• Title/Summary/Keyword: Segment transform

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Modeling of Piano Sound Using Method of Line-Segment Approximation and Curve Fitting (선분 근사법과 곡선의 적합성을 이용한 피아노 음의 모델링)

  • Lim, Hun;Chong, Ui-Pil
    • The Journal of the Acoustical Society of Korea
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    • v.19 no.3
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    • pp.86-91
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    • 2000
  • In this paper, we will discuss the characteristics of the magnitude and the phase of the piano sound in frequency domain by using the FFT(Fast Fourier Transform). The method deciding the parameters representing those sounds through the mathematical model is described. We used the curve fitting method for the modeling of the harmonic part of the sound including the fundamental frequency in order to minimize the errors between original sounds and modeled sounds. furthermore, we used the line segment approximation method for the modeling of the noise part around fundamental frequency. We also applied the same method for the phase model and could get the modeled sound to be similar to the original sound using the parameters. Therefore the high compression ratio comparing the modeled sound to the original sound is achieved.

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Hologram Compression Technique using Motion Compensated Temporal Filtering (움직임보상 시간적 필터링을 이용한 홀로그램 압축 기법)

  • Seo, Young-Ho;Choi, Hyun-Jun;Kim, Dong-Wook
    • The Journal of Korean Institute of Communications and Information Sciences
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    • v.34 no.11B
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    • pp.1296-1302
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    • 2009
  • We propose an efficient coding method of digital holograms using MCTF and standard compression tools for video. The hologram is generated by a computer-generated hologram (CGH) algorithm with both an object image and its depth information. The proposed coding consists of localization by segmenting a hologram, frequency transform using $64\times64$ segment size, 2-D discrete cosine transform DCT for extracting redundancy, motion compensated temporal filtering (MCTF), segment scanning the segmented hologram to form a video sequence, and video coding, which uses H.264/AVC. The proposed algorithm illustrates that it has better properties for reconstruction, 10% higher compression rate than previous research in case of object.

The Analysis of Partial Discharges Pattern using Discrete Wavelet Transform (이산 웨이브렛변환에 의한 부분방전패턴 분석)

  • 이현동;김충년;지승욱;박광서;이광식;이동인
    • Proceedings of the Korean Institute of IIIuminating and Electrical Installation Engineers Conference
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    • 2000.11a
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    • pp.183-187
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    • 2000
  • This paper deals with multiresolution analysis of wavelet transform for partial discharge(PD), both corona and surface discharge. Multiresolution analysis was used for performing discrete wavelet transform. PD signals was decomposed into "approximation" and "detail" components until 4 levels by using discrete wavelet analysis. In this paper, daubechies family is adopted for the research of the characteristics of PD signals. The results show that in corona discharge the segment 7, 8, 9, 10, 11 values of defined variable is increased with discharge process, so phase distribution is characterized by 210~330 ranges. In case surface discharge in expoxy insulator inserted, defined variable values is fairly symmetric discharge pattern because coupled both corona and dielectric bounded discharges. We can confirmly discriminate the type PD source. the type PD source.

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Development of Exercise ECG Analysis Algorithm Using Wavelet Transform (웨이브렛 변환을 이용한 Exercise ECG 신호분석 알고리즘의 개발)

  • Park, G.L.;Lee, K.J.
    • Proceedings of the KOSOMBE Conference
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    • v.1996 no.11
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    • pp.213-216
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    • 1996
  • In this research we would like to develop an exercise ECG signal analysis algorithm using the wavelet transform, which is possible to analyze the time and the frequency simultaneously. Wavelet transform has an advantage of dividing the nonstationary signals into the high frequency and low frequency band successively. Thus, it can separates the unnecessary noises from the frequency band of QRS complex and then using the selected frequency band we could detect the QRS complex and ST segment.

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Line Segment Detection Algorithm Using Improved PPHT (개선된 PPHT를 이용한 선분 인식 알고리즘)

  • Lee, Chanho;Moon, Ji-hyun;Nguyen, Duy Phuong
    • Journal of IKEEE
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    • v.20 no.1
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    • pp.82-88
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    • 2016
  • The detection rate of Progressive Probability Hough Transform(PPHT) is decreased when a lot of noise components exist due to an unclear or complex original image although it is quite a good algorithm that detects line segments accurately. In order to solve the problem, we propose an improved line detecting algorithm which is robust to noise components and recovers slightly damaged edges. The proposed algorithm is based on PPHT and traces a line segments by pixel and checks of it is straight. It increases the detection rate by reducing the effect of noise components and by recovering edge patterns within a limited pixel size. The proposed algorithm is applied to a lane detection method and the false positive detection rate is decreased by 30% and the line detection rate is increased by 15%.

