Pattern Recognition With Weighted Complex Networks

In this paper we introduce a weighted complex networks model to investigate and recognize structures of patterns. The regular treating in pattern recognition models is to describe each pattern as a high-dimensional vector which however is insufficient to express the structural information. Thus, a number of methods are developed to extract the structural information, such as different feature extraction algorithms used in pre-processing steps, or the local receptive fields in convolutional networks. In our model, each pattern is attributed to a weighted complex network, whose topology represents the structure of that pattern. Based upon the training samples, we get several prototypal complex networks which could stand for the general structural characteristics of patterns in different categories. We use these prototypal networks to recognize the unknown patterns. It is an attempt to use complex networks in pattern recognition, and our result shows the potential for real-world pattern recognition. A spatial parameter is introduced to get the optimal recognition accuracy, and it remains constant insensitive to the amount of training samples. We have discussed the interesting properties of the prototypal networks. An approximate linear relation is found between the strength and color of vertexes, in which we could compare the structural difference between each category. We have visualized these prototypal networks to show that their topology indeed represents the common characteristics of patterns. We have also shown that the asymmetric strength distribution in these prototypal networks brings high robustness for recognition. Our study may cast a light on understanding the mechanism of the biologic neuronal systems in object recognition as well.

Method for improving classification performance of neural network based on fuzzy input and network inversion

In order to effectively improve the classification performance of neural network, first architecture of fuzzy neural network with fuzzy input was proposed. Next a cost function of fuzzy outputs and non-fuzzy targets was defined. Then a learning algorithm from the cost function for adjusting weights was derived. And then the fuzzy neural network was inversed and fuzzified inversion algorithm was proposed. Finally, computer simulations on real-world pattern classification problems examine the effectives of the proposed approach. The experiment results show that the proposed approach has the merits of high learning efficiency, high classification accuracy and high generalization capability.

Practical Evaluation of Security against Generalized Interpolation Attack

Interpolation attack was presented by Jakobsen and Knudsen at FSE'97. Interpolation attack is effective against ciphers that have a certain algebraic structure like the PURE cipher which is a prototype cipher, but it is difficult to apply the attack to real-world ciphers. This difficulty is due to the difficulty of deriving a low degree polynomial relation between ciphertexts and plaintexts. In other words, it is difficult to evaluate the security against interpolation attack. This paper generalizes the interpolation attack. The generalization makes easier to evaluate the security against interpolation attack. We call the generalized interpolation attack linear sum attack. We present an algorithm that evaluates the security of byte-oriented ciphers against linear sum attack. Moreover, we show the relationship between linear sum attack and higher order differential attack. In addition, we show the security of CRYPTON, E2, and RIJNDAEL against linear sum attack using the algorithm.

an efficient method to generate feasible paths for basis path testing

Basis path testing is a very powerful structural testing criterion. The number of test paths equals to the cyclomatic complexity of program defined by McCabe. Traditional test generation methods select the paths either without consideration of the constraints of variables or interactively. In this note, an efficient method is presented to generate a set of feasible basis paths. The experiments show that this method can generate feasible basis paths for real-world C programs automatically in acceptable time.

Deterministic Column-Based Matrix Decomposition

In this paper, we propose a deterministic column-based matrix decomposition method. Conventional column-based matrix decomposition (CX) computes the columns by randomly sampling columns of the data matrix. Instead, the newly proposed method (termed as CX_D) selects columns in a deterministic manner, which well approximates singular value decomposition. The experimental results well demonstrate the power and the advantages of the proposed method upon three real-world data sets.

Effect of information transmission on cooperative behavior

Considering the fact, in the real world, that information is transmitted with a time delay, we study an evolutionary spatial prisoner's dilemma game where agents update strategies according to certain information that they have learned. In our study, the game dynamics are classified by the modes of information learning as well as game interaction, and four different combinations, i.e. the mean-field case, case I, case II and local case, are studied comparatively. It is found that the time delay in case II smoothes the phase transition from the absorbing states of C (or D) to their mixing state, and promotes cooperation for most parameter values. Our work provides insights into the temporal behavior of information and the memory of the system, and may be helpful in understanding the cooperative behavior induced by the time delay in social and biological systems.

