• Korean Journal of Environment and Ecology
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• v.12 no.3
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• pp.259-270
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• 1998

This study, as an essential research to develope a mountainous runoff model, was conducted to clarify the hydrologic character and water budget equation of Pinus densiflora and Quercus acutissima. Net rainfall quantity division for two species was investigated at Youngsung experiment forest and Yeungnam University for 30 months(Sep. 1995-Jun. 1998). The results were summarized as follows; 1. The percentages of throughfall and stemflow to gross precipitation are 73.8% and 0.8% in the Pinus densiflora, and 76.9% and 3.8% in the Quercus acutissima, respectively 2. In the Pinus densiflora, regression fomula of stemflow, throughfall, and net rainfall to gross precipitation are S$_{f}$ = 0.01GP-2.05 ($r^2$=0.54) T$_{f}$ = 0.79Gp - 26.04 ($r^2$=0.92), N$_{r}$ = 0.81Gp - 28.09 ($r^2$=0.92). Stemflow and throughfall increased in direct proportion to gross precipitation. 3. In the Quercus acutissima, regression fomula of stemflow, throughfall, and net rainfall to gross precipitation are S$_{f}$ = 0.03Gp + 12.25 ($r^2$=0.74), T$_{f}$ = 0.78Gp + 19.75 ($r^2$=0.96), N$_{r}$ = 0.81Gp + 3199 ($r^2$=0.96), respectively. Comparing with two species, gross precipitation has a much larger effect on the stemflow and throughfall of Quercus acutissima than those of Pinus densiflora. 4. In the analysis of intercorrelation between stemflow and throughfall of each species and crown area(CA), diameter at breast height(DBH), and gross precipitation(Gp), correlation coefficient was higher by following order at each species; Gp>CA>DBH on stemflow of Pinus densinora, Gp>DBH>CA on stemflow of Quercus acutissima, and Gp>CA>DBH on throughfall of Pinus densiflora and Quercus acutissima.ssima.

• Food Science of Animal Resources
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• v.25 no.4
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• pp.365-372
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• 2005

This study was carried out to investigate the Physiochemical Properties of loin and rump in the native horse meat from Jeju. In the analysis of chemical composition of loin and rump, the result showed $72.2\%\;and\;73.8\%$ in moisture content $20.1\%\;and\;21.2\%$ in crude protein, $2.42\%\;and\;3.08\%$ in crude Int and $0.13\%\;and\;0.14\%$ in crude ash respectively. Glutamic acid was 3,275mg/100g and 3,577mg/100g in loin and rump each and it had highest result in amino acid analysis. K content was 388.0mg/100g which showed highest result in mineral analysis and next contents were P>Na>Mg>Ca. Oleic acid had highest result in fatty acid composition which were $62.64\%\;and\;63.77\%$ in loin and rump respectively. Cholesterol content of loin and rump were 43.25 and 43.57 mg/100g but showed no significant differences to the part. pH of loin and rump were 5.60 and 5.75 which had no significant differences. Loin had Higher result than that of rump with no significant differences in WHC and springiness of texture analysis. Redness of rump was higher than that of loin. In the sensory evaluation, there were significant differences in the color and odor. Loin had higher result than that of rump in the overall palatability but showed no significant differences. With the result of this experiment native horse meat from Jeju could be understood as good meat resources.

• Radiation Oncology Journal
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• v.16 no.2
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• pp.99-106
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• 1998

