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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 64 Documents
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Search results for , issue "Vol 27, No 2: August 2022" : 64 Documents clear
Mathematical modelling of vehicle drivetrain to predict energy consumption Latha Ramasamy; Ashok Kumar Loganathan; Rajalakshmi Chinnasamy
Indonesian Journal of Electrical Engineering and Computer Science Vol 27, No 2: August 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v27.i2.pp638-646

Abstract

Nowadays, many firms have started producing electric vehicles (EVs). One of the biggest challenges to broad acceptance of electric vehicles is their limited range EVs. Forecasting future energy usage is one of the way to calculate the driving range. In this paper, a simulation model of the drivetrain has been developed to evaluate the energy flow of a vehicle for the given torque and speed conditions. The energy consumption of an electric vehicle is determined by the vehicle's attributes. Road torque, road speed, motor model, motor controller model, battery model, and PI controller are the primary components of the model. The overall resistive force offered by the vehicle, as well as energy consumption owing to resistive force during motoring and regeneration has been validated through the simulation results. Here, the vehicle model, Mercedes Benz Class C Saloon has been considered.
ThreatNet: advanced threat detection, region-based convolutional neural network framework Anurag Singh; Naresh Kumar; Seifedine Kadry
Indonesian Journal of Electrical Engineering and Computer Science Vol 27, No 2: August 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v27.i2.pp1007-1015

Abstract

It is critical for many countries to ensure public safety in detecting and identifying threats in a night, commercial places, border areas and public places. Majority of past research in this area has focused on the use of image-level categorization and object-level detection techniques. As an X-ray and thermal security image analysis strategy, object separation can considerably improve automatic threat detection when used in conjunction with other techniques. In order to detect possible threats, the effects of introducing segmentation deep learning models into the threat detection pipeline of a large imbalanced X-ray and thermal dataset were investigated. With the purpose of boosting the number of true positives discovered, a faster regional convolutional neural network (R-CNN) model was trained on a balanced dataset to identify probable hazard zones in X-ray and thermal security pictures. In order to get the final results, we combined the two models i.e faster R-CNN with Mask RCNN into a single detection pipeline using the transfer learning technique, which outperforms baseline and end-to-end instance segmentation methods using less number of the practical dataset, with mAPs ranging from 94.88 percent to 91.40 percent helps in detecting the person with guns, knives, pliers to avoid cross border threats.
Early wildfire detection using machine learning model deployed in the fog/edge layers of IoT Mounir Grari; Idriss Idrissi; Mohammed Boukabous; Omar Moussaoui; Mostafa Azizi; Mimoun Moussaoui
Indonesian Journal of Electrical Engineering and Computer Science Vol 27, No 2: August 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v27.i2.pp1062-1073

Abstract

The impact of wildfires, even following the fire's extinguishment, continues to affect harmfully public health and prosperity. Wildfires are becoming increasingly frequent and severe, and make the world's biodiversity in a growing serious danger. The fires are responsible for negative economic consequences for individuals, corporations, and authorities. Researchers are developing new approaches for detecting and monitoring wildfires, that make use of advances in computer vision, machine learning, and remote sensing technologies. IoT sensors help to improve the efficiency of detecting active forest fires. In this paper, we propose a novel approach for predicting wildfires, based on machine learning. It uses a regression model that we train over NASA's fire information for resource management system (FIRMS) dataset to predict fire radiant power in megawatts. The analysis of the obtained simulation results (more than 99% in the R2 metric) shows that the ensemble learning model is an effective method for predicting wildfires using an IoT device equipped with several sensors that could potentially collect the same data as the FIRMS dataset, such as smart cameras or drones.
The enhancement of the dual-layer phosphorus configuration in color uniformity and luminous flux of a light emitting diode Phuc Dang Huu; Phung Ton That; Phan Xuan Le
Indonesian Journal of Electrical Engineering and Computer Science Vol 27, No 2: August 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v27.i2.pp766-772

