Skocz do pozycji: 1.

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

/5

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Skocz do pozycji: 2.

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

/5

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Skocz do pozycji: 3.

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

/5

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Skocz do pozycji: 4.

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

/5

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Skocz do pozycji: 5.

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

/5

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Skocz do pozycji: 6.

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

/5

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Skocz do pozycji: 7.

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

/5

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Skocz do pozycji: 8.

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

/5

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Skocz do pozycji: 9.

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

/5

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Skocz do pozycji: 10.

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

/5

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Skocz do pozycji: 11.

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

/5

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Skocz do pozycji: 12.

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

/5

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Skocz do pozycji: 13.

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

/5

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Skocz do pozycji: 14.

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

/5

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Skocz do pozycji: 15.

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

/5

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Skocz do pozycji: 16.

Tytuł:

Automatic Fault Classification for Journal Bearings Using ANN and DNN

Autorzy:

Narendiranath Babu, T.
Aravind, A.
Rakesh, A.
Jahzan, M.
Rama Prabha, D.
Ramalinga Viswanathan, M.

Powiązania:

https://bibliotekanauki.pl/articles/177579.pdf

Data publikacji:

2018

Wydawca:

Polska Akademia Nauk. Czytelnia Czasopism PAN

Tematy:

journal bearing
fault classification
artificial neural networks
deep neural networks

Opis:

Journal bearings are the most common type of bearings in which a shaft freely rotates in a metallic sleeve. They find a lot of applications in industry, especially where extremely high loads are involved. Proper analysis of the various bearing faults and predicting the modes of failure beforehand are Essentials to increase the working life of the bearing. In the current study, the vibration data of a journal Bering in the healthy condition and in five different fault conditions are collected. A feature extraction metod is employed to classify the different fault conditions. Automatic fault classification is performed using artificial neural networks (ANN). As the probability of a correct prediction goes down for a higher number of faults in ANN, the method is made more robust by incorporating deep neural networks (DNN) with the help of autoencoders. Training was done using the scaled conjugate gradient algorithm and the performance was calculated by the cross entropy method. Due to the increased number of hidden layers in DNN, it is possible to achieve a high efficiency of 100% with the feature extraction method.

Źródło:

Archives of Acoustics; 2018, 43, 4; 727-738
0137-5075

Pojawia się w:

Archives of Acoustics

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 17.

Tytuł:

Effect of process parameters on the condition of the wire electrode in WEDM of Ti6Al4V

Autorzy:

Zaborski, S.
Poroś, D.

Powiązania:

https://bibliotekanauki.pl/articles/1429400.pdf

Data publikacji:

2008

Wydawca:

Wrocławska Rada Federacji Stowarzyszeń Naukowo-Technicznych

Tematy:

titanium alloys
wire electrode
artificial neural networks
WEDM

Opis:

Conventional machining of titanium alloy Ti6Al4V cause high temperature and rapid wear of tool which makes him hardly suitable for machining by machine cutting. The presented experimental study was carried out on a modern wire EDM Sodick AQ327L. Three types of the wire were used. Investigated were the effects of such input parameters as the pulse width and the time between two pulses on the output parameters such as area cutting efficiency, workpiece surface roughness and wear rate of the electrode. The resulting relationships were determined using the conventional regression analysis and neural networks. The results were checked for goodness of fit.

Źródło:

Journal of Machine Engineering; 2008, 8, 2; 52-64
1895-7595
2391-8071

Pojawia się w:

Journal of Machine Engineering

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 18.

Tytuł:

Zastosowanie sztucznych sieci neuronowych do rozpoznawania metali na podstawie wykresu statycznej próby rozciągania
Application of artificial neural networks for recognition of metals on the basis of static tensile test chart

Autorzy:

Ewald, Dawid

Powiązania:

https://bibliotekanauki.pl/articles/41204115.pdf

Data publikacji:

2010

Wydawca:

Uniwersytet Kazimierza Wielkiego w Bydgoszczy

Tematy:

sztuczne sieci neuronowe
FANN
Delphi
artificial neural networks

Opis:

W artykule przedstawia zagadnienie sztucznych sieci neuronowych oraz ich wykorzystania w klasyfikacji metali na podstawie wykresu statycznej próby rozciągania. W pracy opisano działanie sieci neuronowych oraz sposób ich wykorzystania..
In this article presents the issue of artificial neural networks and their use in the classification of metals on the basis of the static tensile test chart. This paper describes the operation of neural networks and how to use them.

Źródło:

Studia i Materiały Informatyki Stosowanej; 2010, 3; 21-30
1689-6300

Pojawia się w:

Studia i Materiały Informatyki Stosowanej

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 19.

Tytuł:

Cognitive Modeling and Formation of the Knowledge Base of the Information System for Assessing the Rating of Enterprises

Autorzy:

Kryvoruchko, Olena
Desiatko, Alona
Karpunin, Igor
Hnatchenko, Dmytro
Lakhno, Myroslav
Malikova, Feruza
Turdaliev, Ayezhan

Powiązania:

https://bibliotekanauki.pl/articles/27311936.pdf

Data publikacji:

2023

Wydawca:

Polska Akademia Nauk. Czasopisma i Monografie PAN

Tematy:

information security
audit
Bayesian network
artificial neural networks

Opis:

A mathematical model is proposed that makes it possible to describe in a conceptual and functional aspect the formation and application of a knowledge base (KB) for an intelligent information system (IIS). This IIS is developed to assess the financial condition (FC) of the company. Moreover, for circumstances related to the identification of individual weakly structured factors (signs). The proposed model makes it possible to increase the understanding of the analyzed economic processes related to the company's financial system. An iterative algorithm for IIS has been developed that implements a model of cognitive modeling. The scientific novelty of the proposed approach lies in the fact that, unlike existing solutions, it is possible to adjust the structure of the algorithm depending on the characteristics of a particular company, as well as form the information basis for the process of assessing the company's FC and the parameters of the cognitive model.

Źródło:

International Journal of Electronics and Telecommunications; 2023, 69, 4; 697--705
2300-1933

Pojawia się w:

International Journal of Electronics and Telecommunications

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 20.

Tytuł:

Rotor blade geometry optimization in kaplan turbine

Autorzy:

Banaszek, M.
Tesch, K.

Powiązania:

https://bibliotekanauki.pl/articles/1943215.pdf

Data publikacji:

2010

Wydawca:

Politechnika Gdańska

Tematy:

fluid mechanics
turbomachinery
genetic algorithms
artificial neural networks

Opis:

This paper presents a description of the method and results of rotor blade shape optimization. The rotor blading constitutes a part of a turbine’s flow path. The optimization consists in selecting a shape that minimizes the polytrophic loss ratio [1]. The shape of the blade is defined by the mean camber line and thickness of the airfoil. The thickness is distributed around the camber line based on the ratio of distribution. A global optimization was done by means of Genetic Algorithms (GA) with the help of Artificial Neural Networks (ANN) for approximations. For the numerical simulation of a flow through the model Kaplan turbine, the geometry employed in the model was based on the actual geometry of the existing test stage. The fluid parameters and the boundary conditions for the model were based on experimental measurements which were carried out at the test stand at the Department of Turbomachinery and Fluid Mechanics at the Gdansk University of Technology. The shape of the blading was optimized for the operational point with a maximum efficiency.

