This study proposes a mathematical model to optimize the fighter aircraft-weapon combinations for the Republic of Korea Air Force. With the recent emergence of the population cliff issue due to declining birth rates in Korea, there is an urgent need for efficient weapon system operations in light of decreasing military personnel. This study aims to enhance operational environments and mission efficiency within the military. The objective is to reduce the workload of pilots and maintenance personnel by operating an optimal number of weapons instead of deploying all possible armaments for each aircraft type. To achieve this, various factors for optimizing the fighter-weapon combinations were identified and quantified. A model was then constructed using goal programming, with the objective functions based on the compatibility, Circular Error Probable (CEP), and fire range of the weapons, along with the planned wartime mission-specific weapon ratios for each aircraft type. Experimental analysis of the proposed model indicated a significant increase in mission performance efficiency compared to the existing system in both operational and maintenance aspects. It is hoped that this model will be applied in military settings.

This study addresses the challenge of optimizing officer assignments by reflecting the mutual preferences of both officers and units. Current officer assignments primarily rely on subjective judgments made during assignment committee meetings, often resulting in officers being placed in undesired positions or locations. This misalignment leads to reduced morale, decreased work efficiency, and even resignation. This issue has become more important at a time when the attrition of junior officers is accelerating. To address this issue, we propose an optimal assignment model that incorporates both officer and unit preferences, aiming to balance organizational needs with personal preferences. Additionally, it discusses methods to improve the mathematical model by considering various demands for practical application in the field, such as minimizing dissatisfaction and addressing the occurrence of preferred or undesirable units. These refinements resolve practical issues such as preventing undesirable unit assignments and managing multiple optimal solutions. Through this approach, the study seeks to deliver a more balanced and satisfying assignment system for officers while enhancing organizational efficiency.

This study focuses on optimizing the uniform pressing process in precision manufacturing, addressing challenges posed by surface roughness and height differences between components. In real-world conditions, such irregularities can lead to non-uniform pressure distribution during pressing, negatively affecting product quality. To mitigate these issues, a buffer protection layer was introduced between the press and components. The optimization process was conducted through finite element analysis (FEA) to determine the ideal material properties, including elastic modulus, Poisson's ratio, and thickness of the buffer layer. Two surface roughness scenarios were examined to assess the impact of surface conditions on pressing uniformity. The results indicate that a higher elastic modulus, Poisson’s ratio, and thicker buffer layers are more effective in achieving uniform pressing, particularly under rougher surface conditions. This study provides a practical solution for improving the precision and reliability of pressing processes, ensuring better product consistency and enhancing overall manufacturing efficiency.

Multidisciplinary Design Optimization(MDO) method that considers principles in various fields affecting big scale structure and system design at the same time is used. Because most variables are connected many engineering phenomena under the classic optimized design method(all-in-one design approach), it is hard to judge the meaning of final design solution obtained, and there are cases where all variables converge before reaching the optimal design value in large-scale design problems with many variables. Collaborative Optimization (CO) method, the most advanced MDO approach, is used to efficiently solve these optimum problems, to efficiently analyze design problems involving numerous design variables and constraints and in which various engineering phenomena occur. However, the application of the MDO problem to CO introduces a number of numerical problems by destroying the numerical properties of the original optimal design problem. Therefore, this study researches one solution by listing the problems of CO after organizing various approaches of MDO.

Liquid hydrogen, a promising energy carrier, necessitates robust storage and transportation systems due to its extremely low boiling point. Consequently, the development of reliable cryogenic adhesives and standardized testing protocols is crucial. This study focused on optimizing the design of a gripper used in single lap shear tests for evaluating cryogenic adhesives, specifically targeting the challenges posed by low-temperature conditions that induce slippage at the gripper interface. The optimal design was performed using a total of five variables, including the position and size of the gripper. By employing the genetic algorithm coupled with finite element analysis, we exhaustively searched through over 1000 models to identify the optimal gripper geometry. We successfully minimized stress concentration at the gripper region while maintaining a uniform stress distribution on the non-bonded surface. Furthermore, the study explored the impact of symmetric versus asymmetric gripper configurations on test results. The findings revealed that symmetric grippers generally yielded more consistent and reliable data. This study's results enable the accurate and stable execution of lap shear tests under the temperature conditions of liquefied hydrogen.

Recently, Car weight reduction has become an important development goal to improve fuel efficiency. Car seat frame is a key part of the weight reduction. Existing steel seat frames have the advantages of high rigidity and durability, but have the disadvantage of heavy weight. Recently, Almag material, which are alloy of aluminum and magnesium, is attracting attention because of excellence in strength and weight reduction. At first, the core stiffness members of the seat frame are selected to optimize the weight of the seat frame. And then strength analysis and natural frequency analysis are performed for the existing steel seat frame and Almag seat frame. Based on these analysis results, optimal thickness of the Almag seat frame are determined by an automation program using a genetic algorithm.

