• Journal of the Korean Society for Precision Engineering
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• v.13 no.12
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• pp.120-128
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• 1996

An attempt is made to characterize and synthesize engineered surfaces. The proposed method is not only an analytical tool to characterize but also to generate/synthesize three-dimensional surfaces. The developed method expresses important engineered surface characteristics such as the autocorrelation or power spectrum density functions in terms of the two-dimensional autoregressive coefficients.

• Journal of Biomedical Engineering Research
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• v.21 no.4
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• pp.339-344
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• 2000

The mucociliary system is one of the most important airway defense mechanisms in human body and impairment of ciliary movement results in various diseases in respiratory tract. In this study, we have developed a system that can measure ciliary movement in vivo and quantified ciliary beat frequency (CBF) through autoregressive (AR) power spectrum. To measure the frequency in vivo, we applied a photoelectric method that was composed of a laser light and a fiber optic probe. Scattered signals are transferred to a PC in which they are displayed on the monitor and its CBF is determined by the AR method in were acquired. For 8 normal subjects, the analyzed CBFs ranged from 5 to 10Hz and its mean was 7.3${\pm}$1.1Hz. This result showed similar aspects to the reported results of CBFs to data. We expect that this result will be applied in various clinical studies such as analysis of CBF changes by drugs or by diseaes.

• 제어로봇시스템학회:학술대회논문집
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• 1986.10a
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• pp.134-136
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• 1986

In this paper, a new power spectral estimation technique is presented. At first, by transforming the original data with the Karhunen-Loeve Transform(KLT), we can reduce the amount of the redundant information. Next, by modeling the transformed data by means of the autoregressive(AR) model and then applying the least-squares parameter estimation algorithm to this model, even more accurate spectrum estimates can be obtained. The KLT is the optimum transform for signal representation with respect to the mean-square error criterion. And the least-squares method is used to overcome the inherent shortcomings of popular burg algorithm.

• Journal of the Institute of Convergence Signal Processing
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• v.1 no.1
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• pp.15-22
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• 2000

The analysis of power spectrum based on linear AR model is applied widely to quantize the response of autonomic nerve noninvasively, In this paper, we estimate the power spectrum density for heartrate variability of the electrocadiogram and pulse wave for short term data(less than two minute), The time series of heart rate variability is obtained from the time interval(RRI, PPI) between the feature point of the electrocadiogram and pulse wave for normal person, The generated time series reconstructed into new time series through polynomial interpolation to apply to the AR mode. The power spectrum density for AR model is calculated by Burg algorithm, After applying AR model, the power spectrum density for heart rate variability of the electrocadiogram and the pulse wave is shown smooth spectrum power at the region of low frequence and high frequence, and that the power spectrum density of electrocadiogram and pulse wave has similar form for same subject.

• Nuclear Engineering and Technology
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• v.49 no.4
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• pp.825-837
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• 2017

Due to the severe impacts of recent earthquakes, the use of seismic isolation is paramount for the safety of nuclear structures. The diversity observed in seismic events demands ongoing research to analyze the devastating attributes involved, and hence to enhance the sustainability of base-isolated nuclear power plants. This study reports the seismic performance of a seismically-isolated nuclear reactor containment building (NRCB) under strong short-period ground motions (SPGMs) and long-period ground motions (LPGMs). The United States Nuclear Regulatory Commission-based design response spectrum for the seismic design of nuclear power plants is stipulated as the reference spectrum for ground motion selection. Within the period range(s) of interest, the spectral matching of selected records with the target spectrum is ensured using the spectral-compatibility approach. NRC-compliant SPGMs and LPGMs from the mega-thrust Tohoku earthquake are used to obtain the structural response of the base-isolated NRCB. To account for the lack of earthquakes in low-to-moderate seismicity zones and the gap in the artificial synthesis of long-period records, wavelet-decomposition based autoregressive moving average modeling for artificial generation of real ground motions is performed. Based on analysis results from real and simulated SPGMs versus LPGMs, the performance of NRCBs is discussed with suggestions for future research and seismic provisions.

