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81.
Ozlem Ozgun Gökhan Apaydin Mustafa Kuzuoglu Levent Sevgi 《Computer Physics Communications》2011,182(12):2638-2654
A MATLAB-based one-way and two-way split-step parabolic equation software tool (PETOOL) has been developed with a user-friendly graphical user interface (GUI) for the analysis and visualization of radio-wave propagation over variable terrain and through homogeneous and inhomogeneous atmosphere. The tool has a unique feature over existing one-way parabolic equation (PE)-based codes, because it utilizes the two-way split-step parabolic equation (SSPE) approach with wide-angle propagator, which is a recursive forward–backward algorithm to incorporate both forward and backward waves into the solution in the presence of variable terrain. First, the formulation of the classical one-way SSPE and the relatively-novel two-way SSPE is presented, with particular emphasis on their capabilities and the limitations. Next, the structure and the GUI capabilities of the PETOOL software tool are discussed in detail. The calibration of PETOOL is performed and demonstrated via analytical comparisons and/or representative canonical tests performed against the Geometric Optic (GO) + Uniform Theory of Diffraction (UTD). The tool can be used for research and/or educational purposes to investigate the effects of a variety of user-defined terrain and range-dependent refractivity profiles in electromagnetic wave propagation.
Program summary
Program title: PETOOL (Parabolic Equation Toolbox)Catalogue identifier: AEJS_v1_0Program summary URL:http://cpc.cs.qub.ac.uk/summaries/AEJS_v1_0.htmlProgram obtainable from: CPC Program Library, Queen?s University, Belfast, N. IrelandLicensing provisions: Standard CPC licence, http://cpc.cs.qub.ac.uk/licence/licence.htmlNo. of lines in distributed program, including test data, etc.: 143 349No. of bytes in distributed program, including test data, etc.: 23 280 251Distribution format: tar.gzProgramming language: MATLAB (MathWorks Inc.) 2010a. Partial Differential Toolbox and Curve Fitting Toolbox requiredComputer: PCOperating system: Windows XP and VistaClassification: 10Nature of problem: Simulation of radio-wave propagation over variable terrain on the Earth?s surface, and through homogeneous and inhomogeneous atmosphere.Solution method: The program implements one-way and two-way Split-Step Parabolic Equation (SSPE) algorithm, with wide-angle propagator. The SSPE is, in general, an initial-value problem starting from a reference range (typically from an antenna), and marching out in range by obtaining the field along the vertical direction at each range step, through the use of step-by-step Fourier transformations. The two-way algorithm incorporates the backward-propagating waves into the standard one-way SSPE by utilizing an iterative forward–backward scheme for modeling multipath effects over a staircase-approximated terrain.Unusual features: This is the first software package implementing a recursive forward–backward SSPE algorithm to account for the multipath effects during radio-wave propagation, and enabling the user to easily analyze and visualize the results of the two-way propagation with GUI capabilities.Running time: Problem dependent. Typically, it is about 1.5 ms (for conducting ground) and 4 ms (for lossy ground) per range step for a vertical field profile of vector length 1500, on Intel Core 2 Duo 1.6 GHz with 2 GB RAM under Windows Vista. 相似文献82.
Bing-Kun Bao Author Vitae Bingbing Ni Author VitaeAuthor Vitae Shuicheng Yan Author Vitae 《Pattern recognition》2011,44(3):598-606
With fast growing number of images on photo-sharing websites such as Flickr and Picasa, it is in urgent need to develop scalable multi-label propagation algorithms for image indexing, management and retrieval. It has been well acknowledged that analysis in semantic region level may greatly improve image annotation performance compared to that in the holistic image level. However, region level approach increases the data scale to several orders of magnitude and proposes new challenges to most existing algorithms. In this work, we present a novel framework to effectively compute pairwise image similarity by accumulating the information of semantic image regions. Firstly, each image is encoded as Bag-of-Regions based on multiple image segmentations. Secondly, all image regions are separated into buckets with efficient locality-sensitive hashing (LSH) method, which guarantees high collision probabilities for similar regions. The k-nearest neighbors of each image and the corresponding similarities can be efficiently approximated with these indexed patches. Lastly, the sparse and region-aware image similarity matrix is fed into the multi-label extension of the entropic graph regularized semi-supervised learning algorithm [1]. In combination they naturally yield the capability of handling large-scale dataset. Extensive experiments on NUS-WIDE (260k images) and COREL-5k datasets validate the effectiveness and efficiency of our proposed framework for region-aware and scalable multi-label propagation. 相似文献
83.
Zhongsheng HuaAuthor Vitae 《Decision Support Systems》2011,51(3):671-681
With the increasing interdependence among supply chain members, bankruptcy of a supply chain member may be caused by operational decisions of other members. To investigate how bankruptcy occurs and propagates in supply chain networks, we build a multi-agent simulation model for a two-stage supply chain that consists of multiple upstream manufacturers and multiple downstream retailers. Based on the developed simulation model, we study impacts of various operational parameters and decisions, such as horizontal competition among retailers, order allocation strategies of retailers, wholesale price of manufacturers, characteristics of market demand and number of retailers, on bankruptcy propagation. Since many operational decisions of a firm are made under financial constraints, we also investigate the linkage between firm's operational risks and financial decisions (e.g., the maximal risk of cash flow that a member is willing to take, and the up-front payment proportion of retailers in a two-period payment policy). Experimental results reveal that operational interactions between supply chain members and operational decisions made by supply chain members are important causes of bankruptcy propagation, but impacts of these operational parameters and decisions depend on financial decisions. These observations indicate that supply chain members can moderately hedge their operational risk through financial decisions. 相似文献
84.
