Emotional Cognitive Expression in Lacquer Colors Based on Prior Knowledge. (30th August 2022)

• Record Type: Journal Article
• Authors: Wei, Ping
• Other Names: Kaifa Zhao Academic Editor.
• Is Part Of: Journal of environmental and public health. Volume 2022(2022)
• Publication Date: 2022-08-30
• Subjects: Environmental health -- Periodicals; Occupational diseases -- Periodicals; Public health -- Periodicals; 613.105
• DOI: 10.1155/2022/1151676
• Languages: English
• ISSNs: 1687-9805

Abstract

Abstract : Since lacquer painting first appeared in the world of art, research into it has grown steadily. People have developed a keen interest in modern lacquer painting as a result of the extensive study of lacquer culture in both domestic and international academic circles. Many artists and art enthusiasts have contributed significantly to the study and research of lacquer painting and have made helpful attempts at modern lacquer painting. But it is challenging to describe the emotion that a lacquer painting's color conveys. This paper presents a decision-making framework for emotional cognitive learning based on the theory of emotional cognitive evaluation because there are relatively few researchers who have specifically studied the relationship between the creation of lacquer paintings and emotions and because there are also few research materials and documents for reference. The assessment of an emotional state is the central component of this framework. The observation module in the model framework is used to gather the emotional data that the lacquer painting expresses. The issue of emotional expression in lacquer painting is resolved by the emotional evaluation system, which combines the preprocessed information with prior knowledge to evaluate. The importance of affective cognitive expression in lacquer painting and the necessity of affective computing in the fields of machine learning and decision control is obtained on the basis of discussing the research status and content of affective cognition and affective computing. The efficiency of expression was increased by 1.3 percent as a result.