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Wyszukujesz frazę "Chin, Jeng Feng" wg kryterium: Autor


Wyświetlanie 1-2 z 2
Tytuł:
Exploring critical success factors for the implementation of lean manufacturing in machinery and equipment SMEs
Autorzy:
Yuik, Chong Jia
Perumal, Puvanasvaran A.
Feng, Chin Jeng
Powiązania:
https://bibliotekanauki.pl/articles/1818984.pdf
Data publikacji:
2020
Wydawca:
Politechnika Białostocka. Oficyna Wydawnicza Politechniki Białostockiej
Tematy:
lean manufacturing
critical success factors
machinery and equipment
small and medium-sized enterprises
kluczowe czynniki sukcesu
maszyny i wyposażenie
małe i średnie przedsiębiorstwa
Opis:
This study aims to explore the predominant critical success factors (CSFs) for the implementation of lean manufacturing (LM) in small and medium-sized enterprises (SMEs) producing machinery and equipment (M&E). The convergent parallel mixedmethods (qualitative and quantitative) were employed in three Malaysian M&E manufacturing SMEs. The study identified four predominant CSFs that significantly impact on the LM application in M&E manufacturing SMEs, namely, leadership and commitment of the top management, training to upgrade skills and expertise, employee involvement and empowerment, and the development of LM implementation framework for SMEs. This study can assist the M&E manufacturing SMEs in prioritising these predominant CSFs so that the management teams can work on the improvement strategy and achieve a higher level of lean sustainability. It offers valuable insights into the LM implementation that could provide a practical reference guide to other industrial companies.
Źródło:
Engineering Management in Production and Services; 2020, 12, 4; 77--91
2543-6597
2543-912X
Pojawia się w:
Engineering Management in Production and Services
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Vision-based biomechanical markerless motion classification
Autorzy:
Liew, Yu Liang
Chin, Jeng Feng
Powiązania:
https://bibliotekanauki.pl/articles/2204259.pdf
Data publikacji:
2023
Wydawca:
Szkoła Główna Gospodarstwa Wiejskiego w Warszawie. Instytut Informatyki Technicznej
Tematy:
vision
single camera
markerless
stick model
human motion
motion classification
data mining
Opis:
This study used stick model augmentation on single-camera motion video to create a markerless motion classification model of manual operations. All videos were augmented with a stick model composed of keypoints and lines by using the programming model, which later incorporated the COCO dataset, OpenCV and OpenPose modules to estimate the coordinates and body joints. The stick model data included the initial velocity, cumulative velocity, and acceleration for each body joint. The extracted motion vector data were normalized using three different techniques, and the resulting datasets were subjected to eight classifiers. The experiment involved four distinct motion sequences performed by eight participants. The random forest classifier performed the best in terms of accuracy in recorded data classification in its min-max normalized dataset. This classifier also obtained a score of 81.80% for the dataset before random subsampling and a score of 92.37% for the resampled dataset. Meanwhile, the random subsampling method dramatically improved classification accuracy by removing noise data and replacing them with replicated instances to balance the class. This research advances methodological and applied knowledge on the capture and classification of human motion using a single camera view.
Źródło:
Machine Graphics & Vision; 2023, 32, 1; 3--24
1230-0535
2720-250X
Pojawia się w:
Machine Graphics & Vision
Dostawca treści:
Biblioteka Nauki
Artykuł
    Wyświetlanie 1-2 z 2

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