基于BP网络的农业主题元搜索引擎研究

为提高传统搜索引擎对农业主题信息的搜索效果,构建了基于BP网络的农业主题搜索引擎,该搜索引擎通过利用多个搜索引擎的搜索结果来提高搜索引擎的查全率,再通过使用BP网络对搜索结果进行二次判断来提高搜索引擎的查准率.     

移动互联网技术标准

从移动应用出发,为确保基本移动应用的互通性,开放移动联盟（OMA）组织制订移动应用层的技术引擎技术规范及实施互通测试;从固定互联网出发,万维网联盟（W3C）制订了基于Web基础应用技术的技术规范,为基于Web技术开发的移动互联网应用奠定了基础。已经有很多支撑移动互联网业务重要的技术引擎,如用于互联网访问和下载的技术引擎、用于提供移动用户和移动终端状态的技术引擎、用于社区和群组管理的技术引擎、用于移动搜索技术引擎、基于分类的内容过滤技术引擎被广泛应用于移动互联网中。     

Models for motion-based video indexing and retrieval

With the rapid proliferation of multimedia applications that require video data management, it is becoming more desirable to provide proper video data indexing techniques capable of representing the rich semantics in video data. In real-time applications, the need for efficient query processing is another reason for the use of such techniques. We present models that use the object motion information in order to characterize the events to allow subsequent retrieval. Algorithms for different spatiotemporal search cases in terms of spatial and temporal translation and scale invariance have been developed using various signal and image processing techniques. We have developed a prototype video search engine, PICTURESQUE (pictorial information and content transformation unified retrieval engine for spatiotemporal queries) to verify the proposed methods. Development of such technology will enable true multimedia search engines that will enable indexing and searching of the digital video data based on its true content.     

元搜索引擎中搜索结果的采集与处理

元搜索引擎与基本成员搜索引擎之间的信息交换,是元搜索引擎技术实现的难题,针对这一问题,选择Google和Baidu基本搜索引擎,实现了对这两个基本搜索引擎的检索、结果采集、生成搜索记录的过程,并对采集、处理后的检索结果进行排序,供用户进行二次检索。     

Feature Selection Based Correlation Attack on HTTPS Secure Searching

Search engine plays an irreplaceable role in web information organizing and accessing. It is very common for Internet users to query a search engine when retrieving web information. Sensitive data about search engine user’s intentions or behavior can be inferred from his query phrases, the returned results pages, and the webpages he visits subsequently. In order to protect contents of communications from being eavesdropped, some search engines adopt HTTPS by default to provide bidirectional encryption. This only provides an encrypted channel between user and search engine, the majority of webpages indexed in search engines’ results pages are still on HTTP enabled websites and the contents of these webpages can be observed by attackers once the user click on these links. Imitating attackers, we propose a novel approach for attacking secure search through correlating analysis of encrypted search with unencrypted webpages. We show that a simple weighted TF–DF mechanism is sufficient for selecting guessing phrase candidates. Imitating search engine users, by querying these candidates and enumerating webpages indexed in results pages, we can hit the definite query phrases and meanwhile reconstruct user’s web-surfing trails through DNS-based URLs comparison and flow feature statistics-based network traffic analysis. In the experiment including 28 search phrases, we achieved 67.86% hit rate at first guess and 96.43% hit rate within three guesses. Our empirical research shows that HTTPS traffic can be correlated and de-anonymized through HTTP traffic and secured search of search engines are not always secure unless HTTPS by default enabled everywhere.     

搜索引擎的检索性能及应用

本文首先讨论了Ｉｎｔｅｒｎｅｔ搜索引擎的三种类型。然后基于 Ｆｏｒｒｅｓｔｅｒ研究和 Ａｌｅｘａｎｄｅｒ　Ｌｅbｅｄｅｖ实验,分析讨论了主要搜索引擎各自的检索特点和性能,并对在科研中单个搜索引擎的应用进行了探讨。这对于科研人员选择检索方法可能会有所帮助。     

基于遗传算法的搜索引擎调度

通用搜索引擎（GSE）通过将用户的请求分发给实际的搜索引擎来为用户提供服务。GSE所选用的实际搜索将直接决定搜索的质量。由于实际搜索引擎的性能及用户的请求是动态改变的,所以固定的实际搜索引擎不利于系统整体性能的优化。文中利用一种模拟自然界生物进化过程的计算模型－－遗传算法来实现GSE中智能代理管理器的调度策略。利用它可以动态优化实际搜索引擎的组合,从而提高GSE的整体性能。     

