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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 8 Documents
 1 
Search results for , issue " Vol 4, No 4 (2016)" : 8 Documents clear
Structure-Texture Decomposition of RGB-D Images Erdem, Aykut
International Journal of Intelligent Systems and Applications in Engineering Vol 4, No 4 (2016)
Publisher : Advanced Technology and Science (ATScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18201/ijisae.2016426381

Abstract

In this paper, we study the problem of separating texture from structure in RGB-D images. Our structure preserving image smoothing operator is based on the region covariance smoothing (RCS) method in [16] that we present a number of modifications to this framework to make it depth-aware and increase its effectiveness. In particular, we propose to incorporate three geometric depth features, namely height above ground, angle with gravity and horizontal disparity to the pool of image features used in that study. We also suggest to use a new kernel function based on KL-divergence between the distributions of extracted features. We demonstrate our approach on challenges images from NYU-Depth v2 Dataset [24], achieving more accurate decompositions than the state-of-the-art approaches which do not utilize any depth information.
The Control of A Non-Linear Chaotic System Using Genetic and Particle Swarm Based On Optimization Algorithms Kose, Ercan; Muhurcu, Aydin
International Journal of Intelligent Systems and Applications in Engineering Vol 4, No 4 (2016)
Publisher : Advanced Technology and Science (ATScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18201/ijisae.2016426386

Abstract

In this study, the control of a non-linear system was realized by using a linear system control strategy. According to the strategy and by using the controller coefficients, system outputs were controlled for all reference points with the same coefficients via focused references. In the framework of this study, the Lorenz chaotic system as non-linear structure, and the discrete-time PI algorithm as the control algorithm has selected. The genetic algorithm and particle swarm optimization methods have used in the optimization process, and the success of both methods has been discussed among themselves. Closed-loop control system has run simultaneously under the Matlab / Simulink programmer. The results have discussed by using the ISE, IAE, ITAE error criteria, and improved dTISDSE purpose functions.
Long Term and Remote Health Monitoring with Smart Phones Kirci, Pinar; Kurt, Gokhan
International Journal of Intelligent Systems and Applications in Engineering Vol 4, No 4 (2016)
Publisher : Advanced Technology and Science (ATScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18201/ijisae.2016426358

Abstract

The basic  aim of our work  is to provide  solutions with monitoring the heart beat rates  of disabled or old people. And also we expect to help the people who have specific heart diseases  like potential cardiac arrests and cardiac pacemaker carriers. Besides in case of emergency situations, our system will produce an immediate alarm to provide urgent help for the patients. In the system, emergency situations depend on the heart beat rates. If the heart beat rates of a person decreases at lower rates compared with normal heart beat rates or if the heart beat rates of a person increases at higher rates compared with normal heart beat rates or if  big heart beat rate changes occur during the predetermined time period then these situations will be evaluated as emergency situations and these situations should be announced to considered people and places like hospitals, patient’s doctor and patient’s family members. The proposed  system colloborates with smartphones and includes sensors to collect data from the patient. Also the system is used to process and compare data with pre defined normal heart beat rates by patient’s doctor and to notice if there is an emergency situation. Besides, in case of an emergengy situation, to inform considered people. But if there is not an emergengy  situation exists, then the system stores the collected data and sends them as  daily and weekly graphics to the patient’s doctor. These graphics are collected as a result of definite daily  activities like sleeping, sitting, standing, walking and jogging. The results are compared with the patient’s doctor’s stated normal heart rate intervals for every activity period. Furthermore, our proposed system structure includes heart pulse sensor, a smartphone screen, bluetooth interface and memory.
The Usage Of Artificial Neural Networks Method In The Diagnosis Of Rheumatoid Arthritis Tok, Kadir; Saritas, Ismail
International Journal of Intelligent Systems and Applications in Engineering Vol 4, No 4 (2016)
Publisher : Advanced Technology and Science (ATScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18201/ijisae.2016426382

Abstract

In this study, artificial neural networks (ANN) method is used for the diagnosis of rheumatoid arthritis in order to support medical diagnostics. For the diagnosis of rheumatoid arthritis, backpropagation algorithm was examined in Matlab R2015b environment in artificial neural networks. With the system, the data in a data set, which are received from the patients with rheumatoid arthritis and from the people who are not suffering from rheumatoid arthritis, are classified successfully. Also, ANN backpropagation algorithm results and the results found by Perceptron algorithm are compared in terms of performance. Whereas %82 accuracy percentage is obtained with the Backpropagation method in performance tests in the data set, the accuracy percentage is calculated %71 with Perceptron method.
A Low Cost Single Board Computer Based Mobile Robot Motion Planning System for Indoor Environments Solak, Serdar; Dogru Bolat, Emine; Tuncer, Adem; Yildirim, Mehmet
International Journal of Intelligent Systems and Applications in Engineering Vol 4, No 4 (2016)
Publisher : Advanced Technology and Science (ATScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18201/ijisae.2016426379

