• The Journal of the Korea Contents Association
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• v.14 no.9
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• pp.598-612
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• 2014

The present study is aimed at inquiring into the meaning of experience of action learning program for adult learners who participated in action learning program of H financial company which was carried out as a means of corporate training. The goal of study is to examine the essential factors of action learning program impacting on the increase of motivation for learning and the improvement of job-related problem-solving ability of the learners who participated in the learning as well as on the increase of motivation for learning and the improvement of job-related problem-solving ability among the components of action learning program. As for research method, 3 main questions and 15 sub-questions about motivation for learning, job-related problem-solving ability, and components of action learning were prepared for 9 learners who participated in the action learning program, and then focus group interviews (F.G.I) were conducted. The results show that action learning program increased motivation for learning by combining concentration of attention and relevance to job, and the degree of organization of learning team was a key element to improving motivation for learning. Also, through development of alternatives and planning/execution, it impacted on improving job-related problem-solving ability of participants. And the interest and support of the administrator were key elements to improving job-related problem-solving ability. In conclusion, the results show that action learning program in corporate job training activities improves motivation for learning of the participants. Therefore, in order to improve job-related problem-solving ability of the participants in job training, more focus should be put on concentration of attention and reinforcement of relevance to the job and more interest and support should be given to organization of appropriate learning teams among components of action learning program. Along with this, the administrator needs to grasp participants' awareness of problems and pay attention and give support to the participants to enhance the performance of planning/execution.

• Atmosphere
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• v.25 no.1
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• pp.155-167
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• 2015

A dynamical seasonal prediction system for boreal winter utilizing cryospheric information was developed. Using the Community Atmospheric Model, version3, (CAM3) as a modeling system, newly developed snow depth initialization method and sea ice concentration treatment were implemented to the seasonal prediction system. Daily snow depth analysis field was scaled in order to prevent climate drift problem before initializing model's snow fields and distributed to the model snow-depth layers. To maximize predictability gain from land surface, we applied one-month-long training procedure to the prediction system, which adjusts soil moisture and soil temperature to the imposed snow depth. The sea ice concentration over the Arctic region for prediction period was prescribed with an anomaly-persistent method that considers seasonality of sea ice. Ensemble hindcast experiments starting at 1st of November for the period 1999~2000 were performed and the predictability gain from the imposed cryospheric informations were tested. Large potential predictability gain from the snow information was obtained over large part of high-latitude and of mid-latitude land as a result of strengthened land-atmosphere interaction in the modeling system. Large-scale atmospheric circulation responses associated with the sea ice concentration anomalies were main contributor to the predictability gain.

• Journal of Advanced Marine Engineering and Technology
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• v.39 no.7
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• pp.685-695
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• 2015

Mud, iron oxide, plaster, salt, minerals, and dissolved metals are sequentially deposited in accordance with the increasing concentration of seawater. In this paper, by using the physical characteristics of the seawater, we propose a new vacuum heat-transfer technology to subsequently obtain the proportion of the dissolved components in a cost-effective manner. Based on the vacuum heat-transfer characteristics of seawater, we comprehensively divide the seawater resource processes into the following four processes: (1) the salt concentration process to the saturation concentration, (2) crystallization process for salt formation, (3) mineral precipitation, and (4) remaining of dissolved metals.

• Journal of Nuclear Fuel Cycle and Waste Technology(JNFCWT)
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• v.2 no.1
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• pp.35-40
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• 2004

Final disposal of radioactive waste generated from Nuclear Power Plant (NPP) requires the detailed information about the characteristics and the quantities of radionuclides in waste package. Most of these radionuclides are difficult to measure and expensive to assay. Thus it is suggested to the indirect method by which the concentration of the Difficult-to-Measure (DTM) nuclide is estimated using the correlations of concentration - it is called the scaling factor - between Easy-to-Measure (Key) nuclides and DTM nuclides with the measured concentration of the Key nuclide. In general, the scaling factor is determined by the log mean average (LMA) method and the regression method. However, these methods are inadequate to apply to fission product nuclides and some activation product nuclides such as 14$^{C}$ and 90$^{Sr}$ . In this study, the artificial neural network (ANN) method is suggested to improve the conventional SF determination methods - the LMA method and the regression method. The root mean squared errors (RMSE) of the ANN models are compared with those of the conventional SF determination models for 14$^{C}$ and 90$^{Sr}$ in two parts divided by a training part and a validation part. The SF determination models are arranged in the order of RMSEs as the following order: ANN model

• Journal of Environmental Health Sciences
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• v.46 no.6
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• pp.702-709
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• 2020

