• The Journal of Korean Association of Computer Education
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• v.6 no.3
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• pp.27-36
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• 2003

Rccommender systems are being used by an ever-increasing number of E-Commerce sites to help consumers find products to purchase. Recommender Systems offer a technology that allows personalized recommendations of items of potential interest to users based on information about similarities and dissimilarities among different user' tastes. However, despite enormous interest in recommender systems, both the number of available published techniques and information about their performance are limited. In this paper. we design and implement an M-Commerce recommendation systems using the past buying behavior of the consumer, consumer information, and association rule mining.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.34 no.6C
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• pp.619-625
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• 2009

This paper proposes a parallel architecture of a Parity Checksum Generator adopted for packet synchronization and error detection in the ITU-T Recommendation J.83 Annex B. The proposed parallel processing architecture removes a performance bottleneck occurred in a conventional serial processing architecture, leading to significant decrease in processing time for generating a Parity Checksum. The implementation results show that the proposed parallel processing architecture reduces the processing time by 83.1% at the expense of 16% area increase.

• Journal of Korea Spatial Information System Society
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• v.6 no.1 s.11
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• pp.59-71
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• 2004

Nearest neighbor query retrieves nearest located target objects, and is very frequently used in mobile environment. In this paper we propose a novel neatest neighbor query processing technique that is able to retrieve nearest located target object from the user who is continuously moving with a direction. The proposed method retrieves objects using the direction property of moving object as well as euclidean distance to target object. The proposed method is applicable to traffic information system, travel information system, and location-based recommendation system which require retrieving nearest located object.

• Journal of Radiation Protection and Research
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• v.18 no.1
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• pp.1-7
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• 1993

In its recent recommendation No. 60(1990), ICRP has newly introduced several terminology which had not existed in its prior recommendation No. 26(1977). Of these, a newly defined quantity 'Equivalent Dose' replacing the 'Dose Equivalent' of the ICRU concept has been recommended to be adopted in the radiation protection programme. However, since the committee still uses the 'Dose Equivalent' and 'Equivalent Dose' in its several publications, it is likely to provoke unnecessary confusions and misuses in applying these two quantities. In this paper were described the definition and difference between these two quantities to help in understanding of these two quantitites among the person involved in the radiation protection activities.

• Journal of Sensor Science and Technology
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• v.25 no.4
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• pp.297-301
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• 2016

ISO/IEC JTC1/SC29WG11 MPEG has been standardizing UD(user description) to give a user personalized recommendation services. Besides, CD(context description), service description(SD), and recommendation description(RD) are recently being standardized by UD Adhoc Group in MPEG with an advanced UD to cope with needs of current and upcoming services such as augmented reality and social network. The descriptions was reflected to MPEG-UD WD(Working Draft) at MPEG $107^{th}$ meeting and the document was finally approved as international standard by national bodies with standard number(ISO/IEC IS 21000-22 UD) at $114^{th}$ MPEG meeting. In addition, reference software WD to validate conformance of UD standard was approved at $113^{th}$ MPEG meeting. In this paper, we developed a demo program for visual communication according to guideline defined in reference software WD to validate the reference software as well as UD standard.

• Journal of the Korea Computer Industry Society
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• v.4 no.10
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• pp.621-630
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• 2003

Web logs are the information recorded by a web server when users access the web sites, and due to a speedy rising of internet usage, the worth of their practical use has become increasingly important. Analyzing such logs can use to determine the patterns representing users' navigational behavior in a Web site and restructure a Web site to create a more effective organizational presence. For these applications, the generally used key methods in many studies are association rules and sequential patterns based by Apriori algorithms, which are widely used to extract correlation among patterns. But Apriori inhere inefficiency in computing cost when applied to large databases. In this paper, we develop a new algorithm for mining interesting patterns which is faster than Apriori algorithm and recommendation agents which could provide a system manager with valuable information that are accessed sequentially by many users.

• 한국HCI학회:학술대회논문집
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• 2009.02a
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• pp.357-362
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• 2009

We propose a method to recommend most likely people to call based on call log of mobile phone user. Call logs of an user can reflect calling pattern of the user include regular calling behavior. When user got a list of people to call with a click of 'send' button on the phone, the time and effort to search a person with several typing or to select a person from the list can be definitely reduced. This paper presents the design process of an algorithm to find most likely people to call at a certain moment and the verification process with recorded call log and Experience Sampling Method(ESM) on mobile phone.

• Journal of Korea Multimedia Society
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• v.19 no.11
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• pp.1878-1890
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• 2016

Web 2.0 has features produced the content through the user of the participation and share. The content production activities have became active since social network service appear. The social bookmark, one of social network service, is service that lets users to store useful content and share bookmarked contents between personal users. Unlike Internet search engines such as Google and Naver, the content stored on social bookmark is searched based on tag keyword information and unnecessary information can be excluded. Social bookmark can make users access to selected content. However, quick access to content that users want is difficult job because of the user of the participation and share. Our paper suggests a method recommending search word to be able to access quickly to content. A method is suggested by using Collaborative Filtering and Jaccard similarity coefficient. The performance of suggested system is verified with experiments that compare by 'Delicious' and "Feeltering' with our system.

• Journal of KIISE:Computing Practices and Letters
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• v.12 no.5
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• pp.326-338
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• 2006

In this paper, we propose the weighted Markov model for recommending the users' prefered contents in the environment with considering the users' transition of their content consumption mind according to the kind of contents providing in time. In general, TV viewers have an intention to consume again the preferred contents consumed in recent by them. In order to take into the consideration, we modify the preference transition matrix by providing weights to the consecutively consumed contents for recommending the users' preferred contents. We applied the proposed model to the recommendation of TV viewer's genre preference. The experimental result shows that our method is more efficient than the typical methods.

• Journal of KIISE
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• v.43 no.3
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• pp.289-295
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• 2016

Malware authors spread malware variants in order to evade detection. It's hard to detect malware variants using static analysis. Therefore dynamic analysis based on API call information is necessary. In this paper, we proposed a malware family recommendation method to assist malware analysts in classifying malware variants. Our proposed method extract API call information of malware families by dynamic analysis. Then the multiple sequence alignment technique was applied to the extracted API call information. A signature of each family was extracted from the alignment results. By the similarity of the extracted signatures, our proposed method recommends three family candidates for unknown malware. We also measured the accuracy of our proposed method in an experiment using real malware samples.