• Journal of Bio-Environment Control
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• v.24 no.3
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• pp.226-234
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• 2015

To provide the data necessary to determine exposure coefficients used for calculating the snow load acting on a greenhouse, we compared the exposure coefficients in the greenhouse structure design standards for various countries. We determined the exposure coefficient for each region and tried to improve on the method used to decide it. Our results are as follows: After comparing the exposure coefficients in the standards of various countries, we could determine that the main factors affecting the exposure coefficient were terrain roughness, wind speed, and whether a windbreak was present. On comparing national standards, the exposure coefficients could be divided into three groups: exposure coefficients of 0.8(0.9) for areas with strong winds, 1.0(1.1) for partially exposed areas, and 1.2 for areas with dense windbreaks. After analyzing the exposure coefficients for 94 areas in South Korea according to the ISO4355 standard, all of the areas had two coefficients (1.0 and 0.8), except Daegwallyeong (0.5) and Yeosu (0.6), which had one coefficient each. In South Korea, the probability of snow is greater inland than in coastal areas and there are fewer days with a maximum wind velocity > $5m{\cdot}s^{-1}$ inland. When determining the exposure coefficients in South Korea, we can subdivide the country into three regions: coastal areas with strong winds have an exposure coefficient of 0.8; inland areas have a coefficient of 1.0; and areas with dense windbreaks have an exposure coefficient of 1.2. Further research that considers the number of days with a wind velocity > $5m{\cdot}s^{-1}$ as the threshold wind speed is needed before we can make specific recommendations for the exposure coefficient for different regions.

• Korean journal of applied entomology
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• v.53 no.4
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• pp.375-380
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• 2014

The sweetpotato whitefly (SPW), Bemisia tabaci Gennadius, is a major pest in tomato greenhouses on Jeju Island because they transmit viral diseases. To develop practical sampling methods for adult SPWs, yellow-color sticky traps were used in commercial tomato greenhouses throughout the western part of Jeju Island in 2011 and 2012. On the basis of the size and growing conditions in the tomato greenhouses, 20 to 30 traps were installed in each greenhouse for developing a sampling plan. Adult SPWs were more attracted to horizontal traps placed 60 cm above the ground than to vertical trap placed 10 cm above the plant canopy. The spatial patterns of the adult SPWs were evaluated using Taylor's power law (TPL) and Iwao's patchiness regression (IPR). The results showed that adult SPWs were aggregated in each surveyed greenhouse. In this study, TPL showed better performance because of the coefficient of determination ($r^2$). On the basis of the fixed-precision level sampling plan using TPL parameters, more traps were required for higher precision in lower SPW densities per trap. A sequential sampling stop line was constructed using TPL parameters. If the treatment threshold was greater than 10 maximum adult SPWs on a trap, the required traps numbered 15 at a fixed-precision level of 0.25. In estimating the mean density per trap, the proportion of traps with two or more adult SPWs was more efficient than whole counting: ${\ln}(m)=1.19+0.90{\ln}(-{\ln}(1-p_T))$. The results of this study could be used to prevent the dissemination of SPW as a viral disease vector by using accurate control decision in SPW management programs.

• Investigative Magnetic Resonance Imaging
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• v.9 no.1
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• pp.16-23
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• 2005

Purpose : In order to investigate the functional brain anatomy associated with mental calculation, functional magnetic resonance imaging was performed. Materials and Methods : In six normal right handed subjects, functional MR images were obtained using a 1.57 MR scanner and the EPI BOLD technique. The study included experiment I and experiment II. Each experiment consisted of five resting and four activation periods with each period of 30 seconds. During the activation period of both experiment I and II, calculation equations[an example: $(4+5)\times8=72$] were presented and the subjects were instructed to decide true or false of them. During the resting period of experiment I, the subjects were instructed to visually fixate on a crosshair. During the resting period of experiment II, two diagrams (an example: $(\bullet,\;\blacksquare)$)were presented and the subjects were instructed to decide they are same or not. For the post-processing of images, the SPM program was used, with the threshold of significance set at p<0.00001. The activated areas during the tasks were assessed. Results : In experiment 1, the inferior frontal gyrus, prefrontal cortex, promoter area, supplementary motor area, and intraparietal sulcus including superior parietal cortex were activated bilaterally. Although these areas were also activated in experiment II, the activated signals in the right frontal and parietal lobes were lessened. Conclusion : The left inferior frontal gyrus and prefrontal cortex and bilateral intraparietal sulci were activated during mental calculation. The right frontal and parietal lobes might be related to attention and decision making.

• 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.