• Geophysics and Geophysical Exploration
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• v.19 no.1
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• pp.11-19
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• 2016

Two representative residual static methods of traveltime decomposition and stack-power maximization are discussed in terms of application to land seismic data. For the model data with synthetic shot/receiver statics (time shift) applied and random noises added, continuities of reflection event are much improved by stack-power maximization method, resulting the derived time-shifts approximately equal to the synthetic statics. Optimal parameters (maximum allowable shift, correlation window, iteration number) for residual statics are effectively chosen with diagnostic displays of CSP (common shot point) stack and CRP (common receiver point) stack as well as CMP gather. In addition to removal of long-wavelength time shift by refraction statics, prior to residual statics, processing steps of f-k filter, predictive deconvolution and time variant spectral whitening are employed to attenuate noises and thereby to minimize the error during the correlation process. The reflectors including horizontal layer of reservoir are more clearly shown in the variable-density section through repicking the velocities after residual statics and inverse NMO correction.

• The KIPS Transactions:PartB
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• v.18B no.3
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• pp.147-156
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• 2011

This paper proposes a 3-stage (preprocessing, feature extraction, and classification) fault detection and classification algorithm for induction motors. In the first stage, a low-pass filter is used to remove noise components in the fault signal. In the second stage, a discrete cosine transform (DCT) and a statistical method are used to extract features of the fault signal. Finally, a back propagation neural network (BPNN) method is applied to classify the fault signal. To evaluate the performance of the proposed algorithm, we used one second long normal/abnormal vibration signals of an induction motor sampled at 8kHz. Experimental results showed that the proposed algorithm achieves about 100% accuracy in fault classification, and it provides 50% improved accuracy when compared to the existing fault detection algorithm using a cross-covariance method. In a real-world data acquisition environment, unnecessary noise components are usually included to the real signal. Thus, we conducted an additional simulation to evaluate how well the proposed algorithm classifies the fault signals in a circumstance where a white Gaussian noise is inserted into the fault signals. The simulation results showed that the proposed algorithm achieves over 98% accuracy in fault classification. Moreover, we developed a testbed system including a TI's DSP (digital signal processor) to implement and verify the functionality of the proposed algorithm.

• Journal of IKEEE
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• v.14 no.2
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• pp.16-23
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• 2010

Since the interest of a brain science research is increased recently, various devices using brain waves have been developed in the field of brain training game, education application and brain computer interface. In this paper, we have developed a portable EEG measurement and a bluetooth based wireless transmission device measuring brain waves from the frontal lob simply and conveniently. The low brain signals about 10~100${\mu}V$ was amplified into several volts and low pass, high pass and notch filter were designed for eliminating unwanted noise and 60Hz power noise. Also, PIC24F192 microcontroller has been used to convert analog brain signal into digital signal and transmit the signal into personal computer wirelessly. The sampling rate of 1KHz and bluetooth based wireless transmission with 38,400bps were used. The LabVIEW programing was used to receive and monitor the brain signals. The power spectrum of commercial biopac MP100 and that of a developed EEG system was compared for performance verification after the simulation signals of sine waves of $1{\mu}V$, 0~200Hz was inputed and processed by FFT transformation. As a result of comparison, the developed system showed good performance because frequency response of a developed system was similar to that of a commercial biopac MP100 inside the range of 30Hz specially.

• Journal of Broadcast Engineering
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• v.26 no.1
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• pp.26-38
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• 2021

In this paper, DLKF (Deep Learning Keypoint Filtering), the deep learning-based keypoint filtering method for the rapidization of the image registration method for remote sensing images is proposed. The complexity of the conventional feature-based image registration method arises during the feature matching step. To reduce this complexity, this paper proposes to filter only the keypoints detected in the artificial structure among the keypoints detected in the keypoint detector by ensuring that the feature matching is matched with the keypoints detected in the artificial structure of the image. For reducing the number of keypoints points as preserving essential keypoints, we preserve keypoints adjacent to the boundaries of the artificial structure, and use reduced images, and crop image patches overlapping to eliminate noise from the patch boundary as a result of the image segmentation method. the proposed method improves the speed and accuracy of registration. To verify the performance of DLKF, the speed and accuracy of the conventional keypoints extraction method were compared using the remote sensing image of KOMPSAT-3 satellite. Based on the SIFT-based registration method, which is commonly used in households, the SURF-based registration method, which improved the speed of the SIFT method, improved the speed by 2.6 times while reducing the number of keypoints by about 18%, but the accuracy decreased from 3.42 to 5.43. Became. However, when the proposed method, DLKF, was used, the number of keypoints was reduced by about 82%, improving the speed by about 20.5 times, while reducing the accuracy to 4.51.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.5
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• pp.518-523
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• 2017

This paper proposes a novel denoising method based on non-local(NL) means. The NL-means algorithm is effective for removing an additive Gaussian noise, but the denoising parameter should be controlled depending on the noise level for proper noise elimination. Therefore, the proposed method optimizes the denoising parameter according to the noise levels. The proposed method consists of two processes: off-line and on-line. In the off-line process, the relations between the noise level and the denoising parameter of the NL-means filter are analyzed. For a given noise level, the various denoising parameters are applied to the NL-means algorithm, and then the qualities of resulting images are quantified using a structural similarity index(SSIM). The parameter with the highest SSIM is chosen as the optimal denoising parameter for the given noise level. In the on-line process, we estimate the noise level for a given noisy image and select the optimal denoising parameter according to the estimated noise level. Finally, NL-means filtering is performed using the selected denoising parameter. As shown in the experimental results, the proposed method accurately estimated the noise level and effectively eliminated noise for various noise levels. The accuracy of noise estimation is 90.0% and the highest Peak Signal-to-noise ratio(PSNR), SSIM value.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.35 no.5A
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• pp.482-489
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• 2010

