• Journal of Environmental Science International
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• v.19 no.6
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• pp.715-735
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• 2010

This study is designed to assess the priority order of the chemicals to cause to generate occupational diseases in order to understand the fundamental data required for the preparation of health protective measure for the workers dealing with chemicals. The 41 types of 51 ones of chemicals to cause to generate the national occupational diseases were selected as the study objects by understanding their domestic use or not, and their occupational diseases' occurrence or not among 110,608 types of domestic and overseas chemicals. To assess their priority order the sum of scores was acquired by understanding the actually classified condition based on a perfect score of physical riskiness(90points) and health toxicity(92points) as a classification standard by GHS, the priority order on GHS riskiness assessment, GHS toxicity assessment, GHS toxic xriskiness assessment(sum of riskiness plus toxicity) was assessed by multiplying each result by each weight of occupational disease's occurrence. The high ranking 5 items of chemicals for GHS riskiness assessment were turned out to be urethane, copper, chlorine, manganese, and thiomersal by order. Besides as a result of GHS toxicity assessment the top fives were assessed to be aluminum, iron oxide, manganese, copper, and cadium(Metal) by order. On the other hand, GHS toxicity riskiness assessment showed that the top fives were assessed to be copper, urethane, iron oxide, chlorine and phenanthrene by order. As there is no material or many uncertain details for physical riskiness or health toxicity by GHS classification though such materials caused to generate the national occupational diseases, it is very urgent to prepare its countermeasure based on the forementioned in order to protect the workers handling or being exposed to chemicals from health.

• Journal of the Korean Institute of Intelligent Systems
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• v.19 no.5
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• pp.730-736
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• 2009

It is more important to analysis of huge gemonics data in Bioinformatics. Here we present a novel datamining approach to predict structure and function using protein's primnary structure only. We propose not also to develope n-Block substring search algorithm in reducing enormous search space effectively in relation to feature selection, but to formulate weighted linear algorithm in a prediction of structure and function of a protein using primary structure. And we show efficient in protein domain characterization and classification by calculation weight value in determining domain association in each selected substring, and also reveal that more efficient results are acquired through claculated model score result in an inference about degree of association with each CDS(coding sequence) in domain.

• KIPS Transactions on Software and Data Engineering
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• v.8 no.1
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• pp.27-36
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• 2019

In this paper, we propose a method to find the most similar answer to the user's response from the question-answer database in order to avoid generating a redundant question in retrieval-based automatic question generation system. As a question of the most similar answer to user's response may already be known to the user, the question should be removed from a set of question candidates. A similarity detector calculates a similarity between two answers by utilizing the same words, paraphrases, and sentential meanings. Paraphrases can be acquired by building a phrase table used in a statistical machine translation. A sentential meaning's similarity of two answers is calculated by an attention-based convolutional neural network. We evaluate the accuracy of the similarity detector on an evaluation set with 100 answers, and can get the 71% Mean Reciprocal Rank (MRR) score.

• Journal of The Korean Society of Agricultural Engineers
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• v.64 no.3
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• pp.63-73
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• 2022

A more accurate understanding of the irrigation water supply is necessary for efficient agricultural water management. Although we measure water levels in an irrigation canal using ultrasonic water level gauges, some errors occur due to malfunctions or the surrounding environment. This study aims to apply CNN (Convolutional Neural Network) Deep-learning-based image classification and segmentation models to the irrigation canal's CCTV (Closed-Circuit Television) images. The CCTV images were acquired from the irrigation canal of the agricultural reservoir in Cheorwon-gun, Gangwon-do. We used the ResNet-50 model for the image classification model and the U-Net model for the image segmentation model. Using the Natural Breaks algorithm, we divided water level data into 2, 4, and 8 groups for image classification models. The classification models of 2, 4, and 8 groups showed the accuracy of 1.000, 0.987, and 0.634, respectively. The image segmentation model showed a Dice score of 0.998 and predicted water levels showed R² of 0.97 and MAE (Mean Absolute Error) of 0.02 m. The image classification models can be applied to the automatic gate-controller at four divisions of water levels. Also, the image segmentation model results can be applied to the alternative measurement for ultrasonic water gauges. We expect that the results of this study can provide a more scientific and efficient approach for agricultural water management.

