Synthesis and Properties of Nanocomposites Based on Iron and Titanium Oxides and Modified with Silver

Materials Science - By the methods of rotational-corrosion dispersion and chemical deposition, we obtain the structures of nanosized composites based on iron and titanium oxides and modified with...     

Analysis of classification accuracy for pre-filtered multichannel remote sensing data

Noise is one of the main factors degrading the quality of original multichannel remote sensing data and its presence influences classification efficiency, object detection, etc. Thus, pre-filtering is often used to remove noise and improve the solving of final tasks of multichannel remote sensing. Recent studies indicate that a classical model of additive noise is not adequate enough for images formed by modern multichannel sensors operating in visible and infrared bands. However, this fact is often ignored by researchers designing noise removal methods and algorithms. Because of this, we focus on the classification of multichannel remote sensing images in the case of signal-dependent noise present in component images. Three approaches to filtering of multichannel images for the considered noise model are analysed, all based on discrete cosine transform in blocks. The study is carried out not only in terms of conventional efficiency metrics used in filtering (MSE) but also in terms of multichannel data classification accuracy (probability of correct classification, confusion matrix). The proposed classification system combines the pre-processing stage where a DCT-based filter processes the blocks of the multichannel remote sensing image and the classification stage. Two modern classifiers are employed, radial basis function neural network and support vector machines. Simulations are carried out for three-channel image of Landsat TM sensor. Different cases of learning are considered: using noise-free samples of the test multichannel image, the noisy multichannel image and the pre-filtered one. It is shown that the use of the pre-filtered image for training produces better classification in comparison to the case of learning for the noisy image. It is demonstrated that the best results for both groups of quantitative criteria are provided if a proposed 3D discrete cosine transform filter equipped by variance stabilizing transform is applied. The classification results obtained for data pre-filtered in different ways are in agreement for both considered classifiers. Comparison of classifier performance is carried out as well. The radial basis neural network classifier is less sensitive to noise in original images, but after pre-filtering the performance of both classifiers is approximately the same.     

Development and Application of a Stainless CrNbMoC Tool Steel

Steels with 15 mass% Cr, 2 mass% Mo and varying contents of C, V, Nb and Ti were investigated by thermodynamic calculations to find a stainless grade promising a wear resistance equal to that of the standard cold work tool steel X153CrMoV12. It was shown that Nb is most suited to form MC carbides for wear resistance thus reducing the content of M₇C₃, which in turn raised the Cr content dissolved in the matrix to a passivating level. Small melts in the vicinity of steel X140CrNbMoTi15‐5‐2 confirmed this concept in respect to hardenability, wear resistance and corrosion resistance. Industrial manufacturing and application of the new grade RN15X® will be discussed.     

Influence of the chemical composition of oxidation feedstock on the yield of black furnace oil,a byproduct of the manufacturing of air-blown asphalts

The effect of the chemical composition of short residues, a feedstock for the manufacturing of airblown asphalt on the yield of the byproduct black furnace oil was studied. It was shown that paraffin-naphthene hydrocarbons present in short residues are the major source of the formation of black furnace oil. It was found that the paraffin-naphthene hydrocarbons remaining unreacted during the oxidation of asphalts make up a considerable portion of the black oil produced upon asphalt manufacturing.     

Synthesis of energetic compounds via the metathesis reaction of 4-methylenespiro[2,3]hexane

Transformations of methylenespiro[2,3]hexane (MSH) on a heterogeneous rhenium-alumina metathesis (disproportionation) catalyst were studied. It was found that, owing to a significant difference in the stability of carbenic complexes of methylene and disubstituted carbenes, MSH undergoes isomerization to 4-methylspiro[2,3]hex-4-ene followed by their cometathesis yielding bis(spiro[2,3]hexylidene-4). The feasibility of selective cometathesis of MSH and dicyclobutylidene on the rhenium-alumina catalyst resulting in the formation of 4-cyclobutylidenespiro[2,3]hexane was shown.     

On experimental characterization of polarization fluctuation dynamics in random optical beams

Polarization correlation functions that characterize the rate of change of the instantaneous polarization state of an arbitrary fluctuating electromagnetic beam were recently introduced. In this work, we describe techniques that enable the measurement of these functions leading to the determination of the so-called polarization time of a random field.