Wyniki wyszukiwania - BazTech

1

Performance of artificial neural networks in an inverse problem of laser beam diagnostics

Pietrak Karol, Muszyński Radosław, Marek Adam, Łapka Piotr

Bulletin of the Polish Academy of Sciences. Technical Sciences

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2022

|

Vol. 70, nr 1

art. no. e140100

EN

The presented results are for the numerical verification of a method devised to identify an unknown spatio-temporal distribution of heat flux that occurs at the surface of a thin aluminum plate, as a result of pulsed laser beam excitation. The presented identification of boundary heat flux function is a part of the newly proposed laser beam profiling method and utilizes artificial neural networks trained on temperature distributions generated with the ANSYS Fluent solver. The paper focuses on the selection of the most effective neural network hyperparameters and compares the results of neural network identification with the Levenberg–Marquardt method used earlier and discussed in previous articles. For the levels of noise measured in physical experiments (0.25–0.5 K), the accuracy of the current parameter estimation method is between 5 and 10%. Design changes that may increase its accuracy are thoroughly discussed.

2

Channel based epilepsy seizure type detection from electroencephalography (EEG) signals with machine learning techniques

Tuncer Erdem, Bolat Emine Doğru

Biocybernetics and Biomedical Engineering

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2022

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Vol. 42, no. 2

575--595

EN

Epileptic seizures result from disturbances in the electrical activity of the brain, classified as focal, generalized, or unknown. Failure to correctly classify epileptic seizures may result in inappropriate treatment and continuation of seizures. Therefore, automatic detection of generalized, focal, and other epileptic seizures from EEG signals is important. In this research article, Focal-Generalized classification method is proposed that compares traditional classification algorithms and deep learning methods. Two different classifications: four-class (Case (I) Complex Partial Seizure (CPSZ) (C4-T4 Onset)-CPSZ (FP2-F8 Onset)-CPSZ (T5-O1 Onset)- Absence Seizure (ABSZ)) and two-class (Case (II) CPSZ-ABSZ) problems are considered. This study includes preprocessing of scalp Electroencephalogram (EEG) data, feature extraction with discrete wavelet method, feature selection using Correlation-based Feature Selection (CFS) method, and classification of data with classifier algorithms (K-Nearest Neighbors (Knn), Support Vector Machine (SVM), Random Forest (RF) and Long Short-Term Memory (LSTM). The proposed method was applied on 23 subjects in the Temple University Hospital (TUH) scalp EEG data set, and a classification success rate of 95,92% for case (I) and 98,08% for case (II) was successfully achieved with deep learning architecture LSTM.

3

Road safety for cyclists based on the calories needed

Bosurgi Gaetano, Pellegrino Orazio, Wachnicka Joanna, Okraszewska Romanika

Transport Problems

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2021

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T. 16, z. 1

113--126

EN

Cyclists are a vulnerable group of road users, especially when no separate infrastructure for cyclists is provided. Then, road factors such as distance and altitude differences can indirectly affect cyclists' safety. Therefore, the authors proposed a procedure based on the geometric characteristics of the road that can determine riding difficulties for cyclists. The proposed procedure can be used both by the public authorities who manage cyclists' safety and as a method of classifying the road network for cycling. The proposed procedure, based on the use of pattern recognition techniques, analyses data from a sample of nine riders who travelled on rural roads within the Municipality of Messina (Italy) to classify the roads according to their cycling difficulty. For each rider, duration, distance, road slope, altitude difference and spent calories have been measured and analysed. The collected data were used for the development of a model capable of predicting the cyclist’s physical effort as a function of the road alignment itself. Knowing the effort required to cycle along a route can contribute to a more complete assessment of road classification, commonly defined according to motor vehicles. Moreover, it may constitute a measure determining the safety of cycling by encouraging cyclists to travel along roads compatible with their physical abilities.