Źródło:

TASK Quarterly. Scientific Bulletin of Academic Computer Centre in Gdansk; 2010, 14, 3; 209-225
1428-6394

Pojawia się w:

TASK Quarterly. Scientific Bulletin of Academic Computer Centre in Gdansk

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 21.

Tytuł:

Person movement prediction using artificial neural networks with dynamic training on a fixed-size training data set

Autorzy:

Mikluščak, T.
Gregor, M.

Powiązania:

https://bibliotekanauki.pl/articles/118229.pdf

Data publikacji:

2011

Wydawca:

Polskie Towarzystwo Promocji Wiedzy

Tematy:

person movement prediction
smart environments
artificial neural networks

Opis:

Significant technical development over the last years has lately been showing more and more promise of making the vision of smart environments come true. The role of future smart environments lies in proactive interaction. Prediction of user’s actions plays a vital role in such interaction. This paper presents a method based on artificial neural networks designed to accommodate the problem of person movement prediction. The paper explores the importance of dynamic training in prediction of nonstationary time series. An approach to dynamic training, based on the so-called on-the-fly training, is presented.

Źródło:

Applied Computer Science; 2011, 7, 2; 33-46
1895-3735

Pojawia się w:

Applied Computer Science

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 22.

Tytuł:

Artificial neural network modelling of cutting force components during AZ91HP alloy milling

Autorzy:

Kulisz, M.
Zagórski, I.
Semeniuk, A.

Powiązania:

https://bibliotekanauki.pl/articles/118253.pdf

Data publikacji:

2016

Wydawca:

Polskie Towarzystwo Promocji Wiedzy

Tematy:

simulation
cutting force
artificial neural networks
magnesium alloys

Opis:

The paper presents simulation of the cutting force components for ma-chining of magnesium alloy AZ91HP. The simulation employs the Black Box model. The closest match to (input and output) data obtained from the machining process was determined. The simulation was performed with the use of the Statistica programme with the application of neural networks: RBF (Radial Basis Function) and MLP (Multi-Layered Perceptron).

Źródło:

Applied Computer Science; 2016, 12, 4; 49-58
1895-3735

Pojawia się w:

Applied Computer Science

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 23.

Tytuł:

An adaptive control system of roadheader with intelligent modelling of mechanical features of mined rock

Autorzy:

Jasiulek, D.
Stankiewicz, K.
Świder, J.

Powiązania:

https://bibliotekanauki.pl/articles/242129.pdf

Data publikacji:

2011

Wydawca:

Instytut Techniczny Wojsk Lotniczych

Tematy:

roadheader
artificial neural networks
rocks properties
control system

Opis:

An idea of use of artificial intelligence technology for determination of selected parameters of roadheader operation, by a direct implementation of artificial neural network in control system of machine, was presented in the paper. The roadheaders operates in hard coal mines underground in extremely difficult environmental conditions. Technological process of driving of roadheader depends on many factors such as technical parameters of machine, mechanical and physical properties of rocks and operator's skills. It is difficult to develop a conventional system that could help in control operation of actuators of the machine, and increase the utilization of machine technical potential and improve rate of roadway development advance, due to mining-and-geological conditions (including mechanical and physical features of rocks), which are variable during mining. Proposed system for control of roadheader, as an adaptive system equipped with artificial neural network, will react to changes in operational space of machine. Improved machine performance will be possible due to use of artificial intelligence technology, which aids analysis of conditions of machine operation such as type of mined rock, size of excavation or web depth, makes possible inference process of introducing adequate changes of actuators control values.

Źródło:

Journal of KONES; 2011, 18, 2; 197-203
1231-4005
2354-0133

Pojawia się w:

Journal of KONES

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 24.

Tytuł:

Optimization of a thin-walled element geometry using a system integrating neural networks and finite element method

Autorzy:

Golewski, P.
Gajewski, J.
Sadowski, T.

Powiązania:

https://bibliotekanauki.pl/articles/351314.pdf

Data publikacji:

2017

Wydawca:

Polska Akademia Nauk. Czytelnia Czasopism PAN

Tematy:

artificial neural networks
numerical modelling
thin-walled element

Opis:

Artificial neural networks [ANNs] are an effective method for predicting and classifying variables. This article presents the application of an integrated system based on artificial neural networks and calculations by the finite element method [FEM] for the optimization of geometry of a thin-walled element of an air structure. To ensure optimal structure, the structure’s geometry was modified by creating side holes and ribs, also with holes. The main criterion of optimization was to reduce the structure’s weight at the lowest possible deformation of the tested object. The numerical tests concerned a fragment of an elevator used in the “Bryza” aircraft. The tests were conducted for networks with radial basis functions [RBF] and multilayer perceptrons [MLP]. The calculations described in the paper are an attempt at testing the FEM - ANN system with respect to design optimization.

Źródło:

Archives of Metallurgy and Materials; 2017, 62, 1; 435-442
1733-3490

Pojawia się w:

Archives of Metallurgy and Materials

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 25.

Tytuł:

Wykorzystanie sztucznych sieci neuronowych do prognozowania wyników meczów piłkarskich
Using artificial neural networks to predict the results of football matches

Autorzy:

BARTMAN, Jacek
BAJDA, Konrad

Powiązania:

https://bibliotekanauki.pl/articles/456764.pdf

Data publikacji:

2014

Wydawca:

Uniwersytet Rzeszowski

Tematy:

Sztuczne Sieci Neuronowe
prognozowania
Artificial Neural Networks
prediction

Opis:

W pracy zaprezentowano koncepcję wykorzystania Sztucznych Sieci Neuronowych do prognozowania wyników meczów. Przedstawiono architekturę sieci oraz skuteczność realizowanych przez nią prognoz. Uzyskane wyniki zestawiono z wynikami otrzymanymi przy wykorzystaniu innych metod.
The paper presents the concept of using Artificial Neural Networks to predict the results of football matches. Autors presents the architecture of the network and the effectiveness of the implementation by the forecasts. The results were compared with results obtained using other methods

Źródło:

Edukacja-Technika-Informatyka; 2014, 5, 2; 425-431
2080-9069

Pojawia się w:

Edukacja-Technika-Informatyka

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 26.

