• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.20 no.4
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• pp.7-16
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• 2020

The mobile electronic notification service delivers notifications through mobile phone text or app-based message push to solve various problems of the paper-based mail service. And it is a service that an electronic document relay company proves to prove delivery. In order for public and administrative agencies to provide mobile electronic notification service, the user's identification information of the mobile phone number or the subscribed app is required. To overcome these limitations, ICT-regulated sandbox system was used to allow collective conversion of users' resident registration numbers into connecting information (CI). Therefore, in this paper, we propose a technical method for safe management of user CI in mobile electronic notice service, identity verification of electronic notice readers, and prevention of forgery and forgery of electronic notices. Through the proposed method, it is confirmed that the service can be activated by minimizing the adverse function of the mobile electronic notification service by securing the user's convenience and technical safety for the CI.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.40 no.5
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• pp.345-353
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• 2003

When designing network processors or implementing network equipments such as routers are implemented, IP lookup operations cause the major impact on their performance. As the organization of the IP address becomes simpler, the speed of the IP lookup operations can go faster. However, since the efficient management of IP address is inevitable due to the increasing number of network users, the address organization should become more complex. Therefore, for both IPv4(IP version 4) and IPv6(IP version 6), it is the essential fact that IP lookup operations are difficult and tedious. Lots of researcher for improving the performance of IP lookups have been presented, but the good solution has not been came out. Software approach alleviates the memory usage, but at the same time it si slow in terms of searching speed when performing an IP lookup. Hardware approach, on the other hand, is fast, however, it has disadvantages of producing hardware overheads and high memory usage. In this paper, conventional researches on IP lookups are shown and their advantages and disadvantages are explained. In addition, by mixing two representative structures, a new hybrid parallel architecture for fast IP lookups is proposed. The performance evaluation result shows that the proposed architecture provides better performance and lesser memory usage.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.2
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• pp.492-497
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• 2019

This study analysed the legal concept of personal information(PI), which was not differentiated from behavioral information, and established it clearly for invigorating online targeted advertising(OTA), which draw attention in big data era; by selecting Guidelines of Assessment of Data Breach Incident Factors and Guidelines of Measures for No-Identifying Personal Information based on Personal Information Protection Act(PIPA) and Enforcement Decree of the PIPA. As a result, PI was defined as any kind of information relating to (1)a living individual(not group, corporate body or things etc.); (2)makes possibly identify the individual by his or her identifiers such as name, resident registration number, image, etc. (not included if not identify the individual); and (3)including information like attribute values which makes possibly identify any specific individual, if not by itself, but combined with other information which can be actually collected and combined). Specifically, PI includes basic, proper distinguishable, sensitive and other PI. It is suggested that PI concept should be researched continually with digital technology development; the effectiveness of the Guidelines of PI Protection in OTA, the legal principles of PI protection from not only users' but business operators' perspectives and the differentiation between PI and behavioral information in OTA should be researched.

• Journal of the Korean Regional Science Association
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• v.38 no.1
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• pp.21-32
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• 2022

The purpose of this study is two things. First, it is to develop and demonstrate a sharong taxi platform. To this end, the implications for platform development were derived by analyzing consumers' perceptions of existing taxi services using IPA. As a result, abnormal business activities and safe services in the maintenance area were found to be safe rides and easy rides in the key improvement area. Safety such as usage fee level and driver information provision were derived in the areas subject to improvement, and friendly response and internal and external cleanliness were derived in the areas of excessive investment. The second purpose of this study is to estimate the value given to users for sharing taxi service platforms using the CVM. As a result of estimating the value of the demonstration service of the shared taxi platform developed through this study, the WTP was 3,621 won/per household/per year when expanding throughout Gimhae-si, and 2,515 won/per household/per year. Compared to the willingness to pay for empirical services, only 69.5% of the willingness to pay for the spread project in Gimhae-si. This is the result of a combination of service spread to an unspecified number of people and concerns about service quality due to spatial expansion. This suggests that it is necessary to build data through continuous demonstration and to carefully build a roadmap for spread by upgrading services based on this.

• Journal of Korean Dental Hygiene Science
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• v.5 no.2
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• pp.97-104
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• 2022

Background: Due to the declining number of students preparing for university entrance exams , the quota of universities has been decreasing continuously. This situation became increasingly diverse as new media used online, mobile, and PR tools to continuously invite students. This study is aimed at offering the helpful data to plan an effective PR strategy by analyzing the correlation between the major selection and satisfaction of the department's social media usage among freshmen majoring in dental hygiene. Methods: The collected data from the self-reported survey with freshmen were analyzed using the SPSS 22.0 program. The survey items were motive to select the major, social media platform that subjects used, reasons to use the media, time to visit the department's social media platform, and satisfaction level on the department's social media platform, using a 5-point Likert Scale. Results: The reasons for choosing a major were given by 32.2% and 15.9% respondents, respectively, as the vision after graduation and practice facilities. 39.9% and 31.4% used Instagram and YouTube for social media platforms, respectively, for using social media platform; 26.9% and 26.3% visited the department's social media before and after entering the university, respectively; 46.4% and 24.9% used Instagram and YouTube for department social media; and they generally satisfied with the contents of the department's social media. 40.9% of them said that information from the department's social media was useful. 33.8% of them said the information from the department's social media exceeded their expectations. 46.8% of them answered that the department's social media made the department's image positive. 33.4% got interested in the major more due to the department's social media. According to 32.1% of respondents, the department's social media was helpful in deciding on a major. With 35.4%, a positive correlation was discovered between the department's practice facilities and satisfaction on the department's social media. Conclusion: It is thought that the department's social media should try continuously by uploading the contents to meet the users' needs on a regular basis and seeking the plans to be able to collect various opinions using surveys through the related social media so that students can select the major and, moreover, lead the positive direction to adapt the university life under the unfamiliar environment after admission.

