Accuracy of SRM and power tap power monitoring systems for bicycling

Purpose: Although manufacturers of bicycle power monitoring devices SRM and Power Tap (PT) claim accuracy to within 2.5%, there are limited scientific data available in support. The purpose of this investigation was to assess the accuracy of SRM and PT under different conditions. Methods: First, 19 SRM were calibrated, raced for 11 months, and retested using a dynamic CALRIG (50-1000 W at 100 rpm). Second, using the same procedure, five PT were repeat tested on alternate days. Third, the most accurate SRM and PT were tested for the influence of cadence (60, 80, 100, 120 rpm), temperature (8 and 21degreesC) and time (1 h at similar to300 W) on accuracy. Finally, the same SRM and PT were downloaded and compared after random cadence and gear surges using the CALRIG and on a training ride. Results: The mean error scores for SRM and PT factory calibration over a range of 50-1000 W were 2.3 +/- 4.9% and -2.5 +/- 0.5%, respectively. A second set of trials provided stable results for 15 calibrated SRM after 11 months (-0.8 +/- 1.7%), and follow-up testing of all PT units confirmed these findings (-2.7 +/- 0.1%). Accuracy for SRM and PT was not largely influenced by time and cadence; however. power output readings were noticeably influenced by temperature (5.2% for SRM and 8.4% for PT). During field trials, SRM average and max power were 4.8% and 7.3% lower, respectively, compared with PT. Conclusions: When operated according to manufacturers instructions, both SRM and PT offer the coach, athlete, and sport scientist the ability to accurately monitor power output in the lab and the field. Calibration procedures matching performance tests (duration, power, cadence, and temperature) are, however, advised as the error associated with each unit may vary.

Ground water model calibration using pilot points and regularization

Use of nonlinear parameter estimation techniques is now commonplace in ground water model calibration. However, there is still ample room for further development of these techniques in order to enable them to extract more information from calibration datasets, to more thoroughly explore the uncertainty associated with model predictions, and to make them easier to implement in various modeling contexts. This paper describes the use of pilot points as a methodology for spatial hydraulic property characterization. When used in conjunction with nonlinear parameter estimation software that incorporates advanced regularization functionality (such as PEST), use of pilot points can add a great deal of flexibility to the calibration process at the same time as it makes this process easier to implement. Pilot points can be used either as a substitute for zones of piecewise parameter uniformity, or in conjunction with such zones. In either case, they allow the disposition of areas of high and low hydraulic property value to be inferred through the calibration process, without the need for the modeler to guess the geometry of such areas prior to estimating the parameters that pertain to them. Pilot points and regularization can also be used as an adjunct to geostatistically based stochastic parameterization methods. Using the techniques described herein, a series of hydraulic property fields can be generated, all of which recognize the stochastic characterization of an area at the same time that they satisfy the constraints imposed on hydraulic property values by the need to ensure that model outputs match field measurements. Model predictions can then be made using all of these fields as a mechanism for exploring predictive uncertainty.

Field-based evaluation of semipermeable membrane devices (SPMDs) as passive air samplers of polyaromatic hydrocarbons (PAHs)

Semipermeable membrane devices (SPMDs) have been used as passive air samplers of semivolatile organic compounds in a range of studies. However, due to a lack of calibration data for polyaromatic hydrocarbons (PAHs), SPMD data have not been used to estimate air concentrations of target PAHs. In this study, SPMDs were deployed for 32 days at two sites in a major metropolitan area in Australia. High-volume active sampling systems (HiVol) were co-deployed at both sites. Using the HiVol air concentration data from one site, SPMD sampling rates were measured for 12 US EPA Priority Pollutant PAHs and then these values were used to determine air concentrations at the second site from SPMD concentrations. Air concentrations were also measured at the second site with co-deployed HiVols to validate the SPMD results. PAHs mostly associated with the vapour phase (Fluorene to Pyrene) dominated both the HiVol and passive air samples. Reproducibility between replicate passive samplers was satisfactory (CV < 20%) for the majority of compounds. Sampling rates ranged between 0.6 and 6.1 m(3) d(-1). SPMD-based air concentrations were calculated at the second site for each compound using these sampling rates and the differences between SPMD-derived air concentrations and those measured using a HiVol were, on average, within a factor of 1.5. The dominant processes for the uptake of PAHs by SPMDs were also assessed. Using the SPMD method described herein, estimates of particulate sorbed airborne PAHs with five rings or greater were within 1.8-fold of HiVol measured values. (C) 2004 Elsevier Ltd. All rights reserved.

