• 한국수자원학회:학술대회논문집
• /
• 한국수자원학회 2022년도 학술발표회
• /
• pp.437-437
• /
• 2022

The prediction of dam inflow rate is crucial for the management of the largest multi-purpose dam in South Korea, the Soyang Dam. The main issue associated with the management of water resources is the stochastic nature of the reservoir inflow leading to an increase in uncertainty associated with the inflow prediction. The Autoregressive (AR) model is commonly used to provide the simulation and forecast of hydrometeorological data. However, because its estimation is based solely on the time-series data, it has the disadvantage of being unable to account for external variables such as climate information. This study proposes the use of the Autoregressive Exogenous Stochastic Volatility (ARXSV) model within a Bayesian modeling framework for increased predictability of the monthly dam inflow by addressing the exogenous and stochastic factors. This study analyzes 45 years of hydrological input data of the Soyang Dam from the year 1974 to 2019. The result of this study will be beneficial to strengthen the potential use of data-driven models for accurate inflow predictions and better reservoir management.

• 한국지구과학회지
• /
• 제38권5호
• /
• pp.345-356
• /
• 2017

It is important to understand the variability of tropospheric ozone since it is both a major pollutant affecting human health and a greenhouse gas influencing global climate. We analyze the characteristics of East Asia tropospheric ozone simulated in a chemistry-climate model. We use a global chemical transport model, driven by the prescribed meteorological fields from an air-sea coupled climate model simulation. Compared with observed data, the ozone simulation shows differences in distribution and concentration levels; in the vicinity of the Korean Peninsula, a large error occurred in summer. Our analysis reveals that this bias is mainly due to the difference in atmospheric circulation, as the anomalous southerly winds lead to the decrease in tropospheric ozone in this region. In addition, observational data have shown that the western North Pacific subtropical high (WNPSH) reduces tropospheric ozone across the southern China/Korean Peninsula/Japan region. In the model, the ozone changes associated with WNPSH are shifted westward relative to the observations. Our findings suggest that the variations in WNPSH should be considered in predicting tropospheric ozone concentrations.

• 한국수자원학회:학술대회논문집
• /
• 한국수자원학회 2002년도 학술발표회 논문집(II)
• /
• pp.1187-1194
• /
• 2002

In this study, we have simulated regional climate over East Asia using dynamical downscaling For dynamic downscaling experiments for regional climate simulation, MM5method. with 27 km horizontal resolution and 18 layers of sigma-coordinate in vertical is nested within global-scale NCEP reanalysis data with 2.5。${\times}$2.5。 resolution in longitude and latitude. In regional simulation, January and July, 1979 monthly mean features have been obtained by both continuous integration and daily restart integration driven by updating the lateral boundary forcing at 6-hr intervals from the NCEP reanalysis data using a nudging scheme with the updating design of initial and boundary conditions in both continuous and restart integrations. In result, we may successfully generated regional detail features which might be forced by topography, lake, coastlines and land use distribution from a regional climate. There is no significant difference in monthly mean features either integrate continuously or integrate with daily restart. For climatologically long integration, the initial condition may not be significantly important. Accordingly, MM5 can be integrated for a long period without restart frequently, if a proper lateral boundary forcing is given.

• Nuclear Engineering and Technology
• /
• 제50권8호
• /
• pp.1306-1313
• /
• 2018

The randomness and incipient nature of certain faults in reactor systems warrant a robust and dynamic detection mechanism. Existing models and methods for fault diagnosis using different mathematical/statistical inferences lack incipient and novel faults detection capability. To this end, we propose a fault diagnosis method that utilizes the flexibility of data-driven Support Vector Machine (SVM) for component-level fault diagnosis. The technique integrates separately-built, separately-trained, specialized SVM modules capable of component-level fault diagnosis into a coherent intelligent system, with each SVM module monitoring sub-units of the reactor coolant system. To evaluate the model, marginal faults selected from the failure mode and effect analysis (FMEA) are simulated in the steam generator and pressure boundary of the Chinese CNP300 PWR (Qinshan I NPP) reactor coolant system, using a best-estimate thermal-hydraulic code, RELAP5/SCDAP Mod4.0. Multiclass SVM model is trained with component level parameters that represent the steady state and selected faults in the components. For optimization purposes, we considered and compared the performances of different multiclass models in MATLAB, using different coding matrices, as well as different kernel functions on the representative data derived from the simulation of Qinshan I NPP. An optimum predictive model - the Error Correcting Output Code (ECOC) with TenaryComplete coding matrix - was obtained from experiments, and utilized to diagnose the incipient faults. Some of the important diagnostic results and heuristic model evaluation methods are presented in this paper.

