• The Journal of the Petrological Society of Korea
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• v.18 no.1
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• pp.31-47
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• 2009

We performed petrological, geochemical, and geochronological study for the Pyeonghae granite gneiss and the Hada leuco-granite gneiss intruding the Paleoproterozoic meta-sedimentary rocks (pyeonghae formation and Wonnam formation) of the Pyeonghae area located in northeastem part of the Yeongnam (Sobaeksan) massif. The Pyeonghae granite gneiss generally has higher abundance of mafic minerals (biotite etc.), and posesses higher ${Fe_2}{O_3}^t$, MgO, CaO, $TiO_2$, $P_{2}O_{5}$ contents but lower $SiO_2$ and $K_{2}O$ contents than the Hada leuco-granite gneiss which tends to have slightly high $Al_{2}O_{3}$ and $Na_{2}O$ contents and slightly high larger negative Eu anomalies. However both gneisses reveal very similar REE concentrations and chondrite-normalized patterns and apparently show differentiation trend affected by crystallization of biotite, plagioclase, apatite and sphene. Their peraluminous and calc-alkaline chemistry suggests tectonic environment of volcanic arc. SHRIMP Zircon U-Pb age determinations yield upper intercept ages of $1990{\pm}23\;Ma$ ($2{\sigma}$) and $1939{\pm}41\;Ma$ ($2{\sigma}$), and weighted mean $^{207}Pb/^{206}Pb$ ages of $1982{\pm}6.3\;Ma$ ($2{\sigma}$) and $1959{\pm}28\;Ma$ ($2{\sigma}$) for the Pyeonghae granite gneiss and the Hada leuco-granite gneiss respectively, showing overlapping ages within the error. Our study suggests that the Precambrian granitoids in this area intruded contemporaneously with the Buncheon granite gneissin volcanic arc environment.

• International Journal of Highway Engineering
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• v.12 no.4
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• pp.93-100
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• 2010

Trip generation is the first step in the conventional four-step model and has great effects on overall demand forecasting, so accuracy really matters at this stage. A linear regression model is widely used as a current trip generation model for such plans as urban transportation and SOC facilities, assuming that the relationship between each socio-economic index and trip generation stays linear. But when rapid urban development or an urban planning structure has changed, socio-economic index data for trip estimation may be lacking to bring many errors in estimated trip. Hence, instead of assuming that a socio-economic index widely used for a general purpose, this study aims to develop a new trip generation model by type based on the market separation for the variables to reflect the characteristics of various zones. The study considered the various characteristics (land use, socio-economic) of zones to enhance the forecasting accuracy of a trip generation model, the first-step in forecasting transportation demands. For a market separation methodology to improve forecasting accuracy, data mining (CART) on the basis of trip generation was used along with a regression analysis. Findings of the study indicated as follows : First, the analysis of zone characteristics using the CART analysis showed that trip production was under the influence of socio-economic factors (men-women relative proportion, age group (22 to 29)), while trip attraction was affected by land use factors (the relative proportion of business facilities) and the socio-economic factor (the relative proportion of third industry workers). Second, model development by type showed as a result that trip generation coefficients revealed 0.977 to 0.987 (trip/person) for "production" 0.692 to 3.256 (trip/person) for "attraction", which brought the necessity for type classifications. Third, a measured verification was conducted, where "production" and "attraction" showed a higher suitability than the existing model. The trip generation model by type developed in this study, therefore, turned out to be superior to the existing one.

• Journal of The Korean Society of Agricultural Engineers
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• v.61 no.6
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• pp.111-121
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• 2019

Evapotranspiration (ET) of vegetation is one of the major components of the hydrologic cycle, and its accurate estimation is important for hydrologic water balance, irrigation management, crop yield simulation, and water resources planning and management. For agricultural crops, ET is often calculated in terms of a short or tall crop reference, such as well-watered, clipped grass (reference crop evapotranspiration, $ET_o$). The Penman-Monteith equation recommended by FAO (FAO 56-PM) has been accepted by researchers and practitioners, as the sole $ET_o$ method. However, its accuracy is contingent on high quality measurements of four meteorological variables, and its use has been limited by incomplete and/or inaccurate input data. Therefore, this study evaluated the applicability of Backpropagation Neural Network (BPNN) model for estimating $ET_o$ from less meteorological data than required by the FAO 56-PM. A total of six meteorological inputs, minimum temperature, average temperature, maximum temperature, relative humidity, wind speed and solar radiation, were divided into a series of input groups (a combination of one, two, three, four, five and six variables) and each combination of different meteorological dataset was evaluated for its level of accuracy in estimating $ET_o$. The overall findings of this study indicated that $ET_o$ could be reasonably estimated using less than all six meteorological data using BPNN. In addition, it was shown that the proper choice of neural network architecture could not only minimize the computational error, but also maximize the relationship between dependent and independent variables. The findings of this study would be of use in instances where data availability and/or accuracy are limited.

