Calibration Interval Research Articles

New Thermocouple-Based Microwave/Millimeter-Wave Power Sensor MMIC Techniques in GaAs

We describe a new RF and microwave power sensor monolithic microwave integrated circuit design. The circuit incorporates a number of advances over existing designs. These include a III-V epitaxial structure optimized for sensitivity, the figure-of-merit applicable to the optimization, a mechanism for in-built detection of load ageing and damage to extend calibration intervals, and a novel symmetrical structure to linearize the high-power end of the scale.

Internet-Enabled Calibration

Calibration of measuring instruments provides the confidence in measurements. It is the obligation of every laboratory to have its measuring equipment calibrated in regular intervals. This obligation means that the laboratory must send its equipment to the calibration laboratory every year or two depending on the calibration intervals. During this time the equipment is not available and this presents a financial burden to the laboratory of the customer. Since many of the modern instruments include some communication interfaces, it was made possible to create an Internet-enabled calibration system. This term encompasses a wide range of possible applications and services. The Internet-enabled calibrations must address several problems not present in standard calibrations, including security issues, since the equipment is not always under direct control of the calibration laboratory personnel who will sign the calibration certificate. As the traceability and integrity of the calibration process directly depends on the measured data, the reliable and secure remote control and monitoring of instruments must be a crucial aspect of Internet-enabled calibration technologies.

Effect of norepinephrine dosage and calibration frequency on accuracy of pulse contour-derived cardiac output

IntroductionContinuous cardiac output monitoring is used for early detection of hemodynamic instability and guidance of therapy in critically ill patients. Recently, the accuracy of pulse contour-derived cardiac output (PCCO) has been questioned in different clinical situations. In this study, we examined agreement between PCCO and transcardiopulmonary thermodilution cardiac output (COTCP) in critically ill patients, with special emphasis on norepinephrine (NE) administration and the time interval between calibrations.MethodsThis prospective, observational study was performed with a sample of 73 patients (mean age, 63 ± 13 years) requiring invasive hemodynamic monitoring on a non-cardiac surgery intensive care unit. PCCO was recorded immediately before calibration by COTCP. Bland-Altman analysis was performed on data subsets comparing agreement between PCCO and COTCP according to NE dosage and the time interval between calibrations up to 24 hours. Further, central artery stiffness was calculated on the basis of the pulse pressure to stroke volume relationship.ResultsA total of 330 data pairs were analyzed. For all data pairs, the mean COTCP (±SD) was 8.2 ± 2.0 L/min. PCCO had a mean bias of 0.16 L/min with limits of agreement of -2.81 to 3.15 L/min (percentage error, 38%) when compared to COTCP. Whereas the bias between PCCO and COTCP was not significantly different between NE dosage categories or categories of time elapsed between calibrations, interchangeability (percentage error <30%) between methods was present only in the high NE dosage subgroup (≥0.1 μg/kg/min), as the percentage errors were 40%, 47% and 28% in the no NE, NE < 0.1 and NE ≥ 0.1 μg/kg/min subgroups, respectively. PCCO was not interchangeable with COTCP in subgroups of different calibration intervals. The high NE dosage group showed significantly increased central artery stiffness.ConclusionsThis study shows that NE dosage, but not the time interval between calibrations, has an impact on the agreement between PCCO and COTCP. Only in the measurements with high NE dosage (representing the minority of measurements) was PCCO interchangeable with COTCP.

A Statistical Evaluation of Calibration Check Intervals

In 2009 the Alcohol Test Committee of the Canadian Society of Forensic Science changed the calibration check interval of Approved Screening Devices from every 2 weeks to every 31 days. Data from 612 calibration checks of a group of Alco-Sensor IV DWF screening devices were analyzed using a contingency table and x2 analysis as well as a significance test for a difference in proportions. There were no statistically significant differences between the length of time of the calibration interval and the frequency of a device requiring recalibration. This analysis shows the Alco-Sensor IV DWF is no more likely to require calibration up to and including a 5 week interval when compared to a 2 week interval, supporting the Alcohol Test Committee decision.

