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Wyświetlanie 1-15 z 67
Tytuł:
Black box dynamic modelling of proton exchange membrane fuel cells with artificial neural networks
Autorzy:
Kapica, J.
Powiązania:
https://bibliotekanauki.pl/articles/411175.pdf
Data publikacji:
2016
Wydawca:
Polska Akademia Nauk. Oddział w Lublinie PAN
Tematy:
PEM fuel cells
neural network model
dynamic behaviour
black box
Opis:
The fuel cells are energy sources which can play an important role in transition of the energy sector into broader use of renewable energy. Numerical modelling provides an easy way to investigate properties of the objects modelled. There are various ways to model dynamic behaviour of the PEM fuel cells including methods using artificial neural networks. There are no clear rules of how a neural network should be configured: how many neurons in the hidden layer and which training algorithm should be used. In a time series modelling task additional parameters including sampling frequency, learning data set duration and number of past data points used for training need to be determined. The paper presents results of research on the influence of various model parameters on the PEM fuel cell modelling accuracy.
Źródło:
ECONTECHMOD : An International Quarterly Journal on Economics of Technology and Modelling Processes; 2016, 5, 4; 85-89
2084-5715
Pojawia się w:
ECONTECHMOD : An International Quarterly Journal on Economics of Technology and Modelling Processes
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Acoustical Assessment of Automotive Mufflers Using FEM, Neural Networks, and a Genetic Algorithm
Autorzy:
Chang, Y.-C.
Chiu, M.-C.
Wu, M.-R.
Powiązania:
https://bibliotekanauki.pl/articles/177901.pdf
Data publikacji:
2018
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
acoustics
finite element method
genetic algorithm
muffler optimization
polynomial neural network model
Opis:
In order to enhance the acoustical performance of a traditional straight-path automobile muffler, a multi-chamber muffler having reverse paths is presented. Here, the muffler is composed of two internally parallel/extended tubes and one internally extended outlet. In addition, to prevent noise transmission from the muffler’s casing, the muffler’s shell is also lined with sound absorbing material. Because the geometry of an automotive muffler is complicated, using an analytic method to predict a muffler’s acoustical performance is difficult; therefore, COMSOL, a finite element analysis software, is adopted to estimate the automotive muffler’s sound transmission loss. However, optimizing the shape of a complicated muffler using an optimizer linked to the Finite Element Method (FEM) is time-consuming. Therefore, in order to facilitate the muffler’s optimization, a simplified mathematical model used as an objective function (or fitness function) during the optimization process is presented. Here, the objective function can be established by using Artificial Neural Networks (ANNs) in conjunction with the muffler’s design parameters and related TLs (simulated by FEM). With this, the muffler’s optimization can proceed by linking the objective function to an optimizer, a Genetic Algorithm (GA). Consequently, the discharged muffler which is optimally shaped will improve the automotive exhaust noise.
Źródło:
Archives of Acoustics; 2018, 43, 3; 517-529
0137-5075
Pojawia się w:
Archives of Acoustics
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Study on maritime logistics warehousing center model and precision marketing strategy optimization based on fuzzy method and neural network model
Autorzy:
Xiao, K.
Hu, X.
Powiązania:
https://bibliotekanauki.pl/articles/260268.pdf
Data publikacji:
2017
Wydawca:
Politechnika Gdańska. Wydział Inżynierii Mechanicznej i Okrętownictwa
Tematy:
maritime logistics warehousing center mode
precision marketing strategy optimization
fuzzy method
neural network model
polarity reversal
Opis:
The bulk commodity, different with the retail goods, has a uniqueness in the location selection, the chosen of transportation program and the decision objectives. How to make optimal decisions in the facility location, requirement distribution, shipping methods and the route selection and establish an effective distribution system to reduce the cost has become a burning issue for the e-commerce logistics, which is worthy to be deeply and systematically solved. In this paper, Logistics warehousing center model and precision marketing strategy optimization based on fuzzy method and neural network model is proposed to solve this problem. In addition, we have designed principles of the fuzzy method and neural network model to solve the proposed model because of its complexity. Finally, we have solved numerous examples to compare the results of lingo and Matlab, we use Matlab and lingo just to check the result and to illustrate the numerical example, we can find from the result, the multi-objective model increases logistics costs and improves the efficiency of distribution time.