基于工作流管理中项目管理的应用

在建立基于工作流管理的项目管理组织模型的基础上,研究项目管理中的任务分解机制。针对产品开发过程,运用面向对象的思想、计算机技术和数据库技术,构建工作流与项目管理的集成结构模型,设计PDM系统中的项目管理功能模块,最后,结合某空调企业的PDM系统,说明了基于工作流管理的项目管理模块的实际应用。     

LIBS与其它原子光谱技术在机油检测中的研究进展

发动机机油中磨损元素在发动机正常运转中发挥着重要的作用,对其进行实时检测可以预知事故的发生,防止发动机进一步磨损,因此实现机油中磨损元素的探测具有重要的应用价值。归纳了利用原子光谱对机油检测的关键问题,指出了原子发射光谱技术中的激光诱导击穿光谱（LIBS）技术和其它原子光谱技术在机油样品检测方面的研究进展,讨论了各种技术的优缺点。介绍了LIBS技术在机油检测方面的优势,引入了间接烧蚀LIBS技术。该技术不仅保存了LIBS技术本身的优势,而且大大提高了检测灵敏度。用间接烧蚀LIBS技术探测机油中磨损元素,具有巨大的应用前景,对该技术的现场探测应用进行了展望。     

自动检测技术在航空发动机方面的应用

本文主要介绍了自动检测技术在航空发动机方面的应用,以及国内外所涌现的最新理论和最新技术.自动检测技术是现在各种行业所必需,应用于军事、工业等过程控制的信号测量等方面,是高科技军事现代工业实现全面自动化的重要基础,航空发动机故障诊断技术是实现航空发动机视情维修的重要一环,在航空发动机的设计、生产、使用和维护中起着非常重要的指导作用.     

Exploring Social Networks and Improving Hypertext Results for Cloud Solutions

Internet technologies are constantly evolving as well as the way people use them. Search engines help users to find higher and better relevant results to their searches. Cloud Computing is an evolution of the Internet services and provides a step further ecosystem that can be used to improve the search of more relevant results. Each search engine is based on different modules in order to retrieve the results expected by users using specific keywords. Social networks appear as a reliable Web technology that can directly support a content search. Several studies have been performed showing the growth of social networks in people lives. Using the cloud computing paradigm it is possible to propose a more scalable and efficient way to explore public information available on online social networks. This paper includes the analyses of several social networks services, available contents, cloud-crawlers, and information extraction. In order to collect relevant data from social networks, a social crawler on cloud is proposed. The new approach provides a cloud-based crawler for low-cost, effective, and personalized search models. Moreover, a new algorithm to rank Web documents is proposed and demonstrated. The proposed system is evaluated in comparison with the top Internet search engine, Google, its behavior is very promising, and it is ready for use.     

基于飞参数据的航空发动机故障预报研究

为能够充分的体现出飞行参数数据的应用价值,本篇文章研究了对飞行参数历史数据的发动机故障预测方法。在对飞行参数数据里面多组数据与发动机性能参数关系进行理论分析的基础上,讨论了四种发动机的可行性和具体预测方法的不足,同时还研究了这个方法在发动机故障预测里面的应用。结合到飞机飞行参数系统记录的实际数据,这个研究的结果对飞行安全的问题具有了重要的意义。     

A Study of the Techniques of Automatic Abstracting and Knowledge Acquisition Systems

A Study of the Techniques of Automatic Abstracting and Knowledge Acquisition Systems     

Measurement and modelling of a linear electromagnetic actuator driven camless valve train for spark ignition IC engines under full load condition

Camless engine valve train for gasoline engines particularly port injection engines offer major improvements over traditional system with fixed valve timing and lift, in terms of efficiency, maximum torque and power, and emissions. Electromagnetic driven valve actuators are very promising in this context, but there are significant control problems. The use of displacement sensor could add extra cost to the camless engine. In this work, a moving coil actuator driven valve train was designed and prototyped and a 1D single-cylinder internal combustion engine model was built to investigate the benefit of without throttle by adopting such camless valve train using Ricardo WAVE under full load operating conditions. A novel low-cost sensor named linear actuator position sensor (LAPS) based on flux linkage change on search coil was constructed in LabVIEW by using current, voltage and proximity sensors to detect the valve lift. The measurement of valve lift and current was reported to prove the feasibility of the camless valve train. The valve lift was used for the single-cylinder engine model and the simulation shows that the peak overall engine efficiency using camless valve train reaches 38.2% and the peak torque increases by 5 Nm compared conventional throttled engine under full load operating conditions. The LAPS model was validated by the experimental valve lift and the high accuracy indicates that the LAPS can be adopted for future development of camless valve train for spark ignition IC engines.     

