• Annual Conference of KIPS
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• 2009.04a
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• pp.348-351
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• 2009

유비쿼터스 사회를 실현하는 핵심기술인 u-GIS 공간정보 기술은 데이터 스트림 처리 시스템(Data Stream Management System)과 지리정보 시스템(Geography Information System)이 결합된 플랫폼인 u-GIS DSMS를 요구한다. u-GIS DSMS는 GeoSeonsor에서 수집되는 센서 테이터와 GIS의 공간정보 데이터를 결합하여 처리하는 공간영역질의가 다수 요구된다. 이런 공간영역질의들은 특정 지역에 밀집하게 등록되는 경향이 있으며, 유사한 프리디킷을 가질 가능성이 높다. 이러한 특징은 공간영역질의가 특정 지역에 밀집되면 다수의 비슷한 연산들이 반복적으로 처리하기 때문에 시스템 성능이 저하 될 것이다. 이를 해결하기 위해 영역질의 색인기법 연구가 활발히 진행되고 있다. 그러나 기존의 VCR-Index와 CQI-Index 기법은 질의영역을 셀 구조나 가상구조로 분할하여 처리하기 때문에 자원 및 연산을 공유 할 수 없어 질의 처리 속도가 현저히 저하되기 때문에 대량의 공간영역질의 처리에는 부적합하다. 그래서 본 논문에서는 공간영역질의의 효율적인 연산 공유를 위한 질의영역 밀집도 기반의 그룹화 기법을 제안한다. 이 기법은 질의영역의 밀집도를 이용하여 공간영역질의들을 그룹화 후 색인을 구성한다. 색인된 영역들의 데이터는 단일 큐로 구성 후 질의들의 프리디킷을 분석하여 자원 및 연산 공유기법을 통해 기존의 기법보다 처리 속도 향상 및 메모리 사용을 감소시켰다.

• Journal of the Korea Society of Computer and Information
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• v.28 no.9
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• pp.55-62
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• 2023

In this paper, we present a method that utilizes geometry-based sound generation techniques to efficiently handle multiple sound sources, wind turbulence, and temperature-dependent interactions. Recently, a method based on reduced raytracing has been proposed to update the sound position and efficiently calculate sound propagation and diffraction without recursive reflection/refraction of many rays, but this approach only considers the propagation characteristics of sound and does not consider the interaction of multiple sound sources, wind currents, and temperature. These limitations make it difficult to create sound scenes in a variety of virtual environments because they only generate static sounds. In this paper, we propose a method for efficiently constructing a sound map in a situation where multiple sounds are placed, and a method for efficiently controlling the movement of an agent through it. In addition, we propose a method for controlling sound propagation by considering wind currents and temperature. The method proposed in this paper can be utilized in various fields such as metaverse environment design and crowd simulation, as well as games that can improve content immersion based on sound.

• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.178-178
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• 2022

일반적으로 하천은 상류에서 하류로 흐르지만, 하구에서 일어나는 해안으로부터의 염수의 역류 혹은 상류에서 홍수 발생 시 순간적으로 나타나는 역류 등 주 흐름을 거스르는 흐름이 종종 발생하는데 이는 예상치 못한 피해를 가져올 수 있으므로 최대한 방지해야 한다. 즉, 양방향 흐름이 일어나는 지역을 단방향 흐름으로 바꿔줄 수 있는 수리 구조물의 설치가 필요하다. 역방향 흐름을 제어하는 대표적인 예로는 보가 있는데, 보는 충분한 수위 확보뿐만 아니라 하구에서 역류하는 해수를 방지하는 역할도 한다. 다만, 상류와 하류를 수직적으로 분리함에 따라 물고기의 자유로운 이동을 제한하는 등 생태계를 단절시키는 부작용이 나타날 뿐만 아니라, 최근 정부의 정책에 따라 세종보, 죽산보 등의 보 해체 결정이 이루어지면서 이를 대체할 방안이 필요하다. 따라서 이번 연구에서는 수직적인 구조물이 아닌 수평적인 수리 구조물을 고안함으로써 생태계에 큰 영향을 주지 않으면서 가장 효과적으로 양방향 흐름을 제어할 수 있는 구조물 설계 모형을 탐구해보았다. 구조물 설계 아이디어는 심장의 판막에서 고안하였다. 판막은 특정한 방향성을 갖는 구조로 이루어져 있으면서 혈액의 역류를 방지하는 기관으로, 비슷한 방식으로 하천에도 특정 각도를 갖는 구조물의 설치를 통해 단방향 흐름을 유도할 수 있다고 판단하였다. 실제 하천 규모에서의 실험은 불가능하다고 판단, 전산 유체 프로그램 OpenFOAM을 이용하여 가상 수로의 모델링을 진행하였다. 얇은 판 형태의 흐름 제어 구조물을 수로 측면에 각각 설치 후, 같은 조건에서 정방향 흐름과 역방향 흐름에 대해 각각 시뮬레이션을 진행하였다. 이때, 두 흐름의 하류 유량 크기의 차이를 단방향 흐름을 정량화하는 수치로 산정한다. 시뮬레이션은 구조물과 흐름 방향이 이루는 각도, 구조물의 개수 및 간격, 구조물의 비대칭성 등 여러 가지 조건을 바꿔가면서 진행하고, 유속 분포 및 후류의 크기 등의 수리학적 현상을 파악하여 계산 결과를 분석한다. 분석 결과를 바탕으로 하류의 유량 차이가 가장 크게 나타나는 수리 구조물의 조건을 결정하고, 해당 구조물의 실제 적용 가능 여부를 판단한다.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.650-652
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• 2022

