Sharif Digital Repository

In modern power systems, technical virtual power plants (TVPPs) play an important role enabling presence of distributed energy resources (DERs) in electricity markets. In this paper, strategy of using the available energy resources for a TVPP is put under investigation. A new optimization framework is presented for problem of TVPP energy scheduling by taking operational constraints of distribution network into account. In the proposed model, photovoltaic (PV) units and micro turbines along with the electric vehicles (EVs) are scheduled in such a way that the profit of TVPP owner would be maximized. The uncertainty in output generation of PV units is modeled by adopting fuzzy c-means (FCM)... 

Cellular learning automata (CLA) is a distributed computational model which was introduced in the last decade. This model combines the computational power of the cellular automata with the learning power of the learning automata. Cellular learning automata is composed from a lattice of cells working together to accomplish their computational task; in which each cell is equipped with some learning automata. Wide range of applications utilizes CLA such as image processing, wireless networks, evolutionary computation and cellular networks. However, the only input to this model is a reinforcement signal and so it cannot receive another input such as the state of the environment. In this paper,... 

This paper introduces a new fuzzy-based design procedure for more efficient application of reward-penalty schemes in distribution sector. To achieve a fair as well as applicable regulation scheme, the fuzzy C-means clustering algorithm is employed to efficiently determine the similarity among distribution companies. As setting procedure of the reward-penalty scheme parameters can significantly affect the income of different companies, a new procedure based on the membership degrees obtained from the fuzzy C-means algorithm is proposed to fairly determine these parameters for each electricity distribution company. Some numerical studies are performed on the Iranian electricity distribution... 

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 currently one of the most crucial techniques for dealing with massive amount of heterogeneous information on the web, which is beyond human being's capacity to digest. Recent studies have shown that the most commonly used partitioning-based clustering algorithm, the K-means algorithm, is more suitable for large datasets. However, the K-means algorithm can generate a local optimal solution. In this paper we present novel harmony search clustering algorithms that deal with documents clustering based on harmony search optimization method. By modeling clustering as an optimization problem, first, we propose a pure harmony search based clustering algorithm that finds near global... 

Failure Mode and Effect Analysis (FMEA) is an effective risk analysis and failure avoidance approach in the design, process, services, and system. With all its benefits, FMEA has three limitations: failure mode risk assessment and prioritization, complex FMEA worksheets, and difficult application of FMEA tables. This paper seeks to overcome the shortcomings of FMEA using an integrated approach based on a developed Pythagorean fuzzy (PF) k-means clustering algorithm and a popular MCDM method called PF-VIKOR. In the first step, Pythagorean fuzzy numbers (PFNs) were used to collect Severity (S), Occurrence (O), and Detection (D) factors for failure modes to incorporate uncertainty and fuzziness... 

In this work, a new automatic workflow for accurate optimal pseudo-component generation from gas condensate mixtures with a large number of components is presented. This workflow has a good insight into thermo-physical and critical properties and introduces only a small amount of loss of information and EOS flexibility. In this regard, the fuzzy clustering is used to classify the components in the mixture based on the similarities in the critical properties. The mixing rules are then applied to find group properties. Two different approaches for components association in clustering process are investigated with several numbers of groups. The mathematical validity of the groups is controlled... 

This paper studies denoising of images contaminated with additive white Gaussian noise (AWGN). In recent years, clustering-based methods have shown promising performances. In this paper we show that low-rank subspace clustering provides a suitable clustering problem that minimizes the lower bound on the MSE of the denoising, which is optimum for Gaussian noise. Solving the corresponding clustering problem is not easy. We study some global and local sub-optimal solutions already presented in the literature and show that those that solve a better approximation of our problem result in better performances. A simple image denoising method based on dictionary learning using the idea of... 

Distance metric learning is a powerful approach to deal with the clustering problem with side information. For semi-supervised clustering, usually a set of pairwise similarity and dissimilarity constraints is provided as supervisory information. Although some of the existing methods can use both equivalence (similarity) and inequivalence (dissimilarity) constraints, they are usually limited to learning a global Mahalanobis metric (i.e., finding a linear transformation). Moreover, they find metrics only according to the data points appearing in constraints, and cannot utilize information of other data points. In this paper, we propose a probabilistic metric learning algorithm which uses... 

