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 Early Warning System of Tennis Sports Injury Risk Based on Mobile Computing

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Yupeng Li ◽  
Kitak Kim ◽  
Yu Ding
Keyword(s):  
Neural Network ◽  
Early Warning ◽  
Early Warning System ◽  
Injury Risk ◽  
Sports Injuries ◽  
Rbf Neural Network ◽  
Warning System ◽  
Sports Injury ◽  
Analysis Method ◽  
Tennis Players

In recent years, sports injuries in professional tennis players have gradually increased and sports injuries will break the sports training system and affect the long-term growth of new tennis players. Avoiding athlete injuries has become an important factor in improving training quality and game performance and ensuring the sustainable development of young tennis players’ competitiveness. Therefore, this article will use the RBF neural network algorithm and cluster analysis method to establish a tennis sports injury risk early warning model and finally establish a tennis sports injury risk early warning system so that tennis players can reduce their injuries. In this article, we use the questionnaire survey method, expert interview method, mathematical statistics method, and logical analysis method to investigate and analyze the results of training injuries of Chinese tennis players and coaches. The experimental results in this article show that among 48 tennis players of different ages, who are participating in formal training and tennis competitions, 15 young tennis players have been injured more than 6 times, accounting for 31.2% of the total; 20 have been injured 3 to 6 times, accounting for 41.7% of the total; 9 of them have been injured several times, accounting for 18.8% of the total; and 4 have been injured, accounting for 8.3% of the total. After using the tennis sports injury risk warning system based on the algorithm of RBF neural network in mobile computing, the tennis sports injury rate has dropped to 5%. It can be seen that the system has high feasibility and practicability.

scholarly journals Early warning of debris flow using optimized self-organizing feature mapping network

2020 ◽  
Vol 20 (7) ◽  
pp. 2455-2470
Author(s):  
Xuedong Wang ◽  
Cui Wang ◽  
Chaobiao Zhang
Keyword(s):  
Neural Network ◽  
Debris Flow ◽  
Early Warning ◽  
Rainfall Threshold ◽  
Liaoning Province ◽  
Feature Mapping ◽  
Threshold Method ◽  
Flow Area ◽  
Competitive Neural Network ◽  
Self Organizing

Abstract Early warning of debris flow is one of the core contents of disaster prevention and mitigation work for debris flow disasters. There are few early warning methods based on the combination of rainfall threshold and geological environment conditions. In this paper, we presented an early warning method for debris flow based on the infinite irrelevance method (IIM) and self-organizing feature mapping (SOFM), and applied it to Liaoning Province, China. The proposed model consisted of three stages. Firstly, eight geological environmental conditions and two rainfall-inducing conditions were selected by analyzing the factors affecting the development of debris flow in the study area, and the rainfall threshold for debris flow outbreak was 150 mm. Secondly, the correlation between various factors was analyzed by IIM, which prevented the blindness of parameter selection and improved the prediction accuracy of the model. Finally, SOFM was employed to predict the test data. Experimental results showed that the IIM-SOFM model had a strong early warning ability. When 25 samples of low-frequency debris flow area were selected, the accuracy rate of the IIM-SOFM model with optimized network structure parameters was 100%, which it was obviously superior to the rainfall threshold method, BP neural network and competitive neural network. Consequently, it is feasible to use the IIM-SOFM model for early warning of debris flow, outperforming traditional machine learning methods.

Temperature Rise Prediction of Oil-Air Lubricated Angular Contact Ball Bearings Using Artificial Neural Network

2019 ◽  
Vol 12 (3) ◽  
pp. 248-261
Author(s):  
Baomin Wang ◽  
Xiao Chang
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Temperature Rise ◽  
High Speed ◽  
Ball Bearing ◽  
Influence Factors ◽  
Ball Bearings ◽  
Ann Model ◽  
Angular Contact Ball Bearing ◽  
Artificial Neural

Background: Angular contact ball bearing is an important component of many high-speed rotating mechanical systems. Oil-air lubrication makes it possible for angular contact ball bearing to operate at high speed. So the lubrication state of angular contact ball bearing directly affects the performance of the mechanical systems. However, as bearing rotation speed increases, the temperature rise is still the dominant limiting factor for improving the performance and service life of angular contact ball bearings. Therefore, it is very necessary to predict the temperature rise of angular contact ball bearings lubricated with oil-air. Objective: The purpose of this study is to provide an overview of temperature calculation of bearing from many studies and patents, and propose a new prediction method for temperature rise of angular contact ball bearing. Methods: Based on the artificial neural network and genetic algorithm, a new prediction methodology for bearings temperature rise was proposed which capitalizes on the notion that the temperature rise of oil-air lubricated angular contact ball bearing is generally coupling. The influence factors of temperature rise in high-speed angular contact ball bearings were analyzed through grey relational analysis, and the key influence factors are determined. Combined with Genetic Algorithm (GA), the Artificial Neural Network (ANN) model based on these key influence factors was built up, two groups of experimental data were used to train and validate the ANN model. Results: Compared with the ANN model, the ANN-GA model has shorter training time, higher accuracy and better stability, the output of ANN-GA model shows a good agreement with the experimental data, above 92% of bearing temperature rise under varying conditions can be predicted using the ANNGA model. Conclusion: A new method was proposed to predict the temperature rise of oil-air lubricated angular contact ball bearings based on the artificial neural network and genetic algorithm. The results show that the prediction model has good accuracy, stability and robustness.

