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Given the increasing complexity of intelligent transportation systems, the joint optimization of warning mechanisms and vehicle control has emerged as a critical challenge. This paper proposes a hierarchical optimization framework that integrates warning range optimization at the upper level with energy–time balanced vehicle control at the lower level. In the upper level, the warning range, which defines the spatial scope of risk perception, is determined based on road conditions and traffic density, while in the lower level, Second-Order Cone Programming (SOCP) is employed to simultaneously minimize energy consumption and travel time within the selected range. To overcome the limitations of static weight-based approaches, an adaptive weight tuning mechanism is introduced, combining grid search with gradient-norm feedback. The proposed framework demonstrates consistent convergence across diverse simulation settings, and its validity is confirmed through analyses of cost function reduction and weight sensitivity. These findings indicate that the framework can go beyond conventional path planning or single-objective optimization, offering a robust control strategy that ensures stability, efficiency, and adaptability in real-world driving environments.

In safety-focused industries such as defense and automotive, MISRA C is widely adopted to ensure reliable software. However, static analysis tools differ in rule coverage and detection performance, and comparative studies remain limited. In this paper, we evaluate a commercial software tool A and an open-source software CPPCHECK using the MISRA C:2012 example suites. We analyze the static analysis results produced by these tools across three categories (Test Case, Rule Category, and Rule Section) using four metrics (accuracy, precision, recall, and F1-score). Experimental results show that Tool A performs better than CPPCHECK in two categories (Test Case and Rule Category). However, in Rule Sections 9, 15, and 16, CPPCHECK achieves better performance, indicating strengths in specific areas.

Fog significantly impacts not only civilian sectors such as logistics and transportation but also the scale and strategy of military operations. However, its complex mechanisms and strong regional variability make accurate prediction challenging. This study proposes a localized fog prediction model that combines a Physics-Informed Neural Network (PINN) with regional meteorological data. Using weather observations from Taebaek, Gangwon Province (2015–2024), the model performs data-driven learning and incorporates three key physical relationships—Clausius-Clapeyron equation, relative humidity, and dew point—into the loss function to reduce error. Results show 84% accuracy and a 59% fog detection rate, surpassing the military’s empirical model and machine learning-based models such as XGB(49%) and LGB(50%). This study demonstrates that embedding physical principles into machine learning enhances the reliability of fog forecasting and supports the development of localized prediction models.