Tytuł:

Automation of Information Security Risk Assessment

Autorzy:

Akhmetov, Berik
Lakhno, Valerii
Chubaievskyi, Vitalyi
Kaminskyi, Serhii
Adilzhanova, Saltanat
Ydyryshbayeva, Moldir

Powiązania:

https://bibliotekanauki.pl/articles/2124744.pdf

Data publikacji:

2022

Wydawca:

Polska Akademia Nauk. Czytelnia Czasopism PAN

Tematy:

information security
audit
Bayesian network
artificial neural networks

Opis:

An information security audit method (ISA) for a distributed computer network (DCN) of an informatization object (OBI) has been developed. Proposed method is based on the ISA procedures automation by using Bayesian networks (BN) and artificial neural networks (ANN) to assess the risks. It was shown that such a combination of BN and ANN makes it possible to quickly determine the actual risks for OBI information security (IS). At the same time, data from sensors of various hardware and software information security means (ISM) in the OBI DCS segments are used as the initial information. It was shown that the automation of ISA procedures based on the use of BN and ANN allows the DCN IS administrator to respond dynamically to threats in a real time manner, to promptly select effective countermeasures to protect the DCS.

Źródło:

International Journal of Electronics and Telecommunications; 2022, 68, 3; 549--555
2300-1933

Pojawia się w:

International Journal of Electronics and Telecommunications

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 27.

Tytuł:

Application of Artificial Neural Networks for Prediction of Air Pollution Levels in Environmental Monitoring

Autorzy:

Pawul, M.
Śliwka, M.

Powiązania:

https://bibliotekanauki.pl/articles/124279.pdf

Data publikacji:

2016

Wydawca:

Polskie Towarzystwo Inżynierii Ekologicznej

Tematy:

environmental monitoring
air pollution
artificial neural networks
prediction

Opis:

Recently, a lot of attention was paid to the improvement of methods which are used to air quality forecasting. Artificial neural networks can be applied to model these problems. Their advantage is that they can solve the problem in the conditions of incomplete information, without the knowledge of the analytical relationship between the input and output data. In this paper we applied artificial neural networks to predict the PM 10 concentrations as factors determining the occurrence of smog phenomena. To create these networks we used meteorological data and concentrations of PM 10. The data were recorded in 2014 and 2015 at three measuring stations operating in Krakow under the State Environmental Monitoring. The best results were obtained by three-layer perceptron with back-propagation algorithm. The neural networks received a good fit in all cases.

Źródło:

Journal of Ecological Engineering; 2016, 17, 4; 190-196
2299-8993

Pojawia się w:

Journal of Ecological Engineering

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 28.

Tytuł:

2D Geometric Surface Structure ANN Modeling after Milling of the AZ91D Magnesium Alloy

Autorzy:

Kulisz, Monika
Zagórski, Ireneusz
Józwik, Jerzy

Powiązania:

https://bibliotekanauki.pl/articles/2172344.pdf

Data publikacji:

2022

Wydawca:

Stowarzyszenie Inżynierów i Techników Mechaników Polskich

Tematy:

magnesium alloys
milling
roughness
artificial neural networks
simulations

Opis:

The paper presents the results of modeling 2D surface roughness parameters in milling by means of an artificial neural network (ANN). The AZ91D magnesium alloy was used. A HSS milling cutter was employed in the research. The main aim of the study was to obtain the lowest possible surface roughness (good quality) using a commonly available HSS cutter. The results of the research work were presented in the form of bar charts, surface charts and graphs depicting the quality of artificial neural networks. The conducted research shows that it is possible to carry out the machining processes that enable obtaining an average surface quality (defined by roughness parameters Ra, Rz, RSm, Rsk). The Ra, Rz, RSm parameters increase along with the machining parameters (fz, ap), as expected. The Rsk parameter takes (in most cases) negative values, which may indicate a surface with more intense friction and indicative of flat-topped distribution. On the other hand, the results of modeling the selected parameters – Ra, Rz, RSm – with the use of artificial neural networks allow concluding that the obtained network models show satisfactory predictive ability (R = 0.99), and thus are an appropriate tool for the prediction of surface roughness parameters.

Źródło:

Advances in Science and Technology. Research Journal; 2022, 16, 2; 131--140
2299-8624

Pojawia się w:

Advances in Science and Technology. Research Journal

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 29.

Tytuł:

Application of EMD ANN and DNN for Self-Aligning Bearing Fault Diagnosis

Autorzy:

Narendiranath, B. T.
Aravind, A.
Rakesh, A.
Jahzan, M.
Rama, P. D.

Powiązania:

https://bibliotekanauki.pl/articles/176889.pdf

Data publikacji:

2018

Wydawca:

Polska Akademia Nauk. Czytelnia Czasopism PAN

Tematy:

self-aligning bearing
fault classification
artificial neural networks
deep neural networks

Opis:

Self-aligning roller bearings are an integral part of the industrial machinery. The proper analysis and prediction of the various faults that may happen to the bearing beforehand contributes to an increase in the working life of the bearing. This study aims at developing a novel method for the analysis of the various faults in self-aligning bearings as well as the automatic classification of faults using artificial neural network (ANN) and deep neural network (DNN). The vibration data is collected for six different faults as well as for the healthy bearing. Empirical mode decomposition (EMD) followed by Hilbert Huang transform is used to extract instantaneous frequency peaks which are used for fault analysis. Time domain and time-frequency domain features are then extracted which are used to implement the neural networks through the pattern recognition tool in MATLAB. A comparative study of the outputs from the two neural networks is also performed. From the confusion matrix, the efficiency of the ANN has been found to be 95.7% and using DNN has been found to be 100%.

Źródło:

Archives of Acoustics; 2018, 43, 2; 163-175
0137-5075

Pojawia się w:

Archives of Acoustics

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 30.

Tytuł:

Modeling of shape memory alloy springs using a recurrent neural network

Autorzy:

Kardan, I
Abiri, R.
Kabganian, M.
Vahabi, M.

Powiązania:

https://bibliotekanauki.pl/articles/279784.pdf

Data publikacji:

2013

Wydawca:

Polskie Towarzystwo Mechaniki Teoretycznej i Stosowanej

Tematy:

artificial neural networks
smart materials
shape memory alloy springs

Opis:

In this paper, a recurrent neural network structure is proposed for the modeling of the behavior of shape memory alloy springs. Numerous mathematical modeling and experimental evaluations show that the force exerted by SMAs, aside from their length and applied voltages, depends on the loading path. Therefore, in addition to the applied voltage and deformation, a feedback of the voltage applied to, and the force exerted by the SMA spring in the previous time step is included in the inputs to this neural network to represent the loading path. Fed by adequate inputs, the NN estimates the output force of the spring. The results of some thermal loadings of the spring at various fixed lengths and mechanical loadings at various constant voltages are used to train the NN. The performance of the NN model is then evaluated for some constant weight loadings which are not learnt by the NN. Simulation results indicate that compared to other neural network structures, the proposed structure learns the behavior of the SMA spring faster (in less iteration). Moreover, it provides a more general model, i.e. this NN model effectively estimates the output force for almost all possible loadings.

Źródło:

Journal of Theoretical and Applied Mechanics; 2013, 51, 3; 711-718
1429-2955

Pojawia się w:

Journal of Theoretical and Applied Mechanics

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 31.

