• Korean Journal of Soil Science and Fertilizer
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• v.21 no.4
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• pp.386-408
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• 1988

A study was conducted to develop a model for estimating evapotranspiration and yield of Chinese cabbages from meteorological factors from 1981 to 1986 in Suweon, Korea. Lysimeters with water table maintained at 50cm depth were used to measure the potential evapotranspiration and the maximum evapotranspiration in situ. The actual evapotranspiration and the yield were measured in the field plots irrigated with different soil moisture regimes of -0.2, -0.5, and -1.0 bars, respectively. The soil water content throughout the profile was monitored by a neutron moisture depth gauge and the soil water potentials were measured using gypsum block and tensiometer. The fresh weight of Chinese cabbages at harvest was measured as yield. The data collected in situ were analyzed to obtain parameters related to modeling. The results were summarized as followings: 1. The 5-year mean of potential evapotranspiration (PET) gradually increased from 2.38 mm/day in early April to 3.98 mm/day in mid-June, and thereafter, decreased to 1.06 mm/day in mid-November. The estimated PET by Penman, Radiation or Blanney-Criddle methods were overestimated in comparison with the measured PET, while those by Pan-evaporation method were underestimated. The correlation between the estimated and the measured PET, however, showed high significance except for July and August by Blanney-Criddle method, which implied that the coefficients should be adjusted to the Korean conditions. 2. The meteorological factors which showed hgih correlation with the measured PET were temperature, vapour pressure deficit, sunshine hours, solar radiation and pan-evaporation. Several multiple regression equations using meteorological factors were formulated to estimate PET. The equation with pan-evaporation (Eo) was the simplest but highly accurate. PET = 0.712 + 0.705Eo 3. The crop coefficient of Chinese cabbages (Kc), the ratio of the maximum evapotranspiration (ETm) to PET, ranged from 0.5 to 0.7 at early growth stage and from 0.9 to 1.2 at mid and late growth stages. The regression equation with respect to the growth progress degree (G), ranging from 0.0 at transplanting day to 1.0 at the harvesting day, were: $$Kc=0.598+0.959G-0.501G^2$$ for spring cabbages $$Kc=0.402+1.887G-1.432G^2$$ for autumn cabbages 4. The soil factor (Kf), the ratio of the actual evapotranspiration to the maximum evapotranspiration, showed 1.0 when the available soil water fraction (f) was higher than a threshold value (fp) and decreased linearly with decreasing f below fp. The relationships were: Kf=1.0 for $$f{\geq}fp$$ Kf=a+bf for f$$I{\leq}Esm$$ Es = Esm for I > Esm 6. The model for estimating actual evapotranspiration (ETa) was based on the water balance neglecting capillary rise as: ETa=PET. Kc. Kf+Es 7. The model for estimating relative yield (Y/Ym) was selected among the regression equations with the measured ETa as: Y/Ym=a+bln(ETa) The coefficients and b were 0.07 and 0.73 for spring Chinese cabbages and 0.37 and 0.66 for autumn Chinese cabbages, respectively. 8. The estimated ETa and Y/Ym were compared with the measured values to verify the model established above. The estimated ETa showed disparities within 0.29mm/day for spring Chinese cabbages and 0.19mm/day for autumn Chinese cabbages. The average deviation of the estimated relative yield were 0.14 and 0.09, respectively. 9. The deviations between the estimated values by the model and the actual values obtained from three cropping field experiments after the completion of the model calibration were within reasonable confidence range. Therefore, this model was validated to be used in practical purpose.

