• 정보과학회 컴퓨팅의 실제 논문지
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• 제22권1호
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• pp.8-19
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• 2016

도로의 결빙과 해빙으로 도로면의 수축과 팽창이 반복되어 도로면에서 침투한 수분이 포장면의 결합력을 약화시켜 노면홈(포트홀)을 발생시킨다. 현재의 포트홀 조사는 현장에서 육안 조사하고 기록하는 수동적인 방식으로 매년 수 만개소의 포트홀이 발생하는 것에 어려움이 발생하고 있다. 포트홀 정보를 자동으로 수집하기 위해 최근까지 가속도 센서를 이용한 기술과 레이저 스캐닝을 이용한 기술이 많이 연구되었다. 하지만, 가속도 센서 기반 기술은 낮은 인식률과 제한된 센싱 영역의 문제가 있고, 레이저 스캐닝 기반 기술은 비용이 너무 큰 문제가 있다. 따라서, 본 논문에서는 대중적으로 사용하는 차량용 블랙박스 카메라를 이용한 자동 포트홀 탐지 기술을 제안한다. 일반적으로 차량용 블랙박스 카메라에 탑재한 연산프로세서는 낮은 컴퓨팅 능력을 가지므로 포트홀 탐지 알고리즘을 그게 맞게 설계할 필요가 있다. 설계된 알고리즘을 블랙박스에 내장하여 도로 주행실험을 실시하며, 포트홀 탐지 성능을 중심으로 한 실험결과는 포트홀 탐지 정밀도, 민감도 등의 지표를 토대로 분석하고, 실시간 포토홀 탐지 기술의 현장 적용성을 확인한다.

• International journal of advanced smart convergence
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• 제7권1호
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• pp.24-32
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• 2018

An outlier detection method using mixed prediction model has been described in this paper. The mixed prediction model consists of time-series model and regression model. The parameter estimation of the prediction model was performed using supervised learning and a genetic algorithm is adopted for a learning method. The experiments were performed in artificial and real data set. The prediction performance is compared with the existing prediction methods using artificial data. Outlier detection is conducted using the real sensor measurements in a dam. The validity of the proposed method was shown in the experiments.

• Journal of Communications and Networks
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• 제12권4호
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• pp.375-381
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• 2010

A flooding attack, such as DoS or Worm, can be easily created or even downloaded from the Internet, thus, it is one of the main threats to servers on the Internet. This paper presents an online real-time network response system, which can determine whether a LAN is suffering from a flooding attack within a very short time unit. The detection engine of the system is based on the incremental mining of fuzzy association rules from network packets, in which membership functions of fuzzy variables are optimized by a genetic algorithm. The incremental mining approach makes the system suitable for detecting, and thus, responding to an attack in real-time. This system is evaluated by 47 flooding attacks, only one of which is missed, with no false positives occurring. The proposed online system belongs to anomaly detection, not misuse detection. Moreover, a mechanism for dynamic firewall updating is embedded in the proposed system for the function of eliminating suspicious connections when necessary.

• 정보보호학회지
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• 제5권2호
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• pp.32-48
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• 1995

Our paper describes an Intrusion Detection Parallel System(IDPS) which detects an anomaly activity corresponding to the actions that interaction between near detection events. IDES uses parallel inductive approaches regarding the problem of real-time anomaly behavior detection on rule-based system. This approach uses sequential rule that describes user's behavior and characteristics dependent on time. and that audits user's activities by using rule base as data base to store user's behavior pattern. When user's activity deviates significantly from expected behavior described in rule base. anomaly behaviors are recorded. Observed behavior is flagged as a potential intrusion if it deviates significantly from the expected behavior or if it triggers a rule in the parallel inductive system.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1993년도 하계학술대회 논문집 A
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• pp.281-283
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• 1993

A new method of EEG spindle waveform detection i s presented. The method combines the signal conditioning in the time-domin and the analysis of local spectrum in the frequency-domain. Fast computation methods, utilizing some effective approximations, are also suggested for the desist and implementation of the filter as well as for the computation of the local spectrum. The presented approach is especially useful for the real-time implementation of the waveform detection system under a general purpose microcomputer environment. The overall detection system is implemented and tested on-line with the total 24 hour data of selected four subjects. The result show the average agreement of 86.7% with the visually inspected result.

• 한국컴퓨터정보학회논문지
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• 제24권3호
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• pp.87-93
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• 2019

In this paper, we propose a real-time drone-based violent protest detection system. Our proposed system uses drones to detect scenes of violent protest in real-time. The important problem is that the victims and violent actions have to be manually searched in videos when the evidence has been collected. Firstly, we focused to solve the limitations of existing collecting evidence devices by using drone to collect evidence live and upload in AWS(Amazon Web Service)[1]. Secondly, we built a Deep Learning based violence detection model from the videos using Yolov3 Feature Pyramid Network for human activity recognition, in order to detect three types of violent action. The built model classifies people with possession of gun, swinging pipe, and violent activity with the accuracy of 92, 91 and 80.5% respectively. This system is expected to significantly save time and human resource of the existing collecting evidence.

