• Journal of the Korea institute for structural maintenance and inspection
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• v.5 no.1
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• pp.237-246
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• 2001

This study is to measure the creep coefficient by 3 days, 7 days and 28 days in the age when loading for the quality assessment of $350kgf/cm^2$ in the high-strength concrete. And it is to analyze the behavior of creep coefficient by applying the experimental data though the compressive strength test, the elastic modulus test and the dry shrinkage test to the ACI-209, AASHTO-94 and CEB/FIP-90, the prediction mode, and the basis of concrete structural design. Also it is to analyze the behavior of short-term creep coefficient during 91 days in the age when loading through the experiment by using the regression analysis, the statistical theory. As applying it to the long-term behavior during 365 days and comparing with the creep prediction mode and examining it, the result from the analysis of the quality of the concrete is as follows. As the result of comparison and analysis about the ACI-209, AASHTO-94 and CEB/FIP-90, the prediction mode, and the basis of concrete structural design, the normal Portland cement class 1 shows the approximate value with the prediction of GEE/PIP-90 and the basis of concrete structural design, but in case of the prediction of ACI-209 and AASHTO-94, there would be worry of underestimation in the application.

• Proceedings of the Korea Concrete Institute Conference
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• 1999.10a
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• pp.285-288
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• 1999

Creep is a major parameter to represent long-term behavior of concrete structures concerning serviceability and durability. The effect of creep is recently taking account into crack resistance analysis of early-age concrete concerning durability evaluation. Since existing creep prediction models were proposed to predict creep for hardened concrete, most of them cannot consider effectively the information on microstructure formation and hydration developed in the early-age concrete. In this study, creep tests for early-age concrete made of the type I cement and the type V cement are carried out respectively and creep prediction models are evaluated for the prediction of creep behavior in early-age concrete. A creep prediction model is modified for the prediction of creep in early-age concrete and also verified by comparing prediction results with results of creep tests on early-age concrete.

• ETRI Journal
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• v.46 no.2
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• pp.205-217
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• 2024

We propose a bandwidth prediction approach based on deep learning. The approach is intended to accurately predict the bandwidth of various types of mobile networks. We first use a machine learning technique, namely, the gradient boosting algorithm, to recognize the connected mobile network. Second, we apply a handover detection algorithm based on network recognition to account for vertical handover that causes the bandwidth variance. Third, as the communication performance offered by 3G, 4G, and 5G networks varies, we suggest a bidirectional long short-term memory model with time distribution for bandwidth prediction per network. To increase the prediction accuracy, pretraining and fine-tuning are applied for each type of network. We use a dataset collected at University College Cork for network recognition, handover detection, and bandwidth prediction. The performance evaluation indicates that the handover detection algorithm achieves 88.5% accuracy, and the bandwidth prediction model achieves a high accuracy, with a root-mean-square error of only 2.12%.

• Ocean and Polar Research
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• v.37 no.1
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• pp.1-9
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• 2015

An approach based on the combined use of a 2D shallow water model and analytical 1D long wave run-up theory is proposed which facilitates the forecasting of tsunami run-up heights in a more rapid way, compared with the statistical or empirical run-up ratio method or resorting to complicated coastal inundation models. Its application is advantageous for long-term tsunami predictions based on the modeling of many prognostic tsunami scenarios. The modeling of the Chilean tsunami on February 27, 2010 has been performed, and the estimations of run-up heights are found to be in good agreement with available observations.

• Journal of Technology Innovation
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• v.16 no.2
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• pp.147-165
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• 2008

The researches on cyclical patterns of R&D investment has a long history in developed economies since the Schumpeterian hypothesis that long-term productivity-enhancing innovative activities increase during recession. But in Korea the cyclical patterns of R&D investment is one of the unexplored academic areas. Unlike theoretical explanation of R&D's cyclical pattern, empirical results has shown that R&D investment is procyclical to business cycles in developed countries. This paper investigates whether Korean R&D investment show procyclical or countercyclical pattern to business cycles. The empirical results show that Korean R&D investment in private area is procyclical to business cycles with statistical significance, which confirms the credit-constraint theory's prediction, while public area's is not sensitive to them. Public R&D investment has long-term investment characteristics and can be utilized to stabilize procyclically-fluctuating private R&D investment.

