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INDONESIA
Indonesian Journal of Electrical Engineering and Computer Science
Published by Institute of Advanced Engineering and Science
ISSN : 25024752     EISSN : 25024760     DOI : -
Core Subject :
Arjuna Subject : -
Articles 9,174 Documents
 610   611   612   613   614 
An automatic system for detecting voltage leaks in houses to save people's lives Hussein M. Haglan; Hussam Jasim Ali
Indonesian Journal of Electrical Engineering and Computer Science Vol 21, No 3: March 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v21.i3.pp1485-1492

Abstract

Many people are exposed to many dangers, such as electrical leakage into parts of the home that lead to death, injury, or loss of material resources. With the great developments that happen daily in the field of technology, one of the most important examples of these developments is the Arduino. The Arduino is a company that produces a software and electronic parts that are open source for companies and students in order to design and build digital devices, design and implement projects and systems that can be linked together or linked with the Internet to facilitate their use to serve society and humanity. It became easier to deploy this technology to solve problems that put people at risk. Many systems that detect leakage of gas, liquids and fires were built using these modern technologies to protect the lives of people and their resources, but no one has actually used these technologies to detect unexpected electrical voltage leaks into the home's water network or walls that resulting from damage in some parts of electrical devices. Therefore, in this paper, we have proposed and designed a system that can detect an unexpected voltage leak from some electric devices to the water network or walls in houses, alarm of house owners by sound, and cut off the electrical current to the house in order to save people's lives and resources.
Quality and energy optimized scheduling technique for executing scientific workload in cloud computing environment Nagendra Prasad S; Subash Kulkarni S
Indonesian Journal of Electrical Engineering and Computer Science Vol 21, No 2: February 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v21.i2.pp1039-1047

Abstract

Modern BigData data-intensive and scientific workload execution is challenging. The major issues are reliable processing, performance efficiency and energy efficacy perquisite of BigData processing framework. This work assume self-aware MC architectures that autonomously adjust or optimize their performance to accommodate users quality of service (QoS) performance requirement, job execution performance, energy efficiency, and resource accessibility. Extensive workload scheduling has been presented to minimize energy consumption in cloud computing (CC) environment. However, the existing workload scheduling model induces higher amount of interaction cost between inter-processors communications. Further, due to poor resource utilization, routing inefficiency these existing model induces higher energy cost and fails to meet workload QoS prerequisite. For overcoming research challenges, this paper presented quality and energy optimized scheduling (QEOS) technique for executing data-intensive workload by employing dynamic voltage and frequency scaling (DVFS) technique. Experiment outcome shows QEOS model attains good trade-off between system performance and energy consumption in multi-core cloud computing (CC) architectures when compared with existing model.
A predictive maintenance system for wireless sensor networks: a machine learning approach Mohammed Almazaideh; Janos Levendovszky
Indonesian Journal of Electrical Engineering and Computer Science Vol 25, No 2: February 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v25.i2.pp1047-1058

Abstract

Predictive maintenance system (PdM) is a new concept that helps system operators evaluate the current status of their systems, and it also assists in predicting the future quality of these systems and scheduling maintenance action. This paper proposes a PdM model that utilizes machine learning to predict the system’s operational status after M active steps based on L previous observations implemented by a feedforward neural network (FFNN). We use quantization and encoding schemes to reduce the complexity of the system. We apply the proposed model to build a PdM system for wireless sensors networks (WSNs), where our concern is to predict the state of the system as far as the quality of data transfer is concerned. The FFNN provides a forward prediction of the operational status of the network after M consecutive time steps in the future, based on the previous L readings of quality of service (QoS) requirements of WSN. We also demonstrate the relation between complexity and accuracy. We found that larger M leads to higher complexity and larger prediction error, where larger L entails higher complexity and smaller prediction error. We also investigate how quantization and encoding can reduce complexity to implement a real-time PdM system.
Analysis of design goals of cryptography algorithms based on different components Ali Mohammad Norouzzadeh Gil Molk; Mohammad Reza Aref; Reza Ramazani Khorshiddoust
Indonesian Journal of Electrical Engineering and Computer Science Vol 23, No 1: July 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v23.i1.pp540-548

