• Journal of the Korea Academia-Industrial cooperation Society
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• v.13 no.1
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• pp.264-273
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• 2012

This cross-sectional study was investigated musculoskeletal discomfort symptoms and related factors on some middle high school teachers. Self-questionnaire of KOSHA CODE H-30-2003 was done with 250 teachers from 1st to 15th October, 2010, the data from 231 teachers (68 male, 163 female) was statistically analyzed to search the factors related to musculoskeletal discomfort symptoms. According to NIOSH rate of musculoskeletal discomfort symptoms by body parts was 36.8%. Musculoskeletal discomfort symptoms related to age, school types, subjective health status, housekeeping time, VDT work time and regular rest. After adjusting for related variables, odds ratio (OR) of musculoskeletal discomfort symptoms was correlation significantly to subjective health status unhealthy (OR 11.75, 95% Confidence Interval, CI, 3.56-378.78). In addition, ORs (95% CI) of age (40-49) and housekeeping time (${\geq}3$) were 4.63 (1.82-26.18) and 4.33 (1.97-19.34). Analysis of the factors influencing the musculoskeletal discomfort symptoms vary in different parts of the body. The most discomfort symptoms by parts was neck (26.0%) and shoulder (30.0%). In the neck region was related to subjective health status and regular rest. In the shoulder and waist region was subjective health status and sex. Age was wrist/finger, leg/foot was related to subjective health status, sex and VDT work time. Age, school types, subjective health status, housekeeping time, VDT work time and regular rest related to musculoskeletal discomfort symptoms and the most discomfort symptoms by parts was neck and shoulder.

• Tuberculosis and Respiratory Diseases
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• v.49 no.5
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• pp.576-584
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• 2000

Background : With the increase of cigarette consumption by women and the young, the incidence of lung cancer is expected to increase during the next three or four decades in Korea. The purpose of this study was to analyze the smoking habits in patients with lung cancer and to identify the gender differences in terms of their susceptibility to cigarette related carcinogens. Method : This investigation was a hospital-based case control study, which included the data of 178 case subjects (72 females, 106 males) with lung cancer and 218 control subjects (97 females, 121 males) with diseases unrelated to smoking. The information was obtained through a direct personal interview and a questionnaire related to personal smoking history. Results : The relative frequency of the squamous cell carcinoma was substantially higher in males than in females (61.3% in males, and 29.2% in females), while adenocarcinoma including bronchoalveolar cell carcinoma was higher in females(31.9% in females, 18.9% in males). Kreyberg I lung cancer was of relatively higher frequencies in males and smokers, while Kreyberg II lung cancer was higher in females and never smokers. The odds ratios (ORs) at each exposure level were consistently higher in females than males. For all cell types, the risk of lung cancer was increased with the quantity of smoked cigarettes, duration of smoking, and depth of inhalation. Odds ratio was distinctly higher in Kreyberg I lung cancer than in total lung cancer and a steeper gradient of risk with increased smoking was observed in females. Conclusion : The relative risk for lung cancer was consistently higher in females than in males at every level of exposure to cigarette smoke. This is believed to be due to the higher susceptibility of females to tobacco carcinogens, such as gender associated differences of carcinogen activation and/or the elimination of smoking related metabolites.

• Korean Journal of Remote Sensing
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• v.39 no.6_2
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• pp.1605-1613
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• 2023

The physical properties of the ocean interior are determined by temperature and salinity. To observe them, we rely on satellite observations for broad regions of oceans. However, the satellite for salinity measurement, Soil Moisture Active Passive (SMAP), has low temporal and spatial resolutions; thus, more is needed to resolve the fast-changing coastal environment. To overcome these limitations, the algorithm to use the Geostationary Ocean Color Imager-II (GOCI-II) of the Geo-Kompsat-2B (GK-2B) was developed as the inputs for a Multi-layer Perceptron Neural Network (MPNN). The result shows that coefficient of determination (R²), root mean square error (RMSE), and relative root mean square error (RRMSE) between GOCI-II based sea surface salinity (SSS) (GOCI-II SSS) and SMAP was 0.94, 0.58 psu, and 1.87%, respectively. Furthermore, the spatial variation of GOCI-II SSS was also very uniform, with over 0.8 of R² and less than 1 psu of RMSE. In addition, GOCI-II SSS was also compared with SSS of Ieodo Ocean Research Station (I-ORS), suggesting that the result was slightly low, which was further analyzed for the following reasons. We further illustrated the valuable information of high spatial and temporal variation of GOCI-II SSS to analyze SSS variation by the 11th typhoon, Hinnamnor, in 2022. We used the mean and standard deviation (STD) of one day of GOCI-II SSS, revealing the high spatial and temporal changes. Thus, this study will shed light on the research for monitoring the highly changing marine environment.

