智能全站仪ATR实测三维精度分析

简述了智能型全站仪TCA2003的自动目标识别（ATR）功能，以琅琊山抽水蓄能电站大坝变形监测网应用ATR功能三维测量实例，基于对其测量成果精度的评定，分析了ATR三维测量的精度，运用数理统计原理探讨了ATR测量的可靠性，对比了常规的人工测量与ATR测量的工效，给出了有益的结论，为同类工程的应用以及拓展ATR应用领域提供了技术依据。

Precision Analysis for Three-dimensional Measurement of Intellective Total Station ATR

Dissolved gas-in-oil analysis (DGA) plays an important role in fault diagnosis of power transformers. An artificial immune network classification algorithm is proposed for insulation fault diagnosis in this paper. To begin with, both antigens and memory antibodies with class information added to artificial immune network are trained to learn the feature of fault samples. In this way, memory antibody cells poll can represent the fault samples better than those obtained without class information. Then the k-nearest neighbor method is used to classify the fault samples. A mass of fault samples are analyzed in the algorithm proposed and the results are compared with those obtained by the IEC three-ratio method and BPNN. The comparison result indicates that the algorithm proposed has better classifying capability for single-fault and multiple-fault samples as well as high diagnosis precision.