• Journal of Intelligence and Information Systems
• /
• v.19 no.3
• /
• pp.57-71
• /
• 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 User interest changes over time and many people have no interest in outdated news.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.

• Korean Parent-Child Health Journal
• /
• v.4 no.1
• /
• pp.68-81
• /
• 2001

This research is to study about the weight-change of a pregnant woman, conditions of the woman and an infant. The weight-change of a mother during pregnancy was observed and that was expressed as the basis on the body mass index of a mother before pregnancy. The effects of weight-changes on both the discomfort, complications of pregnant mother and the condition of an infant were also investigated. Thus we set a purpose that this study would help pregnant woman and an infant to maintain and enhance their health conditions by proper weight control through nursing mediation. This study was performed in a certain hospital of university in seoul from Feb. 1. 2000 to Mar. 31. 2000. We explained the purpose of this study to the hospital institution and obtained consent of investigation. 152 inpatients who were in condition from PA 37 weeks to PA 42 weeks were the subject of this study. The research materials were made through of question paper that inpatients make answer by themselves and investigation paper. The question paper was about general background, weight and height before pregnancy and discomfort of the physical degree. And the investigation paper was about parity, maternal weight(late pregnancy), high pregnancy, delivery method, hemoglobin level, Apgar score, fetal weight. Physical discomfort was measured using the implement made by Kim hae won(1996) (chronbach's ${\alpha}=0.85$). SPSS was used to do statistics for managing and analyzing data. The results of this study were like followings. 1. The mean value of gained weight during pregnancy was about 13.8kg within from 3 kg to 26 kg. Among 152 research candidates, the gained weight of 80(52.6%) candidates remained within an ideal range. But that of 37 candidates(24.3%) became less than the ideal range. Also that of 35 candidates(23.0%) became over than the ideal range. 2. In the investigation of the relation between the weight change of a pregnant woman and her condition, the scores to represent physical discomfort were middle in all candidates. And the physical discomfort of over weight-gained group was more than that of low weight-gained group, but there was no difference in statistics(F=0.234, p=0.791). The weight-changes of pregnant woman didn't have an influence with the high risk of pregnancy(F=0.509, p=0.477). Also, the weight-changes didn't have an influence on delivery method($x^2=3.825$, p=0.148). However, in the investigation of the relation between weight-change and hemoglobin level, the change of hemoglobin level was highest in over weight gained group(F=3.062, p=0.05). 3. In the investigation of the weight-change of pregnant woman and the condition of infant. the weight changes didn't have an influence on both 1 min Apgar score(F=0.157, p=0.855) and 5 min Apgar score(F=0.030, p=0.970) of infant. Also, in the investigation of weight-change of a pregnant woman and weight difference of a infant with Pearson Correlation Coefficient, the weight-change of a pregnant woman affected vastly the weight of a infant. It was also found that the more pregnant woman gained in weight, the more did gain weighty infants. This relation was in net proportion(r=0.256, p=0.001). In conclusion, these results suggest that the weight-changes during pregnancy in Korea women of these days are more increased than that of the past days and individual variation in weight-changes is very high. Also, these results suggest that the changed hemoglobin level of a mother and weight of an infant were meaningfully affected by the weight-changes of a pregnant woman during pregnancy. However, the physical discomfort of a pregnant woman, the high risks of pregnancy, the delivery method and Apgar score of an infant were not affected by the weight-changes during pregnancy. Because the recommendation suggesting the ideal weight-change, used this study, is basis on the subject of American women, therefore, these results also suggest the necessity of such recommendation which is subject to Korean women.

• 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.

• Nuclear Engineering and Technology
• /
• v.53 no.8
• /
• pp.2534-2546
• /
• 2021

Nuclear emergency preparedness and response is an essential part to ensure the safety of nuclear power plant (NPP). Key support technologies of nuclear emergency decision-making usually consist of accident diagnosis, source term estimation, accident consequence assessment, and protective action recommendation. Source term estimation is almost the most difficult part among them. For example, bad communication, incomplete information, as well as complicated accident scenario make it hard to determine the reactor status and estimate the source term timely in the Fukushima accident. Subsequently, it leads to the hard decision on how to take appropriate emergency response actions. Hence, this paper aims to develop a method for rapid source term estimation to support nuclear emergency decision making in pressurized water reactor NPP. The method aims to make our knowledge on NPP provide better support nuclear emergency. Firstly, this paper studies how to build a Bayesian network model for the NPP based on professional knowledge and engineering knowledge. This paper presents a method transforming the PRA model (event trees and fault trees) into a corresponding Bayesian network model. To solve the problem that some physical phenomena which are modeled as pivotal events in level 2 PRA, cannot find sensors associated directly with their occurrence, a weighted assignment approach based on expert assessment is proposed in this paper. Secondly, the monitoring data of NPP are provided to the Bayesian network model, the real-time status of pivotal events and initiating events can be determined based on the junction tree algorithm. Thirdly, since PRA knowledge can link the accident sequences to the possible release categories, the proposed method is capable to find the most likely release category for the candidate accidents scenarios, namely the source term. The probabilities of possible accident sequences and the source term are calculated. Finally, the prototype software is checked against several sets of accident scenario data which are generated by the simulator of AP1000-NPP, including large loss of coolant accident, loss of main feedwater, main steam line break, and steam generator tube rupture. The results show that the proposed method for rapid source term estimation under nuclear emergency decision making is promising.

