• Tuberculosis and Respiratory Diseases
• /
• v.44 no.6
• /
• pp.1343-1352
• /
• 1997

Background : Heat-treated cells are known to be protected from lysis by TNF, which is considered to play a central role in the pathogenesis of sepsis-induced acute lung injury. The objective of the study was to investigate the effect of heat shock response by heat-pretreatment on the acute lung injury of the rats induced by intratracheally administered TNF-$\alpha$, Methods : We intratracheally instilled either saline or TNF (R&D, 500ng) with and without heat pretreatment in Sprague-Dawley rats weighing 250~350 g. The heated rats were raised their rectal temperature to $41^{\circ}C$ and was maintained thereafter for 13 minutes at 18 h before intratracheal administration of saline or TNF. After 5 h of intratracheal treatment, lung leak, lung myeloperoxidase activity (MPO) and heat shock proteins were measured in rats. Lung leak index was defined as counts per minute of $I^{25}$ in the right lung divided by counts per minutes of $I^{25}$ in 1.0 ml of blood. All data are expressed as means ${\pm}$SE. Results : There is no difference in acute lung leak index ($0.099{\pm}0.024$ vs $0.123{\pm}0.005$) among the rats given saline intratracheally with and without heat pretreatment, but MPO activity showed a decreased tendency in heat-pretreated rats ($4.58{\pm}0.79\;U/g$) compared with heat-unpretreated rats ($7.32{\pm}0.97\;U/g$) (P=0.064). Rats administered TNF intratracheally with heat-pretreatment had decreased lung leak index ($0.137{\pm}0.012$) and lung MPO activity ($5.51{\pm}1.04\;U/g$) compared with those of heat-unpretreated and TNF-administered rats ($0.186{\pm}0.016$, $14.34{\pm}1.22\;U/g$) (P<0.05 in each). There were no significant difference of lung leak index and MPO activity between TNF-treated rats with heat-pretreatment and saline-treated rats with and without heat-pretreatment. Conclusion : The heat shock response attenuated neutrophil recruitment and acute lung leak induced by intratracheal instillation of TNF-in rats.

• Journal of Preventive Medicine and Public Health
• /
• v.35 no.2
• /
• pp.141-146
• /
• 2002

Objectives : This study was peformed in order to assess the incidence of hypertension based on two-years follow-up of a rural hypertension-free cohort in Korea. Methods : The study cohen comprised 2,580 subjects aged above 20 (1,107 men and 1,473 women) of Chung-Song County in Kyungpook Province judged to be hypertensive-free at the baseline examination in 1996. For each of two examinations in the two-year follow-up, those subjects free of hypertension were followed for the development of hypertension to the next examination one year (1997) and two years later (1998). The drop-out rate was 24.7% in men and 19.6% in women. Hypertension was defined as follows 1) above mild hypertension as a SBP above 140 mmHg or a DBP above 90 mmMg,2) above moderate hypertension as a SBP above 160 mmHg or a DBP above 100 mmHg or when the participant reported having used antihypertensive medication after beginning this survey. Results : The age-standardized incidence of above mild hypertension was 6 per 100 person years (PYS) in men and that of above moderate hypertension was 1.2. In women, the age-standardized rate for above mild hypertension was 5.7 and 1.5 for above mild and moderate hypertension, respectively. However, the rates of incidence as calculated by the risk method were 4.8% and 1.0% in men and 4.6%, 1.2% in women, respectively. In both genders, incidence was significantly associated with advancing age(p<0.01), In men, the incidences of above moderate hypertension by age group were 0.5 per 100 PYS aged 20-39, 0.7 aged 40-49, 1.7 aged 50-59, 3.6 aged 60-69, and 5.8 aged above 70(p<0.01). In women, those the incidence measured 0.6 per 100 PYS aged 20-39, 1.8 aged 40-49, 1.3 aged 50-59, 3.3 aged 60-69, and 5.6 aged above 70(p<0.01). After age 60, the incidence of hypertension increased rapidly. Conclusions : The incidence data of hypertension reported in this study may serve as a reference data for evaluating the impact of future public efforts in the primary prevention of hypertension in Korea.

