Articles

Access the latest knowledge in applied science, electrical engineering, computer science and information technology, education, and health.

Advanced Search
Filter Icon

Filters article

Years

FAQ Arrow
Single Year
Range Year
0
0

Source Title

FAQ Arrow

Authors

FAQ Arrow

29,939 Article Results

A simple faulted phase-based fault distance estimation algorithm for a loop distribution system

10.11591/ijeecs.v25.i1.pp14-24
Shwe Myint , Warit Wichakool
This paper presents a single ended faulted phase-based traveling wave fault localization algorithm for loop distribution grids which is that the sensor can get many reflected signals from the fault point to face the complexity of localization. This localization algorithm uses a band pass filter to remove noise from the corrupted signal. The arriving times of the faulted phase-based filtered signals can be obtained by using phase-modal and discrete wavelet transformations. The estimated fault distance can be calculated using the traveling wave method. The proposed algorithm presents detail level analysis using three detail levels coefficients. The proposed algorithm is tested with MATLAB simulation single line to ground fault in a 10 kV grounded loop distribution system. The simulation result shows that the faulted phase time delay can give better accuracy than using conventional time delays. The proposed algorithm can give fault distance estimation accuracy up to 99.7% with 30 dB contaminated signal-to-noise ratio (SNR) for the nearest lines from the measured terminal.
Volume: 25
Issue: 1
Page: 14-24
Publish at: 2022-01-01

K-NN supervised learning algorithm in the predictive analysis of the quality of the university administrative service in the virtual environment

10.11591/ijeecs.v25.i1.pp521-528
Omar Freddy Chamorro-Atalaya , Guillermo Morales Romero , Adrián Quispe Andía , Beatriz Caycho Salas , Elizabeth Katerin Auqui Ramos , Primitiva Ramos Salazar , Carlos Palacios Huaraca
The objective of this study is to analyze and discuss the metrics of the predictive model using the K-nearest neighbor (K-NN) learning algorithm, which will be applied to the data on the perception of engineering students on the quality of the virtual administrative service, such as part of the methodology was analyzed the indicators of accuracy, precision, sensitivity and specificity, from the obtaining of the confusion matrix and the receiver operational characteristic (ROC) curve. The collected data were validated through Cronbach's Alpha, finding consistency values higher than 0.9, which allows to continue with the analysis. Through the predictive model through the Matlab R2021a software, it was concluded that the average metrics for all classes are optimal, presenting a precision of 92.77%, sensitivity 86.62%, and specificity 94.7%; with a total accuracy of 85.5%. In turn, the highest level of the area under the curve (AUC) is 0.98, which is why it is considered an optimal predictive model. Having carried out this study, it is possible to contribute significantly to the decision-making of the higher institution in relation to the improvement of the quality of the virtual administrative service.
Volume: 25
Issue: 1
Page: 521-528
Publish at: 2022-01-01

Smart solution for reducing COVID-19 risk using internet of things

10.11591/ijeecs.v25.i1.pp474-480
Akshay Rajeshkumar , Senthilkumar Mathi
The article exposes a smart device designed for mitigating the coronavirus disease (COVID-19) risk using the internet of things. A portable smart alerting device is designed for ensuring safety in public places which can alert people when the guidelines given by the government were not followed and alert health authorities when any abnormalities found. By doing so, the spread of this fatal disease can be stopped. The modules of the proposed system include the face mask detection module, social distance alerting module, crowd detection and analysis module, health screening module and health assessment module. The proposed system can be placed in any public entrances to monitor people without human intervention. Firstly, the human face images are captured for face mask check, then the crowd analysis of the particular entrance where the person is entering is performed, thereafter health screening of the person is done and the values were imported to the health assessment module to check for any abnormalities. Finally, after all the conditions were met the door is opened automatically. The smart device can be installed and effectively used in many scenarios such as malls, stores, crowded places and campuses to avoid the risk of spread of the coronavirus.
Volume: 25
Issue: 1
Page: 474-480
Publish at: 2022-01-01

Systematic literature review on university website quality

10.11591/ijeecs.v25.i1.pp511-520
Ala' Hasan Saleh , Rasimah Che Mohd Yusoff , Nur Azaliah Abu Bakar , Roslina Ibrahim
Website is a necessity for organizations to enable users worldwide to access their information and gain a competitive edge over others. The diversity of websites makes assessing website quality a difficult task. The aim of this paper is to identify the issues faced in the quality evaluation of university websites, the models and the factors used for evaluating university website quality. Systematic literature review was used to identify and synthesize related scholarly research papers. Findings show that there is a lack of study on university website quality compared to business websites; website designers did not have the appropriate knowledge on the interface design; and the website quality evaluation is complex since there is no specific evaluation model. Webqual 4.0 model was used to evaluate the quality of universities' websites. From 24 studies, initially 79 quality factors were extracted. After performing comparison, filtration and memoing, six quality factors were identified: information quality, specific content, usability, web appearance, service interaction quality, and functionality. This study makes a useful contribution in developing university website quality model by extending the Webqual 4.0 model.
Volume: 25
Issue: 1
Page: 511-520
Publish at: 2022-01-01

