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31,456 Article Results

Cuckoo search algorithm based for tunning both PI and FOPID controllers for the DFIG-Wind energy conversion system

10.11591/ijece.v10i6.pp6319-6329
Mostafa A. Al-Gabalawy , N. S. Hosny , Shimaa A. Hussien
Wind Energy has received great attention in this century. It influences the new power systems, adding new challenges to the power system expansion problem. Nowadays, double feed induction generator (DFIG) wind turbines are used majorly in wind farms, due to their advantages over other types. Therefore, the analysis of the system using this type has become very important. In this paper, a wind turbine modelling was introduced with suggested controllers, in order to enhance the system response, with respect to both pitch control and maximum output power. Cuckoo search algorithm (CSA), a meta-heuristic optimization technique, was implemented to determine the gains of a proportional-integral (PI) controller and fractional order proportional-integral-derivative (FOPID) controller to optimize the system, which considered three control loops: pitch, rotor-side converter, and grid-side converter control loop. Simulation results were determined using MATLAB/Simulink. The comparative analysis of the results showed that the PI Controller gave the simplest and the best response in case of the pitch and rotor-side control loops while the FOPID was the best when applied to the grid-side control loop. Based on the results and discussion, a suggestion of using a compination of each controller was introduced.
Volume: 10
Issue: 6
Page: 6319-6329
Publish at: 2020-12-01

Functional magnetic resonance imaging-based brain decoding with visual semantic model

10.11591/ijece.v10i6.pp6682-6690
Piyawat Saengpetch , Luepol Pipanmemekaporn , Suwatchai Kamolsantiroj
The activity pattern of the brain has been activated to identify a person in mind. Using the function magnetic resonance imaging (fMRI) to decipher brain decoding is the most accepted method. However, the accuracy of fMRI-based brain decoder is still restricted due to limited training samples. The limitations of the brain decoder using fMRI are passed through the design features proposed for many label coding and model training to predict these characteristics for a particular label. Moreover, what kind of semantic features for deciphering the neurological activity patterns are unclear. In current work, a new calculation model for learning decoding labels that is consistent with fMRI activity responses. The approach demonstrates the proposed corresponding label's success in terms of accuracy, which is decoded from brain activity patterns and compared with conventional text-derived feature technique. Besides, experimental studies present a training model based on multi-tasking to reduce the problems of limited training data sets. Therefore, the multi-task learning model is more efficient than modern methods of calculation, and decoding features may be easily obtained.
Volume: 10
Issue: 6
Page: 6682-6690
Publish at: 2020-12-01

Evaluation of graphic effects embedded image compression

10.11591/ijece.v10i6.pp6606-6617
Chanintorn Jittawiriyanukoon , Vilasinee Srisarkun
A fundamental factor of digital image compression is the conversion processes. The intention of this process is to understand the shape of an image and to modify the digital image to a grayscale configuration where the encoding of the compression technique is operational. This article focuses on an investigation of compression algorithms for images with artistic effects. A key component in image compression is how to effectively preserve the original quality of images. Image compression is to condense by lessening the redundant data of images in order that they are transformed cost-effectively. The common techniques include discrete cosine transform (DCT), fast Fourier transform (FFT), and shifted FFT (SFFT). Experimental results point out compression ratio between original RGB images and grayscale images, as well as comparison. The superior algorithm improving a shape comprehension for images with grahic effect is SFFT technique.
Volume: 10
Issue: 6
Page: 6606-6617
Publish at: 2020-12-01

Improving keyword extraction in multilingual texts

10.11591/ijece.v10i6.pp5909-5916
Bahare Hashemzahde , Majid Abdolrazzagh-Nezhad
The accuracy of keyword extraction is a leading factor in information retrieval systems and marketing. In the real world, text is produced in a variety of languages, and the ability to extract keywords based on information from different languages improves the accuracy of keyword extraction. In this paper, the available information of all languages is applied to improve a traditional keyword extraction algorithm from a multilingual text. The proposed keywork extraction procedure is an unsupervise algorithm and designed based on selecting a word as a keyword of a given text, if in addition to that language holds a high rank based on the keywords criteria in other languages, as well. To achieve to this aim, the average TF-IDF of the candidate words were calculated for the same and the other languages. Then the words with the higher averages TF-IDF were chosen as the extracted keywords. The obtained results indicat that the algorithms’ accuracis of the multilingual texts in term frequency-inverse document frequency (TF-IDF) algorithm, graph-based algorithm, and the improved proposed algorithm are 80%, 60.65%, and 91.3%, respectively.
Volume: 10
Issue: 6
Page: 5909-5916
Publish at: 2020-12-01

