National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute”
UniversityKyiv, Ukraine
Research output, citation impact, and the most-cited recent papers from National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute” (Ukraine). Aggregated across the NobleBlocks index of 300M+ scholarly works.
Top-cited papers from National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute”
Deep learning (DL) is a powerful state-of-the-art technique for image processing including remote sensing (RS) images. This letter describes a multilevel DL architecture that targets land cover and crop type classification from multitemporal multisource satellite imagery. The pillars of the architecture are unsupervised neural network (NN) that is used for optical imagery segmentation and missing data restoration due to clouds and shadows, and an ensemble of supervised NNs. As basic supervised NN architecture, we use a traditional fully connected multilayer perceptron (MLP) and the most commonly used approach in RS community random forest, and compare them with convolutional NNs (CNNs). Experiments are carried out for the joint experiment of crop assessment and monitoring test site in Ukraine for classification of crops in a heterogeneous environment using nineteen multitemporal scenes acquired by Landsat-8 and Sentinel-1A RS satellites. The architecture with an ensemble of CNNs outperforms the one with MLPs allowing us to better discriminate certain summer crop types, in particular maize and soybeans, and yielding the target accuracies more than 85% for all major crops (wheat, maize, sunflower, soybeans, and sugar beet).
A nonlinear adaptive state feedback input-output linearizing control is designed for a fifth-order model of an induction motor which includes both electrical and mechanical dynamics under the assumptions of linear magnetic circuits. The control algorithm contains a nonlinear identification scheme which asymptotically tracks the true values of the load torque and rotor resistance which are assumed to be constant but unknown. Once those parameters are identified, the two control goals of regulating rotor speed and rotor flux amplitude are decoupled, so that power efficiency can be improved without affecting speed regulation. Full state measurements are required.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">></ETX>
A combination is presented of all inclusive deep \ninelastic cross sections previously published by the H1 and \nZEUS collaborations at HERA for neutral and charged current e± p scattering for zero beam polarisation. The datawere \ntaken at proton beam energies of 920, 820, 575 and 460GeV \nand an electron beam energy of 27.5GeV. The data correspond \nto an integrated luminosity of about 1 fb−1 and span \nsix orders ofmagnitude in negative four-momentum-transfer \nsquared, Q2, and Bjorken x. The correlations of the systematic \nuncertainties were evaluated and taken into account for \nthe combination. The combined cross sections were input \nto QCD analyses at leading order, next-to-leading order and \nat next-to-next-to-leading order, providing a new set of parton \ndistribution functions, called HERAPDF2.0. In addition \nto the experimental uncertainties, model and parameterisation \nuncertainties were assessed for these parton distribution \nfunctions. Variants of HERAPDF2.0 with an alternative \ngluon parameterisation, HERAPDF2.0AG, and using fixedflavour- \nnumber schemes, HERAPDF2.0FF, are presented. \nThe analysiswas extended by includingHERAdata on charm \nand jet production, resulting in the variant HERAPDF2.0Jets. \nThe inclusion of jet-production cross sections made a simultaneous \ndetermination of these parton distributions and the \nstrong coupling constant possible, resulting in αs (M2Z \n) = \n0.1183±0.0009(exp)±0.0005(model/parameterisation)± \n0.0012(hadronisation) \n+0.0037 \n−0.0030(scale).An extraction of xFγ Z \n3 \nand results on electroweak unification and scaling violations \nare also presented.
The Visual Object Tracking challenge VOT2019 is the seventh annual tracker benchmarking activity organized by the VOT initiative. Results of 81 trackers are presented; many are state-of-the-art trackers published at major computer vision conferences or in journals in the recent years. The evaluation included the standard VOT and other popular methodologies for short-term tracking analysis as well as the standard VOT methodology for long-term tracking analysis. The VOT2019 challenge was composed of five challenges focusing on different tracking domains: (i) VOTST2019 challenge focused on short-term tracking in RGB, (ii) VOT-RT2019 challenge focused on "real-time" shortterm tracking in RGB, (iii) VOT-LT2019 focused on longterm tracking namely coping with target disappearance and reappearance. Two new challenges have been introduced: (iv) VOT-RGBT2019 challenge focused on short-term tracking in RGB and thermal imagery and (v) VOT-RGBD2019 challenge focused on long-term tracking in RGB and depth imagery. The VOT-ST2019, VOT-RT2019 and VOT-LT2019 datasets were refreshed while new datasets were introduced for VOT-RGBT2019 and VOT-RGBD2019. The VOT toolkit has been updated to support both standard shortterm, long-term tracking and tracking with multi-channel imagery. Performance of the tested trackers typically by far exceeds standard baselines. The source code for most of the trackers is publicly available from the VOT page. The dataset, the evaluation kit and the results are publicly available at the challenge website.
