Influencing factor analysis of interception probability and classification-regression neural network based estimation

The interception probability of a single missile is the basis for combat plan design and weapon performance evaluation,while its influencing factors are complex and mutually coupled.Existing calculation methods have very limited analysis of the influence mechanism of influencing factors,and none of them has analyzed the influence of the guidance law.This paper considers the influencing factors of both the interceptor and the target more comprehensively.Interceptor parameters include speed,guidance law,guidance error,fuze error,and fragment killing ability,while target performance includes speed,maneuverability,and vulnerability.In this paper,an interception model is established,Monte Carlo simulation is carried out,and the influence mechanism of each factor is analyzed based on the model and simulation results.Finally,this paper proposes a classification-regression neural network to quickly estimate the interception probability based on the value of influencing factors.The proposed method reduces the interference of invalid interception data to valid data,so its prediction accuracy is significantly better than that of pure regression neural networks.

Simulation of phytoplankton biomass in Quanzhou Bay using a back propagation network model and sensitivity analysis for environmental variables

Prediction and sensitivity models,to elucidate the response of phytoplankton biomass to environmental factors in Quanzhou Bay,Fujian,China,were developed using a back propagation(BP) network.The environmental indicators of coastal phytoplankton biomass were determined and monitoring data for the bay from 2008 was used to train,test and build a three-layer BP artificial neural network with multi-input and single-output.Ten water quality parameters were used to forecast phytoplankton biomass(measured as chlorophyll-a concentration).Correlation coefficient between biomass values predicted by the model and those observed was 0.964,whilst the average relative error of the network was-3.46% and average absolute error was 10.53%.The model thus has high level of accuracy and is suitable for analysis of the influence of aquatic environmental factors on phytoplankton biomass.A global sensitivity analysis was performed to determine the influence of different environmental indicators on phytoplankton biomass.Indicators were classified according to the sensitivity of response and its risk degree.The results indicate that the parameters most relevant to phytoplankton biomass are estuary-related and include pH,sea surface temperature,sea surface salinity,chemical oxygen demand and ammonium.

3-D fracture network dynamic simulation based on error analysis in rock mass of dam foundation

Accurate 3-D fracture network model for rock mass in dam foundation is of vital importance for stability,grouting and seepage analysis of dam foundation.With the aim of reducing deviation between fracture network model and measured data,a 3-D fracture network dynamic modeling method based on error analysis was proposed.Firstly,errors of four fracture volume density estimation methods(proposed by ODA,KULATILAKE,MAULDON,and SONG)and that of four fracture size estimation methods(proposed by EINSTEIN,SONG and TONON)were respectively compared,and the optimal methods were determined.Additionally,error index representing the deviation between fracture network model and measured data was established with integrated use of fractal dimension and relative absolute error(RAE).On this basis,the downhill simplex method was used to build the dynamic modeling method,which takes the minimum of error index as objective function and dynamically adjusts the fracture density and size parameters to correct the error index.Finally,the 3-D fracture network model could be obtained which meets the requirements.The proposed method was applied for 3-D fractures simulation in Miao Wei hydropower project in China for feasibility verification and the error index reduced from 2.618 to 0.337.

Seismic Liquefaction Resistance Based on Strain Energy Concept Considering Fine Content Value Effect and Performance Parametric Sensitivity Analysis

Liquefaction is one of the most destructive phenomena caused by earthquakes,which has been studied in the issues of potential,triggering and hazard analysis.The strain energy approach is a common method to investigate liquefaction potential.In this study,two Artificial Neural Network(ANN)models were developed to estimate the liquefaction resistance of sandy soil based on the capacity strain energy concept(W)by using laboratory test data.A large database was collected from the literature.One group of the dataset was utilized for validating the process in order to prevent overtraining the presented model.To investigate the complex influence of fine content(FC)on liquefaction resistance,according to previous studies,the second database was arranged by samples with FC of less than 28%and was used to train the second ANN model.Then,two presented ANN models in this study,in addition to four extra available models,were applied to an additional 20 new samples for comparing their results to show the capability and accuracy of the presented models herein.Furthermore,a parametric sensitivity analysis was performed through Monte Carlo Simulation(MCS)to evaluate the effects of parameters and their uncertainties on the liquefaction resistance of soils.According to the results,the developed models provide a higher accuracy prediction performance than the previously publishedmodels.The sensitivity analysis illustrated that the uncertainties of grading parameters significantly affect the liquefaction resistance of soils.

