• Proceedings of the Korea Multimedia Society Conference
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• 2003.11a
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• pp.32-35
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• 2003

최근 국내외적으로 침해 공격 사고율이 증가에 대한 방안으로 여러 보안 기술이 개발되어 왔다. 그 중 방화벽은 내부의 중요한 자원과 외부 네트워크와의 경계를 생성하고, 정책기반의 접근제어를 효과적으로 제공하고 있지만 DoS공격, 변형 프로토콜을 통한 공격에는 효과적으로 막지 못한다. 또한 침입탐지 시스템은 공격, 침입, 원하지 않는 트래픽을 구별할 수 있다는 점에서 가치가 있지만 정확한 시점에 공격을 차단하지 못하며 침입탐지 이후에 생기는 불법행동에 대한 커다란 위협이 따르며, 실질적인 방어는 관리자의 수동적인 개입을 필요로 하게 된다. 본 논문에서는 이에 대한 해결 방안으로 방화벽의 침입차단 기능과 침입탐지 시스템의 실시간 침입탐지 기능을 갖춘 리눅스 기반의 공개 보안 툴을 결합한 침입방지 시스템을 설계 및 구현한다.

• Proceedings of the Korean Society of Computer Information Conference
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• 2021.01a
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• pp.207-208
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• 2021

코로나 19에 따라 온라인 교육이나 재택근무 등 비대면 서비스에 대한 관심과 이용이 높아지면서, 비대면 서비스의 보안취약점으로 개인정보 유출과 해킹 등의 피해 우려도 제기되고 있는 상황이다. 본 논문에서는 오픈소스 IDS인 SNORT를 이용하여 침입탐지가 발생했을 경우 미리 설정해 놓은 priority에 따라 이메일 또는 문자메시지로 관리자에게 실시간 알림을 보내기 위한 방법을 제안한다. 제안한 시스템은 여러 개의 구성 요소로 이루어져 있다. Snort는 이벤트를 모니터하고 경계시키며(alert), 시스템 규칙을 사용하여 수신된 보안 이벤트로부터 경고를 생성한다. 경고를 파일에 LOG 형식으로 쌓게 되고 쉘 스크립트를 이용해 침입탐지를 분석하여 관리자에게 메일이나 SMS형태로 전송하게 된다.

• Proceedings of the Korean Information Science Society Conference
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• 2002.10d
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• pp.421-423
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• 2002

본 논문에서는 실시간에 강체 운동을 하는 일반적인 모델사이의 효율적인 충돌검사 알고리즘을 제안한다. 기존의 경계볼륨 알고리즘에 계층적 구조를 적용하였다. 이는 볼록한 물체를 위한 보로노이 영역 기반의 충돌검사 알고리즘을 오목한 물체에도 적용할 수 있도록 확장한다. 추가적으로 빠르게 움직이는 물체에 대한 관통을 탐지하기 위해서 물체의 이동 경로에 대한 교차 검사를 진행한다. 구현된 알고리즘은 일반적인 응용에서 기대한 성능 향상을 얻을 수 있다.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2004.05a
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• pp.96-96
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• 2004

고성능 섬유강화 복합재는 비강성과 비강도가 높고 내부식성과 피로특성이 우수하지만 외부에서 가해지는 하중에 의해 수지, 강화섬유와 수지와의 경계면, 적층 경계면 등에 육안으로 식별하기 어려운 손상이 유발될 가능성이 있으며 이로 인해 구조재로서의 역할을 하지 못하는 경우가 발생한다. 최근에는 외부하중으로 인해 복합재 구조재에 손상이 발생한 경우 자가치료제가 저장된 마이크로캡슐을 이용하여 손상을 보수하려는 시도가 행해지고 있다.(중략)

• Korean Journal of Remote Sensing
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• v.37 no.5_1
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• pp.1207-1214
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• 2021

In thisletter, we developed technology which can exclude effect of cloudsto perform remote waterbody detection based on Sentinel-2 optical satellite imagery to calculate the area of ungauged reservoirs and applied to the Hwanggang dam reservoir, a representative ungauged reservoir, to verify usability. The remote waterbody detection technology calculates the cloud blocking ratio by comparing the cloud boundary in the Sentinel-2 imagery and the reservoir boundary first. Next, itselects data whose cloud blocking ratio does not exceed a specific value and calculates NDWI (Normalized Difference Water Index) with selected imagery. In last, it calculatesthe area of the reservoir by counting the number of grids which have NDWI value considered as waterbody within the boundary of the target reservoir and correcting with cloud blocking ratio. To determine cloud blocking ratio threshold forselecting image, we performed the area calculation of Hwanggang dam reservoir from July 2018 to October 2021. As a result, when the cloud blocking ratio threshold wasset 10%, we confirmed that the result with large error due to clouds were filtered well and obtained 114 results that can show changes in Hwanggang dam reservoir area among 220 images.