4

Rozpoznawanie wzorców : projektowanie, ewaluacja zastosowania

Rybak Łukasz, Dudczyk Janusz

Elektronika : konstrukcje, technologie, zastosowania

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2021

|

Vol. 62, nr 11

12--15

PL

Artykuł stanowi przegląd literatury dotyczącej procesu rozpoznawania wzorców. We wstępie pracy zdefiniowano terminy istotne w kontekście rozważań opisanych w kolejnych sekcjach artykułu oraz określono cel, którym było dokonanie przeglądu istotnych zagadnień ściśle związanych z procesem rozpoznawania wzorców. Następnie scharakteryzowano poszczególne etapy wspomnianego procesu podkreślając zależności pomiędzy nimi. W kolejnej sekcji artykułu opisano istotne problemy występujące na etapie ewaluacji systemu rozpoznawania wzorców. Szczegółowo przedstawiono popularną metodę stosowaną do ich rozwiązywania. Następnie przywołano szereg prac, w których autorzy stosowali proces rozpoznawani wzorców do wielu rzeczywistych problemów.

EN

The article contains a literature review concerning the pattern recognition process. The terms, important in the context of the considerations described in the following sections of the article, were defined in the introduction. Moreover the purpose of the article was stated. The aim was to review significant issues closely related to the pattern recognition process. Then, the each stage of the above-mentioned process were characterized, emphasizing the relationships between them. The next section of the article describes the significant problems that occur at the stage of pattern recognition system evaluation. The popular method used to solve them was presented in detail. Subsequently, a series of papers were referenced in which the authors applied the pattern recognition process to many real-world problems.

5

Rgb-D face recognition using LBP-DCT algorithm

Kumar Sunil B L, Kumari Sharmila M

Applied Computer Science

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2021

|

Vol. 17, no 3

73--81

EN

Face recognition is one of the applications in image processing that recognizes or checks an individual's identity. 2D images are used to identify the face, but the problem is that this kind of image is very sensitive to changes in lighting and various angles of view. The images captured by 3D camera and stereo camera can also be used for recognition, but fairly long processing times is needed. RGB-D images that Kinect produces are used as a new alternative approach to 3D images. Such cameras cost less and can be used in any situation and any environment. This paper shows the face recognition algorithms’ performance using RGB-D images. These algorithms calculate the descriptor which uses RGB and Depth map faces based on local binary pattern. Those images are also tested for the fusion of LBP and DCT methods. The fusion of LBP and DCT approach produces a recognition rate of 97.5% during the experiment

6

Optimized Method based on Lattice Sequences for Multidimensional Integrals in Neural Networks

Todorov Venelin, Dimov Ivan, Fidanova Stefka

Annals of Computer Science and Information Systems

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2021

|

Vol. 25

243--246

EN

In this work we investigate advanced stochastic methods for solving a specific multidimensional problem related to neural networks. Monte Carlo and quasi-Monte Carlo techniques have been developed over many years in a range of different fields, but have only recently been applied to the problems in neural networks. As well as providing a consistent framework for statistical pattern recognition, the stochastic approach offers a number of practical advantages including a solution to the problem for higher dimensions. For the first time multidimensional integrals up to 100 dimensions related to this area will be discussed in our numerical study.

7

Deep learning approach for open switch fault diagnosis in matrix frequency converter

Sabour Mustapha

Przegląd Elektrotechniczny

|

2020

|

R. 96, nr 11

155--160

EN

This document proposes a new method for detecting and locating open circuit faults in a matrix frequency converter (MC) based on the technique of pattern recognition by neural networks. The converter input and output current signals are used for this purpose. For this, a database of current signals under healthy conditions and defective for different operating conditions was established. After transforming these signals into a Concordia lair, a process of deep learning by a convolutional neural network was carried out. To verify the robustness of our proposed approach, a simulation of a MC system with a defective power electronic switch supplying an asynchronous motor controlled by DTC-SVM under different conditions of torque and speed was developed. The diagnostic results demonstrate the feasibility and effectiveness of the proposed method. It made it possible to locate the faulty switch precisely and quickly.

