• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.843-845
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• 2003

Shortest Path Problems are among the most studied network flow optimization problems, with interesting applications in various fields. One such field is the route determination service, where various kinds of shortest path problems need to be solved in location-based service. Our research aim is to propose a route technique in real-time locationbased service (LBS) environments according to user’s route preferences such as shortest, fastest, easiest and so on. Turn costs modeling and computation are important procedures in route planning. There are major two kinds of cost parameters in route planning. One is static cost parameter which can be pre-computed such as distance and number of traffic-lane. The other is dynamic cost parameter which can be computed in run-time such as number of turns and risk of congestion. In this paper, we propose a new cost modeling method for turn costs which are traditionally attached to edges in a graph. Our proposed route determination technique also has an advantage that can provide service interoperability by implementing XML web service for the OpenLS route determination service specification. In addition to, describing the details of our shortest path algorithms, we present a location-based service system by using proposed routing algorithms.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.11
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• pp.730-737
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• 2018

With the increasing number of health consumers aiming for a happy quality of life, the O2O medical marketing market is activated by choosing reliable health care facilities and receiving high quality medical services based on the medical information distributed on web's blog. Because unstructured text data used on the Internet, mobile, and social networks directly or indirectly reflects authors' interests, preferences, and expectations in addition to their expertise, it is difficult to guarantee credibility of medical information. In this study, we propose a blog reading system that provides users with a higher quality medical information service by classifying medical information blogs (medical blog, ad blog) using bigdata and MLP processing. We collect and analyze many domestic medical information blogs on the Internet based on the proposed big data and machine learning technology, and develop a personalized health information recommendation system for each disease. It is expected that the user will be able to maintain his / her health condition by continuously checking his / her health problems and taking the most appropriate measures.

• Journal of Convergence for Information Technology
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• v.8 no.6
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• pp.159-164
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• 2018

This paper deals with the comparative analysis the two surveys called term1, term2 by collecting 4-digit password data 1313 for 2006~2011 and 2519 for 2012~ 2017. Numbers lacking prudence were significantly reduced in the term2 survey and over time, the use of four digit PWs became increasingly prudent. There was a difference in the use of digit numbers between male and female. The top five types accounted over 60%, which imply that certain types of preferences are present. It was the outcome of this paper that we can indirectly deduce these facts. Studies such as reuse of four digit PWs in user's convenience will need to be supplemented in the near future.

• Journal of the Architectural Institute of Korea Planning & Design
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• v.34 no.11
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• pp.75-84
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• 2018

This study analyzed users' acceptance and intention to use in addition to needs and preferences of smart home technologies, and identified the differences in technology preference and acceptance by different factors. The subjects were residents in the 40s and 60s residing in the Seoul or suburbs of Seoul, and questionnaires were conducted in the 40s while interviews with questionnaires were conducted in the 60s. A total of 105 questionnaires were used as data, and frequency, mean, crossover, independent sample t test, one-way ANOVA and multiple regression analysis were performaed using SPSS23. The results of this study are as follows. First, hypertension, hyperlipidemia and hypercholesterolemia were the most common diseases among respondents and if there was no discomfort, they would like to continue living in the homes of the current residence. Therefore, the direction of smart home development should support the daily living and health care so that residents can live a healthy life for a long time in their living space. Second, the technologies that residents most need were a control technology of residential environments and a monitoring technology of residents' health and physiological changes. The most preferred sensor types are motion sensors and speech recognition while video cameras have a very low preference. Third, technology anxiety was the most significant factor influencing intention to accept smart home technology. The greater the technology anxiety is, the weaker the acceptance of technology. Fourth, when applying smart residential technology in homes, various resident characteristics should be considered. Age and technology intimacy were the most influential variables, and accordingly there were differences in technology preference and acceptance. Therefore, a user-friendly smart home plan should be done in the consideration of the results.

• Journal of Digital Convergence
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• v.18 no.12
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• pp.577-583
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• 2020

The learning and cognition of preschool children begins with the color of the object, and the game interface is the child's first impression of the game, so reasonable color design is needed. This article selects 10 educational mobile game for preschool children, extracts colors from the game interface and startup icons, and puts them into Photoshop to analyze the three elements of color hue, lightness, and saturation. Finally, three suggestions are put forward for the color design of game UI. 1.Choose a color similar to the actual color of the thing. 2. Choose warm colors according to your child's preferences. 3. When using contrasting colors, please reduce the brightness or purity of the colors. It is hoped that the research conclusions can provide reference materials for the color design of educational game UI for preschool children.

