International Journal of Intelligent Systems and Applications in Engineering

International Journal of Intelligent Systems and Applications in Engineering

Basic info

  • Publisher: IJISAE
  • Country of publisher: turkey
  • Date added to EuroPub: 2017/Apr/13

Subject and more

  • LCC Subject Category: Engineering, Nanotechnology
  • Publisher's keywords: engineering
  • Language of fulltext: english
  • Time from submission to publication: 4 weeks

Publication charges

  • Article Processing Charges (APCs): No
  • Submission charges: No
  • Waiver policy for charges? Yes

Open access & licensing

  • Type of License: CC BY
  • License terms
  • Open Access Statement: Yes
  • Year open access content began: 2013
  • Does the author retain unrestricted copyright? True
  • Does the author retain publishing rights? True

Best practice polices

  • Permanent article identifier: None
  • Content digitally archived in: Nopolicy
  • Deposit policy registered in: None

This journal has '83' articles

Comparative Study of Krill Herd, Firefly and Cuckoo Search Algorithms for Unimodal and Multimodal Optimization

Comparative Study of Krill Herd, Firefly and Cuckoo Search Algorithms for Unimodal and Multimodal Optimization

Authors: Gobind Preet Singh*, Abhay Singh
(22 downloads)
Abstract

Today, in computer science, a computational challenge exists in finding a globally optimized solution from an enormously large search space. Various metaheuristic methods can be used for finding the solution in a large search space.These methods can be explained as iterative search processes that efficiently perform the exploration and exploitation in the solution space. In this context, three such nature inspired metaheuristic algorithms namely Krill Herd Algorithm (KH), Firefly Algorithm (FA) and Cuckoo search Algorithm (CS) can be used to find optimal solutions of various mathematical optimization problems. In this paper, the proposed algorithms were used to find the optimal solution of fifteen unimodal and multimodal benchmark test functions commonly used in the field of optimization and then compare their performances on the basis of efficiency, convergence, time and conclude that for both unimodal and multimodal optimization Cuckoo Search Algorithm via Lévy flight has outperformed others and for multimodal optimization Krill Herd algorithm is superior than Firefly algorithm but for unimodal optimization Firefly is superior than Krill Herd algorithm.

Keywords: Metaheuristic Algorithm, Krill Herd Algorithm, Firefly Algorithm, Cuckoo Search Algorithm, Unimodal Optimization, Multimodal Optimization
Development Of HealthCare System For Smart Hospital Based On UML and XML Technology

Development Of HealthCare System For Smart Hospital Based On UML and XML Technology

Authors: Magdy Shayboub| Computer Science Department, Faculty of Computers and Informatics, Ismailia 41522, Suez Canal University, Egypt, Ali Mahmoud*| Compute...
(24 downloads)
Abstract

The convergence of information technology systems in health care system building is causing us to look at more effective integration of technologies. Facing increased competition, tighter spaces, staff retention and reduced reimbursement, today’s traditional hospitals are looking at strategic ways to use technology to manage their systems called smart hospital. The concept of the smart hospital is a useful system for any hospital; about adding intelligence to the traditional hospital system by covering all resources and locations with patient information. Patient’s information is an important component of the patient privacy in any health care system that is based on the overall quality of each patient in the health care system. The main commitment for any health care system is to improve the quality of the patient and privacy of patient’s information. Today, there is a need of such computer environment where treatment to patients can be given on the basis of his/her previous medical history at the time of emergency at any time, on any place and anywhere. Pervasive and ubiquitous environment and UML (unified modeling language) can bring the boon in this field. For this it's needed to develop the ubiquitous health care computing environment using the UML with traditional hospital environment. This paper is based on the ubiquitous and pervasive computing environment based on UML and XML(The Extensible Markup Language) technology, in which these problems has been tried to improve traditional hospital system into smart hospital in the near future. The key solution of the smart hospital is online identification of all patients, doctors, nurses, staff, medical equipments, medications, blood bags, surgical tools, blankets, sheets, hospital rooms, etc. In this paper efforts is channeled into improving the knowledge-base ontological description for smart hospital system by using UML and XML technology, Our knowledge is represented in XML format from UML modeling(class diagram). Our smart hospital provides access to its system by using a smart card. Finally, the former try to improve health care delivery through development and management of acute care hospital designed; both physically and operationally, for more efficiency and increased patients safety.

