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.

In the manufacturing industry, dispatching systems play a crucial role in enhancing production efficiency and optimizing production volume. However, in dynamic production environments, conventional static dispatching methods struggle to adapt to various environmental conditions and constraints, leading to problems such as reduced production volume, delays, and resource wastage. Therefore, there is a need for dynamic dispatching methods that can quickly adapt to changes in the environment. In this study, we aim to develop an agent-based model that considers dynamic situations through interaction between agents. Additionally, we intend to utilize the Q-learning algorithm, which possesses the characteristics of temporal difference (TD) learning, to automatically update and adapt to dynamic situations. This means that Q-learning can effectively consider dynamic environments by sensitively responding to changes in the state space and selecting optimal dispatching rules accordingly. The state space includes information such as inventory and work-in-process levels, order fulfilment status, and machine status, which are used to select the optimal dispatching rules. Furthermore, we aim to minimize total tardiness and the number of setup changes using reinforcement learning. Finally, we will develop a dynamic dispatching system using Q-learning and compare its performance with conventional static dispatching methods.

생물환경조절학회지 31권 4호에 게재된 논문의 사사가 잘못 기재되어 있어 바로 잡습니다.

과학과 기술의 발달로 복합재료, 합금, 고강도 탄소섬유, 고분자 재료 등 지능형 소재가 개발되고 있다. 다양한 엔지 니어링 분야에서 이러한 첨단 재료의 응용을 연구하기 위해 전 세계적으로 광범위한 연구가 진행되고 있다. 초탄성 형상기억합 금(SSMA)은 깃발 모양의 히스테리시스 거동을 가지며 추가적인 열처리 없이 응력 완화로 인한 잔류 변형이 거의 없는 신뢰성 이 높은 내진 재료이다. 그러나 공학 문제에서 SSMA 효율성을 연구하기 위한 수치 모델의 개발은 여전히 어려운 작업이다. 본 연구에서는 SSMA 인장시험의 실험결과를 통해 유한요소해석 프로그램인 Abaqus와 수치해석 프로그램인 OpenSEES를 이용하여 재료 모델을 구현한 후 해석결과의 거동 특성 및 에너지 소산을 분석하였다.

Ball stud parts are manufactured by a cold forging process, and fastening with other parts is secured through a head part cutting process. In order to improve process quality, stabilization of the forging quality of the head is given priority. To this end, in this study, a predictive model was developed for the purpose of improving forging quality. The prediction accuracy of the model based on 450 data sets acquired from the manufacturing site was low. As a result of gradually multiplying the data set based on FE simulation, it was expected that it would be possible to develop a predictive model with an accuracy of about 95%. It is essential to build automated labeling of forging load and dimensional data at manufacturing sites, and to apply a refinement algorithm for filtering data sets. Finally, in order to optimize the ball stud manufacturing process, it is necessary to develop a quality prediction model linked to the forging and cutting processes.

The lane departure warning device can not detect the lane to be driven in the future by sensing the departure of the lane passing by during driving and warning the driver. Considering the safe operation of the truck, it is also expected that the departure of the future lanes according to the dynamic weight and speed of the current truck should be predicted. This study attempted to predict whether or not to deviate from the lanes of curved roads to be driven in the future according to the current dynamic driving weight and speed in consideration of the safe driving of trucks.

국내외로 첨단 ICT 융합기술이 농업 분야에 적용되기 시작 하면서, 시설원예 설비들이 고도화되고, 스마트팜 구축 기술 및 인력이 축적되기 시작하였다. 그러나 우리나라 농촌의 경 우, 농업생산 연령의 고령화, 국내 농촌 인구의 지속적인 유출, 저출산 등으로 인하여 스마트팜 확대 및 적용에 어려움이 많 은 실정이다. 따라서 공간 및 시간에 구속을 받지 않는 간편한 농업인 교육 프로그램이 필요하며, 최근 부상하고 있는 시뮬 레이션 기술을 활용한다면 농업 교육용 시뮬레이션 툴 개발도 가능할 것으로 판단된다. 온실 환경 제어 모델을 이용한 시뮬 레이션은 다양한 지역과 기상 조건 하에서 대상 온실의 열과 물질에너지의 상호작용을 합리적으로 예측할 수 있게 해준다. 본 연구에서는 온실 환경 제어 모델을 활용하여 외부 기상 데 이터를 통해 온실의 환경 변화를 예측하고 가상의 환경 제어시스템을 통해 환경 제어 시 필요한 에너지값들을 시뮬레이션 할 수 있었다. 이러한 결과를 통해 이용자가 직접 맞춤형 환경 제어를 할 수 있도록 편의성을 고려한 사용자 인터페이스를 구축할 것이며, 실제 파프리카 재배 온실의 제어 요소들을 반 영할 수 있도록 설계될 것이다. 농업용 교육 시뮬레이션 툴을 최근 활발하게 연구가 이루어지고 있는 작물 생육 모델링 기 술 및 전산유체역학 기술과 융합하면 더욱 타당한 결과를 보 일 것이다.

Considering the Fukushima nuclear accident and the marine discharge plan of contaminated (or treated) water, it is necessary a seafood monitoring system for radioactive material screening. Currently, radioactivity tests in seafood are conducting in Korea. Although current method using a HPGe detector can provide very low uncertainty in determining radioactivity, there is a limitation in that rapid inspection cannot be performed because of a time-consuming pretreatment process as well as long measurement time (typically 10,000 s). To overcome this limitation, we are developing an insitu inspection device, a kind of screening system, which can monitor the radioactivity in seafood in a near real-time basis. In this study, the actual seafood with a check source was measured to verify the reliability of the Monte Carlo simulation model. The detector used in the experiment was a 5-cm-thick polyvinyl toluene (PVT) plastic scintillator with a 0.5-cm-thick lead shield for background reduction. A Cs-137 check source was placed within seafood. The seafood used in the experiment was fishcake, raw oyster, and dried laver, which is the representative seafood of fish, shellfish, and seaweed. These three seafood products of the same size and shape as the manufacturing process were used to predict the performance realistically. We compared the energy spectrum of the Cs-137 check source obtained from measurements and simulations. The region of interest (ROI) around the Compton edge was set to reduce the influence of the background and electronic noise. The results showed that the measured and simulated spectrum were in good agreement.