In this paper we have proposed a new architecture of DQ-IRM(Double-Quadrature Image Rejection Mixer) for image rejection in the low-IF receiver. It consist of a frequency-tunable RF PPF(Poly Phase Filter) and the quadrature mixers. The conventional DQ-IRM generates the quadrature RF signals for the RF wide band at once. But the proposed DQ-IRM with the frequency-tuable RF PPF generates the quadrature RF signals for the narrow band of 2~3 channels bandwidth, which is partitioned from the RF wide band. We designed the CMOS RF tuner for T-DMB(Terrestrial Digital Multimedia Broadcasting) with the proposed 3rd DQ-IRM using a 0.18um CMOS technology and verified the performances of the designed receiver such as the image rejection ratio, the noise figure and the power consumption. The overall NF of the RF tuner is about 1.26 dB and the image reject ratio is about 51 dB. The power consumption is 55.8 mW at 1.8 V supply voltage. The chip area is $3.0{\times}2.5mm^2$.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.4 no.4
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• pp.250-257
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• 2011

This experiment present the automatic detection algorithm of gastric cancer that take second place among all cancers. If an inflammation and a cancer are not examined carefully, early ones have difficulty in being diagnosed as illnesses than advanced ones. For diagnosis of gastric cancer, and progressing cancer in this study, present 4 algorithm. research team extracted an abnormal part in stomach through the endoscope image. At first, decide to use shading technique or not in each endoscope image for study. it make easy distinguish to whether tumor is existing or not by putting shading technique in or eliminate it by the color. Second. By passing image subjoin shading technique to erosion filter, eliminate noise and make give attention to diagnose. Third. Analyzing out a line and fillet graph from image adding surface shade and detect RED value according to degree of symptoms. Fourth. By suggesting this algorithm, that making each patient's endscope image into subdivision graph including RED graph value, afterward revers the color, revealing the position of tumor, this study desire to help to diagnosing gastric, other cancer and inflammation.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.29 no.6C
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• pp.793-800
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• 2004

In this paper, we propose a new automatic pipe grading algorithm for an efficient management of transmission pipe under the ground. Since the conventional transmission pipe evaluation was conducted by subjective decision made by an individual operator, it was difficult to grade them by means of numerical methods and also hard to realistically construct numerical database system. To solve these problems, we Int obtain some information on the current condition of pipes' sections by shooting laser beam at a regular rate and then apply grading algorithm after complete calculation of minimum diameter of pipe. We use some of preprocessing techniques to reduce noise and also use various color models to consider special conditions of each inner pipe. The measurement of pipes' minimum diameter and decision of grade are performed through a detailed processing stages. By some experimental results performed in the field, we show that over 90 percent of correct grade decisions are made by the proposed algorithm.

• Journal of Advanced Navigation Technology
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• v.14 no.4
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• pp.553-561
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• 2010

This paper proposed the face tracking method which can be effectively applied to the robot's vision system. The proposed algorithm tracks the facial areas after detecting the area of video motion. Movement detection of video images is done by using median filter and erosion and dilation operation as a method for removing noise, after getting the different images using two continual frames. To extract the skin color from the moving area, the color information of sample images is used. The skin color region and the background area are separated by evaluating the similarity by generating membership functions by using MIN-MAX values as fuzzy data. For the face candidate region, the eyes are detected from C channel of color space CMY, and the mouth from Q channel of color space YIQ. The face region is tracked seeking the features of the eyes and the mouth detected from knowledge-base. Experiment includes 1,500 frames of the video images from 10 subjects, 150 frames per subject. The result shows 95.7% of detection rate (the motion areas of 1,435 frames are detected) and 97.6% of good face tracking result (1,401 faces are tracked).

• Journal of Biomedical Engineering Research
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• v.37 no.5
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• pp.153-160
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• 2016

This study proposes an algorithm for removal of CPR artifact in order that automated external defibrillator (AED) can effectively diagnose ECG rhythm during cardiopulmonary resuscitation (CPR). Current AED required to interrupt chest compression for reliable rhythm analysis to avoid the effect of artifacts produced by CPR. However even temporarily interruption of chest compression during CPR adversely affects the probability of restoration of spontaneous circulation (ROSC) and survival after the delivery of the shock. Therefore, we proposed a method for removal of CPR artifacts using least mean square (LMS) filter. The removal of the CPR artifacts would enable compressions to continue during AED rhythm analysis, thereby increasing the likelihood of resuscitation success. It was tested on 31 segments of shockable and 300 segments of non-shockable ECG signals recorded from three pigs during CPR. In the result, sensitivity (Se) and specificity (Sp) analysis on the test segments showed values of Se = 3.2%, Sp = 66.0% and Se = 96.8%, Sp = 98.7% in the case of unfiltered and filtered signals during CPR. In conclusion, it was shown that the proposed method can be a useful tool to exactly diagnose the ECG rhythm during the CPR.