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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 200 Documents
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A Mitigation Technique for Inrush Currents in Load Transformers for the Series Voltage Sag Compensator Prasad, Besta Hari; Reddy, Poreddy Harshavardhan; Lalitha, M. Padma
International Journal of Intelligent Systems and Applications in Engineering Vol 2, No 2 (2014)
Publisher : Advanced Technology and Science (ATScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

In many countries, high-tech manufacturers concentrate in industry parks. Survey results suggest that 92% of interruption at industrial facilities is voltage sag related. An inrush mitigation technique is proposed and implemented in a synchronous reference frame sag compensator controller. The voltage sag compensator consists of a three phase voltage source inverter and a coupling transformer for serial connection. It is the most cost effective solution against voltage sags. When voltage sag happen, the transformers, which are often installed in front of critical loads for electrical isolation, are exposed to the disfigured voltages and a DC offset will occur in its flux linkage. When the compensator restores the load voltage, the flux linkage will be driven to the level of magnetic saturation and severe inrush current occurs. The compensator is likely to be interrupted because of its own over current protection. This paper proposes an inrush current mitigation technique together with a state-feedback controller for the Voltage sag compensator.
A New Approach to Determine Eps Parameter of DBSCAN Algorithm Ozkok, Fatma Ozge; Celik, Mete
International Journal of Intelligent Systems and Applications in Engineering Vol 5, No 4 (2017)
Publisher : Advanced Technology and Science (ATScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18201/ijisae.2017533899

Abstract

In recent years, data analysis has become important with increasing data volume. Clustering, which groups objects according to their similarity, has an important role in data analysis. DBSCAN is one of the most effective and popular density-based clustering algorithm and has been successfully implemented in many areas. However, it is a challenging task to determine the input parameter values of DBSCAN algorithm, which are neighborhood radius, Eps, and minimum number of points, MinPts. The values of these parameters significantly affect clustering performance of the algorithm. In this study, we propose AE-DBSCAN algorithm, which includes a new method to determine the value of neighborhood radius Eps automatically. The experimental evaluations showed that the proposed method outperformed the analytical DBSCAN.
Economical and Technical Challenges of a Large Scale Solar Plant YAGCI, Mustafa
International Journal of Intelligent Systems and Applications in Engineering Vol 5, No 2 (2017)
Publisher : Advanced Technology and Science (ATScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18201/ijisae.2017529085

Abstract

Turkish government has increased the importance of Konya-Karapınar region by making the area a pilot region for solar energy production.  In the near future, the government plans to have a 4 GW solar energy plant in the area.  According to this master plan, a pilot solar energy production system was established in the Necmettin Erbakan University campus.  A 1000W two-axis tracking and 1000W fixed panels were implemented in this system. In this paper, the performance of these panels are compared and evaluated.  25 years of solar radiation data for Konya is taken from the state meteorology institute, and the produced energy is compared with the predicted values.  Tracking panels are found to be approximately 25% more efficient than the fixed panels.  However, the maintenance of the tracking panels is observed to be more costly.  Moreover periodic cleaning and repairs are found to be very important and another cost factor for both systems.  Using the insight from this pilot system, an economic analysis of a large scale 1MW solar energy plant to be established in the campus is performed.  For this analysis, fixed panels are considered due to the low investment and maintenance costs and the simplicity of the maintenance work.  It is found that the 1MW solar plant will recover the investment cost within 7-8 years and will provide almost 3M$ profit after 20 years.
Estimating of Compressive Strength of Concrete with Artificial Neural Network According to Concrete Mixture Ratio and Age Ozkan, Ilker Ali; ALTIN, Mustafa
International Journal of Intelligent Systems and Applications in Engineering Vol 4, No 3 (2016)
Publisher : Advanced Technology and Science (ATScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18201/ijisae.263977

Abstract

Compressive strength of concrete is one of the most important elements for an existing building and a new structure to be built. While obtaining the desired compressive strength of concrete with an appropriate mix and curing conditions for a new structure, with non-destructive testing methods for an existing structure or by taking core samples the concrete compressive strength are determined. One of the most important factors that affects the concrete compressive strength is age of concrete. In this study, it is attempted to estimate compressive strength, modelling Artificial Neural Networks (ANN) and using different mixture ratios and compressive strength of concrete samples at different ages. In accordance with obtained data’s in the estimation of concrete compressive strength, ANN could be used safely.
Improving an Expert-Supported Dynamic Programming Algorithm and Adaptive-Neuro Fuzzy Inference System for Long-Term Load Forecasting Cetinkaya, Nurettin
International Journal of Intelligent Systems and Applications in Engineering Vol 5, No 4 (2017)
Publisher : Advanced Technology and Science (ATScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18201/ijisae.2017533858

Abstract

Load forecasting is very important to manage the electrical power systems. Load forecasting can be analyzed in three different ways as short-term, medium-term and long-term. Long-term load forecasting (LTLF) is in need to plan and carry on future energy demand and investment such as size of energy plant. LTLF is affected by energy consumption, national incoming per year, rates of civilization, increasing population rates and moreover economical parameters. Some of the forecasting models use mathematical formulas and statistical models such as correlation and regression analysis. In this study, a new effective expert-supported dynamic programming algorithm (ESDP) has been improved. Additionally, adaptive neuro-fuzzy inference system (ANFIS) and mathematical modeling (MM) are used to forecast long term energy demand. ANFIS is one of the famous artificial intelligence and has widely used to solve forecasting problems in literature. In addition to numerical inputs, ANFIS has linguistics inputs. The results obtained from ESDP, ANFIS and MM are compared to show availability. In order to show error levels mean absolute percentage error (MAPE) and (MAE) are used. The obtained results show that the proposed algorithms are available.
Predicting Student Success in Courses via Collaborative Filtering Cakmak, Ali
International Journal of Intelligent Systems and Applications in Engineering Vol 5, No 1 (2017)
Publisher : Advanced Technology and Science (ATScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18201/ijisae.2017526690

