Network Art Image Classification and Print Propagation Extraction Based on Depth Algorithm

Zhang, Hao; Haimin, Sun; Yuan, Tianyang

doi:10.1155/2022/2546015

Cited by 2 publications

(1 citation statement)

References 22 publications

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“…It is mainly divided into four parts: input layer, convolutional layer, pooling layer, and fully connected layer. It is one of the representative algorithms under Deep Learning [ 28 ].…”

Section: Methodsmentioning

confidence: 99%

Painting Classification in Art Teaching under Machine Learning from the Perspective of Emotional Semantic Analysis

Jia

Zhen-qiu

2022

Computational Intelligence and Neuroscience

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This paper aims to explore the Painting Classification in art teaching under Machine Learning. Based on Emotional Semantics and Machine Learning, the Emotional Semantics of the traditional image are expounded. Firstly, Emotional Semantics are applied to figure painting in art teaching. Then, the convolutional sparse automatic encoder model is introduced in Painting Classification. Finally, the accuracies of the Painting Classification of the Support Vector Machine classifier (SVMC) and that of the Naive Bayes classifier are compared, and the relevant conclusions are drawn. The accuracy of Painting Classification is positively correlated with the scale of painting. After analysis, the painting set is classified in a ratio of 2 : 1, with 2/3 as training set and 1/3 as test set, which is conducive to the good accuracy of classification. In Machine Learning, proper whitening can improve the accuracy of Painting Classification to a certain extent. However, when the whitening treatment coefficient is selected, it cannot be too large, and the average pooling is more accurate than maximum pooling. After the comparison of the new SVMC, the Naive Bayes classifier, and the convolutional sparse automatic encoder, the convolutional sparse automatic encoder has the highest accuracy of Painting Classification. Therefore, the Painting Classification in art teaching under Machine Learning is explored, which is of great help to the classification work of students or teachers in the future.

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“…It is mainly divided into four parts: input layer, convolutional layer, pooling layer, and fully connected layer. It is one of the representative algorithms under Deep Learning [ 28 ].…”

Section: Methodsmentioning

confidence: 99%

Painting Classification in Art Teaching under Machine Learning from the Perspective of Emotional Semantic Analysis

Jia

Zhen-qiu

2022

Computational Intelligence and Neuroscience

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Stability Optimization Simulation of Industrial Robot in ROS Environment

Chu¹

2023

HSET

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This paper studies the end actuator motion state of an RRR industrial robot, and gives a bottom up decision-making scheme based on PID algorithm. Under the background of complex industrial control, any environmental factors will have a deep effect on the accuracy. The physical representation of the accuracy of the end effector is the electrical signal. Foscus on the signal difference from joint to joint, based on the ROS and gazebo, the simulation of the RRR mechanical arm model can theoretically and intuitively Reflect the differences. To minimize the error, proportional–integral–derivative controller will be sued in this model and then Adjust PID parameters of each controller. Assume that the external interference is a linear superposition of impulse signals. The impulse response which the model makes should return to normal as soon as possible. In this way, the purpose of anti-interference is achieved. At the same time, it focuses on solving the motion state of industrial manipulator. For general industrial robots, its actuator, a gripper, should have the function of reaching any position and taking any direction in a certain space area. In other words, if a reference frame o 'is fixed on the center of the hand claw, the robot can send o' to any direction at any position in the space area. The position and direction of the gripper are determined by the amount of motion from the base to the gripper pairs. In addition, with the rapid development of the Internet, image has become a common form of information. This paper will use image recognition technology to provide the required detection data and reference values for the PID controller. Thus, the workspace of the industrial robot can be reflected more completely.

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Network Art Image Classification and Print Propagation Extraction Based on Depth Algorithm

Cited by 2 publications

References 22 publications

Painting Classification in Art Teaching under Machine Learning from the Perspective of Emotional Semantic Analysis

Painting Classification in Art Teaching under Machine Learning from the Perspective of Emotional Semantic Analysis

Stability Optimization Simulation of Industrial Robot in ROS Environment

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