• The Research Journal of the Costume Culture
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• v.4 no.2
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• pp.277-302
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• 1996

The purpose of this study is to suggest fundamental information about wearing method and production of brassiere, which is suitable for physiological hygiene and efficient action for elderly women. The subject is 418 elderly women aged 50∼69 years old. Data is processed by a computer(SAS) and is analyzed by using frequency, percentage, x²-test. The main results of this study are as follows. 1. Elderly women'weight and breast size are higher thant their younger counterparts'in their 20's, but the older and poorer women with more children have smaller weight and breast. Most of the elderly women feel that their breasts are drooped or falling apart. I fact, those elderly women who gave birth to more children have more drooped and wider breasts. The major type of elderly women'breast is the 'drooped breast' featured more by those elderly women who have more children. 2. Most of the elderly women began to wear the brassiere for the first time in their 20's the older women with more children began to wear the brassiere earlier, while those less educated and poorer began to protect their breasts with brassiere later. Many elderly women wear the brassiere to be protected from dirt, noise or look more neat, but day tend to wear the brassiere not all day around but at certain times, older, less educated and proper women with more children tend to use the brassiere for etiquette and less often. This group of elderly women feel tedious, stage or uncomfortable for the brassiere. 3. Most of the elderly women know about their brassiere size. Such variables as age, education and income are correlated positively with the interest in and consciousness of the brassiere size. All in all, the statistical distribution of elderly women's brassiere size is very wide, while most of them use 90A, 85A and 95A sizes. On the other hand, the most popular size of the under bust circumference is 85∼90cm, while their primary cup size is A.

• Journal of Biosystems Engineering
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• v.5 no.2
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• pp.58-66
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• 1980

A survey was conducted to investigate the power tiller accidents. Eight provinces were covered for this study, and 278 power tiller owners were selected randomly by computer random generator. The results are summarized as follows : A. Frequency of accident. (1) Each power tiller had an accident 0.98 times a year and once every 361 hours of use. Higher frequency of accident was found during the miscellaneous operations including the preparation for farming operation, and there was one accident for every 92 hours of use. (2) The power tiller, which are more than six years old, met an accident 1.19 times a year , or one every 311 hours of use. This value was the highest one compared with any other group. (3) Kerosene engine power tillers met an accident 0.97 times a year, or one every 389 hours of use. It was one tie a year, or once every 329 hours of use for diesel engine power tillers. (4) Among diesel-engine power tillers, 10 horse-power group showed a higher frequency tillers. B. Cause of accident (1) The accidents of power tiller were mainly of sefety , which occurred due to the lack of attention during the operation and 47.4% of the total accidents. The next was of accidental, which represented 26.3% of the total accidents. (2) High percentage of safety accidents occurred during the preparation for farming operation including adjustment. Most of the accidental accidents occurred during the transportation. (3) Lower frequency of accident was found in the power tiller group which were operated by the 21-40 years old operator in comparison with that of the power tiller which was operated by other age group. Power tillers which were operated by high school graduates experienced less accidents compared to other education levels. C. Damage by accident (1) Eighty seven pescents of the total accidents caused damage to the power tiller operator , and 13 % of the total accidents caused property damage only. (2) With regard to the damage to the power tiller operator, 73.8% of the total accidents caused light injury but 26.3% caused heavy injury. (3) Accidents which occurred during machine preparation , and farming operations caused minor injury to the operator, but the accidents during transportation caused heavy injury which cost more than 15 days for recovery. (4) Among the 39 accidents , which caused property damage 18 accidents were from the transportation . Among the total property damage accidents 53.8% were light one which cost only less than 1,000 won. (5) The property damage from each accident cost 1,017 won, on the average, with regard to the kinds of operation, the highest property damage occurred during transportation work, with the value of 2, 965won.

• Journal of Digital Convergence
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• v.15 no.7
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• pp.111-121
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• 2017

Smartphone use has become a part of everyday life, not only for adolescents but also for elderly and older people. Thus, this study explored the effect of demographic variables, the amount of smartphone usage, type of smartphone use and psychosocial variables on smartphone dependence of the elder group. We analyzed the data of 5627 (50s and 60s) of the Internet overdependence survey data of the Korea Information Society Agency in 2016. As a result of hierarchical multiple regression analysis, More frequency of smartphone use of weekday and weekend, and usage time of smartphone of weekday were related to greater smartphone dependence. More smartphone use for information seeking and games were associated with greater smartphone dependence. And, as loneliness and anxiety increased, smartphone dependence increased, while smartphone dependence decreased, as social capital increased. Through this study, it is necessary to understand characteristics related to smartphone dependence of elder and older group, and to use this knowledge to solve their smartphone dependence. Future studies also need to identify the causal relationship between predictors of smartphone dependence in elderly and older people.

