Sharif Digital Repository

Distribution companies attempt to present better services, while reducing investment costs, by promoting consumers' participation in demand response programs. They increase the rate of consumers' participation in demand response programs by taking advantage of reward and punishment policies. Increasing consumers' participation in demand response programs is justifiable when it does not have negative impacts on network technical parameters nor decrease the electricity sale rate. Therefore, it seems necessary to determine the maximum level of consumers' optimal participation in implementation of demand response programs. In this article, a new model is presented based on distribution... 

Morphological filtering is a useful technique for the processing and analysis of binary and gray scale images. The extension of morphological techniques to color images is not a straightforward task because this extension stems from the multivariate ordering problem. Since multivariate ordering is ambiguous, existing approaches have used known vector ordering schemes for the color ordering purpose. In the. last decade, many different color morphological operators have been introduced in the literature. Some of them have focused on noise suppression purposes. However, none has shown good performance, especially on edgy regions. In this paper, new color morphological operators, based on a... 

In the last decade, there has been a growing interest in distance function learning for semi-supervised clustering settings. In addition to the earlier methods that learn Mahalanobis metrics (or equivalently, linear transformations), some nonlinear metric learning methods have also been recently introduced. However, these methods either allow limited choice of distance metrics yielding limited flexibility or learn nonparametric kernel matrices and scale very poorly (prohibiting applicability to medium and large data sets). In this paper, we propose a novel method that learns low-rank kernel matrices from pairwise constraints and unlabeled data. We formulate the proposed method as a trace... 

The problem of clustering with side information has received much recent attention and metric learning has been considered as a powerful approach to this problem. Until now, various metric learning methods have been proposed for semi-supervised clustering. Although some of the existing methods can use both positive (must-link) and negative (cannot-link) constraints, they are usually limited to learning a linear transformation (i.e., finding a global Mahalanobis metric). In this paper, we propose a framework for learning linear and non-linear transformations efficiently. We use both positive and negative constraints and also the intrinsic topological structure of data. We formulate our metric... 

Recently, distance metric learning has been received an increasing attention and found as a powerful approach for semi-supervised learning tasks. In the last few years, several methods have been proposed for metric learning when must-link and/or cannot-link constraints as supervisory information are available. Although many of these methods learn global Mahalanobis metrics, some recently introduced methods have tried to learn more flexible distance metrics using a kernel-based approach. In this paper, we consider the problem of kernel learning from both pairwise constraints and unlabeled data. We propose a method that adapts a flexible distance metric via learning a nonparametric kernel... 

Distance metric plays an important role in many machine learning algorithms. Recently, there has been growing interest in distance metric learning for semi-supervised setting. In the last few years, many methods have been proposed for metric learning when pairwise similarity (must-link) and/or dissimilarity (cannot-link) constraints are available along with unlabeled data. Most of these methods learn a global Mahalanobis metric (or equivalently, a linear transformation). Although some recently introduced methods have devised nonlinear extensions of linear metric learning methods, they usually allow only limited forms of distance metrics and also can use only similarity constraints. In this... 

Distance metric has an important role in many machine learning algorithms. Recently, metric learning for semi-supervised algorithms has received much attention. For semi-supervised clustering, usually a set of pairwise similarity and dissimilarity constraints is provided as supervisory information. Until now, various metric learning methods utilizing pairwise constraints have been proposed. The existing methods that can consider both positive (must-link) and negative (cannot-link) constraints find linear transformations or equivalently global Mahalanobis metrics. Additionally, they find metrics only according to the data points appearing in constraints (without considering other data... 

Metric learning is a powerful approach for semi-supervised clustering. In this paper, a metric learning method considering both pairwise constraints and the geometrical structure of data is introduced for semi-supervised clustering. At first, a smooth metric is found (based on an optimization problem) using positive constraints as supervisory information. Then, an extension of this method employing both positive and negative constraints is introduced. As opposed to the existing methods, the extended method has the capability of considering both positive and negative constraints while considering the topological structure of data. The proposed metric learning method can improve performance of... 

One of the most famous approaches for the segmentation of color images is finding clusters in the color space. Shapes of these clusters are often complex and the time complexity of the existing algorithms for finding clusters of different shapes is usually high. In this paper, a novel clustering algorithm is proposed and used for the image segmentation purpose. This algorithm distinguishes clusters of different shapes using a two-stage clustering approach in a reasonable time. In the first stage, the mean-shift clustering algorithm is used and the data points are grouped into some sub-clusters. In the second stage, connections between sub-clusters are established according to a dissimilarity... 

Recently, several algorithms have been introduced for enhancing clustering quality by using supervision in the form of constraints. These algorithms typically utilize the pair wise constraints to either modify the clustering objective function or to learn the clustering distance measure. Very few of these algorithms show the ability of discovering clusters of different shapes along with satisfying the provided constraints. In this paper, a novel semi-supervised clustering algorithm is introduced that uses the side information and finds clusters of arbitrary shapes. This algorithm uses a two-stage clustering approach satisfying the pair wise constraints. In the first stage, the data points... 

