Journal of Advances in Military Studies

Rethinking dosimeter assignment for military radiation safety

Hosub Chi, Jungmi Park, Gyoungjun Choi, Kang Ku — Fri, 16 May 2025 00:00:00 +0000

The increasing use of portable X-ray generators in military operations—particularly for explosive detection and counter-terrorism—has highlighted the need for a radiation safety strategy tailored to field-based, low-dose environments. This study evaluates the performance of thermoluminescent dosimeters (TLDs) and electronic personal dosimeters (EPDs) under controlled pulsed X-ray conditions using the XRS-3 generator, focusing on their sensitivity, dose-response characteristics, and operational suitability in military contexts. The results showed that while TLDs exhibited high accuracy in cumulative dose tracking, they failed to detect radiation beyond 3 meters or at low pulse counts. In contrast, EPDs—although they showed some inconsistencies in cumulative dose measurements under low-dose conditions—successfully detected radiation at all distances and pulse settings. Their ability to provide immediate feedback and detect fluctuations in real time makes them more suitable for dynamic radiation monitoring in military field environments. The study provides a solid foundation for revising current military dosimetry protocols in current military radiation safety management practices.

Comparative analysis on few-shot models performance for improving object detection in the military Domain

Junsub Kim, Dongnyeok Choi — Mon, 12 May 2025 00:00:00 +0000

The application of Object Detection (OD) techniques in the military and defense domain is often restricted by stringent security requirements and limited data availability. To overcome these challenges, the present study investigates the potential of Few-Shot Object Detection (FSOD) for military applications. A military vehicle image dataset, composed of real-world defense imagery, was constructed for this purpose. Four representative object detection models—YOLO, DETR, GLIP, and CD-ViTO—were fine-tuned under 1-shot, 5-shot, and 10-shot conditions. The model performance was evaluated using mean Average Precision(mAP). Notably, the CD-ViTO model's cross-domain generalization capability was further examined by comparing its performance on this military dataset against public benchmarks previously used in FSOD studies. Experimental results demonstrate that CD-ViTO achieved superior mAP scores, highlighting the viability of FSOD for efficient and accurate object detection in military and defense applications.

Optimization based machine learning algorithms for software reliability growth models

Myeongguen Shin, Juwon Jung, Jihyun Lee, Insoo Ryu, Sanggun Park — Tue, 13 May 2025 00:00:00 +0000

Software reliability is a critical factor for system performance and safety, especially in defense industries, where operational failures can have severe consequences. To evaluate and improve software reliability, Software Reliability Growth Models (SRGMs) are widely used. However, many previous studies have relied on single optimization methods or deep learning approaches, which are prone to local optima and extrapolation issues, reducing prediction accuracy. To fill this gap, current study employs a broader range of optimization algorithms based on the Least Squares Method (LSM) and Maximum Likelihood Estimation (MLE) to approximate global optima. NASA’s Jet Propulsion Laboratory (JPL) software defect datasets were used, and several widely recognized SRGM models, including Goel-Okumoto, Delayed S-Shape, Inflection S-Shape, Weibull, and Log-Logistic, were evaluated. Experimental results show that the choice of optimization method significantly affects prediction performance, as measured by Mean Squared Error (MSE). For example, in the J2 dataset, the Weibull model exhibited MSE values ranging from 70.778 to 15,767.68—a 222-fold difference—demonstrating the critical role of optimization in prediction accuracy. The findings confirm the risks of relying solely on single-method approaches and highlight the value of diverse optimization strategies for achieving near-global optima. The study presents a practical framework for improving software reliability assessments, contributing to the development of highly reliable software for the defense industry.