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.

The nuclear fuel that melted during the Fukushima nuclear accident in 2011 is still being cooled by water. In this process, contaminated water containing radioactive substances such as cesium and strontium is generated. The total amount of radioactive pollutants released by the natural environment due to the nuclear accident in Fukushima in 2011 is estimated to be 900 PBq, of which 10 to 37 PBq for cesium. Radioactive cesium (137Cs) is a potassium analog that exists in the water in the form of cations with similar daytime behavior and a small hydration radius and is recognized as a radioactive nuclide that has the greatest impact on the environment due to its long half-life (about 30 years), high solubility and diffusion coefficient, and gamma-ray emission. In this study, alginate beads were designed using Prussian blue, known as a material that selectively adsorbs cesium for removal and detection of cesium. To confirm the adsorption performance of the produced Prussian blue, immersion experiments were conducted using Cs standard solution, and MCNP simulations were performed by modeling 1L reservoir to conduct experiments using radioactive Cs in the future. An adsorption experiment was conducted with water containing standard cesium solution using alginate beads impregnated with Prussian blue. The adsorption experiment tested how much cesium of the same concentration was adsorbed over time. As a result, it was found that Prussian blue beads removed about 80% of cesium within 10-15 minutes. In addition, MCNP simulation was performed using a 1 L reservoir and a 3inch NaI detector to optimize the amount of Prussian blue. The results of comparing the efficiency according to the Prussian volume was shown. It showed that our designed system holds great promise for the cleanup and detection of radioactive cesium contaminated seawater around nuclear plants and/or after nuclear accidents. Thus, this work is expected to provide insights into the fundamental MCNP simulation based optimization of Prussian blue for cesium removal and this work based MCNP simulation will pave the way for various practical applications.

This paper deals with solution methods for discrete and multi-valued optimization problems. The objective function of the problem incorporates noise effects generated in case that fitness evaluation is accomplished by computer based experiments such as Monte Carlo simulation or discrete event simulation. Meta heuristics including Genetic Algorithm (GA) and Discrete Particle Swarm Optimization (DPSO) can be used to solve these simulation based multi-valued optimization problems. In applying these population based meta heuristics to simulation based optimization problem, samples size to estimate the expected fitness value of a solution and population (particle) size in a generation (step) should be carefully determined to obtain reliable solutions. Under realistic environment with restriction on available computation time, there exists trade-off between these values. In this paper, the effects of sample and population sizes are analyzed under well-known multi-modal and multi-dimensional test functions with randomly generated noise effects. From the experimental results, it is shown that the performance of DPSO is superior to that of GA. While appropriate determination of population sizes is more important than sample size in GA, appropriate determination of sample size is more important than particle size in DPSO. Especially in DPSO, the solution quality under increasing sample sizes with steps is inferior to constant or decreasing sample sizes with steps. Furthermore, the performance of DPSO is improved when OCBA (Optimal Computing Budget Allocation) is incorporated in selecting the best particle in each step. In applying OCBA in DPSO, smaller value of incremental sample size is preferred to obtain better solutions