Remote Monitoring of Production Operations using a Smart Gateway Device

使用智能网关设备远程监控生产运营

• 批准号: 560406-2020
• 负责人: Rahimi, Afshin
• 金额: $ 2.19万
• 依托单位: University of Windsor
• 依托单位国家: 加拿大
• 项目类别: Alliance Grants
• 财政年份: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=707070
• 关键词: Remote; Monitoring; Production; Operations; using

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.

近年来，深度学习算法在许多认知应用中的准确性已经超过了人类水平。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，例如健康和自动驾驶。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。但在一些应用中引入深度学习，需要对海量数据进行分析，实时处理至关重要；需要新技术来加速这一过程。 由于其低功耗和高效率，边缘设备在加速深度学习算法方面尤其有前景。使用网关设备应用深度学习模型被称为边缘计算。 “边缘”这个名称表示将模型应用在框架的一个边缘，这与云计算不同，将深度学习模型应用在远程服务器上。由于边缘设备的数据管理能力和可靠性较低以及云计算的延迟，引入了雾架构。 该提案旨在开发一种基于计算机视觉和深度学习的模型，在网关设备上实现，以加速视频推理和可靠性。所提出的系统应用于从制造车间记录的视频，以获得过程监控和主动效率提高的实时检查。为了实现网关设备和深度学习的最佳性能，雾计算被提出来有效地使用设备和算法。