近年来,以无人机(Unmanned Aerial Vehicle,UAV)和城市空中交通(Urban Air Mobility,UAM)为代表的低空经济正经历快速发展,并被纳入国家战略层面。UAV凭借其灵活性在多领域应用广泛;UAM则被视为解决城市交通拥堵的革命性方案。然而,该...近年来,以无人机(Unmanned Aerial Vehicle,UAV)和城市空中交通(Urban Air Mobility,UAM)为代表的低空经济正经历快速发展,并被纳入国家战略层面。UAV凭借其灵活性在多领域应用广泛;UAM则被视为解决城市交通拥堵的革命性方案。然而,该产业的可持续繁荣高度依赖于安全保障。安全是UAV和UAM的生命线,是公众接受的前提与行业发展的基石,因此构建全面、高效的安全保障体系至关重要。展开更多
从评价理论的介入系统出发,利用UAM Corpus Tool对选自《卫报》(The Guardian)的4则英语新闻评论进行分析,旨在介绍如何利用UAM Corpus tool来进行英语新闻评论的语篇分析.由此,充分体现出UAM Corpus Tool在语篇分析中的作用.有助于提...从评价理论的介入系统出发,利用UAM Corpus Tool对选自《卫报》(The Guardian)的4则英语新闻评论进行分析,旨在介绍如何利用UAM Corpus tool来进行英语新闻评论的语篇分析.由此,充分体现出UAM Corpus Tool在语篇分析中的作用.有助于提高语篇分析的效率,增加研究发现的说服力.展开更多
本文在评价理论的框架下,借助UAM Corpus Tool软件,对两节高中英语课例的课堂实录转写文本进行话语分析,考察了两位教师课堂互动话语中评价资源的分布特征和课堂互动话语策略。研究发现,两位教师课堂互动话语中评价资源的利用以及教师...本文在评价理论的框架下,借助UAM Corpus Tool软件,对两节高中英语课例的课堂实录转写文本进行话语分析,考察了两位教师课堂互动话语中评价资源的分布特征和课堂互动话语策略。研究发现,两位教师课堂互动话语中评价资源的利用以及教师课堂互动话语策略所体现的评价意义有显著差别。要回归学生在课堂中的主体地位,促进深度学习,未来需要提升教师课堂互动的话语能力,发挥教师的“脚手架”作用,维持话语的可协商性,吸引更多学生参与课堂互动。展开更多
The development of urban air mobility (UAM) systems requires scalable, regulation-aware planning of low-altitude airspace and supporting infrastructure. This study proposes an end-to-end framework for the design,simul...The development of urban air mobility (UAM) systems requires scalable, regulation-aware planning of low-altitude airspace and supporting infrastructure. This study proposes an end-to-end framework for the design,simulation, and iterative optimization of a structured UAM corridor over Brasilia’s central road axis (Eixão-UAM),aligned with the Brazilian unmanned aircraft traffic management (BR-UTM) ecosystem. In addition, this study proposes a multilayered aerial conflguration stratifled by unmanned aerial vehicle class, supported by a modular ground infrastructure composed of vertihubs, vertiports, and vertistops. A takeoff-scheduling simulator is developed to evaluate platform allocation strategies under realistic traffic and weather conditions. Initial experiments compare a round-robin (RR) baseline with a genetic algorithm (GA), and results reveal that RR outperforms GA v1 in terms of the average waiting time. To address this gap, a large language model (LLM) assisted optimization loop is implemented using GPT-4o Mini and Gemini 2.5 Pro. The LLMs act as reasoning partners, supporting the root-cause diagnoses, fltness function redesign, and rapid prototyping of flve GA variants. Among these, GA v5achieves a 59.62%reduction in maximum waiting time and an approximately 10%reduction in average waiting time over GA v1, thereby approaching the robustness of RR. In contrast, GA v2–v4 and GA v6 perform less consistently, showing an importance of fltness function design. These results underscore the role of an iterative,LLM-guided development in enhancing classical optimization, demonstrating that generative artiflcial intelligence(AI) can contribute to simulation acceleration and the cocreation of operational logic. The proposed method provides a replicable blueprint for integrating LLMs into early-stage UAM planning, offering both theoretical insights and architectural guidance for future low-altitude airspace systems.展开更多