PL

Zaproponowano nową metodę wykrywania i lokalizowania uszkodzeń obwodu otwartego w przekształtniku matrycowym (MC) w oparciu o technikę rozpoznawania wzorców przez sieć neuronową. W tym celu wykorzystywane są sygnały wejściowe i wyjściowe prądu przekształtnika. Utworzono bazę danych sygnałów prądowych w warunkach znamionowych i z uszkodzeniem dla różnych warunków pracy. Po przekształceniu tych sygnałów w środowisku Concordia przeprowadzono proces głębokiego uczenia się przez splotową sieć neuronową. Aby zweryfikować Wiarygodność naszego proponowanego podejścia, opracowano model symulacyjny układu MC z uszkodzonym łącznikiem energoelektronicznym zasilającym silnik asynchroniczny sterowany metodą DTC-SVM z róznymi wartościami momentu i prędkości obrotwej. Wyniki diagnostyczne pokazują wykonalność i skuteczność proponowanej metody.

8

Review of Printed Fabric Pattern Segmentation Analysis and Application

Kumah Charles, Raji Rafiu King, Pan Ruru

Autex Research Journal

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2020

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Vol. 20, no. 4

530--538

EN

Image processing of digital images is one of the essential categories of image transformation in the theory and practice of digital pattern analysis and computer vision. Automated pattern recognition systems are much needed in the textile industry more importantly when the quality control of products is a significant problem. The printed fabric pattern segmentation procedure is carried out since human interaction proves to be unsatisfactory and costly. Hence, to reduce the cost and wastage of time, automatic segmentation and pattern recognition are required. Several robust and efficient segmentation algorithms are established for pattern recognition. In this paper, different automated methods are presented to segregate printed patterns from textiles fabric. This has become necessary because quality product devoid of any disturbances is the ultimate aim of the textile printing industry.

9

The regularization method in the classification task according to given examples

Kwiatkowski Włodzimierz

Przegląd Teleinformatyczny

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2019

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T. 7, Nr 3-4 (49)

3--13

EN

The article considers the problem of classification based on the given examples of classes. As a feature vector, a complete characteristic of object is assumed. The peculiarity of the problem being solved is that the number of examples of the class may be less than the dimension of the feature vector, and also most of the coordinates of the feature vector can be correlated. As a consequence, the feature covariance matrix calculated for the cluster of examples may be singular or ill-conditioned. This disenable a direct use of metrics based on this covariance matrix. The article presents a regularization method involving the additional use of statistical properties of the environment.

PL

W artykule rozpatrywany jest problem klasyfikacji na podstawie wskazanych przykładów klas. Jako wektor cech przyjmuje się kompletną charakterystykę obiektów. Osobliwość rozwiązywanego zadania wynika z tego, że liczba przykładów klasy może być mniejsza od wymiaru wektora cech, a także wektor cech może zawierać współrzędne skorelowane. W konsekwencji macierz kowariancji cech obliczana dla klastra przykładów może być osobliwa albo źle uwarunkowana. Uniemożliwia to bezpośrednie stosowanie metryk bazujących na tej macierzy kowariancji. W artykule została przedstawiona metoda regularyzacji polegająca na dodatkowym wykorzystaniu statystycznych właściwości środowiska.

