• Journal of Korea Game Society
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• v.20 no.3
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• pp.75-84
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• 2020

This paper introduces an interactive indirect illumination algorithm which considers indirect visibility. First, a small number of rays are emitted on hemisphere of the current pixel to obtain the first intersection. If this point is directly illuminated by the light source, its illuminated color is collected. Second, in order to approximate the indirect visibility, a 3D ray marching algorithm, which is based on a hierarchy structure, is used to accelerate the ray-voxel intersection. Third, the indirect images are denoised by an edge-avoiding filtering with a local means replacement method.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.37 no.8
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• pp.767-773
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• 2009

This research have introduced wavelet signal processing technic for improving navigation signals. INS signals can be distorted with conventional pre-filtering method such as low-pass filtering by unwanted smoothing on real signals. But in this paper, wavelet thresholding method is implemented to INS signal to denoise for INS-GPS integrated system. This method reduces signal noise but not distorts the rapid varing signal. And this paper applied thresholding to INS-GPS integrated navigation system and improved navigation performance.

• Journal of Korea Game Society
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• v.20 no.6
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• pp.53-62
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• 2020

This paper proposes on the implementation method of depth of field effect based on backward mapping method available in real-time rendering enviroment using calculation of sampling range based on standard normal distribution and alpha blending of color of layers. To implement the effect, this paper describe how to calculate radius of circle of confusion, establish sampling radius using circle of confusion, and determine color through alpha blending of the multiple layer and denoising.

• Journal of the Korean Society of Marine Environment & Safety
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• v.27 no.7
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• pp.1088-1097
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• 2021

Vibration data of mechanical equipment inevitably have noise. This noise adversely af ects the maintenance of mechanical equipment. Accordingly, the performance of a learning model depends on how effectively the noise of the data is removed. In this study, the noise of the data was removed using the Denoising Auto Encoder (DAE) technique which does not include the characteristic extraction process in preprocessing time series data. In addition, the performance was compared with that of the Wavelet Transform, which is widely used for machine signal processing. The performance comparison was conducted by calculating the failure detection rate. For a more accurate comparison, a classification performance evaluation criterion, the F-1 Score, was calculated. Failure data were detected using the One-Class SVM technique. The performance comparison, revealed that the DAE technique performed better than the Wavelet Transform technique in terms of failure diagnosis and error rate.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.30 no.4
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• pp.617-629
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• 2020

In order to overcome the limitations of the rule-based intrusion detection system due to changes in Internet computing environments, the emergence of new services, and creativity of attackers, network anomaly detection (NAD) using machine learning and deep learning technologies has received much attention. Most of these existing machine learning and deep learning technologies for NAD use supervised learning methods to learn a set of training data set labeled 'normal' and 'attack'. This paper presents the feasibility of the unsupervised learning AutoEncoder(AE) to NAD from data sets collecting of secured network traffic without labeled responses. To verify the performance of the proposed AE mode, we present the experimental results in terms of accuracy, precision, recall, f1-score, and ROC AUC value on the NSL-KDD training and test data sets. In particular, we model a reference AE through the deep analysis of diverse AEs varying hyper-parameters such as the number of layers as well as considering the regularization and denoising effects. The reference model shows the f1-scores 90.4% and 89% of binary classification on the KDDTest+ and KDDTest-21 test data sets based on the threshold of the 82-th percentile of the AE reconstruction error of the training data set.

• Journal of the Korea Society of Computer and Information
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• v.29 no.10
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• pp.21-34
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• 2024

In medical images such as MRI(Magnetic Resonance Imaging) and CT(Computed Tomography) images, noise removal has a significant impact on the performance of medical imaging systems. Recently, the introduction of deep learning in image processing technology has improved the performance of noise removal methods. However, there is a limit to removing only noise while preserving details in the image domain. In this paper, we propose a wavelet transform-based CNN(Convolutional Neural Network) model, namely the WT-DnCNN(Wavelet Transform-Denoising Convolutional Neural Network) model, to improve noise removal performance. This model first removes noise by dividing the noisy image into frequency bands using wavelet transform, and then applies the existing DnCNN model to the corresponding frequency bands to finally remove noise. In order to evaluate the performance of the WT-DnCNN model proposed in this paper, experiments were conducted on MRI and CT images damaged by various noises, namely Gaussian noise, Poisson noise, and speckle noise. The performance experiment results show that the WT-DnCNN model is superior to the traditional filter, i.e., the BM3D(Block-Matching and 3D Filtering) filter, as well as the existing deep learning models, DnCNN and CDAE(Convolution Denoising AutoEncoder) model in qualitative comparison, and in quantitative comparison, the PSNR(Peak Signal-to-Noise Ratio) and SSIM(Structural Similarity Index Measure) values were 36~43 and 0.93~0.98 for MRI images and 38~43 and 0.95~0.98 for CT images, respectively. In addition, in the comparison of the execution speed of the models, the DnCNN model was much less than the BM3D model, but it took a long time due to the addition of the wavelet transform in the comparison with the DnCNN model.