Urban Air Mobility(UAM)is an emerging aviation sector which the goal is to transform air transportation with safe,on-demand air travel for both passengers and cargo.UAM flight planning strategically separates flows of...Urban Air Mobility(UAM)is an emerging aviation sector which the goal is to transform air transportation with safe,on-demand air travel for both passengers and cargo.UAM flight planning strategically separates flows of aircraft on intersecting routes vertically by allocating distinct flight levels to them,and aircraft are required to maintain the flight level when crossing the intersection.However,there is a possibility that an aircraft may fail to maintain the assigned flight level,leading to a potential conflict at intersections.This paper aims to address conflicts at intersections in the context of UAM,focusing on decentralized conflict detection and resolution.A novel approach is developed to facilitate information exchange among UAM components,including the provider of services to UAM,UAM operators,and the pilot in command.A receding horizon trajectory planning approach is proposed for the execution of conflict resolution,optimizing trajectory planning by eliminating potential problems and challenges associated with geometric approaches.The proposed trajectory planner considers the model and constraints of UAM aircraft,offering optimal solutions for safe separation at UAM airspace intersections.The significance of the proposed planning framework is demonstrated through simulations considering conflict at intersections by communicating the UAM components through request and replay services and generating resolution maneuvers on-the-fly for each aircraft involved in the conflict.展开更多
针对城市场景下载人电动垂直起降飞行器(electric vertical takeoff and landing,eVTOL)路径规划问题进行了研究。首先,使用危险度栅格法进行三维城市空间建模,对选定型号的eVTOL飞行器,以航程、运行风险和高度变化为目标函数,结合飞行...针对城市场景下载人电动垂直起降飞行器(electric vertical takeoff and landing,eVTOL)路径规划问题进行了研究。首先,使用危险度栅格法进行三维城市空间建模,对选定型号的eVTOL飞行器,以航程、运行风险和高度变化为目标函数,结合飞行器自身特性及环境限制,构建了多约束条件的载人eVTOL路径规划模型。然后,设计了一种改进人工电场算法(im-proved artificial electric field algorithm,IAEFA),在传统人工电场算法(artificial electric field algorithm,AEFA)的基础上增加了自适应库伦参数,并在库伦常数的计算中引入递减系数,以此进行仿真求解。实验结果显示,所构建的模型可以达到预期效果。使用改进算法进行路径规划的求解效果更优,相较传统粒子群算法和人工电场法,航程更短,高度变化更小且运行更为安全。最后,根据对照实验确定递减系数的取值,当递减系数取值为1.5时,改进算法的求解效果最优。展开更多
城市空中交通管理(Urban Air Mobility,UAM)是指通过电动垂直起降飞行器(electric Vertical Take-Off and Landing,eVTOL)在低空空域构建高效、安全的立体交通体系,以缓解地面拥堵并提升城市出行效率。作为UAM的核心基础设施,垂直起降...城市空中交通管理(Urban Air Mobility,UAM)是指通过电动垂直起降飞行器(electric Vertical Take-Off and Landing,eVTOL)在低空空域构建高效、安全的立体交通体系,以缓解地面拥堵并提升城市出行效率。作为UAM的核心基础设施,垂直起降场的布局与容量直接影响系统的服务能力。基于吞吐量将垂直起降场划分为三类,针对区域出行需求特征,建立以最小化设施数量、最大化需求覆盖为目标的优化模型。通过融合多层次遗传算法与贪婪优化退火算法进行求解验证,结果表明:所提方案能有效平衡资源配置与服务效能。研究成果可为UAM运营商提供科学的起降场选址决策依据。展开更多
