• Journal of the Korean Society of Radiology
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• v.13 no.3
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• pp.453-458
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• 2019

In this study we analyzed the tendency of the image characteristic by changing filtering factor for the proposed fast non local means (FNLM) noise reduction algorithm with designed Male Adult mesh (MASH) phantom through Geant4 application for tomographic emission (GATE) simulation program. To accomplish this purpose, MASH phantom for human copy was designed through the GATE simulation program. In addition, we acquired degraded image by adding Gaussian noise with a value of 0.005 using the MATALB program in MASH phantom. Moreover, in degraded image, the FNLM noise reduction algorithm was applied by changing the filtering factors, which set to 0.005, 0.01, 0.05, 0.1, 0.5, and 1.0 value, respectively. To quantitatively evaluate, the coefficient of variation (COV), signal to noise ratio (SNR), and contrast to noise ratio (CNR) were calculated in reconstructed images. Results of the COV, SNR and CNR were most improved in image with a filtering factor of 0.05 value. Especially, the COV was decreased with increasing filtering factor, and showed nearly constant values after 0.05 value of the filtering factor. In addition, SNR and CNR were showed that improvement with increasing filtering factor, and deterioration after 0.05 value of the filtering factor. In conclusion, we demonstrated the significance of setting the filtering factor when applying the FNLM noise reduction algorithm in degraded image.

• Journal of the Korean Society of Radiology
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• v.13 no.4
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• pp.623-628
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• 2019

The aim of this study was to design fast non local means (FNLM) noise reduction algorithm and to confirm its application feasibility in light microscopic image. For that aim, we acquired mouse first molar image and compared between previous widely used noise reduction algorithm and our proposed FNLM algorithm in acquired light microscopic image. Contrast to noise ratio, coefficient of variation, and no reference-based evaluation parameter such as natural image quality evaluator (NIQE) and blind/referenceless image spatial quality evaluator (BRISQUE) were used in this study. According to the result, our proposed FNLM noise reduction algorithm can achieve excellent result in all evaluation parameters. In particular, it was confirmed that the NIQE and BRISQUE evaluation parameters for analyzing the overall morphologcal image of the tooth were 1.14 and 1.12 times better than the original image, respectively. In conclusion, we demonstrated the usefulness and feasibility of FNLM noise reduction algorithm in light microscopic image of small animal tooth.

• Journal of the Korean Society of Radiology
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• v.18 no.2
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• pp.93-99
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• 2024

The digital medical imaging, especially, computed tomography (CT), should necessarily be considered in terms of noise distribution caused by converting to X-ray photon to digital imaging signal. Recently, the denoising technique based on deep learning architecture is increasingly used in the medical imaging field. Here, we evaluated noise reduction effect according to various noise types based on the U-net deep learning model in the lung CT images. The input data for deep learning was generated by applying Gaussian noise, Poisson noise, salt and pepper noise and speckle noise from the ground truth (GT) image. In particular, two types of Gaussian noise input data were applied with standard deviation values of 30 and 50. There are applied hyper-parameters, which were Adam as optimizer function, 100 as epochs, and 0.0001 as learning rate, respectively. To analyze the quantitative values, the mean square error (MSE), the peak signal to noise ratio (PSNR) and coefficient of variation (COV) were calculated. According to the results, it was confirmed that the U-net model was effective for noise reduction all of the set conditions in this study. Especially, it showed the best performance in Gaussian noise.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2009.04a
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• pp.205-208
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• 2009

POD(proper orthogonal decomposition)는 가해지는 하중(입력)의 계측없이 출력(응답)만으로 구조물의 동적특성을 파악할 수 있는 기법이다. 하지만 실제의 경우 측정데이터에 노이즈가 포함되어 있으면 분해가 완전하게 일어나지 않아 동적특성(특히 감쇠비)을 완벽히 파악하기 힘들다. 본 연구에서는 이러한 문제점을 보완하기 위해서 POD기법으로 추출된 각 모드의 자유진동파형에 RD(random decrement)법을 적용하여 노이즈에 의한 영향을 제거하는 방법을 제안하였다. 본 논문에서는 먼저 수치모델을 사용하여 계측노이즈가 있을 경우 제안된 방법을 사용하면 노이즈의 영향을 감소시킬 수 있음을 검증한 후 실험실 규모의 구조물모형에서 얻은 자유진동계측치에 제안된 기법을 적용하여 시스템식별을 수행하여 동특성을 파악하였다.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.16 no.2
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• pp.113-122
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• 2016

In this paper, we suggest a approach which comprises fast Fourier transform inversion by wavelet noise attenuation. It represents an inverse filtering by adopting a factor into the Wiener filtering, and the optimal factor is chosen to minimize the overall mean squared error. in order to apply the Wiener filter, we have to compute the power spectrum of original image from the corrupted figure. Since the Wiener filtering contains the inverse filtering process, it expands the noise when the blurring filter is not invertible. To remove the large noises, the best is to remove the noise using wavelet threshold. Wavelet noise attenuation steps are consisted of inverse filtering and noise reduction by Wavelet functions. experimental results have not outperformed the other methods over the overall restoration performance.

