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1

Application of Recurrence Quantification Analysis in the Detection of Osteoarthritis of the Knee with the Use of Vibroarthrography

Machrowska Anna, Karpiński Robert, Maciejewski Marcin, Jonak Józef, Krakowski Przemysław, Syta Arkadiusz

Advances in Science and Technology. Research Journal

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2024

|

Vol. 18, no 5

EN

Nowadays, the world is struggling with the problems of an aging society. With the increasing share of older people in the population, degenerative joint diseases are a growing problem. The result of progressive degenerative changes in joints is primarily the deterioration of the patients' quality of life and their gradual exclusion from activity and social life. The ability to effectively, non-invasively and quickly detect cases of chondromalacia of the knee joints is a challenge for modern medicine. The possibility of early detection of progressive degenerative changes allows for the appropriate selection of treatment protocols and significantly increases the chances of inhibiting the development of degenerative diseases of the musculoskeletal system. The article presents a non-invasive method for detecting degenerative changes in the knee joints based on recurrence analysis and classification using neural networks. The result of the analyzes was a classification accuracy of 91.07% in the case of MLP neural networks and 80.36% for RBF networks.

2

Comparison of selected classification methods based on machine learning as a diagnostic tool for knee joint cartilage damage based on generated vibroacoustic processes

Karpiński Robert, Krakowski Przemysław, Jonak Józef, Machrowska Anna, Maciejewski Marcin

Applied Computer Science

|

2023

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Vol. 19, no 4

136--150

EN

Osteoarthritis is one of the most common cause of disability among elderly. It can affect every joint in human body, however, it is most prevalent in hip, knee, and hand joints. Early diagnosis of cartilage lesions is essential for fast and accurate treatment, which can prolong joint function. Available diagnostic methods include conventional X-ray, ultrasound and magnetic resonance imaging. However, those diagnostic modalities are not suitable for screening purposes. Vibroarthrography is proposed in literature as a screening method for cartilage lesions. However, exact method of signal acquisition as well as classification method is still not well established in literature. In this study, 84 patients were assessed, of whom 40 were in the control group and 44 in the study group. Cartilage status in the study group was evaluated during surgical treatment. Multilayer perceptron - MLP, radial basis function - RBF, support vector method - SVM and naive classifier – NBC were introduced in this study as classification protocols. Highest accuracy (0.893) was found when MLP was introduced, also RBF classification showed high sensitivity (0.822) and specificity (0.821). On the other hand, NBC showed lowest diagnostic accuracy reaching 0.702. In conclusion vibroarthrography presents a promising diagnostic modality for cartilage evaluation in clinical setting with the use of MLP and RBF classification methods.

3

Neural Network Model for Control of Operating Modes of Crushing and Grinding Complex

Kalinchyk Vasyl, Meita Olexandr, Pobigaylo Vitalii, Borychenko Olena, Kalinchyk Vitalii

Rocznik Ochrona Środowiska

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2022

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Tom 24

26--40

EN

This article investigates the application of neural network models to create automated control systems for industrial processes. We reviewed and analysed works on dispatch control and evaluation of equipment operating modes and the use of artificial neural networks to solve problems of this type. It is shown that the main requirements for identification models are the accuracy of estimation and ease of algorithm implementation. It is shown that artificial neural networks meet the requirements for accuracy of classification problems, ease of execution and speed. We considered the structures of neural networks that can be used to recognise the modes of operation of technological equipment. Application of the model and structure of networks with radial basis functions and multilayer perceptrons for identifying the mode of operation of equipment under given conditions is substantiated. The input conditions for constructing neural network models of two types with a given three-layer structure are offered. The results of training neural models on the model of a multilayer perceptron and a network with radial basis functions are presented. The estimation and comparative analysis of models depending on model parameters are made. It is shown that networks with radial basis functions offer greater accuracy in solving identification problems. The structural scheme of the automated process control system with mode identification based on artificial neural networks is offered.

