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

Publication charges

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

Editorial information

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

A region covariances-based visual attention model for RGB-D images

A region covariances-based visual attention model for RGB-D images

Authors: Erkut Erdem*| Hacettepe University. Department of Computer Engineering, Ankara, Turkey – TR-06800
( 25 downloads)
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.

Keywords: Visual attention, Visual saliency, Depth saliency, RGB-D images, Region covariances
Adaptive Control Solution for a Class of MIMO Uncertain Underactuated Systems with Saturating Inputs

Adaptive Control Solution for a Class of MIMO Uncertain Underactuated Systems with Saturating Inputs

Authors: Ajay Kulkarni*| Medicaps Institute of Technology and Management, Indore, India, Abhay Kumar| School of Electronics, DAVV, Indore, India
( 25 downloads)
Abstract

This paper addresses the issue of controller design for a class of multi-input multi-output (MIMO) uncertain underactuated systems with saturating inputs. A systematic controller framework, composed of a hierarchically generated control term, meant to ensure the stabilization of a particular portion of system dynamics and some dedicated control terms designed to solve the tracking problem of the remaining system dynamics is presented. Wavelet neural networks are used as adaptive tuners to approximate the system uncertainties also to reshape the control terms so as to deal with the saturation nonlinearity in an antiwindup paradigm. Gradient based tuning laws are developed for the online tuning of adjustable parameters of the wavelet network. A Lyapunov based stability analysis is carried out to ensure the uniformly ultimately bounded (UUB) stability of the closed loop system. Finally, a simulation is carried out which supports the theoretical development.

Keywords: Underactuated systems, hierarchical control structure, adaptive control, wavelet neural network, actuator saturation
The Control of A Non-Linear Chaotic System Using Genetic and Particle Swarm Based On Optimization Algorithms

The Control of A Non-Linear Chaotic System Using Genetic and Particle Swarm Based On Optimization Algorithms

Authors: Ercan Kose*| Mechatronic Engineering., Technology Faculty, Mersin University, Mersin, Turkey, Aydin Muhurcu| Electrical and Electronics Engineering, F...
( 25 downloads)
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.

Keywords: Lorenz chaotic system, discrete-time PI controller, genetic algorithm (GA), particle swarm optimization (PSO)
Classification of Neurodegenerative Diseases using Machine Learning Methods

Classification of Neurodegenerative Diseases using Machine Learning Methods

Authors: Fatih Aydin*| [email protected], Zafer Aslan| Computer Engineering, Faculty of Engineering, Istanbul Aydin University,
( 25 downloads)
Abstract

In this study, neurodegenerative diseases (Amyotrophic Lateral Sclerosis, Huntington’s disease, and Parkinson’s disease) were diagnosed and classified using force signals. In the classification, five machine learning algorithms (Averaged 2-Dependence Estimators (A2DE), K* (K star), Multilayer Perceptron (MLP), Diverse Ensemble Creation by Oppositional Relabeling of Artificial Training Examples (DECORATE), Random Forest) were compared by the 10-fold Cross Validation method. K* classifier gave the best outcome among these algorithms. As a result of quad classification of the K* classifier, the best classification accuracy was 99.17%. According to the first three and five principal component qualifications which are created from these 19 features, the best classification accuracies of K* classifier were 95.44% and 96.68% respectively.

Keywords: Neurodegenerative diseases, Machine Learning, K* classifier, Dimension Reduction, Principal Component Analysis
Predicting Student Success in Courses via Collaborative Filtering

Predicting Student Success in Courses via Collaborative Filtering

Authors: Ali Cakmak| Department of Computer Science, Istanbul Sehir University, Kusbakisi Cad. No: 27, 34662, Uskudar, Istanbul, Turkey
( 27 downloads)
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%.

