An intelligent control method based on artificial neural network for numerical flight simulation of the basic finner projectile with pitching maneuver

In this paper,an intelligent control method applying on numerical virtual flight is proposed.The proposed algorithm is verified and evaluated by combining with the case of the basic finner projectile model and shows a good application prospect.Firstly,a numerical virtual flight simulation model based on overlapping dynamic mesh technology is constructed.In order to verify the accuracy of the dynamic grid technology and the calculation of unsteady flow,a numerical simulation of the basic finner projectile without control is carried out.The simulation results are in good agreement with the experiment data which shows that the algorithm used in this paper can also be used in the design and evaluation of the intelligent controller in the numerical virtual flight simulation.Secondly,combined with the real-time control requirements of aerodynamic,attitude and displacement parameters of the projectile during the flight process,the numerical simulations of the basic finner projectile’s pitch channel are carried out under the traditional PID(Proportional-Integral-Derivative)control strategy and the intelligent PID control strategy respectively.The intelligent PID controller based on BP(Back Propagation)neural network can realize online learning and self-optimization of control parameters according to the acquired real-time flight parameters.Compared with the traditional PID controller,the concerned control variable overshoot,rise time,transition time and steady state error and other performance indicators have been greatly improved,and the higher the learning efficiency or the inertia coefficient,the faster the system,the larger the overshoot,and the smaller the stability error.The intelligent control method applying on numerical virtual flight is capable of solving the complicated unsteady motion and flow with the intelligent PID control strategy and has a strong promotion to engineering application.

Investigations of the mechanical response of dummy HTPB propellant grain under ultrahigh acceleration overload conditions using onboard flight-test measurements

In this paper,to study the mechanical responses of a solid propellant subjected to ultrahigh acceleration overload during the gun-launch process,specifically designed projectile flight tests with an onboard measurement system were performed.Two projectiles containing dummy HTPB propellant grains were successfully recovered after the flight tests with an ultrahigh acceleration overload value of 8100 g.The onboard-measured time-resolved axial displacement,contact stress and overload values were successfully obtained and analysed.Uniaxial compression tests of the dummy HTPB propellant used in the gunlaunched tests were carried out at low and intermediate strain rates to characterize the propellant's dynamic properties.A linear viscoelastic constitutive model was employed and applied in finite-element simulations of the projectile-launching process.During the launch process,the dummy propellant grain exhibited large deformation due to the high acceleration overload,possibly leading to friction between the motor case and propellant grain.The calculated contact stress showed good agreement with the experimental results,though discrepancies in the overall displacement of the dummy propellant grain were observed.The dynamic mechanical response process of the dummy propellant grain was analysed in detail.The results can be used to estimate the structural integrity of the analysed dummy propellant grain during the gun-launch process.

The Relation between Mental Workload and Face Temperature in Flight Simulation

In this research, we study the relationship between mental workload and facial temperature of aircraft participants during a simulated takeoff flight. We conducted experiments to comprehend the correlation between work and facial temperature within the flight simulator. The experiment involved a group of 10 participants who played the role of pilots in a simulated A-320 flight. Six different flying scenarios were designed to simulate normal and emergency situations on airplane takeoff that would occur in different levels of mental workload for the participants. The measurements were workload assessment, face temperatures, and heart rate monitoring. Throughout the experiments, we collected a total of 120 instances of takeoffs, together with over 10 hours of time-series data including heart rate, workload, and face thermal images and temperatures. Comparative analysis of EEG data and thermal image types, revealed intriguing findings. The results indicate a notable inverse relationship between workload and facial muscle temperatures, as well as facial landmark points. The results of this study contribute to a deeper understanding of the physiological effects of workload, as well as practical implications for aviation safety and performance.

