💼 I'm a Test Engineer.
👓 Passionate with machine learning in robotics.
💬 Contact me here:
| Laptop | PC |
|---|---|
| M1 Pro 10-Core CPU | AMD Ryzen 7 1700 OC 3.8 Ghz |
| M1 Pro 16-Core GPU | NVIDIA GTX 1070 |
| 32 GB RAM | 32 GB RAM |
Resume
- Fachhochschule Südwestfalen
Master of Science (M.Sc.), System Engineering and Engineering Management
2016 - 2017 - Swiss German University
Bachelor of Engineering (B.Eng.), Mechatronic
2011 - 2015
- Delta Energy Systems (Germany) GmbH
Test Engineer
Oct 2023 - Now - Fachhochschule Südwestfalen
Research Assistant
Jan 2020 - Sep 2023 - Fachhochschule Bielefeld
Research Assistant
Nov 2018 - Dec 2019 - Fachhochschule Südwestfalen
Laboratory Assistant
Nov 2017 - Aug 2018
Projects
- MLPro - A Synoptic Framework for Standardized Machine Learning Tasks in Python
Mar 2021 - now
The project description can be found here
- Implementation of Path Planning for Robot Welding Station
July 2022 - Sep 2023
The methodologies are published here
- Chess Engine with Robot Manipulator
January 2023 - now
- Remote Condition Monitoring for Pump
Oct 2020 - Sep 2021
The project description can be found here
- Cloud based Fault Detection on Automation System
Mar 2018 - May 2018
The publication of this project can be found here
- Robot Operating System (ROS) based Mobile Robot
Apr 2017 - Aug 2018
The Robot Operating System (ROS) was installed on Raspberry-Pi. LIDAR Laser scanner was used to scan the environment around the robot. Embedded motor controllers were used to control the motor and it communicate with the Raspberry-Pi through RS232
Publications
- Integration of ABB Robot Manipulators and Robot Operating System for Industrial Automation
M. R. Diprasetya, S. Yuwono, M. Löppenberg and A. Schwung, “Integration of ABB Robot Manipulators and Robot Operating System for Industrial Automation,” INDIN, Accepted 2023
See publication - MLPro-MPPS—A high-performance simulation framework for customizable production systems
S. Yuwono, M. Löppenberg, D. Arend, M. R. Diprasetya and A. Schwung, “MLPro-MPPS—A high-performance simulation framework for customizable production systems,” Software Impacts, 2023, doi: 10.1016/j.simpa.2023.100509.
See publication - MLPro 1.0 - Standardized reinforcement learning and game theory in Python
D. Arend, S. Yuwono, M. R. Diprasetya and A. Schwung, “MLPro 1.0 - Standardized reinforcement learning and game theory in Python,” Machine Learning with Applications, 2022, doi: 10.1016/j.mlwa.2022.100341.
See publication - MLPro–An integrative middleware framework for standardized machine learning tasks in Python
D. Arend, M. R. Diprasetya, S. Yuwono and A. Schwung, “MLPro–An integrative middleware framework for standardized machine learning tasks in Python,” Software Impacts, 2022, doi: 10.1016/j.simpa.2022.100421.
See publication - Homogeneous Transformation Matrix Based Neural Network for Model Based Reinforcement Learning on Robot Manipulator
M. R. Diprasetya and A. Schwung, "Homogeneous Transformation Matrix Based Neural Network for Model Based Reinforcement Learning on Robot Manipulator," 2022 IEEE International Conference on Industrial Technology (ICIT), Shanghai, China, 2022, pp. 1-6, doi: 10.1109/ICIT48603.2022.10002834.
See publication - Monitoring and Forecasting of Air Emissions with IoT Measuring Stations and a SaaS Cloud Application
F. A. N., M. T. Ibrahim, R. M. Diprasetya, O. O. Flores and A. Schwung, "Monitoring and Forecasting of Air Emissions with IoT Measuring Stations and a SaaS Cloud Application," 2020 2nd International Conference on Societal Automation (SA), 2021, pp. 1-6, doi: 10.1109/SA51175.2021.9507127.
See publication - Fault Detection Assessment using an extended FMEA and a Rule-based Expert System
F. Arévalo, C. Tito, M. R. Diprasetya and A. Schwung, "Fault Detection Assessment using an extended FMEA and a Rule-based Expert System," 2019 IEEE 17th International Conference on Industrial Informatics (INDIN), 2019, pp. 740-745, doi: 10.1109/INDIN41052.2019.8972299.
See publication - A Cloud-based Architecture for Condition Monitoring based on Machine Learning
F. Arévalo, M. R. Diprasetya and A. Schwung, "A Cloud-based Architecture for Condition Monitoring based on Machine Learning," 2018 IEEE 16th International Conference on Industrial Informatics (INDIN), 2018, pp. 163-168, doi: 10.1109/INDIN.2018.8471970.
See publication