• KOREAN JOURNAL OF CROP SCIENCE
• /
• v.63 no.2
• /
• pp.158-163
• /
• 2018

Perilla (Perilla frutescens var.frutescens) is an annual plant of the Lamiaceae family, mainly grown for obtaining oil by press extraction after roasting the seeds. Oil yield is one of its important traits, but evaluating this yield is time-consuming, requires many seeds, and is hard to adjust to pedigrees in a breeding field. The objective of this study was to develop a method for selecting high-oil-yield lines in a breeding population without oil extraction. Twenty-three perilla cultivars were used for evaluating the oil yield and seed traits such as seed hardness, seed coat thickness, seed coat proportion and crude fat. After evaluation of the seed traits of 23 perilla cultivars, the ranges of oil yields, seed hardness, seed coat thickness, seed coat proportion, 100-seed weight, and crude fat were 24.68-38.75%, 157-1166 gf, $24-399{\mu}m$, 15.4-41.5%, 2.79-6.69 g, and 33.0-47.8%, respectively. In an analysis of correlation coefficients, the oil yield negatively correlated with seed length, seed width, the proportion of seed coat, seed hardness, and 1000-seed weight, but positively correlated with crude fat content. It was observed that as the seed coat proportion increased, the seed coat thickness, hardness, and 1000-seed weight also increased. Multiple linear regression (MLR) was employed to find major variables affecting the oil yield. Among the variables, traits crude fat content and seed coat proportion were assumed to be indirect parameters for estimating the potential oil yield, with respect to a significant positive correlation with the observed oil yield ($R^2=0.791$). Using these two parameters, an equation was derived to predict the oil yield. The results of this study show that various seed traits in 23 perilla cultivars positively or negatively correlated with the oil yield. In particular, crude fat and the seed coat proportion can be used for predicting the oil yield with the newly developed equation, and this approach will improve the efficiency of selecting prominent lines for the oil yield.

• Journal of The Korean Society of Grassland and Forage Science
• /
• v.29 no.1
• /
• pp.31-36
• /
• 2009

This study was conducted to find out an alternative way of rapid and accurate analysis of forage quality. Near reflectance infrared spectroscopy (NIRS) was used to evaluate the possibility of forage analysis and collect 258 samples such as barley for whole crop silage, forage corn and sudangrass from 2002 to 2007. The samples were analyzed for CP (crude protein), CF (crude fiber), ADF (acid detergent fiber), NDF (neutral detergent fiber) and IVTD (in vitro true digestibility), and also scanned using NIRSystem with wavelength from $400{\sim}2,400nm$. Multiple linear regression was used with wet analysis data for developing the calibration model and validate unknown samples. The important index In this experiment was SEC and SEP $r^2$ for CF, CP, NDF, ADF and IVTD in calibration set were 0.70, 0.86, 0.94, 0.94 and 0.89, also 0.47, 0.39, 0.89, 0.90 and 0.61 in validation sample, respectively. The results of this experiment indicates that NIRS was reliable analytical method to assess forage quality, specially in CF, ADF and IVTD, sample should be included for respective forage samples to get accurate result. More robust calibrations can be made to cover every forage samples if added representative sample set.

• Journal of The Korean Society of Grassland and Forage Science
• /
• v.37 no.2
• /
• pp.154-160
• /
• 2017

This study was conducted to find out an alternative way of rapid and accurate analysis of chemical composition of permanent pastures in hilly grazing area. Near reflectance infrared spectroscopy (NIRS) was used to evaluate the potential for predicting proximate analysis of permanent pastures in a vegetative stage. 386 pasture samples obtained from hilly grazing area in 2015 and 2016 were scanned for their visible-NIR spectra from 400~2,400nm. 163 samples with different spectral characteristics were selected and analysed for moisture, crude protein (CP), crude ash (CA), acid detergent fiber (ADF) and neutral detergent fiber (NDF). Multiple linear regression was used with wet analysis data and spectra for developing the calibration and validation mode1. Wavelength of 400 to 2500nm and near infrared range with different critical T outlier value 2.5 and 1.5 were used for developing the most suitable equation. The important index in this experiment was SEC and SEP. The $R^2$ value for moisture, CP, CA, CF, Ash, ADF, NDF in calibration set was 0.86, 0.94, 0.91, 0.88, 0.48 and 0.93, respectively. The value in validation set was 0.66, 0.86, 0.83, 0.71, 0.35 and 0.88, respectively. The results of this experiment indicate that NIRS is a reliable analytical method to assess forage quality for CP, CF, NDF except ADF and moisture in permanent pastures when proper samples incorporated into the equation development.