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... 

There is a global tendency towards using Distributed Generation (DG) and renewable energy resources. Considering low utilization cost and low undesirable environmental effects, wind farms have become a considerable resource for producing electrical energy in many countries. Since Wind farms are not programmable and their power output which depends on weather and wind speed is uncertain, they create problems for utilizing electricity system. There are different methods for preserving system stability against the uncertainty of wind power plants. Optimal utilization of pumped storage and gas resources is one of the approaches for reducing production risk of wind farms. In this paper, it is... 

Recently, the problem of integrating side information about classes has emerged in the learning settings like zero-shot learning. Although using multiple sources of information about the input space has been investigated in the last decade and many multi-view and multi-modal learning methods have already been introduced, the attribute learning for classes (output space) is a new problem that has been attended in the last few years. In this paper, we propose an attribute learning method that can use different sources of descriptions for classes to find new attributes that are more proper to be used as class signatures. Experimental results show that the learned attributes by the proposed... 

In many real-world applications, data contain heterogeneous input modalities (e.g., web pages include images, text, etc.). Moreover, data such as images are usually described using different views (i.e. different sets of features). Learning a distance metric or similarity measure that originates from all input modalities or views is essential for many tasks such as content-based retrieval ones. In these cases, similar and dissimilar pairs of data can be used to find a better representation of data in which similarity and dissimilarity constraints are better satisfied. In this paper, we incorporate supervision in the form of pairwise similarity and/or dissimilarity constraints into... 

One of the most studied problems in machine learning is finding reasonable constraints that guarantee the generalization of a learning algorithm. These constraints are usually expressed as some simplicity assumptions on the target. For instance, in the Vapnik–Chervonenkis (VC) theory the space of possible hypotheses is considered to have a limited VC dimension One way to formulate the simplicity assumption is via information theoretic concepts. In this paper, the constraint on the entropy H(X) of the input variable X is studied as a simplicity assumption. It is proven that the sample complexity to achieve an ∊-δ Probably Approximately Correct (PAC) hypothesis is bounded by [Formula... 

In this paper, a novel clustering algorithm using an agent-based architecture along with a force-based clustering algorithm is proposed. To this end, a set of simple mobile agents thai have limited processing power is used. These agents communicate in a pairwise manner to exchange their position information. As opposed to the bio-inspired clustering algorithms that need a set of local rules to specify the agent movements, in this paper the agent motions are driven from attractive and repulsive potential fields that are created by the data points and the other agents respectively. Each agent moves according to the resulted force from applying the potential fields and announces its next... 

Portfolio theory has found its model in numerous engineering applications for optimizing the electrical generation mix of an electricity area. However, to have better performance of this theory, this paper presents a new heuristic method as known modified artificial bee colony (MABC) to portfolio optimization problem. Moreover, we consider both dis-patchable and non-dis-patchable constrains variables and energy sources. Note that the proposed MABC method uses a Chaotic Local Search (CLS) to enhance the self searching ability of the original ABC algorithm. Resulting, in this paper a portfolio theory-based MABC model that explicitly distinguishes between electricity generation (energy),... 

The complex structure of the power system and the pivotal role of electrical energy in determining the socio-economic indicators of any countries lead the policy makers of power industry to take into account the expansion planning of generation system with high priority. Considering the intense fluctuations of costs in electrical energy generation (particularly due to variation of fuel prices within the recent years in the Middle East), finding an optimal generation portfolio, regardless of the costs variations risk looks impossible. The portfolio theory, as an efficient tool for risk management, provides a proper solution to materialize the optimal generation portfolios with the following... 

In this article, a high-accuracy hybrid approach for short-term wind power forecasting is proposed using historical data of wind farm and Numerical Weather Prediction (NWP) data. The power forecasting is carried out in three stages: wind direction forecasting, wind speed forecasting, and wind power forecasting. In all three phases, the same hybrid method is used, and the only difference is in the input data set. The main steps of the proposed method are constituted of outlier detection, decomposition of time series using wavelet transform, effective feature selection and prediction of each time series decomposed using Multilayer Perceptron (MLP) neural network. The combination of automatic... 

The environmental targets set by power sectors throughout the world are the main drivers toward increasing the share of variable renewable energy sources (VRESs). Growth of VRESs will lead to a higher demand for operational flexibility due to their stochastic nature. Traditionally, conventional generation units provide the major share of additional required flexibility that may result in a higher depreciation. Motivated by this challenge, this paper investigates the potential of Demand Response (DR) as an emerging alternative in systems with significant amounts of wind power. To this end, a comprehensive set of DR programs including tariff-based, incentive-based and combinational DR programs...