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Wyświetlanie 1-15 z 335
Tytuł:
Parallel implementation of neural networks with the use of GPGPU technology OpenCL
Autorzy:
Kłyś, M.
Szymczyk, M.
Szymczyk, P.
Gajer, M.
Powiązania:
https://bibliotekanauki.pl/articles/114679.pdf
Data publikacji:
2015
Wydawca:
Stowarzyszenie Inżynierów i Techników Mechaników Polskich
Tematy:
OpenCL
Artificial Neural Networks
GPGPU
Opis:
The article discusses possibilities of implementing a neural network in a parallel way. The issues of implementation are illustrated with the example of the non-linear neural network. Parallel implementation of earlier mentioned neural network is written with the use of OpenCL library, which is a representative of software supporting general-purpose computing on graphics processor units (GPGPU). The obtained results demonstrate that some group of algorithms can be computed faster if they are implemented in a parallel way and run on a multi-core processor (CPU) or a graphics processing unit (GPU). In case of the GPU, the implemented algorithm should be divided into many threads in order to perform computations faster than on a multi-core CPU. In general, computations on a GPU should be performed when there is a need to process a large amount of data with the use of algorithm which is very well suited to parallel implementation.
Źródło:
Measurement Automation Monitoring; 2015, 61, 1; 16-20
2450-2855
Pojawia się w:
Measurement Automation Monitoring
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
The development of Kalman Filter learning technique for Artificial Neural Networks
Autorzy:
Krok, A.
Powiązania:
https://bibliotekanauki.pl/articles/308081.pdf
Data publikacji:
2013
Wydawca:
Instytut Łączności - Państwowy Instytut Badawczy
Tematy:
Artificial Neural Networks
Kalman filter
Opis:
The paper presents an idea of using the Kalman Filtering (KF) for learning the Artificial Neural Networks (ANN). It is shown that KF can be fully competitive or more beneficial method with comparison standard Artificial Neural Networks learning techniques. The development of the method is presented respecting selective learning of chosen part of ANN. Another issue presented in this paper is the author’s concept of automatic selection of architecture of ANN learned by means of KF based on removing unnecessary connection inside the network. The effectiveness of presented ideas is illustrated on the examples of time series modeling and prediction. Considered data came from the experiments and situ measurements in the field of structural mechanics and materials.
Źródło:
Journal of Telecommunications and Information Technology; 2013, 4; 16-21
1509-4553
1899-8852
Pojawia się w:
Journal of Telecommunications and Information Technology
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Solar irradiance forecasting based on long-wave atmospheric radiation
Autorzy:
Piątek, M.
Trajer, J.
Czekalski, D.
Powiązania:
https://bibliotekanauki.pl/articles/298472.pdf
Data publikacji:
2015
Wydawca:
Uniwersytet Warmińsko-Mazurski w Olsztynie
Tematy:
artificial neural networks
irradiance forecasting
cloudiness
Opis:
This work contains information concerning long-wave atmospheric radiation. Artificial neural networks were developed to forecast total mean hourly irradiance based on long-wave atmospheric radiation as cloudiness indicator. It was proved that using this variable in models for forecasting irradiance is wellgrounded. The proof was based on the neural networks sensitivity analysis. It was proved that neural network model is capable to utilize information carried by long wave atmospheric radiation only when the air temperature is provided as additional explanatory variable.
Źródło:
Technical Sciences / University of Warmia and Mazury in Olsztyn; 2015, 18(1); 27-36
1505-4675
2083-4527
Pojawia się w:
Technical Sciences / University of Warmia and Mazury in Olsztyn
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Artificial neural networks for interpolation and identification of underwater object features
Autorzy:
Balicki, J.
Gloza, I.