Źródło:
Polish Maritime Research; 2017, S 2; 30-38
1233-2585
Pojawia się w:
Polish Maritime Research
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Noise Elimination of Reciprocating Compressors Using FEM, Neural Networks Method, and the GA Method
Autorzy:
Chang, Y.-C.
Chiu, M.-C.
Xie, J.-L.
Powiązania:
https://bibliotekanauki.pl/articles/178126.pdf
Data publikacji:
2017
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
finite element method
polynomial neural network model
genetic algorithm
group method of data handling
reciprocating compressor
optimization
Opis:
Industry often utilizes acoustical hoods to block noise emitted from reciprocating compressors. However, the hoods are large and bulky. Therefore, to diminish the size of the compressor, a compact discharge muffler linked to the compressor outlet is considered. Because the geometry of a reciprocating compressor is irregular, COMSOL, a finite element analysis software, is adopted. In order to explore the acoustical performance, a mathematical model is established using a finite element method via the COMSOL commercialized package. Additionally, to facilitate the shape optimization of the muffler, a polynomial neural network model is adopted to serve as an objective function; also, a Genetic Algorithm (GA) is linked to the OBJ function. During the optimization, various noise abatement strategies such as a reverse expansion chamber at the outlet of the discharge muffler and an inner extended tube inside the discharge muffler, will be assessed by using the artificial neural network in conjunction with the GA optimizer. Consequently, the discharge muffler that is optimally shaped will decrease the noise of the reciprocating compressor.
Źródło:
Archives of Acoustics; 2017, 42, 2; 189-197
0137-5075
Pojawia się w:
Archives of Acoustics
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Shape Optimisation of Multi-Chamber Acoustical Plenums Using BEM, Neural Networks, and GA Method
Autorzy:
Chang, Y.-C.
Cheng, H.-C.
Chiu, M.-C.
Chien, Y.-H.
Powiązania:
https://bibliotekanauki.pl/articles/177780.pdf
Data publikacji:
2016
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
boundary element method
plenum
centre-opening baffle
polynomial neural network model
group method of data handling
optimisation
genetic algorithm
Opis:
Research on plenums partitioned with multiple baffles in the industrial field has been exhaustive. Most researchers have explored noise reduction effects based on the transfer matrix method and the boundary element method. However, maximum noise reduction of a plenum within a constrained space, which frequently occurs in engineering problems, has been neglected. Therefore, the optimum design of multi-chamber plenums becomes essential. In this paper, two kinds of multi-chamber plenums (Case I: a two-chamber plenum that is partitioned with a centre-opening baffle; Case II: a three-chamber plenum that is partitioned with two centre-opening baffles) within a fixed space are assessed. In order to speed up the assessment of optimal plenums hybridized with multiple partitioned baffles, a simplified objective function (OBJ) is established by linking the boundary element model (BEM, developed using SYSNOISE) with a polynomial neural network fit with a series of real data – input design data (baffle dimensions) and output data approximated by BEM data in advance. To assess optimal plenums, a genetic algorithm (GA) is applied. The results reveal that the maximum value of the transmission loss (TL) can be improved at the desired frequencies. Consequently, the algorithm proposed in this study can provide an efficient way to develop optimal multi-chamber plenums for industry.
Źródło:
Archives of Acoustics; 2016, 41, 1; 43-53
0137-5075
Pojawia się w:
Archives of Acoustics
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Effect of ship neural domain shape on safe and optimal trajectory
Autorzy:
Lisowski, J.