Flight Time Minimization of UAV for Cooperative Data Collection in Probabilistic LoS Channel

This paper investigates the data collection in an unmanned aerial vehicle(UAV)-aided Internet of Things(IoT) network, where a UAV is dispatched to collect data from ground sensors in a practical and accurate probabilistic line-of-sight(LoS) channel. Especially, access points(APs) are introduced to collect data from some sensors in the unlicensed band to improve data collection efficiency. We formulate a mixed-integer non-convex optimization problem to minimize the UAV flight time by jointly designing the UAV 3D trajectory and sensors’ scheduling, while ensuring the required amount of data can be collected under the limited UAV energy. To solve this nonconvex problem, we recast the objective problem into a tractable form. Then, the problem is further divided into several sub-problems to solve iteratively, and the successive convex approximation(SCA) scheme is applied to solve each non-convex subproblem. Finally,the bisection search is adopted to speed up the searching for the minimum UAV flight time. Simulation results verify that the UAV flight time can be shortened by the proposed method effectively.

Identifying influential spreaders in social networks: A two-stage quantum-behaved particle swarm optimization with Lévy flight

The influence maximization problem aims to select a small set of influential nodes, termed a seed set, to maximize their influence coverage in social networks. Although the methods that are based on a greedy strategy can obtain good accuracy, they come at the cost of enormous computational time, and are therefore not applicable to practical scenarios in large-scale networks. In addition, the centrality heuristic algorithms that are based on network topology can be completed in relatively less time. However, they tend to fail to achieve satisfactory results because of drawbacks such as overlapped influence spread. In this work, we propose a discrete two-stage metaheuristic optimization combining quantum-behaved particle swarm optimization with Lévy flight to identify a set of the most influential spreaders. According to the framework,first, the particles in the population are tasked to conduct an exploration in the global solution space to eventually converge to an acceptable solution through the crossover and replacement operations. Second, the Lévy flight mechanism is used to perform a wandering walk on the optimal candidate solution in the population to exploit the potentially unidentified influential nodes in the network. Experiments on six real-world social networks show that the proposed algorithm achieves more satisfactory results when compared to other well-known algorithms.

Fixed-Time Sliding Mode Control With Varying Exponent Coefficient for Modular Reconfigurable Flight Arrays

The modular system can change its physical structure by self-assembly and self-disassembly between modules to dynamically adapt to task and environmental requirements. Recognizing the adaptive capability of modular systems, we introduce a modular reconfigurable flight array(MRFA) to pursue a multifunction aircraft fitting for diverse tasks and requirements,and investigate the attitude control and the control allocation problem by using the modular reconfigurable flight array as a platform. First, considering the variable and irregular topological configuration of the modular array, a center-of-mass-independent flight array dynamics model is proposed to allow control allocation under over-actuated situations. Secondly, in order to meet the stable, fast and accurate attitude tracking performance of the MRFA, a fixed-time convergent sliding mode controller with state-dependent variable exponent coefficients is proposed to ensure fast convergence rate both away from and near the system equilibrium point without encountering the singularity. It is shown that the controller also has fixed-time convergent characteristics even in the presence of external disturbances. Finally,simulation results are provided to demonstrate the effectiveness of the proposed modeling and control strategies.

Design methodology of a mini-missile considering flight performance and guidance precision

The design of mini-missiles(MMs)presents several novel challenges.The stringent mission requirement to reach a target with a certain precision imposes a high guidance precision.The miniaturization of the size of MMs makes the design of the guidance,navigation,and control(GNC)have a larger-thanbefore impact on the main-body design(shape,motor,and layout design)and its design objective,i.e.,flight performance.Pursuing a trade-off between flight performance and guidance precision,all the relevant interactions have to be accounted for in the design of the main body and the GNC system.Herein,a multi-objective and multidisciplinary design optimization(MDO)is proposed.Disciplines pertinent to motor,aerodynamics,layout,trajectory,flight dynamics,control,and guidance are included in the proposed MDO framework.The optimization problem seeks to maximize the range and minimize the guidance error.The problem is solved by using the nondominated sorting genetic algorithm II.An optimum design that balances a longer range with a smaller guidance error is obtained.Finally,lessons learned about the design of the MM and insights into the trade-off between flight performance and guidance precision are given by comparing the optimum design to a design provided by the traditional approach.