• Korean Journal of Applied Biomechanics
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• v.17 no.2
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• pp.217-226
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• 2007

Putting score counts about 43 % of the golf score. The dominant idea of the putting motion to amateur golfers as well as to many professional golfers is a pendulum-like motion. If a golfer's putting stroke motion is a pendulum-like motion, the putting motion should be straight-back-and-through, the same backswing, downswing, and follow through length and period, and a swing with a fixed hinge joint. If the putting motions of the human are different from the pendulum motion, there could be confusion in understanding and teaching golf putting. The purpose of this study was to examine the center of rotation(COR) of the putter head to reveal whether professional golfers really putt like a pendulum. Thirteen male professional golfers were recruited for the study. Each golfers executed 10.94 m putts six times on an artificial grass mat. Putter head position data were collected through a 60 Hz three-dimensional motion analysis system and low pass filtered with cut-off frequency of 6 Hz. COR of the putter head was mathematically acquired. Each golfer's last five putting motions were considered. The results show that the COR of the putter head was neither fixed nor located inside of the golfer. The medio-lateral directional component of the COR of the putter head fluctuated in the range of 10 cm during downswing and follow through. The anterior-posterior directional component of the COR of the putter head was fixed from the beginning of the downswing through impact. Just after impact, however, it moved to the target up to 60 cm. The superior-inferior directional component of COR of the putter head moved in a superior direction with the beginning of the downswing and showed peak height just prior to impact. During the follow through, it moved back in an inferior direction. The height-normalized peak value of the COR of the putter head was $1.4{\pm}0.3$ height. Technically speaking, male professional golfers' 10.94 m putting motion is not a pendulum-like motion. The dominating idea of a pendulum-like motion in putting might come from the image of the flawless, smooth motion of a pendulum.

• Journal of Ginseng Research
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• v.35 no.1
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• pp.31-38
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• 2011

In order to distinguish the cultivation area of Panax ginseng, principal component analysis (PCA) using quantitative and qualitative data acquired from HPLC was carried out. A new HPLC method coupled with evaporative light scattering detection (HPLC-ELSD) was developed for the simultaneous quantification of ten major ginsenosides, namely $Rh_1$, $Rg_2$, $Rg_3$, $Rg_1$, Rf, Re, Rd, $Rb_2$, Rc, and $Rb_1$ in the root of P. ginseng C. A. Meyer. Simultaneous separations of these ten ginsenosides were achieved on a carbohydrate analytical column. The mobile phase consisted of acetonitrile-water-isopropanol, and acetonitrile-water-isopropanol using a gradient elution. Distinct differences in qualitative and quantitative characteristics for ginsenosides were found between the ginseng roots produced in two different Korean cultivation areas, Ganghwa and Punggi. The ginsenoside profiles obtained via HPLC analysis were subjected to PCA. PCA score plots using two principal components (PCs) showed good separation for the ginseng roots cultivated in Ganghwa and Punggi. PC1 influenced the separation, capturing 43.6% of the variance, while PC2 affected differentiation, explaining 18.0% of the variance. The highest contribution components were ginsenoside $Rg_3$ for PC1 and ginsenoside Rf for PC2. Particularly, the PCA score plot for the small ginseng roots of six-year old, each of which was light than 147 g fresh weight, showed more distinct discrimination. PC1 influenced the separation between different sample sets, capturing 51.8% of the variance, while PC2 affected differentiation, also explaining 28.0% of the variance. The highest contribution component was ginsenoside Rf for PC1 and ginsenoside $Rg_2$ for PC2. In conclusion, the HPLC-ELSD method using a carbohydrate column allowed for the simultaneous quantification of ten major ginsenosides, and PCA analysis of the ginsenoside peaks shown on the HPLC chromatogram would be a very acceptable strategy for discrimination of the cultivation area of ginseng roots.

• Korean Journal of Remote Sensing
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• v.38 no.5_3
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• pp.939-951
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• 2022

The most important factor in designing autonomous driving systems is to recognize the exact location of the vehicle within the surrounding environment. To date, various sensors and navigation systems have been used for autonomous driving systems; however, all have limitations. Therefore, the need for high-definition (HD) maps that provide high-precision infrastructure information for safe and convenient autonomous driving is increasing. HD maps are drawn using three-dimensional point cloud data acquired through a mobile mapping system (MMS). However, this process requires manual work due to the large numbers of points and drawing layers, increasing the cost and effort associated with HD mapping. The objective of this study was to improve the efficiency of HD mapping by segmenting semantic information in an MMS point cloud into six classes: roads, curbs, sidewalks, medians, lanes, and other elements. Segmentation was performed using various machine learning techniques including random forest (RF), support vector machine (SVM), k-nearest neighbor (KNN), and gradient-boosting machine (GBM), and 11 variables including geometry, color, intensity, and other road design features. MMS point cloud data for a 130-m section of a five-lane road near Minam Station in Busan, were used to evaluate the segmentation models; the average F1 scores of the models were 95.43% for RF, 92.1% for SVM, 91.05% for GBM, and 82.63% for KNN. The RF model showed the best segmentation performance, with F1 scores of 99.3%, 95.5%, 94.5%, 93.5%, and 90.1% for roads, sidewalks, curbs, medians, and lanes, respectively. The variable importance results of the RF model showed high mean decrease accuracy and mean decrease gini for XY dist. and Z dist. variables related to road design, respectively. Thus, variables related to road design contributed significantly to the segmentation of semantic information. The results of this study demonstrate the applicability of segmentation of MMS point cloud data based on machine learning, and will help to reduce the cost and effort associated with HD mapping.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.4
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• pp.109-116
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• 2020

