基于Hibernate的关联映射研究

在开发J2EE应用系统中,在进行数据库操作时,使用传统的JDBC来处理数据对象之间的关联关系会非常繁琐。Hibernate通过O/R Mapping将表间关系转化为实体之间的关系,这种关系通过在映射文件中加以声明,较简单地解决了这个问题。Hibernate的关联映射包括一对一,一对多,多对多3种。对这3种关联关系进行系统的分析研究,使得读者对Hibernate的关联映射有一定了解。     

基于用户搜索行为的query-doc关联挖掘

query和doc之间的关联关系是搜索引擎期望获取的一类有价值的信息. query和doc间准确的关联分析不仅可以帮助搜索结果排序,也在query和doc之间的桥接中起到重要作用,以实现相关query和doc之间的信息传递,有利于更深入的query理解和doc理解,并在此基础上开展相关应用.本文提出了一种基于用户搜索行为的query和doc关联关系挖掘算法,该方法首先对用户搜索点击日志中的数据进行整理与分析,构建query与doc间的二部图,再通过采用马尔可夫随机游走模型对二部图数据进行建模,挖掘二部图中的点击数据和session数据,最终挖掘出点击日志中用户没有点击到的doc数据,从而预测出query和doc间的隐含关联关系,同时也可以利用该算法得到query和query潜在的关联关系.基于以上理论基础,我们实现了一套完整的日志挖掘系统,通过大量的实验对比,该系统在各方面均取得了优异的表现,其中对检索结果相关性的性能提升可以达到71.23%,这充分表明,本文所提出的理论和算法能够很好地解决query和doc之间的隐含关系挖掘问题,为提高搜索结果的召回率、实现查询推荐和检索结果聚类奠定了良好的前提基础.     

基于本体可视化的关联数据集表达

近年来不同学科领域的语义数据井喷式增长,关联数据集关联关系的发布形式直接决定了这些数据集能否被快速、准确地理解和使用。目前,数据集关联关系的发布均采用文本,而本论文提出一种基于本体可视化的方法来表达关联数据集,使数据集更容易被理解和使用。设计关联数据集发布方案,基于 OWL2VOWL 项目实现将关联关系转化为 WebVOWL 规定格式的 JSON 元素,并将其结合 WebVOWL 嵌入到发布模型中,完成关联数据集关联关系的发布。依据以微生物为核心的关联数据集的关系可视化表示,完成在数据集上的查询,验证方案带来的便利。通过设计的本体可视化模型来完成关联数据集的表达,丰富了关联数据集的发布形式,并使得关联数据集更容易被专家甚至不懂本体的使用者理解和使用。     

基于全球剖分框架的多源空间信息区位关联与综合表达方法

针对全球海量多源空间信息之间关联关系建立困难的问题,提出了基于全球剖分框架的多源空间信息区位关联模型解决方案。首先,采用GeoSOT剖分框架对多源空间信息进行统一组织与管理;然后,根据提出的“对象-面片-数据”空间信息三层组织机理概念模型,设计并实现了基于全球剖分框架的多源空间信息区位关联模型;最后,进行了多源空间信息区位关联模型的综合表达应用实验。实验结果表明:本模型可以有效地建立起地理对象与多源空间信息之间的区位关联关系,可有效提升海量多源空间信息的空间关联与表达应用效能。     

一种支持服务关联的Web服务选择方法

目前存在的大多数基于QoS(quality of service)的Web服务选择方案都忽略了服务之间的关联关系, 认为服务之间是相互独立的, 使得组合服务的结果与实际有所偏差。为了提高组合Web服务QoS的精确性, 考虑到服务之间的关联关系对组合服务质量的影响, 分别对服务之间的接口关联、业务关联和统计关联三种关联关系进行研究, 提出了一种基于关联度的关联服务集合发现方法, 将支持服务关联的选择问题转换为混合整数规划模型进行求解。实验结果表明, 考虑服务之间的关联关系会提高组合Web服务的质量。     

一种有效的基于关联规则的视频分类方法

视频内容的分类是归纳、理解、检索视频数据的一个重要环节.现有的很多视频分类方法大都采用隐马尔科夫模型(HMM)理论,但是利用HMM模型处理视频数据有其固有的局限性.为了解决HMM模型存在的问题,提出了一种基于关联规则的视频分类方法.该方法在建立了状态模型之后,首先挖掘出各个状态之间的相互关联关系,利用关联距离(AD)的约束来衡量关联关系的可靠程度并形成状态转换模式(STP),进而由这些状态转换模式构成关联规则(AR),最后利用关联规则对视频数据进行分类.实验结果表明,这种分类方法适用于很多类型的视频数据,尤其对体育视频的效果更佳,其性能优于采用HMM理论的分类方法,是一种简单、有效的视频分类方法.     

基于面板数据的灰色B型关联模型及其应用

基于灰色B型关联分析的基本思想,针对现有面板数据灰色关联模型中对象排列顺序变化引起的关联序不一致的问题,以及因未充分考虑对象维度序列关于同一时刻不同对象下均值的变化率导致关联结果失真的问题,从时间维度和对象维度两个方面构建基于面板数据的灰色B型关联模型.在时间维度上,通过引进各指标间的总体位移差、一阶斜率差及二阶斜率差得到横向关联度;在对象维度上,采用各点与同一时刻不同对象下均值之比来刻画纵向关联度,并对两者求加权平均,进而构建出基于面板数据的灰色B型关联模型.讨论模型的规范性、一致保序性等性质.对比分析表明,模型简单有效且不受对象排列顺序的影响.以豫北平原5个市的干旱灾害风险指数为特征指标序列,理清干旱灾害风险指数与其12个影响因素的关联关系,为旱灾风险评估与调控提供理论支持.     