Simple model based on artificial neural network for early prediction and simulation winter rapeseed yield

The aim of the research was to create a prediction model for winter rapeseed yield.The constructed model enabled to perform simulation on 30 June,in the current year,immediately before harvesting.An artificial neural network with multilayer perceptron(MLP) topology was used to build the predictive model.The model was created on the basis of meteorological data(air temperature and atmospheric precipitation) and mineral fertilization data.The data were collected in the period 2008–2017 from 291 productive fields located in Poland,in the southern part of the Opole region.The assessment of the forecast quality created on the basis of the neural model has been verified by defining forecast errors using relative approximation error(RAE),root mean square error(RMS),mean absolute error(MAE),and mean absolute percentage error(MAPE) metrics.An important feature of the created predictive model is the ability to forecast the current agrotechnical year based on current weather and fertilizing data.The lowest value of the MAPE error was obtained for a neural network model based on the MLP network of 21:21-13-6-1:1 structure,which was 9.43%.The performed sensitivity analysis of the network examined the factors that have the greatest impact on the yield of winter rape.The highest rank 1 was obtained by an independent variable with the average air temperature from 1 January to 15 April of 2017(designation by the T1-4_CY model).

A Network Analysis of Skill Game Dynamics

Casino games can be classified in two main categories, i.e. skill games and gambling. Notably, the former refers to games whose outcome is affected by the strategies of players, the latter to those games whose outcome is completely random. For instance, lotteries are easily recognized as pure gambling, while some variants of Poker (e.g. Texas Hold’em) are usually considered as skill games. In both cases, the theory of probability constitutes the mathematical framework for studying their dynamics, despite their classification. Here, it is worth to consider that when games entail the competition between many players, the structure of interactions can acquire a relevant role. For instance, some games as Bingo are not characterized by this kind of interactions, while other games as Poker, show a network structure, i.e. players interact each other and have the opportunity to share or exchange information. In this paper, we analyze the dynamics of a population composed of two species, i.e. strong and weak agents. The former represents expert players, while the latter beginners, i.e. non-expert ones. Here, pair-wise interactions are based on a very simple game, whose outcome is affected by the nature of the involved agents. In doing so, expert agents have a higher probability to succeed when playing with weak agents, while the success probability is equal when two agents of the same kind face each other. Numerical simulations are performed considering a population arranged in different topologies like regular graphs and in scale-free networks. This choice allows to model dynamics that we might observe on online game platforms. Further aspects as the adaptability of agents are taken into account, e.g. the possibility to improve (i.e. to becomean expert). Results show that complex topologies represent a strong opportunity for experts and a risk for both kinds of agents.

Development of a support system for creating disaster prevention maps focusing on road networks and hazardous elements

As a disaster prevention measure based on self-assistance and mutual assistance,disaster prevention maps are being created with citizen participation throughout Japan.The process of creating disaster prevention maps is itself a disaster prevention measure that contributes to raising awareness of disaster prevention by promoting exchange and cooperation within the region.By focusing on relations between road networks and hazardous elements,we developed a system to support disaster prevention map creation that visualizes roads at high risk during a disaster and facilitates the study of evacuation simulations.This system leads to a completed disaster prevention map in three phases.In the first phase,we use a device with GPS logging functions to collect information related to hazardous elements.In the second phase,we use Google Maps(“online map,”below)to visualize roads with high evacuation risk.In the final phase,we perform a regional evaluation through simulations of disaster-time evacuations.In experimental verifications,by conducting usability tests after creating a disaster prevention map in the target area,we evaluated the system in terms of simple operability and visibility.We found that by implementing this series of processes,even users lacking specialized knowledge regarding disaster prevention can intuitively discover evacuation routes while considering the relations between visualized road networks and hazardous elements.These results show that compared with disaster prevention maps having simple site notations using existing WebGIS systems,disaster prevention maps created by residents while inspecting the target area raise awareness of risks present in the immediate vicinity even in normal times and are an effective support system for prompt disaster prevention measures and evacuation drills.

