scholarly journals Development of Novel Thermal Diffusivity Analysis by Spot Periodic Heating and Infrared Radiation Thermometer Method

Materials ◽  
2020 ◽  
Vol 13 (21) ◽  
pp. 4848
Author(s):  
Sho Nagata ◽  
Tsuyoshi Nishi ◽  
Shugo Miyake ◽  
Naoyoshi Azuma ◽  
Kimihito Hatori ◽  
...  
Keyword(s):  
Thermal Diffusivity ◽  
Pure Copper ◽  
Radiation Thermometer ◽  
Contact Method ◽  
Finite Sample ◽  
Periodic Heating ◽  
Sensitivity Distribution ◽  
Transfer Equations ◽  
Finite Sample Size ◽  
Infrared Radiation Thermometer

A spot periodic heating method is a highly accurate, non-contact method for the evaluation of anisotropy and relative thermophysical property distribution. However, accurately evaluating thermal diffusivity is difficult due to the influence of temperature wave reflection from the whole surface of the sample. This study proposes a method to derive thermal diffusivity using a parameter table based on heat transfer equations using the concept of optimum distance between heating-point and measurement point. This method considers finite sample size, sensitivity distribution of infrared ray detector, intensity distribution of heating laser and sample thickness. In these results, the obtained thermal diffusivity of pure copper corresponded well with previous literature values. In conclusion, this method is considered highly effective in evaluating the thermal diffusivity in the horizontal direction.

scholarly journals A Method for Analyzing Local Climates by Using an Infrared Radiation Thermometer together with Topographic Factors

10.5109/24086 ◽  
1995 ◽  
Vol 39 (3/4) ◽  
pp. 243-254
Author(s):  
Makito Mori ◽  
Tetsuo Kobayashi ◽  
Takehiro Takemasa ◽  
Kazuhiro Takeshita ◽  
Kenji Wakimizu
Keyword(s):  
Infrared Radiation ◽  
Radiation Thermometer ◽  
Topographic Factors ◽  
Infrared Radiation Thermometer

Near Observational Equivalence and Theoretical size Problems with Unit Root Tests

1996 ◽  
Vol 12 (4) ◽  
pp. 724-731 ◽  
Author(s):  
Jon Faust
Keyword(s):  
Unit Root ◽  
Sufficient Conditions ◽  
Unit Root Test ◽  
Asymptotic Distributions ◽  
Unit Root Tests ◽  
Finite Sample ◽  
Test Procedures ◽  
Asymptotic Size ◽  
Finite Sample Size ◽  
Topological Sense

Said and Dickey (1984,Biometrika71, 599–608) and Phillips and Perron (1988,Biometrika75, 335–346) have derived unit root tests that have asymptotic distributions free of nuisance parameters under very general maintained models. Under models as general as those assumed by these authors, the size of the unit root test procedures will converge to one, not the size under the asymptotic distribution. Solving this problem requires restricting attention to a model that is small, in a topological sense, relative to the original. Sufficient conditions for solving the asymptotic size problem yield some suggestions for improving finite-sample size performance of standard tests.

scholarly journals On the variance parameter estimator in general linear models

Metrika ◽  
2019 ◽  
Vol 83 (2) ◽  
pp. 243-254
Author(s):  
Mathias Lindholm ◽  
Felix Wahl
Keyword(s):  
Sample Size ◽  
Linear Models ◽  
General Linear ◽  
Finite Sample ◽  
Parameter Estimator ◽  
General Linear Models ◽  
Vector Autoregressive ◽  
Finite Sample Size ◽  
Error Terms ◽  
Variance Parameter

Abstract In the present note we consider general linear models where the covariates may be both random and non-random, and where the only restrictions on the error terms are that they are independent and have finite fourth moments. For this class of models we analyse the variance parameter estimator. In particular we obtain finite sample size bounds for the variance of the variance parameter estimator which are independent of covariate information regardless of whether the covariates are random or not. For the case with random covariates this immediately yields bounds on the unconditional variance of the variance estimator—a situation which in general is analytically intractable. The situation with random covariates is illustrated in an example where a certain vector autoregressive model which appears naturally within the area of insurance mathematics is analysed. Further, the obtained bounds are sharp in the sense that both the lower and upper bound will converge to the same asymptotic limit when scaled with the sample size. By using the derived bounds it is simple to show convergence in mean square of the variance parameter estimator for both random and non-random covariates. Moreover, the derivation of the bounds for the above general linear model is based on a lemma which applies in greater generality. This is illustrated by applying the used techniques to a class of mixed effects models.

