Józef Korbicz

Józef Korbicz
University of Zielona Góra | UZ · Institute of Control and Computational Engineering

Professor of automatic control

About

216
Publications
18,463
Reads
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2,805
Citations
Introduction
Soft computing methods and techniques Artificial neural networks: structure optimization of multi-layer feedforward networks; GMDH (Group Method and Data Hadling) networks and their extentions; networks with the dynamic model of neurons; neural network ensembles; training algorithms: gradient and genetic fuzzy and neuro-fuzzy systems: structure and parameter optimization via evolutionary and gradient algorithms; bounded-error analysis expert systems: integrated knowledge bases
Additional affiliations
May 1975 - present
University of Zielona Góra
Position
  • Professor

Publications

Publications (216)
Research Proposal
Full-text available
This Special Issue focuses on the use of artificial neural networks in the modeling of non-linear systems and fault diagnosis and their use in renewable energy systems such as wind turbines and photovoltaic panels. Both theoretical and experimental work and, especially, the combination of these are welcome. an Open Access Journal by MDPI
Book
This book presents a wide and comprehensive spectrum of issues and problems related to fractional-order dynamical systems. It is meant to be a full-fledge, comprehensive presentation of many aspects related to the broadly perceived fractional-order dynamical systems which constitute an extension of the traditional integer-order-type descriptions. T...
Article
Quantum computation model is regarded as a model which can overcome barriers in calculations efficiency of problems which appear in modern science. In spite of hardware development, in particular a recent emergence of several different physical installations of the pioneering quantum machines, the contemporary and numerical analysis of problems con...
Book
This book presents a wide and comprehensive range of issues and problems in various fields of science and engineering, from both theoretical and applied perspectives. The desire to develop more effective and efficient tools and techniques for dealing with complex processes and systems has been a natural inspiration for the emergence of numerous fie...
Book
This book contains a collection of 13 carefully selected papers contributed by researches in technical and partial medical diagnostics as well as fault-tolerant control and constitutes a comprehensive study of the field. Nowadays technical diagnostics and fault-tolerant control are a field of intensive scientific research that covers well-establish...
Chapter
The complexity of modern systems and industrial installations, along with continuously growing requirements regarding their operation and control quality, is a serious challenge in the development of control theory as well as process and system diagnostics. The dynamic evolution of fault tolerant control theory witnessed in recent years is a partia...
Chapter
World statistics indicate that the breast cancer is the most common worldwide type of cancer among women. The development of computer-aided diagnosis techniques may contribute to a more effective therapy against this type of cancer. In this work, we present preliminary research regarding cell nuclei classification based on the Hausdorff distance. T...
Book
This book gathers 30 papers presented at the 21st PCBBE, which was hosted by the University of Zielona Góra, Poland, and offered a valuable forum for exchanging ideas and presenting the latest developments in all areas of biomedical engineering. Biocybernetics and biomedical engineering are currently considered one of the most promising ways to imp...
Article
Full-text available
The paper is devoted to the problem of counting repetitions and automatic weight stack detection in a weightlifting machine used for weight training. Some weightlifting machines include a weight stack that can be adjusted by a user. For example, the user can choose to increase or reduce the weight load using the weight stack, thus changing the diff...
Article
Full-text available
A new two-stage approach to the identification of polynomial Wiener systems is proposed. It is assumed that the linear dynamic system is described by a transfer function model, the memoryless nonlinear element is invertible and the inverse nonlinear function is a polynomial. Based on these assumptions and by introducing a new extended parametrizati...
Conference Paper
Cytological samples provide useful data for cancer diagnostics but their visual analysis under a microscope is tedious and time-consuming. Moreover, some scientific tests indicate that various pathologists can classify the same sample differently or the same pathologist can classify the sample differently if there is a long interval between subsequ...
