Target maneuver trajectory prediction is an important prerequisite for air combat situation awareness and maneuver decision-making.However,how to use a large amount of trajectory data generated by air combat confronta...Target maneuver trajectory prediction is an important prerequisite for air combat situation awareness and maneuver decision-making.However,how to use a large amount of trajectory data generated by air combat confrontation training to achieve real-time and accurate prediction of target maneuver trajectory is an urgent problem to be solved.To solve this problem,in this paper,a hybrid algorithm based on transfer learning,online learning,ensemble learning,regularization technology,target maneuvering segmentation point recognition algorithm,and Volterra series,abbreviated as AERTrOS-Volterra is proposed.Firstly,the model makes full use of a large number of trajectory sample data generated by air combat confrontation training,and constructs a Tr-Volterra algorithm framework suitable for air combat target maneuver trajectory prediction,which realizes the extraction of effective information from the historical trajectory data.Secondly,in order to improve the real-time online prediction accuracy and robustness of the prediction model in complex electromagnetic environments,on the basis of the TrVolterra algorithm framework,a robust regularized online Sequential Volterra prediction model is proposed by integrating online learning method,regularization technology and inverse weighting calculation method based on the priori error.Finally,inspired by the preferable performance of models ensemble,ensemble learning scheme is also incorporated into our proposed algorithm,which adaptively updates the ensemble prediction model according to the performance of the model on real-time samples and the recognition results of target maneuvering segmentation points,including the adaptation of model weights;adaptation of parameters;and dynamic inclusion and removal of models.Compared with many existing time series prediction methods,the newly proposed target maneuver trajectory prediction algorithm can fully mine the prior knowledge contained in the historical data to assist the current prediction.The rationality and effectiveness of the proposed algorithm are verified by simulation on three sets of chaotic time series data sets and a set of real target maneuver trajectory data sets.展开更多
The unmanned aerial vehicle(UAV)-enabled mobile edge computing(MEC) architecture is expected to be a powerful technique to facilitate 5 G and beyond ubiquitous wireless connectivity and diverse vertical applications a...The unmanned aerial vehicle(UAV)-enabled mobile edge computing(MEC) architecture is expected to be a powerful technique to facilitate 5 G and beyond ubiquitous wireless connectivity and diverse vertical applications and services, anytime and anywhere. Wireless power transfer(WPT) is another promising technology to prolong the operation time of low-power wireless devices in the era of Internet of Things(IoT). However, the integration of WPT and UAV-enabled MEC systems is far from being well studied, especially in dynamic environments. In order to tackle this issue, this paper aims to investigate the stochastic computation offloading and trajectory scheduling for the UAV-enabled wireless powered MEC system. A UAV offers both RF wireless power transmission and computation services for IoT devices. Considering the stochastic task arrivals and random channel conditions, a long-term average energyefficiency(EE) minimization problem is formulated.Due to non-convexity and the time domain coupling of the variables in the formulated problem, a lowcomplexity online computation offloading and trajectory scheduling algorithm(OCOTSA) is proposed by exploiting Lyapunov optimization. Simulation results verify that there exists a balance between EE and the service delay, and demonstrate that the system EE performance obtained by the proposed scheme outperforms other benchmark schemes.展开更多
The research on multiple launch rocket system(MLRS)is now even more demanding in terms of reducing the time for dynamic calculations and improving the firing accuracy,keeping the cost as low as possible.This study emp...The research on multiple launch rocket system(MLRS)is now even more demanding in terms of reducing the time for dynamic calculations and improving the firing accuracy,keeping the cost as low as possible.This study employs multibody system transfer matrix method(MSTMM),to model MLRS.The use of this method provides effective and fast calculations of dynamic characteristics,initial disturbance and firing accuracy.Further,a new method of rapid extrapolation of ballistic trajectory of MLRS is proposed by using the position information of radar tests.That extrapolation point is then simulated and compared with the actual results,which demonstrates a good agreement.The closed?loop fire correction method is used to improve the firing accuracy of MLRS at low cost.展开更多
