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    Wyświetlanie 1-3 z 3
    Tytuł:
    Void fraction and flow regime determination by means of MCNP code and neural network
    Autorzy:
    Rabiei, A.
    Shamsaei, M.
    Kafaee, M.
    Shafaei, M.
    Mahdavi, N.
    Powiązania:
    https://bibliotekanauki.pl/articles/146656.pdf
    Data publikacji:
    2012
    Wydawca:
    Instytut Chemii i Techniki Jądrowej
    Tematy:
    flow regime
    gamma-ray densitometry
    neural network (NN)
    Monte Carlo N-particle (MCNP)
    void fraction
    Opis:
    One of the non-intrusive and accurate methods of measuring void fraction in two-phase gas liquid pipe flows is the use of the gamma-transmission void fraction measurement technique. The goal of this study is to describe low-energy gamma-ray densitometry using an 241Am source for the determination of void fraction and flow regime in water/gas pipes. The MCNP code was utilized to simulate electron and photon transport through materials with various geometries. Then, a neural network was used to convert multi-beam gamma-ray spectra into a classification of the flow regime and void fraction. The simulations cover the full range of void fraction with Bubbly, Annular and Droplet flows. By using simulation data as input to the neural networks, the void fraction was determined with an error less than 3% regardless of the flow regime. It has thus been shown that multi-beam gamma-ray densitometers with a detector response examined by neural networks can analyze a two-phase flow with high accuracy.
    Źródło:
    Nukleonika; 2012, 57, 3; 345-349
    0029-5922
    1508-5791
    Pojawia się w:
    Nukleonika
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Neural network based selection of optimal tool - path in free form surface machining
    Autorzy:
    Korosec, M.
    Kopaz, J.
    Powiązania:
    https://bibliotekanauki.pl/articles/384505.pdf
    Data publikacji:
    2007
    Wydawca:
    Sieć Badawcza Łukasiewicz - Przemysłowy Instytut Automatyki i Pomiarów
    Tematy:
    (NN) Neural Network
    CAD/CAM system
    CAPP
    Intelligent CAM (ICAM)
    milling strategy
    Opis:
    The purpose of the presented paper is to show how with the help of artificial Neural Network (NN) the prediction of milling tool-path strategies could be performed in order to determine which milling tool - path strategies or their sequences will yield the best results (i.e. the most appropriate ones) of free form surface machining, in accordance with a selected technological aim. Usually, the machining task could be completed successfully using different tool-path strategies or their sequences. They can all perform the machining task according to the demands but always only one of the all possible applied strategies is optimal in terms of the desired technological goal (surface quality in most cases). In the presented paper, the best possible surface quality of a machined surface was taken as the primary technological aim. Configuration of the applied Neural Network is presented and the whole procedure of determining the optimal tool-path sequence is shown through an example of a light switch mould. Verification of the machined surface quality, in relation to the average mean roughness Ra is also being performed and compared with the NN predicted results.
    Źródło:
    Journal of Automation Mobile Robotics and Intelligent Systems; 2007, 1, 4; 41-50
    1897-8649
    2080-2145
    Pojawia się w:
    Journal of Automation Mobile Robotics and Intelligent Systems
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Multi-Layer Perceptron Neural Network Utilizing Adaptive Best-Mass Gravitational Search Algorithm to Classify Sonar Dataset
    Autorzy:
    Mosavi, Mohammad Reza
    Khishe, Mohammad
    Naseri, Mohammad Jafar
    Parvizi, Gholam Reza
    Ayat, Mehdi
    Powiązania:
    https://bibliotekanauki.pl/articles/176971.pdf
    Data publikacji:
    2019
    Wydawca:
    Polska Akademia Nauk. Czasopisma i Monografie PAN
    Tematy:
    MLP NN
    Multi-Layer Perceptron Neural Network
    ABGSA
    Adaptive Best Mass Gravitational Search Algorithm
    sonar
    classification
    Opis:
    In this paper, a new Multi-Layer Perceptron Neural Network (MLP NN) classifier is proposed for classifying sonar targets and non-targets from the acoustic backscattered signals. Besides the capabilities of MLP NNs, it uses Back Propagation (BP) and Gradient Descent (GD) for training; therefore, MLP NNs face with not only impertinent classification accuracy but also getting stuck in local minima as well as lowconvergence speed. To lift defections, this study uses Adaptive Best Mass Gravitational Search Algorithm (ABGSA) to train MLP NN. This algorithm develops marginal disadvantage of the GSA using the bestcollected masses within iterations and expediting exploitation phase. To test the proposed classifier, this algorithm along with the GSA, GD, GA, PSO and compound method (PSOGSA) via three datasets in various dimensions will be assessed. Assessed metrics include convergence speed, fail probability in local minimum and classification accuracy. Finally, as a practical application assumed network classifies sonar dataset. This dataset consists of the backscattered echoes from six different objects: four targets and two non-targets. Results indicate that the new classifier proposes better output in terms of aforementioned criteria than whole proposed benchmarks.
    Źródło:
    Archives of Acoustics; 2019, 44, 1; 137-151
    0137-5075
    Pojawia się w:
    Archives of Acoustics
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-3 z 3

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