Keywords: UML, Smart Hospital (SH), Ontology, XML, health care system
A Fuzzy Logic Controller with Tuning Output Scaling Factor for Induction Motor Control Taking Core Loss into Account

A Fuzzy Logic Controller with Tuning Output Scaling Factor for Induction Motor Control Taking Core Loss into Account

Authors: Mohammad Abdul Mannan*| Dept. of EEE, American International University – Bangladesh, 83/B Road 14 Kemal Ataturk Avenue, Banani, Dhaka, Toshiaki Murat...
(24 downloads)
Abstract

This paper presents a design of a fuzzy logic controller (FLC) with tuning output scaling factor for speed control of indirect field oriented induction motor (IM) taking core loss into account. The variation of output scaling factor of FLC depends on the normalized output of FLC. Firstly the speed control of IM taking core loss into account is presented by using FLC with fixed scaling factors (FLC-FSF). Secondly the speed controller based on suggested FLC with tuning output scaling factor (FLC-TOSF) is proposed. The performance of the proposed FLC-TOSF for speed control of IM are investigated and compared to those obtained using FLC-SFS at different operating conditions and variation of parameters. A comparison of simulation results shows that the convergence of actual speed to reference speed is faster by using the proposed FLC-TOSF.

Keywords: Core loss, field oriented induction motor, fuzzy logic controller, fuzzy logic controller with tuning output scaling factor
Application of Angle-Modulated Particle Swarm Optimization Technique in Power System Controlled Separation WAP

Application of Angle-Modulated Particle Swarm Optimization Technique in Power System Controlled Separation WAP

Authors: Almoataz Youssef Abdelaziz*| Ain Shams University, Faculty of Engineering, Electrical Power & Machines Department, Egypt, Walid El-Khattam| Ain Shams...
(23 downloads)
Abstract

One of the recommended preventive plans against the wide area disturbances is WAP, Wide Area Protection, through controlled system splitting or separation. In this paper, authors are proposing three simple algorithms that are intended to be operating online, analyze data from wide-area PMUs placed in different parts of the grid, process the system state and lines’ status and issue disconnecting actions to certain lines in the grid to form islands with minimum imbalances of power between generation and loads. First a simple approach is introduced which performs an extensive search for proper splitting strategies. The authors then present modified approaches which could make the whole system act within much shorter times. All the presented algorithms are analyzed and comments are made on ways to enhance their performances. This will emphasize on how such WAP systems would be designed and developed and if necessary tailored to fit specific systems or applications.

Keywords: Controlled Separation, Particle Swarm Optimization, System Splitting, Wide Area Protection
Speed Control of Direct Torque Controlled Induction Motor By using PI, Anti-Windup PI and Fuzzy Logic Controller

Speed Control of Direct Torque Controlled Induction Motor By using PI, Anti-Windup PI and Fuzzy Logic Controller

Authors: Hakan Açıkgöz*| Kilis 7 Aralik University, Dept. of Electrical Science, Kilis/TURKEY, Ö. Fatih Keçecioğlu| K.Maras Sutcu Imam University, Dept. of Ele...
(24 downloads)
Abstract

In this study, comparison between PI controller, fuzzy logic controller (FLC) and an anti-windup PI (PI+AW) controller used for speed control with direct torque controlled induction motor is presented. Direct torque controlled induction motor drive system is implemented in MATLAB/Simulink environment and the FLC is developed using MATLAB/Fuzzy-Logic toolbox. The proposed control strategy is performed different operating conditions. Simulation results, obtained from PI controller, FLC and PI+AW controller showing the performance of the closed loop control systems, are illustrated in the paper. Simulation results show that FLC is more robust than PI and PI+AW controller against parameter variations and FLC gives better performance in terms of rise time, maximum peak overshoot and settling time.

