An upscaling method using coefficient splitting and its applications to elliptic PDEs

In this paper, we develop an upscaling method using coefficient splitting techniques. Green’s function is constructed using the differential operator associated with the first part of the splitting. An effective upscaling coefficient is recursively calculated by Green’s function. The computation of the upscaling process involves some independent steps. Combining the proposed upscaling method with the stochastic collocation method, we present a stochastic space reduction collocation method, where the stochastic collocation method is performed on a lower dimension stochastic space than the full-dimension stochastic space. We thoroughly analyze the convergence of the proposed upscaling method for both deterministic and stochastic elliptic PDEs. Computation complexity is also addressed for the stochastic upscaling method. A number of numerical tests are presented to confirm the convergence analysis.     

PARLOG and its applications

The key concepts of the parallel logic programming language PARLOG are introduced by comparing the language with Prolog. Some familiarity with Prolog and with the concepts of logic programming is assumed. Two major application areas of PARLOG, systems programming and object-oriented programming, are illustrated. Other applications are briefly surveyed     

LDA merging and splitting with applications to multiagent cooperative learning and system alteration

To adapt linear discriminant analysis (LDA) to real-world applications, there is a pressing need to equip it with an incremental learning ability to integrate knowledge presented by one-pass data streams, a functionality to join multiple LDA models to make the knowledge sharing between independent learning agents more efficient, and a forgetting functionality to avoid reconstruction of the overall discriminant eigenspace caused by some irregular changes. To this end, we introduce two adaptive LDA learning methods: LDA merging and LDA splitting. These provide the benefits of ability of online learning with one-pass data streams, retained class separability identical to the batch learning method, high efficiency for knowledge sharing due to condensed knowledge representation by the eigenspace model, and more preferable time and storage costs than traditional approaches under common application conditions. These properties are validated by experiments on a benchmark face image data set. By a case study on the application of the proposed method to multiagent cooperative learning and system alternation of a face recognition system, we further clarified the adaptability of the proposed methods to complex dynamic learning tasks.     

Higher order operator splitting methods via Zassenhaus product formula: Theory and applications

In this paper, we contribute higher order operator splitting methods improved by Zassenhaus product. We apply the contribution to classical and iterative splitting methods. The underlying analysis to obtain higher order operator splitting methods is presented. While applying the methods to partial differential equations, the benefits of balancing time and spatial scales are discussed to accelerate the methods.The verification of the improved splitting methods are done with numerical examples. An individual handling of each operator with adapted standard higher order time-integrators is discussed. Finally, we conclude the higher order operator splitting methods.     

Zoom tracking and its applications

We present a new active vision technique called zoom tracking. Zoom tracking is the continuous adjustment of a camera's focal length in order to keep a constant-sized image of an object moving along the camera's optical axis. Two methods for performing zoom tracking are presented: a closed-loop visual feedback algorithm based on optical flow, and use of depth information obtained from an autofocus camera's range sensor. We explore two uses of zoom tracking: recovery of depth information and improving the performance of scale-variant algorithms. We show that the image stability provided by zoom tracking improves the performance of algorithms that are scale variant, such as correlation-based trackers. While zoom tracking cannot totally compensate for an object's motion, due to the effect of perspective distortion, an analysis of this distortion provides a quantitative estimate of the performance of zoom tracking. Zoom tracking can be used to reconstruct a depth map of the tracked object. We show that under normal circumstances this reconstruction is much more accurate than depth from zooming, and works over a greater range than depth from axial motion while providing, in the worst case, only slightly less accurate results. Finally, we show how zoom tracking can also be used in time-to-contact calculations. Received: 15 February 2000 / Accepted: 19 June 2000     

人工情感及其应用

自然系统中,情感影响人的行为.目前,人工情感在拟人agent的研究得到了越来越多的重视.为此,本文从工程角度综述了人工情感研究的基本问题,简要介绍了几类情感模型,并着重讨论了基于人工情感的agent体系结构的典型应用.人工情感的研究不仅体现在情感辨识、情感表达等人机交互方面,同时情感的作用机理也影响到智能agent的控制体系结构和算法设计.基于人工情感的体系结构具有混合分层的特点,强调情感和其他过程的联系以增强agent在与动态环境交互中的自适应能力.在这个结构中人工情感的核心作用主要体现在两个方面:首先,人工情感是联系agent的内部状态和外部环境的基础,情感状态影响到agent信息处理的整个过程,包括紧急情况下的快速响应和规划任务时的复杂推理.其次,人工情感作为内部驱动机制对学习过程十分重要,人工情感作为强化刺激促使agent创建更复杂的行为功能.在设计中引入人工情感,对agent实现智能化和个性化至关重要.     

Graph-based induction and its applications

A machine learning technique called Graph-based induction (GBI) efficiently extracts typical patterns from graph data by stepwise pair expansion (pairwise chunking). In this paper, we introduce GBI for general graph structured data, which can handle directed/undirected, colored/uncolored graphs with/without (self) loop and with colored/uncolored links. We show that its time complexity is almost linear with the size of graph. We, further, show that GBI can effectively be applied to the extraction of typical patterns from DNA sequence data and organochlorine compound data from which are to be generated classification rules, and that GBI also works as a feature construction component for other machine learning tools.     