Semi-Automatic Modeling Technique of Torque Converter Flow Passage

The modeling technique of hydrodynamic torque converter flow passage was investigated. The semi-automatic modeling technique of torque converter flow passage was proposed. The flow passage model of each converter wheel is considered as a revolution entity sliced by two curved surfaces. In order to generate the revolution entity, a new approximation method, condition optimum arc approximation, was proposed. The method was used to approximate the meridional streamlines of the inner and outer wall. As a result, the three-dimensional revolution entity can be conveniently generated. In order to create slice surfaces, the central stream surface of flow passage was approximated with a quadric surface. The normal vector of the quadric surface and the thickness/thickness-function of bade were used to calculate the discrete point coordinates of blade surfaces. Via the rotation transformation to the coordinates, the discrete point coordinates of slice surfaces were obtained. A parameterized program code used for the hydrodynamic torque converter design and semi-automatic modeling was developed. Modeling errors were calculated and analyzed. The flow passage model was generated in several minutes with the help of the program code, Auto CAD and Solidworks software. Finally, the model was inputted into Gambit, and the pre-processing task used for the numerical simulation of torque converter flow field was successfully completed. The investigation results show that the semi-automatic modeling not only can ensure the accuracy of modeling, but also librates the research and design workers of torque converter from the time-consuming modeling work, which paves the way for the numerical simulation of the complex flow field of the hydrodynamic torque converter.

基于Gym与Flight Gear的AI模拟飞行训练平台搭建

针对AI模拟飞行研究,提出应用平台Gym与Flightgear模拟飞行软件相结合,构建AI模拟飞行训练平台。通过平台优化,可不断增加飞行动作的难度系数,重新设计奖励机制与神经网络,实现由AI操控模拟飞行软件向AI反馈训练数据的交互闭环。训练结果验证了该训练平台的有效性。

Soil Mapping of a Small Watershed in a Brazilian Savanna Biome: A Semi-Automatic Approach

The objective of this work is to propose a semi-automatic methodology for the creation of a semidetailed to detailed watershed’s soil map. This methodological proposal is based on the traditional method (field work and photo interpretation) and on morphometric variables analysis, using data originated from the Digital Elevation Model (DEM) of the Shuttle Radar Topography Mission (SRTM) program. The steps taken through this methodological proposal were the identification of the hydromorphic areas boundaries through interpretation of aerial orthophotographs;the compartmentalization of the relief through color composition techniques using the morphometric variables—slope, altimetry and horizontal curvature;the development of a pedological database based on data gathered in the field;the elaboration of a preliminary soil map based on the compartmentalized relief and on the dataset of the soils sampled in the field;the elaboration and adjustment of the final soil map according to the interpretation of aerial photographs and also the physical and chemical analyses of the soils. This semi-automatic methodology demonstrated improved efficiency in defining the soil units, reducing operation time and subjectivity of the process, thereby contributing to the systematization of soil mapping at watershed level in scale 1:50,000, as well as, promoting better knowledge between the relation of the geomorphic and soil variables in the Brazilian Savanna Cerrado.

Integration of User-system View for Cyber Crime Analysis： A Semi-automatic Approach

There is a rapid rise in cybercrime cases. There does not exist any effective forensic methods to deal with these eybercrime cases. Investigators are required to study the details of a large amount of tedious source in order to understand the crime model and dig out the evidence. This requires a lot of effort and may result in human errors. In order to overcome these potential errors that may cause by the investigators, we propose a semi-automatic approach that integrates the user view （based on a high level study of the forensic investigator） and the system view （based on the automatic analysis of the source codes） to assist investigators in refining the scope of the investigation. The approach has been verified using a real cybercrime case and the method has been shown to be effective in assisting the investigators in refining the scope of investigation and understanding the crime model. The semi-automatic approach has improved the efficiency and reliability of the digital forensic analysis of cybercrime cases involving large volume of digital evidence from multiple sources.

