• Proceedings of the KIPE Conference
• /
• 2019.11a
• /
• pp.100-101
• /
• 2019

본 논문은 유도전동기 고정자 자속 기준 벡터제어에서, 슬립 관계식과 순환 최소자승법을 이용하여 회전자 저항 및 고정자 과도 인덕턴스를 동시에 추정하는 알고리즘을 제안한다. 모의실험을 수행하여, 추정 회전자 저항과 고정자 과도 인덕턴스가 제안된 방법에 의해 각각 실제 값에 수렴함을 보인다.

• Journal of the Korean Institute of Telematics and Electronics B
• /
• v.33B no.1
• /
• pp.161-169
• /
• 1996

This paper presents a new search region prediction method using vector quantization for the motion estimation. We find motion vectors using the full search BMA from two successive frame images first. Then the motion vectors are used for training a codebook. The trained codebook is the predicted search region. We used the unsupervised neural network for VQ encoding and codebook design. A major advantage of formulating VQ as neural networks is that the large number of adaptive training algorithm that are used for neural networks can be applied to VQ. The proposed method reduces the computation and reduce the bits required to represent the motion vectors because of the smaller search points. The computer simulation results show the increased PSNR as compared with the other block matching algorithms.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.13 no.12
• /
• pp.2625-2632
• /
• 2009

Recently, a speed control method of induction motor by vector control theory is applied to highly efficient industrial field. The speed sensors attached to motor are used for detection of rotating speed. In the case using speed sensor, the installation of cable for minimization of electric noise, weaken maintenance, increase of price are demerit. Therefore the study of speed sensorless vector control theory performed activity. The design of sensorless vector controller for induction motor using tabu search is studied. The proposed sensorless vector control for Induction Motor is composed of two parts. The first part is for optimizing the speed estimation with initial PI parameters. The second part is for optimizing the speed control with initial PI parameters using tabu search. Proposed tabu search is improved by neighbor solution creation using Triangular random distribution. In order to show the usefulness of the proposed method, we apply the proposed controller to the sensorless speed control of an actual AC induction Motor System. The performance of this approach is verified through simulation and the experiment.

• Transactions of the Korean Society for Noise and Vibration Engineering
• /
• v.22 no.8
• /
• pp.777-783
• /
• 2012

Vibration intensity has been used to identify the location of a vibration source in a vibrating system. By using vectors representation, the source of the power flow and the vibration energy transmission paths can be revealed. However, due to the large surface area of a plate-like structure, clear transmission paths cannot be achieved using the vectors representation. Experimentally, for a large surface object, the number of measured points will also be increased. This requires a lot of time for measurement. In this study, streamlines representation is used to clearly indicate the power flow transmission paths at all surface plate for FEM and experiment. To clearly improve the vibration intensity transmission paths, streamlines representation from experimental works and FEM computations are compared. Improved transmission paths visualization for both FEM and experiment are shown in comparison to conventional vectors representation. These streamlines visualization is useful to clearly identify vibration source and detail energy transmission paths especially for large surface plate-like structures. Not only that, this visualization does not need many measured point either for experiment or FEM analysis.

• Journal of the Institute of Electronics Engineers of Korea CI
• /
• v.42 no.6
• /
• pp.75-82
• /
• 2005

The robust feature vector selection method for the multiple frontal face detection is proposed in this paper. The proposed feature vector for the training and classification are integrated by means, amplitude projections, and its 1D Harr wavelet of the input image. And the statistical modeling is performed both for face and nonface classes. Finally, the estimated probability density functions (PDFs) are applied for the detection of multiple frontal faces in the still image. The proposed method can handle multiple faces, partially occluded faces, and slightly posed-angle faces. And also the proposed method is very effective for low quality face images. Experimental results show that detection rate of the propose method is $98.3\%$ with three false detections on the testing data, SET3 which have 227 faces in 80 images.

