• Proceedings of the Korea Electromagnetic Engineering Society Conference
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• 2000.11a
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• pp.259-263
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• 2000

21기 정보화사회를 맞이하여 전과서비스 사용의 증가로 인공전파잡음이 계속 증가하는 추세이므로 잡음원과 전파환경 분포를 파악하기 위하여 각 지역의 전파잡음레벨을 조사하고 있다. 그 중 년도별 전파잡음레벨의 변화와 시간에 따른 잡음레벨 변화를 분석해보니 주파수 대역에 따라 특징적인 변화를 보여주고 있다. 따라서 앞으로 주파수 스펙트럼분포와 잡음원과의 관계를 분석하여 전파환경 보호에 관한 대책을 세우고 원활한 전파서비스가 제공될 수 있도록 할 것이다.

• Journal of the Korean Society of Radiology
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• v.12 no.2
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• pp.193-199
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• 2018

In this research, 15 patients were diagnosed with 1.5T and 3.0T MRI instruments (Philips, Medical System, Achieva) to minize Ferromagnetic artifact and find the optimized Tesla. Based on the theory that the 3.0T, when compared to 1.5T, show relatively high signal-to-ratio(SNR), Scan time can be shortened or adjust the image resolution. However, when using the 3.0T MRI instruments, various artifact due to the magnetic field difference can degrade the diagnostic information. For the analysis condition, area of interest is set at the background of the T1, T2 sagittal image followed by evaluation of L3, L4, L5 SNR, length of 3 parts with Ferromagnetic artifact, and Histogram. The validity evaluation was performed by using the independent t test. As a result, for the SNR evaluation, mere difference in value was observed for L3 between 1.5T and 3.0T, while big differences were observed for both L4, and L5(p<0.05). Shorter length was observed for the 1.5T when observing 3 parts with Ferromagnetic artifact, thus we can conclude that 3.0T can provide more information on about peripheral tissue diagnostic information(p<0.05). Finally, 1.5T showed higher counts values for the Histogram evaluation(p<0.05). As a result, when we have compared the 1.5T and 3.0T with SNR, length of Ferromagnetic artifact, Histogram, we believe that using a Low Tesla for Spine MRI test can achieve the optimal image information for patients with disk operation like PLIF, etc. in the past.

• 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 Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.21 no.11
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• pp.1295-1303
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• 2010

In this paper, an effective method has been proposed for selection of white Gaussian noise(WGN) band for radio background noise measurement system under broad band noise environment. MMN which comes from industrial devices and equipment mostly happens in the shape of broad band noise mostly like impulsive noise and this is the main reason for increasing level in the present radio noise measurements. The existing method based on singular value decomposition has weak point that it cannot give good performance for the broad band signal because it uses signal's white property. The proposed method overcomes such a weakness of singular value decomposition based method by using signal's Gaussian property based method in parallel. Moreover, this proposed method hires a modelling based method which uses parameter estimation algorithm like maximum likelihood estimation(MLE) and gives more accurate result than the method using amplitude probability distribution(APD) graph. Experiment results under the natural environment has done to verify feasibility of the proposed method.

• Journal of Advanced Navigation Technology
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• v.24 no.1
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• pp.47-52
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• 2020

Direction of arrival (DoA) estimation is a scheme of estimating the directions of targets by analyzing signals generated or reflected from the targets and is used in various fields. Artificial neural networks (ANN) is a field of machine learning that mimics the neural network of living organisms. They show good performance in pattern recognition. Although researches has been using ANNs to estimate the DoAs, there are limitationsin dealing with variations of the signal-to-noise ratio (SNR) of the target signals. In this paper, we propose a three-stage ANN algorithm for DoA estimation. The proposed algorithm can minimize the performance reduction by applying the model trained in a single SNR environment to various environments through a 'noise reduction process'. Furthermore, the scheme reduces the difficulty in learning and maintains efficiency in estimation, by employing a process of DoA shift. We compare the performance of the proposed algorithm with Cramer-Rao bound (CRB) and the performances of existing subspace-based algorithms and show that the proposed scheme exhibits better performance than other schemes in some severe environments such as low SNR environments or situations in which targets are located very close to each other.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.43 no.5 s.311
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• pp.79-87
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• 2006

In this study, we evaluated the performance of a companding strategy as a preprocessing for speech enhancement and noise reduction. The proposed algorithm is based on two tone suppression that is human's hearing characteristics. This algorithm enhances spectral peak of speech signal and reduces background noise, however it has tradeoff characteristics between speech distortion and noise reduction due to limited channel number and nonlinear block. Therefore, we designed two different companding structures that have relative characteristics of noise reduction and speech distortion and found suitable companding structures by difference of individual speech perception ability in noise environment. Thus we proposed speech perception enhancement of cochlear implant user in noise environment with low SNR. The performance of the proposed algorithm was evaluated through 5 normal hearing listeners using noise band simulation. Improvement of speech perception was observed for all subjects and each subject preferred the different type of companding structure.