테트로도톡신(tetrodotoxin, TTX)은 강력한 해양생물 유 래 신경독소로, 수산물 내 TTX를 검출하기 위해 기존에 주로 사용되는 mouse bioassay (MBA)와 LC-MS/MS 기법 은 낮은 검출한계와 동물 윤리 문제 등의 한계가 있어 이 를 대체할 새로운 시험법 개발이 필요합니다. Neuro-2a assay는 대표적인 세포기반 대체 시험법으로, 이 방법은 마우스 신경모세포인 Neuro-2a 세포주에 ouabain (O)과 veratridine (V)을 처리하여 과도한 Na+ 유입으로 인한 세 포 사멸을 유도한 후, Na+ 채널 억제제인 TTX가 Na+ 유 입을 차단해 세포를 보호하는 원리를 이용해 TTX를 정량 합니다. 본 연구에서는 Neuro-2a assay를 국내 실험실 환경에 적용하기 위해 TTX 처리 조건과 O/V 농도 등의 매 개변수를 최적화하였습니다. 그 결과, 최적 O/V 농도로 600/60 μM를 설정하였으며, S자형 용량-반응 곡선이 도출 되는 8가지 농도(50-0.195 ng/mL)를 확인하였습니다. 또한, 24번의 반복 실험을 통해 데이터의 신뢰도를 평가할 수 있는 6가지 data criteria를 확립하였으며, 이 중 EC50 값 은 약 3.824-1.268 ng/mL로 나타났습니다. 실험실 간 변동 성 비교 결과, COV+와 Bottom OD값을 제외한 모든 품 질 관리 기준(quality control criteria)과 데이터 기준(data criteria)의 변동계수(CVs)는 1.31-14.92%로 도출되어, 실험 의 적정성과 재현성이 확인되었습니다. 본 연구는 국내에 서 활용 가능한 TTX 검출용 Neuro-2a assay의 최적 조 건과 신뢰성을 평가할 수 있는 quality control criteria와 data criteria를 제시하였습니다. 아울러, TTX뿐만 아니라 유사체인 4,9-anhydroTTX에 대한 TEF 값을 0.2098로 산 출하여, TTX뿐 아니라 다양한 유사체의 검출이 가능함 을 확인하였습니다. 향후, 본 시험법은 국내 수산물 내 TTX 검출을 위한 MBA 대체법으로 활용될 것으로 기대 됩니다.

본 논문에서는 15차 bézier 곡선을 사용하여 기존의 연구보다 더 유연한 빔 형상을 설계하고, 더 넓은 설계 공간에서 최적 설계를 수 행하여 최적의 열전도도를 갖는 빔 형상을 설계한다. 설계 공간이 넓어지면 그 만큼 계산양이 증가하게 되는데, 고차원 변수 공간에서 효율적으로 작동하는 인공신경망을 사용하여 최적 설계를 가속화하여 계산 한계를 극복하였다. 더 나아가 최적의 탄성계수를 갖는 빔의 형상과 비교하였으며 열전도와 탄성학 사이의 수학적 유사성을 이용하여 빔 형상을 설명한다. 본 연구에서는 인공지능을 활용 한 형상 최적설계를 통해 기존의 한계를 뛰어넘는 격자구조의 빔 형상을 제안한다. 먼저, SC(Simple Cubic), BC(Body Centered Cubic) 격자 구조 빔 형상을 bézier 곡선으로 모델링하고 bézier 곡선의 제어점 좌표를 무작위로 설정하여 학습데이터를 확보하였다. NN(Neural Network) 및 GA(Genetic Algorithm)를 통해 우수한 유효 열전도도를 가진 빔 형상을 생성하여 최적의 빔 형상을 설계하였 다. 본 연구를 통해 추후 다양한 열 조건에서 격자구조의 적절한 구조적 해답을 제시할 수 있을 것으로 기대된다.

PURPOSES : In this study, the existence of an optimal pattern among transition methods applied during changes in traffic signal timing was investigated. We aimed to develop this pattern into an artificial intelligence reinforcement-learning model to assess its effectiveness METHODS : By developing various traffic signal transition scenarios and considering 19 different traffic signal transition situations that can be applied to these scenarios, a simulation analysis was performed to identify patterns through statistical analysis. Subsequently, a reinforcement-learning model was developed to select an optimal transition time model suitable for various traffic conditions. This model was then tested by simulating a virtual experimental center environment and conducting performance comparison evaluations on a daily basis. RESULTS : The results indicated that when the change in the traffic signal cycle length was less than 50% in the negative direction, the subtraction method was efficient. In cases where the transition was less than 15% in the positive direction, the proposed center method for traffic signal transition was found to be advantageous. By applying the proposed optimal transition model selection, we observed that the transition time decreased by approximately 70%. CONCLUSIONS : The findings of this study provide guidance for the next level of traffic signal transitions. The importance of traffic signal transition will increase in future AI-based traffic signal control methods, requiring ongoing research in this field.