Camera-sampling field and its applications

In this paper, we propose a novel vector field, called a camera-sampling field, to represent the sampling density distribution of a pinhole camera. We give the derivation and discuss some essential properties of the camera-sampling field, including flux, divergence, curl, gradient, level surface, and sampling patterns. This vector field reveals camera-sampling concisely and facilitates camera sampling analysis. The usage for this vector field in several computer graphics applications is introduced, such as determining the splat kernel for image-based rendering, texture filtering, mipmap level selection, level transition criteria for LOD, and LDI-construction.     

拓扑指数及其应用

拓扑指数是从化合物的结构图衍生出来的一种数学不变量,由于计算简便,取值客观,近年来在各领域的应用中取得了丰硕的成果,备受研究者关注。本文主要介绍几类有代表性的拓扑指数及其在定量结构-性质/活性关系(QSPR/QSAR)研究中的应用,讨论了它们的优缺点,提出了拓扑指数的发展趋势。     

二阶控制论及其应用

本文综述二阶控制论学派的缘起和成就,包括它的创导人冯·福尔斯特和他的BCL实验室,以及它的主要论点和与前期控制论的差异.介绍了二阶控制论在对话和理解协议方面、以及管理和经济系统方面的应用.最后本文作者对学派的工作进行了分析和评论.     

Alternating splitting waveform relaxation method and its successive overrelaxation acceleration

For the large sparse implicit linear initial value problem, we present a block successive overrelaxation scheme for the alternating direction implicit waveform relaxation method to further accelerate its convergence speed, and discuss the convergence property of the resulting iteration method in detail. Numerical implementations about several non-Hermitian implicit linear initial value problems show that the alternating direction implicit waveform relaxation method is very effective, and the block successive overrelaxation technique really accelerates its convergence speed.     

二进制差异演化算法及其应用

针对传统差异演化算法（Differential Evolution）无法求解采用二进制编码问题的缺点,通过引入“少数服从多数”原则,提出一种解决二进制编码问题的差异演化算法—二进制差异演化算法。5个测试函数和4个背包问题的仿真实验表明了文中提出算法在解决二进制编码问题上的有效性。     

Extreme learning machine and its applications

Recently, a novel learning algorithm for single-hidden-layer feedforward neural networks (SLFNs) named extreme learning machine (ELM) was proposed by Huang et al. The essence of ELM is that the learning parameters of hidden nodes, including input weights and biases, are randomly assigned and need not be tuned while the output weights can be analytically determined by the simple generalized inverse operation. The only parameter needed to be defined is the number of hidden nodes. Compared with other traditional learning algorithms for SLFNs, ELM provides extremely faster learning speed, better generalization performance and with least human intervention. This paper firstly introduces a brief review of ELM, describing the principle and algorithm of ELM. Then, we put emphasis on the improved methods or the typical variants of ELM, especially on incremental ELM, pruning ELM, error-minimized ELM, two-stage ELM, online sequential ELM, evolutionary ELM, voting-based ELM, ordinal ELM, fully complex ELM, and symmetric ELM. Next, the paper summarized the applications of ELM on classification, regression, function approximation, pattern recognition, forecasting and diagnosis, and so on. In the last, the paper discussed several open issues of ELM, which may be worthy of exploring in the future.     

The Trace transform and its applications

The Trace transform proposed, a generalization of the Radon transform, consists of tracing an image with straight lines along which certain functionals of the image function are calculated. Different functionals that can be used may be invariant to different transformations of the image. The paper presents the properties the functionals must have in order to be useful in three different applications of the method: construction of invariant features to rotation, translation and scaling of the image, construction of sensitive features to the parameters of rotation, translation and scaling of the image, and construction of features that may correlate well with a certain phenomenon we wish to monitor     

Pivot vector method and its applications

The Gauss transformation method is modified to include simultaneously a whole vector of elements — the pivot vector — instead of a single element. This leads to a generalization of the elimination method and to development of new simplex methods. Complexity of the methods and their application to special problems of linear algebra and linear programming are discussed.Translated from Kibernetika i Sistemnyi Analiz, No. 1, pp. 116–127, January–February, 1992.     

Subdivision connectivity remeshing and its applications

This paper presents a subdivision connectivity remeshing approach for closed genus 0 meshes. It is based on spherical parameterization and umbrella‐operator smoothing. Our main contribution lies in adopting a low‐distortion spherical parameterization approach to generate high‐quality subdivision connectivity meshes. Besides, a simple and efficient point location method on the sphere based on the uniform partition of the rectangle is presented, which is used to find the containing triangle in the spherical mesh for each point on the sphere rapidly. Our method can generate high‐quality subdivision connectivity meshes fast, which can be applied to level of detail and progressive transmission. All the application examples demonstrate that our remeshing procedure is robust and efficient. Copyright © 2011 John Wiley & Sons, Ltd.