• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.16 no.3
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• pp.117-122
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• 2023

Gaze estimation, which automatically recognizes where a user is currently staring, and object detection based on estimated gaze point, can be a more accurate and efficient way to understand human visual behavior. in this paper, we propose a method to detect the objects within the region of interest around the gaze point. Specifically, after estimating the 3D gaze point, a region of interest based on the estimated gaze point is created to ensure that object detection occurs only within the region of interest. In our experiments, we compared the performance of general object detection, and the proposed object detection based on region of interest, and found that the processing time per frame was 1.4ms and 1.1ms, respectively, indicating that the proposed method was faster in terms of processing speed.

• Journal of Digital Convergence
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• v.11 no.12
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• pp.407-415
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• 2013

Recently, Interest variety of IT services and computing resources are increasing. As a result, the interest in the security of cloud environments is also increasing. Cloud environment is stored that to provide services to a large amount of IT resources on the Cloud. Therefore, Cloud is integrity of the stored data and resources that such as data leakage, forgery, etc. security incidents that the ability to quickly process is required. However, the existing developed various solutions or studies without considering their cloud environment for development and research to graft in a cloud environment because it has been difficult. Therefore, we proposed wire-wireless integrated Security management Model in cloud environment.

• Journal of Digital Convergence
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• v.10 no.10
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• pp.203-214
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• 2012

As users of mobile devices such as smart phone are rapidly growing owing to the development of information technology, interest for information retrieval and a variety of services using mobile devices is gradually increasing. Users want to get the tourist information service through the use of mobile devices and accordingly, Korea local governments are trying to provide a variety of services on the mobile tourist information via smart phone. As more interest and requirements on mobile tourist information service, researches on types and preferences of mobile tourist information, measurement of the quality of service, the user's satisfaction and re-use is currently being done. However, meanwhile, the research on the content analysis classified and investigated a wide variety of numerical rating scale such as research topics of research papers, research methodology is wholly lacking. Thus, in terms of the research need on a systematic study of the domestic mobile tourist information, this study presented the research tendencies and implications of yearly research trends, research subjects, statistical analysis techniques, research methods, research models and theories related to the mobile tourist information focusing on journals listed on the National Research Foundation of Korea.

• The Journal of the Korea Contents Association
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• v.11 no.1
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• pp.56-64
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• 2011

It is difficult to discover frequent itemsets based on whole data from data stream since data stream has the characteristics of infinity and continuity. Therefore, a specialized data mining method, which reflects the properties of data and the requirement of users, is required. In this paper, we propose the method of FIMWB discovering the frequent itemsets which are reflecting the property that the recent events are more important than old events. Data stream is splitted into batches according to the given time interval. Our method gives a weighted value to each batch. It reflects user's interestedness for recent events. FP-Digraph discovers the frequent itemsets by using the result of FIMWB. Experimental result shows that FIMWB can reduce the generation of useless items and FP-Digraph method shows that it is suitable for real-time environment in comparison to a method based on a tree(FP-Tree).

• Journal of Korea Multimedia Society
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• v.6 no.6
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• pp.1111-1119
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• 2003

The research of science and engineering needs the latest information from internet resources. But it is a complex and repeated procedure to search and filter web documents from the huge Internet resources. In this paper, we propose the PREA system, which can organize the research paper databases and search World Wide Web documents that the user is interested in. It observes the usage of the local Paper databases and presented web documents and then constructs a profile intelligently. However, to make a profile, we used the modified spreading activation network(MSAN) so that the PREA can search and filter web documents by semantic meaning of user's interest in realtime. The system constructed in multi-agents manner that can cooperate together effectively. The results show the effectiveness of our system to search web documents compared with a commercial search engine.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.46 no.2
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• pp.27-35
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• 2009

A paradigm of medical industry is changing quickly to u-healthcare according to entry toward an aging society and improvement of quality of life(QoL). The change toward u-healthcare is meaningful since meaning of healthcare is redefined by prevention and management instead of medical service such as diagnosis of disease and treatment. However, the interest about u-healthcare is only concentrated to derivation of new healthcare service, development of medical measurement appliances(Sensors), and integration and standardization of medical information. Therefore, in this paper, the main ai of this study is trying to realize and implement u-healthcare technology through primary philosophies of ubiquitous composition such as Disappear Computing, Invisible Computing, and Calm Computing and development of user-centered technology.

