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29,939 Article Results

Web-based software application design for solar PV system sizing

10.12928/telkomnika.v19i6.21666
Lambe Mutalub; Kwara State University Adesina , Olalekan; Kwara State University Ogunbiyi , Mustapha; Kwara State University Mubarak
The solar photovoltaic (PV) energy source systems generally rely on the availability of sunlight, its duration, and the capacity of storage devices if it is not a grid-tie system. The components of the PV sources come in different sizes and capacities, depending on the various applications and available products in the market. Therefore, sizing of PV components becomes important to the functionality and reliability of solar PV sources. This work is aimed at the development of a web-based software application designed for sizing the capacity of solar PV source components that meet required energy demand. A description of photovoltaic system components, available types, and sizing techniques are discussed. Parameter evaluation algorithms with flowcharts were developed for PV components. Consequently, web-based software was developed and simulated for a different case study. The results described the estimated load, average daily load, ratings of PV system components such as inverter, battery, solar panel, and charge controller. The cost estimates of each component, the total estimated cost of the project, and the specification of components’ purchasing store are similarly presented. Thus, the developed application can be applied to size different ranges of microgrid systems meant for several applications.
Volume: 19
Issue: 6
Page: 2009-2019
Publish at: 2021-12-01

Exploring ESL learners’ blended learning experiences and its’ effectiveness through web-based technologies

10.11591/ijere.v10i4.21465
Sangeeth Ramalingam , Melor Md Yunus , Harwati Hashim
The inability to gain employment among the Malaysian graduates became a critical issue due to lack of 21st century skills. Higher learning institutions are urged to play their significant roles in producing graduates who have subject knowledge and relevant 21st century skills. There are many teaching strategies which are currently in practice at tertiary institutions, however more efficient approach is needed to produce well-balanced graduates. This present research proposed a promising approach which is blended learning with web-based technologies to improve students’ 21st century skills. Current literature indicated blended learning has not been sufficiently explored in English as Second Language (ESL) context. Thus, this study aimed to explore ESL learners’ blended learning experiences using innovative web-based technologies and to examine the effectiveness of the blended learning strategy in improving the learners’ 21st-century skills. Qualitative data were collected through phone call interviews with the participants and analyzed through thematic analysis. Working with a sample of bachelor degree students at a tertiary institution revealed the participants’ blended learning experience exceptionally improved their 21st-century skills in various ways. The result of this study contributes to the pedagogical aspects of ESL teaching and learning and the improvement of 21st century skills among the students.
Volume: 10
Issue: 4
Page: 1436-1445
Publish at: 2021-12-01

Overview of microgrid systems

10.11591/ijaas.v10.i4.pp378-391
V. Saravanan , K. M. Venkatachalam , M. Arumugam , M. A. K. Borelessa , K. T. M. U. Hemapala
This research paper discusses the different types of microgrids, their structural arrangements and the technology adopted for different power management projects. It also deals with various control strategies and security plans used for optimal performance. A detailed overview of the direct current (DC) microgrid system is discussed, outlining its configurations and technical-economic aspects. Performance evaluation of microgrid carried out through various reliability codes is also provided.
Volume: 10
Issue: 4
Page: 378-391
Publish at: 2021-12-01

Emotion recognition from syllabic units using k-nearest-neighbor classification and energy distribution

10.11591/ijece.v11i6.pp5438-5449
Abdellah Agrima , Ilham Mounir , Abdelmajid Farchi , Laila Elmaazouzi , Badia Mounir
In this article, we present an automatic technique for recognizing emotional states from speech signals. The main focus of this paper is to present an efficient and reduced set of acoustic features that allows us to recognize the four basic human emotions (anger, sadness, joy, and neutral). The proposed features vector is composed by twenty-eight measurements corresponding to standard acoustic features such as formants, fundamental frequency (obtained by Praat software) as well as introducing new features based on the calculation of the energies in some specific frequency bands and their distributions (thanks to MATLAB codes). The extracted measurements are obtained from syllabic units’ consonant/vowel (CV) derived from Moroccan Arabic dialect emotional database (MADED) corpus. Thereafter, the data which has been collected is then trained by a k-nearest-neighbor (KNN) classifier to perform the automated recognition phase. The results reach 64.65% in the multi-class classification and 94.95% for classification between positive and negative emotions.
Volume: 11
Issue: 6
Page: 5438-5449
Publish at: 2021-12-01

