Sharif Digital Repository

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... 

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... 

Privacy preservation is an important issue in data publishing. Existing approaches on privacy-preserving data publishing rely on tabular anonymization techniques such as k-anonymity, which do not provide appropriate results for aggregate queries. The solutions based on graph anonymization have also been proposed for relational data to hide only bipartite relations. In this paper, we propose an approach for anonymizing multirelation constraints (ternary or more) with (t,k) hypergraph anonymization in data publishing. To this end, we model constraints as undirected hypergraphs and formally cluster attribute relations as hyperedge with the t-means-clustering algorithm. In addition,... 

Operational and planning studies of high-wind penetrated power systems have well come to the light as a major concern of future energy systems. This paper focuses on the procedure of determining required static reserve of the high-wind penetrated power systems which has not been well accompanied by comprehensive analysis and proper modeling tools. To reach this goal, first, a probabilistic algorithm has been proposed to effectively model the variations in output generation of wind turbines. In this algorithm, the fuzzy c-means clustering method (FCM) is exploited as an efficient as well as robust clustering method to find the multi-state model of wind turbines output generation. Based on... 

Energy consumption and performance metrics have become critical issues for scheduling parallel task-based applications in high-performance computing systems such as cloud datacenters. The duplication and clustering strategy, as well as Dynamic Voltage Frequency Scaling (DVFS) technique, have separately been concentrated on reducing energy consumption and optimizing performance parameters such as throughput and makespan. In this paper, a dual-phase algorithm called EATSDCD which is an energy efficient time aware has been proposed. The algorithm uses the combination of duplication and clustering strategies to schedule the precedence-constrained task graph on datacenter processors through DVFS.... 

Energy consumption and performance metrics have become critical issues for scheduling parallel task-based applications in high-performance computing systems such as cloud datacenters. The duplication and clustering strategy, as well as Dynamic Voltage Frequency Scaling (DVFS) technique, have separately been concentrated on reducing energy consumption and optimizing performance parameters such as throughput and makespan. In this paper, a dual-phase algorithm called EATSDCD which is an energy efficient time aware has been proposed. The algorithm uses the combination of duplication and clustering strategies to schedule the precedence-constrained task graph on datacenter processors through DVFS.... 

High-bandwidth network analysis is challenging, resource consuming, and inaccurate due to the high volume, velocity, and variety characteristics of the network traffic. The infinite stream of incoming traffic forms a dynamic environment with unexpected changes, which requires analysing approaches to satisfy the high-bandwidth network processing challenges such as incremental learning, inline processing, and outlier handling. This study proposes an inline high-bandwidth network stream clustering algorithm designed to incrementally mine large amounts of continuously transmitting network traffic when some outliers can be dropped before determining the network traffic behaviour. Maintaining... 

Sparse representation can be extended to high dimensions and can be used in many applications, including three-dimensional (3D) Inverse synthetic aperture radar (ISAR) imaging. In this study, the high-dimensional sparse representation problem and a recovery method called high-dimensional smoothed least zero-norm (HDSL0) are formulated. In this method, the theory and computation of tensors and approximating L0 norm using Gaussian functions are used for sparse recovery of high-dimensional data. To enhance the performance of HDSL0, modified regularised high-dimensional SL0 (MRe-HDSL0) algorithm, which benefits from the regularised form of SL0 and an additional hard thresholding step, is... 

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... 

Some periodic and quasi-periodic pulse trains are emitted by different sources in the environment and a number of sensors receive them through a single channel simultaneously. We are often interested in separating these pulse trains for source identification at sensors. This identification process is termed as deinterleaving pulse trains. Deinterleaving pulse trains has wide applications in communications, radar systems, neural systems, biomedical engineering, and so on. This paper studies the deinterleaving problem with the assumption that both sources and sensors are fixed. In this study, the problem of deinterleaving pulse trains is modeled as a blind source separation (BSS) problem. To... 

Partial discharge (PD) measurement and identification have great importance to condition monitoring of power transformers. In this paper a new method for recognition of single and multi-source of PD based on extraction of high level image features have been introduced. A database, involving 365 samples of phase-resolved PD (PRPD) data, is developed by measurement carried out on transformer artificial defect models (having different sizes of defect) under a specific applied voltage, to be used for proposed algorithm validation. In the first step, each set of PRPD data is converted into grayscale images to represent different PD defects. Two “image feature extraction” methods, the Local Binary... 

