摘要
利用超声法对温度场进行检测是一种新型的传感测温方法,在对温度场进行检测时选择一种适合的超声重建算法非常重要。笔者对截断奇异值分解法、Tikhonov正则化法、代数重建法和联合迭代重建算法等四种重建算法进行比较发现:在重建质量方面,在低噪声的情况下,截断奇异值分解法和Tikhonov正则化法重建质量都是最佳的;在噪声较大的情况下,联合迭代重建算法的结果最为理想;在运算速度方面,Tikhonov正则化法的速度最快。总体来讲,截断奇异值分解法与Tikhonov正则化法优于代数重建法。在实际应用中,针对不同的情况应该选择不同的重建算法。
Using ultrasound to detect temperature field is a new kind of temperature sensing method. It is very important to select a suitable ultrasound reconstruction algorithm when detecting temperature field. The author compares four reconstruction algorithms：truncated singular value decomposition,Tikhonov regularization,algebraic reconstruction and joint iterative reconstruction. It is found that in terms of reconstruction quality,truncated singular value decomposition and Tikhonov regularization have the best reconstruction quality in the case of low noise;in the case of large noise,the reconstruction quality of truncated singular value decomposition and Tikhonov regularization is the best. The results of the joint iterative reconstruction algorithm are the best,and the Tikhonov regularization method is the fastest in the speed of operation. In general,truncated singular value decomposition method and Tikhonov regularization method are better than algebraic reconstruction methods. In practical applications,different reconstruction algorithms should be chosen for different situations.
作者
刘岩
LIU Yan(Institute of Thermal Power Technology,China Datang Corporation Science and Technology Research Institute,Beijing 100043,China)
出处
《现代信息科技》
2018年第12期146-149,152,共5页
Modern Information Technology
关键词
温度测量
反问题
重建算法
成像算法
temperature measurement
inverse problem
reconstruction algorithms
tomography algorithms
作者简介
刘岩（1987-）,男,黑龙江绥滨人,工程师,2015年毕业于华北电力大学,博士.研究方向：电站汽轮机节能优化.
