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An Hybrid Approach to Improve the Standard Classification System in the Domains of Economics, Humanities, and Social Science (하이브리드 방식에 의한 경제.인문.사회 분야 표준분류체계 개선에 관한 연구)

  • Chung, Eun-Kyung;Park, Ji-Yeon
    • Journal of the Korean BIBLIA Society for library and Information Science
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    • v.20 no.3
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    • pp.129-147
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    • 2009
  • The ultimate goal of classification systems is to provide tools for information management and services through collocation of information objects in similar topics. The National Research Council for Economics, Humanities, and Social Sciences(NRCS) aims to organize the research products from 23 research institutes. To manage and organize the research products effectively, the standard classification system has been developed in conjunction of users' survey and the Business Reference Model(BRM). Although the standard classification system consists of users' perspectives and the aspects of organizational functions, there are limits to apply the system into classification practices. In this study, the proposed hybrid approach is to combine a clustering approach with 1,884 keywords from the titles of research products between 2007 and 2008. The clustering approach is performed in a heuristic way according to the KDC due to the lack of digital full texts of research products. The results of this study proposed a revised standard classification system for NRCS with 16 headings and 90 sub-headings. The revised standard classification system will play an important role in managing research products effectively.

Design of a designated lane enforcement system based on deep learning (딥러닝 기반 지정차로제 단속 시스템 설계)

  • Bae, Ga-hyeong;Jang, Jong-wook;Jang, Sung-jin
    • Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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    • 2022.10a
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    • pp.236-238
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    • 2022
  • According to the current Road Traffic Act, the 2020 amendment bill is currently in effect as a system that designates vehicle types for each lane for the purpose of securing road use efficiency and traffic safety. When comparing the number of traffic accident fatalities per 10,000 vehicles in Germany and Korea, the number of traffic accident deaths in Germany is significantly lower than in Korea. The representative case of the German autobahn, which did not impose a speed limit, suggests that Korea's speeding laws are not the only answer to reducing the accident rate. The designated lane system, which is observed in accordance with the keep right principle of the Autobahn Expressway, plays a major role in reducing traffic accidents. Based on this fact, we propose a traffic enforcement system to crack down on vehicles violating the designated lane system and improve the compliance rate. We develop a designated lane enforcement system that recognizes vehicle types using Yolo5, a deep learning object recognition model, recognizes license plates and lanes using OpenCV, and stores the extracted data in the server to determine whether or not laws are violated.Accordingly, it is expected that there will be an effect of reducing the traffic accident rate through the improvement of driver's awareness and compliance rate.

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Classification of Industrial Parks and Quarries Using U-Net from KOMPSAT-3/3A Imagery (KOMPSAT-3/3A 영상으로부터 U-Net을 이용한 산업단지와 채석장 분류)

  • Che-Won Park;Hyung-Sup Jung;Won-Jin Lee;Kwang-Jae Lee;Kwan-Young Oh;Jae-Young Chang;Moung-Jin Lee
    • Korean Journal of Remote Sensing
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    • v.39 no.6_3
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    • pp.1679-1692
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    • 2023
  • South Korea is a country that emits a large amount of pollutants as a result of population growth and industrial development and is also severely affected by transboundary air pollution due to its geographical location. As pollutants from both domestic and foreign sources contribute to air pollution in Korea, the location of air pollutant emission sources is crucial for understanding the movement and distribution of pollutants in the atmosphere and establishing national-level air pollution management and response strategies. Based on this background, this study aims to effectively acquire spatial information on domestic and international air pollutant emission sources, which is essential for analyzing air pollution status, by utilizing high-resolution optical satellite images and deep learning-based image segmentation models. In particular, industrial parks and quarries, which have been evaluated as contributing significantly to transboundary air pollution, were selected as the main research subjects, and images of these areas from multi-purpose satellites 3 and 3A were collected, preprocessed, and converted into input and label data for model training. As a result of training the U-Net model using this data, the overall accuracy of 0.8484 and mean Intersection over Union (mIoU) of 0.6490 were achieved, and the predicted maps showed significant results in extracting object boundaries more accurately than the label data created by course annotations.