Article
Full-text available
Morphometric analysis of nuclei is crucial in cytological examinations. Unfortunately, nuclei segmentation presents many challenges because they usually create complex clusters in cytological samples. To deal with this problem, we are proposing an approach, which combines convolutional neural network and watershed transform to segment nuclei in cyt...
Chapter
Computer-assisted image analysis cytology play an important function in modern cancer diagnostics. A crucial task of such systems is segmentation of cell nuclei. Automatic procedure have to locate their exact position in cytological preparation and determine precise edges in order to extract morphometric features. Unfortunately, segmentation of ind...
Conference Paper
The improvement of computer image analysis techniques in recent years can support the pathologist’s work by the automation of nuclei segmentation, cell population count, computing statistics of morphological features or cell classification. However, due to the complexity of the image representing the cytological preparation this process does not be...
Conference Paper
Full-text available
The paper presents method of nuclei segmentation on cytological images based on the Convolutional Neural Network (CNN) and modified Hough Transform method. It approximates nuclei by ellipses fitted to nuclei regions segmented by CNN. As study data set 50 cytological RGB images were used, divided into training set (50 images) and test set (10 images...
Conference Paper
Full-text available
The automatic detection of nuclei within cytological sample imagery is crucial for quantitative analysis in medical applications. Unfortunately, the classical segmentation algorithms perform poorly for cytological images if precise seeds of nuclei are not given in advance. To tackle this problem, we propose nuclei detection method based on Bayesian...
Article
The paper deals with the problem of simultaneous estimation of sensor and process faults. For that purpose, a novel scheme is proposed and its complete design procedure is described. The approach results in a robust estimation strategy with guaranteed convergence. In particular, apart from simultaneous estimation ability the proposed approach makes...
Conference Paper
Computer-Aided Diagnosis (CAD) in digital pathology very often boils down to examination of nuclei using morphological analysis. To determine the characteristics of nuclei, they need to be segmented from the background or other objects in the image (e.g. red blood cells). Despite a tremendous work that has been done to improve segmentation methods,...
Article
In the paper, a methodology of actuator fault estimation and fault tolerant control for nonlinear systems is proposed. To solve such a challenging problem, a quadratic boundedness (QB) approach is used to design observer in such a way that the state and fault estimation error will converge to the origin (as close as possible) irrespective of the ex...
Conference Paper
The paper presents Iterated Conditional Modes based method for nuclei recognition in cytological images. It approximates nuclei by circles and ellipses. The first step is to find coordinates and sizes of circles. To find good configuration of circles, Iterated Conditional Modes (ICM) approach is employed to maximize the probability of configuration...
Article
Full-text available
This paper proposes an approach for the joint state and fault estimation for a class of uncertain non-linear systems with simultaneous unknown input and actuator faults. This is achieved by designing an unknown input observer combined with a set-membership estimation in the presence of disturbances and measurement noise. The observer is designed us...
Article
Full-text available
The paper deals with the problem of simultaneous state and process fault estimation for non-linear dynamic systems. Instead of estimating the fault directly, its product with state and the state itself are estimated. To derive the fault from the product, a simple algebraic approach is proposed. The estimation strategy is based on the quadratic boun...
Article
Full-text available
The paper presents a deep learning approach for automatic classification of breast tumors based on fine needle cytology. The main aim of the system is to distinguish benign from malignant cases based on microscopic images. Experiment was carried out on cytological samples derived from 50 patients (25 benign cases + 25 malignant cases) diagnosed in...
Conference Paper
Full-text available
The paper presents a deep learning approach for automatic classification of breast tumors based on fine needle cytology. The main aim of the system is to distinguish benign from malignant cases based on microscopic images. Experiment was carried out on cytological samples derived from 50 patients (25 benign cases + 25 malignant cases) diagnosed in...
Conference Paper
Due to uncertain flight conditions as well as faults, an outdoor performance of any unmanned aerial vehicle is a challenging task. Indeed, owing to weather conditions it is entirely different that any laboratory tests. While small and radio controlled drones are relatively common devices, they are still unruly while wind blows. Moreover, process an...
