A deep multimodal fusion and multitasking trajectory prediction model for typhoon trajectory prediction to reduce flight scheduling cancellation

Natural events have had a significant impact on overall flight activity,and the aviation industry plays a vital role in helping society cope with the impact of these events.As one of the most impactful weather typhoon seasons appears and continues,airlines operating in threatened areas and passengers having travel plans during this time period will pay close attention to the development of tropical storms.This paper proposes a deep multimodal fusion and multitasking trajectory prediction model that can improve the reliability of typhoon trajectory prediction and reduce the quantity of flight scheduling cancellation.The deep multimodal fusion module is formed by deep fusion of the feature output by multiple submodal fusion modules,and the multitask generation module uses longitude and latitude as two related tasks for simultaneous prediction.With more dependable data accuracy,problems can be analysed rapidly and more efficiently,enabling better decision-making with a proactive versus reactive posture.When multiple modalities coexist,features can be extracted from them simultaneously to supplement each other’s information.An actual case study,the typhoon Lichma that swept China in 2019,has demonstrated that the algorithm can effectively reduce the number of unnecessary flight cancellations compared to existing flight scheduling and assist the new generation of flight scheduling systems under extreme weather.

Robust fixed-time flight controller for a dual-system convertible UAV in the cruise mode

This paper investigates the attitude tracking control problem for the cruise mode of a dual-system convertible unmanned aerial vehicle(UAV)in the presence of parameter uncertainties,unmodeled uncertainties and wind disturbances.First,a fixed-time disturbance observer(FXDO)based on the bi-limit homogeneity theory is designed to estimate the lumped disturbance of the convertible UAV model.Then,a fixed-time integral sliding mode control(FXISMC)is combined with the FXDO to achieve strong robustness and chattering reduction.Bi-limit homogeneity theory and Lyapunov theory are applied to provide detailed proof of the fixed-time stability.Finally,numerical simulation experimental results verify the robustness of the proposed algorithm to model parameter uncertainties and wind disturbances.In addition,the proposed algorithm is deployed in a open-source UAV autopilot and its effectiveness is further demonstrated by hardware-in-the-loop experimental results.

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.

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.

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.

Research on Teaching Methods of Flight Control Principles

With the rapid development of China’s civil aviation industry,the teaching method of operating knowledge of flight principles has changed greatly,which creates a good implementation environment to improve the safety of civil aviation in our country.At present,the main training content of air route transport pilots in China is basic aviation theory,initial flight training,airline modification,etc.The principles of flight control are an important part of basic aviation theoretical knowledge training,which will involve a large number of flight technology training content,instructors will also be based on the pilot type.Teaching flight control theory and practical knowledge requires relatively high theoretical learning ability of students,and the learning effect of this part of theoretical knowledge will directly affect the quality of subsequent learning,but also directly affect the effectiveness of flight training.This paper focuses on the analysis of the basic concepts of flight control,studies the existing problems in the teaching of flight control principles,summarizes the teaching measures of flight control principles,aiming to provide a reference to teaching personnel.

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。

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.

Time-of-Flight透散射介质成像技术综述

Time-of-Flight (ToF)成像是利用光在目标和相机之间的飞行时间来获取场景的深度信息,具有体积小、成本低、实时成像等优势。在散射环境中,由于散射介质对光的散射作用,ToF成像受到多径干扰的影响,深度测量误差较大,限制了ToF相机在散射场景中的应用。ToF透散射介质成像技术是校正因散射光引起的多径干扰效应,从传感器接收到的混叠信号中分离出目标分量,实现散射场景中的深度信息恢复,其在雾天自动驾驶、水下勘测、生物医学等领域具有广阔的应用前景。依据ToF成像系统的不同,详细介绍了PL-ToF和CW-ToF成像的基本原理,阐述和分析了散射场景中ToF稳态成像和瞬态成像的机理和特点,分别回顾和总结了ToF稳态成像和瞬态成像的透散射介质成像研究现状,并介绍了ToF透散射介质成像的应用前景,最后依据现有ToF透散射介质成像技术的优缺点,对未来发展趋势进行了展望。

ENERGY STATE APPROACH TO THE INTEGRATED FLIGHT PERFORMANCE MANAGEMENT OF COMMERCIAL AIRCRAFT

The integrated aircraft flight performance management techniques are discussed in this paper based on the point-mass energy state approximation principle. The flight performance optimization algorithms, developed with energy state approximation approach, are first introduced, the functionally integrated flight path/speed control system, so called total energy control system (TECS), is then discussed, and the guidance technique and algorithms, which relate the performance optimization results directly with the TECS, are analyzed and developed. Digital simulation results for a specific transport aircraft model demonstrate the satisfactory performances of the resulted flight performance management system.

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.

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.

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.

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.

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.

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.