At present, microelectro mechanical systems (MEMS) sensors have gradually replaced traditional mechanical sensors and are applied to several fields. Many developed countries pay high attention to technological innovat...At present, microelectro mechanical systems (MEMS) sensors have gradually replaced traditional mechanical sensors and are applied to several fields. Many developed countries pay high attention to technological innovation of MEMS sensors, and have applied a large number of patents since 2000. In this study, the patents of MEMS sensor from 2000 to 2015 are researched, the patents data is collected from Derwent Innovation Index (DII), and the method of co-classification analysis is used to investigate the technology cluster evolution of MEMS sensors. Results show that the technology diffusion occurrs in each technical field and the technology relevance between different technical fields is changed over time. On the whole, the evolution process of MEMS sensor is the manufacture and material of sensor chip, the electronic components and measuring function, the computing and control technology, and applications to biochemical field and communication.展开更多
In conjunction with association rules for data mining, the connections between testing indices and strong and weak association rules were determined, and new derivative rules were obtained by further reasoning. Associ...In conjunction with association rules for data mining, the connections between testing indices and strong and weak association rules were determined, and new derivative rules were obtained by further reasoning. Association rules were used to analyze correlation and check consistency between indices. This study shows that the judgment obtained by weak association rules or non-association rules is more accurate and more credible than that obtained by strong association rules. When the testing grades of two indices in the weak association rules are inconsistent, the testing grades of indices are more likely to be erroneous, and the mistakes are often caused by human factors. Clustering data mining technology was used to analyze the reliability of a diagnosis, or to perform health diagnosis directly. Analysis showed that the clustering results are related to the indices selected, and that if the indices selected are more significant, the characteristics of clustering results are also more significant, and the analysis or diagnosis is more credible. The indices and diagnosis analysis function produced by this study provide a necessary theoretical foundation and new ideas for the development of hydraulic metal structure health diagnosis technology.展开更多
基金Supported by the Scientific Monitoring and Key Areas in-depth Investigation Analysis and Research(No.ZD2017-1)Science and Technology Major Specific Project Core Electronic Elements,High-General Chips and Basic Software(No.2015XM54)
文摘At present, microelectro mechanical systems (MEMS) sensors have gradually replaced traditional mechanical sensors and are applied to several fields. Many developed countries pay high attention to technological innovation of MEMS sensors, and have applied a large number of patents since 2000. In this study, the patents of MEMS sensor from 2000 to 2015 are researched, the patents data is collected from Derwent Innovation Index (DII), and the method of co-classification analysis is used to investigate the technology cluster evolution of MEMS sensors. Results show that the technology diffusion occurrs in each technical field and the technology relevance between different technical fields is changed over time. On the whole, the evolution process of MEMS sensor is the manufacture and material of sensor chip, the electronic components and measuring function, the computing and control technology, and applications to biochemical field and communication.
基金supported by the Key Program of the National Natural Science Foundation of China(Grant No.50539010)the Special Fund for Public Welfare Industry of the Ministry of Water Resources of China(Grant No.200801019)
文摘In conjunction with association rules for data mining, the connections between testing indices and strong and weak association rules were determined, and new derivative rules were obtained by further reasoning. Association rules were used to analyze correlation and check consistency between indices. This study shows that the judgment obtained by weak association rules or non-association rules is more accurate and more credible than that obtained by strong association rules. When the testing grades of two indices in the weak association rules are inconsistent, the testing grades of indices are more likely to be erroneous, and the mistakes are often caused by human factors. Clustering data mining technology was used to analyze the reliability of a diagnosis, or to perform health diagnosis directly. Analysis showed that the clustering results are related to the indices selected, and that if the indices selected are more significant, the characteristics of clustering results are also more significant, and the analysis or diagnosis is more credible. The indices and diagnosis analysis function produced by this study provide a necessary theoretical foundation and new ideas for the development of hydraulic metal structure health diagnosis technology.
