• Korean Chemical Engineering Research
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• v.43 no.5
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• pp.571-578
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• 2005

We propose a new optimization framework for the reactive scheduling. The proposed rescheduling scheme is specially focused on how to generate rescheduling results when equipment failure occurs. The approach is based on a continuous-time problem representation that takes into account the schedule in progress, the updated information on the batches still to be processed, the present plant state, the deviations in plant parameters and the time data. To update the predictive scheduling, we used right shift rescheduling and total regeneration when equipment failure occurs. And, a practical solution to the rescheduling problem requires satisfaction of two often confliction measures: the efficiency measure that evaluates the satisfaction of a desired objective function value and the stability measure that evaluates the amount of change between the schedules before and after the disruption. In this paper, the efficiency is measured by the makespan of all jobs in the system. And, the stability is measured by the percentage change in makespan and the modified sequence deviation in the predictive scheduling and rescheduling.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.39 no.1
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• pp.135-146
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• 2019

In this paper, the preventive maintenance strategy in operation stage of a bridge using Bayesian inference is proposed. The proposed technique can be used to predict the variation in the performance (or condition) of the bridge with higher accuracy, considering the uncertainty of monitoring. The applicability of the proposed method to the existing bridges is verified and analyzed that have an advantage in terms of maintenance cost efficiency compared to the conventional periodic maintenance system, which establishes maintenance after damage. It is expected that the proposed preventive maintenance method can be used to overcome the limitation of the conventional periodic maintenance method and to make practical bridge maintenance decision.

• Journal of Korean Society on Water Environment
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• v.36 no.6
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• pp.508-520
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• 2020

Dam reservoirs have been reported to contribute significantly to global carbon emissions, but unlike natural lakes, there is considerable uncertainty in calculating carbon emissions due to the complex of emission pathways. In particular, the method of calculating carbon dioxide (CO2) net atmospheric flux (NAF) based on a simple gas exchange theory from sporadic data has limitations in explaining the spatiotemporal variations in the CO2 flux in stratified reservoirs. This study was aimed to analyze the spatial and temporal CO2 distribution and mass balance in Daecheong Reservoir, located in the mid-latitude monsoon climate zone, by applying a two-dimensional hydrodynamic and water quality model (CE-QUAL-W2). Simulation results showed that the Daecheong Reservoir is a heterotrophic system in which CO2 is supersaturated as a whole and releases CO2 to the atmosphere. Spatially, CO2 emissions were greater in the lacustrine zone than in the riverine and transition zones. In terms of time, CO2 emissions changed dynamically according to the temporal stratification structure of the reservoir and temporal variations of algae biomass. CO2 emissions were greater at night than during the day and were seasonally greatest in winter. The CO2 NAF calculated by the CE-QUAL-W2 model and the gas exchange theory showed a similar range, but there was a difference in the point of occurrence of the peak value. The findings provide useful information to improve the quantification of CO2 emissions from reservoirs. In order to reduce the uncertainty in the estimation of reservoir carbon emissions, more precise monitoring in time and space is required.

• Journal of the Korean Society of Systems Engineering
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• v.18 no.2
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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.

• Journal of the Korean Society of Systems Engineering
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• v.18 no.2
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• pp.94-107
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• 2022

Accidents prevention and mitigation is the highest priority of nuclear power plant (NPP) operation, particularly in the aftermath of the Fukushima Daiichi accident, which has reignited public anxieties and skepticism regarding nuclear energy usage. To deal with accident scenarios more effectively, operators must have ample and precise information about key safety parameters as well as their future trajectories. This work investigates the potential of machine learning in forecasting NPP response in real-time to provide an additional validation method and help reduce human error, especially in accident situations where operators are under a lot of stress. First, a base-case SGTR simulation is carried out by the best-estimate code RELAP5/MOD3.4 to confirm the validity of the model against results reported in the APR1400 Design Control Document (DCD). Then, uncertainty quantification is performed by coupling RELAP5/MOD3.4 and the statistical tool DAKOTA to generate a large enough dataset for the construction and training of neural-based machine learning (ML) models, namely LSTM, GRU, and hybrid CNN-LSTM. Finally, the accuracy and reliability of these models in forecasting system response are tested by their performance on fresh data. To facilitate and oversee the process of developing the ML models, a Systems Engineering (SE) methodology is used to ensure that the work is consistently in line with the originating mission statement and that the findings obtained at each subsequent phase are valid.

