• Journal of Korea Port Economic Association
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• v.33 no.2
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• pp.83-96
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• 2017

As a way to address global warming, among the renewable energy sources, there have been heavy investments worldwide for the development of offshore wind farms. However, such development has a drawback: investment costs are higher than those for onshore wind farms due to required operations such as offshore transportation and installation. In particular, delays in installation due to adverse maritime weather conditions are factors that affect the economics of offshore wind farms' operation. Therefore, in this study, we analyze the optimal schedule of the construction of an offshore wind farm from a macro perspective by considering the weather conditions in Korea. For this purpose, we develop a mathematical model and apply it to a 2.5 GW offshore wind farm project on the southwestern coast of the country. We use data from the Korea Meteorological Agency for maritime weather conditions and attempt to reflect the actual input data based on precedent cases overseas. The results show that it takes 6 months to install 35 offshore wind turbines. More specifically, it is pointed out that it is possible to minimize costs by not working in winter.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.38 no.2
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• pp.349-356
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• 2018

This paper attempts to develop a connection model based on FE analysis that can be applied to the evaluation of earthquake fragility of Steel Storage Racks lacking research in Korea. In order to accomplish this goal, shaking table tests, modal tests, and various member tests (8 case, push-over test) for structural members have been conducted to understand the behavior of steel storage racks. Based on the experimental results, detailed modeling of the joints was conducted using the NX-Nastran program in order to develop a connection model for Steel storage racks to be applied to the seismic vulnerability assessment. Especially, surface to surface contact element and spring element are applied to simulate the connection between the column member and the beam member connected by the simple latch method. Spring element model developed and applied ARX (Auto Regressive eXogenous) based mathematical model. The simulation results based on the FE model showed excellent reliability with a mutual error rate of less than 8% when compared with the member test results. As a result, it was confirmed that the FE model based connection model developed in the study can be applied to the analytical model for the seismic vulnerability assessment of Steel storage racks.

• Journal of Bio-Environment Control
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• v.32 no.4
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• pp.384-395
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• 2023

Since transpiration plays a key role in optimal irrigation management, knowledge of the irrigation demand of crops like tomatoes, which are highly susceptible to water stress, is necessary. One way to determine irrigation demand is to measure transpiration, which is affected by environmental factor or growth stage. This study aimed to estimate the transpiration amount of tomatoes and find a suitable model using mathematical and deep learning models using minute-by-minute data. Pearson correlation revealed that observed environmental variables significantly correlate with crop transpiration. Inside air temperature and outside radiation positively correlated with transpiration, while humidity showed a negative correlation. Multiple Linear Regression (MLR), Polynomial Regression model, Artificial Neural Network (ANN), Long short-term Memory (LSTM), and Gated Recurrent Unit (GRU) models were built and compared their accuracies. All models showed potential in estimating transpiration with R² values ranging from 0.770 to 0.948 and RMSE of 0.495 mm/min to 1.038 mm/min in the test dataset. Deep learning models outperformed the mathematical models; the GRU demonstrated the best performance in the test data with 0.948 R² and 0.495 mm/min RMSE. The LSTM and ANN closely followed with R² values of 0.946 and 0.944, respectively, and RMSE of 0.504 m/min and 0.511, respectively. The GRU model exhibited superior performance in short-term forecasts while LSTM for long-term but requires verification using a large dataset. Compared to the FAO56 Penman-Monteith (PM) equation, PM has a lower RMSE of 0.598 mm/min than MLR and Polynomial models degrees 2 and 3 but performed least among all models in capturing variability in transpiration. Therefore, this study recommended GRU and LSTM models for short-term estimation of tomato transpiration in greenhouses.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.28 no.2
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• pp.63-72
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• 2016

An expected-discounted cost model based on RRP(Renewal Reward Process), referred to as a stochastic decision model, has been developed to estimate the optimal period of in-depth inspection which is one of critical issues in the life-cycle maintenance management of harbor structures such as rubble-mound breakwaters. A mathematical model, which is a function of the probability distribution of the service-life, has been formulated by simultaneously adopting PIM(Periodic Inspection and Maintenance) and CBIM(Condition-Based Inspection and Maintenance) policies so as to resolve limitations of other models, also all the costs in the model associated with monitoring and repair have been discounted with time. From both an analytical solution derived in this paper under the condition in which a failure rate function is a constant and the sensitivity analyses for the variety of different distribution functions and conditions, it has been confirmed that the present solution is more versatile than the existing solution suggested in a very simplified setting. Additionally, even in that case which the probability distribution of the service-life is estimated through the stochastic process, the present model is of course also well suited to interpret the nonlinearity of deterioration process. In particular, a MCS(Monte-Carlo Simulation)-based sample path method has been used to evaluate the parameters of a damage intensity function in stochastic process. Finally, the present stochastic decision model can satisfactorily be applied to armor units of rubble mound breakwaters. The optimal periods of in-depth inspection of rubble-mound breakwaters can be determined by minimizing the expected total cost rate with respect to the behavioral feature of damage process, the level of serviceability limit, and the consequence of that structure.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.33 no.4
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• pp.1413-1424
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• 2013

Recently, the frequency of severe storms increases in Korea. Severe storms occurring in a short time cause huge losses of both life and property. A considerable research has been performed for the flood control system development based on an accurate stream discharge prediction. A physical model is mainly used for flood forecasting and warning. Physical rainfall-runoff models used for the conventional flood forecasting process require extensive information and data, and include uncertainties which can possibly accumulate errors during modelling processes. ANFIS, a data driven model combining neural network and fuzzy technique, can decrease the amount of physical data required for the construction of a conventional physical models and easily construct and evaluate a flood forecasting model by utilizing only rainfall and water level data. A data driven model, however, has a disadvantage that it does not provide the mathematical and physical correlations between input and output data of the model. The characteristics of a data driven model according to functional options and input data such as the change of clustering radius and training data length used in the ANFIS model were analyzed in this study. In addition, the applicability of ANFIS was evaluated through comparison with the results of HEC-HMS which is widely used for rainfall-runoff model in Korea. The neuro-fuzzy technique was applied to a Cheongmicheon Basin in the South Han River using the observed precipitation and stream level data from 2007 to 2011.

