انجمن کامپیوتر ایران

The dynamic nature of mobile nodes in mobile adhoc networks (MANETs), causes their association and disassociation to and from clusters perturb the stability of network and problem becomes worse if nodes are clusterheads (CH). Therefore cluster maintenance schemes are needed to handle new admissions and releases of node in the clusters. In this paper, we introduce a novel cluster maintenance algorithm which selects a new clusterhead from a trusty area that is defined previously based on some maintenance optimization rules. The election process is done before the current clusterhead leaves the cluster. So the routes which include this clusterhead as a middle node are less probable to break and will be more stable. Number of nodes belonging to a cluster is restricted in the proposed algorithm. In order to prevent of overusing of clusterheads' battery power, the CH selection process is invoked whenever the battery power of current CH goes below some threshold.

The traffic congestion problem in urban areas is worsening since traditional traffic signal control systems cannot provide efficient traffic control. Therefore, dynamic traffic signal control in Intelligent Transportation System (ITS) recently has received increasing attention. This study devised an adaptive and cooperative multi-agent fuzzy system for a decentralized traffic signal control. To achieve this goal we have worked on a model, which has three levels of control. Every intersection is controlled by its own traffic situation, its neighboring intersections recommendations and a knowledge base, which provides the traffic pattern of each intersection in any particular day of the week and hour of the day. The proposed architecture comprises a knowledge base, prediction module and a traffic observer that provide data to real traffic data preparation module, then a decision-making layer takes decision to how long should the intersection green light be extended. The proposed architecture can achieve dynamic traffic signal control. We have also developed a NetLogobased traffic simulator to serve as the agents’ world. Our approach is tested with traffic control of a large connected junction and the result obtained is promising; The average delay time can be reduced by 21.76% compared to the conventional fixed sequence traffic signal and 14.77% compared to the vehicle actuated traffic signal control strategy.

One of the important requirements for operational planning of electrical utilities is the prediction of hourly load up to several days, known as Short Term Load Forecasting (STLF). Considering the effect of its accuracy on system security and also economical aspects, there is an on-going attention toward putting new approaches to the task. Recently, Neuro Fuzzy modeling has played a successful role in various applications over nonlinear time series prediction. This paper presents a neuro-fuzzy model for the application of short-term load forecasting. This model is identified through Locally Liner Model Tree (LoLiMoT) learning algorithm. The model is compared to a multilayer perceptron and Kohonen Classification and Intervention Analysis. The models are trained and assessed on load data extracted from EUNITE network competition.

Portfolio optimization under classic mean-variance framework of Markowitz must be revised as variance fails to be a good risk measure. This is especially true when the asset returns are not normal. In this paper, we utilize Value at Risk (VaR) as the risk measure and Historical Simulation (HS) is used to obtain an acceptable estimate of the VaR. Also, a well known multi-objective evolutionary approach is used to address the inherent bi-objective problem; In fact, NSGA-II is incorporated here. This method is tested on a set of past return data of 12 assets on Tehran Stock Exchange (TSE). A comparison of the obtained results, shows that the proposed method offers high quality solutions and a wide range of risk return trade-offs.

A watermarking method in DCT domain is modified to achieve better imperceptibility. Particle Swarm Optimization (PSO) is used to find the best DCT coefficients for embedding the watermark sequence and the Structural Similarity Index is used as the fitness function in order to have a watermarked image with the best possible quality.

A model of episodic memory is derived to propose algorithms of text categorization with semantic space models. Performances of two algorithms are contrasted using textual material of the text-mining context ‘DEFT09’. Results confirm that the episodic memory metaphor provides a convenient framework to propose efficient algorithm for text categorization. One algorithm has already been tested with LSA. The present paper extends these algorithms to another model of Word Vector named Random Indexing.