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Synthetic aperture radar (SAR) has a good ability to detect the microwave scattering characteristics of the target and has a good capability of slant range Doppler positioning. Using multi-view SAR images in combination with image matching and positioning equations, the 3D position of the target can be obtained. In the past, multi-view 3D reconstruction mainly used side-looking images with parallel trajectories but different incident angles. That method is not universal for different configurations and lacks analysis of the relationship between solution and parameter error. This study aims at the problem of multi-view SAR 3D reconstruction. The authors establish general 3D reconstruction equations that can be used for non-parallel track and non-side-looking and the solution method is deduced. Based on this, an analysis method of error sensitivity is proposed and the dependence relationship between the reconstruction error and configuration, velocity, position, and slant range is analysed. The correctness of this method is verified by simulation experiments, which provides guidance for selection of configuration and sensor accuracy when applying 3D reconstruction.

Using PET, brain areas representing the use of a well‐learned tool (chopsticks) were investigated in 10 normal common users. The experimental task was to hold the tool in their right hand and use it to pick up and transport a small pin from a table. Data for the same task performed using only the fingers were also obtained as a control. The results showed an extensive overlap in activated areas with and without the use of the tool. The tool‐use prehension, compared to the finger prehension, was associated with higher activities in the caudal‐ventral premotor, dorsal premotor, superior parietal, posterior intraparietal, middle temporal gyrus, and primary sensory, occipital cortices, and the cerebellum. These are thus considered to be the human cortical and subcortical substrates representing the use of the tool studied. The activity of the posterior intraparietal area was negatively correlated with the number of drops of the pin, whereas occipital activity was positively correlated with the same error parameter. The caudal‐ventral premotor and posterior intraparietal areas are together known to be involved in tool use‐related modulation in peripersonal space. The correlation results suggest that this modulation depends on the level of performance. The coactivated left middle temporal gyrus further suggests that familiarity with a tool as well as the knowledge about its usage plays a role in peripersonal space modulation. Superior parietal activation, along with occipital activation, indicates the involvement of visual‐spatial attention in the tool use, possibly reflecting the effect of interaction between the prehension (task) and the tool. Hum Brain Mapp 2009. © 2009 Wiley‐Liss, Inc.

Methods for power network parameter estimation and correction are discussed in this paper. A sequential-type method for real-time processing of network parameters is proposed. The method exploits the information contained in the measurement residuals to estimate a better network parameter; it uses a linearized sensitivity model to relate the measurement residuals to the parameter error. The proposed method has been coded in FORTRAN using several short-cuts and has been thoroughly tested in the estimation of transformer ratios in the Belgian HV system.

A sensorless driving method of brushless DC motors (BLDCM) using neural networks has been studied in this paper. Considering the nonlinear characteristics and the parameter error of the modeling, neural networks are introduced to estimate the electromotive force (EMF). The results of simulation and experiment using offline trained neural networks show that the proposed method is useful. In addition, the robustness about the parameters is discussed. © 2007 Wiley Periodicals, Inc. Electr Eng Jpn, 162(4): 64–71, 2008; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/eej.20240

This paper reports the results of a study of an error parameter, m, which has been proposed as a measure for determining the effects on accuracy of the basic assumptions upon which TLM modelling of diffusion and heat flow has been founded. The nature of the m parameter is studied in detail by using a two-dimensional thermal diffusion problem. The use of m in the implementation of automatic timestepping is discussed in a practical three-dimensional engineering problem.

Purpose: The purpose of this study is to evaluate delivery parameter errors (DPEs) and their impact on clinical dose variation with the Varian RapidArc technique. Methods: The dynalog files of 16 head-and-neck patients were retrospectively analyzed to characterize three RapidArc DPEs: dose MU, gantry angle, and MLC gap errors. A total of 64 reconstructed plans were created by creating four variants of each of the original 16 plans (three with the DPEs applied individually and one with the three DPEs combined). These reconstructed plans were compared to the original plans to evaluate the impact of the DPEs on the clinical dose distribution. Results: The mean dose MU, gantry angle, and MLC gap error for all patients were 0.00 ± 0.00 MU, −0.36 ± 0.03°, and 0.00 ± 0.01 mm, respectively. The DPEs had no obvious dosimetric impact on any of the studied dosimetric endpoints except the parotid dose. The gantry angle error, MLC gap error, and combined DPEs changed the parotid Dmean (mean dose) and parotid V30 (volume receiving at least 30 Gy) by 1%–2%. Conclusions: It is feasible to use dose distributions reconstructed from dynalog file data as a quality assurance tool. The dose MU, gantry angle, and MLC errors have only minor effects on the accuracy of the delivered dose.

Some model calculations with LAOCOON 3 have been used to show that the mean error in the calculated coupling constants is linearly related to the root mean square error in the transition frequencies. On the basis of this linearity it is shown that the ‘probable’ error parameter of LAOCOON 3 should be increased by a factor of 2·5 to give more realistic errors.

Fur Interpolationsformeln zur numerischen Integration von y″ f(x,y) werden einfache Extrapolationsformeln zur laufenden Ermittlung des fortgepflanzten Abbruchfehlers angegeben. Fur die Interpolationsformeln von Milne und Gaus wird die Brauchbarkeit dieser Fehlerformeln durch Beispiele belegt. Die Fehlerformeln werden auf Interpolationsformeln fur y″ f(x,y,y′) ausgedehnt. Die Interpolationsformeln von Milne und Gaus werden durch neue Formeln fur y′ so erganzt, das die kombinierten Formeln fur die Integration von y″ f(x,y,y′) den gleichen gunstigen Fehlerparameter E besitzen wie die ersteren Formeln fur y″ f(x,y). Beispiele illustrieren die Genauigkeit der Fehlerformeln auch fur diese neuen Interpolationsformeln. Simple extrapolation formulas are presented for the propagation of the truncation error for interpolation formulas integrating y″ f(x,y). These error formulas are applied to the interpolation formulas of Milne and Gaus, and their accuracy is illustrated by examples. The error formulas are extended to cover the case of interpolation formulas for y″ f(x,y,y′). The interpolation formulas of Milne and Gaus are supplemented by new formulas for y' in such a way that the combined formulas have the same small error parameter E as the original formulas of Milne or Gauss. Examples demonstrate the accuracy of the error formulas for these new interpolation formulas.