• 한국지진공학회논문집
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• 제28권4호
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• pp.183-191
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• 2024

Accurate seismic vulnerability assessment requires high quality and large amounts of ground motion data. Ground motion data generated from time series contains not only the seismic waves but also the background noise. Therefore, it is crucial to determine the high-pass cut-off frequency to reduce the background noise. Traditional methods for determining the high-pass filter frequency are based on human inspection, such as comparing the noise and the signal Fourier Amplitude Spectrum (FAS), f² trend line fitting, and inspection of the displacement curve after filtering. However, these methods are subject to human error and unsuitable for automating the process. This study used a deep learning approach to determine the high-pass filter frequency. We used the Mel-spectrogram for feature extraction and mixup technique to overcome the lack of data. We selected convolutional neural network (CNN) models such as ResNet, DenseNet, and EfficientNet for transfer learning. Additionally, we chose ViT and DeiT for transformer-based models. The results showed that ResNet had the highest performance with R² (the coefficient of determination) at 0.977 and the lowest mean absolute error (MAE) and RMSE (root mean square error) at 0.006 and 0.074, respectively. When applied to a seismic event and compared to the traditional methods, the determination of the high-pass filter frequency through the deep learning method showed a difference of 0.1 Hz, which demonstrates that it can be used as a replacement for traditional methods. We anticipate that this study will pave the way for automating ground motion processing, which could be applied to the system to handle large amounts of data efficiently.

• 임상간호연구
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• 제14권1호
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• pp.99-111
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• 2008

Purpose: This study was done to provide basic data for developing an in-service education program to improve nurses' quality. First, frequency of nursing activities and competency levels of emergency nurses according to their career ladders were compared through job analysis and then practical education programs were presented on based of the results. Method: Data were collected from 335 nurses working in emergency rooms in 31 tertiary hospitals. Data collection was done from September to November 2005 using the job analysis questionnaire. Results: There were 41 nursing activities that showed differences in frequency and 78 activities that showed differences in perceived competency level. Irrespective of emergency nurses' careers, activities that show high frequency but low competency were sputum liquefying therapy, assessment of cranial nerve function, identification of diagnostic radiology, and communication with various departments. In-service education content according to nurse's career ladders was presented by adding high frequency nursing activities and activities with low competency level even though having high frequency. Conclusion: There is a need to develop and provide in-service education programs, which consider nurses' difference in frequency and competency level for their career ladders.

• 디지털산업정보학회논문지
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• 제11권4호
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• pp.155-164
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• 2015

In this paper, we propose a new color interpolation method based on trilateral filter approach, which not only preserve the high-frequency components(image edge) while interpolating the missing raw data of color image(bayer data pattern), but also immune to the image noise components and better preserve the detail of the low-frequency components. The method is the trilateral filter approach applying a gradient to the low frequency components of the image signal in order to preserve the high-frequency components and the detail of the low-frequency components through the measure of the freedom of similarity among adjacent pixels. And also we perform Gaussian smoothing to the interpolated image data in order to robust to the noise. In this paper, we compare the conventional demosaicing algorithm and the proposed algorithm using 10 test images in terms of hue MAD, saturation MAD and CPSNR for the objective evaluation, and verify the performance of the proposed algorithm.

• Smart Structures and Systems
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• 제6권5_6호
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• pp.661-673
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• 2010

This paper presents recent developments in an extremely compact, wireless impedance sensor node (the WID3, $\underline{W}$ireless $\underline{I}$mpedance $\underline{D}$evice) for use in high-frequency impedance-based structural health monitoring (SHM), sensor diagnostics and validation, and low-frequency (< ~1 kHz) vibration data acquisition. The WID3 is equipped with an impedance chip that can resolve measurements up to 100 kHz, a frequency range ideal for many SHM applications. An integrated set of multiplexers allows the end user to monitor seven piezoelectric sensors from a single sensor node. The WID3 combines on-board processing using a microcontroller, data storage using flash memory, wireless communications capabilities, and a series of internal and external triggering options into a single package to realize a truly comprehensive, self-contained wireless active-sensor node for SHM applications. Furthermore, we recently extended the capability of this device by implementing low-frequency analog-to-digital and digital-to-analog converters so that the same device can measure structural vibration data. The compact sensor node collects relatively low-frequency acceleration measurements to estimate natural frequencies and operational deflection shapes, as well as relatively high-frequency impedance measurements to detect structural damage. Experimental results with application to SHM, sensor diagnostics and low-frequency vibration data acquisition are presented.

