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INDONESIA
International Journal of Intelligent Systems and Applications in Engineering
Published by Ismail SARITAS
ISSN : 21476799     EISSN : -     DOI : -
Core Subject : Science,
Computer Science & IT
International Journal of Intelligent Systems and Applications in Engineering (IJISAE) is an international and interdisciplinary journal for both invited and contributed peer reviewed articles that intelligent systems and applications in engineering at all levels. The journal publishes a broad range of papers covering theory and practice in order to facilitate future efforts of individuals and groups involved in the field. IJISAE, a peer-reviewed double-blind refereed journal, publishes original papers featuring innovative and practical technologies related to the design and development of intelligent systems in engineering. Its coverage also includes papers on intelligent systems applications in areas such as nanotechnology, renewable energy, medicine engineering, Aeronautics and Astronautics, mechatronics, industrial manufacturing, bioengineering, agriculture, services, intelligence based automation and appliances, medical robots and robotic rehabilitations, space exploration and etc.
Arjuna Subject : -
Articles 53 Documents
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Search results for , issue " 2016: Special Issue" : 53 Documents clear
The Minimization of Torque Ripples of Segmental Type Switched Reluctance Motor by Particle Swarm Optimization Terzioğlu, Hakan; Herdem, Saadetdin; BAL, Güngör
International Journal of Intelligent Systems and Applications in Engineering 2016: Special Issue
Publisher : Prof. Dr. Ismail SARITAS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18201/ijisae.2017Special Issue-146974

Abstract

In this study, we realized a controller design which can reduce torque ripple of 10/8 Switched Reluctance Motor (SRM). To perform the study, a Switched Reluctance Motor with 5 phase, U type segmental rotor was used. The control of the SRM was actualized by bipolar converter used H-bridge topology. The control signals of converter are obtained by control circuit designed by using dsPIC33EP512MU810. One of the reasons of the current ripples in the SRM is ON-OFF times in a period of the control signals. When the ripples of the current reduced, the ripples of torque of the SRM also reduced. Therefore, in this study, the ON-OFF times in a period of phase control signals were determined by an algorithm used particle swarm optimization. When SRM was controlled by this algorithm developed, the decreasing of its torque ripples was determined.
A Hybrid Algorithm for Automated Guided Vehicle Routing Problem Söyleyici, Cansu; Keser, Sinem Bozkurt
International Journal of Intelligent Systems and Applications in Engineering 2016: Special Issue
Publisher : Prof. Dr. Ismail SARITAS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18201/ijisae.2016Special Issue-146965

Abstract

Nowadays, automatic systems become crucial in many factories to achieve some tasks such as minimizing cost, maximizing efficiency, quality, and reliability. The planning is important for manufacturing systems to adopt changing conditions. Also, manufacturers want to obtain fast, reliable, qualified and economic products. Flexible Manufacturing Systems (FMSs) are used to meet this need. FMSs make production fast, qualified, reliable and economic by using computer-controlled structure that includes robots and transportation systems. Automated Guided Vehicles (AGVs) and FMS are thought to be integrated because FMSs use AGVs as a part of transportation in the factory. AGVs are used to carry loads, in other words products, in production areas, warehouses, factories that use magnets, landmarks, laser sensors, lines to know where they are. AGV scheduling and routing is NP-hard and open-ended problems. In the literature, there are many algorithms and methods are proposed to solve these problems. In this study, we present a hybrid algorithm that is composed of simulated annealing (SA) and Dijkstra’s algorithm to solve the routing problem. The hybrid algorithm is compared with SA algorithm in terms of distance cost using benchmark problems in the literature.
Network Traffic Classification via Kernel Based Extreme Learning Machine Ertam, Fatih; Avcı, Engin
International Journal of Intelligent Systems and Applications in Engineering 2016: Special Issue
Publisher : Prof. Dr. Ismail SARITAS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18201/ijisae.267522

Abstract

The classification of data on the internet in order to make internet use more efficient has an important place especially for network administrators managing corporate networks. Studies for the classification of internet traffic have increased recently. By these studies, it is aimed to increase the quality of service on the network, use the network efficiently, create the service packages and offer them to the users. The first classification method used for the classification of the internet traffic was the classification for the use of port numbers. This classification method has already lost its validity although it was an effective and quick method of classification for the first usage times of the internet. Another classification method used for the classification of network traffic is called as load-based classification or deep packet analysis. This approach is based on the principle of classification by identifying signatures on packets flowing on the network. Another method of classification of the internet traffic which is commonly used in our day and has been also selected for this study is the kernel based on extreme learning machine based approaches. In this study, over 95% was achieved accuracies using different activation functions.

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