• The Journal of Korean Institute of Communications and Information Sciences
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• v.31 no.6A
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• pp.586-592
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• 2006

In this paper, a novel detection method called the joint multiple frequency cell (JMFC) detection is addressed for spread spectrum code acquisition in the presence of residual Doppler frequency offset (RDFO). When the RDFO exists, the correlation peak used for detection during the acquisition process is split into several lower neighboring peaks, resulting in severe degradation in the detection performance, and consequently, in the overall acquisition performance. In the JMFC detection, a decision variable for detection is formed by combining several consecutive correlator outputs, so that the reduction in the correlation value due to the RDFO can be alleviated. Numerical results show that the proposed scheme can offer better detection performance over the conventional scheme based on the cell-by-cell detection.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.65 no.9
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• pp.1583-1589
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• 2016

This paper proposes an improved spread spectrum time domain reflectometry (ISSTDR) using time-frequency correlation and reference signal elimination method in order to have more accurate fault determination and location detection than conventional (SSTDR) despite increased signal attenuation due to the long distance to cable fault location. The proposed method has a two-step process: the first step is to detect a peak location of the reference signal using time-frequency correlation analysis, and the second step is to detect a peak location of the correlation coefficient of the reflected signal by removing the reference signal. The proposed method was validated through comparison with existing 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.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.41 no.8
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• pp.914-916
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• 2016

The noise in a power signal degrades the detection rate of the power quality (PQ) event signals. We present a new wavelet de-noising technique for PQ event detection that employs the correlation-based thresholding instead of the wavelet-scale-based thresholding of existing schemes. The simulation results show that the proposed scheme is more robust to Gaussian and impulsive noisy conditions and has further improved detection ratio than existing schemes.

• Journal of Multimedia Information System
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• v.1 no.1
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• pp.45-53
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• 2014

This article describes a two wheelers detection system riding on people based on modified histogram of oriented gradients (HOG) for vision based intelligent vehicles. These features used correlation coefficient parameter are able to classify variable and complicated shapes of a two wheelers according to different viewpoints as well as human appearance. Also our system maintains the simplicity of evaluation of traditional formulation while being more discriminative. In this paper, we propose an evolutionary method trained part-based models to classify multiple view-based detection: frontal, rear and side view (within $60^{\circ}C$). Our experimental results show that a two wheelers riding on people detection system based on proposed approach leads to higher detection accuracy rate than traditional features.

• Proceedings of the KSRS Conference
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• 2008.03a
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• pp.180-184
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• 2008

Preliminary results are reported on ship detection using coherence images computed from crosscorrelating images of multi-look-processed dual-polarization data (HH and HV) of ENVISAT ASAR. The traditional techniques of ship detection by radars such as CFAR (Constant False Alarm Rate) rely on the amplitude data, and therefore the detection tends to become difficult when the amplitudes of ships images are at similar level as the mean amplitude of surrounding sea clutter. The proposed method utilizes the property that the multi-look images of ships are correlated with each other. Because the inter-look images of sea surface are covered by uncorrelated speckle, crosscorrelation of multi-look images yields the different degrees of coherence between the images and water. The polarimetric information of ships, land and intertidal zone are first compared based on the cross-correlation between HH and HV. In the next step, we examine the technique when the dual-polarization data are split into two multi-look images.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.44 no.2
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• pp.55-61
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• 2007

In this paper, we represent the maximum detection probability formulas of symbol synchronization timing at each received delay signal in multipath channel delay profile in the multiplied correlation and difference type correlated symbol synchronization timing detection method. The computer simulation results show that the correlation symbol timing detection method have maximum detection probability at the lead of received delay signal of highest amplitude, but the difference type of correlation symbol timing detection method always have maximum detection probability at the lead of first received delay signal in the multipath channel models. Using this results, we show the BER characteristics difference between the IEEE802.11a OFDM signals which is obtained in case of the symbol synchronization timing is taken at zero error(perfect) timing position and at -1 sample error symbol timing position from perfect timing position in the multipath channel models regardless the length of channel delay spread.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.7
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• pp.3093-3115
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• 2020

Network anomaly detection system plays an essential role in detecting network anomaly and ensuring network security. Anomaly detection system based machine learning has become an increasingly popular solution. However, due to the unbalance and high-dimension characteristics of network traffic, the existing methods unable to achieve the excellent performance of high accuracy and low false alarm rate. To address this problem, a new network anomaly detection method based on data balancing and recursive feature addition is proposed. Firstly, data balancing algorithm based on improved KNN outlier detection is designed to select part respective data on each category. Combination optimization about parameters of improved KNN outlier detection is implemented by genetic algorithm. Next, recursive feature addition algorithm based on correlation analysis is proposed to select effective features, in which a cross contingency test is utilized to analyze correlation and obtain a features subset with a strong correlation. Then, random forests model is as the classification model to detection anomaly. Finally, the proposed algorithm is evaluated on benchmark datasets KDD Cup 1999 and UNSW_NB15. The result illustrates the proposed strategies enhance accuracy and recall, and decrease the false alarm rate. Compared with other algorithms, this algorithm still achieves significant effects, especially recall in the small category.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.10 no.6
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• pp.107-111
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• 2010

The damage of tire by damage material classification method is researched as used ultrasonic wave signal to occur at a tire during vehicle driving. Auto-correlation function after having passed through an envelope detecting preprocess is used for detecting periodicity because of occurring periodic ultrasonic waves signal with tire revolution. One revolution cycle time of a damaged tire and period that calculated auto-correlation function appeared equally in experiment. The result that can classification whether or not there was a tire damage is established.

• Journal of the Korean Society for Precision Engineering
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• v.31 no.4
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• pp.327-334
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• 2014

This paper presents a method of fault detection and fault localization from acoustic noise measurements. In order to detect the presence of noise sources wavelet transform is applied to acoustic signal. In addition, a cross correlation based method is proposed to calculate the exact location of the noise allowing the user to quickly diagnose and resolve the source of the noise. The fault detection system is implemented using two microphones and a computer system. Experimental results show that the system can detect faults due to artifacts accidentally inserted during the manufacturing process and estimate the location of the fault with approximately 1 cm precision.

• Journal of Advanced Marine Engineering and Technology
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• v.30 no.2
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• pp.247-252
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• 2006

Almost ship monitoring systems are event driven alarm system which warn only when the measurement value is over or under set point. These kinds of system cannot warn until signal is growing to abnormal state that the signal is over or under the set point. therefore cannot play a role for preventive maintenance system. This paper proposes fault diagnosis method which is able to diagnose and forecast the fault from present operating condition by analyzing monitored signals with present ship monitoring system without any additional sensors. By analyzing the data with high correlation coefficient(CC), correlation level of interactive data can be defined. Knowledge base of abnormal detection can be built by referring level of CC(Fault Detection CC. FDCC) to detect abnormal data among monitored data from monitoring system and knowledge base of diagnosis built by referring CC among interactive data for related machine each other to diagnose fault part.