Sharif Digital Repository

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... 

Frame aggregation (FA) mechanisms improve the throughput of WLANs. In this paper, the effect of the FA mechanism on the throughput of wireless local area networks (WLANs) has been investigated. To this end, we propose an analytical model in order to analyze an IEEE 802.11n network comprised of an access point (AP) and several conventional nodes (CNs), all in the coverage area of each other. With respect to the heavier download traffic compared to the upload one, in our scenario, only the AP uses an FA mechanism and the other nodes use the basic IEEE 802.11 standard. In our proposed analytical model, the maximum downlink (DL) throughput is derived. Regarding the asymmetry among nodes, our... 

Space robots are inseparable components of many space missions. However, capabilities of space robots with rigid manipulators are restricted in confronting with unknown and confined environments and situations require high safety. To overcome these defects, the potential of bio-inspired soft robots for space applications has recently been addressed in the literature. Dynamic modeling and control of soft manipulators are challenging due to their infinite degrees of freedom. In this paper, a general comprehensive three-dimensional dynamic modeling framework is developed for a floating soft manipulator-spacecraft system employing the Euler-Lagrange method. This framework derives the coupled... 

Space robots have proven their ability to accomplish many space missions impossible or dangerous for the human. However, the conventional space robots suffer from the low adaptability, heavy structures, and the low safety. In this regard, the bio-inspired soft robots, composed of the soft material, have been emerged to overcome these shortcomings and to expand the space missions’ diversity. The potential of soft robots for space applications has recently been investigated in the literature. Although the dynamic modeling and control of soft robotic arms are complicated due to their infinite degrees of freedom, soft manipulators are anticipated to provide the adaptive capture of targets,... 

Persian (Farsi) script is totally cursive and each character is written in several different forms depending on its former and later characters in the word. These complexities make automatic handwriting recognition of Persian a very hard problem and there are few contributions trying to work it out. This paper presents a novel practical approach to online recognition of Persian handwriting which is based on representation of inputs and patterns with very simple visual features and comparison of these simple terms. This recognition approach is tested over a set of Persian words and the results have been quite acceptable when the possible words where unknown and they were almost all correct in... 

k-medoids algorithm is a partitional, centroid-based clustering algorithm which uses pairwise distances of data points and tries to directly decompose the dataset with n points into a set of k disjoint clusters. However, k-medoids itself requires all distances between data points that are not so easy to get in many applications. In this paper, we introduce a new method which requires only a small proportion of the whole set of distances and makes an effort to estimate an upperbound for unknown distances using the inquired ones. This algorithm makes use of the triangle inequality to calculate an upper-bound estimation of the unknown distances. Our method is built upon a recursive approach to... 

Introduction: Cue-induced craving is central to addictive disorders. Most cue-reactivity functional magnetic resonance imaging studies are analyzed statically and report averaged signals, disregarding the dynamic nature of craving and task fatigue. Accordingly, this study investigates temporal dynamics of the neural response to drug cues as a functional magnetic resonance imaging study among methamphetamine users. Methods: A total of 32 early abstinent methamphetamine users underwent functional magnetic resonance imaging while viewing visual methamphetamine cues. A craving > neutral contrast was obtained in regions of interest. To explore the changes over time, the pre-processed signal was... 

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 this study, biocompatible ceramic layers containing TiO2 and hydroxyapatite (HA) nanoparticles (TiO2/HA) were deposited on pure commercial titanium (Grade 2) by using plasma electrolytic oxidation and AC power supply. The coating process was carried out in five different solutions for various times at a current density of 500 mA cm−2. To achieve the optimum conditions for thickness and microstructure, the coating process was conducted in solutions with a 3 g L−1 concentration of HA nanoparticles. FESEM, XRD, and FTIR results showed that HA nanoparticles were successfully incorporated into the pores of the layer. Furthermore, the corrosion behavior of the coating layers in the simulated... 

Although GAN-based methods have received many achievements in the last few years, they have not been entirely successful in generating discrete data. The most crucial challenge of these methods is the difficulty of passing the gradient from the discriminator to the generator when the generator outputs are discrete. Despite the fact that several attempts have been made to alleviate this problem, none of the existing GAN-based methods have improved the performance of text generation compared with the maximum likelihood approach in terms of both the quality and the diversity. In this paper, we proposed a new framework for generating discrete data by an adversarial approach in which there is no... 

Background: We developed transformer-based deep learning models based on natural language processing for early risk assessment of Alzheimer’s disease from the picture description test. Methods: The lack of large datasets poses the most important limitation for using complex models that do not require feature engineering. Transformer-based pre-trained deep language models have recently made a large leap in NLP research and application. These models are pre-trained on available large datasets to understand natural language texts appropriately, and are shown to subsequently perform well on classification tasks with small training sets. The overall classification model is a simple classifier on... 

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... 

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 restructured and de-regulated power systems, generating companies (Gencos) are responsible for supplying electricity for both energy and reserve markets, which usually operate simultaneously. In this condition, the question is how much and for what price must each Genco generate for each market to maximize its profit, so this paper intends to answer to this question. In this paper, first, the combined energy and reserve markets are considered, and the Nash equilibrium points are determined. Then, the bidding strategies for each Genco at these points will be presented. The bids for the energy and 10 min spinning reserve (TMSR) markets are separated in the second stage, and again, the... 

In the restructured power systems, generating companies (Genco) are responsible for selling their product in the energy market. In this condition, the question is how much and for what price must each Genco generate to maximize its profit. Therefore, this paper intends to propose a rational method to answer this question. In the proposed methodology, the hourly forecasted market clearing price (FMCP) is used as a reference to model the possible and probable price strategies of Gencos. The forecasted price is the basis of the bidding strategies of each Genco, which can be achieved by solving a bi-level optimization problem using GAMS (general algebraic modeling system) language. The first... 

In the restructured power systems, they are typically scheduled based on the offers and bids to buy and sell energy and ancillary services (AS) subject to operational and security constraints. Generally, no account is taken of unit reliability when scheduling it. Therefore generating units have no incentive to improve their reliability. This paper proposes a new method to obtain the equilibrium points for reliability and price bidding strategy of units when the unit reliability is considered in the scheduling problem. The proposed methodology employs the supply function equilibrium (SFE) for modeling a unit's bidding strategy. Units change their bidding strategies and improve their... 

On pool market structure, the generating units are typically dispatched in order of lowest to highest bid as needed to meet demand requirement as well as considering network constraints. Generally, no account is taken of generating unit's reliability when scheduling them. This paper proposes a new structure for electricity market to consider generating unit's reliability in the scheduling problem. GAMS (General Algebraic Modeling System) language has been used to solve the social welfare maximization problem using CPLEX optimization software with mixed integer programming. ©2006 IEEE  

This paper presents a new method to analyze the bidding strategies of Generating Companies (GENCOs) with regard to demand elasticity. It is assumed that the available information of each GENCO about its opponents is incomplete and only the minimum and maximum generation levels of their opponents, as well as their fuel type, are known. In the proposed methodology, GENCOs prepare their strategic bids according to a Supply Function Equilibrium (SFE) model. GENCOs will change their bidding strategies until Nash equilibrium points are obtained. The general Algebraic Modeling System (GAMS) has been used to solve the maximization modules using the MINOS optimization software with non Linear...