• Proceedings of the Korean Institute of Building Construction Conference
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• 한국건축시공학회 2023년도 봄 학술논문 발표대회
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• pp.223-224
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• 2023

Optimal fleet management in the planning stage is one of the most critical activities that guarantee successful construction projects. In South Korea, the construction standard production rate database (CSPRD) is normally employed. However, when it comes to a trade-off problem that involves decision-making on optimal sets of equipment to perform a certain task, the method will require the planners' in-depth knowledge and experience regarding the target process and a time consuming estimation of the performance of every possible scenario must be conducted for the deduction of the optimal fleet management. On this account, this research paper proposes a lightweight method of using mixed integer nonlinear programming (MINLP) in multi-objective problems based on CSPRD-based mathematical equations to assist planners in the preplanning stage of choosing the optimal sets of types and size machinery to efficiently arrange the construction scheduling and budgeting.

• Proceedings of the KSRS Conference
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• 대한원격탐사학회 1999년도 Proceedings of International Symposium on Remote Sensing
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• pp.107-114
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• 1999

The large-scale distribution of crops Is usually determined by climate. We present the results of a climate-crop prediction based on spatial bio-physical process model approach, implemented in a GIS (Geographic Information System) environment using several regional and global agriculture-environmental databases. The model utilizes daily climate data like temperature, rainfall, solar radiation being generated stocastically by in-built model weather generator to determine the daily biomass and finally the crop yield. Crops are characterized by their specific growing period requirements, photosynthesis, respiration properties and harvesting index properties. Temperature and radiation during the growing period controls the development of each crop. The model simulates geographic/spatial distribution of climate by which a crop-growing belt can also be determined. The model takes both irrigated and non-irrigated area crop productivity into account and the potential increase in productivity by the technical means like mechanization is not considered. All the management input given at the base year 1995 was kept same for the next twenty-year changes until 2015. The simulated distributions of crops under current climatic conditions coincide largely with the current agricultural or specific crop growing regions. Simulation with assumed weather generated derived climate change scenario illustrate changes in the agricultural potential. There are large regional differences in the response across the country. The north-south and east-west regions responded differently with projected climate changes with increased and decreased productivity depending upon the crops and scenarios separately. When water was limiting or facilitating as non-irrigated and irrigated area crop-production effects of temperature rise and higher $CO_2$ levels were different depending on the crops and accordingly their production. Rise in temperature led to yield reduction in case of maize and rice whereas a gain was observed for wheat crop, doubled $CO_2$ concentration enhanced yield for all crops and their several combinations behaved differently with increase or decrease in yields. Finally, with this spatial modeling approach we succeeded in quantifying the crop productivity which may bring regional disparities under the different climatic scenarios where one region may become better off and the other may go worse off.

• Computers and Concrete
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• 제30권4호
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• pp.277-287
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• 2022

Due to the scarcity of extortionate experimental data, fatigue failure of the reinforced concrete (RC) element might be achieved economically adopting nonlinear finite element (FE) analysis as an alternative approach. However, conventional implicit dynamic analysis is expensive, quasi-static method overlooks interaction effects and inertia, direct cyclic analysis computes stabilized responses. Apart from this, explicit dynamic analysis may provide a numerical operating system for factual long-term responses. The study explores the fatigue behavior based on a simplified explicit dynamic solution employing nonlinear time domain analysis. Among fourteen RC beams, one beam is selected to validate under static loading, one under fatigue with the experimental study and other twelve to check the detail fatigue behavior. The SWOT (Strength, Weakness, Opportunities, Threats) analysis has been carried out to pinpoint the detail scenario in the adoption of numerical approach as an alternative to the experimental study. Excellent agreement of FE and experimental results is seen. The 3D nonlinear RC beam model at service fatigue limits is truthful to be used as an expedient contrivance to envisage the precise fatigue behavior. The simplified analysis approach for RC beam under fatigue offers savings in computation to predict responses providing acceptable accuracy rather than the complicated laboratory investigation. At higher frequency, the flexural failure occurs a bit earlier gradually compared to the repeated loading case of lower frequency. The deflection increases by 6%-10% at the end of first cycle for beams with increasing frequency of cyclic loading. However, at the end of fatigue loading, greater deflection occur earlier for higher load range because of more rapid stiffness degradation. For higher frequency, a slight boost in concrete compressive strains at an initial stage of loading has been seen indicating somewhat stepper increment. Stiffness degradation in larger loading cycle at same duration escalates the upsurge of the rate of strain in case of higher frequency.