scholarly journals FINITE-SAMPLE SIZE CONTROL OF IVX-BASED TESTS IN PREDICTIVE REGRESSIONS

2020 ◽  
pp. 1-25
Author(s):  
Mehdi Hosseinkouchack ◽  
Matei Demetrescu
Keyword(s):  
Size Control ◽  
Finite Sample ◽  
Limiting Distributions ◽  
Valid Inference ◽  
Tuning Parameters ◽  
Predictive Regressions ◽  
Chi Squared ◽  
Standard Normal ◽  
Finite Sample Size

Abstract In predictive regressions with variables of unknown persistence, the use of extended IV (IVX) instruments leads to asymptotically valid inference. Under highly persistent regressors, the standard normal or chi-squared limiting distributions for the usual t and Wald statistics may, however, differ markedly from the actual finite-sample distributions which exhibit in particular noncentrality. Convergence to the limiting distributions is shown to occur at a rate depending on the choice of the IVX tuning parameters and can be very slow in practice. A characterization of the leading higher-order terms of the t statistic is provided for the simple regression case, which motivates finite-sample corrections. Monte Carlo simulations confirm the usefulness of the proposed methods.

scholarly journals Influence of Size of Source Effect on Accuracy of LWIR Radiation Thermometers

2016 ◽  
Vol 23 (4) ◽  
pp. 661-667
Author(s):  
David Cywiak ◽  
Daniel Cárdenas-García ◽  
Hugo Rodriguez-Arteaga
Keyword(s):  
Radiation Thermometer ◽  
Electrical Signal ◽  
Field Of View ◽  
Measured Temperature ◽  
Temperature Deviation ◽  
Source Effect ◽  
Long Wavelength ◽  
Proposed Model ◽  
Long Wavelength Infrared ◽  
Infrared Radiation Thermometer

Abstract Determining the size of source effect of a radiation thermometer is not an easy task and manufacturers of these thermometers usually do not indicate the deviation to the measured temperature due to this effect. It is one of the main uncertainty components when measuring with a radiation thermometer and it may lead to erroneous estimation of the actual temperature of the measured target. We present an empiric model to estimate the magnitude of deviation of the measured temperature with a long-wavelength infrared radiation thermometer due to the size of source effect. The deviation is calculated as a function of the field of view of the thermometer and the diameter of the radiating source. For thermometers whose field of view size at 90% power is approximately equal to the diameter of the radiating source, it was found that this effect may lead to deviations of the measured temperature of up to 6% at 200ºC and up to 14% at 500ºC. Calculations of the temperature deviation with the proposed model are performed as a function of temperature and as a function of the first order component of electrical signal.

scholarly journals Statistics of firn closure: a simulation study

1993 ◽  
Vol 39 (131) ◽  
pp. 133-142 ◽  
Author(s):  
I. G. Enting
Keyword(s):  
Age Distribution ◽  
Atmospheric Composition ◽  
Finite Sample ◽  
Critical Fluctuations ◽  
Effective Age ◽  
Finite Sample Size ◽  
Trapped Gas ◽  
Process Measurements ◽  
Bubble Trapping ◽  
Trapping Process

AbstractThe trapping of bubbles of air in polar ice has provided a unique record of past atmospheric composition. However, the interpretation of measured concentrations depends on the statistics of the trapping process. Measurements of trace atmospheric constituents whose concentrations are changing steadily can be interpreted in terms of an “effective age” of the gas which differs from the age of the ice by a delay which corresponds to the mean trapping time. The statistics of bubble trapping can be modelled as a percolation model which is one of a class of models whose transitions are characterized by large critical fluctuations. These critical fluctuations cause an intrinsic sample-to-sample variability in the delay time and thus in the effective age. Monte Carlo simulations using a lattice model of the firn are presented, showing the effect of finite sample size on the age distribution of trapped gas. For samples containing more than about 103–104 bubbles, the simulations indicate that the range of variability is small compared to the average duration of the trapping process.

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