• Proceedings of the IEEK Conference
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• 2000.09a
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• pp.511-514
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• 2000

디지털 워터마킹이란 디지털 컨텐츠의 저작권을 보호하기 위해 보이지 않는 임의의 데이터를 미디어에 삽입하는 방법이다. 본 논문에서는 주파수 영역으로 변환된 원 데이터를 임의의 방향으로 정의된 데이터와 내적(Inner Product)하여 이를 워터마크 정보에 따라 변화시키는 방법을 제안한다. 이 방법은 비밀키가 워터마크 데이터와 연관된 것이 아니기 때문에 어떠한 종류의 데이터도 삽입할 수 있다는 장점이 있다. 사용되는 비밀키는 주파수 영역으로 변환된 원 데이터와 내적되는 임의의 데이터를 만드는데 사용된다. 또한 워터마크의 견고성을 높이기 위해, 주파수 영역으로 변환된 원 데이터와 내적된 임의의 데이터는 잡음처럼 되므로 이를 인간 시각 특성을 사용하여 모델링 하였다. 제안된 방법은 원 영상 없이 워터마크를 검출할 수 있으며. 워터마크의 견고성 실험을 위해 JPEG, Cropping, Resizing. Gaussian 잡음 등을 적응하였다.

• Proceedings of the Korea Multimedia Society Conference
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• 2002.05d
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• pp.882-885
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• 2002

디지털 영상의 소유권을 보호하기 위해 웨이블렛 변환을 이용하여 시각적으로 인식할 수 없는 부분에 워터마크를 삽입하는 알고리즘을 제안하였다. 본 논문에서는 잡음의 분산이 커질수록 유사도는 감소했으나 분산이 100으로 상당히 큰 범위임에도 불구하고, 제안한 방법의 경우 87%이상의 유사도를 검출해냈다. 이에 비해 Cox 방법의 경우 54%정도로 낮은 유사도를 검출해 내고 있다. 결과적으로 제안한 방법이 COX의 방법보다 잡음에 대한 워터마크의 견고성이 향상되었음을 알수 있었다.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.43 no.2 s.308
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• pp.81-92
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• 2006

This paper proposes a robustness watermarking for 3D mesh model based on projection onto convex sets (POCS). After designing the convex sets for robustness and invisibility among some requirements for watermarking system, a 3D-mesh model is projected alternatively onto two constraints convex sets until the convergence condition is satisfied. The robustness convex set are designed for embedding the watermark into the distance distribution of the vertices to robust against the attacks, such as mesh simplification, cropping, rotation, translation, scaling, and vertex randomization. The invisibility convex set are designed for the embedded watermark to be invisible. The decision values and index that the watermark was embedded with are used to extract the watermark without the original model. Experimental results verify that the watermarked mesh model has invisibility and robustness against the attacks, such as translation, scaling, mesh simplification, cropping, and vertex randomization.

• Journal of the Korea Computer Graphics Society
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• v.7 no.1
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• pp.37-49
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• 2001

We propose an optimization of mutual information for multiresolution image registration to represent useful information as integrated form obtaining from complementary information of multi modality images. The method applies mutual information as cost function to measure the statistical dependency or information redundancy between the image intensities of corresponding pixels in both images, which is assumed to be maximal if the images are geometrically aligned. As experimental results we validate visual inspection for accuracy, changning initial condition and addictive noise for robustness. Since our method uses the native image rather than prior feature extraction, few user interaction is required to perform the registration. In addition it leads to robust density estimation and convergence as applying non-parametric density estimation and stochastic multiresolution optimization.

• Journal of KIISE:Software and Applications
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• v.32 no.7
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• pp.612-624
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• 2005

In this paper, we propose a surface-based registration using a gaussian weighted distance map for PET-CT brain image fusion. Our method is composed of three main steps: the extraction of feature points, the generation of gaussian weighted distance map, and the measure of similarities based on weight. First, we segment head using the inverse region growing and remove noise segmented with head using region growing-based labeling in PET and CT images, respectively. And then, we extract the feature points of the head using sharpening filter. Second, a gaussian weighted distance map is generated from the feature points in CT images. Thus it leads feature points to robustly converge on the optimal location in a large geometrical displacement. Third, weight-based cross-correlation searches for the optimal location using a gaussian weighted distance map of CT images corresponding to the feature points extracted from PET images. In our experiment, we generate software phantom dataset for evaluating accuracy and robustness of our method, and use clinical dataset for computation time and visual inspection. The accuracy test is performed by evaluating root-mean-square-error using arbitrary transformed software phantom dataset. The robustness test is evaluated whether weight-based cross-correlation achieves maximum at optimal location in software phantom dataset with a large geometrical displacement and noise. Experimental results showed that our method gives more accuracy and robust convergence than the conventional surface-based registration.

