• Atmosphere
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• v.29 no.5
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• pp.671-687
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• 2019

This study evaluates the long-term seasonal predictability of summer (June, July and August) heatwaves over South Korea using 30-year (1989~2018) Hindcast data of the Pusan National University Coupled General Circulation Model (PNU CGCM)-Weather Research and Forecasting (WRF) chain. Heatwave indices such as Number of Heatwave days (HWD), Heatwave Intensity (HWI) and Heatwave Warning (HWW) are used to explore the long-term seasonal predictability of heatwaves. The prediction skills for HWD, HWI, and HWW are evaluated in terms of the Temporal Correlation Coefficient (TCC), Root Mean Square Error (RMSE) and Skill Scores such as Heidke Skill Score (HSS) and Hit Rate (HR). The spatial distributions of daily maximum temperature simulated by WRF are similar overall to those simulated by NCEP-R2 and PNU CGCM. The WRF tends to underestimate the daily maximum temperature than observation because the lateral boundary condition of WRF is PNU CGCM. According to TCC, RMSE and Skill Score, the predictability of daily maximum temperature is higher in the predictions that start from the February and April initial condition. However, the PNU CGCM-WRF chain tends to overestimate HWD, HWI and HWW compared to observations. The TCCs for heatwave indices range from 0.02 to 0.31. The RMSE, HR and HSS values are in the range of 7.73 to 8.73, 0.01 to 0.09 and 0.34 to 0.39, respectively. In general, the prediction skill of the PNU CGCM-WRF chain for heatwave indices is highest in the predictions that start from the February and April initial condition and is lower in the predictions that start from January and March. According to TCC, RMSE and Skill Score, the predictability is more influenced by lead time than by the effects of topography and/or terrain feature because both HSS and HR varies in different leads over the whole region of South Korea.

• Journal of rehabilitation welfare engineering & assistive technology
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• v.9 no.2
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• pp.137-144
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• 2015

In this paper, we present an inertial sensor-based gait analysis system to measure and analyze lower-limb movements. We developed an integral AHRS(Attitude Heading Reference System) using a combination of rate gyroscope, accelerometer and magnetometer sensor signals. Several AHRS modules mounted on segments of the patient's body provide the quaternions representing the patient segments's orientation in space. And a method is also proposed for calculating three-dimensional inter-segment joint angle which is an important bio-mechanical measure for a variety of applications related to rehabilitation. To evaluate the performance of our AHRS module, the Vicon motion capture system, which offers millimeter resolution of 3D spatial displacements and orientations, is used as a reference. The evaluation resulted in a RMSE(Root Mean Square Error) of 1.08 and 1.72 degree in yaw and pitch angle. In order to evaluate the performance of our the gait analysis system, we compared the joint angle for the hip, knee and ankle with those provided by Vicon system. The result shows that our system will provide an in-depth insight into the effectiveness, appropriate level of care, and feedback of the rehabilitation process by performing real-time limb or gait analysis during the post-stroke recovery.

• Journal of the Korean Association of Geographic Information Studies
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• v.15 no.1
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• pp.99-118
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• 2012

The aim of this study is to define a correlation between spatial distribution characteristics of FFG(Functional Feeding Groups) of aquatic insects and related environmental factors in the Geum River based on the theory of RCC(River Continuum Concept). For that objective we had used SMRA(Stepwise Multiple Regression Analysis) method to analyze close relationship between the distribution of aquatic insects and the physical and chemical factors that may affect their inhabiting environment in the study area. And then, a probabilistic method named Frequency Ratio Model(FRM) and spatial analysis function of GIS were applied to produce a predictive distribution map of biota community considering their distribution characteristics according to the environmental factors as related variables. As a result of SMRA, the values of decision coefficient for factors of elevation, stream width, flow velocity, conductivity, temperature and percentage of sand showed higher than 0.5. Therefore these 6 environmental factors were considered as major factors that might affect the distribution characteristics of aquatic insects. Finally, we had calculated RMSE(Root Mean Square Error) between the predicted distribution map and prior survey database from other researches to verify the result of this study. The values of RMSE were calculated from 0.1892 to 0.4242 according to each FFG so we could find out a high reliability of this study. The results of this study might be used to develop a new estimation method for aquatic ecosystem with macro invertebrate community and also be used as preliminary data for conservation and restoration of stream habitats.

