The Ocean 4A scatterometer, expected to be launched in 2024, is poised to be the world’s first spaceborne microwave scatterometer utilizing a digital beamforming system. To ensure high-precision measurements and perf...The Ocean 4A scatterometer, expected to be launched in 2024, is poised to be the world’s first spaceborne microwave scatterometer utilizing a digital beamforming system. To ensure high-precision measurements and performance sta-bility across diverse environments, stringent requirements are placed on the dynamic range of its receiving system. This paper provides a detailed exposition of a field-programmable gate array (FPGA)-based automatic gain control (AGC) design for the spaceborne scatterometer. Implemented on an FPGA, the algo-rithm harnesses its parallel processing capabilities and high-speed performance to monitor the received echo signals in real time. Employing an adaptive AGC algorithm, the system gene-rates gain control codes applicable to the intermediate fre-quency variable attenuator, enabling rapid and stable adjust-ment of signal amplitudes from the intermediate frequency amplifier to an optimal range. By adopting a purely digital pro-cessing approach, experimental results demonstrate that the AGC algorithm exhibits several advantages, including fast con-vergence, strong flexibility, high precision, and outstanding sta-bility. This innovative design lays a solid foundation for the high-precision measurements of the Ocean 4A scatterometer, with potential implications for the future of spaceborne microwave scatterometers.展开更多
Autonomous vehicle technology will transform fundamentally urban traffic systems.To better enhance the coming era of connected and autonomous vehicles,effective control strategies that interact wisely with these intel...Autonomous vehicle technology will transform fundamentally urban traffic systems.To better enhance the coming era of connected and autonomous vehicles,effective control strategies that interact wisely with these intelligent vehicles for signalized at-grade intersections are indispensable.Vehicle-to-infrastructure communication technology offers unprecedented clues to reduce the delay at signalized intersections by innovative information-based control strategies.This paper proposes a new dynamic control strategy for signalized intersections with vehicle-to-signal information.The proposed strategy is called periodic vehicle holding(PVH)strategy while the traffic signal can provide information for the vehicles that are approaching an intersection.Under preliminary autonomous vehicle(PAV)environment,left-turning and through-moving vehicles will be sorted based on different information they receive.The paper shows how PVH reorganizes traffic to increase the capacity of an intersection without causing severe spillback to the upstream intersection.Results show that PVH can reduce the delay by approximately 15%at a signalized intersection under relatively high traffic demand.展开更多
In order to balance the temporal-spatial distribution of urban traffic flow, a model is established for combined urban traffic signal control and traffic flow guidance. With consideration of the wide use of fixed sign...In order to balance the temporal-spatial distribution of urban traffic flow, a model is established for combined urban traffic signal control and traffic flow guidance. With consideration of the wide use of fixed signal control at intersections, traffic assignment under traffic flow guidance, and dynamic characteristics of urban traffic management, a tri-level programming model is presented. To reflect the impact of intersection delay on traffic assignment, the lower level model is set as a modified user equilibrium model. The middle level model, which contains several definitional constraints for different phase modes, is built for the traffic signal control optimization. To solve the problem of tide lane management, the upper level model is built up based on nonlinear 0-1 integer programming. A heuristic iterative optimization algorithm(HIOA) is set up to solve the tri-level programming model. The lower level model is solved by method of successive averages(MSA), the middle level model is solved by non-dominated sorting genetic algorithm II(NSGA II), and the upper level model is solved by genetic algorithm(GA). A case study is raised to show the efficiency and applicability of the proposed modelling and computing method.展开更多