Clustering has become an increasingly important and highly complicated research area for targeting useful and relevant information in modern application domains such as the World Wide Web. Recent studies have shown that the most commonly used partitioning-based clustering algorithm, the K-means algorithm, is more suitable for large datasets. However, the K-means algorithm may generate a local optimal clustering. In this paper, we present novel document clustering algorithms based on the Harmony Search (HS) optimization method. By modeling clustering as an optimization problem, we first propose a pure HS based clustering algorithm that finds near-optimal clusters within a reasonable time.... 

Recently, data stream has become popular in many contexts of data mining. Due to the high amount of incoming data, traditional clustering algorithms are not suitable for this family of problems. Many data stream clustering algorithms proposed in recent years considered the scalability of data, but most of them did not attend the following issues: (1) The quality of clustering can be dramatically low over the time. (2) Some of the algorithms cannot handle arbitrary shapes of data stream and consequently the results are limited to specific regions. (3) Most of the algorithms have not been evaluated in multi-density environments. Identifying appropriate clusters for data stream by handling the... 

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  

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... 

Surface electromyogram (sEMG) signals, a noninvasive bioelectric signal, can be used for the rehabilitation and control of artificial extremities. Current sEMG pattern-recognition systems suffer from a limited number of patterns that are frequently intensified by the unsuitable accuracy of the instrumentation and analytical system. To solve these problems, we designed a multistep-based sEMG pattern-recognition system where, in each step, a stronger more capable relevant technique with a noticeable improved performance is employed. In this paper, we utilized the sEMG signals to classify and recognize six classes of hand movements. We employed an adaptive neurofuzzy inference system (ANFIS) to... 

Wireless sensor networks include small nodes which have sensing ability; calculation and connection extend themselves everywhere soon. Such networks have source limitation on connection, calculation and energy consumption. So, since the nodes have limited energy in sensor networks, the optimized energy consumption in these networks is of more importance and has created many challenges. The previous works have shown that by organizing the network nodes in a number of clusters, the energy consumption could be reduced considerably. So the lifetime of the network would be increased. In this paper, we used the Queen-bee algorithm to create energy efficient clusters in wireless sensor networks.... 

Wireless sensor networks (WSNs) comprise a large number of small sensor nodes scattered across limited geographical areas. The nodes in such networks carry sources of limited and mainly unchangeable energy. Therefore, it is necessary that these networks operate under energy efficient protocols and structures. Energy efficient clustering algorithms have been developed to reduce the networks energy consumption and extend its lifetime. This paper presents an innovative cluster-based communication protocol for WSNs. In order to reduce communication overhead, the authors propose an Energy Aware Clustering Hierarchy Protocol that creates a multi-level hierarchical structure to adequately route and... 

Clustering is the process of grouping data objects into set of disjoint classes called clusters so that objects within a class are highly similar with one another and dissimilar with the objects in other classes. The k-means algorithm is a simple and efficient algorithm that is widely used for data clustering. However, its performance depends on the initial state of centroids and may trap in local optima. In order to overcome local optima obstacles, a lot of studies have been done in clustering. The Fashion Algorithm is one effective method for searching problem space to find a near optimal solution. This paper presents a hybrid optimization algorithm based on Generalized Fashion Algorithm... 

Color quantization is a technique for processing and reduction colors in image. The purposes of color quantization are displaying images on limited hardware, reduction use of storage media and accelerating image sending time. In this paper a hybrid algorithm of GA and Particle Swarm Optimization algorithms with FCM algorithm is proposed. Finally, some of color quantization algorithms are reviewed and compared with proposed algorithm. The results demonstrate Superior performance of proposed algorithm in comparison with other color quantization algorithms  

Data aggregation is a technique that is used to decrease extra and repetitive data in cluster based routing protocols. As we know SPEED routing algorithm is based on service quality and dose not perform data aggregation. In this article, we try to add an data aggregation technique to the available techniques without interfering with the functions of previous ones. The idea involves virtual configuration of sensors and specification of an individual ID to the created data by the sensors in each region, then data aggregation in relay node is done by this ID, resulting in less energy consumption, lower traffic and repeated data, an increase in network lifetime and better quality of service  

Image segmentation with clustering approach is widely used in biomedical application. Fuzzy c-means (FCM) clustering is able to preserve the information between tissues in image, but not taking spatial information into account, makes segmentation results of the standard FCM sensitive to noise. To overcome the above shortcoming, a modified FCM algorithm for MRI brain image segmentation is presented in this paper. The algorithm is realized by incorporating the spatial neighborhood information into the standard FCM algorithm and modifying the membership weighting of each cluster by smoothing it by Total Variation (TV) denoising. The proposed algorithm is evaluated with accuracy index in...