搜索引擎原型系统的研究与设计

分析了搜索引擎技术在网络实时教育系统中的运用的可行性 ,对于搜索引擎原型系统框架的实现过程提出了自己的解决方案 ,并且着重对于搜索引擎技术进行了探讨和研究 ,在 link topology和关联规则等的基础上提出了一些新的想法和研究 .其中包括文档预选词的自动取舍和语言字典档的自动生成和维护等 .     

基于常见问题库的多搜索引擎自动问答系统

与传统的搜索引擎相比,自动问答系统支持自然语言提问,返回给用户一个简短而准确的答案,是自然语言处理领域的一个研究热点。文中介绍了一种基于常见问题库的多搜索引擎自动问答系统,它利用常见问题库和两大搜索引擎,快速准确地回答用户的问题,更加智能化地满足用户的检索需求。     

基于吉布斯采样结果的主题文本网络构建方法

挖掘文档集合中主题词的概率分布可对文档内容做概要性了解。进一步探寻给定主题下单词之间的连接关系不仅能丰富主题词的含义,而且能更细致地表现主题的层次和聚集关系。为此,针对带标签的文档集合,基于标注潜在狄利克雷分布(LDA)分析后的吉布斯采样结果,提出一种给定主题下2个单词共现的概率计算方法,并在此基础上构建主题文本网络。与逐点标注LDA(PL-LDA)模型相比,该方法不扩充原始文件,计算量小,耗时短。在航空安全报告数据集上的实验结果表明,对标记单词较多的主题,该方法能够较好地展示主题词的分布情况以及它们之间的复杂联系。     

频谱无混叠采样和信号完全可重构采样

通过对采样定理的分析,指出频谱无混叠采样和信号可完全重构采样并不等同,并给出和证明了实现这两个采样的条件,从而全面地回答了在何种条件下,经典采样定理中的最低采样频率可以等于信号最高频率的二倍.作为最低采样频率问题的最典型例子,本文对正弦信号的采样和重构问题作了进一步的分析,揭示了二倍信号频率的采样导致正弦信号相位信息丢失的原因.本文对如何在应用中正确地确定最低采样频率,提供了有益的参考.     

周期非均匀采样带通信号的采样参数

从采样信号中完全重建原信号的条件为基础,推导了周期非均匀采样的最低采样阶数Ⅳ。与其单通道采样率fs需要满足的关系,给周期非均匀采样的有效应用提供了理论指导。并进一步分析了最小总采样率fM。结论证实了周期非均匀采样能保证任意带通信号以2倍带宽的采样率进行采样。     

基于组网的智能变电站采样同步问题探讨

智能变电站中,为了减少投资,提高数据共享,会采用网络采样方式,但是网络中的电压电流信息需要保持同步,以便实现类似差动和距离保护的功能。在智能站过程层网络化后,各种差动保护（如不出站的母线差动保护、主变差动保护,出站的线路差动保护）、单端量的距离保护与功率测量、合并单元、PMU、故障录波器等二次设备对采样同步提出了更新、更高的要求。所以在网络采样方式下,保护的性能依赖于对时系统。分析了目前的同步采样技术的机制,研究了由于同步信号异常可能导致的问题,最后提出了几种解决方案。     

Spherical Visibility Sampling

Many 3D scenes (e.g. generated from CAD data) are composed of a multitude of objects that are nested in each other. A showroom, for instance, may contain multiple cars and every car has a gearbox with many gearwheels located inside. Because the objects occlude each other, only few are visible from outside. We present a new technique, Spherical Visibility Sampling (SVS), for real‐time 3D rendering of such – possibly highly complex – scenes. SVS exploits the occlusion and annotates hierarchically structured objects with directional visibility information in a preprocessing step. For different directions, the directional visibility encodes which objects of a scene's region are visible from the outside of the regions' enclosing bounding sphere. Since there is no need to store a separate view space subdivision as in most techniques based on preprocessed visibility, a small memory footprint is achieved. Using the directional visibility information for an interactive walkthrough, the potentially visible objects can be retrieved very efficiently without the need for further visibility tests. Our evaluation shows that using SVS allows to preprocess complex 3D scenes fast and to visualize them in real time (e.g. a Power Plant model and five animated Boeing 777 models with billions of triangles). Because SVS does not require hardware support for occlusion culling during rendering, it is even applicable for rendering large scenes on mobile devices.     