Abnormal Flight Passenger Recovery Algorithm Based on Itinerary Acceptance

Under the background of the rapid development of the air transport industry, the abnormal phenomenon of flights has become increasingly serious due to various factors such as the gradual reduction of resources, adverse climatic conditions, problems in air traffic control and mechanical failures. In order to reduce losses, it has become a major problem for airlines to use optimization algorithm to study the recovery of abnormal flights. By upgrading the passenger recovery engine, the purpose of this paper is to provide the optimal recovery scheme for passengers, so as to reduce the risk of transferring overseas flights, and thus reduce the economic loss of airlines. In this paper, the optimization model and algorithm based on network flow, combined with actual business requirements, comprehensively consider multiple optimization objectives to quickly generate passenger recovery solutions, and at the same time achieve the optimal income of airlines and the acceptance rate of passenger recovery, so as to balance the two. The practicability and effectiveness of the proposed model and algorithm are proved by some concrete examples.

Underconstrained Cable-Driven Parallel Suspension System of Virtual Flight Test Model in Wind Tunnel

An underconstrained cable-driven parallel robot(CDPR)suspension system was designed for a virtual flight testing(VFT)model.This mechanism includes two identical upper and lower kinematic chains,each of which comprises a cylindrical pair,rotating pair,and cable parallelogram.The model is pulled via two cables at the top and bottom and fixed by a yaw turntable,which can realize free coupling and decoupling with three rotational degrees of freedom of the model.First,the underconstrained CDPR suspension system of the VFT model was designed according to the mechanics theory,the degrees of freedom were verified,and the support platform was optimized to realize the coincidence between the model’s center of mass and the rotation center of the mechanism during the motion to ensure the stability of the support system.Finally,kinematic and dynamical modeling of the underconstrained CDPR suspension system was conducted;the system stiffness and stability criteria were deduced.Thus,the modeling of an underconstrained,reconfigurable,passively driven CDPR was understood comprehensively.Furthermore,dynamic simulations and experiments were used to verify that the proposed system meets the support requirements of the wind tunnel-based VFT model.This study serves as the foundation for subsequent wind tunnel test research on identifying the aerodynamic parameters of aircraft models,and also provides new avenues for the development of novel support methods for thewind tunnel testmodel.

Flight Behavioural Responses for African Ungulates across Species and Vegetation Covers in a Trophy Hunting Ecosystem: A Case Study from Selous Game Reserve, Tanzania

Trophy hunting has severe consequences on wild animals’ behaviors, which in return has implications for affecting wildlife populations. The Selous Game Reserve is a protected area in Tanzania that has been subjected to commercial trophy hunting for decades, and information about the effects of trophy hunting on animals’ welfare is still scarce. The Flight Initiating Distance (FID) can be a good measure to evaluate the welfare of animals and the level of risk perception towards anthropogenic disturbances, including trophy hunting. The study used linear mixed models to assess the flight responses of twelve commonly hunted species in the Selous game reserve (S.G.R.). The study compared animal vigilance between species, vegetation types, and group size. The FID varied between species, with which more vigilance was observed in zebras, elands, wildebeests, and sable antelopes. The study found a significant influence of vegetation cover on individual species’ FID. Further, the study found a significant influence of group size on animals’ vigilance (L. M. M., 95% CI = 0.590 - 4.762), in which there was a decrease in FID with an increase in group size for wildebeests. At the same time, other species, such as buffaloes, eland, hartebeests, and zebras, had their FIDs increasing with the increase in group size. We conclude that the impact of trophy hunting on savannah ungulates varies between species, vegetation covers, and group size of individual species. Regulatory authorities should consider minimum approach distances by trophy hunters in different vegetation cover to reduce animal biological disturbances.

