• Journal of the Korean Geosynthetics Society
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• v.4 no.4
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• pp.23-37
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• 2005

The factors affecting the long-term design strength of geogrid can be classified into factors on creep deformation, installation damage, temperature, chemical degradation and biological degradation. Especially, creep deformation and installation damage are considered as main factors to determine the long-term design strength of geogrid. Current practice in the design of a reinforced soil structures is to calculate the long-term design strength of a geosynthetic reinforcement damaged during installation by multiplying the two partial safety factors, $RF_{ID}$ and $RF_{CR}$. This method assumes that there is no evaluation of synergy effect between installation damage and creep deformation of geogrids. This paper describes the results of a series of experimental study, which are carried out to assess the combined effect of the installation damage and the creep deformation for the long-term design strength of geogrid reinforcements. A series of field tests was carried out to assess installation damage of various geogrids with respect to different fill materials, and then creep tests are conducted to evaluate the creep deformation of both undamaged and damaged geogrids. The results indicated that the tensile strength reduction factors, RF, considering the combined effect between the installation damage and the creep deformation is less than that calculated by the current design method.

• Journal of Korean Neurosurgical Society
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• v.65 no.1
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• pp.151-160
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• 2022

Continuous monitoring of intracranial pressure is a well established medical procedure. Still, little is known about long-term behavior of intracranial pressure in normal pressure hydrocephalus. The present study is designed to evaluate periodicity of intracranial pressure over long-time scales using intraventricular pressure monitoring in patients with normal pressure hydrocephalus. In addition, the circadian and diurnal patterns of blood pressure and body temperature in those patients are studied. Four patients, selected with "probable" normal pressure hydrocephalus, were monitored for several dozen hours. Intracranial pressure, blood pressure, and body temperature were recorded hourly. Autocorrelation functions were calculated and cross-correlation analysis were carried out to study all the time-series data. Autocorrelation results show that intracranial pressure, blood pressure, and body temperature values follow bimodal (positive and negative) curves over a day. The cross-correlation functions demonstrate causal relationships between intracranial pressure, blood pressure, and body temperature. The results show that long-term fluctuations in intracranial pressure exhibit cyclical patterns with periods of about 24 hours. Continuous intracranial pressure recording in "probable" normal pressure hydrocephalus patients reveals circadian fluctuations not related to the day and night cycle. These fluctuations are causally related to changes in blood pressure and body temperature. The present study reveals the complete loss of the diurnal blood pressure and body temperature rhythmicities in patients with "probable" normal pressure hydrocephalus.

• Journal of Digital Convergence
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• v.10 no.9
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• pp.357-362
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• 2012

We have compared and predicted for non-linear time series data which are real data having different variences using GRCA(1) model and neural network method. In particular, using Korea Composite Stock Price Index rate, mean square errors of prediction are obtained in genaralized random coefficient autoregressive model and neural network method. Neural network method prove to be better in short-term forecasting, however GRCA(1) model perform well in long-term forecasting.

• Journal of the Korean Data and Information Science Society
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• v.23 no.6
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• pp.1309-1317
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• 2012

Stochastic weather generators are commonly used to simulate time series of daily weather, especially precipitation amount. Recently, a generalized linear model (GLM) has been proposed as a convenient approach to fitting these weather generators. In this paper, a stochastic weather generator is considered to model the time series of daily precipitation at Seoul in South Korea. As a covariate, global temperature is introduced to relate long-term temporal scale predictor to short-term temporal predictands. One of the limitations of stochastic weather generators is a marked tendency to underestimate the observed interannual variance of monthly, seasonal, or annual total precipitation. To reduce this phenomenon, we incorporate time series of seasonal total precipitation in the GLM weather generator as covariates. It is veri ed that the addition of these covariates does not distort the performance of the weather generator in other respects.

• Korean Journal of Child Studies
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• v.28 no.5
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• pp.285-296
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• 2007

The purpose of this study was to estimate the future demand for certified teachers at childcare centers. This is an essential step to secure the supply of childcare teachers in the future. To achieve this purpose, the demand for childcare teachers from 2006 to 2020 were estimated using time series techniques with data on the number of childcare teachers from 2002 to 2005. According to time series estimates, the demand for childcare teachers is expected to increase steadily from 1,224 to 1,956 annually. This illustrates the need for mid-term and long-term planing in order to guarantee an adequate supply of childcare teachers.

