• Korean Journal of Agricultural Science
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• v.25 no.2
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• pp.247-259
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• 1998

Analyses which loads were regarded as instant load and gradual step load were performed with data measured on gradually loaded field, and the results were inspected to find effect of load condition, and final settlements predicted by Hyperbolic, Tan's, Asaoka's, and Monden's method were compared with each other. According to above analyses, the following conclusions were obtained. Settlement curves which loads were regarded as instant load and gradual step load were beginning to coincide at time of twice duration of embankment. On the ground installed vertical drain, the result of Hyperbolic, Tan's, Asaoka's, Monden's, curve fitting I, and curve fitting II (simple, Carrillo) methods make conclude that Asaoka, curve fitting I, and curve fitting II methods agree with measured settlement.

• Journal of Korean Society of Transportation
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• v.34 no.2
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• pp.158-167
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• 2016

This study evaluates potential sources of errors in k-NN(k-nearest neighbor) algorithm such as procedures, variables, and input data. Previous research has been thoroughly reviewed for understanding fundamentals of k-NN algorithm that has been widely used for short-term traffic states prediction. The framework of this algorithm commonly includes historical data smoothing, pattern database, similarity measure, k-value, and prediction horizon. The outcomes of this study suggests that: i) historical data smoothing is recommended to reduce random noise of measured traffic data; ii) the historical database should contain traffic state information on both normal and event conditions; and iii) trial and error method can improve the prediction accuracy by better searching for the optimum input time series and k-value. The study results also demonstrates that predicted error increases with the duration of prediction horizon and rapidly changing traffic states.

• Korean Journal of Remote Sensing
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• v.30 no.5
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• pp.587-596
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• 2014

Soil moisture is one of the most important interests in hydrological response and the interaction between the land surface and atmosphere. Estimation of Antecedent Wetness Conditions (AWC) which is soil moisture condition prior to a rainfall in the basin should be considered for rainfall-runoff prediction. In this study, Soil Wetness Index (SWI), Antecedent Precipitation Index ($API_5$), remotely sensed Soil Moisture ($SM_{rs}$), and 5 days ground Soil Moisture ($SM_{g5}$) were selected to estimate the AWC at four study area in the Korean Peninsula. The remotely sensed soil moisture data were taken from the AMSR-E soil moisture archive. The maximum potential retention ($S_{obs}$) was obtained from direct runoff and rainfall using Soil Conservation Service-Curve Number (SCS-CN) method by rainfall data of 2011 for each study area. Results showed the great correlations between the maximum potential retention and SWI with a mean correlation coefficient which is equal to -0.73. The results of time length representing the time scale of soil moisture showed a gap from region to region. It was due to the differences of soil types and the characteristics of study area. Since the remotely sensed soil moisture has been proved as reasonable hydrological variables to predict a wetness in the basin, it should be continuously monitored.

• KDI Journal of Economic Policy
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• v.14 no.1
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• pp.121-145
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• 1992

This paper examines characteristics of time series data related to the construction investment(stationarity and time series components such as secular trend, cyclical fluctuation, seasonal variation, and random change) and surveys predictibility, fitness, and explicability of independent variables of various models to build a short-term construction investment forecasting model suitable for current economic circumstances. Unit root test, autocorrelation coefficient and spectral density function analysis show that related time series data do not have unit roots, fluctuate cyclically, and are largely explicated by lagged variables. Moreover it is very important for the short-term construction investment forecasting to grasp time lag relation between construction investment series and leading indicators such as building construction permits and value of construction orders received. In chapter 3, we explicate 7 forecasting models; Univariate time series model (ARIMA and multiplicative linear trend model), multivariate time series model using leading indicators (1st order autoregressive model, vector autoregressive model and error correction model) and multivariate time series model using National Accounts data (simple reduced form model disconnected from simultaneous macroeconomic model and VAR model). These models are examined by 4 statistical tools that are average absolute error, root mean square error, adjusted coefficient of determination, and Durbin-Watson statistic. This analysis proves two facts. First, multivariate models are more suitable than univariate models in the point that forecasting error of multivariate models tend to decrease in contrast to the case of latter. Second, VAR model is superior than any other multivariate models; average absolute prediction error and root mean square error of VAR model are quitely low and adjusted coefficient of determination is higher. This conclusion is reasonable when we consider current construction investment has sustained overheating growth more than secular trend.

• Journal of the Korean Geotechnical Society
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• v.29 no.1
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• pp.13-22
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• 2013

This paper presents the results of a numerical investigation into the behavior of retaining wall subject to cycles of wetting and drying due to rainfall. The stress-pore pressure coupled finite element modeling strategy was first established for stimulating the wall behavior. A series of finite element analyses were then performed on a range of conditions including different rainfall and backfill conditions. The results indicated that the rainfall intensity was the primary influencing factor for the wall behavior. Also revealed was that the pre-rainfall condition determines the magnitudes and the distribution of matric suction which in fact has a significant impact on the behavior of wall during a major rainfall. This result demonstrates the importance of incorporating the pre-rainfall condition for numerical modeling of walls during heavy rainfall. Practical implications of the findings from this study are discussed in great detail.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.47 no.5
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• pp.43-51
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• 2010

