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Wyświetlanie 1-15 z 31
Tytuł:
Wpływ degradacji urządzeń pomiarowych na pozyskiwanie symptomów niesprawnej pracy złożonych obiektów energetycznych
Influence of measuring equipment degradation on gaining of symptoms of large power units inefficient operation
Autorzy:
Głuch, J.
Powiązania:
https://bibliotekanauki.pl/articles/327488.pdf
Data publikacji:
2008
Wydawca:
Polska Akademia Nauk. Polskie Towarzystwo Diagnostyki Technicznej PAN
Tematy:
symulacja neuronowa
diagnostyka cieplno-przepływowa
turbina parowa
neural simulation
thermal diagnostics
flow diagnostics
steam power plants
measuring system
Opis:
Opisywana jest możliwość budowania relacji cieplno-przepływowych diagnostycznych z zastosowaniem metody sztucznych sieci neuronowych. Są one zastosowane do detekcji zdegradowanych urządzeń pomiarowych w złożonych systemach pomiarowych. Przedstawiono to na przykładzie bloku energetycznego dużej mocy. Wykorzystano obliczenia symulacyjne degradacji. Rozważano zarówno degradacje samego systemu pomiarowego jak i degradacje geometrii urządzeń składowych. Pokazano dobrą jakość określania symptomów degradacji. Wykorzystano przykłady z praktyki eksploatacyjnej.
Possibility of building of diagnostic relations with usage of artificial neural networks ANN is described in the paper. The relations are applied for detection of the degraded measuring devices in steam power cycles of complex electricity generation systems. The example of the large steam turbine power plant is shown in the paper. Neuronal diagnostic relations are created on the basis of simulation calculations. There are taking into account both degradations of that of measuring equipment as well as simultaneously occurring degradations of measuring equipment and components of thermal cycle. Good quality of neuronal calculations is stated. Application of these relations is shown on some examples from exploitation practice.
Źródło:
Diagnostyka; 2008, 2(46); 67-70
1641-6414
2449-5220
Pojawia się w:
Diagnostyka
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Deep learning classification and recognition method for milling surface roughness combined with simulation data
Autorzy:
Lu, Lingli
Yi, Huaian
Shu, Aihua
Qin, Jianhua
Lu, Enhui
Powiązania:
https://bibliotekanauki.pl/articles/2203367.pdf
Data publikacji:
2023
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
milling surface
classification
deep neural network
simulation
Opis:
To address the problem that a deep neural network needs a sufficient number of training samples to have a good prediction performance, this paper firstly used the Z-Map algorithm to generate a simulated profile of the milling surface and construct an optical simulation model of surface imaging to supplement the training sample size of the neural network. Then the Deep CORAL model was used to match the textures of the simulated samples and the actual samples across domains to solve the problem that the simulated samples were not in the same domain as the actual milling samples. Experimental results have shown that high texture matching could be achieved between optical simulation images and actual images, laying the foundation for expanding the actual milled workpiece images with the simulation images. The deep convolutional neural model Xception was used to predict the classification of six classes of data sets with the inclusion of simulation images, and the accuracy was improved from 86.48% to 92.79% compared with the model without the inclusion of simulation images. The proposed method solves the problem of the need for a large number of samples for deep neural networks and lays the foundation for similar methods to predict surface roughness for different machining processes.
Źródło:
Metrology and Measurement Systems; 2023, 30, 1; 117--138
0860-8229
Pojawia się w:
Metrology and Measurement Systems
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Neural networks for function approximation in dynamic modelling
Autorzy:
Nedbálek, J.