This paper will present the results and analyses of a simulation to send a satellite from the Earth to Mars. We use Python to simulate the orbit of the rocket. Our goal is to find the least energy-cost trajectory, wit...This paper will present the results and analyses of a simulation to send a satellite from the Earth to Mars. We use Python to simulate the orbit of the rocket. Our goal is to find the least energy-cost trajectory, with the least initial velocity. We find the date which allows the satellite to go from the Earth to Mars in the shortest distance based on a Hohmann transfer orbit considering the gravity of the Sun, Earth, and Mars.展开更多
Knowing the long-term degradation trajectory of Lithium-ion(Li-ion) battery in its early usage stage is critical for the maintenance of the battery energy storage system(BESS) in reality. Previous battery health diagn...Knowing the long-term degradation trajectory of Lithium-ion(Li-ion) battery in its early usage stage is critical for the maintenance of the battery energy storage system(BESS) in reality. Previous battery health diagnosis methods focus on capacity and state of health(SOH) estimation which can receive only the short-term health status of the cell. This paper proposes a novel degradation trajectory prediction method with synthetic dataset and deep learning, which enables to grasp the characterization of the cell's health at a very early stage of Li-ion battery usage. A transferred convolutional neural network(CNN) is chosen to finalize the early prediction target, and the polynomial function based synthetic dataset generation strategy is designed to reduce the costly data collection procedure in real application. In this thread, the proposed method needs one full lifespan data to predict the overall degradation trajectories of other cells. With only the full lifespan cycling data from 4 cells and 100 cycling data from each cell in experimental validation, the proposed method shows a good prediction accuracy on a dataset with more than 100 commercial Li-ion batteries.展开更多
As the sixth generation network(6G)emerges,the Internet of remote things(IoRT)has become a critical issue.However,conventional terrestrial networks cannot meet the delay-sensitive data collection needs of IoRT network...As the sixth generation network(6G)emerges,the Internet of remote things(IoRT)has become a critical issue.However,conventional terrestrial networks cannot meet the delay-sensitive data collection needs of IoRT networks,and the Space-Air-Ground integrated network(SAGIN)holds promise.We propose a novel setup that integrates non-orthogonal multiple access(NOMA)and wireless power transfer(WPT)to collect latency-sensitive data from IoRT networks.To extend the lifetime of devices,we aim to minimize the maximum energy consumption among all IoRT devices.Due to the coupling between variables,the resulting problem is non-convex.We first decouple the variables and split the original problem into four subproblems.Then,we propose an iterative algorithm to solve the corresponding subproblems based on successive convex approximation(SCA)techniques and slack variables.Finally,simulation results show that the NOMA strategy has a tremendous advantage over the OMA scheme in terms of network lifetime and energy efficiency,providing valuable insights.展开更多
SS型Buck-WPT(Buck-wireless power transfer)系统由Buck电路和基本的SS型无线电能传输电路组成。该电路系统因为结构和控制方式简单、控制效果明显等优点在感应式无线电能传输方面得到广泛应用。但该电路的动态特性并不能满足一些时变...SS型Buck-WPT(Buck-wireless power transfer)系统由Buck电路和基本的SS型无线电能传输电路组成。该电路系统因为结构和控制方式简单、控制效果明显等优点在感应式无线电能传输方面得到广泛应用。但该电路的动态特性并不能满足一些时变系统对快速性的较高要求。例如,系统在启动时会存在较强震荡和较大超调,系统负载改变时稳定状态会发生改变且存在明显抖动,系统极限空载时原边谐振电流会增大,且该电流值远超出安全工作范围。本文提出了一种基于可控电感的SS型Buck-WPT系统。首先,分析了电感值可调的方法并在COMSOL中建立仿真模型验证其电感值可控的特性。其次,对SS型Buck-WPT系统进行数学建模,将SS型WPT系统作为Buck电路的特殊负载,推导SS型Buck-WPT系统状态空间方程。研究其三维空间内相轨迹的降维描述方法,将该系统用二维相轨迹描述系统运行过程。然后,通过分析启动阶段相轨迹运行规律,改进前级Buck电路。将传统Buck电路中的电感换成可控电感,运用其电感值可调的控制系统开通阶段的运行轨迹,使系统在1个开关周期内无超调快速进入稳态。当系统负载改变时,系统的输出电压会改变,且是不断抖动来回反复的过程,利用PI算法对系统进行恒流控制。通过可控电感控制系统相轨迹,使副边输出能无抖动快速进入稳态,保证输出电压不变。针对SS型谐振网络的Buck-WPT系统中出现空载大电流的问题,提出了将可控电感串联接入原边谐振网络的方法。实时检测原边谐振电流值,该值超过正常工作范围,感值就快速增大,减小原边谐振电流,达到空载时维持原边谐振电流安全值以下。最后,验证上述方法在优化SS型Buck-WPT系统动态特性的有效性,在Simulink中搭建仿真电路。该方法能减小工作条件改变时带来的系统抖动,且在不改变系统响应速度前提下减小超调,优化系统动态性能,增强系统抗负载扰动力,提高系统带负载能力有明显效果。展开更多
基金the support of the Fundamental Research Funds for the Air Force Engineering University under Grant No.XZJK2019040。
