Low-cost Sensing Methods and Hybrid Learning Models

低成本传感方法和混合学习模型

• 批准号: DP220101634
• 负责人: Prof Wei Xiang
• 金额: $ 32.41万
• 依托单位: La Trobe University
• 依托单位国家: 澳大利亚
• 项目类别: Discovery Projects
• 财政年份: 2022
• 资助国家: 澳大利亚
• 起止时间: 2022-07-20 至 2025-07-19
• 项目状态: 未结题
• 来源: https://dataportal.arc.gov.au/RGS/Web/Grants/DP220101634
• 关键词: Low; cost; Sensing; Methods; Hybrid

This project aims to revolutionise the theory and practice of sensing and monitoring by developing novel Artificial Intelligence and Internet of Things technologies. This project expects to generate new knowledge in the area of Artificial Intelligence of Things by combining sensing, machine learning, and big data analytics. Expected outcomes of this project include novel low-cost sensing methods and new hybrid machine learning models for predictive sensory data analytics. This should provide significant benefits, such as substantially reduced operating and service costs and improved accuracy for real-time monitoring in the fields where cheap-to-implement and easy-to-service monitoring systems over large geographical areas are imperative.

该项目旨在通过开发新的人工智能和物联网技术来彻底改变传感和监测的理论和实践。该项目希望通过结合传感，机器学习和大数据分析来产生人工智能领域的新知识。该项目的预期成果包括新型低成本传感方法和用于预测传感数据分析的新型混合机器学习模型。这将带来显著的好处，例如大大降低运营和服务成本，并提高在大地理区域内实施廉价和易于服务的监测系统的现场实时监测的准确性。