A statistical algorithm is introduced to improve the false inference of active loci, in the population in which members are admixture. The algorithm uses an advanced clustering algorithm based on a Bayesian approach. The proposed algorithm simultaneously infers the hidden structure of the population. In this regard, the Monte Carlo Markov Chain (MCMC) algorithm has been used to evaluate the posterior probability distribution of the model parameters. The proposed algorithm is implemented in a bundle, and then its performance is widely evaluated in a number of artificial databases. The accuracy of the clustering algorithm is compared with the STRUCTURE method based on certain criterion. © 2019... 

Partial discharge (PD) measurement and identification have great importance to condition monitoring of power transformers. In this paper, a new method for recognition of single and multi-source of PD based on extraction of high level image features has been introduced. A database, involving 365 samples of phase-resolved PD (PRPD) data, is developed by measurement carried out on transformer artificial defect models (having different sizes of defect) under a specific applied voltage, to be used for proposed algorithm validation. In the first step, each set of PRPD data is converted into grayscale images to represent different PD defects. Two 'image feature extraction' methods, the Local Binary... 

Association mapping of genetic diseases has attracted extensive research interest during the recent years. However, most of the methodologies introduced so far suffer from spurious inference of the associated sites due to population inhomogeneities. In this paper, we introduce a statistical framework to compensate for this shortcoming by equipping the current methodologies with a state-of-the-art clustering algorithm being widely used in population genetics applications. The proposed framework jointly infers the disease-associated factors and the hidden population structures. In this regard, a Markov Chain-Monte Carlo (MCMC) procedure has been employed to assess the posterior probability... 

We study the statistical and computational properties of a network Lasso method for local graph clustering. The clusters delivered by nLasso can be characterized elegantly via network flows between cluster boundaries and seed nodes. While spectral clustering methods are guided by a minimization of the graph Laplacian quadratic form, nLasso minimizes the total variation of cluster indicator signals. As demonstrated theoretically and numerically, nLasso methods can handle very sparse clusters (chain-like) which are difficult for spectral clustering. We also verify that a primal-dual method for non-smooth optimization allows to approximate nLasso solutions with optimal worst-case convergence... 

Objective: Joint analysis of multi-subject brain imaging datasets has wide applications in biomedical engineering. In these datasets, some sources belong to all subjects (joint), a subset of subjects (partially-joint), or a single subject (individual). In this paper, this source model is referred to as joint/partially-joint/individual multiple datasets unidimensional (JpJI-MDU), and accordingly, a source extraction method is developed. Method: We present a deflation-based algorithm utilizing higher order cumulants to analyze the JpJI-MDU source model. The algorithm maximizes a cost function which leads to an eigenvalue problem solved with thin-SVD (singular value decomposition)... 

A Mobile Ad Hoc Network (MANET) is an infrastructure-less network composed of mobile devices. In order to meet the foresight of newly developing MANETs, a method of maintaining a real-time flow despite dynamic topology and random movement of users is required. Mobility prediction is one of the keys to successful design of efficient protocols to find stable and reliable routes in MANETs. An important characteristic of a MANET is the distribution of the link distance between communicating users. In this paper a novel mobility model is developed for users of a MANET wandering in an unlimited area and is used to derive an analytical framework to approximate the communication links distance... 

Most positions of the human genome are typically invariant (99%) and only some positions (1%) are commonly variant which are associated with complex genetic diseases. Haplotype reconstruction is to divide aligned SNP fragments, which is the most frequent form of difference to address genetic diseases, into two classes, and thus inferring a pair of haplotypes from them. Minimum error correction (MEC) is an important model for this problem but only effective when the error rate of the fragments is low. MEC/GI as an extension to MEC employs the related genotype information besides the SNP fragments and so results in a more accurate inference. The haplotyping problem, due to its NP-hardness, may... 

Topology control in mobile ad-hoc networks allows better spatial reuse of the wireless channel and control over network resources. Topology control algorithms tend to optimize network power usage by keeping the topology connected. However, few efforts have focused on the issue of topology control with mobility. One of the most efficient mobility aware topology control protocols is the "Mobility Aware Distributed Topology Control Protocol". The major problem with this protocol is the future distance predictor which uses mobility prediction to estimate the future distance of neighboring nodes. The efficiency of this estimator varies in presence of different mobility models, sampling rates and... 

We propose a novel method for the clustering of point-cloud data that originate from single-molecule localization microscopy (SMLM). Our scheme has the ability to infer a hierarchical structure from the data. It takes a particular relevance when quantitatively analyzing the biological particles of interest at different scales. It assumes a prior neither on the shape of particles nor on the background noise. Our multiscale clustering pipeline is built upon graph theory. At each scale, we first construct a weighted graph that represents the SMLM data. Next, we find clusters using spectral clustering. We then use the output of this clustering algorithm to build the graph in the next scale; in...