Tytuł:

Exchange Rates: Predictable but not Explainable? Data Mining with Leading Indicators and Technical Trading Rules
Możliwości modelowania i prognozowania kursów walutowych: wskaźniki wyprzedzające i analiza techniczna

Autorzy:

Brandl, Bernd

Powiązania:

https://bibliotekanauki.pl/articles/907593.pdf

Data publikacji:

2005

Wydawca:

Uniwersytet Łódzki. Wydawnictwo Uniwersytetu Łódzkiego

Tematy:

exchange rates
data mining
artificial neural networks
genetic algorithms

Opis:

This paper presents a data mining approach to forecasting exchange rates. It is assumed that exchange rates are determined by both fundamental and technical factors. The balance of fundamental and technical factors varies for each exchange rate and frequency. It is difficult for forecasters to establish the relative relevance of different kinds of factors given this mixture; therefore the utilization of data mining algorithms is advantageous. The approach applied uses a genetic algorithm and neural networks. Out-of-sample forecasting results are illustrated for five exchange rates on different frequencies and it is shown that data mining is able to produce forecasts that perform well.
W artykule przedstawiono proces eksploracji danych statystycznych w prognozowaniu kursów walutowych. Zakładamy, że kursy walutowe pozostają pod wpływem zarówno czynników o charakterze fundamentalnym, jak i czynników pozaekonomicznych. Równowaga pomiędzy tymi czynnikami różni się w zależności od rodzaju kursu walutowego i częstotliwości jego pomiaru. Prognostykom trudno jest ustalić względną siłę wpływu różnych czynników, stąd analiza polegająca na eksploracji danych ma określone zalety. W proponowanym podejściu wykorzystano algorytmy genetyczne i sztuczne sieci neuronowe. Przedstawiliśmy wyniki eksperymentów prognostycznych poza próbą statystyczną w odniesieniu do pięciu kursów walutowych, obserwowanych z różną częstotliwością. Pokazaliśmy, że metoda eksploracji danych może stanowić skuteczne narzędzie prognostyczne.

Źródło:

Acta Universitatis Lodziensis. Folia Oeconomica; 2005, 192
0208-6018
2353-7663

Pojawia się w:

Acta Universitatis Lodziensis. Folia Oeconomica

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 32.

Tytuł:

Evaluation of models for the dew point temperature determination

Autorzy:

Górnicki, K.
Winiczenko, R.
Kaleta, A.
Choińska, A.

Powiązania:

https://bibliotekanauki.pl/articles/298023.pdf

Data publikacji:

2017

Wydawca:

Uniwersytet Warmińsko-Mazurski w Olsztynie

Tematy:

dew point temperature
relative humidity
model
artificial neural networks

Opis:

The accuracy of the available from the literature models for the dew point temperature determination was compared. The proposal of the modelling using artificial neural networks was also given. The experimental data were taken from the psychrometric tables. The accuracies of the models were measured using the mean bias error MBE, root mean square error RMSE, correlation coefficient R, and reduced chi-square χ² . Model M3, especially with constants A=237, B=7.5, gave the best results in determining the dew point temperature (MBE: -0.0229 – 0.0038 K, RMSE: 0.1259 – 0.1286 K, R=0.9999, χ² : 0.0159 – 0.0166 K² ). Model M1 with constants A=243.5, B=17.67 and A=243.3, B=17.269 can be also considered as appropriate (MBE=-0.0062 and -0.0078 K, RMSE=0.1277 and 0.1261 K, R=0.9999, χ² =0.0163 and 0.0159 K² ). Proposed ANN model gave the good results in determining the dew point temperature (MBE=-0.0038 K, RMSE=0.1373 K, R=0.9999, χ² =0.0189 K² ).

Źródło:

Technical Sciences / University of Warmia and Mazury in Olsztyn; 2017, 20(3); 241--257
1505-4675
2083-4527

Pojawia się w:

Technical Sciences / University of Warmia and Mazury in Olsztyn

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 33.

Tytuł:

An idea of a measurement system for determining thermal parameters of heat insulation materials

Autorzy:

Chudzik, S.
Minkina, W.

Powiązania:

https://bibliotekanauki.pl/articles/220464.pdf

Data publikacji:

2011

Wydawca:

Polska Akademia Nauk. Czytelnia Czasopism PAN

Tematy:

thermal conductivity
artificial neural networks
inverse heat conduction problem

Opis:

The article presents the prototype of a measurement system with a hot probe, designed for testing thermal parameters of heat insulation materials. The idea is to determine parameters of thermal insulation materials using a hot probe with an auxiliary thermometer and a trained artificial neural network. The network is trained on data extracted from a nonstationary two-dimensional model of heat conduction inside a sample of material with the hot probe and the auxiliary thermometer. The significant heat capacity of the probe handle is taken into account in the model. The finite element method (FEM) is applied to solve the system of partial differential equations describing the model. An artificial neural network (ANN) is used to estimate coefficients of the inverse heat conduction problem for a solid. The network determines values of the effective thermal conductivity and effective thermal diffusivity on the basis of temperature responses of the hot probe and the auxiliary thermometer. All calculations, like FEM, training and testing processes, were conducted in the MATLAB environment. Experimental results are also presented. The proposed measurement system for parameter testing is suitable for temporary measurements in a building site or factory.

Źródło:

Metrology and Measurement Systems; 2011, 18, 2; 261-273
0860-8229

Pojawia się w:

Metrology and Measurement Systems

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 34.

Tytuł:

Optimization of flotation efficiency of phosphate minerals in mine tailings using polymeric depressants : experiments and machine learning

Autorzy:

Alsafasfeh, Ashraf
Alagha, Lana
Alzidaneen, Ala
Nadendla, Venkata Sriram Siddhardh

Powiązania:

https://bibliotekanauki.pl/articles/2146912.pdf

Data publikacji:

2022

Wydawca:

Politechnika Wrocławska. Oficyna Wydawnicza Politechniki Wrocławskiej

Tematy:

phosphate tailing
froth flotation
polymers
chitosan
artificial neural networks

Opis:

In this study, direct froth flotation experiments were conducted on silicate-rich phosphate tailing samples. The average grade of P2O5 in the flotation feed was 21.6% as determined using a combination of spectroscopic techniques including X-ray powder diffraction (XRD), mineral liberation analysis (MLA), and scanning electron microscopy/energy dispersive X-ray spectroscopy (SEM/EDS). Two polymers were selected to promote the depression of silicates and enhance the flotation of phosphates: in-house synthesized hybrid polyacrylamide (Hy-PAM) and chitosan. Flotation efficiency of phosphates was evaluated at different flotation conditions including depressant type, depressant dosage, pH, and the flotation time. Results indicated that the optimum flotation efficiency of phosphate minerals (84.6% recovery at 28.6% grade of P2O5) was obtained when Hy-PAM was utilized at the studied range of pH and flotation time. All datasets produced from the flotation experiments were integrated within the framework of machine learning (ML) using artificial neural networks (ANNs). The ANN platform was trained, validated, and successfully employed to predict the process outcomes in relation to the pulp and reagents characteristics, which in turn were used to determine the optimum values of process variables. Coefficient of determination (R2), mean absolute error (MAE), and root-mean-square error (RMSE) were used as model indicators. Optimization results showed that the peak flotation performance could be achieved at higher dosages of both polymers. However, lower pH and shorter flotation time for Hy-PAM, and higher pH and longer flotation time for chitosan, were predicted to give the optimum process efficiency.

