Knowledge base graph embedding module design for Visual question answering model

Liu

et al. 2021

Sensors

Self Cite

A bilateral neural network adaptive controller is designed for a class of teleoperation systems with constant time delay, external disturbance and internal friction. The stability of the teleoperation force feedback system with constant communication channel delay and nonlinear, complex, and uncertain constant time delay is guaranteed, and its tracking performance is improved. In the controller design process, the neural network method is used to approximate the system model, and the unknown internal friction and external disturbance of the system are estimated by the adaptive method, so as to avoid the influence of nonlinear uncertainties on the system.

Section: Space Dynamic Model Of Combined Teleoperation System Jointmentioning

confidence: 99%

“…Moreover, the uncertainty of these teleoperation system models not only affects the performance of the system but also makes the entire system unstable [10][11][12]. Therefore, how to solve the above problems has been a wide concern in the field of control [13,14].…”

Section: Introductionmentioning

confidence: 99%

Adaptive Neural Network Control of Time Delay Teleoperation System Based on Model Approximation

Wang¹,

Liu

et al. 2021

Sensors

Self Cite

“…Because this study realizes feature matching by constructing a convolutional neural network [16,32], it does not need to calculate the descriptors of each frame when tracking the feature points, which avoids the defects caused by the insufficient performance of the descriptors. The feature points of the tracked category are found in each frame of the endoscope video [33], and the 3D coordinates are recovered by the binocular geometric relationship to complete the tracking.…”

Section: Feature Point Trackingmentioning

confidence: 99%

Study on Reconstruction and Feature Tracking of Silicone Heart 3D Surface

Zhang¹,

Liu

et al. 2021

Sensors

Self Cite

At present, feature-based 3D reconstruction and tracking technology is widely applied in the medical field. In minimally invasive surgery, the surgeon can achieve three-dimensional reconstruction through the images obtained by the endoscope in the human body, restore the three-dimensional scene of the area to be operated on, and track the motion of the soft tissue surface. This enables doctors to have a clearer understanding of the location depth of the surgical area, greatly reducing the negative impact of 2D image defects and ensuring smooth operation. In this study, firstly, the 3D coordinates of each feature point are calculated by using the parameters of the parallel binocular endoscope and the spatial geometric constraints. At the same time, the discrete feature points are divided into multiple triangles using the Delaunay triangulation method. Then, the 3D coordinates of feature points and the division results of each triangle are combined to complete the 3D surface reconstruction. Combined with the feature matching method based on convolutional neural network, feature tracking is realized by calculating the three-dimensional coordinate changes of the same feature point in different frames. Finally, experiments are carried out on the endoscope image to complete the 3D surface reconstruction and feature tracking.

“…With the development of machine learning and neural network technology [3][4][5][6][7][8], neural networks have been widely used in environmental science, including all kinds of natural hazards [9][10][11][12][13][14]. In the past century, many researchers abroad have used some neural network knowledge to process atmospheric pollutant data.…”

Section: Introductionmentioning

confidence: 99%

A Haze Prediction Method Based on One-Dimensional Convolutional Neural Network

Zhang¹,

Huang

et al. 2021

Atmosphere

Self Cite

In recent years, more and more people are paying close attention to the environmental problems in metropolitan areas and their harm to the human body. Among them, haze is the pollutant that people are most concerned about. The demand for a method to predict the haze level for the public and academics keeps rising. In order to predict the haze concentration on a time scale in hours, this study built a haze concentration prediction method based on one-dimensional convolutional neural networks. The gated recurrent unit method was used for comparison, which highlights the training speed of a one-dimensional convolutional neural network. In summary, the haze concentration data of the past 24 h are used as input and the haze concentration level on the next moment as output such that the haze concentration level on the time scale in hours can be predicted. Based on the results, the prediction accuracy of the proposed method is over 95% and can be used to support other studies on haze prediction.