• The Journal of Korean Institute of Communications and Information Sciences
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• v.13 no.4
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• pp.332-339
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• 1988

Using a generalized observation model, in which one can express the effects of non-additive noise such as signal-dependent noise and multiplicative noise in addition to purely-additive noise, the problem of weak random-signal detection is investigated. It is shown that the test statistics of locally optimum detectors for detection of weak random signals in signal-dependent noise model are interesting extensions of those in purely-additive noise model. This result is a complement to the result for weak random-signal detction in multiplicative noise model.

• Journal of Electrical Engineering and Technology
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• v.13 no.3
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• pp.1383-1391
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• 2018

A new nonlinear filtering algorithm for effectively denoising images corrupted by the random-valued impulse noise, called dual sliding statistics switching median (DSSSM) filter is presented in this paper. The proposed DSSSM filter is made up of two subunits; i.e. Impulse noise detection and noise filtering. Initially, the impulse noise detection stage of DSSSM algorithm begins by processing the statistics of a localized detection window in sorted order and non-sorted order, simultaneously. Next, the median of absolute difference (MAD) obtained from both sorted statistics and non-sorted statistics will be further processed in order to classify any possible noise pixels. Subsequently, the filtering stage will replace the detected noise pixels with the estimated median value of the surrounding pixels. In addition, fuzzy based local information is used in the filtering stage to help the filter preserves the edges and details. Extensive simulations results conducted on gray scale images indicate that the DSSSM filter performs significantly better than a number of well-known impulse noise filters existing in literature in terms of noise suppression and detail preservation; with as much as 30% impulse noise corruption rate. Finally, this DSSSM filter is algorithmically simple and suitable to be implemented for electronic imaging products.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2001.11b
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• pp.888-891
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• 2001

Effects of backing conditions on the impedance tube measurement are investigated experimentally, by using several pairs of generally employed end conditions. The results show that the measured values are similar for most of pairs, except the case of using an open pipe condition. In addition, the random error is investigated in the viewpoint of the variation of test conditions. The multi-termination method is suggested for minimizing such a random error.

• 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 Korea Institute of Information, Electronics, and Communication Technology
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• v.12 no.6
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• pp.629-635
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• 2019

In Deep Learning models derivative is implemented by error back-propagation which enables the model to learn the error and update parameters. It can find the global (or local) optimal points of parameters even in the complex models taking advantage of a huge improvement in computing power. However, deliberately generated data points can 'fool' models and degrade the performance such as prediction accuracy. Not only these adversarial examples reduce the performance but also these examples are not easily detectable with human's eyes. In this work, we propose the method to detect adversarial datasets with random noise addition. We exploit the fact that when random noise is added, prediction accuracy of non-adversarial dataset remains almost unchanged, but that of adversarial dataset changes. We set attack methods (FGSM, Saliency Map) and noise level (0-19 with max pixel value 255) as independent variables and difference of prediction accuracy when noise was added as dependent variable in a simulation experiment. We have succeeded in extracting the threshold that separates non-adversarial and adversarial dataset. We detected the adversarial dataset using this threshold.

• Proceedings of the Optical Society of Korea Conference
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• 1999.08a
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• pp.226-227
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• 1999

In the fiber-optic gyroscope employing the erbium-doped fiber source, the source excess noise was subtracted through a signal processing to improve the gyroscope sensitivity . As the result, we obtained the improvement of 14 dB(electrical) at the proper frequency, which was measured from the noise floor spectrum . In addition the random walk coefficient in the gyro output was reduced by about factor of three.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.1206-1210
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• 2005

This paper presents performance improvement in optical CDMA system under the presence of beat noise using a cancellation technique. Optical fibers and atmospheric optical communications have been proposed the connection between base stations and central station. The optical signal beat noise is due to interference between lightwave, many optical waves are simultaneously incident on each receiver photodiode. Since the photodiode acts as a square-law detector, beat noise can occur in the receiver. While A two-stage cancellation technique is analyzed and verified via simulation employed here because of its system simplicity. By using the random ingredients of all user signals are estimated, the beat noise is rebuilt and removed from the intended signal. In addition to cancellation technique cancel the inherent multiuser interference (MUI) in CDMA system and nonlinear distortion (NLD) in optical system. It is performed at the receiver of the central station where the random ingredients of all user signals are estimated and the MUI and the NLD are rebuilt and removed from the received signal. The validity of the cancellation technique is theoretically analyzed and shown by numerical results. The increasing of capacity in two stage cancellation are obtained.

• Journal of Korea Society of Digital Industry and Information Management
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• v.15 no.4
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• pp.103-110
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• 2019

A patent contains detailed information of the developed technology and is published to the public. Thus, patents can be used to overcome the limitations of traditional technology trend research and prediction techniques. Recently, due to the advantages of patented analytical methodology, IP R&D is carried out worldwide. The patent is big data and has a huge amount, various domains, and structured and unstructured data characteristics. For this reason, there are many difficulties in collecting and researching patent information. Patent research generally writes the Search formula to collect patent documents from DB. The collected patent documents contain some noise patents that are irrelevant to the purpose of analysis, so they are removed. However, eliminating noise patents is a manual task of reading and classifying technology, which is time consuming and expensive. In this study, we propose a model that automatically classifies The Noise patent for efficient patent information research. The proposed method performs Patent Embedding using Word2Vec and generates Noise seed label. In addition, noise patent classification is performed using the Random forest. The experimental data is published and registered with the USPTO among the patents related to Ocean Surveillance & Tracking Network technology. As a result of experimenting with the proposed model, it showed 73% accuracy with the label actually given by experts.

• JSTS:Journal of Semiconductor Technology and Science
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• v.16 no.2
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• pp.185-190
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• 2016

A 70-${\AA}$ nanowire field-effect transistor (FET) for sub-10-nm CMOS technology is designed and simulated in order to investigate the impact of an oxide trap on random telegraph noise (RTN) in the device. It is observed that the drain current fluctuation (${\Delta}I_D/I_D$) increases up to a maximum of 78 % due to the single electron trapping. In addition, the effect of various trap positions on the RTN in the nanowire FET is thoroughly analyzed at various drain and gate voltages. As the drain voltage increases, the peak point for the ${\Delta}I_D/I_D$ shifts toward the source side. The distortion in the electron carrier density and the conduction band energy when the trap is filled with an electron at various positions in the device supports these results.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.16 no.5
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• pp.445-448
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• 1991

The two-sample locally optimum rank detection scheme is obtained which uses rank and sign statistics for detection of random signals in additive noise. It is shown that the detector is similar in structure to the locally optimum detector for random signals and to the one-sample locally optimum rank detector for random signals. It is also shown that the detector is a generalization of the two-sample locally optimum rank detector for known signals. In addition , the problem of two-sample locally optimum rank detection of random signals in multiple input case is considered briefly.