This paper proposes an associative memory model based on a coupled system of Gaussian maps. A one-dimensional Gaussian map describes a discrete-time dynamical system, and the coupled system of Gaussian maps can genera...This paper proposes an associative memory model based on a coupled system of Gaussian maps. A one-dimensional Gaussian map describes a discrete-time dynamical system, and the coupled system of Gaussian maps can generate various phenomena including asymmetric fixed and periodic points. The Gaussian associative memory can effectively recall one of the stored patterns, which were triggered by an input pattern by associating the asymmetric two-periodic points observed in the coupled system with the binary values of output patterns. To investigate the Gaussian associative memory model, we formed its reduced model and analyzed the bifurcation structure. Pseudo-patterns were observed for the proposed model along with other conventional associative memory models, and the obtained patterns were related to the high-order or quasi-periodic points and the chaotic trajectories. In this paper, the structure of the Gaussian associative memory and its reduced models are introduced as well as the results of the bifurcation analysis are presented. Furthermore, the output sequences obtained from simulation of the recalling process are presented. We discuss the mechanism and the characteristics of the Gaussian associative memory based on the results of the analysis and the simulations conducted.展开更多
By combining of the second gradient operator, the second class of integral theorems, the Gaussian-curvature-based integral theorems and the Gaussian （or spherical） mapping, a series of invariants or geometric conser...By combining of the second gradient operator, the second class of integral theorems, the Gaussian-curvature-based integral theorems and the Gaussian （or spherical） mapping, a series of invariants or geometric conservation quantities under Gaussian （or spherical） mapping are revealed. From these mapping invariants important transformations between original curved surface and the spherical surface are derived. The potential applications of these invariants and transformations to geometry are discussed展开更多
针对现有SLAM算法在渲染真实感、内存占用和复杂场景适应性方面的不足,提出了一种基于3D Gaussians Splatting的密集SLAM算法——TIGO-SLAM(tensor illumination and Gaussian optimization for indoor SLAM)。该算法集成了基于神经网...针对现有SLAM算法在渲染真实感、内存占用和复杂场景适应性方面的不足,提出了一种基于3D Gaussians Splatting的密集SLAM算法——TIGO-SLAM(tensor illumination and Gaussian optimization for indoor SLAM)。该算法集成了基于神经网络的张量光照模型、改进的高斯遮罩算法以及网格化神经场的几何和颜色属性表示,具体创新包括:a)基于神经网络的张量光照模型,增强镜面反射与漫反射效果,从而提升了渲染真实感;b)通过冗余高斯剔除机制改进高斯遮罩算法,有效降低了内存消耗并提高了实时性;c)结合网格化神经场的几何与颜色属性表示,采用优化的码本存储方式,显著提高了渲染性能和场景重建精度。实验结果表明,TIGO-SLAM在室内场景渲染、内存优化和复杂场景适应性方面均有显著提升,特别是在动态室内环境中的渲染和重建效果表现突出,为SLAM技术在资源受限设备上的应用提供了新的可能。展开更多
