Long term rolling prediction model for solar radiation combining empirical mode decomposition (EMD) and artificial neural network (ANN) techniques

2018 ◽  
Vol 10 (1) ◽  
pp. 013704 ◽  
Author(s):  
Fang-Fang Li ◽  
Si-Ya Wang ◽  
Jia-Hua Wei
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Solar Radiation ◽  
Prediction Model ◽  
Empirical Mode Decomposition ◽  
Mode Decomposition ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

A Prediction Model of Deflection of Fixing Thin Parts

2012 ◽  
Vol 217-219 ◽  
pp. 1526-1529
Author(s):  
Yu Mei Liu ◽  
Wen Ping Liu ◽  
Zhao Liang Jiang ◽  
Zhi Li
Keyword(s):  
Neural Network ◽  
Experimental Data ◽  
Artificial Neural Network ◽  
Prediction Model ◽  
Simulated Data ◽  
Artificial Neural ◽  
Predicted Values ◽  
Artificial Neural Network Ann ◽  
Abaqus Software

A prediction model of deflection is presented. The Artificial Neural Network (ANN) is adopted, and ANN establishes the mapping relation between the clamping forces and the position of fixing and the value of deflection. The results of simulation of Abaqus software is used for Training and querying an ANN. The predicted values are in agreement with simulated data and experimental data.

AN ARTIFICIAL NEURAL NETWORK APPROACH TO HOTEL EMPLOYEE SATISFACTION: THE CASE OF CHINA

2008 ◽  
Vol 36 (4) ◽  
pp. 467-482 ◽  
Author(s):  
Xizhou Tian ◽  
Yongjian Pu
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Employee Satisfaction ◽  
High Rate ◽  
Neural Network Approach ◽  
Artificial Neural ◽  
Hotel Employee ◽  
And Gender ◽  
Artificial Neural Network Ann

At present, the hotel employment sector in China has a high rate of employee turnover compared to other services. This is not unlike other countries. The reason for the turnover among hotel employees may be lower worker satisfaction resulting in decreased – or no – loyalty to employers. This study was based on an Artificial Neural Network (ANN). The factors influencing employee satisfaction were examined and the impacts of demographic characteristics on hotel employee satisfaction were analyzed. Results show that hotel employee satisfaction in China is low, hotel employee satisfaction differs by age and gender, and that professional development opportunities for employees and the long-term growth prospects of the hotels themselves are the most important contributors to employee satisfaction. On the basis of these findings, several recommendations for improving employee satisfaction, thereby sustaining the long-term economic health of China's hospitality industry, are provided.

scholarly journals Artificial Neural Network Analyzing Wearable Device Gait Data for Identifying Patients With Stroke Unable to Return to Work

2021 ◽  
Vol 12 ◽  
Author(s):  
Marco Iosa ◽  
Edda Capodaglio ◽  
Silvia Pelà ◽  
Benedetta Persechino ◽  
Giovanni Morone ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Return To Work ◽  
Measurement Unit ◽  
Walking Ability ◽  
Double Support ◽  
Important Input ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

A potential dramatic effect of long-term disability due to stroke is the inability to return to work. An accurate prognosis and the identification of the parameters inflating the possibility of return to work after neurorehabilitation are crucial. Many factors may influence it, such as mobility and, in particular, walking ability. In this pilot study, two emerging technologies have been combined with the aim of developing a prognostic tool for identifying patients able to return to work: a wearable inertial measurement unit for gait analysis and an artificial neural network (ANN). Compared with more conventional statistics, the ANN showed a higher accuracy in identifying patients with respect to healthy subjects (90.9 vs. 75.8%) and also in identifying the subjects unable to return to work (93.9 vs. 81.8%). In this last analysis, the duration of double support phase resulted the most important input of the ANN. The potentiality of the ANN, developed also in other fields such as marketing on social networks, could allow a powerful support for clinicians that today should manage a large amount of instrumentally recorded parameters in patients with stroke.

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