Construction Control and Monitoring of Large Span Continuous Steel Arch Bridge Based on 3G Network

This paper takes the right branch main channel Bridge of Huai River Bridge in Huainan as the engineering background, uses the finite element software Midas and the ANSYS to simulate and analyze the jacking construction of the bridge, and according to the theoretical calculation, the construction monitoring plan is developed, and the stress and deformation of the key section and part of the structure are monitored. Construction monitoring combined with 3 g network and data acquisition module, monitoring data for the real time measurement, the centralized acquisition and wireless transmission, accomplish on-line real-time monitoring of the bridge construction process, effective control of jacking construction and monitoring. The comparison between theoretical analysis and measured results shows that the simulation results are reasonable, and the construction monitoring scheme based on 3G network and data collection can be used as reference.

Optimization Processes of Tangible and Intangible Networks through the Laplace Problems for Regular Lattices with Multiple Obstacles along the Way

A systematic approach is proposed to the theme of safety,reliability and global quality of complex networks(material and immaterial)by means of special mathematical tools that allow an adequate geometric characterization and study of the operation,even in the presence of multiple obstacles along the path.To that end,applying the theory of graphs to the problem under study and using a special mathematical model based on stochastic geometry,in this article we consider some regular lattices in which it is possible to schematize the elements of the network,with the fundamental cell with six,eight or 2(n+2)obstacles,calculating the probability of Laplace.In this way it is possible to measure the“degree of impedance”exerted by the anomalies along the network by the obstacles examined.The method can be extended to other regular and/or irregular geometric figures,whose union together constitutes the examined network,allowing to optimize the functioning of the complex system considered.

Reliability analysis of slope stability by neural network,principal component analysis,and transfer learning techniques

The prediction of slope stability is considered as one of the critical concerns in geotechnical engineering.Conventional stochastic analysis with spatially variable slopes is time-consuming and highly computation-demanding.To assess the slope stability problems with a more desirable computational effort,many machine learning(ML)algorithms have been proposed.However,most ML-based techniques require that the training data must be in the same feature space and have the same distribution,and the model may need to be rebuilt when the spatial distribution changes.This paper presents a new ML-based algorithm,which combines the principal component analysis(PCA)-based neural network(NN)and transfer learning(TL)techniques(i.e.PCAeNNeTL)to conduct the stability analysis of slopes with different spatial distributions.The Monte Carlo coupled with finite element simulation is first conducted for data acquisition considering the spatial variability of cohesive strength or friction angle of soils from eight slopes with the same geometry.The PCA method is incorporated into the neural network algorithm(i.e.PCA-NN)to increase the computational efficiency by reducing the input variables.It is found that the PCA-NN algorithm performs well in improving the prediction of slope stability for a given slope in terms of the computational accuracy and computational effort when compared with the other two algorithms(i.e.NN and decision trees,DT).Furthermore,the PCAeNNeTL algorithm shows great potential in assessing the stability of slope even with fewer training data.

Research of NS dataflow mechanism and its analyzer implementation

This paper analyzes the main elements in NS network simulator, makes adetailed view of dataflow management in a link, a node, and an agent, respectively, and introducesthe information described by its trace file. Based on the analysis of transportation and treatmentof different packets in NS, a dataflow state machine is proposed with its states exchange triggeringevents and a dataflow analyzer is designed and implemented according to it. As the machine statefunctions, the analyzer can make statistic of total transportation flux of a specified dataflow andoffer a general fluctuation diagram. Finally, a concrete example is used to test its performance.

Assessment of bearing capacity of interfering strip footings located near sloping surface considering artificial neural network technique

The bearing capacity of interfering footings located near the slope face suffers from reduced bearing capacity due to the formation of the curtailed passive zone. Depending upon the position of the footing, their spacing and steepness of the slope different extents of bearing capacity reduction can be exhibited. A series of finite element investigation has been done with the aid of Plaxis 3 D v AE.01 to elucidate the influence of various geotechnical and geometrical parameters on the ultimate bearing capacity of interfering surface strip footings located at the crest of the natural soil slope. Based on the large database obtained from the numerical simulation, a6-8-1 Artificial Neural Network architecture has been considered for the assessment of the ultimate bearing capacity of interfering strip footings placed on the crest of natural soil slope. Sensitivity analyses have been conducted to establish the relative significance of the contributory parameters, which exhibited that for the stated problem, apart from shear strength parameters, the setback ratio and spacing of footing are the prime contributory parameters.