Corroded and loosened bolt detection of steel bolted joints based on improved you only look once network and line segment detector

  • Youhao Ni;Jianxiao Mao;Hao Wang;Yuguang Fu;Zhuo Xi
    • Smart Structures and Systems
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    • v.32 no.1
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    • pp.23-35
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    • 2023
  • Steel bolted joint is an important part of steel structure, and its damage directly affects the bearing capacity and durability of steel structure. Currently, the existing research mainly focuses on the identification of corroded bolts and corroded bolts respectively, and there are few studies on multiple states. A detection framework of corroded and loosened bolts is proposed in this study, and the innovations can be summarized as follows: (i) Vision Transformer (ViT) is introduced to replace the third and fourth C3 module of you-only-look-once version 5s (YOLOv5s) algorithm, which increases the attention weights of feature channels and the feature extraction capability. (ii) Three states of the steel bolts are considered, including corroded bolt, bolt missing and clean bolt. (iii) Line segment detector (LSD) is introduced for bolt rotation angle calculation, which realizes bolt looseness detection. The improved YOLOv5s model was validated on the dataset, and the mean average precision (mAP) was increased from 0.902 to 0.952. In terms of a lab-scale joint, the performance of the LSD algorithm and the Hough transform was compared from different perspective angles. The error value of bolt loosening angle of the LSD algorithm is controlled within 1.09%, less than 8.91% of the Hough transform. Furthermore, the proposed framework was applied to fullscale joints of a steel bridge in China. Synthetic images of loosened bolts were successfully identified and the multiple states were well detected. Therefore, the proposed framework can be alternative of monitoring steel bolted joints for management department.

Feature Matching Algorithm Robust To Viewpoint Change (시점 변화에 강인한 특징점 정합 기법)

  • Jung, Hyun-jo;Yoo, Ji-sang
    • The Journal of Korean Institute of Communications and Information Sciences
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    • v.40 no.12
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    • pp.2363-2371
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    • 2015
  • In this paper, we propose a new feature matching algorithm which is robust to the viewpoint change by using the FAST(Features from Accelerated Segment Test) feature detector and the SIFT(Scale Invariant Feature Transform) feature descriptor. The original FAST algorithm unnecessarily results in many feature points along the edges in the image. To solve this problem, we apply the principal curvatures for refining it. We use the SIFT descriptor to describe the extracted feature points and calculate the homography matrix through the RANSAC(RANdom SAmple Consensus) with the matching pairs obtained from the two different viewpoint images. To make feature matching robust to the viewpoint change, we classify the matching pairs by calculating the Euclidean distance between the transformed coordinates by the homography transformation with feature points in the reference image and the coordinates of the feature points in the different viewpoint image. Through the experimental results, it is shown that the proposed algorithm has better performance than the conventional feature matching algorithms even though it has much less computational load.

ST-Segment Analysis of ECG Using Polynomial Approximation (다항식 근사를 이용한 심전도의 ST-Segment 분석)

  • Jeong, Gu-Young;Yu, Kee-Ho;Kwon, Tae-Kyu;Lee, Seong-Cheol
    • Journal of Institute of Control, Robotics and Systems
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    • v.8 no.8
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    • pp.691-697
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    • 2002
  • Myocardial ischemia is a disorder of cardiac function caused by insuficient blood flow to the muscle tissue of the heart. We can diagnose myocardial ischemia by observing the change of ST-segment, but this change is temporary. Our primary purpose is to detect the temporary change of the 57-segment automatically In the signal processing, the wavelet transform decomposes the ECG(electrocardiogram) signal into high and low frequency components using wavelet function. Recomposing the high frequency bands including QRS complex, we can detect QRS complex more easily. Amplitude comparison method is adopted to detect QRS complex. Reducing the effect of noise to the minimum, we grouped ECG by 5 data and compared the amplitude of maximum value. To recognize the ECG .signal pattern, we adopted the polynomial approximation partially and statistical method. The polynomial approximation makes possible to compare some ECG signal with different frequency and sampling period. The ECG signal is divided into small parts based on QRS complex, and then, each part is approximated to the polynomials. After removing the distorted ECG by calculating the difference between the orignal ECG and the approximated ECG for polynomial, we compared the approximated ECG pattern with the database, and we detected and classified abnormality of ECG.

A Study on the Automatic Diagnosis of ECG

  • Jeong, Gu-Young;Yu, Kee-Ho;Kwon, Tae-Kyu;Lee, Seong-Cheol
    • 제어로봇시스템학회:학술대회논문집
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    • 2001.10a
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    • pp.55.4-55
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    • 2001
  • Analyzing the ECG signal, we can find heart disease. Myocardial ischemia is a disorder of cardiac function caused by insufficient blood flow to the muscle tissue of the heart. Myocardial ischemia is inscribed on ST-segment of the ECG during and after patient takes exercise or is under stress, but after long time past, the ECG pattern is return to steady state. Therefore, it is necessary to monitor and analyze the ECG signal continuously for patient or aged people. Our primary purpose is the detection of temporary change of the ST-segment of ECG automatically. In the signal processing, the wavelet transform decomposes the ECG signal into high and low frequency components using wavelet function. Recomposing the high frequency bands including QRS complex, we can detect QRS complex more easily ...

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Non-restricted Measurement and Diagnosis of ECG signals

  • Jeong, Gu-Young;Yu, Kee-Ho
    • 제어로봇시스템학회:학술대회논문집
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    • 2002.10a
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    • pp.77.3-77
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    • 2002
  • In this paper, the algorithm for detecting the transient change of ST-segment and the device for measuring ECG from patient without restriction of activity are introduced. ST-segment elevation and depression is considered as the main characteristic in diagnosis of myocardial ischemia, but the change of pattern is also important. To consider all of the former and the latter, we used polynomial approximation for diagnosis of ECG. The feature points(R, S and T are detected through the signal processing processes including wavelet transform, and then R-S and S-T are approximated to polynomial. This method allows comparison of two signals that have different sampling time or different numbers of...

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