Crack Catcher AI Collaboration

CAPTIVATE members, specifically members in the Renewable Energy and Artificial Intelligence Research Group (REALM), lead by Dr. Endang Djuana has collaborated with team from Oregon Institute of Technology (Oregon Tech) under Prof. Arief Budiman as Principal Investigator of the Crack Catcher AI Project. 

The main contributions of REALM Research Group within CAPTIVATE are within the area of modelling, simulation and development of machine learning prediction models to support research in materials, energy and biomedical applications. Built upon several experience in artificial intelligence and machine learning research in the related fields of energy, agriculture, biomedical and signal processing applications.

The Team has won a place in the National Semifinal round of Department of Energy (DOE)’s American-Made Solar Innovation competition in 2022 (Round 6). The detail of the project can be seen in this Crack Catcher AI Youtube VideoWe have published a Q1 Journal Paper as a result of the first stage of this research project in Solar Energy Materials and Solar Cells Journal and the link to pre-print text can be found here.

Reference:

Budiman AS, Putri DN, Candra H, Djuana E, Sari TK, Aji DP, Putri LR, Sitepu E, Speaks D, Pasang T. Crack Catcher AI–Enabling smart fracture mechanics approaches for damage control of thin silicon cells or wafers. Solar Energy Materials and Solar Cells. 2024 Aug 15;273:112927.

Collaboration Track Records

Dr. Endang Djuana has an extensive collaboration track record with Prof. Arief Budiman started from Smart Dome 4.0 Project which funded by National Grant in Indonesia under Kedaireka Grant Scheme which has built 2 Smart Dome pilots in Tanjung Sari, Sumedang West Java and in Pupuan, Tabanan, Bali – Indonesia and published several papers in International Conferences and Journal. The collaboration is under leadership of Professor Fergyanto Gunawan of Binus University (Binus ASO / Industrial Engineering Postgraduate Department), and consists of 4 Co-PIs – Professor Arief Budiman Industrial Engineering Postgraduate Department, Professor Bens Pardamean (Binus BDSRC / AI Center), Dr. Endang Djuana (Electrical / Computer Systems Engineering Department) Universitas Trisakti and Mr. Sugiarto Romeli from PT Impack Pratama Industry Tbk. Built upon that project Dr. Djuana has several other collaborative projects utilizing Artificial Intelligence / Machine Learning as follows: Agricultural Photovoltaic Dome (Agro PV Dome), Crack Catcher AI, Battery Charging Optimization, and the latest Biomedical and Nanomaterials Consortium jointly lead by Professor Arief Budiman, Professor Bens Pardamean, Professor Derrick Speaks, Professor Rachel Speaks, and Dr. Endang Djuana.

References:

1. Budiman AS, Putri DN, Candra H, Djuana E, Sari TK, Aji DP, Putri LR, Sitepu E, Speaks D, Pasang T. Crack Catcher AI–Enabling smart fracture mechanics approaches for damage control of thin silicon cells or wafers. Solar Energy Materials and Solar Cells. 2024 Aug 15;273:112927.
2. Budiman AS, Gunawan F, Djuana E, Pardamean B, Romeli S, Putri DN, Aji DP, Rahardjo K, Wibowo MI, Daffa N, Owen R. Smart dome 4.0: Low-cost, independent, automated energy system for agricultural purposes enabled by machine learning. InJournal of Physics: Conference Series 2022 Apr 1 (Vol. 2224, No. 1, p. 012118). IOP Publishing.
3. Gunawan FE, Budiman AS, Pardamean B, Djuana E, Romeli S, Cenggoro TW, Purwandari K, Hidayat AA, Redi AA, Asrol M. Multivariate time-series deep learning for joint prediction of temperature and relative humidity in a closed space. Procedia Computer Science. 2023 Jan 1;227:1046-53.
4. Cenggoro TW, Elwirehardja GN, Dominic N, Setiawan KE, Rahutomo R, Djuana E, Gunawan FE, Budiman AS, Romeli S, Pardamean B. Deep Learning with Greedy Layer-Wise Compound Scaling for Temperature and Humidity Prediction in Solar Dryer Dome. Available at SSRN 4123081. 2022.
5. Widjaja RG, Asrol M, Agustono I, Djuana E, Harito C, Elwirehardja GN, Pardamean B, Gunawan FE, Pasang T, Speaks D, Hossain E., Budiman AS, State of charge estimation of lead acid battery using neural network for advanced renewable energy systems. Emerging Science Journal. 2023 May 3;7(3):691-703.
6. Agustono I, Asrol M, Budiman AS, Djuana E, Gunawan FE. State of Charge Prediction of Lead Acid Battery using Transformer Neural Network for Solar Smart Dome 4.0. International Journal of Emerging Technology and Advanced Engineering. 2022. 12(10): 1-10