• Journal of IKEEE
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• v.18 no.4
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• pp.523-531
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• 2014

In this paper, a fast single image blind denoising algorithm is presented, where noise parameters are estimated by local statistics of an observed degraded image without a prior information about the additive noise. The estimated noise parameters are used to define the constraints on the noise detection which is coupled with the 1st-order Markov Random Field. In addition, an adaptive modified weighted Gaussian filter is introduced, where variable window sizes and weighting coefficients defined by the constraints are used to control the degree of the smoothness of the reconstructed image. The experimental results demonstrate the capability of the proposed algorithm. Please put the abstract of paper here.

• The Proceeding of the Korean Institute of Electromagnetic Engineering and Science
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• v.23 no.1
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• pp.37-47
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• 2012

수십 W급 이상의 무선 전력 전송 기술을 활용한 시스템의 증가와 더불어, 전자파 환경 문제가 심각하게 대두되고 있으며, 본 논문에서는 전자파 환경을 고려한 무선 전력 전송 시스템 설계 기술에 관하여 언급한다. 가장 먼저 무선 전력 전송 시스템을 분류하고, 회로 해석을 통하여 동작 특성을 분석하며, 코일 시스템에서 발생하는 전자파 노이즈 예측 및 축소 기술을 소개하고, 마지막으로 제작된 LED TV를 위한 무선 전력 전송 시스템에서 발생하는 전자파 노이즈 측정 결과 분석을 통하여, 설계 단계에서 전자파 노이즈를 예측하고, 축소 기술을 적용하면, 전자파 노이즈 규정을 만족할 수 있는 무선 전력 전송 시스템 설계를 할 수 있음을 확인하였다.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.38A no.2
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• pp.183-190
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• 2013

In this paper, we propose a spatially adaptive noise detection and removal algorithm for a single degraded image. Under the assumption that an observed image is Gaussian-distributed, the noise information is estimated by local statistics of degraded image, and the degree of the additive noise is detected by the local statistics of the estimated noise. In addition, we describe a noise removal method taking a modified Gaussian filter which is adaptively determined by filter parameters and window size. The experimental results demonstrate the capability of the proposed algorithm.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2019.06a
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• pp.272-273
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• 2019

VVC(Versatile Video Coding)는 YUV 입력 영상에 대하여 Luma 성분과 Chroma 성분에 대하여 각각 다른 최적의 방법으로 블록분할 후 해당 블록에 대해서 화면 내 예측 또는 화면 간 예측을 수행하고, 예측영상과 원본영상의 차이를 변환, 양자화하여 압축한다. 이 과정에서 복원영상에는 블록화 노이즈, 링잉 노이즈, 블러링 노이즈 발생한다. 본 논문에서는 인코더에서 원본영상과 복원영상의 잔차신호에 대한 MAE(Mean Absolute Error)를 추가정보로 전송하여 이 추가정보와 복원영상을 이용하여 Deep Learning 기반의 신경망 네트워크로 영상의 품질을 높이는 방법을 제안한다. 복원영상의 노이즈를 감소시키기 위하여 영상을 $32{\times}32$블록의 임의로 분할하고, DenseNet기반의 UNet 구조로 네트워크를 구성하였다.

• Journal of Korea Multimedia Society
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• v.16 no.12
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• pp.1368-1375
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• 2013

In this paper, we presents a spatially adaptive noise detection and removal algorithm. Under the assumption that an observed image and the additive noise have Gaussian distribution, the noise parameters are estimated with local statistics, and the parameters are used to define the constraints on the noise detection process, where the first order Markov Random Field (MRF) is used. In addition, an adaptive low-pass filter having a variable window sizes defined by the constraints on noise detection is used to control the degree of smoothness of the reconstructed image. Experimental results demonstrate the capability of the proposed algorithm.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.32 no.10C
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• pp.975-981
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• 2007

In this paper, we propose a spatially adaptive image restoration algorithm using local and statistics and estimated noise. The ratio of local mean, variance, and maximum values with different window size is used to constrain the solution space, and these parameters are computed at each iteration step using partially restored image. In addition, the additive noise estimated from partially restored image and the local constraints are used to determine a parameter for controlling the degree of local smoothness on the solution. The resulting iterative algorithm exhibits increased convergence speed when compared to the non-adaptive algorithm. In addition, a smooth solution with a controlled degree of smoothness is obtained without a prior knowledge about the noise. Experimental results demonstrate that the proposed algorithm requires the similar iteration number to converge, but there is the improvement of SNR more than 0.2 dB comparing to the previous approach.

