Error calibration

Quality control elements required by the instrumental analyzer method include analyzer calibration error ( 2 percent of instrument span allowed) verifying the absence of bias introduced by the sampling system (less than 5 percent of span for zero and upscale cah-bration gases) and verification of zero and calibration drift over the test period (less than 3 percent of span of the period of each rim). 

We often cannot afford to assemble large numbers of validations samples with concentrations as accurate as the training set concentrations. But since the validation samples are used to test the calibration rather than produce the calibration, errors in validation sample concentrations do not have the same detrimental impact as errors in the training set concentrations. Validation set... 

Uncertainty in Process Measurements. Sensor measurements are always subject to noise, calibration error, and temporary signal loss, as well as various faults that may not be immediately detected. Therefore, data preprocessing will often be required to overcome the inherent limitations of... 

Errors in system output measurements can produce calibration errors because the model user will be attempting to calibrate against inaccurate or missing data. Errors associated with system outputs are discussed below. 

Limitations of indirect calorimetry include limited availability, calibration errors, and other errors. 

Valve open too far Internal valve malfunction. Operator error. Calibration error. Operation High N2 pressure at HF cylinders, HF vaporizer - HF vaporizer vessel rupture - HF released to environment. High HF flow to HF vaporizer - high HF flow to B-l wing -potential liquid HF to B-l wing. Local pressure indication on N2 line. Local pressure indication between rupture disk and PRV-4 at vaporizer. PRV-3 at V-13 outlet. PRVs on N2 feed lines to HF cylinders. PRV-4 at HF vaporizer. ii If N2 line relief valves lift, vaporizer relief valve should not lift. Relief valve discharges piped to D-wing stack. 

Valve closed too far Internal valve malfunction. Operator error. Calibration error. Operation No N2 pressure to HF cylinder - no HF flow to HF vaporizer, B-l wing. Local pressure indication on N2 line. None IV ... 

Physical characterization of polymers is a common activity that research and development technologists at the Dow Chemical Company perform. A material property evaluation that is critical for most polymer systems is a tensile test. Many instruments such as an Instron test frame can perform a tensile test and, by using specialized software, can acquire and process data. Use of an extensometer eliminates calibration errors and allows the console to display strain and deformation in engineering units. Some common results from a tensile test are modulus, percent elongation, stress at break, and strain at yield. These data are then used to better understand the capabilities of the polymer system and in what end-use applications it may be used. 

A thermistor can be made cheaply and relies on the fact that the resistance of certain semiconductor metals falls as temperature increases. Thermistors are fast responding but suffer from calibration error and deteriorate over time. 

Figure 12.26 Plot of the calibration error (RMSEE) and the validation error (RMSEP) as a function of the number of latent variables, for the case where 63 of the styrene-butadiene copolymer samples were selected for calibration, and the remaining seven samples were used for validation.

Instrumental band broadening or axial dispersion can cause calibration errors when employing polydisperse standards. Correction of the polydisperse standard calibration data for instrumental band broadening will minimize the effect on molecular weight analyses of polymer samples. However, as previously demonstrated in this report, when low dispersion SEC columns are employed instrumental band broadening is minimized and the effect on use of linear calibration methodology is negligible. 

The bunching pattern in T plots differ from cycles in two respects in bunching, the changes are precipitous, and they do not have a characteristic repetition frequency. The sudden systematic error shifts are due to apparently random events. These events are most commonly associated with calibration errors and/or operator technique. Rotation of laboratory personnel can produce this pattern if the individuals follow different procedures. Operator related systematic errors can be detected by plotting points with separate ssrmbols for different operators. Bunching may also appear when reagent lots are changed. 

Figure 24.9a shows a plot of measured total carbon (CO plus CO2, mole percent) versus equivalence ratio. The solid line was calculated assuming chemical equilibrium at the measured temperatures. The data points represent the measured CO and CO2 mole fractions (dry basis) using the fast extractive-sampling system. Horizontal bars represent the uncertainty in (f> due to reading and calibration errors vertical bars represent the uncertainty in the CO and CO2 mole-fraction sum due to line strength and absorption measurement uncertainty. The data are consistent to within 4% of the equilibrium predictions at all values of (p, indicating reliable operation of the system. 