10

Textile Fiber Identification Using Near-Infrared Spectroscopy and Pattern Recognition

Zhou Jinfeng, Yu Lingjie, Ding Qian, Wang Rongwu

Autex Research Journal

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2019

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Vol. 19, no. 2

201--209

EN

Fibers are raw materials used for manufacturing yarns and fabrics, and their properties are closely related to the performances of their derivatives. It is indispensable to implement fiber identification in analyzing textile raw materials. In this paper, seven common fibers, including cotton, tencel, wool, cashmere, polyethylene terephthalate (PET), polylactic acid (PLA), and polypropylene (PP), were prepared. After analyzing the merits and demerits of the current methods used to identify fibers, near-infrared (NIR) spectroscopy was used owing to its significant superiorities, the foremost of which is it can capture the tiny information differences in chemical compositions and morphological features to display the characteristic spectral curve of each fiber. First, the fibers’ spectra were collected, and then, the relationships between the vibrations of characteristic chemical groups and the corresponding wavelengths were researched to organize a spectral information library that would be beneficial to achieve quick identification and classification. Finally, to achieve intelligent detection, pattern recognition approaches, including principal component analysis (PCA) (used to extract information of interest), soft independent modeling of class analogy (SIMCA), and linear discrimination analysis (LDA) (defined using two classifiers), assisted in accomplishing fiber identification. The experimental results – obtained by combining PCA and SIMCA – displayed that five of seven target fibers, namely, cotton, tencel, PP, PLA, and PET, were distributed with 100% recognition rate and 100% rejection rate, but wool and cashmere fibers yielded confusing results and led to relatively low recognition rate because of the high proportion of similarities between these two fibers. Therefore, the six spectral bands of interest unique to wool and cashmere fibers were selected, and the absorbance intensities were imported into the classifier LDA, where wool and cashmere were group-distributed in two different regions with 100% recognition rate. Consequently, the seven target fibers were accurately and quickly distinguished by the NIR method to guide the fiber identification of textile materials.

11

Artificial neural networks as a tool for pattern recognition and electrofacies analysis in Polish palaeozoic shale gas formations

Puskarczyk Edyta

Acta Geophysica

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2019

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Vol. 67, no. 6

1991--2003

EN

Unconventional oil and gas reservoirs from the lower Palaeozoic basin at the western slope of the East European Craton were taken into account in this study. The aim was to supply and improve standard well logs interpretation based on machine learning methods, especially ANNs. ANNs were used on standard well logging data, e.g. P-wave velocity, density, resistivity, neutron porosity, radioactivity and photoelectric factor. During the calculations, information about lithology or stratigraphy was not taken into account. We apply different methods of classification: cluster analysis, support vector machine and artificial neural network—Kohonen algorithm. We compare the results and analyse obtained electrofacies. Machine learning method–support vector machine SVM was used for classification. For the same data set, SVM algorithm application results were compared to the results of the Kohonen algorithm. The results were very similar. We obtained very good agreement of results. Kohonen algorithm (ANN) was used for pattern recognition and identification of electrofacies. Kohonen algorithm was also used for geological interpretation of well logs data. As a result of Kohonen algorithm application, groups corresponding to the gas-bearing intervals were found. Analysis showed diversification between gas-bearing formations and surrounding beds. It is also shown that internal diversification in gas-saturated beds is present. It is concluded that ANN appeared to be a useful and quick tool for preliminary classification of members and gas-saturated identification.

12

Wykrywanie anomalii bazujące na wskazanych przykładach

Kwiatkowski W.

Przegląd Teleinformatyczny

|

2018

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T. 6, Nr 1-2 (46)

3--21

PL

Rozpatrywany jest problem wykrywania anomalii na podstawie zarejestrowanych obserwacji zachowania systemu. Problem jest sformułowany jako zadanie rozpoznawania wzorców zachowania normalnego i zachowania nietypowego. Obydwa wzorce są określane przez wskazanie odpowiednich przykładów. Osobliwość rozwiązywanego zadania wynika z faktu, że zwykle liczebność przykładów jest dużo mniejsza od wymiaru wektora obserwacji. W artykule zostały przedstawione dwie metody detekcji anomalii bazujące na wyznaczaniu rzutów obserwacji na podprzestrzenie wzorców. Wyróżnikiem pierwszej metody jest wykorzystywanie odległości wektora obserwacji od podprzestrzeni wzorców. Druga metoda polega na przeniesieniu zadania rozpoznawania wzorców do podprzestrzeni wzorców.