The effects of sediment transport on grain-size distributions

Changes in statistics (mean, sorting, and skewness) describing grain-size distributions have long been used to speculate on the direction of sediment transport. We present a simple model whereby the distributions of sediment in transport are related to their source by a sediment transfer function which defines the relative probability that a grain within each particular class interval will be eroded and transported. A variety of empirically derived transfer functions exhibit negatively skewed distributions (on a phi scale). Thus, when a sediment is being eroded, the probability of any grain going into transport increases with diminishing grain size throughout more than half of its size range. This causes the sediment in transport to be finer and more negatively skewed than its source, whereas the remaining sediment (a lag) must become relatively coarser and more positively skewed. Flume experiments show that the distributions of transfer functions change from having a highly negative skewness to being nearly symmetrical (although still negatively skewed) as the energy of the transporting process increases. We call the two extremes low-energy and high-energy transfer functions , respectively. In an expanded sediment-transport model, successive deposits in the direction of transport are related by a combination of two transfer functions. If energy is decreasing and the transfer functions have low-energy distributions, successive deposits will become finer and more negatively skewed. If, however, energy is decreasing, but the initial transfer function has a high-energy distribution, successive deposits will become coarser and more positively skewed. The variance of the distributions of lags, sediment in transport, and successive deposits in the down-current direction must eventually decrease (i.e., the sediments will become better sorted). We demonstrate that it is possible for variance first to increase, but suggest that, in reality, an increasing variance in the direction of transport will seldom be observed, particularly when grain-size distributions are described in phi units. This model describing changes in sediment distributions was tested in a variety of environments where the transport direction was known. The results indicate that the model has real-world validity and can provide a method to predict the directions of sediment transport

GIS接受外部空间数据的初步探讨——兼论ARC/INFO~的数据接受方式

数据是地理信息系统 (GIS)应用的核心。现实世界的数据具有普遍的多样性 ,关于 GIS接受不同数据的研究已成为当前 GIS研究中的一个难点和热点。从常见空间数据类型的格式、GIS接受外部空间数据的方式以及 GIS接受外部空间数据中的数据精度、比例尺、坐标变换等几个方面 ,对外部空间数据处理系统的数据向 GIS转换的问题进行了探讨 ,同时以通用 GIS工具软件 ARC/INFO为例分析了其接受外部矢量空间数据的方式。

ARC/INFOR~接受Surfer图文数据的实现

数据是地理信息系统 (GIS)应用的核心。现实世界的数据具有普遍的多样性 ,关于 GIS接受不同数据的研究已成为当前 GIS研究中的一个难点和热点。本文根据通用 GIS工具软件 ARC/INFO接受外部空间数据的方式和图形分析软件 Surfer的数据格式特征 ,运用特定的处理方法实现了由 Surfer格式数据向 ARC/ INFO矢量格式数据 (Coverage)的转换 ,使 ARC/ INFO接受外部数据格式的范围拓宽 ,数据处理能力增强。

虚拟现实辅助机器人遥操作技术研究

本文以水下机器人的遥操作作业为应用背景 ,提出并实现了虚拟现实技术和视觉感知信息辅助机器人遥操作实验系统 .该系统使用了 CAD模型和立体视觉信息完成遥操作机器人及其作业环境的几何建模和运动学建模 ,实现了虚拟作业环境的生成和实时动态图形显示 .采用了基于立体视觉的虚拟环境与真实环境的一致性校正、图形图像叠加、作业体与环境位姿关系建立、基于网络的监控通讯等关键技术 .在这个实验系统中 ,操作人员可利用所生成的虚拟环境 ,在多视点、多窗口作业状态图形和图像显示帮助下 ,实时动态地进行作业观测与机器人遥操作与运动规划 ,为先进遥操作机器人系统的实现提供了经验和关键技术 .

