• Journal of the Korean Society of Safety
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• v.31 no.5
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• pp.22-27
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• 2016

The Electrostatic Charge Prevention Technology is a core factor that highly influences the yield of Ultra High Resolution Flat Panel Display and high-integrated semiconductor manufacturing processes. The corona or x-ray ionizations are commonly used in order to eliminate static charges during manufacturing processes. To develop such a revolutionary x-ray ionizer that is free of x-ray radiation and has function to control the volume of ion formation simultaneously is a goal of this research and it absolutely overcomes the current risks of x-ray ionization. Under the International Commission on Radiological Protection, it must have a leakage radiation level that should be lower than a recommended level that is $1{\mu}Sv/hour$. In this research, the new generation of x-ray ionizer can easily control both the volume of ion formation and the leakage radiation level at the same time. In the research, the test constraints were set and the descriptions are as below; First, In order not to leak x-ray radiation while testing, the shielding box was fully installed around the test equipment area. Second, Implement the metallic Ring Electrode along a tube window and applied zero to ${\pm}8kV$ with respect to manage the positive and negative ions formation. Lastly, the ion duty ratio was able to be controlled in different test set-ups along with a free x-ray leakage through the metallic Ring Electrode. In the result of experiment, the maximum x-ray radiation leakage was $0.2{\mu}Sv/h$. These outcome is lower than the ICRP 103 recommended value, which is $1{\mu}Sv/h$. When applying voltage to the metallic ring electrode, the positive decay time was 2.18s at the distance of 300 mm and its slope was 0.272. In addition, the negative decay time was 2.1s at the distance of 300 mm and its slope was 0.262. At the distance of 200 mm, the positive decay time was 2.29s and its slope was 0.286. The negative decay time was 2.35s and its slope was 0.293. At the distance of 100 mm, the positive decay time was 2.71s and its slope was 0.338. The negative decay time was 3.07s and its slope was 0.383. According to these research, the observation was shown that these new concept of ionizer is able to minimize the leakage radiation level and to control the positive and negative ion duty ratio while ionization.

• Journal of Intelligence and Information Systems
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• v.20 no.4
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• pp.89-105
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• 2014

After emergence of Internet, social media with highly interactive Web 2.0 applications has provided very user friendly means for consumers and companies to communicate with each other. Users have routinely published contents involving their opinions and interests in social media such as blogs, forums, chatting rooms, and discussion boards, and the contents are released real-time in the Internet. For that reason, many researchers and marketers regard social media contents as the source of information for business analytics to develop business insights, and many studies have reported results on mining business intelligence from Social media content. In particular, opinion mining and sentiment analysis, as a technique to extract, classify, understand, and assess the opinions implicit in text contents, are frequently applied into social media content analysis because it emphasizes determining sentiment polarity and extracting authors' opinions. A number of frameworks, methods, techniques and tools have been presented by these researchers. However, we have found some weaknesses from their methods which are often technically complicated and are not sufficiently user-friendly for helping business decisions and planning. In this study, we attempted to formulate a more comprehensive and practical approach to conduct opinion mining with visual deliverables. First, we described the entire cycle of practical opinion mining using Social media content from the initial data gathering stage to the final presentation session. Our proposed approach to opinion mining consists of four phases: collecting, qualifying, analyzing, and visualizing. In the first phase, analysts have to choose target social media. Each target media requires different ways for analysts to gain access. There are open-API, searching tools, DB2DB interface, purchasing contents, and so son. Second phase is pre-processing to generate useful materials for meaningful analysis. If we do not remove garbage data, results of social media analysis will not provide meaningful and useful business insights. To clean social media data, natural language processing techniques should be applied. The next step is the opinion mining phase where the cleansed social media content set is to be analyzed. The qualified data set includes not only user-generated contents but also content identification information such as creation date, author name, user id, content id, hit counts, review or reply, favorite, etc. Depending on the purpose of the analysis, researchers or data analysts can select a suitable mining tool. Topic extraction and buzz analysis are usually related to market trends analysis, while sentiment analysis is utilized to conduct reputation analysis. There are also various applications, such as stock prediction, product recommendation, sales forecasting, and so on. The last phase is visualization and presentation of analysis results. The major focus and purpose of this phase are to explain results of analysis and help users to comprehend its meaning. Therefore, to the extent possible, deliverables from this phase should be made simple, clear and easy to understand, rather than complex and flashy. To illustrate our approach, we conducted a case study on a leading Korean instant noodle company. We targeted the leading company, NS Food, with 66.5% of market share; the firm has kept No. 1 position in the Korean "Ramen" business for several decades. We collected a total of 11,869 pieces of contents including blogs, forum contents and news articles. After collecting social media content data, we generated instant noodle business specific language resources for data manipulation and analysis using natural language processing. In addition, we tried to classify contents in more detail categories such as marketing features, environment, reputation, etc. In those phase, we used free ware software programs such as TM, KoNLP, ggplot2 and plyr packages in R project. As the result, we presented several useful visualization outputs like domain specific lexicons, volume and sentiment graphs, topic word cloud, heat maps, valence tree map, and other visualized images to provide vivid, full-colored examples using open library software packages of the R project. Business actors can quickly detect areas by a swift glance that are weak, strong, positive, negative, quiet or loud. Heat map is able to explain movement of sentiment or volume in categories and time matrix which shows density of color on time periods. Valence tree map, one of the most comprehensive and holistic visualization models, should be very helpful for analysts and decision makers to quickly understand the "big picture" business situation with a hierarchical structure since tree-map can present buzz volume and sentiment with a visualized result in a certain period. This case study offers real-world business insights from market sensing which would demonstrate to practical-minded business users how they can use these types of results for timely decision making in response to on-going changes in the market. We believe our approach can provide practical and reliable guide to opinion mining with visualized results that are immediately useful, not just in food industry but in other industries as well.