• Journal of Bio-Environment Control
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• v.18 no.4
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• pp.332-340
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• 2009

In this study, the thermal insulation effectiveness of the greenhouse insulators by the installation methods was investigated to find the right installation way of the insulation materials. Physical properties of the insulators such as thickness, air transmissivity, apparent density, ultraviolet rays cutoff ratio, reflectance, thermal conductivity, moisture absorptivity were evaluated and the insulation ability of the insulators were measured by the module experiments. For the same insulator, the insulation ability of the case with the outward direction of the black colored face, i.e., with the inward direction of the white colored face, was better than that of vice versa. The case of the black colored both surfaces was better than the case of the white colored both surfaces. For aluminium reflection material, the case with the outward direction of the lustre face, i.e., with the inward direction of the non-lustre face, was better than that of vice versa. For the same material with the inner thin polyethylene foam (or polyester) and the chemical wool, the case with the outward direction of the inner thin polyethylene foam (or polyester), i.e., with the inward chemical wool, was better than that of vice versa. Addition of the inner thin polyethylene foam increased the insulation effect very much.

• Journal of Bio-Environment Control
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• v.15 no.4
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• pp.296-305
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• 2006

The heterogeneity of crop transpiration is important to clearly understand the microclimate mechanisms and to efficiently handle the water resource in greenhouses. A computational fluid dynamic program (Fluent CFD version 6.2) was developed to study the internal climate and crop transpiration distributions of greenhouses. Additionally, the global solar radiation model and a crop heat exchange model were programmed together. Those models programmed using $C^{++}$ software were connected to the CFD main module using the user define function (UDF) technology. For the developed CFD validity, a field experiment was conducted at a $17{\times}6 m^2$ plastic-covered mechanically ventilated single-span greenhouse located at Pusan in Korea. The CFD internal distributions of air temperature, relative humidity, and air velocity at 1m height were validated against the experimental results. The CFD computed results were in close agreement with the measured distributions of the air temperature, relative humidity, and air velocity along the greenhouse. The averaged errors of their CFD computed results were 2.2%,2.1%, and 7.7%, respectively.

• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.20 no.3
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• pp.119-130
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• 2015

Impacts of Argo temperature assimilation on the analysis fields in the East Sea is investigated by using DAESROM, the East Sea Regional Ocean Model with a 3-dimensional variational assimilation module (Kim et al., 2009). Namely, we produced analysis fields in 2009, in which temperature profiles, sea surface temperature (SST) and sea surface height (SSH) anomaly were assimilated (Exp. AllDa) and carried out additional experiment by withdrawing Argo temperature data (Exp. NoArgo). When comparing both experimental results using assimilated temperature profiles, Root Mean Square Error (RMSE) of the Exp. AllDa is generally lower than the Exp. NoArgo. In particular, the Argo impacts are large in the subsurface layer, showing the RMSE difference of about $0.5^{\circ}C$. Based on the observations of 14 surface drifters, Argo impacts on the current and temperature fields in the surface layer are investigated. In general, surface currents along the drifter positions are improved in the Exp. AllDa, and large RMSE differences (about 2.0~6.0 cm/s) between both experiments are found in drifters which observed longer period in the southern region where Argo density was high. On the other hand, Argo impacts on the SST fields are negligible, and it is considered that SST assimilation with 1-day interval has dominant effects. Similar to the difference of surface current fields between both experiments, SSH fields also reveal significant difference in the southern East Sea, for example the southwestern Yamato Basin where anticyclonic circulation develops. The comparison of SSH fields implies that SSH assimilation does not correct the SSH difference caused by withdrawing Argo data. Thus Argo assimilation has an important role to reproduce meso-scale circulation features in the East Sea.

• Membrane Journal
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• v.19 no.3
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• pp.252-260
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• 2009

The effects of the treatment of an acidic solution at pH 2 on polyacrylonitrile ultrafiltration (UF) membranes were investigated using a circular cross-flow filtration bench with a membrane module. A substantial reduction in the membrane permeability was observed after 80 hours' treatment of the acidic solution. In addition, the analyses of the sample solutions by ultraviolet/visible absorption spectroscopy and gas chromatography/mass spectrometry (GC/MS), which were taken from the feed tank as a function of the treatment time, showed that a new organic compound was produced in the course of the treatment. From a thorough search of the mass spectral library we presumed the new compound to be 1,6-dioxacyclododecane-7,12-dione (DCD), one of the well-known additives for polyurethane. Based on further experimental results, including the scanning electron microscope (SEM) images and the solid-state NMR spectra of the membranes used for the treatment of the acidic solution, we suggested that the decrease of the permeate flux resulted not from the deformation of the membranes, but from the fouling by DCD eluted from the polyurethane tubes in the filtration bench during the treatment. Those results imply that the reactivity to an acidic solution of the parts comprising the filtration bench is as important as that of the membranes themselves for effective treatments of acidic solutions, for efficient chemical cleaning by strong acids, and also in determining the pH limit of the solutions that can be treated by the membranes.

• The Journal of Korean Academy of Prosthodontics
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• v.47 no.4
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• pp.394-405
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• 2009

Statement of problem: Crestal bone loss, a common problem associated with dental implant, has been attributed to excessive bone stresses. Design of implant's transgingival (TG) part may affect the crestal bone stresses. Purpose: To investigate if concavely designed geometry at a dental implant's TG part reduces peri-implant bone stresses. Material and methods: A total of five differently configured TG parts were compared. Base model was the ITI one piece implant (Straumann, Waldenburg, Switzerland) characterized by straight TG part. Other 4 experimental models, i.e. Model-1 to Model-4, were designed to have concave TG part. Finite element analyses were carried out using an axisymmetric assumption. A vertical load of 50 N or an oblique load of 50 N acting at $30^{\circ}$ with the implant's long axis was applied. For a systematic stress comparison, a total of 19 reference points were defined on nodal points around the implant. The peak crestal bone stress acting at the intersection of implant and crestal bone was estimated using regression analysis from the stress results obtained at 5 reference points defined along the mid plane of the crestal bone. Results: Base Model with straight configuration at the transgingival part created highest stresses on the crestal bone. Stress level was reduced when concavity was imposed. The greater the concavity and the closer the concavity to the crestal bone level, the less the crestal stresses. Conclusion: The transgingival part of dental implant affect the crestal bone stress. And that concavely designed one may be used to reduce bone stress.

• 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.