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    Wyświetlanie 1-9 z 9
    Tytuł:
    Useful energy prediction model of a Lithium-ion cell operating on various duty cycles
    Autorzy:
    Burzyński, Damian
    Powiązania:
    https://bibliotekanauki.pl/articles/2087015.pdf
    Data publikacji:
    2022
    Wydawca:
    Polska Akademia Nauk. Polskie Naukowo-Techniczne Towarzystwo Eksploatacyjne PAN
    Tematy:
    cycle life modelling
    lithium-ion battery
    machine learning
    predictive models
    useful energy prediction
    Opis:
    The paper deals with the subject of the prediction of useful energy during the cycling of a lithium-ion cell (LIC), using machine learning-based techniques. It was demonstrated that depending on the combination of cycling parameters, the useful energy (RUEc) that can be transferred during a full cycle is variable, and also three different types of evolution of changes in RUEc were identified. The paper presents a new non-parametric RUEc prediction model based on Gaussian process regression. It was proven that the proposed methodology enables the RUEc prediction for LICs discharged, above the depth of discharge, at a level of 70% with an acceptable error, which is confirmed for new load profiles. Furthermore, techniques associated with explainable artificial intelligence were applied to determine the significance of model input parameters – the variable importance method – and to determine the quantitative effect of individual model parameters (their reciprocal interaction) on RUEc – the accumulated local effects model of the first and second order.
    Źródło:
    Eksploatacja i Niezawodność; 2022, 24, 2; 317--329
    1507-2711
    Pojawia się w:
    Eksploatacja i Niezawodność
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    On graph mining with deep learning: introducing model r for link weight prediction
    Autorzy:
    Hou, Yuchen
    Holder, Lawrence B.
    Powiązania:
    https://bibliotekanauki.pl/articles/91884.pdf
    Data publikacji:
    2019
    Wydawca:
    Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
    Tematy:
    deep learning
    neural networks
    machine learning
    graph mining
    link weight prediction
    predictive models
    node embeddings
    Opis:
    Deep learning has been successful in various domains including image recognition, speech recognition and natural language processing. However, the research on its application in graph mining is still in an early stage. Here we present Model R, a neural network model created to provide a deep learning approach to the link weight prediction problem. This model uses a node embedding technique that extracts node embeddings (knowledge of nodes) from the known links’ weights (relations between nodes) and uses this knowledge to predict the unknown links’ weights. We demonstrate the power of Model R through experiments and compare it with the stochastic block model and its derivatives. Model R shows that deep learning can be successfully applied to link weight prediction and it outperforms stochastic block model and its derivatives by up to 73% in terms of prediction accuracy. We analyze the node embeddings to confirm that closeness in embedding space correlates with stronger relationships as measured by the link weight. We anticipate this new approach will provide effective solutions to more graph mining tasks
    Źródło:
    Journal of Artificial Intelligence and Soft Computing Research; 2019, 9, 1; 21-40
    2083-2567
    2449-6499
    Pojawia się w:
    Journal of Artificial Intelligence and Soft Computing Research
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    New trends in the prevention of occupational noise-induced hearing loss
    Autorzy:
    Sliwinska-Kowalska, Mariola
    Powiązania:
    https://bibliotekanauki.pl/articles/2116178.pdf
    Data publikacji:
    2020-10-20
    Wydawca:
    Instytut Medycyny Pracy im. prof. dra Jerzego Nofera w Łodzi
    Tematy:
    predictive models
    cochlear neuropathy
    temporary threshold shift
    individual susceptibility to noise
    speech in noise
    medical guidelines
    Opis:
    Noise exposure during lifespan is one of the main causes of hearing loss. The highest risk of noise-induced hearing loss (NIHL) is related to exposures in the workplace, and affects about 7% of the population. Occupational NIHL is irreversible, thus its prevention must be considered a priority. Although current hearing conservation programs (HCPs) have proved to be very beneficial, the incidence of occupational NIHL is still high, reaching about 18% of overexposed workers. This paper reviews recent research on the effects of noise on hearing in pursuit of more effective methods for the prevention of occupational NIHL. The paper discusses the translational significance of noise-induced cochlear neuropathy, as recently shown in animals, and the concept of hidden hearing loss in relation to current NIHL damage risk criteria. The anticipated advantages of monitoring the incidents of the temporary threshold shift (TTS) in workers exposed to high levels of noise have been analyzed in regard to the preclinical diagnostics of NIHL, i.e., at the stage when hearing loss is still reversible. The challenges, such as introducing speech-in-noise audiometry and TTS computational predictive models into HCPs, have been discussed. Finally, the paper underscores the need to develop personalized medical guidelines for the prevention of NIHL and to account for several NIHL risk factors other than these included in the ISO 1999:2013 model. Implementing the steps mentioned above would presumably further reduce the incidence of occupational NIHL, as well as associated social costs.