• Proceedings of the Korea Information Processing Society Conference
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• 2022.11a
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• pp.550-552
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• 2022

최근 사물인터넷(IoT) 기기가 활성화됨에 따라 웨어러블 장치 환경에서 장기간 모니터링 및 수집이 가능해짐에 따라 생체 신호 처리 및 ECG 분석 연구가 활성화되고 있다. 그러나, ECG 데이터는 부정맥 비트의 불규칙적인 발생으로 인한 클래스 불균형 문제와 근육의 떨림 및 신호의 미약등과 같은 잡음으로 인해 낮은 신호 품질이 발생할 수 있으며 훈련용 공개데이터 세트가 작다는 특징을 갖는다. 이 논문에서는 ECG 1D 신호를 2D 스펙트로그램 이미지로 변환하여 잡음의 영향을 최소화하고 전이학습과 메타학습의 장점을 결합하여 클래스 불균형 문제와 소수의 데이터에서도 빠른 학습이 가능하다는 특징을 갖는다. 따라서, 이 논문에서는 ECG 스펙트럼 이미지를 사용하여 2D-CNN 메타-전이 학습 기반 부정맥 분류 기법을 제안한다.

• 전기의세계
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• v.33 no.7
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• pp.412-418
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• 1984

의용생체공학의 한분야인 재활공학의 많은 발전으로 상실된 인간의 사지기관의 일부는 거의 자연스런 기능을 갖는 장치로 대치할 수 있는 가능성이 높아지고 있으며, 일한 연구의 결과는 산업용 로보트의 개발에도 기여를 하고 있다. 그중에서도 핵심이 되고 있는 것이 근전도신호를 이용한 보철제어(Prosthesis Control)에 관한 연구이다. 근전도신호가 인공팔제어에 이용된 것은 1950년대 초 소련에서 처음 시도되었고 그후 유럽, 카나다 미국등에서 계속 이에 관한 연구가 성과를 나타내고 있다. 근전도 신호를 제어신호로 사용할 경우 가장 큰 문제점은 근전도신호의 저주파 잡음인데, 실제로 비례제어신호를 얻기위하여는 이 잡음이 제거되어야 한다. 그러므로 여기에서는 근전도신호 처리방법에 대한 개략적인 것을 소개하고, 잡음의 제거방법등을 검토해 보고자 한다.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.404-406
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• 2021

In modern society, artificial intelligence and automation are being applied in various fields due to the development of the 4th industrial revolution and IoT technology. In particular, systems with a high proportion of image processing, such as automated processes, intelligent CCTV, medical industry, robots, and drones, are susceptible to external factors noise. In this paper, we propose a digital filter based on noise estimation and weights to reconstruct an image in a complex noise environment. The proposed algorithm classifies the types of noise using noise judgment, and determines the noise level of the filtering mask to switch the filtering process to obtain the final output. In order to verify the performance of the proposed algorithm, simulation was conducted, compared with the existing filter algorithm, and the results were analyzed.

• Geophysics and Geophysical Exploration
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• v.2 no.4
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• pp.180-183
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• 1999

A proto-type seismic measurement system with a reference was developed to improve S/N (signal-to-noise ratio) of seismic data, especially in noisy urban areas. Two pairs of correlation measurements (the one for microphone and geophone, and another for electromagnetic (EM) loop and geophone) were carried out near Kimpo Airport and at Kimje. The spectrum analyses were also performed to investigate the correlation of two pairs of time series; one for microphone and geophone, and another for EM loop and geophone. The sound waves measured with the microphone and the geophone are highly correlated. However, differences in the reponses are readily identifiable across 200 Hz; in the vicinity of 100 Hz, the spectral energy for geophone is 20 dB higher than that for microphone, and at near 500 Hz, the spectral energy for microphone is 30 dB higher than that for geophone. Overall, the spectral energy appears concentrated on the frequency window below 600 Hz for geophone. It contrasts with the observation of dominant frequency at the range of above 200 Hz for microphone. The wave forms of EM noise (due to an ACDC inverter) measured with EM loop and geophone are consistently and highly correlated each other. The power spectrum of the EM noise for EM loop shows that the spectral energies at odd harmonic frequencies of 60 Hz are higher than those at even harmonic frequencies of 60 Hz. It is compared to the power spectrum for geophone; the spectral energies at odd harmonics are nearly same as those at even harmonic frequencies.