A Haptic feedback system based on leap motion controller for prosthetic hand application

10.11591/ijece.v10i6.pp5772-5778
Hussam K. Abdul-Ameer , Luma Issa Abdul-Kreem , Huda Adnan , Zahra Sami
Leap Motion Controller (LMC) is a gesture sensor consists of three infrared light emitters and two infrared stereo cameras as tracking sensors. LMC translates hand movements into graphical data that are used in a variety of applications such as virtual/augmented reality and object movements control. In this work, we intend to control the movements of a prosthetic hand via (LMC) in which fingers are flexed or extended in response to hand movements. This will be carried out by passing in the data from the Leap Motion to a processing unit that processes the raw data by an open-source package (Processing i3) in order to control five servo motors using a micro-controller board. In addition, haptic setup is proposed using force sensors (FSR) and vibro-motors in which the speed of these motors is proportional to the amount of the grasp force exerted by the prosthetic hand. Investigation for optimal placement of the FSRs on a prosthetic hand to obtain convenient haptic feedback has been carried out. The results show the effect of object shape and weight on the obtained response of the FSR and how they influence the locations of the sensors.
Volume: 10
Issue: 6
Page: 5772-5778
Publish at: 2020-12-01

Short-term wind speed forecasting system using deep learning for wind turbine applications

10.11591/ijece.v10i6.pp5779-5784
Gokhan Erdemir , Aydin Tarik Zengin , Tahir Cetin Akinci
It is very important to accurately detect wind direction and speed for wind energy that is one of the essential sustainable energy sources. Studies on the wind speed forecasting are generally carried out for long-term predictions. One of the main reasons for the long-term forecasts is the correct planning of the area where the wind turbine will be built due to the high investment costs and long-term returns. Besides that, short-term forecasting is another important point for the efficient use of wind turbines. In addition to estimating only average values, making instant and dynamic short-term forecasts are necessary to control wind turbines. In this study, short-term forecasting of the changes in wind speed between 1-20 minutes using deep learning was performed. Wind speed data was obtained instantaneously from the feedback of the emulated wind turbine's generator. These dynamically changing data was used as an input of the deep learning algorithm. Each new data from the generator was used as both test and training input in the proposed approach. In this way, the model accuracy and enhancement were provided simultaneously. The proposed approach was turned into a modular independent integrated system to work in various wind turbine applications. It was observed that the system can predict wind speed dynamically with around 3% error in the applications in the test setup applications.
Volume: 10
Issue: 6
Page: 5779-5784
Publish at: 2020-12-01

Digital-based instruction: Chances and challenges in English language teaching context

10.11591/ijere.v9i4.20579
Ningtyas Orilina Argawati , Lilis Suryani
The use of Digital Based Instruction (DBI) nowadays seems to have many obstacles and difficulties. It is due to lack of digital literacy both lecturers and students. Meanwhile, many advantages also emerge through the use of DBI. It becomes the reason for the lecturers to keep on trying to develop their digital literacy to occupy the need of DBI to their English Language Teaching (ELT) class. This study aimed at revealing the chances and challenges of the implementation of DBI. Descriptive qualitative was employed in this study which involved the use of observation and interview as the instruments. The participants of this study were five English lecturers and 200 students. The findings showed that the chances of applying DBI cover activating students, elevating digital literacy, provoking students’ creativity, and creating new application. While some challenges are also emerged. It is due to limited devices and access, and limited digital literacy. The implementation of DBI could provoke the elevation of digital literacy and creativity if we could handle the challenges.
Volume: 9
Issue: 4
Page: 1138-1144
Publish at: 2020-12-01

An efficient data masking for securing medical data using DNA encoding and chaotic system

