• Journal of Bio-Environment Control
• /
• v.28 no.1
• /
• pp.78-85
• /
• 2019

This study was designed to examine the performance of an aspirated radiation shield(ARS), which was made at the investigator's lab and characterized by relatively easier making and lower costs based on survey data and reports on errors in its measurements of temperature and relative humidity. The findings were summarized as follows: the ARS and the Jinju weather station made measurements and recorded the range of maximum, average, and minimum temperature at $2.0{\sim}34.1^{\circ}C$, $-6.1{\sim}22.2^{\circ}C$, $-14.0{\sim}15.1^{\circ}C$ and $0.4{\sim}31.5^{\circ}C$, $-5.8{\sim}22.0^{\circ}C$, $-14.1{\sim}16.3^{\circ}C$, respectively. There were no big differences in temperature measurements between the two institutions except that the lowest and highest point of maximum temperature was higher on the campus by $1.6^{\circ}C$ and $2.6^{\circ}C$, respectively. The measurements of ARS were tested against those of a standard thermometer. The results show that the temperature measured by ARS was lower by $-2.0^{\circ}C$ or higher by $1.8^{\circ}C$ than the temperature measured by a standard thermometer. The analysis results of its correlations with a standard thermometer reveal that the coefficient of determination was 0.99. Temperature was compared between fans and no fans, and the results show that maximum, average, and minimum temperature was higher overall with no fans by $0.5{\sim}7.6^{\circ}C$, $0.3{\sim}4.6^{\circ}C$ and $0.5{\sim}3.9^{\circ}C$, respectively. The daily average relative humidity measurements were compared between ARS and the weather station of Jinju, and the results show that the measurements of ARS were a little bit higher than those of the Jinju weather station. The measurements on June 27, July 26 and 29, and August 20 were relatively higher by 5.7%, 5.2%, 9.1%, and 5.8%, respectively, but differences in the monthly average between the two institutions were trivial at 2.0~3.0%. Relative humidity was in the range of -3.98~+7.78% overall based on measurements with ARS and Assman's psychometer. The study analyzed correlations in relative humidity between the measurements of the Jinju weather station and those of Assman's psychometer and found high correlations between them with the coefficient of determination at 0.94 and 0.97, respectively.

• Journal of Biosystems Engineering
• /
• v.6 no.2
• /
• pp.20-32
• /
• 1982

This survey was conducted to investigate the present status of farm tractor utilization for obtaining a basic reference to the establishment of the government's agricultural mechanization strategies. Thirty two counties from the eight provinces except Jeju were covered in this study. From these selected areas, 433 sample farms having farm tractor were taken to obtain the general informations by the enquete, and 93 sample farms among them to investigate the status of daily tractor use in the year of 1980. The analyzed results are summarized as follows: 1. Farm tractors owned by the rice-oriented farms holds 71.5 percent of the total number of tractors the livestock-oriented farms 17.0 percent, and the orchard-oriented farms 7.0 percent. Among the farm tractors 64.3 percent was a large size (46ps) and 35.7 percent a small size(19~23ps). 2. Most of the tractors surveyed were equipped with the essential attachments such as plow and rotavator. About 18 percent of the tractor owners had no trailer, which seemed too high considering the large percentage of tractor use for transportation. The availability of other attachments was very low except a grader on the rice-oriented farms and a hay harvester and a front loader on the livestock-oriented farms. 3. The average size of farm was 3.9 hectare for the rice-oriented farms, 13.9 hectare for the livestock-oriented farms and 7.4 hectare for the orchard-oriented farms. It was obious that the average farm size of was too small compared to the theoretical machine capacity of the tractors. 4. About 70 percent of the tractor operators were in the age of twenties and thirties. About 90 percent of them had an educational level of middle school graduate or above even though their technical level was very low. 5. Any particular problem in tractor use was not found in this survey. From the farmer's preference for purchasing a new tractor, however, it is estimated the demand on a 20-30ps tractor will be more increased. 6. The average annual use of tractor was of about 100 days or 400 hours. It appeared that the rice-oriented farms used most with 412.4 hours per year, and followed by the livestock-oriented farms with 403.6 hours, the orchard oriented farms with 377.7 hours. 7. Among the total hours of tractor use, 47.3 percent was for transportation, and 41.6 percent was for plowing and rotary tillage. The largest portion of the annual tractor use was taken by transportation on the livestock-oriented farms, by land preperation on the rice-oriented farms, and by loading and chemical spraying on the orchard-oriented farms. 8. The hours of tractor use had a peak in May. The hours of use for own farm was remarkably different among the different farm oriented, but there was no considerable difference between the too different sizes of tractor. 9. The hours of tractor use decreased as the age of the operator or the educational level increased. The reason might be that the operators who had a high educational level or were older had a tendency of disliking custom works. 10. The average custom use of tractor was 171.3 hours per year, and the ratio of custom work was 63.7 percent on the rice-oriented farms, 31.7 percent on the livestock-oriented farms and 22.4 percent on the orchard-oriented farms. Among the custom works, the most popular one was the grader leveling. 11. The charge on custom work was about 40,000 Won per hectare for plowing and rotary tillage, and it was the most expensive in the southeastern region, and next followed by the southwestern region. 12. The average plowing capacity of the small tractor was 7.8 hours per hectare in the paddy field, and that of the large tractors was 4.3 hours per hectare. The average rotary-tilling capacities of the small and the large tractors were 6.5 and 4.3 hours per hectare, in the paddy field respectively.