Chapter
The paper is devoted to the issue of a robust predictive control for a class of non-linear discrete-time systems with an application of an ellipsoidal inner-bounding of a robust invariant set. The crucial issue is to maintain the state of the system inside the robust invariant feasible set, which is a set of states guaranteeing the stability of the...
Article
The paper deals with the problem of robust unknown input observer design for the neural-network based models of non-linear discrete-time systems. Authors review the recent development in the area of robust observers for non-linear discrete-time systems and proposes less restrictive procedure for design the H∞ observer. The approach guaranties simul...
Article
Full-text available
The paper deals with the problem of robust predictive fault-tolerant control for nonlinear discrete-time systems described by the Takagi-Sugeno models. The proposed approach is based on a triple stage procedure, i.e. it starts from fault estimation while the fault is compensated with a robust controller. The robust controller is designed without ta...
Conference Paper
The paper is devoted to the issue of a robust predictive control for a class of non-linear discrete-time systems with an application of an ellipsoidal inner-bounding of a robust invariant set. The crucial issue is to maintain the state of the system inside the robust invariant feasible set, which is a set of states guaranteeing the stability of the...
Conference Paper
The paper concerns a problem of robust adaptive fault tolerant control for linear discrete-time systems. The first part concerns the design problem of a robust observer, which can be used for simultaneous state and fault estimation along with decoupling of the so-called unknown input, which can represent modelling uncertainty as well as disturbance...
Article
Full-text available
The paper shows a unified approach for designing both sensor and actuator fault diagnosis with neural networks. In particular, a general scheme of the group method of data handling neural networks is recalled. Subsequently, a unscented Kalman filter approach for designing the network and determining its uncertainty is briefly portrayed. The achieve...
Article
The paper deals with the problem of robust fault diagnosis of industrial systems. The main objective was to develop fault diagnosis scheme based on the parameters identification via Bounded-Error Approach. The effectiveness of the proposed approach was shown on the model of brushless DC motor.
Article
Full-text available
In this paper, the problem of the dynamic GMDH Group Method and Data Handling neural networks and their application in fault detection systems is presented. Such networks can be considered as feedforward networks with a growing structure during the training process. The GMDH networks application in fault detection systems improves their eeciency wi...
Conference Paper
The paper deals with the problem of robust fault-tolerant model predictive control for non-linear discrete-time systems described by the Linear Parameter-Varying model. The proposed approach is based on a multi-stage stage procedure. Robust controller is designed without taking into account the input constraints related with the actuator saturation...
Article
The paper deals with the problem of robust predictive fault-tolerant control for nonlinear discrete-time systems described by the Takagi-Sugeno models. The proposed approach consists of three steps, i.e. it starts from fault estimation, the fault is compensated with a robust controller, and finally, when the compensation is not successful then a su...
Chapter
Full-text available
The robust predictive fault-tolerant control for non-linear discrete-time is the aim of this paper. The preliminary part of the paper describes derivation of fault estimation and then the fault compensation with a robust controller. If the robust fault compensation does not provide satisfactory results, which means that the current state does not b...
Conference Paper
The paper deals with the problem of a robust fault diagnosis of a wind turbine. The preliminary part of the paper describes the Linear Parameter-Varying model derivation with a Recurrent Neural Network. The subsequent part of the paper describes a robust fault detection, isolation and identification scheme, which is based on the observer and H1 fra...
Conference Paper
The paper is concerned with the task of robust fault estimation of non-linear discrete-time systems. The general unknown input observer strategy and the ℋ∞ framework are utilised to design a robust fault estimation scheme. The resulting design procedure guaranties that a prescribed disturbance attenuation level is achieved with respect to the fault...
Book
For many years technical and medical diagnostics has been the area of intensive scientific research. It covers well-established topics as well as emerging developments in control engineering, artificial intelligence, applied mathematics, pattern recognition and statistics. At the same time, a growing number of applications of different fault diagno...
