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Search results for: SPECTRAL ESTIMATION, MULTIVARIATE AUTOREGRESSIVEPROCESS, MODEL AVERAGING, FINAL PREDICTION ERROR
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Lattice filter based multivariate autoregressive spectral estimation with joint model order and estimation bandwidth adaptation
PublicationThe problem of parametric, autoregressive model based estimation of a time-varying spectral density function of a multivariate nonstationary process is considered. It is shown that estimation results can be considerably improved if identification of the autoregressive model is carried out using the two-sided doubly exponentially weighted lattice algorithm which combines results yielded by two one-sided lattice algorithms running...
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On autoregressive spectrum estimation using the model averaging technique
PublicationThe problem of estimating spectral density of a nonstationary process satisfying local stationarity conditions is considered. The proposed solution is a two step procedure based on local autoregressive (AR) modeling. In the first step Bayesian-like averaging of AR models, differing in order, is performed. The main contribution of the paper is development of a new final-prediction-error-like statistic, which can be used to select...
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Identification of nonstationary multivariate autoregressive processes– Comparison of competitive and collaborative strategies for joint selection of estimation bandwidth and model order
PublicationThe problem of identification of multivariate autoregressive processes (systems or signals) with unknown and possibly time-varying model order and time-varying rate of parameter variation is considered and solved using parallel estimation approach. Under this approach, several local estimation algorithms, with different order and bandwidth settings, are run simultaneously and compared based on their predictive performance. First,...
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Lattice filter based autoregressive spectrum estimation with joint model order and estimation bandwidth adaptation
PublicationThe problem of parametric, autoregressive model based estimation of a time-varying spectral density function of a nonstationary process is considered. It is shown that estimation results can be considerably improved if identification of the autoregressive model is carried out using the two-sided doubly exponentially weighted lattice algorithm which combines results yielded by two one-sided lattice algorithms running forward in...
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On adaptive covariance and spectrum estimation of locally stationary multivariate processes
PublicationWhen estimating the correlation/spectral structure of a locally stationary process, one has to make two important decisions. First, one should choose the so-called estimation bandwidth, inversely proportional to the effective width of the local analysis window, in the way that complies with the degree of signal nonstationarity. Too small bandwidth may result in an excessive estimation bias, while too large bandwidth may cause excessive...
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On Noncausal Identification of Nonstationary Multivariate Autoregressive Processes
PublicationThe problem of identification of nonstationary multivariate autoregressive processes using noncausal local estimation schemes is considered and a new approach to joint selection of the model order and the estimation bandwidth is proposed. The new selection rule, based on evaluation of pseudoprediction errors, is compared with the previously proposed one, based on the modified Akaike’s final prediction error criterion.
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Akaike's final prediction error criterion revisited
PublicationWhen local identification of a nonstationary ARX system is carried out, two important decisions must be taken. First, one should decide upon the number of estimated parameters, i.e., on the model order. Second, one should choose the appropriate estimation bandwidth, related to the (effective) number of input-output data samples that will be used for identification/ tracking purposes. Failure to make the right decisions results...
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On Adaptive Spectrum Estimation of Multivariate Autoregressive Locally Stationary Processes
PublicationAutoregressive modeling is a widespread parametricspectrum estimation method. It is well known that, in the caseof stationary processes with unknown order, its accuracy canbe improved by averaging models of different complexity usingsuitably chosen weights. The paper proposes an extension of thistechnique to the case of multivariate locally stationary processes.The proposed solution is based on local autoregressive...
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On adaptive selection of estimation bandwidth for analysis of locally stationary multivariate processes
PublicationWhen estimating the correlation/spectral structure of a locally stationary process, one should choose the so-called estimation bandwidth, related to the effective width of the local analysis window. The choice should comply with the degree of signal nonstationarity. Too small bandwidth may result in an excessive estimation bias, while too large bandwidth may cause excessive estimation variance. The paper presents a novel method...
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Robust Parameter Estimation and Output Prediction for Reactive Carrier-Load Nonlinear Dynamic Networks
PublicationIn this paper an extension of on-line model simplification technique for a class of networked systems, namely reactive carrier-load nonlinear dynamic networked system (RCLNDNS), kept within point-parametric model (PPM) framework is addressed. The PPM is utilised to acquire a piece wise constant time-varying parameter linear structure for the RCLNDNS suitable for the on-line one step ahead prediction that may be applied to monitoring...
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Bounding approach to parameter estimation without priori knowledge on model structure error.