• Journal of Internet Computing and Services
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• 제14권1호
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• pp.53-62
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• 2013

It is common among users to have multiple computing devices as well as to access their files or do work at different locations. To achieve file consistency as well as mobility in this scenario, an efficient approach for workspace synchronization should be used. However, file synchronization alone cannot guarantee the mobility of work environment which allows activities to be resumed at any place and time. This paper proposes a ubiquitous synchronization approach which provides cloud-based access to a user's workspace. Efficient synchronization is achieved by combining session monitoring with file system management. Experimental results show that the proposed mechanism outperforms Cloud Master-replica Synchronization in terms of number of I/O operations, CPU utilization, as well as the average and maximum latencies in responding to client requests.

• Journal of the military operations research society of Korea
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• 제34권1호
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• pp.31-45
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• 2008

This paper describes a development of the operational architecture of a low altitude air defense automation system using a systems engineering approach. The future battlefield is changing to new system of systems that command and control by the network based BM/C4I. Also, it is composed of various sensors and shooters in an single theater. Future threats may be characterized as unmanned mewing bodies that the strategic effect is great such as UAVs, cruise missiles or tactical ballistic missiles. New threats such as low altitude stealth cruise missiles may also appear. The implementation of a low altitude air defense against these future threats is required to complex and integrated approach based on systems engineering. In this view, this work established an operational scenario and derived operational requirements by identifying mission and future operational environments. It is presented the operational architecture of the low altitude air defense automation system by using the CORE 5.0.

• Journal of The Korean Society of Agricultural Engineers
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• 제55권4호
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• pp.95-106
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• 2013

It is now generally known that dynamical climate modeling outputs include systematic biases in reproducing the properties of atmospheric variables such as, preciptation and temerature. There is thus, general consensus among the researchers about the need of bias-correction process prior to using climate model results especially for hydrologic applications. Among the number of bias-correction methods, distribution (e.g., cumulative distribution fuction, CDF) mapping based approach has been evaluated as one of the skillful techniques. This study investigates the uncertainty of using various CDF mapping-based methods for bias-correciton in assessing regional climate change Impacts. Two different dynamicailly-downscaled Global Circulation Model results (CCSM and GFDL under ARES4 A2 scenario) using Regional Spectial Model for retrospective peiod (1969-2000) and future period (2039-2069) were collected over the west central Florida. Total 12 possible methods (i.e., 3 for developing distribution by each of 4 for estimating biases in future projections) were examined and the variations among the results using different methods were evaluated in various ways. The results for daily temperature showed that while mean and standard deviation of Tmax and Tmin has relatively small variation among the bias-correction methods, monthly maximum values showed as significant variation (~2'C) as the mean differences between the retrospective simulations and future projections. The accuracy of raw preciptiation predictions was much worse than temerature and bias-corrected results appreared to be more significantly influenced by the methodologies. Furthermore the uncertainty of bias-correction was found to be relevant to the performance of climate model (i.e., CCSM results which showed relatively worse accuracy showed larger variation among the bias-correction methods). Concludingly bias-correction methodology is an important sourse of uncertainty among other processes that may be required for cliamte change impact assessment. This study underscores the need to carefully select a bias-correction method and that the approach for any given analysis should depend on the research question being asked.

• Journal of the Korean Society of Systems Engineering
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• 제18권2호
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• pp.75-93
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• 2022

Machine learning (ML) data-driven meta-model is proposed as a surrogate model to reduce the excessive computational cost of the physics-based model and facilitate the real-time prediction of a nuclear power plant's transient response. To forecast the transient response three machine learning (ML) meta-models based on recurrent neural networks (RNNs); specifically, Long Short Term Memory (LSTM), Gated Recurrent Unit (GRU), and a sequence combination of Convolutional Neural Network (CNN) and LSTM are developed. The chosen accident scenario is a control element assembly withdrawal at power concurrent with the Loss Of Offsite Power (LOOP). The transient response was obtained using the best estimate thermal hydraulics code, MARS-KS, and cross-validated against the Design and control document (DCD). DAKOTA software is loosely coupled with MARS-KS code via a python interface to perform the Best Estimate Plus Uncertainty Quantification (BEPU) analysis and generate a time series database of the system response to train, test and validate the ML meta-models. Key uncertain parameters identified as required by the CASU methodology were propagated using the non-parametric Monte-Carlo (MC) random propagation and Latin Hypercube Sampling technique until a statistically significant database (181 samples) as required by Wilk's fifth order is achieved with 95% probability and 95% confidence level. The three ML RNN models were built and optimized with the help of the Talos tool and demonstrated excellent performance in forecasting the most probable NPP transient response. This research was guided by the Systems Engineering (SE) approach for the systematic and efficient planning and execution of the research.