On the evaluation of the IEEE 802.11ac WLAN performance with QoS deployment

10.11591/ijeecs.v24.i3.pp1618-1627
Ziyad Khalaf Farej , Omer Mohammed Ali
The increase in the number of users on wireless local area networks (WLAN) and the development of large size applications have increased the demand for high-speed data rate and low latency. The IEEE 802.11ac was developed to provide very high throughput WLANs. Many enhancements are added to the medium access control (MAC) and physical (PHY) layers to increase data rate and improve network performance, these features enable the IEEE 802.11ac standard to provide quality of service (QoS) for multimedia applications. This paper concentrates on the impact of QoS on the system performance in term of delay and throughput. Four scenarios are proposed to investigate the network performance with different (from 1 up to 8) spatial stream (SS). The objective modular network testbed in C++ (OMNet++ modeler v5.5.1) is used to simulate and model these scenarios. For 8×8 SS, the results of simulation show the best throughput (maximum) and delay (minimum) values of (622, 484, 399.3, 382.96 Mbps) and (0.0211, 0.0589, 0.1037, 0.1202 sec) for 5, 15, 30 and 45 node number scenarios respectively. Although the number of nodes increases, the system performance decreases, however when QoS is deployed the performance is enhanced and its best improvement is obtained at the highest (45) node number scenario with values of 94.4% and 56.1% for throughput and delay respectively.
Volume: 24
Issue: 3
Page: 1618-1627
Publish at: 2021-12-01

Mode NASA blade used to calculate the power generator for (VAWT) by drag and lift coefficients

10.12928/telkomnika.v19i6.17074
Ibrahim Amer; University of Baghdad Ibrahim , Mohammed Saeed; Middle Technical University Mohammed , A.; South Ural State University (SUSU) Ibrahim , O. J.; South Ural State University (SUSU) Abdalgbar
One of the confrontations with increasing demand on power in the entire world the methodologies of provided power divided into traditional methods against renewable methods. This article presents a simulation model to estimate the integrated power from vertical access wind turbine (VAWT) stages of development of a simulation model of local power supply system (LPSS) with (VAWT). However, wind power is one of the quickest developing advances for the sustainable power age. Disturbingly, in the ongoing years a few instances of corruption on telecommunication systems frameworks have emerged because of the presence of wind ranches, and costly and in fact complex restorative estimations needed. The grade of variation of power verified according to the grid size. The parameters were taken in the study through the preparation of the model are (efficiency, cost, and system response) compared to the benefits against disadvantages when combining the two systems to achieve a high performance of the power stability.
Volume: 19
Issue: 6
Page: 1992-1999
Publish at: 2021-12-01

Metaheuristic optimization in neural network model for seasonal data

10.12928/telkomnika.v19i6.20409
Budi; Diponegoro University Warsito , Rukun; Diponegoro University Santoso , Hasbi; Diponegoro University Yasin
The use of metaheuristic optimization techniques in obtaining the optimal weights of neural network model for the time series was the main part of this research. The three optimization methods used as experiments were genetic algorithm (GA), particle swarm optimization (PSO), and modified bee colony (MBC). Feed forward neural network (FFNN) was the neural network (NN) architecture chosen in this research. The limitations and weaknesses of gradient-based methods for learning algorithm inspired some researchers to use other techniques. A reasonable choice is non-gradient based method. Neural network is inspired by the characteristics of creatures. Therefore, the optimization techniques which are also resemble the patterns of life in nature will be appropriate. In this study, various scenarios on the three metaheuristic optimization methods were applied to get the best one. The proposed procedure was applied to the rainfall data. The experimental study showed that GA and PSO were recommended as optimization methods at FFNN model for the rainfall data.
Volume: 19
Issue: 6
Page: 1892-1901
Publish at: 2021-12-01

Analysis the digital images by using morphology operators

10.11591/ijeecs.v24.i3.pp1654-1662
Hadeel Amjed Saeed , Sumaya Hamad , Azmi Tawfik Hussain
In this paper, we deal with morphology images that try to improve the use of images. On the one hand, the process is used to obtain the histogram of the image then converted it into a non-color image (gray scale). The next step is to perform the erosion, dilation, open and close operations on the images, how these methods have important effects, and how can be used on a variable number of images, and found the differences between them. These operations were applied on four different images, check images, four basic operations (dilation, erosion, open and close) for each image were performed. Then, retrieving process to the original state of the image (the colored copy) was applied. The results found that retrieving the original images is difficult, and there is the occurrence of some noises on the image when it was retrieved. Finally, conclusions of the work are presented.
Volume: 24
Issue: 3
Page: 1654-1662
Publish at: 2021-12-01