PublicationArtykuł przedstawia estymację parametrów modelu ARMA (Autoregresive moving average) metodą zbiorów ograniczonych. Założono brak wiedzy na temat ograniczeń na błąd struktury modelu lub, że wiedza ta jest bardzo konserwatywna. W celu redukcji tego konserwatyzmu, zaproponowano koncepcje modelu punktowo-parametrycznego. W podejściu tym zakłada się istnienie zbioru parametrów modelu oraz błędu struktury odpowiadających każdej z trajektorii...
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DOP and Pseudorange Error Estimation in Urban Environments for Mobile Android GNSS Applications
PublicationJust a couple of years ago, GNSS (Global Navigation Satellite Systems) were available only for a narrow group of users. Currently, with the outbreak of mobile devices, they are accessible to anyone and everywhere. Urban navigation or searching for POIs (Points of Interest) had become an everyday activity. With the availability of consumer electronics and wireless technologies, each user can obtain information considering his or...
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DOP and Pseudorange Error Estimation in Mobile GNSS Systems for Android OS Applications
PublicationIn the near past, GNSS (Global Navigation Satellite Systems) were only offered for a narrow group of recipients. Nowadays, thanks to mobile devices, they are available to anyone and everywhere. Personal navigation, searching for POI (Point of Interest), etc., had become a basic essential activity. Thanks to the widespread and availability of smartphones each user can obtain information considering his or her location even in an...
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Box-spline histograms for multivariate density estimation
PublicationW pracy podano ujednoliconą konstrukcję estymatora pochodnych gęstości oraz stałych asymptotycznych dla tej gęstości. Posłużono się histogramami box-splinowymi. Skorzystano z twierdzeń asymptotycznych dla operatorów związanych z tymi estymatorami.
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Numerical Estimation of Hull Hydrodynamic Derivatives in Ship Maneuvering Prediction
PublicationPrediction of the maneuvering characteristics of the ship at the design stage can be done by means of model tests, computational simulations or a combination of both. The model tests can be realized as direct simulation of the standard maneuvers with the free running model, which gives the most accurate results, but is also the least affordable as it requires very large tank or natural lake, as well as complex equipment of the...
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NUMERICAL ESTIMATION OF HULL HYDRODYNAMIC DERIVATIVES IN SHIP MANOUVERING PREDICTION
PublicationOperating in crowded waterways pose a risk of accidents and disasters due to maneuvering limitations of the ship. In order to predict ship’s maneuvering characteristics at the design stage, model tests are often executed as the most accurate prediction tool. Two approaches can be distinguished here: free running model tests and numerical simulations based on planar motion model with the use of hydrodynamic derivatives obtained...
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Autonomous Ship Utility Model Parameter Estimation Utilising Extended Kalman Filter
PublicationIn this paper, a problem of autonomous ship utility model identification for control purposes is considered. In particular, the problem is formulated in terms of model parameter estimation (one-step-ahead prediction). This is a complex task due to lack of measurements of the parameter values, their time-variability and structural uncertainty introduced by the available models. In this work, authors consider and compare two utility...
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On the Usefulness of the Generalised Additive Model for Mean Path Loss Estimation in Body Area Networks
PublicationIn this article, the usefulness of the Generalised Additive Model for mean path loss estimation in Body Area Networks is investigated. The research concerns a narrow-band indoor off-body network operating at 2.45 GHz, being based on measurements performed with four different users. The mean path loss is modelled as a sum of four components that depend on path length, antenna orientation angle, absolute difference between transmitting...
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Estimation and Prediction of Vertical Deformations of Random Surfaces, Applying the Total Least Squares Collocation Method
PublicationThis paper proposes a method for determining the vertical deformations treated as random fields. It is assumed that the monitored surfaces are subject not only to deterministic deformations, but also to random fluctuations. Furthermore, the existence of random noise coming from surface’s vibrations is also assumed. Such noise disturbs the deformation’s functional models. Surface monitoring with the use of the geodetic levelling...
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Linear viscoelastic transversely isotropic model based on the spectral decomposition of elasticity tensors
PublicationThe linear viscoelasticity is still a useful model in the engineering for studying the behavior of materials loaded with different loading rates (frequencies). Certain types of materials reveal also an anisotropic behavior: fiber reinforced composites, asphalt concrete mixtures, or wood, to name a few. In general, researchers try to identify experimentally the dependence of engineering constants like: directional Young’s moduli...
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Estimation of blood pressure parameters using ex-Gaussian model
PublicationThe paper presents an example of model-based estimation of blood pressure parameters (onset, systolic and diastolic pressure) from continuous measurements. First, the signal was low pass filtered and its quality was estimated. Good quality periods were divided into beats using an electrocardiogram. Next, the beginning of each beat of the blood pressure signal was approximated basing on the function created from the sum of two independent...