• Korean Journal of Remote Sensing
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• v.33 no.5_1
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• pp.599-605
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• 2017

Accurate cloud discrimination in satellite images strongly affects accuracy of remotely sensed parameter produced using it. Especially, cloud contaminated pixel over ocean is one of the major error factors such as Sea Surface Temperature (SST), ocean color, and chlorophyll-a retrievals,so accurate cloud detection is essential process and it can lead to understand ocean circulation. However, static threshold method using real-time algorithm such as Moderate Resolution Imaging Spectroradiometer (MODIS), Advanced Himawari Imager (AHI) can't fully explained reflectance variability over ocean as a function of relative positions between the sun - sea surface - satellite. In this paper, we assembled a reflectance spectral library as a function of Solar Zenith Angle (SZA) and Viewing Zenith Angle (VZA) from ocean surface reflectance with clear sky condition of Advanced Himawari Imager (AHI) identified by NOAA's cloud products and spectral library is used for applying the Dynamic Time Warping (DTW) to detect cloud pixels. We compared qualitatively between AHI cloud property and our results and it showed that AHI cloud property had general tendency toward overestimation and wrongly detected clear as unknown at high SZA. We validated by visual inspection with coincident imagery and it is generally appropriate.

• Korean Journal of Remote Sensing
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• v.34 no.6_3
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• pp.1427-1443
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• 2018

Object-based image analysis (OBIA) allows higher computation efficiency and usability of information inherent in the image, as it reduces the complexity of the image while maintaining the image properties. Superpixel methods oversegment the image with a smaller image unit than an ordinary object segment and well preserve the edges of the image. SLIC (Simple linear iterative clustering) is known for outperforming the previous superpixel methods with high image segmentation quality. Although the input parameter for SLIC, number of superpixels has considerable influence on image segmentation results, impact analysis for SLIC parameter has not been investigated enough. In this study, we performed optimal parameter analysis and evaluation of change detection for SLIC-based superpixel techniques using KOMPSAT data. Forsuperpixel generation, three superpixel methods (SLIC; SLIC0, zero parameter version of SLIC; SNIC, simple non-iterative clustering) were used with superpixel sizes in ranges of $5{\times}5$ (pixels) to $50{\times}50$ (pixels). Then, the image segmentation results were analyzed for how well they preserve the edges of the change detection reference data. Based on the optimal parameter analysis, image segmentation boundaries were obtained from difference image of the bi-temporal images. Then, DBSCAN (Density-based spatial clustering of applications with noise) was applied to cluster the superpixels to a certain size of objects for change detection. The changes of features were detected for each superpixel and compared with reference data for evaluation. From the change detection results, it proved that better change detection can be achieved even with bigger superpixel size if the superpixels were generated with high regularity of size and shape.

• Journal of Korean Society for Geospatial Information Science
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• v.20 no.4
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• pp.117-125
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• 2012

Although many studies have been conducted to extract buildings from airborne lidar data, most of them assume that all the boundaries of a building are straight line segments. This makes it difficult to model building boundaries containing curved segments correctly. This paper aims to model buildings containing curved segments as combination of straight lines and arcs. First, two sets of boundary points are extracted by adaptive convex hull algorithm and local convex hull algorithm with a larger radius. Then, arc segments are determined by average spacing of boundary points and intersection ratio of perpendicular lines. Finally, building boundary is modeled through regularization of least squares line or circle fitting. The experimental results showed that the proposed method can model the curved building boundaries as arc segments correctly by completeness of 69% and correctness of 100%. The approach will be utilized effectively to create automatically digital map that meets the conditions of the Korean digital mapping.

• Land and Housing Review
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• v.14 no.4
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• pp.95-102
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• 2023

Numerous investigations have explored the integration of machine leaning algorithms with high-resolution drone image for object detection in urban settings. However, a prevalent limitation in vehicle extraction studies involves the reliance on bounding boxes rather than instance segmentation. This limitation hinders the precise determination of vehicle direction and exact boundaries. Instance segmentation, while providing detailed object boundaries, necessitates labour intensive labelling for individual objects, prompting the need for research on automating unsupervised instance segmentation in vehicle extraction. In this study, a novel approach was proposed for vehicle extraction utilizing unsupervised SVM classification applied to vehicle bounding boxes in drone images. The method aims to address the challenges associated with bounding box-based approaches and provide a more accurate representation of vehicle boundaries. The study showed promising results, demonstrating an 89% accuracy in vehicle extraction. Notably, the proposed technique proved effective even when dealing with significant variations in spectral characteristics within the vehicles. This research contributes to advancing the field by offering a viable solution for automatic and unsupervised instance segmentation in the context of vehicle extraction from image.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.39C no.1
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• pp.60-69
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• 2014

Footstep detection using seismic sensors for security is a very meaningful task, but readings can easily fluctuate due to noise in outdoor environment. We propose NSSC method based on nonlinear spectral subtraction and cross-correlation using prime footstep model signal as a footstep signal refining process that enhances the signal-to-noise ratio (SNR) and attenuates noise. After de-noising, a detection event classification method is presented as further refining process to ensure that the detection result is a footstep. To validate the proposed algorithm, representative experiments including sunny and rainy-day cases are demonstrated.