Article
Full-text available
Prompt and widely available diagnostics of breast cancer is crucial for the prognosis of patients. One of the diagnostic methods is the analysis of cytological material from the breast. This examination requires extensive knowledge and experience of the cytologist. Computer-aided diagnosis can speed up the diagnostic process and allow for large-sca...
Conference Paper
Full-text available
On behalf of the International Program Committee and the Organizing Committee of the 2nd International Conference on Control and Fault-Tolerant Systems, SysTol'13, we welcome the participants of the conference to be held in Nice on October 9-11, 2013. The conference is sponsored by the Research Center for Automatic Control of Nancy and Université d...
Conference Paper
The paper presents nonlinear model predictive control designed using recurrent neural network. A recurrent neural network is trained to act as the one-step ahead predictor, which is then used succesively to obtain k-step ahead prediction of the plant output. Based on the neural predictor, the control law is derived solving a constrained optimizatio...
Conference Paper
The paper deals with the problem of robust predictive fault-tolerant control for non-linear discrete-time systems. The proposed approach starts with the fault estimation and then the fault is compensated with a robust controller. If the robust fault compensation does not provide satisfactory results, which means that the current state does not belo...
Conference Paper
Full-text available
The multi-level thresholding is one of the most important issues in image segmentation. It is a time consuming problem, i.e. finding appropriate threshold values could take exceptionally long computational time. In this paper we evaluate and compare three meta-heuristic techniques tackling to this problem: ant colony, fireflies and honey bee mating...
Article
Full-text available
In this paper, the effectiveness of using Artificial Neural Networks (ANNs) for predicting the corrections of the Polish time scale UTC(PL) (Universal Coordinated Time) is presented. In particular, prediction results for the different types of neural networks, i.e., the MLP (MultiLayer Perceprton), the RBF (Radial Basis Function) and the GMDH (Grou...
Conference Paper
The paper deals with the problem of robust fault estimation of non-linear discrete-time systems. In particular, it is shown how to employ the unknown input observer approach and the H∞ strategy to design a robust fault estimation filter. The proposed approach is designed in such a way that a prescribed disturbance attenuation level is achieved with...
Conference Paper
The uncertainty of neural model influences the effectiveness of the neural model-based FDI and FTC systems. The application of the GMDH approach to the state-space neural model structure selection allows reducing the model uncertainty. The state-space representation of the neural model enables to develop a new technique of estimation of the neural...
Article
Full-text available
The paper deals with the problem of designing filters for non-linear discrete-time stochastic systems. In particular, it is shown how to design an unknown input filter for a single (constant) unknown input distribution matrix, which guarantees that the effect of a fault will not be decoupled from the residual. Subsequently, the problem of using one...
Conference Paper
Full-text available
In this paper, the actuators and sensors fault de-tection and localization using a system model is considered. To obtain the system model, the neural network modeling is used. The artificial feedforward neural network with dynamic neurons in the state-space representation is proposed. To estimate the neural network parameters, the Adaptive Random S...
Article
This paper deals with the multilayered approach of the high-order neural network applied in a robust fault detection scheme. To introduce dynamic properties in these networks, a dynamic high-order neural unit is presented. It is shown that these networks can approximate any function with less parameters than in the case of multi-layer perceptron ne...
Conference Paper
Full-text available
This paper deals with the stability analysis of the fault accommodation control system. When a fault is detected, the fault tolerant control tries to compensate the fault effect by adding to the standard control the auxiliary signal. This auxiliary control constitutes the additional control loop which can influence the stability of the entire contr...
Article
Full-text available
Nonlinear model predictive control of a boiler unit: A fault tolerant control study This paper deals with a nonlinear model predictive control designed for a boiler unit. The predictive controller is realized by means of a recurrent neural network which acts as a one-step ahead predictor. Then, based on the neural predictor, the control law is deri...
