• Journal of the Korean Society for Precision Engineering
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• v.12 no.12
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• pp.130-138
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• 1995

Machined graphite/epoxy surfaces were studied by using SEM (Scanning Electron Microscopy), surface profilometry and its analysis to determine suitable surface describing parameters for machined unidirectional and multidirectional laminate composite. The surface roughness and profile are found to be highly depdndent on the fiber layup direction and the measurement direction. It was possible to machine 90 .deg. and -45 .deg. plies due to the adjacent plies, which were holding those plies. It was found that the microgeometrical variations in terms of roughness parameters $R_{a}$ without $D_{y}$(Maximum Damage Depth) region and $D_{y}$are better descriptors of the machined laminate composite surface than commonly used roughness parameters $R_{a}$and $R_{max}$ The characteristics of surface profiles in laminate composite are well represented in CPD (Cumulative Probability Distribution) plot and PPD (Percentage Probability Density) plot. Edge-trimmed multidirectional laminate surfaces are Gaussian and random for profiles measured along the tool movement direction, they are periodic and non-Gaussian in the direction perpendicular to the tool movement.t.ent.t.

• 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.

• Journal of Broadcast Engineering
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• v.22 no.5
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• pp.608-617
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• 2017

Methods of contrast enhancement have problem such as side effect of over-enhancement with non-gaussian histogram distribution, tradeoff enhancement efficiency against brightness preserving. In order to enhance contrast at various histogram distribution, segmentation to region with gaussian distribution and then enhance contrast each region. First, we segment an image into several regions using GMM(Gaussian Mixture Model)fitting by that k-mean clustering and EM(Expectation-Maximization) in $L^*a^*b^*$ color space. As a result region segmentation, we get the region map and probability map. Then we apply local contrast enhancement algorithm that mean shift to minimum overlapping of each region and preserve brightness histogram equalization. Experiment result show that proposed region based contrast enhancement method compare to the conventional method as AMBE(AbsoluteMean Brightness Error) and AE(Average Entropy), brightness is maintained and represented detail information.

• Proceedings of the Korean Vacuum Society Conference
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• 1999.07a
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• pp.37-37
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• 1999

KSTAR(Korea Superconducting Tokamak Advanced Research) 핵융합 토카막 실험 장치의 플라즈마 가열을 위한 수소 중성입자빔 수송라인 내에 설치되는 collimator에 가해지는 열속 및 플라즈마에 전달되는 빔의 통과율을 해석하였다. 43cm$\times$12cm 크기의 이온원으로부터 방출되는 이온빔의 공간적 분산은 기본적으로는 Gaussian 분산(수직바향으로 1.2$^{\circ}$, 수평방향으로 0.5$^{\circ}$)의 형태를 가지지만 이온 가속 전장의 공간적 불균일로 인해 Gaussian 분산에서 다소 벗어나는 형태를 띠게 되는데, 이의 영향을 고려할 수 있는 수학적 모델을 정립하였다. 해석에 고려된 요소들은 다음과 같다. 이온원을 수많은 점원의 집합으로 가정하여 각각의 점원으로부터 주어진 공간적 분산을 가지는 이온들이 방출되는 것으로 가정하였으며, 방출된 이온은 중성화 과정을 거쳐 40%의 이온만이 중성입자화되며, 중성화되지 않은 60%의 이온들은 bending magnet에서 ion dump로 유도되어 사라지며, 나머지 중성입자들은 직진 운동을 하게 된다. 빔 진행 도중 빔 중앙에서 크게 벗어나는 일부 중성입자들은 여러 겹으로 존재하는 빔 collimator에 의해 단계적으로 제거되며, 일부 중성입자들은 잔류 수소기체에 의한 재이온화 과정을 거치기도 한다. 여기서는 정립된 수학적 모델을 이용하여 이들 collimator에서 제거되는 양 및 재이온화 손실들을 고려하여 최종적으로 플라즈마에 입사되는 중성입자 빔을 계산하였다. 한편, 빔 수송라인 설치시에 발생할 수 있는 설치 오차를 이온원 설치시의 오차와 빔 collimator 설치상의 오차로 구분하여 이들의 의한 영향도 계산하였다. Gaussian 분산을 가정하였을 경우, 이온원에 가장 근접하여 설치되는 collimator에 가해지는 수직성분의 열속은 9.7kW/cm2로 계산되었다. 이 열속을 제어 가능한 수준으로 낮추기 위해서 collimator는 빔 라인과 거의 나란하게 설치될 것이다. 빔의 통과율은 약 33%로서 하나의 이온원에서 방출된 7.8MW 중 2.5 MW만이 플라즈마에 전달되는 것을 알 수 있었다. Non-Gaussian 분산의 경우, 최대 열속은 9.1kW/cm2로 다소 낮아졌으나, 빔통과율은 28%정도로 더욱 낮아졌다. 설치상의 오차에 의한 영향을 살펴보면, 이온원이 1$^{\circ}$ 정도 기울어지게 설치된다면 collimaor에 가해지는 최대 열속 및 빔통과율은 약 15kW/cm2, 16.6% 정도로 나타나 매우 심각한 결과를 초래함을 알 수 있었다. 이에 비해 collimator 설치상의 오차의 영향은 이보다 훨씬 작아 5mm 오차가 발생했을 경우에도 최대 열속은 12kW/cm2까지 증가했으나, 빔 통과율의 변화는 거의 없었다.