• Journal of the Computational Structural Engineering Institute of Korea
• /
• v.22 no.1
• /
• pp.45-52
• /
• 2009

In this paper, a guideline for designing breakwater in wave loads and in seismic loads is proposed. A simple model structure in breaking wave zone is examined using Morison equation in consideration with the effect of an impact load, for evaluation of the wave loads. As the impact load effect is not significant, pressure distributions according to Goda are applied for evaluation of wave loads on breakwater. Structural behavior of breakwater in wave loads can be obtained using the Goda method, as well. For seismic analysis, Ofunato and Hachinohe models, as well as an artificial seismic acceleration loads model, are adopted. Soil-structure interaction analysis is carried out to find the seismic load effect. It is found that, in certain cases, structural deformation in wave loads is in the same level as deformation that in seismic loads. Thus, it is our recommendation that these two loads are considered at the same level in breakwater design.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2008.05a
• /
• pp.531-534
• /
• 2008

Recently, by the interest of geographical information and the utilization field of geographical information diversifying. Various GIS(Geographic Information System) was built to manage geographical information efficiently. However, because of interoperability between geographic information system, OGC(Open Geospatial Consortium) standardized GML(Geography Markup Language) of the XML(eXtensible Markup Language) foundation in which an interoperability writes possible geographical information data. GML national standard is applied to various application fields at present, and the use is confirmed. In this paper, GML 3.2.1 encoding standard specification follows requirements of OGC recommendation, we designed GML document editing system using XML standard technology, and generated interoperability GML data.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.38C no.3
• /
• pp.254-262
• /
• 2013

This paper proposes FMT(Filtered Multi-Tone)-based digital radio implementation algorithms and the results obtained by various field tests especially in terms of transmitter characteristics. In this study, we predefined frame structure and protocols used for the CSTDMA(Carrier Sensing Time Division Multiple Access) scheme, designed digital filters and RF front end to fulfill the system characteristics such as the spectral mask and processing delays given by the Recommendation ITU-R M.1842-1. The proposed system supports exchange of data for e-Navigation with the usage of wider channel of 50-100kHz bandwidth, Turbo coding and FMT modulation. Furthermore, the common Ethernet protocol makes connection to local WLAN(Wireless Local Area Network) on board the ship for other data services.

• Journal of Digital Convergence
• /
• v.14 no.5
• /
• pp.309-316
• /
• 2016

While consumption patterns have changed online music, online music platform began to emerge. While people prefer popular music recommendation, they use the online music platform chart or use the SNS Platform to share information. Online platform Ranking is different because of different properties held by members. Meanwhile, we need music charts characteristics of SNS users. So there were a lot of attempts to chart a comprehensive variety of platforms. And continue to emerge theses linking the musical characteristics and SNS. In this paper, We have developed a new chart using the behavior of Twitter Users who listen to music, and studies comparing the results with existing chart.

• The Journal of the Korea Contents Association
• /
• v.12 no.10
• /
• pp.499-509
• /
• 2012

The purpose of this paper is to conceptualize the brand of tourism resources and examine the effects of brand equity on brand loyalty focusing on the brand of tourism resources in cities and counties level. To this end, we reviewed the literatures and analysed 410 surveys from the 8 provinces. The results show that brand awareness and brand image of tourism resources have significant effects on brand preference, and brand awareness, image and preference have effects on brand loyalty measured by revisit and recommendation intention. However the one of most important results is the brand equity(awareness, image, preference) has partially significant effects on brand loyalty under the effects of visitor's satisfaction. Additionally, the positive effect of brand awareness on brand image is examined. More specific results and implications are provided.

• Journal of the Korea Society of Computer and Information
• /
• v.25 no.9
• /
• pp.53-62
• /
• 2020

Product recommendation services that have been researched recently are only recommended through the customer's product purchase history. In this paper, we propose the digital signage service through customers' behavior pattern analysis that is recommending through not only purchase history, but also behavior pattern that customers take when choosing products. This service analyzes customer behavior patterns and extracts interests about products that are of practical interest. The service is learning extracted interest rate and customers' purchase history through the Wide & Deep model. Based on this learning method, the sparse vector of other products is predicted through the MF(Matrix Factorization). After derive the ranking of predicted product interest rate, this service uses the indoor signage that can interact with customers to expose the suitable advertisements. Through this proposed service, not only online, but also in an offline environment, it would be possible to grasp customers' interest information. Also, it will create a satisfactory purchasing environment by providing suitable advertisements to customers, not advertisements that advertisers randomly expose.