Powiązania:
https://bibliotekanauki.pl/articles/2069707.pdf
Data publikacji:
2008
Wydawca:
Uniwersytet Morski w Gdyni. Polskie Towarzystwo Bezpieczeństwa i Niezawodności
Tematy:
reliability
Monte Carlo
RBF neural network
simulation
temperature
Opis:
The paper demonstrates the comparsion of Monte Carlo simulation (MC) algorithm with the Radial Basis Function (RBF) neural network enhancement of the same algorithm in the reliability case study. In our test, we dispose of the tank containing liquid water – its temperature process variable evolves deterministicly according to the differential equation, which solution is known. All component failures are considered as a stochastic events. In the case of surpassing temperature treshhold of the liquid inside the tank, we interpret the situation as the system failure. With regard to process dynamics, we attempt to evaluate the tank system unreliability related to the initiative input parameters setting. The neural network is used in equation coeficients calculation, which is executed in each transient state. Due to the neural networks, for some of the initial component settings, we can achieve the results of computation faster than in classical way of coeficients calculating and substituting into the equation.
Źródło:
Journal of Polish Safety and Reliability Association; 2008, 2; 255--259
2084-5316
Pojawia się w:
Journal of Polish Safety and Reliability Association
Dostawca treści:
Biblioteka Nauki
Artykuł
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
Artykuł
Tytuł:
Self-learning control algorithms used to manage the operating of an internal combustion engine
Autorzy:
Graba, Mariusz
Mamala, Jarosław
Bieniek, Andrzej
Powiązania:
https://bibliotekanauki.pl/articles/242942.pdf
Data publikacji:
2019
Wydawca:
Instytut Techniczny Wojsk Lotniczych
Tematy:
transport
simulation
combustion engines
environmental protection
neural network
Opis:
The article presents the possibility of using self-learning control algorithms to manage subassemblies of an internal combustion engine in order to reduce exhaust emissions to the natural environment. In compression ignition (CI) engines, the issue of emissions mainly concerns two components: particulate matter (PM) and nitrogen oxides (NOx). The work focuses mainly on the possibility of reducing the emission of nitrogen oxides. It is assumed that the particularly problematic points when it comes to excessive emission of harmful substances are the dynamic states in which combustion engines operate constantly. In dynamically changing operating points, it is very difficult to choose the right setting of actuators such as the exhaust gas recirculation (EGR) valve to ensure the correct operation of the unit and the minimum emission of these substances. In the light of the above, an attempt was made to develop a selflearning mathematical model, which can predict estimated emission levels of selected substance basing on current measurement signals (e.g. air, pressure, crankshaft rotational speed, etc.). The article presents the results of the estimation of nitrogen oxides by the trained neural network in comparison to the values measured with the use of a sensor installed in the exhaust system. The presented levels of estimated and measured results are very similar to each other and shifted over time in favour of neural networks, where the information about the emission level appears much earlier. On the basis of the estimated level, it shall be possible to make an appropriate decision about specific settings of recirculation system components, such as the EGR valve. It is estimated that by using the chosen control method it is possible significantly to reduce the emission of harmful substances into the natural environment while maintaining dynamic properties of the engine.
Źródło:
Journal of KONES; 2019, 26, 4; 75-82
1231-4005
2354-0133
Pojawia się w:
Journal of KONES
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Examples of Simulation of the Alloying Elements Effect on Austenite Transformations During Continuous Cooling
Autorzy:
Trzaska, Jacek
Powiązania:
https://bibliotekanauki.pl/articles/2049560.pdf
Data publikacji:
2021
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
CCT diagram
simulation
neural network
heat treatment
steel
Opis:
The article shows examples of simulation of the chemical composition effect on austenite transformation during continuous cooling. The calculations used own neural model of CCT (Continuous Cooling Transformation) diagrams describing austenite transformations that occur during continuous cooling. The model allows to calculate a CCT diagrams of structural steels and engineering steels based on chemical composition of steel and austenitizing temperature. Examples of simulation shown herein are related to the effect of selected elements on the temperatures of phase transformations, hardness and volume fraction of ferrite, pearlite, bainite and martensite in steel.