Motivated by technological advances in power electronics and signal processing, and by the interest in using direct drives for robot manipulators, we investigate the control problem of high-performance drives for switched reluctance motors (SRM's). SRM's are quite simple, low cost, and reliable motors as compared to the widely used dc motors. However, the SRM presents a coupled nonlinear multivariable control structure which calls for complex nonlinear control design in order to achieve high dynamic performances. We first develop a detailed nonlinear model which matches experimental data and establish an electronic commutation strategy. Then, on the basis of recent nonlinear control techniques, we design a state feedback control algorithm which compensates for all the nonlinearities and decouples the effect of stator phase currents in the torque production. The position dependent logic of the electronic commutator assigns control authority to one phase, which controls the motion, while the remaining phase currents are forced to decay to zero. Simulations for a direct drive, single link manipulator with the SRM are reported, which show the control performance of the algorithm we propose in nominal conditions and test its robustness versus the most critical parameter uncertainties of payload mass and stator resistance.
Many applied problems arising in agricultural monitoring and food security require reliable crop maps at national or global scale. Large scale crop mapping requires processing and management of large amount of heterogeneous satellite imagery acquired by various sensors that consequently leads to a “Big Data” problem. The main objective of this study is to explore efficiency of using the Google Earth Engine (GEE) platform when classifying multi-temporal satellite imagery with potential to apply the platform for a larger scale (e.g. country level) and multiple sensors (e.g. Landsat-8 and Sentinel-2). In particular, multiple state-of-the-art classifiers available in the GEE platform are compared to produce a high resolution (30 m) crop classification map for a large territory (~28,100 km2 and 1.0 M ha of cropland). Though this study does not involve large volumes of data, it does address efficiency of the GEE platform to effectively execute complex workflows of satellite data processing required with large scale applications such as crop mapping. The study discusses strengths and weaknesses of classifiers, assesses accuracies that can be achieved with different classifiers for the Ukrainian landscape, and compares them to the benchmark classifier using a neural network approach that was developed in our previous studies. The study is carried out for the Joint Experiment of Crop Assessment and Monitoring (JECAM) test site in Ukraine covering the Kyiv region (North of Ukraine) in 2013. We found that Google Earth Engine (GEE) provides very good performance in terms of enabling access to the remote sensing products through the cloud platform and providing pre-processing; however, in terms of classification accuracy, the neural network based approach outperformed support vector machine (SVM), decision tree and random forest classifiers available in GEE.
Abstract Plasmonic nanostructures hold promise for the realization of ultra-thin sub-wavelength devices, reducing power operating thresholds and enabling nonlinear optical functionality in metasurfaces. However, this promise is substantially undercut by absorption introduced by resistive losses, causing the metasurface community to turn away from plasmonics in favour of alternative material platforms (e.g., dielectrics) that provide weaker field enhancement, but more tolerable losses. Here, we report a plasmonic metasurface with a quality-factor ( Q -factor) of 2340 in the telecommunication C band by exploiting surface lattice resonances (SLRs), exceeding the record by an order of magnitude. Additionally, we show that SLRs retain many of the same benefits as localized plasmonic resonances, such as field enhancement and strong confinement of light along the metal surface. Our results demonstrate that SLRs provide an exciting and unexplored method to tailor incident light fields, and could pave the way to flexible wavelength-scale devices for any optical resonating application.
