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All Journal KAPAL Jurnal Ilmu Pengetahuan dan Teknologi Kelautan IPTEK The Journal for Technology and Science Sinergi IJOCE (International Journal of Offshore and Coastal Engineering) International Journal of Marine Engineering Innovation and Research JMES: The International Journal of Mechanical Engineering and Sciences Warta Penelitian Perhubungan
Dedi Budi Purwanto
Teknik Perkapalan Fakultas Teknologi Kelautan Institut Teknologi Sepuluh Nopember
Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mechanical Engineering Physics Transportation

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Automated Corrosion Detection on Steel Structures Using Convolutional Neural Network Mohammad Khoirul Effendi; Bara Atmaja; Arif Wahjudi; Dedi Budi Purwanto
JMES: The International Journal of Mechanical Engineering and Sciences Vol 7 No 1 (2023)
Publisher : LPPM, Institut Teknologi Sepuluh Nopember, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j25807471.v7i1.15881

Abstract

Steel is a material that is widely used in industry and construction. The tensile and compressive force of steel is relatively high compared to other materials. On the opposite, low corrosion resistance is the main weakness of steel, which can encourage steel deterioration and fatal accidents for the user. Furthermore, regular visual inspection by a human should be performed to prevent catastrophic incidents. However, human visual inspection increases the risk of work accidents and reduces work effectiveness. Therefore, a drone with a camera is one solution to increase efficiency, increase security levels, and minimize difficulties or risks during corrosion inspection. In this research, the drone has been used to capture corroded video of a construction structure. The convolutional neural network (CNN) method is then used to detect the location of the corroded images. This study has been conducted on Surabaya’s Petekan-bridge with the Mobilenet V1 SSD pre-training model. In this study, the distance between a drone and the detected object varied between 1 and 2 m. Next, the drone speed was varied into 0.6 m/s, 0.9m/s, and 1.3m/s. As a result, CNN can detect corrosion on the surface of steel materials with the best accuracy is 84.66% and minimum total loss value of 1.673 by applying 200 images, 200000 epochs, batch size at 4, learning rate at 0.001 and 0.1, the distance at 1 m, drone speed at 0.6 m/s.
A Modest Numerical Simulation for Automatic Ship Collision Avoidance System to Support Green and Autonomous Shipping Concept Yuda Apri Hermawan; Fernanda Wahyu Pratama; Totok Yulianto; Dedi Budi Purwanto
International Journal of Offshore and Coastal Engineering Vol. 7 No. 1 (2023)
Publisher : Department of Ocean Engineering

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j25800914.v7i1.15530

Abstract

The development of autonomous ship is being pursued massively and significantly, either industrially, regulatory, or academic. The autonomous ship is considered to be able to significantly reduce challenges caused by unexpected errors of manual navigation (manned ship). One of the challenges for autonomous ship development is constructing an automatic ship collision avoidance system as a basic system for identifying and avoiding an obstacle object around the ship. The collision avoidance system must consider not only the position of the ship and the obstacle but also the maneuvering characteristic (ship dynamics) and control system of the ship, thereby making its numerical system more complex. This research presents a modest numerical simulation for designing an automatic ship collision avoidance system. However, the numerical model still considers the main necessary elements of the system. The numerical model includes a set of automatic guidance system, collision avoidance system, ship dynamics, and control system. Head on head, overtaking, and crossing collision scenarios are performed to investigate the numerical model. The simulation results show that the modest numerical simulation can be used to perform an automatic ship collision avoidance system in which the ship can automatically avoid a target ship considered as the ship’s obstacle in those three collision scenarios.
Co-Authors Agus Surono Agustiani, Elly Andi Trimulyono Ardi Nugroho Yulianto Aries Sulisetyono Arif Wahjudi Arif Wahjudi Bara Atmaja Bara Atmaja Betty Ariani Danawati Hari Prajitno Eko Sasmito Hadi Fernanda Wahyu Pratama Hafizh, Muhammad Nur Abdullah Hermawan, Yuda Apri IKAP Utama Mohammad Khoirul Effendi Muhammad Iqbal Muhammad Iqbal Muhammad Luqman Hakim, Muhammad Luqman Rahmi, Lista Putri Adinda Samuel Samuel Samuel Samuel Setyawan, Dony Sulaiman, Suardi bin Teguh Putranto Totok Yulianto Wardani, Putri Kusuma Warlinda Eka Triastuti Wicaksono, Ardhana Widjaja, Raden Sjarief Yuda Apri Hermawan Zeinurrohman, Rangga