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Sensitivity of the Baltic Sea level prediction to spatial model resolution
Publicationhe three-dimensional hydrodynamic model of the Baltic Sea (M3D) and...
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An Approach to Mean Path Loss Model Estimation for Off-Body Channels
PublicationThis paper presents an approach to estimation of the mean path loss model parameters in off-body Body Area Networks channels. In this approach, the path loss exponent is constrained to a value obtained for the line-of-sight (LoS) propagation in the co-polarised channel, considering a generalised static scenario. The comparison of the goodness of fit between the proposed approach and other approaches, for a set of measurements obtained...
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International Competitiveness of Czech Manufacturing: A Sectoral Approach with Error Correction Model
PublicationThe main objective of this paper is to find the determinants of the international competitiveness of the manufacturing sectors of the Czech economy, using the database of 13 manufacturing subsectors in 1995–2011, with the aid of ECM model. The authors research the question of how much foreign and domestic demand, the level of labour costs, the level of sector innovation intensity, the level of sector openness to foreign markets...
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The hybrid estimation algorithm for wastewater treatment plant robust model predictive control purposes at medium time scale
PublicationThe paper proposes an approach to designing the hybrid estimation algorithm/module (HEA) with moving measurements window for Wastewater Treatment Plant (WWTP) Robust Model Predictive Control (RMPC) purposes at medium time scale. The RMPC uses a dedicated grey-box model of biological reactor for the system outputs prediction purposes. The grey-box model parameters are dependant on the plant operating point. Hence, these parameters...
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Instrument detection and pose estimation with rigid part mixtures model in video-assisted surgeries
PublicationLocalizing instrument parts in video-assisted surgeries is an attractive and open computer vision problem. A working algorithm would immediately find applications in computer-aided interventions in the operating theater. Knowing the location of tool parts could help virtually augment visual faculty of surgeons, assess skills of novice surgeons, and increase autonomy of surgical robots. A surgical tool varies in appearance due to...
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Application of a hybrid mechanistic/machine learning model for prediction of nitrous oxide (N2O) production in a nitrifying sequencing batch reactor
PublicationNitrous oxide (N2O) is a key parameter for evaluating the greenhouse gas emissions from wastewater treatment plants. In this study, a new method for predicting liquid N2O production during nitrification was developed based on a mechanistic model and machine learning (ML) algorithm. The mechanistic model was first used for simulation of two 15-day experimental trials in a nitrifying sequencing batch reactor. Then, model predictions...
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Minimum mean square error estimation of speech short-term predictor parameters under noisy conditions
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Personalized prediction of the secondary oocytes number after ovarian stimulation: A machine learning model based on clinical and genetic data
PublicationControlled ovarian stimulation is tailored to the patient based on clinical parameters but estimating the number of retrieved metaphase II (MII) oocytes is a challenge. Here, we have developed a model that takes advantage of the patient’s genetic and clinical characteristics simultaneously for predicting the stimulation outcome. Sequence variants in reproduction-related genes identified by next-generation sequencing were matched...
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Parameter estimation of a discrete model of a reinforced concrete slab
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The spectral finite element model for analysis of flexural–shear coupled wave propagation
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Noise spectral density computation based on finite element model of piezoceramic sensor
PublicationThe high sensitivity with wide bandwidth is required for sensor applications in non-destructive testing (NDT). The sensitivity of piezoceramic sensors demands to minimize their noise especially thermal noise, polarisation noise and low frequency 1/f noise,which are the main sources of voltage or current fluctuation in this sort of sensors. For simplicity, only the piezoceramic part of sensor was under study. the theoretical and...
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Bounding approach to parameter estimation without prior knowledge on modeling error and application to quality modeling in drinking water distribution systems
PublicationW artykule rozważana jest estymacja parametrów modelu autoregresji z ruchoma średnią i sygnałem wejściowym (ARMAX) z wykorzystaniem przedziałowego modelu błędu. Zakłada się, że granice błędu struktury modelu są nieznane, bądź znane, ale bardzo konserwatywne. Dla zmniejszenia tego konserwatyzmu proponowane jest idea modeli punktowo-parametrycznych, w której występują zbiory parametrów i błędu modelu odpowiadające wszystkim wejściom....
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Development of a Formability Prediction Model for Aluminium Sandwich Panels with Polymer Core
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Electromagnetic interference frequencies prediction model of flyback converter for snubber design
PublicationSnubber design for flyback converters usually requires experimental prototype measurements or simulation based on accurate and complex models. In this study simplified circuit modelling of a flyback converter has been described to dimension snubbers in early stage of design process. Simulation based prediction of the transistor and diode ringing frequencies has been validated by measurements in a prototype setup. In that way obtained...