    Źródło:
    International Journal of Occupational Medicine and Environmental Health; 2020, 33, 6; 841-848
    1232-1087
    1896-494X
    Pojawia się w:
    International Journal of Occupational Medicine and Environmental Health
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Predictive regression models of monthly seismic energy emissions induced by longwall mining
    Regresyjne modele predykcyjne miesięcznej emisji energii sejsmicznej indukowanej eksploatacją w ścianie
    Autorzy:
    Jakubowski, J.
    Tajduś, A.
    Powiązania:
    https://bibliotekanauki.pl/articles/219968.pdf
    Data publikacji:
    2014
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    sejsmiczność indukowana
    wstrząsy górnicze
    zagrożenie tąpaniami
    eksploatacja ścianowa
    drzewa wzmacniane
    sieci neuronowe
    data mining
    modele regresyjne
    modele predykcyjne
    induced seismicity
    mining tremors
    rockburst hazard
    longwall mining
    boosted trees
    neural networks
    regression models
    predictive models
    Opis:
    This article presents the development and validation of predictive regression models of longwall mining-induced seismicity, based on observations in 63 longwalls, in 12 seams, in the Bielszowice colliery in the Upper Silesian Coal Basin, which took place between 1992 and 2012. A predicted variable is the logarithm of the monthly sum of seismic energy induced in a longwall area. The set of predictors include seven quantitative and qualitative variables describing some mining and geological conditions and earlier seismicity in longwalls. Two machine learning methods have been used to develop the models: boosted regression trees and neural networks. Two types of model validation have been applied: on a random validation sample and on a time-based validation sample. The set of a few selected variables enabled nonlinear regression models to be built which gave relatively small prediction errors, taking the complex and strongly stochastic nature of the phenomenon into account. The article presents both the models of periodic forecasting for the following month as well as long-term forecasting.
    W artykule przedstawiono budowę i walidację predykcyjnych modeli regresyjnych sejsmiczności indukowanej eksploatacją w ścianie, opartych na obserwacjach w 63 ścianach kopalni Bielszowice prowadzonych w 12 pokładach w latach 1992-2012. Zmienna prognozowaną jest logarytm miesięcznej sumy energii sejsmicznej wstrząsów w ścianie. Zestaw predyktorów składa się z siedmiu zmiennych ilościowych i jakościowych opisujących wybrane czynniki górnicze i geologiczne w ścianach. Do budowy modeli zastosowano dwie metody uczenia się maszyn: drzewa wzmacniane oraz sieci neuronowe. Zastosowano dwa rodzaje walidacji modeli: na losowej próbie walidacyjnej oraz na czasowej próbie walidacyjnej. Zestaw kilku wybranych zmiennych pozwolił na zbudowanie nieliniowych modeli regresyjnych, które, biorąc pod uwagę złożoną i silnie stochastyczną naturę zjawiska, dają względnie małe błędy pro gnozy. W artykule przedstawiono zarówno modele do prognozy okresowej na kolejny miesiąc jak i do prognozy długoterminowej.
