Sharif Digital Repository

In this paper, we combine a novel Sequential Graph Coloring Heuristic Algorithm (SGCHA) with a non-systematic method based on a cultural algorithm to solve the graph coloring problem (GCP). The GCP involves finding the minimum number of colors for coloring the graph vertices such that adjacent vertices have distinct colors. In our solving approach, we first use an estimator which is implemented with SGCHA to predict the minimum colors. Then, in the non-systematic part which has been designed using cultural algorithms, we improve the prediction. Various components of the cultural algorithm have been implemented to solve the GCP with a self adaptive behavior in an efficient manner. As a result... 

General Circulation Models (GCMs) are advanced tools for impact assessment and climate change studies. Previous studies show that the performance of the GCMs in simulating climate variables varies significantly over different regions. This study intends to evaluate the performance of the Coupled Model Intercomparison Project phase 5 (CMIP5) GCMs in simulating temperature and precipitation over Iran. Simulations from 37 GCMs and observations from the Climatic Research Unit (CRU) were obtained for the period of 1901–2005. Six measures of performance including mean bias, root mean square error (RMSE), Nash-Sutcliffe efficiency (NSE), linear correlation coefficient (r), Kolmogorov-Smirnov... 

General Circulation Models (GCMs) are advanced tools for impact assessment and climate change studies. Previous studies show that the performance of the GCMs in simulating climate variables varies significantly over different regions. This study intends to evaluate the performance of the Coupled Model Intercomparison Project phase 5 (CMIP5) GCMs in simulating temperature and precipitation over Iran. Simulations from 37 GCMs and observations from the Climatic Research Unit (CRU) were obtained for the period of 1901–2005. Six measures of performance including mean bias, root mean square error (RMSE), Nash-Sutcliffe efficiency (NSE), linear correlation coefficient (r), Kolmogorov-Smirnov... 

Fuel cells belong to an avant-garde technology family for a wide variety of applications including micro-power, transportation power, stationary power for buildings and other distributed generation applications. The first objective of this contribution is to find a suitable reduced model of a Solid Oxide Fuel Cell (SOFC). The derived reduced model is then used to design a state estimator. In the first step, the distributed model of the SOFC that is derived using the first principle balance equations is solved by the method of lines. Since this model is too complex and sluggish for real-time applications, a representation of this model with lower number of states and good accuracy is needed.... 

The past few years have witnessed a growing rate of attraction in adoption of Artificial Intelligence (AI) techniques to solve different engineering problems. Besides, Short Term Electrical Load Forecasting (STLF) is one of the important concerns of power systems and accurate load forecasting is vital for managing supply and demand of electricity. This study estimates short term electricity loads of Iran by means of Adaptive Neuro-Fuzzy Inference System (ANFIS), Artificial Neural Networks (ANN) and Genetic Algorithm (GA) which are the most successful AI techniques in this field. In order to improve forecasting accuracy, all AI techniques are equipped with preprocessing concept, and effects... 

The trend of gold price in the market is the most important consideration for the investors of the gold, and serves as the basis of gaining profit, so there are scholars who try to forecast the gold price. Forecasting accuracy is one of the most important factors involved in selecting a forecasting method. Besides, nowadays artificial intelligence (AI) techniques are becoming more and more widespread because of their accuracy, symbolic reasoning, flexibility and explanation capabilities. Among these techniques, particle swarm optimization (PSO) is one of the best AI techniques for optimization and parameter estimation. In this study a PSO-based time series model for the gold price... 

The past few years have witnessed a growing rate of attraction in adoption of Artificial Intelligence (AI) techniques and combining them to improve forecasting accuracy in different fields. Besides, stock market forecasting has always been a subject of interest for most investors and professional analysts. Stock market forecasting is a tough problem because of the uncertainties involved in the movement of the market. This paper proposes a hybrid artificial intelligence model for stock exchange index forecasting, the model is a combination of genetic algorithms and feedforward neural networks. Actually it evolves neural network weights by using genetic algorithms. We also employ preprocessing... 

