• Journal of the Korea Institute of Building Construction
• /
• v.23 no.5
• /
• pp.607-618
• /
• 2023

Pervasive research underscores the direct correlation between an enhanced safety climate and a marked reduction in accidents. The intricacies of safety climate are governed by three pivotal strata: organizational management, on-site operations, and the broader enterprise framework. Within an organizational context, sustaining optimal performance across these layers poses a considerable challenge, often attributable to the constraints of available managerial bandwidth. It becomes imperative, then, to conceive a phased enhancement blueprint for the safety climate. To orchestrate this blueprint with precision, a discerning understanding of the hierarchy of safety climate metrics is essential, which subsequently guides judicious managerial resource allocation. This investigation is anchored in elucidating the hierarchical significance of safety climate metrics through the Analytical Hierarchy Process(AHP). Implementing the AHP framework, both a questionnaire was disseminated and a subsequent analysis undertaken, culminating in the extraction of relative priorities of safety climate determinants. Consequent to this analysis, "workers' safety prioritization and risk aversion" emerged as the foremost dimension, holding a significance weight of 0.1900. Furthermore, within the detailed elements, "unwavering adherence to safety mandates amidst demanding operational constraints" ranked supreme, manifesting a weight of 0.6663. The findings encapsulated in this study are poised to be foundational in sculpting improvements at an institutional level and devising policies, all with the end goal of fostering an exemplar safety climate within construction arenas.

• KOREAN JOURNAL OF CROP SCIENCE
• /
• v.69 no.2
• /
• pp.97-110
• /
• 2024

This study investigated 575 cultivated soybean landraces from different provinces in Korea, using 17 key agromorphological traits. The studied soybeans showed wide variations in both qualitative and quantitative traits, signifying the existence of genetic diversity. The standardized Shannon-Weaver index (H') ranged from 0.3 to 1.0, with seed-related traits having an H' value higher than 0.7. Similarly, quantitative traits showed significant variation, with the coefficient of variation ranging from 7.2% for days to maturity (DM) to 62.3% for the number of pods per plant (PPP). In terms of province, the Gyeongsangbuk-do and Gyeongsangnam-do accessions differed from the other accessions, with higher proportions of green and yellow seed coats and lower proportion of black hilums. Gyeongsangnam-do accessions also showed early maturation and flowering but had the lowest average one-hundred seeds weight (HSW). In contrast, Jeollanam-do accessions flowered and matured late but had the highest average seed weight per plant (SWPP). Hierarchical cluster analysis grouped the soybeans into 12 clusters, and further statistical analysis showed significant variations in all quantitative traits (p < 0.05). Principal component analysis grouped the accessions based on the clusters. DM, PPP, HSW, and SWPP were identified as major contributors to the observed variance along the axes of the first two principal components. Correlation analysis revealed significant associations between maturity and yield-related traits. Based on their relative performance, 37 promising accessions were identified. Overall, this study highlights the diversity of recently cultivated Korean soybean landraces and provides opportunities for future metabolomic and genomic studies.

• Korean Journal of Food Science and Technology
• /
• v.33 no.1
• /
• pp.33-39
• /
• 2001

To develop a method for separation process using Sep-pak $C_18$, simultaneous and systematic analysis of 8 permitted and 11 non-permitted synthetic food colors in Korea, optimization of analysis conditions for reverse phase ion-pair high performance liquid chromatography was carried out. For the best result of Sep-pak $C_18$ separation the pH of color standard mixture solution was $5{\sim}6$ and 0.1% HCl-methanol solution were set as eluent. The colors eluated from Sep-pak $C_18$ cartridge were determined and confirmed by high performance liquid chromatography with a photodiode array detector at 420 nm for yellow colors type, at 520 nm for red colors type, at 600 nm for blue and green colors type and at 254 nm for mixed colors. Conditions for HPLC analysis were as follows: column, Symmetry $C_18$ (5 m, 3.9 mm $i.d.{\times}150\;mm$); mobile phase, 0.025 M ammonium acetate (containing 0.01 M tetrabutylammonium bromide) : acetonitrile : methanol (65 : 25 : 10) and 0.025 M ammonium acetate(containing 0.01 M tetrabutylammonium bromide) : acetonitrile : methanol (40 : 50 : 10); flow rate, 1 mL/min. It takes 35 minutes for simultaneaus analysis and 18 minutes for systematic analysis. The detection limits range of each colors were $0.01{\sim}0.05\;{\mu}g/g$.

