• Title/Summary/Keyword: AR Model

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The Characteristics of Muscle Fatigue of EMG Signal Using the AR Model (AR 모델을 이용한 EMG 신호의 근육피로 특성)

  • 김홍래;왕문성
    • 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.

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A Study on Analysis of Time Delay Model Using Autoregressive Method for Mobile Communication Channels (AR 모델을 이용한 이동 통신 채널의 시간 지연 해석기법에 관한 연구)

  • 이형권;류은숙;이종길
    • Proceedings of the IEEK Conference
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    • 1999.06a
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    • pp.29-32
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    • 1999
  • In this study, the time delay model were simulated using the well-known AR model. Frequency response of the time delay model can be obtained by mapping AR model to JTC model in the time domain. That is, from the few measurement data in JTC model, the channel frequency response can be obtained by the estimation of AR model parameters. From this channel frequency response, the time delay model can be obtained using Fourier transformation. To prove the validity of the suggested method, three models of JTC were shown and analyzed.

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The Development of the DEA-AR Model using Multiple Regression Analysis and Efficiency Evaluation of Regional Corporation in Korea (다중회귀분석을 이용한 DEA-AR 모형 개발 및 국내 지방공사의 효율성 평가)

  • Sim, Gwang-Sic;Kim, Jae-Yun
    • Journal of the Korean Operations Research and Management Science Society
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    • v.37 no.1
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    • pp.29-43
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    • 2012
  • We design a DEA-AR model using multiple regression analysis with new methods which limit weights. When there are multiple input and single output variables, our model can be used, and the weights of input variables use the regression coefficient and coefficient of determination. To verify the effectiveness of the new model, we evaluate the efficiency of the Regional Corporations in Korea. Accordance with statistical analysis, it proved that there is no difference between the efficiency value of the DEA-AR using AHP and our DEA-AR model. Our model can be applied to a lot of research by substituting DEA-AR model relying on AHP in the future.

Analysis of the Efficiency of the Regional Public Hospitals using DEA-AR/AHP Combined Model (DEA-AR/AHP 결합모형을 이용한 지방의료원의 효율성 분석)

  • Yang, Dong-Hyun
    • Health Policy and Management
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    • v.20 no.4
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    • pp.74-96
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    • 2010
  • The purpose of this empirical study is to evaluate efficiency of the regional public hospitals, using DEA(Data Envelopment Analysis). to do this, we design a DEA-AR/AHP Hybrid model to evaluate efficiency of 34 Regional Public Hospitals. the proposed model is developed by adding Acceptance Region(AR). using analytical hierarchy process(AHP). this model is compared with those of typical DEA models. Financial data used in this study were obtained from Database of the Korea Association Regional Public Hospital and analyzed using DEA model. As a result of analysis, This study found that the DEA-AR/AHP Hybrid model was superior to those typical DEA models in determining the priority among efficient hospitals. the result of this study can provide helpful information to evaluate the efficiency of public hospitals for efficient operational management, to develop more precise measurement for the priority of the efficient hospitals.

Measurement of Muscle Fatigue using AR Parameters (AR 매개 변수를 이용한 근육 피로의 측정)

  • Kim, H.R.;Wang, M.S.;Choi, Y.H.;Park, S.H.
    • 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.

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Uncertainty Estimation of AR Model Parameters Using a Bayesian technique (Bayesian 기법을 활용한 AR Model 매개변수의 불확실성 추정)