• Journal of the Korea Concrete Institute
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• v.23 no.1
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• pp.77-86
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• 2011

The concrete creep deformation of a hybrid composite section can cause additional deformation of the composite section and the stress relaxation of pre-compressive stress on the concrete section due to partial restraint of the deformation. In this study, the stress relaxation coefficient method (SRCM) is derived for simple analysis of complicate hybrid or composite sections for engineering purpose. Also, an equation of the stress relaxation coefficient (SRC) required for the SRCM is proposed. The SRCM is derived with the parameters of a creep coefficient, section and loading properties using the same method as the constant-creep step-by-step method (CC-SSM). The errors of the SRCM is improved by using the proposed SRC equation than the average SRC's which were estimated from the CC-SSM. The root mean square error (RMSE) of the SRCM with the proposed SRC equation for concrete with creep coefficient less than 3 was less than 1.2% to the creep deformation at the free condition and was 3.3% for the 99% reliability. The proposed SRC equation reflects the internal restraint of composite sections, and the effective modulus of elasticity computed with the proposed SRC can be used effectively to estimate the rigidity of a composite section in a numerical analysis which can be applied in analysis of the external restrain effect of boundary conditions.

• Journal of the Korean earth science society
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• v.38 no.2
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• pp.129-140
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• 2017

Global solar radiation was calculated in this research using ground-base measurement data, meteorological satellite data, and GWNU (Gangneung-Wonju National University) solar radiation model. We also analyzed the accuracy of the GWNU model by comparing the observed solar radiation according to the total cloud cover. Our research was based on the global solar radiation of the GWNU radiation site in 2012, observation data such as temperature and pressure, humidity, aerosol, total ozone amount data from the Ozone Monitoring Instrument (OMI) sensor, and Skyview data used for evaluation of cloud mask and total cloud cover. On a clear day when the total cloud cover was 0 tenth, the calculated global solar radiations using the GWNU model had a high correlation coefficient of 0.98 compared with the observed solar radiation, but root mean square error (RMSE) was relatively high, i.e., $36.62Wm^{-2}$. The Skyview equipment was unable to determine the meteorological condition such as thin clouds, mist, and haze. On a cloudy day, regression equations were used for the radiation model to correct the effect of clouds. The correlation coefficient was 0.92, but the RMSE was high, i.e., $99.50Wm^{-2}$. For more accurate analysis, additional analysis of various elements including shielding of the direct radiation component and cloud optical thickness is required. The results of this study can be useful in the area where the global solar radiation is not observed by calculating the global solar radiation per minute or time.

• Korean Chemical Engineering Research
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• v.45 no.1
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• pp.32-38
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• 2007

A batch Process has a multi-way data structure that consists of batch-time-variable axis, so the statistical modeling of a batch process is a difficult and challenging issue to the process engineers. In this study, We applied a statistical process control technique to the general batch process data, and implemented a fault-detection and Statistical process control system that was able to detect, identify and diagnose the fault. Semiconductor etch process and semi-batch styrene-butadiene rubber process data are used to case study. Before the modeling, we pre-processed the data using the multi-way unfolding technique to decompose the data structure. Multivariate regression techniques like support vector regression and partial least squares were used to identify the relation between the process variables and process condition. Finally, we constructed the root mean squared error chart and variable contribution chart to diagnose the faults.

• Journal of Korea Water Resources Association
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• v.51 no.9
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• pp.803-812
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• 2018

The purpose of this study is to develop Particle Swarm Optimization (PSO) automatic calibration algorithm with multi-objective functions by Python, and to evaluate the applicability by applying the algorithm to the Soil and Water Assessment Tool (SWAT) watershed modeling. The study area is the upstream watershed of Gongdo observation station of Anseongcheon watershed ($364.8km^2$) and the daily observed streamflow data from 2000 to 2015 were used. The PSO automatic algorithm calibrated SWAT streamflow by coefficient of determination ($R^2$), root mean square error (RMSE), Nash-Sutcliffe efficiency ($NSE_Q$), and especially including $NSE_{INQ}$ (Inverse Q) for lateral, base flow calibration. The results between automatic and manual calibration showed $R^2$ of 0.64 and 0.55, RMSE of 0.59 and 0.58, $NSE_Q$ of 0.78 and 0.75, and $NSE_{INQ}$ of 0.45 and 0.09, respectively. The PSO automatic calibration algorithm showed an improvement especially the streamflow recession phase and remedied the limitation of manual calibration by including new parameter (RCHRG_DP) and considering parameters range.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.34 no.5
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• pp.525-534
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• 2016

