Research on early warning analysis model for Karst rocky desertification based on neural network: taking Du'an County of Guangxi as an example

2008 ◽  
Author(s):  
Baoqing Hu ◽  
Lian Duan ◽  
Wenbin Yang
Keyword(s):  
Neural Network ◽  
Early Warning ◽  
Analysis Model ◽  
Karst Rocky Desertification ◽  
Rocky Desertification

scholarly journals Construction and Simulation of Injury Early Warning Model for Retired Athletes Based on Improved Self-organizing Neural Network

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Deli Li
Keyword(s):  
Neural Network ◽  
Physical Function ◽  
Early Warning ◽  
High Speed ◽  
Sports Injuries ◽  
Analysis Model ◽  
Injury Data ◽  
Comprehensive Development ◽  
Physical Functions ◽  
Self Organizing

With the progress of sci-tech, the interdisciplinary and comprehensive development, and various advanced sci-tech gradually integrated into the field of sports, it has become possible to study how to reasonably prevent sports injuries, minimize the risk of sports injuries, and maintain the best physical condition of retired athletes. Due to the long-term high-load exercise of retired athletes during their sports career, athletes’ physical functions have been damaged to varying degrees, resulting in more injuries. According to the characteristics that many factors need to be considered in the prediction of retired athletes’ injuries, this paper puts forward an improved self-organizing neural network (SOM) method to predict retired athletes’ injuries. In this paper, an early warning analysis model of retired athletes’ susceptibility to injury based on SOM is proposed, which screens the state of retired athletes’ physical function variables in each stage, considers athletes’ physical function data whose standard deviation is higher than the limit specification of susceptibility to injury as susceptible injury data, quickly judges all vulnerable injury data, and completes the high-speed early warning analysis of retired athletes’ susceptibility to injury.

scholarly journals Testing algorithm for heat transfer performance of nanofluid-filled heat pipe based on neural network

Open Physics ◽  
2020 ◽  
Vol 18 (1) ◽  
pp. 751-760
Author(s):  
Lei Lei
Keyword(s):  
Neural Network ◽  
Heat Transfer ◽  
Heat Pipe ◽  
Heat Transfer Performance ◽  
Volume Fraction ◽  
Heat Pipes ◽  
Transfer Performance ◽  
Analysis Model ◽  
Accurate Analysis ◽  
Testing Algorithm

AbstractTraditional testing algorithm based on pattern matching is impossible to effectively analyze the heat transfer performance of heat pipes filled with different concentrations of nanofluids, so the testing algorithm for heat transfer performance of a nanofluidic heat pipe based on neural network is proposed. Nanofluids are obtained by weighing, preparing, stirring, standing and shaking using dichotomy. Based on this, the heat transfer performance analysis model of the nanofluidic heat pipe based on artificial neural network is constructed, which is applied to the analysis of heat transfer performance of nanofluidic heat pipes to achieve accurate analysis. The experimental results show that the proposed algorithm can effectively analyze the heat transfer performance of heat pipes under different concentrations of nanofluids, and the heat transfer performance of heat pipes is best when the volume fraction of nanofluids is 0.15%.

scholarly journals Safety Warning Model of Coal Face Based on FCM Fuzzy Clustering and GA-BP Neural Network

Symmetry ◽  
2021 ◽  
Vol 13 (6) ◽  
pp. 1082
Author(s):  
Fanqiang Meng
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Early Warning ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Coal Face ◽  
Early Warning Model ◽  
Working Face ◽  
The Neural Network ◽  
Warning Model

Risk and security are two symmetric descriptions of the uncertainty of the same system. If the risk early warning is carried out in time, the security capability of the system can be improved. A safety early warning model based on fuzzy c-means clustering (FCM) and back-propagation neural network was established, and a genetic algorithm was introduced to optimize the connection weight and other properties of the neural network, so as to construct the safety early warning system of coal mining face. The system was applied in a coal face in Shandong, China, with 46 groups of data as samples. Firstly, the original data were clustered by FCM, the input space was fuzzy divided, and the samples were clustered into three categories. Then, the clustered data was used as the input of the neural network for training and prediction. The back-propagation neural network and genetic algorithm optimization neural network were trained and verified many times. The results show that the early warning model can realize the prediction and early warning of the safety condition of the working face, and the performance of the neural network model optimized by genetic algorithm is better than the traditional back-propagation artificial neural network model, with higher prediction accuracy and convergence speed. The established early warning model and method can provide reference and basis for the prediction, early warning and risk management of coal mine production safety, so as to discover the hidden danger of working face accident as soon as possible, eliminate the hidden danger in time and reduce the accident probability to the maximum extent.

scholarly journals Methodology for Developing Hydrological Models Based on an Artificial Neural Network to Establish an Early Warning System in Small Catchments

2016 ◽  
Vol 2016 ◽  
pp. 1-14 ◽  
Author(s):  
Ivana Sušanj ◽  
Nevenka Ožanić ◽  
Ivan Marović
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Early Warning ◽  
Early Warning System ◽  
Warning System ◽  
Small Scale ◽  
Ann Model ◽  
Data Value ◽  
The Mean ◽  
Artificial Neural

In some situations, there is no possibility of hazard mitigation, especially if the hazard is induced by water. Thus, it is important to prevent consequences via an early warning system (EWS) to announce the possible occurrence of a hazard. The aim and objective of this paper are to investigate the possibility of implementing an EWS in a small-scale catchment and to develop a methodology for developing a hydrological prediction model based on an artificial neural network (ANN) as an essential part of the EWS. The methodology is implemented in the case study of the Slani Potok catchment, which is historically recognized as a hazard-prone area, by establishing continuous monitoring of meteorological and hydrological parameters to collect data for the training, validation, and evaluation of the prediction capabilities of the ANN model. The model is validated and evaluated by visual and common calculation approaches and a new evaluation for the assessment. This new evaluation is proposed based on the separation of the observed data into classes based on the mean data value and the percentages of classes above or below the mean data value as well as on the performance of the mean absolute error.

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