Powiązania:
https://bibliotekanauki.pl/articles/24201475.pdf
Data publikacji:
2023
Wydawca:
Uniwersytet Morski w Gdyni. Wydział Nawigacyjny
Tematy:
artificial neural network model
method for optimization
dynamic programming method
ship safety domain
safe ship control
path planning
multi-object decision model
computer simulation
Opis:
This article presents the task of safely guiding a ship, taking into account the movement of many other marine units. An optimally neural modified algorithm for determining a safe trajectory is presented. The possible shapes of the domains assigned to other ships as traffic restrictions for the particular ship were subjected to a detailed analysis. The codes for the computer program Neuro-Constraints for generating these domains are presented. The results of the simulation tests of the algorithm for a navigational situation are presented. The safe trajectories of the ship were compared at different distances, changing the sailing conditions and ship sizes.
Źródło:
TransNav : International Journal on Marine Navigation and Safety of Sea Transportation; 2023, 17, 1; 185--191
2083-6473
2083-6481
Pojawia się w:
TransNav : International Journal on Marine Navigation and Safety of Sea Transportation
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
A Small Wind Turbine Output Model for Spatially Constrained Remote Island Micro-Grids
Autorzy:
Žigman, D.
Meštrović, K.
Tomiša, T.
Powiązania:
https://bibliotekanauki.pl/articles/2172468.pdf
Data publikacji:
2022
Wydawca:
Uniwersytet Morski w Gdyni. Wydział Nawigacyjny
Tematy:
wind turbine
small wind turbine
decision tree model
artificial neural network model
random forest model
micro-grids
spatially constrained remote Island micro-grids
remote Island micro-grid
Opis:
Modelling operation of the power supply system for remote island communities is essential for its operation, as well as a survival of a modern society settled in challenging conditions. Micro-grid emerges as a proper solution for a sustainable development of a spatially constrained remote island community, while at the same time reflecting the power requirements of similar maritime subjects, such as large vessels and fleets. Here we present research results in predictive modelling the output of a small wind turbine, as a component of a remote island micro-grid. Based on a month-long experimental data and the machine learning-based predictive model development approach, three candidate models of a small wind turbine output were developed, and assessed on their performance based on an independent set of experimental data. The Random Forest Model out performed competitors (Decision Tree Model and Artificial Neural Network Model), emerging as a candidate methodology for the all-year predictive model development, as a later component of the over-all remote island micro-grid model.
Źródło:
TransNav : International Journal on Marine Navigation and Safety of Sea Transportation; 2022, 16, 1; 143--146
2083-6473
2083-6481
Pojawia się w:
TransNav : International Journal on Marine Navigation and Safety of Sea Transportation
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Kinetics of the continuous reaction crystallization of barium sulphate in BaCl2 - (NH4)2SO4 - NaCl - H2O system - neural network model
Autorzy:
Piotrowski, K.
Koralewska, J.
Wierzbowska, B.
Matynia, A.
Powiązania:
https://bibliotekanauki.pl/articles/778848.pdf
Data publikacji:
2009
Wydawca:
Zachodniopomorski Uniwersytet Technologiczny w Szczecinie. Wydawnictwo Uczelniane ZUT w Szczecinie
Tematy:
siarczan baru
jony sodu
sole pohartownicze
hartowanie stali
chlorek baru
kinetyka krystalizacji
barium sulphate
sodium ions
used quenching salts
steel hardening
barium chloride
reaction crystallization kinetics
population density distribution
chemical neutralization
solid waste utilization
neural network model
Opis:
One of the main toxic components of post quenching salts formed in large quantities during steel hardening processes is BaCl2. This dangerous ingredient can be chemically neutralized after dissolution in water by means of reaction crystallization with solid ammonium sulphate (NH4)2SO4. The resulting size distribution of the ecologically harmless crystalline product - BaSO4 - is an important criteria deciding about its further applicability. Presence of a second component of binary quenching salt mixture (BaCl2-NaCl) in water solution, NaCl, influences the reaction-crystallization process kinetics affecting the resulting product properties. The experimental 39 input-output data vectors containing the information about the continuous reaction crystallization in BaCl2 - (NH4)2SO4 - NaCl - H2O system ([BaCl2]RM = 10-24 mass %, [NaCl]RM = 0-12 mass %, T = 305-348 K and τ = 900-9000 s) created the database for the neural network training and validation. The applicability of diversified network configurations, neuron types and training strategies were verified. An optimal network structure was used for the process modeling.
