The Cerebellum according to the Ouroboros Model,the ＂Interpolator Hypothesis＂

The Ouroboros Model offers a novel cognitive architecture with an algorithmic backbone of iterative and self-referential processing. All memory content is organized into meaningful pieces of data, i.e.,chunks and schemata, which are laid down as a kind of snapshots of all activations at a relevant point in time. This entails a grainy structure of memory content. Whereas a core process of ＂consumption＂ analysis can naturally be defined taking advantage of this parcellation, it necessitates interpolation for fine nuances, especially for the representation of intermediate values during transients. It is argued that, in the vertebrate brain, essential interpolation functionality is provided by the cerebellum. Findings concerning movement control and perception as well as the involvement of the cerebellum in more abstract, cognitive, tasks can be interpreted as reflecting a function of the cerebellum as a co-processor, i.e., a general-purpose interpolator. The cerebellum is thus boosting representations in cerebral cortex areas, which are reciprocally connected with cerebellar cortex areas. In this paper, it is sketched how the new ＂interpolator hypothesis＂ can explain manifold observations, effectively subsuming previous theories of the function of the cerebellum. Directly relating to the BICA challenge, a general-purpose interpolator is postulated as an efficient device providing fine-grained resolution for the representation of diverse content, potentially useful in any cognitive architecture.     

广角摄像机在机器人视觉定位中的应用

为了保证广角摄像机在现代机器人视觉定位应用中的准确性,以广角摄像机像素坐标的反畸变过程为研究对象,提出一种在畸变对应表的基础上使用中心迭代法以及区域插值法确定物体实际位置的方法。通过实验证明了该方法能够测量物体的位置,其精度能够满足工业使用要求。     

基于Voronoi图的连续反向最近邻查询

为解决动态环境中移动点的连续反向最近邻查询问题,将连续反向最近邻查询分为单色和双色2种情况进行研究。利用移动点Voronoi图,分别给出单色连续反向最近邻查询算法、双色连续反向最近邻查询算法以及相关定理,对算法正确性和可终止性进行证明,分析算法时间复杂性。按照移动点Voronoi图的拓扑结构是否改变分为2种情况,分析每种情况下候选所在区域的变化,在变化区域内进行Voronoi图的重构,得到对应的解决方法。在多数情况下,该算法只需生成局部移动点的Voronoi图即可找到结果,减小了连续反向最近邻查询的代价。     

基于ENVI软件的遥感影像内插方法偏差改正

ENVI是由遥感领域的科学家采用交互式数据语言IDL(Interactive Data Language)开发的一套功能强大的遥感图像处理软件,其能够快速、便捷、准确地从遥感影像中提取所需的信息。为了对影像进行更好的分析和处理,ENVI提供了一套基础的影像内插方法用于对影像进行插值处理。然而,由于理论上的缺陷,其采用的插值方法存在一定的偏差,从而导致后续的影像处理工作中引入了人为的误差影响。因此基于ENVI软件所用的影像内插方法,从理论入手指出其不足之处并对其模型进行改正。通过实验证明,改正后的内插方法能够获得更为准确的插值影像,从而为下一步的处理提供更为准确的数据结果。     

景观设计教学中空间逻辑分析与艺术情感表达的培养

随着社会的发展,人居环境日新月异,景观设计专业顺理成章的成为各大院校的热门专业。但课程设置体系中存在着诸多问题。未能做到因材施教。本文针对艺术院校景观专业的现存问题,提出课程教学应着手于小尺度空间。以工作模型作为主要研究载体,渐进的培养掌生综合分析问题的能力,提高学生的空间感知,运用构成艺术语言转译空间精神,营造个性的艺术空间。     

改进的局域均值分解在机械故障诊断中的应用

为了准确地提取出故障特征,把局域均值分解算法应用于机械故障振动信号的特征提取中.然而在对信号进行局域均值分解时,由于端点的趋势无法预知,在分解时会污染到整个信号序列,而且滑动平均造成了信号的过平滑处理,导致故障特征不能准确地提取.采用波形匹配解决端点处信号的走势,之后再利用线性插值求得信号的局域均值函数与包络函数,通过在Matlab环境下仿真调幅信号验证了该算法在端点处能很好的保持原有信号的特征;并采用实际的机械振动信号进行实验验证,引入端点效应评价指标,根据分解后的生产函数与原信号的相关系数验证了改进的局域均值分解更能准确地提取故障特征.     