EN

The paper considers the issue of anomalies detection based on registered observations of a system behavior. The problem is formulated as recognition of normal and anomalous behavior patterns. Both types of patterns are identified by indication of appropriate examples. A peculiarity of this task is that usually the number of examples is far lower than the dimension of vectors describing the observations. Two methods to solve this task have been presented in the paper, based on projecting the observations on the subspace of examples. The first method is based on a distance of the observation vector from the subspace of examples. The second method is based on transferring the pattern recognition problem to the subspace of examples.

13

Rozpoznawanie ruchu palców na podstawie analizy elektromiogramu

Sikora M., Paszkiel S.

Pomiary Automatyka Robotyka

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2018

|

R. 22, nr 2

11--16

PL

W artykule przedstawiono informacje dotyczące systemu umożliwiającego rozpoznawanie ruchu palców na podstawie dwóch sygnałów elektromiograficznych (EMG). W chwili obecnej system pozwala rozróżnić czy wykonany był ruch palcem wskazującym, środkowym, serdecznym lub małym. W dalszej części artykułu prezentowane są wyniki działania systemu oraz możliwe kierunki rozwoju.

EN

This paper discusses the system that allows to recognition of fingers movement based on a electromyogram (EMG). At the moment it can distinguish between the movement of pinky finger, ring finger, middle finger and index finger. The article presents the results of research on the effectiveness of the system as well as further development possibilities.

14

Estimating the validity of the recognition results of earthquake-prone areas using the ArcMap

Gorshkov A., Novikova O.

Acta Geophysica

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2018

|

Vol. 66, no. 5

843--853

EN

In 1972, V. Keilis-Borok and I. Gelfand introduced the phenomenological approach based on the morphostructural zoning and pattern recognition for identification of earthquake-prone areas. This methodology identifies seismogenic nodes capable of generating strong earthquakes on the basis of geological, morphological, and geophysical data, which do not contain information on past seismicity. In the period 1972–2018, totally, 26 worldwide seismic regions have been studied and maps showing the recognized earthquake-prone areas in each region have been published. After that, 11 of these regions were hit by earthquakes of the relevant sizes. The goal of this work is to analyze the correlation of the post-publication events with seismogenic nodes defined in these 11 regions. The test was performed using the NEIC earthquake catalog because it uniformly defines the location and magnitudes of earthquakes over the globe. The ArcMap facilities were exploited to plot the post-publication events on the maps showing the recognized seismogenic nodes. We found that about 86% of such events fall in the recognized seismogenic nodes. The performed test proved the sufficient validity of the methodology for identifying areas capable of strong earthquakes and confirms the idea on nucleating strong earthquakes at the nodes.

15

Predykcja ataków DDoS za pomocą technik eksploracji danych

Czyczyn-Egird D., Wojszczyk R.

Zeszyty Naukowe Wydziału Elektroniki i Informatyki Politechniki Koszalińskiej

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2017

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Nr 11

49--63

PL

Pojęcie ataków internetowych jest znane w przestrzeni sieci komputerowych od bardzo dawna. Ataki te mają różne cele, najczęstszym powodem jest dążenie sprawcy do unieruchomienia połączenia sieciowego oraz blokady usług. Skutki takich działań mogą być trudne do naprawienia, a także bardzo kosztowne. Warto zatem wykrywać takie szkodliwe ataki w jak najkrótszym czasie, kiedy skutki są jeszcze dość łatwo odwracalne. W artykule przedstawiono wyniki badań nad przewidywaniem wystąpienia ataków typu DoS na wybrane zasoby sieciowe. Wyniki badań zostały uzyskane poprzez wykorzystanie technik eksploracji danych.