Abstract

In this study, a low cost, flexible and modular structure is proposed for mobile robot motion planning systems in an indoor environment with obstacles. In this system, the mobile robot has to follow the shortest path to the target avoiding obstacles. It is designed as three main modules including image processing, path planning and robot motion blocks. These modules are embedded on a single board computer. In the image processing module, the image of the indoor environment, including a mobile robot, obstacles and a target, having different colors is taken to the single board computer with a wireless IP camera. This image is processed to find the locations of the mobile robot, obstacles and the target in C programming language using OpenCV. In path planning module, the shortest and optimal path is generated for the mobile robot. Generated path is applied to the robot motion module to produce necessary angles and distances for the mobile robot to reach the target. Since the structure of the proposed system is designed as modular and flexible, similar or different hardware, software or methods can be applied to these three modules.
A Region Covariances-based Visual Attention Model for RGB-D Images Erdem, Erkut
International Journal of Intelligent Systems and Applications in Engineering Vol 4, No 4 (2016)
Publisher : Advanced Technology and Science (ATScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18201/ijisae.2016426384

Abstract

Existing computational models of visual attention generally employ simple image features such as color, intensity or orientation to generate a saliency map which highlights the image parts that attract human attention. Interestingly, most of these models do not process any depth information and operate only on standard two-dimensional RGB images. On the other hand, depth processing through stereo vision is a key characteristics of the human visual system. In line with this observation, in this study, we propose to extend two state-of-the-art static saliency models that depend on region covariances to process additional depth information available in RGB-D images. We evaluate our proposed models on NUS-3D benchmark dataset by taking into account different evaluation metrics. Our results reveal that using the additional depth information improves the saliency prediction in a statistically significant manner, giving more accurate saliency maps.
Fusion of Target Detection Algorithms in Hyperspectral Images Yuksel, Seniha Esen; Karakaya, Ahmet
International Journal of Intelligent Systems and Applications in Engineering Vol 4, No 4 (2016)
Publisher : Advanced Technology and Science (ATScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18201/ijisae.2016426380

Abstract

Target detection in hyperspectral images is important in many applications including search and rescue operations, defence systems, mineral exploration and border security. For this purpose, several target detection algorithms have been proposed over the years, however, it is not clear which of these algorithms perform best on real data and on sub-pixel targets, and moreover, which of these algorithms have complementary information and should be fused together. The goal of this study is to detect the nine arbitrarily placed sub-pixel targets, from seven different materials from a 1.4km altitude. For this purpose, eight signature-based hyperspectral target detection algorithms, namely the GLRT, ACE, SACE, CEM, MF, AMSD, OSP and HUD, and three anomaly detectors, namely RX, Maxmin and Diffdet, were tested and compared. Among the signature-based target detectors, the three best performing algorithms that have complementary information were identified. Finally these algorithms were fused together using four different fusion algorithms. Our results indicate that with a proper fusion strategy, five of the nine targets could be found with no false alarms.
Adaptive Control Solution for a Class of MIMO Uncertain Underactuated Systems with Saturating Inputs Kulkarni, Ajay; Kumar, Abhay
International Journal of Intelligent Systems and Applications in Engineering Vol 4, No 4 (2016)
Publisher : Advanced Technology and Science (ATScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18201/ijisae.2016426385

Abstract

This paper addresses the issue of controller design for aclass of multi-input multi-output (MIMO) uncertain underactuatedsystems with saturating inputs. A systematic controller framework,composed of a hierarchically generated control term, meant toensure the stabilization of a particular portion of system dynamicsand some dedicated control terms designed to solve the trackingproblem of the remaining system dynamics is presented. Waveletneural networks are used as adaptive tuners to approximate thesystem uncertainties also to reshape the control terms so as to dealwith the saturation nonlinearity in an antiwindup paradigm.Gradient based tuning laws are developed for the online tuning ofadjustable parameters of the wavelet network. A Lyapunov basedstability analysis is carried out to ensure the uniformly ultimatelybounded (UUB) stability of the closed loop system. Finally, asimulation is carried out which supports the theoreticaldevelopment.

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