Truong Nguyen

This paper examines 18th-century Annamese Neo-Confucian discourse on conceptual issues related to zhongguo and the hua-yi dichotomy as expressed across a diversity of 18th-century writings. I engage with Huang Chun-chieh’s theory of “contextual turn” and localized subjectivity in 18th-century East Asian Confucianisms by arguing that 18th-century Annamese Neo-Confucianism operated along a dissimilar ideological trajectory which affirmed “geographic China, political China, and cultural China” as a transdynastic and singular zhongguo from which Annam received its politico-cultural legitimation and prestige. This discourse of dependence on institutional recognition and geographical connection to the Chinese zhongguo distinguished 18th-century Annamese literati not only from contemporaneous modes of Confucian intellectuality in Japan and Joseon, but also from foundational conceptions of Vietic statehood characteristic of the early Le dynasty. My analysis of metaphysical theories invoked by 18th-century Annamese literati in their discussions of zhongguo and the rise of human civilization engages with both Huang Chun-chieh’s theory of a philological turn away from the metaphysical commitments in 18th-century East Asian Confucianisms and Alexander Woodside’s theory of pre-modern Vietnamese Confucianism being characterized by a non-metaphysical “classical primordialism.”

The implementation of an artificial neural network for predicting induction heating region locations is proposed in this research. Steel plate deformations during the induction heating process are produced using an analytical solution derived from electromagnetic and plate theory. The plate transform following vertical displacements in each divided area was used as input of neural following desired shape of the steel plate and the specified heating areas for induction treatment as output parameters to predict and evaluate the model. A dataset used 90% for training and remaining 10% for testing to implement on the efficient models when changing hidden layer and its neurons relatively. The trial and error for analyzing and predicting heating-affected regions with the ANNs model reached the high average accuracy and lowest mean square error at 98.08% and 0.00913, respectively. Consequently, the feasibility test indicates that the developed approach may be well utilized to identify the heating positions by grid area in order to achieve the desired plate deformation. Moreover, the analysis of vertical displacement during induction heating and its response behaviour of steel plate based on thermo-mechanical are also addressed.