Packing measure and dimension of the limit sets of IFSs of generalized complex continued fractions

We consider a family of conformal iterated function systems (for short, CIFSs) of generalized complex continued fractions which is a generalization of the CIFS of complex continued fractions. We show the packing dimension and the Hausdorff dimension of the limit set of each CIFS in the family are equal and the packing measure of the limit set with respect to the packing dimension of the limit set is finite in order to present new and interesting examples of infinite CIFSs. Note that the Hausdorff measure of the limit set with respect to the Hausdorff dimension is zero. To prove the above results, we consider three cases (essentially two cases) and define a `nice' subset of the index set of the CIFS in each case. In addition, we estimate the cardinality of the `nice' subsets and the conformal measure of the CIFSs.Comment: arXiv admin note: text overlap with arXiv:1812.0799

On-machine identification of rotary axis location errors under thermal influence by spindle rotation

Position and orientation errors of rotary axis average lines are often among dominant error contributors in the five-axis kinematics. Although many error calibration schemes are available to identify them on -machine, they cannot be performed when a machine spindle is rotating. Rotary axis location errors are often influenced by the machine’s thermal deformation. This paper presents the application of a non-contact laser light barrier system, widely used in the industry for tool geometry measurement, to the identification of rotary axis location errors, when the spindle rotates in the same speed as in actual machining applications. The effectiveness of the proposed scheme is verified by experimental comparison with the R-Test and a machining test. The uncertainty analysis is also presented.This work was supported by JSPS KAKENHI Grant NumberJP15K05721

N-best Response-based Analysis of Contradiction-awareness in Neural Response Generation Models

Avoiding the generation of responses that contradict the preceding context is a significant challenge in dialogue response generation. One feasible method is post-processing, such as filtering out contradicting responses from a resulting n-best response list. In this scenario, the quality of the n-best list considerably affects the occurrence of contradictions because the final response is chosen from this n-best list. This study quantitatively analyzes the contextual contradiction-awareness of neural response generation models using the consistency of the n-best lists. Particularly, we used polar questions as stimulus inputs for concise and quantitative analyses. Our tests illustrate the contradiction-awareness of recent neural response generation models and methodologies, followed by a discussion of their properties and limitations.Comment: 8 pages, Accepted to The 23rd Annual Meeting of the Special Interest Group on Discourse and Dialogue (SIGDIAL 2022