Erratum: High speed opto-electronic neural network

1990 ◽  
Vol 26 (20) ◽  
pp. 1739
Author(s):  
N.M. Barnes ◽  
P. Healey ◽  
P. McKee ◽  
A.W. O'Neill ◽  
M.A.Z. Rejmangreene ◽  
...  
Keyword(s):  
Neural Network ◽  
High Speed

scholarly journals An Intelligent Evaluation Method to Analyze the Competitiveness of Airlines

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Jun Zhao ◽  
Xumei Chen
Keyword(s):  
Neural Network ◽  
Prior Knowledge ◽  
Evaluation Method ◽  
Standard Sample ◽  
Reference Value ◽  
The Self ◽  
Index Data ◽  
Som Neural Network ◽  
Simulation Results ◽  
Self Organizing

An intelligent evaluation method is presented to analyze the competitiveness of airlines. From the perspective of safety, service, and normality, we establish the competitiveness indexes of traffic rights and the standard sample base. The self-organizing mapping (SOM) neural network is utilized to self-organize and self-learn the samples in the state of no supervision and prior knowledge. The training steps of high convergence speed and high clustering accuracy are determined based on the multistep setting. The typical airlines index data are utilized to verify the effect of the self-organizing mapping neural network on the airline competitiveness analysis. The simulation results show that the self-organizing mapping neural network can accurately and effectively classify and evaluate the competitiveness of airlines, and the results have important reference value for the allocation of traffic rights resources.

scholarly journals Peer-led exercise program for ageing adults to improve physical functions - a randomized trial

2021 ◽  
Vol 18 (1) ◽  
Author(s):  
D. R. Bouchard ◽  
J. V. Olthuis ◽  
V. Bouffard-Levasseur ◽  
C. Shannon ◽  
T. McDonald ◽  
...  
Keyword(s):  
Physical Function ◽  
Controlled Trial ◽  
Intervention Group ◽  
Exercise Program ◽  
Control Group ◽  
Walk Test ◽  
Blood Draw ◽  
Chair Stand ◽  
Significant Difference ◽  
Physical Functions

Abstract Background A peer-led exercise program is one way to empower people sharing similar characteristics to encourage others to be active, but there is a lack of evidence that these programs have physical function and other benefits when delivered to ageing adults. Methods This randomized controlled trial lasting 12 weeks proposed an exercise peer-led program offered to 31 adults aged 50 and above, twice a week, by a trained leader of the same age from March to May 2019. The program was offered for free with limited space and equipment. Valid tests of physical function (e.g., 30-s chair stand, 6-min walk test) were used to assess the functional benefits. Psychosocial outcomes were assessed using self-reported questionnaires and metabolic outcomes via a fasted blood draw. Results A significant difference was found between pre-and post-values in most physical function tests in the intervention group (all p < 0.05). When adjusted for potential confounders, the intervention group was significantly associated with a more significant improvement on the chair stand test (ß = .26; p < 0.001; r2 = 0.26), the arm curl (ß = .29; p < 0.001; r2 = 0.49), as well as the 6-min walk test (ß = -.14; p < 0.001; r2 = 0.62) compared with the control group. Using repetitive measures generalized linear model, the interaction between the changes and the group was significant for all three tests. Benefits were also observed for participants’ stress level and perceived health in the intervention group compared to the control. Finally, no significant difference was observed between groups for metabolic health. Conclusions The current work suggests that a 12-week peer-led exercise program can improve physical function for adults age 50 and above. Trial registration NCT03799952(ClinicalTrials.gov) 12/20/2018.

scholarly journals Influence of hinge length and distribution number on the camber and cornering properties of a non-pneumatic tire

2020 ◽  
Vol 12 (12) ◽  
pp. 168781402098468
Author(s):  
Xianbin Du ◽  
Youqun Zhao ◽  
Yijiang Ma ◽  
Hongxun Fu
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Adaptive Learning ◽  
High Speed ◽  
Back Propagation ◽  
Lateral Force ◽  
Learning Ability ◽  
Vehicle Performance ◽  
Neural Network Theory ◽  
Bp Neural Network Model

The camber and cornering properties of the tire directly affect the handling stability of vehicles, especially in emergencies such as high-speed cornering and obstacle avoidance. The structural and load-bearing mode of non-pneumatic mechanical elastic (ME) wheel determine that the mechanical properties of ME wheel will change when different combinations of hinge length and distribution number are adopted. The camber and cornering properties of ME wheel with different hinge lengths and distributions were studied by combining finite element method (FEM) with neural network theory. A ME wheel back propagation (BP) neural network model was established, and the additional momentum method and adaptive learning rate method were utilized to improve BP algorithm. The learning ability and generalization ability of the network model were verified by comparing the output values with the actual input values. The camber and cornering properties of ME wheel were analyzed when the hinge length and distribution changed. The results showed the variation of lateral force and aligning torque of different wheel structures under the combined conditions, and also provided guidance for the matching of wheel and vehicle performance.

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