• Journal of Intelligence and Information Systems
• /
• v.24 no.4
• /
• pp.1-32
• /
• 2018

In addition to stakeholders including managers, employees, creditors, and investors of bankrupt companies, corporate defaults have a ripple effect on the local and national economy. Before the Asian financial crisis, the Korean government only analyzed SMEs and tried to improve the forecasting power of a default prediction model, rather than developing various corporate default models. As a result, even large corporations called 'chaebol enterprises' become bankrupt. Even after that, the analysis of past corporate defaults has been focused on specific variables, and when the government restructured immediately after the global financial crisis, they only focused on certain main variables such as 'debt ratio'. A multifaceted study of corporate default prediction models is essential to ensure diverse interests, to avoid situations like the 'Lehman Brothers Case' of the global financial crisis, to avoid total collapse in a single moment. The key variables used in corporate defaults vary over time. This is confirmed by Beaver (1967, 1968) and Altman's (1968) analysis that Deakins'(1972) study shows that the major factors affecting corporate failure have changed. In Grice's (2001) study, the importance of predictive variables was also found through Zmijewski's (1984) and Ohlson's (1980) models. However, the studies that have been carried out in the past use static models. Most of them do not consider the changes that occur in the course of time. Therefore, in order to construct consistent prediction models, it is necessary to compensate the time-dependent bias by means of a time series analysis algorithm reflecting dynamic change. Based on the global financial crisis, which has had a significant impact on Korea, this study is conducted using 10 years of annual corporate data from 2000 to 2009. Data are divided into training data, validation data, and test data respectively, and are divided into 7, 2, and 1 years respectively. In order to construct a consistent bankruptcy model in the flow of time change, we first train a time series deep learning algorithm model using the data before the financial crisis (2000~2006). The parameter tuning of the existing model and the deep learning time series algorithm is conducted with validation data including the financial crisis period (2007~2008). As a result, we construct a model that shows similar pattern to the results of the learning data and shows excellent prediction power. After that, each bankruptcy prediction model is restructured by integrating the learning data and validation data again (2000 ~ 2008), applying the optimal parameters as in the previous validation. Finally, each corporate default prediction model is evaluated and compared using test data (2009) based on the trained models over nine years. Then, the usefulness of the corporate default prediction model based on the deep learning time series algorithm is proved. In addition, by adding the Lasso regression analysis to the existing methods (multiple discriminant analysis, logit model) which select the variables, it is proved that the deep learning time series algorithm model based on the three bundles of variables is useful for robust corporate default prediction. The definition of bankruptcy used is the same as that of Lee (2015). Independent variables include financial information such as financial ratios used in previous studies. Multivariate discriminant analysis, logit model, and Lasso regression model are used to select the optimal variable group. The influence of the Multivariate discriminant analysis model proposed by Altman (1968), the Logit model proposed by Ohlson (1980), the non-time series machine learning algorithms, and the deep learning time series algorithms are compared. In the case of corporate data, there are limitations of 'nonlinear variables', 'multi-collinearity' of variables, and 'lack of data'. While the logit model is nonlinear, the Lasso regression model solves the multi-collinearity problem, and the deep learning time series algorithm using the variable data generation method complements the lack of data. Big Data Technology, a leading technology in the future, is moving from simple human analysis, to automated AI analysis, and finally towards future intertwined AI applications. Although the study of the corporate default prediction model using the time series algorithm is still in its early stages, deep learning algorithm is much faster than regression analysis at corporate default prediction modeling. Also, it is more effective on prediction power. Through the Fourth Industrial Revolution, the current government and other overseas governments are working hard to integrate the system in everyday life of their nation and society. Yet the field of deep learning time series research for the financial industry is still insufficient. This is an initial study on deep learning time series algorithm analysis of corporate defaults. Therefore it is hoped that it will be used as a comparative analysis data for non-specialists who start a study combining financial data and deep learning time series algorithm.

