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Wyszukujesz frazę "optical neural network" wg kryterium: Temat

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    Wyświetlanie 1-6 z 6
    Tytuł:
    Noise quantization simulation analysis of optical convolutional networks
    Autorzy:
    Zhang, Ye
    Zhang, Saining
    Zhang, Danni
    Su, Yanmei
    Yi, Junkai
    Wang, Pengfei
    Wang, Ruiting
    Luo, Guangzhen
    Zhou, Xuliang
    Pan, Jiaoqing
    Powiązania:
    https://bibliotekanauki.pl/articles/27310111.pdf
    Data publikacji:
    2023
    Wydawca:
    Politechnika Wrocławska. Oficyna Wydawnicza Politechniki Wrocławskiej
    Tematy:
    optical neural network
    convolutional neural network
    noise
    quantization
    Opis:
    Optical neural network (ONN) has been regarded as one of the most prospective techniques in the future, due to its high-speed and low power cost. However, the realization of optical convolutional neural network (CNN) in non-ideal cases still remains a big challenge. In this paper, we propose an optical convolutional networks system for classification problems by applying general matrix multiply (GEMM) technology. The results show that under the influence of noise, this system still has good performance with low TOP-1 and TOP-5 error rates of 44.26% and 14.51% for ImageNet. We also propose a quantization model of CNN. The noise quantization model reaches a sufficient prediction accuracy of about 96% for MNIST handwritten dataset.
    Źródło:
    Optica Applicata; 2023, 53, 3; 483--493
    0078-5466
    1899-7015
    Pojawia się w:
    Optica Applicata
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Detection of Monocrystalline Silicon Wafer Defects Using Deep Transfer Learning
    Autorzy:
    Ganum, Adriana
    Iskandar, D. N. F. Awang
    Chin, Lim Phei
    Fauzi, Ahmad Hadinata
    Powiązania:
    https://bibliotekanauki.pl/articles/2058502.pdf
    Data publikacji:
    2022
    Wydawca:
    Instytut Łączności - Państwowy Instytut Badawczy
    Tematy:
    automated optical inspection
    machine learning
    neural network
    wafer imperfection identification
    Opis:
    Defect detection is an important step in industrial production of monocrystalline silicon. Through the study of deep learning, this work proposes a framework for classifying monocrystalline silicon wafer defects using deep transfer learning (DTL). An existing pre-trained deep learning model was used as the starting point for building a new model. We studied the use of DTL and the potential adaptation of Mo bileNetV2 that was pre-trained using ImageNet for extracting monocrystalline silicon wafer defect features. This has led to speeding up the training process and to improving performance of the DTL-MobileNetV2 model in detecting and classifying six types of monocrystalline silicon wafer defects (crack, double contrast, hole, microcrack, saw-mark and stain). The process of training the DTL-MobileNetV2 model was optimized by relying on the dense block layer and global average pooling (GAP) method which had accelerated the convergence rate and improved generalization of the classification network. The monocrystalline silicon wafer defect classification technique relying on the DTL-MobileNetV2 model achieved the accuracy rate of 98.99% when evaluated against the testing set. This shows that DTL is an effective way of detecting different types of defects in monocrystalline silicon wafers, thus being suitable for minimizing misclassification and maximizing the overall production capacities.
    Źródło:
    Journal of Telecommunications and Information Technology; 2022, 1; 34--42
    1509-4553
    1899-8852
    Pojawia się w:
    Journal of Telecommunications and Information Technology
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Subpixel localization of optical vortices using artificial neural networks
    Autorzy:
    Popiołek-Masajada, Agnieszka
    Frączek, Ewa
    Burnecka, Emilia
    Powiązania:
    https://bibliotekanauki.pl/articles/1849005.pdf
    Data publikacji:
    2021
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    optical vortex
    spiral phase map
    pseudo phase
    deep learning
    neural network
    Opis:
    Optical vortices are getting attention in modern optical metrology. Because of their unique features, they can be used as precise position markers. In this paper, we show that an artificial neural network can be used to improve vortex localization. A deep neural network with several hidden layers was trained to find subpixel vortex positions on the spiral phase maps. Several thousand training samples, differing by spiral density, its orientation, and vortex position, were generated numerically for teaching purposes. As a result, Best Validation Performance of the order of 10-5 pixel has been reached. To verify the usefulness of the proposed method, a related experiment in the setup of an optical vortex scanning microscope has been reported. It is shown that the vortex can be localized with subpixel accuracy also on experimental phase maps.
