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Wyświetlanie 1-15 z 55
Tytuł:
Incidences of variables in labor absenteeism: an analysis of neural networks
Autorzy:
Pérez-Campdesuñer, Reyner
De Miguel-Guzmán, Margarita
García-Vidal, Gelmar
Sánchez-Rodríguez, Alexander
Martínez-Vivar, Rodobaldo
Powiązania:
https://bibliotekanauki.pl/articles/407357.pdf
Data publikacji:
2020
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
labor absenteeism
neural networks model
human resource management
ANOVA analysis
Opis:
Labor absenteeism is a factor that affects the good performance of organizations in any part of the world, from the instability that is generated in the functioning of the system. This is evident in the effects on quality, productivity, reaction time, among other aspects. The direct causes by which it occurs are generally known and with greater reinforcement the diseases are located, without distinguishing possible classifications. However, behind these or other causes can be found other possible factors of incidence, such as age or sex. This research seeks to explore, through the application of neural networks, the possible relationship between different variables and their incidence in the levels of absenteeism. To this end, a neural networks model is constructed from the use of a population of more than 12,000 employees, representative of various classification categories. The study allowed the characterization of the influence of the different variables studied, supported in addition to the performance of an ANOVA analysis that allowed to corroborate and clarify the results of the neural network analysis.
Źródło:
Management and Production Engineering Review; 2020, 11, 1; 3--12
2080-8208
2082-1344
Pojawia się w:
Management and Production Engineering Review
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Analiza jakościowa modeli neuronowych na przykładzie wytrzymałości kinetycznej granul
Qualitaty models analysis neural networks on the example of the pellet quality
Autorzy:
Rynkiewicz, M.
Powiązania:
https://bibliotekanauki.pl/articles/290587.pdf
Data publikacji:
2006
Wydawca:
Polskie Towarzystwo Inżynierii Rolniczej
Tematy:
wytrzymałość kinetyczna
sztuczna sieć neuronowa
pellets
kinetic strength
neural networks model
Opis:
W pracy dokonano analizy działania modeli neuronowych, które różniły się parametrami tj. liczbą neuronów i liczbą warstw ukrytych. Ocenę jakości działania przeprowadzono w oparciu o wartości uzyskanych błędów względnych i odchylenia standardowego. Do nauczania sieci neuronowych wykorzystano dane, dotyczące zależności pomiędzy średnią średnicą granulowanych cząstek komponentów i temperatury pary wodnej podawanej do kondycjonera granulatora a wytrzymałością kinetyczną granul.
In this work analyses of action of neural model were made. The neural models differed in parameters: number of neurons and the number of hidden layers. The assessment of the quality of action was carried in the support of relative mistakes gotten about value and of standard deviation. Data, concerning the relation was used to teaching neural networks between the average diameter particles of components and the temperature of given steam to conditioner and with kinetic durability of pellets.
Źródło:
Inżynieria Rolnicza; 2006, R. 10, nr 6(81), 6(81); 241-248
1429-7264
Pojawia się w:
Inżynieria Rolnicza
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Environmental analysis of a product manufactured with the use of an additive technology – AI-based vs. traditional approaches
Autorzy:
Dostatni, Ewa
Dudkowiak, Anna
Rojek, Izabela
Mikołajewski, Dariusz
Powiązania:
https://bibliotekanauki.pl/articles/2204511.pdf
Data publikacji:
2023
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
additive manufacturing
AM
eco-design
life cycle assessment
LCA
artificial intelligence
AI
neural networks model
produkcja dodatkowa
zielony design
szacowanie cyklu życia
sztuczna inteligencja
model sieci neuronowej
Opis:
This paper attempts to conduct a comparative life cycle environmental analysis of alternative versions of a product that was manufactured with the use of additive technologies. The aim of the paper was to compare the environmental assessment of an additive-manufactured product using two approaches: a traditional one, based on the use of SimaPro software, and the authors’ own concept of a newly developed artificial intelligence (AI) based approach. The structure of the product was identical and the research experiments consisted in changing the materials used in additive manufacturing (from polylactic acid (PLA) to acrylonitrile butadiene styrene (ABS)). The effects of these changes on the environmental factors were observed and a direct comparison of the effects in the different factors was made. SimaPro software with implemented databases was used for the analysis. Missing information on the environmental impact of additive manufacturing of PLA and ABS parts was taken from the literature for the purpose of the study. The novelty of the work lies in the results of a developing concurrent approach based on AI. The results showed that the artificial intelligence approach can be an effective way to analyze life cycle assessment (LCA) even in such complex cases as a 3D printed medical exoskeleton. This approach, which is becoming increasingly useful as the complexity of manufactured products increases, will be developed in future studies.
