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    Wyświetlanie 1-14 z 14
    Tytuł:
    Metric entropy of convex hulls in Hilbert spaces
    Autorzy:
    Li, Wenbo V.
    Linde, Werner
    Powiązania:
    https://bibliotekanauki.pl/articles/1206124.pdf
    Data publikacji:
    2000
    Wydawca:
    Polska Akademia Nauk. Instytut Matematyczny PAN
    Tematy:
    metric entropy
    convex hull
    majorizing measure
    Gaussian process
    Opis:
    Let T be a precompact subset of a Hilbert space. We estimate the metric entropy of co(T), the convex hull of T, by quantities originating in the theory of majorizing measures. In a similar way, estimates of the Gelfand width are provided. As an application we get upper bounds for the entropy of co(T), $T={t_1,t_2,...}$, $||t_j||≤a_j$, by functions of the $a_j$'s only. This partially answers a question raised by K. Ball and A. Pajor (cf. [1]). Our estimates turn out to be optimal in the case of slowly decreasing sequences $(a_j)_{j=1}^∞$.
    Źródło:
    Studia Mathematica; 2000, 139, 1; 29-45
    0039-3223
    Pojawia się w:
    Studia Mathematica
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Gaussian process dynamic modeling and backstepping sliding mode control for magnetic levitation system of maglev train
    Autorzy:
    Sun, Yougang
    Wang, Sumei
    Lu, Yang
    Xu, Junqi
    Powiązania:
    https://bibliotekanauki.pl/articles/2086959.pdf
    Data publikacji:
    2022
    Wydawca:
    Polskie Towarzystwo Mechaniki Teoretycznej i Stosowanej
    Tematy:
    maglev train
    Gaussian process
    sliding mode control
    parameter perturbations
    Opis:
    The maglev trains are strongly nonlinear and open-loop unstable systems with external disturbances and parameters uncertainty. In this paper, the Gaussian process method is utilized to get the dynamic parameters, and a backstepping sliding mode controller is proposed for magnetic levitation systems (MLS) of maglev trains. That is, for a MLS of a maglev train, a nonlinear dynamic model with accurate parameters is obtained by the Gaussian process regression method, based on which a novel robust control algorithm is designed. Specifically, the MLS is divided into two sub-systems by a backstepping method. The inter virtual control inputs and the Lyapunov function are constructed in the first sub-system. For the second sub-system, the sliding mode surface is constructed to fulfil the design of the whole controller to asymptotically regulate the airgap to a desired trajectory. The stability of the proposed control method is analyzed by the Lyapunov method. Both simulation and experimental results are included to illustrate the superior performance of the presented method to cope with parameters perturbations and external disturbance.
    Źródło:
    Journal of Theoretical and Applied Mechanics; 2022, 60, 1; 49--62
    1429-2955
    Pojawia się w:
    Journal of Theoretical and Applied Mechanics
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Modelling of Li-Ion battery state-of-health with Gaussian processes
    Autorzy:
    Dudek, Adrian
    Baranowski, Jerzy
    Powiązania:
    https://bibliotekanauki.pl/articles/28761940.pdf
    Data publikacji:
    2023
    Wydawca:
    Polska Akademia Nauk. Czasopisma i Monografie PAN
    Tematy:
    lithium-ion batteries
    state of health
    Gaussian process
    diagnostics
    Opis:
    The problem of lithium-ion cells, which degrade in time on their own and while used, causes a significant decrease in total capacity and an increase in inner resistance. So, it is important to have a way to predict and simulate the remaining usability of batteries. The process and description of cell degradation are very complex and depend on various variables. Classical methods are based, on the one hand, on fitting a somewhat arbitrary parametric function to laboratory data and, on the other hand, on electrochemical modelling of the physics of degradation. Alternative solutions are machine learning ones or non-parametric ones like support-vector machines or the Gaussian process (GP), which we used in this case. Besides using the GP, our approach is based on current knowledge of how to use non-parametric approaches for modeling the electrochemical state of batteries. It also uses two different ways of dealing with GP problems, like maximum likelihood type II (ML-II) methods and the Monte Carlo Markov Chain (MCMC) sampling.
    Źródło:
    Archives of Electrical Engineering; 2023, 72, 3; 643--659
    1427-4221
    2300-2506
    Pojawia się w:
    Archives of Electrical Engineering
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Generalized hyperbolic processes autocovariance functions
    Autorzy:
    Troush, N. N.
    Kuzmina, A.