• Journal of agricultural medicine and community health
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• v.44 no.4
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• pp.165-184
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• 2019

Objectives: The purpose of this study was to identify and compare the difference and related factors with general characteristic and health behaviors, a experience of diagnosis and treatment of chronic diseases between rural and urban among elderly in Korea. Methods: We used the data of Community Health Survey 2017 which were collected by the Korean Center for Disease Control and Prevention. The study population comprised 67,835 elderly peopled aged 65 years or older who participated in the survey. The chi-square test, univariate and multivariate logistic regression analysis were used to analyze data. Results: We identified many significant difference of health behaviors, an experience of diagnosis and treatment with chronic diseases between rural and urban. Compared to urban elderly, the odds ratios (ORs) (95% confidence interval) of rural elderly were 1.136 (1.092-1.183) for diagnosis of diabetes, 1.278 (1.278-1.386) for diagnosis of dyslipidemia, 0.940 (0.904-0.977) for diagnosis of arthritis, 0.785(0.736-0.837) for treatment of arthritis, 1.159 (1.116-1.203) for diagnosis of cataracts, and 1.285(1.200-1.375) for treatment of cataracts. In the experience of diagnosis and treatment of chronic diseases, various variables were derived as contributing factors for each disease. Especially, there were statistically significant difference in the experience of diabetes diagnosis, arthritis diagnosis, cataract diagnosis and dyslipidemia except for hypertension diagnosis (p <0.01) between urban and rural elderly. There were statistically significant differences in the experience of treatment for arthritis and cataract (p <0.01), but there was no significant difference in the experience of treatment for hypertension, diabetes, dyslipidemia between urban and rural elderly. Conclusion: Therefore, it would be necessary to implement a strategic health management project for diseases that showed significant experience of chronic diseases with diagnosis and treatment, reflecting the related factors of the elderly chronic diseases among the urban and rural areas.

• Journal of Intelligence and Information Systems
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• v.23 no.4
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• pp.127-146
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• 2017

Services using artificial intelligence have begun to emerge in daily life. Artificial intelligence is applied to products in consumer electronics and communications such as artificial intelligence refrigerators and speakers. In the financial sector, using Kensho's artificial intelligence technology, the process of the stock trading system in Goldman Sachs was improved. For example, two stock traders could handle the work of 600 stock traders and the analytical work for 15 people for 4weeks could be processed in 5 minutes. Especially, big data analysis through machine learning among artificial intelligence fields is actively applied throughout the financial industry. The stock market analysis and investment modeling through machine learning theory are also actively studied. The limits of linearity problem existing in financial time series studies are overcome by using machine learning theory such as artificial intelligence prediction model. The study of quantitative financial data based on the past stock market-related numerical data is widely performed using artificial intelligence to forecast future movements of stock price or indices. Various other studies have been conducted to predict the future direction of the market or the stock price of companies by learning based on a large amount of text data such as various news and comments related to the stock market. Investing on commodity asset, one of alternative assets, is usually used for enhancing the stability and safety of traditional stock and bond asset portfolio. There are relatively few researches on the investment model about commodity asset than mainstream assets like equity and bond. Recently machine learning techniques are widely applied on financial world, especially on stock and bond investment model and it makes better trading model on this field and makes the change on the whole financial area. In this study we made investment model using Support Vector Machine among the machine learning models. There are some researches on commodity asset focusing on the price prediction of the specific commodity but it is hard to find the researches about investment model of commodity as asset allocation using machine learning model. We propose a method of forecasting four major commodity indices, portfolio made of commodity futures, and individual commodity futures, using SVM model. The four major commodity indices are Goldman Sachs Commodity Index(GSCI), Dow Jones UBS Commodity Index(DJUI), Thomson Reuters/Core Commodity CRB Index(TRCI), and Rogers International Commodity Index(RI). We selected each two individual futures among three sectors as energy, agriculture, and metals that are actively traded on CME market and have enough liquidity. They are Crude Oil, Natural Gas, Corn, Wheat, Gold and Silver Futures. We made the equally weighted portfolio with six commodity futures for comparing with other commodity indices. We set the 19 macroeconomic indicators including stock market indices, exports & imports trade data, labor market data, and composite leading indicators as the input data of the model because commodity asset is very closely related with the macroeconomic activities. They are 14 US economic indicators, two Chinese economic indicators and two Korean economic indicators. Data period is from January 1990 to May 2017. We set the former 195 monthly data as training data and the latter 125 monthly data as test data. In this study, we verified that the performance of the equally weighted commodity futures portfolio rebalanced by the SVM model is better than that of other commodity indices. The prediction accuracy of the model for the commodity indices does not exceed 50% regardless of the SVM kernel function. On the other hand, the prediction accuracy of equally weighted commodity futures portfolio is 53%. The prediction accuracy of the individual commodity futures model is better than that of commodity indices model especially in agriculture and metal sectors. The individual commodity futures portfolio excluding the energy sector has outperformed the three sectors covered by individual commodity futures portfolio. In order to verify the validity of the model, it is judged that the analysis results should be similar despite variations in data period. So we also examined the odd numbered year data as training data and the even numbered year data as test data and we confirmed that the analysis results are similar. As a result, when we allocate commodity assets to traditional portfolio composed of stock, bond, and cash, we can get more effective investment performance not by investing commodity indices but by investing commodity futures. Especially we can get better performance by rebalanced commodity futures portfolio designed by SVM model.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.179-196
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• 2019

