潜艇航渡阶段隐身辅助决策系统研究

Stealth security has always been considered as an important guarantee for the vitality and combat effectiveness of submarines.In accordance with the stealth requirements of submarines performing stealth voyage tasks,this paper proposes a stealth assistant decision system.Firstly,the submarine stealth posture is acquired.A fuzzy neural network inference engine based on improved simplified particle swarm optimization is designed.The auxiliary decision-making scheme for state control and maneuver avoidance of submarine and its equipment is automatically generated.Secondly,the simulation and deduction of the assistant decision-making scheme are realized by the calculation modules of sound source level,propagation loss,and stealth situation.The assistant decision-making scheme and simulation result provide decision support for the commander.Thirdly,the simulation experiment platform of the submarine stealth assistant decision system is constructed.The submarine stealth assistant decision system described in this paper can quickly and efficiently produce assistant decision-making schemes,including submarine and equipment control and maneuver avoidance.The scheme is in line with the combat experience and the results of the pre-model simulation experiments,whereas the simulation deduction evaluates the rationality and effectiveness of the selected scheme.The submarine stealth assistant decision system can adapt to a complex battlefield environment in addition to rapidly and accurately providing assistance in decision-making.

Dynamic time prediction for electric vehicle charging based on charging pattern recognition

Overcharging is an important safety issue in the charging process of electric vehicle power batteries,and can easily lead to accelerated battery aging and serious safety accidents.It is necessary to accurately predict the vehicle’s charging time to effectively prevent the battery from overcharging.Due to the complex structure of the battery pack and various charging modes,the traditional charging time prediction method often encounters modeling difficulties and low accuracy.In response to the above problems,data drivers and machine learning theories are applied.On the basis of fully considering the different electric vehicle battery management system(BMS)charging modes,a charging time prediction method with charging mode recognition is proposed.First,an intelligent algorithm based on dynamic weighted density peak clustering(DWDPC)and random forest fusion is proposed to classify vehicle charging modes.Then,on the basis of an improved simplified particle swarm optimization(ISPSO)algorithm,a high-performance charging time prediction method is constructed by fully integrating long short-term memory(LSTM)and a strong tracking filter.Finally,the data run by the actual engineering system are verified for the proposed charging time prediction algorithm.Experimental results show that the new method can effectively distinguish the charging modes of different vehicles,identify the charging characteristics of different electric vehicles,and achieve high prediction accuracy.