    Powiązania:
    https://bibliotekanauki.pl/articles/92840.pdf
    Data publikacji:
    2014
    Wydawca:
    Uniwersytet Przyrodniczo-Humanistyczny w Siedlcach
    Tematy:
    generalized hyperbolic process
    normal inverse Gaussian process
    variance gamma process
    autocovariation function
    Opis:
    Generalized hyperbolic processes are Levy processes which allow an almost perfect fit to financial data. Autocovariance functions of generalized hyperbolic processes such as the normal inverse Gaussian process, the variance gamma process and the hyperbolic process are deduced at this paper.
    Źródło:
    Studia Informatica : systems and information technology; 2014, 1-2(18); 37-45
    1731-2264
    Pojawia się w:
    Studia Informatica : systems and information technology
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    A study of the Inverse Gaussian Process with hazard rate functions-based drifts applied to degradation modelling
    Autorzy:
    Rodríguez-Picón, Luis Alberto
    Méndez-González, Luis Carlos
    Pérez-Olguín, Iván JC
    Hernández-Hernández, Jesús Israel
    Powiązania:
    https://bibliotekanauki.pl/articles/2175136.pdf
    Data publikacji:
    2022
    Wydawca:
    Polska Akademia Nauk. Polskie Naukowo-Techniczne Towarzystwo Eksploatacyjne PAN
    Tematy:
    inverse Gaussian process
    hazard rate function
    degradation rate
    variable drift
    Opis:
    The stochastic modelling of degradation processes requires different characteristics to be considered, such that it is possible to capture all the possible information about a phenomenon under study. An important characteristic is what is known as the drift in some stochastic processes; specifically, the drift allows to obtain information about the growth degradation rate of the characteristic of interest. In some phenomenon’s the growth rate cannot be considered as a constant parameter, which means that the rate may vary from trajectory to trajectory. Given this, it is important to study alternative strategies that allow to model this variation in the drift. In this paper, several hazard rate functions are integrated in the inverse Gaussian process to describe its drift in the aims of individually characterize degradation trajectories. The proposed modelling scheme is illustrated in two case studies, from which the best fitting model is selected via information criteria, a discussion of the flexibility of the proposed models is provided according to the obtained results.
    Źródło:
    Eksploatacja i Niezawodność; 2022, 24, 3; 590--602
    1507-2711
    Pojawia się w:
    Eksploatacja i Niezawodność
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    One-match-ahead forecasting in two-team sports with stacked Bayesian regressions
    Autorzy:
    Lam, M. W. Y.
    Powiązania:
    https://bibliotekanauki.pl/articles/91870.pdf
    Data publikacji:
    2018
    Wydawca:
    Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
    Tematy:
    sports analytics
    one-match-ahead forecasting
    winning probability
    Gaussian process regression
    Opis:
    There is a growing interest in applying machine learning algorithms to real-world examples by explicitly deriving models based on probabilistic reasoning. Sports analytics, being favoured mostly by the statistics community and less discussed in the machine learning community, becomes our focus in this paper. Specifically, we model two-team sports for the sake of one-match-ahead forecasting. We present a pioneering modeling approach based on stacked Bayesian regressions, in a way that winning probability can be calculated analytically. Benefiting from regression flexibility and high standard of performance, Sparse Spectrum Gaussian Process Regression (SSGPR) – an improved algorithm for the standard Gaussian Process Regression (GPR), was used to solve Bayesian regression tasks, resulting in a novel predictive model called TLGProb. For evaluation, TLGProb was applied to a popular sports event – National Basketball Association (NBA). Finally, 85.28% of the matches in NBA 2014/2015 regular season were correctly predicted by TLGProb, surpassing the existing predictive models for NBA.
    Źródło:
    Journal of Artificial Intelligence and Soft Computing Research; 2018, 8, 3; 159-172
    2083-2567
    2449-6499
    Pojawia się w:
    Journal of Artificial Intelligence and Soft Computing Research
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Option pricing by Esscher transforms in the cases of normal inverse Gaussian and variance gamma processes
    Autorzy:
    Troush, N. N.
    Kuzmina, A. V.
    Powiązania:
    https://bibliotekanauki.pl/articles/92926.pdf
    Data publikacji:
    2012
    Wydawca:
    Uniwersytet Przyrodniczo-Humanistyczny w Siedlcach
    Tematy:
    Esscher transforms
    option pricing
    generalized hyperbolic process
    normal inverse Gaussian process
    variance gamma process
    Opis:
    The class of Esscher transforms is an important tool for option pricing Gerber and Shiu (1994) showed that the Esscher transform is an efficient technique for valuing derivative securities if the log returns of the underlying securities are governed by certain stochastic processes with stationary and independent increments. Levy processes are the processes of such type. Special cases of the Levy processes such as the normal inverse Gaussian process and the variance gamma process are considered at this paper. Values of these processes parameters for the existence of Esscher transform are deduced. A new algorithm of a normal inverse Gaussian process and variance gamma process simulation is also presented in this paper. These algorithm is universal and simpler one compared with analogous algorithms.
