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IAES International Journal of Artificial Intelligence (IJ-AI)
Published by Institute of Advanced Engineering and Science
ISSN : 20894872     EISSN : 22528938     DOI : -
Core Subject : Science, Engineering,
Computer Science & IT Engineering
IAES International Journal of Artificial Intelligence (IJ-AI) publishes articles in the field of artificial intelligence (AI). The scope covers all artificial intelligence area and its application in the following topics: neural networks; fuzzy logic; simulated biological evolution algorithms (like genetic algorithm, ant colony optimization, etc); reasoning and evolution; intelligence applications; computer vision and speech understanding; multimedia and cognitive informatics, data mining and machine learning tools, heuristic and AI planning strategies and tools, computational theories of learning; technology and computing (like particle swarm optimization); intelligent system architectures; knowledge representation; bioinformatics; natural language processing; multiagent systems; etc.
Arjuna Subject : -
Articles 4 Documents
 1 
Search results for , issue "Vol 4, No 3: September 2015" : 4 Documents clear
Type2 Fuzzy Soft Computing Technique for Image Enhancement U. Sesadri; B. Siva Sankar; C. Nagaraju
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 4, No 3: September 2015
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (564.43 KB) | DOI: 10.11591/ijai.v4.i3.pp97-104

Abstract

The mainpurpose of Image enhancement is to process an image so that outcome is more appropriate than original image for definite application. The fuzzy logic isone of the soft computing techniques to enhance the images by eliminating uncertainty.In this paper efficient type2 fuzzy logic technique is used to get betterquality image. This method consists of two steps. In the First step fisher criterion function is useful to generate type1 fuzzy membership value. In the second step based on type1 membership value fuzzy rules are derived to enhance the image. The type2 fuzzy method is compared with type1 fuzzy. The table values and graphs provethat the proposed method gives better results compared with fuzzy type1 method.
Fuzzy Logic Controller for Cascaded H-Bridge Multilevel Inverter N. Sivakumar; A. Sumathi
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 4, No 3: September 2015
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (654.167 KB) | DOI: 10.11591/ijai.v4.i3.pp105-112

Abstract

This paper proposes fuzzy logic controller based seven-level hybrid inverter for photovoltaic systems with sinusoidal pulse width-modulation (SPWM) techniques. Multi-Level Inverter technology have been developed in the area of high-power medium-voltage energy scheme, because of their advantages such as devices of high dv/dt rating, higher switching frequency, unlimited power processing, shape of output waveform and desired level of output voltage, current and frequency adjustment.This topology can be used there by enabling the scheme to reduce the Total Harmonic Distortion (THD) for high voltage applications. The Maximum Power Point Tracking algorithm is also used for extracting maximum power from the PV array connected to each DC link voltage level. The Maximum Power Point Tracking algorithm is solved by Perturb and Observer method.It has high performance with low Total Harmonic Distortion and reduced by this control strategy. The proposed system has verified and THD is obtained by using MATLAB/simulink.The result is compared with the hardware prototype working model.
The Optimal Thresholding Technique for Image Segmentaion Using Fuzzy Otsu Method P. Rambabu; C. Naga Raju
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 4, No 3: September 2015
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (759.76 KB) | DOI: 10.11591/ijai.v4.i3.pp81-88

Abstract

Image Segmentation plays a very important role in image processing. The single-mindedness of image segmentation is to partition the image into a set of disconnected regions with the homogeneous and uniform attributes like intensity, tone, color and texture. There are various methods for image segmentation but no method is suitable for low contrast images. In this paper, we are presenting an efficient and optimal thresholding image segmentation technique that can be used to separate the object and background pixels of the image to improve the quality of low contrast images. This innovative method consists of two steps. Firstly fuzzy logics are used to find optimum mean value using S-curve with automatic selection of controlled parameters to avoid the fuzziness in the image. Secondly, the fuzzy logic’s optimal threshold value used in Otsu method to improve the contrast of the image. This method, gives better results than traditional Otsu and Fuzzy logic techniques. The graphs and tables of values show that the proposed method is superior to traditional methods.
A New DG Allocation Approach Based on Biogeography-Based Optimization with Considering Fuzzy Load Uncertainty Mohammad Sedaghat; Esmaeel Rokrok; Mohammad Bakhshipour
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 4, No 3: September 2015
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (510.733 KB) | DOI: 10.11591/ijai.v4.i3.pp89-96

Abstract

A new distributed generation placement method based on biogeography-based optimization (BBO) is investigated in this paper. A significant novelty of this study lies in considering fuzzy load uncertainty. For this purpose a fuzzy backward- forward sweep load flow is proposed. The main objectives of this study is minimizing power losses and improving voltage profile. A comparative study between optimal location and sizing under typical load condition and fuzzy load uncertainty is presented. To verify the efficiency of proposed BBO method, it is conducted on IEEE 33 bus distribution system, also a comparative study between proposed BBO approach and particle swarm optimization (PSO), Technical-learning based optimization (TLBO), Artificial bee colony (ABC), Imperialist competitive algorithm (ICA) is investigated. The simulation results show the excellent and superior performance of proposed BBO approach in comparison with the other intelligent methods.

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