Abstract

Based on their skills and interests, students’ success in courses may differ greatly. Predicting student success in courses before they take them may be important. For instance, students may choose elective courses that they are likely to pass with good grades. Besides, instructors may have an idea about the expected success of students in a class, and may restructure the course organization accordingly. In this paper, we propose a collaborative filtering-based method to estimate the future course grades of students. Besides, we further enhance the standard collaborative filtering by incorporating automated outlier elimination and GPA-based similarity filtering. We evaluate the proposed technique on a real dataset of course grades. The results indicate that we can estimate the student course grades with an average error rate of 0.26, and the proposed enhancements improve the error value by 16%. 
The Classification of Diseased Trees by Using kNN and MLP Classification Models According to the Satellite Imagery Unlersen, Muhammed Fahri; Sabanci, Kadir
International Journal of Intelligent Systems and Applications in Engineering Vol 4, No 2 (2016)
Publisher : Advanced Technology and Science (ATScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18201/ijisae.05552

Abstract

In this study, the Japanese Oak and Pine Wilt in forested areas of Japan was classified into two group as diseased trees and all other land cover area according to the 6 attributes in the spectral data set of the forest. The Wilt Data Set which was obtained from UCI machine learning repository database was used. Weka (Waikato Environment for Knowledge Analysis) software was used for classification of areas in the forests. The classification success rates and error values were calculated and presented for classification data mining algorithms just as Multilayer Perceptron (MLP) and k-Nearest Neighbor (kNN). In MLP neural networks the classification performance for various numbers of neurons in the hidden layer was presented. The highest success rate was obtained as 86.4% when the number of neurons in the hidden layer was 10. The classification performance of kNN method was calculated for various counts of neighborhood. The highest success rate was obtained as 72% when the count of neighborhood number was 2.
Crow Search based Multi-objective Optimization of Irreversible Air Refrigerators Turgut, Oguz Emrah
International Journal of Intelligent Systems and Applications in Engineering Vol 6, No 2 (2018)
Publisher : Advanced Technology and Science (ATScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18201/ijisae.2018642064

Abstract

This study proposes the optimum performance of the irreversible air refrigerators through recently developed metaheuristic algorithm called Crow search algorithm by means of finite time thermodynamics. Finite time thermodynamics is based on choosing the optimum pathways for any kind of thermodynamic system in order to reach the maximum efficiency of the thermodynamic cycle. Handful of objectives for assessing the performance of the irreversible air refrigerators such as coefficient of performance (COP), exergetic efficiency (ηII)  , ecological coefficient of  performance (ECOP), thermoeconomic optimization (F), and thermoecologic optimization functions (ECF) have been  successfully applied on the system. Three optimization scenarios have been studied for the multi objective optimization of irreversible air refrigerators. First scenario evaluates the concurrent optimization of objectives including exergetic efficiency (ηII), coefficient of performance (COP), and ecological coefficient of performance (ECOP). In second scenario, coefficient of performance (COP), thermoeconomic parameter (F), and  thermoecological coefficient of  performance (ECOP) have been simultaneously maximized to retain optimum working point of the cycle. Third case studies the simultaneous optimization of the imposed objectives such as second law efficiency (ηII), coefficient of performance (COP), and thermoecological function (ECF).  Widely known decision-making theorems of LINMAP, TOPSIS, and Shannon’s entropy theorem have been applied on the Pareto curve constructed by the non-dominated solutions to decide the most favorable solution on the frontier. 
Grade prediction improved by regular and maximal association rules Udristoiu, Anca Loredana; Udristoiu, Stefan
International Journal of Intelligent Systems and Applications in Engineering Vol 3, No 2 (2015)
Publisher : Advanced Technology and Science (ATScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18201/ijisae.17210

Abstract

In this paper we propose a method of predicting student scholar performance using the power of regular and maximal association rules. Due to the large number of generated rules, traditional data mining algorithms can become difficult and inappropriate to educational systems. Thus, we use some methods to overcome this problem, discovering rules useful in educational process. These methods are applied to the e-learning system Moodle, for “Database” course.
The Classification of Eye State by Using kNN and MLP Classification Models According to the EEG Signals Sabancı, Kadir; Koklu, Murat
International Journal of Intelligent Systems and Applications in Engineering Vol 3, No 4 (2015)
Publisher : Advanced Technology and Science (ATScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18201/ijisae.75836

Abstract

What is widely used for classification of eye state to detect human’s cognition state is electroencephalography (EEG). In this study, the usage of EEG signals for online eye state detection method was proposed. In this study, EEG eye state dataset that is obtained from UCI machine learning repository database was used. Continuous 14 EEG measurements forms the basic of the dataset. The duration of the measurement is 117 seconds (each measurement has14980 sample). Weka (Waikato Environment for Knowledge Analysis) program is used for classification of eye state. Classification success was calculated by using k-Nearest Neighbors algorithm and multilayer perceptron neural networks models. The obtained success of classification methods were compared. The classification success rates were calculated for various number of neurons in the hidden layer of a multilayer perceptron neural network model. The highest classification success rate have been obtained when the number of neurons in the hidden layer was equal to 7. And it was 56.45%. The classification success rates were calculated with k-nearest neighbors algorithm for different neighbourhood values. The highest success was achieved in the classification made with kNN algorithm.  In kNN models, the success rate for 3 nearest neighbor were calculated as 84.05%.

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