• Journal of Intelligence and Information Systems
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• v.23 no.1
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• pp.23-46
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• 2017

Although there have been cases of evaluating the value of specific companies or projects which have centralized on developed countries in North America and Europe from the early 2000s, the system and methodology for estimating the economic value of individual technologies or patents has been activated on and on. Of course, there exist several online systems that qualitatively evaluate the technology's grade or the patent rating of the technology to be evaluated, as in 'KTRS' of the KIBO and 'SMART 3.1' of the Korea Invention Promotion Association. However, a web-based technology valuation system, referred to as 'STAR-Value system' that calculates the quantitative values of the subject technology for various purposes such as business feasibility analysis, investment attraction, tax/litigation, etc., has been officially opened and recently spreading. In this study, we introduce the type of methodology and evaluation model, reference information supporting these theories, and how database associated are utilized, focusing various modules and frameworks embedded in STAR-Value system. In particular, there are six valuation methods, including the discounted cash flow method (DCF), which is a representative one based on the income approach that anticipates future economic income to be valued at present, and the relief-from-royalty method, which calculates the present value of royalties' where we consider the contribution of the subject technology towards the business value created as the royalty rate. We look at how models and related support information (technology life, corporate (business) financial information, discount rate, industrial technology factors, etc.) can be used and linked in a intelligent manner. Based on the classification of information such as International Patent Classification (IPC) or Korea Standard Industry Classification (KSIC) for technology to be evaluated, the STAR-Value system automatically returns meta data such as technology cycle time (TCT), sales growth rate and profitability data of similar company or industry sector, weighted average cost of capital (WACC), indices of industrial technology factors, etc., and apply adjustment factors to them, so that the result of technology value calculation has high reliability and objectivity. Furthermore, if the information on the potential market size of the target technology and the market share of the commercialization subject refers to data-driven information, or if the estimated value range of similar technologies by industry sector is provided from the evaluation cases which are already completed and accumulated in database, the STAR-Value is anticipated that it will enable to present highly accurate value range in real time by intelligently linking various support modules. Including the explanation of the various valuation models and relevant primary variables as presented in this paper, the STAR-Value system intends to utilize more systematically and in a data-driven way by supporting the optimal model selection guideline module, intelligent technology value range reasoning module, and similar company selection based market share prediction module, etc. In addition, the research on the development and intelligence of the web-based STAR-Value system is significant in that it widely spread the web-based system that can be used in the validation and application to practices of the theoretical feasibility of the technology valuation field, and it is expected that it could be utilized in various fields of technology commercialization.

• Journal of agricultural medicine and community health
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• v.29 no.1
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• pp.65-75
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• 2004

Objectives: Immunizations have been one of the most effective measures preventing from infectious diseases. It is quite important national infectious disease prevention policy to keep the immunizations rate high and monitor the immunizations rate continuously. To do this, Korean CDC introduced the National Immunization Registry Program(NIRP) which has been implementing since 2000 at the Public Health Centers(PHC). The National Immunization Registry Program will be near completed after sharing, connecting and transfering vaccination data between public and private sector. The aims of this study was to evaluate the immunization module of non-chart system in private clinic with health information system of public health center(made by POSDATA Co., LTD) and immunization registry program(made by BIT Computer Co., LTD). Methods: The analysis and survey were done by specialists in medical, health field, and health information fields from 2001. November to 2002. January. We made the analysis and recommendation about the immunization module of non-chart system in private clinic. Results and Conclusions: To make improvement on immunization module, the system will be revised on various function like receipt and registration, preliminary medical examination, reference and inquiry, registration of vaccine, print-out various sheet, function of transfer vaccination data, issue function of vaccination certification, function of reminder and recall, function of statistical calculation, and management of vaccine stock. There are needs of an accurate assessment of current immunization module on each private non-chart system. And further studies will be necessary to make it an accurate system under changing health policy related national immunization program. We hope that the result of this study may contribute to establish the National Immunization Registry Program.

• 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.

