An efficient implementation of lattice-ladder multilayer perceptrons in field programmable gate arrays

The implementation efficiency of electronic systems is a combination of conflicting requirements, as increasing volumes of computations, accelerating the exchange of data, at the same time increasing energy consumption forcing the researchers not only to optimize the algorithm, but also to quickly implement in a specialized hardware. Therefore in this work, the problem of efficient and straightforward implementation of operating in a real-time electronic intelligent systems on field-programmable gate array (FPGA) is tackled. The object of research is specialized FPGA intellectual property (IP) cores that operate in a real-time. In the thesis the following main aspects of the research object are investigated: implementation criteria and techniques. The aim of the thesis is to optimize the FPGA implementation process of selected class dynamic artificial neural networks. In order to solve stated problem and reach the goal following main tasks of the thesis are formulated: rationalize the selection of a class of Lattice-Ladder Multi-Layer Perceptron (LLMLP) and its electronic intelligent system test-bed – a speaker dependent Lithuanian speech recognizer, to be created and investigated; develop dedicated technique for implementation of LLMLP class on FPGA that is based on specialized efficiency criteria for a circuitry synthesis; develop and experimentally affirm the efficiency of optimized FPGA IP cores used in Lithuanian speech recognizer. The dissertation contains: introduction, four chapters and general conclusions. The first chapter reveals the fundamental knowledge on computer-aideddesign, artificial neural networks and speech recognition implementation on FPGA. In the second chapter the efficiency criteria and technique of LLMLP IP cores implementation are proposed in order to make multi-objective optimization of throughput, LLMLP complexity and resource utilization. The data flow graphs are applied for optimization of LLMLP computations. The optimized neuron processing element is proposed. The IP cores for features extraction and comparison are developed for Lithuanian speech recognizer and analyzed in third chapter. The fourth chapter is devoted for experimental verification of developed numerous LLMLP IP cores. The experiments of isolated word recognition accuracy and speed for different speakers, signal to noise ratios, features extraction and accelerated comparison methods were performed. The main results of the thesis were published in 12 scientific publications: eight of them were printed in peer-reviewed scientific journals, four of them in a Thomson Reuters Web of Science database, four articles – in conference proceedings. The results were presented in 17 scientific conferences

Neural Network Potential Simulations of Copper Supported on Zinc Oxide Surfaces

Heterogeneous catalysis is an area of active research, because many industrially relevant reactions involve gaseous reactants and are accelerated by solid phase catalysts. In recent years, activity in the field has become more intense due to the development of surface science and simulation techniques that allow for acquiring deeper insight into these catalysts, with the goal of producing more active, cheaper and less toxic catalytic materials. One particularly crucial case study for heterogeneous catalysis is the synthesis of methanol from synthesis gas, composed of H2, CO and CO2. The reaction is catalyzed by a mixture of Cu and ZnO nanoparticles with Al2O3 as a support material. This process is important not only due to methanol’s many uses as a solvent, raw material for organic synthesis, and possible energy and carbon capture material, but also as an example for many other metal/metal oxide catalysts. A plethora of experimental studies are available for this catalyst, as well as for simpler model systems of Cu clusters supported on ZnO surfaces. Unfortunately, there is still a lack of theoretical studies that can support these experi- mental results by providing an atom-by-atom representation of the system. This scarcity of atomic level simulations is due to the absence of fast but ab-initio level accurate potentials that would allow for reaching larger systems and longer simulated time scales. A promising possibility to bridge this gap in potentials is the rise of machine learning potentials, which utilize the tools of machine learning to reproduce the potential energy surface of a system under study, as sampled by an expensive electronic structure reference method of choice. One early and fruitful example of such machine learning force fields are neural network potentials, as initially developed by Behler and Parrinello. In this thesis, a neural network potential of the Behler-Parrinello type has been constructed for ternary Cu/Zn/O systems, focusing on supported Cu clusters on the ZnO(10-10) surface, as a model for the industrial catalyst. This potential was subsequently utilized to perform a number of simulations. Small supported Cu clusters between 4 and 10 atoms were optimized with a genetic algorithm, and a number of structural trends observed. These clusters revealed the first hints of the structure of the Cu/ZnO interface, where Cu prefers to interact with the support through configurations in the continuum between Cu(110) and Cu(111). Simulated annealing runs for Cu clusters between 200 and 500 atoms reinforced this observation, with these larger clusters also adopting this sort of interface with the support. Additionally, in these simulations the effect of strain induced by the support can be observed, with deviations from ideal lattice constants reaching the top of all of the clusters. To further investigate the influence of strain in this system, large coincident surfaces of Cu were deposited on ZnO supports. Due to the lattice mismatch present between the two materials, this requires straining the Cu overlayer. This analysis confirmed once again that Cu(110) and Cu(111) are the most stable surfaces when de- posited on ZnO(10-10). During this thesis a number of new algorithm and programs were developed. Of particular interest is the bin and hash algorithm, which was designed to aid in the construction and curating of reference sets for the neural network potential, and can also be used to evaluate the quality of atomic descriptor sets.2021-10-0