• Journal of Bio-Environment Control
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• v.27 no.2
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• pp.166-172
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• 2018

This study set out to conduct a field survey with smart greenhouse-based farms in seven types to figure out the actual state of smart greenhouses distributed across the nation before selecting a system to implement an optimal greenhouse environment and doing a research on higher productivity based on data related to crop growth, development, and environment. The findings show that the farms were close to an intelligent or advanced smart farm, given the main purposes of leading cases across the smart farm types found in the field. As for the age of farmers, those who were in their forties and sixties accounted for the biggest percentage, but those who were in their fifties or younger ran 21 farms that accounted for approximately 70.0%. The biggest number of farmers had a cultivation career of ten years or less. As for the greenhouse type, the 1-2W type accounted for 50.0%, and the multispan type accounted for 80.0% at 24 farms. As for crops they cultivated, only three farms cultivated flowers with the remaining farms growing only fruit vegetables, of which the tomato and paprika accounted for approximately 63.6%. As for control systems, approximately 77.4% (24 farms) used a domestic control system. As for the control method of a control system, three farms regulated temperature and humidity only with a control panel with the remaining farms adopting a digital control method to combine a panel with a computer. There were total nine environmental factors to measure and control including temperature. While all the surveyed farms measured temperature, the number of farms installing a ventilation or air flow fan or measuring the concentration of carbon dioxide was relatively small. As for a heating system, 46.7% of the farms used an electric boiler. In addition, hot water boilers, heat pumps, and lamp oil boilers were used. As for investment into a control system, there was a difference in the investment scale among the farms from 10 million won to 100 million won. As for difficulties with greenhouse management, the farmers complained about difficulties with using a smart phone and digital control system due to their old age and the utter absence of education and materials about smart greenhouse management. Those difficulties were followed by high fees paid to a consultant and system malfunction in the order.

• Journal of Intelligence and Information Systems
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• v.22 no.4
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• pp.157-176
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• 2016

For a long time, mobile phone had a sole function of communication. Recently however, abrupt innovations in technology allowed extension of the sphere in mobile phone activities. Development of technology enabled realization of almost computer-like environment even on a very small device. Such advancement yielded several forms of new high-tech devices such as smartphone and tablet PC, which quickly proliferated. Simultaneously with the diffusion of the mobile devices, mobile applications for those devices also prospered and soon became deeply penetrated in consumers' daily lives. Numerous mobile applications have been released in app stores yielding trillions of cumulative downloads. However, a big majority of the applications are disregarded from consumers. Even after the applications are purchased, they do not survive long in consumers' mobile devices and are soon abandoned. Nevertheless, it is imperative for both app developers and app-store operators to understand consumer behaviors and to develop marketing strategies aiming to make sustainable business by first increasing sales of mobile applications and by also designing surviving strategy for applications. Therefore, this research analyzes consumers' mobile application usage behavior in a frame of substitution/supplementary of application categories and several explanatory variables. Considering that consumers of mobile devices use multiple apps simultaneously, this research adopts multivariate probit models to explain mobile application usage behavior and to derive correlation between categories of applications for observing substitution/supplementary of application use. The research adopts several explanatory variables including sociodemographic data, user experiences of purchased applications that reflect future purchasing behavior of paid applications as well as consumer attitudes toward marketing efforts, variables representing consumer attitudes toward rating of the app and those representing consumer attitudes toward app-store promotion efforts (i.e., top developer badge and editor's choice badge). Results of this study can be explained in hedonic and utilitarian framework. Consumers who use hedonic applications, such as those of game and entertainment-related, are of young age with low education level. However, consumers who are old and have received higher education level prefer utilitarian application category such as life, information etc. There are disputable arguments over whether the users of SNS are hedonic or utilitarian. In our results, consumers who are younger and those with higher education level prefer using SNS category applications, which is in a middle of utilitarian and hedonic results. Also, applications that are directly related to tangible assets, such as banking, stock and mobile shopping, are only negatively related to experience of purchasing of paid app, meaning that consumers who put weights on tangible assets do not prefer buying paid application. Regarding categories, most correlations among categories are significantly positive. This is because someone who spend more time on mobile devices tends to use more applications. Game and entertainment category shows significant and positive correlation; however, there exists significantly negative correlation between game and information, as well as game and e-commerce categories of applications. Meanwhile, categories of game and SNS as well as game and finance have shown no significant correlations. This result clearly shows that mobile application usage behavior is quite clearly distinguishable - that the purpose of using mobile devices are polarized into utilitarian and hedonic purpose. This research proves several arguments that can only be explained by second-hand real data, not by survey data, and offers behavioral explanations of mobile application usage in consumers' perspectives. This research also shows substitution/supplementary patterns of consumer application usage, which then explain consumers' mobile application usage behaviors. However, this research has limitations in some points. Classification of categories itself is disputable, for classification is diverged among several studies. Therefore, there is a possibility of change in results depending on the classification. Lastly, although the data are collected in an individual application level, we reduce its observation into an individual level. Further research will be done to resolve these limitations.