Sampling Archimedean copulas

The challenge of efficiently sampling exchangeable and nested Archimedean copulas is addressed. Specific focus is put on large dimensions, where methods involving generator derivatives are not applicable. Additionally, new conditions under which Archimedean copulas can be mixed to construct nested Archimedean copulas are presented. Moreover, for some Archimedean families, direct sampling algorithms are given. For other families, sampling algorithms based on numerical inversion of Laplace transforms are suggested. For this purpose, the Fixed Talbot, Gaver Stehfest, Gaver Wynn rho, and Laguerre series algorithm are compared in terms of precision and runtime. Examples are given, including both exchangeable and nested Archimedean copulas.     

Fast Filtered Sampling

Traffic sampled from the network backbone using uniform packet sampling is commonly utilized to detect heavy hitters, estimate flow level statistics, as well as identify anomalies like DDoS attacks and worm scans. Previous work has shown however that this technique introduces flow bias and truncation which yields inaccurate flow statistics and “drowns out” information from small flows, leading to large false positives in anomaly detection.In this paper, we present a new sampling design: Fast Filtered Sampling (FFS), which is comprised of an independent low-complexity filter, concatenated with any sampling scheme at choice. FFS ensures the integrity of small flows for anomaly detection, while still providing acceptable identification of heavy hitters. This is achieved through a filter design which suppresses packets from flows as a function of their size, “boosting” small flows relative to medium and large flows. FFS design requires only one update operation per packet, has two simple control parameters and can work in conjunction with existing sampling mechanisms without any additional changes. Therefore, it accomplishes a lightweight online implementation of the “flow-size dependent” sampling method. Through extensive evaluation on traffic traces, we show the efficacy of FFS for applications such as portscan detection and traffic estimation.     

取样脉冲对平衡式取样积分微分电路性能影响

取样脉冲是影响超宽带无线电引信接收机性能的关键因素。在分析取样积分微分电路工作原理的基础上,建立了其频域和时域数学模型。结合无载波信号的时域多普勒效应,提出了基于Matlab的超宽带无线电引信接收机输出信号时域仿真方法。研究了取样脉冲宽度和幅度对电路输出波形的影响,结果表明:电路完全对称时,输出信号幅度只与取样脉冲宽度有关,但并非单调关系;非对称时,电路达到稳态前产生一个脉冲信号,脉冲幅度与取样脉冲的幅度成正比,宽度成反比。     

Multi-Scaling Sampling: An Adaptive Sampling Method for Discovering Approximate Association Rules

One of the obstacles of the efficient association rule mining is the explosive expansion of data sets since it is costly or impossible to scan large databases, esp., for multiple times. A popular solution to improve the speed and scalability of the association rule mining is to do the algorithm on a random sample instead of the entire database. But how to effectively define and efficiently estimate the degree of error with respect to the outcome of the algorithm, and how to determine the sample size needed are entangling researches until now. In this paper, an effective and efficient algorithm is given based on the PAC (Probably Approximate Correct) learning theory to measure and estimate sample error. Then, a new adaptive, on-line, fast sampling strategy - multi-scaling sampling - is presented inspired by MRA (Multi-Resolution Analysis) and Shannon sampling theorem, for quickly obtaining acceptably approximate association rules at appropriate sample size. Both theoretical analysis and empirical study have showed that the sampling strategy can achieve a very good speed-accuracy trade-off.     