文摘Target maneuver trajectory prediction is an important prerequisite for air combat situation awareness and maneuver decision-making.However,how to use a large amount of trajectory data generated by air combat confrontation training to achieve real-time and accurate prediction of target maneuver trajectory is an urgent problem to be solved.To solve this problem,in this paper,a hybrid algorithm based on transfer learning,online learning,ensemble learning,regularization technology,target maneuvering segmentation point recognition algorithm,and Volterra series,abbreviated as AERTrOS-Volterra is proposed.Firstly,the model makes full use of a large number of trajectory sample data generated by air combat confrontation training,and constructs a Tr-Volterra algorithm framework suitable for air combat target maneuver trajectory prediction,which realizes the extraction of effective information from the historical trajectory data.Secondly,in order to improve the real-time online prediction accuracy and robustness of the prediction model in complex electromagnetic environments,on the basis of the TrVolterra algorithm framework,a robust regularized online Sequential Volterra prediction model is proposed by integrating online learning method,regularization technology and inverse weighting calculation method based on the priori error.Finally,inspired by the preferable performance of models ensemble,ensemble learning scheme is also incorporated into our proposed algorithm,which adaptively updates the ensemble prediction model according to the performance of the model on real-time samples and the recognition results of target maneuvering segmentation points,including the adaptation of model weights;adaptation of parameters;and dynamic inclusion and removal of models.Compared with many existing time series prediction methods,the newly proposed target maneuver trajectory prediction algorithm can fully mine the prior knowledge contained in the historical data to assist the current prediction.The rationality and effectiveness of the proposed algorithm are verified by simulation on three sets of chaotic time series data sets and a set of real target maneuver trajectory data sets.
基金supported in part by the U.S. National Science Foundation under Grant CNS-2007995in part by the National Natural Science Foundation of China under Grant 92067201,62171231in part by Jiangsu Provincial Key Research and Development Program under Grant BE2020084-1。
文摘The unmanned aerial vehicle(UAV)-enabled mobile edge computing(MEC) architecture is expected to be a powerful technique to facilitate 5 G and beyond ubiquitous wireless connectivity and diverse vertical applications and services, anytime and anywhere. Wireless power transfer(WPT) is another promising technology to prolong the operation time of low-power wireless devices in the era of Internet of Things(IoT). However, the integration of WPT and UAV-enabled MEC systems is far from being well studied, especially in dynamic environments. In order to tackle this issue, this paper aims to investigate the stochastic computation offloading and trajectory scheduling for the UAV-enabled wireless powered MEC system. A UAV offers both RF wireless power transmission and computation services for IoT devices. Considering the stochastic task arrivals and random channel conditions, a long-term average energyefficiency(EE) minimization problem is formulated.Due to non-convexity and the time domain coupling of the variables in the formulated problem, a lowcomplexity online computation offloading and trajectory scheduling algorithm(OCOTSA) is proposed by exploiting Lyapunov optimization. Simulation results verify that there exists a balance between EE and the service delay, and demonstrate that the system EE performance obtained by the proposed scheme outperforms other benchmark schemes.
基金supported by the Na- tional Natural Science Foundation of China (No. 11472135)the Science Challenge Project (No. JCKY2016212A506- 0104)
文摘The research on multiple launch rocket system(MLRS)is now even more demanding in terms of reducing the time for dynamic calculations and improving the firing accuracy,keeping the cost as low as possible.This study employs multibody system transfer matrix method(MSTMM),to model MLRS.The use of this method provides effective and fast calculations of dynamic characteristics,initial disturbance and firing accuracy.Further,a new method of rapid extrapolation of ballistic trajectory of MLRS is proposed by using the position information of radar tests.That extrapolation point is then simulated and compared with the actual results,which demonstrates a good agreement.The closed?loop fire correction method is used to improve the firing accuracy of MLRS at low cost.
文摘This paper will present the results and analyses of a simulation to send a satellite from the Earth to Mars. We use Python to simulate the orbit of the rocket. Our goal is to find the least energy-cost trajectory, with the least initial velocity. We find the date which allows the satellite to go from the Earth to Mars in the shortest distance based on a Hohmann transfer orbit considering the gravity of the Sun, Earth, and Mars.
