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    Wyświetlanie 1-4 z 4
    Tytuł:
    The study of co-design in the area of manufacturing
    Autorzy:
    Krawczyk-Dembicka, Elżbieta
    Urban, Wiesław
    Łukaszewicz, Krzysztof
    Powiązania:
    https://bibliotekanauki.pl/articles/2173723.pdf
    Data publikacji:
    2022
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    co-design
    manufacturing
    sustainable production
    Industry 4.0
    simulation
    współprojektowanie
    produkcja
    zrównoważona produkcja
    Przemysł 4.0
    symulacja
    Opis:
    The study is devoted to the co-design concept which is not widely studied in the manufacturing industry area. The concept is just practiced but not theorized and not investigated enough, although it greatly deserves it because of its perspectives and advantages potential in the technology changes era. This study aims to present an investigation of literature views on co-design in manufacturing operations, with the comparison to service literature where it is widely discussed; the study also aims at in-depth investigations of co-design occurrences in two industrial cases of product development to understand their nature and circumstances. In addition, the influence of Industry 4.0 technologies and their coexistence with the concept of sustainability will also be strongly taken into consideration in the empirical part of this study. The process of the individualized production of the industrial line for animal food packing and cardboard packaging production has been studied according to case study methodology. The study demonstrates that co-design could contribute to bettering the process of new product development and achieving products more accurate for the final users’ requirements. It goes hand in hand with one of the core ideas of sustainability, which is to have long-lasting products, exploited by the customer with a high level of satisfaction for a longer time. The study implies that the technologies of Industry 4.0 could support wider and more effective co-design exploitation by manufacturing entities.
    Źródło:
    Bulletin of the Polish Academy of Sciences. Technical Sciences; 2022, 70, 6; art. no. e143930
    0239-7528
    Pojawia się w:
    Bulletin of the Polish Academy of Sciences. Technical Sciences
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Conceptualization of Industry 4.0 technology for the production of tailor-made furniture – a case study
    Autorzy:
    Łukaszewicz, Krzysztof
    Urban, Wiesław
    Krawczyk-Dembicka, Elżbieta
    Powiązania:
    https://bibliotekanauki.pl/articles/2204519.pdf
    Data publikacji:
    2023
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    product development
    customized manufacturing
    furniture
    conceptual study
    Industry 4.0
    rozwój produktu
    Przemysł 4.0
    produkcja na zamówienie
    meble
    studium koncepcyjne
    Opis:
    Production is becoming more customer-focused as it departs from delivering standardized mass products to market segments, and the emerging Industry 4.0 technologies render this much easier than before. These technologies enable two-way information exchange with customers throughout all the steps of product development, particularly in terms of tailor-made products. This study aims at presenting proposals of implementing Industry 4.0 technologies into the process of tailored products, where the product is customized for the customer from the start and where adjustments are also made at the manufacturing stage. The study also aims to build a concept of intensification of customer contact and to improve the process flow by applying Industry 4.0 technologies. The study’s subject is tailor-made furniture production, with individually designed products that are manufactured and installed at a customer’s facilities. The company in the study operates on a small scale. The study employs a case study methodology that shows how the process can be improved in terms of real-time effective customer contact and process flow. The huge potential of 3D visualization as well as augmented and virtual reality technologies are also demonstrated. The study concludes with several directions for further development of existing technology solutions.
