An Innovative Deep Architecture for Flight Safety Risk Assessment Based on Time Series Data

With the development of the integration of aviation safety and artificial intelligence,research on the combination of risk assessment and artificial intelligence is particularly important in the field of risk management,but searching for an efficient and accurate risk assessment algorithm has become a challenge for the civil aviation industry.Therefore,an improved risk assessment algorithm(PS-AE-LSTM)based on long short-term memory network(LSTM)with autoencoder(AE)is proposed for the various supervised deep learning algorithms in flight safety that cannot adequately address the problem of the quality on risk level labels.Firstly,based on the normal distribution characteristics of flight data,a probability severity(PS)model is established to enhance the quality of risk assessment labels.Secondly,autoencoder is introduced to reconstruct the flight parameter data to improve the data quality.Finally,utilizing the time-series nature of flight data,a long and short-termmemory network is used to classify the risk level and improve the accuracy of risk assessment.Thus,a risk assessment experimentwas conducted to analyze a fleet landing phase dataset using the PS-AE-LSTMalgorithm to assess the risk level associated with aircraft hard landing events.The results show that the proposed algorithm achieves an accuracy of 86.45%compared with seven baseline models and has excellent risk assessment capability.

聚苯胺/二氧化锰/氧化石墨烯复合电极的制备及性能

采用电化学的方法,以氧化石墨纸为支撑一步法制备导电聚苯胺(PANI)/二氧化锰(MnO2)/氧化石墨烯(GO)复合材料;采用四因素三水平正交设计,研究制备复合电极的最佳工艺;采用扫描电镜、X射线衍射、红外光谱和电化学测试对复合电极进行表征和电化学性能进行研究。结果表明,石墨纸经100 min氧化,在硫酸锰浓度0.3 mol/L、磺基水杨酸(SSA)浓度为30 g/L的硫酸-苯胺溶液里以聚合电流密度为18 mA/cm^2制备出最佳的PANI/MnO2/GO复合电极,以0.2 A/g恒流放电,其比能量可达336 Wh/kg。复合电极的微观形貌为三维网状结构,具有大的比表面积和孔体积;二氧化锰、聚苯胺和石墨纸形成复合良好的三维网状结构,不仅有利于降低极化,还能使电解液与电极充分接触,提高活性物质的利用率,其电化学性能远远优于PANI压制电极和氯化银电极。

ECG Classification Using Deep CNN Improved by Wavelet Transform

Atrial fibrillation is the most common persistent form of arrhythmia.A method based on wavelet transform combined with deep convolutional neural network is applied for automatic classification of electrocardiograms.Since the ECG signal is easily inferred,the ECG signal is decomposed into 9 kinds of subsignals with different frequency scales by wavelet function,and then wavelet reconstruction is carried out after segmented filtering to eliminate the influence of noise.A 24-layer convolution neural network is used to extract the hierarchical features by convolution kernels of different sizes,and finally the softmax classifier is used to classify them.This paper applies this method of the ECG data set provided by the 2017 PhysioNet/CINC challenge.After cross validation,this method can obtain 87.1%accuracy and the F1 score is 86.46%.Compared with the existing classification method,our proposed algorithm has higher accuracy and generalization ability for ECG signal data classification.

Optimization design of multi-gathering mode for the surface system in coalbed methane field

As a potential resource for emerging clean energy with abundant reserves,coalbed methane(CBM)has risen rapidly in recent years,and the construction of rational and economical CBM gathering system plays a vital role in the development of the oil and gas industry.At present,there is no literature that considers the optimization of the multi-gathering mode of coalbed methane pipe network system.Due to the complexity and high investment,this paper establishes a unified mixed-integer nonlinear programming model to determine the gathering modes(including liquified natural gas,compressed natural gas,and gas gathering station)of gathering system to reduce the cost of coalbed methane collection and export.The objective function is the maximization of total profit during the period of the whole project,and such constraints,like network structure,facility number,location,node flow balance,capacity and variable value,are taken into consideration.The solution strategy and heuristic algorithm is proposed and verified by the field data from Shanxi province(China).The results show that the model can solve the problem for optimization design of the surface system in complicated CBM fields.

Optimization of fracturing parameters for tight oil production based on genetic algorithm

It is difficult to determine the main controlling factors of tight oil production.In addition to the problem of uncontrollable prediction accuracy,the numerical prediction model established by the main controlling factors will also make the correctly predicted low production samples lose the value of development.Applying the optimization algorithm with fast convergence speed and global optimization to optimize the controllable parameters in the high-precision numerical prediction model can effectively improve the productivity of low production wells with timeliness,and bring greater economic value while saving development cost.Using PCA-GRA method,the sample weight and the weighted correlation ranking results of parameters affecting tight oil production were obtained.Thereupon then the main controlling factors of tight oil production were determined.Then we set up a BP neural network model with by taking the main controlling factors as input and tight oil production as output.The prediction effect of the network was good and can be put into use.The results of sensitivity analysis showed that the network was stable,and the total fracturing fluid volume had the greatest impact on the production of tight oil.Finally,by using genetic algorithm,we optimized the fracturing parameters of all low production well samples in the field data.Combined with the fracturing parameters of all high production well samples and the optimized fracturing parameters of low production wells,the optimal interval of fracturing parameters was given,which can provide guidance for the field fracturing operation.