Recently, the "Smart Consumer" has been emerging. He or she is increasingly inclined to search for and purchase products by taking into account personal judgment or expert reviews rather than by relying on information delivered through manufacturers' advertising. This is especially true when purchasing cosmetics. Because cosmetics act directly on the skin, consumers respond seriously to dangerous chemical elements they contain or to skin problems they may cause. Above all, cosmetics should fit well with the purchaser's skin type. In addition, changes in global cosmetics consumer trends make it necessary to study this field. The desire to find one's own individualized cosmetics is being revealed to consumers around the world and is known as "Finding the Holy Grail." Many consumers show a deep interest in customized cosmetics with the cultural boom known as "K-Beauty" (an aspect of "Han-Ryu"), the growth of personal grooming, and the emergence of "self-culture" that includes "self-beauty" and "self-interior." These trends have led to the explosive popularity of cosmetics made in Korea in the Chinese and Southeast Asian markets. In order to meet the customized cosmetics needs of consumers, cosmetics manufacturers and related companies are responding by concentrating on delivering premium services through the convergence of ICT(Information, Communication and Technology). Despite the evolution of companies' responses regarding market trends toward customized cosmetics, there is no "Intelligent Data Platform" that deals holistically with consumers' skin condition experience and thus attaches emotions to products and services. To find the Holy Grail of customized cosmetics, it is important to acquire and analyze consumer data on what they want in order to address their experiences and emotions. The emotions consumers are addressing when purchasing cosmetics varies by their age, sex, skin type, and specific skin issues and influences what price is considered reasonable. Therefore, it is necessary to classify emotions regarding cosmetics by individual consumer. Because of its importance, consumer emotion analysis has been used for both services and products. Given the trends identified above, we judge that consumer emotion analysis can be used in our study. Therefore, we collected and indexed data on consumers' emotions regarding their cosmetics experiences focusing on consumers' language. We crawled the cosmetics emotion data from SNS (blog and Twitter) according to sales ranking ($1^{st}$ to $99^{th}$), focusing on the ample/serum category. A total of 357 emotional adjectives were collected, and we combined and abstracted similar or duplicate emotional adjectives. We conducted a "Consumer Sentiment Journey" workshop to build a "Consumer Sentiment Dictionary," and this resulted in a total of 76 emotional adjectives regarding cosmetics consumer experience. Using these 76 emotional adjectives, we performed clustering with the Self-Organizing Map (SOM) method. As a result of the analysis, we derived eight final clusters of cosmetics consumer sentiments. Using the vector values of each node for each cluster, the characteristics of each cluster were derived based on the top ten most frequently appearing consumer sentiments. Different characteristics were found in consumer sentiments in each cluster. We also developed a cosmetics experience pattern map. The study results confirmed that recommendation and classification systems that consider consumer emotions and sentiments are needed because each consumer differs in what he or she pursues and prefers. Furthermore, this study reaffirms that the application of emotion and sentiment analysis can be extended to various fields other than cosmetics, and it implies that consumer insights can be derived using these methods. They can be used not only to build a specialized sentiment dictionary using scientific processes and "Design Thinking Methodology," but we also expect that these methods can help us to understand consumers' psychological reactions and cognitive behaviors. If this study is further developed, we believe that it will be able to provide solutions based on consumer experience, and therefore that it can be developed as an aspect of marketing intelligence.

