eng
Ahar Branch,Islamic Azad University, Ahar,Iran
Journal of Artificial Intelligence in Electrical Engineering
2345-4652
2345-4652
2014-09-01
3
10
1
7
514431
Implementation of VlSI Based Image Compression Approach on Reconfigurable Computing System - A Survey
Shahin Shafei
shahin_shafei@yahoo.com
1
Image data require huge amounts of disk space and large bandwidths for transmission. Hence, imagecompression is necessary to reduce the amount of data required to represent a digital image. Thereforean efficient technique for image compression is highly pushed to demand. Although, lots of compressiontechniques are available, but the technique which is faster, memory efficient and simple, surely hits theuser requirements. In this paper, the image compression, need of compression, its principles, how imagedata can be compressed, and the image compression techniques are reviewed and discussed. Also,wavelet-based image compression algorithm using Discrete Wavelet Transform (DWT) based on B-splinefactorization technique is discussed in detail. Based on the review, some general ideas to choose the bestcompression algorithm for an image are recommended. Finally, applications and future scopes of imagecompression techniques are discussed considering its development on FPGA systems.
http://jaiee.iau-ahar.ac.ir/article_514431_f0ce29e7b5eebb7bd762602bea812e9a.pdf
Image compression
Discrete wavelet transform
Decomposed lifting algorithm (DLA)
Huffman-coding
eng
Ahar Branch,Islamic Azad University, Ahar,Iran
Journal of Artificial Intelligence in Electrical Engineering
2345-4652
2345-4652
2014-09-01
3
10
8
15
514432
Reinforcement Learning Based PID Control of Wind Energy Conversion Systems
Mohammad Esmaeil akbari
m-akbari@iau-ahar.ac.ir
1
Noradin Ghadimi
noradin.1364@gmail.com
2
In this paper an adaptive PID controller for Wind Energy Conversion Systems (WECS) has been developed. Theadaptation technique applied to this controller is based on Reinforcement Learning (RL) theory. Nonlinearcharacteristics of wind variations as plant input, wind turbine structure and generator operational behaviordemand for high quality adaptive controller to ensure both robust stability and safe performance. Thus, areinforcement learning algorithm is used for online tuning of PID coefficients in order to enhance closed loopsystem performance. In this study, at start the proposed controller is applied to two pure mathematical plants,and then the closed loop WECS behavior is discussed in the presence of a major disturbance.
http://jaiee.iau-ahar.ac.ir/article_514432_fe4a9b1ce870b521c462bf74d3e2e596.pdf
Adaptive Control
WECS
reinforcement learning
eng
Ahar Branch,Islamic Azad University, Ahar,Iran
Journal of Artificial Intelligence in Electrical Engineering
2345-4652
2345-4652
2014-09-01
3
10
16
19
514433
Training Set of Data Bin for Small Black Pixels Neighborhood Recognition of Each Boundary
Roya Abdollahi
roya.abdollahi6666@yahoo.com
1
We first describe how to “fuzzify” the estimated binary columns to create a [0,1]-valued column. Werefer to this [0,1] -valued column as the soft segmentation column of the noisy spectrogram column.Similarly to the collection of soft segmentation columns as the soft segmentation image, or simply asthe soft segmentation. The band-dependent posterior probability that the hard segmentation columnvalue of pixel is 1, given that bin and the binary values in the neighborhood configuration of the pixelare equal. Symbolically, each pixel of the soft segmentation column is set to the soft segmentationcolumn value of the pixel in a row was set to zero.
http://jaiee.iau-ahar.ac.ir/article_514433_3556cd5188d360081946e825ad3ea21d.pdf
eng
Ahar Branch,Islamic Azad University, Ahar,Iran
Journal of Artificial Intelligence in Electrical Engineering
2345-4652
2345-4652
2014-09-01
3
10
20
23
514434
Fuel Cell Voltage Control for Load Variations Using Neural Networks
Zolekh Teadadi
sh.teadadi@gmail.com
1
Hassan Changiziyan
2
In the near future the use of distributed generation systems will play a big role in the production ofelectrical energy. One of the most common types of DG technologies , fuel cells , which can be connectedto the national grid by power electronic converters or work alone Studies the dynamic behavior andstability of the power grid is of crucial importance. These studies need to know the exact model of dynamicelements. In this paper, a new method based on a neural network algorithm for controlling the fuel cellvoltage is provided. The effects of load change the output voltage characteristic of the fuel cell unit ischecked Simulations in MATLAB / SIMULINK. The results show that the prosecution is conducted in anappropriate manner Voltage Stabilization time.
http://jaiee.iau-ahar.ac.ir/article_514434_9897a9ce118d79a55582a25f4a68b3b1.pdf
Fuel cell
Dynamic Behavior
Neural networks
hydrogen
neural network controller
eng
Ahar Branch,Islamic Azad University, Ahar,Iran
Journal of Artificial Intelligence in Electrical Engineering
2345-4652
2345-4652
2014-09-01
3
10
24
36
514435
Load Frequency Control in Two Area Power System Using Sliding Mode Control
Milad Babakhani Qazijahan
babakhani.milad@yahoo.com
1
In this article, the sliding mode control of frequency load control of power systems is studied. The study areaconsists of a system of water and heat. First, a mathematical model of the proposed system disturbances ismade and then sliding control mode for frequency load control is provided. By the system simulation andsliding mode control, it can be shown that the damping of oscillations is well led.
http://jaiee.iau-ahar.ac.ir/article_514435_e3385c15cf4d3d2f1d12004bcf4eb733.pdf
frequency Load control
Sliding mode control
dual zone system
eng
Ahar Branch,Islamic Azad University, Ahar,Iran
Journal of Artificial Intelligence in Electrical Engineering
2345-4652
2345-4652
2014-09-01
3
10
37
50
514436
Neural Networks in Electric Load Forecasting:A Comprehensive Survey
Vahid Mansouri
vahidmansouri2010@gmail.com
1
Mohammad Esmaeil akbari
2
Review and classification of electric load forecasting (LF) techniques based on artificial neuralnetworks (ANN) is presented. A basic ANNs architectures used in LF reviewed. A wide range of ANNoriented applications for forecasting are given in the literature. These are classified into five groups:(1) ANNs in short-term LF, (2) ANNs in mid-term LF, (3) ANNs in long-term LF, (4) Hybrid ANNs inLF, (5) ANNs in Special applications of LF. The major research articles for each category are brieflydescribed and the related literature reviewed. Conclusions are made on future research directions.
http://jaiee.iau-ahar.ac.ir/article_514436_40c94157e14d3cf7890e1d964fdf1bf9.pdf
Artificial Neural Networks (ANNs)
Load Forecasting(LF)
Short Term LF
Mid Term
LF
Long Term LF
Peak LF
Unit Commitment(UC)