Marilyn Monroe knew that "diamonds are a girl's best friend" but, in the meantime, many chemists have realized that they are also extremely attractive objects in contemporary chemistry. The chemist's diamonds are usually quite small (herein: nanometer-sized "diamondoids") and as a result of their unique structure are unusual chemical building blocks. Since lower diamondoids (up to triamantane) have recently become available in large amounts from petroleum and higher diamondoids (starting from tetramantane) are now also accessible from crude oil new research involving them has begun to emerge. Having well-defined structures makes these cage compounds so special compared to other nanometer-scale diamonds. Selective and high-yielding synthetic approaches to the functionalization of diamondoids gives derivatives that can find applications in, for example, polymers, coating materials, drugs, and molecular electronics.
The topic covered in this book is the study of metric and other close characteristics of different spaces and classes of random variables and the application of the entropy method to the investigation of properties of stochastic processes whose values, or increments, belong to given spaces. The following processes appear in detail: pre-Gaussian processes, shot noise processes representable as integrals over processes with independent increments, quadratically Gaussian processes, and, in particular, correlogram-type estimates of the correlation function of a stationary Gaussian process, jointly strictly sub-Gaussian processes, etc. The book consists of eight chapters divided into four parts: The first part deals with classes of random variables and their metric characteristics. The second part presents properties of stochastic processes "imbedded" into a space of random variables discussed in the first part. The third part considers applications of the general theory. The fourth part outlines the necessary auxiliary material. Problems and solutions presented show the intrinsic relation existing between probability methods, analytic methods, and functional methods in the theory of stochastic processes. The concluding sections, "Comments" and "References", gives references to the literature used by the authors in writing the book.
ADVERTISEMENT RETURN TO ISSUEPREVArticleNEXTSelective Alkane Transformations via Radicals and Radical Cations: Insights into the Activation Step from Experiment and TheoryAndrey A. Fokin and Peter R. SchreinerView Author Information Department of Organic Chemistry, Kiev Polytechnic Institute, 37 Pobedy Avenue, 03056 Kiev, Ukraine, Department of Chemistry, University of Georgia, Athens, Georgia 30602-2556 Cite this: Chem. Rev. 2002, 102, 5, 1551–1594Publication Date (Web):April 17, 2002Publication History Received6 September 2001Published online17 April 2002Published inissue 1 May 2002https://pubs.acs.org/doi/10.1021/cr000453mhttps://doi.org/10.1021/cr000453mresearch-articleACS PublicationsCopyright © 2002 American Chemical SocietyRequest reuse permissionsArticle Views6480Altmetric-Citations360LEARN ABOUT THESE METRICSArticle Views are the COUNTER-compliant sum of full text article downloads since November 2008 (both PDF and HTML) across all institutions and individuals. These metrics are regularly updated to reflect usage leading up to the last few days.Citations are the number of other articles citing this article, calculated by Crossref and updated daily. Find more information about Crossref citation counts.The Altmetric Attention Score is a quantitative measure of the attention that a research article has received online. Clicking on the donut icon will load a page at altmetric.com with additional details about the score and the social media presence for the given article. Find more information on the Altmetric Attention Score and how the score is calculated. Share Add toView InAdd Full Text with ReferenceAdd Description ExportRISCitationCitation and abstractCitation and referencesMore Options Share onFacebookTwitterWechatLinked InRedditEmail Other access optionsGet e-Alertsclose SUBJECTS:Cations,Chemical structure,Hydrocarbons,Oxidation,Selectivity Get e-Alerts
This work has been carried out to support the investigation of the electroencephalogram (EEG) Fourier power spectral, coherence, and detrended fluctuation characteristics during performance of mental tasks. To this aim, the presented dataset contains International 10/20 system EEG recordings from subjects under mental cognitive workload (performing mental serial subtraction) and the corresponding reference background EEGs. Based on the subtraction task performance (number of subtractions and accuracy of the result), the subjects were divided into good counters and bad counters (for whom the mental task required excessive efforts). The data was recorded from 36 healthy volunteers of matched age, all of whom are students of Educational and Scientific Centre “Institute of Biology and Medicine”, National Taras Shevchenko University of Kyiv (Ukraine); the recordings are available through Physiobank platform. The dataset can be used by the neuroscience research community studying brain dynamics during cognitive workload.
