• 한국제4기학회지
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• 제17권2호
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• pp.161-161
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• 2003

During the last century, most scientific questions related to climate change were focused on the evidence of anthropogenic global warming (IPCC, 2001). There are robust evidences of warming and also human-induced climate change. We now understand the global, mean change a little bit better; however, the uncertainties for regional climate change still remains large. The purpose of this study is to understand the past climate change over Korea based on the observational data and to project future regional climate change over East Asia using ECHAM4/HOPE model and MM5 for downscaling. There are significant evidences on regional climate change in Korea, from several variables. The mean annual temperature over Korea has increased about 1.5∼$1.7^{\circ}C$ during the 20th century, including urbanization effect in large cities which can account for 20-30% of warming in the second half of the 20th century. Cold extreme temperature events occurred less frequently especially in the late 20th century, while hot extreme temperature events were more common than earlier in the century. The seasonal and annual precipitation was analyzed to examine long-term trend on precipitation intensity and extreme events. The number of rainy days shows a significant negative trend, which is more evident in summer and fall. Annual precipitation amount tends to increase slightly during the same period. This suggests an increase of precipitation intensity in this area. These changes may influence on growing seasons, floods and droughts, diseases and insects, marketing of seasonal products, energy consumption, and socio-economic sectors. The Korean Peninsular is located at the eastern coast of the largest continent on the earth withmeso-scale mountainous complex topography and itspopulation density is very high. And most people want to hear what will happen in their back yards. It is necessary to produce climate change scenario to fit forhigh-resolution (in meteorological sense, but low-resolution in socio-economic sense) impact assessment. We produced one hundred-year, high-resolution (∼27 km), regional climate change scenario with MM5 and recognized some obstacles to be used in application. The boundary conditions were provided from the 240-year simulation using the ECHAM4/HOPE-G model with SRES A2 scenario. Both observation and simulation data will compose past and future regional climate change scenario over Korea.

• 대한토목학회논문집
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• 제28권3D호
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• pp.423-429
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• 2008

본 연구의 목적은 기후변화와 토지이용에 의한 미래 토양침식을 추정하는 것이다. 기후모형인 CCCma (Canadian Centre for Climate Modelling and Analysis)에 의해 예측된 강우자료 중 2030년에서 2050년까지의 자료를 이용하여 토양침식 모의를 수행한 후 관측값과 비교하였다. 즉, 현재의 토양침식 관측값과 예측된 미래의 조건에 따른 토양침식 결과에 대한 상대비교를 통해 기후변화가 토양침식에 미치는 영향을 분석하였다. 사회-경제 변화에 의해 예상되는 토지이용 변화와 기온 및 의 증가에 따른 식물성장에 대하여 포괄적으로 고려하였다. A2 시나리오와 B2 시나리오에 의해 예측된 2030년에서 2050년 기간의 모의된 강우평균을 1966년에서 1998년 사이의 관측 강우평균과 비교한 결과 각각 17.7%와 24.5% 증가하는 것으로 나타났다. B2 시나리오에 의한 토양침식량이 A2 시나리오에 의한 값보다 크게 예측되는 것을 확인할 수 있었으며, 총 6개 시나리오(일부 농촌 지역의 도시화 2개 시나리오, 전 농촌 지역의 도시화 2개 시나리오, 식물성장을 가정한 시나리오 2개) 중 일부 농촌 지역이 순차적 도시화가 이루어지는 시나리오를 제외한 나머지의 경우 토양침식이 48%에서 90%까지 증가하는 것을 알 수 있었다. 온도에 의한 식물성장속도의 가속, 높은 증발산을, 그리고 거름효과가 미치는 영향 등을 가정한 시나리오가 토양침식결과는 이를 가정하지 않은 시나리오보다 약 25% 정도 작게 예측되는 것을 확인할 수 있었다. 연구결과 본 대상유역의 미래에는 강우량과 토양침식량이 증가할 것으로 사료되므로, 이에 대한 관심을 가져야 할 것이다.

• Journal of Information Processing Systems
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• 제18권3호
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• pp.443-456
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• 2022

An efficient and reasonable resource allocation strategy can greatly improve the service quality of Internet of Vehicles (IoV). However, most of the current allocation methods have overestimation problem, and it is difficult to provide high-performance IoV network services. To solve this problem, this paper proposes a network resource allocation strategy based on deep learning network model DDQN. Firstly, the method implements the refined modeling of IoV model, including communication model, user layer computing model, edge layer offloading model, mobile model, etc., similar to the actual complex IoV application scenario. Then, the DDQN network model is used to calculate and solve the mathematical model of resource allocation. By decoupling the selection of target Q value action and the calculation of target Q value, the phenomenon of overestimation is avoided. It can provide higher-quality network services and ensure superior computing and processing performance in actual complex scenarios. Finally, simulation results show that the proposed method can maintain the network delay within 65 ms and show excellent network performance in high concurrency and complex scenes with task data volume of 500 kbits.

