A novel method of systematic error compensation for a position and orientation system

A novel method is introduced for self-calibration and elimination of systematic errors for a position and orientation system （POS）. The method uses a combined bundle block adjustment with POS data （named the POS-supported bundle block adjustment） without a calibration field. On the basis of delivering strict observation equations for POS data, the specific scheme of compensating the translation and drift systematic errors in a POS is given, and a prototype system WuCAPS is developed. The effects of eliminating POS systematic errors using the POS-supported bundle block adjustment for different ground control conditions are tested using two sets of actual aerial photos. The first set was taken over a fiat region in the suburbs of the city of Yingkou in China and tested at a scale of 1：2500. The second set was taken over a high mountainous region in the desert region of Xinjiang in China and tested at a scale of 1：32,000. The empirical results verified that the POS systematic errors can be completely eliminated and the photo elements of exterior orientation obtained by the POS-supported bundle block adjustment can satisfy the requirements of aerial photogrammetric topographic mapping when four full ground control points （GCPs） are emplaced in the comers of the adjustment block for large-scale images of flat regions, but only one full GCP emplaced in the center of the adjustment block is needed for medium-small scale images of mountainous regions. This not only demonstrates the validity of the established mathematical model and the feasibility of the method proposed in this paper, but also avoids the use of a special calibration field. Therefore, it can simplify the existing POS operation rules and dramatically save on practical application costs, laying the theoretical foundation for widesoread use of POS.     

大中型零售商业企业计算机CMIS分析与设计

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HMM和负反馈模型在词性标注中的应用

词性标注是词法分析的基础．HMM是一个数学模型，具有算法成熟、效率高、易于训练的优点；负反馈是一个控制模型，在规则中引入负反馈，可以增强规则的客观性．本文探讨了两个模型，然后将它们应用于词性标注中．实验结果表明两者的结合是有效的和成功的．