• Journal of Intelligence and Information Systems
• /
• v.26 no.4
• /
• pp.27-65
• /
• 2020

Many companies on information and communication technology make public their own developed AI technology, for example, Google's TensorFlow, Facebook's PyTorch, Microsoft's CNTK. By releasing deep learning open source software to the public, the relationship with the developer community and the artificial intelligence (AI) ecosystem can be strengthened, and users can perform experiment, implementation and improvement of it. Accordingly, the field of machine learning is growing rapidly, and developers are using and reproducing various learning algorithms in each field. Although various analysis of open source software has been made, there is a lack of studies to help develop or use deep learning open source software in the industry. This study thus attempts to derive a strategy for adopting the framework through case studies of a deep learning open source framework. Based on the technology-organization-environment (TOE) framework and literature review related to the adoption of open source software, we employed the case study framework that includes technological factors as perceived relative advantage, perceived compatibility, perceived complexity, and perceived trialability, organizational factors as management support and knowledge & expertise, and environmental factors as availability of technology skills and services, and platform long term viability. We conducted a case study analysis of three companies' adoption cases (two cases of success and one case of failure) and revealed that seven out of eight TOE factors and several factors regarding company, team and resource are significant for the adoption of deep learning open source framework. By organizing the case study analysis results, we provided five important success factors for adopting deep learning framework: the knowledge and expertise of developers in the team, hardware (GPU) environment, data enterprise cooperation system, deep learning framework platform, deep learning framework work tool service. In order for an organization to successfully adopt a deep learning open source framework, at the stage of using the framework, first, the hardware (GPU) environment for AI R&D group must support the knowledge and expertise of the developers in the team. Second, it is necessary to support the use of deep learning frameworks by research developers through collecting and managing data inside and outside the company with a data enterprise cooperation system. Third, deep learning research expertise must be supplemented through cooperation with researchers from academic institutions such as universities and research institutes. Satisfying three procedures in the stage of using the deep learning framework, companies will increase the number of deep learning research developers, the ability to use the deep learning framework, and the support of GPU resource. In the proliferation stage of the deep learning framework, fourth, a company makes the deep learning framework platform that improves the research efficiency and effectiveness of the developers, for example, the optimization of the hardware (GPU) environment automatically. Fifth, the deep learning framework tool service team complements the developers' expertise through sharing the information of the external deep learning open source framework community to the in-house community and activating developer retraining and seminars. To implement the identified five success factors, a step-by-step enterprise procedure for adoption of the deep learning framework was proposed: defining the project problem, confirming whether the deep learning methodology is the right method, confirming whether the deep learning framework is the right tool, using the deep learning framework by the enterprise, spreading the framework of the enterprise. The first three steps (i.e. defining the project problem, confirming whether the deep learning methodology is the right method, and confirming whether the deep learning framework is the right tool) are pre-considerations to adopt a deep learning open source framework. After the three pre-considerations steps are clear, next two steps (i.e. using the deep learning framework by the enterprise and spreading the framework of the enterprise) can be processed. In the fourth step, the knowledge and expertise of developers in the team are important in addition to hardware (GPU) environment and data enterprise cooperation system. In final step, five important factors are realized for a successful adoption of the deep learning open source framework. This study provides strategic implications for companies adopting or using deep learning framework according to the needs of each industry and business.

