Claim Missing Document
Check
Articles

Found 2 Documents
Search
Journal : JAIS (Journal of Applied Intelligent System)

Adaptive Learning Model for Social Robots Using Visual and Proximity Sensors in Dynamic Educational Environments Tamamy, Aries Jehan; Pambudi, Arga Dwi; Arifin, Zaenal; Harsono, Budi
(JAIS) Journal of Applied Intelligent System Vol. 10 No. 1 (2025): April 2025
Publisher : LPPM Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/jais.v10i1.12997

Abstract

Social robots are increasingly being integrated into educational environments to support learning and engagement. However, most existing systems lack the adaptability required to respond appropriately to dynamic human behavior in real-time classroom settings. This paper presents an adaptive learning framework for social robots that utilizes visual and proximity sensor data to perceive human spatial context and adjust interaction strategies accordingly. A Deep Q-Network (DQN)-based reinforcement learning algorithm is employed to map environmental states to socially appropriate actions such as maintaining distance, initiating interaction, or retreating. The robot was trained in a simulated classroom environment consisting of dynamic student agents with randomized behaviors. Experimental results show that the robot achieved a cumulative reward improvement of over 500%, reduced its average distance error from 0.45 m to 0.18 m, and increased its interaction success rate from 50% to 88% over 100 training episodes. These results confirm the effectiveness of the proposed model in enabling real-time behavioral adaptation. The framework contributes to the development of context-aware, socially intelligent robotic systems capable of enhancing Human-Robot Interaction (HRI) in educational applications. Future work includes extending the model to incorporate emotional cues and real-world validation with physical robot platforms. Keywords - social robots, adaptive learning, reinforcement learning, human-robot interaction, sensor fusion, educational robotics
Temperature Monitoring of Lithium Battery Using Kalman Filter: A Simulation-Based Study Arifin, Zaenal; Islahudin, Nur; Tamamy, Aries Jehan; Heryanto, M Ary
(JAIS) Journal of Applied Intelligent System Vol. 10 No. 1 (2025): April 2025
Publisher : LPPM Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/jais.v10i1.13469

Abstract

Battery temperature plays a vital role in determining the performance, safety, and lifespan of lithium-ion batteries in electric vehicle (EV) applications. This study presents a simulation-based approach for monitoring surface temperature using Kalman filter estimation, which integrates air temperature, current load, and battery characteristics. A mathematical model of thermal dynamics is developed and used for real-time temperature prediction. The results demonstrate that the Kalman filter is effective in estimating the surface temperature accurately, even with uncertain measurements. This work also discusses the integration of an actuator (fan/cooler) and PID control to maintain the temperature around the ideal level of 25°C, showcasing the potential of this system for smart thermal battery management in cost-constrained embedded systems.   Keywords - Temperature Monitoring; Kalman Filter; Thermal Modeling; Estimation Algorithm; State Estimation; Simulation;