• International Journal of Highway Engineering
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• v.14 no.3
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• pp.77-85
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• 2012

Most of Automatic Accident Detection Algorithm has a problem of detecting an accident as traffic congestion. Actually, center's managers deal with accidents depend on watching CCTV or accident report by drivers even though they run the Automatic Accident Detection system. It is because of the system's detecting errors such as detecting non-accidents as accidents, and it makes decreasing in the system's overall reliability. It means that Automatic Accident Detection Algorithm should not only have high detection probability but also have low false alarm probability, and it has to detect accurate accident spot. The study tries to verify and evaluate the effectiveness of using Gaussian Mixture Model and individual vehicle tracking to adapt Accident Detection Algorithm to Center Management System by measuring accident detection probability and false alarm probability's frequency in the real accident.

• Journal of Korea Multimedia Society
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• v.12 no.1
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• pp.26-30
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• 2009

We propose an adaptive Recursive Least Rank(RLR) L-filter which uses an L-estimator in order statistics and is based on rank estimate in robust statistics. The proposed RLR L-filter is a non-linear adaptive filter using non-linear adaptive algorithm and adapts itself to optimal filter in the sense of least dispersion measure of errors with non-homogeneous step size. Therefore the filter may be suitable for applications when the transmission channel is nonlinear channels such as Gaussian noise or impulsive noise, or when the signal is non-stationary such as image signal.

• Speech Sciences
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• v.10 no.3
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• pp.225-239
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• 2003

In this paper, we develop a keyword spotting system using vocabulary-independent speech recognition technique, and investigate several non-keyword modeling and post-processing methods to improve its performance. In order to model non-keyword speech segments, monophone clustering and Gaussian Mixture Model (GMM) are considered. We employ likelihood ratio scoring method for the post-processing schemes to verify the recognition results, and filler models, anti-subword models and N-best decoding results are considered as an alternative hypothesis for likelihood ratio scoring. We also examine different methods to construct anti-subword models. We evaluate the performance of our system on the automatic telephone exchange service task. The results show that GMM-based non-keyword modeling yields better performance than that using monophone clustering. According to the post-processing experiment, the method using anti-keyword model based on Kullback-Leibler distance and N-best decoding method show better performance than other methods, and we could reduce more than 50% of keyword recognition errors with keyword rejection rate of 5%.

• Journal of Korea Society of Digital Industry and Information Management
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• v.16 no.3
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• pp.49-58
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• 2020

Image interpolation, a technology that converts low resolution images into high resolution images, has been widely used in various image processing fields such as CCTV, web-cam, and medical imaging. This technique is based on the fact that the statistical distributions of the white Gaussian noise and the difference between the interpolated image and the original image is similar to each other. The proposed algorithm is composed of three steps. In first, the interpolated image is derived by random image interpolation. In second, we derive weighting functions that are used to apply non-local mean filtering. In the final step, the prediction error is corrected by performing non-local mean filtering by applying the selected weighting function. It can be considered as a post-processing algorithm to further reduce the prediction error after applying an arbitrary image interpolation algorithm. Simulation results show that the proposed method yields reasonable performance.

• The Journal of Bigdata
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• v.3 no.2
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• pp.19-33
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• 2018

Owing to the increase of FTA, food trade, and versatile preferences of consumers, food import has increased at tremendous rate every year. While the inspection check of imported food accounts for about 20% of the total food import, the budget and manpower necessary for the government's import inspection control is reaching its limit. The sudden import food accidents can cause enormous social and economic losses. Therefore, predictive system to forecast the compliance of food import with its preemptive measures will greatly improve the efficiency and effectiveness of import safety control management. There has already been a huge data accumulated from the past. The processed foods account for 75% of the total food import in the import food sector. The analysis of big data and the application of analytical techniques are also used to extract meaningful information from a large amount of data. Unfortunately, not many studies have been done regarding analyzing the import food and its implication with understanding the big data of food import. In this context, this study applied a variety of classification algorithms in the field of machine learning and suggested a data preprocessing method through the generation of new derivative variables to improve the accuracy of the model. In addition, the present study compared the performance of the predictive classification algorithms with the general base classifier. The Gaussian Naïve Bayes prediction model among various base classifiers showed the best performance to detect and predict the nonconformity of imported food. In the future, it is expected that the application of the abnormality detection model using the Gaussian Naïve Bayes. The predictive model will reduce the burdens of the inspection of import food and increase the non-conformity rate, which will have a great effect on the efficiency of the food import safety control and the speed of import customs clearance.

• Journal of the Institute of Electronics and Information Engineers
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• v.49 no.10
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• pp.202-208
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• 2012

Fetal heart rate monitoring is important information to assess fetal well-being. Non-invasive fetal ECG (electrocardiography) can be derived from maternal abdominal signal. And various promising signal processing methods have been introduced to extract fetal ECG from mother's composite abdominal signal. However, non-invasive fetal ECG monitoring still has not been widely used in clinical practice due to insufficient reliable measurement and difficulty of signal processing. In application of signal processing method to extract fetal ECG, it might be lower signal to noise ratio due to time varying white Gaussian noise. In this paper, time varying Kalman smoother is proposed to remove white noise in fetal ECG and its feasibility is confirmed. Wiener process was set as Kalman system model and covariance matrix was modified according to white Gaussian noise level. Modified error covariance matrix changed Kalman gain and degree of smoothness. Optimal covariance matrix according to various amplitude in Gaussian white noise was extracted by 5 channel fetal ECG model, and feasibility of proposed method could be confirmed.