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Search results for: STATE ESTIMATION
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Asynchronous distributed state estimation based on covariance intersection
PublicationW artykule rozważa się problem estymacji stanu w rozproszonym systemach wieloczujnikowym. W systemach takich stan obserwowanego obiektu jest estymowany przez pewien zbiór estymatorów lokalnych. Każdy estymator lokalny wykonuje fuzję danych pochodzących ze skojarzonego z nim czujnika lub czujników z danymi pochodzącymi z innych lokalnych estymatorów. W wyniku operacji fuzji oraz dodatkowo operacji filtracji korzystając np. z filtru...
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Asynchronous distributed state estimation for continuous-time stochastic processes
PublicationWe consider the problem of state estimation of a continuous-time stochastic process using an asynchronous distributed multi-sensor estimation system (ADES). In an ADES the state of a process of interest is estimated by a group of local estimators. Each local estimator based, for example, on a Kalman filter, performs single sensor filtration but also fusion of its local results and results from other (remote) processors to compute...
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Distributed state estimation using a network of asynchronous processing nodes
PublicationWe consider the problem of distributed state estimation of continuous-time stochastic processes using a~network of processing nodes. Each node performs measurement and estimation using the Kalman filtering technique, communicates its results to other nodes in the network, and utilizes similar results from the other nodes in its own computations. We assume that the connection graph of the network is not complete, i.e. not all nodes...
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Distributed state estimation using a network of asynchronous processing nodes
PublicationWe consider the problem of distributed state estimation of continuous-time stochastic processes using a~network of processing nodes. Each node performs measurement and estimation using the Kalman filtering technique, communicates its results to other nodes in the network, and utilizes similar results from the other nodes in its own computations. We assume that the connection graph of the network is not complete, i.e. not all nodes...
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Mixed algorithm in searches of mechanical system steady-state conditions for low precision of the state estimation
PublicationW pracy zaprezentowano algorytm poszukiwania rozwiązania układu równań nieliniowych. Nieliniowe funkcje lewych stron znane są z ograniczoną dokładnością, a wzory określające ich pochodne względem czasu nie są znane. Wartości pochodnych wyznaczane są numerycznie za pomocą różnic skończonych. Z uwagi na niską precyzje wyznaczania wartości funkcji, wartości pochodnych znane są jedynie z ograniczoną dokładnością., pochodne zawierają...
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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 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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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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Channel State Estimation in LTE-Based Heterogenous Networks Using Deep Learning
PublicationFollowing the continuous development of the information technology, the concept of dense urban networks has evolved as well. The powerful tools, like machine learning, break new ground in smart network and interface design. In this paper the concept of using deep learning for estimating the radio channel parameters of the LTE (Long Term Evolution) radio interface is presented. It was proved that the deep learning approach provides...
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An adaptive-noise Augmented Kalman Filter approach for input-state estimation in structural dynamics
PublicationThe establishment of a Digital Twin of an operating engineered system can increase the potency of Structural Health Monitoring (SHM) tools, which are then bestowed with enhanced predictive capabilities. This is particularly relevant for wind energy infrastructures, where the definition of remaining useful life is a main driver for assessing the efficacy of these systems. In order to ensure a proper representation of the physical...
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Set-Bounded joined parameter and state estimation for model predictive control of integrated wastewater treatment plant systems at medium time scale.
PublicationW artykule opisano moduł łącznej estymacji w postaci zbiorów ograniczonych (ang. set-bounded) parametrów i stanu systemu dla potrzeb sterowania predykcyjnego zintegrowanym systemem ściekowym w średniej skali czasu. Jest to jeden ze składowych elementów Inteligentnej Hierarchicznej Struktury Sterowania opracowanej w celu pokonania następujących problemów, które występują w kontrolowanym systemie: różne skale czasowe procesów, silnie...
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Direct estimation of linear and nonlinear functionals of quantum state
PublicationWe present a simple quantum network, based on the controlled-SWAP gate, that can extract certain properties of quantum states without recourse to quantum tomography. It can be used as a basic building block for direct quantum estimations of both linear and nonlinear functionals of any density operator. The network has many potential applications ranging from purity tests and eigenvalue estimations to direct characterization of...
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From limits of quantum operations to multicopy entanglement witnesses and state spectrum estimation.
PublicationBadano ograniczenia na nieliniowe transformacje stanu kwantowego. Wprowadzono strukturalne fizyczne przybliżenia niefizycznych odwzorowań liniowych.Zdefiniowano świadków splątania działających na wielu kopiach danego stanu.Pokazano zastosowanie obserwabli kwantowych w detekcji entropii Tsallisa.
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<title>Selection of GRNN network parameters for the needs of state vector estimation of maneuvering target in ARPA devices</title>
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Estimation of wastewater treatment plant state for model predictive control of N-P remowal at medium time scale.
