• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.10
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• pp.1397-1402
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• 2021

The overlay2 file system is one of the union file systems that mounts multiple directories into one. The source directory used for this overlay2 file system mount has a characteristic that it operates independently of the write-able layer after mounting, so it is often used for container platforms for application delivery. However, the overlay2 file system has a security vulnerability that the write-able layer is also modified when file in the source directory is modified. In this study, I proposed the overlay2 file system protection technology to remove the security vulnerabilities of the overlay2 file system. As a result of empirically implementing the proposed overlay2 file system protection technology and verifying the function, the protection technology proposed in this study was verified to be effective. However, since the method proposed in this study is a passive protection method, a follow-up study is needed to automatically protect it at the operating system level.

• Journal of Platform Technology
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• v.9 no.1
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• pp.46-57
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• 2021

Recently, research has been actively conducted to utilize blockchain technology in various fields. In particular, blockchain-based smart contracts are applied to various automation systems that require reliability as they have the characteristics of recording data in a distributed ledger environment to verify the integrity and validity of data. However, blockchain does not provide data access control and information security because data is shared among network participants. In this paper, we propose a user dynamic access control mechanism utilizing smart contracts in blockchain environments. The proposed mechanism identifies the user's contextual information when accessing data, allocating the user's role and dynamically controlling the data access range. This can increase the security of the system and the efficiency of data management by granting data access dynamically at the time of user authentication, rather than providing the same services in roles assigned to each user group of the network system. The proposed mechanism is expected to provide flexible authentication capabilities through dynamic data access control by users to enhance the security of data stored within blockchain networks.

• Proceedings of the Korea Water Resources Association Conference
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• 2016.05a
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• pp.593-593
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• 2016

최근 기후변화에 따른 국내 기상특성이 변화함에 따라 가뭄, 하천건천화, 홍수 등 물 관련 재해 발생 빈도 및 규모가 점점 커지고 있으며, 세계적으로 홍수 발생 빈도뿐만 아니라 태풍 및 가뭄 발생 빈도도 꾸준히 증가하고 있어 광범위한 관측과 정확한 예측 및 즉각적 대처능력 확보를 위한 수재해 관리가 필요한 실정이다. 아울러 급격한 도시화에 따라 내수범람이 빈번하게 발생하기 때문에 재난발생 시 그 피해가 극대화로 직결되고 있어 도시에서 발생할 수 있는 내수범람을 정확하게 예 경보하기 위한 고해상도 실시간 강수관측이 필요하다. 또한, 유역차원의 홍수범람이 빈번하게 발생하고 있으며, 홍수기 홍수통제소의 댐수문 관리에 어려움이 따라 기상관련 재난이 발생할 수 있어, 유역 차원의 정확한 홍수량 예측과 예 경보 시스템 구축을 위해서는 고해상도의 실시간 강수관측을 통한 강수예측 기술이 중요하다. 이를 위해 위성, 레이더, AWS 등 각종 광역 관측 장비로부터 표출되는 데이터를 통합하고, 이를 종합적으로 분석하여 수리수문인자 및 기상인자로 전환할 수 있는 시스템을 개발 할 경우 가뭄, 하천 건천화, 홍수 등을 실시간으로 감시하고 평가 예측 할 수 있는 정보 생산이 가능할 것으로 판단된다. 이에 본 연구에서는 수재해 정보 플랫폼 서비스를 위하여 전체적인 시스템을 개발하기에 앞서 가능성을 타진하고 검증할 필요가 있는 주요 구간을 시험하는 개발 기법인 프로토타입을 우선적으로 개발한다. 주요 항목으로는 (1) 지속 산출 격자 자료에 대한 OGC WxS 또는 LOD 서비스 자동 연계, (2) 격자 자료 ?춤형 제공(해상도, 좌표계 등), (3) 기초, 분석 정보 제공 시스템 등을 프로토타입 대상으로 설정하고 웹 기반 수재해 정보 플랫폼 인터페이스를 구현한다. 개발된 플랫폼은 수재해 예측정보의 정확도를 향상시키고 국지적 침수재해 평가 예측 및 지역 맞춤형 재해평가 체계를 실현함으로써 국가 물 관련 재해를 혁신할 수 있는 기술을 확보하는 소중한 토대가 될 것으로 사료된다.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.20 no.4
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• pp.117-134
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• 2010

A passive RFID tag which received attention as a future technology for automatic and quick identification faces some difficulties about security problems such as tag authentication, reader authentication, data protection, and untraceability in addition to cost and reliable identification. A representative passive RFID technology is the ISO/IEC 18000-6 Type C which is an international standard for 900 MHz UHF-band. This standard has some difficulties in applying to the security services such as originality verification, tag's internal information protection, and untraceability, because it does not provide high-level security solution. In this paper, we summarize security requirements of ISO/IEC ITC 1/SC 31 international standardization group, propose security protocols suitable for the UHF-band passive RFID system using a crypto engine, and analyze its security strength. In addition, we verify that it is possible to implement a tag conforming with the proposed security protocols by presenting concrete command/response pairs and cryptographic method.

