查询词典 filtering flask

The dissertation is focus on establishing a user model for personalized information retrieval based on user partialness keyword list, including classified dictionary, study module, user behavior monitor module, Internet change detecting module, document analysis and information filtering module, and with the communications and collaboration of these modules, the model obtains on load of user partialness information while in inquiring, retrieving and results filtering. Thus the precision of information retrieval increases, and it can keep up with the change of webpage, and personalized information retrieval service realized.

本文的主要工作是在客户端建立基于用户偏好关键词表的用户个性化信息检索模型，包括分类词典、学习模块、用户行为监测模块、Internet变化检测模块及文档分析与信息过滤模块，并通过各模块之间的通信协作来实现用户查询提问时、信息检索过程中和检索结果过滤时用户偏好信息的加载，来提高检索结果的精度和跟踪网页信息的变化，从而实现个性化信息服务。

At the rest of this paper, the following information are given: introduction to the variation of user status on the server; process of dynamic modifying to rule of packet filtering; the application of design patterner in implementation of rule table of packet filtering.

同时，本文还详细地介绍了使用原始网络插口技术接收和发送网络数据包的技术，客户端服务器端之间加密通信实现的过程，使用设计模式技术保证包过滤规则列表的唯一性等一些该方案在具体设计和实现中涉及的技术、问题和解决方法。

Preprocessing consists of data selection, baseline adjustment, noise filtering and peak identification. Data selection removes preprimer data section which contains little useful information. Baseline adjustment adjusts the baselines of four channels to the same level. Noise filtering removes the impulse noise and white noise. Peak identification selects the peak data for use in cross matrix determination.

预处理包括数据段选取、基线调整、噪声滤除、峰值识别四个模块：数据段选取删除前段冗余数据；基线调整使得四色荧光信号的基线高度一致；噪声滤除去除信号中的脉冲噪声和白噪声；峰值识别选取峰值数据，作为求解串扰矩阵的数据。

The experiments indicated that this method could not only eliminate white noise and specking noise effectively and also reserve details on image ,its filtering effect is better than median filtering and wavelet soft thresholding method .

实验表明该方法不仅对高斯噪声和椒盐噪声去噪效果明显，而且保留了影像的细节信息，其滤波效果优于中值滤波和小波软阈值法。

The experiments indicated that this method could not only eliminate white noise and specking noise effectively and also reserve details on image ,its filtering effect is better than median filtering and wavelet soft thresholding method .

本文提出一种在小波域进行高斯函数滤波的去噪方法，此方法是在小波域内对高频子带分别进行高斯函数滤波，达到消噪的目的，实验表明该算法不仅能够消除高斯噪声和椒盐噪声，而且能够较好地保留影像的细节，其滤波效果优于中值滤波和小波软阈值滤波方法。

Dynamic Packet Filtering, also named Stateful Inspection, is the extension of function of traditional Packet Filtering.

动态包过滤又称状态检测，是在传统包过滤上的功能扩展，最早由CheckPoint提出。

This paper presented an approach to perform text content filtering based on content-based and collaborative filtering, using the Probability Model. Introducing the idea of Stereotypic inference, it classified the users into different types and built the model for each type.

采用了内容过滤和协同过滤相结合的混合过滤模式，利用高级语义知识进行Web文档的主题归类，并引入了典型推荐的思想把用户进行归类。

In the context of adaptive filtering, the subband adaptive filtering is known to enjoy a fast convergence rate.

在过去关於适应性滤波技术的研究，都能够发现次频带适应性滤波技术可以享有快速的收敛速度。

Due to the complexity of the cell jitter, the NonSynchronous Tining Recovery methods are currently not mature With the emphasis being given to the Class A CBR traffic, this paper analyzes the performance of the queueing delay and cell jitter at the source node and intermediate nodes, and discusses the Source Timing Recovery at the destination node in ATM networks Firstly, this paper presents a description of the cell jitter of CBR traffic, and gives the definitions of two kinds of cell jitter regarding the Source Timing Recovery for CBR traffic Then, by using exact mathematical models and analysis methods, this paper analyzes the impact of the factors, such as the capacity of the queueing buffer, the randomness, the deterministic nature and the correlation in cell arrivals of the background traffic sources, on the queueing delay and cell jitter performance of the CBR traffic through Statistical Multiplexitng To obtain an insight into the power spectral distribution and look for better schemes for the depression and filtering of the cell jitter, within the analyses we succeed deriving the power spectrum of the cell jitter for CBR traffic Hence, not only the power spectral distribution of the cell jitter can in the frequency domain be qualitatively understood, but also can the rms (root-meansquare) value of the cell jitter be quantitatively obtained so as to more accurately measure the amplitude of the jitter In the end-to-end performance analysis of the queueing delay and cell jitter, we propose a kind of quasi-periodic cell stream model to characterize the jittered CBR traffic, and present an initial queueing analysis of the CBR traffic following such a model at a generic intermediate node Additionally, we briefly discuss the buildout/playout and Source Timing Recovery functions of the destination node Finally, regarding the Source Timing Recovery of CBR traffic, this paper systematically discusses several important principles of the cell jitter filtering and depression reported in the literature, introduces several implementation schemes of the Source Timing Recovery e.

由于信元抖动的复杂性，非同步定时恢复方法目前还很不成熟。本文针对A类CBR业务流在ATM网络源节点和中间节点的排队时延和信元抖动性能，以及在目的节点的源定时恢复问题作了较为全面的研究。首先，文中描述了CBR业务流的信元抖动，并具体地给出了两种与CBR业务源定时恢复有关的信元抖动的定义。然后，采用了精确的数学模型和分析方法，有针对性地分析了业务背景中信元到达的纯随机性、确定性和相关性以及排队缓存器容量等因素对CBR业务流经过统计复用后的排队时延和信元抖动性能的影响。为了了解信元抖动的功率频谱分布和寻求更好的抑制和滤除抖动的方法，在性能分析中，我们成功地完成了CBR业务流信元抖动的功率谱分析，使得不但可以从频域定性地认识信元抖动的能量分布特性，而且还可以定量地求出信元抖动的均方根值(rms：root-mean-square)，以更为准确地衡量抖动的大小。在CBR业务流的多节点端-端排队时延和信元抖动性能分析中，我们提出了一种准周期性(quasi-periodic)信元流模型来描述感染了信元抖动的CBR业务流，并基于这一模型进行了CBR业务流中间节点的初步排队分析。

The rotation of the drum produces a relative motion between the filtering screen and the surrounding water making the filtering screen self-cleaning. Nauplii and rotifers thus could be concentrated at high speed to a high density with little damage.

滚筒的旋转会在滤网与周围的水体间产生相对运动而使得滤网面自净，丰年虾无节幼虫与轮虫因此可以在高速下被浓缩到高密度而不会有太大的损伤。

Panorama Many people are dancing in a large sitting room.

在一个大客厅里，许多人在跳舞。

We fully understand how a microwave oven cooks food, it is first necessary to talk ...

我们充分理解一个微波炉煮的食物，这是首先要谈。。。