• 정보보호학회논문지
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• 제13권3호
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• pp.81-90
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• 2003

침입탐지시스템의 침입 여부는 감사로그를 기반으로 판단되며, 그 성능은 감사로그를 바탕으로 침입 패턴에 대해 얼마나 정확하고 효율적으로 기술했느냐에 달려있다. 본 논문에서는 시스템 호출, 네트워크 패킷, Syslog의 정보를 통해 상관성을 도출하고, 상태전이 기반의 패턴과 이에 대한 규칙기반의 상관관계 패턴을 작성하였다. 이러한 상관관계를 이용하여 탐지율의 정확성을 높일 수 있었다. 특히, covert channel의 탐지 실험을 통해 상관관계 패턴을 통한 탐지가 가능함을 보였다.

• 한국음향학회지
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• 제36권5호
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• pp.300-304
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• 2017

원통형 소나 시스템에서 기존의 광대역탐지 기법으로는 인접한 표적을 분리 탐지하는데 한계가 있다. 본 논문에서는 방위분해능을 향상을 위해 상호상관을 이용한 광대역탐지 기법을 원통형 소나 시스템에 적용한다. 제안된 기법은 상호상관을 이용한 광대역탐지 처리 이전에 분리 빔형성을 이용하여 동일 지향방위의 좌.우 반 빔을 생성한다. 생성된 좌 우 반 빔간의 상관관계 결과를 방위 값으로 변환하여 표적의 방위를 추정한다. 시뮬레이션을 통하여 제안된 기법이 기존의 광대역탐지 기법보다 성능이 우수함을 검증하였다.

• 한국음향학회지
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• 제20권2호
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• pp.86-92
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• 2001

소음원 위치와 방위를 규명하기 위해 다양한 배열처리기술이 발전되어 왔다. 배열처리기술의 기본은 두 개의 수신센서에 수신된 신호의 시간차를 이용하여 소음원의 위치와 방위를 구하는 것으로 응용분야나 신호처리방법에 따라 고유의 특성을 갖는 빔형성기법, 상관함수기법 및 NAH (Near-Field Acoustic Holography) 등이 있다 본 연구에서는 이러한 기법들 중 광대역 소음원 탐지에 적용되는 상관함수기법을 채택하여 소음원의 대역폭과 측정 잡음원 간의 상관 관계가 위치나 방위 탐지 정확도에 미치는 영향을 분석하여 효과적인 소음원 탐지기법을 제안한다. 본 연구에서 채택한 배열의 기하학적 형상은 위치나 방위의 3차원적 모호성을 없애기 위한 3차원 비선형이며 제안된 기법의 타당성은 수치모의 실험 및 실제 실험으로 검증되었다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제12권3호
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• pp.1224-1242
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• 2018

Aiming at motion analysis and compensation, it is essential to conduct motion detection with images. However, motion detection and tracking from low-altitude images obtained from an unmanned aerial system may pose many challenges due to degraded image quality caused by platform motion, image instability and illumination fluctuation. This research tackles these challenges by proposing a modified joint transform correlation algorithm which includes two preprocessing strategies. In spatial domain, a modified fuzzy edge detection method is proposed for preprocessing the input images. In frequency domain, to eliminate the disturbance of self-correlation items, the cross-correlation items are extracted from joint power spectrum output plane. The effectiveness and accuracy of the algorithm has been tested and evaluated by both simulation and real datasets in this research. The simulation experiments show that the proposed approach can derive satisfactory peaks of cross-correlation and achieve detection accuracy of displacement vectors with no more than 0.03pixel for image pairs with displacement smaller than 20pixels, when addition of image motion blurring in the range of 0~10pixel and 0.002variance of additive Gaussian noise. Moreover,this paper proposes quantitative analysis approach using tri-image pairs from real datasets and the experimental results show that detection accuracy can be achieved with sub-pixel level even if the sampling frequency can only attain 50 frames per second.

• Journal of Communications and Networks
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• 제12권6호
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• pp.582-591
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• 2010

Probabilistic modeling and analysis of correlation metrics have been receiving considerable interest for a long period of time because they can be used to evaluate the performance of communication receivers, including satellite broadcasting receivers. Although differential correlators have a simple structure and practical importance over channels with severe frequency offsets, closedform expressions for the output distribution of differential correlators do not exist. In this paper, we present detection error probability expressions for frame synchronization using differential correlation, and demonstrate their accuracy over channel parameters of practical interest. The derived formulas are presented in terms of the Marcum Q-function, and do not involve numerical integration, unlike the formulas derived in some previous studies. We first determine the distributions and error probabilities for single-span differential correlation metric, and then extend the result to multispan differential correlation metric with certain approximations. The results can be used for the performance analysis of various detection strategies that utilize the differential correlation structure.

