An efficient numerical simulation technique is introduced to extract the propagation characteristics of a millimeter guided wave structure. The method is based on the application of the Krylov subspace model order red...An efficient numerical simulation technique is introduced to extract the propagation characteristics of a millimeter guided wave structure. The method is based on the application of the Krylov subspace model order reduction technique (Padé via Lanczos) to the compact finite difference frequency domain (FDFD) method. This new technique speeds up the solution by decreasing the originally larger system matrix into one lower order system matrix. Numerical experiments from several millimeter guided wave structures demonstrate the efficiency and accuracy of this algorithm.展开更多
Background Automatic guided vehicles(AGVs)have developed rapidly in recent years and have been used in several fields,including intelligent transportation,cargo assembly,military testing,and others.A key issue in thes...Background Automatic guided vehicles(AGVs)have developed rapidly in recent years and have been used in several fields,including intelligent transportation,cargo assembly,military testing,and others.A key issue in these applications is path planning.Global path planning results based on known environmental information are used as the ideal path for AGVs combined with local path planning to achieve safe and rapid arrival at the destination.Using the global planning method,the ideal path should meet the requirements of as few turns as possible,a short planning time,and continuous path curvature.Methods We propose a global path-planning method based on an improved A^(*)algorithm.The robustness of the algorithm was verified by simulation experiments in typical multiobstacle and indoor scenarios.To improve the efficiency of the path-finding time,we increase the heuristic information weight of the target location and avoid invalid cost calculations of the obstacle areas in the dynamic programming process.Subsequently,the optimality of the number of turns in the path is ensured based on the turning node backtracking optimization method.Because the final global path needs to satisfy the AGV kinematic constraints and curvature continuity condition,we adopt a curve smoothing scheme and select the optimal result that meets the constraints.Conclusions Simulation results show that the improved algorithm proposed in this study outperforms the traditional method and can help AGVs improve the efficiency of task execution by planning a path with low complexity and smoothness.Additionally,this scheme provides a new solution for global path planning of unmanned vehicles.展开更多
Ubiquitous computing plays an increasing role in our lives. Typically, applications in ubiquitous computing environ-ments are context aware, namely, they react to the situations of their users at a given moment in tim...Ubiquitous computing plays an increasing role in our lives. Typically, applications in ubiquitous computing environ-ments are context aware, namely, they react to the situations of their users at a given moment in time. One example for such environment is visitor’s guides in cultural heritage sites, supporting visits of individuals or small groups, such as families or friends. In such environments, it is well known that interaction among visitors enhances the overall visit experience. Recently, some research prototypes of visitor’s guides have started supporting such interaction through textual communication services embedded in them. However, these applications have so far been developed separately in an ad-hoc manner, despite common features and infrastructures they share. The research described here generalizes communication services offered by different visitor’s guides and suggests a systematic and generic framework for developing context-aware communication services for visitor’s guides. The specific communication services are abstracted into a domain model, later used in practice for adapting and tailoring the different concepts to the specific requirements of the applications. The framework is demonstrated in the specific setting of a multi-agent museum visitor’s guide system. We also show that the suggested framework is not limited to the specific museum visitor’s guide system but may facilitate the development of context-aware communication applications in general.展开更多
Particulate nitrate,a key component of fine particles,forms through the intricate gas-to-particle conversion process.This process is regulated by the gas-to-particle conversion coefficient of nitrate(ε(NO_(3)^(-))).T...Particulate nitrate,a key component of fine particles,forms through the intricate gas-to-particle conversion process.This process is regulated by the gas-to-particle conversion coefficient of nitrate(ε(NO_(3)^(-))).The mechanism betweenε(NO_(3)^(-))and its drivers is highly complex and nonlinear,and can be characterized by machine learning methods.However,conventional machine learning often yields results that lack clear physical meaning and may even contradict established physical/chemical mechanisms due to the influence of ambient factors.It urgently needs an alternative approach that possesses transparent physical interpretations and provides deeper insights into the impact ofε(NO_(3)^(-)).Here we introduce a supervised machine learning approachdthe multilevel nested random forest guided by theory approaches.Our approach robustly identifies NH4 t,SO_(4)^(2-),and temperature as pivotal drivers forε(NO_(3)^(-)).Notably,substantial disparities exist between the outcomes of traditional random forest analysis and the anticipated actual results.Furthermore,our approach underscores the significance of NH4 t during both daytime(30%)and nighttime(40%)periods,while appropriately downplaying the influence of some less relevant drivers in comparison to conventional random forest analysis.This research underscores the transformative potential of integrating domain knowledge with machine learning in atmospheric studies.展开更多
文摘An efficient numerical simulation technique is introduced to extract the propagation characteristics of a millimeter guided wave structure. The method is based on the application of the Krylov subspace model order reduction technique (Padé via Lanczos) to the compact finite difference frequency domain (FDFD) method. This new technique speeds up the solution by decreasing the originally larger system matrix into one lower order system matrix. Numerical experiments from several millimeter guided wave structures demonstrate the efficiency and accuracy of this algorithm.