Abstract

A solid-state process was used to generate the green phosphor Ca3Si2O4N2:Eu2+. The luminescence characteristics, dispersed reflection spectra, and heat quenching were investigated initially, followed by the white light emitting diodes (wLED’s) manufacture by the Eu2+ stimulated Ca3Si2O4N2 phosphor. Based on the concentration of ion Eu2+, a wide green emission range localized between 510 and 550 nm was seen in Eu2+ -doped Ca3Si2O4N2. In Ca3Si2O4N2, the best doping concentration of Eu2+ was 1 mol%. An electric multipolar interaction process conveys energy among Eu2+ ions, with a necessary conversion distance of around 30.08 Å. Blending a near-ultraviolet (n-UV) light emitting diodes (LED) which has a GaN basis (380 nm) with the blue BaMgAl10O17:Eu2+, the green  Ca3Si2O4N2:Eu2+, and the red Ca3Si2O4N2:Eu2+ phosphors yielded a wLED with a 88.25 color-rendering indice Ra at 6029 K correlating color temperature.  Ca3Si2O4N2:Eu2+ appears to be a promising option to apply as a converting phosphor in wLED applications.
Classification of focal liver disease in egyptian patients using ultrasound images and convolutional neural networks Rania Mohamed Abd-ElGhaffar; Mahmoud El-Zalabany; Hossam El-Din Moustafa; Mervat El-Seddek
Indonesian Journal of Electrical Engineering and Computer Science Vol 27, No 2: August 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v27.i2.pp793-802

Abstract

Recently, computer-aided diagnostic systems for various diseases have received great attention. One of the latest technologies used is deep learning architectures for analyzing and classifying medical images. In this paper, a new system that uses deep learning to classify three focal diseases in the liver besides the normal liver is proposed. A pre-trained convolutional neural network is utilized. Two types of networks are used, ResNet50 and AlexNet with fully connected networks (FCNs). After extracting the deep features using deep learning, FCNs can input images in different states of the disease, such as Normal, Hem, HCC, and Cyst. Dataset is obtained from the Egyptian Liver Research Institute. Two classifiers are utilized, the first includes two classes (Normal/Cyst, Normal/Hem, Normal/HCC, HCC/Cyst, HCC/Hem, Cyst/Hem) and the second contains four classes (Normal/Cyst/ HCC/Hem) to distinguish liver images. Using performance criteria, it has been shown that the two-category classifiers have given better results than the four-class classifier, and accordingly a hybrid classifier was suggested to merge the weighted probabilities of the classes obtained by each singular classifier. Experimental results have achieved an accuracy of 96.1% using ResNet50 which means that it can be used as an assistive diagnostic method for classification of focal liver disease.
Hindi to English transliteration using multilayer gated recurrent units Mohd Zeeshan Ansari; Tanvir Ahmad; Mirza Mohd Sufyan Beg; Faiyaz Ahmad
Indonesian Journal of Electrical Engineering and Computer Science Vol 27, No 2: August 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v27.i2.pp1083-1090

Abstract

Transliteration is the task of translating text from source script to target script provided that the language of the text remains the same. In this work, we perform transliteration on less explored Devanagari to Roman Hindi transliteration and its back transliteration. The neural transliteration model in this work is based on a sequence-to-sequence neural network that is composed of two major components, an encoder that transforms source language words into a meaningful representation and the decoder that is responsible for decoding the target language words. We utilize gated recurrent units (GRU) to design the multilayer encoder and decoder network. Among the several models, the multilayer model shows the best performance in terms of coupon equivalent rate (CER) and word error rate (WER). The method generates quite satisfactory predictions in Hindi-English bilingual machine transliteration with WER of 64.8% and CER of 20.1% which is a significant improvement over existing methods.
Luminescence and transfer for power characteristics of Sr4La(PO4)3O:Ce3+,Tb3+,Mn2+ phosphor for WLEDs My Hanh Nguyen Thi; Phan Xuan Le
Indonesian Journal of Electrical Engineering and Computer Science Vol 27, No 2: August 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v27.i2.pp722-727