    Źródło:
    Archives of Mining Sciences; 2014, 59, 3; 705-720
    0860-7001
    Pojawia się w:
    Archives of Mining Sciences
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Risk associated with heavy metals in children playground soils of Owerri metropolis, Imo State, Nigeria
    Autorzy:
    Wirnkor, Verla Andrew
    Evelyn Ngozi, Verla Evelyn
    Powiązania:
    https://bibliotekanauki.pl/articles/1118068.pdf
    Data publikacji:
    2017
    Wydawca:
    Przedsiębiorstwo Wydawnictw Naukowych Darwin / Scientific Publishing House DARWIN
    Tematy:
    Bioconcentration
    Metal fractions
    Models
    Predictive
    Risk
    Opis:
    Despite recording the worst heavy metal disaster involving children, there is still scarcity of information on risk assessment of playground soils in Nigeria. In this study, thirty-six soil samples at 0-5 cm depth were collected from nine playgrounds in Owerri metropolis during the dry and rainy seasons. Five heavy metals were fractionated into six chemical fractions using a modified sequential extraction scheme and mean concentrations quantified by AAnalyst 400 Perkin Elmer AAS. Predictive risk models were used to obtain information about the risk of metals contamination to children using these playgrounds for longer periods. These reveal that there were no significant differences in the mean values of bioconcentration factors of all five metals in the various playgrounds for the two years of data. Even though risk values for both dry and rainy season followed the same trend, it was observed that the Zinc showed highest bioconcentration factors (1.6), average daily dose (230.08 mg/kg/day) and risk (5095593 mg/kg/6years). Over all, playgrounds UPS, TSO and SCP had highest mean risk values, respectively. Though with no clear trend, mobility factors showed a weak and positive correlation with risk. Children in playgrounds of public schools within Owerri metropolis could, therefore, be at risk of Mn, Cu and Zn toxicity problems as projected risk values were high for all studied playgrounds. This assessment could help identify playgrounds with urgent need for heavy metals reduction goals, consequently contributing to preserving children’s health.
    Źródło:
    World News of Natural Sciences; 2017, 10; 49-69
    2543-5426
    Pojawia się w:
    World News of Natural Sciences
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Real-time implementation of multiple model based predictive control strategy to air/fuel ratio of a gasoline engine
    Autorzy:
    Wojnar, S
    Polóni, T
    Šimončič, P
    Rohal’-Ilkiv, B
    Honek, M
    Csambál, J
    Powiązania:
    https://bibliotekanauki.pl/articles/229632.pdf
    Data publikacji:
    2013
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    model predictive control
    multiple models
    air/fuel ratio
    spark ignition engine
    ARX models
    Opis:
    Growing safety, pollution and comfort requirements influence automotive industry ever more. The use of three-way catalysts in exhaust aftertreatment systems of combustion engines is essential in reducing engine emissions to levels demanded by environmental legislation. However, the key to the optimal catalytic conversion level is to keep the engine air/fuel ratio (AFR) at a desired level. Thus, for this purposes more and more sophisticated AFR control algorithms are intensively investigated and tested in the literature. The goal of this paper is to present for a case of a gasoline engine the model predictive AFR controller based on the multiple-model approach to the engine modeling. The idea is to identify the engine in particular working points and then to create a global engine's model using Sugeno fuzzy logic. Opposite to traditional control approaches which lose their quality beside steady state, it enables to work with satisfactory quality mainly in transient regimes. Presented results of the multiple-model predictive air/fuel ratio control are acquired from the first experimental real-time implementation on the VW Polo 1390 cm3 gasoline engine, at which the original electronic control unit (ECU) has been fully replaced by a dSpace prototyping system which execute the predictive controller. Required control performance has been proven and is presented in the paper.