Keywords: Anti-windup PI controller, Direct torque control, Fuzzy logic controller, Induction motor
Optimal Energy Management System for PV/Wind/Diesel-Battery Power Systems for Rural Health Clinic

Optimal Energy Management System for PV/Wind/Diesel-Battery Power Systems for Rural Health Clinic

Authors: Ani Vincent Anayochukwu*| Department of Electronic Engineering, University of Nigeria, Nsukka, Nigeria
(22 downloads)
Abstract

Good operation of a hybrid system can be achieved only by a suitable control of the interaction in the operation of the different devices. This paper proposed a supervisory control system that will be used to control and supervise the operations of PV/Wind-Diesel hybrid power generation system. The controller was developed in such a way that it coordinates when power should be generated by renewable energy (PV panels and Wind turbine) and when it should be generated by diesel generator and is intended to maximize the use of renewable system while limiting the use of diesel generator. Diesel generator is allocated only when the demand cannot be met by the renewable energy sources including battery bank. The structural analysis of the supervisory control is described in details through data flow diagrams. The developed control system was used to study the operations of the hybrid PV/Wind-Diesel energy system for the three hypothetical off-grid remote health clinics at various geographical locations in Nigeria. It was observed that the hybrid controller allocates the sources optimally according to the demand and availability. From the control simulation, we were able to see the performance of the system over the course of the year to see which mode(s) the system spends most time in, the power supplied by each of the energy sources over the year, and the power required by the load over the year. This is a very useful manner to check how the system is being supplied and which source of energy is the most proficient in supplying the load.

Keywords: Hybrid System, Supervisory control, Power Consumption, Power Supply, Health Clinic
SLAM – Map Building and Navigation via ROS#

SLAM – Map Building and Navigation via ROS#

Authors: Arbnor Pajaziti*| IDEA Teknoloji Çözümleri, Sun plaza BBDO Blok Dereboyu Cd. Bilim Sk. No:5, 34398, Maslak /İstanbul / Turkey, Petrit Avdullahu| IDEA...
(27 downloads)
Abstract

The presented work describes a ROS based control system of a Turtlebot robot for mapping and navigation in indoor environments. It presents the navigation of Turtlebot in self-created environment. The mapping process is done by using the GMapping algorithm, which is an open source algorithm and the localization process is done by using the AMCL pack. There are ROS built functions used in order to perform navigation of Turtlebot. The SLAM method implemented in ROS has proven a way for robots to do localization and mapping autonomously. The aim defined in paper to fulfill mapping, localization and navigation of Turtlebot in new and unknown environment is achieved.

Keywords: Map Building, Navigation, ROS, Simulation, Turtlebot
Rainfall Runoff Modelling Using Generalized Neural Network and Radial Basis Network

Rainfall Runoff Modelling Using Generalized Neural Network and Radial Basis Network

Authors: C Chandre Gowda*| Department of Applied Mechanics and Hydraulics NITK Surathkal, Mangalore, India., S G Mayya| Department of Applied Mechanics and Hyd...
(22 downloads)
Abstract

Rainfall runoff study has a wide scope in water resource management. To provide a reliable prediction model is of paramount importance. Runoff prediction is carried out using generalized regression neural network and radial basis neural network. Daily Rainfall runoff model was developed for Nethravathi river basin located at the west coast of Karnataka, India. The comparative study showed Radial basis neural network performed better than generalized neural network during its evaluation by performance indicators.

Keywords: Generalized regression neural network, Radial basis neural network, Runoff Modelling
Avoiding Premature Convergence of Genetic Algorithm in Informational Retrieval Systems

Avoiding Premature Convergence of Genetic Algorithm in Informational Retrieval Systems

Authors: Ammar Sami Aldallal*| Ahlia University – Bahrain
(23 downloads)
Abstract

Genetic algorithm is been adopted to implement information retrieval systems by many researchers to retrieve optimal document set based on user query. However, GA is been critiqued by premature convergence due to falling into local optimal solution. This paper proposes a new hybrid crossover technique that speeds up the convergence while preserving high quality of the retrieved documents. The proposed technique is applied to HTML documents and evaluated using precision measure. The results show that this technique is efficient in balancing between fast convergence and high quality outcome.

