This study investigates the risk reduction effect and identifies the optimal capacity of Multi-barrier Accident Coping Strategy (MACST) facilities for nuclear power plants (NPPs) under seismic hazard. The efficacy of MACST facilities in OPR1000 and APR1400 NPP systems is evaluated by utilizing the Improved Direct Quantification of Fault Tree with Monte Carlo Simulation (I-DQFM) method. The analysis encompasses a parametric study of the seismic capacity of two MACST facilities: the 1.0 MW large-capacity mobile generator and the mobile low-pressure pump. The results demonstrate that the optimal seismic capacity of MACST facilities for both NPP systems is 1.5g, which markedly reduces the probability of core damage. In particular, the core damage risk is reduced by approximately 23% for the OPR1000 system, with the core damage fragility reduced by approximately 72% at 1.0g seismic intensity. For the APR1400 system, the implementation of MACST is observed to reduce the core damage risk by approximately 17% and the core damage fragility by approximately 44% under the same conditions. These results emphasize the significance of integrating MACST facilities to enhance the resilience and safety of NPPs against seismic hazard scenarios, highlighting the necessity for continuous adaptation of safety strategies to address evolving natural threats.

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.

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.

In this study, hybrid devices were developed to simultaneously remove odor and particulate matter (PM) emitted during meat grilling, and their performance was evaluated. A ceramic filter system and surfactant microbubble plasma system were used to reduce particulate matter. For odor reduction, an electro-oxidation system, an ozone-active catalytic oxidation system, and a multi-adsorption filter system were used. By combining the above particulate matter reduction and odor reduction devices, the reduction efficiency of odor and particulate matter generated during meat grilling was analyzed. As a result, most of the six combined device conditions showed a reduction efficiency of more than 90% for particulate matter. The combined odor also showed a high reduction efficiency of less than 200 times the emission concentration standard. This study also evaluated 22 types of odorous substances, of which ammonia (NH3) and hydrogen sulfide (H2S) showed removal efficiencies of more than 99%. Therefore, it is expected that the combination of these technologies can be used and applied directly to the sites where meat grilling restaurants are located to effectively contribute to the simultaneous reduction of particulate matter and odor.

This study proposes a construction plan for the Korea Navy's next-generation TSCE(Total Ship Computing Environment) based destroyers to address rapidly evolving maritime threats and decreasing military manpower. It focuses on system integrated ship construction based on TSCE for quick response time with fewer operators, improving the efficiency of systems and Equipments installed in the ship. The methodology includes analyzing TSCE-based system integration theories and levels. also analyze system integration in U.S. Navy’s Zumwalt destroyers and Littoral Combat Ships, conducting expert surveys to build consensus on system integration methods, proposing operational efficiency improvements through TSCE-based system integration. Additionally, we propose an architecture of TSCE with real time OA(Open Architecture) from both functional and physical perspectives, verified through Python simulations. The study suggests optimal crew sizes for next-generation destroyers through comparative analysis of TSCE based integration types. It emphasizes the importance of system integration in naval ship construction, presenting specific measures to enhance operational efficiency and optimize crew operations. The findings are expected to contribute significantly to enhance the future naval capabilities of the Korea Navy.

Airpower is a crucial force for suppressing military threats and achieving victory in wars. This study evaluates newly introduced fighter forces, considering factors such as fighter performance and power index, operational environment, capacity of each airbase, survivability, and force sustainment capability to determine the optimal deployment plan that maximizes operational effectiveness and efficiency. Research methods include optimization techniques such as MIP(mixed integer programming), allocation problems, and experimental design. This optimal allocation mathematical model is constructed based on various constraints such as survivability, mission criticality, and aircraft's performance data. The scope of the study focuses the fighter force and their operational radius is limited to major Air Force and joint operations, such as air interdiction, defensive counter-air operations, close air support, maritime operations and so on. This study aims to maximize the operational efficiency and effectiveness of fighter aircraft operations. The results of proposed model through experiments showed that it was for superior to the existing deployment plan in terms of operation and sustainment aspects when considering both wartime and peacetime.

구절초(Chrysanthemum zawadskii var. latilobum (Maxim.) Kitam.)는 linarin, chlorogenic acid 등의 유용성분들을 함유하고 있으며, 산업적으 로 이용가치가 있는 식물이다. 구절초의 산업화를 위해 유용성분인 linarin과 chlorogenic acid의 최적 추출 조건을 구하기 위해 반응표면분석을 수행하였으며, Linarin의 최적 추출 조건은 에탄올 농도 100%, 추출 시간 30h, 추출 온도 75℃였다. 최적 추출 조건에서 예상된 linarin의 값은 33.36 mg/g였으며 실험값은 38.33±2.66 mg/g였다. Chlorogenic acid의 최적 추출 조건은 에탄올 농도 48%, 추출 시간 30h, 추출 온도 65℃로 예상된 chlorogenic acid의 함량은 42.19 mg/g였으며 실험값은 44.34±2.09 mg/g이었다. 반응표면분석법으로 도출된 최적 추출 조건을 통해 구절초에서 linarin과 chlorogenic acid의 효율적인 추출이 가능함을 확인하였다.