Applications of Machine Learning to Cognitive Radio Networks

Cognitive radio offers the promise of intelligent radios that can learn from and adapt to their environment. To date, most cognitive radio research has focused on policy-based radios that are hard-coded with a list of rules on how the radio should behave in certain scenarios. Some work has been done on radios with learning engines tailored for very specific applications. This article describes a concrete model for a generic cognitive radio to utilize a learning engine. The goal is to incorporate the results of the learning engine into a predicate calculus-based reasoning engine so that radios can remember lessons learned in the past and act quickly in the future. We also investigate the differences between reasoning and learning, and the fundamentals of when a particular application requires learning, and when simple reasoning is sufficient. The basic architecture is consistent with cognitive engines seen in AI research. The focus of this article is not to propose new machine learning algorithms, but rather to formalize their application to cognitive radio and develop a framework from within which they can be useful. We describe how our generic cognitive engine can tackle problems such as capacity maximization and dynamic spectrum access.     

大数据技术在通信工程项目管理中的应用

当前,大数据、移动互联网以及云计算等技术,是社会发展的重要力量,尤其是大数据技术。传统的工程项目管理方式,不利于保障工程项目管理效果,而利用大数据技术,则有利于优化通信工程项目管理的效果,提高通信工程的质量。文中分析了大数据技术在通信工程项目管理中的应用,提出了有针对性的应用措施,希望能对实际工作起到参考作用,优化整体管理效果。     

Arranging multicast forwarding table in class sequence in ternary-cam for line-speed lookup

PIM-SM (Protocol Independent Multicast-Sparse Mode) is a main multicast routing protocol in the IPv6 (Internet Protocol version 6). It can use either a shared tree or a shortest path tree to deliver data packets, consequently the multicast IP lookup engine requires, in some cases, two searches to get a correct lookup result according to its multicast forwarding rule, and it may result in a new requirement of doubling the lookup speed of the lookup engine. The ordinary method to satisfy this requirement in TCAM (Ternary Content Addressable Memory) based lookup engines is to exploit parallelism among multiple TCAMs. However, traditional parallel methods always induce more resources and higher design difficulty. We propose in this paper a novel approach to solve this problem. By arranging multicast forwarding table in class sequence in TCAM and making full use of the intrinsic characteristic of the TCAM, our approach can get the right lookup result with just one search and a single TCAM, while keeping the hardware of lookup engine unchanged. Experimental results have shown that the approach make it possible to satisfy forwarding IPv6 multicast packets at the full link rate of 20 Gb/s with just one TCAM with the current TCAM chip.     

一种基于信息推送的搜索引擎模型

随着信息时代的来临,海量信息不断地涌入因特网,人们想要获得自己所需要的信息,越来越离不开搜索引擎。但目前的搜索引擎往往无法满足人们搜索信息的根本要求,常常有人抱怨,我所搜索的信息都是我不需要的。针对现有搜索引擎的不足,把信息推送技术引入到搜索引擎中,建立了一种基于信息推送的搜索引擎模型,解决了现有搜索引擎在搜索过程中存在的资源迷向问题,真正实现了信息找人的搜索方式。     

基于DMD的真三维显示系统及其三维成像引擎设计

结合在体元式三维显示(Volumetric 3D display)中比较有代表性的3种显示系统,在说明了体元式真三维显示系统的实现思路和方法的基础上,从应用的角度解析以DMD为核心构成的三维成像引擎在体元式真三维显示系统中的应用,并给出3种基于DMD的三维成像引擎架构,能够在不同层次上很好地满足真三维显示对海量数据实时、高速、精确处理的要求。     

A Personalized Search Model Using Online Social Network Data Based on a Holonic Multiagent System

Personalized search utilizes user preferences to optimize search results,and most existing studies obtain user preferences by analyzing user behaviors in search engines that provide click-through data.However,the behavioral data are noisy because users often clicked some irrelevant documents to find their required information,and the new user cold start issue represents a serious problem,greatly reducing the performance of personalized search.This paper attempts to utilize online social network data to obtain user preferences that can be used to personalize search results,mine the knowledge of user interests,user influence and user relationships from online social networks,and use this knowledge to optimize the results returned by search engines.The proposed model is based on a holonic multiagent system that improves the adaptability and scalability of the model.The experimental results show that utilizing online social network data to implement personalized search is feasible and that online social network data are significant for personalized search.