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 "Łukasik, Tomasz" wg kryterium: Autor

  Lista filtrów
Wyrównaj
    Wyświetlanie 1-3 z 3
    Tytuł:
    Intelligent management in the age of Industry 4.0 – an example of a polymer processing company
    Autorzy:
    Łukasik, Katarzyna
    Stachowiak, Tomasz
    Powiązania:
    https://bibliotekanauki.pl/articles/407205.pdf
    Data publikacji:
    2020
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    intelligent manufacturing
    Industry 4.0
    modern technologies
    polymer processing company
    Opis:
    In the article, the significance and essence of management of intelligent manufacturing in the era of the fourth industrial revolution has been presented. The current revolution has a large impact on the operation of the company. Through the changes resulting from the application of modern technologies, production processes are also undergoing revolutions, which results in changes in such indicators of business development. Management of intelligent manufacturing is also a challenge for socially responsible activities; due to solutions of Industry 4.0, enterprises directly and indirectly influence environmental protection, which results in benefits for all mankind. In the article, the analysis and assessment of management of intelligent manufacturing, using modern technologies during the production process, has been carried out, with particular emphasis on the components of management such as: monitoring, control, autonomy, optimization. Moreover, the impact of the above components of management on changes in the following indicators (KPI – Key Performance Indictors) has been evaluated, i.e. (1) quality, (2) rapidity of the production process implementation, (3) performance and (4) productivity, (5) decrease in waste generated during the technological process and (6) amount of consumed electricity. For the purposes of conducting the research, a case study has been used, developed due to the information shared by the company manufacturing machinery and equipment for the polymer processing industry, in which intelligent solutions of Industry 4.0 are being applied. The presented article is a significant contribution to the current development of knowledge in the field of implementing Industry 4.0 solutions for polymer processing. The article is a combination of theoretical and practical knowledge in the field of management and practical industrial applications. It refers to the most current research trends.
    Źródło:
    Management and Production Engineering Review; 2020, 11, 2; 38-49
    2080-8208
    2082-1344
    Pojawia się w:
    Management and Production Engineering Review
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Recognition of handwritten Latin characters with diacritics using CNN
    Autorzy:
    Lukasik, Edyta
    Charytanowicz, Małgorzata
    Milosz, Marek
    Tokovarov, Michail
    Kaczorowska, Monika
    Czerwinski, Dariusz
    Zientarski, Tomasz
    Powiązania:
    https://bibliotekanauki.pl/articles/2173581.pdf
    Data publikacji:
    2021
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    handwritten documents
    diacritic
    neural networks
    character recognition
    deep learning
    dokument odręczny
    znaki diakrytyczne
    sieci neuronowe
    rozpoznawanie znaków
    głęboka nauka
    Opis:
    Convolutional Neural Networks (CNN) have achieved huge popularity in solving problems in image analysis and in text recognition. In this work, we assess the effectiveness of CNN-based architectures where a network is trained in recognizing handwritten characters based on Latin script. European languages such as Dutch, French, German, etc., use different variants of the Latin script, so in the conducted research, the Latin alphabet was extended by certain characters with diacritics used in Polish language. To evaluate the recognition results under the same conditions, a handwritten Latin dataset was also developed. The proposed CNN architecture produced an accuracy of 96% for the extended character set. This is comparable to state-of-the-art results found in the domain of identifying handwritten characters. The presented approach extends the usage of CNN-based recognition to different variants of the Latin characters and shows it can be successfully used for a set of languages based on that script. It seems to be an effective technique for a set of languages written using the Latin script.
    Źródło:
    Bulletin of the Polish Academy of Sciences. Technical Sciences; 2021, 69, 1; art. no. e136210
    0239-7528
    Pojawia się w:
    Bulletin of the Polish Academy of Sciences. Technical Sciences
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Recognition of handwritten Latin characters with diacritics using CNN
    Autorzy:
    Lukasik, Edyta
    Charytanowicz, Małgorzata
    Milosz, Marek
    Tokovarov, Michail
    Kaczorowska, Monika
    Czerwinski, Dariusz
    Zientarski, Tomasz
    Powiązania:
    https://bibliotekanauki.pl/articles/2090714.pdf
    Data publikacji:
    2021
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    handwritten documents
    diacritic
    neural networks
    character recognition
    deep learning
    dokument odręczny
    znaki diakrytyczne
    sieci neuronowe
    rozpoznawanie znaków
    głęboka nauka
    Opis:
    Convolutional Neural Networks (CNN) have achieved huge popularity in solving problems in image analysis and in text recognition. In this work, we assess the effectiveness of CNN-based architectures where a network is trained in recognizing handwritten characters based on Latin script. European languages such as Dutch, French, German, etc., use different variants of the Latin script, so in the conducted research, the Latin alphabet was extended by certain characters with diacritics used in Polish language. To evaluate the recognition results under the same conditions, a handwritten Latin dataset was also developed. The proposed CNN architecture produced an accuracy of 96% for the extended character set. This is comparable to state-of-the-art results found in the domain of identifying handwritten characters. The presented approach extends the usage of CNN-based recognition to different variants of the Latin characters and shows it can be successfully used for a set of languages based on that script. It seems to be an effective technique for a set of languages written using the Latin script.
    Źródło:
    Bulletin of the Polish Academy of Sciences. Technical Sciences; 2021, 69, 1; e136210, 1--12
    0239-7528
    Pojawia się w:
    Bulletin of the Polish Academy of Sciences. Technical Sciences
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-3 z 3

      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