• Radiation Oncology Journal
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• v.19 no.1
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• pp.66-73
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• 2001

Purpose : For the research of Boron Neutron Capture Therapy (BNCT), fast neutrons generated from the MC-50 cyclotron with maximum energy of 34.4 MeV in Korea Cancer Center Hospital were moderated by 70 cm paraffin and then the dose characteristics were investigated. Using these results, we hope to establish the protocol about dose measurement of epi-thermal neutron, to make a basis of dose characteristic of epi-thermal neutron emitted from nuclear reactor, and to find feasibility about accelerator-based BNCT. Method and Materials : For measuring the absorbed dose and dose distribution of fast neutron beams, we used Unidos 10005 (PTW, Germany) electrometer and IC-17 (Far West, USA), IC-18, ElC-1 ion chambers manufactured by A-150 plastic and used IC-l7M ion chamber manufactured by magnesium for gamma dose. There chambers were flushed with tissue equivalent gas and argon gas and then the flow rate was S co per minute. Using Monte Carlo N-Particle (MCNP) code, transport program in mixed field with neutron, photon, electron, two dimensional dose and energy fluence distribution was calculated and there results were compared with measured results. Results : The absorbed dose of fast neutron beams was $6.47\times10^{-3}$ cGy per 1 MU at the 4 cm depth of the water phantom, which is assumed to be effective depth for BNCT. The magnitude of gamma contamination intermingled with fast neutron beams was $65.2{\pm}0.9\%$ at the same depth. In the dose distribution according to the depth of water, the neutron dose decreased linearly and the gamma dose decreased exponentially as the depth was deepened. The factor expressed energy level, $D_{20}/D_{10}$, of the total dose was 0.718. Conclusion : Through the direct measurement using the two ion chambers, which is made different wall materials, and computer calculation of isodose distribution using MCNP simulation method, we have found the dose characteristics of low fluence fast neutron beams. If the power supply and the target material, which generate high voltage and current, will be developed and gamma contamination was reduced by lead or bismuth, we think, it may be possible to accelerator-based BNCT.

• Radiation Oncology Journal
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• v.27 no.4
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• pp.201-209
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• 2009

Purpose: To investigate the care patterns for radiation therapy and to determine inter-hospital differences for patients with laryngeal carcinoma in Korea. Materials and Methods: A total of 237 cases of laryngeal carcinoma (glottis, 144; supraglottis, 93) assembled from 23 hospitals, who underwent irradiation in the year of 1998 and 1999, were retrospectively analyzed to investigate inter-hospital differences with respect to radiotherapy treatment. We grouped the 23 hospitals based on the number of new patients annually irradiated in 1998; and designated them as group A (${\geq}$900 patients), group B (${\geq}$400 patients and <900 patients), and group C (<400 patients). Results: The median age of the 237 patients was 62 years (range, 25 to 88 years), of which 216 were male and 21 were female. The clinical stages were distributed as follows: for glottis cancer, I; 61.8%, II; 21.5%, III; 4.2%, IVa; 11.1%, IVb; 1.4%, and in supraglottic cancer, I; 4.3%, II; 19.4%, III; 28.0%, IVa; 43.0%, IVb; 5.4%, respectively. Some differences were observed among the 3 groups with respect to the dose calculation method, radiation energy, field arrangement, and use of an immobilization device. No significant difference among 3 hospital groups was observed with respect to treatment modality, irradiation volume, and median total dose delivered to the primary site. Conclusion: This study revealed that radiotherapy process and patterns of care are relatively uniform in laryngeal cancer patients in Korean hospitals, and we hope this nationwide data can be used as a basis for the standardization of radiotherapy for the treatment of laryngeal cancer.

• Korean Journal of Agricultural Science
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• v.2 no.1
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• pp.61-100
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• 1975