Environmental pollution has led to global warming, which threatens human life. In response, hydrogen is gaining attention as a next-generation energy source that does not emit carbon. Due to its explosive nature, special care must be taken in the safe storage and transportation of hydrogen. Among various storage methods, liquefied storage, which can reduce its volume to 1/800, is considered efficient. However, since its boiling point reaches -253°C, the design of an insulation system is essential. For the design of insulation systems applied to large containers, a membrane-type design is required, which necessitates the use of cryogenic adhesives. To evaluate whether the cryogenic adhesive is properly implemented, assessments such as tensile and shear tests are necessary. This study presents a methodology for shear evaluation. Conventional methods for shear evaluation of adhesives result in slippage, preventing proper assessment. Therefore, a method involving drilling holes in the gripper and pulling from the holes must be applied. Optimal design concerning the size and location of the holes is required, and this study derives optimal values based on finite element analysis. By conducting experiments based on the results of this study, it is expected that the risk of gripper damage will be minimized, allowing for accurate evaluation of the adhesive’s performance.

Abstract Handling imbalanced datasets in binary classification, especially in employment big data, is challenging. Traditional methods like oversampling and undersampling have limitations. This paper integrates TabNet and Generative Adversarial Networks (GANs) to address class imbalance. The generator creates synthetic samples for the minority class, and the discriminator, using TabNet, ensures authenticity. Evaluations on benchmark datasets show significant improvements in accuracy, precision, recall, and F1-score for the minority class, outperforming traditional methods. This integration offers a robust solution for imbalanced datasets in employment big data, leading to fairer and more effective predictive models.

본 논문에서는 다목적 구조물인 다중연결 해양부유체를 대상으로 변형 기반 모드 차수축소법을 적용하고 차수축소모델의 구조응 답 예측 성능을 향상시키기 위해 유전 알고리즘 기반의 센서 배치 최적화를 수행하였다. 다중연결 해양부유체의 차수축소모델 생성 에 필요한 변형 기반 모드 데이터를 얻기 위해 다양한 규칙파랑하중조건에 대한 유체-구조 연성 수치해석을 수행하고 변형 기반 모드 의 직교성, 자기상관계수를 이용하여 주요 변형 기반 모드를 선정하였다. 다중연결 해양부유체의 경우 차수축소모델의 구조응답 예 측 성능이 계측 및 예측 구조응답 위치에 따라 민감하기 때문에 유전 알고리즘 기반의 최적화를 수행하여 최적의 센서 배치를 도출하 였다. 최적화 결과, 모든 센서 배치 조합에 대한 차수축소모델 생성 및 예측 성능 평가 대비 약 8배의 계산 비용을 절감하였으며, 예측 성능 평가 지표인 평균 제곱근 오차가 초기 센서 배치보다 84% 감소하였다. 또한, 다중연결 해양부유체 모형시험 결과를 이용하여 불 규칙파랑하중에 대한 최적화된 센서 배치의 차수축소모델의 구조응답 예측 성능을 평가 및 검증하였다.

This study introduces and experimentally validates a novel approach that combines Instruction fine-tuning and Low-Rank Adaptation (LoRA) fine-tuning to optimize the performance of Large Language Models (LLMs). These models have become revolutionary tools in natural language processing, showing remarkable performance across diverse application areas. However, optimizing their performance for specific domains necessitates fine-tuning of the base models (FMs), which is often limited by challenges such as data complexity and resource costs. The proposed approach aims to overcome these limitations by enhancing the performance of LLMs, particularly in the analysis precision and efficiency of national Research and Development (R&D) data. The study provides theoretical foundations and technical implementations of Instruction fine-tuning and LoRA fine-tuning. Through rigorous experimental validation, it is demonstrated that the proposed method significantly improves the precision and efficiency of data analysis, outperforming traditional fine-tuning methods. This enhancement is not only beneficial for national R&D data but also suggests potential applicability in various other data-centric domains, such as medical data analysis, financial forecasting, and educational assessments. The findings highlight the method's broad utility and significant contribution to advancing data analysis techniques in specialized knowledge domains, offering new possibilities for leveraging LLMs in complex and resource- intensive tasks. This research underscores the transformative potential of combining Instruction fine-tuning with LoRA fine-tuning to achieve superior performance in diverse applications, paving the way for more efficient and effective utilization of LLMs in both academic and industrial settings.

This paper presents a path planning optimization model for the engineering units to install obstacles in the shortest time during wartime. In a rapidly changing battlefield environment, engineering units operate various engineering obstacles to fix, bypass, and delay enemy maneuvers, and the success of the operation lies in efficiently planning the obstacle installation path in the shortest time. Existing studies have not reflected the existence of obstacle material storage that should be visited precedence before installing obstacles, and there is a problem that does not fit the reality of the operation in which the installation is continuously carried out on a regional basis. By presenting a Mixed Integrer Programming optimization model reflecting various constraints suitable for the battlefield environment, this study attempted to promote the efficient mission performance of the engineering unit during wartime.

This research presented the procedural framework of developing and optimizing an artificial intelligence model for predicting the change of bread texture by different baking enhancers. Emphasis was placed on the impact of various baking enhancers on the Mixolab thermo-mechanical properties of wheat flour and consequent alterations in bread texture. The application of baking enhancers positively contributed to dough formation and stability, producing bread with a soft texture. However, a relatively low Pearson correlation coefficient was observed between a single Mixolab parameter and bread texture (r<0.59). To more accurately predict the texture of bread from the thermo-mechanical features of wheat flour with baking enhancers, five AI models (multiple linear regression, decision tree, stochastic gradient descent, random forest, and multilayer perceptron neural network) were applied, and their prediction performance was compared. The multilayer perceptron neural network model was further utilized to enhance the prediction of bread texture by mitigating overfitting risks. Finally, the hyperparameter tuning (activation function [Leaky ReLU], regularization [0.0001], and dropout [0.1]) led to enhanced model performance (R2 = 0.8109 and RMSE = 0.1096).