• Geophysics and Geophysical Exploration
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• v.19 no.2
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• pp.67-75
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• 2016

To investigate spatial and temporal variations of low-frequency (${\leq}5Hz$) ambient seismic noise, we analyzed the noise data recorded for one whole year of 2014 at surface accelerometer stations in South Korea. After decomposed into low-frequency (LF; < 1 Hz) and high-frequency (HF; ${\geq}1Hz$) components, the root-mean-squared (RMS) amplitudes and power spectral densities (PSD) of the noise data were computed. The RMS amplitudes were larger on islands and near-shore stations, but also large RMS amplitudes were observed at inland stations in large cities only for HF components. The RMS amplitudes of HF components were larger in the daytime than at nighttime and during weekdays than on Sunday and holidays. This indicates the HF components are closely related to human activities. On the contrary, daily and weekly variations were not clear in the LF components while they showed seasonal variations with its maximum during the winter and a good correlation with significant wave height. Therefore, we interpret the mechanism of LF components is closely related to natural phenomena such as sea. The amplitude of LF components decreased as an exponential function of the distance to the center of typhoons. The exponential index of -0.76 suggested that ambient seismic noise included both surface and body waves. Peak frequencies of the PSD curves were near 0.34 Hz indicating the double frequency. No temporal variation in the peak frequency was clearly noticed.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.6 no.4
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• pp.639-644
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• 2002

Nonlinear filters which are utilized rank-order information and temporal-order information, have many proposed, in order to restore nonstationary signals which are corrupted by additive noise. In this paper, we propose a data-dependent LWOS filter whose coefficients change based on local statistics. LWOS(Linear Combination of Weighted Order Statistics) filters［1］which also utilized two informations, and have properties of efficient impulsive and nonimpulsive noise attenuation and sufficiently details and edges preservation. DD-LWOS filters can remove non-impulsive oises while preserving signal details. DD-LWOS2 filter gets more better performance than DD-LWOS filter when input image corrupted by additive noise which includes Impulsive noise components.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.35 no.8C
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• pp.709-712
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• 2010

In this paper, the two sample locally optimum rank detector is obtained in the weakly dependent noise with non-zero temporal correlation between noise observations. The test statistic of the locally optimum rank detector is derived from the Neyman-Pearson lemma suitable for the two sample observation models, where it is assumed that reference observations are available in addition to regular observations. Two-sample locally optimum rank detecter shows the same performance with the one-sample locally optimum rank detector asymptotically. The structure of the two-sample rank detector is simpler than that of the one-sample rank detector because the sign statistic is not processed separately.

• Atmosphere
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• v.26 no.3
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• pp.461-471
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• 2016

A quality check algorithm for the Weather Information Service Engine pulsed Doppler wind lidar is developed from a view point of spatial and temporal consistencies of observed wind speed. Threshold values for quality check are determined by statistical analysis on the standard deviation of 3-component of wind speed obtained by a wind lidar, and the vertical gradient of horizontal wind speed obtained by a radiosonde system. The algorithm includes carrier-to-noise ratio (CNR) check, data availability check, and vertical gradient of horizontal wind speed check. That is, data sets whose CNR is less than -29 dB, data availability is less than 90%, or vertical gradient of horizontal wind speed is less than $-0.028s^{-1}$ or larger than $0.032s^{-1}$ are classified as 'doubtful', and flagged. The developed quality check algorithm is applied to data obtained at Bucheon station for the period from 1 to 30 September 2015. It is found that the number of 'doubtful' data shows maxima around 2000 m high, but the ratio of 'doubtful' to height-total data increases with increasing height due to atmospheric boundary height, cloud, or rainfall, etc. It is also found that the quality check by data availability is more effective than those by carrier to noise ratio or vertical gradient of horizontal wind speed to remove an erroneous noise data.

• Publications of The Korean Astronomical Society
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• v.30 no.2
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• pp.433-437
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• 2015

We show the results of a time series analysis of the long-term light curves of four blazars. 3C 279, 3C 345, 3C 446, and BL Lacertae. We used densely sampled light curves spanning 32 years at three frequency bands (4.8, 8, 14.5 GHz), provided by the University of Michigan Radio Astronomy Observatory monitoring program. The spectral indices of our sources are mostly flat or inverted (-0.5 < ${\alpha}$ < 0), which is consistent with optically thick emission. Strong variability was seen in all light curves on various time scales. From the analyses of time lags between the light curves from different frequency bands and the evolution of the spectral indices with time, we find that we can distinguish high-peaking flares and low-peaking flares according to the Valtaoja et al. classification. The periodograms (temporal power spectra) of the light curves are in good agreement with random-walk power-law noise without any indication of (quasi-)periodic variability. We note that random-walk noise light curves can originate from multiple shocks in jets. The fact that all our sources are in agreement with being random-walk noise emitters at radio wavelengths suggests that such behavior is a general property of blazars. We are going to generalize our approach by applying our methodology to a much larger blazar sample in the near future.