Źródło:
Bulletin of the Polish Academy of Sciences. Technical Sciences; 2023, 71, 1; art. no. e144478
0239-7528
Pojawia się w:
Bulletin of the Polish Academy of Sciences. Technical Sciences
Dostawca treści:
Biblioteka Nauki
Artykuł
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
Artykuł
Tytuł:
Single-ended quality measurement of a music content via convolutional recurrent neural networks
Autorzy:
Organiściak, Kamila
Borkowski, Józef
Powiązania:
https://bibliotekanauki.pl/articles/1849158.pdf
Data publikacji:
2020
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
audio data analysis
artefacts detection
convolutional neural networks
recurrent neural networks
classification model
Opis:
The paper examines the usage of Convolutional Bidirectional Recurrent Neural Network (CBRNN) for a problem of quality measurement in a music content. The key contribution in this approach, compared to the existing research, is that the examined model is evaluated in terms of detecting acoustic anomalies without the requirement to provide a reference (clean) signal. Since real music content may include some modes of instrumental sounds, speech and singing voice or different audio effects, it is more complex to analyze than clean speech or artificial signals, especially without a comparison to the known reference content. The presented results might be treated as a proof of concept, since some specific types of artefacts are covered in this paper (examples of quantization defect, missing sound, distortion of gain characteristics, extra noise sound). However, the described model can be easily expanded to detect other impairments or used as a pre-trained model for other transfer learning processes. To examine the model efficiency several experiments have been performed and reported in the paper. The raw audio samples were transformed into Mel-scaled spectrograms and transferred as input to the model, first independently, then along with additional features (Zero Crossing Rate, Spectral Contrast). According to the obtained results, there is a significant increase in overall accuracy (by 10.1%), if Spectral Contrast information is provided together with Mel-scaled spectrograms. The paper examines also the influence of recursive layers on effectiveness of the artefact classification task.
Źródło:
Metrology and Measurement Systems; 2020, 27, 4; 721-733
0860-8229
Pojawia się w:
Metrology and Measurement Systems
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Hybrid demand forecasting models: pre-pandemic and pandemic use studies
Autorzy:
Kolkova, Andrea
Rozehnal, Petr
Powiązania:
https://bibliotekanauki.pl/articles/22443157.pdf
Data publikacji:
2022
Wydawca:
Instytut Badań Gospodarczych
Tematy:
forecastHybrid
demand forecasting
statistic model
neural networks
Opis:
Research background: In business practice and academic sphere, the question of which of the prognostic models is the most accurate is constantly present. The accuracy of models based on artificial intelligence and statistical models has long been discussed. By combining the advantages of both groups, hybrid models have emerged. These models show high accuracy. Moreover, the question remains whether data in a dynamically changing economy (for example, in a pandemic period) have changed the possibilities of using these models. The changing economy will continue to be an important element in demand forecasting in the years to come. In business, where the concept of just in time already proves to be insufficient, it is necessary to open new research questions in the field of demand forecasting. Purpose of the article: The aim of the article is to apply hybrid models to bicycle sales e-shop data with a comparison of accuracy models in the pre-pandemic period and in the pandemic period. The paper examines the hypothesis that the pandemic period has changed the accuracy of hybrid models in comparison with statistical models and models based on artificial neural networks. Models: In this study, hybrid models will be used, namely the Theta model and the new forecastHybrid, compared to the statistical models ETS, ARIMA, and models based on artificial neural networks. They will be applied to the data of the e-shop with the cycle assortment in the period from 1.1. 2019 to 5.10 2021. Whereas the period will be divided into two parts, pre-pandemic, i.e. until 1 March 2020 and pandemic after that date. The accuracy evaluation will be based on the RMSE, MAE, and ACF1 indicators. Findings & value added: In this study, we have concluded that the prediction of the Hybrid model was the most accurate in both periods. The study can thus provide a scientific basis for any other dynamic changes that may occur in demand forecasting in the future. In other periods when there will be volatile demand, it is essential to choose models in which accuracy will decrease the least. Therefore, this study provides guidance for the use of methods in future periods as well. The stated results are likely to be valid even in an international comparison.
Źródło:
Equilibrium. Quarterly Journal of Economics and Economic Policy; 2022, 17, 3; 699-725
1689-765X
2353-3293
Pojawia się w:
Equilibrium. Quarterly Journal of Economics and Economic Policy
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Software implementation of multiple model neural filter for radar target tracking
Autorzy:
Kazimierski, W.
Wawrzyniak, N.
