• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 한국정보통신학회 2015년도 추계학술대회
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• pp.661-663
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• 2015

It is currently being used or being developed mobile fitness application systems associated with the most obese. These systems give or notify the user to reduce the weight, check your momentum to inform your calories burned. However, the accuracy is low because it provides and manages the common data without considering the individual characteristics or the environment of the user. In this paper analyzes the disadvantage of mobile fitness system. To solve this problem it presents the data sharing mechanism to alert you to the exercise equipment with the other users belonging to the BMI group, such as yourself. And it proposes the design of a mobile fitness system which is based on the data sharing mechanism.

• KIISE Transactions on Computing Practices
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• 제21권9호
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• pp.596-603
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• 2015

A concept network is an essential knowledge base for semantic search engines, personalized search systems, recommendation systems, and text mining. Recently, studies of extending concept representation using external ontology have been frequently conducted. We thus propose a new way of building 3-dimensional text model-based concept networks using the world knowledge-level Wikipedia ontology. In fact, it is desirable that 'concepts' derived from text documents are defined according to the theoretical framework of formal concept analysis, since relationships among concepts generally change over time. In this paper, concept networks hidden in a given document collection are extracted more reasonably by representing a concept as a term-by-document matrix.

• Journal of the Korean Institute of Intelligent Systems
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• 제16권5호
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• pp.616-621
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• 2006

Recently many cars are equipping a navigation system. The main purpose of the early system guides a user through the route. A navigation system includes various abilities by development of various technologies and it has given more convenience to user. It can play various records on the tape and announces which are useful information about each road. Also it can use various multi-media contents by DMB device during driving. However, guide function of basic and important road in the navigation system has not grown greatly yet. In this paper, we proposed recommendation method of human-friendly road considering user's condition through various information of outside environment, user's velocity intention, a driver's emotion and a preference of the road. Modules consists of hierarchical structure that can easily correct and add each algorithm and those use fuzzy logic algorithm.

• Journal of Korea Society of Digital Industry and Information Management
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• 제14권4호
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• pp.35-45
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• 2018

Users are sharing many of contents such as text, image, video, and so on in SNS. There are various information as like as personal interesting, opinion, and relationship in social media contents. Therefore, many of recommendation systems or search systems are being developed through analysis of social media contents. In order to extract subject-related topics of social context being collected from social media channels in developing those system, it is necessary to develop ontologies for semantic analysis. However, it is difficult to develop formal ontology because social media contents have the characteristics of non-formal data. Therefore, we develop a social topic system based on semantic and subject correlation. First of all, an extracting system of social topic based on semantic relationship analyzes semantic correlation and then extracts topics expressing semantic information of corresponding social context. Because the possibility of developing formal ontology expressing fully semantic information of various areas is limited, we develop a self-extensible architecture of ontology for semantic correlation. And then, a classifier of social contents and feed back classifies equivalent subject's social contents and feedbacks for extracting social topics according semantic correlation. The result of analyzing social contents and feedbacks extracts subject keyword, and index by measuring the degree of association based on social topic's semantic correlation. Deep Learning is applied into the process of indexing for improving accuracy and performance of mapping analysis of subject's extracting and semantic correlation. We expect that proposed system provides customized contents for users as well as optimized searching results because of analyzing semantic and subject correlation.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 한국정보통신학회 2014년도 춘계학술대회
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• pp.413-416
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• 2014

Recently, demand for various leisure sports gradually increases, as people's sense of values changes into leisure-centered one according to the change of given social circumstance and the change of customer needs all over the world. The actual condition is that an interest and participation rate especially in water leports during the summer increases. And needs for various hull design of standardized boat for water leisure increase. Therefore, this paper is intended to develop a recommendation system to design a boat for water leisure by using the collaborative filtering technique in order to make it possible to actively cope with the change of various customer needs for hull design. To this end, emotion relating to kayak design was selected through consumer survey, and emotion was derived by factor analysis and assessment, and then a kayak design layout in the aspect of customer's emotional preference was presented. Besides, an analysis was made according to the elements such as hull, body, and propulsion system of kayak in order to select emotional words according to the kayak design reflecting user's preference, and then a boat model for water leisure in conformance with user's preference was presented.

• Journal of Korea Society of Industrial Information Systems
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• 제27권5호
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• pp.1-11
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• 2022

With the development of deep learning technology, various research and development are underway to estimate preference rankings through learning, and it is used in various fields such as web search, gene classification, recommendation system, and image search. Approximation algorithms are used to estimate deep learning-based preference ranking, which builds more than k comparison sets on all comparison targets to ensure proper accuracy, and how to build comparison sets affects learning. In this paper, we propose a k-disjoint comparison set generation algorithm and a k-chain comparison set generation algorithm, a novel algorithm for generating paired comparison sets for crowd-sourcing-based deep learning affinity measurements. In particular, the experiment confirmed that the k-chaining algorithm, like the conventional circular generation algorithm, also has a random nature that can support stable preference evaluation while ensuring connectivity between data.