• Journal of Digital Convergence
• /
• v.14 no.2
• /
• pp.295-303
• /
• 2016

This study aimed to assess the influence of nicotine dependence with satisfaction of basic psychological needs, social support, attitude on smoking among middle school students. From Sep, 2015 to Nov, 2015, participants included 150 middle school students from D city and K province in Korea. Data for basic psychological needs, social support, attitude on smoking and nicotine dependence were collected through a self-reported questionnaire and were analyzed with independent t-test and analysis of variance, Pearson's correlation analysis, and multiple linear regression analysis. This study shows negative correlations between nicotine dependence and satisfaction of basic psychological needs(r=-.221, P=.008), competency(r=-.194, P=.021), relatedness(r=-.219, P=.009). The variables predicting nicotine dependence were satisfaction of basic psychological needs(${\beta}=.221$, p=.008). These variables accounted for 42% of the variance of nicotine dependence in smoking middle school students. Our results indicated that it is necessary to increase basic psychological needs to decrease nicotine dependence. Therefore we should develop programs in order to increase satisfaction of basic psychological needs.

• Smart Media Journal
• /
• v.7 no.1
• /
• pp.16-23
• /
• 2018

In this paper, we used the Deep Neural Network (DNN) to predict the number of daily spectators of Gwangju - KIA Champions Field in order to provide marketing data for the team and related businesses and for managing the inventories of the facilities in the stadium. In this study, the DNN model, which is based on an artificial neural network (ANN), was used, and four kinds of DNN model were designed along with dropout and batch normalization model to prevent overfitting. Each of four models consists of 10 DNNs, and we added extra models with ensemble model. Each model was evaluated by Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE). The learning data from the model randomly selected 80% of the collected data from 2008 to 2017, and the other 20% were used as test data. With the result of 100 data selection, model configuration, and learning and prediction, we concluded that the predictive power of the DNN model with ensemble model is the best, and RMSE and MAPE are 15.17% and 14.34% higher, correspondingly, than the prediction value of the multiple linear regression model.

• The Journal of the Korea Contents Association
• /
• v.15 no.11
• /
• pp.486-500
• /
• 2015

This study focuses on customer loyalty that explains how firms can maintain long-term relationship with customers, in the internet shopping and mobile shopping contexts. Specifically, this study examines the key determinants of customer loyalty from two perspectives: service quality and shopping value. Concerning service quality, previous studies have long argued that it is a starting point of building customer loyalty. Shopping value is a key variable in capturing consumers' shopping motives. In this study, we consider two types of shopping value: usefulness as utilitarian value and enjoyment as hedonic value. Moreover, this study examines whether the effects of service quality and shopping value on customer loyalty differ depending on internet and mobile shopping groups. To test the proposed hypotheses, we conducted multiple linear regression analysis and chow test with a total of 199 data collected on users who have experience in internet shopping and mobile shopping. The key findings are as follows: First, in the internet shopping group, customer loyalty depends on service quality (responsiveness and empathy) and usefulness, whereas in the mobile shopping, it only depends on enjoyment. Second, the impacts of service quality and shopping value on customer loyalty are different depending on internet shopping and mobile shopping. The results imply that e-tailors should develop differential methods suitable for internet shopping or mobile shopping to enhance customer loyalty.

