查看更多>>摘要：Autonomous umanned aerial vehicle(UAV)manipula-tion is necessary for the defense department to execute tactical missions given by commanders in the future unmanned battle-field.A large amount of research has been devoted to improving the autonomous decision-making ability of UAV in an interactive environment,where finding the optimal maneuvering decision-making policy became one of the key issues for enabling the intelligence of UAV.In this paper,we propose a maneuvering decision-making algorithm for autonomous air-delivery based on deep reinforcement learning under the guidance of expert expe-rience.Specifically,we refine the guidance towards area and guidance towards specific point tasks for the air-delivery pro-cess based on the traditional air-to-surface fire control methods.Moreover,we construct the UAV maneuvering decision-making model based on Markov decision processes(MDPs).Specifi-cally,we present a reward shaping method for the guidance towards area and guidance towards specific point tasks using potential-based function and expert-guided advice.The pro-posed algorithm could accelerate the convergence of the maneuvering decision-making policy and increase the stability of the policy in terms of the output during the later stage of training process.The effectiveness of the proposed maneuvering deci-sion-making policy is illustrated by the curves of training para-meters and extensive experimental results for testing the trained policy.
查看更多>>摘要：Natural events have had a significant impact on over-all 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 con-tinues,airlines operating in threatened areas and passengers having travel plans during this time period will pay close atten-tion 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 pre-diction 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 depend-able data accuracy,problems can be analysed rapidly and more efficiently,enabling better decision-making with a proactive ver-sus 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 cancel-lations compared to existing flight scheduling and assist the new generation of flight scheduling systems under extreme weather.
查看更多>>摘要：In the context of big data,many large-scale know-ledge graphs have emerged to effectively organize the explosive growth of web data on the Internet.To select suitable know-ledge graphs for use from many knowledge graphs,quality assessment is particularly important.As an important thing of quality assessment,completeness assessment generally refers to the ratio of the current data volume to the total data volume.When evaluating the completeness of a knowledge graph,it is often necessary to refine the completeness dimension by set-ting different completeness metrics to produce more complete and understandable evaluation results for the knowledge graph.However,lack of awareness of requirements is the most prob-lematic quality issue.In the actual evaluation process,the exist-ing completeness metrics need to consider the actual applica-tion.Therefore,to accurately recommend suitable knowledge graphs to many users,it is particularly important to develop rele-vant measurement metrics and formulate measurement schemes for completeness.In this paper,we will first clarify the concept of completeness,establish each metric of completeness,and finally design a measurement proposal for the completeness of knowledge graphs.
查看更多>>摘要：To solve the problem of risk identification and quanti-tative assessment for human-computer interaction(HCI)in com-plex avionics systems,an HCI safety analysis framework based on system-theoretical process analysis(STPA)and cognitive reli-ability and error analysis method(CREAM)is proposed.STPA-CREAM can identify unsafe control actions and find the causal path during the interaction of avionics systems and pilot with the help of formal verification tools automatically.The common per-formance conditions(CPC)of avionics systems in the aviation environment is established and a quantitative analysis of human failure is carried out.Taking the head-up display(HUD)system interaction process as an example,a case analysis is carried out,the layered safety control structure and formal model of the HUD interaction process are established.For the interactive behavior"Pilots approaching with HUD",four unsafe control actions and 35 causal scenarios are identified and the impact of common performance conditions at different levels on the pilot decision model are analyzed.The results show that HUD's HCI level gra-dually improves as the scores of CPC increase,and the quality of crew member cooperation and time sufficiency of the task is the key to its HCI.Through case analysis,it is shown that STPA-CREAM can quantitatively assess the hazards in HCI and iden-tify the key factors that impact safety.
查看更多>>摘要：As the"engine"of equipment continuous operation and repeated operation,equipment maintenance support plays a more prominent role in the confrontation of symmetrical combat systems.As the basis and guide for the planning and implemen-tation of equipment maintenance tasks,the equipment damage measurement is an important guarantee for the effective imple-mentation of maintenance support.Firstly,this article compre-hensively analyses the influence factors to damage measure-ment from the enemy's attributes,our attributes and the battle-field environment starting from the basic problem of wartime equipment damage measurement.Secondly,this article deter-mines the key factors based on fuzzy comprehensive evaluation(FCE)and performed principal component analysis(PCA)on the key factors.Finally,the principal components representing more than 85%of the data features are taken as the input and the equipment damage quantity is taken as the output.The data are trained and tested by artificial neural network(ANN)and ran-dom forest(RF).In a word,FCE-PCA-RF can be used as a refer-ence for the research of equipment damage estimation in wartime.