10.11591/ijece.v10i6.pp6008-6018
Siddartha B. K. , Ravikumar G. K.
Data security is utmost important for ubiquitous computing of medical/diagnostic data or images. Along with must consider preserving privacy of patients. Recently, deoxyribose nucleic acid (DNA) sequences and chaotic sequence are jointly used for building efficient data masking model. However, the state-of-art model are not robust against noise and cropping attack (CA). Since in existing model most digits of each pixel are not altered. This work present efficient data masking (EDM) method using chaos and DNA based encryption method for securing health care data. For overcoming research challenges effective bit scrambling method is required. Firstly, this work present an efficient bit scrambling using logistic sine map and pseudorandom sequence using chaotic system. Then, DNA substitution is performed among them to resist against differential attack (DA), statistical attack (SA) and CA. Experiment are conducted on standard considering diverse images. The outcome achieved shows proposed model efficient when compared to existing models.
Volume: 10
Issue: 6
Page: 6008-6018
Publish at: 2020-12-01

Hiding text in speech signal using K-means, LSB techniques and chaotic maps

10.11591/ijece.v10i6.pp5726-5735
Iman Qays Abduljaleel , Amal Hameed Khaleel
In this paper, a new technique that hides a secret text inside a speech signal without any apparent noise is presented. The technique for encoding the secret text is through first scrambling the text using Chaotic Map, then encoding the scraped text using the Zaslavsky map, and finally hiding the text by breaking the speech signal into blocks and using only half of each block with the LSB, K-means algorithms. The measures (SNR, PSNR, Correlation, SSIM, and MSE) are used on various speech files (“.WAV”), and various secret texts. We observed that the suggested technique offers high security (SNR, PSNR, Correlation, and SSIM) of an encrypted text with low error (MSE). This indicates that the noise level in the speech signal is very low and the speech purity is high, so the suggested method is effective for embedding encrypted text into speech files.
Volume: 10
Issue: 6
Page: 5726-5735
Publish at: 2020-12-01

Student performance prediction using simple additive weighting method

10.11591/ijai.v9.i4.pp630-637
Harco Leslie Hendric Spits Warnars , Arif Fahrudin , Wiranto Herry Utomo
In the world of student education is an important component where the role of students is as someone who is psychologically ready to receive lessons or other input from the school. However, each student has different performance and development, therefore it is important to do monitoring so that student performance will always be monitored by the school for improving student quality maintenance. Also, in the process of valuing education for students needs to be done by giving an appreciation in the form of giving gifts or just giving words and motivation so that students can perform better in learning and participating in other activities at school. In terms of selecting students with good performance or those who have a very declining development using the school method not only assess students by one criterion but with several criteria to produce a decision that can be accepted by many people. Performance Students must also be monitored by the school or the related rights. In this paper, the student performance prediction was assessed with 5 criteria components and the result shows there are 10 very satisfy students, 10 satisfying students, 10 well students, and 10 Enough students from sample 40 students.
Volume: 9
Issue: 4
Page: 630-637
Publish at: 2020-12-01

The assesement of the shunt active filter efficiency under varied power supply source and load parameters

10.11591/ijece.v10i6.pp5621-5630
Yuriy Sychev , Boris Abramovich , Veronika Prokhorova
The results of theoretical and experimental studies on the problems of effective application of shunt active filters for power quality improvement and electromagnetic compatibility ensuring were presented in this article. Based on the different theories and methods, the most effective and well-studied control algorithms of shunt active filters were determined and analyzed to ensure their effective application in distributed generation systems and combined power supply systems. Mathematical and computer simulation models of shunt active filters with different control algorithms in distributed generation systems and combined power supply systems were developed. According to the results of mathematical modeling and computer simulation, the dependences were detected, reflecting the influences of internal and external parameters on the factors, determining the efficiency of the correction of power quality indicators. The range of permissible changes in the parameters of shunt active filters by saving the required efficiency level of power quality correction was detected under varied power supply system characteristics, load parameters and also in hybrid structures. The adaptive algorithm of the shunt active filter functioning under varied application conditions for automated power quality improvement is developed on the basis
Volume: 10
Issue: 6
Page: 5621-5630
Publish at: 2020-12-01

Strengthening programming skills among engineering students through experiential learning based robotics project