• Proceedings of the KIEE Conference
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• 2003.07d
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• pp.2693-2695
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• 2003

M진 직교 진폭 복조기(M-ary QAM Demodulator)를 위한 개선된 이중모드 MCMA 블라인드 등화기법을 제안하였다. 최소 교란 원리(the principle of minimum disturbance)를 MCMA에 적용함으로써 개선된 블라인드 등화기법과 반송파 위상 복구 능력을 얻으며 dicision-directed algorithm과 함께 이중모드로 동작함으로써 향상된 안전성(stability)과 견고성(robustness)얻을 수 있다. 잡음 특성과 시변채널에 대해 컴퓨터 모의 실험을 통하여 제안된 이중모드 알고리즘의 개선된 성능을 검증하였다.

• Journal of Korea Society of Industrial Information Systems
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• v.12 no.5
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• pp.1-13
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• 2007

In this research, we found out that bridging nodes have great effect on the robustness of protein-protein interaction networks. Until now, many researchers have focused on node's degree as node's essentiality. Hub nodes in the scale-free network are very essential in the network robustness. Some researchers have tried to relate node's essentiality with node's betweenness centrality. These approaches with betweenness centrality are reasonable but there is a positive relation between node's degree and betweenness centrality value. So, there are no differences between two approaches. We first define a bridging node as the node with low connectivity and high betweenness value, we then verify that such a bridging node is a primary factor in the network robustness. For a biological network database from Internet, we demonstrate that the removal of bridging nodes defragment an entire network severally and the importance of the bridging nodes in the network robustness.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2013.05a
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• pp.911-913
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• 2013

The user's context awareness is important to the reliability of sensors data. The sensor data is constantly change to external temp, internal& external environment and vibration. This noise environment is affecting that the data collected information from sensors. Of course this method of digital filter and inference algorithm specifically request for the use of ripple noise and action inference. In this paper, experiment was a comparison of the KF(Kalman Filter) and WMAF(Weight Moving Average Filter) for noise decrease and distortion prevention according to user behavior. And, we compared the EWDF(Extended Weight Dual Filter) with several filer. In an experiment, in contrast to other filter, the proposed filter is robust in a noise-environment.

• KIPS Transactions on Software and Data Engineering
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• v.8 no.5
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• pp.193-204
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• 2019

The explosion of data due to the improvement of sensor technology and computing performance has become the basis for analyzing the situation in the industrial fields, and various attempts to detect events based on such data are increasing recently. In particular, sound signals collected from sensors are used as important information to classify events in various application fields as an advantage of efficiently collecting field information at a relatively low cost. However, the performance of sound-event classification in the field cannot be guaranteed if noise can not be removed. That is, in order to implement a system that can be practically applied, robust performance should be guaranteed even in various noise conditions. In this study, we propose a system that can classify the sound event after generating the enhanced sound signal based on the deep learning algorithm. Especially, to remove noise from the sound signal itself, the enhanced sound data against the noise is generated using SEGAN applied to the GAN with a VAE technique. Then, an end-to-end based sound-event classification system is designed to classify the sound events using the enhanced sound signal as input data of CNN structure without a data conversion process. The performance of the proposed method was verified experimentally using sound data obtained from the industrial field, and the f1 score of 99.29% (railway industry) and 97.80% (livestock industry) was confirmed.

• The KIPS Transactions:PartB
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• v.8B no.2
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• pp.155-163
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• 2001

In this paper, we propose an image registration method by optimization of mutual information to provide a significant infonnation from multimodality images. The method applies mutual infonnation to measure the statistical dependency'r information redundancy between the image intensities of corresponding pixels in both images, which is assumed to be maximal if the images are geometrically aligned. We show the registration results optimizing mutual information between brain MR image and brain CT image and the comparison results with additive gaussian noise. Since our method uses the native image rather than prior segmentation or feature extraction, no user interaction is required and the accuracy of registration is improved. In addition, it shows the robustness against the noise.