    Źródło:
    Studia Informatica : systems and information technology; 2012, 1-2(16); 35-43
    1731-2264
    Pojawia się w:
    Studia Informatica : systems and information technology
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    A probabilistic approach for approximation of optical and opto-electronic properties of an opto-semiconductor wafer under consideration of measuring inaccuracy and model uncertainty
    Autorzy:
    Stroka, Stefan M.
    Heumann, Christian
    Suhrke, Fabian
    Meindl, Kathrin
    Powiązania:
    https://bibliotekanauki.pl/articles/2204192.pdf
    Data publikacji:
    2023
    Wydawca:
    Polska Akademia Nauk. Stowarzyszenie Elektryków Polskich
    Tematy:
    Gaussian process regression
    machine learning
    uncertainty quantification
    photoluminescence
    opto-semiconductor wafer measuring
    Opis:
    This paper presents a probabilistic machine learning approach to approximate wavelength values for unmeasured positions on an opto-semiconductor wafer after epitaxy. Insufficient information about optical and opto-electronic properties may lead to undetected specification violations and, consequently, to yield loss or may cause product quality issues. Collection of information is restricted because physical measuring points are expensive and in practice samples are only drawn from 120 specific positions. The purpose of the study is to reduce the risk of uncertainties caused by sampling and measuring inaccuracy and provide reliable approximations. Therefore, a Gaussian process regression is proposed which can determine a point estimation considering measuring inaccuracy and further quantify estimation uncertainty. For evaluation, the proposed method is compared with radial basis function interpolation using wavelength measurement data of 6-inch InGaN wafers. Approximations of these models are evaluated with the root mean square error. Gaussian process regression with radial basis function kernel reaches a root mean square error of 0.814 nm averaged over all wafers. A slight improvement to 0.798 nm could be achieved by using a more complex kernel combination. However, this also leads to a seven times higher computational time. The method further provides probabilistic intervals based on means and dispersions for approximated positions.
    Źródło:
    Opto-Electronics Review; 2023, 31, 2; art. no. e145863
    1230-3402
    Pojawia się w:
    Opto-Electronics Review
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Use of machine learning algorithm for the better prediction of SR peculiarities of WEDM of Nimonic-90 superalloy
    Autorzy:
    Singh Nain, S.
    Sai, R.
    Sihag, P.
    Vambol, S.
    Vambol, V.
    Powiązania:
    https://bibliotekanauki.pl/articles/378951.pdf
    Data publikacji:
    2019
    Wydawca:
    Stowarzyszenie Komputerowej Nauki o Materiałach i Inżynierii Powierzchni w Gliwicach
    Tematy:
    support vector machine
    Gaussian process
    artificial neural network
    WEDM
    maszyna wektorów nośnych
    proces gaussowski
    sztuczna sieć neuronowa
    Opis:
    Purpose: With the end goal to fulfil stringent structural shape of the component in aeronautics industry, machining of Nimonic-90 super alloy turns out to be exceptionally troublesome and costly by customary procedures, for example, milling, grinding, turning, etc. For that reason, the manufacture and design engineer worked on contactless machining process like EDM and WEDM. Based on previous studies, it has been observed that rare research work has been published pertaining to the use of machine learning in manufacturing. Therefore the current research work proposed the use of SVM, GP and ANN methods to evaluate the WEDM of Nimonic-90. Design/methodology/approach: The experiments have been performed on the WEDM considering five process variables. The Taguchi L 18 mixed type array is used to formulate the experimental plan. The surface roughness is checked by using surface contact profilometre. The evolutionary algorithms like SVM, GP and ANN approaches have been used to evaluate the SR of WEDM of Nimonic-90 super alloy. Findings: The entire models present the significant results for the better prediction of SR peculiarities of WEDM of Nimonic-90 superalloy. The GP PUK kernel model is dominating the entire model. Research limitations/implications: The investigation was carried for the Nimonic-90 super alloy is selected as a work material. Practical implications: The results of this study provide an opportunity to conduct contactless processing superalloy Nimonic-90. At the same time, this contactless process is much cheaper, faster and more accurate. Originality/value: An experimental work has been reported on the WEDM of Udimet-L605 and use of advance machine learning algorithm and optimization approaches like SVM, and GRA is recommended. A study on WEDM of Inconel 625 has been explored and optimized the process using Taguchi coupled with grey relational approach. The applicability of some evolutionary algorithm like random forest, M5P, and SVM also tested to evaluate the WEDM of Udimet-L605.The fuzzy- inference and BP-ANN approached is used to evaluate the WEDM process. The multi-objective optimization using ratio analysis approach has been utilized to evaluate the WEDM of high carbon & chromium steel. But this current research work proposed the use of SVM, GP and ANN methods to evaluate the WEDM of Nimonic-90.
