An adaptive weighted stereo matching algorithm with multilevel and bidirectional dynamic programming based on ground control points (GCPs) is presented. To decrease time complexity without losing matching precision, u...An adaptive weighted stereo matching algorithm with multilevel and bidirectional dynamic programming based on ground control points (GCPs) is presented. To decrease time complexity without losing matching precision, using a multilevel search scheme, the coarse matching is processed in typical disparity space image, while the fine matching is processed in disparity-offset space image. In the upper level, GCPs are obtained by enhanced volumetric iterative algorithm enforcing the mutual constraint and the threshold constraint. Under the supervision of the highly reliable GCPs, bidirectional dynamic programming framework is employed to solve the inconsistency in the optimization path. In the lower level, to reduce running time, disparity-offset space is proposed to efficiently achieve the dense disparity image. In addition, an adaptive dual support-weight strategy is presented to aggregate matching cost, which considers photometric and geometric information. Further, post-processing algorithm can ameliorate disparity results in areas with depth discontinuities and related by occlusions using dual threshold algorithm, where missing stereo information is substituted from surrounding regions. To demonstrate the effectiveness of the algorithm, we present the two groups of experimental results for four widely used standard stereo data sets, including discussion on performance and comparison with other methods, which show that the algorithm has not only a fast speed, but also significantly improves the efficiency of holistic optimization.展开更多
Nonlinear loads in the power distribution system cause non-sinusoidal currents and voltages with harmonic components.Shunt active filters(SAF) with current controlled voltage source inverters(CCVSI) are usually used t...Nonlinear loads in the power distribution system cause non-sinusoidal currents and voltages with harmonic components.Shunt active filters(SAF) with current controlled voltage source inverters(CCVSI) are usually used to obtain balanced and sinusoidal source currents by injecting compensation currents.However,CCVSI with traditional controllers have a limited transient and steady state performance.In this paper,we propose an adaptive dynamic programming(ADP) controller with online learning capability to improve transient response and harmonics.The proposed controller works alongside existing proportional integral(PI) controllers to efficiently track the reference currents in the d-q domain.It can generate adaptive control actions to compensate the PI controller.The proposed system was simulated under different nonlinear(three-phase full wave rectifier) load conditions.The performance of the proposed approach was compared with the traditional approach.We have also included the simulation results without connecting the traditional PI control based power inverter for reference comparison.The online learning based ADP controller not only reduced average total harmonic distortion by 18.41%,but also outperformed traditional PI controllers during transients.展开更多
In this paper we discuss policy iteration methods for approximate solution of a finite-state discounted Markov decision problem, with a focus on feature-based aggregation methods and their connection with deep reinfor...In this paper we discuss policy iteration methods for approximate solution of a finite-state discounted Markov decision problem, with a focus on feature-based aggregation methods and their connection with deep reinforcement learning schemes. We introduce features of the states of the original problem, and we formulate a smaller "aggregate" Markov decision problem, whose states relate to the features. We discuss properties and possible implementations of this type of aggregation, including a new approach to approximate policy iteration. In this approach the policy improvement operation combines feature-based aggregation with feature construction using deep neural networks or other calculations. We argue that the cost function of a policy may be approximated much more accurately by the nonlinear function of the features provided by aggregation, than by the linear function of the features provided by neural networkbased reinforcement learning, thereby potentially leading to more effective policy improvement.展开更多
飞轮混合动力系统(planetary gear set based flywheel hybrid electric powertrain,PGS-FHEP)在提高车辆性能和能源利用率方面具有巨大优势。本文研究对其主要部件进行了设计和匹配,并在等效能耗最小控制策略(equivalent consumption m...飞轮混合动力系统(planetary gear set based flywheel hybrid electric powertrain,PGS-FHEP)在提高车辆性能和能源利用率方面具有巨大优势。本文研究对其主要部件进行了设计和匹配,并在等效能耗最小控制策略(equivalent consumption minimization strategy,ECMS)的基础上,引入动态规划(dynamic programming,DP)控制策略获取最优电池荷电状态(state of charge,SOC)轨迹,通过实时调整遗传算法(genetic algorithm,GA)求得的初始最优等效因子,确保实际SOC轨迹与最优轨迹相符,从而搭建了一种可实时控制的自适应等效能耗最小控制策略(adaptive equivalent consumption minimization strategy,A-ECMS),最终在中国轻型商用车行驶工况(China light-duty commercial vehicle test cycle,CLTC-C)工况下对三种控制策略进行了仿真对比。结果表明,在A-ECMS控制下,较传统ECMS相比,加装PGS-FHEP的飞轮混合动力汽车(flywheel hybrid electric vehicle,FHEV)综合能耗降低了2.51%,控制效果更接近DP控制策略;系统能量回收率可达57.72%,其中,飞轮以机械能形式回收占比23.64%。此外,能量回收过程中,飞轮的参与使电池的峰值功率显著降低。展开更多
