Informacja

Drogi użytkowniku, aplikacja do prawidłowego działania wymaga obsługi JavaScript. Proszę włącz obsługę JavaScript w Twojej przeglądarce.

English (EN)·Polski (PL)·Yкраїнський (UK)
Przejdź do strony domowej biblioteki Centralna Biblioteka Wojskowa Link otwiera się w nowym oknie Logo biblioteki Prolib Integro - strona głównaCentralna Biblioteka Wojskowa

Wyszukujesz frazę "detection process" wg kryterium: Wszystkie pola

  Lista filtrów
Wyrównaj
    Wyświetlanie 1-2 z 2
    Tytuł:
    The Hough transform in the classification process of inland ships
    Autorzy:
    Bobkowska, Katarzyna
    Wawrzyniak, Natalia
    Powiązania:
    https://bibliotekanauki.pl/articles/135214.pdf
    Data publikacji:
    2019
    Wydawca:
    Akademia Morska w Szczecinie. Wydawnictwo AMSz
    Tematy:
    Hough transform
    k Nearest Neighbors (kNN)
    image processing
    classification
    ship recognition
    line detection
    Opis:
    This article presents an analysis of the possibilities of using image processing methods for feature extraction that allows kNN classification based on a ship’s image delivered from an on-water video surveillance system. The subject of the analysis is the Hough transform which enables the detection of straight lines in an image. The recognized straight lines and the information about them serve as features in the classification process. Above all, this approach allows ships to be recognized, which can then be characterized by a specific representation and shape. Recreational units that are often seen on inland waters were classified correctly using this method. Each analyzed camera image was previously prepared – brought to the form where the ship was visible from the side and the background removed (they were monochromatic – white). The results obtained in this work will allow for the development of the final ship classification method based on camera images. This method is a significant part of the emerging system prototype, which is implemented as part of the Automatic Ship Recognition and Identification (SHREC) project.
    Źródło:
    Zeszyty Naukowe Akademii Morskiej w Szczecinie; 2019, 58 (130); 9-15
    1733-8670
    2392-0378
    Pojawia się w:
    Zeszyty Naukowe Akademii Morskiej w Szczecinie
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    A Data Mining Approach for Analysis of a Wire Electrical Discharge Machining Process
    Autorzy:
    Dandge, Shruti Sudhakar
    Chakraborty, Shankar
    Powiązania:
    https://bibliotekanauki.pl/articles/2023974.pdf
    Data publikacji:
    2021-09
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    wire electrical discharge machining
    data mining
    classification and regression tree
    chi-squared
    automatic interaction detection
    classification
    Opis:
    Wire electrical discharge machining (WEDM) is a non-conventional material-removal process where a continuously travelling electrically conductive wire is used as an electrode to erode material from a workpiece. To explore its fullest machining potential, there is always a requirement to examine the effects of its varied input parameters on the responses and resolve the best parametric setting. This paper proposes parametric analysis of a WEDM process by applying non-parametric decision tree algorithm, based on a past experimental dataset. Two decision tree-based classification methods, i.e. classification and regression tree (CART) and Chi-squared automatic interaction detection (CHAID) are considered here as the data mining tools to examine the influences of six WEDM process parameters on four responses, and identify the most preferred parametric mix to help in achieving the desired response values. The developed decision trees recognize pulse-on time as the most indicative WEDM process parameter impacting almost all the responses. Furthermore, a comparative analysis on the classification performance of CART and CHAID algorithms demonstrates the superiority of CART with higher overall classification accuracy and lower prediction risk.
    Źródło:
    Management and Production Engineering Review; 2021, 13, 3; 116-128
    2080-8208
    2082-1344
    Pojawia się w:
    Management and Production Engineering Review
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-2 z 2

      Ta witryna wykorzystuje pliki cookies do przechowywania informacji na Twoim komputerze. Pliki cookies stosujemy w celu świadczenia usług na najwyższym poziomie, w tym w sposób dostosowany do indywidualnych potrzeb. Korzystanie z witryny bez zmiany ustawień dotyczących cookies oznacza, że będą one zamieszczane w Twoim komputerze. W każdym momencie możesz dokonać zmiany ustawień dotyczących cookies