• Journal of Intelligence and Information Systems
• /
• v.24 no.3
• /
• pp.67-88
• /
• 2018

Until recently, as we recognize the significance of sustainable growth and competitiveness of small-and-medium sized enterprises (SMEs), governmental support for tangible resources such as R&D, manpower, funds, etc. has been mainly provided. However, it is also true that the inefficiency of support systems such as underestimated or redundant support has been raised because there exist conflicting policies in terms of appropriateness, effectiveness and efficiency of business support. From the perspective of the government or a company, we believe that due to limited resources of SMEs technology development and capacity enhancement through collaboration with external sources is the basis for creating competitive advantage for companies, and also emphasize value creation activities for it. This is why value chain network analysis is necessary in order to analyze inter-company deal relationships from a series of value chains and visualize results through establishing knowledge ecosystems at the corporate level. There exist Technology Opportunity Discovery (TOD) system that provides information on relevant products or technology status of companies with patents through retrievals over patent, product, or company name, CRETOP and KISLINE which both allow to view company (financial) information and credit information, but there exists no online system that provides a list of similar (competitive) companies based on the analysis of value chain network or information on potential clients or demanders that can have business deals in future. Therefore, we focus on the "Value Chain Network System (VCNS)", a support partner for planning the corporate business strategy developed and managed by KISTI, and investigate the types of embedded network-based analysis modules, databases (D/Bs) to support them, and how to utilize the system efficiently. Further we explore the function of network visualization in intelligent value chain analysis system which becomes the core information to understand industrial structure ystem and to develop a company's new product development. In order for a company to have the competitive superiority over other companies, it is necessary to identify who are the competitors with patents or products currently being produced, and searching for similar companies or competitors by each type of industry is the key to securing competitiveness in the commercialization of the target company. In addition, transaction information, which becomes business activity between companies, plays an important role in providing information regarding potential customers when both parties enter similar fields together. Identifying a competitor at the enterprise or industry level by using a network map based on such inter-company sales information can be implemented as a core module of value chain analysis. The Value Chain Network System (VCNS) combines the concepts of value chain and industrial structure analysis with corporate information simply collected to date, so that it can grasp not only the market competition situation of individual companies but also the value chain relationship of a specific industry. Especially, it can be useful as an information analysis tool at the corporate level such as identification of industry structure, identification of competitor trends, analysis of competitors, locating suppliers (sellers) and demanders (buyers), industry trends by item, finding promising items, finding new entrants, finding core companies and items by value chain, and recognizing the patents with corresponding companies, etc. In addition, based on the objectivity and reliability of the analysis results from transaction deals information and financial data, it is expected that value chain network system will be utilized for various purposes such as information support for business evaluation, R&D decision support and mid-term or short-term demand forecasting, in particular to more than 15,000 member companies in Korea, employees in R&D service sectors government-funded research institutes and public organizations. In order to strengthen business competitiveness of companies, technology, patent and market information have been provided so far mainly by government agencies and private research-and-development service companies. This service has been presented in frames of patent analysis (mainly for rating, quantitative analysis) or market analysis (for market prediction and demand forecasting based on market reports). However, there was a limitation to solving the lack of information, which is one of the difficulties that firms in Korea often face in the stage of commercialization. In particular, it is much more difficult to obtain information about competitors and potential candidates. In this study, the real-time value chain analysis and visualization service module based on the proposed network map and the data in hands is compared with the expected market share, estimated sales volume, contact information (which implies potential suppliers for raw material / parts, and potential demanders for complete products / modules). In future research, we intend to carry out the in-depth research for further investigating the indices of competitive factors through participation of research subjects and newly developing competitive indices for competitors or substitute items, and to additively promoting with data mining techniques and algorithms for improving the performance of VCNS.