This study has been made to find out the possibilty of successfully using the following $K_2O$ recommended equation $K_2O\;kg/10a=(Ko/\sqrt{Ca+Mg}-Ks/\sqrt{Ca+Mg})sqrt{Ca+Mg}.\;47.\;B\;D$. where $Ko/sqrt{Ca+Mg}=0.03518+0.0007658\;Sio_2/O.M$. $K_Ssqrt{Ca+Mg}$=Exchangeable K me/100g/$\sqrt{Total\;soluble(Ca+Mg)me/100g\;in\;Soil}$ B. D. =Bulk density of top soil, when the dose of Nitrogen for rice is estimated from the following equation: $N\;kg/10a=(4.2+0.096\;SiO_2/O.M).F$ where $F=0.907+0.263x-0.013x^2$ $SiO_2/O.M=(available\;SiO_2=ppm)/(organic\;matter\;%)$in soil For this. two field experiments. one in sandy and the other in clay paddy soil. have been conducted using 3 levels of wollastonite (0, 500, 100kg/10a) as main treatments; 3 levels of $K_2O$ application were used as sub-plots. These were as follows : (1) 8kg of $K_2O$/10a regardless of the K activity-$K/\sqrt{Ca+Mg}$; (2) kg of $K_2O$/10a estimated from the above equation. and (3) same as (2) above plus additional 30% of $K_2O$. The dose of N kg/ 10a was determined from the above equation based on the value of $SiO_2$/O.M. ratio in each treatment. There were three replications. The leading variety of rice in Chung Chong Nam Do area. Akibare (introduced from Japan) was used. The data obtained. through soil and plant analysis and growth and yield observations. have been throughly examined to attain the following summarized conclusions. 1. The nitrogen dose. estimated from the above equation. was in excess for optimum growth of the rice variety Akibare; indicating the necessity of modification onthe value of "F" or the constants in the equation. The concept of using $SiO_2$/O.M. in the equation was shown to be applicable. 2. The dose of potash. estimated from the respective equation given above. also was in excess of the rice requirements indicating the necessity of minor change in the estimation of $Ko/\sqrt{Ca+Mg}$ value and some great modification in the calculation of $Ks/\sqrt{Ca+Mg}$ value for the equation; however the concept of using $K/\sqrt{Ca+Mg}$ as a basis of $K_2O$ recommendation was shown to be quite reasonable. 3. It was found. from the correlation study using the data of paddy yield and amount of $K_2O$ absorbed by rice plants that the substitution of the value of $Ks/\sqrt{Ca+Mg}$ in the equation for the vaule $Ks/\sqrt{Ca+Mg}=0.037+0.78K\;me/100g$ soil was much more applicable than using the value calculated from the data of soil and wollastonite analysis.

• Journal of Intelligence and Information Systems
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• v.19 no.3
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• pp.141-156
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• 2013

Social media is a representative form of the Web 2.0 that shapes the change of a user's information behavior by allowing users to produce their own contents without any expert skills. In particular, as a new communication medium, it has a profound impact on the social change by enabling users to communicate with the masses and acquaintances their opinions and thoughts. Social media data plays a significant role in an emerging Big Data arena. A variety of research areas such as social network analysis, opinion mining, and so on, therefore, have paid attention to discover meaningful information from vast amounts of data buried in social media. Social media has recently become main foci to the field of Information Retrieval and Text Mining because not only it produces massive unstructured textual data in real-time but also it serves as an influential channel for opinion leading. But most of the previous studies have adopted broad-brush and limited approaches. These approaches have made it difficult to find and analyze new information. To overcome these limitations, we developed a real-time Twitter trend mining system to capture the trend in real-time processing big stream datasets of Twitter. The system offers the functions of term co-occurrence retrieval, visualization of Twitter users by query, similarity calculation between two users, topic modeling to keep track of changes of topical trend, and mention-based user network analysis. In addition, we conducted a case study on the 2012 Korean presidential election. We collected 1,737,969 tweets which contain candidates' name and election on Twitter in Korea (http://www.twitter.com/) for one month in 2012 (October 1 to October 31). The case study shows that the system provides useful information and detects the trend of society effectively. The system also retrieves the list of terms co-occurred by given query terms. We compare the results of term co-occurrence retrieval by giving influential candidates' name, 'Geun Hae Park', 'Jae In Moon', and 'Chul Su Ahn' as query terms. General terms which are related to presidential election such as 'Presidential Election', 'Proclamation in Support', Public opinion poll' appear frequently. Also the results show specific terms that differentiate each candidate's feature such as 'Park Jung Hee' and 'Yuk Young Su' from the query 'Guen Hae Park', 'a single candidacy agreement' and 'Time of voting extension' from the query 'Jae In Moon' and 'a single candidacy agreement' and 'down contract' from the query 'Chul Su Ahn'. Our system not only extracts 10 topics along with related terms but also shows topics' dynamic changes over time by employing the multinomial Latent Dirichlet Allocation technique. Each topic can show one of two types of patterns-Rising tendency and Falling tendencydepending on the change of the probability distribution. To determine the relationship between topic trends in Twitter and social issues in the real world, we compare topic trends with related news articles. We are able to identify that Twitter can track the issue faster than the other media, newspapers. The user network in Twitter is different from those of other social media because of distinctive characteristics of making relationships in Twitter. Twitter users can make their relationships by exchanging mentions. We visualize and analyze mention based networks of 136,754 users. We put three candidates' name as query terms-Geun Hae Park', 'Jae In Moon', and 'Chul Su Ahn'. The results show that Twitter users mention all candidates' name regardless of their political tendencies. This case study discloses that Twitter could be an effective tool to detect and predict dynamic changes of social issues, and mention-based user networks could show different aspects of user behavior as a unique network that is uniquely found in Twitter.