• The Journal of Asian Finance, Economics and Business
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• v.9 no.4
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• pp.83-92
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• 2022

Due to the uncertainty in the order of the integrated model, the SARIMA-LSTM model, SARIMA-SVR model, LSTM-SARIMA model, and SVR-SARIMA model are constructed respectively to determine the best-combined model for forecasting the China-Russia trade turnover. Meanwhile, the effect of the order of the combined models on the prediction results is analyzed. Using indicators such as MAPE and RMSE, we compare and evaluate the predictive effects of different models. The results show that the SARIMA-LSTM model combines the SARIMA model's short-term forecasting advantage with the LSTM model's long-term forecasting advantage, which has the highest forecast accuracy of all models and can accurately predict the trend of China-Russia trade turnover in the post-epidemic period. Furthermore, the SARIMA - LSTM model has a higher forecast accuracy than the LSTM-ARIMA model. Nevertheless, the SARIMA-SVR model's forecast accuracy is lower than the SVR-SARIMA model's. As a result, the combined models' order has no bearing on the predicting outcomes for the China-Russia trade turnover time series.

• Computers and Concrete
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• v.15 no.4
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• pp.687-707
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• 2015

A finite element computer code for short-term analysis of steel-concrete composite structures is extended to study long-term effects under service loads, in the present work. Long-term effects are important in engineering design because they influence stress and strain distribution of the structural system and therefore contribute to the increment of deflections in these structures. For creep analysis, a rheological model based on a Kelvin chain, with elements placed in series, was employed. The parameters of the Kelvin chain were obtained using Dirichlet series. Creep and shrinkage models, proposed by the CEB FIP 90, were used. The shear-lag phenomenon that takes place at the concrete slab is usually neglected or not properly taken into account in the formulation of beam-column finite elements. Therefore, in this work, a three-dimensional numerical model based on the assemblage of shell finite elements for representing the steel beam and the concrete slab is used. Stud shear connectors are represented for special beam-column elements to simulate the partial interaction at the slab-beam interface. The two-dimensional representation of the concrete slab permits to capture the non-uniform shear stress distribution in the horizontal plane of the slab due to shear-lag phenomenon. The model is validated with experimental results of two full-scale continuous composite beams previously studied by other authors. Results are given in terms of displacements, bending moments and cracking patterns in order to shown the influence of long-term effects in the structural response and also the potentiality of the present numerical code.

• The Journal of Asian Finance, Economics and Business
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• v.7 no.5
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• pp.91-102
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• 2020

The study investigates the relationship between the inflation rate and economic growth to find out the optimal inflation threshold for economic growth. Therefore, this study applied an ordinary least square model (OLS) and the ordinal regression model, and collected the time-series data from 1996 to 2017 to test the relationship between inflation and economic growth in the short-term and long-term. The sample fits the model and is statistically significant. The study showed that 96.6% of correlation between inflation rate and economic growth are close and 4.5% of optimal inflation threshold is appropriate for economic growth. It finds that the optimal inflation threshold is base to perform economic growth, besides the inflation rate is positively related to economic growth. The results support the monetary policy appropriately. This study identifies issues for Government to consider: have a comprehensive solution among macroeconomic policies, monetary policy, fiscal policy and other policies to control and maintain the inflation and stimulate growth; have appropriate policies to regulate inflation to stimulate economic growth over the long term; set a priority goal for sustainable economic growth; not pursue economic growth by maintaining the inflation rate in the long term, but take appropriate measures to stabilize the inflation at the optimal inflation threshold.

• Korean Journal of Metals and Materials
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• v.47 no.10
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• pp.613-620
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• 2009

The Kachanov-Rabotnov (K-R) creep model was proposed to accurately model the long-term creep curves above $10^5$ hours of Alloy 617. To this end, a series of creep data was obtained from creep tests conducted under different stress levels at $950^{\circ}C$. Using these data, the creep constants used in the K-R model and the modified K-R model were determined by a nonlinear least square fitting (NLSF) method, respectively. The K-R model yielded poor correspondence with the experimental curves, but the modified K-R model provided good agreement with the curves. Log-log plots of ${\varepsilon}^{\ast}$-stress and ${\varepsilon}^{\ast}$-time to rupture showed good linear relationships. Constants in the modified K-R model were obtained as ${\lambda}$=2.78, and $k=1.24$, and they showed behavior close to stress independency. Using these constants, long-term creep curves above $10^5$ hours obtained from short-term creep data can be modeled by implementing the modified K-R model.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.16 no.12
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• pp.8576-8584
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• 2015

Load forecasting is needed to make supply and demand plan for a stable supply of electricity. It is also necessary for optimal operational plan of the power system planning. In particular, in order to ensure stable power supply, long-term load forecasting is important. And regional load forecasting is important for tightening supply stability. Regional load forecasting is known to be an essential process for the optimal state composition and maintenance of the electric power system network including transmission lines and substations to meet the load required for the area. Therefore, in this paper we propose a forecasting method using SARIMA during the 12 months (long-term/mid-term) load forecasting by 16 regions of the South Korea.