• 한국환경보건학회지
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• 제41권6호
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• pp.425-437
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• 2015

Objectives: Risk assessment is a tool for predicting and reducing uncertainty related to the effects of future activities. Probability approaches are the main elements in risk assessment, but confusion about the interpretation and use of assessment factors often undermines the message of the analyses. The aim of this study is to provide a guideline for systematic reduction plans regarding uncertainty in risk assessment. Methods: Articles and reports were collected online using the key words "uncertainty analysis" on risk assessment. Uncertainty analysis was conducted based on reports focusing on procedures for analysis methods by the World Health Organization (WHO) and U.S. Environmental Protection Agency (USEPA). In addition, case studies were performed in order to verify suggested methods qualitatively and quantitatively with exposure data, including measured data on toluene and styrene in residential spaces and multi-use facilities. Results: Based on an analysis of the data on uncertainty, three major factors including scenario, model, and parameters were identified as the main sources of uncertainty, and tiered approaches were determined. In the case study, the risk of toluene and styrene was evaluated and the most influential factors were also determined. Five reduction plans were presented: providing standard guidelines, using reliable exposure factors, possessing quality controls for analysis and scientific expertise, and introducing a peer review system. Conclusion: In this study, we established a method for reducing uncertainty by taking into account the major factors. Also, we showed a method for uncertainty analysis with tiered approaches. However, uncertainties are difficult to define because they are generated by many factors. Therefore, further studies are needed for the development of technical guidelines based on the representative scenario, model, and parameters developed in this study.

• 제어로봇시스템학회논문지
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• 제7권8호
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• pp.689-696
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• 2001

Large-scale Multi-disciplinary Systems(LMS) such as transportation, aerospace, defense etc. are complex systems in which there are many subsystems, interfaces, functions and demanding performance requirements. Because many contractors participate in the development, it is necessary to apply methods of sharing common objectives and communicating design status effectively among all of the stakeholders. The processes and methods of systems engineering which includes system requirement analysis; functional analysis; architecting; system analysis; interface control; and system specification development provide a success-oriented disciplined approach to the project. This paper shows not only the methodology and the results of model-based systems engineering to Automated Guided Transit(AGT) system as one of LMS systems, but also propose the extension of the model-based tool to help manage a project by linking WBS (Work Breakdown Structure), work organization, and PBS (Product Breakdown Structure). In performing the model-based functional analysis, the focus was on the operation concept of an example rail system at the top-level and the propulsion/braking function, a key function of the modern automated rail system. The model-based behavior analysis approach that applies a discrete-event simulation method facilitates the system functional definition and the test and verification activities. The first application of computer-aided tool, RDD-100, in the railway industry demonstrates the capability to model product design knowledge and decisions concerning key issues such as the rationale for architecting the top-level system. The model-based product design knowledge will be essential in integrating the follow-on life-cycle phase activities. production through operation and support, over the life of the AGT system. Additionally, when a new generation train system is required, the reuse of the model-based database can increase the system design productivity and effectiveness significantly.

• 정보처리학회논문지A
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• 제9A권4호
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• pp.519-524
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• 2002

본 논문에서는 분산 시뮬레이션 기법 중에 하나인 MRIP(Multiple Replications In Parallel) 시나리오에서 각종 순차적인 시뮬레이션 분석 방법들의 성능을 측정할 수 있는 포함범위(Coverage)에 대한 신뢰구간(confidence intervals) 및 속도향상(Speedup)에 대해 살펴보았다. F-분포를 기반으로 한 신뢰구간에 대한 추정기(estimator)를 단일 프로세서와 다중 프로세서 상에서 참조모델(reference model)로 $M/M/1/{\infty},\;M/D/I/{\infty}과\;M/H_{2}/1/{\infty}$큐잉 시스템을 활용하여 정상상태(steady-state)에서의 평균치를 추정하는 시뮬레이션에 적용하였다. 순차적인 포함범위 분석을 위해서는 수많은 시뮬레이션 실행(Run)들이 요구되는데, MRIP 분산 시뮬레이션 시나리오에서 다중 프로세서를 이용하여 시뮬레이션을 수행하여 최종 시뮬레이션 결과를 얻는데 걸리는 신간을 감소시켰다. 또한, LNA으로 연결된 분산 컴퓨팅 시스템에 시뮬레이션을 동시에 수행시킴으로써 쉽게 필요한 수의 시뮬레이션 실행결과(Run)를 수집할 수 있다. 이는 샘플의 수가 증가됨으로써 좀더 신뢰도가 높은 최종 신뢰구간을 시뮬레이션 수행자가 얻을 수 있게 해준다.