10.11591/ijere.v9i4.20653
Mohd Faisal Ibrahim , Aqilah Baseri Huddin , Fazida Hanim Hashim , Mardina Abdullah , Ashrani Aizzuddin Abd Rahni , Seri Mastura Mustaza , Aini Hussain , Mohd Hairi Mohd Zaman
This study examined the educational effects in strengthening programming skills among university’s undergraduate engineering students via integration of a robotics project and an experiential learning approach. In this study, a robotics project was conducted to close the gap of students’ difficulty in relating the theoretical concepts of programming and real-world problems. Hence, an experiential learning approach using the Kolb model was proposed to investigate the problem. In this project, students were split into groups whereby they were asked to develop codes for controlling the navigation of a wheeled mobile robot. They were responsible for managing their group’s activities, conducting laboratory tests, producing technical reports and preparing a video presentation. The statistical analysis performed on the students’ summative assessments of a programming course revealed a remarkable improvement in their problem-solving skills and ability to provide programming solutions to a real-world problem.
Volume: 9
Issue: 4
Page: 939-946
Publish at: 2020-12-01

A proposed system for opinion mining using machine learning, NLP and classifiers

10.11591/ijai.v9.i4.pp726-733
Poonam Tanwar , Priyanka rai
In today’s life consumer reviews are the part of everyday life. User read the reviews before purchase, or stores it for finding the best product through comparison of the product review. From customers view point the reviews play vital role to make a decision regarding an online purchase as well as spammers to write the fake reviews which can increase or defame the reputation of any product. Spammers are using these platforms illegally for financial benefits/incentives are involved in writing fake reviews and they are trying to achieve their motive in terms of financial or to defeat the competitor which causes an explosive growth of sentiment/opinion spamming of writing forged/fake reviews. The present studies and research are used to analyse and categorize the opinion spamming into three different detection targets opinion spam, spammers, and to find the collusive opinion spammer groups so that false opinions can be avoided. Opinion spamming further divided into three different types based on textual and linguistic, behavioral, and relational features. The motivation behind this work is to study the dynamics of spam diffusion and extract the latent features that fuel the diffusion process. The user-based features and content-based features have been used for the categorization of spam/non-spam content. The contributions of this work are building the datasetwhich assists as the ground-truth for classifying/analyzing the variation of fraud/genuine and non-spam/spam information diffusion and to analyze the effects of topics over the diffusibility of non-spam and spam evidences/information. The paper, carried out an in-depth analysis of Twitter Spam diffusion.
Volume: 9
Issue: 4
Page: 726-733
Publish at: 2020-12-01

An effective identification of crop diseases using faster region based convolutional neural network and expert systems

10.11591/ijece.v10i6.pp6531-6540
P. Chandana , G. S. Pradeep Ghantasala , J. Rethna Virgil Jeny , Kaushik Sekaran , Deepika N. , Yunyoung Nam , Seifedine Kadry
The majority of research Study is moving towards cognitive computing, ubiquitous computing, internet of things (IoT) which focus on some of the real time applications like smart cities, smart agriculture, wearable smart devices. The objective of the research in this paper is to integrate the image processing strategies to the smart agriculture techniques to help the farmers to use the latest innovations of technology in order to resolve the issues of crops like infections or diseases to their crops which may be due to bugs or due to climatic conditions or may be due to soil consistency. As IoT is playing a crucial role in smart agriculture, the concept of infection recognition using object recognition the image processing strategy can help out the farmers greatly without making them to learn much about the technology and also helps them to sort out the issues with respect to crop. In this paper, an attempt of integrating kissan application with expert systems and image processing is made in order to help the farmers to have an immediate solution for the problem identified in a crop.
Volume: 10
Issue: 6
Page: 6531-6540
Publish at: 2020-12-01

Evaluation of lightweight battery management system with field test of electric bus in campus transit system

10.11591/ijece.v10i6.pp6202-6213
Watcharin Srirattanawichaikul , Paramet Wirasanti
A battery management system is a crucial part of a battery-powered electric vehicle, which functions as a monitoring system, state estimation, and protection for the vehicle. Among these functions, the state estimation, i.e., state of charge and remaining battery life estimation, is widely researched in order to find an accuracy estimation methodology. Most of the recent researches are based on the study of the battery cell level and the complex algorithm. In practice, there is a statement that the method should be simple and robust. Therefore, this research work is focused on the study of lightweight methodology for state estimation based on the battery pack. The discrete Coulomb counting method and the data-driven approach, based on the Palmgren-Miner method, are proposed for the estimation of the state of charge and remaining battery life, respectively. The proposed methods are evaluated through a battery-powered electric bus under real scenario-based circumstances in the campus transit system. In addition, the battery life-cycle cost analysis is also investigated. The tested bus has currently been in operation in the transit system for more than one year.
Volume: 10
Issue: 6
Page: 6202-6213
Publish at: 2020-12-01
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