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Information measurement systems in the digital society

机译:数字社会中的信息测量系统

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The transition to the next technological order requires robotics and automation in the industry. Managing such process of production is impossible without scenario modeling, forecasting and diagnostic of possible emergencies based on measurement information received from various sensors. The models used must be adequate to the real state of the object, and not to its project, on the basis of which it was designed and manufactured. Such models are called twin models of physical objects. The objective of the research is to obtain the approaches to the creation of information-measuring systems capable of processing large volumes of measurement information for implementing dynamically changing twin models of physical objects, and to carry out continuous training and adaptation to changing conditions. The proposed method allows continuous extraction of knowledge from the measurement information, accumulation knowledge about the object during its life. It was shown that hybrid models with physical and mathematical substantiation and capability of learning based on measurement data are the possible solution.
机译:到下一个技术顺序的过渡需要行业中的机器人和自动化。在没有从各种传感器接收的测量信息的情况下,在没有情景建模,预测和诊断的情况下,不可能管理这些生产过程。使用的模型必须足够的对象的真实状态,而不是其项目的设计和制造。这些模型称为物理对象的双重模型。该研究的目的是获得能够处理能够处理大量测量信息的信息测量系统来实现用于实现物理对象的动态变化的动态测量信息的方法,以及对改变条件进行连续训练和调整。该方法允许从测量信息中连续提取知识,在其生命中累积关于对象的知识。结果表明,具有基于测量数据的物理和数学证实和学习能力的混合模型是可能的解决方案。

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