光伏阵列在局部阴影条件下P-U曲线会出现多个峰值,传统的粒子群优化PSO(particle swarm optimization)算法无法快速精确地搜寻到最大功率点。针对这种情况,本文提出1种基于混沌映射和高斯扰动的改进粒子群优化算法最大功率点跟踪MPPT(ma...光伏阵列在局部阴影条件下P-U曲线会出现多个峰值,传统的粒子群优化PSO(particle swarm optimization)算法无法快速精确地搜寻到最大功率点。针对这种情况,本文提出1种基于混沌映射和高斯扰动的改进粒子群优化算法最大功率点跟踪MPPT(maximum power point tracking)控制策略。首先引入混沌Sine映射构造1种非线性随机递增惯性权重,并在粒子群的“个体认知”部分引入高斯扰动,同时利用对数函数构造学习因子,形成基于混沌映射和高斯扰动的改进粒子群算法;通过对6种典型单峰、多峰函数的测试,证明该算法收敛速度更快,不易陷入局部最优;将算法应用于MPPT控制中,并进一步通过不同算法MPPT控制进行对比仿真研究。对比仿真结果表明:在均匀光照强度、局部静态遮荫和动态遮荫3种情况下,基于混沌映射和高斯扰动的改进粒子群优化算法MPPT控制策略均具有更快的收敛速度和更小的搜索振荡幅度,能准确地搜寻到最大功率点,具有更高的寻优精度,从而提高了MPPT系统的发电效率。展开更多
文摘This paper proposes an associative memory model based on a coupled system of Gaussian maps. A one-dimensional Gaussian map describes a discrete-time dynamical system, and the coupled system of Gaussian maps can generate various phenomena including asymmetric fixed and periodic points. The Gaussian associative memory can effectively recall one of the stored patterns, which were triggered by an input pattern by associating the asymmetric two-periodic points observed in the coupled system with the binary values of output patterns. To investigate the Gaussian associative memory model, we formed its reduced model and analyzed the bifurcation structure. Pseudo-patterns were observed for the proposed model along with other conventional associative memory models, and the obtained patterns were related to the high-order or quasi-periodic points and the chaotic trajectories. In this paper, the structure of the Gaussian associative memory and its reduced models are introduced as well as the results of the bifurcation analysis are presented. Furthermore, the output sequences obtained from simulation of the recalling process are presented. We discuss the mechanism and the characteristics of the Gaussian associative memory based on the results of the analysis and the simulations conducted.
基金Project supported by the National Natural Science Foundation of China (No.10572076)
文摘By combining of the second gradient operator, the second class of integral theorems, the Gaussian-curvature-based integral theorems and the Gaussian （or spherical） mapping, a series of invariants or geometric conservation quantities under Gaussian （or spherical） mapping are revealed. From these mapping invariants important transformations between original curved surface and the spherical surface are derived. The potential applications of these invariants and transformations to geometry are discussed
文摘针对现有SLAM算法在渲染真实感、内存占用和复杂场景适应性方面的不足,提出了一种基于3D Gaussians Splatting的密集SLAM算法——TIGO-SLAM(tensor illumination and Gaussian optimization for indoor SLAM)。该算法集成了基于神经网络的张量光照模型、改进的高斯遮罩算法以及网格化神经场的几何和颜色属性表示,具体创新包括:a)基于神经网络的张量光照模型,增强镜面反射与漫反射效果,从而提升了渲染真实感;b)通过冗余高斯剔除机制改进高斯遮罩算法,有效降低了内存消耗并提高了实时性;c)结合网格化神经场的几何与颜色属性表示,采用优化的码本存储方式,显著提高了渲染性能和场景重建精度。实验结果表明,TIGO-SLAM在室内场景渲染、内存优化和复杂场景适应性方面均有显著提升,特别是在动态室内环境中的渲染和重建效果表现突出,为SLAM技术在资源受限设备上的应用提供了新的可能。
文摘光伏阵列在局部阴影条件下P-U曲线会出现多个峰值,传统的粒子群优化PSO(particle swarm optimization)算法无法快速精确地搜寻到最大功率点。针对这种情况,本文提出1种基于混沌映射和高斯扰动的改进粒子群优化算法最大功率点跟踪MPPT(maximum power point tracking)控制策略。首先引入混沌Sine映射构造1种非线性随机递增惯性权重,并在粒子群的“个体认知”部分引入高斯扰动,同时利用对数函数构造学习因子,形成基于混沌映射和高斯扰动的改进粒子群算法;通过对6种典型单峰、多峰函数的测试,证明该算法收敛速度更快,不易陷入局部最优;将算法应用于MPPT控制中,并进一步通过不同算法MPPT控制进行对比仿真研究。对比仿真结果表明:在均匀光照强度、局部静态遮荫和动态遮荫3种情况下,基于混沌映射和高斯扰动的改进粒子群优化算法MPPT控制策略均具有更快的收敛速度和更小的搜索振荡幅度,能准确地搜寻到最大功率点,具有更高的寻优精度,从而提高了MPPT系统的发电效率。