EN

The notion of Internet attacks has been well-known in the area of computer networks for a long time now. These attacks have different goals; the most frequent one is when perpetrator aims at disabling a network connection and denying service. The effects of these actions can be difficult to rectify and also very expensive. Therefore, these harmful attacks should be detected in the shortest time possible when the effects are still quite easily reversible. The article presented the results of the research on predicting the occurrence of DoS attacks on the selected network resources. The research results were obtained by using data mining techniques.

16

Multimodal face recognition method with two-dimensional hidden Markov model

Bobulski J.

Bulletin of the Polish Academy of Sciences. Technical Sciences

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2017

|

Vol. 65, nr 1

121—128

EN

The paper presents a new solution for the face recognition based on two-dimensional hidden Markov models. The traditional HMM uses one-dimensional data vectors, which is a drawback in the case of 2D and 3D image processing, because part of the information is lost during the conversion to one-dimensional features vector. The paper presents a concept of the full ergodic 2DHMM, which can be used in 2D and 3D face recognition. The experimental results demonstrate that the system based on two dimensional hidden Markov models is able to achieve a good recognition rate for 2D, 3D and multimodal (2D+3D) face images recognition, and is faster than ICP method.

17

Reconstruction of high-dimensional data using the method of probabilistic features combination

Jakóbczak D. J.

Zeszyty Naukowe Wydziału Elektroniki i Informatyki Politechniki Koszalińskiej

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2016

|

Nr 9

37--50

EN

Proposed method, called Probabilistic Features Combination (PFC), is the method of multi-dimensional data modeling, extrapolation and interpolation using the set of high-dimensional feature vectors. This method is a hybridization of numerical methods and probabilistic methods. Identification of faces or fingerprints need modeling and each model of the pattern is built by a choice of multi-dimensional probability distribution function and feature combination. PFC modeling via nodes combination and parameter γ as N-dimensional probability distribution function enables data parameterization and interpolation for feature vectors. Multidimensional data is modeled and interpolated via nodes combination and different functions as probability distribution functions for each feature treated as random variable: polynomial, sine, cosine, tangent, cotangent, logarithm, exponent, arc sin, arc cos, arc tan, arc cot or power function.

PL

Autorska metoda Probabilistycznej Kombinacji Cech - Probabilistic Features Combination (PFC) jest wykorzystywana do interpolacji i modelowania wielowymiarowych danych. Węzły traktowane są jako punkty charakterystyczne N-wymiarowej informacji, która ma być odtwarzana (np. obraz). Wielowymiarowe dane są interpolowane lub rekonstruowane z wykorzystaniem funkcji rozkładu prawdopodobieństwa: potęgowych, wielomianowych, wykładniczych, logarytmicznych, trygonometrycznych, cyklometrycznych.

18

Identyfikacja wzorców w finansowych szeregach czasowych z wykorzystaniem hierarchicznych metod grupowania na przykładzie kursu BTC/PLN

Kądziołka K.

Zeszyty Naukowe Warszawskiej Wyższej Szkoły Informatyki

|

2016

|

nr 14

37--48

PL

W artykule przedstawiono zastosowanie metody Warda do identyfikacji wzorców w finansowych szeregach czasowych, na przykładzie kursu waluty kryptograficznej bitcoin. Wykorzystując zidentyfikowane wzorce, generowano prognozy zmian kursu w analizowanym szeregu dla danych zbioru testowego, które nie zostały wykorzystane w procesie identyfikacji wzorców. Przeciętny absolutny oraz maksymalny błąd prognozy na danych zbioru testowego był niewielki, natomiast zgodność kierunku zmian kursu BTC/PLN na zbiorze testowym wynosiła tylko 60%.

EN

The aim of this article was to present the use of Ward’s method to identify patterns in BTC/PLN exchange rate. Identified patterns were used to predict BTC/PLN movement direction. Mean absolute percentage error and maximal percentage error on the test set were small, but the movement direction was correctly predicted only in 60% of cases.