4

Prediction of the compressive strength of environmentally friendly concrete using artificial neural network

Kulisz Monika, Kujawska Justyna, Aubakirova Zulfiya, Zhairbaeva Gulnaz, Warowny Tomasz

Applied Computer Science

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2022

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Vol. 18, no 4

68--81

EN

The paper evaluated the possibility of using artificial neural network models for predicting the compressive strength (Fc) of concretes with the addition of recycled concrete aggregate (RCA). The artificial neural network (ANN) approaches were used for three variable processes modeling (cement content in the range of 250 to 400 kg/m3, percentage of recycled concrete aggregate from 25% to 100% and the ratios of water contents 0.45 to 0.6). The results indicate that the compressive strength of recycled concrete at 3, 7 and 28 days is strongly influenced by the cement content, %RCA and the ratios of water contents. It is found that the compressive strength at 3, 7 and 28 days decreases when increasing RCA from 25% to 100%. The obtained MLP and RBF networks are characterized by satisfactory capacity for prediction of the compressive strength of concretes with recycled concrete aggregate (RCA) addition. The results in statistical terms; correlation coefficient (R) reveals that the both ANN approaches are powerful tools for the prediction of the compressive strength.

5

The influence of rainfall time series fractality on forecasting models’ efficiency

Rahmani Farhang, Fattahi Mohammad Hadi

Acta Geophysica

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2022

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Vol. 70, no 3

1349--1361

EN

Forecasting rainfall time series is of great significance for hydrologists and geoscientists. Thus, this study represents a contribution to understanding the impact of the fractal time series variety on forecasting model performance. Multiple fractal series were generated via p-model and used for modeling. Subsequently, the forecasting was delivered based on existing observed monthly rainfall data (three stations in the UK, from 1865 to 2002) through five forecasting models. Finally, the association between series fractality and models’ performance was examined. The results indicated that the forecasting based on the mono-fractal series resulted in the most reliable results (R2=1 and RMSE less than 0.02). In the case of multifractal series, modeling based on series with the right side of the asymmetric curve of the multifractal spectrum presented series with the lowest RMSE (0.96) and highest R2 (0.99) (desirable performance). In contrast, the forecasting based on series with the left side of the asymmetric curve of the multifractal spectrum suggested the most unreliable outcomes (R2 range [−0.0007 ~ 0.988] and RMSE range [0.8526 ~ 39.3]). The forecasting based on the symmetric curve of the multifractal spectrum series delivered regular performance. Accordingly, high and low errors are expected from forecasting based on the time series with a left-skewed multifractal spectrum and right-skewed multifractal spectrum (and mono-fractal time series), respectively. Hybrid models were the best options for forecasting mono-fractal and multifractal time series with right side asymmetric and symmetric multifractal spectrum curves. The ARIMA model was suitable to predict multifractal time series with left side asymmetric multifractal spectrum curves.

6

Training subset selection for support vector regression

Liu Cenru, Cen Jiahao

Annals of Computer Science and Information Systems

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2019

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Vol. 18

11--14

EN

As more and more data are available, training a machine learning model can be extremely intractable, especially for complex models like Support Vector Regression (SVR) train- ing of which requires solving a large quadratic programming optimization problem. Selecting a small data subset that can effectively represent the characteristic features of training data and preserve their distribution is an efficient way to solve this problem. This paper proposes a systematic approach to select the best representative data for SVR training. The distribution of both predictor and response variables are preserved in the selected subset via a 2-layer data clustering strategy. A 2-layer step-wise greedy algorithm is introduced to select best data points for constructing a reduced training set. The proposed method has been applied for predicting deck's win rates in the Clash Royale Challenge, in which 10 subsets containing hundreds of data examples were selected from 100k for training 10 SVR models to maximize their prediction performance evaluated using R-squared metric. Our final submission having a R2 score of 0.225682 won the 3rd place among over 1200 solutions submitted by 115 teams.

7

Dynamically positioned ship steering making use of backstepping method and artificial neural networks

Witkowska A., Rynkiewicz T. N.

Polish Maritime Research

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2018

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nr 4

5--12

EN

The article discusses the issue of designing a dynamic ship positioning system making use of the adaptive vectorial backstepping method and RBF type artificial neural networks. In the article, the backstepping controller is used to determine control laws and neural network weight adaptation laws. The artificial neural network is applied at each time instant to approximate nonlinear functions containing parametric uncertainties. The proposed control system does not require precise knowledge of the model of ship dynamics and external disturbances, it also eliminates the problem of analytical determination of the regression matrix when designing the control law with the aid of the adaptive backstepping procedure.

8

An Efficient ANN Interference Cancelation for High Order Modulation over Rayleigh Fading Channel

Bouguerra F., Saidi L.