This study was undertaken to identify factors affecting the mental health of nursing students, including career motivation, sense of humor, and problem-solving ability, and to provide data for improving mental health of nursing students. This research enrolled 235 nursing students in B and Y cities. Data were collected from October 22 to October 31, 2018, and analyzed by applying t-test, ANOVA, Pearson's correlation coefficient, and multiple regression, using the SPSS/WIN 22.0. The average score for mental health was determined to be 2.19±0.77. Ed. Negative correlation was obtained between mental health and career motivation (r=-0.443, p<0.001), mental health and sense of humor (r=0.400, p<0.001), and mental health and problem-solving ability (r=0.465, p<0.001). Regression analysis showed that 33.4% variance in the mental health of nursing students is affected by gender (β=-0.121, p=0.033), pocket money (β=0.123, p=0.028), peer relation (β=0.165, p=0.004), sense of humor (β=-0.168, p=0.012), and problem-solving ability (β=-0.186, p=0.006). Data from this study indicates a necessity to repeat the study for identifying general characteristics and psychological factors that control an individual, and technical factors that can be acquired through learning. Taken together, we believe that the factors included and indicated in this study influence the mental health of nursing students.

• Journal of the Korean Association of Geographic Information Studies
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• v.15 no.1
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• pp.119-132
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• 2012

Recently, forest soil sediment disasters resulting from locally concentrated heavy rainfall have been occurring frequently in steep slope areas. The importance of landslide hazard map is emerging to analyze landslide vulnerable areas. This study was carried out to develop HyGIS-Landslide based on Hydro Geographic Information System in order to analyze forest soil sediment disaster in the mountainous river basin. HyGIS-Landslide is one of HyGIS components designed by considering the landslide hazard criteria of Korea Forest Service. It could show the distribution of landslide hazard areas after calculating the spatial data. In this system, the user could reset the weight of hazard criteria to reflect the regional characteristics of the landslide area. This component provided user interface that could make the latest spatial data available in the area of interest. HyGIS-Landslide could be applied to the surveyor's compensation score and it was possible to reflect the landslide risk exactly through it. Also, it could be used in topographic analysis techniques providing spatial analysis and making topographical parameters in HyGIS. Finally the accuracy could be acquired by calculating the landslide hazard grade map and landslide mapping data. This study applied HyGIS-Landslide at the Gangwon-do province sample site. As a result, HyGIS-Landslide could be applied to a decision support system searching for mountainous disaster risk region; it could be classified more effectively by re-weighting the landslide hazard criteria.

• Journal of Nutrition and Health
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• v.54 no.2
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• pp.199-210
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• 2021

Purpose: This study investigated the food consumption behaviors in Korean adults, according to the agrifood consumer competency index (ACCI). Methods: Data obtained from the 2019 Consumption Behaviors Survey for Food were analyzed. A total of 6,176 adults (2,783 males, 3,393 females) aged ≥ 19 years, were included in the study. Based on the score of agrifood consumer competency index, the subjects were classified into three groups. The dietary habits, eating-out and food-delivery/take-out behaviors, opinion of food labeling, and concerns for domestic products were compared among the 3 groups. Results: The ACCI scores of the male and female subjects were 63.6 and 64.8, respectively. Subjects of both genders in the highest tertile of the ACCI were more likely to have a higher education level and higher health concerns, as compared to subjects in the lowest tertile (p < 0.05). Male subjects having highest tertile of the ACCI reported significantly more exercise and alcohol consumption, as compared to subjects in the lowest tertile (p < 0.05). A higher score of the ACCI also portrayed a higher satisfaction in own diet and greater checking of the food label. Moreover, subjects with a higher score of the ACCI showed greater satisfaction and reliability in the food label, as well as increased concerns for domestic agrifoods, local foods, and eco-friendly foods. Subjects in the lowest tertile of the ACCI acquired their dietary information from acquaintances, whereas subjects in the highest tertile of the ACCI learnt the information from food labels themselves. Conclusion: These results are indicative of the food consumption and behaviors of Korean adults according to their ACCI scores, and provide basic data that will be useful for implementing an effective food policy.