Journal of Aerospace Information Systems, Ahead of Print.
航空航天信息系统期刊》，提前印刷。
{"title":"Research on Optical Site Diversity for Space Communications over Asia-Pacific Region","authors":"Tatsuya Mukai, Yoshihisa Takayama","doi":"10.2514/1.i010996","DOIUrl":"https://doi.org/10.2514/1.i010996","url":null,"abstract":"Journal of Aerospace Information Systems, Ahead of Print. <br/>","PeriodicalId":50260,"journal":{"name":"Journal of Aerospace Information Systems","volume":"20 1","pages":""},"PeriodicalIF":1.5,"publicationDate":"2023-12-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139068885","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Journal of Aerospace Information Systems, Ahead of Print.
航空航天信息系统期刊》，提前印刷。
{"title":"Risk Assessment Procedure of Final Approach to Landing Using Deep Learning","authors":"Pei-Chen Tsai, Ying-Chih Lai","doi":"10.2514/1.i011177","DOIUrl":"https://doi.org/10.2514/1.i011177","url":null,"abstract":"Journal of Aerospace Information Systems, Ahead of Print. <br/>","PeriodicalId":50260,"journal":{"name":"Journal of Aerospace Information Systems","volume":"53 1","pages":""},"PeriodicalIF":1.5,"publicationDate":"2023-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138560128","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Journal of Aerospace Information Systems, Ahead of Print.
航空航天信息系统杂志，出版前。
{"title":"Multi-Agent Task Allocation with Interagent Distance Constraints","authors":"Euihyeon Choi, Woohyuk Chang","doi":"10.2514/1.i011272","DOIUrl":"https://doi.org/10.2514/1.i011272","url":null,"abstract":"Journal of Aerospace Information Systems, Ahead of Print. <br/>","PeriodicalId":50260,"journal":{"name":"Journal of Aerospace Information Systems","volume":"131 5","pages":""},"PeriodicalIF":1.5,"publicationDate":"2023-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138519968","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Kimoon Lee, Dong-Jin Kim, Dae-Won Chung, Seonho Lee
Journal of Aerospace Information Systems, Ahead of Print.
航空航天信息系统杂志，出版前。
{"title":"Optimal Mission Planning for Multiple Agile Satellites Using Modified Dynamic Programming","authors":"Kimoon Lee, Dong-Jin Kim, Dae-Won Chung, Seonho Lee","doi":"10.2514/1.i011270","DOIUrl":"https://doi.org/10.2514/1.i011270","url":null,"abstract":"Journal of Aerospace Information Systems, Ahead of Print. <br/>","PeriodicalId":50260,"journal":{"name":"Journal of Aerospace Information Systems","volume":"45 4","pages":""},"PeriodicalIF":1.5,"publicationDate":"2023-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138519957","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Previous approaches for small fixed-wing unmanned air systems that carry strapdown rather than gimbaled cameras achieved satisfactory ground target tracking performance using both standard and deep reinforcement learning algorithms. However, these approaches have significant restrictions and abstractions to the dynamics of the vehicle, such as constant airspeed and constant altitude, because the number of states and actions was necessarily limited. Thus, extensive tuning was required to obtain good tracking performance. The expansion from 4 state–action degrees of freedom to 15 enabled the agent to exploit previous reward functions that produced novel yet undesirable emergent behavior. This paper investigates the causes of and various potential solutions to undesirable emergent behavior in the ground target tracking problem. A combination of changes to the environment, reward structure, action space simplification, command rate, and controller implementation provides insight into obtaining stable tracking results. Consideration is given to reward structure selection and refinement to mitigate undesirable emergent behavior. Results presented in the paper for a simulated environment of a single unmanned air system tracking a randomly moving single ground target show that a soft actor–critic algorithm can produce feasible tracking trajectories without limiting the state space and action space, provided that the environment is properly posed.
