are transferred to ANSYS 14.5  to calculate the deformations. The deformation data are used to set up the parametric pylon model in FEKO 6.0  to simulate pylon deformation during RCS measurement and calculate the electromagnetic scattering including the translating target and deformed pylon. Finally, the simulation results of the amplitude and phase from FEKO are processed by circle ﬁtting. The detailed procedures are introduced in reference  and can be depicted as a ﬂowchart in Fig. 4. The basic structural sizes of the metal pylon in the simulation are shown in Fig. 5, and the RCS of the target used to calculate the relative error is set as − 30 dB. Diﬀerent wall thicknesses (5, 7.5, and 10 mm) and target weights (1000, 2000, and 3000 kg) are used as the variables in the simulations. The above simulation is performed under the frequency of 1 GHz. Another set of simulations whose frequencies are extend to 1, 2, 2.7, and 4 GHz is carried out, focusing on the status with 1000 kg target load and 5 mm wall thickness. Translation range is modiﬁed according to the frequency to hold the phase shift of target. Note that there will be strong coupling at 3 GHz because the wavelength matches the length of the top of the pylon model. Thus, 2.7 GHz is employed instead of 3 GHz to prevent such coupling.
Twelve-hour fasting blood was collected according to the ARIC study wide protocol. The Minneapolis field center conducted fatty acid analysis in plasma phospholipid and cholesteryl ester fractions for visit 1 blood specimens (1987–89) among the white segment of the study popu- lation in that center. The procedure is described in detail elsewhere . The identity of 28 fatty acid peaks were revealed by gas chromatography by comparing each peak's retention time to the retention times of fatty acids in synthetic standards of known compositions. The rela- tive amount of each fatty acid (as a percent of all fatty acids) could be calculated by integrating the area under the peak and dividing the result by the total area for all fatty acids and multiplying by 100. To minimize transcrip- tion errors, data from the chromatogram was transferred electronically to a computer for analysis. Two concentra- tion biomarkers, consisting of the plasma phospholipids and cholesteryl ester level of fatty acids in each of the groups described above, were used to assess measurementerror in the FFQ and correct for that error.
combination in (8) are formed by carrier phase measure- ments, so we should detect cycle slips before subsequent analysis. If cycle slips were found, we mark it and make these data as a segment. In a data segment of no cycle slips, the GF-IF combination mainly consists of receiver measurement errors and multipath on the respective fre- quencies plus a constant determined by the carrier phase ambiguities. In the case of no cycle slips the ambiguity is a constant and easy to handle . Therefore the combi- nation (8) can be used to evaluate the phase multipath. The carrier phase measurement errors and phase multi- path are with the same order, and linear combination will amplify measurement errors, so denoising processing can be utilized to extract more accurate phase multipath.
A misalignment between the artifact and axis of rotation leads to eccentric error in the probe signals. Two primary methods exist to eliminate this effect, such as the least quadratic circle and the Fourier analysis to remove the fundamental frequency. However, little attention is given to the fact that lateral component of eccentric movement vector of artifact ball may lead to additional reading error of capacitive probe. Set the eccentric error to be e . At angular position θ , the lateral and the radial components of eccentric error are e ∗ cos θ and e ∗ sin θ respectively. Assuming the initial lateral offset of the probe e 0 relative to the ball
Many studies or writings have been published on the effect of measurementerror on regression ana- lysis. However, to the author knowledge, none of them relates the value of reliability coefficient of a predictor’s measurement on the size of the bias and type 1 error comprehensively. For example, some studies only mentioned the effect of the measure- ment error in general without discussing the tole- rable size of bias resulting from predictors’ measure- ment with certain reliabilities (Berry, 1993; Pedhazur, 1997; Ree & Carretta, 2006). The study conducted by Shear & Zumbo (2013) only showed the effect of measurementerror only on the type 1 error rate when the measurement has a large reliability.
F.C Campbell (2013) mentioned that calibration is one of important things that related to measurement. If the calibration is wrong or not been done well, all the measurement later could be wrong too. This will affect all the production if it had been used in industry. The measurementerror that been traced using human vision then need to be inspect and solve the error. There are some disturbance that often unnoticed, such as the humidity, air pressure and also temperature. The people who need to do the inspection should aware of this condition so the source of error can be found.
measurement time of typical impedance spectra acquisition. In conclusion, when the membrane is well humidified (close to the saturation value), small humidity variations do not produce any significant ohmic resistance variation, but they might produce temporary flooding (localized outside the membrane in the gas diffusion layer or catalyst layer) with consequent significant voltage drop for a very short time (typically less than 1 s). This phenomenon does not represent a problem from the measurement point of view because it is easily recognizable and the measurement can be repeated after a very short time (few seconds). On the contrary, when the membrane humidity is lower than the saturation level (a condition which is frequently adopted in practice to reduce flooding occurrence due to unexpected operating fluctuations), uncontrolled humidity variations produce a continuous ohmic resistance variation that affects impedance measurements, par- ticularly at low frequency for the reasons explained above.
