Detection of insulation defects in the wire through measuring changes in its capacitance

The paper describes the technique to detect local wire insulation defects through measuring the wire capacitance. The operating principle of the CAP-10 device is explained. The principal possibility of this device to detect local defects in wire insulation is shown. The experiments showed that the device can be used to detect defects of different types

Machine learning technique for morphological classification of galaxies at z<0.1 from the SDSS

Methods. We used different galaxy classification techniques: human labeling, multi-photometry diagrams, Naive Bayes, Logistic Regression, Support Vector Machine, Random Forest, k-Nearest Neighbors, and k-fold validation. Results. We present results of a binary automated morphological classification of galaxies conducted by human labeling, multiphotometry, and supervised Machine Learning methods. We applied its to the sample of galaxies from the SDSS DR9 with redshifts of 0.02 < z < 0.1 and absolute stellar magnitudes of 24m < Mr < 19.4m. To study the classifier, we used absolute magnitudes: Mu, Mg, Mr , Mi, Mz, Mu-Mr , Mg-Mi, Mu-Mg, Mr-Mz, and inverse concentration index to the center R50/R90. Using the Support vector machine classifier and the data on color indices, absolute magnitudes, inverse concentration index of galaxies with visual morphological types, we were able to classify 316 031 galaxies from the SDSS DR9 with unknown morphological types. Conclusions. The methods of Support Vector Machine and Random Forest with Scikit-learn machine learning in Python provide the highest accuracy for the binary galaxy morphological classification: 96.4% correctly classified (96.1% early E and 96.9% late L types) and 95.5% correctly classified (96.7% early E and 92.8% late L types), respectively. Applying the Support Vector Machine for the sample of 316 031 galaxies from the SDSS DR9 at z < 0.1, we found 141 211 E and 174 820 L types among them.Comment: 10 pages, 5 figures. The presentation of these results was given during the EWASS-2017, Symposium "Astroinformatics: From Big Data to Understanding the Universe at Large". It is vailable through \url{http://space.asu.cas.cz/~ewass17-soc/Presentations/S14/Dobrycheva_987.pdf

Astronomy in Ukraine

The current and prospective status of astronomical research in Ukraine is discussed. A brief history of astronomical research in Ukraine is presented and the system organizing scientific activity is described, including astronomy education, institutions and staff, awarding higher degrees/titles, government involvement, budgetary investments and international cooperation. Individuals contributing significantly to the field of astronomy and their accomplishments are mentioned. Major astronomical facilities, their capabilities, and their instrumentation are described. In terms of the number of institutions and personnel engaged in astronomy, and of past accomplishments, Ukraine ranks among major nations of Europe. Current difficulties associated with political, economic and technological changes are addressed and goals for future research activities presented.Comment: Paper to be published in ``Organizations and Strategies in Astronomy'' -- Vol. 7, Ed. A. Heck, 2006, Springer, Dordrecht; 25 pages, 2 figs, 2 table

In-process Measuring of Capacitance Per Unit Length for Single-core Electric Wires

The paper describes technical in-process implementation of the electrical method to measure the electrical capacitance per unit length of a single core electric wire. The basic design values of the electro-capacitive measuring transducer are determined. The impact of changes in water conductivity on measurement results is analyzed. Techniques to offset from the impact of changes in water conductivity on the results of the electrical capacitance per unit length control based on indirect electrical conductivity measurement are considered. An appropriate correction of the conversion function is made

Machine-learning computation of distance modulus for local galaxies

Quickly growing computing facilities and an increasing number of extragalactic observations encourage the application of data-driven approaches to uncover hidden relations from astronomical data. In this work we raise the problem of distance reconstruction for a large number of galaxies from available extensive observations. We propose a new data-driven approach for computing distance moduli for local galaxies based on the machine-learning regression as an alternative to physically oriented methods. We use key observable parameters for a large number of galaxies as input explanatory variables for training: magnitudes in U, B, I, and K bands, corresponding colour indices, surface brightness, angular size, radial velocity, and coordinates. We performed detailed tests of the five machine-learning regression techniques for inference of $m-M$: linear, polynomial, k-nearest neighbours, gradient boosting, and artificial neural network regression. As a test set we selected 91 760 galaxies at $z<0.2$ from the NASA/IPAC extragalactic database with distance moduli measured by different independent redshift methods. We find that the most effective and precise is the neural network regression model with two hidden layers. The obtained root-mean-square error of 0.35 mag, which corresponds to a relative error of 16\%, does not depend on the distance to galaxy and is comparable with methods based on the Tully-Fisher and Fundamental Plane relations. The proposed model shows a 0.44 mag (20\%) error in the case of spectroscopic redshift absence and is complementary to existing photometric redshift methodologies. Our approach has great potential for obtaining distance moduli for around 250 000 galaxies at $z<0.2$ for which the above-mentioned parameters are already observed.Comment: 8 pages, 5 figures, Accepted for publication in A&