A Kinetic Analysis of “Suyangsumurepdipimu” Movement for Korean Dance “Cheoyong-Mu”

Lee, Jung Hrun; Kim, Hyo-Sub; Shin, Eui su

doi:10.35159/kjss.2020.04.29.2.1121

Cited by 2 publications

(2 citation statements)

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“…LSTM has pro-duced excellent results. It is commonly used to solve a range of time series problems [21]. The first step in the LSTM procedure is to decide whether you want to use it; data from the cell should be discarded.…”

Section: Methodsmentioning

confidence: 99%

A Hybrid Density Network-Based Dance Movement Generation Algorithm and Internet of Things for Young Children

Fang

2022

Wireless Communications and Mobile Computing

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The music development matching model and the quantifiable planning model have an undesirable fit between the dance produced by the model and the music self in terms of music-driven dance development age (e.g., generated dance development is lacking, and long-distance dance arrangements are lacking in perfection and discernment). The traditional methodology cannot produce new dance moves or other associated concerns. To address these concerns, we are working on a dance age estimation based on technological developments and neural networks that will eliminate the need for voice and development planning. The first stage uses the prosody elements and sound beat highlights extracted from music as music highlights, while the second stage uses the directions of essential human body issues derived from dance recordings as movement highlights. The model’s generating module acknowledges the vital planning of music and dance advancements to build a smooth dance posture in the next stage; the discriminator module acknowledges the autoencoder module agent has improved sound characteristics and the consistency of dance and music. In the third and final step, the model’s transformed form changes the dance act succession into a good diversity of dance. Finally, a reasonable rendition of the dance that matches the music has been found (e.g., trial data is gathered from online dance recordings, and the exploratory outcomes are examined from five perspectives: poor work esteem, correlation of various baselines, assessment of grouping age influence, client examination, and genuine dance recording quality assessment). The proposed dance age model has a reasonable impact on converting into actual dance recordings, according to the results.

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Section: Methodsmentioning

confidence: 99%

A Hybrid Density Network-Based Dance Movement Generation Algorithm and Internet of Things for Young Children

Fang

2022

Wireless Communications and Mobile Computing

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“…The correct understanding of their behavior and the perception of dancers' movements can be divided into several categories: static characteristics, which are mainly manifested in size, color, body shape, depth, etc. [16,17]. The target is of the dancer's face.…”

Section: Dance Movement Recognitionmentioning

confidence: 99%

Dance Movement Recognition Technology Based on Multifeature Information Fusion

Liu¹,

Hu²

2021

Journal of Sensors

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Multifeature data integration technology uses computer technology to automatically analyze and process the observation data obtained by multiple sensors in chronological order according to a defined threshold. And it can complete the necessary decision-making and evaluation tasks in the data processing process. This article is aimed at studying the effect of multifeature information fusion on dance movement recognition technology. The article mainly adopts the principle of structural risk mitigation and the image thresholding method to effectively obtain the binarized image of the dancer’s moving image, so as to select an appropriate threshold, use the threshold as its distinguishing standard, and then segment the pixel threshold. The method of multifeature information fusion and dance movement recognition technology is introduced. The performance of image classification algorithms is compared, and the experiment of dance video movement, human behavior recognition, and gait phase recognition are carried out. The experimental data show that in the power identification algorithm of this article, when the peak value is 1000 bits, the experimental data is 256 bits, the analysis and comparison data is 457 bits, and the experimental environment is 812 bits. When the peak value is 2000 bits, the experimental data is 451 bits, the analysis and comparison data is 635 bits, and the experimental environment is 947 bits. The experimental data is 562 bits, the analysis and comparison data is 546 bits, and the experimental environment is 774 bits. The experimental results show that the dance action recognition technique based on multifeature information fusion is studied well.

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A Kinetic Analysis of “Suyangsumurepdipimu” Movement for Korean Dance “Cheoyong-Mu”

Cited by 2 publications

References 0 publications

A Hybrid Density Network-Based Dance Movement Generation Algorithm and Internet of Things for Young Children

A Hybrid Density Network-Based Dance Movement Generation Algorithm and Internet of Things for Young Children

Dance Movement Recognition Technology Based on Multifeature Information Fusion

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