• 전기전자학회논문지
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• 제25권4호
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• pp.619-624
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• 2021

Vehicle orientation detection is a challenging task because the orientations of vehicles can vary in a wide range in captured images. The existing methods for oriented vehicle detection require too much computation time to be applied to a real-time system. We propose Rotate YOLO, which has a set of anchor boxes with multiple scales, ratios, and angles to predict bounding boxes. For estimating the orientation angle, we applied angle-related IoU with CIoU loss to solve the underivable problem from the calculation of SkewIoU. Evaluation results on three public datasets DLR Munich, VEDAI and UCAS-AOD demonstrate the efficiency of our approach.

• 한국동물위생학회지
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• 제34권4호
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• pp.321-331
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• 2011

Mycobacterium bovis, a member of the M. tuberculosis complex (MTC), is the causative agent of bovine tuberculosis. Detection of M. bovis and M. tuberculosis using conventional culture- and biochemical-based assays is time-consuming. Therefore, a simple and sensitive molecular assay for rapid detection would be of great help in specific situations such as faster diagnosis of bovine tuberculosis (bTB) infection in the abattoirs. We developed a novel multiplex real-time PCR assay which was applied directly to biological samples with evidence of bTB and it was allowed to differentiate between M. bovis and M. tuberculosis. The primers and TaqMan probes were designed to target the IS1081 gene, the multi-copy insertion element in the MTC and the 12.7-kb fragment which presents in M. tuberculosis, not in the M. bovis genome. The assay was optimized and validated by testing 10 species of mycobacteria including M. bovis and M. tuberculosis, and 10 other bacterial species such as Escherichia coli, and cattle lymph nodes (n=113). The tests identified 96.4% (27/28) as M. bovis from the MTC-positive bTB samples using conventional PCR for specific insertion elements IS1081. And MTC-negative bTB samples (n=85) were tested using conventional PCR and the real-time PCR. When comparative analyses were conducted on all bovine samples, using conventional PCR as the gold standard, the relative accuracy of real-time PCR was 99.1%, the relative specificity was 100%, and the agreement quotient (kappa) was 0.976. The detection limits of the real-time PCR assays for M. bovis and M. tuberculosis genomic DNA were 10 fg and 0.1 pg per PCR reaction, respectively. Consequently, this multiplex real-time PCR assay is a useful diagnotic tool for the identification of MTC and differentiation of M. bovis and M. tuberculosis, as well as the epidemiologic surveillance of animals slaughtered in abattoir.

• 한국식품위생안전성학회지
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• 제34권6호
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• pp.557-561
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• 2019

본 연구에서는 가축분퇴비에 존재할 수 있는 식중독균의 검출을 위하여 기존의 배양을 이용한 방법을 대체할 수 있는 real-time PCR을 적용하고자 하였으며, 이에 따라 유전자 증폭에 영향을 미치는 DNA 추출 방법에 따른 식중독균 검출 효율을 비교하였다. 적용한 방법은 가열 처리, 유기용매 및 흡착제 처리, 효소 처리의 3가지로 구분할 수 있으며, 각 방법에 따른 DNA의 검출 효율을 실험 결과로 나타내었다. 가열 처리 방법에서는 가열 시간의 증가에 따라 DNA 검출 효율이 높아지는 경향을 나타냈으며, 유기용매 및 흡착제는 효과를 나타내지 않았고, 효소 처리의 경우에는 그람 양성균 보다는 그람 음성균의 DNA가 추출 효율이 더 높은 것으로 나타났다. 결론적으로 퇴비에서 30분 이상의 가열 처리와 효소의 처리를 통한 DNA 추출 방법은 real-time PCR을 적용한 식중독균 검출에 적합한 것으로 판단된다.

• 한국식품위생안전성학회지
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• 제23권2호
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• pp.121-128
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• 2008

본 연구는 Lightcycler (Roche)를 이용한 Real-Time PCR(LC-PCR)기법을 통하여 원유시료에서 신속, 정확하게 황색포도상구균을 검출하는 기법을 개발하고자 하였다. coagulase 전구체를 coding하는 113 bp의 coa 유전자의 증폭, melting curve 분석 및 DNA염기서열을 분석하여 황색포도상구균 특유의 유전자 검출하는 기법을 개발하였다. 또한 분리된 균주중 메치실린에 내성을 나타내는 균주를 검출하고자 penicillin-binding protein, PBP2a (mecA)를 coding 하는 209 bp의 mecA 유전자의 증폭, melting curve 분석 및 DNA염기서열을 분석하여 메치실린내성 황색 포도상구균을 real-time PCR 기법으로 검출하는 기술을 개발하였다. 본 실험에 따르면 647개의 원유시료중 6개의 시료에서 황색포도상구균이 검출되었으며 이중 2개의 시료에서 분리된 황생포도상구균이 메치실린내성 황색포도상구균임을 확인하였다. 또한 DNA 검출한계는 10 fg으로 기존 PCR에 비해 매우 감도가 우수한 것을 확인하였다. 또한 3개의 원유시료에서 돼지나 소의 삼출성 피부염의 원인균인 Staphylococcus chromogenes가 분리되었다.