{"title":"Design, Selection, and Evaluation of Reinforcement Learning Single Agents for Ground Target Tracking","authors":"Hannah Lehman, John Valasek","doi":"10.2514/1.i011284","DOIUrl":"https://doi.org/10.2514/1.i011284","url":null,"abstract":"Previous approaches for small fixed-wing unmanned air systems that carry strapdown rather than gimbaled cameras achieved satisfactory ground target tracking performance using both standard and deep reinforcement learning algorithms. However, these approaches have significant restrictions and abstractions to the dynamics of the vehicle, such as constant airspeed and constant altitude, because the number of states and actions was necessarily limited. Thus, extensive tuning was required to obtain good tracking performance. The expansion from 4 state–action degrees of freedom to 15 enabled the agent to exploit previous reward functions that produced novel yet undesirable emergent behavior. This paper investigates the causes of and various potential solutions to undesirable emergent behavior in the ground target tracking problem. A combination of changes to the environment, reward structure, action space simplification, command rate, and controller implementation provides insight into obtaining stable tracking results. Consideration is given to reward structure selection and refinement to mitigate undesirable emergent behavior. Results presented in the paper for a simulated environment of a single unmanned air system tracking a randomly moving single ground target show that a soft actor–critic algorithm can produce feasible tracking trajectories without limiting the state space and action space, provided that the environment is properly posed.","PeriodicalId":50260,"journal":{"name":"Journal of Aerospace Information Systems","volume":"14 6","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134991444","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Unmanned aerial system (UAS) traffic management of airspace is a domain that demands strategic management of unmanned aerial vehicles (UAVs) for smooth and conflict-free movement in the uncharted low-altitude G airspace. In the context of the previously proposed CORRIDRONE structure, UAV traffic has to be organized in a shared volume of airspace connecting two or more corridors for a network of multilane corridors, resulting in the formation of aerial intersections. In this work, an intersection planning algorithm is proposed that aims to provide no-conflict paths to the UAVs inside the intersection volume. Paths are modeled as a function of the lanes involved in the transition, and conflict resolution is achieved by changing lanes. Optimized solutions are found among the conflicted UAV paths, such that only a few paths need modifying, optimizing the number of lane changes and time spent in the intersection. Simulation results, including random starting time intervals, various UAV sizes, corridor sizes, and differing numbers of lanes in intersecting corridors, are presented to demonstrate the concepts discussed in the paper.
{"title":"Intersection Planning for Multilane Unmanned Aerial Vehicle Traffic Management","authors":"Samiksha Rajkumar Nagrare, Ashwini Ratnoo, Debasish Ghose","doi":"10.2514/1.i011307","DOIUrl":"https://doi.org/10.2514/1.i011307","url":null,"abstract":"Unmanned aerial system (UAS) traffic management of airspace is a domain that demands strategic management of unmanned aerial vehicles (UAVs) for smooth and conflict-free movement in the uncharted low-altitude G airspace. In the context of the previously proposed CORRIDRONE structure, UAV traffic has to be organized in a shared volume of airspace connecting two or more corridors for a network of multilane corridors, resulting in the formation of aerial intersections. In this work, an intersection planning algorithm is proposed that aims to provide no-conflict paths to the UAVs inside the intersection volume. Paths are modeled as a function of the lanes involved in the transition, and conflict resolution is achieved by changing lanes. Optimized solutions are found among the conflicted UAV paths, such that only a few paths need modifying, optimizing the number of lane changes and time spent in the intersection. Simulation results, including random starting time intervals, various UAV sizes, corridor sizes, and differing numbers of lanes in intersecting corridors, are presented to demonstrate the concepts discussed in the paper.","PeriodicalId":50260,"journal":{"name":"Journal of Aerospace Information Systems","volume":"102 21","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135137855","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Open AccessTechnical NotesAssessment of Approach and Departure Paths for Vertical Takeoff and Landing AircraftSuyoung Shin and Keumjin LeeSuyoung ShinKorea Aerospace University, Goyang 412-791, Republic of Korea*Graduate Student, Department of Air Transportation; currently Junior Engineer, Hanwha