• Proceedings of the KAIS Fall Conference
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• 2010.05b
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• pp.961-964
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• 2010

A semi-empirical fluctuation term is presented to improve a classical equation of state (EOS) for volumetric properties in the critical region. The term is based on the two assumptions: (1) The Helmholtz energy is individually divided into classical and long-range density fluctuation contribution (2) All molecules form cluster near the critical region due to long-range density fluctuation. To formulate such molecular cluster, we extended the Veytsman statistics originally developed for the cluster due to hydrogen bonding. The probability function in the statistics is modified to represent the characteristics of long-range density fluctuation vanishing far from critical region. The proposed fluctuation contribution was incorporated into the Sanchez-Lacombe EOS and the combined model with 6 adjustable parameters has been tested against experimental VLE data. The combined model is found to well represent flatten critical isotherm for methane and top of the coexistence curve for the tested components. The prediction results for caloric data are in good agreement with the experimental data.

• Proceedings of the Korea Concrete Institute Conference
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• 2006.11a
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• pp.1-12
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• 2006

For advanced civil engineering structures a service life of hundred up to hundred fifty and even two hundred years is sometimes required. The prediction of the performance of concrete structures over such a long period requires accurate and reliable predictive models. Most of the presently used, mostly experience based models don't have the quality and reliability that is required for reliable long-term predictions. The models designers are searching for should be based on an accurate description of the relevant degradation mechanisms. The starting point of such models is a realistic description of the microstructure of the concrete. In this presentation the need and the role of fundamental microstructural models for predicting the performance of concrete structures will be presented. An example will be given of a microstructural model with a proven potential for long-term predictions. Besides this also the role of models in general, i.e. in the whole design and execution process of concrete structures, will be dealt with. Finally recent trends in concrete research will be presented, like the research on self-healing cement-bases systems.

• The Journal of Bigdata
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• v.5 no.1
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• pp.1-9
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• 2020

The study was conducted with funding from the government (Ministry of Agriculture, Food and Rural Affairs) in 2018 with support from the Agricultural, Food, and Rural Affairs Agency, 318069-03-HD040, and in based on artificial intelligence-based HPAI spread analysis and patterning. The model that is actively used in time series and text mining recently is LSTM (Long Short-Term Memory Models) model utilizing deep learning model structure. The LSTM model is a model that emerged to resolve the Long-Term Dependency Problem that occurs during the Backpropagation Through Time (BPTT) process of RNN. LSTM models have resolved the problem of forecasting very well using variable sequence data, and are still widely used.In this paper study, we used the data of the Call Detailed Record (CDR) provided by KT to identify the migration path of people who are expected to be closely related to the virus. Introduce the results of predicting the path of movement by learning the LSTM model using the path of the person concerned. The results of this study could be used to predict the route of HPAI propagation and to select routes or areas to focus on quarantine and to reduce HPAI spread.

• Proceedings of the Korea Water Resources Association Conference
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• 2016.05a
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• pp.394-398
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• 2016

Numerical analysis was conducted using Delft3D developed by Deltares in Netherlands to predict long-term river bed changes in Saemangeum Area. Tidal flow, discharge through the drainage gates and river bed changes in numerical model was verified by comparing to the results of field observation and hydraulic experiments. We calculated long-term river bed changes in Saemangeum area for 10 years from 2031 to 2040 after completion of development in Saemangeum. It is shown that 70 cm and 139 cm of accumulation occur in estuaries of Dongjin River and Mankyong River, respectively. Variation of flood level was also investigated considering long-term river bed changes. There was no change in estuary of Dongjin River but maximum flood level in estuary of Mankyong River increased 81 cm.

• Nuclear Engineering and Technology
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• v.52 no.7
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• pp.1429-1435
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• 2020

Applying an accurate parametric prediction model to identify abnormal or false pressurizer water levels (PWLs) is critical to the safe operation of marine pressurized water reactors (PWRs). Recently, deep-learning-based models have proved to be a powerful feature extractor to perform high-accuracy prediction. However, the effectiveness of models still suffers from two issues in PWL prediction: the correlations shifting over time between PWL and other feature parameters, and the example imbalance between fluctuation examples (minority) and stable examples (majority). To address these problems, we propose a cost-sensitive mechanism to facilitate the model to learn the feature representation of later examples and fluctuation examples. By weighting the standard mean square error loss with a cost-sensitive factor, we develop a Cost-Sensitive Long Short-Term Memory (CSLSTM) model to predict the PWL of PWRs. The overall performance of the CSLSTM is assessed by a variety of evaluation metrics with the experimental data collected from a marine PWR simulator. The comparisons with the Long Short-Term Memory (LSTM) model and the Support Vector Regression (SVR) model demonstrate the effectiveness of the CSLSTM.