Abstract

Cryptography algorithms are a fundamental part of a cryptographic system that is designed and implemented to increase information security. They are the center of attention of experts in the information technology domains. Although the cryptography algorithms are implemented to attainthe goals such as confidentially, integrity and authenticity of designing, but other matters that must be noticed by designers include speed, resource consumption, reliability, flexibility, usage type and so on. For the useful allocation of hardware, software, human resources, etc., it is important to identify the role of each of the factors influencing the design of cryptographic algorithms to invest in the development of cryptographic knowledge.This paper examines 1700 papers, documents and technical reports of international journals in the specific lengthy period (1978-2019), and the goal of the design and implementation of cryptography algorithms in a different period is extracted. Using a statistical population that consists of time and the number of documents in a long time and also avariety of data, leads this study to have a reliable result and attract the attention of designers.The results show that in recent years, attention tonew usage such as IoT and telemedicine, as well as lightweight cryptography, has increased to achieve the main goals.
An approach to categorize chest X-ray images using sparse categorical cross entropy Chaithanya B. N.; Swasthika Jain T. J.; A. Usha Ruby; Ayesha Parveen
Indonesian Journal of Electrical Engineering and Computer Science Vol 24, No 3: December 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v24.i3.pp1700-1710

Abstract

The Coronavirus disease (COVID-19) pandemic is the most recent threat to global health. Reverse transcription-polymerase chain reaction (RT-PCR) testing, computed tomography (CT) scans, and chest X-ray (CXR) images are being used to identify Coronavirus, one of the most serious community viruses of the twenty-first century. Because CT scans and RT-PCR analyses are not available in most health divisions, CXR images are typically the most time-saving and cost-effective tool for physicians in making decisions. Artificial intelligence and machine learning have become increasingly popular because of recent technical advancements. The goal of this project is to combine machine learning, deep learning, and the health-care sector to create a categorization technique for detecting the Coronavirus and other respiratory disorders. The three conditions evaluated in this study were COVID-19, viral Pneumonia, and normal lungs. Using X-ray pictures, this research developed a sparse categorical cross-entropy technique for recognizing all three categories. The proposed model had a training accuracy of 91% and a training loss of 0.63, as well as a validation accuracy of 81% and a validation loss of 0.7108.
Intelligent machine for sorting semi-precious minerals Mikhail Polishchuk; Mikhail Tkach; Igor Parkhomey; Juliy Boiko; Yevhenii Batrak; Oleksander Eromenko
Indonesian Journal of Electrical Engineering and Computer Science Vol 22, No 3: June 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v22.i3.pp1354-1364

Abstract

This paper proposes is devoted to solving the problem of sorting workpieces from minerals and semi-precious stones that are used in jewelry production. Intensive development of production leads to a reduction of natural resources, including semi-precious minerals. Therefore, the task of saving the use of minerals is relevant. The solution is based on the idea a new method for sorting semi-precious minerals is proposed, as well as the design of an intelligent installation for implementing this method. An increase in the accuracy of sorting semiprecious minerals has been achieved through the use of charge-coupled device (CCD) matrices, which automatically determine the volume of each mineral in 3D space. From the simulation work, it was found in comparison with manual sorting, the proposed method has reduced the amount of waste in the manufacture of jewelry by 15% ... 20%. Experimental studies were carried out using the example of such a semiprecious mineral as amber. The cost of raw amber in the amount of one kilogram on the modern market is in the range of 1200…2200 US dollars. The result confirms that the method is superior to the alternative method, with the achieved average waste saving of 20%, the proposed sorting technology makes it possible to obtain a profit of 240...440 US dollars per kilogram of amber. Ultimately, these solutions can improve the efficiency of jewelry production.
Combining feature selection and hybrid approach redefinition in handling class imbalance and overlapping for multi-class imbalanced Hartono Hartono; Erianto Ongko; Yeni Risyani
Indonesian Journal of Electrical Engineering and Computer Science Vol 21, No 3: March 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v21.i3.pp1513-1522

