基于分布式智能移动Agent的信息检索系统

随着网络的迅速发展和信息时代的到来,从网络中快速准确地检索到自己所需要的信息变得更为困难,于是提出了分布式移动多Agent的概念来接受此挑战。首先对Agent理论的研究现状进行了综述和讨论,主要内容包括智能Agent的概念、特性、多智能Agent系统等;然后在研究了现有的检索系统的基础上,提出了其存在的问题,并进而提出了解决方案,即基于分布式智能多A-gent的智能检索系统的设计和实现;该系统可及时有效地根据用户的需求实现智能地、自主地、协作地、快速地检索、过滤和融合网络信息。

智能主体在构件检索中的知识处理研究

分布式构件库系统中,智能主体可通过协作联合完成用户的检索要求。对构件检索来说,确定哪些构件库拥有目标构件是一个需要首先解决的数据源选择问题。智能主体拥有自己的知识库,并且需要具备学习能力,能更新其知识库以保持检索结果的有效性。文章讨论了可更新的主体,它可以将新的信息合并到给定的知识库中。我们提出了一种基于逻辑程序设计的知识表达和更新方法,该方法遵从声明式更新策略以执行更新任务。我们所提出的可更新主体具有清晰的语义,也能够以恰当的方式处理不一致的信息,从而较智能地解决了数据源选择问题。

电子病历智能检索系统设计与实现

以海南省人民医院为例,阐述电子病历智能检索系统设计思路、技术路线、架构以及具体功能,指出该系统应用有助于提升对临床科研的支撑保障能力,为实现精准医疗和智慧医疗服务体系奠定坚实基础。

面向海量植物图像的智能检索系统设计

针对传统软件技术设计的植物图像检索系统中存在无法实现智能检索、植物图像数量增长慢、检索系统难以扩容,以及当植物图像数量达到百万级以上时检索效率低和检索请求高并发时植物图像加载慢等问题,提出利用百度AI技术、Image Sharp图像分割技术和CV2颜色识别技术实现植物图像的智能检索。利用Fast DFS技术实现检索系统的动态扩容、负载均衡和植物图像的快速加载,利用Solr搜索引擎技术提高海量植物图像的检索效率,利用Python爬虫技术不断丰富检索系统的植物图像从而实现检索系统的可持续化发展。实验结果表明,通过上述技术能够构建一个面向海量植物图像的智能检索系统。

厦门人才网分布式全文检索与推荐系统设计

基于Lucene.Net框架,利用.Net Remoting技术,结合多种数据挖掘和推荐算法构建面向大规模文本数据的厦门人才网分布式全文检索与推荐系统.提出把大索引切割成小索引,采用多台服务器并行处理的分布式解决方案,完成招聘企业、职位、应聘人员信息的整合与聚集,实现信息的智能推荐.实验结果表明,本系统取得了较高的索引效率、较快的检索速度以及较为精准的推荐结果,并在实际应用中取得了良好的效果.

基于移动Agent的分布式地理信息检索系统研究

如何从海量的空间信息中快速准确地找出用户感兴趣的信息,是地理信息空间检索服务所要解决的主要问题。为此,本文提出了一种基于移动Agent的分布式地理信息检索框架,并对其中的关键技术进行了研究。移动Agent技术的引入使得空间检索更加智能化,能有效解决网络带宽问题和离线计算问题,同时极大地提高了检索效率,为分布式GIS系统建设提供了一种新的思路和解决方法。

一种基于多维学习的分布式智能检索模型

介绍智能检索Agent和分布式信息系统的发展状况;在分析用户偏好知识、专家经验知识和网络信息分类特点的基础上,结合分布式网络系统,提出一种新的基于多维学习的分布式智能检索模型。

Innovative Analysis Ready Data(ARD)product and process requirements,software system design,algorithms and implementation at the midstream as necessary-but-notsufficient precondition of the downstream in a new notion of Space Economy 4.0-Part 2:Software developments