为探索中美主流媒体在报道“第三届‘一带一路’国际合作高峰论坛”时的评价差异与介入资源分布特征,基于评价理论介入系统框架,运用UAM Corpus Tool语料库工具,就China Daily和CNN网站上所收集整理的新闻报道作介入资源分析,进而得出...为探索中美主流媒体在报道“第三届‘一带一路’国际合作高峰论坛”时的评价差异与介入资源分布特征,基于评价理论介入系统框架,运用UAM Corpus Tool语料库工具,就China Daily和CNN网站上所收集整理的新闻报道作介入资源分析,进而得出中美主流媒体在报道中的关注侧重点、评价差异,以及介入资源的特征。研究结果发现:双方都有大量使用“自言”资源,而在扩展和收缩对话空间上,双方呈现出不同的倾向。同时,CNN和China Daily的报道在“自言”中,所表达的观点截然不同。展开更多
In the future smart cities,unmanned aerial vehicles(UAVs)or electric vertical take-off and landing aircraft(e VTOL)are widely employed for urban air mobility(UAM).Considering such real-world scenarios,a deep reinforce...In the future smart cities,unmanned aerial vehicles(UAVs)or electric vertical take-off and landing aircraft(e VTOL)are widely employed for urban air mobility(UAM).Considering such real-world scenarios,a deep reinforcement learning based communication resource allocation method is proposed for UAVs to provide communication services for e VTOL swarms to ensure their reliable communication and safe operation.To save energy consumption,UAVs can ride on a moving interaction station(MIS),such as an urban bus.By using UAV trajectory control and communication power allocation,a joint fair optimization problem is formulated to maximize the channel capacity while optimizing radar sensing performance.To address the optimization problem,a Point Cloud based deep Q-network(PCDQN)algorithm is proposed.It contains a point neural network that can determine the action space of the UAV directly originating from the threedimensional(3D)radar point clouds,and a deep reinforcement learning based decision model for deciding the action from action spaces.Simulation results demonstrate that the proposed method exhibits competitive performance compared to the benchmarks.展开更多
随着城市空中交通(Urban Air Mobility,UAM)商业化运输的兴起,简化飞行操纵(SVO)的概念被引入航空器设计,旨在简化操作流程以适应未来运输需求。然而,基于SVO设计的电动垂直起降飞行器(eVTOL)是否满足适航性条件,以及其操纵界面是否符...随着城市空中交通(Urban Air Mobility,UAM)商业化运输的兴起,简化飞行操纵(SVO)的概念被引入航空器设计,旨在简化操作流程以适应未来运输需求。然而,基于SVO设计的电动垂直起降飞行器(eVTOL)是否满足适航性条件,以及其操纵界面是否符合人机交互的友好性标准,目前尚存在不确定性。为了解决这一问题,对基于简化操纵设计的eVTOL进行了任务科目基元(MTE)的实验,以评估其操纵品质。实验共招募了20名被试人员,通过库珀-哈珀量表收集了他们对eVTOL操纵品质的主观评分,并进行了半结构化访谈,同时记录了他们的眼动数据和肌电数据。本研究在结合主观评估和可解释的手工特征的基础上,提出了基于格拉米角场法和卷积神经网络的操纵品质评估模型。研究结果表明,不良的操纵界面设计显著影响了参与者的肌电信号和眼动信号。基于此结论设计的卷积神经网络模型能够以93.67%的准确率评估eVTOL的操纵品质。研究不仅为航空器操纵品质的提供了更为客观的新视角,而且为未来简化操纵eVTOL的设计提供了重要的改进依据。展开更多
文摘近年来,以无人机(Unmanned Aerial Vehicle,UAV)和城市空中交通(Urban Air Mobility,UAM)为代表的低空经济正经历快速发展,并被纳入国家战略层面。UAV凭借其灵活性在多领域应用广泛;UAM则被视为解决城市交通拥堵的革命性方案。然而,该产业的可持续繁荣高度依赖于安全保障。安全是UAV和UAM的生命线,是公众接受的前提与行业发展的基石,因此构建全面、高效的安全保障体系至关重要。
文摘从评价理论的介入系统出发,利用UAM Corpus Tool对选自《卫报》(The Guardian)的4则英语新闻评论进行分析,旨在介绍如何利用UAM Corpus tool来进行英语新闻评论的语篇分析.由此,充分体现出UAM Corpus Tool在语篇分析中的作用.有助于提高语篇分析的效率,增加研究发现的说服力.