• Asia Marketing Journal
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• v.14 no.2
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• pp.157-176
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• 2012

Brand switching data frequently used in market structure analysis is adequate to analyze non- durable goods, because it can capture competition between specific two brands. But brand switching data sometimes can not be used to analyze goods like automobiles having long term duration because one of main assumptions that consumer preference toward brand attributes is not changed against time can be violated. Therefore a new type of data which can precisely capture competition among durable goods is needed. Another problem of using brand switching data collected from actual purchase behavior is short of explanation why consumers consider different set of brands. Considering above problems, main purpose of this study is to analyze market structure for durable goods with consideration set. The author uses exploratory approach and latent class clustering to identify market structure based on heterogeneous consideration set among consumers. Then the relationship between some factors and consideration set formation is analyzed. Some benefits and two demographic variables - age and income - are selected as factors based on consumer behavior theory. The author analyzed USA automotive market with top 11 brands using exploratory approach and latent class clustering. 2,500 respondents are randomly selected from the total sample and used for analysis. Six models concerning market structure are established to test. Model 1 means non-structured market and model 6 means market structure composed of six sub-markets. It is exploratory approach because any hypothetical market structure is not defined. The result showed that model 1 is insufficient to fit data. It implies that USA automotive market is a structured market. Model 3 with three market structures is significant and identified as the optimal market structure in USA automotive market. Three sub markets are named as USA brands, Asian Brands, and European Brands. And it implies that country of origin effect may exist in USA automotive market. Comparison between modal classification by derived market structures and probabilistic classification by research model was conducted to test how model 3 can correctly classify respondents. The model classify 97% of respondents exactly. The result of this study is different from those of previous research. Previous research used confirmatory approach. Car type and price were chosen as criteria for market structuring and car type-price structure was revealed as the optimal structure for USA automotive market. But this research used exploratory approach without hypothetical market structures. It is not concluded yet which approach is superior. For confirmatory approach, hypothetical market structures should be established exhaustively, because the optimal market structure is selected among hypothetical structures. On the other hand, exploratory approach has a potential problem that validity for derived optimal market structure is somewhat difficult to verify. There also exist market boundary difference between this research and previous research. While previous research analyzed seven car brands, this research analyzed eleven car brands. Both researches seemed to represent entire car market, because cumulative market shares for analyzed brands exceeds 50%. But market boundary difference might affect the different results. Though both researches showed different results, it is obvious that country of origin effect among brands should be considered as important criteria to analyze USA automotive market structure. This research tried to explain heterogeneity of consideration sets among consumers using benefits and two demographic factors, sex and income. Benefit works as a key variable for consumer decision process, and also works as an important criterion in market segmentation. Three factors - trust/safety, image/fun to drive, and economy - are identified among nine benefit related measure. Then the relationship between market structures and independent variables is analyzed using multinomial regression. Independent variables are three benefit factors and two demographic factors. The result showed that all independent variables can be used to explain why there exist different market structures in USA automotive market. For example, a male consumer who perceives all benefits important and has lower income tends to consider domestic brands more than European brands. And the result also showed benefits, sex, and income have an effect to consideration set formation. Though it is generally perceived that a consumer who has higher income is likely to purchase a high priced car, it is notable that American consumers perceived benefits of domestic brands much positive regardless of income. Male consumers especially showed higher loyalty for domestic brands. Managerial implications of this research are as follow. Though implication may be confined to the USA automotive market, the effect of sex on automotive buying behavior should be analyzed. The automotive market is traditionally conceived as male consumers oriented market. But the proportion of female consumers has grown over the years in the automotive market. It is natural outcome that Volvo and Hyundai motors recently developed new cars which are targeted for women market. Secondly, the model used in this research can be applied easier than that of previous researches. Exploratory approach has many advantages except difficulty to apply for practice, because it tends to accompany with complicated model and to require various types of data. The data needed for the model in this research are a few items such as purchased brands, consideration set, some benefits, and some demographic factors and easy to collect from consumers.