• Title, Summary, Keyword: 유사 동적 실험

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A Reconfigurable, General-purpose DSM-CC Architecture and User Preference-based Cache Management Strategy (재구성이 가능한 범용 DSM-CC 아키텍처와 사용자 선호도 기반의 캐시 관리 전략)

  • Jang, Jin-Ho;Ko, Sang-Won;Kim, Jung-Sun
    • The KIPS Transactions:PartC
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    • v.17C no.1
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    • pp.89-98
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    • 2010
  • In current digital broadcasting systems, GEM(Globally Executable MHP)-based middlewares such as MHP(Multimedia Home Platform), OCAP(OpenCable Application Platform), ACAP(Advanced Common Application Platform) are the norm. Despite much of the common characteristics shared, such as MPEG-2 and DSM-CC(Digital Storage Media-Command and Control) protocols, the information and data structures they need are slightly different, which results in incompatibility issues. In this paper, in line with an effort to develop an integrated DTV middleware, we propose a general-purpose, reconfigurable DSM-CC architecture for supporting various standard GEM-based middlewares without code modifications. First, we identify DSM-CC components that are common and thus can be shared by all GEM-based middlewares. Next, the system is provided with middleware-specific information and data structures in the form of XML. Since the XML information can be parsed dynamically at run time, it can be interchanged either statically or dynamically for a specific target middleware. As for the performance issues, the response time and usage frequency of DSM-CC module highly contribute to the performance of STB(Set-Top-Box). In this paper, we also propose an efficient application cache management strategy and evaluate its performance. The performance result has shown that the cache strategy reflecting user preferences greatly helps to reduce response time for executing application.

Content-based Recommendation Based on Social Network for Personalized News Services (개인화된 뉴스 서비스를 위한 소셜 네트워크 기반의 콘텐츠 추천기법)

  • Hong, Myung-Duk;Oh, Kyeong-Jin;Ga, Myung-Hyun;Jo, Geun-Sik
    • 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.

Investigating Dynamic Mutation Process of Issues Using Unstructured Text Analysis (비정형 텍스트 분석을 활용한 이슈의 동적 변이과정 고찰)

  • Lim, Myungsu;Kim, Namgyu
    • Journal of Intelligence and Information Systems
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    • v.22 no.1
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    • pp.1-18
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    • 2016
  • Owing to the extensive use of Web media and the development of the IT industry, a large amount of data has been generated, shared, and stored. Nowadays, various types of unstructured data such as image, sound, video, and text are distributed through Web media. Therefore, many attempts have been made in recent years to discover new value through an analysis of these unstructured data. Among these types of unstructured data, text is recognized as the most representative method for users to express and share their opinions on the Web. In this sense, demand for obtaining new insights through text analysis is steadily increasing. Accordingly, text mining is increasingly being used for different purposes in various fields. In particular, issue tracking is being widely studied not only in the academic world but also in industries because it can be used to extract various issues from text such as news, (SocialNetworkServices) to analyze the trends of these issues. Conventionally, issue tracking is used to identify major issues sustained over a long period of time through topic modeling and to analyze the detailed distribution of documents involved in each issue. However, because conventional issue tracking assumes that the content composing each issue does not change throughout the entire tracking period, it cannot represent the dynamic mutation process of detailed issues that can be created, merged, divided, and deleted between these periods. Moreover, because only keywords that appear consistently throughout the entire period can be derived as issue keywords, concrete issue keywords such as "nuclear test" and "separated families" may be concealed by more general issue keywords such as "North Korea" in an analysis over a long period of time. This implies that many meaningful but short-lived issues cannot be discovered by conventional issue tracking. Note that detailed keywords are preferable to general keywords because the former can be clues for providing actionable strategies. To overcome these limitations, we performed an independent analysis on the documents of each detailed period. We generated an issue flow diagram based on the similarity of each issue between two consecutive periods. The issue transition pattern among categories was analyzed by using the category information of each document. In this study, we then applied the proposed methodology to a real case of 53,739 news articles. We derived an issue flow diagram from the articles. We then proposed the following useful application scenarios for the issue flow diagram presented in the experiment section. First, we can identify an issue that actively appears during a certain period and promptly disappears in the next period. Second, the preceding and following issues of a particular issue can be easily discovered from the issue flow diagram. This implies that our methodology can be used to discover the association between inter-period issues. Finally, an interesting pattern of one-way and two-way transitions was discovered by analyzing the transition patterns of issues through category analysis. Thus, we discovered that a pair of mutually similar categories induces two-way transitions. In contrast, one-way transitions can be recognized as an indicator that issues in a certain category tend to be influenced by other issues in another category. For practical application of the proposed methodology, high-quality word and stop word dictionaries need to be constructed. In addition, not only the number of documents but also additional meta-information such as the read counts, written time, and comments of documents should be analyzed. A rigorous performance evaluation or validation of the proposed methodology should be performed in future works.