Conference Paper
Full-text available
This paper presents an identification method of dynamic systems based on the Group Method of Data Handing. In particular, a new structure of the dynamic neuron in pole representation is proposed. Moreover, a new training algorithm based on the Unscented Kalman Filter is presented. The final part of this work contains an illustrative example regardi...
Conference Paper
Full-text available
The paper deals with the problem of designing an unknown input filter for non-linear discrete-time stochastic systems. In particular, it is shown how to design an unknown input filter for a single (constant) unknown input distribution matrix. Subsequently, the interacting multiple model algorithm is employed to tackle the problem of selecting an ap...
Article
In this paper, a virtual actuator-based active fault-tolerant control strategy is presented. After a short introduction to Takagi-Sugeno fuzzy systems, it is shown how to design a fault-tolerant control strategy for this particular class of non-linear systems. The key contribution of the proposed approach is an integrated fault-tolerant control des...
Article
Full-text available
This paper presents an automatic computer system to breast cancer diagnosis. System was designed to distinguish benign from malignant tumors based on fine needle biopsy microscope images. Studies conducted focus on two different problems, the first concern the extraction of morphometric and colorimetric parameters of nuclei from cytological images...
Chapter
The chapter focuses on selected methods of diagnostics which have been implemented in the DiaSter system. Taking into account the specificity of industrial processes diagnostics, the robust fault detection problem is presented in the first part of the chapter. Considering the robust neural model, the passive approach based on model error modeling i...
Book
Modern control systems are complex in the sense of implementing numerous functions, such as process variable processing, digital control, process monitoring and alarm indication, graphic visualization of process running, or data exchange with other systems or databases. This book conveys a description of the developed DiaSter system as well as char...
Conference Paper
This paper presents an identification method of Multi-Input Multi-Output (MIMO) dynamic systems based on the Group Method of Data Handing. In particular, a new structure of the dynamic neuron is proposed. The synthesis of the neural model is performed with the application of the pole estimation approach. The final part of this work contains an illu...
Conference Paper
This paper deals with a nonlinear model predictive control designed for a boiler unit. The predictive controller is realized by means of a recurrent neural network which acts as an one-step ahead predictor. Then, based on the neural predictor, the control law is derived solving an optimization problem. Fault tolerant properties of the proposed cont...
Article
In this paper, an active FTC scheme is proposed. First, it is developed in the context of linear systems and then it is extended to non-linear systems with the differential mean value theorem. The key contribution of the proposed approach is an integrated FTC design procedure of the fault identification and fault-tolerant control schemes. Fault ide...
Article
Summary form only given. The lecture starts with the discussion of the methodology of Fault Detection and Isolation (FDI) for dynamic systems. Then recent model-based approaches to FDI analytical ones and those based on soft computing are surveyed. Taking into account many limitations of analytical methods, the main attention is focused on the use...
Conference Paper
This work presents a proposition of the Hough transform’s accumulator on-line compression method in the context of music tunes identification system. One can also find here a short discussion on the method’s properties as well as experimental results obtained during the research.
Article
In this paper, an active FTC strategy is proposed. First, it is developed in the context of linear systems and then it is extended to Lipschitz non-linear systems. The key contribution of the proposed approach is an integrated FTC design procedure of the fault identification and fault-tolerant control schemes. Fault identification is based on the u...
Chapter
Process models in the problems of advanced automatic control, diagnostics or design and investigation of alternative solutions are the grounds for the implementation of intended aims. This chapter is devoted in general to the creation of the investigated process model in a form that would be useful for the conducted investigations or designs. It co...
Chapter
This document presents the stage of research concerning an automatic diagnosis system of breast cancer based on cytological images of FNB (Fine Needle Biopsy). The work concentrates on the image segmentation phase, which is employed to find nucleus in cytological images. The accuracy and correctness of the image segmentation algorithm is a critical...