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Estimation of the parameters of the discrete model of a steel–concrete composite beam
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Tree-based homogeneous ensemble model with feature selection for diabetic retinopathy prediction
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Asynchronous distributed state estimation based on a continuous time stochastic model
PublicationGłównym zadaniem systemu estymacji jest szacowanie stanu obserwowanego obiektu. W rozproszonych wieloczujnikowych systemach estymacji stan obiektu jest estymowany przez pewien zbiór estymatorów lokalnych. Każdy estymator lokalny wykonuje filtrację (np opartą na filtracji Kalmana) danych pochodzących ze skojarzonego z nim czujnika bądź czujników oraz fuzję przetworzonych danych z czujników z danymi pochodzącymi z innych lokalnych...
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Asynchronous distributed state estimation based on a continuous-time stochastic model
PublicationW artykule rozważa się ogólny problem estymacji stanu w asynchronicznych rozłożonych systemach (ADE) opartych na wielu czujnikach. W takich systemach stan obiektu jest oceniany przez grupę lokalnych estymatorów, z których każdy oparty zwykle na filtrze Kalmana, dokonuje fuzji danych zebranych poprzez jego lokalne czujniki oraz danych uzyskanych od innych (zdalnych) procesorów, w celu wyznaczenia możliwie najlepszych estymat. Przeprowadzając...
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Asynchronous distributed state estimation based on a continuous-time stochastic model
PublicationW artykule rozważa się problem estymacji stanu w asynchronicznych rozłożonych systemach (ADE) opartych na wielu czujnikach pomiarowych. W systemach takich stan obiektu jest oceniany przez grupę lokalnych estymatorów, z których każdy (oparty zwykle na filtrze Kalmana) dokonuje fuzji danych zebranych poprzez jego lokalne czujniki oraz danych odebranych od innych zdalnych procesorów, w celu wyznaczenia możliwie najlepszych estymat....
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Novel method of estimation of inertial and dissipative parameters of a railway pantograph model
PublicationAn increase in electric railway vehicles service velocity requires that correct interaction between the pantograph and the catenary is ensured. This implies the need for developing mathematical models of pantographs and catenaries and determining their parameters. The article presents a method to determine parameters of mechanical joints of a railway pantograph based on analysis of pantograph subassemblies in swinging motion. The...
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Determinants of the European Union’s Trade - evidence from a panel estimation of the gravity model
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Prediction Model for Hemorrhagic Complications after Laparoscopic Sleeve Gastrectomy: Development of SLEEVE BLEED Calculator
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Respiration rate estimation for model predictive control of dissolved oxygen in wastewater treatment plant
PublicationRespiration rate is very important parameter for biological processes in wastewater treatment plant (WWTP). The sequential algorithm for estimate the respiration rate is proposed and investigated. The Kalman filter (KF) is used. Simulation tests for the benchmark WWTP are presented.Respiracja jest bardzo ważnym parametrem dla prawidłowego przebiegu procesów biologicznych w oczyszczalni ścieków. W artykule przedstawiono i zbadano...
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Controlled Random Search Applied to Parameters Estimation of the Longitudinal Solutes Transport Model for Rivers
PublicationNumerical computations are presented for the longitudinal transport of passive, conservative solutes in an actual river with the inclusion of geometrical complexities of river channels. A special emphasis is put on the method of the identification of model parameters which is based on a specially designed optimisation procedure using random control search algorithm. Two different situations are considered namely a linear version...
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A Proposed Soft Computing Model for Ultimate Strength Estimation of FRP-Confined Concrete Cylinders
PublicationIn this paper, the feed-forward backpropagation neural network (FFBPNN) is used to propose a new formulation for predicting the compressive strength of fiber-reinforced polymer (FRP)-confined concrete cylinders. A set of experimental data has been considered in the analysis. The data include information about the dimensions of the concrete cylinders (diameter, length) and the total thickness of FRP layers, unconfined ultimate concrete...
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Short clinically-based prediction model to forecast transition to psychosis in individuals at clinical high risk state
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Experimental investigations and prediction of WEDMed surface of nitinol SMA using SinGAN and DenseNet deep learning model
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Solubility of sulfanilamide in binary solvents containing water: Measurements and prediction using Buchowski-Ksiazczak solubility model
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Model Based Monitoring of Dynamic Loads and Remaining Useful Life Prediction in Rolling Mills and Heavy Machinery
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