• 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.

• Proceedings of the KIEE Conference
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• 1989.07a
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• pp.158-161
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• 1989

This paper describes the AR model of EMG signal during maximum voluntary contraction. By comparing the AR coefficients and the reflection coefficients of the AR model with the median frequency of power spectrum, it if proved that muscle fatigue can be measured by the AR and the reflection coefficients. In the estimation procedure of AR model parameter, the auto-correlation method is superior to the covariance method, and it is determined that the optimal order is six. As the muscle becomes fatigue, the median frequency of power spectrum is declined, and the AR coefficient [$a_1$ ] and the reflection coefficient [$k_1$ ] are also decreased. Therefore the muscle fatigue can be measured by the AR parameter.

• Journal of Biomedical Engineering Research
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• v.10 no.1
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• pp.11-16
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• 1989

This paper describes the AR model of EMG signal during maximum voluntary contraction. By comparing the AR coefficients and the reflection coefficients of the AR model with the median frequency of power spectrum, it is proved that muscle fatigue can be measured by the AR and the reflection coefficients. In the estimation procedure of AR model parameter, the autocorrelation method is superior to the covariance method, and it is determined that the optimal order is six. As the muscle becomes fatigue, the median frequency of power spectrum is declined, and the AR coefficient [$a_1$] and the reflection coefficient [$k_1$] are also decreased. Therefore the muscle fatigue can be measured by the AR parameter.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.4
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• pp.1489-1503
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• 2018

Spectrum reuse has attracted much concern of researchers and scientists, however, the dynamic spectrum access is challenging, since an individual secondary user usually just has limited sensing abilities. One key insight is that spectrum usage forecasting among secondary users, this inspiration enables users to obtain more informed spectrum opportunities. Therefore, spectrum usage forecasting is vital to cognitive radio networks (CRNs). With this insight, a spectrum usage forecasting model for the occurrence of primary users prediction is derived in this paper. The proposed model is based on auto regressive enhanced primary user emergence reasoning (AR-PUER), which combines linear prediction and primary user emergence reasoning. Historical samples are selected to train the spectrum usage forecasting model in order to capture the current distinction pattern of primary users. The proposed scheme does not require the knowledge of signal or of noise power. To verify the performance of proposed spectrum usage forecasting model, we apply it to the data during the past two months, and then compare it with some other sensing techniques. The simulation results demonstrate that the spectrum usage forecasting model is effective and generates the most accurate prediction of primary users occasion in several cases.

• Korean Journal of Optics and Photonics
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• v.14 no.3
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• pp.286-291
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• 2003

Asymmetric multiple quantum well ridge waveguide laser diodes (AMQW RWG LDs) with a wide and flat gain spectrum were designed and fabricated. The operating parameters and gain spectra were measured and analyzed for uncoated and anti-reflection (AR) coated LDs. For AR coated 500 mm-long RWG LOs, the extremely flat gain spectrum over a spectral range of 90 nm was obtained at the current 75 ㎃.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.24 no.4
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• pp.10-16
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• 2010

In this paper, synthesis here Mn add to Ar injection the state and a vacuum an atomosphere $ZnGa_2O_4$ : Mn, ZnO and $Ga_2O_3$ power of 1 : 1 mole ratio mixture. Manufacture a close examination of oxygen a component variation luminescence a specific character reach an in fluence of $ZnGa_2O_4$ : Mn, luminescence spectrum observation also an explanation of Mn site symmetry and at luminescence spectrum reach an influence from low temperature photoluminescence spectrum.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2008.10a
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• pp.628-631
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• 2008

For the remote sensing purpose, radar systems extract the target information, such as the magnitude of reflectivity and the velocity from the spectrum analysis of return echoes through the Doppler filter bank. This conventional spectrum estimation method, FFT(Fast fourier Transform) is widely used in most radar systems. However, the frequency resolution of return echoes can be seriously degraded in fast moving targets because of the short acquisition time. Since the high Doppler frequency resolution is important in the detection and tracking of fast moving targets, it can cause very unsatisfactory results. Therefore, in this paper, the parameter spectrum estimation method called AR(Autoregressive) spectrum estimation, is investigated to overcome these problems.

• Proceedings of the Korean Society of Computer Information Conference
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• 2021.07a
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• pp.529-530
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• 2021

본 논문에서는 MindWave와 AR 헤드셋 기기를 연동하여 자폐 스펙트럼 장애 아동이 불안감을 느낄 때 발산되는 뇌파 신호를 실시간으로 감지한다. 또한 실시간 객체 검출을 위한 YOLOv5 알고리즘을 통해 시각적 정보를 수집하여 해당 아동이 불안감을 느끼는 원인을 파악하고 이에 맞는 해결책을 AR 형태로 제시하며 자폐 스펙트럼 장애 아동이 불안감을 느끼면 보호자에게 알림을 전송하는 앱을 구현한다. 이를 통해 자폐 스펙트럼 장애 아동의 뇌파 안정과 정서 회복을 돕고 실생활에서 발생할 수 있는 돌발 상황을 방지할 수 있는 시스템을 제안한다.

• The Korean Journal of Applied Statistics
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• v.24 no.6
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• pp.995-1006
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• 2011

A time series can be decomposed into simple components with a multiscale method. Empirical mode decomposition(EMD) is a recently invented multiscale method in Huang et al. (1998). It is natural to apply a classical prediction method such a vector autoregressive(AR) model to the obtained simple components instead of the original time series; in addition, a prediction procedure combining a classical prediction model to EMD and Hilbert spectrum is proposed in Kim et al. (2008). In this paper, we suggest to adopt principal component analysis(PCA) to the prediction procedure that enables the efficient selection of input variables among obtained components by EMD. We discuss the utility of adopting PCA in the prediction procedure based on EMD and Hilbert spectrum and analyze the daily worm account data by the proposed PCA adopted prediction method.

• Communications of the Korean Mathematical Society
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• v.13 no.3
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• pp.461-464
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• 1998

We generalize the Artin-Rees Theorem about the AR-property and study linking diagram in the set of prime ideals of the coordinate ring of quantum affine space.