Neuro-Optimal Guidance Control for Lunar Soft Landing

Returning to moon has become a top topic recently. Many studies have shown that soft landing is a challenging problem in lunar exploration. The lunar soft landing in this paper begins from a 100 km circular lunar parking orbit. Once the landing area has been selected and it is time to deorbit for landing, a ΔV burn of 19.4 m/s is performed to establish a 100×15 km elliptical orbit. At perilune, the landing jets are ignited, and a propulsive landing is performed. A guidance and control scheme for lunar soft landing is proposed in the paper, which combines optimal theory with nonlinear neuro-control. Basically, an optimal nonlinear control law based on artificial neural network is presented, on the basis of the optimum trajectory from perilune to lunar surface in terms of Pontryagin's maximum principle according to the terminal boundary conditions and performance index. Therefore some optimal control laws can be carried out in the soft landing system due to the nonlinear mapping function of the neural network. The feasibility and validity of the control laws are verified in a simulation experiment.

Constrain-based analysis of gene deletion on the metabolic flux redistribution of Saccharomyces Cerevisiae

Based on the gene-protein-reaction (GPR) model of S. cerevisiae_iND750 and the method of constraint-based analysis, we first calculated the metabolic flux distribution of S. cere-visiae_iND750. Then we calculated the deletion impact of 438 calculable genes, one by one, on the metabolic flux redistribution of S. cere-visiae_iND750. Next we analyzed the correlation between v (describing deletion impact of one gene) and d (connection degree of one gene) and the correlation between v and Vgene (flux sum controlled by one gene), and found that both of them were not of linear relation. Furthermore, we sought out 38 important genes that most greatly affected the metabolic flux distribution, and determined their functional subsystems. We also found that many of these key genes were related to many but not several subsystems. Because the in silico model of S. cere-visiae_iND750 has been tested by many ex-periments, thus is credible, we can conclude that the result we obtained has biological sig-nificance.

PHYSICAL SIMULATION BASED INTELLIGENT SYSTEM FOR THE PREDICTION OF SHEET METAL DRAWING CAPABILITY

With the combination of a new theoretical formula, physical simulation experiments, the technology of artificial neural network and database, an intelligent system for the prediction of sheet metal drawing capability is constructed for the first time. A modified criterion for sheet metal drawing capability is proposed in this paper, namely, the Technological Limiting Drawing Ratio, TLDR = f(R, n, s, t, F, μ,r_d,r_p…). Based on the studies of other scholars, a new formula is derived to predict the TLDR in this paper. Then a series of orthogonal physical simulation experiments are designed to investigate the effect of technological parameters on the TLDR, and the results are analyzed in the paper. Then the predicting system is constructed with the combination of the theoretical formula, orthogonal experiments, the technology of artifocial neural network and database. The predicted results show good agreements with experimental data, so it can be used to avoid the blindness in the selection of sheet metal before stamping. The system operates under the Windows operating system, and it supports the mechanism of Client/Server as well as Intranet, so the system has high engineering value.

Structural and dynamical mechanisms of a naturally occurring variant of the human prion protein in preventing prion conversion

Prion diseases are associated with the misfolding of the normal helical cellular form of prion protein (PrPC) into the β-sheet-rich scrapie form (PrPSc) and the subsequent aggregation of PrPSc into amyloid fibrils. Recent studies demonstrated that a naturally occurring variant V127 of human PrPC is intrinsically resistant to prion conversion and aggregation, and can completely prevent prion diseases. However, the underlying molecular mechanism remains elusive. Herein we perform multiple microsecond molecular dynamics simulations on both wildtype (WT) and V127 variant of human PrPC to understand at atomic level the protective effect of V127 variant. Our simulations show that G127V mutation not only increases the rigidity of the S2–H2 loop between strand-2 (S2) and helix-2 (H2), but also allosterically enhances the stability of the H2 C-terminal region. Interestingly, previous studies reported that animals with rigid S2–H2 loop usually do not develop prion diseases, and the increase in H2 C-terminal stability can prevent misfolding and oligomerization of prion protein. The allosteric paths from G/V127 to H2 C-terminal region are identified using dynamical network analyses. Moreover, community network analyses illustrate that G127V mutation enhances the global correlations and intra-molecular interactions of PrP, thus stabilizing the overall PrPC structure and inhibiting its conversion into PrPSc. This study provides mechanistic understanding of human V127 variant in preventing prion conversion which may be helpful for the rational design of potent anti-prion compounds.