PURPOSES : Driving simulations are widely used for safety assessment because they can minimize the time and cost associated with collecting driving behavior data compared to real-world road environments. Simulator-based driving behavior data do not necessarily represent the actual driving behavior data. An evaluation must be performed to determine whether driving simulations accurately reflect road safety conditions. The main objective of this study was to establish a methodology for assessing whether simulation-based driving behavior data represent real-world safety characteristics. METHODS : A 500-m spatial window size and a 100-m moving size were used to aggregate and match the driving behavior indicators and crash data. A correlation analysis was performed to identify statistically significant indicators among the various evaluation metrics correlated with crash frequency on the road. A set of driving behavior evaluation indicators highly correlated with crash frequency was used as inputs for the negative binomial and decision tree models. Negative binomial model results revealed the indicators used to estimate the number of predicted crashes. The decision-tree model results prioritized the driving behavior indicators used to classify high-risk road segments. RESULTS : The indicators derived from the negative binomial model analysis were the standard deviation of the peak-to-peak jerk and the time-varying volatility of the yaw rate. Their importance was ranked first and fifth, respectively, using the proposed decision tree model. Each indicator has a significant importance among all indicators, suggesting that certain indicators can accurately reflect actual road safety. CONCLUSIONS : The proposed indicators are expected to enhance the reliability of driving-simulator-based road safety evaluations.

다중 운집 사고는 주로 도시 내 밀집된 공간에서 발생하며, 보행자의 자유로운 이동이 제한될 때 더욱 위험하다. 이러한 상황에서 군중의 물리적 압력이 더해지면 대형 참사로 이어질 수 있어 예방과 신속한 대응이 필수적이다. 사고 발생 가능성을 최소화하기 위해 서는 실시간으로 군중 밀도를 모니터링하고, 위험 상황을 사전에 경고할 수 있는 예측 시스템 구축이 필요하다. 그러나 현재 사용되는 CCTV 기반 모니터링 시스템은 특정 구역에 국한되며, 설치 및 유지 비용이 높아 광범위한 모니터링에는 한계가 있다. 이에 본 연구 에서는 Cell Transmission Model(CTM)을 기반으로 한 양방향 보행 시뮬레이션 프레임워크를 개발하고, 이를 모바일 통신 데이터로 검증하였다. 연구 과정에서는 먼저 1)단방향 보행 CTM을 구축하고, 2)이를 양방향 보행 CTM으로 확장하여 경계 셀을 재설정하고 유 입량을 조정하는 방식으로 진행했다. 또한, 다중 운집 사고를 구현하기 위해 체류 개념을 추가했다. 검증 단계는 1)대상지 선정, 2)보행 네트워크 구축, 3)시뮬레이션 적용, 4)모바일 통신 데이터와의 비교 검증 순으로 이루어졌다. 대상지는 이태원 참사가 발생했던 이태원 역 부근으로, 20×20m 셀 단위로 보행 네트워크를 구축했다. 시뮬레이션 결과, 모바일 통신 데이터와의 높은 유사도를 보였다. 본 연구 에서 개발한 시뮬레이션은 대규모 행사나 혼잡한 보행 환경에서 군중 밀집을 예측하고, 사고 가능성을 조기에 경고하는 데 활용될 수 있다. 특히, 대형 이벤트나 도시 재난 관리에서 실시간 대응 시스템의 기초 자료로 사용할 수 있다.

공항은 다른 어떤 기반시설보다 복잡하고 사고시 매우 치명적이기 때문에 공항 계획/설계시 운영적인 측면을 고려한 면밀한 검토가 필요하다. 공항 건설이후 실제 항공기가 어떻게 운영되는지 시뮬레이션하고 문제점을 사전에 예측함으로써 항공기 운항 안전성을 확보할 수 있기 때문이다. 최근 도로/공항의 경우 디지털 트윈 기반의 시뮬레이션 프로그램으로 설계, 분석하는 사례가 많다. 이러한 기조에 맞춰 공항에서도 시뮬레이션 프로그램인 AviPLAN을 활용하여 에어사이드 배치 설계를 수행하고 있으며, 인천국제공항공사와 한국공항공사에서도 활용하고 있다. 본 연구에서는 기존 국내외 공항에 AviPLAN 프로그램을 활용하여 최적화 설계를 수행하였고 산출된 포장물량 절감사례를 바탕으로 에어사이드 시설 배치가 얼마나 중요한지 확인하고자 하였다.