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Focus system sensor characterisation by mechanistic-empirical modelling

机译:机械-经验模型表征聚焦系统传感器

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摘要

Focus system sensor characterisation involves the determination of physical quantities from experiment. Herewith an experimental model will need to specify how the sensor is expected to respond to input data and the quality of information obtained depends directly on the performance of the model. With this concern novel model building and solving techniques have been created for sensor comprehensive characterisation. These techniques incorporate a priori knowledge-based approach in mechanistic model elaboration and for attainment the enhanced level of adequacy, accuracy, and precision carry out the approximation of the exact unknown model simultaneously by available mechanistic and polynomial empirical functions. Two different approaches for mechanistic-empirical model solving have been fashioned. These techniques allow the achievement of the high degree of completeness in elaborated models. From models' validation it has been found that the novel techniques in comparison with pure empirical modelling permit to attain less discrepancy to empirical evidence for the whole region of interest for concerned explanatory variables.
机译:聚焦系统传感器的表征涉及根据实验确定物理量。因此,实验模型将需要指定期望传感器如何响应输入数据,并且所获得信息的质量直接取决于模型的性能。出于这种考虑,已经创建了用于传感器综合表征的新型模型构建和求解技术。这些技术在机械模型的详细说明中采用了基于先验知识的方法,并且为了获得更高水平的充分性,准确性和精确度,可以通过可用的机械和多项式经验函数同时对精确的未知模型进行逼近。机械-经验模型求解的两种不同方法已经形成。这些技术允许在详尽的模型中实现高度的完整性。从模型的验证中发现,与纯经验模型相比,新技术可以使感兴趣的解释变量在整个感兴趣区域中与经验证据的差异较小。

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