Subsequently we consider uncertainties due to imperfect inspection techniques (measurementerror in this case) to estimate the Gamma process param- eters. For measured defect size we assume normal distribution function with age dependent standard deviation and mean value equal to the 0.076 (Marsh & Frangopol, 2008). Two inspection techniques are assumed to provide the corrosion information. The measurementerror definitions of each technique are demonstrated in Table 3.
Mathiowetz and Duncan (1988) used the same dataset as Duncan and Hill and paint a rather di ff erent picture of ME and its implications, depending on what forms of ME are considered. Respondents o ff ered very accurate answers when asked to report the total time spent unemployed in the last year. However, when required to identify and time each spell of unemployment, results are much less accurate: 66% of spells were omitted. In addition, Mathiowetz and Duncan modelled the probability of misclassifying unemployment status for different socio-demographic groups and showed that associations with demographic variables such as ethnic- ity, education, age and gender were not statistically signif- icant if other variables capturing saliency and interference were controlled for. Saliency was measured by the length of the spell and interference by the number of spells of unem- ployment that the respondent experienced during the period of analysis. The authors therefore speculated that it is not the condition of being younger or a woman that is associated with ME but their more complex work histories. Another finding from this paper challenges the hypothesis that accu- racy deteriorates as the time between the period to be recalled and the date of the interview grows. The authors found that the e ff ect of elapsed time is not linear but quadratic, with the probability of committing an error growing the closer the pe- riod is to the interview date up to a point, about five months, where it falls sharply.
cancer or not. One of the important covariates is a measure of exposure to radiation at the time of explosion. The amount of radiation exposure is not observable but one can use the estimated dose using DS86 dosimetry (Roesch 1987, Fujita 1989) as the surrogate. Also the cause of death viz., cancer or not, may be misclassified (Sposto et al.(1992)). The binary regression modeling when the responses (death from cancer or not) are subject to classification errors and covariates (exposure to radiation) are subject to measurementerror is considered by Roy et al. (2005).
Diagnostic biomarkers are used frequently in epidemiologic and clinical work. The ability of a diagnostic biomarker to discriminate between subjects who develop disease (cases) and subjects who do not (controls) is often measured by the area under the receiver operating characteristic curve (AUC). The diagnostic biomarkers are usually measured with error. Ignoring measurementerror can cause biased estimation of AUC, which results in misleading interpretation of the efficacy of a diagnostic biomarker. Several methods have been proposed to correct AUC for measurementerror, most of which required the normality assumption for the distributions of diagnostic biomarkers. In this article, we propose a new method to correct AUC for measurementerror and derive approximate confidence limits for the corrected AUC. The proposed method does not require the normality assumption. Both real data analyses and simulation studies show good performance of the proposed measurementerror correction method.
Concern about the possible health effects from exposure to Extremely Low Frequency (ELF) electromagnetic fields is increasing worldwide. This paper presents analyses of measurements of low frequency magnetic and electric fields caused by conductors carrying current in the Oman electric power system. Measurements were taken during the summer period at around 3 PM to capture peak loads. Field measurements were from several high voltage environments which include different types of electricity infrastructure comprising overhead lines, underground cables and substations. The measured values were compared to the International Commission on Non-Ionizing Radiation Protection (ICNIRP) standards for both general public and occupational exposure. All the Oman values of electric and magnetic fields are lower than the recommended values provided by ICNIRP.
