RESUMO
The technology of in situ measurement of cylindrical shapes is an important means of improving the surface machining accuracy of cylindrical workpieces. As a method of cylindricity measurement, the principle of the three-point method has not been fully studied and applied, so it is seldom used in the field of high-precision cylindrical topography measurement. Since the three-point method has the advantages of a simpler measurement structure and smaller system error compared with other multi-point methods, the research on it is still of great significance. Based on the existing research results of the three-point method, this paper proposes the in situ measurement and reconstruction technology of the cylindrical shape of a high-precision mandrel by means of a three-point method. The principle of the technology is deduced in detail and an in situ measurement and reconstruction system is built to carry out the experiments. Experiment results are verified using a commercial roundness meter and the deviation of cylindricity measurement results is 10 nm, which is 2.56% of the measurement results of commercial roundness meters. This paper also discusses the advantages and application prospects of the proposed technology.
RESUMO
The radial error is an important parameter to evaluate the performance of ultra-precision spindles. The three-point method has not yet been well applied in nanometer-scale measurement due to its disadvantages of harmonic suppression and the complicated error separation process. In order to verify that the three-point method can realize the nanometer-scale measurement of the radial error in the machining environment, an in situ measurement and evaluation system is established. Experiments are performed using the system, and a comparative experiment is conducted to verify the accuracy of the system. The average value and standard deviation of the measurement results are 23.096 nm and 0.556 nm, respectively. The in situ measurement result was in good agreement with the Donaldson reversal method using a commercially available spindle analyzer.
RESUMO
The increasing frequency of floods and the lack of protective measures have the potential to cause severe damage. Working from the perspective of network public opinion is an effective way to understand flood disasters. However, the existing research tends to focus on a single perspective, such as the characteristics of the text, algorithm optimization, or spatial location recognition, while scholars have paid much less attention to the impact of social-psychological differences in space on network public opinion. This research is based on the following hypothesis: When public opinions break out, the differences of network public opinions in geography will form spatially different centers of geographical public opinions in flood disasters (CGeoPOFDs). These centers represent the cities that receive the most attention from network public opinion. Based on this hypothesis, this study proposes a new way of identifying and analyzing CGeoPOFDs. First, two optimization strategies were applied to enhance a naïve Bayes network: syntactic parsing, which was used to optimize the selection of feature word vectors, and ensemble learning, which enabled multi-classifier fusion optimization. Social media data were classified through the improved algorithm, and then, various methods (hotspot analysis, geographic mapping, and sentiment analysis) were used to identify CGeoPOFDs. Finally, analysis was performed in terms of spatiotemporal, virtual, and real dimensions. In addition, microblog social data and real disaster data were used to arrive at empirical results. According to the study findings, the identified CGeoPOFDs offered traditional characteristics of network public opinion while also featuring unique spatiotemporal characteristics. Over time, CGeoPOFDs demonstrated spatial aggregation and bias diffusion and an overall positive emotional tendency.