• Smart Structures and Systems
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• v.33 no.2
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• pp.145-163
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• 2024

The evaluation of dynamic characteristics of bridges under operational traffic loads is a crucial aspect of bridge structural health monitoring. In the vehicle-bridge interaction (VBI) system, the vibration responses of bridge exhibit time-varying characteristics. To address this issue, an accurate time-frequency analysis method that combines the autoregressive power spectrum based empirical wavelet transform (AR-EWT) and local maximum synchrosqueezing transform (LMSST) is proposed to identify the time-varying instantaneous frequencies (IFs) of the bridge in the VBI system. The AR-EWT method decomposes the vibration response of the bridge into mono-component signals. Then, LMSST is employed to identify the IFs of each mono-component signal. The AR-EWT combined with the LMSST method (AR-EWT+LMSST) can resolve the problem that LMSST cannot effectively identify the multi-component signals with weak amplitude components. The proposed AR-EWT+LMSST method is compared with some advanced time-frequency analysis techniques such as synchrosqueezing transform (SST), synchroextracting transform (SET), and LMSST. The results demonstrate that the proposed AR-EWT+LMSST method can improve the accuracy of identified IFs. The effectiveness and applicability of the proposed method are validated through a multi-component signal, a VBI numerical model with a four-degree-of-freedom half-car, and a VBI model experiment. The effect of vehicle characteristics, vehicle speed, and road surface roughness on the identified IFs of bridge are investigated.

• Proceedings of the KOSOMBE Conference
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• v.1998 no.11
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• pp.78-79
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• 1998

This paper describes a real time algorithm for blood flow estimation of LDF(laser Doppler flowmeter). Many algorithms for blood flow estimation are using power spectral density of Doppler signal by blood flow. In these research, the fast Fourier transformation is used to estimate power spectral density. This is a block processing procedure rather than real time processing. The algorithm in this paper used parametric spectral estimation. This has real time capability by estimation of AR(autoregressive) parameters sample by sample, and has smoothing power spectrum. Also, the frequency resolution is not limited by number of samples used to estimate AR parameter. Another advantage of this algorithm is that AR model enhance SNR.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2001.11b
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• pp.699-702
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• 2001

본 연구에서는 생활공간에서의 음 환경이 인체에 미치는 영향을 조사하기 위하여 긍정감성 유발 음환경으로서 시냇물 흐르는 소리를 배경으로한 명상음악을 제시하고 부정감성을 유발하는 음환경으로서 '헬리콥터소음'과 '마루삐그덕 소음'을 제시하여 자율신경계 생리반응으로서 심전도의 HRV를 분석하였다. HRV는 AR(autoregressive) 모델로 구하였으며 Power spectrum을 LF(0.01 - 0.08 Hz), MF(0.08 - 0.15 Hz), HF(0.15 - 0.5 Hz) 영역으로 나누어 LF. MF. HF 영역의 Power 및 LF/HF와 MF/(LF+HF) Power Ratio를 분석하였다.

• Proceedings of the KOSOMBE Conference
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• v.1998 no.11
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• pp.199-200
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• 1998

The mucociliary system is one of the most important airway defense mechanisms, and knowledge of the ciliary beat frequency(CBF) is important in the understanding of this system. Using a laser light scattering method and fiber optic probe, we developed a simple and practical instrument for real-time in vivo measurements of CBF of cells in human nasal cavity. From the ciliated epithelium cells in an anterior end of middle terminator in nasal cavity, the signals of ciliary movement are transferred into a PC and analyzed by a autoregressive(AR) power spectrum. The mean CBF of 8 normal subjects was $7.1{\pm}1.1$(Hz). This instrument provided a convenient and reliable method of studying the mucociliary activity in the respiratory tract.

• Korean Journal of Artificial Intelligence
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• v.12 no.1
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• pp.17-24
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• 2024

This paper introduces a novel approach to time-series estimation for energy load forecasting within Virtual Power Plant (VPP) systems, leveraging advanced artificial intelligence (AI) algorithms, namely Long Short-Term Memory (LSTM) and Seasonal Autoregressive Integrated Moving Average (SARIMA). Virtual power plants, which integrate diverse microgrids managed by Energy Management Systems (EMS), require precise forecasting techniques to balance energy supply and demand efficiently. The paper introduces a hybrid-method forecasting model combining a parametric-based statistical technique and an AI algorithm. The LSTM algorithm is particularly employed to discern pattern correlations over fixed intervals, crucial for predicting accurate future energy loads. SARIMA is applied to generate time-series forecasts, accounting for non-stationary and seasonal variations. The forecasting model incorporates a broad spectrum of distributed energy resources, including renewable energy sources and conventional power plants. Data spanning a decade, sourced from the Korea Power Exchange (KPX) Electrical Power Statistical Information System (EPSIS), were utilized to validate the model. The proposed hybrid LSTM-SARIMA model with parameter sets (1, 1, 1, 12) and (2, 1, 1, 12) demonstrated a high fidelity to the actual observed data. Thus, it is concluded that the optimized system notably surpasses traditional forecasting methods, indicating that this model offers a viable solution for EMS to enhance short-term load forecasting.