PublicationPrzy ograniczonych możliwościach pomiarowych estymaty stanu są potrzebne w sterowaniu optymalizującym, opartym na sterowaniu predykcyjnym, sterującym usuwaniem azotu i fosforu, w biologicznej oczyszczalni ścieków. Optymalizator MPC do implementacji sprzężenia zwrotnego z obiektu potrzebuje tych estymat. Dodatkowo aktualizowane muszą być parametry modelu Gray-Box wykorzystywanego w module MPC. Wtedy estymaty stanu są używane przez...
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Asynchronous Networked Estimation System for Continuous Time Stochastic Processes
PublicationIn this paper we examine an asynchronous networked estimation system for state estimation of continuous time stochastic processes. Such a system is comprised of several estimation nodes connected using a possibly incomplete communication graph. Each of the nodes uses a Kalman filter algorithm and data from a local sensor to compute local state estimates of the process under observation. It also performs data fusion of local estimates...
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Discrete-time estimation of nonlinear continuous-time stochastic systems
PublicationIn this paper we consider the problem of state estimation of a dynamic system whose evolution is described by a nonlinear continuous-time stochastic model. We also assume that the system is observed by a sensor in discrete-time moments. To perform state estimation using uncertain discrete-time data, the system model needs to be discretized. We compare two methods of discretization. The first method uses the classical forward Euler...
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Discrete-time estimation of nonlinear continuous-time stochastic systems
PublicationIn this paper we consider the problem of state estimation of a dynamic system whose evolution is described by a nonlinear continuous-time stochastic model. We also assume that the system is observed by a sensor in discrete-time moments. To perform state estimation using uncertain discrete-time data, the system model needs to be discretized. We compare two methods of discretization. The first method uses the classical forward Euler...
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Estimation of a Stochastic Burgers' Equation Using an Ensemble Kalman Filter
PublicationIn this work, we consider a difficult problem of state estimation of nonlinear stochastic partial differential equations (SPDE) based on uncertain measurements. The presented solution uses the method of lines (MoL), which allows us to discretize a stochastic partial differential equation in a spatial dimension and represent it as a system of coupled continuous-time ordinary stochastic differential equations (SDE). For such a system...
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A hierarchical observer for a non-linear uncertain CSTR model of biochemical processes
PublicationThe problem of estimation of unmeasured state variables and unknown reaction kinetic functions for selected biochemical processes modelled as a continuous stirred tank reactor is addressed in this paper. In particular, a new hierarchical (sequential) state observer is derived to generate stable and robust estimates of the state variables and kinetic functions. The developed hierarchical observer uses an adjusted asymptotic observer...
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Robust asymptotic super twisting sliding mode observer for non-linear uncertain biochemical systems
PublicationThe problem of state estimation (reconstruction of the state vector) for a given class of biochemical systems under uncertain system dynamics has been addressed in this paper. In detail, the bioreactor at a water resource recovery facility represents the considered biochemical systems. The biochemical processes taking place in the bioreactor have been modelled using an activated sludge model. Based on this model, an appropriate...
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A new method of wind farm active power curve estimation based on statistical approach
PublicationThe purpose of this paper is to solve the wind farm active power estimation problem, introducing the method which is based on a statistical approach and robust fitting. The proposed algorithm uses a statistical approach and compared to existing ones- includes a wind direction as well as the influence of turbine start-up procedure on the estimation. The results show that additional estimation inputs i.e. the wind direction and the...
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Estimation of structural stiffness with the use of Particle Swarm Optimization
PublicationThe paper presents the theoretical background and four applications examples of the new method for the estimation of support stiffness coefficients of complex structures modelled discretely (e.g. with the use of the Finite Element Model (FEM) method based on the modified Particle Swarm Optimization (PSO) algorithm. In real-life cases, exact values of the supports’ stiffness coefficients may change for various reasons...
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State Observer for Doubly-fed Induction Generator
PublicationIn the paper a new state observer for doubly-fed generator has been proposed. In the new approach an extended mathematical model of the doubly fed generator is used to form equations of the introduced z type observer. Stability of the observer has been verified through poles placement analyses. The active and reactive powers of the generator are controlled by a nonlinear multiscalar control method. Simulation and experimental results...
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IFE: NN-aided Instantaneous Pitch Estimation
PublicationPitch estimation is still an open issue in contemporary signal processing research. Nowadays, growing momentum of machine learning techniques application in the data-driven society allows for tackling this problem from a new perspective. This work leverages such an opportunity to propose a refined Instantaneous Frequency and power based pitch Estimator method called IFE. It incorporates deep neural network based pitch estimation...