• Journal of Intelligence and Information Systems
• /
• v.23 no.2
• /
• pp.107-122
• /
• 2017

Volatility in the stock market returns is a measure of investment risk. It plays a central role in portfolio optimization, asset pricing and risk management as well as most theoretical financial models. Engle(1982) presented a pioneering paper on the stock market volatility that explains the time-variant characteristics embedded in the stock market return volatility. His model, Autoregressive Conditional Heteroscedasticity (ARCH), was generalized by Bollerslev(1986) as GARCH models. Empirical studies have shown that GARCH models describes well the fat-tailed return distributions and volatility clustering phenomenon appearing in stock prices. The parameters of the GARCH models are generally estimated by the maximum likelihood estimation (MLE) based on the standard normal density. But, since 1987 Black Monday, the stock market prices have become very complex and shown a lot of noisy terms. Recent studies start to apply artificial intelligent approach in estimating the GARCH parameters as a substitute for the MLE. The paper presents SVR-based GARCH process and compares with MLE-based GARCH process to estimate the parameters of GARCH models which are known to well forecast stock market volatility. Kernel functions used in SVR estimation process are linear, polynomial and radial. We analyzed the suggested models with KOSPI 200 Index. This index is constituted by 200 blue chip stocks listed in the Korea Exchange. We sampled KOSPI 200 daily closing values from 2010 to 2015. Sample observations are 1487 days. We used 1187 days to train the suggested GARCH models and the remaining 300 days were used as testing data. First, symmetric and asymmetric GARCH models are estimated by MLE. We forecasted KOSPI 200 Index return volatility and the statistical metric MSE shows better results for the asymmetric GARCH models such as E-GARCH or GJR-GARCH. This is consistent with the documented non-normal return distribution characteristics with fat-tail and leptokurtosis. Compared with MLE estimation process, SVR-based GARCH models outperform the MLE methodology in KOSPI 200 Index return volatility forecasting. Polynomial kernel function shows exceptionally lower forecasting accuracy. We suggested Intelligent Volatility Trading System (IVTS) that utilizes the forecasted volatility results. IVTS entry rules are as follows. If forecasted tomorrow volatility will increase then buy volatility today. If forecasted tomorrow volatility will decrease then sell volatility today. If forecasted volatility direction does not change we hold the existing buy or sell positions. IVTS is assumed to buy and sell historical volatility values. This is somewhat unreal because we cannot trade historical volatility values themselves. But our simulation results are meaningful since the Korea Exchange introduced volatility futures contract that traders can trade since November 2014. The trading systems with SVR-based GARCH models show higher returns than MLE-based GARCH in the testing period. And trading profitable percentages of MLE-based GARCH IVTS models range from 47.5% to 50.0%, trading profitable percentages of SVR-based GARCH IVTS models range from 51.8% to 59.7%. MLE-based symmetric S-GARCH shows +150.2% return and SVR-based symmetric S-GARCH shows +526.4% return. MLE-based asymmetric E-GARCH shows -72% return and SVR-based asymmetric E-GARCH shows +245.6% return. MLE-based asymmetric GJR-GARCH shows -98.7% return and SVR-based asymmetric GJR-GARCH shows +126.3% return. Linear kernel function shows higher trading returns than radial kernel function. Best performance of SVR-based IVTS is +526.4% and that of MLE-based IVTS is +150.2%. SVR-based GARCH IVTS shows higher trading frequency. This study has some limitations. Our models are solely based on SVR. Other artificial intelligence models are needed to search for better performance. We do not consider costs incurred in the trading process including brokerage commissions and slippage costs. IVTS trading performance is unreal since we use historical volatility values as trading objects. The exact forecasting of stock market volatility is essential in the real trading as well as asset pricing models. Further studies on other machine learning-based GARCH models can give better information for the stock market investors.