Źródło:

Physicochemical Problems of Mineral Processing; 2022, 58, 4; art. no. 150477
1643-1049
2084-4735

Pojawia się w:

Physicochemical Problems of Mineral Processing

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 35.

Tytuł:

Semiautomatic land cover mapping according to the 2nd level of the CORINE Land Cover legend

Autorzy:

Golenia, M.
Zagajewski, B.
Ochtyra, A.
Hościło, A.

Powiązania:

https://bibliotekanauki.pl/articles/92466.pdf

Data publikacji:

2015

Wydawca:

Oddział Kartograficzny Polskiego Towarzystwa Geograficznego

Tematy:

classification
Corine Land Cover
Landsat
artificial neural networks
Warsaw

Opis:

Actual land cover maps are a very good source of information on present human activities. It increases value of actual spatial databases and it is a key element for decision makers. Therefore, it is important to develop fast and cheap algorithms and procedures of spatial data updating. Every day, satellite remote sensing deliver vast amount of new data, which can be semi-automatically classified. The paper presents a method of land cover classification based on a fuzzy artificial neural network simulator and Landsat TM satellite images. The latest CORINE Land Cover 2012 polygons were used as reference data. Three satellite images acquired 21 April 2011, 5 June 2010, 27 August 2011 over Warsaw and surrounding areas were processed. As an outcome of classification procedure, the maps, error matrices and a set of overall, producer and user accuracies and a kappa coefficient were achieved. The classification accuracy oscillates around 76% and confirms that artificial neural networks can be successfully used for forest, urban fabric, arable land, pastures, inland waters and permanent crops mapping. Low accuracies were obtained in case of heterogenic land cover units.

Źródło:

Polish Cartographical Review; 2015, 47, 4; 203-212
2450-6974

Pojawia się w:

Polish Cartographical Review

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 36.

Tytuł:

The use of artificial neural networks in supporting the annual training in 400 meter hurdles

Autorzy:

Iskra, Janusz
Przednowek, Krzysztof
Wiktorowicz, Krzysztof
Krzeszowski, Tomasz

Powiązania:

https://bibliotekanauki.pl/articles/1054811.pdf

Data publikacji:

2017

Wydawca:

Uniwersytet Szczeciński. Wydawnictwo Naukowe Uniwersytetu Szczecińskiego

Tematy:

400 meter hurdles
artificial neural networks
planning training loads

Opis:

This paper presents an evaluation of the annual cycle for 400 m hurdles using artificial neural networks. The analysis included 21 Polish national team hurdlers. In planning the annual cycle, 27 variables were used, where 5 variables describe the competitor and 22 variables represent the training loads. In the presented solution, the task of generating training loads for the assumed result were considered. The neural models were evaluated by cross-validation method. The smallest error was obtained for the radial basis function network with nine neurons in the hidden layer. The performed analysis shows that at each phase of training the structure of training loads is different.

Źródło:

Central European Journal of Sport Sciences and Medicine; 2017, 17, 1; 15-24
2300-9705
2353-2807

Pojawia się w:

Central European Journal of Sport Sciences and Medicine

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 37.

Tytuł:

Neuro-wavelet damage detection technique in beam, plate and shell structures with experimental validation
Wykrywanie uszkodzeń w konstrukcjach belkowych, płytowych i powłokowych przy użyciu systemu neuro-wavelet

Autorzy:

Rucka, M.
Wilde, K.

Powiązania:

https://bibliotekanauki.pl/articles/279993.pdf

Data publikacji:

2010

Wydawca:

Polskie Towarzystwo Mechaniki Teoretycznej i Stosowanej

Tematy:

damage detection
continuous wavelet transform (CWT)
artificial neural networks

Opis:

The paper presents a new neuro-wavelet damage detection technique for structural health monitoring. The proposed method combines the ability of the continuous wavelet transform to detect abnormalities in the structure dynamic parameters with the artificial neural network possibility of learning, remembering and recognition. The effectiveness of the method is verified on experimental mode shapes of a beam, plate and shell structures. The results of the study show that the neural network trained on the data from a simple structure can effectively improve the search of the location of the same type of damage in complex structures.
Niniejsza praca poświęcona jest technice diagnostyki konstrukcji bazującej na transformacie falkowej oraz sztucznych sieciach neuronowych (tzw. system neuro- wavelet). Zastosowanie analizy falkowej pozwala na lokalizację uszkodzeń wymagającą minimalnej ilości danych wejściowych. W tym celu niezbędna jest tylko odpowiedź konstrukcji pomierzona w wielu punktach. Poprawę efektywności lokalizacji zniszczeń uzyskano poprzez użycie sztucznej sieci neuronowej. Nauczona sieć neuronowa poprawnie rozpoznaje miejsce położenia uszkodzeń, nawet w przypadkach, gdy określenie położenia uszkodzenia nie było możliwe bezpośrednio z obliczonych współczynników falkowych. Zaproponowana metoda została sprawdzona eksperymentalnie na przykładach konstrukcji belkowych, płytowych i powłokowych.

Źródło:

Journal of Theoretical and Applied Mechanics; 2010, 48, 3; 579-604
1429-2955

Pojawia się w:

Journal of Theoretical and Applied Mechanics

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 38.

Tytuł:

Impulse artificial neural networks in internal transport

Autorzy:

Ochelska-Mierzejewska, J.

Powiązania:

https://bibliotekanauki.pl/articles/407219.pdf

Data publikacji:

2014

Wydawca:

Polska Akademia Nauk. Czytelnia Czasopism PAN

Tematy:

internal transport
impulse artificial neural networks
dynamic path planning

Opis:

The second most important function of a warehouse, apart from the storing of goods, is internal transport with a focus on time-effectiveness. When there is a time gap between the production and export of products, the goods need to be stored until they are dispatched to the consumers. An important problem that concerns both large and small warehouses is the selection of priorities, that is handling the tasks in order of importance. Another problem is to identify the most efficient routes for forklift trucks to transport goods from a start-point to a desired destination and prevent the routes from overlapping. In automated warehouses, the transport of objects (the so called pallets of goods) is performed by machines controlled by a computer instead of a human operator. Thus, it is the computer, not the man, that makes the difficult decisions regarding parallel route planning, so that the materials are transported within the warehouse in near-optimal time. This paper presents a method for enhancing this process.