The general design of the automation for multiple fields using reinforcement learning algorithm

10.11591/ijeecs.v25.i1.pp481-487
Vijaya Kumar Reddy Radha , Anantha N. Lakshmipathi , Ravi Kumar Tirandasu , Paruchuri Ravi Prakash
Reinforcement learning is considered as a machine learning technique that is anxious with software agents should behave in particular environment. Reinforcement learning (RL) is a division of deep learning concept that assists you to make best use of some part of the collective return. In this paper evolving reinforcement learning algorithms shows possible to learn a fresh and understable concept by using a graph representation and applying optimization methods from the auto machine learning society. In this observe, we stand for the loss function, it is used to optimize an agent’s parameter in excess of its knowledge, as an imputational graph, and use traditional evolution to develop a population of the imputational graphs over a set of uncomplicated guidance environments. These outcomes in gradually better RL algorithms and the exposed algorithms simplify to more multifaceted environments, even though with visual annotations.
Volume: 25
Issue: 1
Page: 481-487
Publish at: 2022-01-01

Development of computer-based learning system for learning behavior analytics

10.11591/ijeecs.v25.i1.pp460-473
Kanyalag Phodong , Thepchai Supnithi , Rachada Kongkachandra
This paper aims to analyze the learning behavior of Thai learners by using a computer-based learning system for English writing. Three main objectives were set: the development of a computer-based learning system, automatic behavior data collection, and learning behavior analytics. Firstly, the system is developed under a multidisciplinary idea that is designed to integrate two concepts between the self-regulated learning model and components of natural language processing. The integration design encourages self-learning in the digital learning environment and supports appropriate English writing by the provided component selection. Second, the system automatically collects the writing behavior of a group of Thai learners. The data collected are necessary input for the process of learning analytics. Third, the writing behaviors data were analyzed to find the learning behavioral patterns of the learners. For learning analytics, behavior sequential analysis was used to analyze the learning logs from the system. The 31 undergraduate students are participated to record writing behaviors via the system. The learning patterns in relation to grammatical skills were compared between three groups: basic, intermediate, and advanced levels. The learning behavior patterns of the three groups are different that use for reflecting learners and improving the learning materials or curriculum.
Volume: 25
Issue: 1
Page: 460-473
Publish at: 2022-01-01

Feature extraction to predict quality of segregating sweet tamarind using image processing

10.11591/ijeecs.v25.i1.pp339-346
Panana Tangwannawit , Sakchai Tangwannawit
In this modern age, several new methods have been developed, especially in image processing for agriculture business, which consists of technologies derived from artificial intelligence (AI) capabilities called machine learning. Classify is a widely used method to analyze patterns, trends, as well as the body of knowledge from the data visualization. Image classification application improves discrimination and prediction efficiency. The objective of this research was to feature extraction of sweet tamarind and compare the algorithm for classification. This research used images from golden sweet tamarind species with the use of MATLAB and Python language. The steps of this research consisted of 1) preprocessing step for finding the distance to appropriate of the image quality, 2) feature extracting for finding the number of black pixels and the number of white pixels, perimeter, diameter, and centroid, and 3) classifying for algorithms' comparison. The results showed that the camera's distance to the image was 60 cm. The coefficient of determination was at 0.9956, and the Standard Error of Estimate was 7,424.736 pixels. The conclusion of classification found that the random forest had the highest accuracy at 92.00%, SD. = 8.06, precision = 90.12, recall = 92.86, and F1-score = 91.36.
Volume: 25
Issue: 1
Page: 339-346
Publish at: 2022-01-01