    Źródło:
    Metrology and Measurement Systems; 2021, 28, 3; 497-508
    0860-8229
    Pojawia się w:
    Metrology and Measurement Systems
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Subpixel localization of optical vortices using artificial neural networks
    Autorzy:
    Popiołek-Masajada, Agnieszka
    Frączek, Ewa
    Burnecka, Emilia
    Powiązania:
    https://bibliotekanauki.pl/articles/1849096.pdf
    Data publikacji:
    2021
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    optical vortex
    spiral phase map
    pseudo phase
    deep learning
    neural network
    Opis:
    Optical vortices are getting attention in modern optical metrology. Because of their unique features, they can be used as precise position markers. In this paper, we show that an artificial neural network can be used to improve vortex localization. A deep neural network with several hidden layers was trained to find subpixel vortex positions on the spiral phase maps. Several thousand training samples, differing by spiral density, its orientation, and vortex position, were generated numerically for teaching purposes. As a result, Best Validation Performance of the order of 10-5 pixel has been reached. To verify the usefulness of the proposed method, a related experiment in the setup of an optical vortex scanning microscope has been reported. It is shown that the vortex can be localized with subpixel accuracy also on experimental phase maps.
    Źródło:
    Metrology and Measurement Systems; 2021, 28, 3; 497-508
    0860-8229
    Pojawia się w:
    Metrology and Measurement Systems
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Graph-based segmentation with homogeneous hue and texture vertices
    Autorzy:
    Ngo, Lua
    Han, Jae-Ho
    Powiązania:
    https://bibliotekanauki.pl/articles/2033896.pdf
    Data publikacji:
    2021
    Wydawca:
    Politechnika Wrocławska. Oficyna Wydawnicza Politechniki Wrocławskiej
    Tematy:
    image segmentation
    deep neural network
    electron microscopy
    optical coherence tomography
    pattern recognition
    Opis:
    This work presents an automated segmentation method, based on graph theory, which processes superpixels that exhibit spatially similarities in hue and texture pixel groups, rather than individual pixels. The graph shortest path includes a chain of neighboring superpixels which have minimal intensity changes. This method reduces graphics computational complexity because it provides large decreases in the number of vertices as the superpixel size increases. For the starting vertex prediction, the boundary pixel in first column which is included in this starting vertex is predicted by a trained deep neural network formulated as a regression task. By formulating the problem as a regression scheme, the computational burden is decreased in comparison with classifying each pixel in the entire image. This feasibility approach, when applied as a preliminary study in electron microscopy and optical coherence tomography images, demonstrated high measures of accuracy: 0.9670 for the electron microscopy image and 0.9930 for vitreous/nerve-fiber and inner-segment/outer-segment layer segmentations in the optical coherence tomography image.
    Źródło:
    Optica Applicata; 2021, 51, 4; 541-549
    0078-5466
    1899-7015
    Pojawia się w:
    Optica Applicata
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Sieć neuronowa lokalizująca wiry optyczne rozmieszczone w regularnej strukturze
    Artificial neural network for localization of optical vortices positioned in a regular structure
    Autorzy:
    Guszkowski, T.
    Frączek, E.
    Powiązania:
    https://bibliotekanauki.pl/articles/157563.pdf
    Data publikacji:
    2010
    Wydawca:
    Stowarzyszenie Inżynierów i Techników Mechaników Polskich
    Tematy:
    wir optyczny
    lokalizacja
    sztuczna sieć neuronowa
    optical vortex
    localization
    artificial neural network
    Opis:
    Praca prezentuje opartą na sztucznej sieci neuronowej metodę określania położenia wirów optycznych rozmieszczonych w regularnej strukturze i powstałych w wyniku interferencji trzech fal płaskich. Do uczenia i oceny jakości działania sieci neuronowej wykorzystano zestaw symulowanych obrazów, na które dodatkowo nałożono szum pomiarowy oraz zniekształcenia geometryczne wynikające z symulowanych drgań układu pomiarowego. W wyniku uczenia sieci neuronowej uzyskano neuronowy lokalizator wirów optycznych o medianie błędu lokalizacji poniżej 0,4 piksela.
    The paper presents a short introduction to the optical vortices localization problem and an Artificial Neural Network (ANN) for localization of the optical vortices positioned in a regular structure of honeycomb. The analyzed vortices form as an effect of interference of three planar waves. A set of 1800 simulated images with added noise and geometric distortions modelling experimental setup vibrations was used for ANN learning and evaluation. As a result an unidirectional ANN with 100 inputs which correspond with pixels in a 10x10 image matrix; one non-linear hidden layer of 5 neuron and 2 outputs representing the coordinates of the vortex were created. The learning criterion was Mean Square Error (MSE) and the net was taught with Levenberg-Marquardt algorithm implemented in MATLAB. Final tests were performed with 180 images excluded from ANN learning. As a result a neuronal localizator of optical vortices was obtained with the worst-case localization error less than 2.1 pixel and localization error median less than 0.4 pixel.
    Źródło:
    Pomiary Automatyka Kontrola; 2010, R. 56, nr 9, 9; 1074-1076
    0032-4140
    Pojawia się w:
    Pomiary Automatyka Kontrola
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-6 z 6

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