基金supported in part by the National Natural Science Foundation of China (52107229, 62203423, and 61903114)in part by the Fujian Provincial Natural Science Foundation (2022J01504)。
文摘Knowing the long-term degradation trajectory of Lithium-ion(Li-ion) battery in its early usage stage is critical for the maintenance of the battery energy storage system(BESS) in reality. Previous battery health diagnosis methods focus on capacity and state of health(SOH) estimation which can receive only the short-term health status of the cell. This paper proposes a novel degradation trajectory prediction method with synthetic dataset and deep learning, which enables to grasp the characterization of the cell's health at a very early stage of Li-ion battery usage. A transferred convolutional neural network(CNN) is chosen to finalize the early prediction target, and the polynomial function based synthetic dataset generation strategy is designed to reduce the costly data collection procedure in real application. In this thread, the proposed method needs one full lifespan data to predict the overall degradation trajectories of other cells. With only the full lifespan cycling data from 4 cells and 100 cycling data from each cell in experimental validation, the proposed method shows a good prediction accuracy on a dataset with more than 100 commercial Li-ion batteries.
基金supported by National Natural Science Foundation of China(No.62171158)the project“The Major Key Project of PCL(PCL2021A03-1)”from Peng Cheng Laboratorysupported by the Science and the Research Fund Program of Guangdong Key Laboratory of Aerospace Communication and Networking Technology(2018B030322004).
文摘As the sixth generation network(6G)emerges,the Internet of remote things(IoRT)has become a critical issue.However,conventional terrestrial networks cannot meet the delay-sensitive data collection needs of IoRT networks,and the Space-Air-Ground integrated network(SAGIN)holds promise.We propose a novel setup that integrates non-orthogonal multiple access(NOMA)and wireless power transfer(WPT)to collect latency-sensitive data from IoRT networks.To extend the lifetime of devices,we aim to minimize the maximum energy consumption among all IoRT devices.Due to the coupling between variables,the resulting problem is non-convex.We first decouple the variables and split the original problem into four subproblems.Then,we propose an iterative algorithm to solve the corresponding subproblems based on successive convex approximation(SCA)techniques and slack variables.Finally,simulation results show that the NOMA strategy has a tremendous advantage over the OMA scheme in terms of network lifetime and energy efficiency,providing valuable insights.
文摘SS型Buck-WPT(Buck-wireless power transfer)系统由Buck电路和基本的SS型无线电能传输电路组成。该电路系统因为结构和控制方式简单、控制效果明显等优点在感应式无线电能传输方面得到广泛应用。但该电路的动态特性并不能满足一些时变系统对快速性的较高要求。例如,系统在启动时会存在较强震荡和较大超调,系统负载改变时稳定状态会发生改变且存在明显抖动,系统极限空载时原边谐振电流会增大,且该电流值远超出安全工作范围。本文提出了一种基于可控电感的SS型Buck-WPT系统。首先,分析了电感值可调的方法并在COMSOL中建立仿真模型验证其电感值可控的特性。其次,对SS型Buck-WPT系统进行数学建模,将SS型WPT系统作为Buck电路的特殊负载,推导SS型Buck-WPT系统状态空间方程。研究其三维空间内相轨迹的降维描述方法,将该系统用二维相轨迹描述系统运行过程。然后,通过分析启动阶段相轨迹运行规律,改进前级Buck电路。将传统Buck电路中的电感换成可控电感,运用其电感值可调的控制系统开通阶段的运行轨迹,使系统在1个开关周期内无超调快速进入稳态。当系统负载改变时,系统的输出电压会改变,且是不断抖动来回反复的过程,利用PI算法对系统进行恒流控制。通过可控电感控制系统相轨迹,使副边输出能无抖动快速进入稳态,保证输出电压不变。针对SS型谐振网络的Buck-WPT系统中出现空载大电流的问题,提出了将可控电感串联接入原边谐振网络的方法。实时检测原边谐振电流值,该值超过正常工作范围,感值就快速增大,减小原边谐振电流,达到空载时维持原边谐振电流安全值以下。最后,验证上述方法在优化SS型Buck-WPT系统动态特性的有效性,在Simulink中搭建仿真电路。该方法能减小工作条件改变时带来的系统抖动,且在不改变系统响应速度前提下减小超调,优化系统动态性能,增强系统抗负载扰动力,提高系统带负载能力有明显效果。