An efficient forward model of the climate controls on interannual variation in tree-ring width

We present a simple, efficient, process-based forward model of tree-ring growth, called Vaganov–Shashkin-Lite (VS-Lite), that requires as inputs only latitude and monthly temperature and precipitation. Simulations of six bristlecone pine ring-width chronologies demonstrate the interpretability of model output as an accurate representation of the climatic controls on growth. Ensemble simulations by VS-Lite of two networks of North American ring-width chronologies correlate with observations at higher significance levels on average than simulations formed by regression of ring width on the principal components of the same monthly climate data. VS-Lite retains more skill outside of calibration intervals than does the principal components regression approach. It captures the dominant low- and high-frequency spatiotemporal ring-width signals in the network with an inhomogeneous, multivariate relationship to climate. Because continuous meteorological data are most widely available at monthly temporal resolution, our model extends the set of sites at which forward-modeling studies are possible. Other potential uses of VS-Lite include generation of synthetic ring-width series for pseudo-proxy studies, as a data level model in data assimilation-based climate reconstructions, and for bias estimation in actual ring-width index series.

Diagnostic tools to evaluate a spatial land change projection along a gradient of an explanatory variable

This paper proposes a method to quantify the goodness-of-fit of a land change projection along a gradient of an explanatory variable, by classifying pixels as one of four types: null successes, false alarms, hits, and misses. The method shows: (1) how the correctness and error of a land change projection are distributed along the gradient of an explanatory variable, (2) how the gradient of the explanatory variable relates to the stationarity of the land transition processes, and (3) how to use the insights from the previous two points to search for additional explanatory variables. The paper illustrates the method through a case study that applies the model Geomod in Central Massachusetts, USA. Results reveal that the model predicts more than the observed amount of change on flat slopes and less than the observed amount of change on steep slopes. One reason for these types of errors is that the land change process during the calibration interval is different than the process during the prediction interval with respect to slope. The method allows modelers to use the validation step as a diagnostic tool to search for potentially influential missing variables and to gain insight into land transition processes. The technique is designed to be applicable to a variety of types of land change models.

Statistical Calibration of ASTM C150 Bogue-Derived Phase Limits to Directly Determined Phases by Quantitative X-Ray Powder Diffraction

Abstract Statistical analyses of companion Bogue (ASTM C150-07) and quantitative X-ray powder diffraction (QXRD) estimates for cement phases are used to establish the most likely linear relationship between these two measurement techniques for alite, belite, aluminate, ferrite, and for the combinations (C3S+4.75⋅C3A) and (C4AF+2⋅C3A) used to characterize heat of hydration and sulfate resistance, respectively. This cross-calibration was performed using published data from more than 194 Portland cements, spanning more than 50 years of manufacture. Each resulting calibration is a linear relationship, reported with 99 % Working–Hotelling–Scheffé confidence limits from the least-squares regression. Fieller calibration intervals are used to report bounds for the QXRD values corresponding to the Bogue limits given in ASTM C150-07, Table 1. Such bounds could allow either technique to be used for phase estimation to assess the compliance of a Portland cement.

Quantifying Sources of Low-Frequency Drift During Single-Molecule Experiments

Single-molecule techniques have evolved to the point where quantitative force measurements on biological systems can be performed down into the femtonewton range. As resolution is constantly improving, the pinpointing and elimination of noise sources become increasingly important. Complementary to Fourier analysis, Allan-variance analysis is ideally suited for this task; adjacent time series are recorded and the variations between observation intervals are calculated. Here, we provide a comprehensive toolbox consisting of acquisition and analysis software as well as fitting scripts to directly extract parameters of noise and low-frequency drift sources [1].Furthermore, the validity and robustness of Allan-variance analysis is demonstrated in data obtained from various optical-tweezers setups wherein laboratory-specific noise sources are detected. This allows for a quantitative discrimination as well of common detection systems as of different calibration methods. In addition, we demonstrate how our toolbox can be applied during single-molecule experiments. Here, we determine the optimal calibration interval for any setup, suitable settings for variance and update rates in force-feedback loops, and variations due to the geometrical constraints of the sample chamber.As outlook, we present data from other single-molecule techniques such as solid-state nanopores and magnetic tweezers. These emphasize the fact that Allan-variance analysis can be used as a standard tool enabling precise quantification of noise and drift effects.[1] F. Czerwinski, A.C. Richardson, and L.B. Oddershede, “Quantifying Noise in Optical Tweezers by Allan Variance,” Opt. Express 17, 13255-13269 (2009)