Purpose : We conducted clonogenic assay using human cancer cell lines (MKN-45, PC-14, Y-79, HeLa) to investigate a correlation between the parameters of radiosensitivity. Materials and Methods : Human cancer cell lines were irradiated with single doses of 1, 2, 3, 5, 7 and 10Gy for the study of radiosensitivity and subrethal damage repair capacity was assessed with two fractions of 5Gy separated with a time interval of 0, 1, 2, 3, 4, 6 and 24 hours. Surviving fraction was assessed with clonogenic assay using $Sperman-H\"{a}rbor$ method and mathematical analysis of survival curves was done with linear-quadratic (LQ) , multitarget-single hit(MS) model and mean inactivation dose$(\v{D})$. Results : Surviving fractions at 2Gy(SF2) were variable among the cell lines, ranged from 0.174 to 0.85 The SF2 of Y-79 was lowest and that of PC-14 was highest(p<0.05, t-test). LQ model analysis showed that the values of $\alpha$ for Y-79, MKN-45, HeLa and PC-14 were 0.603, 0.356, 0.275 and 0.102 respectively, and those of $\beta$ were 0.005, 0.016, 0.025 and 0.027 respectively. Fitting to MS model showed that the values of Do for Y-79. MKN-45, HeLa and PC-14 were 1.59. 1.84. 1.88 and 2.52 respectively, and those of n were 0.97, 1.46, 1.52 and 1 69 respectively. The $\v{D}s$ calculated by Gauss-Laguerre method were 1.62, 2.37, 2,01 and 3.95 respectively So the SF2 was significantly correlated with $\alpha$, Do and $\v{D}$. Their Pearson correlation coefficiencics were -0.953 and 0,993. 0.999 respectively(p<0.05). Sublethal damage repair was saturated around 4 hours and recovery ratios (RR) at plateau phase ranged from 2 to 3.79. But RR was not correlated with SF2, ${\alpha}$, ${\beta}$, Do, $\v{D}$. Conclusion : The intrinsic radiosensitivity was very different among the tested human cell lines. Y-79 was the most sensitive and PC-l4 was the least sensitive. SF2 was well correlated with ${\alpha}$, Do, and $\v{D}$. RR was high for MKN-45 and HeLa but had nothing to do with radiosensitivity parameters. These basic parameters can be used as baseline data for various in vitro radiobiological experiments.

• Journal of Intelligence and Information Systems
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• v.26 no.1
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• pp.1-21
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• 2020

With the rapid development of artificial intelligence technology, various techniques have been developed to extract meaningful information from unstructured text data which constitutes a large portion of big data. Over the past decades, text mining technologies have been utilized in various industries for practical applications. In the field of business intelligence, it has been employed to discover new market and/or technology opportunities and support rational decision making of business participants. The market information such as market size, market growth rate, and market share is essential for setting companies' business strategies. There has been a continuous demand in various fields for specific product level-market information. However, the information has been generally provided at industry level or broad categories based on classification standards, making it difficult to obtain specific and proper information. In this regard, we propose a new methodology that can estimate the market sizes of product groups at more detailed levels than that of previously offered. We applied Word2Vec algorithm, a neural network based semantic word embedding model, to enable automatic market size estimation from individual companies' product information in a bottom-up manner. The overall process is as follows: First, the data related to product information is collected, refined, and restructured into suitable form for applying Word2Vec model. Next, the preprocessed data is embedded into vector space by Word2Vec and then the product groups are derived by extracting similar products names based on cosine similarity calculation. Finally, the sales data on the extracted products is summated to estimate the market size of the product groups. As an experimental data, text data of product names from Statistics Korea's microdata (345,103 cases) were mapped in multidimensional vector space by Word2Vec training. We performed parameters optimization for training and then applied vector dimension of 300 and window size of 15 as optimized parameters for further experiments. We employed index words of Korean Standard Industry Classification (KSIC) as a product name dataset to more efficiently cluster product groups. The product names which are similar to KSIC indexes were extracted based on cosine similarity. The market size of extracted products as one product category was calculated from individual companies' sales data. The market sizes of 11,654 specific product lines were automatically estimated by the proposed model. For the performance verification, the results were compared with actual market size of some items. The Pearson's correlation coefficient was 0.513. Our approach has several advantages differing from the previous studies. First, text mining and machine learning techniques were applied for the first time on market size estimation, overcoming the limitations of traditional sampling based- or multiple assumption required-methods. In addition, the level of market category can be easily and efficiently adjusted according to the purpose of information use by changing cosine similarity threshold. Furthermore, it has a high potential of practical applications since it can resolve unmet needs for detailed market size information in public and private sectors. Specifically, it can be utilized in technology evaluation and technology commercialization support program conducted by governmental institutions, as well as business strategies consulting and market analysis report publishing by private firms. The limitation of our study is that the presented model needs to be improved in terms of accuracy and reliability. The semantic-based word embedding module can be advanced by giving a proper order in the preprocessed dataset or by combining another algorithm such as Jaccard similarity with Word2Vec. Also, the methods of product group clustering can be changed to other types of unsupervised machine learning algorithm. Our group is currently working on subsequent studies and we expect that it can further improve the performance of the conceptually proposed basic model in this study.