A hybrid deep learning approach towards building an intelligent system for pneumonia detection in chest X-ray images

10.11591/ijece.v11i6.pp5530-5540
Ihssan S. Masad , Amin Alqudah , Ali Mohammad Alqudah , Sami Almashaqbeh
Pneumonia is a major cause for the death of children. In order to overcome the subjectivity and time consumption of the traditional detection of pneumonia from chest X-ray images; this work hypothesized that a hybrid deep learning system that consists of a convolutional neural network (CNN) model with another type of classifiers will improve the performance of the detection system. Three types of classifiers (support vector machine (SVM), k-nearest neighbor (KNN), and random forest (RF) were used along with the traditional CNN classification system (Softmax) to automatically detect pneumonia from chest X-ray images. The performance of the hybrid systems was comparable to that of the traditional CNN model with Softmax in terms of accuracy, precision, and specificity; except for the RF hybrid system which had less performance than the others. On the other hand, KNN hybrid system had the best consumption time, followed by the SVM, Softmax, and lastly the RF system. However, this improvement in consumption time (up to 4 folds) was in the expense of the sensitivity. A new hybrid artificial intelligence methodology for pneumonia detection has been implemented using small-sized chest X-ray images. The novel system achieved a very efficient performance with a short classification consumption time.
Volume: 11
Issue: 6
Page: 5530-5540
Publish at: 2021-12-01

Blockchain-based adoption framework for authentic land registry system in Malaysia

10.12928/telkomnika.v19i6.19276
Abdulaziz; University of Kuala Lumpur Aborujilah , Muhammad Naqib Bin Mohd; University of Kuala Lumpur Yatim , Abdulaleem; University of Kuala Lumpur Al-Othmani
Land registration systems are very essential for property ownership management. The exited land registry systems are less efficient and time-consuming and expose to human errors. By using blockchain technology, most of the principles of good governance in land administration such as transparency and efficiency can be fulfilled. However, there is a lack of experience in developing blockchain-based land registry systems. This paper proposes a blockchain-based adoption framework for land registry management in Malaysia. It elaborates more on developing a prototype that fulfills the main functions of current land registration by using smart contract functionalities. Also, this paper illustrates the main challenges of adopting this technology such as expertise shortage of software developers, implementation difficulties due to scalability of the land transactions, data sharing with different types of blockchain and lack of security attacks resistance. Therefore, there is a need to form an agreed-upon blockchain development platform that meet such constraints.
Volume: 19
Issue: 6
Page: 2038-2049
Publish at: 2021-12-01

Optimization of wind solar and battery hybrid renewable system using backtrack search algorithm

10.11591/ijeecs.v24.i3.pp1269-1277
Ingudam Chitrasen Meitei , Rajen Pudur
Penetration of renewable sources to the grid is always a problem for electrical engineers, apart from reliability and efficiency, cost optimization is also a big concern among them. Wind, solar and battery hybrid combinations (WSB-HPS) are also very common among hybrid systems, but this WSBHPS combines wind and solar energy power generation reduces the charge and discharge time of the battery. Therefore, this system improves the reliability of the power supply by fully utilizing the wind and solar power generation and improves the charging and discharging state of the battery and hence reduces the whole cost as the investment in battery is reduced. backtrack search algorithm (BSA) is the highly efficient and powerful algorithm to solve combinatorial optimization problems. In this paper an attempt is made to optimize the hybrid combination using BSA in the matrix laboratory (MATLAB) environment and comparable study is made using HOMER. A complete optimised data is generated for a particular area in Manipur and reduced cost is suggested.
Volume: 24
Issue: 3
Page: 1269-1277
Publish at: 2021-12-01

Assessing naive Bayes and support vector machine performance in sentiment classification on a big data platform