Źródło:

Management and Production Engineering Review; 2014, 5, 2; 33-44
2080-8208
2082-1344

Pojawia się w:

Management and Production Engineering Review

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 39.

Tytuł:

Artificial Neural Networks vs Spatial Regression Approach in Property Valuation

Autorzy:

Przekop, Damian

Powiązania:

https://bibliotekanauki.pl/articles/2119889.pdf

Data publikacji:

2022

Wydawca:

Polska Akademia Nauk. Czytelnia Czasopism PAN

Tematy:

artificial neural networks
spatial regression
SDEM
GNS
property valuation

Opis:

The purpose of this paper is to compare two approaches applied in property valuation: artificial neural networks and spatial regression. Despite the fact that artificial neural networks are often the first choice for modeling in the big data era, spatial econometrics methods offer incorporation of information on dependences between multiple objects in the studied space. Although this dependency structure can be incorporated into artificial neural network via feature engineering, this study is focused on abilities of reproducing it with machine learning method from crude coordinate data. The research is based on the database of 18,166 property sale transactions in Warsaw, Poland. According to this study, such volume of data does not allow artificial neural networks to compete in reflecting spatial dependence structure with spatial regression models.

Źródło:

Central European Journal of Economic Modelling and Econometrics; 2022, 2; 99-223
2080-0886
2080-119X

Pojawia się w:

Central European Journal of Economic Modelling and Econometrics

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 40.

Tytuł:

Analiza komputerowa diagnozowania defektów alternatora z wykorzystaniem sztucznej sieci neuronowej
Computer analysis of alternator defects diagnosing using artificial neural network

Autorzy:

Jastriebow, A.
Gad, S.
Słoń, G.

Powiązania:

https://bibliotekanauki.pl/articles/328147.pdf

Data publikacji:

2002

Wydawca:

Polska Akademia Nauk. Polskie Towarzystwo Diagnostyki Technicznej PAN

Tematy:

sztuczna sieć neuronowa
alternator
diagnostyka
artificial neural networks
diagnostic

Opis:

Opracowano analizę komputerową do prowadzenia symulacyjnych badań statystycznych diagnozowania defektów alternatora za pośrednictwem sztucznych sieci neuronowych w postaci wielowarstwowych perceptronów. Na podstawie zbudowanego programu i generatora danych uczących przeanalizowano możliwość diagnostyki kilku defektów alternatora. Przedstawione wyniki symulacji dają pełną gwarancję efektywnego rozwiązania postawionego problemu.
The computer analysis for conducting of statistical simulating research of alternator defects diagnosing through artificial neural networks in the form of multi layer perceptrons has been worked out. On the basis of built program and teaching data generator, a possibility of some alternator defects diagnosing has been analyzed. Presented simulation results give full guarantee of effective solution of tested problem.

Źródło:

Diagnostyka; 2002, 27; 7-10
1641-6414
2449-5220

Pojawia się w:

Diagnostyka

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 41.

Tytuł:

A Comparative Evaluation of the Use of Artificial Neural Networks for Modeling the Rainfall-Runoff Relationship in Water Resources Management

Autorzy:

Turhan, Evren

Powiązania:

https://bibliotekanauki.pl/articles/1838400.pdf

Data publikacji:

2021

Wydawca:

Polskie Towarzystwo Inżynierii Ekologicznej

Tematy:

rainfall-runoff model
artificial neural networks
MLR
Nergizlik Dam

Opis:

Recently, Artificial Neural Network (ANN) methods, which have been successfully applied in many fields, have been considered for a large number of reliable streamflow estimation and modeling studies for the design and project planning of hydraulic structures. The present study aimed to model the rainfall-runoff relationship using different ANN methods. The Nergizlik Dam, located in the Seyhan sub-basin and one of the important basins in Turkey, was chosen as the study area. Analyses were carried out based on streamflow estimation with the help of observed precipitation and runoff data at certain time intervals. Feed Forward Backpropagation Neural Network (FFBPNN) and Generalized Regression Neural Network (GRNN) methods were adopted, and obtained results were compared with Multiple Linear Regression (MLR) method, which is accepted as the traditional method. Also, the models were performed using three different transfer functions to create optimum ANN modeling. As a result of the study, it was seen that ANN methods showed statistically good results in rainfall-runoff modeling, and the developed models can be successfully applied in the estimation of average monthly flows.

Źródło:

Journal of Ecological Engineering; 2021, 22, 5; 166-178
2299-8993

Pojawia się w:

Journal of Ecological Engineering

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 42.

Tytuł:

Enhancing constructive neural network performance using functionally expanded input data

Autorzy:

Bertini, Jr., J. R.
Carmo Nicoletti, do, M.

Powiązania:

https://bibliotekanauki.pl/articles/91786.pdf

Data publikacji:

2016

Wydawca:

Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych

Tematy:

constructive neural networks
functional link artificial neural networks
functionally expanded input data

Opis:

Constructive learning algorithms are an efficient way to train feedforward neural networks. Some of their features, such as the automatic definition of the neural network (NN) architecture and its fast training, promote their high adaptive capacity, as well as allow for skipping the usual pre-training phase, known as model selection. However, such advantages usually come with the price of lower accuracy rates, when compared to those obtained with conventional NN learning approaches. This is, perhaps, the reason for conventional NN training algorithms being preferred over constructive NN (CoNN) algorithms. Aiming at enhancing CoNN accuracy performance and, as a result, making them a competitive choice for machine learning based applications, this paper proposes the use of functionally expanded input data. The investigation described in this paper considered six two-class CoNN algorithms, ten data domains and seven polynomial expansions. Results from experiments, followed by a comparative analysis, show that performance rates can be improved when CoNN algorithms learn from functionally expanded input data.

Źródło:

Journal of Artificial Intelligence and Soft Computing Research; 2016, 6, 2; 119-131
2083-2567
2449-6499

Pojawia się w:

Journal of Artificial Intelligence and Soft Computing Research

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 43.

Tytuł:

The recognition of partially occluded objects with support vector machines, convolutional neural networks and deep belief networks

Autorzy:

Chu, J. L.
Krzyżak, A.

Powiązania:

https://bibliotekanauki.pl/articles/91650.pdf

Data publikacji:

2014

Wydawca:

Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych

Tematy:

neural networks
belief networks
convolutional neural networks
artificial neural networks
Deep Belief Network
generative model

Opis:

Biologically inspired artificial neural networks have been widely used for machine learning tasks such as object recognition. Deep architectures, such as the Convolutional Neural Network, and the Deep Belief Network have recently been implemented successfully for object recognition tasks. We conduct experiments to test the hypothesis that certain primarily generative models such as the Deep Belief Network should perform better on the occluded object recognition task than purely discriminative models such as Convolutional Neural Networks and Support Vector Machines. When the generative models are run in a partially discriminative manner, the data does not support the hypothesis. It is also found that the implementation of Gaussian visible units in a Deep Belief Network trained on occluded image data allows it to also learn to effectively classify non-occluded images.