• Korean Journal of Food Science and Technology
• /
• v.31 no.6
• /
• pp.1471-1476
• /
• 1999

A simple and practical method for determination of caffeine in foods was developed. The analysis of caffeine was performed by reverse phase high performance liquid chromatography using a ${\mu}-Bondapak\;C_{18}$ column at isocratic condition with methanol-acetic acid-water(20 : 1 : 79) on UV detector at 280 nm. The clean-up and extraction of caffeine in samples were based on a simple pretreatment using a Sep-Pak $C_{18}$ cartridge. Recovery rates obtained with this method for cider, candy, cookie, milk, ice cream and persimmon leaf tea were 99.23%, 99.50%, 99.17%, 99.37%, 98.93% and 99.10% respectively. And the detection limit of caffeine was $0.1\;{\mu}g/mL$. With this method, the range of caffeine contents extracted from coffee, green tea, black tea, Oolong tea(tea bag), soft drinks, ice cream, milk and commercial confectionery were $3.38{\sim}37.50\;mg/g,\;16.30{\sim}26.10\;mg/g,\;10.80{\sim}16.65\;mg/g,\;11.25\;mg/g,\;0.06{\sim}0.11\;mg/g,\;0.04{\sim}0.44\;mg/g,\;0.04{\sim}0.39\;mg/g\;and\;0.10{\sim}1.80\;mg/g$, respectively. But caffeine was not detected in the other tea such as Acanthopanax sessiliflorum tea, Angelica gigas tea, Angelica tea, Arrow root tea, Duchu'ng tea, Dunggulle tea, Ganoerma lucidum tea, Ginger tea powder, Persimmon leaf tea, Ssanghwa tea and Cocoa mix powder.

• The Korean Journal of Applied Statistics
• /
• v.36 no.1
• /
• pp.49-62
• /
• 2023

Solar energy, which is rapidly increasing in proportion, is being continuously developed and invested. As the installation of new and renewable energy policy green new deal and home solar panels increases, the supply of solar energy in Korea is gradually expanding, and research on accurate demand prediction of power generation is actively underway. In addition, the importance of solar radiation prediction was identified in that solar radiation prediction is acting as a factor that most influences power generation demand prediction. In addition, this study can confirm the biggest difference in that it attempted to predict solar radiation using medium-term forecast weather data not used in previous studies. In this paper, we combined the multi-linear regression model, KNN, random fores, and SVR model and the clustering technique, K-means, to predict solar radiation by hour, by calculating the probability density function for each cluster. Before using medium-term forecast data, mean absolute error (MAE) and root mean squared error (RMSE) were used as indicators to compare model prediction results. The data were converted into daily data according to the medium-term forecast data format from March 1, 2017 to February 28, 2022. As a result of comparing the predictive performance of the model, the method showed the best performance by predicting daily solar radiation with random forest, classifying dates with similar climate factors, and calculating the probability density function of solar radiation by cluster. In addition, when the prediction results were checked after fitting the model to the medium-term forecast data using this methodology, it was confirmed that the prediction error increased by date. This seems to be due to a prediction error in the mid-term forecast weather data. In future studies, among the weather factors that can be used in the mid-term forecast data, studies that add exogenous variables such as precipitation or apply time series clustering techniques should be conducted.

• Journal of the Korean Institute of Landscape Architecture
• /
• v.52 no.2
• /
• pp.51-63
• /
• 2024

While the government policy to fully adopt BIM in the construction sector is being implemented, the construction and utilization of landscape BIM models are facing challenges due to problems such as limitations in BIM authoring tools, difficulties in modeling natural materials, and a shortage in BIM content including libraries. In particular, plants, fundamental design elements in the field of landscape architecture, must be included in BIM models, yet they are often omitted during the modeling process, or necessary information is not included, which further compromises the quality of the BIM data. This study aimed to contribute to the construction and utilization of landscape BIM models by developing a plant library that complies with BIM standards and is applicable to the landscape industry. The plant library of trees and shrubs was developed in Revit by modeling 3D shapes and collecting attribute items. The geometric information is simplified to express the unique characteristics of each plant species at LOD200, LOD300, and LOD350 levels. The attribute information includes properties on plant species identification, such as species name, specifications, and quantity estimation, as well as ecological attributes and environmental performance information, totaling 24 items. The names of the files were given so that the hierarchy of an object in the landscape field could be revealed and the object name could classify the plant itself. Its usability was examined by building a landscape BIM model of an apartment complex. The result showed that the plant library facilitated the construction process of the landscape BIM model. It was also confirmed that the library was properly operated in the basic utilization of the BIM model, such as 2D documentation, quantity takeoff, and design review. However, the library lacked ground cover, and had limitations in those variables such as the environmental performance of plants because various databases for some materials have not yet been established. Further efforts are needed to develop BIM modeling tools, techniques, and various databases for natural materials. Moreover, entities and systems responsible for creating, managing, distributing, and disseminating BIM libraries must be established.