• Journal of the Korea Society of Computer and Information
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• v.27 no.7
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• pp.93-100
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• 2022

In the era of information overload on the Internet, the recommendation system, which is an indispensable function, is a service that recommends products that a user may prefer, and has been successfully provided in various commercial sites. Recently, studies to reflect the rating time of items to improve the performance of collaborative filtering, a representative recommendation technique, are active. The core idea of these studies is to generate the recommendation list by giving an exponentially lower weight to the items rated in the past. However, this has a disadvantage in that a time function is uniformly applied to all items without considering changes in users' preferences according to the characteristics of the items. In this study, we propose a time-aware collaborative filtering technique from a completely different point of view by developing a new similarity measure that integrates the change in similarity values between items over time into a weighted sum. As a result of the experiment, the prediction performance and recommendation performance of the proposed method were significantly superior to the existing representative time aware methods and traditional methods.

• Journal of Software Assessment and Valuation
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• v.16 no.1
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• pp.81-88
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• 2020

Various methods of differentiating and servicing digital content for individual users have been studied. Searching for association rules is a very useful way to discover individual preferences in digital content services. The Apriori algorithm is useful as an association rule extractor using frequent itemsets. However, the Apriori algorithm is not suitable for application to an actual content service because it considers only the reference count of each content. In this paper, we propose a new algorithm based on the Apriori that searches association rules by using purchase history and usage-time for each item. The proposed algorithm utilizes the usage time with the weight value according to purchase items. Thus, it is possible to extract the exact preference of the actual user. We implement the proposed algorithm and verify the performance through the actual data presented in the actual content service system.

• Smart Media Journal
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• v.12 no.4
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• pp.27-40
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• 2023

Many researchers have investigated ways to enhance recommender systems by integrating heterogeneous data to address the data sparsity problem. However, only a few studies have successfully integrated heterogeneous data using knowledge graph. Additionally, most of the knowledge graphs built in these studies only incorporate explicit relationships between entities and lack additional information. Therefore, we propose a method for expanding knowledge graphs by using deep learning to model latent relationships between heterogeneous data from multiple knowledge bases. Our extended knowledge graph enhances the quality of entity features and ultimately increases the accuracy of predicted user preferences. Experiments using real music data demonstrate that the expanded knowledge graph leads to an increase in recommendation accuracy when compared to the original knowledge graph.

• Convergence Security Journal
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• v.24 no.2
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• pp.133-140
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• 2024

The spread of Direct-to-Consumer (DTC) genetic testing, where users request tests directly, has been increasing. With growing demand, certification systems have been implemented to grant testing qualifications to non-medical institutions, and the scope of tests has been expanded. However, unlike cases in less regulated foreign countries, disease-related tests are still excluded from the domestic regulations. The existing de-identification method does not adequately ensure the uniqueness and familial sharing of genomic information, limiting its practical utility. Therefore, this study proposes the application of fully homomorphic encryption in the analysis process to guarantee the usefulness of genomic information while minimizing the risk of leakage. Additionally, to safeguard the individual's right to self-determination, a privacy preservation model based on Opt-out is suggested. This aims to balance genomic information protection with maintainability of usability, ensuring the availability of information in line with the user's preferences.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.24 no.3
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• pp.139-145
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• 2024

Since COVID-19, most of people's main living spaces have been moved indoors. Due to this influence, many people's interest in companion plants continues to rise. People who raise companion plants often raise them for the purpose of emotional stability or air purification. In fact, plants have the effect of giving people a sense of emotional stability and the ability to purify indoor air is excellent depending on what kind of plant they are. However, if you do not have knowledge of plants, you will not know which plants have excellent air purification effects, and even if you grow them, you will face a problem that withers quickly. Therefore, in this paper, we develop an app that provides users who do not have prior knowledge to store and manage their MBTI and member information in a database using databases and MBTI, and based on this, recommend plant data that fits their preferences with the user and manage their schedules through calendars.