• The Journal of Korean Society for Radiation Therapy
• /
• v.24 no.2
• /
• pp.157-165
• /
• 2012

Purpose: We evaluated usefulness of abdominal compressor for stereotactic body radiotherapy (SBRT) with unresectable hepatocellular carcinoma (HCC) patients and hepato-biliary cancer and metastatic liver cancer patients. Materials and Methods: From November 2011 to March 2012, we selected HCC patients who gained reduction of diaphragm movement >1 cm through abdominal compressor (diaphragm control, elekta, sweden) for HT (Hi-Art Tomotherapy, USA). We got planning computed tomography (CT) images and 4 dimensional (4D) images through 4D CT (somatom sensation, siemens, germany). The gross tumor volume (GTV) included a gross tumor and margins considering tumor movement. The planning target volume (PTV) included a 5 to 7 mm safety margin around GTV. We classified patients into two groups according to distance between tumor and organs at risk (OAR, stomach, duodenum, bowel). Patients with the distance more than 1 cm are classified as the 1st group and they received SBRT of 4 or 5 fractions. Patients with the distance less than 1 cm are classified as the 2nd group and they received tomotherapy of 20 fractions. Megavoltage computed tomography (MVCT) were performed 4 or 10 fractions. When we verify a MVCT fusion considering priority to liver than bone-technique. We sent MVCT images to Mim_vista (Mimsoftware, ver .5.4. USA) and we re-delineated stomach, duodenum and bowel to bowel_organ and delineated liver. First, we analyzed MVCT images to check the setup variation. Second we compared dose difference between tumor and OAR based on adaptive dose through adaptive planning station and Mim_vista. Results: Average setup variation from MVCT was $-0.66{\pm}1.53$ mm (left-right) $0.39{\pm}4.17$ mm (superior-inferior), $0.71{\pm}1.74$ mm (anterior-posterior), $-0.18{\pm}0.30$ degrees (roll). 1st group ($d{\geq}1$) and 2nd group (d<1) were similar to setup variation. 1st group ($d{\geq}1$) of $V_{diff3%}$ (volume of 3% difference of dose) of GTV through adaptive planing station was $0.78{\pm}0.05%$, PTV was $9.97{\pm}3.62%$, $V_{diff5%}$ was GTV 0.0%, PTV was $2.9{\pm}0.95%$, maximum dose difference rate of bowel_organ was $-6.85{\pm}1.11%$. 2nd Group (d<1) GTV of $V_{diff3%}$ was $1.62{\pm}0.55%$, PTV was $8.61{\pm}2.01%$, $V_{diff5%}$ of GTV was 0.0%, PTV was $5.33{\pm}2.32%$, maximum dose difference rate of bowel_organ was $28.33{\pm}24.41%$. Conclusion: Despite we saw diaphragm movement more than 5 mm with flouroscopy after use an abdominal compressor, average setup_variation from MVCT was less than 5 mm. Therefore, we could estimate the range of setup_error within a 5 mm. Target's dose difference rate of 1st group ($d{\geq}1$) and 2nd group (d<1) were similar, while 1st group ($d{\geq}1$) and 2nd group (d<1)'s bowel_organ's maximum dose difference rate's maximum difference was more than 35%, 1st group ($d{\geq}1$)'s bowel_organ's maximum dose difference rate was smaller than 2nd group (d<1). When applicating SBRT to HCC, abdominal compressor is useful to control diaphragm movement in selected patients with more than 1 cm bowel_organ distance.

• The Journal of Korean Society for Radiation Therapy
• /
• v.27 no.2
• /
• pp.157-166
• /
• 2015

