• Design Convergence Study
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• v.15 no.5
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• pp.1-19
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• 2016

The emergence of digital media has enabled visitors' active intervention based on the existing art information archiving. Accordingly, this study was conducted to analyze how archiving data that art museums have developed so farcan be provided significantly to visitors and generate a contemporary appreciation code. This study intended to solve the problems in case study analysis through the restructuralization of information that reflected visitors' appreciation activities as the principal agent. The structure of information, which was reframed for multilayered information approach and use by keyword, is meaningful as it has developed an information structure to mainly enhance visitors' understanding. It is expected that the use of art information proposed in this study will be helpful for setting the specific direction of actual transmedia storytelling service by art museums based on web later on.

• The Journal of the Convergence on Culture Technology
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• v.9 no.3
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• pp.221-237
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• 2023

This study was conducted as part of a phased study aimed at closely examining the mutual ecosystem between creators and viewers through paying attention to the personal media environment that is in the center of rapid changes in the media industry and particularly exploring the areas of activity of the single-person or minority media creators who lead the mobile media environment that could be accessed, viewed and produced anywhere. In particular, this study aimed to not only pay attention to the situation in which the potential to expand professional YouTuber areas encounters the changing desire and demand of content service users who continue to become more evolved as time passes, but also examine the influence of the reputation of professional YouTubers on users' loyalty and subscription intention, examine the moderating effect of sincerity on the relationship between YouTubers and users, and demonstrate such relationship formation process based on concrete data. In the conclusion section, implications that can be drawn from the study results and suggestions for further studies in the future were proposed.

• Proceedings of the Korea Information Processing Society Conference
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• 2022.11a
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• pp.377-379
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• 2022

POI refers to the point of Interest in Location-Based Social Networks (LBSNs). With the rapid development of mobile devices, GPS, and the Web (web2.0 and 3.0), LBSNs have attracted many users to share their information, physical location (real-time location), and interesting places. The tremendous demand of the user in LBSNs leads the recommendation systems (RSs) to become more widespread attention. Recommendation systems assist users in discovering interesting local attractions or facilities and help social network service (SNS) providers based on user locations. Therefore, it plays a vital role in LBSNs, namely POI recommendation system. In the machine learning model, most of the training data are stored in the centralized data storage, so information that belongs to the user will store in the centralized storage, and users may face privacy issues. Moreover, sharing the information may have safety concerns because of uploading or sharing their real-time location with others through social network media. According to the privacy concern issue, the paper proposes a recommendation model to prevent user privacy and eliminate traditional RS problems such as cold-start and data sparsity.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.10a
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• pp.585-587
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• 2022

The visual motivation between AP devices is NTP-based visual motivation through the access of the Internet through the internal wired LAN, but this has several seconds of visual difference in hundreds of milliseconds (msec) depending on the network. The frame for the output of the video will vary depending on the application, but usually 24 (image) frames are output to the screen in one second. Therefore, the visual synchronization between peripheral devices can be performed through the adjacent moving camera device, not the wired method. The programming method of generating API for synchronization command when creating an application for visual synchronization and delivering it to AP through MAC may differ from the time in synchronization command according to the environment of the operating system at the transmission side and the situation of the buffer queue of the MAC. Therefore, as a method to solve this problem, the renewal of visual information in the device driver terminal controlling MAC can be much more effective.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2023.05a
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• pp.104-105
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• 2023

해양 관측과 위성 원격탐사를 이용하여 시공간적으로 다양하게 변하는 생태 어장 환경 및 선박 관련 자료를 획득할 수 있다. 이번 연구의 주요 목적은 야간 불빛 위성 자료를 이용하여 광범위한 해역에 대한 어선의 위치 분포를 파악하는 딥러닝 기반 모델을 제안하는 것이다. 제안한 모델의 정확성을 평가하기 위해 야간 조업 어선의 위치를 포함하고 있는 AIS(Automatic Identification System) 정보와 상호 비교 평가 하였다. 이를 위해, 먼저 AIS 자료를 획득 및 분석하는 방법을 소개한다. 해양안전종합시스템(General Information Center on Maritime Safety & Security, GICOMS)으로부터 제공받은 AIS 자료는 동적정보와 정적정보로 나뉜다. 동적 정보는 일별 자료로 구분되어있으며, 이 정보에는 해상이동업무식별번호(Maritime Mobile Service Identity, MMSI), 선박의 시간, 위도, 경도, 속력(Speed over Ground, SOG), 실침로(Course over Ground, COG), 선수방향(Heading) 등이 포함되어 있다. 정적정보는 1개의 파일로 구성되어 있으며, 선박명, 선종 코드, IMO Number, 호출부호, 제원(DimA, DimB, DimC, Dim D), 홀수, 추정 톤수 등이 포함되어 있다. 이번 연구에서는 선박의 정보에서 어선의 정보를 추출하여 비교 자료로 사용하였으며, 위성 자료는 구름의 영향이 없는 깨끗한 날짜의 영상 자료를 선별하여 사용하였다. 야간 불빛 위성 자료, 구름 정보 등을 이용하여 야간 조업 어선의 불빛을 감지하는 심층신경망(Deep Neural Network; DNN) 기반 모델을 제안하였다. 본 연구의결과는 야간 어선의 분포를 감시하고 한반도 인근 어장을 보호하는데 기여할 것으로 기대된다.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.33 no.5
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• pp.737-751
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• 2023

