Military rotorcraft must adhere to internal noise standards to minimize hearing loss during operation and enable effective voice communication in noisy environments. This study predicted the internal noise levels and suggested noise reduction of a coaxial rotorcraft under high-speed maneuvering conditions to ensure compliance with these standards. To achieve this, Statistical Energy Analysis(SEA) models for helicopters were validated, and a new SEA model specific to the high-speed coaxial rotorcraft was suggested. Also, frequency spectrum of internal noise sources such as coaxial rotor, engines, and main gear box was scaled for the coaxial rotorcraft in high-speed maneuvering. As the sources were scaled for a high-speed coaxial rotorcraft, the internal noise levels at cockpit and cabin was predicted to exceed the design limit. This study proposed sound-proof panel modeling and position to satisfy the design limit.

This study presents an integrated machine learning framework for separating narrowband components and extracting Lloyd's mirror interference patterns from ship-radiated noise(SRN) spectrograms. The proposed methodology employs Independent Vector Analysis to separate narrowband spectral components from multi-hydrophone acoustic signals, subsequently applying DBSCAN and RANSAC algorithms for robust identification of parabolic Lloyd's mirror patterns in residual spectrograms. Experimental validation utilizing SRN data acquired during the SAVEX-15 sea trials demonstrates effective narrowband component separation, as verified through DEMON and LOFAR analyses, alongside accurate pattern extraction capabilities. The unsupervised framework exhibits enhanced reliability under adverse noise conditions and enables precise closest point of approach(CPA) estimation. The developed methodology offers an automated and robust solution for SRN analysis, significantly improving acoustic signal interpretation and target identification capabilities in maritime defense applications.

Despite their continued operation in legacy aircraft, CRT(Cathode-Ray Tube)-based Azimuth Indicators exhibit significant limitations, prompting the need for digital modernization. The upgraded display unit maintains mechanical compatibility with existing 3-ATI(Aircraft Type Indicator) interfaces while achieving reduced size and weight. Functionally, it extends beyond basic RWR(Radar Warning Receiver) symbology to integrate MAWS(Missile Approach Warning System) and DIRCM(Directional Infrared Counter Measures) alerts as well as BIT(Built In Test) information, and it supports NVIS(Night Vision Imaging System) operation for night missions. The prototype was subjected to functional and environmental validation, confirming compliance with operational requirements. The results demonstrate a practical modernization approach that enhances maintainability, situational awareness, and survivability of legacy aircraft.

The Two-Sting Rig(TSR) is a wind tunnel test device designed to simulate the six degrees of freedom(6-DOF) motion of a store separating from its carrier platform. In such confined and high-risk environments, real-time collision detection is essential to prevent equipment damage and ensure operational safety. This paper proposes a real-time collision detection method that models the TSR and surrounding wind tunnel structures, such as aircraft models and support stings, using simplified geometric primitives. The method is specifically designed for enclosed test environments where external sensing is impractical due to structural constraints, and instead relies solely on internal motor feedback. The proposed algorithm is integrated into the TSR control software and validated across multiple operational scenarios. Experimental results confirm that the method reliably detects proximity risks in real-time, with execution times averaging under 0.95 ms, ensuring seamless integration into the 20 ms control loop. These findings demonstrate the algorithm's effectiveness and practical suitability for real-time integration in wind tunnel operations.

Experimental investigations were conducted to measure airblast parameters from TNT surface bursts at scaled distances of 0.94 to 6.79 m/kg^1/3. Pressure-time histories of incident airblast waves were recorded using PCB sensors and processed to determine peak overpressure, impulse, and positive phase duration. These parameters were compared with Kingery-Bulmash(K-B) blast curves. Experimental peak overpressures were approximately 1.17 times higher than K-B predictions, while impulses showed excellent agreement(0.96 times K-B values). The elevated peak overpressures are likely due to improved precision in modern pressure sensors and digital data acquisition systems. These findings suggest that K-B blast parameters, limited by the technology of their time, require updating with data from contemporary instrumentation to enhance the accuracy of blast analysis and protective design applications.