Źródło:
Archives of Metallurgy and Materials; 2021, 66, 1; 331-337
1733-3490
Pojawia się w:
Archives of Metallurgy and Materials
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Research on the risk classification of cruise ship fires based on an attention-BP neural network
Autorzy:
Xiong, Zhenghua
Xiang, Bo
Chen, Ye
Chen, Bin
Powiązania:
https://bibliotekanauki.pl/articles/32912853.pdf
Data publikacji:
2022
Wydawca:
Politechnika Gdańska. Wydział Inżynierii Mechanicznej i Okrętownictwa
Tematy:
cruise fire
simulation modeling
ensemble learning
BP neural network
Opis:
Due to the relatively closed environment, complex internal structure, and difficult evacuation of personnel, it is more difficult to prevent ship fires than land fires. In this paper, taking the large cruise ship as the research object, the physical model of a cruise cabin fire is established through PyroSim software, and the safety indexes such as smoke temperature, CO concentration, and visibility are numerically simulated. An Attention-BP neural network model is designed for realizing the intelligent identification of a cabin fire and dividing the risk level, which integrates the diagnosis results of multiple neural network models through the self-Attention mechanism and adaptively distributes the weight of each BP neural network model. The proposed model can provide decision-making reference for subsequent fire-fighting measures and personnel evacuation. Experimental results show that the proposed Attention-BP neural network model can effectively realize the early warning of the fire risk level. Compared with other machine learning algorithms, it has the highest stability and accuracy and reduces the uncertainty of early cabin fire warning.
Źródło:
Polish Maritime Research; 2022, 3; 61-68
1233-2585
Pojawia się w:
Polish Maritime Research
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Neural modeling of the electric power stock market in usage of MATLAB and Simulink tools for the day ahead market data
Autorzy:
Ruciński, D.
Tchórzewski, J.
Powiązania:
https://bibliotekanauki.pl/articles/94831.pdf
Data publikacji:
2016
Wydawca:
Szkoła Główna Gospodarstwa Wiejskiego w Warszawie. Wydawnictwo Szkoły Głównej Gospodarstwa Wiejskiego w Warszawie
Tematy:
neuronal modelling
MATLAB
Simulink environment
simulation research
artificial neural network
Opis:
The work contains selected results of the modelling of neural Electric Power Exchange (EPE) in Poland. For modelling EPE system, artificial neural network (ANN) was constructed. ANN was learned and tested using of the next day market data. Generated neural model was used for simulation tests and susceptibility tests. Suitable model was implemented in Simulink. As a result of simulation tests and susceptibility testing a lot of interesting research results were obtained.
Źródło:
Information Systems in Management; 2016, 5, 2; 215-226
2084-5537
2544-1728
Pojawia się w:
Information Systems in Management
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
A Novel GPU-Enabled Simulator for Large Scale Spiking Neural Networks
Autorzy:
Szynkiewicz, P.
Powiązania:
https://bibliotekanauki.pl/articles/307680.pdf
Data publikacji:
2016
Wydawca:
Instytut Łączności - Państwowy Instytut Badawczy
Tematy:
GPU computing
OpenCL programming technology
parallel simulation
spiking neural networks
Opis:
The understanding of the structural and dynamic complexity of neural networks is greatly facilitated by computer simulations. An ongoing challenge for simulating realistic models is, however, computational speed. In this paper a framework for modeling and parallel simulation of biological-inspired large scale spiking neural networks on high-performance graphics processors is described. This tool is implemented in the OpenCL programming technology. It enables simulation study with three models: Integrate-andfire, Hodgkin-Huxley and Izhikevich neuron model. The results of extensive simulations are provided to illustrate the operation and performance of the presented software framework. The particular attention is focused on the computational speed-up factor.