An input-output signal selection based on Phillips-Heffron model of a parallel high voltage alternative current/high voltage direct current(HVAC/HVDC) power system is presented to study power system stability. It is w...An input-output signal selection based on Phillips-Heffron model of a parallel high voltage alternative current/high voltage direct current(HVAC/HVDC) power system is presented to study power system stability. It is well known that appropriate coupling of inputs-outputs signals in the multivariable HVDC-HVAC system can improve the performance of designed supplemetary controller. In this work, different analysis techniques are used to measure controllability and observability of electromechanical oscillation mode. Also inputs–outputs interactions are considered and suggestions are drawn to select the best signal pair through the system inputs-outputs. In addition, a supplementary online adaptive controller for nonlinear HVDC to damp low frequency oscillations in a weakly connected system is proposed. The results obtained using MATLAB software show that the best output-input for damping controller design is rotor speed deviation as out put and phase angle of rectifier as in put. Also response of system equipped with adaptive damping controller based on HVDC system has appropriate performance when it is faced with faults and disturbance.展开更多
In this paper, a traffic signal control method based on fuzzy logic (FL), fuzzy-neuro (FN) and multi-objective genetic algorithms (MOGA) for an isolated four-approach intersection with through and left-turning movemen...In this paper, a traffic signal control method based on fuzzy logic (FL), fuzzy-neuro (FN) and multi-objective genetic algorithms (MOGA) for an isolated four-approach intersection with through and left-turning movements is presented. This method has an adaptive signal timing ability, and can make adjustments to signal timing in response to observed changes.The 'urgency degree' term, which can describe the different user's demand for green time is used in decision-making by which strategy of signal timing can be determined. Using a fuzzy logic controller, we can determine whether to extend or terminate the current signal phase and select the sequences of phases. In this paper, a method based on fuzzy-neuro can be used to predict traffic parameters used in fuzzy logic controller. The feasibility of using a multi-objective genetic algorithm ( MOGA) to find a group of optimizing sets of parameters for fuzzy logic controller depending on different objects is also demonstrated. Simulation results show that the proposed methed is effecfive to adjust the signal timing in response to changing traffic conditions on a real-time basis, and the controller can produce lower vehicle delays and percentage of stopped vehicles than a traffic-actuated controller.展开更多
针对在信号交叉口前由于车辆转向和换道操作频繁容易引发冲突、降低通行效率的问题,构建基于深度强化学习(DQN)的车辆群体控制模型,优化车辆车道选择.首先,利用传感器和网联设备等获取周围车辆及交叉口信号灯实时状态信息,基于共享DQN...针对在信号交叉口前由于车辆转向和换道操作频繁容易引发冲突、降低通行效率的问题,构建基于深度强化学习(DQN)的车辆群体控制模型,优化车辆车道选择.首先,利用传感器和网联设备等获取周围车辆及交叉口信号灯实时状态信息,基于共享DQN模型进行车道选择,并根据该结果计算下一时刻位置、速度和转向角;进一步以效率及安全性指标建立奖励函数对车道选择决策实施评价,将状态信息、决策信息及奖励评价信息整合形成经验,存入同一经验池用于共享DQN模型参数迭代更新;最后,使用SUMO(simulation of urban mobility)与Python联合仿真搭建不同交通流量环境对训练后的模型进行验证.研究表明:相较于SUMO中的车道选择模型,基于共享DQN模型的信号交叉口前车辆群体车道选择模型,在低、中、高流量测试场景的平均速度均有提高,交叉口前排队长度分别减少了9.6%、22.5%和24.8%.本文模型可以有效减少信号交叉口的排队长度、提高信号交叉口前的路段平均速度、增强车辆从上游到达交叉口的效率,为未来车路协同的应用提供理论借鉴和技术支持.展开更多
在异质交通流背景下,针对交通信号调度与车辆轨迹规划协同问题,本文提出集信号和轨迹于一体的融合控制模型。该模型采用竞争双深度Q网络算法(Dueling Double Deep Q Network, D3QN),通过深度强化学习技术对交通信号和车辆轨迹进行同步优...在异质交通流背景下,针对交通信号调度与车辆轨迹规划协同问题,本文提出集信号和轨迹于一体的融合控制模型。该模型采用竞争双深度Q网络算法(Dueling Double Deep Q Network, D3QN),通过深度强化学习技术对交通信号和车辆轨迹进行同步优化,旨在实现交通效率与生态驾驶的双重目标,并基于SUMO(Simulation of Urban Mobility)仿真平台对模型进行全面验证。仿真结果表明:与基准模型相比,单一优化策略虽然能在一定程度上改善交叉口性能,但存在整体效率提升受限的问题;本文提出的融合控制模型结合了宏观交通流与微观车辆行为的优化,使车均延误降低66.99%,燃油消耗减少11.26%,同时CO_(2)等污染物排放量也显著减少。进一步的敏感性分析揭示了系统性能随网联自动驾驶汽车(Connected and Autonomous Vehicles, CAV)渗透率的变化规律修正:当渗透率达到一定水平后,性能提升幅度逐渐减小,且模型在不同交通流量条件下均展现出稳定的优化效果,这一结果证实了该控制方法在城市交叉口环境中的适应性和鲁棒性。展开更多
