scholarly journals Numerical Simulation of Wave Overtopping on Breakwater with an Armor Layer of Accropode Using SWASH Model

Water ◽  
2020 ◽  
Vol 12 (2) ◽  
pp. 386 ◽  
Author(s):  
Na Zhang ◽  
Qinghe Zhang ◽  
Keh-Han Wang ◽  
Guoliang Zou ◽  
Xuelian Jiang ◽  
...  
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Error Rate ◽  
Empirical Formula ◽  
Physical Model Test ◽  
Roughness Coefficient ◽  
Wave Overtopping ◽  
Mean Error ◽  
The Mean

In this paper, a new method for predicting wave overtopping discharges of Accropode armored breakwaters using the non-hydrostatic wave model Simulating WAves till SHore (SWASH) is presented. The apparent friction coefficient concept is proposed to allow the bottom shear stress term calculated in the momentum equation to reasonably represent the effect of comprehensive energy dissipation caused by the roughness and seepage during the wave overtopping process. A large number of wave overtopping cases are simulated with a calibrated SWASH model to determine the values of equivalent roughness coefficients so that the apparent friction coefficients can be estimated to achieve the conditions with good agreement between numerical overtopping discharges and those from the EurOtop neural network model. The relative crest freeboard and the wave steepness are found to be the two main factors affecting the equivalent roughness coefficient. A derived empirical formula for the estimation of an equivalent roughness coefficient is presented. The simulated overtopping discharges by the SWASH model using the values of the equivalent roughness coefficient estimated from the empirical formula are compared with the physical model test results. It is found that the mean error rate from the present model predictions is 0.24, which is slightly better than the mean error rate of 0.26 from the EurOtop neural network model.

scholarly journals Computational Fluid Dynamic Design and Experimental Study of a Temperature Sensor Array Used in Climate Reference Station

2019 ◽  
Vol 36 (9) ◽  
pp. 1835-1847
Author(s):  
Jie Yang ◽  
Qingquan Liu ◽  
Wei Dai
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Air Temperature ◽  
Neural Network Model ◽  
Temperature Sensor ◽  
Sensor Array ◽  
The Neural Network ◽  
Temperature Observation ◽  
The Mean ◽  
Cfd Method

Accurate air temperature measurements are demanded for climate change research. However, air temperature sensors installed in a screen or a radiation shield have traditionally resisted observation accuracy due to a number of factors, particularly solar radiation. Here we present a novel temperature sensor array to improve the air temperature observation accuracy. To obtain an optimum design of the sensor array, we perform a series of analyses of the sensor array with various structures based on a computational fluid dynamics (CFD) method. Then the CFD method is applied to obtain quantitative radiation errors of the optimum temperature sensor array. For further improving the measurement accuracy of the sensor array, an artificial neural network model is developed to learn the relationship between the radiation error and environment variables. To assess the extent to which the actual performance adheres to the theoretical CFD model and the neural network model, air temperature observation experiments are conducted. An aspirated temperature measurement platform with a forced airflow rate up to 20 m s−1 served as an air temperature reference. The average radiation errors of a temperature sensor equipped with a naturally ventilated radiation shield and a temperature sensor installed in a screen are 0.42° and 0.23°C, respectively. By contrast, the mean radiation error of the temperature sensor array is approximately 0.03°C. The mean absolute error (MAE) between the radiation errors provided by the experiments and the radiation errors given by the neural network model is 0.007°C, and the root-mean-square error (RMSE) is 0.009°C.

APPLICATION OF ARTIFICIAL NEURAL NETWORKS FOR PREDICTION OF POSITIVE DYNAMICS FOR TREATMENT OF PATIENTS WITH TUBERCULOSIS

2020 ◽  
Author(s):  
Marina Ermolickaya
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Mean Square Error ◽  
Network Model ◽  
Neural Network Model ◽  
Test Sample ◽  
Mean Square ◽  
Tuberculosis Patients ◽  
The Mean

Using the RStudio program, a neural network model has been developed that predicts positive dynamics in the treatment of tuberculosis patients in a tuberculosis dispensary hospital. The accuracy of the presented model on the test sample is 99.4%, the mean square error (MSE) is 0.013.

Modeling and Analysis of Plunge Milling Force Based on Orthogonal Experiment

2013 ◽  
Vol 391 ◽  
pp. 372-375 ◽  
Author(s):  
Xing Hua Niu ◽  
Xian Li Meng ◽  
Zhen Tao Zhang ◽  
Rui Zhao ◽  
Bo Fei Shen
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Bp Neural Network ◽  
Empirical Formula ◽  
Orthogonal Experiment ◽  
Force Model ◽  
Plunge Milling ◽  
Milling Force ◽  
Bp Neural Network Model

Plunge milling force experiment was designed based on the method of orthogonal experiment, selecting Cr12 mold steel as the experimental material for obtaining the measurement data. Combined with the experimental data, the empirical formula of the milling force model and BP neural network model were established respectively. The two types model are analyzed and compared. The results show that the BP neural network model has a better prediction effect than traditional empirical formula.

scholarly journals Meteorological Variables and Prediction of Road Traffic Accident Severity in Suzhou city of Anhui Province of China

2020 ◽  
Author(s):  
Mingming Liang ◽  
Yun Zhang ◽  
ZhenHai Yao ◽  
Guagbo Qu ◽  
Tingting Shi ◽  
...  
Keyword(s):  
Neural Network ◽  
Random Forest ◽  
Network Model ◽  
Neural Network Model ◽  
Error Rate ◽  
Traffic Accident ◽  
Traffic Accidents ◽  
Meteorological Variables ◽  
Forest Model ◽  
The Neural Network

Abstract Background: The prediction of the severity of traffic accidents is concerned by researchers and law enforcement. In order to simulate the relationship between road severity results and meteorological factors, a large number of models have been proposed. This study purpose is to conduct a machine learning model to investigate the impact of meteorological variables on the severity of road traffic accidents. Methods: Using data from the 2007 and 2008 -2017 the Traffic Police Detachment of the Public Security Bureau of Suzhou, 7,795 traffic accidentswere included in this study. We attempted to use a random forest model to convey the nonlinear relationship between meteorological variables and the severity of traffic accidents, and to compare the prediction accuracy of the neural network model. The model is constructed by the randomForest package and the neuralnet package in the R software. 75% of the training samples were divided from the data to establish a prediction model, and the remaining 25% of the test samples were used for testing. In addition, in order to understand the accuracy of the model prediction, the predicted results were calculated and compared with the actual results. Results: In the random forest model, the most optimal mtry parameter value was 5, the number of decision trees is 400. The weight of wind direction, atmospheric pressure and temperature might be higher than other variables. The OOB (out of bag) estimate of error rate was 51.09%, and the error rate for general traffic accident prediction is the lowest (45.97%). Similarly, in the neural network model, the calculated error rate is 61.01%, with the lowest error rate for minor traffic accidents (35.84%). Conclusions: The results of this study show that how using meteorological data predicts the severity of a traffic accident with relative accuracy, and the random forest model may be more suitable than the neural network model. Research and application of machine learning algorithms in the field of traffic accidents should be further explored.

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