Powiązania:
https://bibliotekanauki.pl/articles/332167.pdf
Data publikacji:
2008
Wydawca:
Polskie Towarzystwo Akustyczne
Tematy:
artificial neural networks
underwater object
hydroacoustic
Opis:
Artificial neural networks can be applied for interpolation of function with multiple variables. Because of concurrent processing of data by neurons, that approach can be seen as hopeful alternative for numerical algorithms. From these reasons, the analysis of capabilities for some models of neural networks has been carried out in the purpose for identification of the underwater object properties. Features of the underwater objects can be recognized by characteristics of a amplitude according to the frequency of measured signals. The feed-forward multi-layer networks with different transfer functions have been applied. Those network models have been trained by some versions of back-propagation algorithm as well as the Levenberg-Marquardt gradient optimization technique. Finally, for determination of the amplitude for the frequency of signal by the two-layer network with the hidden layer of the radial neurons has been proposed.
Źródło:
Hydroacoustics; 2008, 11; 1-10
1642-1817
Pojawia się w:
Hydroacoustics
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Implementacja sztucznych sieci neuronowych w środowisku LabVIEW
Artificial neural networks in LabVIEW
Autorzy:
Rafiński, L.
Powiązania:
https://bibliotekanauki.pl/articles/268930.pdf
Data publikacji:
2008
Wydawca:
Politechnika Gdańska. Wydział Elektrotechniki i Automatyki
Tematy:
sztuczne sieci neuronowe
artificial neural networks
Opis:
Przedstawiono możliwości oraz strukturę zrealizowanego przez autora modułu do implementacji sztucznych sieci neuronowych w środowisku LabVIEW.
The article shows the structure and capabilities of a LabVIEW module for the artficial neural networks implementation designed by the author.
Źródło:
Zeszyty Naukowe Wydziału Elektrotechniki i Automatyki Politechniki Gdańskiej; 2008, 25; 141-143
1425-5766
2353-1290
Pojawia się w:
Zeszyty Naukowe Wydziału Elektrotechniki i Automatyki Politechniki Gdańskiej
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Correction of gas sensor dynamic errors by means of neural networks
Autorzy:
Roj, J.
Urzędniczok, H.
Powiązania:
https://bibliotekanauki.pl/articles/114150.pdf
Data publikacji:
2015
Wydawca:
Stowarzyszenie Inżynierów i Techników Mechaników Polskich
Tematy:
gas sensors
artificial neural networks
dynamic correction
Opis:
The paper presents a method based on artificial neural network (ANN) technique applied for correction of dynamic error of gas concentration measuring transducer. Its response time is about 8 minutes. The results obtained in the research of this transducer were used for learning and testing ANN, which were implemented in the dynamic correction task. The described method allowed for significant reduction of the transducer’s response time – the output signal was practically fixed after a time equal to one sampling period of output signal provided that the stimulus is a step function. In addition, the use of ANN allows reducing the impact of the transducer dynamic non-linearity on the correction effectiveness.
Źródło:
Measurement Automation Monitoring; 2015, 61, 12; 538-541
2450-2855
Pojawia się w:
Measurement Automation Monitoring
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Simultaneous measurement of Cr, Mn and Fe diffusion in chromium-manganese steels
Autorzy:
Dudała, J.
Gilewicz-Wolter, J.
Stęgowski, Z.
Powiązania:
https://bibliotekanauki.pl/articles/148882.pdf
Data publikacji:
2005
Wydawca:
Instytut Chemii i Techniki Jądrowej
Tematy:
radiotracers
diffusion
steel
spectrum
artificial neural networks
Opis:
The paper presents an application of multitracer method to diffusion measurement in Cr-Mn steels. Radioisotope tracers of chromium 51Cr, manganese 54Mn and iron 59Fe were used simultaneously in the diffusion process. Measurements of gamma-ray spectra and the proper analysis enabled evaluation of concentration distribution for each tracer. As a new tool, artificial neural networks (ANN) method was used for analysis of spectra. The proper solution of the diffusion model was applied to the experimental tracers' distribution data and diffusion coefficients were determined.
Źródło:
Nukleonika; 2005, 50, 2; 67-71
0029-5922
1508-5791
Pojawia się w:
Nukleonika
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Attempt to utilise histogram of vibration cepstrum of engine body for setting up the clearance model of the piston-cylinder assembly for PNN neural classifier
Autorzy:
Madej, H.
Czech, P.
