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.
This article presents a machine learning-based method for detecting micro-sleep. The method is simple, efficient, and can be applied in practical scenarios without the need for large-scale equipment such as servers. We recorded the physiological characteristics of 16 young adults in a driving simulation laboratory, mainly consisting of electroencephalogram (EEG) and driver behaviour videos, and used machine learning to detect micro-sleep events. We compared different machine learning algorithms (SVM, KNN, ANN) and ultimately adopted a combination of ANN and SVM algorithms (pre-processing small-scale data), which reduced the recognition error rate from an initial 4.5% to 0.2%. This combination accelerated the recognition speed and improved the accuracy, making it a practical approach.
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