Effects of Feed Protein Quality on the Protein Metabolism of Growing Pigs - Using a Simulation Model - (성장기 돼지의 단백질대사에 사료단백질의 질이 미치는 영향 -수치모델을 사용하여-)

  • 이옥희
    • Journal of the Korean Society of Food Science and Nutrition
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    • v.26 no.4
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    • pp.704-713
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    • 1997
  • This study was conducted to describe qualitatively the protein metabolism of pigs during growth depending on the feed protein quality and to describe quantitatively amino acids requirements, using a simulation model. The used model has a non-linear structure. In the used model, the protein utilization system of a pig, which is in the non-steady-state, is described with 15 flux equations and 11 differential equations and is composed with two compartments. Protein deposition(g/day) of pigs on the 30th, 60th, 90th, and 120th day of feeding duration with three-quality protein, beginning with body weight 20kg, were calculated according to the empirical model, PAF(the product of amino acid functions) of Menke, and was used as object function for the simulation. The mean of relative difference between the simulated protein deposition and PAF calculated values, lied in a range of 8.8%. The simulated protein deposition showed different behavior according to feed protein quality. In the high-quality protein, it showed paraboloidal form with extending growth simulation up to 150eh day. So the maximum of protein deposition was acquired on the 105th day of simulate growth time and then it decreased fast. In the low-quality protein, this form of protein deposition in the course of simulated growth did not appear until 150th day. The simulated protein mass also showed a difference in accordance with feed protein quality. The difference was small on the 30th day of simulated growth, but with duration of the simulated growth it was larger. On the 150th day the simulated protein deposition of high quality protein was 1.5 times higher as compared to the low-quality protein. The simulated protein synthesis and break-down rates(g/day) in the whole body showed a parallel behavior in the course of growth, according to feed protein quality. It was found that the improvement of feed protein quality increased protein deposition in the whole body through a increase of both protein synthesis and breakdown during growth. Also protein deposition efficiency, which was calculated from simulated protein deposition and protein synthesis, showed a difference in dependence on the protein qualify of feed protein. The protein deposition efficiency was higher in pigs fed with high quality protein, especially at the simulation time 30th day. But this phenomena disappeared with growth, so on the 150th day of growth, the protein deposition of the high feed protein quality was lowest among the three different quality of feed protein. The simulated total requirement of the 10 essential amino acids for the growth of pigs was 28.1(g/100g protein), similar to NRC. The requirement of lysine was 4.2(g/100g protein).

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Recognition of Resident Registration Card using ART2-based RBF Network and face Verification (ART2 기반 RBF 네트워크와 얼굴 인증을 이용한 주민등록증 인식)

