• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2022.06a
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• pp.246-249
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• 2022

최근 들어 세계적으로 크게 관심을 받는 메타버스 및 몰입형(가상현실, 확장현실, 및 라이트필드) 콘텐츠 서비스의 응용 범위를 확대하기 위해서는 3D 객체의 실시간 전송을 위한 압축 기술이 필요하다. ISO/IEC 23090 MPEG-I Part 5 로 2021 년 표준화 완료된 V-PCC (Video-based Point Cloud Compression)는 이러한 산업계의 관심 및 필요에 의해서 국제 표준화된 동적 3D 포인트 클라우드 객체 부호화 기술이다. V-PCC 기술의 압축 성능은 기존 산업계 기술에 비해 매우 우수하나, 부호화기의 연산 복잡도가 매우 높다는 단점을 가지고 있다. 본 논문에서는 V-PCC 부호화기에서 가장 높은 연산 복잡도를 갖는 법선 추정 알고리즘의 결합 고속화 기법을 제안한다. 법선 추정은 2 개의 알고리즘으로 구성되어 있다. 첫번째는 "방향을 무시하는 법선 추정 알고리즘(normal estimation)"이고, 두번째는 첫번째 알고리즘에서 추정된 법선들을 대상으로 하는 "법선 방향 추정 알고리즘(normal orientation)"이다. 본 논문에서 제안하는 고속화 기법은 2 개 알고리즘을 결합하여 첫번째 법선 추정 알고리즘에서 획득한 부가 정보를 두번째 법선 방향 추정 알고리즘에서 활용함으로써 연산량을 대폭 줄이고, 또한 법선 방향 추정 알고리즘 내의 우선순위 큐 자료구조를 변경하여 추가적인 고속화를 달성한다. 7 개 테스트 영상에 대한 실험 결과, 압축 효율 저하 없이 법선 방향 추정 알고리즘의 속도를 평균 89.2% 향상시킬 수 있다.

• The Journal of the Convergence on Culture Technology
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• v.10 no.4
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• pp.613-625
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• 2024

This study analyzed the convergence case of AI painting and NFT art, art content created based on digital technology, an innovative technology of the 4th industrial technology, and explored its characteristics. Digital technology that innovates the paradigm of life in the 21st century is being used in creative art, and AI painting and NFT art that use it as an expression tool are changing the way they perceive and accept art. AI painting using big data and artificial intelligence technology is evolving into interactive daily art, and NFT art using blockchain and NFT technology is becoming the art of the metaverse with economic and cultural values. Therefore, this study attempted to explore various aspects and values of these digital convergence arts. For the study, representative examples of AI painting and NFT art were classified into cognitive creative AI painting and language generative AI, art economic NFTs, and art and cultural NFTs, and their characteristics, contents, and meanings were analyzed. It is hoped that the results of this study will contribute to the development of AI painting and NFT art, which are digital convergence arts.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.11 no.2
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• pp.89-94
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• 2011

Today, the utilization of communication devices is being increased including information terminals, cell phones, handheld personal digital assistants (PDA) caused by the development of information and communication technology. The development of information and services is speeding up, whereas most communication devices have provided a inefficient hierarchical menu and sequential searching structure. In this study, the Adaptive User Interface is applied to the Bus Information Terminal(BIT) which is one of communication equipment installed in the bus stop. It will be based on analysis of unspecified individuals' preferences and user's directly personalization in the BIT prototype. We expect the results of this study to be possible to provide users with efficient and convenient information acquisition and contribute to the development of public transport use by improving the accessibility and usability of BIT.

• KIPS Transactions on Computer and Communication Systems
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• v.1 no.3
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• pp.171-180
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• 2012

In the development of MDMS(Meter Data Management System) based on CIM(Common Information Model), which is international standard in information model and data exchange on power system, the two focused issues are the effective management of data collected in a shorter time period and the way to integrate services supporting legacy system to use the AMI(AMI, Advanced Metering Infrastructure) data. In this paper, we propose MDMS implementation methods and functions in AMI environment which are differ from existing AMR system environments in that the methods support bi-directional service infrastructure. The proposed MDMS in this paper has two unique features, one is the secure of interoperability by utilizing the CIM and ESB, the other is the improvement of field application by implementing system module based on components. On an implementation of smart grid, the result of proposed methods is expected to contribute to the efficient development and operation of CIM-based power system.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.24 no.2
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• pp.79-86
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• 2024

In this paper, we propose a virtual learning platform based on various interactions that occur during real class activities, rather than the existing content delivery-oriented learning metaverse platform. In this study, we provide a learning environment that combines AI and a virtual environment to solve problems by talking to real-time AI. Also, we applied G-learning techinques to improve class immersion. The Virtual Edu platform developed through this study provides an effective learning experience combining self-directed learning, simulation of interest through games, and PBL teaching method. And we propose a new educational method that improves student participation learning effectiveness. Experiment, we test performance on learninng activity based on real-time video classroom. As a result, it was found that the class progressing stably.