    Źródło:
    Archives of Materials Science and Engineering; 2019, 95, 1; 12-19
    1897-2764
    Pojawia się w:
    Archives of Materials Science and Engineering
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Efficient learning variable impedance control for industrial robots
    Autorzy:
    Li, C.
    Zhang, Z.
    Xia, G.
    Xie, X.
    Zhu, Q.
    Powiązania:
    https://bibliotekanauki.pl/articles/200716.pdf
    Data publikacji:
    2019
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    variable impedance control
    reinforcement learning
    efficient
    Gaussian process
    industrial robots
    impedancja
    poprawa efektywności
    wydajność
    model Gaussa
    roboty przemysłowe
    Opis:
    Compared with the robots, humans can learn to perform various contact tasks in unstructured environments by modulating arm impedance characteristics. In this article, we consider endowing this compliant ability to the industrial robots to effectively learn to perform repetitive force-sensitive tasks. Current learning impedance control methods usually suffer from inefficiency. This paper establishes an efficient variable impedance control method. To improve the learning efficiency, we employ the probabilistic Gaussian process model as the transition dynamics of the system for internal simulation, permitting long-term inference and planning in a Bayesian manner. Then, the optimal impedance regulation strategy is searched using a model-based reinforcement learning algorithm. The effectiveness and efficiency of the proposed method are verified through force control tasks using a 6-DoFs Reinovo industrial manipulator.
    Źródło:
    Bulletin of the Polish Academy of Sciences. Technical Sciences; 2019, 67, 2; 201-212
    0239-7528
    Pojawia się w:
    Bulletin of the Polish Academy of Sciences. Technical Sciences
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Kernel function based regression approaches for estimating the oxygen transfer performance of plunging hollow jet aerator
    Autorzy:
    Kumar, M.
    Tiwari, N. K.
    Ranjan, S.
    Powiązania:
    https://bibliotekanauki.pl/articles/368684.pdf
    Data publikacji:
    2019
    Wydawca:
    Stowarzyszenie Komputerowej Nauki o Materiałach i Inżynierii Powierzchni w Gliwicach
    Tematy:
    volumetric oxygen transfer coefficient
    multiple nonlinear regression
    Gaussian process regression
    support vector regression
    współczynnik wnikania tlenu
    regresja nieliniowa
    proces gaussowski
    regresja wektora wsparcia
    Opis:
    Purpose: To evaluate the capability of various kernels employed with support vector regression (SVR) and Gaussian process regression (GPR) techniques in estimating the volumetric oxygen transfer coefficient of plunging hollow jets. Design/methodology/approach: In this study, a data set of 81 observations is acquired from laboratory experiments of hollow jets plunging on the surface of water in the tank. The jet variables: jet velocity, jet thickness, jet length, and water depth are varied accordingly and the values of volumetric oxygen transfer coefficient is computed. An empirical relationship expressing the oxygenation performance of plunging hollow jet aerator in terms of jet variables is formulated using multiple nonlinear regression. The performance of this nonlinear relationship is compared with various kernel function based SVR and GPR models. Models developed with the training data set (51 observations) are checked on testing data set (24 observations) for performance comparison. Sensitivity analysis is carried out to examine the influence of jet variables in effecting the oxygen transfer capabilities of plunging hollow jet aerator. Findings: The overall comparison of kernels yielded good estimation performance of Radial Basis Function kernel (RBF) and Pearson VII Function kernel (PUK) using the SVR technique which is followed by nonlinear regression, and other kernel function based regression models. Research limitations/implications: The results of the study pertaining to the performance of kernels are based on the current experimental conditions and the estimation potential of the regression models may fluctuate beyond the selection of current data range due to datadependant learning of the soft computing models. Practical implications: Volumetric oxygen transfer coefficient of plunging hollow jets can be predicted precisely using SVR model by employing RBF as kernel function as compared to empirical correlation and other kernel function based regression models. Originality/value: The comparative analysis of kernel functions is conducted in this study. In previous studies, the predictive modelling approaches are implemented in simulating the aeration properties of cylindrical solid jets only, while this paper simulates the volumetric oxygen transfer coefficient of diverging hollow jets with the jet variables by utilizing polynomial, normalized polynomial, PUK, and RBF kernels in SVR and GPR.