Źródło:
Journal of Telecommunications and Information Technology; 2016, 2; 34-42
1509-4553
1899-8852
Pojawia się w:
Journal of Telecommunications and Information Technology
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Modelling cross-sectional tabular data using convolutional neural networks: Prediction of corporate bankruptcy in Poland
Autorzy:
Dzik-Walczak, Aneta
Odziemczyk, Maciej
Powiązania:
https://bibliotekanauki.pl/articles/1965119.pdf
Data publikacji:
2021-11-27
Wydawca:
Uniwersytet Warszawski. Wydział Nauk Ekonomicznych
Tematy:
convolutional neural networks
machine learning
simulation
bankruptcy prediction
financial indicators
Opis:
The paper deals with the topic of modelling the probability of bankruptcy of Polish enterprises using convolutional neural networks. Convolutional networks take images as input, so it was thus necessary to apply the method of converting the observation vector to a matrix. Benchmarks for convolutional networks were logit models, random forests, XGBoost, and dense neural networks. Hyperparameters and model architecture were selected based on a random search and analysis of learning curves and experiments in folded, stratified cross-validation. In addition, the sensitivity of the results to data preprocessing was investigated. It was found that convolutional neural networks can be used to analyze cross-sectional tabular data, especially for the problem of modelling the probability of corporate bankruptcy. In order to achieve good results with models based on parameters updated by a gradient (neural networks and logit), it is necessary to use appropriate preprocessing techniques. Models based on decision trees have been shown to be insensitive to the data transformations used.
Źródło:
Central European Economic Journal; 2021, 8, 55; 352-377
2543-6821
Pojawia się w:
Central European Economic Journal
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Neural state estimator for complex mechanical part of electrical drive: neural network size and performance of state estimation
Autorzy:
Łuczak, D.
Wójcik, A.
Powiązania:
https://bibliotekanauki.pl/articles/1193680.pdf
Data publikacji:
2018
Wydawca:
Politechnika Wrocławska. Oficyna Wydawnicza Politechniki Wrocławskiej
Tematy:
two-mass drive
neural network
simulation studies
non-linear state estimation
Opis:
This paper presents the results of simulation research of an off-line-trained, feedforward neural-network-based state estimator. The investigated system is the mechanical part of an electrical drive characterised by elastic coupling with a working machine, modelled as a dual-mass system. The aim of the research was to find a set of neural network structures giving useful and repeatable results of the estimation. The mechanical resonance frequency of the system has been adopted at the level of 9.3-10.3 Hz. The selected state variables of the mechanical system are load, speed and stiffness torque of the shaft.
Źródło:
Power Electronics and Drives; 2018, 3, 38; 205-216
2451-0262
2543-4292
Pojawia się w:
Power Electronics and Drives
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Model procesu sortowania obiektów przy wykorzystaniu podejścia neuronowego
Модель процесу сортування об`єктів з використанням нейропідходу
The model of objects’ sorting process by using neuro approach
Autorzy:
Lotysh, J.
Powiązania:
https://bibliotekanauki.pl/articles/408026.pdf
Data publikacji:
2015
Wydawca:
Politechnika Lubelska. Wydawnictwo Politechniki Lubelskiej
Tematy:
automatyczna kontrola
sieci neuronowe
modelowanie komputerowe
automatic control
neural networks
computer simulation
Opis:
W artykule zaproponowane zostały imitacyjne komputerowe modele sortowania za pomocą normalnego trybu pracy oraz trybu neuronowego. Na podstawie modelu opracowano algorytm oraz otrzymane oprogramowanie, które implementuje system kontroli sortowania obiektów za pomocą wykorzystania podejścia neuronowego.
В роботі пропонуютьcя імітаційні комп’ютерні моделі сортування зі звичайним режимом роботи та з режимом нейроуправління. На базі моделі розроблено алгоритм та отримано програмне забезпечення, яке реалізує систему управління сортуванням об`єктів з використанням нейропідходів.
Imitational sorting computer models with ordinary operating regime and with neurooperating regime are proposed in the article. On the basis of the model the algorithm is developed and the software is received, which realizes the system of sorting operating of the objects by using neuro approaches.
Źródło:
Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska; 2015, 4; 92-98
2083-0157
2391-6761
Pojawia się w:
Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Adaptive neural network control of mechatronics objects
Autorzy:
Nemtsev, E.
Zukov, Y.
