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Journal : EPI International Journal of Engineering

Improved Performance of Silicon Rubber Insulation with Coal Fly Ash Micro Filler Tajuddin Waris; Yoshinobu Murakami; Naohiro Hozumi; Tomohiro Kawashima; Salama Manjang; Ikhlas Kitta
EPI International Journal of Engineering Vol 1 No 2 (2018): Volume 1 Number 2, August 2018 with Special Issue on Railway Engineering
Publisher : Center of Techonolgy (COT), Engineering Faculty, Hasanuddin University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25042/epi-ije.082018.13

Abstract

This paper presents the effect of coal fly Ash filler (CFA) on the electrical properties of silicon rubber composite. The observed electrical properties are volume resistance, surface resistance, and the surface breakdown voltage. The temperature The effect on the surface breakdown voltage of silicon rubber with CFA filler also was observed. The type of silicon used is RTV 683. The filler concentrations of the matrix are 0,​​10.20,30,40 per-hundred gram of resin (phr). The test results show that the surface resistance increased significantly with the increasing of CFA loading filler. The increasing of volume resistance is nonlinear; the volume resistance tends to decrease when the CFA loading filler exceeds 30 phr. Flashover test results show that flashover inception voltage (FOIV) increases with the increasing of CFA filler concentration and the increasing of temperature.
Processing of Drone’s Digital Image for Determining Border of Rice Fields with Edge Detection Method Suhardiman Diman; Zahir Zainuddin; Salama Manjang
EPI International Journal of Engineering Vol 2 No 2 (2019): Volume 2 Number 2, August 2019 with Special Issue on Natural Disaster and Mitigat
Publisher : Center of Techonolgy (COT), Engineering Faculty, Hasanuddin University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25042/epi-ije.082019.08

Abstract

Edge detection was the basic thing used in most image processing applications to get information from the image frame as a beginning for extracting the features of the segmentation object that will be detected. Nowadays, many edge detection methods create doubts in choosing the right edge detection method and according to image conditions. Based on the problems, a study was conducted to compare the performance of edge detection using methods of canny, Sobel and laplacian by using object of rice field. The program was created by using the Python programming language on OpenCV. The result of the study on one image test that the Canny method produces thin and smooth edges and did not omit the important information on the image while it has required a lot of computing time. Classification is generally started from the data acquisition process; pre-processing and post-processing. Canny edge detection can detect actual edges with minimum error rates and produce optimal image edges. The threshold value obtained from the Canny method was the best and optimal threshold value for each method. The result of a test by comparing the three methods showed that the Canny edge detection method gives better results in determining the rice field boundary, which was 90% compared to Sobel 87% and laplacian 89%.