With the advancement of industry and the advent of Industry 4.0, the original traditional factories also need information and intelligent changes to adapt to the times. Our new system demonstrates a support system for mid/low-volume production, designed to help employees assemble different products with visual or tactile feedback in a traditional factory. The system records the entire workflow using multiple sensors (mainly image, motion, and electrical contact sensors) and learns to analyze each production stage through deep learning networks to set optimal values. The system compares the current state of the job with the learned target state and provides information to the employee when deviations occur so that corrections can be made on-site. After testing, the system has improved the product quality and production efficiency of the factory, meeting the standards of modern factories.
With the rapid development of e-commerce, online shopping, and the Internet of Things, regional mixed warehousing has replaced single small warehousing, not only becoming the first choice of various transportation companies, but also the mainstream configuration of various malls and supermarkets. Constant temperature is an indicator that must be achieved for basic storage, however, a large-scale storage system also poses great challenges to the local heating network. The adaptive machine learning algorithm proposed in this paper can quickly and efficiently generate a model of a large-area warehouse heating network, accurately simulating heat demand.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.