    Źródło:
    Bulletin of the Polish Academy of Sciences. Technical Sciences; 2023, 71, 1; art. no. e144587
    0239-7528
    Pojawia się w:
    Bulletin of the Polish Academy of Sciences. Technical Sciences
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Deep learning-based framework for tumour detection and semantic segmentation
    Autorzy:
    Kot, Estera
    Krawczyk, Zuzanna
    Siwek, Krzysztof
    Królicki, Leszek
    Czwarnowski, Piotr
    Powiązania:
    https://bibliotekanauki.pl/articles/2173573.pdf
    Data publikacji:
    2021
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    deep learning
    medical imaging
    tumour detection
    semantic segmentation
    image fusion
    technika deep learning
    głęboka nauka
    obrazowanie medyczne
    wykrywanie guza
    segmentacja semantyczna
    połączenie obrazu
    Opis:
    For brain tumour treatment plans, the diagnoses and predictions made by medical doctors and radiologists are dependent on medical imaging. Obtaining clinically meaningful information from various imaging modalities such as computerized tomography (CT), positron emission tomography (PET) and magnetic resonance (MR) scans are the core methods in software and advanced screening utilized by radiologists. In this paper, a universal and complex framework for two parts of the dose control process – tumours detection and tumours area segmentation from medical images is introduced. The framework formed the implementation of methods to detect glioma tumour from CT and PET scans. Two deep learning pre-trained models: VGG19 and VGG19-BN were investigated and utilized to fuse CT and PET examinations results. Mask R-CNN (region-based convolutional neural network) was used for tumour detection – output of the model is bounding box coordinates for each object in the image – tumour. U-Net was used to perform semantic segmentation – segment malignant cells and tumour area. Transfer learning technique was used to increase the accuracy of models while having a limited collection of the dataset. Data augmentation methods were applied to generate and increase the number of training samples. The implemented framework can be utilized for other use-cases that combine object detection and area segmentation from grayscale and RGB images, especially to shape computer-aided diagnosis (CADx) and computer-aided detection (CADe) systems in the healthcare industry to facilitate and assist doctors and medical care providers.
    Źródło:
    Bulletin of the Polish Academy of Sciences. Technical Sciences; 2021, 69, 3; art. no. e136750
    0239-7528
    Pojawia się w:
    Bulletin of the Polish Academy of Sciences. Technical Sciences
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Deep learning-based framework for tumour detection and semantic segmentation
    Autorzy:
    Kot, Estera
    Krawczyk, Zuzanna
    Siwek, Krzysztof
    Królicki, Leszek
    Czwarnowski, Piotr
    Powiązania:
    https://bibliotekanauki.pl/articles/2128156.pdf
    Data publikacji:
    2021
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    deep learning
    medical imaging
    tumour detection
    semantic segmentation
    image fusion
    technika deep learning
    głęboka nauka
    obrazowanie medyczne
    wykrywanie guza
    segmentacja semantyczna
    połączenie obrazu
    Opis:
    For brain tumour treatment plans, the diagnoses and predictions made by medical doctors and radiologists are dependent on medical imaging. Obtaining clinically meaningful information from various imaging modalities such as computerized tomography (CT), positron emission tomography (PET) and magnetic resonance (MR) scans are the core methods in software and advanced screening utilized by radiologists. In this paper, a universal and complex framework for two parts of the dose control process – tumours detection and tumours area segmentation from medical images is introduced. The framework formed the implementation of methods to detect glioma tumour from CT and PET scans. Two deep learning pre-trained models: VGG19 and VGG19-BN were investigated and utilized to fuse CT and PET examinations results. Mask R-CNN (region-based convolutional neural network) was used for tumour detection – output of the model is bounding box coordinates for each object in the image – tumour. U-Net was used to perform semantic segmentation – segment malignant cells and tumour area. Transfer learning technique was used to increase the accuracy of models while having a limited collection of the dataset. Data augmentation methods were applied to generate and increase the number of training samples. The implemented framework can be utilized for other use-cases that combine object detection and area segmentation from grayscale and RGB images, especially to shape computer-aided diagnosis (CADx) and computer-aided detection (CADe) systems in the healthcare industry to facilitate and assist doctors and medical care providers.
    Źródło:
    Bulletin of the Polish Academy of Sciences. Technical Sciences; 2021, 69, 3; e136750, 1--7
    0239-7528
    Pojawia się w:
    Bulletin of the Polish Academy of Sciences. Technical Sciences
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-4 z 4

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