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Menampilkan 1–3 dari 3 artikel
Integrated Digital Twin and Physics Informed Machine Learning Model for Real Time Performance Prediction of Industrial Mechanical Systems
Irlon Irlon
; Siti Shofiah
; Helmi Wibowo
; Erick Fernando
; Genrawan Hoendarto
; Mursalim Mursalim
International Journal of Mechanical, Industrial and Control Systems Engineering
Vol 2
, No 2
(2025)
Background: The rapid advancement of digital technologies in the Industry 4.0 era has transformed industrial mechanical systems into highly interconnected and data driven environments through the integration of sensors, the Internet of Things (IoT), data analytics, and cyber physical systems. This increasing complexity requires more adaptive and accurate monitoring and prediction methods than conventional simulation approaches, which often face limitations in capturing real time dynamic system b...
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Autonomous Mobile Robot Navigation Optimization in Dynamic Warehouse Environments Using Reinforcement Learning and Sensor Fusion Techniques
Yogiek Indra Kurniawan
; Siti Shofiah
; Rosalina Yani Widiastuti
; Teguh Arifianto
; Ribut Julianto
International Journal of Industrial Innovation and Mechanical Engineering
Vol 1
, No 2
(2024)
Background: The rapid growth of warehouse automation and autonomous mobile robots has increased the need for adaptive navigation systems capable of operating safely and efficiently in dynamic industrial environments. Classical path planning algorithms such as A* and RRT perform well in structured settings but exhibit limitations when handling moving obstacles and environmental uncertainty. Objective: This study aims to develop and evaluate a reinforcement learning based navigation framework inte...
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Explainable Artificial Intelligence Techniques for Enhancing Interpretability and Trustworthiness in Autonomous Vehicle Decision Making Systems
Ahmad Jurnaidi Wahidin
; Siti Shofiah
; Siska Narulita
; Deny Prasetyo
; Ardy Wicaksono
; Teguh Arifianto
; Muhamad Furqon
International Journal of Computer Technology and Science
Vol 1
, No 2
(2024)
Autonomous vehicles (AVs) are revolutionizing transportation by relying on advanced AI techniques like deep learning and reinforcement learning for decision-making and navigation. However, concerns about the opacity of traditional AI models in safety-critical applications such as autonomous driving raise issues related to safety, accountability, and trust. This study explores the integration of Explainable AI (XAI) techniques in AV systems to enhance transparency and interpretability while maint...
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