Slantlet transform used for faults diagnosis in robot arm

10.11591/ijeecs.v25.i1.pp281-290
Muhamad Azhar Abdilatef Alobaidy , Jassim Mohammed Abdul-Jabbar , Saad Zaghlul Al-khayyt
The robot arm systems are the most target systems in the fields of faults detection and diagnosis which are electrical and the mechanical systems in many fields. Fault detection and diagnosis study is presented for two robot arms. The disturbance due to the faults at robot's joints causes oscillations at the tip of the robot arm. The acceleration in multi-direction is analysed to extract the features of the faults. Simulations for planar and space robots are presented. Two types of feature (faults) detection methods are used in this paper. The first one is the discrete wavelet transform, which is applied in many research's works before. The second type, is the Slantlet transform, which represents an improved model of the discrete wavelet transform. The multi-layer perceptron artificial neural network is used for the purpose of faults allocation and classification. According to the obtained results, the Slantlet transform with the multi-layer perceptron artificial neural network appear to possess best performance (4.7088e-05), lower consuming time (71.017308 sec) and higher accuracy (100%) than the results obtained when applying discrete wavelet transform and artificial neural network for the same purpose.
Volume: 25
Issue: 1
Page: 281-290
Publish at: 2022-01-01

Automatic delivery-scam prevention using Raspberry-Pi

10.11591/ijeecs.v25.i1.pp113-119
Bindu Bhaskaran , Barath Krishna Gunasekaran , Srinivasan Velumani
The word ‘automatic’ is unavoidable in this modern technical era. Automation facilitates not only technical advancement and time reduction to several processes, but also provides protection in various aspects. Delivery scam is a commonly occurring crime and it has to be reduced. Product delivery is a long process which involves various people to ensure correct delivery to the customer, providing chances for scam to occur. This paper discusses on an automatic delivery-scam prevention system with the help of Raspberry-Pi controller. This system provides safety to the ordered goods by limiting the authorisation of opening the packages to company and the customer only. It assures the safe and correct delivery of the ordered product.
Volume: 25
Issue: 1
Page: 113-119
Publish at: 2022-01-01

Multi-scale 3D-convolutional neural network for hyperspectral image classification

10.11591/ijeecs.v25.i1.pp307-316
Murali Kanthi , Thogarcheti Hitendra Sarma , Chigarapalle Shoba Bindu
Deep Learning methods are state-of-the-art approaches for pixel-based hyperspectral images (HSI) classification. High classification accuracy has been achieved by extracting deep features from both spatial-spectral channels. However, the efficiency of such spatial-spectral approaches depends on the spatial dimension of each patch and there is no theoretically valid approach to find the optimum spatial dimension to be considered. It is more valid to extract spatial features by considering varying neighborhood scales in spatial dimensions. In this regard, this article proposes a deep convolutional neural network (CNN) model wherein three different multi-scale spatial-spectral patches are used to extract the features in both the spatial and spectral channels. In order to extract these potential features, the proposed deep learning architecture takes three patches various scales in spatial dimension. 3D convolution is performed on each selected patch and the process runs through entire image. The proposed is named as multi-scale three-dimensional convolutional neural network (MS-3DCNN). The efficiency of the proposed model is being verified through the experimental studies on three publicly available benchmark datasets including Pavia University, Indian Pines, and Salinas. It is empirically proved that the classification accuracy of the proposed model is improved when compared with the remaining state-of-the-art methods.
Volume: 25
Issue: 1
Page: 307-316
Publish at: 2022-01-01

Fixed point theorem between cone metric space and quasi-cone metric space

10.11591/ijeecs.v25.i1.pp540-549
Abdullah Al-Yaari , Hamzah Sakidin , Yousif Alyousifi , Qasem Al-Tashi
This study involves new notions of continuity of mapping between quasi-cone metrics spaces (QCMSs), cone metric spaces (CMSs), and vice versa. The relation between all notions of continuity were thoroughly studied and supported with the help of examples. In addition, these new continuities were compared with various types of continuities of mapping between two QCMSs. The continuity types are 𝒇𝒇-continuous, 𝒃𝒃-continuous, 𝒇𝒃-continuous, and 𝒃𝒇-continuous. The results demonstrated that the new notions of continuity could be generalized to the continuity of mapping between two QCMSs. It also showed a fixed point for this continuity map between a complete Hausdorff CMS and QCMS. Overall, this study supports recent research results.
Volume: 25
Issue: 1
Page: 540-549
Publish at: 2022-01-01

Comparative study of low power wide area network based on internet of things for smart city deployment in Bandung city