Źródło:
Polish Journal of Chemical Technology; 2009, 11, 4; 13-19
1509-8117
1899-4741
Pojawia się w:
Polish Journal of Chemical Technology
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Membrain neural network for visual pattern recognition
Autorzy:
Popko, A.
Jakubowski, M.
Wawer, R.
Powiązania:
https://bibliotekanauki.pl/articles/103198.pdf
Data publikacji:
2013
Wydawca:
Stowarzyszenie Inżynierów i Techników Mechaników Polskich
Tematy:
neural network
pattern recognition
neuron model
Opis:
Recognition of visual patterns is one of significant applications of Artificial Neural Networks, which partially emulate human thinking in the domain of artificial intelligence. In the paper, a simplified neural approach to recognition of visual patterns is portrayed and discussed. This paper is dedicated for investigators in visual patterns recognition, Artificial Neural Networking and related disciplines. The document describes also MemBrain application environment as a powerful and easy to use neural networks’ editor and simulator supporting ANN.
Źródło:
Advances in Science and Technology. Research Journal; 2013, 7, 18; 54-59
2299-8624
Pojawia się w:
Advances in Science and Technology. Research Journal
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Neural networks as performance improvement models in intelligent CAPP systems
Autorzy:
Rojek, I.
Powiązania:
https://bibliotekanauki.pl/articles/971020.pdf
Data publikacji:
2010
Wydawca:
Polska Akademia Nauk. Instytut Badań Systemowych PAN
Tematy:
classification model
neural network
tool
manufacturing operation
Opis:
The paper presents neural networks as performance improvement models in intelligent computer aided process planning systems (CAPP systems). For construction of these models three types of neural networks were used: linear network, multi-layer network with error backpropagation, and the Radial Basis Function network (RBF). The models were compared. Due to the comparison, we can say which type of neural network is the best for selection of tools for manufacturing operations. Tool selection for manufacturing operation is a classification problem. Hence, neural networks were built as classification models, meant to improve tool selection for manufacturing. The study was done for selected manufacturing operations: turning, milling and grinding. Models for the milling operation were presented in detail.
Źródło:
Control and Cybernetics; 2010, 39, 1; 54-68
0324-8569
Pojawia się w:
Control and Cybernetics
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
A Neural Network Model for Object Mask Detection in Medical Images
Autorzy:
Tereikovskyi, Igor
Korchenko, Oleksander
Bushuyev, Sergey
Tereikovskyi, Oleh
Ziubina, Ruslan
Veselska, Olga
Powiązania:
https://bibliotekanauki.pl/articles/2200721.pdf
Data publikacji:
2023
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
model
neural network
object mask
medical images
Opis:
In modern conditions in the field of medicine, raster image analysis systems are becoming more widespread, which allow automating the process of establishing a diagnosis based on the results of instrumental monitoring of a patient. One of the most important stages of such an analysis is the detection of the mask of the object to be recognized on the image. It is shown that under the conditions of a multivariate and multifactorial task of analyzing medical images, the most promising are neural network tools for extracting masks. It has also been determined that the known detection tools are highly specialized and not sufficiently adapted to the variability of the conditions of use, which necessitates the construction of an effective neural network model adapted to the definition of a mask on medical images. An approach is proposed to determine the most effective type of neural network model, which provides for expert evaluation of the effectiveness of acceptable types of models and conducting computer experiments to make a final decision. It is shown that to evaluate the effectiveness of a neural network model, it is possible to use the Intersection over Union and Dice Loss metrics. The proposed solutions were verified by isolating the brachial plexus of nerve fibers on grayscale images presented in the public Ultrasound Nerve Segmentation database. The expediency of using neural network models U-Net, YOLOv4 and PSPNet was determined by expert evaluation, and with the help of computer experiments, it was proved that U-Net is the most effective in terms of Intersection over Union and Dice Loss, which provides a detection accuracy of about 0.89. Also, the analysis of the results of the experiments showed the need to improve the mathematical apparatus, which is used to calculate the mask detection indicators.
