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ELM-based control system applications: A bibliometric analysis and review Pratiwi, Enggar Banifa; Prajitno, Prawito; Kurniawan, Edi
Journal of Mechatronics, Electrical Power, and Vehicular Technology Vol 15, No 1 (2024)
Publisher : National Research and Innovation Agency

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55981/j.mev.2024.889

Abstract

This study conducts a bibliometric analysis of the extreme learning machine (ELM) research, with a particular emphasis on ELM-based control systems and applications. The objective of this study is to identify research trends, collaboration opportunities, and challenges in ELM applications. The analysis comprises the identification and retrieval of 3,174 articles from Scopus between 2018 and 2023. VOSviewer 1.6.20 is used for data interpretation, identifying six distinct keyword clusters and revealing both well-established research areas and emerging fields with significant potential for future exploration. Key research trends indicate a shift towards advanced or hybrid approaches, with recent interest in integrating optimization techniques. In the analysis, opportunities for collaboration with leading researchers are also highlighted. The findings emphasize the wide range of applications for ELM in improving the robustness of control systems while also highlighting important issues that need to be addressed. Finally, this study provides valuable insights into the current state and future directions of ELM research, especially ELM-based control systems.
Radionuclide identification system using convolution neural network for environmental radiation monitoring Istofa, Istofa; Kusuma, Gina; Ningsih, Firliyani Rahmatia; Triyanto, Joko; Susila, I Putu; Prajitno, Prawito
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i2.pp2282-2290

Abstract

Radionuclide identification is an important task for nuclear safety and security aspects, especially to environmental radiation monitoring systems. This study aims to build an automatic radionuclide identification system that can be applied in environmental radiation monitoring stations. The gamma energy spectrum was obtained by varying radionuclide types, measurement time and source distance using a scintillation detector. The dataset was collected by converting gamma energy spectrum into images, data pre-processing by removing background noise and normalizing the gamma spectrum. Automatic identification is demonstrated as a development method based on convolutional neural network (CNN) algorithm, where the images come from gamma-ray spectrum in the form of photoelectric peak characteristic. Three CNN architectures are used to train the model, which are VGG-16, AlexNet and Xception. The performance of each model is evaluated using accuracy, precision and recall to find the appropriate architecture. The most optimum results are shown by VGG-16 with an accuracy of 97.72%, a precision of 97.75% and a recall of 97.71%. The models are critically reviewed and it is concluded that the developed models can be further implemented on embedded devices utilizing the tiny machine learning (TinyML) platform in environmental radiation monitoring systems.
Multivariate Imputation Chained Equation on Solar Radiation in Automatic Weather Station Akbar, Gema; Prajitno, Prawito; Ariffudin; Ananda, Naufal
Jurnal Penelitian Pendidikan IPA Vol 10 No 7 (2024): July
Publisher : Postgraduate, University of Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jppipa.v10i7.7679

Abstract

Solar radiation is one of the crucial weather observation variables Its variable has a role in renewable energy solutions, agriculture, meteorology, and hydrology. AWS is one of instrument that use to observing weather especially solar radiation. AWS has a pyranometer sensor used to measure solar radiation. Unfortunately, the instrument has problem like the igh cost of supplying, installing, maintaining, and calibrating the equipment. Due to this, there is a lot of empty data, and the actual data cannot be properly measured.  Imputation of solar radiation data using MICE algorithm can be solution. This study using BLR, NRR and RFR estimator to estimating solar radiation data. AWS Staklim Banten as target and other AWS as input. The period from January 1, 2018 - February 12, 2024. The performance evaluation of the solar radiation imputation estimator is still according to WMO operational requirements for solar radiation measurements, which can be seen from the resulting MAPE value < 8%.
A Review: Flight Envelope Monitoring and Protection as One of the Flight Safety System Sarah, Annisa; Prajitno, Prawito; Hasim, Fadillah; Hakim, Teuku Mohd Ichwanul
Indonesian Journal of Aerospace Vol. 22 No. 2 (2024): Indonesian Journal Of Aerospace
Publisher : BRIN Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55981/ijoa.2024.4671

Abstract

The development of civil aircraft technology, especially commercial aircraft, is now very advanced and complex. This development is intended to assist the pilots to control aircraft in various flight conditions with the main goal to achieved is flight safety. This literature review will discuss research on the flight warning system, which will support the flight envelope protection system development study, which has been conducted over the past few years. The review includes an analysis of existing literature, case studies of aviation accidents, and the evaluation of flight envelope protection systems in current aircraft models. This review shows that the development of technologies and systems can make a significant contribution to flight safety and pilot situational awareness of flight envelope boundaries, so the possibility of aircraft accidents due to loss of control can be reduced