Chemical spills on complex geometry are difficult to model due to the uneven concentration distribution caused by air flow over ground obstacles. Computational fluid dynamics(CFD) is one of the powerful tools to estim...Chemical spills on complex geometry are difficult to model due to the uneven concentration distribution caused by air flow over ground obstacles. Computational fluid dynamics(CFD) is one of the powerful tools to estimate the building-resolving wind flow as well as pollutant dispersion. However, it takes too much time and requires enormous computational power in emergency situations. As a time demanding task, the estimation of the chemical spill consequence for emergency response requires abundant wind field information. In this paper, a comprehensive wind field reconstruction framework is proposed, providing the ability of parameter tuning for best reconstruction accuracy. The core of the framework is a data regression model built on principal component analysis(PCA) and extreme learning machine(ELM). To improve the accuracy, the wind field estimation from the regression model is further revised from local wind observations. The optimal placement of anemometers is provided based on the maximum projection on minimum eigenspace(MPME) algorithm. The fire dynamic simulator(FDS) generates high-resolution data of wind flow over complex geometries for the framework to be implemented. The reconstructed wind field is evaluated against simulation data and an overall reconstruction error of 9% is achieved. When used in real case,the error increases to around 12% since no convergence check is available. With parameter tuning abilities,the proposed framework provides an efficient way of reconstructing the wind flow in congested areas.展开更多
Controller placement problem(CPP)is a critical issue in software defined wireless networks(SDWN).Due to the limited power of wireless devices,CPP is facing the challenge of energy efficiency in SDWN.Nevertheless,the r...Controller placement problem(CPP)is a critical issue in software defined wireless networks(SDWN).Due to the limited power of wireless devices,CPP is facing the challenge of energy efficiency in SDWN.Nevertheless,the related research on CPP in SDWN hasn’t modeled the energy consumption of controllers so far.To prolong the lifetime of SDWN and improve the practicability of research,we rebuilt a CPP model considering the minimal transmitted power of controllers.An adaptive controller placement algorithm(ACPA)is proposed with the following two stages.First,data field method is adopted to determine sub-networks for different network topologies.Second,for each sub-network we adopt an exhaustive method to find the optimal location which meets the minimal average transmitted power to place controller.Compared with the other algorithms,the effectiveness and efficiency of the proposed scheme are validated through simulation.展开更多
Partial Reconfigurable FPGAs （Field Programmable Gate Array） allow tasks to be placed and removed dynamically at runtime. One of the challenging problems is the placement of modules on reconfigurable resources. Seve...Partial Reconfigurable FPGAs （Field Programmable Gate Array） allow tasks to be placed and removed dynamically at runtime. One of the challenging problems is the placement of modules on reconfigurable resources. Several modules placement techniques have been introduced in the literature to solve the temporal placement problem. This paper presents a temporal placement approach that manages the resources of a reconfigurable device. In fact, the authors＇ contribution focuses on introducing a new temporal placement algorithm that aims to minimize the communication cost between modules. Results show an important improvement in communication cost compared with other approaches.展开更多
基金Supported by the National Natural Science Foundation of China(21706069and 61751305)the Fundamental Research Funds for the Central Universities(222201814039).
文摘Chemical spills on complex geometry are difficult to model due to the uneven concentration distribution caused by air flow over ground obstacles. Computational fluid dynamics(CFD) is one of the powerful tools to estimate the building-resolving wind flow as well as pollutant dispersion. However, it takes too much time and requires enormous computational power in emergency situations. As a time demanding task, the estimation of the chemical spill consequence for emergency response requires abundant wind field information. In this paper, a comprehensive wind field reconstruction framework is proposed, providing the ability of parameter tuning for best reconstruction accuracy. The core of the framework is a data regression model built on principal component analysis(PCA) and extreme learning machine(ELM). To improve the accuracy, the wind field estimation from the regression model is further revised from local wind observations. The optimal placement of anemometers is provided based on the maximum projection on minimum eigenspace(MPME) algorithm. The fire dynamic simulator(FDS) generates high-resolution data of wind flow over complex geometries for the framework to be implemented. The reconstructed wind field is evaluated against simulation data and an overall reconstruction error of 9% is achieved. When used in real case,the error increases to around 12% since no convergence check is available. With parameter tuning abilities,the proposed framework provides an efficient way of reconstructing the wind flow in congested areas.
基金supported by the Open Research Fund of Key Laboratory of Space Utilization,Chinese Academy of Sciences(No.LSU-KFJJ-2018-06)the International Research Cooperation Seed Fund of Beijing University of Technology(No.2018B41)
文摘Controller placement problem(CPP)is a critical issue in software defined wireless networks(SDWN).Due to the limited power of wireless devices,CPP is facing the challenge of energy efficiency in SDWN.Nevertheless,the related research on CPP in SDWN hasn’t modeled the energy consumption of controllers so far.To prolong the lifetime of SDWN and improve the practicability of research,we rebuilt a CPP model considering the minimal transmitted power of controllers.An adaptive controller placement algorithm(ACPA)is proposed with the following two stages.First,data field method is adopted to determine sub-networks for different network topologies.Second,for each sub-network we adopt an exhaustive method to find the optimal location which meets the minimal average transmitted power to place controller.Compared with the other algorithms,the effectiveness and efficiency of the proposed scheme are validated through simulation.
文摘Partial Reconfigurable FPGAs （Field Programmable Gate Array） allow tasks to be placed and removed dynamically at runtime. One of the challenging problems is the placement of modules on reconfigurable resources. Several modules placement techniques have been introduced in the literature to solve the temporal placement problem. This paper presents a temporal placement approach that manages the resources of a reconfigurable device. In fact, the authors＇ contribution focuses on introducing a new temporal placement algorithm that aims to minimize the communication cost between modules. Results show an important improvement in communication cost compared with other approaches.