Źródło:
International Journal of Electronics and Telecommunications; 2023, 69, 1; 41--46
2300-1933
Pojawia się w:
International Journal of Electronics and Telecommunications
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Neural modeling of plant tissue cultures: a review
Autorzy:
Zielinska, S.
Kepczynska, E.
Powiązania:
https://bibliotekanauki.pl/articles/81293.pdf
Data publikacji:
2013
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
artificial neural network
biomass
plant tissue
neural model
tissue culture
in vitro condition
micropropagation
radial neural network
neural network
somatic embryo
Źródło:
BioTechnologia. Journal of Biotechnology Computational Biology and Bionanotechnology; 2013, 94, 3
0860-7796
Pojawia się w:
BioTechnologia. Journal of Biotechnology Computational Biology and Bionanotechnology
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Demand forecasting: an alternative approach based on technical indicator Pbands
Autorzy:
Kolková, Andrea
Ključnikov, Aleksandr
Powiązania:
https://bibliotekanauki.pl/articles/19233720.pdf
Data publikacji:
2021
Wydawca:
Instytut Badań Gospodarczych
Tematy:
demand forecasting
neural network
BATS
hybrid model
Pbands
Opis:
Research background: Demand forecasting helps companies to anticipate purchases and plan the delivery or production. In order to face this complex problem, many statistical methods, artificial intelligence-based methods, and hybrid methods are currently being developed. However, all these methods have similar problematic issues, including the complexity, long computing time, and the need for high computing performance of the IT infrastructure. Purpose of the article: This study aims to verify and evaluate the possibility of using Google Trends data for poetry book demand forecasting and compare the results of the application of the statistical methods, neural networks, and a hybrid model versus the alternative possibility of using technical analysis methods to achieve immediate and accessible forecasting. Specifically, it aims to verify the possibility of immediate demand forecasting based on an alternative approach using Pbands technical indicator for poetry books in the European Quartet countries. Methods: The study performs the demand forecasting based on the technical analysis of the Google Trends data search in case of the keyword poetry in the European Quartet countries by several statistical methods, including the commonly used ETS statistical methods, ARIMA method, ARFIMA method, BATS method based on the combination of the Cox-Box transformation model and ARMA, artificial neural networks, the Theta model, a hybrid model, and an alternative approach of forecasting using Pbands indicator.  The study uses MAPE and RMSE approaches to measure the accuracy. Findings & value added: Although most currently available demand prediction models are either slow or complex, the entrepreneurial practice requires fast, simple, and accurate ones. The study results show that the alternative Pbands approach is easily applicable and can predict short-term demand changes. Due to its simplicity, the Pbands method is suitable and convenient to monitor short-term data describing the demand. Demand prediction methods based on technical indicators represent a new approach for demand forecasting. The application of these technical indicators could be a further forecasting models research direction. The future of theoretical research in forecasting should be devoted mainly to simplifying and speeding up. Creating an automated model based on primary data parameters and easily interpretable results is a challenge for further research.