• Journal of agricultural medicine and community health
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• v.43 no.4
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• pp.234-249
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• 2018

This study was performed to identify the impact of awareness and educational experiences on cardiopulmonary resuscitation in the ability to execute of cardiopulmonary resuscitation among Korean adults. This study used original data of 2014 Community Health Data Survey. 228,712 participants in this survey were resident in South Korea who is aged 19 or older on July 2014. Participants in this survey were sampled an average of 900 residents(target error ${\pm}3percent$) per community health center of Korea. Data were analyzed by using R 3.1.3 employing chi-squared test, fisher's exact analysis, and logistic regression analysis. Ability to execute CPR was significantly higher in males(3.34 time), higher the education level (1.61 times), the white color occupation (1.14 times), the higher the income level (1.07 times), the higher the education level (0.91 times), non-hypertensive patients (1.12 times), non-diabetic patients (1.16 times), non-dyslipidemic patients (0.86 times), non-stroke patients (0.30 times), CPR education experience group (3.25 times), CPR experience group with manikin-based training (4.30 times), higher subjective health status (1.08 times, 1.16 times) respectively. This study identified that awareness, educational experience, and mannequin-based learning experience of CPR impacted on the ability to execute CPR. Responding to education-related factors could contribute to reducing the rate of out-of-hospital acute cardiac arrest by improving the ability to execute CPR of the general public.

• Journal of Korea Water Resources Association
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• v.53 no.12
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• pp.1159-1172
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• 2020

This study applied deep convolution neural network based on U-Net and SegNet using long period weather radar data to very short-term rainfall prediction. And the results were compared and evaluated with the translation model. For training and validation of deep neural network, Mt. Gwanak and Mt. Gwangdeoksan radar data were collected from 2010 to 2016 and converted to a gray-scale image file in an HDF5 format with a 1km spatial resolution. The deep neural network model was trained to predict precipitation after 10 minutes by using the four consecutive radar image data, and the recursive method of repeating forecasts was applied to carry out lead time 60 minutes with the pretrained deep neural network model. To evaluate the performance of deep neural network prediction model, 24 rain cases in 2017 were forecast for rainfall up to 60 minutes in advance. As a result of evaluating the predicted performance by calculating the mean absolute error (MAE) and critical success index (CSI) at the threshold of 0.1, 1, and 5 mm/hr, the deep neural network model showed better performance in the case of rainfall threshold of 0.1, 1 mm/hr in terms of MAE, and showed better performance than the translation model for lead time 50 minutes in terms of CSI. In particular, although the deep neural network prediction model performed generally better than the translation model for weak rainfall of 5 mm/hr or less, the deep neural network prediction model had limitations in predicting distinct precipitation characteristics of high intensity as a result of the evaluation of threshold of 5 mm/hr. The longer lead time, the spatial smoothness increase with lead time thereby reducing the accuracy of rainfall prediction The translation model turned out to be superior in predicting the exceedance of higher intensity thresholds (> 5 mm/hr) because it preserves distinct precipitation characteristics, but the rainfall position tends to shift incorrectly. This study are expected to be helpful for the improvement of radar rainfall prediction model using deep neural networks in the future. In addition, the massive weather radar data established in this study will be provided through open repositories for future use in subsequent studies.