Quick Training Algorithm for Extra Reduced Size Lattice-Ladder Multilayer Perceptrons

Abstract. A quick gradient training algorithm for a specific neural network structure called an extra reduced size lattice-ladder multilayer perceptron is introduced. Presented derivation of the algorithm utilizes recently found by author simplest way of exact computation of gradients for rotation parameters of lattice-ladder filter. Developed neural network training algorithm is optimal in terms of minimal number of constants, multiplication and addition operations, while the regularity of the structure is also preserved. Key words: lattice-ladder filter, lattice-ladder multilayer perceptron, adaptation, gradient adaptive lattice algorithms

Aeronautical engineering: A continuing bibliography with indexes (supplement 323)

This bibliography lists 518 reports, articles, and other documents introduced into the NASA scientific and technical information system in November 1995. Subject coverage includes: design, construction and testing of aircraft and aircraft engines; aircraft components, equipment, and systems; ground support systems; and theoretical and applied aspects of aerodynamics and general fluid dynamics

Measurement of the Triple-Differential Cross-Section for the Production of Multijet Events using 139 fb^{-1} of Proton-Proton Collision Data at \sqrt{s} = 13 TeV with the ATLAS Detector to Disentangle Quarks and Gluons at the Large Hadron Collider

At hadron-hadron colliders, it is almost impossible to obtain pure samples in either quark- or gluon-initialized hadronic showers as one always deals with a mixture of particle jets. The analysis presented in this dissertation aims to break the aforementioned degeneracy by extracting the underlying fractions of (light) quarks and gluons through a measurement of the relative production rates of multijet events. A measurement of the triple-differential multijet cross section at a centre-of-mass energy of 13 TeV using an integrated luminosity of 139 fb −1 of data collected with the ATLAS detector in proton-proton collisions at the Large Hadron Collider (LHC) is presented. The cross section is measured as a function of the transverse momentum p T , two categories of pseudorapidity η rel defined by the relative orientation between the jets, as well as a Jet Sub-Structure (JSS) observable O JSS , sensitive to the quark- or gluon-like nature of the hadronic shower of the two leading-p T jets with 250 GeV < p T < 4.5 TeV and |η| < 2.1 in the event. The JSS variables, which have been studied within the context of this thesis, can broadly be divided into two categories: one set of JSS observables is constructed by iteratively declustering and counting the jet’s charged constituents; the second set is based on the output predicted by Deep Neural Networks (DNNs) derived from the “deep sets” paradigm to implement permutation invariant functions over sets, which are trained to discriminate between quark- and gluon- initialized showers in a supervised fashion. All JSS observables are measured based on Inner Detector tracks with p T > 500 MeV and |η| < 2.5 to maintain strong correlations between detector- and particle-level objects. The reconstructed spectra are fully corrected for acceptance and detector effects, and the unfolded cross section is compared to various state-of-the-art parton shower Monte Carlo models. Several sources of systematic and statistical uncertainties are taken into account that are fully propagated through the entire unfolding procedure onto the final cross section. The total uncertainty on the cross section varies between 5 % and 20 % depending on the region of phase space. The unfolded multi-differential cross sections are used to extract the underlying fractions and probability distributions of quark- and gluon-initialized jets in a solely data-driven, model- independent manner using a statistical demixing procedure (“jet topics”), which has originally been developed as a tool for extracting emergent themes in an extensive corpus of text-based documents. The obtained fractions are model-independent and are based on an operational definition of quark and gluon jets that does not seek to assign a binary label on a jet-to-jet basis, but rather identifies quark- and gluon-related features on the level of individual distributions, avoiding common theoretical and conceptional pitfalls regarding the definition of quark and gluon jets. The total fraction of gluon-initialized jets in the multijet sample is (IRC-safely) measured to be 60.5 ± 0.4(Stat) ⊕ 2.4(Syst) % and 52.3 ± 0.4(Stat) ⊕ 2.6(Syst) % in central and forward region, respectively. Furthermore, the gluon fractions are extracted in several exclusive regions of transverse momentum

Social work with airports passengers

Social work at the airport is in to offer to passengers social services. The main methodological position is that people are under stress, which characterized by a particular set of characteristics in appearance and behavior. In such circumstances passenger attracts in his actions some attention. Only person whom he trusts can help him with the documents or psychologically