Attenuation coefficients of medical ultrasound not only reflect the pathological information of tissues scanned but also provide the quantitative information to compensate the decay of backscattered signals for other medical ultrasound parameters. Based on the frequency-selective attenuation property of human tissues, attenuation estimation methods in spectral domain have difficulties for real-time implementation due to the complexicity while estimation methods in time domain do not achieve the compensation for the diffraction effect effectively. In this paper, we propose the modified VSA method, which compensates the diffraction with reference phantom in time domain, using adaptive bandpass filters with decreasing center frequencies along depths. The adaptive bandpass filtering technique minimizes the distortion of relative echogenicity of wideband transmit pulses and maximizes the signal-to-noise ratio due to the random scattering, especially at deeper depths. Since the filtering center frequencies change according to the accumulated attenuation, the proposed algorithm improves estimation accuracy and precision comparing to the fixed filtering method. Computer simulation and experimental results using tissue-mimicking phantoms demonstrate that the distortion of relative echogenicity is decreased at deeper depths, and the accuracy of attenuation estimation is improved by 5.1% and the standard deviation is decreased by 46.9% for the entire scan depth.

• Journal of Navigation and Port Research
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• v.44 no.6
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• pp.517-523
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• 2020

Pyeongtaek·Dangjin port handles the largest volume of finished vehicles in Korea, including more than 95% of imported cars. However, since the volume of imported cars has been stagnant since 2015, officials planning to invest in port development or automobile-related industries must make new forecasts. Economic variables such as the GDP often have been used in predicting automobile volume, but prior research showed that the impact of these economic variables on automobile volume I has been gradually decreasing in developed countries. These variables remain important predictors, however, in developing countries that experience rapid economic growth. In this study, predicting the volume of imported passenger cars at Pyeongtaek·Dangjin port, the decreasing Korean population was a major factor we considered. Our forecast showed that the volume of imported passenger cars at Pyeongtaek·Dangjin port will gradually decrease -by 2021. The Mean Absolute Percentage Error (MAPE) verification was performed to measure the accuracy of the predicted results, and the scenario analysis was performed on the share of imported passenger cars.

• Journal of Navigation and Port Research
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• v.44 no.6
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• pp.564-570
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• 2020

In order to propose a realistic demand forecast for harbor pilots, define a direction for securing a supply of pilots for the betterment of national logistic services, and ensure the competitiveness of Korean ports, this study intended first to propose a new forecasting process for harbor pilot requirements through conducting analysis of determining factors affecting harbor pilot demand. Additionally, analyzing relevant previous studies allowed us to estimate the number of pilots required in the past and asses the studies limitations. Our second purpose was to propose a more stable allocation method among different pilot areas after forecasting the demand of harbor pilots until 2027 through application of the new forecasting process. From this application, the total number of pilots required was forecasted at 270, suggesting the total demand for harbor pilots will be increased by 7.57% compared with 251 pilots in 2018.

• Journal of Digital Convergence
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• v.15 no.11
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• pp.245-250
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• 2017

Flash floods is defined as the flooding of intense rainfall over a relatively small area that flows through river and valley rapidly in short time with no advance warning. So that it can cause damage property and casuality. This study is to establish the flash-flood warning system using 38 accident data, reported from the National Disaster Information Center and Land Surface Model(TOPLATS) between 2009 and 2012. Three variables were used in the Land Surface Model: precipitation, soil moisture, and surface runoff. The three variables of 6 hours preceding flash flood were reduced to 3 factors through factor analysis. Decision tree, random forest, Naive Bayes, Support Vector Machine, and logistic regression model are considered as big data methods. The prediction performance was evaluated by comparison of Accuracy, Kappa, TP Rate, FP Rate and F-Measure. The best method was suggested based on reproducibility evaluation at the each points of flash flood occurrence and predicted count versus actual count using 4 years data.

• Journal of the Korean Geotechnical Society
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• v.28 no.8
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• pp.5-19
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• 2012

Vertical drains along with the preloading technique have been commonly used to enhance the consolidation rate of dredged placement formation. In practice, vertical drains are usually installed in the process of self-weight consolidation of a dredged soil deposit because this process takes considerable time to be completed, which makes conventional analytical or numerical models difficult to quantify the consolidation behavior. In this paper, we propose a governing partial differential equation and develop a numerical model for 2-D axisymmetric non-linear finite strain consolidation considering self-weight consolidation to predict the behavior of a vertical drain in the dredged placement foundation which is installed during the self-weight consolidation. In order to verify the developed model in this paper, results of the numerical analysis are compared with that of the lab-scaled self-weight consolidation test. In addition, the model verification has been carried out by comparing with the simplified method. The comparisons show that the developed model can properly simulate the consolidation of the dredged placement formation with the vertical drains installed during the self-weight consolidation. Finally, the effect of construction schedule of vertical drains and of pre-loading during the self-weight consolidation is examined by simulating an imaginary dredged material placement site with a thickness of 10 m and 20 m, respectively. This simulation infers the applicability of the proposed method in this research for designing a soil improvement in a soft dredged deposit when vertical drains and pre-loading are implemented before the self-weight consolidation ceases.