Remote Monitoring of Production Operations using a Smart Gateway Device

近年来，深度学习算法在许多认知应用中已经超过了人类水平的准确性。这一趋势促使研究人员使用深度学习算法来分析从传感器收集的数据，用于许多应用，如健康和自动驾驶。在深度学习算法的设计中，优化的主要焦点是准确性和吞吐量。然而，通过在某些应用中引入深度学习，需要分析大量数据，实时处理至关重要；需要新技术来加速这一进程。

In recent years, deep learning algorithms have surpassed human-level accuracy for many cognitive applications. This trend has motivated researchers to use deep learning algorithms to analyze collected data from sensors for many applications, such as health and autonomous driving. In the design of deep learning algorithms, the primary focus of optimization has been accuracy and throughput. However, by introducing deep learning in some applications, the massive amount of data should be analyzed, and real-time processing is critical; new techniques are needed to accelerate the process. Edge devices are especially promising for accelerating deep learning algorithms due to their low power budget and high efficiency. Using gateway devices to apply deep learning models is referred to as edge computing. The name edge indicates applying the model at one edge of a framework, which is different from cloud computing, applying the deep learning model on a remote server. Because of the low capability and reliability of the edge devices on data management and latency on cloud computing, fog architecture is introduced. This proposal aims to develop a computer vision and deep learning-based model to be implemented on a gateway device to accelerate the video inferencing and reliability. The proposed system is applied to the video recorded from manufacturing floors to obtain real-time inspection for process monitoring and proactive efficiency improvement. To achieve the best performance in gateway device and deep learning, fog computing is proposed to use the device and algorithms effectively.