We demonstrate the possibility to drive an antiferromagnetic domain wall at high velocities by fieldlike Néel spin-orbit torques. Such torques arise from current-induced local fields that alternate their orientation on each sublattice of the antiferromagnet and whose orientation depends primarily on the current direction, giving them their fieldlike character. The domain wall velocities that can be achieved by this mechanism are 2 orders of magnitude greater than the ones in ferromagnets. This arises from the efficiency of the staggered spin-orbit fields to couple to the order parameter and from the exchange-enhanced phenomena in antiferromagnetic texture dynamics, which leads to a low domain wall effective mass and the absence of a Walker breakdown limit. In addition, because of its nature, the staggered spin-orbit field can lift the degeneracy between two 180° rotated states in a collinear antiferromagnet, and it provides a force that can move such walls and control the switching of the states.
The theory of parabolic equations, a well-developed part of the contemporary partial differential equations and mathematical physics, is the subject theory of of an immense research activity. A contin
The authors design a global adaptive output feedback control for a fifth-order model of induction motors, which guarantees asymptotic tracking of smooth speed references on the basis of speed and stator current measurements, for any initial condition and for any unknown constant value of torque load and rotor resistance. The proposed seventh-order nonlinear compensator generates estimates both for the unknown parameters (torque load and rotor resistance) and for the unmeasured state variables (rotor flux); they converge to the corresponding true values under persistency of excitation which actually holds in typical operating conditions. The control algorithm generates references for the magnetizing flux component and for the torque component of stator current which lead to significant simplification for current-fed motors. Simulations show that the proposed controller is suitable for high dynamic performance applications.
For many applied problems in agricultural monitoring and food security, it is important to provide reliable crop classification maps. Satellite imagery is extremely valuable source of data to provide crop maps in a timely way at moderate and high spatial resolution. Information on parcel boundaries that takes into account the spatial context may improve the quality of maps compared to pixel-based classification approaches. In general, parcels may contain several plots with different crops and such situations should be taken into account when using parcel boundaries. In this paper, we aim to compare pixel-based and parcel-based approaches to crop classification from multitemporal optical (Landsat-8) and synthetic-aperture radar (SAR) Sentinel-1 imagery. For this, we propose a parcel-based approach that involves a pixel-based classification map and specifically designed rules to account for several plots within parcel. The study is carried out for the Joint Experiment of Crop Assessment and Monitoring test site in Ukraine covering the Kyiv oblast (North of Ukraine) in 2013-2015, and the Odessa oblast (South of Ukraine) in 2014-2015. We found that pixel-based overall classification accuracy can be increased from 85.32% to 89.40% when using parcel boundaries. Among tested parcel-based approaches, the one that relied on pixel-based classification map and a procedure to select multiple plots within the parcel yielded the best performance.
The metal-induced coupling of tertiary diamondoid bromides gave highly sterically congested hydrocarbon (hetero)dimers with exceptionally long central C-C bonds of up to 1.71 Å in 2-(1-diamantyl)[121]tetramantane. Yet, these dimers are thermally very stable even at temperatures above 200 °C, which is not in line with common C-C bond length versus bond strengths correlations. We suggest that the extraordinary stabilization arises from numerous intramolecular van der Waals attractions between the neighboring H-terminated diamond-like surfaces. The C-C bond rotational dynamics of 1-(1-adamantyl)diamantane, 1-(1-diamantyl)diamantane, 2-(1-adamantyl)triamantane, 2-(1-diamantyl)triamantane, and 2-(1-diamantyl)[121]tetramantane were studied through variable-temperature (1)H- and (13)C NMR spectroscopies. The shapes of the inward (endo) CH surfaces determine the dynamic behavior, changing the central C-C bond rotation barriers from 7 to 33 kcal mol(-1). We probe the ability of popular density functional theory (DFT) approaches (including BLYP, B3LYP, B98, B3LYP-Dn, B97D, B3PW91, BHandHLYP, B3P86, PBE1PBE, wB97XD, and M06-2X) with 6-31G(d,p) and cc-pVDZ basis sets to describe such an unusual bonding situation. Only functionals accounting for dispersion are able to reproduce the experimental geometries, while most DFT functionals are able to reproduce the experimental rotational barriers due to error cancellations. Computations on larger diamondoids reveal that the interplay between the shapes and the sizes of the CH surfaces may even allow the preparation of open-shell alkyl radical dimers (and possibly polymers) that are strongly held together exclusively by dispersion forces.