An Efficient Approach Based on Remora Optimization Algorithm and Levy Flight for Intrusion Detection

With the recent increase in network attacks by threats,malware,and other sources,machine learning techniques have gained special attention for intrusion detection due to their ability to classify hundreds of features into normal system behavior or an attack attempt.However,feature selection is a vital preprocessing stage in machine learning approaches.This paper presents a novel feature selection-based approach,Remora Optimization Algorithm-Levy Flight(ROA-LF),to improve intrusion detection by boosting the ROA performance with LF.The developed ROA-LF is assessed using several evaluation measures on five publicly available datasets for intrusion detection:Knowledge discovery and data mining tools competition,network security laboratory knowledge discovery and data mining,intrusion detection evaluation dataset,block out traffic network,Canadian institute of cybersecu-rity and three engineering problems:Cantilever beam design,three-bar truss design,and pressure vessel design.A comparative analysis between developed ROA-LF,particle swarm optimization,salp swarm algorithm,snake opti-mizer,and the original ROA methods is also presented.The results show that the developed ROA-LF is more efficient and superior to other feature selection methods and the three tested engineering problems for intrusion detection.

Enhanced Multi-Objective Grey Wolf Optimizer with Lévy Flight and Mutation Operators for Feature Selection

The process of selecting features or reducing dimensionality can be viewed as a multi-objective minimization problem in which both the number of features and error rate must be minimized.While it is a multi-objective problem,current methods tend to treat feature selection as a single-objective optimization task.This paper presents enhanced multi-objective grey wolf optimizer with Lévy flight and mutation phase(LMuMOGWO)for tackling feature selection problems.The proposed approach integrates two effective operators into the existing Multi-objective Grey Wolf optimizer(MOGWO):a Lévy flight and a mutation operator.The Lévy flight,a type of random walk with jump size determined by the Lévy distribution,enhances the global search capability of MOGWO,with the objective of maximizing classification accuracy while minimizing the number of selected features.The mutation operator is integrated to add more informative features that can assist in enhancing classification accuracy.As feature selection is a binary problem,the continuous search space is converted into a binary space using the sigmoid function.To evaluate the classification performance of the selected feature subset,the proposed approach employs a wrapper-based Artificial Neural Network(ANN).The effectiveness of the LMuMOGWO is validated on 12 conventional UCI benchmark datasets and compared with two existing variants of MOGWO,BMOGWO-S(based sigmoid),BMOGWO-V(based tanh)as well as Non-dominated Sorting Genetic Algorithm II(NSGA-II)and Multi-objective Particle Swarm Optimization(BMOPSO).The results demonstrate that the proposed LMuMOGWO approach is capable of successfully evolving and improving a set of randomly generated solutions for a given optimization problem.Moreover,the proposed approach outperforms existing approaches in most cases in terms of classification error rate,feature reduction,and computational cost.

Nonlinear direct data-driven control for UAV formation flight system

This paper proposes the nonlinear direct data-driven control from theoretical analysis and practical engineering,i.e.,unmanned aerial vehicle(UAV)formation flight system.Firstly,from the theoretical point of view,consider one nonlinear closedloop system with a nonlinear plant and nonlinear feed-forward controller simultaneously.To avoid the complex identification process for that nonlinear plant,a nonlinear direct data-driven control strategy is proposed to design that nonlinear feed-forward controller only through the input-output measured data sequence directly,whose detailed explicit forms are model inverse method and approximated analysis method.Secondly,from the practical point of view,after reviewing the UAV formation flight system,nonlinear direct data-driven control is applied in designing the formation controller,so that the followers can track the leader’s desired trajectory during one small time instant only through solving one data fitting problem.Since most natural phenomena have nonlinear properties,the direct method must be the better one.Corresponding system identification and control algorithms are required to be proposed for those nonlinear systems,and the direct nonlinear controller design is the purpose of this paper.