基于深度神经网络的电液伺服泵控系统健康评估研究

电液伺服泵控系统具备功重比高、响应快等优点,在多领域得到广泛应用,但如何针对该系统开展更有效健康评估,进一步保障系统的安全性和可靠性成为必须面对的问题。按照明确原理、建立数学模型、建立仿真模型、仿真分析的思路针对健康评估方法开展研究,提出油液体积含气量、气隙磁密、泄漏系数3个健康评估指标并确定阈值,构建了LGA(LSTM-GRNN-ANN)深度神经网络健康评估方法并进行仿真分析,结果显示该方法准确率约为97.48%,比LSTM、GRNN健康评估方法具有更高的准确率,为继续深入开展电液伺服泵控系统健康评估的研究提供了理论支持。

知识-数据驱动的沉管隧道接头安全状态分析方法——以港珠澳大桥海底沉管隧道为例

为解决沉管隧道接头安全状态不断变化且难以直接感知的技术难点,以港珠澳大桥海底沉管隧道为工程背景,通过分析既有监测数据,总结管节接头的变形模式,揭示管节接头张合量与结构温度的强相关性,明确潮位变化对接头剪切变形的显著影响。在此基础上提出一种基于知识-数据驱动的沉管隧道接头变形快速推演方法,通过建立沉管隧道精细化有限元模型,开展海量典型变形模式下的沉管隧道结构力学行为分析,构建沉管隧道变形服役行为数据集;利用BP神经网络,建立基于仿真接头服役行为特征的沉管隧道接头全断面变形推演模型,实现基于有限实测数据的接头全断面变形快速重构。该方法在港珠澳大桥海底沉管隧道的现场管养中得到成功应用。以2023年台风“苏拉”为例,基于台风登陆过程中接头的局部位移实测数据,推演接头剪力键及止水带关键点位处管节接头的变形情况。结果表明,该沉管隧道接头系统整体受台风影响较小。

复杂金融网络级联失效模型仿真及可靠性分析

复杂网络中级联失效现象会导致网络大面积崩溃进而导致一系列灾难性后果,通过对其建模、仿真和定量分析可以有效预防和解决级联失效问题。本文在复杂网络理论基础上,基于复杂网络的动力学特征和级联失效机理,运用图论语言建立了金融网络模型;首次将级联失效经典模型运用到复杂金融网络研究中,提出了金融网络级联失效模型。然后通过仿真定量分析了不同条件下金融网络级联失效模型的可靠性,验证了模型的有效性,有助于复杂金融网络失效的预防及进一步研究。研究发现:第一、在金融网络级联失效模型仿真过程中,增加迭代次数,会导致网络效率下降,节点失效率上升。当网络受到蓄意攻击时,网络效率迅速下降,节点失效率快速上升;网络受到极大冲击,且这种冲击远大于随机攻击情况下。第二、同一种攻击方式下,提升风险容忍系数α,有助于提高网络效率、降低失效率,提升网络可靠性,甚至可以避免网络级联失效的发生,但这会付出相应的成本。

连续油管井下牵引器自适应控制设计及仿真

当连续油管在水平段受到过大摩擦阻力而无法达到预定位置或出现自锁时,需要采用牵引器来增加连续油管的极限下入深度,使连续油管达到预定作业位置。针对井下牵引器控制系统的调节速度与误差抑制问题,设计了基于模糊神经网络PID控制策略,调整了其模糊规则;最后,在阶跃响应和正弦信号下进行Simulink仿真,验证其动态性能与静态性能。通过实验分析,结果表明:在阶跃响应下的上升时间为1.8s,系统无超调,调节时间为2s;正弦响应的最大滞后时间在0.3s左右,最大幅值误差在0.05kPa,幅值误差小,快速性好,跟随性能优越。基于模糊神经网络PID控制超调量减少51.3%,调节时间减少3.6s,可以更好的控制牵引器,使连续油管更加稳定准确快速的达到预定作业位置。