Źródło:

Journal of Artificial Intelligence and Soft Computing Research; 2014, 4, 1; 5-19
2083-2567
2449-6499

Pojawia się w:

Journal of Artificial Intelligence and Soft Computing Research

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 44.

Tytuł:

A Comparative Analysis of the Semantic Field of ‘deception’: A Case Study Of Russian And American Imageboard Messages

Autorzy:

Lykova, Olga
Gordeev, Denis

Powiązania:

https://bibliotekanauki.pl/articles/2028581.pdf

Data publikacji:

2021-03-30

Wydawca:

Uniwersytet Łódzki. Wydawnictwo Uniwersytetu Łódzkiego

Tematy:

artificial neural networks
deception
semantic field
anonymity
word2vec

Opis:

This article uses the material of anonymous Internet forums to analyse the semantic field of deception by the instrumentality of artificial neural networks. Two major imageboards were investigated: 2ch.hk and 4chan.org, being the most popular Russian and American imageboards. For the experiment an algorithm called Word2vec was used to examine 30 million word usages for either of the languages. This analysis revealed 10 words with the greatest semantic proximity to terms from semantic fields of «deception» for Russian and American English. The results showed the tendency among native Russian imageboard users to link the concept of deception with religion and spiritual sphere, while American forum users associate deception with politics and related concepts.

Źródło:

Research in Language; 2021, 19, 1; 95-106
1731-7533

Pojawia się w:

Research in Language

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 45.

Tytuł:

A Neural Network Approach for Predicting Production Volume of Biofuels in Poland
Zastosowanie sieci neuronowych do prognozowania wielkości produkcji biopaliw w Polsce

Autorzy:

Siuda, Krzysztof

Powiązania:

https://bibliotekanauki.pl/articles/2024082.pdf

Data publikacji:

2021

Wydawca:

Wydawnictwo Uniwersytetu Ekonomicznego we Wrocławiu

Tematy:

artificial neural networks
biofuels
prediction
sztuczne sieci neuronowe
biopaliwa
predykcja

Opis:

This article focuses on the creation of artificial neural networks (ANN) and their use in predicting the volume of biofuel production in Poland on the basis of historical data. Artificial neural networks are extremely useful in predicting events in which it is difficult to find determinism and cause-effect relationships. For this purpose 30 artificial neural networks of different topology were created. The analysed artificial neural networks had: one or two layers, from 4 to 8 neurons on the first layer and 4 or 6 neurons on the second layer. Moreover, the effect of delayed inputs and the effect of learning set size on prediction quality were analysed. The quality of each structure was evaluated based on the coefficient of determination, mean error, and mean square error. The stability of prediction was evaluated based on the sample standard deviation of RMSE and MAE. All the presented ANN structures were simulated five times and the best individual results included in the tables. The best results were obtained for an artificial neural network with two layers, four neurons in each layer and one delay. Overall, the second layer increased the stability of the prediction. Streszczenie: W artykule skupiono się na tworzeniu sztucznych sieci neuronowych i ich wykorzystaniu do prognozowania wielkości produkcji biopaliw w Polsce na podstawie danych historycznych. Sztuczne sieci neuronowe są niezwykle przydatne w prognozowaniu zdarzeń, w których trudno doszukać się determinizmu i związków przyczynowo-skutkowych. W tym celu stworzono 30 sztucznych sieci neuronowych o różnej topologii. Analizowane
W artykule skupiono się na tworzeniu sztucznych sieci neuronowych i ich wykorzystaniu do prognozowania wielkości produkcji biopaliw w Polsce na podstawie danych historycznych. Sztuczne sieci neuronowe są niezwykle przydatne w prognozowaniu zdarzeń, w których trudno doszukać się determinizmu i związków przyczynowo-skutkowych. W tym celu stworzono 30 sztucznych sieci neuronowych o różnej topologii. Analizowane sztuczne sieci neuronowe miały: jedną lub dwie warstwy, od 4 do 8 neuronów w warstwie pierwszej oraz 4 lub 6 neuronów w warstwie drugiej. Ponadto przeanalizowano wpływ opóźnionych wejść oraz wpływ wielkości zbioru uczącego na jakość predykcji. Jakość każdej ze struktur oceniono na podstawie współczynnika determinacji, błędu średniego oraz błędu średniokwadratowego. Stabilność prognozowania była oceniana na podstawie odchylenia standardowego próby RMSE oraz MAE. Wszystkie przedstawione struktury ANN były symulowane pięciokrotnie, a najlepsze pojedyncze wyniki zamieszczono w tabelach. Najlepsze wyniki uzyskano dla sztucznej sieci neuronowej z dwiema warstwami, czterema neuronami w każdej warstwie i jednym opóźnieniem. Druga warstwa zwiększyła stabilność predykcji.

Źródło:

Ekonomia XXI Wieku; 2021, 24; 7-26
2353-8929

Pojawia się w:

Ekonomia XXI Wieku

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 46.

Tytuł:

Discrete Fractional Order Artificial Neural Network

Autorzy:

Sierociuk, D.
Sarwas, G.
Dzieliński, A.

Powiązania:

https://bibliotekanauki.pl/articles/386578.pdf

Data publikacji:

2011

Wydawca:

Politechnika Białostocka. Oficyna Wydawnicza Politechniki Białostockiej

Tematy:

sztuczne sieci neuronowe
systemy nieliniowe
artificial neural networks
nonlinear systems

Opis:

In this paper the discrete time fractional order artificial neural network is presented. This structure is proposed for simulating the dynamics of non-linear fractional order systems. In the second part of this paper several numerical examples are shown. The final part of the paper presents the discussion on the use of fractional or integer discrete time neural network for modelling and simulating fractional order non-linear systems. The simulation results show the advantages of the proposed solution over the classical (integer) neural network approach to modelling of non-linear fractional order systems.

Źródło:

Acta Mechanica et Automatica; 2011, 5, 2; 128-132
1898-4088
2300-5319

Pojawia się w:

Acta Mechanica et Automatica

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 47.

Tytuł:

Analiza możliwości prognozowania przemieszczeń gleby podczas orki za pomocą klasycznych metod statystycznych oraz sztucznych sieci neuronowych
The analysis of possibilities of predictions of soil dislocations during ploughing using standard statistical methods as well as artificial neural networks

Autorzy:

Niedbała, G.
Klejna, K.