Journal of Telecommunications and Information Technology

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2018

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nr 4

75--80

EN

High order modulation (HOM) presents a key challenge in increasing spectrum efficiency in 4G and upcoming 5G communication systems. In this paper, two non-linear adaptive equalizer techniques based on multilayer perceptron (MLP) and radial basis function (RBF) are designed and applied on HOM to optimize its performance despite its high sensitivity to noise and channel distortions. The artiﬁcial neural network’s (ANN) adaptive equalizer architectures and learning methods are simpliﬁed to avoid more complexity and to ensure greater speed in symbol decision making. They will be compared with the following popular adaptive ﬁlters: least mean square (LMS) and recursive least squares (RLS), in terms of bit error rate (BER) and minimum square error (MSE) with 16, 64, 128, 256, 512 and 1024 quadrature amplitude modulation (QAM). By that, this work will show the advantage that the MLP equalizer has, in most cases, over RBF and traditional linear equalizers.

9

Comparative Application of Radial Basis Function and Multilayer Perceptron Neural Networks to Predict Traffic Noise Pollution in Tehran Roads

Mansourkhaki A., Berangi M., Haghiri M.

Journal of Ecological Engineering

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2018

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Vol. 19, nr 1

113--121

EN

Noise pollution is a level of environmental noise which is considered as a disturbing and annoying phenomenon for human and wildlife. It is one of the environmental problems which has not been considered as harmful as the air and water pollution. Compared with other pollutants, the attempts to control noise pollution have largely been unsuccessful due to the inadequate knowledge of its effects on humans, as well as the lack of clear standards in previous years. However, with an increase of traveling vehicles, the adverse impact of increasing noise pollution on human health is progressively emerging. Hence, investigators all around the world are seeking to find new approaches for predicting, estimating and controlling this problem and various models have been proposed. Recently, developing learning algorithms such as neural network has led to novel solutions for this challenge. These algorithms provide intelligent performance based on the situations and input data, enabling to obtain the best result for predicting noise level. In this study, two types of neural networks – multilayer perceptron and radial basis function – were developed for predicting equivalent continuous sound level (LAeq) by measuring the traffic volume, average speed and percentage of heavy vehicles in some roads in west and northwest of Tehran. Then, their prediction results were compared based on the coefficient of determination (R2) and the Mean Squared Error (MSE). Although both networks are of high accuracy in prediction of noise level, multilayer perceptron neural network based on selected criteria had a better performance.

10

Prediction of industrial pollution by radial basis function networks

Djebbri N., Rouainia M.

Environment Protection Engineering

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2018

|

Vol. 44, nr 3

153--164

EN

Atmospheric pollution has been receiving a significant interest for several decades since industries cause more and more pollution. Thanks to the development of many prediction techniques, scientists and industries are focusing more on pollution prediction. The aim of this work is to predict the two pollutant concentrations (NOx and CO) in industrial sites by a modified radial basis function (RBF) based neural network. The modification considered the spread parameter h of the activation function in the RBF network. In order to get the best network, the variations of this parameter for three cases were considered. In the first case, only pollutants concentrations variables were used, while in the second one, only the meteorological variables were utilized. In the third case, pollutants' concentrations were connected with meteorological variables. Based on calculation errors, the best model that ensures the best monitoring of pollutants concentration could be identified.

11

Porównanie skuteczności wybranych klasyﬁkatorów neuronowych w identyﬁkacji grup macerałów węgla kamiennego oraz materii nieorganicznej

Skiba M., Młynarczuk M.

Prace Instytutu Mechaniki Górotworu PAN

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2016

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T. 18, nr 4

133--138

PL

Jednym z podstawowych zastosowań sztucznych sieci neuronowych jest rozpoznawanie i klasyfikacja wzorców. W ramach pracy przeprowadzono automatyczną identyfikację grup macerałów oraz materii nieorganicznej za pomocą trzech klasyfikatorów neuronowych: dwuwarstwowej sieci jednokierunkowej (Multi-Layer Perceptron, MLP), sieci o radialnych funkcjach bazowych (Radial Basis Function, RBF) oraz samoorganizującej mapy Kohonena (Self- -Organizing Maps, SOM). Do analiz wykorzystano zbiór 3000 mikroskopowych zdjęć próbek węgla kamiennego. Każde z nich opisano 12 – wymiarowym wektorem cech. Dla każdej z rozpatrywanych sieci dokonano 100 – krotnego powtórzenia losowego wyboru ciągu uczącego, treningu sieci oraz rozpoznania badanych obiektów. Analizy wykazały wysoką skuteczność zastosowanych klasyfikatorów neuronowych w identyfikacji grup macerałów oraz materii nieorganicznej. Najlepsze rezultaty, na poziomie przekraczającym 98% poprawnych rozpoznań, uzyskano dla klasyfikatorów bazujących na uczeniu nadzorowanym (MLP oraz RBF). Nieznacznie niższą skuteczność rozpoznań otrzymano w przypadku sieci SOM – 95,9% klasyfikacji zgodnych z decyzjami obserwatora.

