Description: 水合物分解时的温度----应变计算程序。海底石油工程非常有用。-Gas hydrates may play an important role in global climate change, carbon sequestration, energy production and seafloor stability. However, formation and dissociation pathways in geologically complex systems are poorly defined. We present a new approach to processing large amounts of data from a LUNA distributed sensing system (DSS) in the seafloor process simulator (SPS) at Oak Ridge National Laboratory to monitor and visualize gas hydrate formation and dissociation in heterogeneous sediments. The DSS measures relative temperature/strain change with a high spatial resolution allowing the heat of reaction during gas hydrate formation/dissociation to be used to locate clathrate processes in space and time within the vessel. Optical fibers are placed in the sediment following an Archimedean spiral design and the position of each sensor is determined iteratively over the arc length using Newton s method. The DSS data are then gridded with a natural neighbor interpolation algorithm to allow Platform: |
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Author:张开 |
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Description: 有时间序列方法和技术的兴趣大增。从人，自然收集的信息几乎每一件，和生物过程是容易随时间的变化，以及这些变化如何发生的研究是一个中心问题充分理解这样的过程。所有的时间序列数据挖掘任务的分类中，可能是最突出的一个。在时间序列的分类有大量的实证研究，在时间域表明近邻规则是非常有效的。然而，一定的时间序列特征不在这个领域很容易地识别和表达的变化可能揭示了一些重要的和未知的特征。在这项工作中我们提出了递归图的使用对于时间序列的分类表示域。我们的方法复发措施地块使用坎帕纳基奥之间的相似性（CK-1）的距离，一个基于Kolmogorov复杂性的距离，利用视频压缩算法来估计图像的相似性。我们表明，递归图与CK-1距离导致比欧氏距离和动态时间翘曲在几个数据集的准确率显著提高。虽然复发地块不能用于所有的数据集提供准确率最高的，我们证明了我们可以预测未来的时间，我们的方法将跑赢时间表示欧氏距离和动态时间弯曲距离。-There is a huge increase of interest for time series methods and techniques. Virtually every piece of information collected human, natural, and biological processes is susceptible to changes over time, and the study of how these changes occur is a central issue to fully understand such processes. Among all time series mining tasks, classification is likely to be the most prominent one. In time series classification there is a significant body of empirical research that indicates that k-nearest neighbor rule in the time domain is very effective. However, certain time series features are not easily identified in this domain and a change in representation may reveal some significant and unknown features. In this work we propose the use of recurrence plots as representation domain for time series classification. Our approach measures the similarity between recurrence plots using Campana-Keogh (CK-1) distance, a Kolmogorov complexity-based distance that uses video compression algorithms to Platform: |
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Author:裴孟菲 |
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Description: 利用自然梯度算法，构成不同频率的调制信号，包含位置式PID算法、积分分离式PID，一些自适应信号处理的算法，包括最小二乘法、SVM、神经网络、1_k近邻法,使用起来非常方便，合成孔径雷达（SAR)目标成像仿真。- Use of natural gradient algorithm, Constituting the modulated signals of different frequencies, It contains positional PID algorithm, integral separate PID, Some adaptive signal processing algorithms, Including the least squares method, the SVM, neural networks, 1 _k neighbor method, Very convenient to use, Synthetic Aperture Radar (SAR) imaging simulation target. Platform: |
Size: 6144 |
Author:anhhstujh |
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Description: 最小均方误差（MMSE）的算法，包括AHP，因子分析，回归分析，聚类分析，代码里有很完整的注释和解释，包括最小二乘法、SVM、神经网络、1_k近邻法,采用累计贡献率的方法，利用自然梯度算法，用于时频分析算法。- Minimum mean square error (MMSE) algorithm, Including AHP, factor analysis, regression analysis, cluster analysis, Code, there are very complete notes and explanations Including the least squares method, the SVM, neural networks, 1 _k neighbor method, The method of cumulative contribution rate Use of natural gradient algorithm, For time-frequency analysis algorithm. Platform: |
Size: 6144 |
Author:aaxmyg |
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Description: 考虑雨衰 阴影 和多径影响，利用自然梯度算法，非归零型差分相位调制信号建模与仿真分析 ，能量熵的计算，借鉴了主成分分析算法（PCA），基于负熵最大的独立分量分析，包括最小二乘法、SVM、神经网络、1_k近邻法。- Consider shadow rain attenuation and multipath effects Use of natural gradient algorithm, NRZ type differential phase modulation signal modeling and simulation analysis, Energy entropy calculation, It draws on principal component analysis algorithm (PCA), Based on negative entropy largest independent component analysis, Including the least squares method, the SVM, neural networks, 1 _k neighbor method. Platform: |
Size: 4096 |
Author:朱艳利 |
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Description: 用MATLAB实现的压缩传感，包括最小二乘法、SVM、神经网络、1_k近邻法,利用自然梯度算法。- Using MATLAB compressed sensing, Including the least squares method, the SVM, neural networks, 1 _k neighbor method, Use of natural gradient algorithm. Platform: |
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Author:师浩 |
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Description: ML-KNN，这是来自传统的K-近邻（KNN）算法。详细地，为每一个看不见的实例中，首先确定了训练集中的k近邻。之后，基于从标签集获得的统计信息。这些相邻的实例，即属于每个可能类的相邻实例的数量，最大后验（MAP）原理。用于确定不可见实例的标签集。三种不同现实世界中多标签学习问题的实验研究，即酵母基因功能分析、自然场景分类和网页自动分类，表明ML-KNN实现了卓越的性能(ML-KNN which is derived from the traditional K-nearest neighbor (KNN) algorithm. In detail, for each unseen
instance, its K nearest neighbors in the training set are firstly identified. After that, based on statistical information gained from the label sets of
these neighboring instances, i.e. the number of neighboring instances belonging to each possible class, maximum a posteriori (MAP) principle
is utilized to determine the label set for the unseen instance. Experiments on three different real-world multi-label learning problems, i.e. Yeast
gene functional analysis, natural scene classification and automatic web page categorization, show that ML-KNN achieves superior performance
to some well-established multi-label learning algorithms.
2007 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved.) Platform: |
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Author:玖
|
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