This research focuses on optimizing the airfoil shape tailored for cardboard drones, a significant asset in the Russia-Ukraine war due to their cost-effectiveness and operational impact. Despite their advantages in storage and transport, these drones require manual assembly, with considerable time spent on connecting wing ribs and spars. To enhance both ease of assembly and aerodynamic performance, we formulated an airfoil shape optimization problem incorporating manufacturability constraints specific to cardboard drone construction. We utilized NeuralFoil, a physics-informed machine learning approach, to overcome the limitations of traditional tools. By integrating NeuralFoil with a gradient-based optimization method, the proposed approach resulted in a new airfoil that not only improves aerodynamic efficiency over existing shapes but also simplifies the assembly process.

Electronic warfare equipment operates in various environments such as fixed-wing, rotary-wing aircraft, ships, and ground systems, and controls different types of sensors and countermeasures based on the concept of equipment operation. As the concept of weapon system operation becomes more advanced, electronic warfare equipment must meet a wide range of requirements, and the software installed on it must be developed on time while ensuring reliability and quality. Software product line engineering is a methodology for efficiently developing and maintaining various software products used in specific domains by systematically managing commonality and variability, which helps reduce development costs and time while improving quality. In this paper, we apply software product line engineering to the electronic warfare software family and model the variability of electronic warfare software.

The Network Enabled Weapon(NEW) possesses weapon data link communication capabilities, enabling precision strikes through various operational processes, such as re-targeting and in-flight target update(IFTU), which breaks away from the conventional fire-and-forget method. In Link-K, messages and protocols are designed to support directives and responses, and transmit NEW status and profile. NEW profile transmission, one of the NEW operational concepts, involves transmitting various data or files, including target imagery, during NEW operations. NEW profile transmission concept, not found in existing tactical data links such as Link-16, is newly introduced in Link-K. To achieve this, we designed messages and protocols for transmitting NEW profiles, as well as messages and protocols for transmitting real-time data in stream form to address its limitations. Furthermore, to intensively transmit this data during NEW operations in time division multiple access(TDMA)-based tactical data link network and enhance network resource efficiency, we introduce the concept of shared time slot and propose a network design plan that incorporates these features. Then, we present the results of measuring the transmission time when transmitting the NEW profile with three sample files. The experimental results are expected to serve as a criterion for judging the target imagery transmission capability in NEW in the future.

Traditional single task-multi robot-instantaneous assignment(ST-MR-IA) methods often suffer from rigidity under resource constraints and unbalanced resource usage. To address these issues, this paper proposes a genetic algorithm-based approach for coalition formation and task allocation in heterogeneous multi-robot systems(MRS) operating in unstructured environments. Leveraging GA flexibility, the proposed method identifies near-optimal solutions even with insufficient resources. By incorporating spatial cohesion, it groups proximal robots to minimize path deviation, ensuring consistent travel distances and balanced resource consumption. Gazebo simulations across three mission scenarios validate that this approach significantly improves mission feasibility and efficiency compared to linear programming-based algorithms.

This study proposes an optimal deployment model for long-range air-control radars using a Benders decomposition approach. The model integrates GIS-based terrain visibility analysis and incorporates probabilistic scenarios reflecting radar destruction and failure rates to represent realistic operational uncertainty. A two-stage stochastic optimization framework was developed to minimize total installation cost and detection deficiency across the Korean Air Defense Identification Zone(KADIZ). Computational experiments show that the deterministic model achieved better performance in detection coverage and cost minimization. However, the stochastic model demonstrated its practical value by reflecting uncertainty in radar availability and providing more robust deployment decisions under potential disruption scenarios. The proposed framework contributes to improving the resilience and adaptability of national air surveillance planning under uncertain environments.