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A robust sliding mode observer for non-linear uncertain biochemical systems
PublicationA problem of state estimation for a certain class of non-linear uncertain systems has been addressed in this paper. In particular, a sliding mode observer has been derived to produce robust and stable estimates of the state variables. The stability and robustness of the proposed sliding mode observer have been investigated under parametric and unstructured uncertainty in the system dynamics. In order to ensure an unambiguous non-linear...
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Rafał Łangowski dr inż.
PeopleRafał Łangowski received the M.Sc. and the Ph.D. degrees (Hons.) in control engineering from the Faculty of Electrical and Control Engineering at the Gdańsk University of Technology in 2003 and 2015, respectively. From 2007 to 2014, he held the specialist as well as manager positions at ENERGA, one of the biggest energy enterprises in Poland. He is currently an Assistant Professor with the Department of Intelligent Control and...
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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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Hierarchical Estimation of Human Upper Body Based on 2D Observation Utilizing Evolutionary Programming and 'Genetic Memory'
PublicationNew method of the human body pose estimation based on single camera 2D observation is presented. It employs 3D model of the human body, and genetic algorithm combined with annealed particle filter for searching the global optimum of model state, best matching the object's 2D observation. Additionally, motion cost metric is employed, considering current pose and history of the body movement, favouring the estimates with the lowest...
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Direction-of-Arrival Estimation Methods in Interferometric Echo Sounding
PublicationNowadays, there are two leading sea sounding technologies: the multibeam echo sounder and the multiphase echo sounder (also known as phase-dierence side scan sonar or bathymetric side scan sonar). Both solutions have their advantages and disadvantages, and they can be perceived as complementary to each other. The article reviews the development of interferometric echo sounding array configurations and the various methods applied...
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The concept of estimation of elevator shaft control measurement results in the local 3D coordinate system.
PublicationGeodetic control measurements play an important part because they provide information about the current state of repair of the construction, which has a direct impact on the safety assessment of its exploitation. Authors in this paper have focused on control measurements of the elevator shaft. The article discusses the problem of determining the deviation of elevator shaft walls from the vertical plane in the local 3D coordinate...
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Interval estimator for chlorine monitoring in drinking water distribution systems under uncertain system dynamics, inputs and state measurement errors
PublicationThe design of interval observer for estimation of unmeasured state variables for application to drinking water distribution systems is described in this paper. In particular, it considers the design of such observer for estimation of water quality described by free chlorine concentration. An interval observer is derived to produce robust interval bounds on the estimated water quality state variables. The stability and robustness...
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A Universal Gains Selection Method for Speed Observers of Induction Machine
PublicationProperties of state observers depend on proper gains selection. Each method of state estimation may require the implementation of specific techniques of finding those gains. The aim of this study is to propose a universal method of automatic gains selection and perform its verification on an induction machine speed observer. The method utilizes a genetic algorithm with fitness function which is directly based on the impulse response...
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TASTE QUALITIES ESTIMATION BY TASTE SENSE AND ELECTROCHEMICAL SENSORS
PublicationThe principles of taste sense and kinds of tastes (sweet, salty, sour, bitter, umami and fat) have been described. Food quality estimation by taste sense: organoleptic testing and sensory analysis has been presented. The principle of operation of potentiom etric sensor composed of several Ion Selective Electrodes or All Solid State Electrodes with polymeric membranes and macromolecular chemical compounds sensitive to substances...
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Potentiometric taste sensor application for liquid product taste estimation
PublicationThe principles of taste sense and kinds of tastes (sweet, salty, sour, bitter, umami and fat) have been described. Food quality estimation by taste sense: organoleptic testing and sensory analysis has been presented. The principle of operation of potentiometric sensor composed of several Ion Selective Electrodes or All Solid State Electrodes with polymeric membranes and macromolecular chemical compounds sensitive to substances...
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Genetic programming extension to APF-based monocular human body pose estimation
PublicationNew method of the human body pose estimation based on a single camera 2D observation is presented, aimed at smart surveillance related video analysis and action recognition. It employs 3D model of the human body, and genetic algorithm combined with annealed particle filter for searching the global optimum of model state, best matching the object's 2D observation. Additionally, new motion cost metric is employed, considering current...
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Respiration rate estimation using non-linear observers in application to wastewater treatment plant
PublicationA problem of respiration rate estimation using two new non-linear observers for a wastewater treatment plant is addressed in this paper. In particular, a non-linear adaptive Luenberger-like observer and a super twisting sliding mode observer have been derived to produce stable and bounded estimates of the respiration rate. During the synthesis of the particular observer, an appropriate mathematical utility model was used. The observability...