• Journal of Animal Science and Technology
• /
• v.50 no.6
• /
• pp.807-818
• /
• 2008

This study was conducted to prove the optimum feeding method of Lactobacillus in broiler chickens by investigating the intestinal viability of ingested Lactobacillus and the effect of feeding levels and frequency of Lactobacillus on growth performance in broiler chickens. In experiment 1, A total of one hundred, 5 weeks old male broiler chickens(Abor Acre) were fed Lactobacillus reuteri avibro2 expressed green fluorescent protein(GFP) at 104cfu/g diet to investigate the retention time of ingested Lactobacillus in the intestine for 1 day. The percentage of Lactobacillus expressed GFP in intestinal contents was 26% at 1 day after fed Lactobacillus expressed GFP. The percentage of Lactobacillus expressed GFP in intestinal contents was decreased in length of time. In experiment 2, A total of four hundred eighty, 1-d-old male broiler chicks(Abor Acre) were randomly divided into 4 groups with 4 replicates of 30 birds each to prove the optimum feeding level of Lactobacillus. The treatments were control(free antibiotics), Lactobacillus reuteri avibro2 5.0×10cfu/mL, 5.0×103cfu/mL, and 5.0×105cfu/mL. The final body weight and body wight gain of Lactobacillus reuteri avibro2 5.0×103cfu/mL were the highest in all groups(P<0.05). Feed conversion ratio was not significantly difference among the groups. The number of intestinal lactic acid bacteria in Lactobacillus treated groups tended to be improved or significantly increased as compared to that of control(P<0.05). Protein and fat digestibility in Lactobacillus 5.0×103cfu/mL and 5.0×105cfu/mL treated groups were significantly improved(P<0.05). No significant differences were observed on the availability of dry matter and crude ash in Lactobacillus treatments compared to those of control. In experiment 3, A total of six hundred 1-d-old male broiler chicks(Abor Acre) were randomly divided into 4 groups with 4 replicates of 30 birds each and were fed Lactobacillus reuteri avibro2 at intervals of 1, 2, 3, and 5 day for five weeks. Feeding level of Lactobacillus was 5.0×103cfu/mL The final body weight and body wight gain of Lactobacillus reuteri avibro2 5.0×103cfu/mL were the highest in all groups(P<0.05). The final body weight and body weight gain were significantly increased, when Lactobacillus was fed at intervals of 1 days, or 2 days. There were no significant differences in feed intake and feed conversion ratio among the all groups. The number of intestinal lactic acid bacteria in Lactobacillus treated groups tended to be improved or significantly increased as compared to that of control(P<0.05). No significant differences were observed on the number of coliform bacteria and Salmonella of ileum and cecum. Consequently, supplemental Lactobacillus influenced positive effects on the growth performance, nutrient availability and intestinal microflora. The optimum feeding level of Lactobacillus was 5.0×103cfu/mL, and the constant feeding of Lactobacillus was effective.

• Journal of Intelligence and Information Systems
• /
• v.27 no.3
• /
• pp.139-156
• /
• 2021

