In precision machining of complex curved surface parts with high performance, geometry accuracy is not the only constraint, but the performance should also be met. Performance of this kind of parts is closely related ...In precision machining of complex curved surface parts with high performance, geometry accuracy is not the only constraint, but the performance should also be met. Performance of this kind of parts is closely related to the geometrical and physical parameters, so the final actual size and shape are affected by multiple source constraints, such as geometry, physics, and performance. These parts are rather difficult to be manufactured and new manufacturing method according to performance requirement is urgently needed. Based on performance and manufacturing requirements for complex curved surface parts, a new classification method is proposed, which divided the complex curved surface parts into two categories： surface re-design complex curved surface parts with multi-source constraints（PRCS） and surface unique complex curved surface parts with pure geometric constraints（PUCS）. A correlation model is constructed between the performance and multi-source constraints for PRCS, which reveals the correlation between the performance and multi-source constraints. A re-design method is also developed. Through solving the correlation model of the typical paws performance-associated surface, the mapping relation between the performance-associated surface and the related removal amount is obtained. The explicit correlation model and the method for the corresponding related removal amount of the performance-associated surface are built based on the classification of surface re-design complex curved surface parts with multi-source constraints. Research results have been used in the actual processing of the typical parts such as radome, common bottom components, nozzle, et al., which shows improved efficiency and accuracy of the precision machining for the surface re-design parts with complex curved surface.展开更多
Compressibility factor （z-factor） values of natural gases are necessary in most petroleum engineering calculations.Necessity arises when there are few available experimental data for the required composition,pressur...Compressibility factor （z-factor） values of natural gases are necessary in most petroleum engineering calculations.Necessity arises when there are few available experimental data for the required composition,pressure and temperature conditions.One of the most common methods of calculating z-factor values is empirical correlation.Firstly,a new correlation based on the famous Standing-Katz （S-K） Chart is presented to predict z-factor values.The advantage of this correlation is that it is explicit in z and thus does not require an iterative solution as is required by other methods.Secondly,the comparison between new one and other correlations is carried out and the results indicate the superiority of the new correlation over the other correlations used to calculate z-factor.展开更多
Generating emotional talking faces from a single portrait image remains a significant challenge. The simultaneous achievement of expressive emotional talking and accurate lip-sync is particularly difficult, as express...Generating emotional talking faces from a single portrait image remains a significant challenge. The simultaneous achievement of expressive emotional talking and accurate lip-sync is particularly difficult, as expressiveness is often compromised for lip-sync accuracy. Prevailing generative works usually struggle to juggle to generate subtle variations of emotional expression and lip-synchronized talking. To address these challenges, we suggest modeling the implicit and explicit correlations between audio and emotional talking faces with a unified framework. As human emotional expressions usually present subtle and implicit relations with speech audio, we propose incorporating audio and emotional style embeddings into the diffusion-based generation process, for realistic generation while concentrating on emotional expressions. We then propose lip-based explicit correlation learning to construct a strong mapping of audio to lip motions, assuring lip-audio synchronization. Furthermore, we deploy a video-to-video rendering module to transfer expressions and lip motions from a proxy 3D avatar to an arbitrary portrait. Both quantitatively and qualitatively, MagicTalk outperforms state-of-the-art methods in terms of expressiveness, lip-sync, and perceptual quality.展开更多
Gas compressibility factor is a critical thermodynamic property that is a required input in the estimation of many reservoir fluid properties and reservoir engineering calculations.Experimentally derived values are co...Gas compressibility factor is a critical thermodynamic property that is a required input in the estimation of many reservoir fluid properties and reservoir engineering calculations.Experimentally derived values are considered the best,but these are very expensive and time-consuming.In this work,we have developed a new simplified explicit compressibility factor correlation based on a large dataset using a hybrid nonlinear optimization technique.The new model has a correlation coefficient of 0.9997 and very low average relative error and root-mean-square errors.Statistical analysis shows that this new correlation outperforms all of the existing correlations within the range of 0.2<Ppr<15 and 1.05<Tpr<2).展开更多
基金supported by Key Program of National Natural Science Foundation of China(Grant No.50835001)Program for New Century Excellent Talents in University,China(Grant No.NCET-13-0081)
文摘In precision machining of complex curved surface parts with high performance, geometry accuracy is not the only constraint, but the performance should also be met. Performance of this kind of parts is closely related to the geometrical and physical parameters, so the final actual size and shape are affected by multiple source constraints, such as geometry, physics, and performance. These parts are rather difficult to be manufactured and new manufacturing method according to performance requirement is urgently needed. Based on performance and manufacturing requirements for complex curved surface parts, a new classification method is proposed, which divided the complex curved surface parts into two categories： surface re-design complex curved surface parts with multi-source constraints（PRCS） and surface unique complex curved surface parts with pure geometric constraints（PUCS）. A correlation model is constructed between the performance and multi-source constraints for PRCS, which reveals the correlation between the performance and multi-source constraints. A re-design method is also developed. Through solving the correlation model of the typical paws performance-associated surface, the mapping relation between the performance-associated surface and the related removal amount is obtained. The explicit correlation model and the method for the corresponding related removal amount of the performance-associated surface are built based on the classification of surface re-design complex curved surface parts with multi-source constraints. Research results have been used in the actual processing of the typical parts such as radome, common bottom components, nozzle, et al., which shows improved efficiency and accuracy of the precision machining for the surface re-design parts with complex curved surface.
基金financed by the National Iranian Gas Company through the Gas Research Center of Ahwaz Petroleum University of Technology
文摘Compressibility factor （z-factor） values of natural gases are necessary in most petroleum engineering calculations.Necessity arises when there are few available experimental data for the required composition,pressure and temperature conditions.One of the most common methods of calculating z-factor values is empirical correlation.Firstly,a new correlation based on the famous Standing-Katz （S-K） Chart is presented to predict z-factor values.The advantage of this correlation is that it is explicit in z and thus does not require an iterative solution as is required by other methods.Secondly,the comparison between new one and other correlations is carried out and the results indicate the superiority of the new correlation over the other correlations used to calculate z-factor.
文摘Generating emotional talking faces from a single portrait image remains a significant challenge. The simultaneous achievement of expressive emotional talking and accurate lip-sync is particularly difficult, as expressiveness is often compromised for lip-sync accuracy. Prevailing generative works usually struggle to juggle to generate subtle variations of emotional expression and lip-synchronized talking. To address these challenges, we suggest modeling the implicit and explicit correlations between audio and emotional talking faces with a unified framework. As human emotional expressions usually present subtle and implicit relations with speech audio, we propose incorporating audio and emotional style embeddings into the diffusion-based generation process, for realistic generation while concentrating on emotional expressions. We then propose lip-based explicit correlation learning to construct a strong mapping of audio to lip motions, assuring lip-audio synchronization. Furthermore, we deploy a video-to-video rendering module to transfer expressions and lip motions from a proxy 3D avatar to an arbitrary portrait. Both quantitatively and qualitatively, MagicTalk outperforms state-of-the-art methods in terms of expressiveness, lip-sync, and perceptual quality.
文摘Gas compressibility factor is a critical thermodynamic property that is a required input in the estimation of many reservoir fluid properties and reservoir engineering calculations.Experimentally derived values are considered the best,but these are very expensive and time-consuming.In this work,we have developed a new simplified explicit compressibility factor correlation based on a large dataset using a hybrid nonlinear optimization technique.The new model has a correlation coefficient of 0.9997 and very low average relative error and root-mean-square errors.Statistical analysis shows that this new correlation outperforms all of the existing correlations within the range of 0.2<Ppr<15 and 1.05<Tpr<2).