EN

One of the main applications of artificial neural networks is the recognition and classification of different patterns. In the framework of the work an automatic identification of maceral groups and inorganic matter was carried. Three neural classifiers were used: a Multi-Layer Perceptron (MLP), a network of Radial Basis Function (RBF) and Kohonen Self-Organizing Maps (SOM). For the purposes of the analysis a collection of 3,000 images of microscopic samples of coal was used. Each image was described by 12-dimensional feature vector. For each network were carried out: a hundredfold draw of learning set, the network training and classification of objects under study. The analyses have shown high effectiveness of the neural classifi ers used to identify maceral groups and inorganic matter. The best results were obtained for the classifiers based on supervised learning (MLP and RBF). They were at a level exceeding 98% of correct diagnoses. Slightly lower efficiency of diagnosis was obtained in the case of SOM network – 95.9% of classification compatible with the observer decisions.

12

Zastosowanie sieci RBF w analizie pękania elementów maszyn

Różyło P., Gajewski J., Nowakowski P., Machrowska A.

Logistyka

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2015

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nr 3

4176--4182, CD 1

PL

W pracy przedstawiono numeryczne badanie procesu pękania elementu maszynowego oraz zastosowanie sieci RBF do analizy wyników badań. W ramach analizy numerycznej zaprojektowano element maszynowy w oparciu o dane materiałowe stali C45. Projekt i badanie wykonano w oprogramowaniu ABAQUS. Analiza pozwoliła na dostarczenie informacji odnośnie stopnia wytężenia badanego elementu.

EN

The paper presents a numerical study of the cracking process of the machine component and use RBF network to analyze the test results. In the context of numerical analysis machine element is designed based on the data of the steel C45. Design and testing was performed in the software ABAQUS. The analysis made it possible to provide information on the degree of effort of the test piece.

13

Właściwości metrologiczne radialnych i sigmoidalnych sieci neuronowych zastosowanych do korekcji błędów statycznych w przetworniku próbkującym

Roj J.

Przegląd Elektrotechniczny

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2013

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R. 89, nr 1a

84--87

PL

W artykule przedstawiono wyniki symulacyjnych badań sieci neuronowych o radialnych oraz sigmoidalnych funkcjach aktywacji. Zamodelowano sytuację pomiarową, w której sieci neuronowe realizują korekcję błędów statycznych nieliniowego czujnika pomiarowego w przetworniku próbkującym. Niedokładność neuronowej realizacji korekcji wyrażono za pomocą niepewności. Porównano właściwości metrologiczne obu rodzajów sieci w zależności od ich struktury oraz liczebności zbiorów uczących.

EN

The paper describes the results of simulation studies of RBF and MLP neural networks. There was modeled a measurement situation in which neural networks performed static error correction of a non-linear sensor in a sampling transducer. The correction inaccuracy by neural networks is expressed by the uncertainty. There were compared metrological properties of both types of networks depending on their structures and size of learning sets.

14

Sztuczne sieci neuronowe - podstawowe struktury sieciowe i algorytmy uczące

Osowski S.

Przegląd Elektrotechniczny

|

2009

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R. 85, nr 8

1-8

PL

Praca przedstawia struktury i algorytmy uczenia trzech podstawowych rodzajów sieci neuronowych: sieci sigmoidalnej wielowarstwowej MLP, sieci RBF oraz SVM. Sieci te pełnią podobną rolę uniwersalnego aproksymatora zmiennych wielowymiarowych, różniąc się przede wszystkim rodzajem zastosowanych neuronów i algorytmem uczącym. Pokazano uniwersalność tych rozwiązań i ich użyteczność w wielu problemach praktycznych występujących w technice.

EN

The paper presents the overview of the basic structures and learning algorithms of the most typical supervised neural networks. To the most important belong multilayer perceptron, radial basis function network and support vector machine. All of them fulfill the role of the universal approximators. The paper shows the universal character of these solutions and their applicability in solving different problems of various characters.

