In future complex and diverse battlefield situations, the existing command system faces the challenge of delayed human judgement of strategy and low objectivity. This paper proposes an artificial intelligence model that takes situation information and course of action simulation results as input and automatically assigns scores to various evaluation elements and a comprehensive score. This tool is expected to assist the commander in making decisions, reduce the time required for making judgments, and promote impartial decision-making.

This paper addresses a novel approach to performance enhancement of the naval gun fire control system(FCS) by using the projectile tracking filter without any distortion of radar measurements. Under the assumption that the maneuvering between the projectile and the ship equipped with the radar is not quite large, this method is based on the concept of polar-coordinate target tracking, which separates the range estimation filter and the direction cosine estimation filter. Note that using polar-coordinates allows tracking to be performed in the same coordinate system from which the radar line-of-sight(LOS) measurements are obtained, unlike the conventional tracking process in Cartesian. Also, it is easy to implement in real-time and guarantees consistent estimates due to its linear filter structure. With the help of the above method, therefore, the proposed filter is able to improve the overall performance of FCS which requires stability of projectile estimates within a short engagement time. The effectiveness of the presented scheme is validated through computer simulations.

The Remote Control Munition System is a anti-personnel munitions system to replace land mines that are hard to retrieve and can inflict damage on friendly forces and civilians. As operating environments and methods change, quality improvement is necessary to ensure appropriate durability. Therefore, corrosion resistance evaluation was performed according to the surface treatment of ALDC12, the main assembly material. We conduct the potentiodynamic polarization, cyclic corrosion test to perform analysis on corrosion behavior. Additionally, we try to observe the pitting on the surface through SEM analysis. In conclusion, among the three surface treatments, Anodizing surface treatment is judged to be the most suitable for corrosion durability in a field environment.

An Emergency Destruction System is inevitable for ensuring safety both at sea and in populated areas, particularly during emergency detonations triggered by abnormal missile flight or upon mission completion. This paper introduces a novel method for developing an Emergency Destruction System capable of precisely calculating the Instantaneous Impact Point(IIP) during high-speed, maneuverable long-range surface-to-air missile flight tests. The Emergency Destruction System designed for long-range surface-to-air missile flight tests generates impact position tables that meticulously incorporate wind errors and navigation equations based on the Earth's ellipsoidal model. Factors such as the Coriolis effect and the direction of the gravitational acceleration vector are accounted for, significantly enhancing the accuracy of IIP determination amidst highly variable missile speed and attitude.

Reinforcement learning, which are also studied in the field of defense, face the problem of sample efficiency, which requires a large amount of data to train. Transfer learning has been introduced to address this problem, but its effectiveness is sometimes marginal because the model does not effectively leverage prior knowledge. In this study, we propose a stochastic initial state randomization(SISR) method to enable robust knowledge transfer that promote generalized and sufficient knowledge transfer. We developed a simulation environment involving a cooperative robot transportation task. Experimental results show that successful tasks are achieved when SISR is applied, while tasks fail when SISR is not applied. We also analyzed how the amount of state information collected by the agents changes with the application of SISR.

In this paper, polar converting logic of bank-to-turn with skidding is provided for small size UAV. In general, original polar converting logic has problems of singularity, safety and flight data analysis when acceleration commands diverge at the end of terminal guidance. To solve those problems, new methodology of polar converting logic of bank-to-turn with skidding is presented. It can easily solve singularity problem, increase safety and provide intuition of data analysis. Simulation and flight results are presented to verify proposed polar converting logic for bank-to-turn with skidding method.

In this study, we propose the terrain following algorithm using model predictive control and nonlinear disturbance observer-based backstepping sliding mode controller for an aircraft system. Terrain following is important for military missions because it helps the aircraft avoid detection by the enemy radar. The model predictive control is used to replace the generating trajectory and guidance with the flight path angle constraint. In addition, the aircraft is affected to the parameter uncertainty and unknown disturbance such as wind near the mountainous terrain. Therefore, we suggest the nonlinear disturbance-based backstepping sliding mode control method for the aircraft that has highly nonlinearity to enhance flight path angle tracking performance. Through the numerical simulation, the proposed method showed the better tracking performance than the traditional backstepping method. Furthermore, the proposed method presented the terrain following maneuver maintaining the desired altitude.

Microbial forensics is a scientific discipline for analyzing evidence related to biological crimes by identifying the origin of microorganisms. Multiple locus variable number tandem repeat analysis(MLVA) is one of the microbiological analysis methods used to specify subtypes within a species based on the number of tandem repeat in the genome, and advances in next generation sequencing(NGS) technology have enabled in silico anlysis of full-length whole genome sequences. In this paper, we analyzed unknown samples provided by Robert Koch Institute(RKI) through The United Nations Secretary-General's Mechanism(UNSGM)'s external quality assessment exercise(EQAE) project, which we officially participated in 2023. We confirmed that the 3 unknown samples were B. anthracis through nucleic acid isolation and genetic sequence analysis studies. MLVA results on 32 loci of B. anthracis were analysed by using genome sequences obtained from NGS(NextSeq and MinION) and Sanger sequencing. The MLVA typing using short-reads based NGS platform(NextSeq) showed a high probability of causing assembly error when a size of the tandem repeats was grater than 200 bp, while long-reads based NGS platform(MinION) showed higher accuracy than NextSeq, although insertion and deletion was observed. We also showed hybrid assembly can correct most indel error caused by MinION. Based on the MLVA results, genetic identification was performed compared to the 2,975 published MLVA databases of B. anthracis, and MLVA results of 10 strains were identical with 3 unkonwn samples. As a result of whole genome alignment of the 10 strains and 3 unknown samples, all samples were identified as B. anthracis strain A4564 which is associated with injectional anthrax isolates in heroin users.

Rapid, early, accurate detection and identification of the various pathogenic agents associated with the development of biological weapons is critical in preventing loss of life and limiting the impact of these organisms when used against civilian or military targets. The aim of this study was to produce a system for the simple, rapid, accurate and simultaneous detection and identification of Ricin, Botulinum toxin B and Staphylococcal enterotoxin B as a proof of principle for developing field appropriate reverse transcription loop-mediated isothermal amplification systems for the accurate identification of potential biological threats. These systems were designed to facilitate the identification of potential threats even in remote or resource-limited locations.

In continuous research of detecting highly toxic chemical warfare agents to ensure preparedness for the future battlefield, flexible and wearable sensor platforms with high sensitivity are still demanding. Herein we demonstrate a facile fabrication of polypyrrole-based chemiresistors on cellulose paper for the detection of nerve gas simulants. In order to optimize electrical properties of sensor platform, conducting polymer made of polypyrrole were first synthesized on flexible cellulose paper and interdigitated electrodes were formed thereon. Following confirmation of polypyrrole and/or oxime moiety through FT-IR analyses, electrical characteristics were measured in the various ratio of monomers between simple pyrrole and oxime-modified one. Typically for the optimized chemiresistor(2:8 molar ratio of simple pyrrole and oxime-modified one), eleven species of chemical warfare agents were examined and enhanced conductivity(10⁴~10⁵ order) was observed for three simulants(diethyl cyanophosphonate, diisopropyl fluorophosphonate and diethyl chlorophosphonate), which was mainly attributed to intermolecular hydrogen bonding, while no significant responses was recorded against sixteen common volatile organic chemicals.