• 대한전기학회논문지:전력기술부문A
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• 제54권5호
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• pp.242-250
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• 2005

This paper proposes a new protective relaying algorithm using Neuro-Fuzzy and wavelet transform. To organize advanced nuero-fuzzy algorithm, it is important to select target data reflecting various transformer transient states. These data are made of changing-rates of Dl coefficient and RSM value within half cycle after fault occurrence. Subsequently, advanced neuro-fuzzy algorithm is obtained by converging the target data. As a result of applying the advanced neuro-fuzzy algorithm, discrimination between internal fault and inrush is correctly distinguished within 1/2 after fault occurrence. Accordingly, it is evaluated that the proposed algorithm can effectively protect a transformer by correcting discrimination between winding fault and inrushing state.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2004년도 추계학술대회 논문집 전력기술부문
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• pp.293-296
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• 2004

This paper proposes a new protective relaying algorithm using ACI(Advanced Computational Intelligence) and wavelet transform. To organize the advanced neuro-fuzzy algorithm, it is important to select target data reflecting various transformer transient states. These data are made of changing-rates of D1 coefficient and RSM value within half cycle after fault occurrence. Subsequently, the advanced neuro-fuzzy algorithm is obtained by converging the target data. As a result of applying the advanced neuro-fuzzy algorithm, discrimination between internal fault and inrush is correctly distinguished within half cycle after fault occurrence. Accordingly, it is evaluated that the proposed algorithm can effectively protect a transformer by correcting discrimination between winding fault and inrushing state.

• 대한수의학회지
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• 제39권3호
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• pp.513-522
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• 1999

DNA hybridization assay using probes prepared from liver was carried out to identify species characterization of the domestic animals. Gel electrophoresis showed that the target DNA extracted from raw muscle were 1kb and uniform pattern while fragments size of heated muscle were irregular. Hybridization was performed by adding 200ng/ml probe in hybridization solution and incubating for 12 hours at $68^{\circ}C$. To obtain good discrimination, applied washing buffer and washing step differently depending on the species. The probes of pig, horse and dog formed the specific hybrids with each target DNA respectively. Although cross reaction was detected in cattle, goat and sheep but signal intensity among these species made the discrimination possible each other. Such pattern was the same in the cases of chicken, turkey and duck. The hybridization pattern of heated muscle was similar to that of raw muscle in general, but the signal intensity was inferior to that of raw muscle. Species identification between closely related animal species, hybridized using the target DNA of such closely related animal species as a blocking agent, remarkable increase of discrimination from the evident decrease of non specific reaction compared with the control group. In addition, in the admixture where certain meat was included in the beef, pork, chicken meat, we could find whether any unjust meat was admixed or not. In this case, detection limit of certain meat in admixture was 1%.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1998년도 하계학술대회 논문집 B
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• pp.657-659
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• 1998

In this paper we discuss an algorithm to discriminate a target under track against multiple acoustic counter-measure (ACM) sources, based on sequential testings of multiple hypotheses. The ACM sources are separated from the target under track and generate, while drifting, measurements with false range and Doppler information. The purpose of the ACM is to mislead the target tracking and to help the true target evade a pursuer. The proposed algorithm uses as a test statistic a function of both the sequences of processed waveform signature and the innovation sequences from extended Kalman filters to estimate the target dynamics and the drifting positions of the ACM sources. Numerical experiments on various scenarios show that the proposed algorithm discriminates the target faster with a higher probability of success than the algorithm using only the innovation sequences from extended Kalman filters.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 2004년도 추계학술대회 논문집
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• pp.277-280
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• 2004

In recent die industry, web-based production control system is applied widely because of the improvement of IT technology. In result, many researches are published about remote monitoring at a long distance. The target of this study is to develop Die Discrimination System using web-based vision, and CAD API when client discriminates die in process at a long distance. Special feature of this system is to use 2D vision image and to match with DB. We can get discrimination result enough to want with short time and a little low precision in web-monitoring by development of this system.