文摘The Ocean 4A scatterometer, expected to be launched in 2024, is poised to be the world’s first spaceborne microwave scatterometer utilizing a digital beamforming system. To ensure high-precision measurements and performance sta-bility across diverse environments, stringent requirements are placed on the dynamic range of its receiving system. This paper provides a detailed exposition of a field-programmable gate array (FPGA)-based automatic gain control (AGC) design for the spaceborne scatterometer. Implemented on an FPGA, the algo-rithm harnesses its parallel processing capabilities and high-speed performance to monitor the received echo signals in real time. Employing an adaptive AGC algorithm, the system gene-rates gain control codes applicable to the intermediate fre-quency variable attenuator, enabling rapid and stable adjust-ment of signal amplitudes from the intermediate frequency amplifier to an optimal range. By adopting a purely digital pro-cessing approach, experimental results demonstrate that the AGC algorithm exhibits several advantages, including fast con-vergence, strong flexibility, high precision, and outstanding sta-bility. This innovative design lays a solid foundation for the high-precision measurements of the Ocean 4A scatterometer, with potential implications for the future of spaceborne microwave scatterometers.
文摘Autonomous vehicle technology will transform fundamentally urban traffic systems.To better enhance the coming era of connected and autonomous vehicles,effective control strategies that interact wisely with these intelligent vehicles for signalized at-grade intersections are indispensable.Vehicle-to-infrastructure communication technology offers unprecedented clues to reduce the delay at signalized intersections by innovative information-based control strategies.This paper proposes a new dynamic control strategy for signalized intersections with vehicle-to-signal information.The proposed strategy is called periodic vehicle holding(PVH)strategy while the traffic signal can provide information for the vehicles that are approaching an intersection.Under preliminary autonomous vehicle(PAV)environment,left-turning and through-moving vehicles will be sorted based on different information they receive.The paper shows how PVH reorganizes traffic to increase the capacity of an intersection without causing severe spillback to the upstream intersection.Results show that PVH can reduce the delay by approximately 15%at a signalized intersection under relatively high traffic demand.
基金Project(2014BAG01B0403)supported by the High-Tech Research and Development Program of China
文摘In order to balance the temporal-spatial distribution of urban traffic flow, a model is established for combined urban traffic signal control and traffic flow guidance. With consideration of the wide use of fixed signal control at intersections, traffic assignment under traffic flow guidance, and dynamic characteristics of urban traffic management, a tri-level programming model is presented. To reflect the impact of intersection delay on traffic assignment, the lower level model is set as a modified user equilibrium model. The middle level model, which contains several definitional constraints for different phase modes, is built for the traffic signal control optimization. To solve the problem of tide lane management, the upper level model is built up based on nonlinear 0-1 integer programming. A heuristic iterative optimization algorithm(HIOA) is set up to solve the tri-level programming model. The lower level model is solved by method of successive averages(MSA), the middle level model is solved by non-dominated sorting genetic algorithm II(NSGA II), and the upper level model is solved by genetic algorithm(GA). A case study is raised to show the efficiency and applicability of the proposed modelling and computing method.