基于速度矢量可行度的移动机器人多行为综合决策方法

为了提高机器人对真实环境的适应能力 ,基于行为思想越来越多地被用于自主机器人的在线运动决策 .由此 ,产生了多行为综合管理问题 .本文分析了常用的基于矢量合成的并行行为的综合方法 .在此基础上 ,提出基于速度矢量可行度的自主移动机器人多行为综合决策方法 .该方法可较完整地保留子行为的决策意图 ,得到更合理的行为综合结果 .

图象噪声的模糊聚类平滑方法

模糊C-means算法在聚类分析中已得到了成功的应用,本文提出一种利用模糊C-means算法消除噪声的新方法。一般来说,图象中的噪声点就是其灰度值与其周围象素的灰度值之差超过某个门限值的点。根据这个事实,首先利用模糊C-means算法分类,再利用标准核函数检测出噪声点,然后将噪声点去掉。由于只修改噪声点处的象素灰度值,而对于其它象素的灰度值不予改变,所以本算法能够很好地保护细节和边缘。本方法每次处理3×3个点,而以往的方法只能每次处理一个点,所以本方法能提高运算速度。文中给出了利用本方法对实际图象处理的结果,并与梯度倒数权值法进行了定量的比较。

一种模糊petri网仿真工具的设计实现

文章将面向对象思想应用于模糊Petri网仿真工具的设计和实现过程,探讨了模糊Petri网建模与仿真的可视化问题,提出了基于网格可视化技术解决方案及具体实现方法。通过对变速箱的自动建模和诊断仿真实例,证明该系统具有良好的实用性,为模糊Petri网理论的普遍应用提供了工具平台。

空间电流体系地磁反演及理论研究

The space currents definitely take effects on electromagnetic environment and also are scientific highlight in the space research. Space currents as a momentum and energy provider to Geospace Storm, disturb the varied part of geomagnetic field, distort magnetospheric configuration and furthermore take control of the coupling between magnetosphere and ionosphere. Due to both academic and commercial objectives above, we carry on geomagnetic inverse and theoretical studies about the space currents by using geomagnetic data from INTERMAGNET. At first, we apply a method of Natural Orthogonal Components (NOC) to decomposition the solar daily variation, especially for (solar quiet variation). NOC is just one of eign mode analysis, the most advantage of this method is that the basic functions (BFs) were not previously designated, but naturally came from the original data so that there are several BFs usually corresponding to the process really happened and have more physical meaning than the traditional spectrum analysis with the fixed BFs like Fourier trigonometric functions. The first two eign modes are corresponding to the and daily variation and their amplitudes both have the seasonal and day-to-day trend, that will be useful for evaluating geomagnetic activity indices. Because of the too strict constraints of orthogonality, we try to extend orthogonal contraints to the non-orthogonal ones in order to give more suitable and appropriate decomposition of the real processes when the most components did not satisfy orthogonality. We introduce a mapping matrix which can transform the real physical space to a new mathematical space, after that process, the modified components which associated with the physical processes have satisfied the orthogonality in the new mathematical space, furthermore, we can continue to use the NOC decomposition in the new mathematical space, and then all the components inversely transform back to original physical space, so that we would have finished the non-orthogonal decomposition which more generally in the real world. Secondly, geomagnetic inverse of the ring current’s topology is conducted. Configurational changes of the ring current in the magnetosphere lead to different patterns of disturbed ground field, so that the global configuration of ring current can be inferred from its geomagnetic perturbations. We took advantages of worldwide geomagnetic observatories network to investigate the disturbed geomagnetic field which produced by ring current. It was found that the ring current was not always centered at geomagnetic equator, and significantly deviated off the equator during several intense magnetic storms. The deviation owing to the tilting and latitudinal shifting of the ring current with respect to the earth’s dipole can be estimated from global geomagnetic survey. Furthermore those two configurational factors which gave a quantitative description of the ring current configuration, will be helpful to improve the Dst calibration and understand the dependence of ring current’s configuration on the plasma sheet location relative to the equator when magnetotail field warped. Thirdly, the energization and physical acceleration process of ring current during magnetic storm has been proposed. When IMF Bz component increase, the enhanced convection electric field drive the plasma injection into the inner magnetosphere. During the transport process, a dynamic heating is happened which make the particles more ‘hot’ when the injection is more deeply inward. The energy gradient along the injection path is equivalent to a kind of force, which resist the plasma more earthward injection, as a diamagnetic effect of the magnetosphere anti and repellent action to the exotically injected plasma. The acceleration efficiency has a power law form. We use analytical way to quantitatively describe the dynamical process by introducing a physical parameter: energization index, which will be useful to understand how the particle is heated. At the end, we give a scheme of how to get the from storm time geomagnetic data. During intense magnetic storms, the lognormal trend of geomagnetic Dst decreases depend on the heating dynamic of magnetosphere controlling ring current. The descending pattern of main phase is governed by the magnetospheric configuration, which can be describled by the energization index. The amplitude of Dst correlated with convection electric field or south component of the solar wind. Finally, the Dst index is predicted by upstream solar wind parameter. As we known space weather have posed many chanllenges and impacts on techinal system, the geomagnetic index for evaluating the activity space weather. We review the most popular Dst prediction method and repeat the Dst forecasting model works. A concise and convnient Key Points model of the polar region is also introduced to space weather. In summary, this paper contains some new quantitative and physical description of the space currents with special focus on the ring current. Whatever we do is just to gain a better understanding of the natural world, particularly the space environment around Earth through analytical deduction, algorithm designing and physical analysis, to quantitative interpretation. Applications of theoretical physics in conjunction with data analysis help us to understand the basic physical process govering the universe.

