• Korean Chemical Engineering Research
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• v.62 no.1
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• pp.118-124
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• 2024

A neural network is utilized for preprocessing of de-noizing in electrocardiogram signals, retinal images, seismic waves, etc. However, the de-noizing process could provoke increase of computational time and distortion of the original signals. In this study, we investigated a neural network architecture to analyze measurement data without additional de-noizing process. From the dynamical behaviors of DNA in aqueous solution, our neural network model aimed to predict the mole fraction of each DNA in the solution. By adding white noise to the dynamics data of DNA artificially, we investigated the effect of the noise to neural network's predictions. As a result, our model was able to predict the DNA mole fraction with an error of O(0.01) when signal-to-noise ratio was O(1). This work can be applied as a efficient artificial intelligence methodology for analyzing DNA related to genetic disease or cancer cells which would be sensitive to background measuring noise.

• Geophysics and Geophysical Exploration
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• v.23 no.4
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• pp.230-242
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• 2020

We performed a magnetotelluric (MT) survey to delineate the geological structures below the depth of 20 km in the Gyeongju area where an earthquake with a magnitude of 5.8 occurred in September 2016. The measured MT data were severely distorted by electrical noise caused by subways, power lines, factories, houses, and farmlands, and by vehicle noise from passing trains and large trucks. Using machine-learning methods, we classified the MT time series data obtained near the railway and highway into two groups according to the inclusion of traffic noise. We applied three schemes, stochastic gradient descent, support vector machine, and random forest, to the time series data for the highspeed train noise. We formulated three datasets, Hx, Hy, and Hx & Hy, for the time series data of the large truck noise and applied the random forest method to each dataset. To evaluate the effect of removing the traffic noise, we compared the time series data, amplitude spectra, and apparent resistivity curves before and after removing the traffic noise from the time series data. We also examined the frequency range affected by traffic noise and whether artifact noise occurred during the traffic noise removal process as a result of the residual difference.

• Geophysics and Geophysical Exploration
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• v.22 no.3
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• pp.116-131
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• 2019

In order to obtain high-quality high-resolution marine seismic data, the survey needs to be carried out at very low-sea condition. However, the survey is often performed with a slight wave, which degrades the quality of data. In this case, it is possible to improve the quality of seismic data by detecting the exact location of the sea bottom signal and eliminating the influence of waves or swells automatically during data processing. However, if noise is included or the sea bottom signal is weakened due to sea waves, sea bottom detection errors are likely to occur. In this study, we applied a method reducing such errors by estimating the sea bottom location, setting a narrow detection range and detecting the sea bottom location within this range. The expected location of the sea bottom was calculated using previously detected sea bottom locations for each channel of multi-channel data. The expected location calculated in each channel is also compared and verified with expected locations of other channels in a shot gather. As a result of applying this method to the noisy 8-channel high-resolution air-gun seismic data acquired off Yeosu, the errors in selecting the strong noise before sea bottom or the strong subsurface reflected signal after the sea bottom signal are remarkably reduced and it is possible to produce the high-quality seismic section with the correction of ~ 2.5 m swell effect.

• Geophysics and Geophysical Exploration
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• v.25 no.2
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• pp.71-84
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• 2022

When acquiring seismic data, various types of simultaneously recorded seismic noise hinder accurate interpretation. Therefore, it is essential to attenuate this noise during the processing of seismic data and research on seismic noise attenuation. For this purpose, machine learning is extensively used. This study attempts to attenuate noise in prestack seismic data using unsupervised machine learning. Three unsupervised machine learning models, N2NUNET, PATCHUNET, and DDUL, are trained and applied to synthetic and field prestack seismic data to attenuate the noise and leave clean seismic data. The results are qualitatively and quantitatively analyzed and demonstrated that all three unsupervised learning models succeeded in removing seismic noise from both synthetic and field data. Of the three, the N2NUNET model performed the worst, and the PATCHUNET and DDUL models produced almost identical results, although the DDUL model performed slightly better.

• The Korean Journal of Applied Statistics
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• v.18 no.1
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• pp.115-127
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• 2005

DNA microarray experiments allow us to study expression of thousands of genes simultaneously, Normalization is a process for removing noises occurred during the microarray experiment, Print-tip is regarded as one main sources of noises, In this paper, we review normalization methods most commonly used in the microarray experiments, Especially, we investigate the effects of print-tips through simulated data sets.

