• Journal of the Korean Institute of Intelligent Systems
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• v.9 no.4
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• pp.426-435
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• 1999

A high impedance fault(HIF) is one of the serious problems facing the electric utility industry today. Because of the high impedance of a downed conductor under some conditions these faults are not easily detected by over-current based protection devices and can cause fires and personal hazard. In this paper a new method for detection of HIF which uses adaptive neuro-fuzzy inference system (ANFIS) is proposed. Since arcing fault current shows different changes during high and low voltage portion of conductor voltage waveform we firstly divided one cycle of fault current into equal spanned four data windows according to the mangnitude of conductor voltage. Fast fourier transform(FFT) is applied to each data window and the frequency spectrum of current waveform are chosen asinputs of ANFIS after input selection method is preprocessed. Using staged fault and normal data ANFIS is trained to discriminate between normal and HIF status by hybrid learning algorithm. This algorithm adapted gradient descent and least square method and shows rapid convergence speed and improved convergence error. The proposed method represent good performance when applied to staged fault data and HIFLL(high impedance like load)such as arc-welder.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.40 no.12
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• pp.24-30
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• 2003

In this paper, we proposed an adaptive ATC(Automatic Threshold Control) circuit with more decreased settling time by improving the structure of the peak detector. We showed that it could reduce a good deal of the settling time because it showed less than half the error voltage ratio that the ATC circuit with conventional structure showed in analysis. We also designed a burst-mode ATC circuit for the 1.25Gbps EPON system using a commercial foundry. It produced the reference levels in very short time, 6㎱ in 40 ㏈ input dynamic range.

• Proceedings of the IEEK Conference
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• 2002.07c
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• pp.1519-1522
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• 2002

In this paper, we propose a new adaptive dissolve detection method based on the analysis of a dissolve modeling error that is the difference between an ideally modeled dissolve curve without any correlation and an actual variance curve with a correlation. The dissolve modeling error is determined based on a correlation between two scenes and variances for each scene. First, Candidate regions are extracted by using the characteristics of a parabola that is downward convex, then the candidate region will be verified based on a dissolve modeling error. If a dissolve modeling error on a candidate region is less than a threshold that is defined by a dissolve modeling error with a target correlation, the candidate region should be a dissolve region with a correlation less than the target correlation. The threshold is adaptively determined based on the variances between the candidate regions and the target correlation. By considering the correlation between neighbor scenes, the proposed method is able to be a semantic scene-change detector. The proposed algorithm was tested on various types of data and its performance proved to be more accurate and reliable when compared with other commonly used methods

• The Korean Journal of Applied Statistics
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• v.21 no.2
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• pp.233-245
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• 2008

In this work, a new adaptive weighted mean filter is proposed for preserving image details while effectively suppressing impulse noise. The proposed filter is based on a noise pixel detection-estimation strategy. All the pixels are first detected using an impulse noise detector. Then the detected noise pixels are replaced with the output of the weighted mean filter over adaptive working window according to the rate of corrupted neighborhood pixels, while noise-free pixels are left unaltered. We compare the proposed filter to other existing filters in the qualitative measure and quantitative measures such as PSNR and MAE as well as computation time to verify the capability of the proposed filter. Extensive simulations show that the proposed filter performs better than other filters in impulse noise suppression and detail preservation without increasing of running time.

• The Journal of the Acoustical Society of Korea
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• v.19 no.7
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• pp.34-41
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• 2000

This paper proposes a new double-talk detection algorithm which is based on the fuzzy rules, in the adaptive echo canceller of telecommunication system. In this method, the two inputs of the fuzzy inference for detecting double-talk condition are used. One is the cross-correlation coefficient between the error signal and the primary signal which is the summation of the real echo signal and the near-end signal. The other one is the cross-correlation coefficient between the estimation error signal and the primary signal. The fuzzy controller makes a fuzzification for two inputs by the membership functions of trapezoid does the max-min composition using if-then rules. The composed result is defuzzificated by the center gravity method. And by defuzzificated values, the double-talt the echo path variance, and the echo path variance during the double-talk are detected. It is confirmed by computer simulation that this fuzzy double-talk detector is able to estimate the double talk and the echo path variation condition, and even track echo path variation more accurately than the conventional algorithm during the double-talk period.

• Journal of Sensor Science and Technology
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• v.1 no.2
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• pp.155-163
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• 1992

The phased array antenna has the ability to perform adaptive sampling by directing the radar beam without inertia in any direction. The adaptive sampling capability of the phased array antenna allows each sampling time interval to be varied for each target, depending on the acceleration of each target at any time. In this paper we design a three-dimensional adaptive tracking algorithm for the phased array radar system with a given set of measurement parameters. The tracking algorithm avoids taking unnecessarily frequent samples, while keeping the angular prediction error within a fraction of antenna beamwidth so that the probability of detection will not be degraded during a track update illuminations. In our algorithm, the target model and the sampling rate are selected depending on the target range and the target maneuver status which is determined by a maneuver detector. A detailed simulation is conducted to test the validity of our tracking algorithm for encounter geometries under various conditions of maneuver.