Abstract

In the classification process that contains class imbalance problems. In addition to the uneven distribution of instances which causes poor performance, overlapping problems also cause performance degradation. This paper proposes a method that combining feature selection and hybrid approach redefinition (HAR) method in handling class imbalance and overlapping for multi-class imbalanced. HAR was a hybrid ensembles method in handling class imbalance problem. The main contribution of this work is to produce a new method that can overcome the problem of class imbalance and overlapping in the multi-class imbalance problem.  This method must be able to give better results in terms of classifier performance and overlap degrees in multi-class problems. This is achieved by improving an ensemble learning algorithm and a preprocessing technique in HAR using minimizing overlapping selection under SMOTE (MOSS). MOSS was known as a very popular feature selection method in handling overlapping. To validate the accuracy of the proposed method, this research use augmented R-Value, Mean AUC, Mean F-Measure, Mean G-Mean, and Mean Precision. The performance of the model is evaluated against the hybrid method (MBP+CGE) as a popular method in handling class imbalance and overlapping for multi-class imbalanced. It is found that the proposed method is superior when subjected to classifier performance as indicate with better Mean AUC, F-Measure, G-Mean, and precision.
A review on supervised learning methodologies for detection of exudates in diabetic retinopathy Ujwala W. Wasekar; R. K. Bathla
Indonesian Journal of Electrical Engineering and Computer Science Vol 23, No 2: August 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v23.i2.pp837-846

Abstract

Diabetic retinopathy has become one of the major reasons for blindness in the world. Early and precise diagnosis of the disease may save one’s eyesight from irreversible damage. Manual detection of lesions is time consuming and may not be as accurate as desirable. Many automated systems have been developed recently to help ophthalmologists in their endeavors. Exudates are one of the early signs of manifestation of diabetic retinopathy. In this paper, the methodologies detecting exudates in retinal fundus images were reviewed. These methods were categorized into deep learning, machine learning and methods primarily focusing on image processing techniques. The comprehensive view of the performances of the methods was given. Several datasets were described briefly. Most of the researchers preferred combination of multiple publically available databases. Also, the potential areas of research were discussed. It was found that sensitivity which identifies the abnormal images correctly, is the most widely used performance measure. The study will be helpful to the researchers wanting to explore more in this field.
Experimental simplified rule of self tuning fuzzy logic-model reference adaptive speed controller for induction motor drive M.Z. Ismail; M.H.N. Talib; Z. Ibrahim; J. Mat Lazi; Z. Rasin
Indonesian Journal of Electrical Engineering and Computer Science Vol 20, No 3: December 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v20.i3.pp1653-1664

Abstract

Fuzzy logic controller (FLC) has shown excellent performance in dealing with the non-linearity and complex dynamic model of the induction motor. However, a conventional constant parameter FLC (CPFL) will not be able to provide–good coverage performance for a wide speed range operation with a single tuning parameter. Therefore, this paper proposed a self tuning mechanism FLC approach by model reference adaptive controller (ST-MRAC) to continuously allow to adjust the parameters. Due to real time hardware application, the dominant rules selection method for simplified rules has been implemented as part of the reducing computational burden. Experiment results validate a good performance of the ST-MRAC compared to the CPFL for the   speed performance in terms of the wide range of operations and disturbance showed remarkable performance.
First order surface grating fiber coupler under the period chirp and apodization functions variations effects Alsharef Mohammad; Mohammed S. Alzaidi; Mahmoud M. A. Eid; Vishal Sorathiya; Sunil Lavadiya; Shobhit K. Patel; Ahmed Nabih Zaki Rashed
Indonesian Journal of Electrical Engineering and Computer Science Vol 25, No 2: February 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v25.i2.pp1020-1029

Abstract

The paper has demonstrated the first order surface grating fiber coupler under the period chirp and apodization functions variations effects. The Fiber coupler transmittivity/reflectivity, the fiber coupler grating index change and the fiber coupler mesh transmission cross-section are clarified against the grating length with the quadratic/cubic root period chirp and Gaussian/uniform apodization functions. The fiber coupler delay and dispersion are simulated and demonstrated with grating wavelength with quadratic/cubic root period chirp and Gaussian/uniform apodization function. As well as the fiber coupler output pulse intensity is simulated against the time period with the quadratic/cubic root period chirp and Gaussian/uniform apodization function. The fiber coupler peak intensity variations against the transmission range variations is also demonstrated by OptiGrating simulation software.

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