• The Journal of Korean Institute of Communications and Information Sciences
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• v.41 no.6
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• pp.628-630
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• 2016

Compressive sensing can recover an original sparse signal from a few measurements. Its performance is affected by the number of non-zero elements in the signal. The knowledge of partial locations of non-zero elements can improve the recovery performance. In this paper, we apply the partial location knowledge to the multipath matching pursuit. The numerical results show it improves the signal recovery performance and the channel estimation performance in the ITU-VB channel.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.12 no.20
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• pp.77-80
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• 1989

A rectifying sampling plan which assumes a prior distribution on the lot percent defective is considered. This sampling is developed for finite lot size N with matching OC curves and generated from an initial plan selected from single sampling by attributes.

• Journal of the Korean Statistical Society
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• v.31 no.1
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• pp.17-31
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• 2002

This paper considers noninformative priors for the power law process under failure truncation. Jeffreys'priors as well as reference priors are found when one or both parameters are of interest. These priors are compared in the light of how accurately the coverage probabilities of Bayesian credible intervals match the corresponding frequentist coverage probabilities. It is found that the reference priors have a definite edge over Jeffreys'prior in this respect.

• International journal of advanced smart convergence
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• v.10 no.3
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• pp.163-171
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• 2021

Recently, Machine Learning-based visualization approaches have been proposed to combat the problem of malware detection. Unfortunately, these techniques are exposed to Adversarial examples. Adversarial examples are noises which can deceive the deep learning based malware detection network such that the malware becomes unrecognizable. To address the shortcomings of these approaches, we present Block-matching and 3D filtering (BM3D) algorithm and deep image prior based denoising technique to defend against adversarial examples on visualization-based malware detection systems. The BM3D based denoising method eliminates most of the adversarial noise. After that the deep image prior based denoising removes the remaining subtle noise. Experimental results on the MS BIG malware dataset and benign samples show that the proposed denoising based defense recovers the performance of the adversarial attacked CNN model for malware detection to some extent.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.28 no.8C
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• pp.803-810
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• 2003

This paper addresses an image processing technique for the human papillomavirus (HPV) DNA chip to discriminate whether the probes are hybridized with the target DNA. HPV DNA chip is designed to determine HPV gene-types by using DNA probes for 22 HPV types. In addition to the probes, the HPV DNA chip has markers that always react with the sample DNA. The positions of probe-dots in the final scanned image are fixed relative to the marker- dot locations with a small variation attributable to the accuracy of the dotter and the scanner. The probes are quadruplicated to enhance diagnostic fidelity. frier knowledge including the marker relative distance and the replication information of probes is integrated into the template matching technique with normalized covariance measure. It was demonstrated that the employment of both of the prior knowledges can be accomplished by simply averaging the template matching measures over the positions of the markers and probes. The resulting proposed scheme yields stable marker locating and probe classification.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.37 no.4
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• pp.16-25
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• 2000

In this paper, a new disparity detection algorithm which is robust to noise is presented. It detects the disparity of an arbitrary pixel through the iterative competition with neighbor pixels in the range of disparity. A diffusion process to improve stereo matching confidence is used prior to detecting disparity of an arbitrary pixel. It is used for aggregating initial matching measure of the difference map. If the image region for matching is too small, a wrong match might be found due to noise. On the contrary, the region is too big, it results in blurring of object boundaries. Therefore, we decide the image region for matching by using the diffusion process for aggregating matching measure, then detect the true disparity with proposed competition method to the distribution of matching measure. Through the proposed method we get the result of improving matching rate of 6.96% with real stereo imge. From the simulation with the stereo imge, the proposed disparity detection method significantly outperforms the conventional method to matching rate.

• Journal of Service Research and Studies
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• v.13 no.1
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• pp.1-26
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• 2023

We analyze the effects and related factors of Smart Factory adoption. 110 and 325 samples were collected by median-size-industry matching method, respectively, of adopting and non-adopting companies. We use financial statement data (ROA, etc.) from the year before adoption to the fourth year after adoption. Abnormal operating performance and annual abnormal changes are obtained according to event study method, and analyzed by Wilcoxon signed-rank test and t-test. ROA and sales growth rate demonstrate short-term effects after adoption, but not long-term effects. As a result of regression analysis to examine if the three factors of labor intensity, R&D intensity, and prior financial performance have moderating effect, the moderating effect of R&D intensity and prior financial performance is confirmed. In addition, we perform regression analysis to confirm performance effects of early and late adoptions and whether prior financial performance and organization size have moderating effect. It is confirmed that the later the time of adoption, the greater the effect of adoption in the long term and the moderating effect of prior financial performance and organization size is confirmed.

• Proceedings of the IEEK Conference
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• 2008.06a
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• pp.863-864
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• 2008

To predict the iris matching performance and guarantee its reliability, image quality measure prior to matching is desired. An analysis on iris image degradation factors which deteriorate matching performance is a basic step for iris image quality measure. We considered five degradation factors-white-out, black-out, noise, blur, and occlusion by specular reflection-which happen generally during the iris image acquisition process. Experimental results show that noise and white-out degraded the EER most significantly, while others on EER were either insignificant or degradation images resulted in even better performance in some cases of blur. This means that degradation factors that affect the performance can be different from those based on human perception or image degradation evaluation.

• ETRI Journal
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• v.28 no.2
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• pp.162-174
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• 2006

Several photogrammetric and geographic information system applications such as surface matching, object recognition, city modeling, environmental monitoring, and change detection deal with multiple versions of the same surface that have been derived from different sources and/or at different times. Surface registration is a necessary procedure prior to the manipulation of these 3D datasets. This need is also applicable in the field of medical imaging, where imaging modalities such as magnetic resonance imaging (MRI) can provide temporal 3D imagery for monitoring disease progression. This paper will present a general automated surface registration procedure that can establish correspondences between conjugate surface elements. Experimental results using light detection and ranging (LIDAR) and MRI data will verify the feasibility, robustness, and accuracy of this approach.

• Journal of Information Processing Systems
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• v.15 no.2
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• pp.410-421
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• 2019

Distributed compressed sensing (DCS) states that we can recover the sparse signals from very few linear measurements. Various studies about DCS have been carried out recently. In many practical applications, there is no prior information except for standard sparsity on signals. The typical example is the sparse signals have block-sparse structures whose non-zero coefficients occurring in clusters, while the cluster pattern is usually unavailable as the prior information. To discuss this issue, a new algorithm, called backtracking-based adaptive orthogonal matching pursuit for block distributed compressed sensing (DCSBBAOMP), is proposed. In contrast to existing block methods which consider the single-channel signal reconstruction, the DCSBBAOMP resorts to the multi-channel signals reconstruction. Moreover, this algorithm is an iterative approach, which consists of forward selection and backward removal stages in each iteration. An advantage of this method is that perfect reconstruction performance can be achieved without prior information on the block-sparsity structure. Numerical experiments are provided to illustrate the desirable performance of the proposed method.