Systems; .Search for more papers by this author and Keumjin Lee https://orcid.org/0000-0002-3938-449XKorea Aerospace University, Goyang 412-791, Republic of Korea†Professor, Department of Air Transportation; . Member AIAA (Corresponding Author).Search for more papers by this authorPublished Online:8 Nov 2023https://doi.org/10.2514/1.I011278SectionsRead Now ToolsAdd to favoritesDownload citationTrack citations ShareShare onFacebookTwitterLinked InRedditEmail AboutNomenclatureAset of available approach and departure directionsFATObackback distance of obstacle-free volume on final approach and takeoff areaFATOfrontfront distance of obstacle-free volume on final approach and takeoff areaFATOwidthwidth of obstacle-free volume on final approach and takeoff areah1low hover height of obstacle-free volumeh2high hover height of obstacle-free volumeIobsset of indies of the voxels that Pobs occupiesIOFV(ψ)set of indies of the voxels that POFV(ψ) occupiesKnumber of buildingsLnumber of pointslix-axis index of the voxel where pi occupiesMnumber of IOFVmiy-axis index of the voxel where pi occupiesNnumber of IobsNxsize of voxelated space in x directionNysize of voxelated space in y directionNzsize of voxelated space in z directionniz-axis index of the voxel where pi occupiesPobsset of points that represent obstacle dataPOFV(ψ)set of points that represent obstacle-free volume in a specific orientation angle ψpiith point in Psxvoxel size in x directionsyvoxel size in y directionszvoxel size in z directionTObackback distance of obstacle-free volume at h2TOfrontfront distance obstacle-free volume at h2TOwidthwidth of obstacle-free volume at h2Vset of voxels for the region of interestvlmnvoxel located at l, m, and n in the x, y, and z directions, respectivelyxix-axis coordinate of pixox-axis coordinate of reference point of voxelated spaceyiy-axis coordinate of piyoy-axis coordinate of reference point of voxelated spaceziz-axis coordinate of pizoz-axis coordinate of reference point of voxelated spaceα(Θ)directional availability under Θδappdivergence of approach surfaceδdepdivergence of departure surfaceΘset of the specification parameters of obstacle-free volumeθappslope of approach surfaceθdepslope of departure surfaceΨset of orientation angles of obstacle-free volumeψorientation angle between the true north and the centerline of approach/departure surfaceI. IntroductionUrban air mobility (UAM) is a new form of transportation to take passengers and cargo over urban areas, in turn promoting reduced traffic congestion and CO2 emissions [1,2]. Yet, the safety of low-altitude flights in congested areas must be addressed before UAM is used commercially. Especially, identifying av
{"title":"Assessment of Approach and Departure Paths for Vertical Takeoff and Landing Aircraft","authors":"Suyoung Shin, Keumjin Lee","doi":"10.2514/1.i011278","DOIUrl":"https://doi.org/10.2514/1.i011278","url":null,"abstract":"Open AccessTechnical NotesAssessment of Approach and Departure Paths for Vertical Takeoff and Landing AircraftSuyoung Shin and Keumjin LeeSuyoung ShinKorea Aerospace University, Goyang 412-791, Republic of Korea*Graduate Student, Department of Air Transportation; currently Junior Engineer, Hanwha Systems; .Search for more papers by this author and Keumjin Lee https://orcid.org/0000-0002-3938-449XKorea Aerospace University, Goyang 412-791, Republic of Korea†Professor, Department of Air Transportation; . Member AIAA (Corresponding Author).Search for more papers by this authorPublished Online:8 Nov 2023https://doi.org/10.2514/1.I011278SectionsRead Now ToolsAdd to favoritesDownload citationTrack citations ShareShare onFacebookTwitterLinked InRedditEmail AboutNomenclatureAset of available approach and departure directionsFATObackback distance of obstacle-free volume on final approach and takeoff areaFATOfrontfront distance of obstacle-free volume on final approach and takeoff areaFATOwidthwidth of obstacle-free volume on final approach and takeoff areah1low hover height of obstacle-free volumeh2high hover height of obstacle-free volumeIobsset of indies of the voxels that Pobs occupiesIOFV(ψ)set of indies of the voxels that POFV(ψ) occupiesKnumber of buildingsLnumber of pointslix-axis index of the voxel where pi occupiesMnumber of IOFVmiy-axis index of the voxel where pi occupiesNnumber of IobsNxsize of voxelated space in x directionNysize of voxelated space in y directionNzsize of voxelated space in z directionniz-axis index of the voxel where pi occupiesPobsset of points that represent obstacle dataPOFV(ψ)set of points that represent obstacle-free volume in a specific orientation angle ψpiith point in Psxvoxel size in x directionsyvoxel size in y directionszvoxel size in z directionTObackback distance of obstacle-free volume at h2TOfrontfront distance obstacle-free volume at h2TOwidthwidth of obstacle-free volume at h2Vset of voxels for the region of interestvlmnvoxel located at l, m, and n in the x, y, and z