• Proceedings of the KIEE Conference
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• 2007.04b
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• pp.5-9
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• 2007

본 논문에서는 실제 운용 중인 현장에서 노이즈 및 서지 신호를 측정 분석하여, 그 결과를 토대로 이들 노이즈 신호를 제거할 수 있는 접지시스템 및 내부 보호 설비를 제안하여 시공하였다. 실제 운용 중인 설비 내에 유도 흑은 침입하는 전원 계통의 sag, Swell, Transient, 서지 그리고 고조파와 같은 노이즈와 접지시스템을 통해 유입되는 다양한 노이즈 신호를 차단하여 빠르고 안전하게 제거하는 방안을 연구하였다. 이를 위해 운용 중인 설비의 다양한 노이즈 신호를 실측하였고, 실측된 결과로부터 전달 및 침입 경로를 예측하여, 기존 접지 구성 및 내부 배선의 문제점의 개선하고 노이즈 및 서지의 실제적 차단을 위한 접지시스템과 내부 보호 설비를 설계 제안하였다. 또한 설계 시뮬레이션 결과와 현장 시공 결과를 비교하여 제안된 설비의 성능을 확인하였고, 시공 후 설비 운용 중에 노이즈 및 서지 신호를 재 측정하여 기존 설비 운용시 측정했던 결과를 비교 분석하여 제안 보호 설비의 개선된 성능을 최종 확인하였다.

• Journal of the Korean Data Analysis Society
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• v.20 no.6
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• pp.2853-2864
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• 2018

Discriminant analysis predicts a class label of a new observation with an unknown label, using information from the existing labeled data. Hence, observed labels play a critical role in the analysis and we usually assume that these labels are correct. If the observed label contains an error, the data has label noise. Label noise can frequently occur in real data, which would affect classification performance. In order to resolve this, a comparative study was carried out using simulated data with label noise. In particular, we considered 4 different classification techniques such as LDA (linear discriminant analysis classifiers), QDA (quadratic discriminant analysis classifiers), KNN (k-nearest neighbour), and SVM (support vector machine). Then we evaluated each method via average accuracy using generated data from various scenarios. The effect of label noise was investigated through its occurrence rate and type (noise location). We confirmed that the label noise is a significant factor influencing the classification performance.

• The Transactions of the Korean Institute of Power Electronics
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• v.4 no.6
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• pp.579-588
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• 1999

This paper presents a technique for predicting the conductLu EMI(Electro Magnetic Interference) produced b by PWM inverter-induction motor drive system. To obtain the simulation models for prediction of conduct떠 n noise, high frequency model of an inverter leg with parasitic elements and multi-coil model of stator winding M are designed. Finally, the results are confirmLu from simulation and experiments.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.607-610
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• 2021

When learning a model in supervised learning, input data and the label of the data are required. However, labeling is high cost task and if automated, there is no guarantee that the label will always be correct. In the case of supervised learning in such a noisy labels environment, the accuracy of the model increases at the initial stage of learning, but decrease significantly after a certain period of time. There are various methods to solve the noisy label problem. But in most cases, the probability predicted by the model is used as the pseudo label. So, we proposed a method to predict the true label more quickly by refining the probabilities predicted by the model. Result of experiments on the same environment and dataset, it was confirmed that the performance improved and converged faster. Through this, it can be applied to methods that use the probability distribution predicted by the model among existing studies. And it is possible to reduce the time required for learning because it can converge faster in the same environment.