• Korean Journal of Optics and Photonics
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• v.19 no.1
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• pp.1-8
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• 2008

Shack-Hartmann sensors are widely employed as a wavefront measuring device in various applications. Adaptive optics is one of the major applications. Since an adaptive optics system should be operated in real-time, high-speed wavefront sensing is essential. In high-speed operation, integration time of an image detector is very short. In this case, noises such as readout noise and photon noise greatly influence the accuracy of wavefront sensing. Therefore a fast and noise-insensitive centroid finding algorithm is required for the real-time wavefront sensing. In this paper, the multi-resolution correlation method is proposed. By employing multi-resolution images, this method greatly reduces the computation time when compared to the fast Fourier transform (FFT) correlation method. The verification is performed through the computational simulation. In this paper, the center of mass method, correlation method and multi-resolution correlation method are employed to compare the measurement accuracy of the centroid finding algorithms. The accuracy of a Shack-Hartmann wavefront sensor using the proposed algorithm is proved to be comparable to that of the conventional correlation method.

• The Journal of the Acoustical Society of Korea
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• v.22 no.2
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• pp.121-128
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• 2003

Acoustic Echo Cancellation(m) is very active research topic having many applications like teleconference and hands-free communication and it employs Double Talk Detector(DTD) to indicate whether the near-end speaker is active or not. However. the DTD is very sensitive to the variation of acoustical environment and it sometimes provides wrong information about the near-end speaker. In this paper, we are focusing on the development of robust DTD algorithm which is a basic building block for reliable AEC system. The proposed AEC system consists of delayless subband AEC and narrow-band DTD. Delayless subband AEC has proven to have excellent performance of echo cancellation with a low complexity and high convergence speed. In addition, it solves the signal delay problem in the existing subband AEC. On the other hand, the proposed narrowband DTD is operating on low frequency subband. It can take most advantages from the narrow subband such as a low computational complexity due to the down-sampling and the reliable DTD decision making procedure because of the low-frequency nature of the subband signal. From the simulation results of the proposed narrowband DTD and wideband DTD, we confirm that the proposed DTD outperforms the wideband DTD in a sense of removing possible false decision making about the near-end speaker activity.

• Journal of the Institute of Electronics and Information Engineers
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• v.50 no.7
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• pp.258-266
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• 2013

We propose adaptive detection algorithm to reduce a false alarm by considering the characteristics of the random noise on the detection system based on a seismic sensor. The proposed algorithm consists of the first step detection using kernel function and the second step detection using detection classes. Kernel function of the first step detection is obtained from the threshold of the Neyman-Pearon decision criterion using the probability density functions varied along the noise from the measured signal. The second step detector consists of 4 step detection class by calculating the occupancy time of the footstep using the first detected samples. In order to verify performance of the proposed algorithm, the detection of the footsteps using measured signal of targets (walking and running) are performed experimentally. The detection results are compared with a fixed threshold detector. The first step detection result has the high detection performance of 95% up to 10m area. Also, the false alarm probability is decreased from 40% to 20% when it is compared with the fixed threshold detector. By applying the detection class(second step detector), it is greatly reduced to less than 4%.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.26 no.8A
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• pp.1291-1297
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• 2001

본 논문에서는 DS-CDMA 환경의 다중 사용자 검출에서 문제시되는 다중 접속 간섭을 제거시켜주기 위해 사용되는 간섭 제거기에 대하여 소개한다. 기존의 간섭 제거 기술 중 병렬형 간섭 제거기는 장치적으로 복잡하고, 순차적 간섭 제거기는 지연 시간이 길다는 단점을 가지고 이다. 무엇보다도, 적응형 간섭 제거기는 수렴하기 위하여 많은 반복수가 필요하다. 우리는 이러한 간섭 제거기들의 성능을 개선시키기 위해서 CMA(Constant Modulus Algorithm)을 이용한 적응형 간섭 제거기에 간섭 제거 기법을 적용시킨 새로운 간섭 제거기를 제안한다. 제안된 간섭 제거기는 적응 필터 처리후 간섭 제거 기법을 적용시키기 때문에 동일한 BER(Bit Error Rate)을 얻고자 할 때 기존의 적응형 간섭 제거기에 비해 수렴을 위한 반복수를 줄여 준다. 그리고, 적응 필터를 사용함으로써 기존의 비선형 간섭 제거기에 비해 하드웨어적인 복잡도를 줄일 수 있는 효과가 있다.