• Title/Summary/Keyword: 선형다변회귀

Search Result 40, Processing Time 0.026 seconds

Voice Conversion Using Linear Multivariate Regression Model and LP-PSOLA Synthesis Method (선형다변회귀모델과 LP-PSOLA 합성방식을 이용한 음성변환)

  • 권홍석;배건성
    • The Journal of the Acoustical Society of Korea
    • /
    • v.20 no.3
    • /
    • pp.15-23
    • /
    • 2001
  • This paper presents a voice conversion technique that modifies the utterance of a source speaker as if it were spoken by a target speaker. Feature parameter conversion methods to perform the transformation of vocal tract and prosodic characteristics between the source and target speakers are described. The transformation of vocal tract characteristics is achieved by modifying the LPC cepstral coefficients using Linear Multivariate Regression (LMR). Prosodic transformation is done by changing the average pitch period between speakers, and it is applied to the residual signal using the LP-PSOLA scheme. Experimental results show that transformed speech by LMR and LP-PSOLA synthesis method contains much characteristics of the target speaker.

  • PDF

Multivariate quantile regression tree (다변량 분위수 회귀나무 모형에 대한 연구)

  • Kim, Jaeoh;Cho, HyungJun;Bang, Sungwan
    • Journal of the Korean Data and Information Science Society
    • /
    • v.28 no.3
    • /
    • pp.533-545
    • /
    • 2017
  • Quantile regression models provide a variety of useful statistical information by estimating the conditional quantile function of the response variable. However, the traditional linear quantile regression model can lead to the distorted and incorrect results when analysing real data having a nonlinear relationship between the explanatory variables and the response variables. Furthermore, as the complexity of the data increases, it is required to analyse multiple response variables simultaneously with more sophisticated interpretations. For such reasons, we propose a multivariate quantile regression tree model. In this paper, a new split variable selection algorithm is suggested for a multivariate regression tree model. This algorithm can select the split variable more accurately than the previous method without significant selection bias. We investigate the performance of our proposed method with both simulation and real data studies.

Evaluation of applicability of pan coefficient estimation method by multiple linear regression analysis (다변량 선형회귀분석을 이용한 증발접시계수 산정방법 적용성 검토)

  • Rim, Chang-Soo
    • Journal of Korea Water Resources Association
    • /
    • v.55 no.3
    • /
    • pp.229-243
    • /
    • 2022
  • The effects of monthly meteorological data measured at 11 stations in South Korea on pan coefficient were analyzed to develop the four types of multiple linear regression models for estimating pan coefficients. To evaluate the applicability of developed models, the models were compared with six previous models. Pan coefficients were most affected by air temperature for January, February, March, July, November and December, and by solar radiation for other months. On the whole, for 12 months of the year, the effects of wind speed and relative humidity on pan coefficient were less significant, compared with those of air temperature and solar radiation. For all meteorological stations and months, the model developed by applying 5 independent variables (wind speed, relative humidity, air temperature, ratio of sunshine duration and daylight duration, and solar radiation) for each station was the most effective for evaporation estimation. The model validation results indicate that the multiple linear regression models can be applied to some particular stations and months.

Multivariate Analysis for Clinicians (임상의를 위한 다변량 분석의 실제)

  • Oh, Joo Han;Chung, Seok Won
    • Clinics in Shoulder and Elbow
    • /
    • v.16 no.1
    • /
    • pp.63-72
    • /
    • 2013
  • In medical research, multivariate analysis, especially multiple regression analysis, is used to analyze the influence of multiple variables on the result. Multiple regression analysis should include variables in the model and the problem of multi-collinearity as there are many variables as well as the basic assumption of regression analysis. The multiple regression model is expressed as the coefficient of determination, $R^2$ and the influence of independent variables on result as a regression coefficient, ${\beta}$. Multiple regression analysis can be divided into multiple linear regression analysis, multiple logistic regression analysis, and Cox regression analysis according to the type of dependent variables (continuous variable, categorical variable (binary logit), and state variable, respectively), and the influence of variables on the result is evaluated by regression coefficient${\beta}$, odds ratio, and hazard ratio, respectively. The knowledge of multivariate analysis enables clinicians to analyze the result accurately and to design the further research efficiently.

Prediction of Gas Chromatographic Retention Times of PAH Using QSRR (기체크로마토그래피에서 QSRR을 통한 PAH 용리시간 예측)

  • Kim, Young Gu
    • Journal of the Korean Chemical Society
    • /
    • v.45 no.5
    • /
    • pp.422-428
    • /
    • 2001
  • Retention relative times(RRTs) of PAH molecules and their derivatives in gas chromatography are trained and predicted in testing sets using a multiple linear regression(MLR) and an artificial neural network(ANN). The main descriptors of PAHs and their derivatives in QSRR are the square root of molecular weight(sqmw), molecular connectivity($^1{\chi}_v$), molecular dipole moment(D) and length-to-breadth ratios(L/B). The results of MLR shows that a heavy molecule has a propensity for long retention time. L/B closely related with slot model is a good descriptor in MLR. On the other hand, ANN which is not effected by the linear dependencies among the descriptors were exclusively based on molecular weight and molecular dipole moment. The variances which shows the accuracy of prediction for retention times in testing sets are 1.860, 0.206 for MLR and ANN, respectively. It was shown that ANN can exceed the MLR in prediction accuracy.