Purpose : Cyber-Knife tumor tracking system, based on the correlation relationship between the position of a tumor which moves in response to the real time respiratory cycle signal and respiration was obtained by the LED marker attached to the outside of the patient, the location of the tumor to predict in advance, the movement of the tumor in synchronization with the therapeutic device to track real-time tumor, is a system for treating. The purpose of this study, in the cyber knife tumor tracking radiation therapy, trying to evaluate the accuracy of tumor tracking radiation therapy system due to the change in the form of unpredictable sudden breathing due to cough and sleep. Materials and Methods : Breathing Log files that were used in the study, based on the Respiratory gating radiotherapy and Cyber-knife tracking radiosurgery breathing Log files of patients who received herein, measured using the Log files in the form of a Sinusoidal pattern and Sudden change pattern. it has been reconstituted as possible. Enter the reconstructed respiratory Log file cyber knife dynamic chest Phantom, so that it is possible to implement a motion due to respiration, add manufacturing the driving apparatus of the existing dynamic chest Phantom, Phantom the form of respiration we have developed a program that can be applied to. Movement of the phantom inside the target (Ball cube target) was driven by the displacement of three sizes of according to the size of the respiratory vertical (Superior-Inferior) direction to the 5 mm, 10 mm, 20 mm. Insert crosses two EBT3 films in phantom inside the target in response to changes in the target movement, the End-to-End (E2E) test provided in Cyber-Knife manufacturer depending on the form of the breathing five times each. It was determined by carrying. Accuracy of tumor tracking system is indicated by the target error by analyzing the inserted film, additional E2E test is analyzed by measuring the correlation error while being advanced. Results : If the target error is a sine curve breathing form, the size of the target of the movement is in response to the 5 mm, 10 mm, 20 mm, respectively, of the average $1.14{\pm}0.13mm$, $1.05{\pm}0.20mm$, with $2.37{\pm}0.17mm$, suddenly for it is variations in breathing, respective average $1.87{\pm}0.19mm$, $2.15{\pm}0.21mm$, and analyzed with $2.44{\pm}0.26mm$. If the correlation error can be defined by the length of the displacement vector in the target track is a sinusoidal breathing mode, the size of the target of the movement in response to 5 mm, 10 mm, 20 mm, respective average $0.84{\pm}0.01mm$, $0.70{\pm}0.13mm$, with $1.63{\pm}0.10mm$, if it is a variant of sudden breathing respective average $0.97{\pm}0.06mm$, $1.44{\pm}0.11mm$, and analyzed with $1.98{\pm}0.10mm$. The larger the correlation error values in both the both the respiratory form, the target error value is large. If the motion size of the target of the sine curve breathing form is greater than or equal to 20 mm, was measured at 1.5 mm or more is a recommendation value of both cyber knife manufacturer of both error value. Conclusion : There is a tendency that the correlation error value between about target error value magnitude of the target motion is large is increased, the error value becomes large in variation of rapid respiration than breathing the form of a sine curve. The more the shape of the breathing large movements regular shape of sine curves target accuracy of the tumor tracking system can be judged to be reduced. Using the algorithm of Cyber-Knife tumor tracking system, when there is a change in the sudden unpredictable respiratory due patient coughing during treatment enforcement is to stop the treatment, it is assumed to carry out the internal target validation process again, it is necessary to readjust the form of respiration. Patients under treatment is determined to be able to improve the treatment of accuracy to induce the observed form of regular breathing and put like to see the goggles monitor capable of the respiratory form of the person.

• Journal of Intelligence and Information Systems
• /
• v.24 no.1
• /
• pp.227-252
• /
• 2018