基金Supported by the Natural Science Foundation of Jiangsu Province (BK20211037)the Science and Technology Development Fund of Wuxi (N20201011)the Nanjing University of Information Science and Technology Wuxi Campus District graduate innovation Project。
文摘Background Automatic guided vehicles(AGVs)have developed rapidly in recent years and have been used in several fields,including intelligent transportation,cargo assembly,military testing,and others.A key issue in these applications is path planning.Global path planning results based on known environmental information are used as the ideal path for AGVs combined with local path planning to achieve safe and rapid arrival at the destination.Using the global planning method,the ideal path should meet the requirements of as few turns as possible,a short planning time,and continuous path curvature.Methods We propose a global path-planning method based on an improved A^(*)algorithm.The robustness of the algorithm was verified by simulation experiments in typical multiobstacle and indoor scenarios.To improve the efficiency of the path-finding time,we increase the heuristic information weight of the target location and avoid invalid cost calculations of the obstacle areas in the dynamic programming process.Subsequently,the optimality of the number of turns in the path is ensured based on the turning node backtracking optimization method.Because the final global path needs to satisfy the AGV kinematic constraints and curvature continuity condition,we adopt a curve smoothing scheme and select the optimal result that meets the constraints.Conclusions Simulation results show that the improved algorithm proposed in this study outperforms the traditional method and can help AGVs improve the efficiency of task execution by planning a path with low complexity and smoothness.Additionally,this scheme provides a new solution for global path planning of unmanned vehicles.
文摘Ubiquitous computing plays an increasing role in our lives. Typically, applications in ubiquitous computing environ-ments are context aware, namely, they react to the situations of their users at a given moment in time. One example for such environment is visitor’s guides in cultural heritage sites, supporting visits of individuals or small groups, such as families or friends. In such environments, it is well known that interaction among visitors enhances the overall visit experience. Recently, some research prototypes of visitor’s guides have started supporting such interaction through textual communication services embedded in them. However, these applications have so far been developed separately in an ad-hoc manner, despite common features and infrastructures they share. The research described here generalizes communication services offered by different visitor’s guides and suggests a systematic and generic framework for developing context-aware communication services for visitor’s guides. The specific communication services are abstracted into a domain model, later used in practice for adapting and tailoring the different concepts to the specific requirements of the applications. The framework is demonstrated in the specific setting of a multi-agent museum visitor’s guide system. We also show that the suggested framework is not limited to the specific museum visitor’s guide system but may facilitate the development of context-aware communication applications in general.
基金supported by the National Natural Science Foundation of China(42077191)the National Key Research and Development Program of China(2022YFC3703400)+1 种基金the Blue Sky Foundation,Tianjin Science and Technology Plan Project(18PTZWHZ00120)Fundamental Research Funds for the Central Universities(63213072 and 63213074).
文摘Particulate nitrate,a key component of fine particles,forms through the intricate gas-to-particle conversion process.This process is regulated by the gas-to-particle conversion coefficient of nitrate(ε(NO_(3)^(-))).The mechanism betweenε(NO_(3)^(-))and its drivers is highly complex and nonlinear,and can be characterized by machine learning methods.However,conventional machine learning often yields results that lack clear physical meaning and may even contradict established physical/chemical mechanisms due to the influence of ambient factors.It urgently needs an alternative approach that possesses transparent physical interpretations and provides deeper insights into the impact ofε(NO_(3)^(-)).Here we introduce a supervised machine learning approachdthe multilevel nested random forest guided by theory approaches.Our approach robustly identifies NH4 t,SO_(4)^(2-),and temperature as pivotal drivers forε(NO_(3)^(-)).Notably,substantial disparities exist between the outcomes of traditional random forest analysis and the anticipated actual results.Furthermore,our approach underscores the significance of NH4 t during both daytime(30%)and nighttime(40%)periods,while appropriately downplaying the influence of some less relevant drivers in comparison to conventional random forest analysis.This research underscores the transformative potential of integrating domain knowledge with machine learning in atmospheric studies.
