具體功能: The documentation of Windows system service Dependence, it also introduce a service software and brief implementation of service dependency
標(biāo)簽： service documentation Dependence introduce
上傳時(shí)間： 2015-08-27
上傳用戶：yulg
％ Measure Statistical Dependence 統(tǒng)計(jì)依賴測(cè)量 ％ 兩個(gè)時(shí)間序列間統(tǒng)計(jì)依賴性測(cè)量 ％ 參考文獻(xiàn)：時(shí)間序列間統(tǒng)計(jì)依賴性測(cè)量的一種改進(jìn)方法（王海燕 李 文① 陳文彥）
標(biāo)簽： Statistical Dependence Measure 測(cè)量
上傳時(shí)間： 2013-12-31
上傳用戶：guanliya
The Fat Fs module is a middleware that written in ANSI C. There is no platform Dependence, so long as the compiler is in compliance with ANSI C. However it handles the system portable FAT structures. You must take the endian into consideration. It must be changed for your platform first or the compiler will abort with an error.
標(biāo)簽： C. Dependence middleware platform
上傳時(shí)間： 2014-01-12
上傳用戶：cccole0605
Robustnesstochangesinilluminationconditionsaswellas viewing perspectives is an important requirement formany computer vision applications. One of the key fac-ors in enhancing the robustness of dynamic scene analy-sis that of accurate and reliable means for shadow de-ection. Shadowdetectioniscriticalforcorrectobjectde-ection in image sequences. Many algorithms have beenproposed in the literature that deal with shadows. How-ever,acomparativeevaluationoftheexistingapproachesisstill lacking. In this paper, the full range of problems un-derlyingtheshadowdetectionareidenti?edanddiscussed.Weclassifytheproposedsolutionstothisproblemusingaaxonomyoffourmainclasses, calleddeterministicmodeland non-model based and statistical parametric and non-parametric. Novelquantitative(detectionanddiscrimina-ionaccuracy)andqualitativemetrics(sceneandobjectin-Dependence,?exibilitytoshadowsituationsandrobustnesso noise) are proposed to evaluate these classes of algo-rithms on a benchmark suite of indoor and outdoor videosequences.
標(biāo)簽： Robustnesstochangesinillumination conditionsaswellas perspectives requirement
上傳時(shí)間： 2014-01-23
上傳用戶：whenfly
The kernel-ica package is a Matlab program that implements the Kernel ICA algorithm for independent component analysis (ICA). The Kernel ICA algorithm is based on the minimization of a contrast function based on kernel ideas. A contrast function measures the statistical Dependence between components, thus when applied to estimated components and minimized over possible demixing matrices, components that are as independent as possible are found.
標(biāo)簽： independent kernel-ica implements algorithm
上傳時(shí)間： 2014-01-17
上傳用戶：yiwen213
Computational models are commonly used in engineering design and scientific discovery activities for simulating complex physical systems in disciplines such as fluid mechanics, structural dynamics, heat transfer, nonlinear structural mechanics, shock physics, and many others. These simulators can be an enormous aid to engineers who want to develop an understanding and/or predictive capability for complex behaviors typically observed in the corresponding physical systems. Simulators often serve as virtual prototypes, where a set of predefined system parameters, such as size or location dimensions and material properties, are adjusted to improve the performance of a system, as defined by one or more system performance objectives. Such optimization or tuning of the virtual prototype requires executing the simulator, evaluating performance objective(s), and adjusting the system parameters in an iterative, automated, and directed way. System performance objectives can be formulated, for example, to minimize weight, cost, or defects; to limit a critical temperature, stress, or vibration response; or to maximize performance, reliability, throughput, agility, or design robustness. In addition, one would often like to design computer experiments, run parameter studies, or perform uncertainty quantification (UQ). These approaches reveal how system performance changes as a design or uncertain input variable changes. Sampling methods are often used in uncertainty quantification to calculate a distribution on system performance measures, and to understand which uncertain inputs contribute most to the variance of the outputs. A primary goal for Dakota development is to provide engineers and other disciplinary scientists with a systematic and rapid means to obtain improved or optimal designs or understand sensitivity or uncertainty using simulationbased models. These capabilities generally lead to improved designs and system performance in earlier design stages, alleviating Dependence on physical prototypes and testing, shortening design cycles, and reducing product development costs. In addition to providing this practical environment for answering system performance questions, the Dakota toolkit provides an extensible platform for the research and rapid prototyping of customized methods and meta-algorithms
標(biāo)簽： Optimization and Uncertainty Quantification
上傳時(shí)間： 2016-04-08
上傳用戶：huhu123456
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