Iranian Journal of Science and Technology Transactions of Electrical Engineering
Volume:36 Issue: 1, 2012

Distributed spectrum sensing (DSS) is of great importance in Cognitive Radio, especially under fading or shadowing effects. In order to evaluate the performance of a distributed system, it is commonly compared with the centralized system as an upper performance bound. Now the question is whether or not one can obtain a distributed strategy serving as an upper bound to benchmark any distributed strategy, tighter than that of the centralized scheme. Here, we suggest employing the Neyman-Pearson (NP) fusion rule to achieve an upper bound. Furthermore, the analysis of a randomized fusion rule has been provided, which is a long-existing problem in this field. For this purpose, theoretical analysis on the performance of the NP fusion rule is carried out. Next, we compare the traditional fusion rules with the proposed bound and observe in which special cases of the probability of false alarm at the fusion center these counting rules are optimum. We further study the effects of varying the number of participating sensors on fusion performance in detail. Remarkably, simulation results in some applicable examples illustrate the significant cooperative gain achieved by the proposed NP fusion rule.

In today's world of numerous sources of multimedia content, recommender systems help users find relevant content items. In our research the reasoning behind the recommendations generated by such systems was explored to check whether presenting users with explanations of recommended content increases their trust in the system. A content-based recommender for TV content has been developed which focuses on items attribute values. The system predicts user's ratings by classifying the vector of similarities between the user model and the items attributes. User's trust is increased by identifying attribute values that are the most relevant for them. User's feedback to the identified attribute values was used to improve the performance of the recommender algorithm. Tests in our experimental platform showed that the developed algorithms produce good results. The accuracy of the system was around 75% in the basic version and it further increased in the enhanced, while the identification of relevant attribute values achieved 86% precision.

Many efficient bi-level thresholding techniques have been proposed in recent years. Usually, the objective functions, which are used by them, are not appropriate for the multilevel thresholding owing to exponential growth of computational complexity. This work presents a new multilevel thresholding algorithm using Artificial Bee Colony algorithm (ABC) with the Otsu’s objective function. Also, a strategy is used to guess suitable thresholds for initializing the proposed method. This initializing phase used the bi-level Otsu method to find the initial thresholds. These guessed thresholds are used to create a food source around each of them for use in the ABC algorithm as initial population. The presented thresholding method is tested on four popular images. The results show that this method has competitive performance compared to other well-known methods such as Gaussian-smoothing, Symmetry-duality, GA-based and PSO-based algorithms.

Distribution feeder reconfiguration (DFR) is one of the well-known and effective strategies adopted in distribution network. The goal of DFR problem is to obtain a new topological structure for distribution feeders by rearranging the status of switches such that an optimal configuration would be obtained. The existence of Distributed Generation (DG) can affect the entire power system and especially distribution networks. This paper presents an efficient approach for multi-objective DFR problem considering the simultaneous effect of DG units. The objective functions to be investigated are 1) power losses, 2) voltage deviation of buses, 3) emission produced by DG units and distribution companies and 4) the total cost of the active power generated by DG units and distribution companies. The new evolutionary method is based on an efficient multi-objective hybrid honey bee mating optimization (HBMO) and shuffled frog leaping algorithm (SFLA) called MHBMO-SFLA. The proposed hybrid algorithm integrates the outstanding characteristics of SFLA to improve the performance of HBMO algorithm sufficiently. In the proposed MHBMO-SFLA, an external repository is considered to save non-dominated solutions which are found during the search process. Also, since the objective functions are not the same, a fuzzy clustering technique is utilized to control the size of the repository within the limits. A distribution test feeder is considered to evaluate the feasibility and effectiveness of the proposed approach.

A new adaptive dynamic under frequency load shedding scheme for a large industrial power system with large cogeneration units is presented. The adaptive LD- method with variable load shedding amount based on the disturbance magnitude is applied to have a minimum load shedding and a proper frequency recovery for different disturbances. To increase the speed of the load shedding scheme and to have an optimum response at different loading conditions, the artificial neural network (ANN) algorithm is developed. The Levenberg–Marquardt algorithm has been used for designed feed-forward neural network training. To prepare the training data set for the designed ANN, transient stability analysis has been performed to determine the minimum load shedding in the industrial power system at various operation scenarios. The ANN inputs are selected to be total in-house power generation, total load demand and initial frequency decay, while the minimum amount of load shedding at each step is selected for the output neurons. The proposed method is applied to the Mobarakeh steelmaking company (M.S.C) at different loading conditions. The performance of the presented ANN load shedding algorithm is demonstrated by the LD- method. Numerical results show the effectiveness of the proposed method.

This paper proposes an intelligent control technique for fuel injection control of Compressed Natural Gas (CNG) engines. Recurrent Neuro-Fuzzy Networks are used to estimate and control air to fuel ratio (AFR) of CNG engines. To reasonably handle such a complicated control problem, a precise experimental test has been done on a real CNG fuelled vehicle and the process input output data have been collected by running the vehicle in transient conditions. To determine the proper amount of gas to be injected, a controller has been designed based on nonlinear inverse dynamics of AFR. The results show that the predicted results are in line with the measured fuel injection commands produced by the real electronic control unit (ECU). This evaluated and validated the efficiency of the controller. The control strategy has the advantage that control actions can be calculated analytically, avoiding the costly and time-consuming calibration efforts required in conventional fuel injection control strategies.