Abstract

We apply the high-heat solid-state technique to create a sequence of phosphors Sr4La(PO4)3O:Ce3+,Tb3+,Mn2+. In Sr4La(PO4)3Othe luminescence characteristics, heat resistance, and transfer of energy between Ce3+ and Tb3+ as well as Mn2+ were thoroughly studied. The insertion of the sensitizer Ce3+ ions can significantly flatter the emission of the color green from Tb3+ and the emission of the color red from Mn2+ via energy transfer. By modifying the Ce3+/Tb3+ and Ce3+/Mn2+ ion ratios, it can change the emission color. White light, which has color coordinates of 0.3326, 0.3298, were produced by the Sr4La(PO4)3O: 0.12Ce3+, 0.3Mn2+ 0.12Ce3+, 0.3Mn2+ specimen, showing Sr4La(PO4)3O:Ce3+,Tb3+,Mn2+ might be profitable in white LEDs.
Comparative study of net energy metering and feed-in tariff for the 496kWp UiTM segamat solar photovoltaic system Muhamad Firdaus Zambri; Muhammad Murtadha Othman; Kamrul Hasan; Muhamad Nabil Hidayat; Abdul Kadir Ismail; Ismail Musirin
Indonesian Journal of Electrical Engineering and Computer Science Vol 27, No 2: August 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v27.i2.pp601-610

Abstract

The Energy and natural resources ministry (KeTSA) of Malaysia has introduced the net energy metering (NEM) 3.0, which provides an opportunity for consumers to install solar photovoltaic (PV) systems to reduce electricity bills. The NEM 3.0 introduces three new initiatives that offer 500 MW quota from 2021 till 2023. NEM has been implemented since 2016, replacing the feed-in tariff (FiT) strategy by promoting the users to utilize the generated energy in the first place before selling any surplus to the utility. As in the FiT strategy, users can only sell the generated energy at a fixed rate without utilizing it. This paper presents the comparative study between NEM and FiT for 496 kWp solar photovoltaic system in UiTM Segamat, Johor in the perspective of economy and energy practice based on the simulation result of MATLAB/Simulink software.
Multiuser-scheduling and resource allocation using max-min technique in wireless powered communication networks Richu Mary Thomas; Malarvizhi Subramani
Indonesian Journal of Electrical Engineering and Computer Science Vol 27, No 2: August 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v27.i2.pp832-841

Abstract

Wireless powered communication network (WPCN) is a promising research area for improving network security and speed. The transmission power of the source during the uplink functions as a random variable in WPCN due to the intrinsic power transfer process, whereas it is a constant in typical cooperative networks, culminating in the signal to noise ratio of the source-access point and all the source-relay-access point being mutually correlated. As a result of the massive increase in communication devices powered by battery, the goal of prolonging their life is critical. To get the most throughput in the shortest amount of time, the best uplink and downlink time allocations were calculated. For high throughput and secrecy performance, the proportional max-min fairness algorithm was used in this paper for secure communication in hybrid relays integrated with WPCN. This method allows for multi-user scheduling with optimum targets to provide reliable hybrid outage probability, secrecy outage probability, and energy outage probability. Efficacy of the proposed system was demonstrated regarding throughput, outage probability, and confidentiality. The performance of the model was compared to that of various energy harvesting models like random user scheduling, best user scheduling, and several others, and was found to outperform them for secure transmission.
The combination of user experience evaluation method in asessing the application of suicide risk idea identification Tenia Wahyuningrum; Gita Fadila Fitriana; Dyah Wahyuningsih
Indonesian Journal of Electrical Engineering and Computer Science Vol 27, No 2: August 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v27.i2.pp1025-1033

Abstract

The global pandemic COVID-19 has led to extreme loss of human life worldwide. The pandemic has made a profound impact on the psyche of some people, and it has led to suicide. The need for confident handling of specific risk factors for suicide is needed. Therefore, to support Indonesian governments to control the increment of suicidal ideas for adolescents using a combination of unmoderated remote usability testing (URUT) and usability metric for user experience (UMUX)-Lite. Then we adopted the school-based mental health program to make a prototype mobile application using the risk factor for suicidal ideation (RFSI) instrument to identify suicidal ideas in adolescents. Participants’ characteristics included late adolescents aged 19-22 years, male and female. The results show that the time-based efficiency on the registered task obtained from the calculation is 0.025 goals/sec. In one second, participants could complete a 0.025 of the job; although all participants could complete all tasks well and quickly, they provided good satisfaction scores. Some design improvements are needed on the prototype by considering user input.

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