• Proceedings of the KSRS Conference
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• v.2
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• pp.772-775
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• 2006

Muilti-dimensional feature vector matching algorithm uses multiple features such as intensity, gradient, variance, first or second derivative of a pixel to find correspondence pixels in stereo images. In this paper, we proposed a new method for adjusting automatically the weight of feature in multi-dimensional feature vector matching considering sharpeness of a pixel in feature vector distance curve. The sharpeness consists of minimum and maximum vector distances of a small window mask. In the experiment we used IKONOS satellite stereo imagery and obtained accurate matching results comparable to the manual weight-adjusting method.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.7 no.4
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• pp.767-778
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• 2003

This paper presents a method for generating optimal fuzzy rules using a genetic algorithm. Fuzzy rules are generated from the training data in the first stage. In this stage, fuzzy c-Means clustering method and cluster validity are used to determine the structure and initial parameters of the fuzzy inference system. A cluster validity is used to determine the number of clusters, which can be the number of fuzzy rules. Once the structure is figured out in the first stage, parameters relating the fuzzy rules are optimized in the second stage. Weights and variance parameters are tuned using genetic algorithms. Variance parameters are also managed with left and right for asymmetrical Gaussian membership function. The method ensures convergence toward a global minimum by using genetic algorithms in weight and variance spaces.

• Journal of the Korean Society for Nondestructive Testing
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• v.34 no.4
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• pp.290-298
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• 2014

Ultrasonic thickness measurement is a non-destructive method to measure the local thickness of a solid element, based on the time taken for an ultrasound wave to return to the surface. When an element is very thin, it is difficult to measure thickness with the conventional ultrasonic thickness method. This is because the method measures the time delay by using the peak of a pulse, and the pulses overlap. To solve this problem, we propose a method for measuring thickness by using the power cepstrum and the minimum variance cepstrum. Because the cepstrums processing can divides the ultrasound into an impulse train and transfer function, where the period of the impulse train is the traversal time, the thickness can be measured exactly. To verify the proposed method, we performed experiments with steel and, acrylic plates of variable thickness. The conventional method is not able to estimate the thickness, because of the overlapping pulses. However, the cepstrum ultrasonic signal processing that divides a pulse into an impulse and a transfer function can measure the thickness exactly.

• Journal of the Institute of Electronics and Information Engineers
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• v.50 no.12
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• pp.191-196
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• 2013

In this paper, the interaural intensity difference (IID)-based sounr source separation method in closely spaced stereo omnidirectional microphones is proposed. First, in order to improve the channel separability, a minimum variance distortionless response (MVDR) beamformer is employed to increase the intensity difference between stereo channels. After that, IID-based sound source separation method is applied. In order to evaluate the performance of the proposed method, source-to-distortion ratio (SDR), source-to-interference ratio (SIR), and sources-to-artifacts ratio (SAR), which are defined as objective evaluation criteria in stereo audio source separation evaluation campaign (SASSEC), are measured. As a result, it was shown from the objective evaluation that the proposed method outperforms a sound source separation method without applying a beamformer.

• Proceedings of the Acoustical Society of Korea Conference
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• spring
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• pp.145-148
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• 2004

수중음향 시스템에서는 빔형성(Beamforming) 기법을 이용하여 목표물로부터 신호를 수신하고 이에 대한 정보를 얻어낼 수 있는데, 이러한 빔형성에 있어서 주엽(Mainlobe)의 빔폭(Beamwidth)과 부엽(Sidelobe)의 크기(Level)를 설계자가 목표하는 값에 최적화 시킬 수 있는 방법을 찾는 것이 무엇보다도 중요하다 최근 연구된 빔형성 기법의 대표적인 결과 중 하나로는 Phi1ip의 Weighting Function Method를 들 수 있다. 이러한 Phi1ip의 방법은 목표 주엽폭과 부엽 준위를 정하고 이를 적응필터 설계방식과 유사하게 적응시켜 나가는 과정을 이용한다. 그러나 이 방법은 주엽폭과 부엽 준위간의 상관관계로 인해서 원하는 부엽준위에 다다르지 못하거나 결과값을 얻는데 상당한 시간이 소요되는 경우가 자주 발생하는데, 이러한 단점을 보완하기 위하여 본 논문에서는 부엽 준위에 미치지 못하는 일부에 대하여 부분 최적화를 시켜 비교적 쉽게 설계를 만족시키는 새로운 알고리듬을 제안하고자 한다.