Gryparis et al., suggest that the smoothing inherent in spatio-temporal models effectively converts classical error into Berkson error, so that the latter is more of a concern. Thus for modelled pollution data a more realistic scenario maybe one where the overall variance of the model predictions is less than that of the “ true ” expo- sures ( λ < 1); and under the scenarios of, λ = 0.5 and λ = 0.75, (Fig. 1) attenuation in the health effect estimate ap- peared to be less marked than for λ = 1, λ = 1.25 or λ = 2.0. However, for λ = 0.5 combined with a high correlation coefficient of 0.9, bias away from the null was observed for both short and long-term exposure ranging from 27% to 40%. In trying to explain these findings we note that the scenario effectively sets the covariance between the model and “ true ” data equal to 1.27 times (i.e. p 0:9 ﬃﬃﬃﬃﬃ 0:5 ) the variance of the model data. This relationship is indicative of positive bias (based on simple regression calibration) [10, 25] but may only occur in practice if there is a lack of independence between the Berkson component of meas- urement error and the modelled data [9, 10]. While, in general Berkson error is not thought to introduce bias into the health effect estimate, some studies have shown that bias away from the null can occur due to Berkson error if additive on a log scale [9, 10].
There exist various applications for indoor positioning among which indoor positioning and tracking in urban environments has gained significant attention. Some user communities, like fire fighters, ideally require indoor accuracy of less than one meter, with accuracies of less than six meters acceptable by some other user communities. Achieving this level of accuracy requires a detailed profiling of error sources so that they can be better understood and indoor positioning accuracy in presence of these errors can be further improved upon. Some well-known error sources like multipath, NLOS, oscillator drift, Dilution of Precision and others have been studied and can be found in the literature. A less well-known error source that can substantially affect indoor positioning accuracy are the effects of the dielectric properties of building materials on propagation delay.
demand function for food, keeping the prices constant, to depict the consumption behavior of households with different levels of total expenditure. In line with demand theory, this study was anchored on two principles. The first is Engle’s law which describes the non-linear relationship between household food expenditure and total household expenditure. The second is the classical test theory of measurement that is concerned about the accuracy of observed variables which important in decision making. Engel’s law describes how household expenditure of a good varies with household income. According to Engel (1857) “the poorer the family, the greater the proportion of its total expenditure that must be devoted to the provision of food” i.e. as income increases, the share of expenditure for food declines, demonstrating the shares of income spent on food are inversely related to income levels (Chen and Wallace, 2009). The theory does not imply that food budget decreases with increase in income, but rather that the proportion of income devoted to food increases at a slower rate than increase in income. Informed by this theory, this study employed household expenditure data to put forward a case for Mandera County in Kenya in estimating the extent of food insecurity. Until recently many studies have used linear functions of budget shares based on working-Lesser specification (Deaton 1980, Mwenjeri, 2009), however studies using non-linear relationships in budget share specifications are being done especially in non- food items (Hausman et al, 1995, Lewbel, 1991).
Dollar Cost Averaging (DCA) is an investment strategy which remains very popular even though previous research has long since established that it is mean-variance inefficient. More recent research has focused on identifying alternative investor objectives which can explain why the strategy nevertheless remains popular. In this chapter I demonstrate that some of these explanations (eg. loss aversion/prospect theory) must be rejected, since DCA is an inefficient strategy for investing available funds regardless of the risk weighting that is applied to the probability distribution of possible terminal wealth outturns. Plausible explanations for DCA's popularity also need to address the arguments that are commonly used by those who recommend this strategy. Regret avoidance and the need for investor discipline are sometimes alluded to, but the argument that is almost invariably made is that DCA achieves an average purchase cost which is always lower than the average price of the securities concerned over the investment period. It seems intuitively obvious that a lower average purchase cost must raise expected returns, and previous research has failed to identify the error in this argument. This chapter explicitly identifies the error in this argument, and hence suggests that cognitive error rather than behavioural motives is the most plausible explanation for DCA’s continued popularity.
In high dimensional settings, the presence of measurementerror can have severe con- sequences on the lasso estimator: the number of non-zero estimates can be inflated, sometimes dramatically, and as such the true sparsity pattern is not recovered Rosen- baum et al. (2010); see Appendix 4.8.1 for an illustration. To correct for measurementerror in the high-dimensional setting, Rosenbaum et al. (2010) proposed a matrix uncer- tainty selector (MU) for linear models. Rosenbaum et al. (2013) proposed an improved version of the MU selector, while Belloni et al. (2017) proved its near-optimal minimax properties and developed a conic programming estimator that can achieve the minimax bound. The conic estimators require selection of three tuning parameters, a difficult task in practice. Another approach for handling measurementerror is to modify the loss and conditional score functions used with the lasso, see Loh and Wainwright (2011), Sørensen et al. (2015) and Datta et al. (2017). Additionally, Sørensen et al. (2018) developed the generalized matrix uncertainty selector (GMUS) for generalized linear models. Both the conditional score approach and GMUS require subjective choices of tuning parameters.