Powiązania:
https://bibliotekanauki.pl/articles/359024.pdf
Data publikacji:
2012
Wydawca:
Akademia Morska w Szczecinie. Wydawnictwo AMSz
Tematy:
radar target tracking
multiple model filters
neural networks
Opis:
The paper presents a software implementation of multiple model neural filter for radar target tracking. Such a filter may be proposed as an interesting alternative for numerical filters. The main purpose of software implementation is to provide a tool for complex research of the filter possibilities and adjusting options. A concept of a filter is briefly mentioned, however the main body of paper is focused on user-approach detailed description of application with UML use-case diagrams. Examples of detailed presentation of usecases are given and the general use-case diagram for application is included. The application itself is to be an advanced tool for researchers interested in analyzing target tracking process, providing different tracking methods and the possibility of adjusting their parameters. The possibility of simulating any scenario, as well as working with real data (also on-line) was ensured. The research was financed by Polish National Centre of Science under the research project “Development of radar target tracking methods of floating targets with the use of multiple model neural filtering”.
Źródło:
Zeszyty Naukowe Akademii Morskiej w Szczecinie; 2012, 32 (104) z. 2; 88-93
1733-8670
2392-0378
Pojawia się w:
Zeszyty Naukowe Akademii Morskiej w Szczecinie
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
An artificial neural networks approach to product cost estimation. The case study for electric motor
Autorzy:
Leszczyński, Zbigniew
Jasiński, Tomasz
Powiązania:
https://bibliotekanauki.pl/articles/432073.pdf
Data publikacji:
2018
Wydawca:
Wydawnictwo Uniwersytetu Ekonomicznego we Wrocławiu
Tematy:
cost estimation model
artifical neural networks
product cost
Opis:
The aim of this paper is to present, in theoretical and application terms, artificial neural networks (ANNs) as a method of estimating the product cost. The first part of the article reviews the methods used to estimate the product cost. The basic approaches to the problem of product cost estimation, presented by various authors, were described. In the second part an empirical study using artificial neural networks was conducted. Two research methods were used in this paper: literature analysis and empirical research carried out in the form of an extensive case study. The test object is a new generation induction motor. The main research problem of the article is the modelling of artificial neural networks for the estimation process of product costs with advanced production technology. The test procedures focus on the application aspects. The conclusions discuss the usefulness and advantages of using ANN models in estimating the costs of products
Źródło:
Informatyka Ekonomiczna; 2018, 1(47); 72-84
1507-3858
Pojawia się w:
Informatyka Ekonomiczna
Dostawca treści:
Biblioteka Nauki
Artykuł
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
Artykuł
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
Artykuł
Tytuł:
A MLMVN with arbitrary complex-valued inputs and a hybrid testability approach for the extraction of lumped models using FRA
Autorzy:
Aizenberg, Igor
Luchetta, Antonio
Manetti, Stefano
Piccirilli, Maria Cristina
Powiązania:
https://bibliotekanauki.pl/articles/91696.pdf
Data publikacji:
2019
Wydawca:
Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
Tematy:
analog circuits
complex-valued neural networks
lumped model
testability
Opis:
A procedure for the identification of lumped models of distributed parameter electromagnetic systems is presented in this paper. A Frequency Response Analysis (FRA) of the device to be modeled is performed, executing repeated measurements or intensive simulations. The method can be used to extract the values of the components. The fundamental brick of this architecture is a multi-valued neuron (MVN), used in a multilayer neural network (MLMVN); the neuron is modified in order to use arbitrary complex-valued inputs, which represent the frequency response of the device. It is shown that this modification requires just a slight change in the MLMVN learning algorithm. The method is tested over three completely different examples to clearly explain its generality.
Źródło:
Journal of Artificial Intelligence and Soft Computing Research; 2019, 9, 1; 5-19
2083-2567
2449-6499
Pojawia się w:
Journal of Artificial Intelligence and Soft Computing Research
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Application perspective of digitalneural networks in the context of marine technologies
Autorzy:
Konon, V.
Konon, N.
Powiązania:
https://bibliotekanauki.pl/articles/24201415.pdf
Data publikacji:
2022
Wydawca:
Uniwersytet Morski w Gdyni. Wydział Nawigacyjny
Tematy:
marine technology
multi-layer perceptron
neural networks
digital neural networks
maritime industry
MLP algorithm
3D model
Artificial Neural Network
Opis:
This study is focused on the issue of digital neural networks’ implementation in the context of maritime industry. Various algorithms of such networks in the terms of the marine technologies have been reviewed in the current study in order to evaluate the effectiveness of the methodology and to propose a new concept of an artificial neural network’s application in this way. Fire-detection system simulation based on the thermal imagers’ data input had been developed to assess the efficiency of the concept suggested with a multi-layer perceptron (MLP) algorithm integrated into the designed 3d-model.
Źródło:
TransNav : International Journal on Marine Navigation and Safety of Sea Transportation; 2022, 16, 4; 743--747
2083-6473
2083-6481
Pojawia się w:
TransNav : International Journal on Marine Navigation and Safety of Sea Transportation
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Porównanie modeli GRNN utworzonych z wykorzystaniem modułów sieci neuronowych pakietów MATLAB i STATISTICA
Comparison of the GRNN models developed by using neural network moduli of the MATLAB and STATISTICA packets
Autorzy:
Białobrzewski, I.