• Korean Journal of Agricultural Science
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• v.5 no.2
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• pp.149-157
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• 1978

This study was carried out to obtain basic data for the type selection of furrow openers for the no-tillage soybean planter trailed by the two-wheel tractor from the standpoint of minimum draft and good performance of furrowing. For this study, two models of furrow opener, hoe and disc type, were tested on the artificial soil stuffed in the moving soil bin. The results obtained were as follows. In the case of disc furrow opener, the drafts were measured according to various diameters of discs under the condition of disc angle $8^{\circ}$ and $16^{\circ}$, working depth 3cm and 6cm, working speed 2.75cm/sec. Minimum draft appeared when the diameter of disc was about 28cm and the drafts increased as the diameter of discs became larger or smaller than this diameter. Specific draft showed almost same tendencies as above but showed the minimum when the diameter was about 30cm. For the purpose of controlling the seeding depth, the relationships between draft and working depths, 3cm and 6cm, were tested. The variations of draft concerning the various working depths showed linear changes and were affected in higher degree by depths than other factors. There were general tendencies at both working depths, 3cm and 6cm, that total draft showed the minimum with the disc diameter of about 28cm and specific draft showed it with the disc diameter of about 30cm regardless of disc angle and working speed. For the purpose of controlling the working width and speed, the relationships among drafts, disc angle and working speed were investigated and there were general tendencies that the draft increased as the angle and speed were increased and the draft was affected more by speed than by angle. To compare the hoe-type with disc-type opener, the specific drafts of hoe openers were compared with those of disc opener of $16^{\circ}$ angle and 30cm diameter. The specific draft of disc-type opener with the diameter of 30cm was $0.35{\sim}0.5kg/cm^2$, while $0.71{\sim}1.02kg/cm^2$ in the case of hoe type with the lift angle of $20^{\circ}$ which is 2 times as much as that of disc type in average value. And the furrows opened by disc openers were cleaner than those opened by hoe openers.

• Journal of the Korean Institute of Landscape Architecture
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• v.51 no.3
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• pp.70-82
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• 2023

The recent surge of IT and data acquisition is shifting the paradigm in all aspects of life, and these advances are also affecting academic fields. Research topics and methods are being improved through academic exchange and connections. In particular, data-based research methods are employed in various academic fields, including landscape architecture, where continuous research is needed. Therefore, this study aims to investigate the possibility of developing a landscape preference evaluation and prediction model using machine learning, a branch of Artificial Intelligence, reflecting the current situation. To achieve the goal of this study, machine learning techniques were applied to the landscaping field to build a landscape preference evaluation and prediction model to verify the simulation accuracy of the model. For this, wind power facility landscape images, recently attracting attention as a renewable energy source, were selected as the research objects. For analysis, images of the wind power facility landscapes were collected using web crawling techniques, and an analysis dataset was built. Orange version 3.33, a program from the University of Ljubljana was used for machine learning analysis to derive a prediction model with excellent performance. IA model that integrates the evaluation criteria of machine learning and a separate model structure for the evaluation criteria were used to generate a model using kNN, SVM, Random Forest, Logistic Regression, and Neural Network algorithms suitable for machine learning classification models. The performance evaluation of the generated models was conducted to derive the most suitable prediction model. The prediction model derived in this study separately evaluates three evaluation criteria, including classification by type of landscape, classification by distance between landscape and target, and classification by preference, and then synthesizes and predicts results. As a result of the study, a prediction model with a high accuracy of 0.986 for the evaluation criterion according to the type of landscape, 0.973 for the evaluation criterion according to the distance, and 0.952 for the evaluation criterion according to the preference was developed, and it can be seen that the verification process through the evaluation of data prediction results exceeds the required performance value of the model. As an experimental attempt to investigate the possibility of developing a prediction model using machine learning in landscape-related research, this study was able to confirm the possibility of creating a high-performance prediction model by building a data set through the collection and refinement of image data and subsequently utilizing it in landscape-related research fields. Based on the results, implications, and limitations of this study, it is believed that it is possible to develop various types of landscape prediction models, including wind power facility natural, and cultural landscapes. Machine learning techniques can be more useful and valuable in the field of landscape architecture by exploring and applying research methods appropriate to the topic, reducing the time of data classification through the study of a model that classifies images according to landscape types or analyzing the importance of landscape planning factors through the analysis of landscape prediction factors using machine learning.