• 한국철도학회:학술대회논문집
• /
• 한국철도학회 2011년도 정기총회 및 추계학술대회 논문집
• /
• pp.2885-2890
• /
• 2011

본 연구에서는 최근 증가하고 있는 대심도역사의 화재위치에 따른 연기확산영향에 대하여 분석하였다. 시뮬레이션모델은 신금호 역사(5호선, 깊이 46m)를 대상으로 하였으며, 화재위치에 따른 연기확산 영향을 분석 하였다. 현장조사 및 실측을 통하여 계측된 실제 역사의 제연팬에 관한 데이터를 화재시뮬레이션 조건으로 적용하였다. 역사전체를 해석 대상으로 하여 총 400만개의 격자를 사용하였으며, 화재위치에 따른 연기확산 영향 비교를 위하여 화재 시나리오를 작성하여 Case별로 화재해석을 수행하였다. 계산 효율을 높이기 위하여 MPI병렬처리기법을 사용하였으며 해석코드는 LES(large eddy simulation) 기법을 주로 사용하는 FDS5 code를 사용하였다.

• Journal of Ecology and Environment
• /
• 제47권2호
• /
• pp.49-62
• /
• 2023

Background: Over the past three decades, gradual eustatic sea-level rise has been considered a primary exogenous factor in the increased frequency of flooding and biological changes in several salt marshes. Under this paradigm, the potential importance of short-term events, such as ocean storminess, in coastal hydrology and ecology is underrepresented in the literature. In this study, a simulation was developed to evaluate the influence of wind waves driven by atmospheric oscillations on sedimentary and vegetation dynamics at the Skallingen salt marsh in southwestern Denmark. The model was built based on long-term data of mean sea level, sediment accretion, and plant species composition collected at the Skallingen salt marsh from 1933-2006. In the model, the submergence frequency (number yr^-1) was estimated as a combined function of wind-driven high water level (HWL) events (> 80 cm Danish Ordnance Datum) affected by the North Atlantic Oscillation (NAO) and changes in surface elevation (cm yr^-1). Vegetation dynamics were represented as transitions between successional stages controlled by flooding effects. Two types of simulations were performed: (1) baseline modeling, which assumed no effect of wind-driven sea-level change, and (2) experimental modeling, which considered both normal tidal activity and wind-driven sea-level change. Results: Experimental modeling successfully represented the patterns of vegetation change observed in the field. It realistically simulated a retarded or retrogressive successional state dominated by early- to mid-successional species, despite a continuous increase in surface elevation at Skallingen. This situation is believed to be caused by an increase in extreme HWL events that cannot occur without meteorological ocean storms. In contrast, baseline modeling showed progressive succession towards the predominance of late-successional species, which was not the then-current state in the marsh. Conclusions: These findings support the hypothesis that variations in the NAO index toward its positive phase have increased storminess and wind tides on the North Sea surface (especially since the 1980s). This led to an increased frequency and duration of submergence and delayed ecological succession. Researchers should therefore employ a multitemporal perspective, recognizing the importance of short-term sea-level changes nested within long-term gradual trends.

• Nuclear Engineering and Technology
• /
• 제55권6호
• /
• pp.2096-2106
• /
• 2023

Rotating machinery is widely applied in important equipment of nuclear power plants (NPPs), such as pumps and valves. The research on intelligent fault diagnosis of rotating machinery is crucial to ensure the safe operation of related equipment in NPPs. However, in practical applications, data-driven fault diagnosis faces the problem of small and imbalanced samples, resulting in low model training efficiency and poor generalization performance. Therefore, a deep convolutional conditional generative adversarial network (DCCGAN) is constructed to mitigate the impact of imbalanced samples on fault diagnosis. First, a conditional generative adversarial model is designed based on convolutional neural networks to effectively augment imbalanced samples. The original sample features can be effectively extracted by the model based on conditional generative adversarial strategy and appropriate number of filters. In addition, high-quality generated samples are ensured through the visualization of model training process and samples features. Then, a deep convolutional neural network (DCNN) is designed to extract features of mixed samples and implement intelligent fault diagnosis. Finally, based on multi-fault experimental data of motor and bearing, the performance of DCCGAN model for data augmentation and intelligent fault diagnosis is verified. The proposed method effectively alleviates the problem of imbalanced samples, and shows its application value in intelligent fault diagnosis of actual NPPs.