Similarly, if the temperature randomly fluctuates during the experiment, the effect can be applied to the initial and final volumes. However, for a constant effect, any calibration error cancels completely, and cancels to some extent for a proportional effect. Thus if a reading of volume vobs is in reality vtrue + Av, the difference between two readings, vobsl and... 

Figure 5.7 depicts how the calibration error of the net (calculated using the calibration set) evolved as a function of the number of epochs. It is obvious... 

Figure 5.7 Evolution of the overall calibration error as a function of the number of epochs the ANN is trained. The inset shows the prediction capabilities of the net as a function of the number of neurons in the hidden layer.

Precision in the measured diffusivities is limited by the reproducibility of the echo height measurements and by field gradient calibration. Under favorable circumstances, both of these can be kept below about 1 %, although for less time-consuming and routine measurements, 2-3 % random uncertainty is more typical. If comparisons among different samples are more important than comparisons with other work on similar samples, then calibration error is quasisystematic and secondary. In that case, perhaps 4% calibration uncertainty may be acceptable, requiring measurement of G or G0 to within 2% (see Eqs. 1, 2). 

Linear regression is undoubtedly the most widely used statistical method in quantitative analysis (Fig. 21.3). This approach is used when the signal y as a function of the concentration x is linear. It stems from the principle that if many samples are used (generally dilutions of a stock solution), it becomes possible to perform variance analysis and estimate calibration error or systematic errors. 

Method Time Uncertaintya Altitudeb Sources Calibration Error Sources... 

The J=2-l, v=0 emissions of ZBSiO,Z9SiO, and 30SiO from Villi Oph, NML Tau, and X Cyg were observed with the Nobeyama 45-m telescope in Jaunuary 19871 At 86 GHz, the half-power beamwidth was 20 , and the aperture efficiency 0.37. The three lines were simultaneously observed using two receivers with instantaneous bandwidths of 2 GHz and 0.5 GHz to reduce errors in relative intensities due to pointing and intensity calibration errors. The intensity scale reported here is the antenna temperature T, corrected for atmospheric and ohmic losses. 

Currently one issue, limiting the exploitation of the retrieval of ozone profiles from the GOME data set is the presence of systematic radiometric calibration errors, which have been identified but not yet eliminated from the operational geophysical irradiance and radiance data products. Some schemes have been developed, which... 

Here one has the same problem as with the calculation of calibration errors only using the members of the set of calibration measurements. One gets an optimistic estimate of the future error. 

Accuracy can also be demonstrated through participation in properly conducted interlaboratory studies, which are also useful to detect systematic errors (Gtinzler 1996) related to, e.g. sample pretreatment (e.g. extraction, clean-up), final measurement (e.g. calibration error, spectral interference) and laboratory competence. As described below, interlaboratory studies are organised in such a way that several laboratories analyse a common material which is distributed by a central laboratory responsible for the data collection and evaluation. 

All experimental measurements are affected by errors. In general, experimental errors are made out of systematic errors and random errors. Systematic errors show a dependence on the operating conditions and may be caused, e.g., by calibration errors of sensors. Since these errors are absent in a well-performed experimental campaign and can be corrected by an improved experimental practice, they are not considered any more in this context. 

Note that such a calibration only measures the ultrasonic energy that actually enters the vessel and is converted into heat in the vessel. The waste heat generated in the transducer as it produces the ultrasonic output does not introduce a calibration error. Nor does ultrasonic energy, which is lost in the transit from the transducer to the window (or reflected back into the cooling water from the window), introduce any calibration error. 

The average diameters obtained by HDC are consistently high. This may be due to a combination of a small calibration error and the relativly coarse (10 percent) spacing of size classifications used in the HDC quantification by Micromeritics. 

Direct comparison using a figure of merit for calibration error between a model built using an E-optimal subset and a model built using the complete set is impossible because of the different number of points in both sets. To ensure an objective comparison between the E-optimal model, we chose ten random sets of N= 10 points and computed the average C. Comparing these values with the Cp values for the E-optimal set in Table 8.20, it can be seen that E-optimal calibrations are in most cases close to or better (i.e., lower) than the average Cp values of the random sets. 

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