Powiązania:
https://bibliotekanauki.pl/articles/243660.pdf
Data publikacji:
2008
Wydawca:
Instytut Techniczny Wojsk Lotniczych
Tematy:
diagnostics
combustion engines
artificial neural networks
vibration
Opis:
The paper presents an attempt to evaluate the wear of piston-cylinder assembly with the aid of vibration signal recorded on spark ignition (SI) engine body. The subject of the study was a four-cylinder combustion engine 1.2 dm3. Diagnosing combustion engines with vibration methods is specifically difficult due to the presence of multiple sources of vibration interfering with the symptoms of damages. Diagnosing engines with vibroacustic methods is difficult also due to the necessity to analyse non-stationary and transient signals [5]. Various methods for selection of usable signal are utilised in the diagnosing process. Changes of the engine technical condition resulting from early stages of wear are difficult to detect for the effect of mechanical defect masking by adaptive engine control systems [3]. According to the studies carried out, it is possible to utilise artificial neural networks for the evaluation of the clearance in piston-cylinder assembly. It was proven that it is possible to set up a properly operating neural classifier able to identify the degree of wear in the piston-cylinder assembly, based on the signal of vibration acceleration in the engine body. Faultless classification was successfully obtained with the use of probabilistic neural network with properly selected value of y coefficient. At the same time, based on the experiments carried out, the crucial role was confirmed for the selection of proper method for pre-treatment of data intended for neural network teaching.
Źródło:
Journal of KONES; 2008, 15, 3; 305-311
1231-4005
2354-0133
Pojawia się w:
Journal of KONES
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Development of a Committee of Artificial Neural Networks for the Performance Testing of Compressors for Thermal Machines in Very Reduced Times
Autorzy:
Coral, R.
Flesch, C. A.
Penz, C. A.
Borges, M. R.
Powiązania:
https://bibliotekanauki.pl/articles/221092.pdf
Data publikacji:
2015
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
refrigeration compressor
artificial neural networks
performance test
Opis:
This paper presents a new test method able to infer - in periods of less than 7 seconds - the refrigeration capacity of a compressor used in thermal machines, which represents a time reduction of approximately 99.95% related to the standardized traditional methods. The method was developed aiming at its application on compressor manufacture lines and on 100% of the units produced. Artificial neural networks (ANNs) were used to establish a model able to infer the refrigeration capacity based on the data collected directly on the production line. The proposed method does not make use of refrigeration systems and also does not require using the compressor oil.
Źródło:
Metrology and Measurement Systems; 2015, 22, 1; 79-88
0860-8229
Pojawia się w:
Metrology and Measurement Systems
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
A repeatability study of artificial neural network predictions in flow stress model development for a magnesium alloy
Autorzy:
Siewior, Hubert
Madej, Łukasz
Powiązania:
https://bibliotekanauki.pl/articles/29520089.pdf
Data publikacji:
2021
Wydawca:
Akademia Górniczo-Hutnicza im. Stanisława Staszica w Krakowie. Wydawnictwo AGH
Tematy:
flow stress
artificial neural networks
feedforward
recursive
Opis:
This work is devoted to an evaluation of the capabilities of artificial neural networks (ANN) in terms of developing a flow stress model for magnesium ZE20. The learning procedure is based on experimental flow-stress data following inverse analysis. Two types of artificial neural networks are investigated: a simple feedforward version and a recursive one. Issues related to the quality of input data and the size of the training dataset are presented and discussed. The work confirms the general ability of feedforward neural networks in flow stress data predictions. It also highlights that slightly better quality predictions are obtained using recursive neural networks.
Źródło:
Computer Methods in Materials Science; 2021, 21, 4; 209-218
2720-4081
2720-3948
Pojawia się w:
Computer Methods in Materials Science
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Application of artificial neural networks in parametrical investigations of the energy flow and synchronization
Aplikacje sztucznych sieci neuronowych w badaniach parametrycznych przepływu i synchronizacji energii
Autorzy:
Dąbrowski, A.
Jach, A.
Kapitaniak, T.
