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Improving Damage Identification Reliability by Combining Classification on Distinct Feature Spaces that the identification of the monitored features is a fundamental step for a correct implementation of a damage identification strategy. They also show that the distinct sensitivity of the classifiers can be exploited to improve the reliability and responsiveness of the DGNSA against many possible unknown damage scenarios.

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Book 2017ctured in the Australian Curriculum, along with the locating of the study in international trends in inquiry learning over time. It will be of great interest to researchers, higher degree students and practicing professionals working in Education and Sociology. .

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