An approach to license plate recognition in real time using multi-stage computational intelligence classifier

Automatic car license plate recognition (LPR) is widely used nowadays. It involves plate localization in the image, character segmentation and optical character recognition. In this paper, a set of descriptors of image segments (characters) was proposed as well as a technique of multi-stage classification of letters and digits using cascade of neural network and several parallel Random Forest or classification tree or rule list classifiers. The proposed solution was applied to automated recognition of number plates which are composed of capital Latin letters and Arabic numerals. The paper presents an analysis of the accuracy of the obtained classifiers. The time needed to build the classifier and the time needed to classify characters using it are also presented

An Approach to License Plate Recognition in Real Time Using Multi-stage Computational Intelligence Classifier

Automatic car license plate recognition (LPR) is widely used nowadays. It involves plate localization in the image, character segmentation and optical character recognition. In this paper, a set of descriptors of image segments (characters) was proposed as well as a technique of multi-stage classification of letters and digits using cascade of neural network and several parallel Random Forest or classification tree or rule list classifiers. The proposed solution was applied to automated recognition of number plates which are composed of capital Latin letters and Arabic numerals. The paper presents an analysis of the accuracy of the obtained classifiers. The time needed to build the classifier and the time needed to classify characters using it are also presented

Application of selected computational intelligence methods to sound level modelling based on traffic intensity in thoroughfare

The aim of the paper was to build the models of sound pressure level as a function of traffic intensity in thoroughfare. The models were built by using artificial analytical models or regression trees. The former included Nordic Prediction Method. The latter were represented by Random Forest and Cubist. The analysis of accuracy of all obtained models was conducted. The best models can be used in the process of reconstruction of equivalent sound level data

Missing values reconstruction in sound level monitoring station by means of intelligent computing

The aim of the paper was to reconstruct the missing data by applying the model which describes variability of sound level in the whole period from 2013 to 2016. To build the model, the computational intelligence methods, like fuzzy systems, or regression trees can be used. The latter approach was applied and we built the model with Cubist regression tree software, using equivalent sound levels recorded in 2013. For the reconstruction of sound level data in short period of time (several days), time series values and day_of_week values together should be used in the training dataset. For the reconstruction of sound level data in long period of time (several months) day_of_week values should be used in the training dataset

Klasyfikacja i modelowanie sygnałów dżwięków w wybranych systemach tribologicznych

The paper presents an analysis of the sound level recorded during dry sliding friction conditions. Balls with a diameter of 6 mm placed on pins were made of 100Cr6 steel, silicon carbide (SiC), and corundum (Al2 O3 ), while rotating discs with a height of 6 mm and a diameter of 42 mm were made of 100Cr6 steel. Each pin and disc system was tested for two values of the relative humidity of the air (50 ± 5% and 90 ± 5%). Models of the A-sound level were developed using regression trees and random forest. The paper presents an analysis of the accuracy of the models obtained. Classifications of the six tests performed on the basis of sound level descriptors were also carried out.W pracy przedstawiono analizę poziomu dźwięku zarejestrowanego podczas tarcia technicznie suchego w ruchu ślizgowym. Podczas sześciu testów tribologicznych stosowano próbkę wykonaną ze stali 100Cr6 oraz trzy przeciwpróbki, wykonane ze stali 100Cr6, węglika krzemu (SiC) i korundu (Al2 O3 ), przy czym każdy układ próbka – przeciwpróbka był testowany dla dwóch wartości wilgotności względnej powietrza (50 ± 5% i 90 ± 5%). Opracowano modele poziomu dźwięku A z użyciem drzew regresji i lasu losowego. W pracy zamieszczono analizę dokładności otrzymanych modeli. Została również przeprowadzona klasyfikacja sześciu wykonanych testów w oparciu o deskryptory poziomu dźwięku

Missing values reconstruction in sound level monitoring station by means of intelligent computing

The aim of the paper was to reconstruct the missing data by applying the model which describes variability of sound level in the whole period from 2013 to 2016. To build the model, the computational intelligence methods, like fuzzy systems, or regression trees can be used. The latter approach was applied and we built the model with Cubist regression tree software, using equivalent sound levels recorded in 2013. For the reconstruction of sound level data in short period of time (several days), time series values and day_of_week values together should be used in the training dataset. For the reconstruction of sound level data in long period of time (several months) day_of_week values should be used in the training dataset

Modeling of acoustic pressure variability at thoroughfare

The work presents the analysis of variability of acoustic pressure, calculated from values of equivalent sound level, recorded in monitoring station. The models of acoustic pressure variability were built in the form of regression trees and random forest. The analysis of accuracy of obtained models was carried out. These models were subsequently used for reconstruction of equivalent sound level for periods of monitoring station inactivity

Modeling of acoustic pressure variability at thoroughfare

The work presents the analysis of variability of acoustic pressure, calculated from values of equivalent sound level, recorded in monitoring station. The models of acoustic pressure variability were built in the form of regression trees and random forest. The analysis of accuracy of obtained models was carried out. These models were subsequently used for reconstruction of equivalent sound level for periods of monitoring station inactivity