文摘本文在评价理论的框架下,借助UAM Corpus Tool软件,对两节高中英语课例的课堂实录转写文本进行话语分析,考察了两位教师课堂互动话语中评价资源的分布特征和课堂互动话语策略。研究发现,两位教师课堂互动话语中评价资源的利用以及教师课堂互动话语策略所体现的评价意义有显著差别。要回归学生在课堂中的主体地位,促进深度学习,未来需要提升教师课堂互动的话语能力,发挥教师的“脚手架”作用,维持话语的可协商性,吸引更多学生参与课堂互动。
基金Project supported by the Brazilian National Council for Scientific and Technological Development(CNPq)(No.309545/2021-8)。
文摘The development of urban air mobility (UAM) systems requires scalable, regulation-aware planning of low-altitude airspace and supporting infrastructure. This study proposes an end-to-end framework for the design,simulation, and iterative optimization of a structured UAM corridor over Brasilia’s central road axis (Eixão-UAM),aligned with the Brazilian unmanned aircraft traffic management (BR-UTM) ecosystem. In addition, this study proposes a multilayered aerial conflguration stratifled by unmanned aerial vehicle class, supported by a modular ground infrastructure composed of vertihubs, vertiports, and vertistops. A takeoff-scheduling simulator is developed to evaluate platform allocation strategies under realistic traffic and weather conditions. Initial experiments compare a round-robin (RR) baseline with a genetic algorithm (GA), and results reveal that RR outperforms GA v1 in terms of the average waiting time. To address this gap, a large language model (LLM) assisted optimization loop is implemented using GPT-4o Mini and Gemini 2.5 Pro. The LLMs act as reasoning partners, supporting the root-cause diagnoses, fltness function redesign, and rapid prototyping of flve GA variants. Among these, GA v5achieves a 59.62%reduction in maximum waiting time and an approximately 10%reduction in average waiting time over GA v1, thereby approaching the robustness of RR. In contrast, GA v2–v4 and GA v6 perform less consistently, showing an importance of fltness function design. These results underscore the role of an iterative,LLM-guided development in enhancing classical optimization, demonstrating that generative artiflcial intelligence(AI) can contribute to simulation acceleration and the cocreation of operational logic. The proposed method provides a replicable blueprint for integrating LLMs into early-stage UAM planning, offering both theoretical insights and architectural guidance for future low-altitude airspace systems.
基金support from the NASA University Leadership Initiative(ULI)under agreement number 2 CFR 200.514 Grant Number 80NSSC20M0161the U.S.Department of Transportation(USDOT)University Transportation Program(UTC)under Grant No.69A3552348304.
文摘Urban Air Mobility(UAM)is an emerging aviation sector which the goal is to transform air transportation with safe,on-demand air travel for both passengers and cargo.UAM flight planning strategically separates flows of aircraft on intersecting routes vertically by allocating distinct flight levels to them,and aircraft are required to maintain the flight level when crossing the intersection.However,there is a possibility that an aircraft may fail to maintain the assigned flight level,leading to a potential conflict at intersections.This paper aims to address conflicts at intersections in the context of UAM,focusing on decentralized conflict detection and resolution.A novel approach is developed to facilitate information exchange among UAM components,including the provider of services to UAM,UAM operators,and the pilot in command.A receding horizon trajectory planning approach is proposed for the execution of conflict resolution,optimizing trajectory planning by eliminating potential problems and challenges associated with geometric approaches.The proposed trajectory planner considers the model and constraints of UAM aircraft,offering optimal solutions for safe separation at UAM airspace intersections.The significance of the proposed planning framework is demonstrated through simulations considering conflict at intersections by communicating the UAM components through request and replay services and generating resolution maneuvers on-the-fly for each aircraft involved in the conflict.