Powiązania:
https://bibliotekanauki.pl/articles/279946.pdf
Data publikacji:
2010
Wydawca:
Polskie Towarzystwo Mechaniki Teoretycznej i Stosowanej
Tematy:
nonlinear dynamics
chaos synchronization
artificial neural networks
Opis:
Dynamics of nonlinear systems is a very complicated problem with many aspects to be recognized. Numerous methods are used to investigate such systems. Their careful analysis is connected with long-time simulations. Thus, there is great need for methods that would simplify these processes. In the paper, an application of Artificial Neural Networks (ANNs) supporting the recognition of the energy flow and the synchronization with use of Impact Maps is introduced. This connection applies an idea of the Energy Vector Space in the system with impacts. An energy flow direction change with the synchronization as a transitional state is shown. A new type of the index allowing one to control the system dynamic state is introduced. Results of the numerical simulations are used in the neural network teaching process. Results of a comparison of the straight impact map simulation and the neural network prediction are shown. Prediction of system parameters for the energy flow synchronization state with use of the neural network is presented.
Dynamika układów nieliniowych jest bardzo komplikowanymzagadnieniem z wieloma aspektami wciąż pozostającymi bez rozwiązania. Do badań takich układów stosuje się wiele różnych metod. Wnikliwa analiza związana jest najczęściej z bardzo czasochłonnymi symulacjami numerycznymi. Istnieje w związku z tym duże zapotrzebowanie na opracowanie metod upraszczających ten proces. W artykule pokazano zastosowanie sztucznych sieci neuronowych (ANN) wspomagających badania przepływu i synchronizacji energii. W badaniach zastosowano Mapy Uderzeń, będące efektem przedstawienia dynamiki układu z uderzeniami w przestrzeni energetyczno-wektorowej. Pokazano zmiany przepływu energii z przejściowym stanem synchronizacji. Wprowadzono nowy rodzaj parametru pozwalającego na określanie stanu dynamicznego układu z uderzeniami. Wyniki przeprowadzonych symulacji numerycznych zostały wykorzystane w procesie uczenia sztucznej sieci neuronowej. Przedstawiono następnie porównanie wyników symulacji i rozwiązania uzyskanego z sieci neuronowej oraz przewidywania parametrów układu, dla których występuje synchronizacja przepływu energii.
Źródło:
Journal of Theoretical and Applied Mechanics; 2010, 48, 4; 871-896
1429-2955
Pojawia się w:
Journal of Theoretical and Applied Mechanics
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Modelling of the dynamics of a gyroscope using artificial neural networks
Zastosowanie sztucznych sieci neuronowych do modelowania dynamiki giroskopu
Autorzy:
Łacny, Ł.
Powiązania:
https://bibliotekanauki.pl/articles/281929.pdf
Data publikacji:
2012
Wydawca:
Polskie Towarzystwo Mechaniki Teoretycznej i Stosowanej
Tematy:
artificial neural networks
dynamical systems
emulation
gyroscopes
Opis:
It this paper, a neural network was utilized in order to create an emulator, which could mimic the behaviour and nonlinear dynamics of a gyroscope with two axes of freedom, subjected to both low- and high-frequency excitation. For this purpose, several known learning methods, such as the gradient and Levenberg-Margquardt method, were used. Three different models of neural networks were considered and compared for their effectiveness: NNFIR, NNARX and the recurrent network NNARMAX.
W niniejszej pracy przedstawiono, w jaki sposób przy użyciu sztucznej sieci neuronowej możliwe jest stworzenie emulatora, który naśladuje zachowanie i nieliniową dynamikę giroskopu o dwóch osiach swobodnych, poddanego wymuszeniom zarówno o niskiej, jak i wysokiej częstotliwości. W celu nauczenia sieci neuronowej, wykorzystano szereg dostępnych algorytmów uczących (m.in. gradientowy, Levenberga-Margquadta). Przetestowano oraz porównano trzy różniące się od siebie modele sieci neuronowych: NNFIR, NNARX oraz sieć rekurencyjną NNARMAX.
Źródło:
Journal of Theoretical and Applied Mechanics; 2012, 50, 1; 85-97
1429-2955
Pojawia się w:
Journal of Theoretical and Applied Mechanics
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
IC engine valve fault detection using energy distribution of different resolution levels of dwt as a input data to PNN classifier
Autorzy:
Madej, H.