• Knowledge Management Research
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• v.23 no.3
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• pp.129-152
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• 2022

As a key infrastructure for digital transformation, the spread of data, network, artificial intelligence (D.N.A.) fields and the emergence of promising industries are laying the groundwork for active digital innovation throughout the economy. In this study, by applying the text mining methodology, major topics were derived by using the abstract, publication year, and research field of the study corresponding to the SCIE, SSCI, and A&HCI indexes of the WoS database as input variables. First, main keywords were identified through TF and TF-IDF analysis based on word appearance frequency, and then topic modeling was performed using g-DMR. With the advantage of the topic model that can utilize various types of variables as meta information, it was possible to properly explore the meaning beyond simply deriving a topic. According to the analysis results, topics such as business intelligence, manufacturing production systems, service value creation, telemedicine, and digital education were identified as major research topics in digital transformation. To summarize the results of topic modeling, 1) research on business intelligence has been actively conducted in all areas after COVID-19, and 2) issues such as intelligent manufacturing solutions and metaverses have emerged in the manufacturing field. It has been confirmed that the topic of production systems is receiving attention once again. Finally, 3) Although the topic itself can be viewed separately in terms of technology and service, it was found that it is undesirable to interpret it separately because a number of studies comprehensively deal with various services applied by combining the relevant technologies.

• Industry Promotion Research
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• v.8 no.2
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• pp.21-27
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• 2023

This study studies the policies and use cases of the government and the financial sector for artificial intelligence, and the future policy tasks of the financial sector. want to derive According to Gartner, noteworthy technologies leading the financial industry in 2022 include 'generative AI', 'autonomous system', 'Privacy Enhanced Computation (PEC) was selected. The financial sector is developing new technologies such as artificial intelligence, big data, and blockchain. Developments are spurring innovation in the financial sector. Data loss due to the spread of telecommuting after the corona pandemic As interests in sharing and personal information protection increase, companies are expected to change in new digital technologies. Global financial companies also utilize new digital technology to develop products or manage and operate existing businesses. I n order to promote process innovation, I T expenses are being expanded. The financial sector utilizes new digital technology to prevent money laundering, improve work efficiency, and strengthen personal information protection. are applying In the era of Big Blur, where the boundaries between industries are disappearing, the competitive edge in the challenge of new entrants In order to preoccupy the market, financial institutions must actively utilize new technologies in their work.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.21 no.6
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• pp.225-240
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• 2022

Gyeonggi-do comprises several types of areas, including urban, semi-urban, and rural areas. The availability of public transportation services varies depending on the area types, but the fare structure is based on a simple transfer rule, i.e., a transfer is free when completed within 30 minutes. As a result, users in non-urban areas with a poor frequency of public transportation services do not receive transfer discounts because most of the bus routes in these areas have a gap of more than 30 minutes between services. In terms of equality of opportunity, the transfer rule is being applied unfavorably and, as a result, equality of opportunity of the non-urban commuter is severely affected. Therefore, this study analyzed the user benefits mainly stemming from transfer fares using the smart card data of commuters using public transportation in Gyeonggi-do. An index called the beneficiary rate of the free transfer was developed and a scenario analysis was conducted based on the various levels of the rate. The results of this analysis proved that the users of public transportation services in non-urban areas in Gyeonggi-do can only receive transfer benefits by the extended time for free transfer and not by the implementation of a uniform policy irrespective of the type of area. The study also suggested an equitable fare transfer system and policy alternatives.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.38 no.4
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• pp.353-361
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• 2020

Due to the rapid urbanization, various traffic problems such as traffic jams during commute and regular traffic jams are occurring. In order to solve these traffic problems, it is necessary to quickly and accurately estimate and analyze traffic volume. ITS (Intelligent Transportation System) is a system that performs optimal traffic management by utilizing the latest ICT (Information and Communications Technology) technologies, and research has been conducted to analyze fast and accurate traffic volume through various techniques. In this study, we proposed a deep learning-based vehicle detection method using UAV (Unmanned Aerial Vehicle) video for real-time traffic analysis with high accuracy. The UAV was used to photograph orthogonal videos necessary for training and verification at intersections where various vehicles pass and trained vehicles by classifying them into sedan, truck, and bus. The experiment on UAV dataset was carried out using YOLOv3 (You Only Look Once V3), a deep learning-based object detection technique, and the experiments achieved the overall object detection rate of 90.21%, precision of 95.10% and the recall of 85.79%.

• Journal of Intelligence and Information Systems
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• v.28 no.4
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• pp.1-25
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• 2022

After the first Covid-19 confirmed case occurred in Korea in January 2020, interest in personal transportation such as public bicycles not public transportation such as buses and subways, increased. The demand for 'Ddareungi', a public bicycle operated by the Seoul Metropolitan Government, has also increased. In this study, a demand prediction model of a GRU(Gated Recurrent Unit) was presented based on the rental history of public bicycles by time zone(2019~2021) in Seoul. The usefulness of the GRU method presented in this study was verified based on the rental history of Around Exit 1 of Yeouido, Yeongdengpo-gu, Seoul. In particular, it was compared and analyzed with multiple linear regression models and recurrent neural network models under the same conditions. In addition, when developing the model, in addition to weather factors, the Seoul living population was used as a variable and verified. MAE and RMSE were used as performance indicators for the model, and through this, the usefulness of the GRU model proposed in this study was presented. As a result of this study, the proposed GRU model showed higher prediction accuracy than the traditional multi-linear regression model and the LSTM model and Conv-LSTM model, which have recently been in the spotlight. Also the GRU model was faster than the LSTM model and the Conv-LSTM model. Through this study, it will be possible to help solve the problem of relocation in the future by predicting the demand for public bicycles in Seoul more quickly and accurately.