• Journal of Electrical Engineering and Technology
• /
• 제13권6호
• /
• pp.2530-2544
• /
• 2018

In this paper, we propose an enhanced LiDAR-camera calibration method that extracts the marker plane from 3D point cloud information. In previous work, we estimated the straight line of each board to obtain the vertex. However, the errors in the point information in relation to the z axis were not considered. These errors are caused by the effects of user selection on the board border. Because of the nature of LiDAR, the point information is separated in the horizontal direction, causing the approximated model of the straight line to be erroneous. In the proposed work, we obtain each vertex by estimating a rectangle from a plane rather than obtaining a point from each straight line in order to obtain a vertex more precisely than the previous study. The advantage of using planes is that it is easier to select the area, and the most point information on the board is available. We demonstrated through experiments that the proposed method could be used to obtain more accurate results compared to the performance of the previous method.

• 대기
• /
• 제28권2호
• /
• pp.163-174
• /
• 2018

In order to assimilate MHS satellite data into the convective scale model at KMA, ATOVS data are reprocessed to utilize the original high-resolution data. And then to improve the preprocessing experiments for cloud detection were performed and optimized to convective-scale model. The experiment which is land scattering index technique added to Observational Processing System to remove contaminated data showed the best result. The analysis fields with assimilation of MHS are verified against with ECMWF analysis fields and fit to other observations including Sonde, which shows improved results on relative humidity fields at sensitive level (850-300 hPa). As the relative humidity of upper troposphere increases, the bias and RMSE of geopotential height are decreased. This improved initial field has a very positive effect on the forecast performance of the model. According to improvement of model field, the Equitable Threat Score (ETS) of precipitation prediction of $1{\sim}20mm\;hr^{-1}$ was increased and this impact was maintained for 27 hours during experiment periods.

• 한국측량학회지
• /
• 제37권2호
• /
• pp.45-53
• /
• 2019

RGB-D SLAM (Simultaneous Localization and Mapping) refers to the technology of using deep camera as a visual sensor for SLAM. In view of the disadvantages of high cost and indefinite scale in the construction of maps for laser sensors and traditional single and binocular cameras, a method for creating three-dimensional map of indoor environment with deep environment data combined with RGB-D SLAM scheme is studied. The method uses a mobile robot system equipped with a consumer-grade RGB-D sensor (Kinect) to acquire depth data, and then creates indoor three-dimensional point cloud maps in real time through key technologies such as positioning point generation, closed-loop detection, and map construction. The actual field experiment results show that the average error of the point cloud map created by the algorithm is 0.0045m, which ensures the stability of the construction using deep data and can accurately create real-time three-dimensional maps of indoor unknown environment.

• 전기전자학회논문지
• /
• 제27권4호
• /
• pp.439-449
• /
• 2023

In an era marked by the increasing use of drones and the growing demand for indoor surveillance, the development of a robust application for detecting and tracking both drones and humans within indoor spaces becomes imperative. This study presents an innovative application that uses FMCW radar to detect human and drone motions from the cloud point. At the outset, the DBSCAN (Density-based Spatial Clustering of Applications with Noise) algorithm is utilized to categorize cloud points into distinct groups, each representing the objects present in the tracking area. Notably, this algorithm demonstrates remarkable efficiency, particularly in clustering drone point clouds, achieving an impressive accuracy of up to 92.8%. Subsequently, the clusters are discerned and classified into either humans or drones by employing a deep learning model. A trio of models, including Deep Neural Network (DNN), Residual Network (ResNet), and Long Short-Term Memory (LSTM), are applied, and the outcomes reveal that the ResNet model achieves the highest accuracy. It attains an impressive 98.62% accuracy for identifying drone clusters and a noteworthy 96.75% accuracy for human clusters.