• Proceedings of the Korea Water Resources Association Conference
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• 한국수자원학회 2022년도 학술발표회
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• pp.305-305
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• 2022

A better approach for assessing meteorological drought occurrences is increasingly important in mitigating and adapting to the impacts of climate change, as well as strategies for developing early warning systems. The present study defines meteorological droughts as a period with an abnormal precipitation deficit based on monthly precipitation data of 18 gauging stations for the Han River watershed in the past (1974-2015). This study utilizes a Bayesian parameter estimation approach to analyze the effects of climate change on future drought (2025-2065) in the Han River Basin using the Coupled Model Intercomparison Project Phase 6 (CMIP6) with four bias-corrected general circulation models (GCMs) under the Shared Socioeconomic Pathway (SSP)2-4.5 scenario. Given that drought is defined by several dependent variables, the evaluation of this phenomenon should be based on multivariate analysis. Two main characteristics of drought (severity and duration) were extracted from precipitation anomalies in the past and near-future periods using the copula function. Three parameters of the Archimedean family copulas, Frank, Clayton, and Gumbel copula, were selected to fit with drought severity and duration. The results reveal that the lower parts and middle of the Han River basin have faced severe drought conditions in the near future. Also, the bivariate analysis using copula showed that, according to both indicators, the study area would experience droughts with greater severity and duration in the future as compared with the historical period.

• Journal of Food Hygiene and Safety
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• 제25권1호
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• pp.16-23
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• 2010

Although concerns about Arsenic (As) contamination in agricultural foods have currently increased, there in on adequate international risk management standards for As particularly on agricultural commodities and processed agricultural products. This scenario holds true also in Korea. Australia, and New Zealand has determined the As maximum level (ML) but only on cereals grains which is based on total As contents. ln addition, Japan has regulated the ML based on trivalent As contents in agricultural commodities, which do not have legal restrictions. On the other hand, China has developed a systemic risk management to restrict the As contamination above MLs in agricultural commodities and processed agricultural products based on inorganic and total As contents. The establishment of an adequate analytical method for As specification in agricultural foods is essential to determine the acceptable level of As in agricultural food. Probabilistic approach may remove some uncertainties in calculating human risk assessment from As. It should be reviewed in terms of maximum levels to set the best scenario based on a realiability and availability to achieve effective As management on agricultural foods in Korea.

• Nuclear Engineering and Technology
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• 제53권8호
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• pp.2534-2546
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• 2021

Nuclear emergency preparedness and response is an essential part to ensure the safety of nuclear power plant (NPP). Key support technologies of nuclear emergency decision-making usually consist of accident diagnosis, source term estimation, accident consequence assessment, and protective action recommendation. Source term estimation is almost the most difficult part among them. For example, bad communication, incomplete information, as well as complicated accident scenario make it hard to determine the reactor status and estimate the source term timely in the Fukushima accident. Subsequently, it leads to the hard decision on how to take appropriate emergency response actions. Hence, this paper aims to develop a method for rapid source term estimation to support nuclear emergency decision making in pressurized water reactor NPP. The method aims to make our knowledge on NPP provide better support nuclear emergency. Firstly, this paper studies how to build a Bayesian network model for the NPP based on professional knowledge and engineering knowledge. This paper presents a method transforming the PRA model (event trees and fault trees) into a corresponding Bayesian network model. To solve the problem that some physical phenomena which are modeled as pivotal events in level 2 PRA, cannot find sensors associated directly with their occurrence, a weighted assignment approach based on expert assessment is proposed in this paper. Secondly, the monitoring data of NPP are provided to the Bayesian network model, the real-time status of pivotal events and initiating events can be determined based on the junction tree algorithm. Thirdly, since PRA knowledge can link the accident sequences to the possible release categories, the proposed method is capable to find the most likely release category for the candidate accidents scenarios, namely the source term. The probabilities of possible accident sequences and the source term are calculated. Finally, the prototype software is checked against several sets of accident scenario data which are generated by the simulator of AP1000-NPP, including large loss of coolant accident, loss of main feedwater, main steam line break, and steam generator tube rupture. The results show that the proposed method for rapid source term estimation under nuclear emergency decision making is promising.