Comparative Analysis of Self-supervised Deephashing Models for Efficient Image Retrieval System (효율적인 이미지 검색 시스템을 위한 자기 감독 딥해싱 모델의 비교 분석)

  • Kim Soo In;Jeon Young Jin;Lee Sang Bum;Kim Won Gyum
    • KIPS Transactions on Software and Data Engineering
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    • v.12 no.12
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    • pp.519-524
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    • 2023
  • In hashing-based image retrieval, the hash code of a manipulated image is different from the original image, making it difficult to search for the same image. This paper proposes and evaluates a self-supervised deephashing model that generates perceptual hash codes from feature information such as texture, shape, and color of images. The comparison models are autoencoder-based variational inference models, but the encoder is designed with a fully connected layer, convolutional neural network, and transformer modules. The proposed model is a variational inference model that includes a SimAM module of extracting geometric patterns and positional relationships within images. The SimAM module can learn latent vectors highlighting objects or local regions through an energy function using the activation values of neurons and surrounding neurons. The proposed method is a representation learning model that can generate low-dimensional latent vectors from high-dimensional input images, and the latent vectors are binarized into distinguishable hash code. From the experimental results on public datasets such as CIFAR-10, ImageNet, and NUS-WIDE, the proposed model is superior to the comparative model and analyzed to have equivalent performance to the supervised learning-based deephashing model. The proposed model can be used in application systems that require low-dimensional representation of images, such as image search or copyright image determination.

A Case Study of Software Architecture Design by Applying the Quality Attribute-Driven Design Method (품질속성 기반 설계방법을 적용한 소프트웨어 아키텍처 설계 사례연구)

  • Suh, Yong-Suk;Hong, Seok-Boong;Kim, Hyeon-Soo
    • The KIPS Transactions:PartD
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    • v.14D no.1 s.111
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    • pp.121-130
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    • 2007
  • in a software development, the design or architecture prior to implementing the software is essential for the success. This paper presents a case that we successfully designed a software architecture of radiation monitoring system (RMS) for HANARO research reactor currently operating in KAERI by applying the quality attribute-driven design method which is modified from the attribute-driven design (ADD) introduced by Bass[1]. The quality attribute-driven design method consists of following procedures: eliciting functionality and quality requirements of system as architecture drivers, selecting tactics to satisfy the drivers, determining architectures based on the tactics, and implementing and validating the architectures. The availability, maintainability, and interchangeability were elicited as duality requirements, hot-standby dual servers and weak-coupled modulization were selected as tactics, and client-server structure and object-oriented data processing structure were determined at architectures for the RMS. The architecture was implemented using Adroit which is a commercial off-the-shelf software tool and was validated based on performing the function-oriented testing. We found that the design method in this paper is an efficient method for a project which has constraints such as low budget and short period of development time. The architecture will be reused for the development of other RMS in KAERI. Further works are necessary to quantitatively evaluate the architecture.

A Comparative Study on the Possibility of Land Cover Classification of the Mosaic Images on the Korean Peninsula (한반도 모자이크 영상의 토지피복분류 활용 가능성 탐색을 위한 비교 연구)

  • Moon, Jiyoon;Lee, Kwang Jae
    • Korean Journal of Remote Sensing
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    • v.35 no.6_4
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    • pp.1319-1326
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    • 2019
  • The KARI(Korea Aerospace Research Institute) operates the government satellite information application consultation to cope with ever-increasing demand for satellite images in the public sector, and carries out various support projects including the generation and provision of mosaic images on the Korean Peninsula every year to enhance user convenience and promote the use of satellite images. In particular, the government has wanted to increase the utilization of mosaic images on the Korean Peninsula and seek to classify and update mosaic images so that users can use them in their businesses easily. However, it is necessary to test and verify whether the classification results of the mosaic images can be utilized in the field since the original spectral information is distorted during pan-sharpening and color balancing, and there is a limitation that only R, G, and B bands are provided. Therefore, in this study, the reliability of the classification result of the mosaic image was compared to the result of KOMPSAT-3 image. The study found that the accuracy of the classification result of KOMPSAT-3 image was between 81~86% (overall accuracy is about 85%), while the accuracy of the classification result of mosaic image was between 69~72% (overall accuracy is about 72%). This phenomenon is interpreted not only because of the distortion of the original spectral information through pan-sharpening and mosaic processes, but also because NDVI and NDWI information were extracted from KOMPSAT-3 image rather than from the mosaic image, as only three color bands(R, G, B) were provided. Although it is deemed inadequate to distribute classification results extracted from mosaic images at present, it is believed that it will be necessary to explore ways to minimize the distortion of spectral information when making mosaic images and to develop classification techniques suitable for mosaic images as well as the provision of NIR band information. In addition, it is expected that the utilization of images with limited spectral information could be increased in the future if related research continues, such as the comparative analysis of classification results by geomorphological characteristics and the development of machine learning methods for image classification by objects of interest.