• Journal of Intelligence and Information Systems
• /
• v.19 no.1
• /
• pp.57-77
• /
• 2013

Social media has become one of the most popular media in web and mobile application. In 2011, social networks and blogs are still the top destination of online users, according to a study from Nielsen Company. In their studies, nearly 4 in 5active users visit social network and blog. Social Networks and Blogs sites rule Americans' Internet time, accounting to 23 percent of time spent online. Facebook is the main social network that the U.S internet users spend time more than the other social network services such as Yahoo, Google, AOL Media Network, Twitter, Linked In and so on. In recent trend, most of the companies promote their products in the Facebook by creating the "Facebook Page" that refers to specific product. The "Like" option allows user to subscribed and received updates their interested on from the page. The film makers which produce a lot of films around the world also take part to market and promote their films by exploiting the advantages of using the "Facebook Page". In addition, a great number of streaming service providers allows users to subscribe their service to watch and enjoy movies and TV program. They can instantly watch movies and TV program over the internet to PCs, Macs and TVs. Netflix alone as the world's leading subscription service have more than 30 million streaming members in the United States, Latin America, the United Kingdom and the Nordics. As the matter of facts, a million of movies and TV program with different of genres are offered to the subscriber. In contrast, users need spend a lot time to find the right movies which are related to their interest genre. Recent years there are many researchers who have been propose a method to improve prediction the rating or preference that would give the most related items such as books, music or movies to the garget user or the group of users that have the same interest in the particular items. One of the most popular methods to build recommendation system is traditional Collaborative Filtering (CF). The method compute the similarity of the target user and other users, which then are cluster in the same interest on items according which items that users have been rated. The method then predicts other items from the same group of users to recommend to a group of users. Moreover, There are many items that need to study for suggesting to users such as books, music, movies, news, videos and so on. However, in this paper we only focus on movie as item to recommend to users. In addition, there are many challenges for CF task. Firstly, the "sparsity problem"; it occurs when user information preference is not enough. The recommendation accuracies result is lower compared to the neighbor who composed with a large amount of ratings. The second problem is "cold-start problem"; it occurs whenever new users or items are added into the system, which each has norating or a few rating. For instance, no personalized predictions can be made for a new user without any ratings on the record. In this research we propose a clustering method according to the users' genre interest extracted from social network service (SNS) and user's movies rating information system to solve the "cold-start problem." Our proposed method will clusters the target user together with the other users by combining the user genre interest and the rating information. It is important to realize a huge amount of interesting and useful user's information from Facebook Graph, we can extract information from the "Facebook Page" which "Like" by them. Moreover, we use the Internet Movie Database(IMDb) as the main dataset. The IMDbis online databases that consist of a large amount of information related to movies, TV programs and including actors. This dataset not only used to provide movie information in our Movie Rating Systems, but also as resources to provide movie genre information which extracted from the "Facebook Page". Formerly, the user must login with their Facebook account to login to the Movie Rating System, at the same time our system will collect the genre interest from the "Facebook Page". We conduct many experiments with other methods to see how our method performs and we also compare to the other methods. First, we compared our proposed method in the case of the normal recommendation to see how our system improves the recommendation result. Then we experiment method in case of cold-start problem. Our experiment show that our method is outperform than the other methods. In these two cases of our experimentation, we see that our proposed method produces better result in case both cases.