    Źródło:
    Journal of Achievements in Materials and Manufacturing Engineering; 2019, 95, 2; 74-84
    1734-8412
    Pojawia się w:
    Journal of Achievements in Materials and Manufacturing Engineering
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Fault tolerant control using Gaussian processes and model predictive control
    Autorzy:
    Yang, X.
    Maciejowski, J. M.
    Powiązania:
    https://bibliotekanauki.pl/articles/330117.pdf
    Data publikacji:
    2015
    Wydawca:
    Uniwersytet Zielonogórski. Oficyna Wydawnicza
    Tematy:
    fault tolerant control
    Gaussian process
    model predictive control
    aircraft control
    probabilistic modelling
    sterowanie tolerujące uszkodzenia
    proces gaussowski
    sterowanie predykcyjne
    sterowanie samolotem
    modelowanie probabilistyczne
    Opis:
    Essential ingredients for fault-tolerant control are the ability to represent system behaviour following the occurrence of a fault, and the ability to exploit this representation for deciding control actions. Gaussian processes seem to be very promising candidates for the first of these, and model predictive control has a proven capability for the second. We therefore propose to use the two together to obtain fault-tolerant control functionality. Our proposal is illustrated by several reasonably realistic examples drawn from flight control.
    Źródło:
    International Journal of Applied Mathematics and Computer Science; 2015, 25, 1; 133-148
    1641-876X
    2083-8492
    Pojawia się w:
    International Journal of Applied Mathematics and Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Rezultaty razrabotki i testovyh ispytanij diagnostičeskoj sistemy ocenki kačestva vnutricilindrovyh processov dizelâ
    Results of the development of diagnostic tests and test quality assessment processes intracylinder Diesel
    Autorzy:
    Vališhin, A.
    Powiązania:
    https://bibliotekanauki.pl/articles/360405.pdf
    Data publikacji:
    2013
    Wydawca:
    Akademia Morska w Szczecinie. Wydawnictwo AMSz
    Tematy:
    diesel
    diagnosis
    intracylinder process
    indicator chart
    statistical methods
    normal Gaussian distributions
    skewness
    kurtosis
    Opis:
    The paper presents the results of the development of diagnostic quality systems intracylinder process of marine Diesel. Diagnostic system is designed based on software product Excel, which greatly simplifies its use on vessels and reduces the cost of its acquisition. In the developed system it uses the methods of statistical evaluation of the Diesel engine by comparing skewness and kurtosis of the reference and the actual indicator diagrams.
    Źródło:
    Zeszyty Naukowe Akademii Morskiej w Szczecinie; 2013, 34 (106); 85-90
    1733-8670
    2392-0378
    Pojawia się w:
    Zeszyty Naukowe Akademii Morskiej w Szczecinie
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Generating placated random shapes for an area estimation study
    Autorzy:
    Almowanes, A.
    Kakiashvili, T.
    Koczkodaj, W. W.
    Powiązania:
    https://bibliotekanauki.pl/articles/123002.pdf
    Data publikacji:
    2013
    Wydawca:
    Politechnika Częstochowska. Wydawnictwo Politechniki Częstochowskiej
    Tematy:
    image processing
    Gaussian blur
    random polygon
    computational geometry
    cognitive process
    software testing
    przetwarzanie obrazów
    geometria obliczeniowa
    proces poznawczy
    testowanie oprogramowania
    Opis:
    Random but visually nice shapes are often needed for cognitive experiments and processes. This study describes a heuristic for generating random but nice shapes. We call them placated shapes. These shapes are produced by applying the Gaussian blur to randomly generated polygons. Subsequently, the threshold is set to transform pixels to black and white from different shades of gray. This transformation produces placated shapes for easier estimation of areas. Randomly generated placated shapes are used for testing the accuracy of cognitive processes by pairwise comparisons. They can also be used in many other areas such as computer games or software testing. Such shapes could also be used for camouflaging heavy army equipment.
    Źródło:
    Journal of Applied Mathematics and Computational Mechanics; 2013, 12, 2; 5-13
    2299-9965
    Pojawia się w:
    Journal of Applied Mathematics and Computational Mechanics
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-14 z 14

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