• Journal of Environmental Impact Assessment
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• v.29 no.6
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• pp.403-413
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• 2020

A mathematical model comprised with eight simultaneous quasi-linear partial differential equations was suggested to provide optimal chlorination strategy. Upstream weighted finite element method was employed to construct multidimensional numerical code. The code was verified against measured concentrations in three type of reactors. Boundary conditions and reaction rate were calibrated for the sixteen cases of experimental results to regenerate the measured values. Eight reaction rate coefficients were estimated from the modeling result. The reaction rate coefficients were expressed in terms of pH and temperature. Automatic optimal algorithm was invented to estimate the reaction rate coefficients by minimizing the sum of squares of the numerical errors and combined with the model. In order to minimize the concentration of chlorine and pollutants at the final usage sites, a real-time predictive control system is imperative which can predict the water quality variables from the chlorine disinfection process at the water purification plant to the customer by means of a model and operate the disinfection process according to the influent water quality. This model can be used to build such a system in water treatment plants.

• Journal of the Korean School Mathematics Society
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• v.11 no.1
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• pp.69-96
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• 2008

The purpose of this paper is to perceive the problems of current geometry education in the middle school mathematics, to develop some learning materials fitted for the mathematising activities based on Freudenthal's learning theories and to analyze the mathematising process followed by teaching-learning activities. For this purpose, we design activity-oriented learning materials for geometry based on Freudenthal's learning theories, and appropriate teaching-learning models are established for the middle school geometry at the 8-NA stage level according to the theory of van Hiele's geometry learning steps. After applied to the practical lessons, the effects of mathematical activities are analyzed.

• The Bulletin of The Korean Astronomical Society
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• v.40 no.1
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• pp.64.3-64.3
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• 2015

We have developed a set of daily solar flare peak flux forecast models for strong flares using multiple linear regression and artificial neural network methods. We consider input parameters as solar activity data from January 1996 to December 2013 such as sunspot area, X-ray flare peak flux and weighted total flux of previous day, and mean flare rates of McIntosh sunspot group (Zpc) and Mount Wilson magnetic classification. For a training data set, we use the same number of 61 events for each C-, M-, and X-class from Jan. 1996 to Dec. 2004, while other previous models use all flares. For a testing data set, we use all flares from Jan. 2005 to Nov. 2013. The best three parameters related to the observed flare peak flux are weighted total flare flux of previous day (r = 0.51), X-ray flare peak flux (r = 0.48), and Mount Wilson magnetic classification (r = 0.47). A comparison between our neural network models and the previous models based on Heidke Skill Score (HSS) shows that our model for X-class flare is much better than the models and that for M-class flares is similar to them. Since all input parameters for our models are easily available, the models can be operated steadily and automatically in near-real time for space weather service.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.3
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• pp.1040-1057
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• 2014

In this paper, a new bit rate estimation scheme is proposed to determine the bit rate for each subclass in an MPEG-2 TS to H.264/AVC transcoder after dividing an input MPEG-2 TS sequence into several subclasses. Video format transcoding in conventional IPTV and Smart TV services is a time-consuming process since the input sequence should be fully transcoded several times with different bit-rates to decide the bit-rate suitable for a service. The proposed scheme can automatically decide the bit-rate for the transcoded video sequence in those services which can be stored on a video streaming server as small as possible without losing any subject quality loss. In the proposed scheme, an input sequence to the transcoder is sub-classified by hierarchical clustering using a parameter value extracted from each frame. The candidate frames of each subclass are used to estimate the bit rate using a statistical analysis and a mathematical model. Experimental results show that the proposed scheme reduces the bit rate by, on an average approximately 52% in low-complexity video and 6% in high-complexity video with negligible degradation in subjective quality.

• Computers and Concrete
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• v.2 no.3
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• pp.189-202
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• 2005

This paper presents an analysis of some experimental results concerning micro-structural tests, permeability measurements and strain-stress tests of four types of High-Performance Concrete, exposed to elevated temperatures (up to $700^{\circ}C$). These experimental results, obtained within the "HITECO" research programme are discussed and interpreted in the context of a recently developed mathematical model of hygro-thermal behaviour and degradation of concrete at high temperature, which is briefly presented in the Part 2 paper (Gawin, et al. 2005). Correlations between concrete permeability and porosity micro-structure, as well as between damage and cracks' volume, are found. An approximate decomposition of the thermally induced material damage into two parts, a chemical one related to cement dehydration process, and a thermal one due to micro-cracks' development caused by thermal strains at micro- and meso-scale, is performed. Constitutive relationships describing influence of temperature and material damage upon its intrinsic permeability at high temperature for 4 types of HPC are deduced. In the Part II of this paper (Gawin, et al. 2005) effect of two different damage-permeability coupling formulations on the results of computer simulations concerning hygro-thermo-mechanical performance of concrete wall during standard fire, is numerically analysed.