Keywords: Collaborative Filtering, Educational Data Mining, Student Success Estimation, Outlier Elimination
Artificial Neural Network Models for Predicting the Energy Consumption of the Process of Crystallization Syrup in Konya Sugar Factory

Artificial Neural Network Models for Predicting the Energy Consumption of the Process of Crystallization Syrup in Konya Sugar Factory

Authors: Abdullah Erdal Tümer*| Necmettin Erbakan University, Dept.of Comp. Eng., Konya,Turkey, Bilgen Ayan Koç| Necmettin Erbakan University, Dept.of Industry...
( 25 downloads)
Abstract

In this study, artificial neural network models have been developed from the sugar production process stages in Konya Sugar Factory using artificial neural networks to estimate the energy consumption of the process of crystallization syrup. Models developing specific enthalpy, mass and pressure as input layer parameters and consumption energy as output layer were used. 124 different data are taken from Konya Sugar Factory during January 2016. Feedforward back propagation algorithm was used in the training phase of the network. Learning function LEARNGDM and the number of hidden layer kept constant as 2 and transfer functions are modified. In the developed 27 ANN model, 2-5-1 network architecture was determined as the best suitable network architecture and transfer function is determined logsig function as the optimal transfer function. Optimum results of the model taken in the coefficient of determination was found R = 0.98 neural network training, testing and validate was also found to be R = 0.98, the performance of the network for not shown data to network was found R=0,99.

Keywords: Artificial Neural Network, Modelling, Energy Consumption, Process Of Crystallization Syrup
A robust adaptive control of interleaved boost converter with power factor correction in wind energy systems

A robust adaptive control of interleaved boost converter with power factor correction in wind energy systems

Authors: Fatih Karik| Technology Faculty, Energy Systems Engineering Department, Gazi University, Ankara, Turkey, Ceyhun Yildiz| Elbistan Vocational School, Ka...
( 25 downloads)
Abstract

Power converters are generally utilized to convert the power from the wind sources to match the load demand and grid requirement to improve the dynamic and steady-state characteristics of wind generation systems and to integrate the energy storage system to solve the challenge of the discontinuous character of the renewable energy. In the low-voltage wind energy systems, interleaved boost converters (IBC) are often used to operate high currents in the system. IBCs are extremely sensitive to the constantly changing loading conditions. These situations require a robust control operation which can ensure a sufficient performance of the IBC over a large-scale changing load. Neural networks (NN) have emerged over the years and have found applications in many engineering fields, including control. In this paper, the adaptive control of interleaved boost converter with power factor correction (PFC) is investigated for grid-connected synchronous generator of wind energy system. For this purpose, a model reference adaptive control (MRAC) based on NN is proposed. Analysis results show that the proposed control strategy for the IBCs achieves near unity power factor (PF) and low total harmonic distortion (THD) in a wide operating range.

Keywords: Model reference adaptive control, Wind energy systems, Boost converter, Power factor correction
Vulnerability Analysis of Multiple Critical Fault Outages and Adaptive Under Voltage Load Shedding Scenarios in Marmara Region Electrical Power Grid

Vulnerability Analysis of Multiple Critical Fault Outages and Adaptive Under Voltage Load Shedding Scenarios in Marmara Region Electrical Power Grid

Authors: Nihat Pamuk*| Turkish Electricity Transmission Company, Maltepe District, Orhangazi Street, No: 74, 54100, Adapazarı–SAKARYA,TURKEY
( 27 downloads)
Abstract

The utilization of electrical power system has been rising frequently from past to now and there is a need of dependable electrical transmission and distribution networks so as to ensure continuous and balanced energy. Besides, conventional energy governance systems have been forced to change as a result of rises in the usage of renewable energy resources and the efficiency of demand-side on the market. In this regard, electrical power systems should be planned and operated, appropriately and the balance of production and consumption demand should be provided within the nominal voltage limits. In this study, firstly, the current status of Marmara region interconnected power grid in Turkey is evaluated. Afterwards, the multiple cascading failure outages scenarios are modeled by “DIgSILENT Power Factory V14” software. The critical transmission line scenarios are implemented on the high voltage power grid model improved. These scenarios are based on the period of maximum and minimum production and consumption demand and the effects of demand response in this period. As a result of grid vulnerability analyses performed, several findings has been obtained about the impacts of different line scenarios on the high voltage transmission system, the optimization of power grid voltage profile and the role of production and consumption demand response on voltage regulation.

Keywords: Power flow, Electrical power grid, Cascading outages scenarios, Voltage regulation, Dig-silent power factory V14 software

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