Investigations on Measurement Uncertainty and Stability of Pressure Dial Gauges and Transducers

Several commercial instruments are available for pressure measurements. As per ISO stipulations, whenever such instruments are used for precise and accurate pressure measurements, it is obligatory on the part of measurement authority to indicate the quality of results. Stability of the pressure measuring instruments over the years is one of the important parameters in defining the quality of results quantitatively. Also, it helps the users to decide the optimum calibration interval of the particular instrument. In the present investigation, we have studied a number of analogue / digital pressure transducers / transmitters / calibrators and pressure dial gauges. The present paper describes the results of the studies carried out on several pressure dial gauges and transducers in the pressure range up to 500 MPa, calibrated several times over the years, as examples. A new approach is proposed for the establishment of measurement uncertainty for such instruments by characterizing the data obtained during calibration over the years using curve fitting. Keywords: Pressure metrology, pressure dial gauge, pressure transducer, calibration, measurement uncertainty, stability

Optimised uncertainty and cost operating characteristics: new tools for conformity assessment. Application to geometrical product control in automobile industry

Translating measurement uncertainty into terms of effective impact associated with manufacture, testing and incorrect assessment gives a more “stakeholder” motivated (and ultimately optimised) approach to decision-making in conformance assessment. Recently developed decision-theory tools include the “optimized uncertainty” methodology and the “operating cost characteristic”. Overall costs, E , consisting of a sum of testing costs, D , and the costs, C , associated with customer risk, can be calculated with the expression: $E(\eta ,\sigma )=D(\eta ,\sigma )+C(\eta )=\frac{D}{\sigma ^2}+\int_{\eta \notin R_{PV} } {C(\eta )\cdot g} (\eta \left| {\hat {\eta }} \right.)\cdot d\eta $ E ( η,σ ) = D ( η,σ ) + C ( η ) = D σ 2 + ∫ η ∉ R PV C ( η ) · g ( η η ) · dη with , where R PV denotes the region of permissible values and σ is a measure of dispersion. A complete, 3D surface of overall cost can indicate the optimum level of measurement effort of these two ranges, as recently published by the author in a wide range of applications: optimized acceptance sampling; optimized testing of measurement instruments; and an analysis of optimised calibration intervals and “guard-banding”. This approach is illustrated in the present work for the example of geometrical product control in the car industry, specifically the gap in vehicle closure panels taking account of customer dissatisfaction.

Calibration of pulse contour continuous cardiac output analysis

We evaluated the effect of the calibration interval on the reliability of pulse contour cardiac output (COpc) measurement [1].

On the Origin of the Standardization Sensitivity in RegEM Climate Field Reconstructions*