• Aerospace Engineering and Technology
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• v.1 no.2
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• pp.91-104
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• 2002

Jet noise of propulsion systems is major source of acoustic loads of launch vehicles and sounding rockets. The investigation of characteristics of jet noise is inevitable for successful missions. In this paper, the mechanism of generation of acoustic loads due to jet noise was investigated. The major parameters that change the characteristics of acoustic loads were also suggested so that effects of the parameters could be investigated. The temporal and spatial characteristics of acoustic loads of KSR-III was demonstrated. The results show that the maximum value of the acoustic loads is found in the octave bands whose center frequencies are 250 Hz and 500 Hz. Finally, the methods and the facilities for the further investigation of acoustic loads were proposed.

• Journal of radiological science and technology
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• v.39 no.4
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• pp.543-547
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• 2016

Acute stroke is a one of common disease that require fast diagnosis and treatment to save patients life. however, the acute stroke may cause lifelong disability due to brain damage with no prompt surgical procedure. In order to diagnose the Stroke, brain perfusion CT examination and possible rapid implementation of 3D angiography has been widely used. However, a low-dose technique should be applied for the examination since a lot of radiation exposure to the patient may cause secondary damage for the patients. Therefore, the degradation of the measured CT images may interferes with a clinical check in that blood vessel shapes on the CT image are significantly affected by gaussian noise. In this study, we employed the spatio-temporal technique to analyze dynamic (brain perfusion) CT data to improve an image quality for successful clinical diagnosis. As a results, proposed technique could remove gaussian noise successfully, demonstrated a possibility of new image segmentation technique for CT angiography. Qualitative evaluation was conducted by skilled radiological technologists, indicated significant quality improvement of dynamic CT images. the proposed technique will be useful tools as a clinical application for brain perfusion CT examination.

• Journal of Broadcast Engineering
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• v.16 no.5
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• pp.873-882
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• 2011

In this paper, we propose an efficient deinterlacing algorithm which interpolates the missing scan lines by weighted summing of the intra and the inter interpolation pixels according to the spatio-temporal variation. In the spatial interpolation, we adopt a new edge based spatial interpolation method which includes edge directional refinement. The conventional edge dependent interpolation algorithms are very sensitive to noise due to the failure of estimating edge direction. In order to exactly detect edge direction, our method first finds the edge directions around the pixel to be interpolated and then refines edge direction of the pixel using weighted maximun frequent filter. Futhermore, we improve the accuracy of motion detection by reducing the possibility of motion detection error using 3 tab median filter. In the final interpolation step, we adopt weighted sum of intra and inter interpolation pixels according to spatio-temporal variation ratio, thereby improving the quality in slow moving area. Simulation results show the efficacy of the proposed method with significant improvement over the previous methods in terms of the objective PSNR quality as well as the subjective image quality.

• Smart Media Journal
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• v.13 no.7
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• pp.9-17
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• 2024

Due to the increase in energy consumption, and eco-friendly policies, there is a need for efficient energy consumption in buildings. Anomaly power detection based on deep learning are being used. Because of the difficulty in collecting anomaly data, anomaly detection is performed using reconstruction error with a Recurrent Neural Network(RNN) based autoencoder. However, there are some limitations such as the long time required to fully learn temporal features and its sensitivity to noise in the train data. To overcome these limitations, this paper proposes the TCN-USAD, combined with Temporal Convolution Network(TCN) and UnSupervised Anomaly Detection for multivariate data(USAD). The proposed model using TCN-based autoencoder and the USAD structure, which uses two decoders and adversarial training, to quickly learn temporal features and enable robust anomaly detection. To validate the performance of TCN-USAD, comparative experiments were performed using two building energy datasets. The results showed that the TCN-based autoencoder can perform faster and better reconstruction than RNN-based autoencoder. Furthermore, TCN-USAD achieved 20% improved F1-Score over other anomaly detection models, demonstrating excellent anomaly detection performance.

• Journal of the Korean Society of Radiology
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• v.12 no.4
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• pp.491-496
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• 2018

The purpose of this study is to analyze the noise spectra in DWI (diffusion-weighted imaging) pulse sequences of 1.5 Tesla and 3.0 Tesla MRI, The ACR (American College of Radiology) phantom and noise spectrum were analyzed by FFT (fast Fourier transform) and TFFT (temporal frequency analysis) using WavePad sound editor version 8.13 (NCH software, Greenwood Village, CO, USA). Noise spectra, FFT and TFFT were analyzed for laboratory 1.5Tesla and 3.0Tesla DWI MR pulse sequences. The noise threshold of the frequency amplitude in the FFT and TFFT at 3.0Tesla compared to 1.5Tesla was between 1.5Tesla and -6 dB, and between 3.0Tesla and 0 dB, the DWI pulse sequence for the patient's noise reduction was appropriately MR examination needs to be applied.