The importance of digital forensics in the cloud environment is increasing as businesses and individuals move their data from On-premise to the cloud. Cloud data can be stored on various devices, including mobile devices and desktops, and encompasses a variety of user behavior artifacts, such as information generated from linked accounts and cloud services. However, there are limitations in securing and analyzing digital evidence due to environmental constraints of the cloud, such as distributed storage of data and lack of artifact linkage. One solution to address this is archiving services, and Google's Takeout is prime example. In this paper, user behavior data is analyzed for cloud forensics based on archiving data and necessary items are selected from an investigation perspective. Additionally, we propose the process of analyzing selectively collected data based on time information and utilizing web-based visualization to meaningfully assess artifact associations and multi-behaviors. Through this, we aim to demonstrate the value of utilizing archiving data in response to the increasing significance of evidence collection for cloud data.

• Korea Trade Review
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• v.48 no.1
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• pp.263-285
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• 2023

The purpose of this study is to examine the business model, growth process, and success factors of the successful super app Gojek in the Southeast Asian and Indonesian markets. Gojek's business model analysis focused on the business canvas (9 blocks) that many companies have used to establish business models. Gojek's growth process was analyzed based on timeline. Gojek 1.0 is from the start of the ride hailing service. Gojek 2.0 is a leap forward into a life-friendly platform. Gojek 3.0 is a process of expanding and diversifying domestic and foreign businesses. Gojek 4.0 is a stage of changing to GoTo through mergers with Tokopedia, and setting a higher leap forward. Based on this, the success factors of Gojek are ① Hyperlocalization ② First mobile ③ A company that is loved through mission execution ④ Provide financial inclusion ⑤ Business expansion and diversification through mergers and acquisitions and partnerships ⑥ Entry into overseas markets ⑦ Attracting various partners and investment ⑧ Lock-in effect and hyperpersonalization. The implications obtained through this study and the limitations and direction of the study were discussed.

• Journal of Internet Computing and Services
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• v.24 no.5
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• pp.107-120
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• 2023

The public bicycle of Seoul, Ttareungyi, was launched at October 2015 to reduce traffic and carbon emissions in downtown Seoul and now, 2023 Oct, the cumulative number of user is upto 4 million and the number of bike is about 43,000 with about 2700 stations. However, super growth of Ttareungyi has caused the several problems, especially demand/supply mismatch, and thus the Seoul citizen has been complained about out of stock. In this point, this study conducted a real time demand forecasting model to prevent stock out bike at stations. To develop the model, the research team gathered the rental·return transaction data of 20,000 bikes in whole 1600 stations for 2019 year and then analyzed bike usage, user behavior, bike stations, and so on. The forecasting model using machine learning is developed to predict the amount of rental/return on each bike station every hour through daily learning with the recent 90 days data with the weather information. The model is validated with MAE and RMSE of bike stations, and tested as a prototype service on the Seoul Bike Management System(Mobile App) for the relocation team of Seoul City.

• Smart Media Journal
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• v.12 no.9
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• pp.159-170
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• 2023

It takes a lot of time and manpower to search for the missing. As part of the solution, a missing person search AI system was implemented using a YOLO-based model. In order to train object detection models, the model was learned by collecting recognition images (road fixation) of drone mobile objects from AI-Hub. Additional mountainous terrain datasets were also collected to evaluate performance in training datasets and other environments. In order to optimize the missing person search AI system, performance evaluation based on model size and hyperparameters and additional performance evaluation for concerns about overfitting were conducted. As a result of performance evaluation, it was confirmed that the YOLOv5-L model showed excellent performance, and the performance of the model was further improved by applying data augmentation techniques. Since then, the web service has been applied with the YOLOv5-L model that applies data augmentation techniques to increase the efficiency of searching for missing people.

• Journal of Korea Society of Industrial Information Systems
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• v.29 no.2
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• pp.69-80
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• 2024

In this paper, we studied a discrete-time queuing system that operates under a mixed situation of D-policy and T-policy, one of the representative server control policies in queuing theory. A single server serves customers arriving by Bernoulli arrival process on a first-in, first-out basis(FIFO). If there are no customers to serve in the system, the server goes on vacation and returns, until the total service time (i.e., total amount of workload) of waiting customers exceeds predetermined workload threshold D. The operation of the system covered in this study can be used to model the efficient resource utilization of mobile devices using secondary batteries. In addition, it is significant in that the steady state waiting time and system sojourn time of the queuing system under a flexible mixed control policy were derived within a unified framework.