A nowcasting model for the prediction of typhoon tracks based on a long short term memory neural network

It is of vital importance to reduce injuries and economic losses by accurate forecasts of typhoon tracks. A huge amount of typhoon observations have been accumulated by the meteorological department, however, they are yet to be adequately utilized. It is an effective method to employ machine learning to perform forecasts. A long short term memory（LSTM） neural network is trained based on the typhoon observations during 1949–2011 in China＇s Mainland, combined with big data and data mining technologies, and a forecast model based on machine learning for the prediction of typhoon tracks is developed. The results show that the employed algorithm produces desirable 6–24 h nowcasting of typhoon tracks with an improved precision.

Limit Requirements Simulation of Sundial Solar Tracking Machine

Sundial solar tracking machines are machines that tracking the sun,and can promote sunshine receiving efficiency of solar panels.Their operations are strongly influenced by wind load.Previous studies were focused on tracking accuracy and tracking methods,the influence of wind load to the operation of the tracking machines has not caused enough attention,so that many tracking machines did not have the reasonable design basis,which led to unreasonable design and high maintenance costs,and had seriously influenced the application and popularization of the tracking machines.Therefore,the 16 m2 sundial solar tracking machine is taken as research object from the perspective of wind load.A series of computational fluid dynamics（CFD） analyses are carried out on the model of the 16 m2 sundial solar tracking machine.Firstly,in order to make CFD analyses carry on smoothly,after the three-dimensional solid model is established,the model is simplified,and grids are meshed on the simplified model.Then,in the virtual environment,to make the simulation closer to real but at the same time not too complex to make simulation hard to realize,assumptions of the nature of air flow are conducted,boundary conditions of the analyses are set reasonably,and appropriate CFD analysis solver is also chosen.Finally,the results of the CFD analyses are also analyzed and sorted;and limit requirements（i.e.,force conditions of limit case）,such as the maximum load and the maximum total torque,are provided for the further finite element analyses（FEA） and the optimization design of the products.This paper presents an effective computer simulation analysis method for the design and optimization of this type of solar tracking machine,and this method can greatly shorten the development cycle and cost.

Evaluation of a laying-hen tracking algorithm based on a hybrid support vector machine

Background： Behavior is an important indicator reflecting the welfare of animals. Manual analysis of video is the most commonly used method to study animal behavior. However, this approach is tedious and depends on a subjective judgment of the analysts. There is an urgent need for automatic identification of individual animals and automatic tracking is a fundamental part of the solution to this problem.Results： In this study, an algorithm based on a Hybrid Support Vector Machine（HSVM） was developed for the automated tracking of individual laying hens in a layer group. More than 500 h of video was conducted with laying hens raised under a floor system by using an experimental platform. The experimental results demonstrated that the HSVM tracker outperformed the Frag（fragment-based tracking method）, the TLD（Tracking-Learning-Detection）,the PLS（object tracking via partial least squares analysis）, the Mean Shift Algorithm, and the Particle Filter Algorithm based on their overlap rate and the average overlap rate.Conclusions： The experimental results indicate that the HSVM tracker achieved better robustness and state-of-theart performance in its ability to track individual laying hens than the other algorithms tested. It has potential for use in monitoring animal behavior under practical rearing conditions.