Application of Golden Section Method to Channel Transect Design

During the desing of channel transect,the paper brings forward golden section method,which is 0.618 methods.In order to reduce the calculation volume of the natural depth of water h 0 and bottomˉwidth b which apply trial calculation method and graphic method,and improve the calculate precision,the mathematical model has been built up,the writer combines example to explain the train of thought,the result shows that the calculation precision is high,the correctness is tested and verified by the result which is calculated by hand.It can be referred to the hydroelectric works.

Optimal flight parameters of unmanned helicopter for tea plantation frost protection

To determine proper flight parameters of an unmanned helicopter for tea plantation frost protection,field experiments were conducted to study the impact of flight height,speed and interval on airflow disturbance and temperature rise around tea canopies based on the analysis and simulation of frost protection with a certain helicopter.The relationship between temperature rise after flight and the above flight parameters was established through a regression orthogonal experiment,based on which the optimal combination of flight parameters was obtained through the single-factor golden section method.The results showed that wind speed around tea canopies decreased with the increase of flight height when flight speed was constant.There was a multivariate linear relationship between temperature rise and flight parameters,and the sequence of flight parameters’influence on frost protection effect was flight interval,flight height,flight speed.The optimal combination of flight parameters were flight height of 4.0 m,flight speed of 6.0 m/s and flight interval of 20 min.After the flight with the above parameters air temperature around tea canopies increased 1.6℃ when background thermal inversion strength was 3.8℃.

An improved neural network model for battery smarter state-of-charge estimation of energy-transportation system

The safety and reliability of battery storage systems are critical to the mass roll-out of electrified transportation and new energy generation.To achieve safe management and optimal control of batteries,the state of charge(SOC)is one of the important parameters.The machine-learning based SOC estimation methods of lithium-ion batteries have attracted substantial interests in recent years.However,a common problem with these models is that their estimation performances are not always stable,which makes them difficult to use in practical applications.To address this problem,an optimized radial basis function neural network(RBF-NN)that combines the concepts of Golden Section Method(GSM)and Sparrow Search Algorithm(SSA)is proposed in this paper.Specifically,GSM is used to determine the optimum number of neurons in hidden layer of the RBF-NN model,and its parameters such as radial base center,connection weights and so on are optimized by SSA,which greatly improve the performance of RBF-NN in SOC estimation.In the experiments,data collected from different working conditions are used to demonstrate the accuracy and generalization ability of the proposed model,and the results of the experiment indicate that the maximum error of the proposed model is less than 2%.