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An interval estimator for chlorine monitoring in drinking water distribution systems under uncertain system dynamics, inputs and chlorine concentration measurement errors
PublicationThe design of an interval observer for estimation of unmeasured state variables with application to drinking water distribution systems is described. In particular, the design process of such an observer is considered for estimation of the water quality described by the concentration of free chlorine. The interval observer is derived to produce the robust interval bounds on the estimated water quality state variables. The stability...
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Thermal Image Processing for Respiratory Estimation from Cubical Data with Expandable Depth
PublicationAs healthcare costs continue to rise, finding affordable and non-invasive ways to monitor vital signs is increasingly important. One of the key metrics for assessing overall health and identifying potential issues early on is respiratory rate (RR). Most of the existing methods require multiple steps that consist of image and signal processing. This might be difficult to deploy on edge devices that often do not have specialized...
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Quantum privacy witness
PublicationWhile it is usually known that the mean value of a single observable is enough to detect entanglement or its distillability, the counterpart of such an approach in the case of quantum privacy has been missing. Here we develop the concept of a privacy witness, i.e., a single observable that may detect the presence of the secure key even in the case of bound entanglement. Then we develop the notion of secret-key estimation based...
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Piotr Paradowski dr
PeopleDr Piotr Paradowski's areas of expertise in quantitative social science methods include truncated and censored models, quantile regressions, survival analysis, panel data models, discrete regressions and qualitative choice models, instrumental variable estimation, and hierarchical modeling. He is also an expert in statistical matching and statistical methods to handle missing data. In addition, he conducts research on income and...
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On the preestimation technique and its application to identification of nonstationary systems
PublicationThe problem of noncausal identification of a nonstationary stochastic FIR (finite impulse response) sys- tem is reformulated, and solved, as a problem of smoothing of preestimated parameter trajectories. Three approaches to preestimation are critically analyzed and compared. It is shown that optimization of the smoothing operation can be performed adaptively using the parallel estimation technique. The new approach is computationally...
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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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Particle Filter Modification using Kalman Optimal Filtering Method as Applied to Road Detection from Satellite Images
PublicationIn the paper recursive state estimation approach is presented as applied to satellite images. Especially, a model of dynamic systems of the non-linear and non-Gaussian systems is presented, and finally the Kalman filter and particle filter and an integration of both is figured out. Special attention is paid to the application for satellite image analysis.
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Detecting Apples in the Wild: Potential for Harvest Quantity Estimation
PublicationKnowing the exact number of fruits and trees helps farmers to make better decisions in their orchard production management. The current practice of crop estimation practice often involves manual counting of fruits (before harvesting), which is an extremely time-consuming and costly process. Additionally, this is not practicable for large orchards. Thanks to the changes that have taken place in recent years in the field of image...
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On the lower smoothing bound in identification of time-varying systems
PublicationIn certain applications of nonstationary system identification the model-based decisions can be postponed, i.e. executed with a delay. This allows one to incorporate in the identification process not only the currently available information, but also a number of ''future'' data points. The resulting estimation schemes, which involve smoothing, are not causal. Assuming that the infinite observation history is available, the paper...
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An Assessment of RASSCF and TDDFT Energies and Gradients on an Organic Donor−Acceptor Dye Assisted by Resonance Raman Spectroscopy
PublicationThe excitation energies and gradients in the ground and the first excited state of a novel donor−(π- bridge)−acceptor 4-methoxy-1,3-thiazole-based chromophore were investigated by means of MS-RASPT2/RASSCF and TDDFT in solution. Within both methods, the excitation energies strongly depend on the employed equilibrium structures, whose differences can be rationalized in terms of bond length alternation indexes. It is shown that functionals with...
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Machine learning-based prediction of seismic limit-state capacity of steel moment-resisting frames considering soil-structure interaction
PublicationRegarding the unpredictable and complex nature of seismic excitations, there is a need for vulnerability assessment of newly constructed or existing structures. Predicting the seismic limit-state capacity of steel Moment-Resisting Frames (MRFs) can help designers to have a preliminary estimation and improve their views about the seismic performance of the designed structure. This study improved data-driven decision techniques in...
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Identification of Fast Time-varying Communication Channels Using the Preestimation Technique
PublicationAccurate identification of stochastic systems with fast-varying parameters is a challenging task which cannot be accomplished using model-free estimation methods, such as weighted least squares, which assume only that system coefficients can be regarded as locally constant. The current state-of-the-art solutions are based on the assumption that system parameters can be locally approximated by a linear combination of appropriately...
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Krzysztof Armiński dr inż.
PeopleKrzysztof Armiński received his PhD in Control System Enginiering from the Gdańsk University of Technology in 2014 Currently he is a assistant professor of Control Enginiering at the Gdańsk University of Technology. His research interests are in the field of set bounded state estimation and modeling for control purpose.