Keywords: Crossover, genetic algorithm, convergence rate, information retrieval, premature convergence
Artificial Bee Colony Algorithm Based Linear Quadratic Optimal Controller Design for a Nonlinear Inverted Pendulum

Artificial Bee Colony Algorithm Based Linear Quadratic Optimal Controller Design for a Nonlinear Inverted Pendulum

Authors: Baris Ata*| Department of Computer Engg. Cukurova University, Adana, Turkey, Ramazan Coban| Department of Computer Engg. Cukurova University, Adana, T...
(23 downloads)
Abstract

This paper presents a linear quadratic optimal controller design for a nonlinear inverted pendulum. Linear Quadratic Regulator (LQR), an optimal control method, is usually used for control of the dynamical systems. Main design parameters in LQR are the weighting matrices; however there is no relevant systematic techniques presented to choose these matrices. Generally, selecting weighting matrices is performed by trial and error method since there is no direct relation between weighting matrices and time domain specifications like overshoot percentage, settling time, and steady state error. Also it is time consuming and highly depends on designer’s experience. In this paper LQR is used to control an inverted pendulum as a nonlinear dynamical system and the Artificial Bee Colony (ABC) algorithm is used for selecting weighting matrices to overcome LQR design difficulties. The ABC algorithm is a swarm intelligence based optimization algorithm and it can be used for multivariable function optimization efficiently. The simulation results justify that the ABC algorithm is a very efficient way to determine LQR weighting matrices in comparison with trial and error method.

Keywords: ABC, LQR, Inverted Pendulum, Optimal Control, Weighting Matrices
An Efficient Document Categorization Approach for Turkish Based Texts

An Efficient Document Categorization Approach for Turkish Based Texts

Authors: Sevinç İlhan Omurca*| Kocaeli University, Faculty of Engineering, Computer Engineering Department Umuttepe Campus, Kocaeli – 41380, Turkey, Semih Baş|...
(24 downloads)
Abstract

Since, it is infeasible to classify all the documents with human effort due to the rapid and uncontrollable growth in textual data, automatic methods have been approached in order to organize the data. Therefore a support vector machine (SVM) classifier is used for text categorization in this study. In text categorization applications, the text representation process could take a huge computation time on weighting the huge size of terms. So far, lexicons that contain less number of terms are used for the solution in the literature. However it has been observed that these kinds of solutions reduce the accuracy of the text classification. In this paper, the term-document matrix is constructed as user dependent according to the purpose of classification. Since the number of terms is still relatively large, we used a hash table for efficient search of terms. Hereby an efficient and rapid TF-IDF method is introduced to construct a weight-matrix to represent the term-document relations and a study concerning classification of the documents in Turkish based news and Turkish columnists is conducted. With the proposed study, the computational time that is required for term-weighting process is reduced substantially; also 99% accuracy is achieved in determination of the news categories and 98% accuracy is achieved in detection of the columnists.

Keywords: Document categorization, SVM, TF-IDF, User dependent term selecting, Hash table
Epileptic State Detection: Pre-ictal, Inter-ictal, Ictal

Epileptic State Detection: Pre-ictal, Inter-ictal, Ictal

Authors: Apdullah Yayik*| Turkish Army Forces, Turkey, Esen Yildirim| Department of Computer Engineering Mustafa Kemal University, Turkey, Yakup Kutlu| Departm...
(23 downloads)
Abstract

Epileptic seizure detection and prediction from electroencephalography (EEG) is a vital area of research. In this study, Second-Order Difference Plot (SODP) is used to extract features based on consecutive difference of time domain values from three states of EEG (pre-ictal, ictal and inter-ictal), and Multi-Layer Neural Network classifier is used to classify these three classes. The proposed technique is tested on a publicly available EEG database and classified with Naive Bayes and k-nearest neighbor classifiers. As a result, it is shown that overall accuracy of 98.70% can be achieved by using the proposed system with Neural Network classifier.

