The IMAGES Model

The spatial distributions of the depo.sited species, NO, NO2, IINO3, and O3, depend on interactions of the transport and chemical schemes with both wet and dry deposition. IMAGES parameterizes wet deposition or wash out as a first-order loss rate calculated as a function of the precipitation rate (Muller and 

Brasseur, 1995). The precipitation rate was taken from the climatology of Shea (1986). IMAGES expresses dry deposition as a function of a prescribed deposition velocity (which is specific to vegetation type). Wet deposition depends on the rate of precipitation, but the formulation accounts for the different types of precipitations and the species-dependent solubility coefficients. Dry deposition velocities vary considerably from species to species. HNO3 has by far the largest deposition velocity. 

The IMAGES model has been used to assess the impact of human activities on the chemical composition of the global troposphere. To tiuantify past, current, and future changes in tropospheric composition, IMAGES was used to simulate the preindustrial atmosphere (year 1850), a contemporary atmosphere (year 1990), and a future atmosphere (year 2050), respectively. As expected, the largest increase in ozone occurs in the lower troposphere in the Northern Hemisphere with changes of more than 70% at mid- 

FIGURE 1 The calculated change (%) in the July zonally averaged concentration of tropospheric ozone between 1850 and 1990 simulated by the IMAGES model using the IS92a emission scenario. 

Calculated change in January surface ozone concentration between 1990 and 2050 [%] 

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Polar M/C interfaces have only been studied sparsely. The polar (111) and nonpolar (100) Cu/MgO interfaces were compared in a slab calculation within the LDA. ° As expected from the image model theory, the polar interface displayed a considerably higher work of separation. In addition, these authors found a larger charge transfer and Cu-MgO orbital mixing for the polar interface. 

To this group belong several models of very different kinds such as the image model (RE-IE), the charge transfer (RE-CT) model, the electron-hole excitation model (RE-EH), and the Raman reflectivity model (RE-RF). These models have very little in common except that they all lead to enhancements by virtue of a resonance scattering mechanism. The validity of the last statement is not always realized by people, but it will be shown below to hold true. 

The image models describe SERS in terms of molecular states modified by the surface. The CT model focused on joint molecule-metal states. The Raman reflectivity envisages the scattering as coming from the metal, modified by the presence of the molecule, and its internal vibrations. 

The importance of distinct a priori knowledge account becomes more perceptible if noisy data are under restoration. The noise / ( shifts the solution of (1) from the Maximum Likelihood (ML) to the so called Default Model for which the function of the image constraint becomes more significant. 

To prepare simulated free-of-noise 3D images of a complex body For this target the image of an internal pore in the real welding joint with extracted noise was used. The ray tracer model was applied for the simulation of five projections of the selected image. 

In many cases, the methods used to solve identification problems are based on an iterative minimization of some performance criterion measuring the dissimilarity between the experimental and the synthetic data (generated by the current estimate of the direct model). In our case, direct quantitative comparison of two Bscan images at the pixels level is a very difficult task and involves the solution of a very difficult optimization problem, which can be also ill-behaved. Moreover, it would lead to a tremendous amount of computational burden. Segmented Bscan images may be used as concentrated representations of the useful... 

There are several important partial results. (1) Definition of quality of the CT-data in relation to the imaging task, including a model of the X-ray paths and how it is used to predict the optimal performance. (2) A model and method to determine how the information of the imaged object transfer from the detector entrance screen through the detector chain to CT... 

To verify the modelling of the data eolleetion process, calculations of SAT 4, in the entrance window of the XRII was compared to measurements of RNR p oj in stored data as function of tube potential. The images object was a steel cylinder 5-mm) with a glass rod 1-mm) as defect. X-ray spectra were filtered with 0.6-mm copper. Tube current and exposure time were varied so that the signal beside the object. So, was kept constant for all tube potentials. Figure 8 shows measured and simulated SNR oproj, where both point out 100 kV as the tube potential that gives a maximum. Due to overestimation of the noise in calculations the maximum in the simulated values are normalised to the maximum in the measured values. Once the model was verified it was used to calculate optimal choice of filter materials and tube potentials, see figure 9. 

The a priori information involved by this modified Beta law (5) does not consider the local correlation between pixels, however, the image f is mainly constituted from locally constant patches. Therefore, this a priori knowledge can be introduced by means of a piecewise continuous function, the weak membrane [2]. The energy related to this a priori model is ... 

To describe the X-ray imaging system the projection of 3D object points onto the 2D image plane, and nonlinear distortions inherent in the image detector system have to, be modelled. A parametric camera model based on a simple pinhole model to describe the projection in combination with a polynomal model of the nonlinear distortions is used to describe the X-ray imaging system. The parameters of the model are estimated using a two step approach. First the distortion parameters for fixed source and detector positions are calculated without any knowledge of the projection parameters. In a second step, the projection parameters are calculated for each image taken with the same source and detector positions but with different sample positions. 

In this section the camera model based on a projection model and the polynomal model of the image detector distortions will be described (figure 2). 

The projection model (equations 1, 2) describes the mapping of 3D points = m,yw,2wY to 2D image points Pi = (ii,j/i) in a virtual, undistorted image plane. The... 

The distortion model (equations 3) describes the mapping of virtual, undistorted image points Pi = xi,yiY to distorted image points P = (i , y Y the real image using third order (2-dimensional) polynomals. 

The changes described above also allowed much easier access to the high voltage cable for routine (6-month) owner directed, service operations, and provided better upper and lower x-ray cabinet and control cabinet ventilation. With the exception of the x-ray tubes, all the individual manufactured components, on all four systems are identical. There are very subtle differences in the warm-up/start-up sequence on the x-ray controllers on the newer systems due to model/year and x-ray tube differences. The last three systems were supplied with environmental type key-boards for the image processors and base-mounted , rather than conduit-mounted exterior warning indicators. The first system was subsequently upgraded to include the better keyboard and the external warning appliances/features. 

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