真实和虚拟现实环境下场景识别中的空间更新

Currently，one of the important research areas in Spatial updating is the role of external (for instance visual) and internal (for instance proprioceptive or vestibular) information in spatial updating of scene recognition. Our study uses the paradigm of classic spatial updating research and the experimental design of investigation of Burgess(2004),first, we will explore the concrete influence of locomotion on scene recognition in real world; next, we will use virtual reality technology, which can control many spatial learning parameters and exclude the influence of extra irrelevant variables, to explore the influence of pure locomotion without visual cue on scene recognition, and furthermore, we will explore whether the ability of spatial updating can be transferred to new situations in a short period of time and compare the result pattern in real word with that in virtual reality to test the validity of virtual reality technology in spatial updating of scene recognition research. The main results of this paper can be summarized as follows： 1. In real world, we found two effects: the spatial updating effect and the viewpoint dependent effect, this result indicated that the spatial updating effect based on locomotion does not eliminate the viewpoint dependent effect during the scene recognition process in physical environment. 2. In virtual reality environment, we still found two effects: the spatial updating effect and the viewpoint dependent effect, this result showed us that the spatial updating effect based on locomotion does not eliminate the viewpoint dependent effect during the scene recognition process in virtual reality environment either. 3. The spatial updating effect based on locomotion plays double role in scene recognition: When subjects were tested in different viewpoint, spatial updating based on locomotion promoted scene recognition; while subjected were tested in same viewpoint, spatial updating based on locomotion had a negative influence on scene recognition, these results show us that spatial updating based on locomotion is automated and can not be ignored. 4. The ability of spatial updating can be transferred to new situations in a short period of time , and the experiment in the immersed virtual reality environment got the same result pattern with that in the physical environment, suggesting VR technology is a very effective method to do research on spatial updating of the scene recognition studies. 5. This study about scene recognition provides evidence to double system model of spatial updating in the immersed virtual reality environment.