Rational Identification of Lightning Derivative Disasters by Strong Convective Weather Monitoring Data in Southern China——A Case Study in Guangzhou Development Region

Analytic method and identification direction for rational identification of lightning derivative disasters by strong convective weather monitoring data in southern China were introduced. Taking identification cases of lightning disaster in Guangzhou Development Region as the background,according to the characteristics in the region that large high-precision enterprises were more,lightning derivative disasters occurred frequently in thunderstorm season,and the actual situation that time of the affected enterprise applying for lightning disaster scene identification lagged,combining Technical Specifications of Lightning Disaster Investigation（ QX / T103-2009）,qualitative analysis method of lightning derivative disaster was put forward under the weather condition of strong convection in southern China by using weather monitoring data（ Doppler sounding radar data,lightning positioning monitoring data,atmospheric electric field data,rainfall data,wind direction and force）,and was optimized by technical means（ ＂ metallographic method＂ and ＂ remanence law＂）. The research could put forward efficient and convenient analytical thinking and method for lightning derivative disaster,and further optimize accuracy and credibility of lightning disaster investigation.

A reliability-oriented genetic algorithm-levenberg marquardt model for leak risk assessment based on time-frequency features

Since leaks in high-pressure pipelines transporting crude oil can cause severe economic losses,a reliable leak risk assessment can assist in developing an effective pipeline maintenance plan and avoiding unexpected incidents.The fast and accurate leak detection methods are essential for maintaining pipeline safety in pipeline reliability engineering.Current oil pipeline leakage signals are insufficient for feature extraction,while the training time for traditional leakage prediction models is too long.A new leak detection method is proposed based on time-frequency features and the Genetic Algorithm-Levenberg Marquardt(GA-LM)classification model for predicting the leakage status of oil pipelines.The signal that has been processed is transformed to the time and frequency domain,allowing full expression of the original signal.The traditional Back Propagation(BP)neural network is optimized by the Genetic Algorithm(GA)and Levenberg Marquardt(LM)algorithms.The results show that the recognition effect of a combined feature parameter is superior to that of a single feature parameter.The Accuracy,Precision,Recall,and F1score of the GA-LM model is 95%,93.5%,96.7%,and 95.1%,respectively,which proves that the GA-LM model has a good predictive effect and excellent stability for positive and negative samples.The proposed GA-LM model can obviously reduce training time and improve recognition efficiency.In addition,considering that a large number of samples are required for model training,a wavelet threshold method is proposed to generate sample data with higher reliability.The research results can provide an effective theoretical and technical reference for the leakage risk assessment of the actual oil pipelines.

A rapid compensation method for launch data of long-range rockets under influence of the Earth＇s disturbing gravity field

Regarding the rapid compensation of the influence of the Earth＇ s disturbing gravity field upon trajectory calculation,the key point lies in how to derive the analytical solutions to the partial derivatives of the state of burnout point with respect to the launch data.In view of this,this paper mainly expounds on two issues：one is based on the approximate analytical solution to the motion equation for the vacuum flight section of a long-range rocket,deriving the analytical solutions to the partial derivatives of the state of burnout point with respect to the changing rate of the finalstage pitch program;the other is based on the initial positioning and orientation error propagation mechanism,proposing the analytical calculation formula for the partial derivatives of the state of burnout point with respect to the launch azimuth.The calculation results of correction data are simulated and verified under different circumstances.The simulation results are as follows：（1） the accuracy of approximation between the analytical solutions and the results attained via the difference method is higher than 90%,and the ratio of calculation time between them is lower than 0.2%,thus demonstrating the accuracy of calculation of data corrections and advantages in calculation speed;（2） after the analytical solutions are compensated,the longitudinal landing deviation of the rocket is less than 20 m and the lateral landing deviation of the rocket is less than 10 m,demonstrating that the corrected data can meet the requirements for the hit accuracy of a long-range rocket.

Research on the dynamic management of cloud simulation derived data

Cloud simulation derived data is defined as the data related to service version,characteristics,relationships,runtime environments and cross-domain communication during service execution in cloud simulation environment,collectively.It is of great value and significance in cloud simulation for service description,service composition and resource management.The types of derived data are abundant and the amount of it is huge.Existing studies on cloud simulation usually assume all of the derived data required for a specific task is well organized and available anytime,which is of course impossible.Derived data needs to be expressed and managed in terms of knowledge to make sure the smooth execution of cloud simulation platform.Therefore,this paper presents a derived data management method in cloud simulation platform to enable derived data collection and dynamic knowledge storage.The prototype system of the proposed method are established and a virtual prototype of double girder crane is taken as an example to verify the effectiveness of the method.