• Analytical Science and Technology
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• v.30 no.4
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• pp.213-219
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• 2017

Polyalkylsiloxane has been spotlighted in pressure-sensitive adhesive (PSA) application due to excellent physical properties and good biocompatibility. Thermal behaviour of polyalkylsiloxane mixtures, such as thermal stability and heat flow, were studied using TG-DTA during catalytic hydrosilation. To understand reaction kinetics of cross-linking, catalytic hydrosilation of polyalkylsiloxane was monitored using variable temperature nuclear magnetic resonance (VT-NMR) as increased temperature. The formation of cross-linking bond $Si-CH_2-CH_2-Si$ was directly observed using distortionless enhanced by polarization transfer (DEPT) technique. Successfully polyalkylsiloxane PSA samples exhibited excellent adhesion properties by cross-linking reaction.

• Journal of Korean Society of Steel Construction
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• v.12 no.5 s.48
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• pp.475-485
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• 2000

In this study, an automated optimum design program for steel box girder bridges has been developed for the optimum design of composite steel box girder bridges. The design constraints required for the optimum design of steel box girder bridges are based on the Korean standard bridge specification. Considering characteristics of steel box girder bridges, several approximation techniques, such as artificial constraint deletion, variable linking and stress reanalysis technique etc. are also introduced to enhance the efficiency of optimization. The developed program is mainly composed of major sub-system modules including structural analysis module using commercial structural analysis program such as RM-SPACEFRAME, optimum design module, pre-process module for friendly user input, and post-processor module for office automation. In addition, in order to demonstrate the efficiency and applicability of the developed optimum design program for steel box girder bridges, a few numerical examples are applied. Based on the results of the application, it may be stated that the automatic optimum design program developed in this study can be a prototype model for the developement of optimum design program for other type of bridge.

• Journal of Korea Water Resources Association
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• v.44 no.3
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• pp.199-208
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• 2011

The objective of this study is to develop real-time river flow forecast model by linking continuous rainfall-runoff model with ensemble Kalman filter technique. Andong dam basin is selected as study area and the model performance is evaluated for two periods, 2006. 7.1~8.18 and 2007. 8.1~9.30. The model state variables for data assimilation are defined as soil water content, basin storage and channel storage. This model is designed so as to be updated the state variables using measured inflow data at Andong dam. The analysing result from the behavior of the state variables, predicted state variable as simulated discharge is updated 74% toward measured one. Under the condition of assuming that the forecasted rainfall is equal to the measured one, the model accuracy with and without data assimilation is analyzed. The model performance of the former is better than that of the latter as much as 49.6% and 33.1% for 1 h-lead time during the evaluation period, 2006 and 2007. The real-time river flow forecast model using rainfall-runoff model linking with data assimilation process can show better forecasting result than the existing methods using rainfall-runoff model only in view of the results so far achieved.

• Journal of Korean Society on Water Environment
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• v.36 no.5
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• pp.353-363
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• 2020

Hydrologic models can be classified into two types: those for understanding physical processes and those for predicting hydrologic quantities. This study deals with how to use the model to predict today's stream flow based on the system's knowledge of yesterday's state and the model parameters. In this regard, for the model to generate accurate predictions, the uncertainty of the parameters and appropriate estimates of the state variables are required. In this study, a relatively simple hydrologic partitioning model is proposed that can explicitly implement the hydrologic partitioning process, and the posterior distribution of the parameters of the proposed model is estimated using the Markov chain Monte Carlo approach. Further, the application method of the ensemble Kalman filter is proposed for updating the normalized soil moisture, which is the state variable of the model, by linking the information on the posterior distribution of the parameters and by assimilating the observed steam flow data. The stochastically and recursively estimated stream flows using the data assimilation technique revealed better representation of the observed data than the stream flows predicted using the deterministic model. Therefore, the ensemble Kalman filter in conjunction with the Markov chain Monte Carlo approach could be a reliable and effective method for forecasting daily stream flow, and it could also be a suitable method for routinely updating and monitoring the watershed-averaged soil moisture.