• 한국정보시스템학회지:정보시스템연구
• /
• 제30권1호
• /
• pp.151-177
• /
• 2021

Purpose This paper aims to improve management performance by effectively responding to production needs and reducing inventory through synchronizing production planning and procurement in the aviation industry. In this study, the differences in production planning and execution were first analyzed in terms of demand, supply, inventory, and process using the big data collected from a domestic aircraft manufacturers. This paper analyzed the problems in procurement and inventory management using legacy big data from ERP system in the company. Based on the analysis, we performed a simulation to derive an efficient procurement and inventory management plan. Through analysis and simulation of operational data, we were able to discover procurement and inventory policies to effectively respond to production needs. Design/methodology/approach This is an empirical study to analyze the cause of decrease in inventory turnover and increase in inventory cost due to dis-synchronize between production requirements and procurement. The actual operation data, a total of 21,306,611 transaction data which are 18 months data from January 2019 to June 2020, were extracted from the ERP system. All them are such as basic information on materials, material consumption and movement history, inventory/receipt/shipment status, and production orders. To perform data analysis, it went through three steps. At first, we identified the current states and problems of production process to grasp the situation of what happened, and secondly, analyzed the data to identify expected problems through cross-link analysis between transactions, and finally, defined what to do. Many analysis techniques such as correlation analysis, moving average analysis, and linear regression analysis were applied to predict the status of inventory. A simulation was performed to analyze the appropriate inventory level according to the control of fluctuations in the production planing. In the simulation, we tested four alternatives how to coordinate the synchronization between the procurement plan and the production plan. All the alternatives give us more plausible results than actual operation in the past. Findings Based on the big data extracted from the ERP system, the relationship between the level of delivery and the distribution of fluctuations was analyzed in terms of demand, supply, inventory, and process. As a result of analyzing the inventory turnover rate, the root cause of the inventory increase were identified. In addition, based on the data on delivery and receipt performance, it was possible to accurately analyze how much gap occurs between supply and demand, and to figure out how much this affects the inventory level. Moreover, we were able to obtain the more predictable and insightful results through simulation that organizational performance such as inventory cost and lead time can be improved by synchronizing the production planning and purchase procurement with supply and demand information. The results of big data analysis and simulation gave us more insights in production planning, procurement, and inventory management for smart manufacturing and performance improvement.

• Journal of the Korean Statistical Society
• /
• 제30권3호
• /
• pp.453-465
• /
• 2001

It is often the case that one wants to estimate parameters of the distribution which follows certain parametric model, while the dta are contaminated. it is well known that the maximum likelihood estimators are not robust to contamination. Basuet al.(1998) proposed a robust method called the minimum density power divergence estimation. In this paper, we investigate data-driven selection of the tuning parameter $\alpha$ in the minimum density power divergence estimation. A criterion is proposed and its performance is studied through the simulation. The simulation includes three cases of estimation problem.

• 한국자기공명학회논문지
• /
• 제1권2호
• /
• pp.79-94
• /
• 1997

The solution structure of bovine pancreatic trypsin inhibitor(BPTI) has been refined by NMR chemical shift data of C${\alpha}$H using classical molecular dynamics simulation. The structure dependent part of the observable chemical shift was modeled by ring current effect, magnetic anisotropy effect from the nearby groups, whereas the structure independent part was replaced with the random coil shift. A new harmonic function derived from the differences between the observed and calculated chemical shifts was added into physical force field as an pseudo potential energy term with force constant of 250 kJmol-1 ppm-2. During the 1.5 ns molecular dynamics simulation with chemical shift restraints BPTI has accessed different conformation space compared to crystal and NOE driven structure.