The evolution of instant communication has mirrored the development of the Internet and messenger applications are among the most representative manifestations of instant communication technologies. In messenger applications, senders use emoticons to supplement the emotions conveyed in the text of their messages. The fact that communication via messenger applications is not face-to-face makes it difficult for senders to communicate their emotions to message recipients. Emoticons have long been used as symbols that indicate the moods of speakers. However, at present, emoticon-use is evolving into a means of conveying the psychological states of consumers who want to express individual characteristics and personality quirks while communicating their emotions to others. The fact that companies like KakaoTalk, Line, Apple, etc. have begun conducting emoticon business and sales of related content are expected to gradually increase testifies to the significance of this phenomenon. Nevertheless, despite the development of emoticons themselves and the growth of the emoticon market, no suitable emoticon recommendation system has yet been developed. Even KakaoTalk, a messenger application that commands more than 90% of domestic market share in South Korea, just grouped in to popularity, most recent, or brief category. This means consumers face the inconvenience of constantly scrolling around to locate the emoticons they want. The creation of an emoticon recommendation system would improve consumer convenience and satisfaction and increase the sales revenue of companies the sell emoticons. To recommend appropriate emoticons, it is necessary to quantify the emotions that the consumer sees and emotions. Such quantification will enable us to analyze the characteristics and emotions felt by consumers who used similar emoticons, which, in turn, will facilitate our emoticon recommendations for consumers. One way to quantify emoticons use is metadata-ization. Metadata-ization is a means of structuring or organizing unstructured and semi-structured data to extract meaning. By structuring unstructured emoticon data through metadata-ization, we can easily classify emoticons based on the emotions consumers want to express. To determine emoticons' precise emotions, we had to consider sub-detail expressions-not only the seven common emotional adjectives but also the metaphorical expressions that appear only in South Korean proved by previous studies related to emotion focusing on the emoticon's characteristics. We therefore collected the sub-detail expressions of emotion based on the "Shape", "Color" and "Adumbration". Moreover, to design a highly accurate recommendation system, we considered both emotion-technical indexes and emoticon-emotional indexes. We then identified 14 features of emoticon-technical indexes and selected 36 emotional adjectives. The 36 emotional adjectives consisted of contrasting adjectives, which we reduced to 18, and we measured the 18 emotional adjectives using 40 emoticon sets randomly selected from the top-ranked emoticons in the KakaoTalk shop. We surveyed 277 consumers in their mid-twenties who had experience purchasing emoticons; we recruited them online and asked them to evaluate five different emoticon sets. After data acquisition, we conducted a factor analysis of emoticon-emotional factors. We extracted four factors that we named "Comic", Softness", "Modernity" and "Transparency". We analyzed both the relationship between indexes and consumer attitude and the relationship between emoticon-technical indexes and emoticon-emotional factors. Through this process, we confirmed that the emoticon-technical indexes did not directly affect consumer attitudes but had a mediating effect on consumer attitudes through emoticon-emotional factors. The results of the analysis revealed the mechanism consumers use to evaluate emoticons; the results also showed that consumers' emoticon-technical indexes affected emoticon-emotional factors and that the emoticon-emotional factors affected consumer satisfaction. We therefore designed the emoticon recommendation system using only four emoticon-emotional factors; we created a recommendation method to calculate the Euclidean distance from each factors' emotion. In an attempt to increase the accuracy of the emoticon recommendation system, we compared the emotional patterns of selected emoticons with the recommended emoticons. The emotional patterns corresponded in principle. We verified the emoticon recommendation system by testing prediction accuracy; the predictions were 81.02% accurate in the first result, 76.64% accurate in the second, and 81.63% accurate in the third. This study developed a methodology that can be used in various fields academically and practically. We expect that the novel emoticon recommendation system we designed will increase emoticon sales for companies who conduct business in this domain and make consumer experiences more convenient. In addition, this study served as an important first step in the development of an intelligent emoticon recommendation system. The emotional factors proposed in this study could be collected in an emotional library that could serve as an emotion index for evaluation when new emoticons are released. Moreover, by combining the accumulated emotional library with company sales data, sales information, and consumer data, companies could develop hybrid recommendation systems that would bolster convenience for consumers and serve as intellectual assets that companies could strategically deploy.

• Journal of Sasang Constitutional Medicine
• /
• v.10 no.2
• /
• pp.533-564
• /
• 1998