Abstract The regularized expectation maximization (RegEM) method has been used in recent studies to derive climate field reconstructions of Northern Hemisphere temperatures during the last millennium. Original pseudoproxy experiments that tested RegEM [with ridge regression regularization (RegEM-Ridge)] standardized the input data in a way that improved the performance of the reconstruction method, but included data from the reconstruction interval for estimates of the mean and standard deviation of the climate field—information that is not available in real-world reconstruction problems. When standardizations are confined to the calibration interval only, pseudoproxy reconstructions performed with RegEM-Ridge suffer from warm biases and variance losses. Only cursory explanations of this so-called standardization sensitivity of RegEM-Ridge have been published, but they have suggested that the selection of the regularization (ridge) parameter by means of minimizing the generalized cross validation (GCV) function is the source of the effect. The origin of the standardization sensitivity is more thoroughly investigated herein and is shown not to be associated with the selection of the ridge parameter; sets of derived reconstructions reveal that GCV-selected ridge parameters are minimally different for reconstructions standardized either over both the reconstruction and calibration interval or over the calibration interval only. While GCV may select ridge parameters that are different from those that precisely minimize the error in pseudoproxy reconstructions, RegEM reconstructions performed with truly optimized ridge parameters are not significantly different from those that use GCV-selected ridge parameters. The true source of the standardization sensitivity is attributable to the inclusion or exclusion of additional information provided by the reconstruction interval, namely, the mean and standard deviation fields computed for the complete modeled dataset. These fields are significantly different from those for the calibration period alone because of the violation of a standard EM assumption that missing values are missing at random in typical paleoreconstruction problems; climate data are predominantly missing in the preinstrumental period when the mean climate was significantly colder than the mean of the instrumental period. The origin of the standardization sensitivity therefore is not associated specifically with RegEM-Ridge, and more recent attempts to regularize the EM algorithm using truncated total least squares could theoretically also be susceptible to the problems affecting RegEM-Ridge. Nevertheless, the principal failure of RegEM-Ridge arises because of a poor initial estimate of the mean field, and therefore leaves open the possibility that alternative methods may perform better.

Calibration Intervals in Linear Regression Models

Many of the existing methods of finding calibration intervals in simple linear regression rely on the inversion of prediction limits. In this article, we propose an alternative procedure which involves two stages. In the first stage, we find a confidence interval for the value of the explanatory variable which corresponds to the given future value of the response. In the second stage, we enlarge the confidence interval found in the first stage to form a confidence interval called, calibration interval, for the value of the explanatory variable which corresponds to the theoretical mean value of the future observation. In finding the confidence interval in the first stage, we have used the method based on hypothesis testing and percentile bootstrap. When the errors are normally distributed, the coverage probability of resulting calibration interval based on hypothesis testing is comparable to that of the classical calibration interval. In the case of non normal errors, the coverage probability of the calibration interval based on hypothesis testing is much closer to the target value than that of the calibration interval based on percentile bootstrap.

Use of microwave induced plasma spectrometry as detector for the determination of O, N and H traces after carrier gas hot extraction

The determination of O, N and H using a microwave induced plasma coupled to carrier gas hot extraction was exploratory investigated. The signal intensities versus time of blanks and calibration materials were recorded. To check for interferences the signal intensities versus wavelength were recorded at the times just before increase and at the maximum of the time dependent analytical signal. O, N and H were investigated at their prominent wavelengths of 777 nm, 174 nm and 486 nm, respectively. Calibration was performed for O, N and H in the ranges of 0–27 μg, 35–1000 μg and 3–43 μg respectively. For concentration values in the middle of the linear part of the investigated calibration interval, a relative precision of 5% at 13 μg, 2% at 74 μg and 0.6% at 23 μg for O, N and H respectively was found. The maximum matrix load to the plasma used was found to be 150 μg min−1. From the signal to noise ratio and the sensitivity obtained, instrumental limits of detection (3s) of 0.01 μg for O, 1 μg for N and 0.1 μg for H were found. Assuming a typical sample mass of 1 g this corresponds to relative LODs of 0.01 μg g−1, 1 μg g−1 and 0.1 μg g−1 for O, N and H respectively.

Efficient Reduction of Landsat TM Memory Effect Using Differential State Equation

In Landsat Thematic Mapper (TM) images, banding due to memory effect (ME) often appears every 16 lines at both sides of a bright object such as a cloud, which not only visually degrades the images but also makes quantitative analysis difficult. The ME is induced by an electric circuit connecting the detectors to the analog/digital converter. Based on the impulse response of the circuit, a restoration filter was proposed to correct the banding, but it must be truncated for improving computational efficiency. This paper proposes an efficient and exact correction algorithm by modeling an offset voltage of a preamplifier as a first-order differential equation. It is shown that the same impulse response is derived from the differential equation. As the differential equation can keep track of the offset voltage as a state variable, the ME of the preamplifier circuit can be corrected not only effectively but also seamlessly by taking the internal calibration interval and calibration lamp status into consideration. The parameters of the circuit are estimated from nighttime unprocessed TM data of 1996, and they are used to correct daytime data of the same year. It is shown that the proposed state-space approach is more computationally efficient than the previous restoration-filter approach.