• Nuclear Engineering and Technology
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• 제55권9호
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• pp.3409-3416
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• 2023

Licensing the next-generation of nuclear reactor designs requires extensive use of Modeling and Simulation (M&S) to investigate system response to many operational conditions, identify possible accidental scenarios and predict their evolution to undesirable consequences that are to be prevented or mitigated via the deployment of adequate safety barriers. Deep Learning (DL) and Artificial Intelligence (AI) can support M&S computationally by providing surrogates of the complex multi-physics high-fidelity models used for design. However, DL and AI are, generally, low-fidelity 'black-box' models that do not assure any structure based on physical laws and constraints, and may, thus, lack interpretability and accuracy of the results. This poses limitations on their credibility and doubts about their adoption for the safety assessment and licensing of novel reactor designs. In this regard, Physics Informed Neural Networks (PINNs) are receiving growing attention for their ability to integrate fundamental physics laws and domain knowledge in the neural networks, thus assuring credible generalization capabilities and credible predictions. This paper presents the use of PINNs as surrogate models for accidental scenarios simulation in Nuclear Power Plants (NPPs). A case study of a Loss of Heat Sink (LOHS) accidental scenario in a Nuclear Battery (NB), a unique class of transportable, plug-and-play microreactors, is considered. A PINN is developed and compared with a Deep Neural Network (DNN). The results show the advantages of PINNs in providing accurate solutions, avoiding overfitting, underfitting and intrinsically ensuring physics-consistent results.

• Journal of Power Electronics
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• 제13권5호
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• pp.884-895
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• 2013

In this paper, the Fourier-based PLL (Phase-locked Loop) is introduced with a new structure, capable of selective harmonic detection in single and three-phase systems. The application of the FB-PLL to harmonic detection is discussed and a new model applicable to three-phase systems is introduced. An analysis of the convergence of the FB-PLL based on a linear model is presented. Simulation and experimental results are included for performance analysis and to support the theoretical development. The decomposition of an input signal in its harmonic components using the Fourier theory is based on previous knowledge of the signal fundamental frequency, which cannot be easily implemented with input signals with varying frequencies or subjected to phase-angle jumps. In this scenario, the main contribution of this paper is the association of a phase-locked loop system, with a harmonic decomposition and reconstruction method, based on the well-established Fourier theory, to allow for the tracking of the fundamental component and desired harmonics from distorted input signals with a varying frequency, amplitude and phase-angle. The application of the proposed technique in three-phase systems is supported by results obtained under unbalanced and voltage sag conditions.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제16권5호
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• pp.1708-1734
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• 2022

In the development of logistics system, the breakthrough of important technologies such as technology platform for logistics information management and control is the key content of the study. Based on Javascript and JQuery, the logistics system realizes real-time monitoring, collection of historical status data, statistical analysis and display, intelligent recommendation and other functions. In order to strengthen the cooperation of warehouse storage, enhance the utilization rate of resources, and achieve the purpose of real-time and visual supervision of transportation equipment and cargo tracking, this paper studies the VNF dynamic deployment and SFC routing problem in the network load change scenario based on the logistics system. The BIP model is used to model the VNF dynamic deployment and routing problem. The optimization objective is to minimize the total cost overhead generated by each SFCR. Furthermore, the application of the SFC mapping algorithm in the routing topology solving problem is proposed. Based on the concept of relative cost and the idea of topology transformation, the SFC-map algorithm can efficiently complete the dynamic deployment of VNF and the routing calculation of SFC by using multi-layer graph. In the simulation platform based on the logistics system, the proposed algorithm is compared with VNF-DRA algorithm and Provision Traffic algorithm in the network receiving rate, throughput, path end-to-end delay, deployment number, running time and utilization rate. According to the test results, it is verified that the test results of the optimization algorithm in this paper are obviously improved compared with the comparison method, and it has higher practical application and promotion value.

• 전기학회논문지
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• 제67권1호
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• pp.8-15
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• 2018

New methodology for probabilistic reliability based grid expansion planning of HVDC in power system including Wind Turbine Generators(WTG) is developed in this paper. This problem is focused on scenario based optimal selection technique to decide best connection bus of new transmission lines of HVDC in view point of adequacy reliability in power system including WTG. This requires two kinds of modeling and simulation for reliability evaluation. One is how is reliability evaluation model and simulation of WTG. Another is to develop a failure model of HVDC. First, reliability evaluation of power system including WTG needs multi-state simulation methodology because of intermittent characteristics of wind speed and nonlinear generation curve of WTG. Reliability methodology of power system including WTG has already been developed with considering multi-state simulation over the years in the world. The multi-state model already developed by authors is used for WTG reliability simulation in this study. Second, the power system including HVDC includes AC/DC converter and DC/AC inverter substation. The substation is composed of a lot of thyristor devices, in which devices have possibility of failure occurrence in potential. Failure model of AC/DC converter and DC/AC inverter substation in order to simulate HVDC reliability is newly proposed in this paper. Furthermore, this problem should be formulated in hierarchical level II(HLII) reliability evaluation because of best bus choice problem for connecting new HVDC and transmission lines consideration. HLII reliability simulation technique is not simple but difficult and complex. CmRel program, which is adequacy reliability evaluation program developed by authors, is extended and developed for this study. Using proposed method, new HVDC connected bus point is able to be decided at best reliability level successfully. Methodology proposed in this paper is applied to small sized model power system.