A rotating laser positioning system (RLPS) is an efficient measurement method for large-scale metrology. Due to multiple transmitter stations, which consist of a measurement network, the position relationship of these stations must be first calibrated. However, with such auxiliary devices such as a laser tracker, scale bar, and complex calibration process, the traditional calibration methods greatly reduce the measurement efficiency. This paper proposes a self-calibration method for RLPS, which can automatically obtain the position relationship. The method is implemented through interscanning technology by using a calibration bar mounted on the transmitter station. Each bar is composed of three RLPS receivers and one ultrasonic sensor whose coordinates are known in advance. The calibration algorithm is mainly based on multiplane and distance constraints and is introduced in detail through a two-station mathematical model. The repeated experiments demonstrate that the coordinate measurement uncertainty of spatial points by using this method is about 0.1 mm, and the accuracy experiments show that the average coordinate measurement deviation is about 0.3 mm compared with a laser tracker. The accuracy can meet the requirements of most applications, while the calibration efficiency is significantly improved.
In this paper, Lifted Wavelet Transform (LWT) and BP neural network are used for
automatic flaw classification of pipeline girth welds. LWT is proposed to extract flaw feature from
ultrasonic echo signals, ideally matched local characteristics of original signal and increasing the
computational speed and flaw classification efficiency. After extracting features of all flaw echoes, a
feature library is constructed. A modified BP neural network is followed as a classifier, trained by the
library. When feature of any flaw echo is extracted and sent to BP network, flaw type is the output,
realizing automatic flaw classification. Experiment results prove the proposed method, LWT with BP
neural network, is more fit for automatic flaw classification than traditional methods.
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