Seung Hwan Park

Satellites are required to operate reliably under extreme space environments characterized by severe temperature variations and ultra-high vacuum conditions. Thermal vacuum tests (TVTs) are essential ground-based verification processes conducted prior to launch, during which large volumes of time-series data are collected over several weeks. As satellite systems become more complex, effectively utilizing these high-volume experimental data for real-time thermal state assessment remains a significant challenge.This study proposes a data-driven real-time satellite temperature prediction framework using a Long Short-Term Memory (LSTM) network trained on thermal vacuum test data. To improve prediction accuracy and physical consistency, a physics-guided data preprocessing strategy is introduced based on thermal engineering principles. By considering conductive and radiative heat transfer mechanisms, irrelevant variables are screened out, and physically meaningful temperature sensor data are selected to constrain the learning process.The proposed methodology is validated using actual thermal vacuum test data from a medium-sized satellite tested at the Korea Aerospace Research Institute (KARI). Prediction performance is evaluated through residual analysis by comparing measured temperatures with LSTM predictions. The results demonstrate that prediction accuracy improves as the training data volume increases: with more than 800,000 seconds of training data, prediction errors are reduced to within ±3°C, and further decrease to below ±1°C with extended training.The proposed approach enables real-time temperature estimation, early anomaly detection, and data recovery during satellite testing, demonstrating the practical applicability of machine learning to satellite hardware verification.

Measuring technology is used in various ways in the logistics industry for defect inspection and loading optimization. Recently, in the context of the fourth industrial revolution, research has focused on measurement automation combining AI, IoT technologies, and measuring equipment. The 3D scanner used for field logistics measurements offers high performance and can handle large volumes quickly; however, its high unit price limits adoption across all lines. Entry-level sensors are challenging to use due to measurement reliability issues: their performance varies with changes in object location, shape, and logistics environment. To bridge this gap, this study proposes a systematic framework for geometry measurement that enables reliable length and width estimation using only a single entry-level distance sensor. We design and build a conveyor-belt-based data acquisition setup that emulates realistic logistics transfer scenarios and systematically varies transfer conditions to capture representative measurement disturbances. Based on the collected data, we perform robust feature extraction tailored to noisy, condition-dependent signals and train an artificial neural network to map sensor observations to geometric dimensions. We then verified the model’s performance in measuring object length and width using test data. The experimental results show that the proposed method provides reliable measurement results even under varying transfer conditions.

In this study, we propose a real-time contrasts control chart based on reinforcement learning (RL-RTC). Effective process monitoring, which directly influences productivity and yield, has become increasingly important. The traditional RTC control chart offers a promising approach to process monitoring by transforming the monitoring problem into a real-time classification task, enabling more timely and accurate detection of issues. However, the parameters of the traditional RTC control chart are typically set based on empirical methods, which limits the ability to fine-tune them effectively. To address this limitation and improve the performance of the RTC method, we propose the RL-RTC control chart, which leverages reinforcement learning for more adaptive parameter control. The proposed method takes the data itself as a state of reinforcement learning (RL) and adaptively decides the parameter of the RTC control chart in real-time. In this paper, the control of the moving window size is defined as an action taken by the RL agent. By automatically learning an optimal policy, the RL-RTC method eliminates the need for manual parameter tuning and enhances adaptability in dynamic process environments. The RL-RTC approach can detect shifts more quickly while maintaining the ability to identify the causes of faults. Compared to a conventional RTC control chart, experimental results demonstrate that the RL-RTC method offers improved performance by fine-tuning the window size in response to changing process conditions. Therefore, a wide scope of applications is expected for adaptively controlling the other RTC control chart parameters.

3D 계측은 물체의 3차원 공간에서의 기하학적 구조를 측정하는 기법으로 정밀도, 이동성, 안전성에 대한 현장 환경의 요구에 따라 적절한 측정이 필요하다. 이러한 상황에서 가장 일반적으로 사용되는 것이 3D 스캐너이다. 반면에 본 연구에서는 트럭이나 창고와 같은 특정 공간에서 최적화된 적재 공정을 수행하는 자동화된 물류 흐름 라인을 위한 레이저 기반 거리 계측 시스템을 제안한다. 본 연구에서 제안한 시스템의 효과를 평가하기 위해 컨베이어 벨트에 설치된 센서 시스템을 활용하여 다양한 실험을 진행하였고, 실제 물류 상자를 이송시켜 다양한 종류의 데이터를 획득하였다. 높은 성능과 객관성을 충족하기 위해 여러 유형의 물류 상자를 토대로 실험을 진행하였고 예측 오차가 1cm 미만임을 확인하였다. 본 연구에서 제안된 시스템은 실용성과 비용 관점에서 기존 3D 스캐너의 단점을 개선할 수 있다는 가능성을 보여주었다.