• Journal of the Korea institute for structural maintenance and inspection
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• v.25 no.3
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• pp.31-39
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• 2021

In this study, the fiber blending ratio and strain rate effect on the tensile properties synergy effect of hybrid fiber reinforced cement composite was evaluated. Hooked steel fiber(HSF) and smooth steel fiber(SSF) were used for reinforcing fiber. The fiber blending ratio of HSF+SSF were 1.5+0.5, 1.0+1.0 and 0.5+1.5vol.%. As a results, in the cement composite(HSF2.0) reinforced with HSF, as the strain rate increases, the tensile stress sharply decreased after the peak stress because of the decrease in the number of straightened pull-out fibers by increase of micro cracks in the matrix around HSF. When 0.5 vol.% of SSF was mixed, the micro cracks was effectively controlled at the static rate, but it was not effective in controlling micro cracks and improving the pull-out resistance of HSF at the high rate. On the other hand, the specimen(HSF1.0SSF1.0) in which 1.0vol.% HSF and 1.0vol.% SSF were mixed, each fibers controls against micro and macro cracks, and SSF improves the pull-out resistance of HSF effectively. Thus, the fiber blending effect of the strain capacity and energy absorption capacity was significantly increased at the high rate, and it showed the highest dynamic increase factor of the tensile strength, strain capacity and peak toughness. On the other hand, the incorporation of 1.5 vol.% SSF increases the number of fibers in the matrix and improves the pull-out resistance of HSF, resulting in the highest fiber blending effect of tensile strength and softening toughness. But as a low volume fraction of HSF which controlling macro crack, it was not effective for synergy of strain capacity and peak toughness.

• Journal of Intelligence and Information Systems
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• v.19 no.3
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• pp.57-71
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• 2013

Over a billion people in the world generate new news minute by minute. People forecasts some news but most news are from unexpected events such as natural disasters, accidents, crimes. People spend much time to watch a huge amount of news delivered from many media because they want to understand what is happening now, to predict what might happen in the near future, and to share and discuss on the news. People make better daily decisions through watching and obtaining useful information from news they saw. However, it is difficult that people choose news suitable to them and obtain useful information from the news because there are so many news media such as portal sites, broadcasters, and most news articles consist of gossipy news and breaking news. User interest changes over time and many people have no interest in outdated news. From this fact, applying users' recent interest to personalized news service is also required in news service. It means that personalized news service should dynamically manage user profiles. In this paper, a content-based news recommendation system is proposed to provide the personalized news service. For a personalized service, user's personal information is requisitely required. Social network service is used to extract user information for personalization service. The proposed system constructs dynamic user profile based on recent user information of Facebook, which is one of social network services. User information contains personal information, recent articles, and Facebook Page information. Facebook Pages are used for businesses, organizations and brands to share their contents and connect with people. Facebook users can add Facebook Page to specify their interest in the Page. The proposed system uses this Page information to create user profile, and to match user preferences to news topics. However, some Pages are not directly matched to news topic because Page deals with individual objects and do not provide topic information suitable to news. Freebase, which is a large collaborative database of well-known people, places, things, is used to match Page to news topic by using hierarchy information of its objects. By using recent Page information and articles of Facebook users, the proposed systems can own dynamic user profile. The generated user profile is used to measure user preferences on news. To generate news profile, news category predefined by news media is used and keywords of news articles are extracted after analysis of news contents including title, category, and scripts. TF-IDF technique, which reflects how important a word is to a document in a corpus, is used to identify keywords of each news article. For user profile and news profile, same format is used to efficiently measure similarity between user preferences and news. The proposed system calculates all similarity values between user profiles and news profiles. Existing methods of similarity calculation in vector space model do not cover synonym, hypernym and hyponym because they only handle given words in vector space model. The proposed system applies WordNet to similarity calculation to overcome the limitation. Top-N news articles, which have high similarity value for a target user, are recommended to the user. To evaluate the proposed news recommendation system, user profiles are generated using Facebook account with participants consent, and we implement a Web crawler to extract news information from PBS, which is non-profit public broadcasting television network in the United States, and construct news profiles. We compare the performance of the proposed method with that of benchmark algorithms. One is a traditional method based on TF-IDF. Another is 6Sub-Vectors method that divides the points to get keywords into six parts. Experimental results demonstrate that the proposed system provide useful news to users by applying user's social network information and WordNet functions, in terms of prediction error of recommended news.