• Geophysics and Geophysical Exploration
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• v.25 no.1
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• pp.26-37
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• 2022

Recently, multiple methods using small aircraft for geophysical exploration have been suggested as a result of the development of information and communication technology. In this study, we introduce the hybrid unmanned aircraft electromagnetic system of the Korea Institute of Geosciences and Mineral resources, which is under development. Additionally, data processing and interpretation methods are suggested via the analysis of datasets obtained using the system under development to verify the system. Because the system uses a three-component receiver hanging from a drone, the effects of rotation on the obtained data are significant and were therefore corrected using a rotation matrix. During the survey, the heights of the source and the receiver and their offsets vary in real time and the measured data are contaminated with noise. The noise makes it difficult to interpret the data using the conventional method. Therefore, we developed a recurrent neural network (RNN) model to enable rapid predictions of the apparent resistivity using magnetic field data. Field data noise is included in the training datasets of the RNN model to improve its performance on noise-contaminated field data. Compared with the results of the electrical resistivity survey, the trained RNN model predicted similar apparent resistivities for the test field dataset.

• Proceedings of the Korean Information Science Society Conference
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• 2000.04b
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• pp.247-249
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• 2000

본 논문에서는 대개 잡음이 포함되어 있고 불규칙적인 특성을 갖고 있는 시계열 자료에 대해 신경 트리 모델을 사용하여 시계열 예측 문제를 해결하고자 한다. 주어진 시계열 자료에 적합한 구조와 가중치를 갖는 신경트리를 찾기 위해 베이지안 진화 알고리즘을 적용한 결과, 자료의 개수가 적어 과적합될 우려가 있는 경우 제안된 방법은 모델의 복잡도가 커지는 것을 억제하고 일반화 성능이 급격하게 나빠지지 않는다는 것을 확인하였다.

• The Bulletin of The Korean Astronomical Society
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• v.39 no.1
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• pp.50.2-50.2
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• 2014

지상에서 관측한 태양계 천체의 분광자료에는 여러 가지 자료들이 포함되어 있다. 태양계 천체는 태양빛을 받아 반사되는 빛이 관측되기 때문에 태양 분광선도 포함되어 있고, 지구 대기를 통과하기 때문에 지구 대기 흡수선 및 방출선도 포함되어 있다. 특히 지구 대기에 의한 분광선은 관측지의 위치, 관측일의 날씨 등이 영향을 미칠 수 있다. 그 외에도 기기에서 발생하는 여러 잡음들이 합쳐진 관측 자료가 획득된다. 이렇게 얻어진 관측 결과로부터 태양 분광선, 지구 대기 흡수선, 기기로부터의 잡음 등을 제거해서 최종적으로 순수한 태양계 천체의 분광선을 획득하게 된다. 본 연구에서는 현재 개발중인 우주탐사선용 중적외선 분광기 지상모델의 현장 검증과정에서 생산될 수 있는 관측 자료에 대한 모사를 하고자 한다. 이 자료는 향후 관측 당시의 대기 상태 및 기기 상태에 따라 발생되는 관측 결과를 예상할 수 있기 때문에 관측 날짜 지정 및 기기 상태 점검에 유용하게 사용될 것이라고 기대한다.

• Geophysics and Geophysical Exploration
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• v.18 no.1
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• pp.9-13
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• 2015

All the resistivity data contain various kinds of noise. The major sources of noise in DC resistivity measurement are high contact resistance, measurement errors, and sporadic background noise. Thus, it is required to measure data noise to accurately interpret resistivity data. Reciprocal measurements can provide a measure of data precision and noise. In this study, we proposed a data-weighting method from reciprocity measurement. Furthermore, a data-weighting method using both the reciprocity error and data-misfit in the inversion process was studied. Applying the data-weighting method to the inversion of 3D resistivity data, it was confirmed that local anomalies are slightly suppressed in the final inversion results.

• 한국해양학회지
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• v.30 no.2
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• pp.116-124
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• 1995

Marine, shallow seismic data have been acquired and processed by newly developed multi-channel(6 channel), PC-based digital recording and processing system. The digital processing system includes pre-processing, swell-compensation filter, frequency filter, gain correction, deconvolution, stacking, migration, and plotting. The quality of processed sections is greatly enhanced in terms of signal-to-noise ratio and vertical/horizontal resolution. The multi-channel, digital recording, acquisition and processing system proved to be and economical, efficient and easy-to-use marine shallow seismic tool.