This paper analyzes Park Saengkwang(1904-1985)’s artwork,created in the 1980s and influenced by Musok,Korean Shamanism.It explores Musok’s thematic significance in the development of his distinctive style and the in...This paper analyzes Park Saengkwang(1904-1985)’s artwork,created in the 1980s and influenced by Musok,Korean Shamanism.It explores Musok’s thematic significance in the development of his distinctive style and the inspiration behind his stylistic changes.Park’s ink paintings are done in bold and intense colors and create an intriguing,mysterious mood,inviting the viewers to the primordial visual experience and exposing its viewers to Korean Shamanism,which has endured the perception that fluctuated between positive and negative throughout Korean history.The practice became a fitting cultural emblem associated with the national identity during the 1970s and 1980s,and thus became a way for Park to explicitly articulate his cultural roots,creating a visual connotation of“Korean.”His art,portraying gut,Korean shamanistic communal rituals,could be conceived as a pictorial rendering of the idea of kibok,praying for good fortune,and served as a pujŏk,talisman paper,that possesses magical healing and protecting power.By striving to overcome the stylistic conflicts between Korean and Japanese,or traditional and Western,Park’s art accomplished the visual rhetoric of national aesthetic sensitivity that built on the communal thoughts and cultural experience of shamanism in the modern history of Korea.展开更多
This paper proposes a CNN-Transformer hybrid model for ink formulation prediction,named CTNet.The model leverages Convolutional Neural Networks(CNN)to extract local features from the spectral reflectance of sample sur...This paper proposes a CNN-Transformer hybrid model for ink formulation prediction,named CTNet.The model leverages Convolutional Neural Networks(CNN)to extract local features from the spectral reflectance of sample surfaces and incorporates the self-attention mechanism of the Transformer to achieve efficient mapping between color and formulation.In addition,Bayesian optimization is introduced for hyperparameter tuning,further enhancing model performance.Experimental results demonstrate that CTNet outperforms CNN,RNN,LSTM,and the standard Transformer model in terms of Mean Absolute Error(MAE),achieving higher prediction accuracy.This provides an effective solution for high-precision and automated ink color matching,showing promising potential for industrial applications.展开更多
文摘This paper analyzes Park Saengkwang(1904-1985)’s artwork,created in the 1980s and influenced by Musok,Korean Shamanism.It explores Musok’s thematic significance in the development of his distinctive style and the inspiration behind his stylistic changes.Park’s ink paintings are done in bold and intense colors and create an intriguing,mysterious mood,inviting the viewers to the primordial visual experience and exposing its viewers to Korean Shamanism,which has endured the perception that fluctuated between positive and negative throughout Korean history.The practice became a fitting cultural emblem associated with the national identity during the 1970s and 1980s,and thus became a way for Park to explicitly articulate his cultural roots,creating a visual connotation of“Korean.”His art,portraying gut,Korean shamanistic communal rituals,could be conceived as a pictorial rendering of the idea of kibok,praying for good fortune,and served as a pujŏk,talisman paper,that possesses magical healing and protecting power.By striving to overcome the stylistic conflicts between Korean and Japanese,or traditional and Western,Park’s art accomplished the visual rhetoric of national aesthetic sensitivity that built on the communal thoughts and cultural experience of shamanism in the modern history of Korea.
文摘This paper proposes a CNN-Transformer hybrid model for ink formulation prediction,named CTNet.The model leverages Convolutional Neural Networks(CNN)to extract local features from the spectral reflectance of sample surfaces and incorporates the self-attention mechanism of the Transformer to achieve efficient mapping between color and formulation.In addition,Bayesian optimization is introduced for hyperparameter tuning,further enhancing model performance.Experimental results demonstrate that CTNet outperforms CNN,RNN,LSTM,and the standard Transformer model in terms of Mean Absolute Error(MAE),achieving higher prediction accuracy.This provides an effective solution for high-precision and automated ink color matching,showing promising potential for industrial applications.