Czech, P.
Powiązania:
https://bibliotekanauki.pl/articles/245388.pdf
Data publikacji:
2009
Wydawca:
Instytut Techniczny Wojsk Lotniczych
Tematy:
diagnostics
combustion engines
artificial neural networks
vibration
Opis:
This article presents the attempt to detect the valve faults in the engine by using the vibroacoustic signal registered on the si engine block. the object of the research was 4-cylinder 4-stroke with eight valves 1.3 l SI engine. the vibration energy casedby combustion process depends on the average rotation speed and the crankshaft position. Mechanical faults which are having an impact on combustion pressure and misfire cause temporary changes of the rotational speed and instantaneous energy spectral density. Form the research analyzed it shows that there is apossibility of using artificial neural networks to assess the condition of the valves in the combustion engines. As part of the study, the descriptors calculated on the basis of the vibration acceleration signal registered on the engine block were proposed to serve as the source of information on the engine condition. The results have corroborated effectiveness of using the signal approximation and detail energy, acquired from the discrete wavelet decomposition, as the base for building models of engine operation. the use of a probabilistic neural network with a correctly selected value of coefficient gamma enables obtaining a faultless classification.
Źródło:
Journal of KONES; 2009, 16, 4; 307-313
1231-4005
2354-0133
Pojawia się w:
Journal of KONES
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
From Linear Classifier to Convolutional Neural Network for Hand Pose Recognition
Autorzy:
Rościszewski, P.
Powiązania:
https://bibliotekanauki.pl/articles/305776.pdf
Data publikacji:
2017
Wydawca:
Akademia Górniczo-Hutnicza im. Stanisława Staszica w Krakowie. Wydawnictwo AGH
Tematy:
machine learning
artificial neural networks
computer vision
Opis:
Recently gathered image datasets and new capabilities of high performance computing systems allowed developing new artificial neural network models and training algorithms. Using the new machine learning models, computer vision tasks can be accomplished based on the raw values of image pixels, instead of specific features. The principle of operation of deep artificial neural networks is more and more resembling of what we believe to be happening in the human visual cortex. In this paper we build up an understanding of convolutional neural networks through investigating supervised machine learning methods suchas K-Nearest Neighbors, linear classifiers and fully connected neural networks. We provide examples and accuracy results based on our implementation aimed for the problem of hand pose recognition.
Źródło:
Computer Science; 2017, 18 (4); 341-356
1508-2806
2300-7036
Pojawia się w:
Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Learning from heterogeneously distributed data sets using artificial neural networks and genetic algorithms
Autorzy:
Peteiro-Barral, D.
Guijarro-Berdiñas, B.
Pérez-Sánchez, B.
Powiązania:
https://bibliotekanauki.pl/articles/91888.pdf
Data publikacji:
2012
Wydawca:
Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
Tematy:
artificial neural networks
genetic algorithm
Devonet algorithm
Opis:
It is a fact that traditional algorithms cannot look at a very large data set and plausibly find a good solution with reasonable requirements of computation (memory, time and communications). In this situation, distributed learning seems to be a promising line of research. It represents a natural manner for scaling up algorithms inasmuch as an increase of the amount of data can be compensated by an increase of the number of distributed locations in which the data is processed. Our contribution in this field is the algorithm Devonet, based on neural networks and genetic algorithms. It achieves fairly good performance but several limitations were reported in connection with its degradation in accuracy when working with heterogeneous data, i.e. the distribution of data is different among the locations. In this paper, we take into account this heterogeneity in order to propose several improvements of the algorithm, based on distributing the computation of the genetic algorithm. Results show a significative improvement of the performance of Devonet in terms of accuracy.
Źródło:
Journal of Artificial Intelligence and Soft Computing Research; 2012, 2, 1; 5-20
2083-2567
2449-6499
Pojawia się w:
Journal of Artificial Intelligence and Soft Computing Research
Dostawca treści:
Biblioteka Nauki
Artykuł
Wyświetlanie 1-15 z 335

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