• The Journal of the Acoustical Society of Korea
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• v.20 no.7
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• pp.94-100
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• 2001

When using a sample covariance matrix data in paucity of snapshots, adaptive matched field processing will have problem in inverting covariance matrix due to the rank deficiency. The general solutions are diagonal loading and eigenanalysis methods, but there is a significant bias in the power output. This paper presents a quantitative study of bias of power output and the performance of source localization through the simulation and the measured data analysis in fixed source case using the diagonal loading method for the minimum variance processor. Results show that the bias in power output is reduced and the performance of source localization is improved when the number of snapshots is greater than the number of array sensors.

• Journal of Mechanical Science and Technology
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• v.18 no.3
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• pp.379-387
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• 2004

A novel flatness sensing system which is called the Flatness Sensing Inter-stand Looper(FlatSIL) system is suggested and a self-tuning PI control system using the FlatSIL is designed for improving the flatness of hot strip in finishing mill processes. The FlatSIL system measures the tension along the direction of the strip width by using segmented rolls, and the tension profile is approximated through the tension of each segmented roll. The flatness control system is operated by using the tension profile. The proposed flatness control system as far as the tension profile-measuring device works for the full strip length during the strip rolling in finishing mills. The generalized minimum variance self-tuning (GMV S-T) PI control method is applied to control the flatness of hot strip which has a design parameter as weighting factor for updating the PI gains. Optimizing the design parameter in the GMV S-T PI controller, the Robbins-Monro algorithm is used. It is shown by the computer simulation and experiment that the proposed GMV S-T PI flatness control system has better performance than the fixed PI flatness control system.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.39 no.6
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• pp.1-8
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• 2002

This paper presents a generalized minimum-variance self-tuning controller with a PID structure using neural network which adapts to the changing parameters of the nonlinear system with nonminimum phase behavior and time delays. The neural network is used to estimate the controller parameters, and the control output is obtained through estimated controller parameter. In order to demonstrate the effectiveness of the proposed algorithm, the computer simulation is done to adapt the nonlinear nonminimum phase system with time delays and changed system parameter after a constant time. The proposed method compared with direct adaptive controller using neural network.

• Journal of Institute of Control, Robotics and Systems
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• v.13 no.1
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• pp.52-57
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• 2007

In this paper we proposed multi-directional matching windows combined by multi-dimensional feature vector matching, which uses not only intensity values but also multiple feature values, such as variance, first and second derivative of pixels. Multi-dimensional feature vector matching has the advantage of compensating the drawbacks of area-based stereo matching using one feature value, such as intensity. We define matching cost of a pixel by the minimum value among eight multi-dimensional feature vector distances of the pixels expanded in eight directions having the interval of 45 degrees. As best stereo matches, we determine the two points with the minimum matching cost within the disparity range. In the experiment we used aerial imagery and IKONOS satellite imagery and obtained more accurate matching results than that of conventional matching method.

• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.9
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• pp.148-156
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• 2014

In the MVDR (minimum variance distortionless response) adaptive array, its performance could be greatly deteriorated in the presence of steering vector errors as the desired signal is treated as an interference. This paper suggests an computationally simple adaptive beamforming method which is robust against these errors. In the proposed method, a minimization problem that is formulated according to the DCB (doubly constrained beamforming) principle is solved to find a solution vector, which is in turn projected onto a subspace to obtain a new steering vector. The minimization problem and the subspace projection are dealt with using some principal eigenpairs, which are obtained using a modified PASTd(projection approximation subspace tracking with deflation). We improve the existing MPASTd(modified PASTd) algorithm such that the computational complexity is reduced. The proposed beamforming method can significantly reduce the complexity as compared with the conventional ones directly eigendecomposing an estimate of the corelation matrix to find all eigenvalues and eigenvectors. Moreover, the proposed method is shown, through simulation, to provide performance improvement over the conventional ones.