基于可能模型方法 ( possible m odel approach,简称 PMA) ,提出了面向行动的信念更新的概念 ,证明了在信息完备的情境演算系统中 ,一个一阶公式在情境 s下成立当且仅当它属于情境 s下的信念集 .这一结果为有效避免情境演算推理中二阶归纳公理的使用提供了一条可行的途径 ,也为基于意向驱动的 agent模型的建立以及面向 agent的程序设计语言 AOPL ID( agent-oriented programm ing language with intention driver)的提出提供了必要的理论基础. 相似文献
85.
86.
The main goal of this paper is to show how relatively minor modifications of well-known algorithms (in particular, back propagation) can dramatically increase the performance of an artificial neural network (ANN) for time series prediction. We denote our proposed sets of modifications as the 'self-momentum', 'Freud' and 'Jung' rules. In our opinion, they provide an example of an alternative approach to the design of learning strategies for ANNs, one that focuses on basic mathematical conceptualization rather than on formalism and demonstration. The complexity of actual prediction problems makes it necessary to experiment with modelling possibilities whose inherent mathematical properties are often not well understood yet. The problem of time series prediction in stock markets is a case in point. It is well known that asset price dynamics in financial markets are difficult to trace, let alone to predict with an operationally interesting degree of accuracy. We therefore take financial prediction as a meaningful test bed for the validation of our techniques. We discuss in some detail both the theoretical underpinnings of the technique and our case study about financial prediction, finding encouraging evidence that supports the theoretical and operational viability of our new ANN specifications. Ours is clearly only a preliminary step. Further developments of ANN architectures with more and more sophisticated 'learning to learn' characteristics are now under study and test. 相似文献
87.
Legal reasoning with subjective logic 总被引:1,自引:0,他引:1
Judges and jurors must make decisions in an environment of ignoranceand uncertainty for example by hearing statements of possibly
unreliable ordishonest witnesses, assessing possibly doubtful or irrelevantevidence, and enduring attempts by the opponents
to manipulate thejudge's and the jurors' perceptions and feelings. Three importantaspects of decision making in this environment
are the quantificationof sufficient proof, the weighing of pieces of evidence, and therelevancy of evidence. This paper proposes
a mathematical frameworkfor dealing with the two first aspects, namely the quantification ofproof and weighing of evidence.
Our approach is based on subjectivelogic, which is an extension of standard logic and probability theory,in which the notion
of probability is extended by including degrees ofuncertainty. Subjective Logic is a framework for modelling humanreasoning
and we show how it can be applied to legalreasoning. 相似文献
88.
关于BP算法推导过程与讨论 总被引:1,自引:0,他引:1
本文通过对多层神经网络模型的BP算法的推导,讨论用BP算法对网络进行优化后误差精度、网络结构和学习次数之间的关系,最后指出BP算法的不足之处,提出加快学习过程的一些方法和技巧。 相似文献
89.
重复传播网络的改进及其在遥感分类中的应用研究 总被引:3,自引:0,他引:3
对重复传播网络在权值初始化、网络学习进行修改,实现在一定警戒条件下竞争层节点的动
态分配,并在遥感分类上进行实验。重复传播网络中学习次数影响了竞争层节点数目和学习精度,
学习精度随学习次数的增加而有限度的增加;警戒参数决定了节点调整的搜索范围,随学习过程进
行,节点搜索范围变小,保证了分类精度和网络的稳定性;两层竞争层结构分类精度要优于单层,其
总精度和Kappa系数分别提高1.1%,0.02。实验结果表明该方法是一可行的遥感分类方法。 相似文献
态分配,并在遥感分类上进行实验。重复传播网络中学习次数影响了竞争层节点数目和学习精度,
学习精度随学习次数的增加而有限度的增加;警戒参数决定了节点调整的搜索范围,随学习过程进
行,节点搜索范围变小,保证了分类精度和网络的稳定性;两层竞争层结构分类精度要优于单层,其
总精度和Kappa系数分别提高1.1%,0.02。实验结果表明该方法是一可行的遥感分类方法。 相似文献
90.
On the Sequential Accumulation of Evidence 总被引:1,自引:0,他引:1
In this paper, we introduce a method for sequentially accumulating evidence as it pertains to an active observer seeking to identify an object in a known environment. We develop a probabilistic framework, based on a generalized inverse theory, where assertions are represented by conditional probability density functions. This leads to a sequential recognition strategy in which evidence is accumulated over successive viewpoints using Bayesian chaining until a definitive assertion can be made. To illustrate the theory we show how the characteristics of belief distributions can be exploited in a model-based recognition problem, where the task is to identify an unknown model from a database of known objects on the basis of parameter estimates. We illustrate the robustness of the algorithm through recognition experiments in two very different contexts: (1) a highly structured recognition context where 3-D parametric models can be estimated directly from range data, (2) a complex environment, where the relationship between the data and the model is learned through an appearance-based strategy. Specifically, the flow fields computed through the object's motion are used as structural signatures for recognition. 相似文献