• Journal of the Korean Magnetics Society
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• v.14 no.5
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• pp.174-179
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• 2004

Attenuation of conduction noise through microstrip line attached with the high lossy iron flakes-rubber composites has been investigated in GHz frequencies. Microstrip line was designed with characteristic impedance of 50 $\Omega$ and a length corresponding to the center frequency of 3 GHz. Iron flakes were fabricated by mechanical forging of spherical iron powders using an attrition mill. The fabricated microstrip line shows a ideal propagation characteristics of S$\sub$11/ < -60 dB and S$\sub$21/ = 0 dB. Attaching a noise absorbing sheet on the microstrip line, S$\sub$11/ increases to about -10 dB and S$\sub$21/ decreases to -20～-60 dB depending on the length of absorbing sheet. The calculated power loss is as high as 80% in the frequency range 2～8 GHz. It is suggested that the most critical material parameter is magnetic loss for the enhancement of noise attenuation.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2010.10a
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• pp.598-598
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• 2010

와이퍼 작동 중에 발생하는 진동소음 중 1000Hz 이상의 스퀼 소음은 발생 메커니즘이 정확하게 알려지지 않았으며 발생하는 조건도 불규칙하다. 이 스퀼 소음의 발생 빈도 및 주파수를 변경하는 설계를 위하여 스퀼 진동 소음의 발생 메커니즘의 원인분석이 우선적으로 이루어져야 한다. 이 논문에서는 자동차 와이퍼 시스템에서 워셔액을 분사하였을 때 발생하는 스퀼 소음을 측정하고 인자 별로 분석하였다. 스퀼 소음이 발생하는 인자들을 마찰계수와 연관이 있는 인자, 기하학적인 인자, 와이퍼의 운동과 관련된 인자들로 나누어 분석하였다. 실제 와이퍼 시스템을 구현하기 위하여 모터와 원판 지지대 등을 이용하였고, 마이크로폰, 레이저 바이브로미터, 노이즈북, 아르테미스를 이용하여 측정 및 분석하였다.

• Journal of the Korea Society of Computer and Information
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• v.25 no.4
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• pp.37-45
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• 2020

The development process of deep learning is an iterative task that requires a lot of manual work. Among the steps in the development process, pre-processing of learning data is a very costly task, and is a step that significantly affects the learning results. In the early days of AI's algorithm research, learning data in the form of public DB provided mainly by data scientists were used. The learning data collected in the real environment is mostly the operational data of the sensors and inevitably contains various noises. Accordingly, various data cleaning frameworks and methods for removing noises have been studied. In this paper, we proposed a method for detecting and removing noises from time-series data, such as sensor data, that can occur in the IoT environment. In this method, the linear regression method is used so that the system repeatedly finds noises and provides data that can replace them to clean the learning data. In order to verify the effectiveness of the proposed method, a simulation method was proposed, and a method of determining factors for obtaining optimal cleaning results was proposed.

• Proceedings of the KIEE Conference
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• 2006.07d
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• pp.2061-2062
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• 2006

a-Si(amorphous silicon) TFT(thin film transistor)는 TFT-LCD(liquid crystal display)의 화소 스위칭(switching) 소자로 폭넓게 이용되고 있다. 현재는 a-Si을 이용하여 gate drive IC를 기판에 집적하는 ASG(amorphous silicon gate) 기술이 연구, 적용되고 있는데 이때 가장 큰 제약은 문턱 전압(Vth)의 이동이다. 특히 고온에서는 문턱 전압의(Vth) 이동이 가속화 되고, Ioff current가 증가 하게 되고, 저온($0^{\circ}C$)에서는 전류 구동능력이 상온($25^{\circ}C$) 상태에서 같은 게이트 전압(Vg)에 대해서 50% 수준으로 감소하게 된다. 특히 ASG 회로는 여러 개의 TFT로 구성되는데, 각각의 TFT가 고온에서 Vth shift 값이 다르게 되어 설계시 예상하지 못 한 고온에서의 화면 무너짐 현상 즉 고온 노이즈 불량이 발생 할 수 있다. 고온 노이즈 불량은 고온에서의 각 TFT의 문턱전압 및 $I_D-V_G$ 특성을 측정한 결과 고온 노이즈 불량에 영향을 주는 인자가 TFT의 width와 기생 capacitor비 hold TFT width가 영향을 주는 것으로 실험 및 시뮬레이션 결과 확인이 되었다. 발생 mechanism은 ASG 회로는 AC 구동을 하기 때문에 Voff 전위에 ripple이 발생 되는데 특히 고온에서 ripple이 크게 증가 하여 출력 signal에 영향을 주어 불량이 발생하는 것을 규명하였다.

• International Journal of Highway Engineering
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• v.3 no.4 s.10
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• pp.137-146
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• 2001

Pavement distresses are one of the most important data for pavement management systems. Inspection machines and its related programs have been used for operating tools in PMS developed in advanced countries. In Korea imported machines and programs for the length price ale utilized to get information of pavement condition from the field This study is launched for developing the program which can detect cracks on asphalt pavement due to many drawbacks in current PMS operation such as improper maintenance work and long resting period when it was broken. The focus of this study is to define principles to analyze pavement surface with digital image processing techniques, to test property of pavement images and to suggest an algorithm that reduces noises at test. To test images, the camera attached on the Automatic Road Analyser(ARAN) was used. Through the FFT images, histogram and statistical values of pavement images, it was found that the images had many noises with high-frequency components against general images, and it was difficult to subdivide pavement images into background or crack. Through several testing with various filters for noise reduction a 3X3 median filter was suggested to reduce noises effectively.