A utility function is an important tool for representing a DM’s preference. We adjoin utility functions to multi-objective optimization problems. In current studies, usually one utility function is used for each objective function. Situations may arise for a goal to have multiple utility functions. Here, we consider a constrained multi-objective problem with each objective having multiple utility functions. We induce the probability of the utilities for each objective function using Bayesian theory. Illustrative examples considering dependence and independence of variables are worked through to demonstrate the usefulness of the proposed model. © 2017, The Author(s)  

In the current paper, we have investigated the generalized FitzHugh-Nagumo model. We have shown that symmetric bursting behaviors of different types could be observed in this model with an appropriate recovery term. A modified version of this system is used to construct bursting activities. Furthermore, we have shown some numerical examples of delayed Hopf bifurcation and canard phenomenon in the symmetric bursting of super-Hopf/homoclinic type near its super-Hopf and homoclinic bifurcations, respectively  

Ornamental stone sawability is important in predicting the prime cost of production and planning of the stone quarries and factories. Ornamental stone sawability with two standard methods, wire and circular sawing, is dependent on many parameters including; the stone that be sawn and machine characteristics and saw operating characteristic. The investigation of these parameters and the determination of the optimum working conditions for sawing machines in the stone industry is significant for establishing the most suitable and economic usage of the sawing method in the future. Up to now, various attempts have been made to determine these parameters. In this review paper, the important... 

Meteorological droughts due to the concurrent occurrence of low-precipitation and high-temperature events can lead to severe negative impacts on agriculture, economy, ecosystem, and society. This study proposes a novel framework to characterize such drought conditions based on the joint variability of precipitation–temperature, particularly under climate change. Generalized hierarchical linear model is used to downscale precipitation and temperature at multiple stations from the outputs of nine General Circulation Models (GCMs) under Representative Concentration Pathways (RCPs) 4.5 and 8.5. A bivariate drought index called Precipitation–Temperature Deciles Index (PTDI) is developed using... 

Meteorological droughts due to the concurrent occurrence of low-precipitation and high-temperature events can lead to severe negative impacts on agriculture, economy, ecosystem, and society. This study proposes a novel framework to characterize such drought conditions based on the joint variability of precipitation–temperature, particularly under climate change. Generalized hierarchical linear model is used to downscale precipitation and temperature at multiple stations from the outputs of nine General Circulation Models (GCMs) under Representative Concentration Pathways (RCPs) 4.5 and 8.5. A bivariate drought index called Precipitation–Temperature Deciles Index (PTDI) is developed using... 

The reliability of data driven prognostics algorithms severely depends on the volume of data. Therefore in case of limited data availability, life estimations usually are not acceptable; because the quantity of run to failure data is not sufficient to train prognostics model efficiently. To board this problem, a life clustering prognostics (LCP) framework is proposed. LCP regenerates the train data at different ages and outcomes to increment of the training data volume. So, the method is useful for limited data conditions. In this research, initially LCP performance is studied in normal situation is; successively robustness of the framework under limited data conditions is considered. For... 

Mooring lines may be damaged under severe conditions during the life of an offshore platform. In the event of a mooring failure, it is possible to break remaining mooring lines in a severe condition, which ultimately leads to the total structural failure; Therefore, the post-failure analysis is very important for the mooring system. In this study, the Amir-Kabir semi-submersible platform has been modeled which is installed in 700 m’ depth of the Caspian Sea, and random waves have been generated towards the structure in the form of the JONSWAP wave spectrum according to the conditions of the Caspian Sea. For analysis, four different mooring configurations have been considered. Then, in each... 

Meteorological droughts due to the concurrent occurrence of low-precipitation and high-temperature events can lead to severe negative impacts on agriculture, economy, ecosystem, and society. This study proposes a novel framework to characterize such drought conditions based on the joint variability of precipitation–temperature, particularly under climate change. Generalized hierarchical linear model is used to downscale precipitation and temperature at multiple stations from the outputs of nine General Circulation Models (GCMs) under Representative Concentration Pathways (RCPs) 4.5 and 8.5. A bivariate drought index called Precipitation–Temperature Deciles Index (PTDI) is developed using... 

Accurate forecasting of outpatient visits aids in decision-making and planning for the future and is the foundation for greater and better utilization of resources and increased levels of outpatient care. It provides the ability to better manage the ways in which outpatient's needs and aspirations are planned and delivered. This study presents a hybrid artificial intelligence (AI) model to develop a Mamdani type fuzzy rule based system to forecast outpatient visits with high accuracy. The hybrid model uses genetic algorithm for evolving knowledge base of fuzzy system. Actually it extracts useful patterns of information with a descriptive rule induction approach based on Genetic Fuzzy Systems...