  • PDF

Prediction of Retention Time for PAH Molecule in HPLC (고속액체 크로마토그래피에서 PAH분자의 구조에 따른 용리시간 예측)

  • Kim, Young-Gu
    • Journal of the Korean Chemical Society
    • /
    • v.44 no.2
    • /
    • pp.102-108
    • /
    • 2000
  • Relative retention times (RRTs) of RAH molecules in HPLC are trained and predicted intesting sets using a multiple linear regression (NLR) and an artificial neural network (ANN). The maindescriptors in QSRR are molecular connectivity ($^1X_v,\;^2X_v$), the length-to-breadth ratios (L/B), and molecular dipole moment(D). L/B which is related with slot model is a good descripter in ANN, but isn't in MLR. Varainces which show the accuracy of prediction times in testing sets are 0.0099, 0.0114 for ANN and MLR, respectively. It was shown that ANN can exceed the MLR in prediction accuracy.

  • PDF

Study of Polymor Properties Prediction Using Nonlinear SEM Based on Gaussian Process Regression (가우시안 프로세서 회귀 기반의 비선형 구조방정식을 활용한 고분자 물성거동 예측 연구)

  • Moon Kyung-Yeol;Park Kun-Wook
    • KIPS Transactions on Computer and Communication Systems
    • /
    • v.13 no.1
    • /
    • pp.1-9
    • /
    • 2024
  • In the development and mass production of polymers, there are many uncontrollable variables. Even small changes in chemical composition, structure, and processing conditions can lead to large variations in properties. Therefore, Traditional linear modeling techniques that assume a general environment often produce significant errors when applied to field data. In this study, we propose a new modeling method (GPR-SEM) that combines Structural Equation Modeling (SEM) and Gaussian Process Regression (GPR) to study the Friction-Coefficient and Flexural-Strength properties of Polyacetal resin, an engineering plastic, in order to meet the recent trend of using plastics in industrial drive components. And we also consider the possibility of using it for materials modeling with nonlinearity.

Applicability evaluation of aerodynamic approaches for evaporation estimation using pan evaporation data (증발접시 증발량자료를 이용한 공기동력학적 증발량 산정 방법의 적용성 평가)

  • Rim, Chang-Soo
    • Journal of Korea Water Resources Association
    • /
    • v.50 no.11
    • /
    • pp.781-793
    • /
    • 2017
  • In this study, applicabilities of aerodynamic approaches for the estimation of pan evaporation were evaluated on 56 study stations in South Korea. To accomplish this study purpose, previous researchers' evaporation estimation equations based on aerodynamic approaches were grouped into seven generalized evaporation models. Furthermore, four multiple linear regression (MLR) models were developed and tested. The independent variables of MLR models are meteorological variables such as wind speed, vapor pressure deficit, air temperature, and atmospheric pressure. These meteorological variables are required for the application of aerodynamic approaches. In order to consider the effect of autocorrelation, MLR models were developed after differencing variables. The applicability of MLR models with differenced variables was compared with that of MLR models with undifferenced variables and the comparison results showed no significant difference between the two methods. The study results have indicated that there is strong correlation between estimated pan evaporation (using aerodynamic models and MLR models) and measured pan evaporation. However, pan evaporation are overestimated during August, September, October, November, and December. Most of meteorological variables that are used for MLR models show statistical significance in the estimation of pan evaporation. Vapor pressure deficit was turned out to be the most significant meteorological variable. The second most significant variable was air temperature; wind speed was the third most significant variable, followed by atmospheric pressure.

Multivariate Statistical Analysis and Prediction for the Flash Points of Binary Systems Using Physical Properties of Pure Substances (순수 성분의 물성 자료를 이용한 2성분계 혼합물의 인화점에 대한 다변량 통계 분석 및 예측)

  • Lee, Bom-Sock;Kim, Sung-Young
    • Journal of the Korean Institute of Gas
    • /
    • v.11 no.3
    • /
    • pp.13-18
    • /
    • 2007
  • The multivariate statistical analysis, using the multiple linear regression(MLR), have been applied to analyze and predict the flash points of binary systems. Prediction for the flash points of flammable substances is important for the examination of the fire and explosion hazards in the chemical process design. In this paper, the flash points are predicted by MLR based on the physical properties of pure substances and the experimental flash points data. The results of regression and prediction by MLR are compared with the values calculated by Raoult's law and Van Laar equation.

  • PDF