As an important non-ferrous metal structural material most used in industry and production,aluminum(Al) alloy shows its great value in the national economy and industrial manufacturing.How to classify Al alloy rapidly...As an important non-ferrous metal structural material most used in industry and production,aluminum(Al) alloy shows its great value in the national economy and industrial manufacturing.How to classify Al alloy rapidly and accurately is a significant, popular and meaningful task.Classification methods based on laser-induced breakdown spectroscopy(LIBS) have been reported in recent years. Although LIBS is an advanced detection technology, it is necessary to combine it with some algorithm to reach the goal of rapid and accurate classification. As an important machine learning method, the random forest(RF) algorithm plays a great role in pattern recognition and material classification. This paper introduces a rapid classification method of Al alloy based on LIBS and the RF algorithm. The results show that the best accuracy that can be reached using this method to classify Al alloy samples is 98.59%, the average of which is 98.45%. It also reveals through the relationship laws that the accuracy varies with the number of trees in the RF and the size of the training sample set in the RF. According to the laws, researchers can find out the optimized parameters in the RF algorithm in order to achieve,as expected, a good result. These results prove that LIBS with the RF algorithm can exactly classify Al alloy effectively, precisely and rapidly with high accuracy, which obviously has significant practical value.展开更多
Fiber sensors have been developed for industry application with significant advantages.In this paper,Fiber sensors for oil field service and harsh environment monitoring which have been investigated in Tsinghua Univer...Fiber sensors have been developed for industry application with significant advantages.In this paper,Fiber sensors for oil field service and harsh environment monitoring which have been investigated in Tsinghua University are demonstrated.By discussing the requirements of practical applications,the key technologies of long-period fiber grating(LPFG)based fiber sensor,optical spectrum analyzer for oil detection,laser induced breakdown spectroscopy(LIBS)system for soil contamination monitoring,and seismic sensor arrays are described.展开更多
基金supported by National High Technology Research and Development Program of China (863 Program. No. 2013AA102402)
文摘As an important non-ferrous metal structural material most used in industry and production,aluminum(Al) alloy shows its great value in the national economy and industrial manufacturing.How to classify Al alloy rapidly and accurately is a significant, popular and meaningful task.Classification methods based on laser-induced breakdown spectroscopy(LIBS) have been reported in recent years. Although LIBS is an advanced detection technology, it is necessary to combine it with some algorithm to reach the goal of rapid and accurate classification. As an important machine learning method, the random forest(RF) algorithm plays a great role in pattern recognition and material classification. This paper introduces a rapid classification method of Al alloy based on LIBS and the RF algorithm. The results show that the best accuracy that can be reached using this method to classify Al alloy samples is 98.59%, the average of which is 98.45%. It also reveals through the relationship laws that the accuracy varies with the number of trees in the RF and the size of the training sample set in the RF. According to the laws, researchers can find out the optimized parameters in the RF algorithm in order to achieve,as expected, a good result. These results prove that LIBS with the RF algorithm can exactly classify Al alloy effectively, precisely and rapidly with high accuracy, which obviously has significant practical value.
基金The work was supported by NSFC of China through grant 60629401 and 10776016,and also supported by the national 863 project of ocean area with grant number of 2006AA0AA102-03 and 863 project with grant number of 2006AA10Z209.
文摘Fiber sensors have been developed for industry application with significant advantages.In this paper,Fiber sensors for oil field service and harsh environment monitoring which have been investigated in Tsinghua University are demonstrated.By discussing the requirements of practical applications,the key technologies of long-period fiber grating(LPFG)based fiber sensor,optical spectrum analyzer for oil detection,laser induced breakdown spectroscopy(LIBS)system for soil contamination monitoring,and seismic sensor arrays are described.