• Korean Journal of Construction Engineering and Management
• /
• v.9 no.5
• /
• pp.75-85
• /
• 2008

The construction industry has been leading the growth of the nation's economy not only by providing with various infrastructure projects but also by positively impaction related industries such as crating numerous job opportunities. Relevant statistics show the production amount of construction taking about 17.5% of the GDP (Gross Domestic Product). In spite of its positive impacts on the economy, image of Korea construction industry is generally reflected as negative mainly because of its environmental disruption, low payment, bribe, fraudulent work and inefficiency. It brings students to be reluctant choosing the construction industry as their carrier path and governmental and principal research status. Therefore it has been difficult to recruit highly qualitied human resources to the industry while the morale of the whole industry has gradually become demoralized. To improve this stand, many domestic researchers carried out research projects for improving the image of Korea Construction Industry. This study also sympathizes with necessity of improving the negative image of construction industry to remain as one of the leading industry in the 21st century. Especially, this study focused on finding important factors which have significant influences on the image of the industry. Through out the research, image influence factors was identified by rigorous literature review and interviews as industrial and academic experts. Factors, then, categorized and used as the main framework for the survey which designed to fine the degree of impacts on the image of the construction industry. In analyzing the survey results, various statistical techniques was employed including factor analysis, Chi-Square-Test, Correlation Analysis and Multiple Linear Regression. Identified as the most influent factors to the image of the construction industry include morale of construction employee, and prospects the industry which of the judgement by payment, impacts on nation's economy, future of the industry, etc.

• Journal of Digital Convergence
• /
• v.14 no.8
• /
• pp.343-353
• /
• 2016

This study was done to identify variables predicting of nursing performance with basic psychological need, self-leadership and job stress in clinical nurses. The participants were 160 clinical nurses, located in P city and D province in korea. Data were collected from Feb to March, 2016 by questionnaire survey. Data analysis was done by using SPSS WIN 18.0 program with independent t-test and analysis of variance, Pearson's correlation analysis, and multiple linear regression. This study shows positive correlations between nursing performance and basic psychological needs(r=.59, p<.001), autonomy(r=.31, p<.001), competency(r=.68, p<.001), relatedness(r=.48, p<.001), self-leadership(r=.58, p<.001), job stress(r=.19, p<.05). Basic psychological needs(${\beta}$=.43), self-leadership(${\beta}$=.33), job stress(${\beta}$=.21) have a 47.3%(Adj $R^2$ .473) explanatory power for the nursing performance in clinical nurses. This study confirmed that basic psychological needs, self-leadership and job stress were identified to improve nursing performance. Therefore we should develop programs in order to increase basic psychological needs and self-leadership.

• Journal of Digital Convergence
• /
• v.15 no.9
• /
• pp.273-281
• /
• 2017

This study was to investigate the effect of job stress and job satisfaction on professional self-concept in nurses. A questionnaire was completed by 196 general hospital employed nurses from October to November 2016. The study instruments comprised professional job stress and job satisfaction, professional self-concept. The data were analyzed by descriptive statistics, t-test, ANOVA, Pearson's correlation coefficients, and multiple linear regression using SPSS/WIN 21.0 program. Professional self-concept was closely related to age(F=4.356, p=.014), marital status(t=4.345, p<.001), education(F=33.411, p<.001). The significant factors influencing professional self-concept were job stress(${\beta}=-0.456$, p<.001), job satisfaction(${\beta}=0.409$, p<.001), education(${\beta}=0.106$, p=.019), and with the explanation power of 67.1%. It is necessary to prepare early identification and resolution of factors related to job stress, and this will help to enhance the professional self concept as well as job satisfaction.

• Journal of Digital Convergence
• /
• v.14 no.11
• /
• pp.379-388
• /
• 2016

This study was aimed at identifying the actual state of workplace violence based on a survey conducted to 876 subway workers in Seoul and determining the relationship between workplace violence and mental health of workers. Data were collected via web site, using a structured questionnaire and for the analysis of the data, a multiple linear regression analysis was carried out by the statistical program SPSS 20.0. According to the results, the perpetrators of violence turned out to be "passengers" in all types of workplace violence: physical violence, verbal violence, sexual harassment and disregard for personality. As for the relationship between workplace violence and the mental health of the workers, statistically significant differences were shown between all the above mentioned workplace violence types and sub-areas of mental health. Also, as for the impact of workplace violence on the mental health of the workers, significant differences were found in physical violence, sexual harassment and disregard for personality, with 8.3 percent of explanatory power. Based on these findings, the study suggests the establishment and the application of customer interaction guidelines to protect subway workers from workplace violence along with specific measures customized for each work environment to prevent violence.