• 대한전기학회논문지:전력기술부문A
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• 제55권3호
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• pp.116-122
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• 2006

The underground transmission lines is continuously expanded in power systems. Therefore the fault of underground transmission lines are increased every year because of the complication of systems. However the studies dealing with fault location in the case of the underground transmission lines are rarely reported except for few papers using traveling wave method and calculating underground cable impedance. This paper describes the algorithm using fuzzy system and travelling wave method in the underground transmission line. Fuzzy inference is used for fault discrimination. To organize fuzzy algorithm, it is important to select target data reflecting various underground transmission line transient states. These data are made of voltage and average of RMS value on zero sequence current within one cycle after fault occurrence. Travelling wave based on wavelet transform is used for fault location. In this paper, a variety of underground transmission line transient states are simulated by EMTP/ATPDraw and Matlab. The input which is used to fault location algorithm are Detail 1(D1) coefficients of differential current. D1 coefficients are obtained by wavelet transform. As a result of applying the fuzzy inference and travelling wave based on wavelet transform, fault discrimination is correctly distinguished within 1/2 cycle after fault occurrence and fault location is comparatively correct.

• Journal of electromagnetic engineering and science
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• 제13권3호
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• pp.151-157
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• 2013

Marine radars are affected by sea and rain clutters, which can make target discrimination difficult. The clutter standard deviation and improvement factor are applied using multiple parameters-moving speed of radar, antenna speed, angle, etc. When a radar signal is processed, a Data Matrix Bank (DMB) filter can be applied to remove sea clutters. This filter allows detection of a target, and since it is not affected by changes in adjacent clutters resulting from a multi- target signal, sea state clutters can be removed. In this paper, we study the level for clutter removal and the method for target detection. In addition, we design a signal processing algorithm for marine radars, analyze the performance of the DMB filter algorithm, and provide a DMB filter algorithm design. We also perform a DMB filter algorithm analysis and simulation, and then apply this to the DMB filter and cell-average constant false alarm rate design to show comparative results.

• 전자공학회논문지S
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• 제35S권3호
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• pp.110-119
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• 1998

In this paper we discuss an algorithm to discriminate a garget under track against multiple acoustic counter-measure (ACM) sources, based on sequential testings of multiple hypotheses. The ACM sources are separated from the target under track and generate, while drifting, measurements with false range and Doppler information. The purpose of the ACM is to mislead the target tracking and to help the true target evade a pursuer. The proposed algorithm uses as a test statistic a function of the innovation sequences from extended Kalman filters to estimate the target dynamics and the drifting positions of the ACM sources. results of numerical experimenats are presented to show a performance profile of the proposed algorithm.

• ETRI Journal
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• 제41권4호
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• pp.483-493
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• 2019

Considering the high dimensions of video sequences, it is often challenging to acquire a sufficient dataset to train the tracking models. From this perspective, we propose to revisit the idea of hand-crafted feature learning to avoid such a requirement from a dataset. The proposed tracking approach is composed of two phases, detection and tracking, according to how severely the appearance of a target changes. The detection phase addresses severe and rapid variations by learning a new appearance model that classifies the pixels into foreground (or target) and background. We further combine the raw pixel features of the color intensity and spatial location with convolutional feature activations for robust target representation. The tracking phase tracks a target by searching for frame regions where the best pixel-level agreement to the model learned from the detection phase is achieved. Our two-phase approach results in efficient and accurate tracking, outperforming recent methods in various challenging cases of target appearance changes.

• 전자공학회논문지A
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• 제31A권5호
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• pp.76-84
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• 1994

Recently, Optical BJTC as a new approach for real-time multi-target tracking has been intensively studied. But the conventional system has some problems in the practical applications such as the false alarm and target missing and low correlation efficiency, and these poor performances are analyzed to be deeply dependent on the binarization method. So, in this paper, a new BJTC system which has the improved performances in target discrimination and diffraction efficiency is suggested, which is based on the JTPS having the same properties with those of the matched filter and new power spectrum binarization method to use effectively the high frequency components of the JTPS signal. Through the computer simulation and some experiments, the performances of the new BJTC tracking system are analyzed and proved to be superior to those of the conventional system baseds on Median method in multi- target tracking problems.