• KSCE Journal of Civil and Environmental Engineering Research
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• v.28 no.3D
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• pp.423-429
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• 2008

The object of the present study is to estimate the potential effects of climate change and land use on soil erosion in the mid-east Korea. Simulated precipitation by CCCma climate model during 2030-2050 is used to model predicted soil erosion, and results are compared to observation. Simulation results allow relative comparison of the impact of climate change on soil erosion between current and predicted future condition. Expected land use changes driven by socio-economic change and plant growth driven by the increase of temperature and are taken into accounts in a comprehensive way. Mean precipitation increases by 17.7% (24.5%) for A2 (B2) during 2030-2050 compared to the observation period (1966-1998). In general predicted soil erosion for the B2 scenario is larger than that for the A2 scenario. Predicted soil erosion increases by 48%~90% under climate change except the scenario 1 and 2. Predicted soil erosion under the influence of temperature-induced fast plant growth, higher evapotranspiration rate, and fertilization effect (scenario 5 and 6) is approximately 25% less than that in the scenario 3 and 4. On the basis of the results it is said that precipitation and the corresponding soil erosion is likely to increase in the future and care needs to be taken in the study area.

• Journal of Information Processing Systems
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• v.18 no.3
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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.

• Journal of Environmental Health Sciences
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• v.41 no.6
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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.

• Journal of Institute of Control, Robotics and Systems
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• v.7 no.8
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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.

• The KIPS Transactions:PartA
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• v.9A no.4
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• pp.519-524
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• 2002

In this paper we study the qualify of sequential coverage analysis under a scenario of distributed stochastic simulation known as MRIP(Multiple Replications In Parallel) in terms of the confidence intervals of coverage and the speedup. The estimator based in the F-distribution was applied to the sequential coverage analysis of steady-state means. in simulations of the $M/M/1/{\infty},\;M/D/I/{\infty}\;and\;M/H_{2}/1/{\infty}$ queueing systems on a single processor and multiple processors. By using multiple processors under the MRIP scenario, the time for collecting many replications needed in sequential coverage analysis is reduced. One can also easily collect more replications by executing it in distributed computers or clusters linked by a local area network.

• Nuclear Engineering and Technology
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• v.55 no.9
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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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• v.13 no.5
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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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• v.16 no.5
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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.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.67 no.1
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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.

• Journal of Nutrition and Health
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• v.46 no.1
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• pp.50-60
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• 2013

The aims of this study were to estimate daily intake of macrominerals from beverages, liquid teas, and liquid coffees and to evaluate their potential health risks for Korean children and adolescents (1-to 19 years old). Assessment of dietary intake was conducted using the actual level of sodium, calcium, phosphorus, potassium, and magnesium in non-alcoholic beverages and (207 beverages, 19 liquid teas, and 24 liquid coffees) the food consumption amount drawn from "The Fourth Korea National Health and Nutrition Examination Survey (2007-2009)". To estimate the dietary intake of non-alcoholic beverages, 6,082 children and adolescents (Scenario I) were compared with 1,704 non-alcoholic beverage consumption subjects among them (Scenario II). Calculation of the estimated daily intake of macrominerals was based on point estimates and probabilistic estimates. The values of probabilistic macromineral intake, which is a Monte-Carlo approach considering probabilistic density functions of variables, were presented using the probabilistic model. The level of safety for macrominerals was evaluated by comparison with population nutrient intake goal (Goal, 2.0 g/day) for sodium, tolerable upper intake level (UL) for calcium (2,500 mg/day) and phosphorus (3,000-3,500 mg/day) set by the Korean Nutrition Society (Dietary Reference Intakes for Koreans, KDRI). For total children and adolescents (Scenario I), mean daily intake of sodium, calcium, phosphorus, potassium, and magnesium estimated by probabilistic estimates using Monte Carlo simulation was, respectively, 7.93, 10.92, 6.73, 23.41, and 1.11, and 95th percentile daily intake of those was, respectively, 28.02, 44.86, 27.43, 98.14, and 3.87 mg/day. For consumers-only (Scenario II), mean daily intake of sodium, calcium, phosphorus, potassium, and magnesium estimated by probabilistic estimates using Monte Carlo simulation was, respectively, 19.10, 25.77, 15.83, 56.56, and 2.86 mg/day, and 95th percentile daily intake of those was, respectively, 62.67, 101.95, 62.09, 227.92, and 8.67 mg/day. For Scenarios I II, sodium, calcium, and phosphorus did not have a mean an 95th percentile intake that met or exceeded the 5% of Goal and UL.