• The Journal of the Acoustical Society of Korea
• /
• v.43 no.1
• /
• pp.72-77
• /
• 2024

Recently, the necessity for sound source localization has grown significantly across various industrial sectors. Among the sound source localization methods, sound intensimetry has the advantage of having high accuracy even with a small microphone array. However, the increase in localization error at high Helmholtz numbers have been pointed out as a limitation of this method. The study proposes a method to compensate for the bias error of the measured sound intensity vector according to the Helmholtz numbers by applying deep learning. The method makes it possible to estimate the accurate direction of arrival of the source by applying a dense layer-based deep learning model that derives compensated sound intensity vectors when inputting the sound intensity vectors measured by a tetrahedral microphone array for the Helmholtz numbers. The model is verified based on simulation data for all sound source directions with 0.1 < kd < 3.0. One can find that the deep learning-based approach expands the measurement frequency range when implementing the sound intensimetry-based sound source localization method, also one can make it applicable to various microphone array sizes.

• Proceedings of the KSME Conference
• /
• 2000.04a
• /
• pp.115-120
• /
• 2000

This paper examines that it is possible to apply RWCIM for determining eigenvector coefficients associated with eigenvalues for V-notched cracks in anisotropic dissimilar materials using the complex stress function. To verify the RWCIM algorithm, two tests will be shown. First it is performed to ascertain whether predicted coefficients associated with eigenvectors is obtained exactly. Second, it makes an examination of the state of stress for FEM and RWCIM according to a number of eigenvectors at a location far away from the V-notched crack tip.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.26 no.8B
• /
• pp.1141-1151
• /
• 2001

본 논문에서는 효과적으로 선택된 초기 탐색 위치를 이용한 움직임 추정의 고속 알고리즘을 제안하였다. 제안된 알고리즘에서는 2$\times$2화소 블록 평균으로 부표본화 영상에서 움직임 벡터를 얻어 원영상 비율로 확대하고, 주위 블록의 움직임으로부터 예측 움직임 벡터를 구하여, 이 중에서 정합오차가 작은 것을 초기 탐색 위치로 선택한다. 그리고 선택된 초기 탐색 위치를 중심으로 회전 탐색을 시작하여, 연속 소거 알고리즘으로 탐색할 후보 블록을 선택하고, 부분 정합 왜곡 소거법을 사용하여 블록간 정합오차 계산량을 줄이면서, 고속으로 움직임 벡터를 추정한다. 알고리즘의 실제 적용에 있어서는 선택된 초기 탐색 위치를 중심으로 회전 탐색 패턴의 탐색 범위를 조절하거나, 매크로 블록 당 복잡도를 제한하여 계산량을 줄일 수 있다. 실험 결과, 제안된 알고리즘은 전역탐색 블록정합 알고리즘에 대하여 0.2dB 이하의 미소한 평균 PSNR 저하만을 발생하면서, FBMA 복잡도의 3% 이하의 평균 복잡도를 소요하였다. 이것은 3단계 탐색법에 대하여 40% 이하의 계산량이다. 그리고 실험 영상들의 각 프레임에 대해서도 비슷한 성능을 보임을 확인하였다.

• Transactions of the Korean Society of Mechanical Engineers A
• /
• v.25 no.9
• /
• pp.1368-1375
• /
• 2001

This paper examines that it is possible to apply RWCIM for determining eigenvector coefficients associated with eigenvalues for V-notched cracks in anisotropic dissimilar materials using the complex stress function. To verify the RWCIM algorithm, two tests will be shown. First, it is performed to ascertain whether predicted coefficients associated with eigenvectors are obtained exactly. Second, it makes an examination of the state of stresses for FEM and RWCIM according to a number of eigenvectors at a location far away from the v-notched crack tip.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2004.11a
• /
• pp.855-858
• /
• 2004

본 논문에서는 원영상 영역내 포함된 우성의 에지에 대한 구체적 정보를 이용하기 위하여 Haar 웨이블릿을 이용한 에지영상 추출한다. 추출된 에지영상에 얼굴영역을 검출하기위해 이진화된 영상에 설정된 임계값을 통하여 얻은 이진영상으로부터 얼굴영역을 검출하기 위하여 얼굴의 일반적인 구조적 정보와 처리시간이 빠른 수평, 수직히스토그램 분석법을 이용하였다. 얼굴영역을 분리한 영상에 얼굴영역의 특징벡터를 구하기 위하여 26개의 특징벡터를 사용한 효율적인 고차 국소 자동 상관함수를 사용하였다. 계산된 특징벡터는 BP 신경망의 학습을 통하여 얼굴인식을 위한 데이터로 사용하여 제안된 알고리즘에 의한 인식률향상과 속도 향상을 입증한다.