19

Supervised and unsupervised learning process in damage classification of rolling element bearings

Strączkiewicz M., Czop P., Barszcz T.

Diagnostyka

|

2016

|

Vol. 17, No. 2

71--80

EN

Damage classification plays a crucial role in the process of management in nearly every branch of industry. In fact, is becomes equally important as damage detection, since it can provide information of malfunction severity and hence lead to improvement of a production or manufacturing process. Within this paper selected supervised and unsupervised pattern recognition methods are employed for this purpose. The attention of the authors is given to assessment of selection, performance benchmarking and applicability of selected pattern recognition methods. The investigation is performed on the data collected using an experimental test grid and rolling element bearing with deteriorating condition of an outer race.

PL

Klasyfikacja uszkodzeń odgrywa ważną rolę w procesie zarządzania w niemalże każdej gałęzi przemysłu. W rzeczywistości staje się ona równie istotna co samo wykrywanie uszkodzenia ponieważ pozwala określić stopień uszkodzenia, a co za tym idzie, poprawić efektywność zarządzania zakładem przemysłowym. W tym celu wykorzystano wybrane nadzorowane i nienadzorowane metody rozpoznawania wzorców. W artykule zwrócono uwagę na ocenę wyboru, porównanie wydajności oraz możliwości wykorzystania tych metod. Analiza przeprowadzona została na danych zgromadzonyh na eksperymentalnym stanowisku testowym, gdzie obserwowany jest stan łożyska tocznego z pogłębiającym się uszkodzeniem bieżni zewnętrznej.

20

Wykorzystanie sztucznych sieci neuronowych do wykrywania i rozpoznawania tablic rejestracyjnych na zdjęciach pojazdów

Huzarek M., Rutkowski T. A.

Zeszyty Naukowe Wydziału Elektrotechniki i Automatyki Politechniki Gdańskiej

|

2015

|

Nr 47

67--70

PL

W artykule przedstawiono koncepcję algorytmu wykrywania i rozpoznawania tablic rejestracyjnych (AWiRTR) na obrazach cyfrowych pojazdów. Detekcja i lokalizacja tablic rejestracyjnych oraz wyodrębnienie z obrazu tablicy rejestracyjnej poszczególnych znaków odbywa się z wykorzystaniem podstawowych technik przetwarzania obrazu (przekształcenia morfologiczne, wykrywanie krawędzi) jak i podstawowych danych statystycznych obiektów wykrytych w obrazie (np. stosunek szerokość do wysokość obiektu). Natomiast za rozpoznawanie poszczególnych znaków odpowiedzialna jest wielowarstwowa, jednokierunkowa sztuczna sieć neuronowa. Przedstawiony algorytm został zaimplementowany i zweryfikowany w środowisku Matlab/Simulink. Pomimo wykorzystania w algorytmie AWiRTR dobrze znanych z literatury metod lokalizacji, segmentacji i rozpoznawania wzorców, otrzymane w trakcie weryfikacji algorytmu wyniki wskazują jego efektywność na poziomie 96,26%. Jest ona porównywalna do efektywności innych algorytmów AWiRTR opisywanych w literaturze.

EN

A license plate detection and recognition system has basically three modules for: localization of the plate region using the digital image of the car, extraction of the characters from digital image of the license plate, and recognition of the characters using a suitable identification method. In this paper, an algorithm is designed that can localize of the plate and extract of the characters from digital image of the license plate with the basics image processing techniques (morphological transformations, edge detection) and with the statistical data (e.g. width height ratio) of the objects identified in the analyzed digital image. It is done at the second and third stage of the presented algorithm, respectively. Finally, at the fourth stage of the presented algorithm, the character recognition is done by multilayer, one directional artificial neural network. Algorithm was implemented and verified in the Matlab/Simulink environment. Experimental results demonstrate promising efficiency of the proposed algorithm: 98% in the task of license plate localization, 95,69% in the task of characters extraction, and 95,11% in the task of characters recognition.