Powiązania:

https://bibliotekanauki.pl/articles/288992.pdf

Data publikacji:

2007

Wydawca:

Polskie Towarzystwo Inżynierii Rolniczej

Tematy:

erozja uprawowa
sztuczna sieć neuronowa
tillage erosion
artificial neural networks

Opis:

Erozja uprawowa, obok erozji wietrznej i wodnej, może prowadzić do degradacji gleby w szczególności na skłonach pól. Nowoczesne odkładnice stosowane obecnie w trakcie orki charakteryzują się dużymi prędkościami roboczymi, co w konsekwencji może doprowadzić do szybkiego przebiegu tego procesu. Określenie najistotniejszych warunków uprawy i parametrów pracy agregatu umożliwi dokonanie prognozy wielkości poziomego przemieszczenia gleby. W tym celu można zastosować metody statystyczne oraz metody modelowania neuronowego. Obydwie metody dały zadawalający wynik prognozy oraz wykazały największy wpływ prędkości orki na poziome przemieszczenie gleby. Wyniki uzyskane za pomocą modeli neuronowych charakteryzują się większą dokładnością prognozy oraz wyższym współczynnikiem korelacji i determinacji.
Tillage erosion as well as wind and water erosion can lead to the degradation of the soil especially on the inclinations of the fields. Modern mouldboards used nowadays during ploughing are marked by high working speeds which can also accelerate the process of the soil degradation. Determining the most relevant conditions for crops as well as the parameters of the plough work with enable us to predict the size of the horizontal soil dislocations. In order to this, we can either use statistical or neural modelling methods. Both methods gave satisfying predictions results and also showed the huge influence of the ploughing speed on the horizontal soil dislocation. The results obtained from the neural modelling, are marked by higher precision and have a higher correlation and determination coefficient.

Źródło:

Inżynieria Rolnicza; 2007, R. 11, nr 2 (90), 2 (90); 217-224
1429-7264

Pojawia się w:

Inżynieria Rolnicza

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 48.

Tytuł:

Algorytmy genetyczne jako narzędzie optymalizacyjne stosowane w sieciach neuronowych
Genetic algorithms as a optimization tool applied in neural networks

Autorzy:

Olszewski, T.
Boniecki, P.
Weres, J.

Powiązania:

https://bibliotekanauki.pl/articles/289865.pdf

Data publikacji:

2005

Wydawca:

Polskie Towarzystwo Inżynierii Rolniczej

Tematy:

sztuczne sieci neuronowe
algorytmy genetyczne
artificial neural networks
genetic algorithms

Opis:

Rewolucyjne wynalazki człowieka bardzo często powstają w wyniku obserwacji przyrody. Korzysta ona z rozwiązań najlepszych i optymalnych, tak więc wartych naśladowania. Niestety czasami jest to bardzo trudne. Przykładem może być mózg ludzki, którego funkcjonowania nadal nie rozumiemy do końca. Obserwując jego budowę stworzono Sztuczne Sieci Neuronowe, które są jego bardzo uproszczonym modelem mającym wykorzystywać jego najważniejsze cechy czyli zdolność uczenia i kojarzenia. Ewolucja naturalna jest swoistym procesem optymalizacyjnym mającym na celu najlepsze przystosowanie osobników do otaczającego świata, a co się z tym wiąże - przetrwania gatunku. Również mechanizmy ewolucyjne zostały wykorzystane przez człowieka. Jedną z metod odwzorowującą te mechanizmy są algorytmy genetyczne pozwalające na optymalne rozwiązanie różnych problemów. W artykule zostało przedstawione połączenie obu idei.
Revolutionary human inventions very often arise as a result of nature observation. Nature use the best and optimal solutions therefore deserves to copy. Unfortunately, sometimes it’s very hard. Human’s brain can be example, whose functions we don’t fully understand. As a result of observations of the build of human’s brain made artificial neural networks. They are its very simplified model, which use its main features: ability to learn and associate. Natural evolution is peculiar optimization process which purpose is the best adaptation of specimen to the surrounding world and it is in connection with survival of the species. Evolutionary mechanics were exploit by the human as well. Genetic algorithms are one of many methods which model evolutionary mechanics. They allow to find optimal solution for different problems. This article presents the combination both ideas.

Źródło:

Inżynieria Rolnicza; 2005, R. 9, nr 2, 2; 137-143
1429-7264

Pojawia się w:

Inżynieria Rolnicza

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 49.

Tytuł:

Możliwości stosowania sztucznych sieci neuronowych przy doborze motywatorów dla kadry menedżerskiej
Using artificial neural network to choose motivators for managers

Autorzy:

Matwiejczuk, T.
Tomaszuk, A.

Powiązania:

https://bibliotekanauki.pl/articles/399048.pdf

Data publikacji:

2011

Wydawca:

Politechnika Białostocka. Oficyna Wydawnicza Politechniki Białostockiej

Tematy:

motywacja
kierownik
sztuczne sieci neuronowe
motivation
manager
artificial neural networks

Opis:

In the article the concept of motivating the management staff was mentioned. It was pointed that the appropriate choice of personal motivators is one of the most crucial elements in the motivating process. It was suggested to choose motivators by Artificial Neural Network. ANN can be widely used. It can find the sens and rules in difficult structures of data. The ways of using tools in the company were also presented.

Źródło:

Ekonomia i Zarządzanie; 2011, 3, 2; 130-137
2080-9646

Pojawia się w:

Ekonomia i Zarządzanie

Dostawca treści:

Biblioteka Nauki

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Skocz do pozycji: 50.

Tytuł:

Determination of the lower calorific and ash values of the lignite coal by using artificial neural networks and multiple regression analysis

Autorzy:

Gulec, Mahmut
Gulbandilar, Eyyup

Powiązania:

https://bibliotekanauki.pl/articles/109973.pdf

Data publikacji:

2019

Wydawca:

Politechnika Wrocławska. Oficyna Wydawnicza Politechniki Wrocławskiej

Tematy:

lignite chemical analyses
artificial neural networks
Seyitomer lignite
Tuncbilek lignite

Opis:

The calorific value of coal varies depending on type of coal and foreign matter content. The calorific value of coal from pits is determined by analyzing moisture, volatile matter, ash and sulfur content in laboratories. This analysis process imposes a burden on businesses both in terms of time and cost. However, calorific value, in particular, can be determined through simpler methods by using ash and moisture values. The aim of this study was to develop a model that reduces the time and labor costs of coal companies by determining the calorific value and ash content of coal with the back-propagation algorithm of artificial neural networks (ANN). The model design was developed based on the data that was obtained from the laboratory analyses of raw coals from the pits of Tuncbilek and Seyitomer mining areas in Turkey. The values of moisture, volatile matter, original ash and sulfur were determined as input variables, and the lower calorific values and ash content were selected as output variables. The lower calorific values (LCV) and Ash estimated by the developed model were compared with the LCV obtained in the laboratory tests and the results showed a correlation. In addition, two different ANN models and multiple regression analysis (MRA) were developed to obtain the single output of the LCV and ash parameters with similar features. As a result, the ANN model and MRA equation models proposed in this study was shown to successfully estimate the LCV and ash content of coals without performing laboratory analyses.

Źródło:

Physicochemical Problems of Mineral Processing; 2019, 55, 2; 400-406
1643-1049
2084-4735

Pojawia się w:

Physicochemical Problems of Mineral Processing

Dostawca treści:

Biblioteka Nauki

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