在分析典型冷热电联供(combined cooling,heat and power,CCHP)系统的基础上,提出描述其组成和结构的母线式结构,并围绕该系统结构设计了微网调度优化模型构架。在该结构中,选取电气、烟气、蒸汽、热水、空气作为基本母线,与源、负荷、...在分析典型冷热电联供(combined cooling,heat and power,CCHP)系统的基础上,提出描述其组成和结构的母线式结构,并围绕该系统结构设计了微网调度优化模型构架。在该结构中,选取电气、烟气、蒸汽、热水、空气作为基本母线,与源、负荷、储能和转换装置联接形成微网。使用该结构对各设备进行独立建模,有助于CCHP系统的灵活配置和通用建模。围绕该结构,建立联供型微网日前动态经济调度的0-1混合整数线性规划模型,最后通过测试算例证实了所提框架的合理性和有效性。展开更多
基金supported by the National Natural Science Foundation of China(No.60605023,60775048)Specialized Research Fund for the Doctoral Program of Higher Education(No.20060141006)
文摘An adaptive weighted stereo matching algorithm with multilevel and bidirectional dynamic programming based on ground control points (GCPs) is presented. To decrease time complexity without losing matching precision, using a multilevel search scheme, the coarse matching is processed in typical disparity space image, while the fine matching is processed in disparity-offset space image. In the upper level, GCPs are obtained by enhanced volumetric iterative algorithm enforcing the mutual constraint and the threshold constraint. Under the supervision of the highly reliable GCPs, bidirectional dynamic programming framework is employed to solve the inconsistency in the optimization path. In the lower level, to reduce running time, disparity-offset space is proposed to efficiently achieve the dense disparity image. In addition, an adaptive dual support-weight strategy is presented to aggregate matching cost, which considers photometric and geometric information. Further, post-processing algorithm can ameliorate disparity results in areas with depth discontinuities and related by occlusions using dual threshold algorithm, where missing stereo information is substituted from surrounding regions. To demonstrate the effectiveness of the algorithm, we present the two groups of experimental results for four widely used standard stereo data sets, including discussion on performance and comparison with other methods, which show that the algorithm has not only a fast speed, but also significantly improves the efficiency of holistic optimization.
文摘Nonlinear loads in the power distribution system cause non-sinusoidal currents and voltages with harmonic components.Shunt active filters(SAF) with current controlled voltage source inverters(CCVSI) are usually used to obtain balanced and sinusoidal source currents by injecting compensation currents.However,CCVSI with traditional controllers have a limited transient and steady state performance.In this paper,we propose an adaptive dynamic programming(ADP) controller with online learning capability to improve transient response and harmonics.The proposed controller works alongside existing proportional integral(PI) controllers to efficiently track the reference currents in the d-q domain.It can generate adaptive control actions to compensate the PI controller.The proposed system was simulated under different nonlinear(three-phase full wave rectifier) load conditions.The performance of the proposed approach was compared with the traditional approach.We have also included the simulation results without connecting the traditional PI control based power inverter for reference comparison.The online learning based ADP controller not only reduced average total harmonic distortion by 18.41%,but also outperformed traditional PI controllers during transients.
基金Supported by National Natural Science Foundation of China(61304079,61125306,61034002)the Open Research Project from SKLMCCS(20120106)+1 种基金the Fundamental Research Funds for the Central Universities(FRF-TP-13-018A)the China Postdoctoral Science.Foundation(201_3M_5305_27)
文摘In this paper we discuss policy iteration methods for approximate solution of a finite-state discounted Markov decision problem, with a focus on feature-based aggregation methods and their connection with deep reinforcement learning schemes. We introduce features of the states of the original problem, and we formulate a smaller "aggregate" Markov decision problem, whose states relate to the features. We discuss properties and possible implementations of this type of aggregation, including a new approach to approximate policy iteration. In this approach the policy improvement operation combines feature-based aggregation with feature construction using deep neural networks or other calculations. We argue that the cost function of a policy may be approximated much more accurately by the nonlinear function of the features provided by aggregation, than by the linear function of the features provided by neural networkbased reinforcement learning, thereby potentially leading to more effective policy improvement.
文摘飞轮混合动力系统(planetary gear set based flywheel hybrid electric powertrain,PGS-FHEP)在提高车辆性能和能源利用率方面具有巨大优势。本文研究对其主要部件进行了设计和匹配,并在等效能耗最小控制策略(equivalent consumption minimization strategy,ECMS)的基础上,引入动态规划(dynamic programming,DP)控制策略获取最优电池荷电状态(state of charge,SOC)轨迹,通过实时调整遗传算法(genetic algorithm,GA)求得的初始最优等效因子,确保实际SOC轨迹与最优轨迹相符,从而搭建了一种可实时控制的自适应等效能耗最小控制策略(adaptive equivalent consumption minimization strategy,A-ECMS),最终在中国轻型商用车行驶工况(China light-duty commercial vehicle test cycle,CLTC-C)工况下对三种控制策略进行了仿真对比。结果表明,在A-ECMS控制下,较传统ECMS相比,加装PGS-FHEP的飞轮混合动力汽车(flywheel hybrid electric vehicle,FHEV)综合能耗降低了2.51%,控制效果更接近DP控制策略;系统能量回收率可达57.72%,其中,飞轮以机械能形式回收占比23.64%。此外,能量回收过程中,飞轮的参与使电池的峰值功率显著降低。
文摘在分析典型冷热电联供(combined cooling,heat and power,CCHP)系统的基础上,提出描述其组成和结构的母线式结构,并围绕该系统结构设计了微网调度优化模型构架。在该结构中,选取电气、烟气、蒸汽、热水、空气作为基本母线,与源、负荷、储能和转换装置联接形成微网。使用该结构对各设备进行独立建模,有助于CCHP系统的灵活配置和通用建模。围绕该结构,建立联供型微网日前动态经济调度的0-1混合整数线性规划模型,最后通过测试算例证实了所提框架的合理性和有效性。