Źródło:
Oeconomia Copernicana; 2021, 12, 4; 1063-1094
2083-1277
Pojawia się w:
Oeconomia Copernicana
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Sieci neuronowe typu MLP oraz RBF jako narzędzia klasyfikacyjne w analizie obrazu
The neural network type the MLP and RBF as classifying tools in picture analysis
Autorzy:
Boniecki, P.
Powiązania:
https://bibliotekanauki.pl/articles/337163.pdf
Data publikacji:
2006
Wydawca:
Sieć Badawcza Łukasiewicz - Przemysłowy Instytut Maszyn Rolniczych
Tematy:
sieć neuronowa
sieć neuronowa MLP
sieć neuronowa RBF
analiza obrazu
identyfikacja neuronowa
model neuronowy
neural network
MLP neural network
RBF neural network
picture analysis
neuronal identification
neuronal model
Opis:
Neuronowa identyfikacja danych obrazowych, ze szczególnym naciskiem na analizę ilościową oraz jakościową, coraz częściej wykorzystywana jest do pozyskiwania oraz zgłębiania wiedzy zawartej w danych empirycznych. Ekstrakcja, a następnie klasyfikacja wybranych cech obrazu, pozawala na wytworzenie informatycznych narzędzi do identyfikacji wybranych obiektów, prezentowanych np. w postaci obrazu cyfrowego. W związku z tym, celowym wydaje się być poszukiwanie nowoczesnych metod wspomagających proces edukacyjny w zakresie konstrukcji oraz eksploatacji modeli neuronowych w kontekście ich wykorzystania w procesie analizy obrazu. Dodatkowym celem pracy było porównanie jakości sieci MLP oraz RBF mające na względzie wskazanie optymalnego instrumentu klasyfikacyjnego.
The neuronal identification of pictorial data, with special emphasis on both quantitative & qualitative analysis, is more frequently utilized to gain & deepen the empirical data knowledge. Extraction & then classification of selected picture features, enables one to create computer tools in order to identify these objects presented as, for example, digital pictures. In relationship from this, it seems to be purposeful the search of the modern methods helping educational process in the range of construction as well as exploitation of neuronal models in context of their utilization in picture analysis process. The additional aim of the work was the comparison of neural network of the type MLP and RBF for indication of the optimum classification tool.
Źródło:
Journal of Research and Applications in Agricultural Engineering; 2006, 51, 4; 34-39
1642-686X
2719-423X
Pojawia się w:
Journal of Research and Applications in Agricultural Engineering
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
The Influence of the Artificial Neural Network type on the quality of learning on the Day-Ahead Market model at Polish Power Exchange joint-stock company
Autorzy:
Ruciński, Dariusz
Powiązania:
https://bibliotekanauki.pl/articles/1819257.pdf
Data publikacji:
2019
Wydawca:
Uniwersytet Przyrodniczo-Humanistyczny w Siedlcach
Tematy:
Perceptron Artificial Neural Network
Radial Artificial Neural Network
Recursive Artificial Neural Network
neural model quality
Day-Ahead Market
Polish Power Exchange
Mean square error
determination index
Opis:
The work contains the results of the Day-Ahead Market modeling research at Polish Power Exchange taking into account the numerical data on the supplied and sold electricity in selected time intervals from the entire period of its operation (from July 2002 to June 2019). Market modeling was carried out based on three Artificial Neural Network models, ie: Perceptron Artificial Neural Network, Recursive Artificial Neural Network, and Radial Artificial Neural Network. The examined period of the Day-Ahead Market operation on the Polish Power Exchange was divided into sub-periods of various lengths, from one month, a quarter, a half a year to the entire period of the market's operation. As a result of neural modeling, 1,191 models of the Market system were obtained, which were assessed according to the criterion of the least error MSE and the determination index R2.
Źródło:
Studia Informatica : systems and information technology; 2019, 1-2(23); 77--93
1731-2264
Pojawia się w:
Studia Informatica : systems and information technology
Dostawca treści:
Biblioteka Nauki
Artykuł
Wyświetlanie 1-15 z 67

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