Powiązania:
https://bibliotekanauki.pl/articles/287990.pdf
Data publikacji:
2005
Wydawca:
Polskie Towarzystwo Inżynierii Rolniczej
Tematy:
model regresyjny
sieci neuronowe GRNN
MATLAB
Statistica
regressive model
GRNN neural networks
Opis:
Przedstawiono wyniki badań wpływu modeli GRNN, utworzonych z wykorzystaniem modułów Sieci Neuronowych pakietów MATLAB i STATISTICA, na dokładność estymacji wartości temperatury powietrza atmosferycznego. Stwierdzono, że model neuronowy GRNN, powstały na bazie Toolbox Neural Networks v.4 pakietu MATLAB, lepiej aproksymuje temperaturę powietrza atmosferycznego niż modele powstałe na bazie modułu Neural Networks pakietu STATISTICA 6.1. Wśród modeli GRNN powstałych na bazie modułu Neural Networks pakietu STATISTICA 6.1 uzyskano lepszą aproksymuję temperatury powietrza atmosferycznego, wykorzystując dostępne opcje funkcji związanych z modułem Projektant sieci użytkownika.
The effects of GRNN models, developed by using the neural network moduli of the MATLAB and STATISTICA packets on the accuracy of atmospherical air temperature estimation, were studied. It was stated that the GRNN neural model developed on the basis of Toolbox Neural Network v.4 of the MATLAB packet approximated the temperature of atmospherical air better than the models based on Neural Network modulus of STATISTICA 6.1 packet. Among the GRNN models developed on the basis of Neural Network modulus of STATISTICA 6.1 packet, the better approximation of air temperature was obtained by using available options of the functions bound to modulus of the “User’s network designer …”
Źródło:
Inżynieria Rolnicza; 2005, R. 9, nr 8, 8; 15-22
1429-7264
Pojawia się w:
Inżynieria Rolnicza
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Genetic algorithms and neural networks for solving water quality model of the Egyptian research reactor
Autorzy:
El-Sayed Wahed, M.
Ibrahim, W. Z.
Effat, A. M.
Powiązania:
https://bibliotekanauki.pl/articles/148150.pdf
Data publikacji:
2009
Wydawca:
Instytut Chemii i Techniki Jądrowej
Tematy:
genetic algorithm
neural networks
model calibration
water distribution system
water quality model
Opis:
The second Egyptian research reactor ETRR-2 became critical on 27th November, 1997. The National Center of Nuclear Safety and Radiation Control (NCNSRC) has the responsibility for the evaluation and assessment of safety of this reactor. Modern managements of water distribution system (WDS) need water quality models that are able to accurately predict the dynamics of water quality variations within the distribution system environment. Before water quality models can be applied to solve system problems, they should be calibrated. The purpose of this paper is to present an approach which combines both macro and detailed models to optimize the water quality parameters. For an efficient search through the solution space, we use a multi-objective genetic algorithm which allows us to identify a set of Pareto optimal solutions providing the decision maker with a complete spectrum of optimal solutions with respect to the various targets. This new combinative algorithm uses the radial basis function (RBF) metamodeling as a surrogate to be optimized for the purpose of decreasing the times of time-consuming water quality simulation and can realize rapidly the calibration of pipe wall reaction coefficients of chlorine model of large-scaled WDS.
Źródło:
Nukleonika; 2009, 54, 4; 239-245
0029-5922
1508-5791
Pojawia się w:
Nukleonika
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Estimation, Decoding and Forecasting in HMM and Hybrid HMM/ANN Models : a Case of Seismic Events in Poland
Autorzy:
Bijak, K.
Powiązania:
https://bibliotekanauki.pl/articles/92872.pdf
Data publikacji:
2006
Wydawca:
Uniwersytet Przyrodniczo-Humanistyczny w Siedlcach
Tematy:
infrastructure subsystem
hybrid HMM/ANN model
neural networks
seismic events
Opis:
This paper compares performance of a hidden Markov model (HMM) and a hybrid HMM/ANN model in seismic events modeling. Observation variables are assumed to follow a Poisson distribution. Parameters of the discrete-time two-state models are estimated on the basis of data on seismic events that were recorded in Poland from 1991 to 1995. Then, on the basis of the estimation results, the most likely sequences of states of the hidden Markov chains are found and forecasts for January 1996 are made. It is shown that the hybrid model fits better to the data.
Źródło:
Studia Informatica : systems and information technology; 2006, 1(7); 7-17
1731-2264
Pojawia się w:
Studia Informatica : systems and information technology
Dostawca treści:
Biblioteka Nauki
Artykuł
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