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    Wyświetlanie 1-4 z 4
    Tytuł:
    The model of multifunctional neural element of intelligent systems
    Autorzy:
    MARTYNIUK, Tetiana
    KOZHEMIAKO, Andriy
    BUDA, Antonina
    KUPERSHTEIN, Leonid
    Powiązania:
    https://bibliotekanauki.pl/articles/457084.pdf
    Data publikacji:
    2013
    Wydawca:
    Uniwersytet Rzeszowski
    Tematy:
    neural element
    threshold parallel processing
    parallel-pipeline processor
    neural network
    perсeptron
    neural chip
    difference cut
    programmable logic device
    Opis:
    The article shows the features of realization of multioperand processing in neural structures on the base of difference cuts, that allow to expand functional capabilities and to reduce time consumptions in neural processing. The structural organization of the parallel-pipeline processor for neural-like vector data processing on the DCs base are proposed. This parallel-pipeline processor on CPLD base are implemented, which allow realize neural chip with a fragment of the neural network layer.
    Źródło:
    Edukacja-Technika-Informatyka; 2013, 4, 2; 366-371
    2080-9069
    Pojawia się w:
    Edukacja-Technika-Informatyka
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Test Procedures for Synchronized Oscillators Network CMOS VLSI Chip
    Autorzy:
    Kowalski, J.
    Strzelecki, M.
    Powiązania:
    https://bibliotekanauki.pl/articles/226984.pdf
    Data publikacji:
    2015
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    neural networks chip testing
    synchronized oscillator network
    parallel image segmentation
    analog-digital VLSI CMOS implementation
    Opis:
    The paper presents test procedures designed for application - specific integrated circuit (ASIC) CMOS VLSI chip prototype that implements a synchronized oscillator neural network with a matrix size of 32×32 for object detecting in binary images. Networks of synchronized oscillators are recently developed tool for image segmentation and analysis. This paper briefly introduces synchronized oscillators network. Basic chip analog building blocks with their test procedures and measurements results are presented. In order to do measurements, special basic building blocks test structures have been implemented in the chip. It let compare Spectre simulateions results to measurements results. Moreover, basic chip analog building blocks measurements give precious information about their imperfections caused by MOS transistor mismatch. This information is very usable during design and improvement of a special setup for chip functional tests. Improvement of the setup is a digitally assisted analog technique. It is an original idea of oscillators tuning procedure used during chip prototype testing. Such setup, oscillators tuning procedure and segmentation of sample binary images are presented.
    Źródło:
    International Journal of Electronics and Telecommunications; 2015, 61, 1; 101-107
    2300-1933
    Pojawia się w:
    International Journal of Electronics and Telecommunications
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Automated evaluation of continuous and segmented chip geometries based on image processing methods and a convolutional neural network
    Autorzy:
    Klippel, Hagen
    Pflaum, Samuel
    Kuffa, Michal
    Wegener, Konrad
    Powiązania:
    https://bibliotekanauki.pl/articles/2171775.pdf
    Data publikacji:
    2022
    Wydawca:
    Wrocławska Rada Federacji Stowarzyszeń Naukowo-Technicznych
    Tematy:
    machining
    AI
    computer vision
    image processing
    chip
    chip shape
    chip segmentation
    Opis:
    The aim of this work is to present a new methodology for the automated analysis of the cross-sections of experimental chip shapes. It enables, based on image processing methods, the determination of average chip thicknesses, chip curling radii and for segmented chips the extraction of chip segmentation lengths, as well as minimum and maximum chip thicknesses. To automatically decide whether a chip at hand should be evaluated using the proposed methods for continuous or segmented chips, a convolutional neural network is proposed, which is trained using supervised learning with available images from embedded chip cross-sections. Data from manual measurements are used for comparison and validation purposes.
    Źródło:
    Journal of Machine Engineering; 2022, 22, 4; 115--132
    1895-7595
    2391-8071
    Pojawia się w:
    Journal of Machine Engineering
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Modeling and optimization of cutting parameters when turning EN-AW-1350 aluminum alloy
    Autorzy:
    Khrouf, F.
    Tebassi, H.
    Yallese, M. A.
    Chaoui, K.
    Haddad, A.
    Powiązania:
    https://bibliotekanauki.pl/articles/2174201.pdf
    Data publikacji:
    2022
    Wydawca:
    Uniwersytet Zielonogórski. Oficyna Wydawnicza
    Tematy:
    sztuczna sieć neuronowa
    optymalizacja
    ANOVA
    artificial neural networks
    chip shape
    optimization
    response surface methodology
    Opis:
    An experimental investigation is carried out to examine the effects of various cutting parameters on the response criteria when turning EN-AW-1350 aluminum alloy under dry cutting conditions. The experiments related to the analysis of the influence of turning parameters on the surface roughness (Ra) and material removal rate (MRR) were carried out according to the Taguchi L27 orthogonal array (313) approach. The analysis of variance (ANOVA) was applied to characterizing the main elements affecting response parameters. Finally, the desirability function (DP) was applied for a bi-objective optimization of the machining parameters with the objective of achieving a better surface finish (Ra) and a higher productivity (MRR). The results showed that the cutting speed is the most dominant factor affecting Ra followed by the feed rate and the depth of cut. Moreover, the Artificial Neural Network (ANN) approach is found to be more reliable and accurate than its Response Surface methodology (RSM) counterpart in terms of predicting and detecting the non-linearity of the surface roughness and material removal rate mathematical models. ANN provided prediction models with a precision benefit of 8.21% more than those determined by RSM. The latter is easier to use, and provides more information than ANN in terms of the impacts and contributions of the model terms.
    Źródło:
    International Journal of Applied Mechanics and Engineering; 2022, 27, 2; 124--142
    1734-4492
    2353-9003
    Pojawia się w:
    International Journal of Applied Mechanics and Engineering
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-4 z 4

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