Airborne sparse flight array SAR 3D imaging based on compressed sensing in frequency domain

In airborne array synthetic aperture radar(SAR), the three-dimensional(3D) imaging performance and cross-track resolution depends on the length of the equivalent array. In this paper, Barker sequence criterion is used for sparse flight sampling of airborne array SAR, in order to obtain high cross-track resolution in as few times of flights as possible. Under each flight, the imaging algorithm of back projection(BP) and the data extraction method based on modified uniformly redundant arrays(MURAs) are utilized to obtain complex 3D image pairs. To solve the side-lobe noise in images, the interferometry between each image pair is implemented, and compressed sensing(CS) reconstruction is adopted in the frequency domain. Furthermore, to restore the geometrical relationship between each flight, the phase information corresponding to negative MURA is compensated on each single-pass image reconstructed by CS. Finally,by coherent accumulation of each complex image, the high resolution in cross-track direction is obtained. Simulations and experiments in X-band verify the availability.

Assessment of the Impacts of Thunderstorm on Flight Operations at Murtala Mohammed International Airport,Ikeja,Lagos State,Nigeria

The inefficiency of the aviation industry and the persistent rise in aviation hazards have been linked to weather phenomena.As a result,researchers are looking for better solutions to the problem.The study examined the impact of thunderstorms on flight operations at Murtala Mohammed International Airport,Lagos.The data on thunderstorms and flight operations were sourced from Nigerian Meteorological Agency(NiMet)and Nigerian Airspace Management Agency(NAMA)respectively.In order to meet the research target,descriptive statistics(mean,standard deviation,and charts)and inferential statistics(Pearson’s Product Moment Correlation(PPMC)and Regression)were used.The significance level for all inferential analyses was set at 5%(0.05).The study revealed that 77.4%of thunderstorms occurred during the rainy season(April-October)while 22.6%occurred during the dry season(November-March).It also revealed some fluctuating movements of a thunderstorm in the study area.According to the findings,thunderstorms occur most frequently at the airport in June and less frequently in January and December.The study also discovered that thunderstorms at the airport are positively and significantly related to flight delays and cancellations,while the association between flight diversions and thunderstorm occurrence is positive but statistically insignificant.Furthermore,flight delays,flight diversions,and flight cancellations interact positively among themselves.The regression result of the study revealed that a 1%increase in thunderstorm occurrence leads to a 19.4%increase in flight delay,a 7.1%increase in flight cancellation,and a 4.3%increase in flight diversion.As a result,the study presented various regression models that may be utilized to make predictions.The study proposes consistent thunderstorm observation at the airport and steady forecasts using the regression models,based on the findings.However,it further recommends that pilots,air traffic controllers,and meteorologists be trained and retrained so that they can provide better and more efficient services.

A Flight Trajectory Prediction Method Based on Internal Relationships between Attributes

The rapid development of the aviation industry urgently requires airspace traffic management,and flight trajectory prediction is a core component of airspace traffic management.Flight trajectory is a multidimensional time series with rich spatio-temporal characteristics,and existing flight trajectory prediction methods only target the trajectory point temporal relationships,but not the implicit interrelationships among the trajectory point attributes.In this paper,a graph convolutional network(AR-GCN)based on the intra-attribute relationships is proposed for solving the flight track prediction problem.First,the network extracts the temporal features of each attribute and fuses them with the original features of the attribute to obtain the enhanced attribute features,then extracts the implicit relationships between attributes as inter-attribute relationship features.Secondly,the enhanced attribute features are used as nodes and the inter-attribute relationship features are used as edges to construct the inter-attribute relationship graph.Finally,the graph convolutional network is used to aggregate the attribute features.Based on the full fusion of the above features,we achieved high accuracy prediction of the trajectory.In this paper,experiments are conducted on ADS-B historical track data.We compare our method with the classical method and the proposed method.Experimental results show that our method achieves significant improvement in prediction accuracy.