Blank

Abstract: Zhengxiao Du, Yujie Qian, Xiao Liu, Ming Ding, Jiezhong Qiu, Zhilin Yang, Jie Tang. Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2022.

Abstract: A method for producing at least one three-dimensional body. Conditions for sensing at least one model are preset. The conditions includes at least an angle of at least one sensor with respect to a longitudinal axis of the model and a relationship between a rotation speed of the model and a speed of movement of the sensor and the model relative to each other in a direction parallel to the longitudinal axis of the model. The model is rotated while simultaneously moving at least one of the model and the sensor relative to each other in a direction parallel to the longitudinal axis of the model while simultaneously sensing a plurality of distances generated by interaction between the sensor and a surface of the model to obtain a first set of data. The preset conditions and the first set of data are transmitted to at least one control for controlling at least one working tool for producing the three-dimensional body. The preset conditions of the at least one sensor and the at least one model substantially duplicated for at least one working tool and at least one blank respectively. The three-dimensional body is produced by varying a position of the working tool relative to the blank corresponding to the sensed distances generated by interaction between the sensor and the model, such that the working tool selectively removes material from the blank to substantially reproduce the surface of the model on the blank.

Abstract: An experimental comparison of several simple inexpensive ways of doing pattern recognition when some data elements are missing (blank) is presented. Pattern recognition methods are usually designed to deal with perfect data, but in the real world data elements are often missing due to error, equipment failure, change of plans, etc. Six methods of dealing with blanks are tested on five data sets. Blanks were inserted at random locations into the data sets. A version of the K-nearest neighbor technique was used to classify the data and evaluate the six methods. Two methods were found to be consistently poor. Four methods were found to be generally good. Suggestions are given for choosing the best method for a particular application.