• Journal of the Korea Convergence Society
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• v.8 no.7
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• pp.175-181
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• 2017

The purpose of this study is to investigate what factors buyers buy in smart band purchasing. Especially, The study was conducted the perception of smart bands, focusing on personal healthcare, which is one of the biggest concern of today's people, and investigating the needs of users. SmartBand is the product with the highest market share in the wearable device market. It is an indicator of how much modern people are interested in their healthcare. Therefore, this study investigates non-users who are not currently using smart bands, and what factors to consider when buying smart bands. As a result, it was found that the design of the product and the hardware performance are more important than the smart band's personal health care function in purchasing the smart band, and fundamentally, the smart band itself was not needed. Especially, people aged 20-30 years have been burdened with using smart bands continuously.

• Journal of Korea Society of Industrial Information Systems
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• v.11 no.2
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• pp.86-97
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• 2006

From the mid 1990's, the scale of IS had been growing, which resulted in increasing number of failure projects. As a result, the measurement of IS performance was a growing concern. Moreover, the need for research on the relation between IT investment and performance that isfd the concern of CEOs has been raised. In this study, the correlation between the IS functionality and investment was discovered based on the IS success model suggested by DeLone & McLean(1992), a business information system evaluation model by Lee Kuk Hie(1992), a balanced score card by Kaplan & Norton(1992). As a result of the LISREL analysis, the investment in the IS has a positive impact on the quality of both information systems and information. In turn, the quality of both information systems and information have a positive impact on the end-user satisfaction, the end-user satisfaction on the financial performance, customer satisfaction, and internal business processes. This study showed that sufficient investment in IS improved business performance

• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.5
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• pp.495-507
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• 2019

Data sparsity is one of the main challenges for the recommender system. The recommender system contains massive data in which only a small part is the observed data and the others are missing data. Most studies assume that missing data is randomly missing from the dataset. Therefore, they only use observed data to train recommendation model, then recommend items to users. In actual case, however, missing data do not lost randomly. In our research, treat these missing data as negative examples of users' interest. Three sample methods are seamlessly integrated into SVD++ algorithm and then propose SVD++_W, SVD++_R and SVD++_KNN algorithm. Experimental results show that proposed sample methods effectively improve the precision in Top-N recommendation over the baseline algorithms. Among the three improved algorithms, SVD++_KNN has the best performance, which shows that the KNN sample method is a more effective way to extract the negative examples of the users' interest.

• Journal of Broadcast Engineering
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• v.17 no.2
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• pp.270-283
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• 2012

With the advent of multi-channel TV, IPTV and smart TV services, excessive amounts of TV program contents become available at users' sides, which makes it very difficult for TV viewers to easily find and consume their preferred TV programs. Therefore, the service of automatic TV recommendation is an important issue for TV users for future intelligent TV services, which allows to improve access to their preferred TV contents. In this paper, we present a recommendation model based on statistical machine learning using a collaborative filtering concept by taking in account both public and personal preferences on TV program contents. For this, users' preference on TV programs is modeled as a latent topic variable using LDA (Latent Dirichlet Allocation) which is recently applied in various application domains. To apply LDA for TV recommendation appropriately, TV viewers's interested topics is regarded as latent topics in LDA, and asymmetric Dirichlet distribution is applied on the LDA which can reveal the diversity of the TV viewers' interests on topics based on the analysis of the real TV usage history data. The experimental results show that the proposed LDA based TV recommendation method yields average 66.5% with top 5 ranked TV programs in weekly recommendation, average 77.9% precision in bimonthly recommendation with top 5 ranked TV programs for the TV usage history data of similar taste user groups.