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    Wyświetlanie 1-3 z 3
    Tytuł:
    Trajectory determination for pipelines using an inspection robot and pipeline features
    Autorzy:
    Zhang, Shuo
    Dubljevic, Stevan
    Powiązania:
    https://bibliotekanauki.pl/articles/1849012.pdf
    Data publikacji:
    2021
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    trajectory determination
    pipeline inspection robot
    pipeline feature
    path reconstruction algorithm
    Opis:
    Geographic trajectory of a pipeline is important information for pipeline maintenance and leak detection. Although accurate trajectory of a ground pipeline usually can be directly measured by using global positioning system technology, it is much difficult to determine trajectory for an underground pipeline where global positioning system signal cannot be received. In this paper, a new method to determine trajectory for an underground pipeline by using a pipeline inspection robot is proposed. The robot is equipped with a low-cost inertial measurement unit and odometers. The kinematic model, measurement model and error propagation model are established for estimating position, velocity and attitude of the robot. The path reconstruction algorithm for the robot is proposed to improve accuracy of trajectory determination based on pipeline features. The experiment is given to illustrate that the position errors of the proposed method are less than 40% of that of the standard extended Kalman filter.
    Źródło:
    Metrology and Measurement Systems; 2021, 28, 3; 439-453
    0860-8229
    Pojawia się w:
    Metrology and Measurement Systems
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Trajectory determination for pipelines using an inspection robot and pipeline features
    Autorzy:
    Zhang, Shuo
    Dubljevic, Stevan
    Powiązania:
    https://bibliotekanauki.pl/articles/1849098.pdf
    Data publikacji:
    2021
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    trajectory determination
    pipeline inspection robot
    pipeline feature
    path reconstruction algorithm
    Opis:
    Geographic trajectory of a pipeline is important information for pipeline maintenance and leak detection. Although accurate trajectory of a ground pipeline usually can be directly measured by using global positioning system technology, it is much difficult to determine trajectory for an underground pipeline where global positioning system signal cannot be received. In this paper, a new method to determine trajectory for an underground pipeline by using a pipeline inspection robot is proposed. The robot is equipped with a low-cost inertial measurement unit and odometers. The kinematic model, measurement model and error propagation model are established for estimating position, velocity and attitude of the robot. The path reconstruction algorithm for the robot is proposed to improve accuracy of trajectory determination based on pipeline features. The experiment is given to illustrate that the position errors of the proposed method are less than 40% of that of the standard extended Kalman filter.
    Źródło:
    Metrology and Measurement Systems; 2021, 28, 3; 439-453
    0860-8229
    Pojawia się w:
    Metrology and Measurement Systems
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    FSPL: A meta-learning approach for a filter and embedded feature selection pipeline
    Autorzy:
    Lazebnik, Teddy
    Rosenfeld, Avi
    Powiązania:
    https://bibliotekanauki.pl/articles/2201020.pdf
    Data publikacji:
    2023
    Wydawca:
    Uniwersytet Zielonogórski. Oficyna Wydawnicza
    Tematy:
    feature selection pipeline
    meta learning
    no free lunch
    autoML
    genetic algorithm
    wybór funkcji
    metauczenie
    algorytm genetyczny
    Opis:
    There are two main approaches to tackle the challenge of finding the best filter or embedded feature selection (FS) algorithm: searching for the one best FS algorithm and creating an ensemble of all available FS algorithms. However, in practice, these two processes usually occur as part of a larger machine learning pipeline and not separately. We posit that, due to the influence of the filter FS on the embedded FS, one should aim to optimize both of them as a single FS pipeline rather than separately. We propose a meta-learning approach that automatically finds the best filter and embedded FS pipeline for a given dataset called FSPL. We demonstrate the performance of FSPL on n = 90 datasets, obtaining 0.496 accuracy for the optimal FS pipeline, revealing an improvement of up to 5.98 percent in the model’s accuracy compared to the second-best meta-learning method.
    Źródło:
    International Journal of Applied Mathematics and Computer Science; 2023, 33, 1; 103--115
    1641-876X
    2083-8492
    Pojawia się w:
    International Journal of Applied Mathematics and Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-3 z 3

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