10.11591/ijeecs.v25.i1.pp425-439
Muhammad Imam Nashiruddin , Maruli Tua Baja Sihotang , Muhammad Ary Murti
Smart city implementation, such as smart energy and utilities, smart mobility & transportation, smart environment, and smart living in urban areas is expanding rapidly worldwide. However, one of the biggest challenges that need to be solved is the selection of the appropriate internet of things (IoT) connectivity technologies. This research will seek for the best candidate low power wide area network (LPWAN) technologies such as long-range wide area network (LoRaWAN), narrow-band internet of things (NB-IoT), and random phase multiple access (RPMA) for IoT smart city deployment in Bandung city is based on IoT network connectivity between with six technical evaluation criteria: gateway requirements, traffic/data projection, the best signal level area distribution, and overlapping zones. Bass model is carried out to determine the capacity forecast. While in coverage prediction, LoRaWAN and NB-IoT use the Okumura-Hata propagation, and Erceg-Greenstein (SUI) model is used for RPMA. Based on the simulation and performance evaluation results, RPMA outperforms LoRaWAN and NB-IoT. It required the least gateway number to cover Bandung city with the best signal levels and overlapping zones.
Volume: 25
Issue: 1
Page: 425-439
Publish at: 2022-01-01

An efficient look up table based approximate adder for field programmable gate array

10.11591/ijeecs.v25.i1.pp144-151
Hadise Ramezani , Majid Mohammadi , Amir Sabbagh Molahosseini
The approximate computing is an alternative computing approach which can lead to high-performance implementation of audio and image processing as well as deep learning applications. However, most of the available approximate adders have been designed using application specific integrated circuits (ASICs), and they would not result in an efficient implementation on field programmable gate arrays (FPGAs). In this paper, we have designed a new approximate adder customized for efficient implementation on FPGAs, and then it has been used to build the Gaussian filter. The experimental results of the implementation of Gaussian filter based on the proposed approximate adder on a Virtex-7 FPGA, indicated that the resource utilization has decreased by 20-51%, and the designed filter delay based on the modified design methodology for building approximate adders for FPGA-based systems (MDeMAS) adder has improved 10-35%, due to the obtained output quality.
Volume: 25
Issue: 1
Page: 144-151
Publish at: 2022-01-01

Digital image processing methods for estimating leaf area of cucumber plants

10.11591/ijeecs.v25.i1.pp317-328
Uoc Quang Ngo , Duong Tri Ngo , Hoc Thai Nguyen , Thanh Dang Bui
Increasingly emerging technologies in agriculture such as computer vision, artificial intelligence technology, not only make it possible to increase production. To minimize the negative impact on climate and the environment but also to conserve resources. A key task of these technologies is to monitor the growth of plants online with a high accuracy rate and in non-destructive manners. It is known that leaf area (LA) is one of the most important growth indexes in plant growth monitoring system. Unfortunately, to estimate the LA in natural outdoor scenes (the presence of occlusion or overlap area) with a high accuracy rate is not easy and it still remains a big challenge in eco-physiological studies. In this paper, two accurate and non-destructive approaches for estimating the LA were proposed with top-view and side-view images, respectively. The proposed approaches successfully extract the skeleton of cucumber plants in red, green, and blue (RGB) images and estimate the LA of cucumber plants with high precision. The results were validated by comparing with manual measurements. The experimental results of our proposed algorithms achieve 97.64% accuracy in leaf segmentation, and the relative error in LA estimation varies from 3.76% to 13.00%, which could meet the requirements of plant growth monitoring systems.
Volume: 25
Issue: 1
Page: 317-328
Publish at: 2022-01-01

A new function of stereo matching algorithm based on hybrid convolutional neural network

10.11591/ijeecs.v25.i1.pp223-231
Mohd Saad Hamid , Nurulfajar Abd Manap , Rostam Affendi Hamzah , Ahmad Fauzan Kadmin , Shamsul Fakhar Abd Gani , Adi Irwan Herman
This paper proposes a new hybrid method between the learning-based and handcrafted methods for a stereo matching algorithm. The main purpose of the stereo matching algorithm is to produce a disparity map. This map is essential for many applications, including three-dimensional (3D) reconstruction. The raw disparity map computed by a convolutional neural network (CNN) is still prone to errors in the low texture region. The algorithm is set to improve the matching cost computation stage with hybrid CNN-based combined with truncated directional intensity computation. The difference in truncated directional intensity value is employed to decrease radiometric errors. The proposed method’s raw matching cost went through the cost aggregation step using the bilateral filter (BF) to improve accuracy. The winner-take-all (WTA) optimization uses the aggregated cost volume to produce an initial disparity map. Finally, a series of refinement processes enhance the initial disparity map for a more accurate final disparity map. This paper verified the performance of the algorithm using the Middlebury online stereo benchmarking system. The proposed algorithm achieves the objective of generating a more accurate and smooth disparity map with different depths at low texture regions through better matching cost quality.
Volume: 25
Issue: 1
Page: 223-231
Publish at: 2022-01-01
1 2 3
...
1995 1996
Show 867 of 1996

Discover Our Library

Embark on a journey through our expansive collection of articles and let curiosity lead your path to innovation.

Explore Now
Library 3D Ilustration