Keywords: Epileptic State Detection, Second-Order Difference Plot, Neural Network
Rainfall estimation based on NAW approach using MSG-SEVIRI images: An application in north Algeria

Rainfall estimation based on NAW approach using MSG-SEVIRI images: An application in north Algeria

Authors: Fatiha Mokdad*| Laboratory of Image Processing and Radiation, University of Sciences and Technology Houari Boumediene (U.S.T.H.B.) B.P. 32, El Alia, B...
(22 downloads)
Abstract

In this work, we will adapt the NAW (Nagri, Adler and Wetzel) precipitation, estimation approach to the north Algeria events using the Meteosat Second Generation (MSG) satellite images. The tests are carried out on seven areas of northern Algeria: Sidi Bel Abbes, Oran Port, Algiers Port, Dar El Beida, Bedjaia, Jijel-Achouat and Annaba, in winter 2006. The NAW approach is applied by thresholding to temperature from 253 K. The validation is performed by comparaison the estimated rainfall to in situ measures collected by the National Office of Meteorology in Dar El Beida (Algeria). We use the infrared data (10.8µm channel) of SEVIRI sensor in this study. The results obtained indicate that the NAW approach gives satisfactory results for the rain rates: 4mm/h assigned to the coldest 10%, 2mm/h assigned to the next 40% and 0mm/h given to the remaining 50% of the area defined as cloud. The rain rate 8mm/h assigned to the coldest 10% of the pixels in the cloud applied for the convective clouds observed for tropical regions are not valid for the Algerian climate, especially for the stratiform clouds type.

Keywords: Precipitation, NAW approach, Stratiform cloud, Convective cloud, Meteosat Second Generation
Diagnosis of Anemia in Children via Artificial Neural Network

Diagnosis of Anemia in Children via Artificial Neural Network

Authors: Esra KAYA| Yildiz Technical University, Faculty of Control – Automat. Eng. – Turkey, Mehmet Emin AKTAN*| Yildiz Technical University, Faculty of Mecha...
(23 downloads)
Abstract

In this paper, a neural network algorithm, which diagnosis of anemia for children under 18 years of age, is presented. The network is trained by using data from hemogram test results from 30 patients and an expert doctor. The network has 5 inputs (HGB, HCT, MCV, MCH, MCHC) and an output. Simulations on 20 different patients show that the artificial neural network detects disease with high accuracy. In this paper, it is shown that anemia diagnosis can be made via neural network methods.

Keywords: Anemia, Diagnose, Artificial neural network
Intrusion Detection Forecasting Using Time Series for Improving Cyber Defence

Intrusion Detection Forecasting Using Time Series for Improving Cyber Defence

Authors: Azween Abdullah *| School of Computing and IT, Taylors University, Subang Jaya, Selangor, Malaysia, Thulasy Ramiah Pillai| School of Computing and IT,...
(23 downloads)
Abstract

The strength of time series modeling is generally not used in almost all current intrusion detection and prevention systems. By having time series models, system administrators will be able to better plan resource allocation and system readiness to defend against malicious activities. In this paper, we address the knowledge gap by investigating the possible inclusion of a statistical based time series modeling that can be seamlessly integrated into existing cyber defense system. Cyber-attack processes exhibit long range dependence and in order to investigate such properties a new class of Generalized Autoregressive Moving Average (GARMA) can be used. In this paper, GARMA (1, 1; 1, ±) model is fitted to cyber-attack data sets. Two different estimation methods are used. Point forecasts to predict the attack rate possibly hours ahead of time also has been done and the performance of the models and estimation methods are discussed. The investigation of the case-study will confirm that by exploiting the statistical properties, it is possible to predict cyber-attacks (at least in terms of attack rate) with good accuracy. This kind of forecasting capability would provide sufficient early-warning time for defenders to adjust their defense configurations or resource allocations.

Keywords: Intrusion forecasting, Predictive modeling, Generalized Autoregressive Moving Average, Long range dependence

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