A planar microwave imaging system with step-frequency synthesized pulse using different calibration methods

An experimental study of a planar microwave imaging system with step-frequency synthesized pulse for possible use in medical applications is described. Simple phantoms, consisting of a cylindrical plastic container with air or oil imitating fatty tissues and small highly reflective objects emulating tumors, are scanned with a probe antenna over a planar surface in the X-band. Different calibration schemes are considered for successful detection of these objects. (c) 2006 Wiley Periodicals, Inc.

Field evaluation of passive samplers: Monitoring polycyclic aromatic hydrocarbons (PAHs) in stormwater

The aim of this study was to evaluate the feasibility of using semipermeable membrane devices (SPMDs) and polyethylene-based passive sampler devices (PSDs) for monitoring PAHs in stormwater. Firstly, SPMDs were deployed at one site and SPMD-derived water concentrations were compared with water concentration measured from grab samples. In a subsequent deployment the performance of SPMDs and PSDs was compared. Finally PSDs of multiple surface area to volume ratios were used to compare PAH concentrations at the two sites. The results obtained in this study show that SPMDs can be used to measure the water concentration of PAHs with reasonable accuracy, when compared with grab samples collected at the same site. Importantly, several PAHs which could not be detected in a 10 L grab sample could be detected in the SPMDs. PSD and SPMD samplers produced similar results when deployed at the same site, with most estimated water concentrations within a factor of 1.5. The use of PSDs in multiple surface area to volume ratios proved to be an effective means of characterizing the uptake kinetics for PAHs in situ. Overall passive water samplers proved to be an efficient technique for monitoring PAHs in stormwater.

The cost of uniqueness in groundwater model calibration

Calibration of a groundwater model requires that hydraulic properties be estimated throughout a model domain. This generally constitutes an underdetermined inverse problem, for which a Solution can only be found when some kind of regularization device is included in the inversion process. Inclusion of regularization in the calibration process can be implicit, for example through the use of zones of constant parameter value, or explicit, for example through solution of a constrained minimization problem in which parameters are made to respect preferred values, or preferred relationships, to the degree necessary for a unique solution to be obtained. The cost of uniqueness is this: no matter which regularization methodology is employed, the inevitable consequence of its use is a loss of detail in the calibrated field. This, ill turn, can lead to erroneous predictions made by a model that is ostensibly well calibrated. Information made available as a by-product of the regularized inversion process allows the reasons for this loss of detail to be better understood. In particular, it is easily demonstrated that the estimated value for an hydraulic property at any point within a model domain is, in fact, a weighted average of the true hydraulic property over a much larger area. This averaging process causes loss of resolution in the estimated field. Where hydraulic conductivity is the hydraulic property being estimated, high averaging weights exist in areas that are strategically disposed with respect to measurement wells, while other areas may contribute very little to the estimated hydraulic conductivity at any point within the model domain, this possibly making the detection of hydraulic conductivity anomalies in these latter areas almost impossible. A study of the post-calibration parameter field covariance matrix allows further insights into the loss of system detail incurred through the calibration process to be gained. A comparison of pre- and post-calibration parameter covariance matrices shows that the latter often possess a much smaller spectral bandwidth than the former. It is also demonstrated that, as all inevitable consequence of the fact that a calibrated model cannot replicate every detail of the true system, model-to-measurement residuals can show a high degree of spatial correlation, a fact which must be taken into account when assessing these residuals either qualitatively, or quantitatively in the exploration of model predictive uncertainty. These principles are demonstrated using a synthetic case in which spatial parameter definition is based oil pilot points, and calibration is Implemented using both zones of piecewise constancy and constrained minimization regularization. (C) 2005 Elsevier Ltd. All rights reserved.