Aiming at the convergence between Earth observation(EO)Big Data and Artificial General Intelligence(AGI),this paper consists of two parts.In the previous Part 1,existing EO optical sensory imagederived Level 2/Analysis Ready Data(ARD)products and processes are critically compared,to overcome their lack of harmonization/standardization/interoperability and suitability in a new notion of Space Economy 4.0.In the present Part 2,original contributions comprise,at the Marr five levels of system understanding:(1)an innovative,but realistic EO optical sensory image-derived semantics-enriched ARD co-product pair requirements specification.First,in the pursuit of third-level semantic/ontological interoperability,a novel ARD symbolic(categorical and semantic)co-product,known as Scene Classification Map(SCM),adopts an augmented Cloud versus Not-Cloud taxonomy,whose Not-Cloud class legend complies with the standard fully-nested Land Cover Classification System’s Dichotomous Phase taxonomy proposed by the United Nations Food and Agriculture Organization.Second,a novel ARD subsymbolic numerical co-product,specifically,a panchromatic or multispectral EO image whose dimensionless digital numbers are radiometrically calibrated into a physical unit of radiometric measure,ranging from top-of-atmosphere reflectance to surface reflectance and surface albedo values,in a five-stage radiometric correction sequence.(2)An original ARD process requirements specification.(3)An innovative ARD processing system design(architecture),where stepwise SCM generation and stepwise SCM-conditional EO optical image radiometric correction are alternated in sequence.(4)An original modular hierarchical hybrid(combined deductive and inductive)computer vision subsystem design,provided with feedback loops,where software solutions at the Marr two shallowest levels of system understanding,specifically,algorithm and implementation,are selected from the scientific literature,to benefit from their technology readiness level as proof of feasibility,required in addition to proven suitability.To be implemented in operational mode at the space segment and/or midstream segment by both public and private EO big data providers,the proposed EO optical sensory image-derived semantics-enriched ARD product-pair and process reference standard is highlighted as linchpin for success of a new notion of Space Economy 4.0.

Innovative Analysis Ready Data(ARD)product and process requirements,software system design,algorithms and implementation at the midstream as necessary-but-notsuffcient precondition of the downstream in a new notion of Space Economy 4.0-Part 1:Problem background in Artificial General Intelligence(AGI)

Aiming at the convergence between Earth observation(EO)Big Data and Artificial General Intelligence(AGI),this two-part paper identifies an innovative,but realistic EO optical sensory imagederived semantics-enriched Analysis Ready Data(ARD)productpair and process gold standard as linchpin for success of a new notion of Space Economy 4.0.To be implemented in operational mode at the space segment and/or midstream segment by both public and private EO big data providers,it is regarded as necessarybut-not-sufficient“horizontal”(enabling)precondition for:(I)Transforming existing EO big raster-based data cubes at the midstream segment,typically affected by the so-called data-rich information-poor syndrome,into a new generation of semanticsenabled EO big raster-based numerical data and vector-based categorical(symbolic,semi-symbolic or subsymbolic)information cube management systems,eligible for semantic content-based image retrieval and semantics-enabled information/knowledge discovery.(II)Boosting the downstream segment in the development of an ever-increasing ensemble of“vertical”(deep and narrow,user-specific and domain-dependent)value–adding information products and services,suitable for a potentially huge worldwide market of institutional and private end-users of space technology.For the sake of readability,this paper consists of two parts.In the present Part 1,first,background notions in the remote sensing metascience domain are critically revised for harmonization across the multidisciplinary domain of cognitive science.In short,keyword“information”is disambiguated into the two complementary notions of quantitative/unequivocal information-as-thing and qualitative/equivocal/inherently ill-posed information-as-data-interpretation.Moreover,buzzword“artificial intelligence”is disambiguated into the two better-constrained notions of Artificial Narrow Intelligence as part-without-inheritance-of AGI.Second,based on a betterdefined and better-understood vocabulary of multidisciplinary terms,existing EO optical sensory image-derived Level 2/ARD products and processes are investigated at the Marr five levels of understanding of an information processing system.To overcome their drawbacks,an innovative,but realistic EO optical sensory image-derived semantics-enriched ARD product-pair and process gold standard is proposed in the subsequent Part 2.