Objectives: Hand sanitizer is made with ethyl alcohol as the main ingredient. Problems related to the use of hand sanitizers and cases of harm caused by the use of hand sanitizers are occurring. This study investigated the usage behavior and recognition level of people using hand sanitizer and identified the chemical components listed in the component label of hand sanitizer. In addition, the methanol and isopropanol contained in hand sanitizer were quantified using HS-GC-MSD. Methods: The investigation of the behavior and recognition of hand sanitizer usage was conducted through a survey of 143 college students and adults. The components marked on 34 types of hand sanitizers were investigated, and methanol and isopropanol concentrations were analyzed using the HS-GC-MSD method. Results: According to the survey, 57% of respondents use hand sanitizers two to three times per day, 92.3% of them do so when in public places and 41.3% of them do so at home. Ethanol, purified water, carbomer, glycerin, and triethanolamine were the ingredients listed in the hand sanitizer. Among the 34 samples, methanol and isopropyl alcohol were detected in 33 samples, the concentration range for methanol was ND-567 ppm, and the concentration range of isopropyl alcohol was ND-2121 ppm. Conclusion: The results of this study have shown that hand sanitizers are being used constantly every day, and methanol, which is not included in the marked content, was detected in a significant concentration compared to wet tissue. It has been found that maintenance of hand sanitizer manufacturing standards and training on how to use them are needed.

• Korean Journal of Plant Resources
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• v.35 no.2
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• pp.192-200
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• 2022

Nitrogen is the most essential macronutrient for the growth of fruit trees and is important factor determining the fruit yield. In order to produce high-quality fruits, it is necessary to supply the appropriate nitrogen fertilizer at the right time. For this, it is a prerequisite to accurately diagnose the nitrogen status of fruit trees. The fastest and most accurate way to determine the nitrogen deficiency of fruit trees is to measure the nitrogen concentration in leaves. However, it is not easy for citrus growers to measure nitrogen concentration through leaf analysis. In this study, several machine learning models were developed to classify the nitrogen deficiency based on the concentration measurement of mineral nutrients in the leaves of tangor Shiranuhi (Citrus unshiu × C. sinensis). The data analyzed from the leaves were increased to about 1,000 training dataset through the bootstrapping method and used to train the models. As a result of testing each model, gradient boosting model showed the best classification performance with an accuracy of 0.971.

• The Journal of the Korea Contents Association
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• v.12 no.8
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• pp.343-354
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• 2012

The purpose of this research was to compare the concentration variation between the sport talented children who have been applied with a sport program and the ordinary children who did not. The experiment group was composed of 59(male:32, female:27) sport talented children who were selected from the center of Busan University sport talent in April 2010. The control group was made up of 148 students who participated in 3 elementary schools located in Busan. Among these ordinary students 80 participants were finally used, as some of the students were excluded who were playing as athlete or studying in Science gifted program, FAIR concentration test sheet was used for this study, which was reformed for Korean version by Oh(2002). Covariance analysis was applied for using SAS 9.1 package, and the following conclusions were drawn. First, both the sport talented group and ordinary group of FAIR concentration's 3 subfactors were improved significantly after 8 months. Second, no difference between the sport talent group and ordinary group were found in the variations of post test of concentration's 3 subfactors. Especially there was no difference among groups in oneway-ANOVA using data of post test.

• Bulletin of the Korean Chemical Society
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• v.33 no.2
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• pp.613-619
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• 2012

We applied several machine learning methods for developing QSAR models for prediction of acute toxicity to fathead minnow. The multiple linear regression (MLR) and artificial neural network (ANN) method were applied to predict 96 h $LC_{50}$ (median lethal concentration) of 555 chemical compounds. Molecular descriptors based on 2D chemical structure were calculated by PreADMET program. The recursive partitioning (RP) model was used for grouping of mode of actions as reactive or narcosis, followed by MLR method of chemicals within the same mode of action. The MLR, ANN, and two RP-MLR models possessed correlation coefficients ($R^2$) as 0.553, 0.618, 0.632, and 0.605 on test set, respectively. The consensus model of ANN and two RP-MLR models was used as the best model on training set and showed good predictivity ($R^2$=0.663) on the test set.

• Journal of Korea Society of Digital Industry and Information Management
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• v.6 no.1
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• pp.197-209
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• 2010

Supply Chain Management have been introduced and used as strategic weapon for many companies. Large investments in the SCM was made, but many companies are not fully getting the performance from the systems. The purpose of this study is to find the determinants of SCM difussion and performance in the perspective of Innovation and Information technology Acceptance. In developing the research model, The model consists of eight independent variables(Management support, Decision Making concentration, IS strategy, training education, relative advantage, technological compatibility, task compatibility, SCM cost), two moderator variables(interorganizational and intraorganizational diffusion), three dependant variables(efficiency, effectiveness, strategic advantage).

• Journal of Korean Society of Water and Wastewater
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• v.21 no.3
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• pp.305-314
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• 2007

Forecasting of water quality variation is not an easy process due to the complicated nature of various water quality factors and their interrelationships. The objective of this study is to test the applicability of neural network models to the forecasting of the water quality at Dalchun station in Han River. Input data is consist of monthly data of concentration of DO, BOD, COD, SS and river flow. And this study selected optimal neural network model through changing the number of hidden layer based on input layer(n) from n to 6n. After neural network theory is applied, the models go through training, calibration and verification. The result shows that the proposed model forecast water quality of high efficiency and developed web-based water quality forecasting system after extend model