In this paper, we present the research achievements of a computer vision system that surveys the behavior of workers in an industrial environment. The proposed system applies weakly supervised learning algorithms able to detect objects, then to extract the behaviors of workers with respect to the predefined assembly construction for a task and adopts adaptation mechanisms able to dynamically modified the performance of a system with respect to the environmental changes. The proposed architecture has been tested on real-life applications.
The manufacturing industries are now experiencing fierce pressure of competition. The advancement in computer networks and information technologies has been gradually reshaping the manufacturing companies by shifting from the industrial age to the information era. A number of new manufacturing and management strategies have emerged for the innovation of manufacturing enterprises. Process and workflow planning and scheduling are considered as two separate and distinct phases in manufacturing. Traditional approaches to these problems do not consider the constraints of both domains simultaneously and can only result in sub-optimal solutions. This separation becomes a barrier to further improving manufacturing performance. This paper proposes a multi agent framework for industrial plants and its related architecture and technological aspects in order to be designed and implemented for enhancing the performance of industrial environments and plants.
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