Powiązania:
https://bibliotekanauki.pl/articles/386408.pdf
Data publikacji:
2008
Wydawca:
Politechnika Białostocka. Oficyna Wydawnicza Politechniki Białostockiej
Tematy:
sieci neuronowe
symulacja komputerowa
obiekt mechatroniczny
neural network
computer simulation
mechatronics objects
Opis:
This paper presents an adaptive neural network approach to control of mechatronics objects. This approach is applied in adaptive control of DC motor in SISO-system and 3-DOF robot arm actuators in MIMO system. Results of computer simulation and comparison with other control techniques are introduced.
Źródło:
Acta Mechanica et Automatica; 2008, 2, 4; 81-85
1898-4088
2300-5319
Pojawia się w:
Acta Mechanica et Automatica
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Simulation investigation of dynamics and some operational situations of a helicopter with the use of neural networks
Symulacyjne badania dynamiki i eksploatacji śmigłowca z zastosowaniem sieci neuronowych
Autorzy:
Stanisławski, J.
Powiązania:
https://bibliotekanauki.pl/articles/212527.pdf
Data publikacji:
2006
Wydawca:
Sieć Badawcza Łukasiewicz - Instytut Lotnictwa
Tematy:
simulation investigation of dynamics
some operational situations of a helicopter
neural networks
Opis:
The work presents results of simulation investigations of dynamics of a helicopter and loads on its main (lifting) rotor, as well as some operational situations like power failure and defects of rotor blades. A package of software has been elaborated which enables the analysis of work of deformable blades. This package contains some additional procedures to construct model of atmosphere turbulence, influence of the elastic rotor support and effect of operation of the turbine power unit. Simulation programs have been used to generate data later applied for training (process of teaching) neural networks. Presented are results of functioning of neural networks in performing the following tasks: recongnizing rotor blade defects, establishing the height reserve for continuation of flight in case of partial power unit failure and assessing the magnitude of selected components of rotor blade loads.
W pracy przedstawiono wyniki badań symulacyjnych dotyczących dynamiki śmigłowca, obciążeń wirnika nośnego, stanów lotu w przypadku awarii napędu oraz uszkodzeń łopat wirnika. Opracowano pakiet oprogramowania umożliwiający analizę pracy odkształcalnych łopat wirnika uzupełniony dodatkowymi procedurami modelującymi turbulencję atmosfery, wpływ sprężystego podparcia wirnika oraz turbinowy zespół napędowy. Programy symulacyjne użyto do generacji danych, wykorzystanych następnie do treningu sieci neuronowych. Przedstawiono wyniki działania sieci neuronowych do następujacych zadań: rozpoznawanie uszkodzeń łopat wirnika, wyznaczanie zapasu wysokości do manewru kontynuacji lotu przy częściowej awarii napedu oraz oszacowanie wielkości wybranych składowych obciążenia łopat wirnika.
Źródło:
Prace Instytutu Lotnictwa; 2006, 3 (186); 5-51
0509-6669
2300-5408
Pojawia się w:
Prace Instytutu Lotnictwa
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Correction of the parametric model of the Day-Ahead Market system using the Artificial Neural Network
Autorzy:
Marlęga, Radosław
Powiązania:
https://bibliotekanauki.pl/articles/2175158.pdf
Data publikacji:
2022
Wydawca:
Uniwersytet Przyrodniczo-Humanistyczny w Siedlcach
Tematy:
artificial neural network
day-ahead market
modeling
simulation
comparative research
model sensitivity testing
Opis:
The paper shows that it is possible to correct the identification model of the Day-Ahead Market system by employing the Perceptron Artificial Neural Network. First, a simulation model of the DAM system at the POLPX has been built, and then it has been shown how the model can be corrected so that the weighted average electricity prices obtained are close enough to the exchange-quoted ones. Next, simulation, comparative and sensitivity studies of the model were carried out for forecast data for four characteristic hours: 6, 12, 18, and 24 of the following year. Many interesting research results were obtained, including a result of sensitivity testing it was shown that the obtained models can be used in forecasting studies.
Źródło:
Studia Informatica : systems and information technology; 2022, 1(26); 85--105
1731-2264
Pojawia się w:
Studia Informatica : systems and information technology
Dostawca treści:
Biblioteka Nauki
Artykuł
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