• Journal of the Korea Institute of Information Security & Cryptology
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• v.13 no.3
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• pp.81-90
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• 2003

The presence of the intrusion is judged by intrusion detection system based on the audit log and the Performance of this system depends on how correctly and effectively it has been described about the intrusion pattern with audit log. In this paper, the relativity concerning intrusion is demonstrated among the information those are ‘System call, Network packet and Syslog’ and the related pattern of the state-transition-based method and those rule-based pattern is identified. By applying this correlation to them, the accuracy rate of detection was able to be improved. Especially, the availability of detection with correlation pattern through Covert Channel detection test has been substantiated.

• Journal of Sensor Science and Technology
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• v.33 no.5
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• pp.298-304
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• 2024

In Korea, fires are the second most common type of disaster, causing large-scale damages. The installation of fire detectors is legislated to prevent fires and minimize damage. Conventional fire detectors have limitations in initial suppression of failures because they detect fires when large amounts of smoke and heat are generated. Additionally, frequent malfunctions in fire detectors may cause users to turn them off. To address these issues, recent studies focus on accurately detecting even small-scale fires using multi-sensor and deep-learning technologies. They also aim at quick fire detection and thermal decomposition using gas. However, these studies are not practical because they overlook the heavy computations involved. Therefore, we propose a fast and accurate fire detection system based on multi-sensor and deep-learning technologies. In addition, we propose a computation-reduction method for selecting sensors suitable for detection using the Pearson correlation coefficient. Specifically, we use a moving average to handle outliers and two-stage labeling to reduce false detections during preprocessing. Subsequently, a deep-learning model is selected as LSTM for analyzing the temporal sequence. Then, we analyze the data using a correlation analysis. Consequently, the model using a small data group with low correlation achieves an accuracy of 99.88% and a false detection rate of 0.12%.

• The Journal of the Acoustical Society of Korea
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• v.36 no.5
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• pp.300-304
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• 2017

In a cylindrical sonar, a conventional broadband energy detector has limitations in the separation of adjacent targets. In this paper, a broadband detection algorithm using cross-correlation is applied to the cylindrical sonar to improve the bearing resolution. The proposed algorithm uses split beamforming before broadband detection processing using cross-correlation to generate half beams. The time delay obtained from the peak of correlation between half beams is used to estimate the bearing of target. Simulations demonstrate the improved performance of the proposed algorithm against the conventional algorithm.

• The Journal of the Acoustical Society of Korea
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• v.20 no.2
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• pp.86-92
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• 2001

Various array processing techniques to identify the noise source position or bearing have been developed. Typical array processing techniques which are based on time delay between received signals at two sensors, are classified as conventional beamforming, correlation function and NAH (Near-Field Acoustic Holography) techniques which have their own characteristics with respect to application field and signal processing method. In this study, correlation function technique which could be applied for broadband noise source detection, is adopted and the effective detection technique is proposed considering the effects of source bandwidth and measurement noise correlation of noise sources. The validity of the Proposed technique is evaluated using the 3-dimensional nonlinear any which does not give 3-dimensional Position or bearing ambiguity

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.3
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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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• v.12 no.6
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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.

• Journal of the Korea Society of Computer and Information
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• v.22 no.12
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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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• v.16 no.6
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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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• v.43 no.1
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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.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.64 no.3
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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.