The sense of being within a three-dimensional (3D) space and interacting with virtual 3D objects in a computer-generated virtual environment (VE) often requires essential image, vision and sensor signal processing...The sense of being within a three-dimensional (3D) space and interacting with virtual 3D objects in a computer-generated virtual environment (VE) often requires essential image, vision and sensor signal processing techniques such as differentiating and denoising. This paper describes novel implementations of the Gaussian filtering for characteristic signal extraction and waveletbased image denoising algorithms that run on the graphics processing unit (GPU). While significant acceleration over standard CPU implementations is obtained through exploiting data parallelism provided by the modern programmable graphics hardware, the CPU can be freed up to run other computations more efficiently such as artificial intelligence (AI) and physics. The proposed GPU-based Gaussian filtering can extract surface information from a real object and provide its material features for rendering and illumination. The wavelet-based signal denoising for large size digital images realized in this project provided better realism for VE visualization without sacrificing real-time and interactive performances of an application.展开更多
虚拟同步发电机(virtual synchronous generator,VSG)在引入同步机二阶转子运动方程,增大电力系统等效惯量的同时,也引入了同步发电机的振荡特性,有功低频振荡等动态稳定性问题也随之而来。引入调速侧电力系统稳定器(governor power sys...虚拟同步发电机(virtual synchronous generator,VSG)在引入同步机二阶转子运动方程,增大电力系统等效惯量的同时,也引入了同步发电机的振荡特性,有功低频振荡等动态稳定性问题也随之而来。引入调速侧电力系统稳定器(governor power system stabilizer,GPSS)能有效抑制VSG的功率低频振荡,但其在超调量及调节时间方面的控制效果仍有待提高。通过建立VSG的小信号模型从极点配置角度分析其稳定性,揭示基于GPSS的VSG控制策略在功率动态响应上存在较高超调和较长调节时间的原因。基于此,参考GPSS控制思想,提出了一种基于超前滞后环节附加前馈阻尼补偿的虚拟同步发电机控制策略。并从理论上分析验证了所提控制策略在不影响系统稳态特性的前提下,能够提供调整自由度更高的正阻尼,在有效地抑制功率超调的同时提高了系统的调节速度,从而更好地抑制了有功功率的低频振荡。最后通过MATLAB/Simulink进行对比仿真,仿真结果与理论分析结果一致,证明了所提控制策略的正确性和有效性。展开更多
国防科技大学自主研制的高性能加速器采用中央处理器(CPU)+通用数字信号处理器(GPDSP)的片上异构融合架构,使用超长指令集(VLIW)+单指令多数据流(SIMD)的向量化结构的GPDSP是峰值性能主要支撑的加速核。主流编译器在密集的数据计算指令...国防科技大学自主研制的高性能加速器采用中央处理器(CPU)+通用数字信号处理器(GPDSP)的片上异构融合架构,使用超长指令集(VLIW)+单指令多数据流(SIMD)的向量化结构的GPDSP是峰值性能主要支撑的加速核。主流编译器在密集的数据计算指令排布、为指令静态分配硬件执行单元、GPDSP特有的向量指令等方面不能很好地支持高性能加速器。基于低级虚拟器(LLVM)编译框架,在前寄存器分配调度阶段,结合峰值寄存器压力感知方法(PERP)、蚁群优化(ACO)算法与GPDSP结构特点,优化代价模型,设计支持寄存器压力感知的指令调度模块;在后寄存器分配阶段提出支持静态功能单元分配的指令调度策略,通过冲突检测机制保证功能单元分配的正确性,为指令并行执行提供软件基础;在后端封装一系列丰富且规整的向量指令接口,实现对GPDSP向量指令的支持。实验结果表明,所提出的LLVM编译架构优化方法从功能和性能上实现了对GPDSP的良好支撑,GCC testsuite测试整体性能平均加速比为4.539,SPEC CPU 2017浮点测试整体性能平均加速比为4.49,SPEC CPU 2017整型测试整体性能平均加速比为3.24,使用向量接口的向量程序实现了平均97.1%的性能提升率。展开更多
Traffic jam in large signalized road network presents a complex nature.In order to reveal the jam characteristics,two indexes,SVS(speed of virtual signal) and VOS(velocity of spillover),were proposed respectively.SVS ...Traffic jam in large signalized road network presents a complex nature.In order to reveal the jam characteristics,two indexes,SVS(speed of virtual signal) and VOS(velocity of spillover),were proposed respectively.SVS described the propagation of queue within a link while VOS reflected the spillover velocity of vehicle queue.Based on the two indexes,network jam simulation was carried out on a regular signalized road network.The simulation results show that:1) The propagation of traffic congestion on a signalized road network can be classified into two stages:virtual split driven stage and flow rate driven stage.The former stage is characterized by decreasing virtual split while the latter only depends on flow rate; 2) The jam propagation rate and direction are dependent on traffic demand distribution and other network parameters.The direction with higher demand gets more chance to be jammed.Our findings can serve as the basis of the prevention of the formation and propagation of network traffic jam.展开更多
基金supported by Research Funding of Huddersfield University:GPU-based High Performance Computing for Signal Processing (No. 1008/REU117)
文摘The sense of being within a three-dimensional (3D) space and interacting with virtual 3D objects in a computer-generated virtual environment (VE) often requires essential image, vision and sensor signal processing techniques such as differentiating and denoising. This paper describes novel implementations of the Gaussian filtering for characteristic signal extraction and waveletbased image denoising algorithms that run on the graphics processing unit (GPU). While significant acceleration over standard CPU implementations is obtained through exploiting data parallelism provided by the modern programmable graphics hardware, the CPU can be freed up to run other computations more efficiently such as artificial intelligence (AI) and physics. The proposed GPU-based Gaussian filtering can extract surface information from a real object and provide its material features for rendering and illumination. The wavelet-based signal denoising for large size digital images realized in this project provided better realism for VE visualization without sacrificing real-time and interactive performances of an application.