广义多周期采样法

目前，单片机在宽带非正弦信号智能化测量系统的应用中，因受Ａ／Ｄ转换速度以及机器周期占用时间的限制，被测信号频率不能太高，否则单片机速度跟不上。现有资料表明被测信号的带宽在１６ＨＺ～１０ｋＨＺ左右，远远不能适应工程需要。广义多周期采样法在不改变硬件结构的基础上，以全新的思维方式提出了一种新的适合于智能化测量仪器的测量方法，使被测信号频率可达到１００ｋＨＺ。     

The Joy of Sampling

A standard method for handling Bayesian models is to use Markov chain Monte Carlo methods to draw samples from the posterior. We demonstrate this method on two core problems in computer vision—structure from motion and colour constancy. These examples illustrate a samplers producing useful representations for very large problems. We demonstrate that the sampled representations are trustworthy, using consistency checks in the experimental design. The sampling solution to structure from motion is strictly better than the factorisation approach, because: it reports uncertainty on structure and position measurements in a direct way; it can identify tracking errors; and its estimates of covariance in marginal point position are reliable. Our colour constancy solution is strictly better than competing approaches, because: it reports uncertainty on surface colour and illuminant measurements in a direct way; it incorporates all available constraints on surface reflectance and on illumination in a direct way; and it integrates a spatial model of reflectance and illumination distribution with a rendering model in a natural way. One advantage of a sampled representation is that it can be resampled to take into account other information. We demonstrate the effect of knowing that, in our colour constancy example, a surface viewed in two different images is in fact the same object. We conclude with a general discussion of the strengths and weaknesses of the sampling paradigm as a tool for computer vision.     

并行多维自适应采样

针对现有自适应采样方法绘制效果差和速度慢的问题,提出一种并行的多维自适应采样方法.首先对多维空间进行粗采样,将其自适应地分割为多个子空间;然后扩展各子空间边界,根据噪声评价值分配每个子空间所需的采样点数;在各子空间上构建KD树以并行地对其进行自适应采样;最后根据各采样点间的梯度值重构图像.实验结果表明,该方法能够以更少的样本绘制高质量的景深、运动模糊和软阴影效果,并控制绘制图像时的内存消耗,支持高分辨率的真实感图像生成.     

分组采样技术研究

随着网络带宽的不断提高,分组采样技术作为网络测量的手段越来越受到重视。因为在高速网络中对所有的分组进行实时的统计分析代价太大,而通过分组采样可以大大减少测量的代价,从而具有更好的可扩展性。本文对近来提出的一些分组采样技术进行了系统的分析和研究,主要对它们的原理、精度和效果进行了详细分析,并对其中存在的问题提出了一些改进的措施。     

采样方法研究综述

Nyquist Shannon采样定理从提出至今已经过了近70年的发展历程,为模拟 世界到数字世界的转变作出了巨大贡献。在这70年的进程中,围绕如何在不丢失信号中有用 信息的前提下,降低采样速率和减少采集数据量的主题、新的采样理论和采样方法也不断涌现 。本文在总结和回顾现有采样技术原理和框架的基础上,结合个人见解对单通道随机 采样、压缩传感采样、有限新息率采样及X 采样等稀疏采样方法的特点、适用场合、最新 进展和公开问题及难点进行了评价,并对稀疏采样发展前景予以展望,并为采样技术改进 和拓展等应用领域提供参考。     

Hero Wavelength Spectral Sampling

We present a spectral rendering technique that offers a compelling set of advantages over existing approaches. The key idea is to propagate energy along paths for a small, constant number of changing wavelengths. The first of these, the hero wavelength, is randomly sampled for each path, and all directional sampling is solely based on it. The additional wavelengths are placed at equal distances from the hero wavelength, so that all path wavelengths together always evenly cover the visible range. A related technique, spectral multiple importance sampling, was already introduced a few years ago. We propose a simplified and optimised version of this approach which is easier to implement, has good performance characteristics, and is actually more powerful than the original method. Our proposed method is also superior to techniques which use a static spectral representation, as it does not suffer from any inherent representation bias. We demonstrate the performance of our method in several application areas that are of critical importance for production work, such as fidelity of colour reproduction, sub‐surface scattering, dispersion and volumetric effects. We also discuss how to couple our proposed approach with several technologies that are important in current production systems, such as photon maps, bidirectional path tracing, environment maps, and participating media.