  • Park, Chan-Young;Park, Jong-Hyeon;Park, Min-Woo;Kwon, Hyun-Han
    • Proceedings of the Korea Water Resources Association Conference
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    • 2016.05a
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    • pp.280-280
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    • 2016
  • 특정 자료의 시간의 흐름에 따른 예측치를 추정하는 방법으로 AR Model 즉, 자기회귀모형이 많이 사용되고 있다. AR Model은 변수의 현재 값을 과거 값의 함수로 나타내게 되는데, 이런 시계열 분석 모델을 사용할 때 매개변수의 추정 과정이 필수적으로 요구된다. 일반적으로 매개변수를 추정하는 방법에는 확률적근사법(stochastic approximation), 최소제곱법(method of least square), 자기상관법(method of autocorrelation method), 최우도법(method of maximum likelihood) 등이 있다. AR Model에서 가장 많이 사용되는 최우도법은 표본크기가 충분히 클 때 가장 효율적인 방법으로 평가되지만 수치적으로 해를 구하는 과정이 복잡한 경우가 많으며, 해를 구하지 못하는 어려움이 따르기도 한다. 또한 표본 크기가 작을 때 일반적으로 잘 일치하지 않은 결과를 얻게 된다. 우리나라의 강우, 유량 등의 자료는 자료의 수가 적은 경우가 많기 때문에 최우도법을 통한 매개변수 추정 시 불확실성이 내재되어있지만 그것을 정량적으로 제시하는데 한계가 있다. 본 연구에서는 AR Model의 매개변수 추정 시 Bayesian 기법으로 매개변수의 사후분포(posterior distribution)를 제공하여 매개변수의 불확실성 구간을 정량적으로 표현하게 됨으로써, 시계열 분석을 통해 보다 신뢰성 있는 예측치를 얻을 수 있으리라 판단된다.

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Bayesian Method for the Multiple Test of an Autoregressive Parameter in Stationary AR(L) Model (AR(1)모형에서 자기회귀계수의 다중검정을 위한 베이지안방법)

  • 김경숙;손영숙
    • The Korean Journal of Applied Statistics
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    • v.16 no.1
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    • pp.141-150
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    • 2003
  • This paper presents the multiple testing method of an autoregressive parameter in stationary AR(1) model using the usual Bayes factor. As prior distributions of parameters in each model, uniform prior and noninformative improper priors are assumed. Posterior probabilities through the usual Bayes factors are used for the model selection. Finally, to check whether these theoretical results are correct, simulated data and real data are analyzed.

A Study on the AR Game Analysis and Business Model (AR게임 분석과 비즈니스 모델에 관한 연구)

  • Kang, Ye-seul;Kang, Ji-yeong;Yoon, Hee-young;Hwang, Jin-ju;Chang, Young-hyun;Ko, Chang-Bae
    • The Journal of the Convergence on Culture Technology
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    • v.2 no.4
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    • pp.49-54
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    • 2016
  • Development of AR/VR has expanded computer games from indoor static contents to outdoor dynamic contents. The syndrome of 'Pokemon Go' which is a representative AR game was born, and Nintendo's stock price shot up a massive 120%. Although some people gained profits, new damages are emerging including illegal intrusion or road accidents because it is a type of game to visit real locations. There are reported about 114 thousand road accidents related to the game. This study aims to examine the trends of AR game in 2016 and analyze damages and positive cases to suggest a method for preventing accidents due to the AR game, and a business model.

A Study on the Quality Model for Testing of Augmented Reality Contents (AR 콘텐츠의 테스팅을 위한 품질모델 연구)

  • Lee, Jong-Won
    • Proceedings of the Korean Society of Computer Information Conference
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    • 2022.01a
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    • pp.65-66
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    • 2022
  • AR을 기반으로 하는 현실과 가상세계가 공존하는 콘텐츠들이 증가하고 있다. 이에 따라 AR 콘텐츠에 대한 체계적인 테스팅에 대한 필요성도 증가하고 있다. 소프트웨어 테스팅과 관련한 품질특성은 ISO/IEC 25010에서 정의하고 있다. 기존의 연구에서 ISO/IEC 25010을 기반으로 AR의 테스팅에 적용할 수 있는 품질특성을 제안하였다. 본 논문에서는 기존의 연구에서 제안한 AR 테스팅 품질특성에 신뢰성과 보안성을 보완할 것을 제안한다. 이러한 품질특성으로 고려하여 AR의 테스팅에 필요한 테스트 케이스를 개발하고 실행한다면 보다 체계적인 테스팅이 가능할 것이다.

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Combining Regression Model and Time Series Model to a Set of Autocorrelated Data

  • Jee, Man-Won
    • Journal of the military operations research society of Korea
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    • v.8 no.1
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    • pp.71-76
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    • 1982
  • A procedure is established for combining a regression model and a time series model to fit to a set of autocorrelated data. This procedure is based on an iterative method to compute regression parameter estimates and time series parameter estimates simultaneously. The time series model which is discussed is basically AR(p) model, since MA(q) model or ARMA(p,q) model can be inverted to AR({$\infty$) model which can be approximated by AR(p) model. The procedure discussed in this articled is applied in general to any combination of regression model and time series model.

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