In order to know the effect of proliferation and differentiation on edipocyte 3T3-L1 by prescriptions and herbs, Taeyangin(太陽人)'s Okapijangcheok-tang(五加皮壯脊湯) Mihudeungsikjangtang Acanthopanacis Cortex(五加皮) Phragmitis Rhizoma(蘆根) and Taeumin(太陰人)'s Taeumjowi-tang(太陰調胃湯) Cheongsimyonja-tang(淸心蓮子湯) Cheongpaesagan-tang(淸肺瀉肝湯) Galkeunbupyong-tang(葛根浮萍湯) Coicis Semen(薏苡仁) Rhei Undulati Rhizoma(大黃) Mori Cortex(桑白皮) Ulmi Cortex(楡根白皮) Holotrichia Vermiculus Kalopanaxii Cortex(海桐皮) Ephedrae Herba(麻黃) Imperatae Rhizoma(白茅根), were used and had some effects. 1. The proliferation effect of edipocyte 1) At the Taeyangin(太陽人)'s prescriptions and herbs, Okapijangcheok-tang(五加皮壯脊湯) Mihudeungsikjang-tang Acanthopanacis Cortex(五加皮) have a control effect at the boiling water-extract and ethyl alcohol-extract. Phragmitis Rhizoma(蘆根) have a control effect at the ethyl alcohol-extract. 2) At the Taeyangin(太陽人)'s prescriptions and herbs, Taeumjowi-tang(太陰調胃湯) Cheongsimyonja-tang(淸心蓮子湯) Cheongpaesagan-tang(淸肺瀉肝湯) Galkeunbupyong-tang(葛根浮萍湯) have a control effect at the boiling water-extract and ethyl alcohol-extract. Coicis Semen(薏苡仁) Rhei Undulati Rhizoma(大黃) Morl Cortex(桑白皮) Ulmi Cortex(楡根白皮) Kalopanaxii Cortex(海桐皮) · Ephedrae Herba(麻黃) of the boiling water-extract, Holotrichia Vermiculus Kalopanaxii Cortex(海桐皮) of ethyl alcohol-extract have a control effect on edipocytes. Rhei Undulati Rhizoma(大黃) Ulmi Cortex(楡根白皮) Ephedrae Herba(麻黃) of high-density have a cyto-toxicity. 2. The differentiation effect of edipocyte 1) At the Taeyangin(太陽人)'s prescriptions and herbs during the natural differentiation, Phragmitis Rhizoma(蘆根) of the boiling water-extract, Okapijangchek-tang(五加皮壯脊湯) Acanthopanacis Cortex(五加皮) of the ethyl alcohol-extract have a cyto-toxicity on the first-differentiation. 2) At the Taeumin(太陰人)'s prescriptions and herbs during the natural differentiation, Ulmi Cortex (楡根白皮) Kalopanaxii Cortex(海桐皮) of the boiling water-extract have a cyto-toxicity on the first-differentiation. Cheongsimyonja-tang(淸心蓮子湯) Ephedrae Herba(麻黃) of ethyl alcohol-extract have a control effect on the redifferentiation. 3) At the Taeyangin(太陽人)'s prescriptions and herbs on the first-differentiation during the induced differentiation, Acanthopanacis Cortex(五加皮) of ethyl alcohol-extract has a control effect. Okapijangchek-tang(五加皮壯脊湯) Acanthopanacis Cortex(五加皮) Phragmitis Rhizoma(蘆根) of the boiling water-extract have a cyto-toxicity. 4) At the Taeumin(太陰人)'s prescriptions and herbs on the first-differentiation during the induced differentiation, Coicis Semen(薏苡仁) Ephedrae Herba(麻黃) Imperatae Rhizoma(白茅根) of the boiling water-extract and Ephedrae Herba(麻黃) of the ethyl alcohol-extract have a control effect. Kalopanaxii Cortex(海桐皮) of the boiling water-extract and the ethyl alcohol-extract has a cyto-toxicity. Considering this result, the Taeyangin(太陽人) Taeumin(太陰人)'s prescriptions and herbs have a control effect on edipocytes during the proliferation. Acanthopanacis Cortex(五加皮), Coicis Semen(薏苡仁) Ephedrae Herba(麻黃) Imperatae Rhizoma(白茅根) have a control effect on edipocytes during the induced differentiation. In the future, for treating a obesity need a vivo assay and hope this study to help to know the mechanisms of obesity.