文摘虚拟同步发电机(virtual synchronous generator,VSG)在引入同步机二阶转子运动方程,增大电力系统等效惯量的同时,也引入了同步发电机的振荡特性,有功低频振荡等动态稳定性问题也随之而来。引入调速侧电力系统稳定器(governor power system stabilizer,GPSS)能有效抑制VSG的功率低频振荡,但其在超调量及调节时间方面的控制效果仍有待提高。通过建立VSG的小信号模型从极点配置角度分析其稳定性,揭示基于GPSS的VSG控制策略在功率动态响应上存在较高超调和较长调节时间的原因。基于此,参考GPSS控制思想,提出了一种基于超前滞后环节附加前馈阻尼补偿的虚拟同步发电机控制策略。并从理论上分析验证了所提控制策略在不影响系统稳态特性的前提下,能够提供调整自由度更高的正阻尼,在有效地抑制功率超调的同时提高了系统的调节速度,从而更好地抑制了有功功率的低频振荡。最后通过MATLAB/Simulink进行对比仿真,仿真结果与理论分析结果一致,证明了所提控制策略的正确性和有效性。
文摘国防科技大学自主研制的高性能加速器采用中央处理器(CPU)+通用数字信号处理器(GPDSP)的片上异构融合架构,使用超长指令集(VLIW)+单指令多数据流(SIMD)的向量化结构的GPDSP是峰值性能主要支撑的加速核。主流编译器在密集的数据计算指令排布、为指令静态分配硬件执行单元、GPDSP特有的向量指令等方面不能很好地支持高性能加速器。基于低级虚拟器(LLVM)编译框架,在前寄存器分配调度阶段,结合峰值寄存器压力感知方法(PERP)、蚁群优化(ACO)算法与GPDSP结构特点,优化代价模型,设计支持寄存器压力感知的指令调度模块;在后寄存器分配阶段提出支持静态功能单元分配的指令调度策略,通过冲突检测机制保证功能单元分配的正确性,为指令并行执行提供软件基础;在后端封装一系列丰富且规整的向量指令接口,实现对GPDSP向量指令的支持。实验结果表明,所提出的LLVM编译架构优化方法从功能和性能上实现了对GPDSP的良好支撑,GCC testsuite测试整体性能平均加速比为4.539,SPEC CPU 2017浮点测试整体性能平均加速比为4.49,SPEC CPU 2017整型测试整体性能平均加速比为3.24,使用向量接口的向量程序实现了平均97.1%的性能提升率。
基金Project(2012CB725402)supported by the State Key Development Program for Basic Research of ChinaProject(2012MS21175)supported by the National Science Foundation for Post-doctoral Scientists of ChinaProject(Bsh1202056)supported by the Excellent Postdoctoral Science Foundation of Zhejiang Province,China
文摘Traffic jam in large signalized road network presents a complex nature.In order to reveal the jam characteristics,two indexes,SVS(speed of virtual signal) and VOS(velocity of spillover),were proposed respectively.SVS described the propagation of queue within a link while VOS reflected the spillover velocity of vehicle queue.Based on the two indexes,network jam simulation was carried out on a regular signalized road network.The simulation results show that:1) The propagation of traffic congestion on a signalized road network can be classified into two stages:virtual split driven stage and flow rate driven stage.The former stage is characterized by decreasing virtual split while the latter only depends on flow rate; 2) The jam propagation rate and direction are dependent on traffic demand distribution and other network parameters.The direction with higher demand gets more chance to be jammed.Our findings can serve as the basis of the prevention of the formation and propagation of network traffic jam.