基于分形理论的短期电力负荷预测*

电力负荷预测是电力系统规划的重要组成部分,也是电力系统经济运行的基础,其对电力系统规划和运行都极其重要。具有精确的预测方法可以保证能源的节约,避免资源的浪费。同时也可以缓解电能短缺所造成的重大影响。论文为了实现更好的电力负荷预测方法,研究了分形理论在电力负荷上的应用。将分形拼贴定理和分形插值法应用到电力负荷预测之中。提出了分形理论电力负荷预测模型,得到了分形插值预测模型。为了证明分形插值法的有效性,论文将分形预测模型与传统的BP神经网络电力负荷预测模型相比较。最后通过仿真算例说明了论文提出的分形插值预测模型预测精度更高,预测相对误差较小。并且分形理论可以很好地应用在电力系统负荷预测中,预测效果要好于BP神经网络预测模型。     

基于云计算的 Excel 数据处理效率优化研究*

为了提高Excel软件的数据处理效率,需建立一个能满足高精确度、高效率、易使用的计算环境。论文建立圆柱螺线优化插值模型,在Excel中通过五组测斜数据计算井眼曲率,再根据云计算可弹性增加计算节点的特点,将Sympho‐ny DE并行软件搭建在本地私有云环境下,通过增加计算节点数目对比运算效率与计算精度。结果表明：随着计算节点数的增加,运算效率大大提高,计算结果更加精确。     

优化变形能的平面多边形同构剖分

平面多边形间的同构三角剖分是平面形状渐进过渡与插值的基础,降低对应三角形的变形程度是获得高质量应用的关键.文中提出一种基于变形能优化的2个平面多边形的同构剖分算法,其中包含同构剖分生成和变形能最小化2个模块.首先根据用户指定的对应特征点对多边形进行顶点重采样,得到顶点一一对应的2个多边形;然后利用带约束的Delaunay剖分对其中的一个多边形进行三角化,得到源网格;再用重心坐标将源网格的内部顶点嵌入到另一个多边形得到同构剖分(目标网格);最后逐一检查三角形的变形能,对源网格中变形能超过阈值的三角形进行细分,用同构剖分模块生成新的目标网格.实验及数据统计分析表明,该算法可以得到较好的同构三角剖分,提升网格质量,并能很好地避免纹理细节失真.     

A surrogate-based optimization method with RBF neural network enhanced by linear interpolation and hybrid infill strategy

In engineering, it is computationally prohibitive to directly employ costly models in optimization. Therefore, surrogate-based optimization is developed to replace the accurate models with cheap surrogates during optimization for efficiency. The two key issues of surrogate-based optimization are how to improve the surrogate accuracy by making the most of the available training samples, and how to sequentially augment the training set with certain infill strategy so as to gradually improve the surrogate accuracy and guarantee the convergence to the real global optimum of the accurate model. To address these two issues, a radial basis function neural network (RBFNN) based optimization method is proposed in this paper. First, a linear interpolation (LI) based RBFNN modelling method, LI-RBFNN, is developed, which can enhance the RBFNN accuracy by enforcing the gradient match between the surrogate and the trend observed from the training samples. Second, a hybrid infill strategy is proposed, which uses the surrogate prediction error based surrogate lower bound as the optimization objective to locate the promising region and meanwhile employs a linear interpolation-based sequential sampling approach to improve the surrogate accuracy globally. Finally, extensive tests are investigated and the effectiveness and efficiency of the proposed methods are demonstrated.