1,527 research outputs found
Deep convolutional neural networks for estimating porous material parameters with ultrasound tomography
We study the feasibility of data based machine learning applied to ultrasound
tomography to estimate water-saturated porous material parameters. In this
work, the data to train the neural networks is simulated by solving wave
propagation in coupled poroviscoelastic-viscoelastic-acoustic media. As the
forward model, we consider a high-order discontinuous Galerkin method while
deep convolutional neural networks are used to solve the parameter estimation
problem. In the numerical experiment, we estimate the material porosity and
tortuosity while the remaining parameters which are of less interest are
successfully marginalized in the neural networks-based inversion. Computational
examples confirms the feasibility and accuracy of this approach
Cuticular Needle Erosion and Winter Drought in Polluted Environments - A Model Analysis
A method is developed for analyzing the consequences of pollutant-imposed cuticular erosion for the tolerance of winter drought in coniferous trees. The erosion rate of cuticular wax is modeled in terms of the contact angle of water droplets, as a function of sulphur dioxide, air temperature and relative humidity. Whole tree transpiration during drought is considered, assuming that the state of erosion affects the cuticular resistance of each needle age class. A formula is derived to compare transpiration with the water available in foliage and stem storage. The derivations are applied to a numerical example concerning the transpiration during a warm spell in the spring. Under certain assumptions, increased cuticular transpiration may well give rise to increased winter drought damage. However, many of the parameters and processes still need to be more thoroughly investigated. The most critical open question appears to be the quantitative relationship between cuticular resistance and the state of erosion of the cuticle
Ozone sensitivity of wild field layer plant species of northern Europe.
The increasing tropospheric ozone (O3) concentration constitutes a potential threat to nature. Plants are known to
react to O3, but knowledge of the sensitivity and type of responses of different species and plant communities is
widely lacking. This review focuses on the ecological effects of O3 on northern wild field layer plant species.
Most of the 65 species examined thus far have proven to be quite tolerant of O3. Visible symptoms were observed
in 54% of the 61 species studied, and growth reduction in 31% of the 55 species studied for growth.
There were no signs to suggest that certain families or vegetation types are more sensitive or tolerant than others.
There were, however, clear differences in sensitivity between the different species. It seems that forbs are usually
more sensitive than grasses. It should be kept in mind, however, that we still lack knowledge on the responses of
many common and abundant key species. The long-term effects are also far from clear. Hardly any field examinations
have been carried out on the effects of O3 on plant communities
PRUNE: Dynamic and Decidable Dataflow for Signal Processing on Heterogeneous Platforms
The majority of contemporary mobile devices and personal computers are based
on heterogeneous computing platforms that consist of a number of CPU cores and
one or more Graphics Processing Units (GPUs). Despite the high volume of these
devices, there are few existing programming frameworks that target full and
simultaneous utilization of all CPU and GPU devices of the platform.
This article presents a dataflow-flavored Model of Computation (MoC) that has
been developed for deploying signal processing applications to heterogeneous
platforms. The presented MoC is dynamic and allows describing applications with
data dependent run-time behavior. On top of the MoC, formal design rules are
presented that enable application descriptions to be simultaneously dynamic and
decidable. Decidability guarantees compile-time application analyzability for
deadlock freedom and bounded memory.
The presented MoC and the design rules are realized in a novel Open Source
programming environment "PRUNE" and demonstrated with representative
application examples from the domains of image processing, computer vision and
wireless communications. Experimental results show that the proposed approach
outperforms the state-of-the-art in analyzability, flexibility and performance.Comment: This is the author's version of an article that has been published in
this journal. Changes were made to this version by the publisher prior to
publicatio
Geoeffectiveness and efficiency of CIR, Sheath and ICME in generation of magnetic storms
We investigate relative role of various types of solar wind streams in
generation of magnetic storms. On the basis of the OMNI data of interplanetary
measurements for the period of 1976-2000 we analyze 798 geomagnetic storms with
Dst < -50 nT and their interplanetary sources: corotating interaction regions
(CIR), interplanetary CME (ICME) including magnetic clouds (MC) and Ejecta and
compression regions Sheath before both types of ICME. For various types of
solar wind we study following relative characteristics: occurrence rate; mass,
momentum, energy and magnetic fluxes; probability of generation of magnetic
storm (geoeffectiveness) and efficiency of process of this generation. Obtained
results show that despite magnetic clouds have lower occurrence rate and lower
efficiency than CIR and Sheath they play an essential role in generation of
magnetic storms due to higher geoeffectiveness of storm generation (i.e higher
probability to contain large and long-term southward IMF Bz component).Comment: 23 pages, 4 figures, 3 tables, submitted to JGR special issue
"Response of Geospace to High-Speed Streams
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