The biggest reason for using a deep learning model in image classification is that it is possible to consider the relationship between each region by extracting each region's features from the overall information of the image. However, the CNN model may not be suitable for emotional image data without the image's regional features. To solve the difficulty of classifying emotion images, many researchers each year propose a CNN-based architecture suitable for emotion images. Studies on the relationship between color and human emotion were also conducted, and results were derived that different emotions are induced according to color. In studies using deep learning, there have been studies that apply color information to image subtraction classification. The case where the image's color information is additionally used than the case where the classification model is trained with only the image improves the accuracy of classifying image emotions. This study proposes two ways to increase the accuracy by incorporating the result value after the model classifies an image's emotion. Both methods improve accuracy by modifying the result value based on statistics using the color of the picture. When performing the test by finding the two-color combinations most distributed for all training data, the two-color combinations most distributed for each test data image were found. The result values were corrected according to the color combination distribution. This method weights the result value obtained after the model classifies an image's emotion by creating an expression based on the log function and the exponential function. Emotion6, classified into six emotions, and Artphoto classified into eight categories were used for the image data. Densenet169, Mnasnet, Resnet101, Resnet152, and Vgg19 architectures were used for the CNN model, and the performance evaluation was compared before and after applying the two-stage learning to the CNN model. Inspired by color psychology, which deals with the relationship between colors and emotions, when creating a model that classifies an image's sentiment, we studied how to improve accuracy by modifying the result values based on color. Sixteen colors were used: red, orange, yellow, green, blue, indigo, purple, turquoise, pink, magenta, brown, gray, silver, gold, white, and black. It has meaning. Using Scikit-learn's Clustering, the seven colors that are primarily distributed in the image are checked. Then, the RGB coordinate values of the colors from the image are compared with the RGB coordinate values of the 16 colors presented in the above data. That is, it was converted to the closest color. Suppose three or more color combinations are selected. In that case, too many color combinations occur, resulting in a problem in which the distribution is scattered, so a situation fewer influences the result value. Therefore, to solve this problem, two-color combinations were found and weighted to the model. Before training, the most distributed color combinations were found for all training data images. The distribution of color combinations for each class was stored in a Python dictionary format to be used during testing. During the test, the two-color combinations that are most distributed for each test data image are found. After that, we checked how the color combinations were distributed in the training data and corrected the result. We devised several equations to weight the result value from the model based on the extracted color as described above. The data set was randomly divided by 80:20, and the model was verified using 20% of the data as a test set. After splitting the remaining 80% of the data into five divisions to perform 5-fold cross-validation, the model was trained five times using different verification datasets. Finally, the performance was checked using the test dataset that was previously separated. Adam was used as the activation function, and the learning rate was set to 0.01. The training was performed as much as 20 epochs, and if the validation loss value did not decrease during five epochs of learning, the experiment was stopped. Early tapping was set to load the model with the best validation loss value. The classification accuracy was better when the extracted information using color properties was used together than the case using only the CNN architecture.

• Korean Journal of Agricultural and Forest Meteorology
• /
• v.23 no.4
• /
• pp.329-339
• /
• 2021

Soybeans (Glycine max), one of major upland crops, require precise management of environmental conditions, such as temperature, water, and soil, during cultivation since they are sensitive to environmental changes. Application of spectral technologies that measure the physiological state of crops remotely has great potential for improving quality and productivity of the soybean by estimating yields, physiological stresses, and diseases. In this study, we developed and validated a soybean growth prediction model using multispectral imagery. We conducted a linear regression analysis between vegetation indices and soybean growth data (fresh weight and LAI) obtained at Miryang fields. The linear regression model was validated at Goesan fields. It was found that the model based on green ratio vegetation index (GRVI) had the greatest performance in prediction of fresh weight at the calibration stage (R²=0.74, RMSE=246 g/m², RE=34.2%). In the validation stage, RMSE and RE of the model were 392 g/m² and 32%, respectively. The errors of the model differed by cropping system, For example, RMSE and RE of model in single crop fields were 315 g/m² and 26%, respectively. On the other hand, the model had greater values of RMSE (381 g/m²) and RE (31%) in double crop fields. As a result of developing models for predicting a fresh weight into two years (2018+2020) with similar accumulated temperature (AT) in three years and a single year (2019) that was different from that AT, the prediction performance of a single year model was better than a two years model. Consequently, compared with those models divided by AT and a three years model, RMSE of a single crop fields were improved by about 29.1%. However, those of double crop fields decreased by about 19.6%. When environmental factors are used along with, spectral data, the reliability of soybean growth prediction can be achieved various environmental conditions.

• Korean Journal of Poultry Science
• /
• v.37 no.4
• /
• pp.383-388
• /
• 2010

The chicken eye can discriminate light color, and different light wavelengths may affect reproduction ability. This study was carried out to identify effect of monochromatic light using light emitting diode (LED) in laying hens. Chickens were exposed to white light (WL), blue light (BL), yellow light (YL), green light (GL) and red light (RL) made by using LED as well as incandescent light (IL) (control). All light sources were equalized to a light intensity of 20 lux. The results indicated that the age of first egg laying and 50 % egg laying in laying hens treated under RL is significantly younger than under other lights (P<0.05). And the ovary weight of laying hens reared under RL is significantly heavier than under other at from 16 to 20 wks (P<0.05). The largest number of eggs production was produced in a group with treated with RL by 59 wks of age compared with any other group. The egg weight of YL was greater than other treatment groups from 26 to 45 wks (P<0.05). The egg shell from hens treated with RL was the strongest and thickest at 20 wk (P<0.05). These results suggest that the egg quality of laying hens reared in different spectrum of LED can be different and RL may enhance the laying performance.