In South Korea, paddy rice has been consumed over the entire region and it is the main source of income for farmers, thus mathematical model for the estimation of rice yield is required for such decision-making processes in agriculture. The objectives of our study are to: (1) develop rice yield estimation model using Convolutional Neural Networks(CNN), (2) choose hyper-parameters for the model which show the best performance and (3) investigate whether CNN model can effectively predict the rice yield by the comparison with the model using Artificial Neural Networks(ANN). Weather and MODIS(The MOderate Resolution Imaging Spectroradiometer) products from April to September in year 2000~2013 were used for the rice yield estimation models and cross-validation was implemented for the accuracy assessment. The CNN and ANN models showed Root Mean Square Error(RMSE) of 36.10kg/10a, 48.61kg/10a based on rice points, respectively and 31.30kg/10a, 39.31kg/10a based on 'Si-Gun-Gu' districts, respectively. The CNN models outperformed ANN models and its possibility of application for the field of rice yield estimation in South Korea was proved.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.11 no.2
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• pp.204-208
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• 2018

In this paper, we proposed and evaluated the time series deep learning prediction model for learning fluctuation pattern of stock price. Recurrent neural networks, which can store previous information in the hidden layer, are suitable for the stock price prediction model, which is time series data. In order to maintain the long - term dependency by solving the gradient vanish problem in the recurrent neural network, we use LSTM with small memory inside the recurrent neural network. Furthermore, we proposed the stock price prediction model using bidirectional LSTM recurrent neural network in which the hidden layer is added in the reverse direction of the data flow for solving the limitation of the tendency of learning only based on the immediately preceding pattern of the recurrent neural network. In this experiment, we used the Tensorflow to learn the proposed stock price prediction model with stock price and trading volume input. In order to evaluate the performance of the stock price prediction, the mean square root error between the real stock price and the predicted stock price was obtained. As a result, the stock price prediction model using bidirectional LSTM recurrent neural network has improved prediction accuracy compared with unidirectional LSTM recurrent neural network.

• Journal of Biosystems Engineering
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• v.41 no.4
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• pp.357-364
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• 2016

Purpose: This study was performed to define the drying characteristics of sorghum by developing thin layer drying equations and evaluating various grain drying equations. Thin layer drying equations lay the foundation characteristics to establish the thick layer drying equations, which can be adopted to determine the design conditions for an agricultural dryer. Methods: The drying rate of sorghum was measured under three levels of drying temperature ($40^{\circ}C$, $50^{\circ}C$, and $60^{\circ}C$) and relative humidity (30%, 40%, and 50%) to analyze the drying process and investigate the drying conditions. The drying experiment was performed until the weight of sorghum became constant. The experimental constants of four thin layer drying models were determined by developing a non-linear regression model along with the drying experiment results. Result: The half response time (moisture ratio = 0.5) of drying, which is an index of the drying rate, was increased as the drying temperature was high and relative humidity was low. When the drying temperature was $40^{\circ}C$ at a relative humidity (RH) of 50%, the maximum half response time of drying was 2.8 h. Contrastingly, the maximum half response time of drying was 1.2 h when the drying temperature was $60^{\circ}C$ at 30% RH. The coefficient of determination for the Lewis model, simplified diffusion model, Page model, and Thompson model was respectively 0.9976, 0.9977, 0.9340, and 0.9783. The Lewis model and the simplified diffusion model satisfied the drying conditions by showing the average coefficient of determination of the experimental constants and predicted values of the model as 0.9976 and Root Mean Square Error (RMSE) of 0.0236. Conclusion: The simplified diffusion model was the most suitable for every drying condition of drying temperature and relative humidity, and the model for the thin layer drying is expected to be useful to develop the thick layer drying model.