• 한국컴퓨터정보학회논문지
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• 제22권12호
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• pp.55-61
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• 2017

In this paper, we propose a novel tracking method using target separation and detection that are based on discriminative correlation filter (DCF), which is studied a lot recently. 'Retainability' is one of the most important factor of tracking. There are some factors making retainability of tracking worse. Especially, fast movement and occlusion of a target frequently occur in image data, and when it happens, it would make target lost. As a result, the tracking cannot be retained. For maintaining a robust tracking, in this paper, separation of a target is used so that normal tracking is maintained even though some part of a target is occluded. The detection algorithm is executed and find new location of the target when the target gets out of tracking range due to occlusion of whole part of a target or fast movement speed of a target. A variety of experiments with various image data sets are conducted. The algorithm proposed in this paper showed better performance than other conventional algorithms when fast movement and occlusion of a target occur.

• Journal of Communications and Networks
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• 제16권6호
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• pp.600-612
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• 2014

In this paper, we consider the problem of spectrum sensing for advanced television systems committee (ATSC) digital television (DTV) signal detection. To exploit the cyclostationarity of the ATSC DTV signals, we employ spectral correlation density (SCD) as the decision statistic and propose an optimal detection algorithm. The major difficulty is in obtaining the probability distribution functions of the SCD. To overcome the difficulty, we probabilistically model the pilot frequency location and employ Gaussian approximation for the SCD distribution. Then, we obtain a practically implementable detection algorithm that outperforms the industry leading systems by 2-3 dB. We also propose various techniques that greatly reduce the system complexity with performance degradation by only a few tenths of decibels. Finally, we show how robust the system is to the estimation errors of the noise power spectral density level and the probability distribution of the pilot frequency location.

• ETRI Journal
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• 제43권1호
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• pp.31-39
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• 2021

One of the major challenges in wireless body area networks (WBANs) is sensor fault detection. This paper reports a method for the precise identification of faulty sensors, which should help users identify true medical conditions and reduce the rate of false alarms, thereby improving the quality of services offered by WBANs. The proposed sensor fault detection (SFD) algorithm is based on Pearson correlation coefficients and simple statistical methods. The proposed method identifies strongly correlated parameters using Pearson correlation coefficients, and the proposed SFD algorithm detects faulty sensors. We validated the proposed SFD algorithm using two datasets from the Multiparameter Intelligent Monitoring in Intensive Care database and compared the results to those of existing methods. The time complexity of the proposed algorithm was also compared to that of existing methods. The proposed algorithm achieved high detection rates and low false alarm rates with accuracies of 97.23% and 93.99% for Dataset 1 and Dataset 2, respectively.

• 전기학회논문지
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• 제64권3호
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• pp.498-504
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• 2015

This paper proposed an Spread Spectrum Time Domain Reflectometry (SSTDR) using time-frequency correlation analysis in order to have more accurate fault determination and location detection than classical SSTDR despite increased signal attenuation due to the long distance to cable fault location. The proposed method was validated through comparison with classical SSTDR methods in open- and short-circuit fault detection experiments of low-voltage power cables. The experimental results showed that the proposed method can detect correlation coefficients at fault locations accurately despite reflected signal attenuation so that cable faults can be detected more accurately and clearly in comparison to existing methods.

• 대한임베디드공학회논문지
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• 제16권6호
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• pp.277-283
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• 2021

This paper addresses the problem of vision-based mask filter inspection for mask production systems. Machine learning-based approaches can be considered to solve the problem, but they may not be applicable to mask filter inspection if normal and anomaly mask filter data are not sufficient. In such cases, handcrafted image processing methods have to be considered to solve the problem. In this paper, we propose a hierarchical correlation-based approach that combines handcrafted image processing methods to detect anomaly mask filters. The proposed approach combines image rotation, cropping and resizing, edge detection of mask filter parts, average blurring, and correlation-based decision. The proposed approach was tested and analyzed with real mask filters. The results showed that the proposed approach was able to successfully detect anomalies in mask filters.