10.11591/ijai.v10.i4.pp990-996
Redouane Karsi , Mounia Zaim , Jamila El Alami
Nowadays, mining user reviews becomes a very useful mean for decision making in several areas. Traditionally, machine learning algorithms have been widely and effectively used to analyze user’s opinions on a limited volume of data. In the case of massive data, powerful hardware resources (CPU, memory, and storage) are essential for dealing with the whole data processing phases including, collection, pre-processing, and learning in an optimal time. Several big data technologies have emerged to efficiently process massive data, like Apache Spark, which is a distributed framework for data processing that provides libraries implementing several machine learning algorithms. In order to evaluate the performance of Apache Spark's machine learning library (MLlib) on a large volume of data, classification accuracies and processing time of two machine learning algorithms implemented in spark: naive Bayes and support vector machine (SVM) are compared to the performance achieved by the standard implementation of these two algorithms on large different size datasets built from movie reviews. The results of our experiment show that the performance of classifiers running under spark is higher than traditional ones and reaches F-measure greater than 84%. At the same time, we found that under spark framework, the learning time is relatively low.
Volume: 10
Issue: 4
Page: 990-996
Publish at: 2021-12-01

A performance evaluation of convolutional neural network architecture for classification of rice leaf disease

10.11591/ijai.v10.i4.pp1069-1078
Afis Julianto , Andi Sunyoto
Plant disease is a challenge in the agricultural sector, especially for rice production. Identifying diseases in rice leaves is the first step to wipe out and treat diseases to reduce crop failure. With the rapid development of the convolutional neural network (CNN), rice leaf disease can be recognized well without the help of an expert. In this research, the performance evaluation of CNN architecture will be carried out to analyze the classification of rice leaf disease images by classifying 5932 image data which are divided into 4 disease classes. The comparison of training data, validation, and testing are 60:20:20. Adam optimization with a learning rate of 0.0009 and softmax activation was used in this study. From the experimental results, the InceptionV3 and InceptionResnetV2 architectures got the best accuracy, namely 100%, ResNet50 and DenseNet201 got 99.83%, MobileNet 99.33%, and EfficientNetB3 90.14% accuracy.
Volume: 10
Issue: 4
Page: 1069-1078
Publish at: 2021-12-01

Image retrieval based on swarm intelligence

10.11591/ijece.v11i6.pp5390-5401
Shahbaa I. Khaleel , Ragad W. Khaled
To keep pace with the development of modern technology in this information technology era, and the immense image databases, whether personal or commercial, are increasing, is requiring the management of these databases to strong and accurate systems to retrieve images with high efficiency. Because of the swarm intelligence algorithms are great importance in solving difficult problems and obtaining the best solutions. Here in this research, a proposed system is designed to retrieve color images based on swarm intelligence algorithms. Where the algorithm of the ant colony optimization (ACOM) and the intelligent water drop (IWDM) was used to improve the system's work by conducting the clustering process in these two methods on the features extracted by annular color moment method (ACM) to obtain clustered data, the amount of similarity between them and the query image, is calculated to retrieve images from the database, efficiently and in a short time. In addition, improving the work of these two methods by hybridizing them with fuzzy method, fuzzy gath geva clustering algorithm (FGCA) and obtaining two new high efficiency hybrid algorithms fuzzy ant colony optimization method (FACOM) and fuzzy intelligent water drop method (FIWDM) by retrieving images whose performance values are calculated by calculating the values of precision, recall and the f-measure. It proved its efficiency by comparing it with fuzzy method, FGCA and by methods of swarm intelligence without hybridization, and its work was excellent.
Volume: 11
Issue: 6
Page: 5390-5401
Publish at: 2021-12-01

Robotic dry cleaner for photovoltaic solar panels: an implemented design that evaluated in iraq's weather

10.12928/telkomnika.v19i6.20505
Mahmood H.; Middle Technical University Salman , Ahmed J.; Middle Technical University Abid , Adel A.; Middle Technical University Obed
Arabian desert areas are suffered from high mitigation in the produced photovoltaic (PV) power due to high dusty weather. This article presents a robotic cleaner that will significantly reduce the impact of dust on the installed PV systems in these areas. The proposed robotic cleaner is simple, low cost, standalone, self-powered, portable, and connected to the cloud. ESP32 used as a controller that manages the cleaning process and monitors its PV power production, the battery's state of charge, time of the day, and weather conditions. Thanks to the ESP32 features and its ability to connect to the cloud, as an internet of things (IoT), via the ThingSpeak website. All the electrical, mechanical, and electronic design aspects are presented and implemented in this article. The results show the effectiveness and performance enhancement due to periodic cleaning using the proposed robotic cleaner. The results also show that the total percentage of the monthly normalized accumulated losses for the two scheduled cleaning photovoltaic strings with a performance improvement of 15.54% for the weekly cleaned string (WCS) 83.04% for the never cleaned string (NCS) through the tested month.
Volume: 19
Issue: 6
Page: 2050-2058
Publish at: 2021-12-01
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