• 한국의류산업학회지
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• 제20권5호
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• pp.533-547
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• 2018

The objective of this study is to examine the perception of golfwear and related trends based on major keywords and associated words related to golfwear utilizing big data. For this study, the data was collected from blogs, Jisikin and Tips, news articles, and web $caf{\acute{e}}$ from two of the most commonly used search engines (Naver & Daum) containing the keywords, 'Golfwear' and 'Golf clothes'. For data collection, frequency and matrix data were extracted through Textom, from January 1, 2016 to December 31, 2017. From the matrix created by Textom, Degree centrality, Closeness centrality, Betweenness centrality, and Eigenvector centrality were calculated and analyzed by utilizing Netminer 4.0. As a result of analysis, it was found that the keyword 'brand' showed the highest rank in web visibility followed by 'woman', 'size', 'man', 'fashion', 'sports', 'price', 'store', 'discount', 'equipment' in the top 10 frequency rankings. For centrality calculations, only the top 30 keywords were included because the density was extremely high due to high frequency of the co-occurring keywords. The results of centrality calculations showed that the keywords on top of the rankings were similar to the frequency of the raw data. When the frequency was adjusted by subtracting 100 and 500 words, it showed different results as the low-ranking keywords such as J. Lindberg in the frequency analysis ranked high along with changes in the rankings of all centrality calculations. Such findings of this study will provide basis for marketing strategies and ways to increase awareness and web visibility for Golfwear brands.

• 응용통계연구
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• 제29권3호
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• pp.505-512
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• 2016

본 연구에서는 금융시계열의 일간 변동성 측정을 위해 가중 융합 방법을 제안하고 있다. 고빈도(high frequency)자료에 기반을 둔 조정된 실현변동성을 계산하고 이를 참 값으로 간주하여 제안된 가중 융합 변동성에서 최적 가중치를 결정하는 과정을 서술하였다. 국내 KOSPI200자료의 1분 단위 고빈도 주가로부터 조정된 실현변동성을 구한 후 최적의 가중 융합 변동성을 제안해 보았다.

• 한국멀티미디어학회논문지
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• 제15권4호
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• pp.526-539
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• 2012

현재까지 연구된 대부분의 정보은닉 기법관련 알고리듬들은 커버영상(cover image)의 변경 또는 조작을 통해 비밀데이터를 삽입(embedding)하여 스테고영상(stego image)을 생성하고, 생성된 스테고영상으로부터 비밀데이터를 추출(extraction)하였다. 이러한 알고리듬은 PSNR의 수치가 높고, 비밀데이터의 수용량(capacity)이 많을수록 좋은 것으로 간주한다. 본 논문에서는 비밀데이터의 패턴(pattern)을 분석하여 숨기는 비밀데이터의 양이 많고, PSNR의 값이 우수한 효율적인 정보은닉 알고리듬을 제안한다. 제안하는 정보은닉 알고리듬은 비밀데이터를 분석하여 비밀데이터 내의 빈도수가 높은 값들을 찾고, 이들의 좌표 값과 인덱스(index)정보를 이용해 커버영상에 삽입한다. 이를 통해 커버영상과 스테고영상 간의 차이는 줄이면서 기존의 제안되었던 알고리듬에 비해 높은 수용량을 보여줌을 실험을 통해 비교한다. 실험결과에서는 5 종류의 비밀 데이터와 8 가지 이하의 패턴을 이용해 커버영상에 삽입하여 생성된 스테고영상과의 차이를 측정한 PSNR과 숨겨진 비밀데이터의 양의 결과를 통해 기존에 제안되었던 알고리듬들 비해 제안하는 정보은닉 알고리듬이 우수함을 보여준다.

• 응용통계연구
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• 제25권6호
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• pp.925-932
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• 2012

The article introduces a method to estimate a cointegrated vector autoregressive model, using mixed-frequency data, in terms of a state-space representation of the vector error correction(VECM) of the model. The method directly estimates the parameters of the model, in a state-space form of its VECM representation, using the available data in its mixed-frequency form. Then it allows one to compute in-sample smoothed estimates and out-of-sample forecasts at their high-frequency intervals using the estimated model. The method is applied to a mixed-frequency data set that consists of the quarterly real gross domestic product and three monthly coincident indicators. The result shows that the method produces accurate smoothed and forecasted estimates in comparison to a method based on single-frequency data.

• Smart Structures and Systems
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• 제11권1호
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• pp.123-134
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• 2013

Identifying structural modal parameters, especially those modes within high frequency range, from ambient data is still a challenging problem due to various kinds of uncertainty involved in vibration measurements. A procedure applying an ensemble empirical mode decomposition (EEMD) method is proposed for accurate and robust structural modal identification. In the proposed method, the EEMD process is first implemented to decompose the original ambient data to a set of intrinsic mode functions (IMFs), which are zero-mean time series with energy in narrow frequency bands. Subsequently, a Sub-PolyMAX method is performed in narrow frequency bands by using IMFs as primary data for structural modal identification. The merit of the proposed method is that it performs structural identification in narrow frequency bands (take IMFs as primary data), unlike the traditional method in the whole frequency space (take original measurements as primary data), thus it produces more accurate identification results. A numerical example and a multiple-span continuous steel bridge have been investigated to verify the effectiveness of the proposed method.

• Journal of Electrical Engineering and Technology
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• 제1권4호
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• pp.534-542
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• 2006

This paper presents results of frequency domain spectroscopy (FDS) measurements on oil-impregnated pressboard insulation, their analyses and use of the data for modeling high frequency response (FRA) of transformers. The dielectric responses were measured in a broad frequency range, i.e. from 0.1 mHz to 1 MHz, on model samples containing different amount of moisture. The responses were parameterized with terms representing dc conductivity, low frequency dispersion and Cole-Cole polarization mechanisms and they were thereafter used to model the FRA response of a three-phase transformer.