directions, respectivelyxix-axis coordinate of pixox-axis coordinate of reference point of voxelated spaceyiy-axis coordinate of piyoy-axis coordinate of reference point of voxelated spaceziz-axis coordinate of pizoz-axis coordinate of reference point of voxelated spaceα(Θ)directional availability under Θδappdivergence of approach surfaceδdepdivergence of departure surfaceΘset of the specification parameters of obstacle-free volumeθappslope of approach surfaceθdepslope of departure surfaceΨset of orientation angles of obstacle-free volumeψorientation angle between the true north and the centerline of approach/departure surfaceI. IntroductionUrban air mobility (UAM) is a new form of transportation to take passengers and cargo over urban areas, in turn promoting reduced traffic congestion and CO2 emissions [1,2]. Yet, the safety of low-altitude flights in congested areas must be addressed before UAM is used commercially. Especially, identifying av","PeriodicalId":50260,"journal":{"name":"Journal of Aerospace Information Systems","volume":"30 S94","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135343232","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Unmanned aerial vehicles (UAVs) are forecast to be widely used in the military and civilian domains. The remaining flying time is a critical parameter to monitor during a flight to ensure the safety of electric UAVs (e-UAVs). However, accurate remaining flying time prediction under different load conditions requires a large amount of data and is computationally expensive for online applications. To address these issues, a deep learning approach based on temporal convolutional networks and transfer learning is developed for lithium-ion battery systems for e-UAVs. A temporal convolutional network is used to extract features from monitoring data and predict the remaining flying time of flights under one load condition. A layer transfer strategy is then used to transfer the knowledge learned from one load condition to another load condition. Battery health monitoring data collected from a fixed-wing e-UAV are used to demonstrate the effectiveness of the proposed method. Experimental results show that the proposed temporal convolutional network with the transfer learning strategy can predict the remaining flying time of the e-UAV under two load conditions more efficiently and accurately than a temporal convolutional network without transfer learning.
{"title":"Remaining Flying Time Prediction of Unmanned Aerial Vehicles Under Different Load Conditions","authors":"Junchuan Shi, Wendy A. Okolo, Dazhong Wu","doi":"10.2514/1.i011198","DOIUrl":"https://doi.org/10.2514/1.i011198","url":null,"abstract":"Unmanned aerial vehicles (UAVs) are forecast to be widely used in the military and civilian domains. The remaining flying time is a critical parameter to monitor during a flight to ensure the safety of electric UAVs (e-UAVs). However, accurate remaining flying time prediction under different load conditions requires a large amount of data and is computationally expensive for online applications. To address these issues, a deep learning approach based on temporal convolutional networks and transfer learning is developed for lithium-ion battery systems for e-UAVs. A temporal convolutional network is used to extract features from monitoring data and predict the remaining flying time of flights under one load condition. A layer transfer strategy is then used to transfer the knowledge learned from one load condition to another load condition. Battery health monitoring data collected from a fixed-wing e-UAV are used to demonstrate the effectiveness of the proposed method. Experimental results show that the proposed temporal convolutional network with the transfer learning strategy can predict the remaining flying time of the e-UAV under two load conditions more efficiently and accurately than a temporal convolutional network without transfer learning.","PeriodicalId":50260,"journal":{"name":"Journal of Aerospace Information Systems","volume":"106 4","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135540395","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper presents a fuzzy adaptive terminal sliding mode control for an unmanned miniature helicopter including uncertainties and external disturbances, and a terminal sliding surface is utilized to provide faster convergence. Due to the fact that the presented controlled system is facing a singularity problem, the controlling structure is developed to a nonsingular one. In the proposed controlling structure, a continuous nonsingular terminal sliding mode control is combined with an adaptive learning algorithm and fuzzy logic system to estimate the uncertainties; and the parameter adaptation law is obtained based on the Lyapunov stability theorem. Analytical results show that the proposed approach enables a faster and more accurate tracking performance as compared to recent controlling methods.
