iranian journal of electrical and electronic engineering 

About: iranian journal of electrical and electronic engineering is an academic journal. The journal publishes majorly in the area(s): Electric power system & Inverter. It has an ISSN identifier of 1735-2827. Over the lifetime, 564 publications have been published receiving 2646 citations. The journal is also known as: IJEEE.

Different Methods of Long-Term Electric Load Demand Forecasting a Comprehensive Review

Abstract: Long-term demand forecasting presents the first step in planning and developing future generation, transmission and distribution facilities. One of the primary tasks of an electric utility accurately predicts load demand requirements at all times, especially for long-term. Based on the outcome of such forecasts, utilities coordinate their resources to meet the forecasted demand using a least-cost plan. In general, resource planning is performed subject to numerous uncertainties. Expert opinion indicates that a major source of uncertainty in planning for future capacity resource needs and operation of existing generation resources is the forecasted load demand. This paper presents an overview of the past and current practice in long- term demand forecasting. It introduces methods, which consists of some traditional methods, neural networks, genetic algorithms, fuzzy rules, support vector machines, wavelet networks and expert systems.

Experimental Study and Steady State Stability Analysis of CLL-T Series Parallel Resonant Converter with Fuzzy Controller using State Space Analysis

Abstract: This paper presents a Closed Loop control of CLL-T (Capacitor Inductor Inductor). Series Parallel Resonant Converter (SPRC) has been simulated and the performance is analysised. A three element CLL-T SPRC working under load independent operation (voltage type and current type load) is presented in this paper. The Steady State Stability Analysis of CLL-T SPRC has been developed using State Space Technique and the regulation of output voltage is done by using Fuzzy controller. The simulation study indicates the superiority of fuzzy control over the conventional control methods. The proposed approach is expected to provide better voltage regulation for dynamic load conditions. A prototype 300 W, 100 kHz converter is designed and built to experimentally demonstrate, dynamic and steady state performance for the CLL-T SPRC are compared from the simulation studies.

A Comparative Study of Gender and Age Classification in Speech Signals

Abstract: Accurate gender classification is useful in speech and speaker recognition as well as speech emotion classification, because a better performance has been reported when separate acoustic models are employed for males and females. Gender classification is also apparent in face recognition, video summarization, human-robot interaction, etc. Although gender classification is rather mature in applications dealing with images, it is still in its infancy in speech processing. Age classification, on the other hand, is also concerned as a useful tool in different applications, like issuing different permission levels for different aging groups. This paper concentrates on a comparative study of gender and age classification algorithms applied to speech signal. Experimental results are reported for the Danish Emotional Speech database (DES) and English Language Speech Database for Speaker Recognition (ELSDSR). The Bayes classifier using sequential floating forward selection (SFFS) for feature selection, probabilistic Neural Networks (PNNs), support vector machines (SVMs), the K nearest neighbor (K-NN) and Gaussian mixture model (GMM), as different classifiers, are empirically compared in order to determine the best classifier for gender and age classification when speech signal is processed. It is proven that gender classification can be performed with an accuracy of 95\% approximately using speech signal either from both genders or male and female separately. The accuracy for age classification is about 88%.

A new fuzzy-based maximum power point tracker for photovoltaic applications

Abstract: (Δ ) are used to generate the optimal MPPT converter duty cycle, such that solar panel maximum power is generated under different operating conditions. A photovoltaic system including a solar panel, a fuzzy MPP tracker and a resistive load is designed, simulated and constructed. The fuzzy MPP tracker includes a buck dc/dc converter, fuzzy controller and interfacing circuits. Theoretical and experimental results are used to indicate the advantages and limitations of the proposed technique.

A New Fast and Efficient HMM-Based Face Recognition System Using a 7-State HMM Along With SVD Coefficients

Abstract: In this paper, a new Hidden Markov Model (HMM)-based face recognition system is proposed. As a novel point despite of five-state HMM used in pervious researches, we used 7-state HMM to cover more details. Indeed we add two new face regions, eyebrows and chin, to the model. As another novel point, we used a small number of quantized Singular Values Decomposition (SVD) coefficients as features describing blocks of face images. This makes the system very fast. The system has been evaluated on the Olivetti Research Laboratory (ORL) face database. In order to additional reduction in computational complexity and memory consumption the images are resized to 64×64 jpeg format. Before anything, an order-statistic filter is used as a preprocessing operation. Then a top-down sequence of overlapping sub-image blocks is considered. Using quantized SVD coefficients of these blocks, each face is considered as a numerical sequence that can be easily modeled by HMM. The system has been examined on 400 face images of the Olivetti Research Laboratory (ORL) face database. The experiments showed a recognition rate of 99%, using half of the images for training. The system has been evaluated on 64×64 jpeg resized YALE database too. This database contains 165 face images with 231×195 pgm format. Using five training image, we obtained 97.78% recognition rate where for six training images the recognition rate was 100%, a record in the literature. The proposed method is compared with the best researches in the literature. The results show that the proposed method is the fastest one, having approximately 100% recognition rate.