• Journal of Acupuncture Research
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• 제27권1호
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• pp.1-10
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• 2010

Objectives : The mechanical properties of Korean electric cupping systems are studied via experimental measurements. The study aimed at establishing the fundamentals of industrialization and systemization of oriental medicine device industry, as well as improving the quality of life for many Koreans. Methods : We reviewed the studies on traditional cupping as well as modern one to fine necessary factors for electric cupping systems. To characterize the mechanical properties of Korean electric cupping systems, we measured the pressure characteristics of commercially available electric cupping system by using an automatic pressure acquisition system and a standard cup. The pumping capability was checked at 40 seconds, and the stability of the suction cup was checked at 600 seconds. We also acquired the noise level of each system in clinical setting. To check the portability of each system, we also measured its physical dimensions. We scrutinized system manuals provided by the system manufacturers. Results : It took less than 5 second to reach the pressure if the connection between the air hose and the vacuum valve of the cupping system was secure. Pressure diminished to no more than 10% for 600s for all systems. Noise levels were 55~70 dB. Increase in pressure was too fast to control for a designated vacuum level except for one product. Conclusions : The Pumping ability of the systems is impressive and reliable. Pressure retention ability of each cup is quite reliable and reproducible. Therefore, their mechanical performances were worthy of recommendation. Some of them had noise level higher than 60 dB and they were bothersome. It was also suggested that the control for low to middle pressure needed to be accomplished by the cupping system.

• Journal of Navigation and Port Research
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• 제31권10호
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• pp.807-812
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• 2007

With the advent of Electronic Chart Display and Information Systems(ECDIS) and Automatic Identification Systems (AIS) as the principal navigation equipment of E-navigation strategy, mariners will begin to practice "e-navigation" and increasingly rely upon these systems to navigate safely and efficiently. However, these electronic systems require "e-inputs" in order to function. At present, the choices for e-input are limited, and they are installation dependent. This means that the mariner must be suitably equipped in order to use an alternative e-input. If the primary e-input is lost, and the vessel is not equipped to make use of suitable alternative e-inputs, then continued operations will have to be done the "old fashioned way" using conventional navigation The final objective is a recommendation of dual harbor positioning system on the most appropriate mix of positioning systems to satisfy the marine needs for radionavigation, positioning services.

• KIPS Transactions on Computer and Communication Systems
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• 제2권6호
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• pp.279-290
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• 2013

In digital forensics log files have been stored as a form of large data for the purpose of tracing users' past behaviors. It is difficult for investigators to manually analysis the large log data without clues. In this paper, we propose a text mining technique for extracting intrusion logs from a large log set to recommend reliable evidences to investigators. In the training stage, the proposed method extracts intrusion association words from a training log set by using Apriori algorithm after preprocessing and the probability of intrusion for association words are computed by combining support and confidence. Robinson's method of computing confidences for filtering spam mails is applied to extracting intrusion logs in the proposed method. As the results, the association word knowledge base is constructed by including the weights of the probability of intrusion for association words to improve the accuracy. In the test stage, the probability of intrusion logs and the probability of normal logs in a test log set are computed by Fisher's inverse chi-square classification algorithm based on the association word knowledge base respectively and intrusion logs are extracted from combining the results. Then, the intrusion logs are recommended to investigators. The proposed method uses a training method of clearly analyzing the meaning of data from an unstructured large log data. As the results, it complements the problem of reduction in accuracy caused by data ambiguity. In addition, the proposed method recommends intrusion logs by using Fisher's inverse chi-square classification algorithm. So, it reduces the rate of false positive(FP) and decreases in laborious effort to extract evidences manually.

• Journal of KIISE:Computing Practices and Letters
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• 제15권3호
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• pp.189-199
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• 2009

The amount of information on the Web is explosively growing as the Internet gains in popularity. However, only a small portion of the information on the Web is truly relevant or useful to the user. Thus, offering suitable information according to user demand is an important subject in information retrieval. In e-commerce, the recommender system is essential to revitalize commercial transactions, raise user satisfaction and loyalty towards the information provider. The existing recommender systems are mostly based on user data collected at servers, so user data are dispersed over several servers. Therefore, web servers that lack sufficient user behavior data cannot easily infer user preferences. Also, if the user visits the server infrequently, it may be hard to reflect the dynamically changing user's interest. This paper proposes a novel personalization system analyzing the user preference based on web documents that are accessed by the user on a user device. The system also identifies non-content blocks appearing repeatedly in the dynamically generated web documents, and adds weight to the keywords extracted from the hyperlink sentence selected by the user. Therefore, the system establishes at an early stage recommendation strategies for the web server that has little user data. Also, user profiles are generated rapidly and more accurately by identifying the information blocks. In order to evaluate the proposed system, this study collected web data and purchase history from users who have current purchase activity. Then, we computed the similarity between purchase data and the user profile. We confirm the accuracy of the generated user profile since the web page containing the purchased item has higher correlation than other item pages.