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All Journal Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Sinergi InComTech: Jurnal Telekomunikasi dan Komputer Indonesian Journal of Electrical Engineering and Computer Science Journal of Integrated and Advanced Engineering (JIAE)
Mirna Yunita
Beijing Institute of Technology
Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Mechanical Engineering

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Modification of Control Oil Feeding with PLC Using Simulation Visual Basic and Neural Network Analysis Yuliza Yuliza; Rachmat Muwardi; Danang Widya Pratama; Makmur Heri Santoso; Mirna Yunita
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 8 No. 1 (2022): March
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v8i1.22336

Abstract

The oil feeding system is an oil distribution system used in engine lubrication by flowing it directly to the engine parts to be lubricated through pipes. In addition, it is also a raw material for the production process by collecting the oil first in the storage tank, then weighing it on the oil scale before use in the production process. The current control is still using the conventional model. The operating system is still manual, and the absence of identity and damage information makes it difficult for the engineer to troubleshoot. The research method is to modify the oil feeding system control using PLC (Programmable Logic Controller) and Visual Basic to display process information. This process uses the Neural Network (NN) method. The simulation results show that the PLC program and visual basic software can be connected properly. The speed of the data transfer test connection that can be obtained is 32 ms. The prediction process of the oil feeding system using the backpropagation algorithm Neural Network and the activation function, which uses the binary sigmoid function (logsig) with the 17-10-1 architecture having very good performance getting the MSE value below the error value of 0.001 maximum epoch 961 and hidden layer 10 with an MSE value of 0.00099915.
Optimize Image Processing Algorithm on ARM Cortex-A72 and A53 Rachmat Muwardi; Mirna Yunita; Harun Usman Ghifarsyam; Hendy Juliyanto
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 8 No. 3 (2022): September
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v8i3.24457

Abstract

This work presents a technique to optimize processing image algorithms. The increasing demand for video applications like context-aware computing on mobile embedded systems requires the use of computationally intensive image processing algorithms. The system engineer has the mandate to optimize them so as to meet real-time deadlines. A methodology to take advantage of the asymmetric dual-core processor, which includes an ARM supported by shared memory, is presented with implementation details. The target platform chosen is the NanoPi M4V2. It has a dual-core and quad-core architecture with an ARM Cortex-A72 and Cortex-A53. The basic image correlation algorithm is chosen for benchmarking as it finds widespread application for various template-matching tasks such as face recognition. The basic algorithm prototypes conform to OpenCV, a popular computer vision library. OpenCV algorithms can be easily ported to the ARM core, which runs a popular operating system such as Linux. The algorithms are tested on a variety of images, and performance results are presented, measuring the speedup obtained due to dual-core and quad-core implementation. A major advantage of this approach is that it allows the ARM processor to perform important real-time tasks.
Co-Authors Andika Arrizki Danang Widya Pratama Diah Septiyana Firman Andika Galang P. N. Hakim Galang Persada Nurani Hakim Galatia Erica Yehezkiel Harun Usman Ghifarsyam Harun Usman Ghifarsyam Hendy Juliyanto Heri Santoso, Makmur Hongmin Gao Hongmin Gao Inggis Kurnia Trisiawan Joe Mada Ranseda Permana Julpri Andika Kadek Dwi Suyasmini Khoirul Aziz Lenni Lenni Mhd Adanan Purba Mustain Rhozaly Rachmat Muwardi Septiyana, Diah Yuliza Yuliza Yuliza Yuliza Zendi Iklima