• Journal of IKEEE
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• v.25 no.1
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• pp.193-200
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• 2021

In order to prevent illegal fishing and reduce lost fishing gear, it is necessary to develop a constant and continuous fishing gear monitoring system in the marine environment. In this paper, we design a long-term operational, reliable system model with communication coverage of more than 25Km considering the reality of gradually expanding fishing activity due to the depletion of fishery resources and marine environments. The design results are implemented to verify the operability of the system by separating the communication success rate of SKT and private LoRa networks and verifying the control function of each control system through the collected location information, respectively.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.38 no.6
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• pp.611-622
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• 2020

As the advancement of technologies on indoor positioning systems and measuring devices such as LiDAR (Light Detection And Ranging) and cameras, the demands on analyzing and searching indoor spaces and visualization services via virtual and augmented reality have rapidly increasing. To this end, it is necessary to model 3D objects from measured data from real-world structures. In addition, it is important to store these structured data in standardized formats to improve the applicability and interoperability. In this paper, we propose a method to construct IndoorGML data, which is an international standard for indoor modeling, from point cloud data acquired from LiDAR sensors. After examining considerations that should be addressed in IndoorGML data, we present a construction method, which consists of free space extraction and connectivity detection processes. With experimental results, we demonstrate that the proposed method can effectively reconstruct the 3D model from point cloud.

• Smart Media Journal
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• v.10 no.1
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• pp.32-38
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• 2021

Image classification through deep learning on the image from photographs has been a very active research field for the past several years. In this paper, we propose a method of automatically discriminating stone images from domestic source through deep learning, which is to use Python's hash library to scan 300×300 pixel photo images of granites such as Hwangdeungseok, Goheungseok, and Pocheonseok, performing data preprocessing to create learning images by examining duplicate images for each stone, removing duplicate images with the same hash value as a result of the inspection, and deep learning by stone. In addition, to utilize VGGNet, the size of the images for each stone is resized to 224×224 pixels, learned in VGG16 where the ratio of training and verification data for learning is 80% versus 20%. After training of deep learning, the loss function graph and the accuracy graph were generated, and the prediction results of the deep learning model were output for the three kinds of stone images.

• Journal of the Institute of Convergence Signal Processing
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• v.22 no.3
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• pp.104-109
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• 2021

In this paper, we experimentally demonstrate a self-seeding FP-LD (Fabry Perot Laser Diode) to verify the possibility of a new tunable light source that can be used in WDM-PON (Wavelength Division Multiplexing - Passive Optical Network) system. The conventional implementation of WDM-PON using a tunable light source has a disadvantage that the center wavelength of the AWG (Arrayed Waveguide Grating) device and the tunable light source must be precisely aligned. However, the proposed tunable light source has the advantage that the tunable wavelength is automatically aligned with the center wavelength of the AWG as well as simple structure. The implemented tunable light source had a tunable band of about 14 nm or more, and the maximum RIN (Relative Intensity Noise) of about -124 dB/Hz, which showed the possibility of modulating 10 Gb/s signal by an external modulator.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.22 no.5
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• pp.75-86
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• 2022

This study aims to solve the problem of class imbalance in numerical data by using a deep learning-based Variational AutoEncoder and to improve the performance of the learning model by augmenting the learning data. We propose 'D-VAE' to artificially increase the number of records for a given table data. The main features of the proposed technique go through discretization and feature selection in the preprocessing process to optimize the data. In the discretization process, K-means are applied and grouped, and then converted into one-hot vectors by one-hot encoding technique. Subsequently, for memory efficiency, sample data are generated with Variational AutoEncoder using only features that help predict with RFECV among feature selection techniques. To verify the performance of the proposed model, we demonstrate its validity by conducting experiments by data augmentation ratio.

• Journal of Biomedical Engineering Research
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• v.44 no.1
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• pp.11-18
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• 2023

Currently, studies to predict the risk of rectal cancer surgery select MRI image slices based on the clinical experience of surgeons. The purpose of this study is to semi-automatically select and classify 2D MRI image slides to predict the risk of rectal cancer surgery using biomarkers. The data used were retrospectively collected MRI imaging data of 50 patients who underwent laparoscopic surgery for rectal cancer at Gachon University Gil Medical Center. Expert-selected MRI image slices and non-selected slices were screened and radiomics was used to extract a total of 102 features. A total of 16 approaches were used, combining 4 classifiers and 4 feature selection methods. The combination of Random Forest and Ridge performed with a sensitivity of 0.83, a specificity of 0.88, an accuracy of 0.85, and an AUC of 0.89±0.09. Differences between expert-selected MRI image slices and non-selected slices were analyzed by extracting the top five significant features. Selected quantitative features help expedite decision making and improve efficiency in studies to predict risk of rectal cancer surgery.