometric data points collected from 40 normal and 40 injured subjects. The system consisted of 5 layers and 8 rules, based on the results of subtractive data clustering, and trained using the hybrid algorithm method. The performance of the system was evaluated in four runs, in the framework of a 4-fold cross validation algorithm. The results indicated a definite correct diagnosis for typical injured and normal... 

Supplier selection is a decision-making process to identify and evaluate suppliers for making contracts. Here, we use interval type-2 fuzzy values to show the decision makers' preferences and also introduce a new formula to compute the distance between two interval type-2 fuzzy sets. The performance of the proposed distance formula in comparison with the normalized Hamming, normalized Hamming based on the Hausdorff metric, normalized Euclidean and the signed distances is evaluated. The results show that the signed distance has the same trend as our method, but the other three methods are not appropriate for interval type-2 fuzzy sets. Using this approach, we propose a hierarchical... 

Minimization of the number of cluster heads in a wireless sensor network is a very important problem to reduce channel contention and to improve the efficiency of the algorithm when executed at the level of cluster-heads. In this paper, we propose an efficient method based on genetic algorithms (GAs) to solve a sensor network optimization problem. Long communication distances between sensors and a sink in a sensor network can greatly drain the energy of sensors and reduce the lifetime of a network. By clustering a sensor network into a number of independent clusters using a GA, we can greatly minimize the total communication distance, thus prolonging the network lifetime. Simulation results... 

The Internet of Things (IoT) is a new concept in the world of technology and information and has many applications in industry, communications, and various other fields. In the lowest layer of the IoT, wireless sensor networks (WSNs) play an important and pivotal role. WSN consists of a large number of sensors and is commonly used to monitor a target. It is important to reduce energy consumption in WSNs to extend network life, since it is usually impossible to replace sensor batteries. In this paper, a novel clustering and routing method is proposed. It is mainly based on genetic algorithms and equilibrium optimization. To improve scalability, the sensor nodes are clustered in the first... 

Wireless sensor networks consist of a large number of nodes which are distributed sporadically in a geographic area. The energy of all nodes on the network is limited. For this reason, providing a method of communication between nodes and network administrator to manage energy consumption is crucial. For this purpose, one of the proposed methods with high performance, is clustering methods. The big challenge in clustering methods is dividing network into several clusters that each cluster is managed by a cluster head (CH). In this paper, a centralized genetic-based clustering (CGC) protocol using onion approach is proposed. The CGC protocol selects the appropriate nodes as CHs according to... 

We study the 2-center problem with outliers in high-dimensional data streams. Given a stream of points in arbitrary d dimensions, the goal is to find two congruent balls of minimum radius covering all but at most z points. We present a (1.8+ε)-approximation streaming algorithm, improving over the previous (4+ε)-approximation algorithm available for the problem. The space complexity and update time of our algorithm are poly(d,z,1/ε), independent of the size of the stream. © 2016 Elsevier B.V  

Clustering is the problem of finding relations in a data set in an supervised manner. These relations can be extracted using the density of a data set, where density of a data point is defined as the number of data points around it. To find the number of data points around another point, region queries are adopted. Region queries are the most expensive construct in density based algorithm, so it should be optimized to enhance the performance of density based clustering algorithms specially on large data sets. Finding the optimum set of region queries to cover all the data points has been proven to be NP-complete. This optimum set is called the skeletal points of a data set. In this paper, we... 

Clustering is the problem of partitioning a (large) set of data using unsupervised techniques. Today, there exist many clustering techniques. The most important characteristic of a clustering technique is the shape of the cluster it can find. In this paper, we propose a method that is capable to find arbitrary shaped clusters and uses simple geometric constructs, Circlusters. Circlusters are different radius sectored circles. Circlusters can be used to create many hybrid approaches in mixture with density based or partitioning based methods. We also proposed two new clustering methods that are capable to find complex clusters in O(n), where n is the size of the data set. Both of the methods... 

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,... 

The different characteristics of UWSN and trade off between UWSN and WSN, have been discussed in many researches. Here, we aim to propose a new architecture for very large scale underwater sensor network. In deployment part of sensors, topology plays a crucial role in issues like communication performance, power consumption, network reliability and fault tolerance capabilities. Hence, it is so critical and should be analyzed how we deploy sensors in targets environment. For instance, to improve reliability of our networks in harsh conditions, it is so important to avoid deploying underwater sensors with single point of failure and bottleneck. For this purpose, we present enhanced clustering... 

Clustering Web data is one important technique for extracting knowledge from the Web. In this paper, a novel method is presented to facilitate the clustering. The method determines the appropriate number of clusters and provides suitable representatives for each cluster by inference from a Bayesian network. Furthermore, by means of the Bayesian network, the contents of the Web pages are converted into vectors of lower dimensions. The method is also extended for hierarchical clustering, and a useful heuristic is developed to select a good hierarchy. The experimental results show that the clusters produced benefit from high quality