The term “diamondoid” describes cage hydrocarbon molecules that are superimposable on the diamond lattice. Diamondoids that are formally built by face-fusing of adamantane units, namely diamantane, triamantane, tetramantane, etc., have fascinated chemists since the beginning of the last century. The functionalization of these perfectly defined (C,H)-molecules is described here. Thus, diamondoid halides and diamondoid alcohols are first rank precursors for amino and phosphine-substituted diamondoids that have proved to be highly useful in therapeutic applications and metal catalysis, respectively. The extent of functionalization and polyfunctionalization achieved for adamantane and diamantane, and the synthesis and applications of the resulting organohybrids are illustrated, revealing their high potential in fields such as organocatalysis, polymers, molecular electronics and mechanics.
The extensive application of antibiotics in human and veterinary medicine has led to their widespread occurrence in a natural aquatic environment. Global health crisis is associated with the fast development of antimicrobial resistance, as more and more infectious diseases cannot be treated more than once. Sulfamethoxazole, trimethoprim and ciprofloxacin are the most commonly detected antibiotics in water systems worldwide. The persistent and toxic nature of these antibiotics makes their elimination by conventional treatment methods at wastewater treatment plants almost impossible. The application of advanced oxidation processes and heterogeneous photocatalysis over TiO2-based materials is a promising solution. This highly efficient technology has the potential to be sustainable, cost-efficient and energy-efficient. A comprehensive review on the application of various TiO2-based photocatalysts for the degradation of sulfamethoxazole, trimethoprim and ciprofloxacin is focused on highlighting their photocatalytic performance under various reaction conditions (different amounts of pollutant and photocatalyst, pH, light source, reaction media, presence of inorganic ions, natural organic matter, oxidants). Mineralization efficiency and ecotoxicity of final products have been also considered. Further research needs have been presented based on the literature findings. Among them, design and development of highly efficient under sunlight, stable, recyclable and cost-effective TiO2-based materials; usage of real wastewaters for photocatalytic tests; and compulsory assessment of products ecotoxicity are the most important research tasks in order to meet requirements for industrial application.
Ukraine is one of the most developed agricultural countries in the world. For many applications, it is extremely important to provide reliable crop maps taking into account diversity of cropping systems used in Ukraine. The use of optical imagery only is limited due to cloud cover, and previous studies showed particular difficulties in discriminating summer crops in Ukraine such as maize, soybeans, sunflower, and sugar beet. This paper focuses on exploring feasibility and assessing efficiency of using multitemporal satellite synthetic-aperture radar (SAR) acquired in C-band and optical images for crop classification in Ukraine. Both optical (Landsat-8/OLI) and SAR (Radarsat-2) images are used to assess the impact of adding backscattering intensity from SAR images for classification purposes. SAR intensity information is very important due to availability of Sentinel-1 imagery over Ukraine starting March 2015. Different combinations of optical and SAR images, as well as SAR modes and polarizations, are assessed for better discrimination of crops. A committee of neural networks, in particular multilayer perceptrons (MLPs), is used to improve classification accuracy compared to several standard classifiers. It is found that using backscatter coefficients from SAR images alone provides the same performance for winter crops (wheat and rapeseed) as surface reflectance from optical images. Considering the summer crops, the major impact of adding backscatter intensity information from SAR images is in better separation of sunflower, soybeans, and maize.
Antiferromagnetic spintronics is an emerging research field whose focus is on the electrical, optical or other means of control of the antiferromagnetic order parameter and its utility in information technology devices. An example of recently discovered new concepts is the Néel spin–orbit torque which allows for the antiferromagnetic order parameter to be controlled by an electrical current in common microelectronic circuits. In this review we discuss the utility of antiferromagnets as active and supporting materials for spintronics, the interplay of antiferromagnetic spintronics with other modern research fields in condensed matter physics, and its utility in future ”More than Moore” information technologies.