{"title":"Fuzzy Adaptive Nonsingular Terminal Sliding Mode Control of a Miniature Helicopter","authors":"Reihaneh Kardehi Moghaddam, Javad Baratpoor","doi":"10.2514/1.i011156","DOIUrl":"https://doi.org/10.2514/1.i011156","url":null,"abstract":"This paper presents a fuzzy adaptive terminal sliding mode control for an unmanned miniature helicopter including uncertainties and external disturbances, and a terminal sliding surface is utilized to provide faster convergence. Due to the fact that the presented controlled system is facing a singularity problem, the controlling structure is developed to a nonsingular one. In the proposed controlling structure, a continuous nonsingular terminal sliding mode control is combined with an adaptive learning algorithm and fuzzy logic system to estimate the uncertainties; and the parameter adaptation law is obtained based on the Lyapunov stability theorem. Analytical results show that the proposed approach enables a faster and more accurate tracking performance as compared to recent controlling methods.","PeriodicalId":50260,"journal":{"name":"Journal of Aerospace Information Systems","volume":"2 10","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135479597","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper examines a couple of realizations of autonomous landing hazard avoidance technology of parafoil: a reinforcement-learning-based approach and a rule-based approach, advocating the former. Furthermore, comparative advantages and behavioral analogies between the two approaches are presented. In the data-driven approach, a decision process observing only a series of nadir-pointing images is designed without explicit augmentation of vehicle dynamics for the homogeneity of observation data. An agent then learns the hazard avoidance steering law in an end-to-end fashion. On the contrary, the rule-based approach is facilitated via explicit notions of guidance-control hierarchy, vehicle dynamic states, and metric details of ground obstacles. The soft actor–critic method is applied to learn a policy that maps the down-looking images to parafoil brakes, whereas a vector field guidance law is employed in the rule-based approach, considering each hazard as a repulsive source. This paper then presents empirical equivalences in designing both approaches and their distinctions. Numerical experiments in multiple test cases validate the reinforcement learning method and present comparisons between the approaches regarding their resultant trajectories. The interesting behaviors of the resultant policy of the data-driven approach are emphasized.
{"title":"Data-Driven Hazard Avoidance Landing of Parafoil: A Deep Reinforcement Learning Approach","authors":"Junwoo Park, Hyochoong Bang","doi":"10.2514/1.i011281","DOIUrl":"https://doi.org/10.2514/1.i011281","url":null,"abstract":"This paper examines a couple of realizations of autonomous landing hazard avoidance technology of parafoil: a reinforcement-learning-based approach and a rule-based approach, advocating the former. Furthermore, comparative advantages and behavioral analogies between the two approaches are presented. In the data-driven approach, a decision process observing only a series of nadir-pointing images is designed without explicit augmentation of vehicle dynamics for the homogeneity of observation data. An agent then learns the hazard avoidance steering law in an end-to-end fashion. On the contrary, the rule-based approach is facilitated via explicit notions of guidance-control hierarchy, vehicle dynamic states, and metric details of ground obstacles. The soft actor–critic method is applied to learn a policy that maps the down-looking images to parafoil brakes, whereas a vector field guidance law is employed in the rule-based approach, considering each hazard as a repulsive source. This paper then presents empirical equivalences in designing both approaches and their distinctions. Numerical experiments in multiple test cases validate the reinforcement learning method and present comparisons between the approaches regarding their resultant trajectories. The interesting behaviors of the resultant policy of the data-driven approach are emphasized.","PeriodicalId":50260,"journal":{"name":"Journal of Aerospace Information Systems","volume":"2 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135935855","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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