• Korean Journal of Mineralogy and Petrology
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• v.35 no.1
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• pp.41-50
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• 2022

To study the properties of Sr²⁺ exchange into zeolite A with increasing the molar concentration of Na⁺ in given exchange solution, four single crystals of fully dehydrated Sr²⁺- and Na⁺- exchanged zeolite A were prepared by the bath method using mixed ion-exchange solutions. The Sr(NO₃)₂:NaNO₃ molar rations of the ion exchange solution were 1:1(crystal 1), 1:100(crystal 2), 1:250(crystal 3), and 1:500 (crystal 4), respectively, with a total concentration of 0.05 M. The single-crystals were then vacuum dehydration at 623 K and 1×10^-4 Pa for 2 days. Their single-crystal structures were determined by single-crystal synchrotron X-ray diffraction techniques in the cubic space group Pm3-m, at 100(1) K, and were then refined to the final error indices of R₁/wR₂=0.047/0.146, 0.048/0.142, 0.036/0.128, and 0.040/0.156 for crystals 1, 2, 3, and 4, respectively. In crystals 1 and 2, the 6 Sr²⁺ ions are found at three different crystallographic sites. In crystal 3, 1 Sr²⁺ and 10 Na⁺ ions are found in large cavity and sodalite unit. In crystal 4, only 12 Na⁺ ions occupy three equipoints. The degree of Sr²⁺ ion-exchange decreased sharply from 100 to 16.7 to 0% as the initial Na⁺ concentration increase and the Sr²⁺ concentration decrease. In addition, the unit cell constant of the zeolite framework decreased with this lower level of Sr²⁺ exchange.

• Korean Journal of Remote Sensing
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• v.38 no.6_3
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• pp.1827-1836
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• 2022

Disasters that occur unexpectedly are difficult to predict. In addition, the scale and damage are increasing compared to the past. Sometimes one disaster can develop into another disaster. Among the four stages of disaster management, search and rescue are carried out in the response stage when an emergency occurs. Therefore, personnel such as firefighters who are put into the scene are put in at a lot of risk. In this respect, in the initial response process at the disaster site, robots are a technology with high potential to reduce damage to human life and property. In addition, Light Detection And Ranging (LiDAR) can acquire a relatively wide range of 3D information using a laser. Due to its high accuracy and precision, it is a very useful sensor when considering the characteristics of a disaster site. Therefore, in this study, development and experiments were conducted so that the robot could perform real-time monitoring at the disaster site. Multi-sensor module was developed by combining LiDAR, Inertial Measurement Unit (IMU) sensor, and computing board. Then, this module was mounted on the robot, and a customized Simultaneous Localization and Mapping (SLAM) algorithm was developed. A method for stably mounting a multi-sensor module to a robot to maintain optimal accuracy at disaster sites was studied. And to check the performance of the module, SLAM was tested inside the disaster building, and various SLAM algorithms and distance comparisons were performed. As a result, PackSLAM developed in this study showed lower error compared to other algorithms, showing the possibility of application in disaster sites. In the future, in order to further enhance usability at disaster sites, various experiments will be conducted by establishing a rough terrain environment with many obstacles.

• Journal of Korea Water Resources Association
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• v.57 no.3
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• pp.151-164
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• 2024

In water treatment plants supplying potable water, the management of chlorine concentration in water treatment processes involving pre-chlorination or intermediate chlorination requires process control. To address this, research has been conducted on water quality prediction techniques utilizing AI technology. This study developed an AI-based predictive model for automating the process control of chlorine disinfection, targeting the prediction of residual chlorine concentration downstream of sedimentation basins in water treatment processes. The AI-based model, which learns from past water quality observation data to predict future water quality, offers a simpler and more efficient approach compared to complex physicochemical and biological water quality models. The model was tested by predicting the residual chlorine concentration downstream of the sedimentation basins at Plant, using multiple regression models and AI-based models like Random Forest and LSTM, and the results were compared. For optimal prediction of residual chlorine concentration, the input-output structure of the AI model included the residual chlorine concentration upstream of the sedimentation basin, turbidity, pH, water temperature, electrical conductivity, inflow of raw water, alkalinity, NH₃, etc. as independent variables, and the desired residual chlorine concentration of the effluent from the sedimentation basin as the dependent variable. The independent variables were selected from observable data at the water treatment plant, which are influential on the residual chlorine concentration downstream of the sedimentation basin. The analysis showed that, for Plant, the model based on Random Forest had the lowest error compared to multiple regression models, neural network models, model trees, and other Random Forest models. The optimal predicted residual chlorine concentration downstream of the sedimentation basin presented in this study is expected to enable real-time control of chlorine dosing in previous treatment stages, thereby enhancing water treatment efficiency and reducing chemical costs.