文摘针对城市场景下载人电动垂直起降飞行器(electric vertical takeoff and landing,eVTOL)路径规划问题进行了研究。首先,使用危险度栅格法进行三维城市空间建模,对选定型号的eVTOL飞行器,以航程、运行风险和高度变化为目标函数,结合飞行器自身特性及环境限制,构建了多约束条件的载人eVTOL路径规划模型。然后,设计了一种改进人工电场算法(im-proved artificial electric field algorithm,IAEFA),在传统人工电场算法(artificial electric field algorithm,AEFA)的基础上增加了自适应库伦参数,并在库伦常数的计算中引入递减系数,以此进行仿真求解。实验结果显示,所构建的模型可以达到预期效果。使用改进算法进行路径规划的求解效果更优,相较传统粒子群算法和人工电场法,航程更短,高度变化更小且运行更为安全。最后,根据对照实验确定递减系数的取值,当递减系数取值为1.5时,改进算法的求解效果最优。
文摘城市空中交通管理(Urban Air Mobility,UAM)是指通过电动垂直起降飞行器(electric Vertical Take-Off and Landing,eVTOL)在低空空域构建高效、安全的立体交通体系,以缓解地面拥堵并提升城市出行效率。作为UAM的核心基础设施,垂直起降场的布局与容量直接影响系统的服务能力。基于吞吐量将垂直起降场划分为三类,针对区域出行需求特征,建立以最小化设施数量、最大化需求覆盖为目标的优化模型。通过融合多层次遗传算法与贪婪优化退火算法进行求解验证,结果表明:所提方案能有效平衡资源配置与服务效能。研究成果可为UAM运营商提供科学的起降场选址决策依据。
文摘为探索中美主流媒体在报道“第三届‘一带一路’国际合作高峰论坛”时的评价差异与介入资源分布特征,基于评价理论介入系统框架,运用UAM Corpus Tool语料库工具,就China Daily和CNN网站上所收集整理的新闻报道作介入资源分析,进而得出中美主流媒体在报道中的关注侧重点、评价差异,以及介入资源的特征。研究结果发现:双方都有大量使用“自言”资源,而在扩展和收缩对话空间上,双方呈现出不同的倾向。同时,CNN和China Daily的报道在“自言”中,所表达的观点截然不同。
基金supported in part by the National Natural Science Foundation of China(No.62401032)in part by the Postdoctoral Fellowship Program of China Postdoctoral Science Foundation(CPSF)(No.GZC20242161)in part by the Open Project Program of State Key Laboratory of CNS/ATM,China(No.2024A03)。
文摘In the future smart cities,unmanned aerial vehicles(UAVs)or electric vertical take-off and landing aircraft(e VTOL)are widely employed for urban air mobility(UAM).Considering such real-world scenarios,a deep reinforcement learning based communication resource allocation method is proposed for UAVs to provide communication services for e VTOL swarms to ensure their reliable communication and safe operation.To save energy consumption,UAVs can ride on a moving interaction station(MIS),such as an urban bus.By using UAV trajectory control and communication power allocation,a joint fair optimization problem is formulated to maximize the channel capacity while optimizing radar sensing performance.To address the optimization problem,a Point Cloud based deep Q-network(PCDQN)algorithm is proposed.It contains a point neural network that can determine the action space of the UAV directly originating from the threedimensional(3D)radar point clouds,and a deep reinforcement learning based decision model for deciding the action from action spaces.Simulation results demonstrate that the proposed method exhibits competitive performance compared to the benchmarks.
文摘随着城市空中交通(Urban Air Mobility,UAM)商业化运输的兴起,简化飞行操纵(SVO)的概念被引入航空器设计,旨在简化操作流程以适应未来运输需求。然而,基于SVO设计的电动垂直起降飞行器(eVTOL)是否满足适航性条件,以及其操纵界面是否符合人机交互的友好性标准,目前尚存在不确定性。为了解决这一问题,对基于简化操纵设计的eVTOL进行了任务科目基元(MTE)的实验,以评估其操纵品质。实验共招募了20名被试人员,通过库珀-哈珀量表收集了他们对eVTOL操纵品质的主观评分,并进行了半结构化访谈,同时记录了他们的眼动数据和肌电数据。本研究在结合主观评估和可解释的手工特征的基础上,提出了基于格拉米角场法和卷积神经网络的操纵品质评估模型。研究结果表明,不良的操纵界面设计显著影响了参与者的肌电信号和眼动信号。基于此结论设计的卷积神经网络模型能够以93.67%的准确率评估eVTOL的操纵品质。研究不仅为航空器操纵品质的提供了更为客观的新视角,而且为未来简化操纵eVTOL的设计提供了重要的改进依据。