The crisis of climate change aroused international needs to reduce the greenhouse gas emission in energy sector. Government of South Korea formulated an agenda of carbon neutrality through announcing 2050 Net-Zero Carbon Scenario A and B in October 2021. As the power supply from renewable energy increases, it becomes a core element to take into account the daily intermittency of renewable energy in analyzing the upcoming energy plans. However, the existing yearly Load Duration Curve is insufficient for applying day and night power change in daily scale into energy mix analysis, since it derives the energy mix for whole year on the basis of classifying annual base load and peak load. Therefore, a new energy mix simulation model based on the daily power load and supply simulation is needed for the future energy analysis. In this study we developed a new model which simulates the average power supply and demand daily (over a 24 hour period) for each season. The model calculates the excess and shortage power during day and night by integrating each energy’s daily power pattern. The 2050 Net-Zero Carbon Scenario A was used for the model verification, during which the same amounts of power production from each energy source were applied: nuclear, renewable, carbon-free gas turbine, fuel cell and byproduct gas. Total power demand pattern and renewable energy production pattern were drawn from the data of 2017 power production, and Pumped-storage Hydroelectricity and Energy Storage System were used as day-to-night conversion. Detailed assumptions for each energy were based on the Basis of Calculation for Net-Zero Carbon Scenario from Government. The model was verified with three cases which were divided depending on the method of hydrogen production and whether the Curtailment and Conversion Loss (CCL) of renewable energy were considered or not. Case 1 assumed production of hydrogen occurred for 24 hours while not considering CCL, had 0% relative error in comparison of total annual power production, and case 2, considering CCL, had a 1.741% relative error. Case 3 assumed production of hydrogen occurred only during daytime with excess power and CCL consideration, yielded 0.493% relative error in total amount of hydrogen production, confirming that the model sufficiently describes the Government’s Scenario A with the input of total power production. This model is expected to be used for analyzing further energy mix with different ratios of each energy source, with special focus on nuclear and renewable energy sources.

Purpose: This study aimed to construct and test a hypothetical model to explain predictive factors affecting nursing students' satisfaction and self-confidence in simulation-based education based on the National League for Nursing Jeffries Simulation Theory. Methods: A cross-sectional study was conducted on 305 fourth-year nursing students with experience in simulation-based education enrolled at universities in Gangwon-do, Gyeongsangbuk-do, and Gyeonggi-do. Data were analyzed using SPSS 25.0 and AMOS 25.0. Results: The hypothetical model showed good fit with the empirical data: χ2/df 2.17, RMSEA=.01, RMR=.01, GFI=.95, AGFI=.91, NFI=.94, TLI=.95, CFI=.97, and PNFI=.68. Simulation design characteristics, teaching efficiency, and flow were found to affect satisfaction and self-confidence directly. A bootstrap test indicated that teaching efficiency and flow mediated the relationship between simulation design characteristics and satisfaction and self-confidence. Conclusion: Simulation educators should apply best practice that enhance teaching efficiency and flow through well-organized simulation designs, nursing students can attain satisfaction and self-confidence through simulation-based education.

Gases such as hydrogen can generate from the disposal canister in high-level radioactive waste disposal systems owing to the corrosion of cooper container in anoxic conditions. The gas can be accumulated in the voids of bentonite buffer around the disposal canister if gas generation rates become larger than the gas diffusion rate of bentonite buffer with the low-permeability. Continuous gas accumulations result in the increase in gas pressure, causing sudden dilation flow of gases with the gas pressure exceeding the gas breakthrough pressure. Given that the gas dilation flow can cause radionuclide leakage out of the engineered barrier system, it is necessary to consider possible damages affected by the radionuclide leakage and to properly understand the complicated behaviors of gas flow in the bentonite buffer with low permeability. In this study, the coupled hydro-mechanical model combined with the damage model that considers two-phase fluid flow and changes in hydraulic properties affected by mechanical deformations is applied to numerical simulations of 1-D gas injection test on saturated bentonite samples (refer to DECOVALEX-2019 Task A Stage 1A). To simulate the mechanical behavior of microcracks which occur due to the dilation flow caused by increase in gas pressure, a concept of elastic damage constitutive law is considered in the coupled hydro-mechanical model. When the TOUGH-FLAC coupling-based model proposed in this study is applied, changes in hydraulic properties affected by mechanical deformations combined with the mechanical damage are appropriately considered, and changes in gas injection pressure, pore pressures at radial filters and outlet, and stress recorded during the gas injection test are accurately simulated.

Recently, traffic accidents have continued to occur due to the failure to secure a safe distance for trucks. Unlike passenger cars, freight cars have a large fluctuation in the weight of the vehicle's shaft depending on the load, and the fatality of accidents and the possibility of accidents are high. In this study, a braking distance prediction model according to the driving speed and loading weight of a three-axis truck was implemented to prevent a forward collision accident. Learning data was generated based on simulation, and a prediction model based on machine learning was implemented to finally verify accuracy. The extra trees algorithm was selected based on the most frequently used R2 Score among regression analyses, and the accuracy of the braking distance prediction model was 98.065% through 10 random scenarios.