15

Identyfikacja rekurencyjnych modeli neuronowych typu RBF

Ławryńczuk M.

Pomiary Automatyka Robotyka

|

2008

|

R. 12, nr 2

558--570

PL

W pracy omówiono algorytm identyfikacji (uczenia) rekurencyjnych sieci neuronowych typu RBF (ang. Radial Basis Function), które mogą być zastosowanie w modelowaniu nieliniowych procesów dynamicznych. W porównaniu z powszechnie stosowanym klasycznym algorytmem wstecznej propagacji błędów, który prowadzi do wyznaczenia modeli służących do predykcji jednokrokowej, proponowany algorytm umożliwia identyfikację predyktorów wielokrokowych. Przedstawiony algorytm wykorzystano do modelowania reaktora chemicznego.

EN

This paper details an identification (training) algorithm of RBF (Radial Basis Function) recurrent neural networks which can be used for modelling nonlinear dynamic processes. In comparison with the widely used classical backpropagation algorithm, which leads to one-step ahead predictors, the described one results in many-steps ahead predictors. The algorithm is used for modelling a chemical reactor.

16

Klasyfikator neuronowy typu RBF uczony za pomocą deskryptorów uszkodzeń zębów kół otrzymanych przy użyciu analizy CWT

Czech P.

Zeszyty Naukowe. Transport / Politechnika Śląska

|

2007

|

z. 61

107-112

PL

W opracowaniu przedstawiono wyniki eksperymentu mającego na celu wykorzystanie sztucznych sieci neuronowych typu RBF do diagnozowania stopnia pęknięcia w stopie zęba koła. Do nauki sieci radialnych wykorzystywano deskryptory wyznaczone na podstawie rozkładów uzyskanych z ciągłej transformaty falkowej. Badania oparto na zidentyfikowanym modelu przekładni zębatej pracującej w układzie napędowym

EN

The work presents results of an experiment that employs the artificial neuronal network in the task of identification of the degree of tooth root cracking. In the experiment was used continuous wavelets analysis (CWT) and RBF neural network. This experiment was based on a simulation experiment

17

A meshless method for non-linear Poisson problems with high gradients

Algahtani H. I.

Computer Assisted Mechanics and Engineering Sciences

|

2006

|

Vol. 13, No. 3

367-377

EN

A meshless method for the solution of linear and non-linear Poisson-type problems involving high gradients is presented. The proposed method is based on collocation with 3rd order polynomial radial basis function coupled with the fundamental solution. The linear problem is solved by satisfying the boundary conditions and the governing differential equations over selected points over the boundary and inside the domain, respectively. In the case of the non-linear case, the resulted equations are highly non-linear and therefore, they are solved using an incremental-iterative procedure. The accuracy and efficiency of the method is verified through several numerical examples.

18

Wykorzystanie sztucznych sieci neuronowych typu RBF do predykcji plonu wybranych roślin zbożowych

Boniecki P.

Journal of Research and Applications in Agricultural Engineering

|

2005

|

Vol. 50, nr 2

15-19

PL

Pojawiające się ostatnio metody, mające cechy sztucznej inteligencji, pozwalają na budowę modeli symulacyjnych, które realizują postawione zadania w oparciu o wzorce zaczerpnięte bezpośrednio z obserwacji przyrody [1]. Szczególną grupę stanowią techniki przetwarzania oparte na sztucznych sieciach neuronowych, będące w istocie komputerowymi symulatorami pracy mózgu [3]. Za pomocą modeli neuronowych można m.in. dokonać predykcji wielkości plonów płodów rolnych w oparciu o posiadane empiryczne dane, dotyczące zbiorów w latach ubiegłych. W pracy proponuje się wykorzystanie technik predykcyjnych, jakie m.in. reprezentują wybrane topologie sieci neuronowych, w szczególności sieci neuronowe typu RBF (Radial Basis Functions).

EN

Appearing recently methods, having guilds of artificial intelligence, permit on building of simulating models which realize assigned tasks on the basis of patterns taken directly with nature observation [1]. The processing techniques based on artificial neural networks create a special group, being in fact a computer simulators of brain work [3]. With the help of neuronal models it is possible to predict the expected crops yield on the basis of empirical data regarding crop yields in last summers. This work proposes utilization of prediction methods, which represent chosen topologies of neuronal nets among others, the RBF (Radial Basis Functions) neural network peculiarly.