• 디지털콘텐츠학회 논문지
• /
• 제19권4호
• /
• pp.837-844
• /
• 2018

최근 클라우드 기술은 물리적인 네트워크를 구축하지 않고, 동적으로 논리적인 네트워크를 구축할 수 있게 만들 수 있다는 특징으로 인해 각광받고 있다. 최근의 클라우드 분야의 연구에도 불구하고, 가입자가 공개적으로 VNF를 이용한 서비스를 제공 받을 수 있는 NFV 환경의 특성으로 공격의 타겟이 될 수 있기 때문에 가짜 VNF에 대한 식별과 개체 간의 통신 암호화에 대한 연구가 필요하다. 따라서 본 논문에서는 가짜 VNF를 탐지하고, VNF 간의 상호 인증을 통해서 통신 구간의 보안성을 향상시킬 수 있는 Virtual PKI를 이용한 보안 메커니즘을 제안한다. 그리고 NFV 환경에서 DDoS 공격에 대한 공격의 탐지율을 향상시키기 위한 다수의 인공지능 알고리즘을 비교 분석함으로써 공격탐지에 효과적인 인공지능 알고리즘을 도출하였다.

• 정보보호학회논문지
• /
• 제28권2호
• /
• pp.429-436
• /
• 2018

전자문서는 생성 및 관리가 효율적이나 유통 및 전달 과정에서 사본이 생성되기 때문에 원본성을 상실하기 쉽다. 이러한 이유로 전자문서에 대한 다양한 보안 기술이 적용되었으나, 현재 사용되고 있는 보안 기술은 대부분 파일 접근 권한 제어, 파일 버전 및 이력 관리 등과 같은 문서 관리에 대한 것이므로 기밀문서와 같이 원본성 확보가 절대적으로 요구되어지는 환경에서는 사용이 불가능하다. 따라서 본 논문에서는 클라우드 컴퓨팅 환경에서 인스턴스 운영체제 내부에 별도의 에이전트 설치 없이 파일시스템 분석을 통하여 문서 위/변조를 탐지하는 기법을 제안한다. BubbleDoc은 인스턴스의 가상 볼륨 스토리지의 최소 영역을 모니터링하기 때문에 문서에 대한 위/변조를 효율적으로 탐지할 수 있다. 실험 결과에 따르면 본 논문에서 제안한 기술은 문서 위/변조 탐지를 위한 모니터링 수행에 있어서 1,000ms 주기로 설정하였을 때 0.16%의 디스크 읽기 연산 오버헤드를 보였다.

• 융합보안논문지
• /
• 제19권5호
• /
• pp.47-53
• /
• 2019

네트워크를 통한 정보의 공유는 오늘날 클라우드 서비스 환경으로 발전하여 그 이용자수를 빠르게 증가시키고 있지만 네트워크를 기반으로 하는 불법적인 공격자들의 주요 표적이 되고 있다. 아울러 공격자들의 다양한 공격 기법 중 IP 스푸핑은 그 공격 특성상 일반적으로 자원고갈 공격을 수반하기 때문에 이에 대한 빠른 탐지와 대응 기법이 요구 된다. IP 스푸핑 공격에 대한 기존의 탐지 방식은 연결 요청을 시도한 클라이언트의 트레이스 백 정보 분석과 그 일치 여부에 따라 최종적인 인증과정을 수행 한다. 그렇지만 트레이스 백 정보의 단순 비교 방식은 서비스 투명성을 요구하는 환경에서 빈번한 False Positive로 인하여 과도한 OTP 발생을 요구할 수 있다. 본 논문에서는 이러한 문제를 개선하기 위해 트레이스 백 정보 기반의 대칭키 암호화 기법을 적용하여 상호 인증 정보로 사용하고 있다. 즉, 트레이스 백 기반의 암호화 키를 생성한 후 정상적인 복호화 과정의 수행 여부로 상호 인증이 가능하도록 하였다. 아울러 이러한 과정을 통하여 False Positive에 의한 오버헤드도 개선할 수 있었다.