CAS 500-1/2 Image Utilization Technology and System Development: Achievement and Contribution (국토위성정보 활용기술 및 운영시스템 개발: 성과 및 의의)

  • Yoon, Sung-Joo;Son, Jonghwan;Park, Hyeongjun;Seo, Junghoon;Lee, Yoojin;Ban, Seunghwan;Choi, Jae-Seung;Kim, Byung-Guk;Lee, Hyun jik;Lee, Kyu-sung;Kweon, Ki-Eok;Lee, Kye-Dong;Jung, Hyung-sup;Choung, Yun-Jae;Choi, Hyun;Koo, Daesung;Choi, Myungjin;Shin, Yunsoo;Choi, Jaewan;Eo, Yang-Dam;Jeong, Jong-chul;Han, Youkyung;Oh, Jaehong;Rhee, Sooahm;Chang, Eunmi;Kim, Taejung
    • Korean Journal of Remote Sensing
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    • v.36 no.5_2
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    • pp.867-879
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    • 2020
  • As the era of space technology utilization is approaching, the launch of CAS (Compact Advanced Satellite) 500-1/2 satellites is scheduled during 2021 for acquisition of high-resolution images. Accordingly, the increase of image usability and processing efficiency has been emphasized as key design concepts of the CAS 500-1/2 ground station. In this regard, "CAS 500-1/2 Image Acquisition and Utilization Technology Development" project has been carried out to develop core technologies and processing systems for CAS 500-1/2 data collecting, processing, managing and distributing. In this paper, we introduce the results of the above project. We developed an operation system to generate precision images automatically with GCP (Ground Control Point) chip DB (Database) and DEM (Digital Elevation Model) DB over the entire Korean peninsula. We also developed the system to produce ortho-rectified images indexed to 1:5,000 map grids, and hence set a foundation for ARD (Analysis Ready Data)system. In addition, we linked various application software to the operation system and systematically produce mosaic images, DSM (Digital Surface Model)/DTM (Digital Terrain Model), spatial feature thematic map, and change detection thematic map. The major contribution of the developed system and technologies includes that precision images are to be automatically generated using GCP chip DB for the first time in Korea and the various utilization product technologies incorporated into the operation system of a satellite ground station. The developed operation system has been installed on Korea Land Observation Satellite Information Center of the NGII (National Geographic Information Institute). We expect the system to contribute greatly to the center's work and provide a standard for future ground station systems of earth observation satellites.

A Mobile Landmarks Guide : Outdoor Augmented Reality based on LOD and Contextual Device (모바일 랜드마크 가이드 : LOD와 문맥적 장치 기반의 실외 증강현실)

  • Zhao, Bi-Cheng;Rosli, Ahmad Nurzid;Jang, Chol-Hee;Lee, Kee-Sung;Jo, Geun-Sik
    • Journal of Intelligence and Information Systems
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    • v.18 no.1
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    • pp.1-21
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    • 2012
  • In recent years, mobile phone has experienced an extremely fast evolution. It is equipped with high-quality color displays, high resolution cameras, and real-time accelerated 3D graphics. In addition, some other features are includes GPS sensor and Digital Compass, etc. This evolution advent significantly helps the application developers to use the power of smart-phones, to create a rich environment that offers a wide range of services and exciting possibilities. To date mobile AR in outdoor research there are many popular location-based AR services, such Layar and Wikitude. These systems have big limitation the AR contents hardly overlaid on the real target. Another research is context-based AR services using image recognition and tracking. The AR contents are precisely overlaid on the real target. But the real-time performance is restricted by the retrieval time and hardly implement in large scale area. In our work, we exploit to combine advantages of location-based AR with context-based AR. The system can easily find out surrounding landmarks first and then do the recognition and tracking with them. The proposed system mainly consists of two major parts-landmark browsing module and annotation module. In landmark browsing module, user can view an augmented virtual information (information media), such as text, picture and video on their smart-phone viewfinder, when they pointing out their smart-phone to a certain building or landmark. For this, landmark recognition technique is applied in this work. SURF point-based features are used in the matching process due to their robustness. To ensure the image retrieval and matching processes is fast enough for real time tracking, we exploit the contextual device (GPS and digital compass) information. This is necessary to select the nearest and pointed orientation landmarks from the database. The queried image is only matched with this selected data. Therefore, the speed for matching will be significantly increased. Secondly is the annotation module. Instead of viewing only the augmented information media, user can create virtual annotation based on linked data. Having to know a full knowledge about the landmark, are not necessary required. They can simply look for the appropriate topic by searching it with a keyword in linked data. With this, it helps the system to find out target URI in order to generate correct AR contents. On the other hand, in order to recognize target landmarks, images of selected building or landmark are captured from different angle and distance. This procedure looks like a similar processing of building a connection between the real building and the virtual information existed in the Linked Open Data. In our experiments, search range in the database is reduced by clustering images into groups according to their coordinates. A Grid-base clustering method and user location information are used to restrict the retrieval range. Comparing the existed research using cluster and GPS information the retrieval time is around 70~80ms. Experiment results show our approach the retrieval time reduces to around 18~20ms in average. Therefore the totally processing time is reduced from 490~540ms to 438~480ms. The performance improvement will be more obvious when the database growing. It demonstrates the proposed system is efficient and robust in many cases.