文摘An input-output signal selection based on Phillips-Heffron model of a parallel high voltage alternative current/high voltage direct current(HVAC/HVDC) power system is presented to study power system stability. It is well known that appropriate coupling of inputs-outputs signals in the multivariable HVDC-HVAC system can improve the performance of designed supplemetary controller. In this work, different analysis techniques are used to measure controllability and observability of electromechanical oscillation mode. Also inputs–outputs interactions are considered and suggestions are drawn to select the best signal pair through the system inputs-outputs. In addition, a supplementary online adaptive controller for nonlinear HVDC to damp low frequency oscillations in a weakly connected system is proposed. The results obtained using MATLAB software show that the best output-input for damping controller design is rotor speed deviation as out put and phase angle of rectifier as in put. Also response of system equipped with adaptive damping controller based on HVDC system has appropriate performance when it is faced with faults and disturbance.
基金This project was supported by China Postdoctoral Science Foundation: "Research on Traffic Signal Control Method for Urban Intersection Based on Intelligent Techniques, 2001" .
文摘In this paper, a traffic signal control method based on fuzzy logic (FL), fuzzy-neuro (FN) and multi-objective genetic algorithms (MOGA) for an isolated four-approach intersection with through and left-turning movements is presented. This method has an adaptive signal timing ability, and can make adjustments to signal timing in response to observed changes.The 'urgency degree' term, which can describe the different user's demand for green time is used in decision-making by which strategy of signal timing can be determined. Using a fuzzy logic controller, we can determine whether to extend or terminate the current signal phase and select the sequences of phases. In this paper, a method based on fuzzy-neuro can be used to predict traffic parameters used in fuzzy logic controller. The feasibility of using a multi-objective genetic algorithm ( MOGA) to find a group of optimizing sets of parameters for fuzzy logic controller depending on different objects is also demonstrated. Simulation results show that the proposed methed is effecfive to adjust the signal timing in response to changing traffic conditions on a real-time basis, and the controller can produce lower vehicle delays and percentage of stopped vehicles than a traffic-actuated controller.
文摘针对在信号交叉口前由于车辆转向和换道操作频繁容易引发冲突、降低通行效率的问题,构建基于深度强化学习(DQN)的车辆群体控制模型,优化车辆车道选择.首先,利用传感器和网联设备等获取周围车辆及交叉口信号灯实时状态信息,基于共享DQN模型进行车道选择,并根据该结果计算下一时刻位置、速度和转向角;进一步以效率及安全性指标建立奖励函数对车道选择决策实施评价,将状态信息、决策信息及奖励评价信息整合形成经验,存入同一经验池用于共享DQN模型参数迭代更新;最后,使用SUMO(simulation of urban mobility)与Python联合仿真搭建不同交通流量环境对训练后的模型进行验证.研究表明:相较于SUMO中的车道选择模型,基于共享DQN模型的信号交叉口前车辆群体车道选择模型,在低、中、高流量测试场景的平均速度均有提高,交叉口前排队长度分别减少了9.6%、22.5%和24.8%.本文模型可以有效减少信号交叉口的排队长度、提高信号交叉口前的路段平均速度、增强车辆从上游到达交叉口的效率,为未来车路协同的应用提供理论借鉴和技术支持.
文摘在异质交通流背景下,针对交通信号调度与车辆轨迹规划协同问题,本文提出集信号和轨迹于一体的融合控制模型。该模型采用竞争双深度Q网络算法(Dueling Double Deep Q Network, D3QN),通过深度强化学习技术对交通信号和车辆轨迹进行同步优化,旨在实现交通效率与生态驾驶的双重目标,并基于SUMO(Simulation of Urban Mobility)仿真平台对模型进行全面验证。仿真结果表明:与基准模型相比,单一优化策略虽然能在一定程度上改善交叉口性能,但存在整体效率提升受限的问题;本文提出的融合控制模型结合了宏观交通流与微观车辆行为的优化,使车均延误降低66.99%,燃油消耗减少11.26%,同时CO_(2)等污染物排放量也显著减少。进一步的敏感性分析揭示了系统性能随网联自动驾驶汽车(Connected and Autonomous Vehicles, CAV)渗透率的变化规律修正:当渗透率达到一定水平后,性能提升幅度逐渐减小,且模型在不同交通流量条件下均展现出稳定的优化效果,这一结果证实了该控制方法在城市交叉口环境中的适应性和鲁棒性。