• Journal of Genetic Medicine
• /
• 제12권2호
• /
• pp.79-84
• /
• 2015

Purpose: We developed and validated a fetal trisomy detection method for use as a noninvasive prenatal test (NIPT) including a Clinical Laboratory Improvement Amendments (CLIA)-certified bioinformatics pipeline on a cloud-based computing system using both Illumina and Life Technology sequencing platforms for 221 Korean clinical samples. We determined the necessary proportions of the fetal fraction in the cell-free DNA (cfDNA) sample for NIPT of trisomies 13, 18, and 21 through a limit of quantification (LOQ) test. Materials and Methods: Next-generation sequencing libraries from 221 clinical samples and three positive controls were generated using Illumina and Life Technology chemistries. Sequencing results were uploaded to a cloud and mapped on the human reference genome (GRCh37/hg19) using bioinformatics tools. Based on Z-scores calculated by normalization of the mapped read counts, final aneuploidy reports were automatically generated for fetal aneuploidy determination. Results: We identified in total 29 aneuploid samples, and additional analytical methods performed to confirm the results showed that one of these was a false-positive. The LOQ test showed that the proportion of fetal fraction in the cfDNA sample would affect the interpretation of the aneuploidy results. Conclusion: Noninvasive chromosome examination (NICE), a CLIA-certified NIPT with a cloud-based bioinformatics platform, showed unambiguous success in fetus aneuploidy detection.

• 대기
• /
• 제22권2호
• /
• pp.163-174
• /
• 2012

Brightness temperature (BT) difference between sea fog and sea surface is small, because the top height of fog is low. Therefore, it is very difficult to detect sea fog with infrared (IR) channels in the nighttime. To overcome this difficulty, we have developed a new algorithm for detection of sea fog that consists in three tests. Firstly, both stratus and sea fog were discriminated from the other clouds by using the difference between BTs $3.7{\mu}m$ and $11{\mu}m$. Secondly, stratus occurring at a level higher than sea fog was removed when the difference between cloud top temperature and sea surface temperature (SST) is smaller than 3 K. In this process, we used daily SST data from AMSR-E microwave measurements that is available even in the presence of cloud. Then, the SST was converted to $11{\mu}m$ BT based on the regressed relationship between AMSR-E SST and MTSAT-1R $11{\mu}m$ BT at 1733 UTC over clear sky regions. Finally, stratus was further removed by using the homogeneity test based on the difference in cloud top texture between sea fog and stratus. Comparison between the retrievals from our algorithm and that from Korea Meteorological Administration (KMA) algorithm, shows that the KMA algorithm often misconceived sea fog as stratus, resulting in underestimating the occurrence of sea fog. Monthly distribution of sea fog over northeast Asia in 2008 was derived from the proposed algorithm. The frequency of sea fog is lowest in winter, and highest in summer especially in June. The seasonality of the sea fog occurrence between East and West Sea was comparable, while it is not clearly identified over South Sea. These results would serve to prevent the possible occurrence of marine accidents associated with sea fog.

• 대한원격탐사학회:학술대회논문집
• /
• 대한원격탐사학회 1998년도 Proceedings of International Symposium on Remote Sensing
• /
• pp.236-241
• /
• 1998

The NOAA AVHRR remote sense SST data, collected by the National Fisheries Research and Development Institute (NFRDI), are analyzed in order to understand the spatial and temporal distributions of SST in the seas adjacent to Korea. Our study is based on 10-day SST images during last 7 years (1991-1997). For a time series analysis of multiple 557 images, all of images must be aligned exactly at the same position by adjusting the scales and positions of each SST image. We devised an algorithm which yields automatic detections of cloud pixels from multiple SST images. The cloud detection algorithm is based on a physical constraint that SST anomalies in the ocean do not exceed certain limits (we used $\pm$ 3$^{\circ}C$ as a criterion of SST anomalies). The remote sense SST data are tuned by comparing remote sense data with observed SST at coastal stations. Seasonal variations of SST are studied by harmonic fit of SST normals at each pixel. The SST anomalies are studied by statistical method. We found that the SST anomalies are rather persistent with time scales between 1 and 2 months. Utilizing the persistency of SST anomalies, we devised an algorithm for a prediction of future SST Model fit of SST anomalies to the Markov process model yields that autoregression coefficients of SST anomalies during a time elapse of 10 days are between 0.5 and 0.7. We plan to improve our algorithms of automatic cloud pixel detection and prediction of future SST. Our algorithm is expected to be incorporated to the operational real time service of SST around Korea.