• Journal of the Korea institute for structural maintenance and inspection
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• v.12 no.4
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• pp.133-140
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• 2008

The design method of the reinforced concrete structures is converting from the current limit state design method to the reliability based design method and active studies have been done in the US, Europe, and Japan etc. Performance based design method is considering lots of uncertainty of current design provision rationally and make sure that structure have a reliable reliability and safety. The main area of these studies is to secure the non-linear analysis technology with high reliability. The data for reinforced concrete columns tested by many researchers are used to verify the applicability of the nonlinear finite element analysis program (RCAHEST, Reinforced Concrete Analysis in Higher Evaluation System Technology). A comparison is made between analysis and test, calculated safety factor based on reliability theories to applies to analysis result.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.17 no.12 s.115
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• pp.1164-1171
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• 2006

In this paper, a simple and nonlinear MMSE detector is derived from the conventional linear MMSE detector to improve the system performance in turbo processing MIMO system and a new turbo processing MIMO system with nonlinear MMSE detector and Gaussian approximation is proposed. In turbo coded turbo processing MIMO system, the proposed system of M=N=4 performs about 0.5 dB better than the conventional system and the proposed system of M=N=8 performs about 0.4 dB better than conventional system at 1 % FER. In addition, the average number of outer iterations of proposed system shows lower than that of conventional system. This is caused by the fact that the nonlinear MMSE detector of proposed system decides soft-outputs of coded bits based on the observation of received signals, so that it can reduce the uncertainty region in estimating the coded bits.

• International Journal of Advanced Culture Technology
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• v.3 no.1
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• pp.120-128
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• 2015

In this paper, Residential roof top photovoltaic system with 9.9 kW design is proposed. The system composed of 200 Watts solar array 33 panels connecting in series 10 strings and parallels 3 strings which have maximum voltage and current are 350 V and 23.8 A. The 10 kW sinusoidal grid-connected inverter with window voltage about 270-350 is selected to convert and transfer DC Power to AC Power at PCC (Point of Common Coupling) of power system following to utility standard. However the impact of fluctuation and uncertainty of weather condition of PV may decrease the voltage stability and voltage collapse of power system. In order to solve this problem the energy storage such 120 V 1200 Ah battery bank and 30 kVAR capacitor are designed for voltage stability control. The other expensed for installing the system such battery charger, cable, accessories and maintenance cost are concerned. The economic analysis by using investment from money loan with interest about 7% and use own money which loss income of deposit about 3% are calculated as 671,844 and 547,044 for PV system with energy storage and non energy storage respectively. The solar energy from PV is about 101,616 Bath per year which evaluated by using the value of $5kWh/m^2/day$ from average peak sun hour (PSH) of the Thailand and 6.96 Bath/kWh of Feed in Tariff Incentive. The payback periods of four scenarios are proposed follow as i) PV system with energy storage and use loan money is 15 years ii) PV system with no energy storage and use loan money is 10 years iii) PV system with energy storage and use deposit money is 9 years iv) PV system with energy storage and use deposit money is 7 years. In addition, the other scenarios of economic analysis such no FIT support and other type of economic analysis such NPV and IRR are proposed in this paper.

• Journal of the Earthquake Engineering Society of Korea
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• v.11 no.6
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• pp.69-78
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• 2007

In this study, for the evaluation of seismic safety of the isolated Emergency Diesel Generator (EDG) System more quantitatively, the seismic fragility analysis method were proposed. Using the proposed method, seismic fragility analysis performed and a seismic risk of EDG system was present. The fragility analysis performed not for an existing EDG system but also for an isolated EDG system which increases the seismic capacity. At first, numerical models for existing and isolated EDG system were constructed and seismic response analysis performed according to input seismic waves and peak ground accelerations. An uncertainty factors and failure modes of both fixed and isolated EDG system were assumed for fragility analysis. The HCLPF values were evaluated for the compare the improvement effect using the isolation system. As a result, the isolation system can make better the seismic fragility of EDG system, but the failure of isolation system was govern the behavior of whole system.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.28 no.12
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• pp.1833-1844
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• 2004

A controller is proposed for the robust backstepping control of a class of nonlinear multiple-input multiple-output (MIMO) systems which can be converted to a strict feedback form. The proposed robust backstepping control scheme follows a systematic procedure for the design of control laws and uses time delay estimation (TDE) to estimate the uncertainties such as parameter variations, unknown disturbances, and unmodeled dynamics, etc. The proposed controller can be also applied to nonlinear MIMO systems with unmatched uncertainties. Stability analysis of the closed-loop system which contains the plant and the proposed controller is also studied and hereby a sufficient stability condition for the closed-loop system is proposed. The simulation results show that the control scheme works well with uncertainties and the proposed stability condition is valid. The controller is experimentally verified on a single-link flexible arm to show the effectiveness of the proposed scheme in the complicated systems with uncertainties.