• Korean Journal of Soil Science and Fertilizer
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• v.44 no.6
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• pp.1195-1206
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• 2011

This study was conducted to apply LCA (Life cycle assessment) methodology to lettuce (Lactuca sativa L.) production systems in Namyang-ju as a case study. Five lettuce growing farms with three different farming systems (two farms with organic farming system, one farm with a system without agricultural chemicals and two farms with conventional farming system) were selected at Namyangju city of Gyeonggi-province in Korea. The input data for LCA were collected by interviewing with the farmers. The system boundary was set at a cropping season without heating and cooling system for reducing uncertainties in data collection and calculation. Sensitivity analysis was carried out to find out the effect of type and amount of fertilizer and energy use on GHG (Greenhouse Gas) emission. The results of establishing GTG (Gate-to-Gate) inventory revealed that the quantity of fertilizer and energy input had the largest value in producing 1 kg lettuce, the amount of pesticide input the smallest. The amount of electricity input was the largest in all farms except farm 1 which purchased seedlings from outside. The quantity of direct field emission of $CO_2$, $CH_4$ and $N_2O$ from farm 1 to farm 5 were 6.79E-03 (farm 1), 8.10E-03 (farm 2), 1.82E-02 (farm 3), 7.51E-02 (farm 4) and 1.61E-02 (farm 5) kg $kg^{-1}$ lettuce, respectively. According to the result of LCI analysis focused on GHG, it was observed that $CO_2$ emission was 2.92E-01 (farm 1), 3.76E-01 (farm 2), 4.11E-01 (farm 3), 9.40E-01 (farm 4) and $5.37E-01kg\;CO_2\;kg^{-1}\;lettuce$ (farm 5), respectively. Carbon dioxide contribute to the most GHG emission. Carbon dioxide was mainly emitted in the process of energy production, which occupied 67~91% of $CO_2$ emission from every production process from 5 farms. Due to higher proportion of $CO_2$ emission from production of compound fertilizer in conventional crop system, conventional crop system had lower proportion of $CO_2$ emission from energy production than organic crop system did. With increasing inorganic fertilizer input, the process of lettuce cultivation covered higher proportion in $N_2O$ emission. Therefore, farms 1 and 2 covered 87% of total $N_2O$ emission; and farm 3 covered 64%. The carbon footprints from farm 1 to farm 5 were 3.40E-01 (farm 1), 4.31E-01 (farm 2), 5.32E-01 (farm 3), 1.08E+00 (farm 4) and 6.14E-01 (farm 5) kg $CO_2$-eq. $kg^{-1}$ lettuce, respectively. Results of sensitivity analysis revealed the soybean meal was the most sensitive among 4 types of fertilizer. The value of compound fertilizer was the least sensitive among every fertilizer imput. Electricity showed the largest sensitivity on $CO_2$ emission. However, the value of $N_2O$ variation was almost zero.