Comments on “Testing the Fidelity of Methods Used in Proxy-Based Reconstructions of Past Climate”: The Role of the Standardization Interval*

Mann et al. (2005, hereafter M05) conclude that they “find no evidence for the suggestion that real-world proxy-based temperature reconstructions are likely to suffer from any systematic underestimate of lowfrequency variability.” This conclusion is based on multiple pseudoproxy experiments using the National Center for Atmospheric Research (NCAR) Climate System Model (CSM) millennial integration and the climate field reconstruction (CFR) method known as regularized expectation maximization (RegEM; Schneider 2001). RegEM was used by Rutherford et al. (2005, hereafter R05) to reconstruct historical Northern Hemisphere climate from the Mann et al. (1998) proxy network, which prompted the follow-up study by M05 to test, in part, the veracity of the R05 millennial climate reconstruction. We have used the publicly available codes published by R05 and M05 to perform a new suite of pseudoproxy reconstructions with the CSM data. Our findings contradict the M05 conclusion and highlight an important methodological choice that was different from R05, not reported by M05, and has significant impacts on the derived reconstructions. Testing climate reconstruction methods with simulated climates relies on proper application of real-world constraints. For instance, it is important to perturb pseudoproxy networks with realistic noise models such that the noise is representative of actual proxy records. A variety of colored noise models have been adopted (Mann and Rutherford 2002; von Storch et al. 2004, 2006; M05), but these may not fully mimic the nonlinear, multivariate, nonstationary characteristics of noise in many proxy series (e.g., Jacoby and D’Arrigo 1995; Briffa et al. 1998; Esper et al. 2005; Evans et al. 2002; Anchukaitis et al. 2006). Therefore, improving the representation of noise in pseudoproxy networks is an ongoing and important area of research. What is more obvious, however, is that the methodological constraints of real-world climate reconstructions must be preserved in pseudoproxy tests if they are to have any direct applicability to actual reconstructions of historical climate. Using information or techniques that would never be possible in real-world settings sheds little light on climate reconstruction methods. The principal motivation of this comment is to note that M05 used information prior to the period of widespread observational evidence, thereby significantly affecting the outcome of their reconstructions. RegEM requires an input data matrix that is a composite of both instrumental and proxy data. A time-byspace matrix for the instrumental data is first formed in which rows correspond to years in the calibration and reconstruction periods, and columns correspond to grid cells in the instrumental field. For instance, a reconstruction for the Equator–70°N region of the NH on a 5° 5° latitude–longitude grid and spanning A.D. 850– 1980 would fill a matrix of 1131 rows by 1008 columns. This matrix of course would be initially empty, except for the instrumental data in the calibration period (rows 1007–1131 for an 1856–1980 calibration interval). The second part of the composite matrix is formed from the proxy data, composing a matrix of 1131 rows and n columns, where n is the number of proxies (104 in the case of M05). Thus, the instrumental and proxy matrices are concatenated by column and compose the input matrix for the RegEM algorithm (Fig. 1). As is standard with most reconstruction procedures, * Lamont-Doherty Earth Observatory Contribution Number 7077.

A Remotely Controlled Calibrator for Chemical Pollutant Measuring-Units

The increasing diffusion of pollutant measuring units, which are installed over wide areas, along with the short calibration interval of several sensors for pollutant quantities, requires new calibration infrastructures to be developed. This paper describes an attempt to develop an innovative calibration system which is based on traveling standards and which does not require units to be removed from the measuring site during the calibration process. The calibration system is based on a traveling standard, which is composed of one or more cylinders that contain gas mixtures, a cell with standard sensors, and a control unit with networking capabilities, which allows the traveling standard to be remotely exercised. A prototype of the proposed system is described and the preliminary results reported.