Frame rate up-conversion (FRUC) algorithm is an image interpolation technology that improves the frame rate of moving pictures. This solves problems such as screen shake or blurry motion caused by low frame rate video in high-definition digital video systems, and provides viewers with a more free and smooth visual experience. In this paper, we propose a video compression technique using deep learning-based FRUC algorithm. The proposed method compresses and transmits after excluding some images from the original video, and uses a deep learning-based interpolation method in the decoding process to restore the excluded images, thereby compressing them with high efficiency. In the experiment, the compression performance was evaluated using the decoded image and the image restored by the FRUC algorithm after encoding the video by skipping 1 or 3 pages. When 1 and 3 sheets were excluded, the average BD-rate decreased by 81.22% and 27.80%. The reason that excluding three images has lower encoding efficiency than excluding one is because the PSNR of the image reconstructed by the FRUC method is low.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.2-5
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• 2022

With the advent of deep learning and the rapid development of ICT (Information and Communication Technology), research using artificial intelligence is being actively conducted in various fields of society such as politics, economy, and culture and so on. Deep learning-based artificial intelligence technology is subdivided into various domains such as natural language processing, image processing, speech processing, and recommendation system. In particular, as the industry is advanced, the need for a recommendation system that analyzes market trends and individual characteristics and recommends them to consumers is increasingly required. In line with these technological developments, this paper extracts and classifies attribute information from structured or unstructured text and image big data through deep learning-based technology development of 'language processing intelligence' and 'image processing intelligence', and We propose an artificial intelligence-based 'customized fashion advisor' service integration system that analyzes trends and new materials, discovers 'market-consumer' insights through consumer taste analysis, and can recommend style, virtual fitting, and design support.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.263-265
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• 2022

In online exhibitions, the artist's differentiated exhibition composition is important because the exhibition immersion decreases compared to offline exhibitions. Therefore, the purpose of this paper is to propose a new online exhibition platform by developing a PC APP service that exhibits the work production process led by the artist. To this end, literature surveys, visitor surveys, and in-depth interviews were conducted. Based on this, a prototype was implemented with Figma and Unity. As a result of the study, visitors answered that online exhibition planning needs to be improved, and the artist said that if the artist's autonomy is guaranteed in the composition of the exhibition hall, the understanding of the work can be improved. By applying this, a system was developed that allows artists to display their own production process data. The significance of this paper is to help visitors improve their understanding of the work and improve the delivery of the artist's work.

• Smart Media Journal
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• v.11 no.9
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• pp.56-63
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• 2022

Recently, the frequency and range of algae occurrence in major rivers and lakes are increasing due to the increase in water temperature due to climate change, the inflow of excessive nutrients, and changes in the river environment. Abnormal algae include green algae and red algae. Green algae is a phenomenon in which blue-green algae such as chlorophyll (Chl-a) in the water grow excessively and the color of the water changes to dark green. In this study, a 3D virtual world of digital twin was built to monitor and control water quality information measured in ecological rivers and lakes in the living environment in real time from a remote location, and a sensor measuring device for water quality information based on the Internet of Things (IOT) sensor. We propose to build a smart water environment service platform that can provide algae warning and water quality forecasting by predicting the causes and spread patterns of water pollution such as algae based on AI machine learning-based collected data analysis.

• Smart Media Journal
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• v.11 no.11
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• pp.25-31
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• 2022

Deep learning-based image processing is essential for autonomous vehicles. To process road images in real-time in a System-on-Chip (SoC) environment, we need to execute deep learning models on a NPU (Neural Procesing Units) specialized for deep learning operations. In this study, we imported seven open-source image processing deep learning models, that were developed on GPU servers, to Texas Instrument Deep Learning (TIDL) NPU environment. We confirmed that the models imported in this study operate normally in the SoC virtual environment through performance evaluation and visualization. This paper introduces the problems that occurred during the migration process due to the limitations of NPU environment and how to solve them, and thereby, presents a reference case worth referring to for developers and researchers who want to port deep learning models to SoC environments.

• The Journal of the Convergence on Culture Technology
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• v.9 no.1
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• pp.669-678
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• 2023

The aim of this study is to explore 'how is ICT being used in social work education and are they effective?' In order to answer the questions, a content analysis was conducted. Three academic databases, DBPIA, Kiss, EBSCO were searched to look for the articles that investigated ICT and social work education between Jan. 2016 and July, 2022. No study was found from DBPIA and Kiss. Based on inclusion criteria, thirteen articles were selected from EBSCO. Those articles were analyzed following 9 analytic themes: Country, Undergraduate or Graduate Student, Pedagogical Theory, Course Goal, Course Content, Evaluation Method, Outcome, Applied Software or Program. As results of the study, the platforms primarily used for social work education were VR, Second Life, PeopleSIM, course-tailored Applications. In addition, the following was illustrated as effectiveness of ICT in social work education; immersiveness, course satisfaction, improved knowledge, etc. Study limitations and recommendations of future applications were also discussed.

• Journal of the Institute of Convergence Signal Processing
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• v.23 no.3
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• pp.173-178
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• 2022

Recently, various computing technologies such as artificial intelligence, big data, and IoT are developing. In particular, artificial intelligence-based deepfake technology is being used in various fields such as the content and medical industry. Deepfake technology is a combination of deep learning and fake, and is a technology that synthesizes a person's face or body through deep learning, which is a core technology of AI, to imitate accents and voices. This paper uses deepfake technology to study the creation of virtual characters through the synthesis of animation models and real person photos. Through this, it is possible to minimize various cost losses occurring in the animation production process and support writers' work. In addition, as deepfake open source spreads on the Internet, many problems emerge, and crimes that abuse deepfake technology are prevalent. Through this study, we propose a new perspective on this technology by applying the deepfake technology to children's material rather than adult material.