A Sea Ice Recognition Algorithm in Bohai Based on Random Forest

As an important maritime hub,Bohai Sea Bay provides great convenience for shipping and suffers from sea ice disasters of different severity every winter,which greatly affects the socio-economic and development of the region.Therefore,this paper uses FY-4A(a weather satellite)data to study sea ice in the Bohai Sea.After processing the data for land removal and cloud detection,it combines multi-channel threshold method and adaptive threshold algorithm to realize the recognition of Bohai Sea ice under clear sky conditions.The random forests classification algorithm is introduced in sea ice identification,which can achieve a certain effect of sea ice classification recognition under cloud cover.Under non-clear sky conditions,the results of Bohai Sea ice identification based on random forests have been improved,and the algorithm can effectively identify Bohai Sea Ice and can improve the accuracy of sea ice identification,which lays a foundation for the accuracy and stability of sea ice identification.It realizes sea ice identification in the Bohai Sea and provides data support and algorithm support for marine climate forecasting related departments.

Federated Learning Based on Data Divergence and Differential Privacy in Financial Risk Control Research

In the financial sector, data are highly confidential and sensitive,and ensuring data privacy is critical. Sample fusion is the basis of horizontalfederation learning, but it is suitable only for scenarios where customershave the same format but different targets, namely for scenarios with strongfeature overlapping and weak user overlapping. To solve this limitation, thispaper proposes a federated learning-based model with local data sharing anddifferential privacy. The indexing mechanism of differential privacy is used toobtain different degrees of privacy budgets, which are applied to the gradientaccording to the contribution degree to ensure privacy without affectingaccuracy. In addition, data sharing is performed to improve the utility ofthe global model. Further, the distributed prediction model is used to predictcustomers’ loan propensity on the premise of protecting user privacy. Usingan aggregation mechanism based on federated learning can help to train themodel on distributed data without exposing local data. The proposed methodis verified by experiments, and experimental results show that for non-iiddata, the proposed method can effectively improve data accuracy and reducethe impact of sample tilt. The proposed method can be extended to edgecomputing, blockchain, and the Industrial Internet of Things (IIoT) fields.The theoretical analysis and experimental results show that the proposedmethod can ensure the privacy and accuracy of the federated learning processand can also improve the model utility for non-iid data by 7% compared tothe federated averaging method (FedAvg).

Parameter optimization of control system design for uncertain wireless power transfer systems using modified genetic algorithm

The closed-loop wireless power transfer(WPT)system can realize constant voltage output in the presence of perturbation.However,the parameter design of the controller is a difficult problem.The traditional trial-and-error method is time-consuming and difficult to find optimal parameters.A parameter optimization strategy of control systems for uncertain WPT systems using the modified genetic algorithm(MGA)is proposed.Firstly,because the system has different characteristics at different periods,the simulation process is divided into three stages.The first one is the start-up stage,in which we mainly consider the overshoot and the rate of the voltage rise.The second one is the tracking stage,in which the tracking time and switching loss are mainly considered.The third one is the stabilisation stage,in which the steady-state error and switching loss are mainly considered.Secondly,three cost functions are designed according to the characteristics of the three stages,and then the optimal controller parameters of each stage are obtained by using MGA.Finally,the effectiveness of the proposed method is verified by simulation.The optimization results show that compared with the previous parameter optimization method,the optimal controller parameters obtained by the proposed method make the WPT system achieve better performance.