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Application of advanced learning methods for detecting network configuration in a smart water distribution system

机译:高级学习方法在智能配水系统中检测网络配置的应用

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Water distribution is one of the main pillars of modern society and accounts for a constant need of innovative solutions to age-old problems. There are many challenges associated to the aging infrastructure in large metropolitan areas as well as efficient operation of control structures, requiring improved decision support systems and autonomous operation based on available data. An extension of traditional methods with modern concepts is required such as using learning algorithms, smart meters and reactive programming for improving the quality of service in water distribution systems. This paper extends an IoT-based model with Deep Learning and automated test scenarios, while showing the effective application and comparison of learning techniques on experimental data in this domain. The experimental model is described from the hardware level to the IoT platform in a modern approach using the current state of software development and architectures for real-time data management.
机译:水资源分配是现代社会的主要支柱之一,并不断需要针对老问题的创新解决方案。大城市地区基础设施老化以及控制结构的有效运行带来了许多挑战,需要改进的决策支持系统和基于可用数据的自主运行。需要对传统方法进行现代概念的扩展,例如使用学习算法,智能电表和反应式编程来提高供水系统的服务质量。本文扩展了具有深度学习和自动化测试场景的基于IoT的模型,同时展示了该领域实验数据的有效应用和学习技术的比较。该实验模型以现代方法从硬件级别到IoT平台进行了描述,使用软件开发的当前状态和用于实时数据管理的体系结构。

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