• Knowledge Management Research
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• v.24 no.1
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• pp.147-172
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• 2023

This study aims to review the competitiveness of 'TVING', a Korean OTT player in Korean market. For this, this study extracts core business resources of TVING focusing on resource-based theory, and analyzes their competitiveness under VRIO framework. TVING has 4 tangible and intangible resources respectively and 1 human resource. Tangible resources of TVING are technology(recommendation, compression), content(original, exclusive, discriminative non-exclusive), production studio, and paid-subscribers. Intangible resources are content planing capability, distribution network(local, global), marketing promotion·PR, sales), brand preference. And human resource of TVING creates IP professionalism of the company. As a result of VRIO analysis, discriminative non-exclusive content, production studios, and domestic service channels are the most powerful resources of TVING to 'sustain competitive advantage'. Technology, original content, planning capability and IT professionalism bring 'temporary competitive advantage', while exclusive content, the number of paid-subscribers, global service network, and promotion capabilities are not core resources with staying in competitive rank stages. By the way, TVING has potential users for sales of KT and LGU+ thanks to partnership and M&A, but this is not effectively used at the organization level yet. The meaning of this study can be found in that this evaluated competitiveness by each resource of TVING and arranged its implications.

• The Journal of Korean Institute of Next Generation Computing
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• v.13 no.5
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• pp.7-18
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• 2017

Recently, the number of SNS user has rapidly increased due to smart device industry development and also the amount of generated data is exponentially increasing. In the twitter, Text data generated by user is a key issue to research because it involves events, accidents, reputations of products, and brand images. Twitter has become a channel for users to receive and exchange information. An important characteristic of Twitter is its realtime. Earthquakes, floods and suicides event among the various events should be analyzed rapidly for immediately applying to events. It is necessary to collect tweets related to the event in order to analyze the events. But it is difficult to find all tweets related to the event using normal keywords. In order to solve such a mentioned above, this paper proposes A Generation and Matching Method of Normal-Transient Dictionary for realtime topic detection. Normal dictionaries consist of general keywords(event: suicide-death-loop, death, die, hang oneself, etc) related to events. Whereas transient dictionaries consist of transient keywords(event: suicide-names and information of celebrities, information of social issues) related to events. Experimental results show that matching method using two dictionary finds more tweets related to the event than a simple keyword search.

• Journal of the Institute of Convergence Signal Processing
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• v.24 no.4
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• pp.178-185
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• 2023

Rapid urbanization and advancements in technology have led to a surge in the number of automobiles, resulting in frequent traffic accidents, and consequently, an increase in human casualties and economic losses. Therefore, there is a need for technology that can predict the risk of traffic accidents to prevent them and minimize the damage caused by them. Traffic accidents occur due to various factors including traffic congestion, the traffic environment, and road conditions. These factors give traffic accidents spatiotemporal characteristics. This paper analyzes traffic accident data to understand the main characteristics of traffic accidents and reconstructs the data in a time series format. Additionally, an LSTM-MLP based model that excellently captures spatiotemporal characteristics was developed and utilized for traffic accident prediction. Experiments have proven that the proposed model is more rational and accurate in predicting the risk of traffic accidents compared to existing models. The traffic accident risk prediction model suggested in this paper can be applied to systems capable of real-time monitoring of road conditions and environments, such as navigation systems. It is expected to enhance the safety of road users and minimize the social costs associated with traffic accidents.

• Knowledge Management Research
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• v.25 no.3
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• pp.231-251
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• 2024

For Airbnb, one of the most successful examples of the sharing economy, to continuously grow, it needs a steady supply of attractive listings. To this end, this study draws on existing research on Airbnb and signaling theory to examine the factors that influence the performance of Airbnb listings. From a dynamic perspective, we expect the importance of factors affecting listings' performance to differ between new and existing listings. Analyzing Airbnb data from the 10 most active US cities using negative binomial regression, we find that dynamic attributes that require a time investment have a stronger impact on existing listings, while static attributes that require less of a time investment have a similar effect on both types. The host's membership duration and number of listings were expected to have positive effects, but showed negative effects. While the instant booking policy increased users' convenience, some hosts were reluctant to use it. The results of the analysis suggest that the importance of the factors varies depending on the type of Airbnb listing (new vs. existing), and the need for differentiated policies and complementary measures based on the type of listing is necessary in a practical perspective.

• Journal of the Korea Computer Graphics Society
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• v.12 no.4
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• pp.23-32
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• 2006

In recent years, since the digital camera has been popularized, so users can easily collect hundreds of photos in a single usage. Thus the managing of hundreds of digital photos is not a simple job comparing to the keeping paper photos. We know that managing and classifying a number of digital photo files are burdensome and annoying sometimes. So people hope to use an automated system for managing digital photos especially for their own purposes. The previous studies, e.g. content-based image retrieval, were focused on the clustering of general images, which it is not to be applied on digital photo clustering and classification. Recently, some specialized clustering algorithms for images clustering digital camera images were proposed. These algorithms exploit mainly the statistics of time gap between sequent photos. Though they showed a quite good result in image clustering for digital cameras, still lots of improvements are remained and unsolved. For example the current tools ignore completely the image transformation with the different focal lengths. In this paper, we present a photo considering focal length information recorded in EXIF. We propose an algorithms based on MVA(Matching Vector Analysis) for classification of digital images taken in the every day activity. Our experiment shows that our algorithm gives more than 95% success rates, which is competitive among all available methods in terms of sensitivity, specificity and flexibility.