Research and improvement of feature detection algorithm based on FAST

Li, Yulin; Zheng, Wenfeng; Liu, Xiangjun; Mou, Yuanyuan; Yin, Lirong; Yang, Bo

doi:10.1007/s12210-021-01020-1

Cited by 40 publications

(20 citation statements)

References 34 publications

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“…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,

Tian

Huang

et al. 2021

Atmosphere

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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.

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Section: Introductionmentioning

confidence: 99%

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

Zhang¹,

Tian

Huang

et al. 2021

Atmosphere

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“…RNN (recurrent neural network) and LSTM (long short-term memory) [22][23][24][25][26][27] have been gradually applied to haze prediction. Qin et al [28] proposed the new concentration prediction scheme of urban PM 2.5 based on CNN (convolutional neural network) and LSTM.…”

Section: Introductionmentioning

confidence: 99%

A Haze Prediction Model in Chengdu Based on LSTM

Liu

Yin

et al. 2021

Atmosphere

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Air pollution with fluidity can influence a large area for a long time and can be harmful to the ecological environment and human health. Haze, one form of air pollution, has been a critical problem since the industrial revolution. Though the actual cause of haze could be various and complicated, in this paper, we have found out that many gases’ distributions and wind power or temperature are related to PM2.5/10’s concentration. Thus, based on the correlation between PM2.5/PM10 and other gaseous pollutants and the timing continuity of PM2.5/PM10, we propose a multilayer long short-term memory haze prediction model. This model utilizes the concentration of O3, CO, NO2, SO2, and PM2.5/PM10 in the last 24 h as inputs to predict PM2.5/PM10 concentrations in the future. Besides pre-processing the data, the primary approach to boost the prediction performance is adding layers above a single-layer long short-term memory model. Moreover, it is proved that by doing so, we could let the network make predictions more accurately and efficiently. Furthermore, by comparison, in general, we have obtained a more accurate prediction.

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“…The frame diagram is shown in Figure 1. However, in the actual teleoperation mechanical system, it is difficult to obtain accurate mechanical parameters of the robot, such as mass, length, center of mass or moment of inertia, etc., resulting in the system dynamics parameters (inertia vector matrix, centrifugal force matrix and gravity term matrix) not being accurate [5], as well as uncertain external interference and mechanical internal friction, which are common in robot workspace control [6][7][8][9]. The complex working environment or the robot's mechanical structure is, therefore, more complicated or can be destroyed.…”

Section: Introductionmentioning

confidence: 99%

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

Wang¹,

Tian

Liu

et al. 2021

Sensors

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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.

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Research and improvement of feature detection algorithm based on FAST

Cited by 40 publications

References 34 publications

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

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

A Haze Prediction Model in Chengdu Based on LSTM

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

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