Image analytical determination of particle size distribution characteristics of natural and industrial bulk aggregates

Image Analysis combined with multivariate regression on Angle Measure Technique (AMT) transformed imagery and the Theory of Sampling (TOS) is here presented as a comprehensive for Image Analysis Sampling (IAS), which takes all aspects of sampling representativity into account — especially 2-dimensional image versus 3-dimensioanl bulk compositions issues. Every IAS application has to be based on optimized image acquisition parameters: camera and illumination type, illumination angle, sample thickness as well as image post-processing, which are all examined here in order to delineate the general requirements for optimal prediction models for particle size distribution of natural and industrial bulk solid aggregates. We present a complete optimization study in order to show its intrinsic problem-dependent nature. This optimization allowed an original 60-sample data set to be compressed to an essential 22 natural coastal sands array with equally varying composition ranges — which was subjected to IAS in order to characterize the specific particle size distribution curves. In addition to D 50 (50%-tile), six other size classes were successfully predicted, while extreme size classes (extreme low or high particle sizes) showed a too narrow training data set span, illustrating a critical grain size contrast which will always bracket successful models of particulate matter being imaged for grain size characterization. All classes with a satisfactory (representative) calibration interval span can be quantitatively predicted due to the powerful scale-dependency of the central AMT feature extraction combined with PLS multivariate calibration. The present application to natural sand aggregate size distributions forms a vehicle to illustrate the full potential of image analysis in general, IAS in particular, also for technological/industrial manufacturing on-line product and process monitoring applications, or quality control purposes, with similar grain-size prediction objectives. There is a significant carrying-over potential to parallel industrial scenarios.

A multicoral calibration method to approximate a universal equation relating Sr/Ca and growth rate to sea surface temperature

Combining strontium‐to‐calcium ratios (Sr/Ca) with mean annual growth rates in Bermuda Diploria labyrinthiformis (brain corals) is shown to improve sea surface temperature (SST) calibrations relative to instrumental data. Growth‐corrected Sr/Ca–SST calibrations based on single‐coral colonies over the same calibration interval, however, are found to be poorly suited for application to data from different coral colonies. This raises concerns about the accuracy of SST reconstructions from fossil coral measurements that involve extrapolation beyond the range of values seen during the calibration period. Here we pursue a novel approach to this problem by incorporating data from multiple coral colonies into a single growth‐corrected Sr/Ca–SST calibration equation, effectively expanding the range of modern values constraining the model. The use of a multiple‐colony calibration model for reconstructing SST yields greater precision and accuracy relative to instrumental data than single‐colony models, providing greater confidence for applications to fossil coral samples.

Univariate linear calibration via replicated errors-in-variables model

In this paper, we deal with the comparative calibration problem, i.e. with the situation when one instrument or measurement technique is calibrated against another, each of which is subject to the measurement error. We propose an approximate, small sample, calibration confidence interval of the unknown true value of the measured substance in units of the more precise instrument, given measurement in units of the less precise instrument. Here we deal with the simplest case—single-use linear univariate calibration, i.e. the case in which we assume linear relationship between the two measurement techniques (instruments), and, further, that the calibration procedure is conducted in order to obtain one value for an unknown, reported together with an interval estimate. The method for deriving the approximate confidence interval is based on estimation of the calibration line via the replicated errors-in-variables model. The model is locally linearized and the Wald-type F-statistic is constructed. An essential point in this approach is the use of the F-approximation of the distribution of the F-statistic suggested by Kenward and Roger [Kenward, M.G. and Roger, J.H., 1997, Small sample inference for fixed effects from restricted maximum likelihood. Biometrics, 53, 983–997]. The statistical properties of the proposed interval estimator are verified by simulations for wide spectrum of experimental designs and compared with two standard univariate interval estimates—the classical approach for deriving the calibration interval was proposed by Eisenhart [Eisenhart, C., 1939, The interpretation of certain methods and their use in biological and industrial research. Annals of Mathematical Statistics, 10, 162–186] and the inverse method was proposed by Krutchkoff [Krutchkoff, R.G., 1967, Classical and inverse methods of calibration. Technometrics, 9, 425–439].