  • Kim Kwang-Baek;Kim Young-Ju
    • Journal of Intelligence and Information Systems
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    • v.12 no.1
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    • pp.1-15
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    • 2006
  • In Korea, a resident registration card has various personal information such as a present address, a resident registration number, a face picture and a fingerprint. A plastic-type resident card currently used is easy to forge or alter and tricks of forgery grow to be high-degree as time goes on. So, whether a resident card is forged or not is difficult to judge by only an examination with the naked eye. This paper proposed an automatic recognition method of a resident card which recognizes a resident registration number by using a refined ART2-based RBF network newly proposed and authenticates a face picture by a template image matching method. The proposed method, first, extracts areas including a resident registration number and the date of issue from a resident card image by applying Sobel masking, median filtering and horizontal smearing operations to the image in turn. To improve the extraction of individual codes from extracted areas, the original image is binarized by using a high-frequency passing filter and CDM masking is applied to the binaried image fur making image information of individual codes better. Lastly, individual codes, which are targets of recognition, are extracted by applying 4-directional contour tracking algorithm to extracted areas in the binarized image. And this paper proposed a refined ART2-based RBF network to recognize individual codes, which applies ART2 as the loaming structure of the middle layer and dynamicaly adjusts a teaming rate in the teaming of the middle and the output layers by using a fuzzy control method to improve the performance of teaming. Also, for the precise judgement of forgey of a resident card, the proposed method supports a face authentication by using a face template database and a template image matching method. For performance evaluation of the proposed method, this paper maked metamorphoses of an original image of resident card such as a forgey of face picture, an addition of noise, variations of contrast variations of intensity and image blurring, and applied these images with original images to experiments. The results of experiment showed that the proposed method is excellent in the recognition of individual codes and the face authentication fur the automatic recognition of a resident card.

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PIRS : Personalized Information Retrieval System using Adaptive User Profiling and Real-time Filtering for Search Results (적응형 사용자 프로파일기법과 검색 결과에 대한 실시간 필터링을 이용한 개인화 정보검색 시스템)

  • Jeon, Ho-Cheol;Choi, Joong-Min
    • Journal of Intelligence and Information Systems
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    • v.16 no.4
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    • pp.21-41
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    • 2010
  • This paper proposes a system that can serve users with appropriate search results through real time filtering, and implemented adaptive user profiling based personalized information retrieval system(PIRS) using users' implicit feedbacks in order to deal with the problem of existing search systems such as Google or MSN that does not satisfy various user' personal search needs. One of the reasons that existing search systems hard to satisfy various user' personal needs is that it is not easy to recognize users' search intentions because of the uncertainty of search intentions. The uncertainty of search intentions means that users may want to different search results using the same query. For example, when a user inputs "java" query, the user may want to be retrieved "java" results as a computer programming language, a coffee of java, or a island of Indonesia. In other words, this uncertainty is due to ambiguity of search queries. Moreover, if the number of the used words for a query is fewer, this uncertainty will be more increased. Real-time filtering for search results returns only those results that belong to user-selected domain for a given query. Although it looks similar to a general directory search, it is different in that the search is executed for all web documents rather than sites, and each document in the search results is classified into the given domain in real time. By applying information filtering using real time directory classifying technology for search results to personalization, the number of delivering results to users is effectively decreased, and the satisfaction for the results is improved. In this paper, a user preference profile has a hierarchical structure, and consists of domains, used queries, and selected documents. Because the hierarchy structure of user preference profile can apply the context when users perfomed search, the structure is able to deal with the uncertainty of user intentions, when search is carried out, the intention may differ according to the context such as time or place for the same query. Furthermore, this structure is able to more effectively track web documents search behaviors of a user for each domain, and timely recognize the changes of user intentions. An IP address of each device was used to identify each user, and the user preference profile is continuously updated based on the observed user behaviors for search results. Also, we measured user satisfaction for search results by observing the user behaviors for the selected search result. Our proposed system automatically recognizes user preferences by using implicit feedbacks from users such as staying time on the selected search result and the exit condition from the page, and dynamically updates their preferences. Whenever search is performed by a user, our system finds the user preference profile for the given IP address, and if the file is not exist then a new user preference profile is created in the server, otherwise the file is updated with the transmitted information. If the file is not exist in the server, the system provides Google' results to users, and the reflection value is increased/decreased whenever user search. We carried out some experiments to evaluate the performance of adaptive user preference profile technique and real time filtering, and the results are satisfactory. According to our experimental results, participants are satisfied with average 4.7 documents in the top 10 search list by using adaptive user preference profile technique with real time filtering, and this result shows that our method outperforms Google's by 23.2%.