查看更多>>摘要：Based on the wave attack task planning method in static complex environment and the rolling optimization frame-work,an online task planning method in dynamic complex envi-ronment based on rolling optimization is proposed.In the pro-cess of online task planning in dynamic complex environment,online task planning is based on event triggering including tar-get information update event,new target addition event,target failure event,weapon failure event,etc.,and the methods include defense area reanalysis,parameter space update,and mission re-planning.Simulation is conducted for different events and the result shows that the index value of the attack scenario after re-planning is better than that before re-planning and according to the probability distribution of statistical simulation method,the index value distribution after re-planning is obvi-ously in the region of high index value,and the index value gap before and after re-planning is related to the degree of posture change.
查看更多>>摘要：In this study,the problem of measuring noise pollu-tion distribution by the intertial-based integrated navigation sys-tem is effectively suppressed.Based on nonlinear inertial naviga-tion error modeling,a nested dual Kalman filter framework struc-ture is developed.It consists of unscented Kalman filter(UKF)master filter and Kalman filter slave filter.This method uses non-linear UKF for integrated navigation state estimation.At the same time,the exact noise measurement covariance is esti-mated by the Kalman filter dependency filter.The algorithm based on dual adaptive UKF(Dual-AUKF)has high accuracy and robustness,especially in the case of measurement information interference.Finally,vehicle-mounted and ship-mounted inte-grated navigation tests are conducted.Compared with tradi-tional UKF and the Sage-Husa adaptive UKF(SH-AUKF),this method has comparable filtering accuracy and better filtering stability.The effectiveness of the proposed algorithm is verified.
查看更多>>摘要：Tracking the fast-moving object in occlusion situa-tions is an important research topic in computer vision.Despite numerous notable contributions have been made in this field,few of them simultaneously incorporate both object's extrinsic features and intrinsic motion patterns into their methodologies,thereby restricting the potential for tracking accuracy improve-ment.In this paper,on the basis of efficient convolution opera-tors(ECO)model,a speed-accuracy-balanced model is put for-ward.This model uses the simple correlation filter to track the object in real-time,and adopts the sophisticated deep-learning neural network to extract high-level features to train a more complex filter correcting the tracking mistakes,when the trac-king state is judged to be poor.Furthermore,in the context of scenarios involving regular fast-moving,a motion model based on Kalman filter is designed which greatly promotes the tracking stability,because this motion model could predict the object's future location from its previous movement pattern.Additionally,instead of periodically updating our tracking model and training samples,a constrained condition for updating is proposed,which effectively mitigates contamination to the tracker from the background and undesirable samples avoiding model degrada-tion when occlusion happens.From comprehensive experi-ments,our tracking model obtains better performance than ECO on object tracking benchmark 2015(OTB100),and improves the area under curve(AUC)by about 8%and 32%compared with ECO,in the scenarios of fast-moving and occlusion on our own collected dataset.
查看更多>>摘要：Detumbling operation toward a rotating target with nutation is meaningful for debris removal but challenging.In this study,a deformable end-effector is first designed based on the requirements for contacting the nutating target.A dual-arm robotic system installed with the deformable end-effectors is modeled and the movement of the end-tips is analyzed.The complex operation of the contact toward a nutating target places strict requirements on control accuracy and controller robustness.Thus,an improvement of the tracking error transfor-mation is proposed and an adaptive sliding mode controller with prescribed performance is designed to guarantee the fast and precise motion of the effector during the contact detumbling.Finally,by employing the proposed effector and the controller,numerical simulations are carried out to verify the effectiveness and efficiency of the contact detumbling toward a nutating tar-get.
查看更多>>摘要：This study presents a kinematic calibration method for exoskeletal inertial motion capture(EI-MoCap)system with considering the random colored noise such as gyroscopic drift.In this method,the geometric parameters are calibrated by the traditional calibration method at first.Then,in order to calibrate the parameters affected by the random colored noise,the expectation maximization(EM)algorithm is introduced.Through the use of geometric parameters calibrated by the traditional ca-libration method,the iterations under the EM framework are decreased and the efficiency of the proposed method on embedded system is improved.The performance of the pro-posed kinematic calibration method is compared to the tradi-tional calibration method.Furthermore,the feasibility of the pro-posed method is verified on the EI-MoCap system.The simula-tion and experiment demonstrate that the motion capture preci-sion is significantly improved by 16.79%and 7.16%respectively in comparison to the traditional calibration method.
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