    Źródło:
    Archives of Control Sciences; 2013, 23, 1; 93-106
    1230-2384
    Pojawia się w:
    Archives of Control Sciences
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Efficient MPC algorithms with variable trajectories of parameters weighting predicted control errors
    Autorzy:
    Nebeluk, Robert
    Marusak, Piotr
    Powiązania:
    https://bibliotekanauki.pl/articles/230077.pdf
    Data publikacji:
    2020
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    model predictive control
    nonlinear systems
    nonlinear models
    nonlinear control
    simulation
    optimization
    Opis:
    Model predictive control (MPC) algorithms brought increase of the control system performance in many applications thanks to relatively easily solving issues that are hard to solve without these algorithms. The paper is focused on investigating how to further improve the control system performance using a trajectory of parameters weighting predicted control errors in the performance function of the optimization problem. Different shapes of trajectories are proposed and their influence on control systems is tested. Additionally, experiments checking the influence of disturbances and of modeling uncertainty on control system performance are conducted. The case studies were done in control systems of three control plants: a linear non-minimumphase plant, a nonlinear polymerization reactor and a nonlinear thin film evaporator. Three types of MPC algorithms were used during research: linear DMC, nonlinear DMC with successive linearization (NDMC–SL), nonlinear DMC with nonlinear prediction and linearization (NDMC–NPL). Results of conducted experiments are presented in greater detail for the control system of the polymerization reactor, whereas for the other two control systems only the most interesting results are presented, for the sake of brevity. The experiments in the control system of the linear plant were done as preliminary experiments with the modified optimization problem. In the case of control system of the thin film evaporator the researched mechanisms were used in the control system of a MIMO plant showing possibilities of improving the control system performance.
    Źródło:
    Archives of Control Sciences; 2020, 30, 2; 325-363
    1230-2384
    Pojawia się w:
    Archives of Control Sciences
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Predicting length of fatigue cracks by means of machine learning algorithms in the small-data regime
    Autorzy:
    Badora, Maciej
    Sepe, Marzia
    Bielecki, Marcin
    Graziano, Antonino
    Szolc, Tomasz
    Powiązania:
    https://bibliotekanauki.pl/articles/2038115.pdf
    Data publikacji:
    2021
    Wydawca:
    Polska Akademia Nauk. Polskie Naukowo-Techniczne Towarzystwo Eksploatacyjne PAN
    Tematy:
    empirical models
    fatigue cracks
    predictive maintenance
    regression analysis
    small data
    statistical learning
    turbomachinery
    Opis:
    In this paper several statistical learning algorithms are used to predict the maximal length of fatigue cracks based on a sample composed of 31 observations. The small-data regime is still a problem for many professionals, especially in the areas where failures occur rarely. The analyzed object is a high-pressure Nozzle of a heavy-duty gas turbine. Operating parameters of the engines are used for the regression analysis. The following algorithms are used in this work: multiple linear and polynomial regression, random forest, kernel-based methods, AdaBoost and extreme gradient boosting and artificial neural networks. A substantial part of the paper provides advice on the effective selection of features. The paper explains how to process the dataset in order to reduce uncertainty; thus, simplifying the analysis of the results. The proposed loss and cost functions are custom and promote solutions accurately predicting the longest cracks. The obtained results confirm that some of the algorithms can accurately predict maximal lengths of the fatigue cracks, even if the sample is small.
    Źródło:
    Eksploatacja i Niezawodność; 2021, 23, 3; 575-585
    1507-2711
    Pojawia się w:
    Eksploatacja i Niezawodność
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Elman neural network for modeling and predictive control of delayed dynamic systems
    Autorzy:
    Wysocki, A.
    Ławryńczuk, M.
    Powiązania:
    https://bibliotekanauki.pl/articles/229646.pdf
    Data publikacji:
    2016
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    dynamic models
    process control
    model predictive control
    neural networks
    Elman neural network
    delayed systems
    Opis:
    The objective of this paper is to present a modified structure and a training algorithm of the recurrent Elman neural network which makes it possible to explicitly take into account the time-delay of the process and a Model Predictive Control (MPC) algorithm for such a network. In MPC the predicted output trajectory is repeatedly linearized on-line along the future input trajectory, which leads to a quadratic optimization problem, nonlinear optimization is not necessary. A strongly nonlinear benchmark process (a simulated neutralization reactor) is considered to show advantages of the modified Elman neural network and the discussed MPC algorithm. The modified neural model is more precise and has a lower number of parameters in comparison with the classical Elman structure. The discussed MPC algorithm with on-line linearization gives similar trajectories as MPC with nonlinear optimization repeated at each sampling instant.
    Źródło:
    Archives of Control Sciences; 2016, 26, 1; 117-142
    1230-2384
    Pojawia się w:
    Archives of Control Sciences
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-9 z 9

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