基于变关联分析的复杂产品设计模型简化

针对复杂产品设计模型中设计变量与设计目标间关联关系复杂且存在变关联关系的问题,提出一种基于变关联的模型简化方法.该方法根据敏感度分析和信息熵理论将设计变量划分为强关联变量、弱关联变量和变关联变量,将弱关联变量简化后的设计模型分解为层次模型;在层次模型的每个计算步求解中,根据关联矩阵法计算变关联变量与设计目标的变关联强度,若为强关联则计算该变关联变量,若为弱关联则简化该变关联变量.最后以换热器结构优化设计为例,验证了文中方法的有效性.     

一种支持事件驱动数据关联的业务服务对象模型

为了解决不同业务流程在实时联动的过程中由于人工操作导致延迟的问题,提出一种支持事件驱动数据关联的业务服务对象模型根据领域的内聚性和稳定性,领域专家建模业务服务对象,其中包括业务流程抽象·业务服务和数据抽象-数据对象,业务人员利用业务服务对象定义数据关联的业务规则,这些业务规则通过事件驱动的方式实现不同业务流程的实时联动.实验数据表明,基于业务服务对象的分布式状态变迁算法在业务流程联动中不仅具有良好的容错性而且高效.     

基于面板数据的灰色指标关联模型构建及其应用

针对现有面板数据灰色关联模型中对象的排列顺序变换引起灰色关联序不一致的缺陷,提出一种基于面板数据的新型灰色指标关联模型.考虑到面板数据的时间维度和对象维度差异特征,在时间维上利用增量表征指标的发展水平,对象维上引入离差表征指标的分布特征,同时将两个维度上的方向差异作为正负关联判断依据,构建时间维度和对象维度的关联系数,得到面板数据的灰色指标关联模型,并对模型的唯一性、对称性和可比性等性质进行证明.最后将该模型应用于苏南五市空气质量与主要污染物浓度的关联分析中,验证了灰色指标关联度不受对象排列顺序的影响,且能够反映指标间的正负关联关系.     

Diagnostic system with an artificial neural network in diagnostics of an analogue technical object

This paper presents a method to control an operation process of a complex technical object, for example a car engine, with the use of trivalent diagnostic information. Also, a general diagram of the complex technical object was presented, and its internal structure was described. A diagnostic analysis was conducted, as a result of which sets of the functional elements of the object and its diagnostic signals were determined. Also, the methodology of the diagnostic examination of the technical system was presented. The result was a functional and diagnostic model, which constituted the basis for initial diagnostic information, which is provided by the sets of information concerning the elements of the basic modules and their output signals. The article also covers a diagnostic system which uses a DIAG computer programme for the recognition of the states of technical objects in trivalent logics. A programme was presented and described in an analytical form for diagnosis through an artificial neural network (ANN), which recognises the states of reparable technical objects in trivalent logics. The final results obtained from the computations conducted by the DIAG programme are presented in the table of the states of the object.     

面向产业链协同SaaS平台的事件协同感知技术研究

为了解决业务关联的产业链协同SaaS平台不同企业对象间基于事件的动态协同感知问题,建立了基于业务事件的平台协同对象感知模型,提出了基于AOP技术的业务事件动态捕获机制、业务关联的协同对象列表产生器实现算法和基于GSM信号增强技术的事件消息发布方法。该模型和算法已在汽车零部件产业链协同SaaS平台上进行了应用,能满足汽车售后服务等复杂业务过程中跨单核企业联盟的业务协同需求。     

自适应在线判别外观学习的分层关联多目标跟踪

目的 复杂场景下目标频繁且长时间的遮挡、跟踪目标外观相似引起身份转换等问题给多目标跟踪带来许多挑战。针对多目标跟踪在复杂场景中因长时间遮挡引起身份转换和轨迹分段的问题,提出一种基于自适应在线判别外观学习的分层关联多目标跟踪算法。方法 利用轨迹置信度将多目标跟踪分为局部关联和全局关联两个层次。在局部关联中,置信度高的可靠轨迹利用外观、位置-大小相似度与当前帧检测点进行关联;在全局关联中,置信度低的不可靠轨迹引入运动模型和有效关联范围进一步关联分段的轨迹。在提取目标外观特征时引入增量线性可判别分析方法以解决身份转换问题,依据新增样本与目标样本均值的外观特征差异自适应地更新目标外观模型。结果 在公开数据集2D MOT2015中的PETS09-S2L1、TUD-Stadmitte、Town-Center 3个数据集中与当前10种多目标跟踪算法进行比较,该方法对各个数据集身份转换和轨迹分段都有减少,其中在Town-Center数据集中,身份转换减少了60个,轨迹分段减少了84个,跟踪准确度提高了5.2%以上。结论 本文多目标跟踪方法,能够在复杂场景中稳定有效地实现多目标跟踪,减少轨迹分段现象,其中引入的在线线性可判别外观学习对遮挡产生的身份转换具有良好的解决效果。     

Trajectory association across multiple airborne cameras

A camera mounted on an aerial vehicle provides an excellent means for monitoring large areas of a scene. Utilizing several such cameras on different aerial vehicles allows further flexibility, in terms of increased visual scope and in the pursuit of multiple targets. In this paper, we address the problem of associating objects across multiple airborne cameras. Since the cameras are moving and often widely separated, direct appearance-based or proximity-based constraints cannot be used. Instead, we exploit geometric constraints on the relationship between the motion of each object across cameras, to test multiple association hypotheses, without assuming any prior calibration information. Given our scene model, we propose a likelihood function for evaluating a hypothesized association between observations in multiple cameras that is geometrically motivated. Since multiple cameras exist, ensuring coherency in association is an essential requirement, e.g. that transitive closure is maintained between more than two cameras. To ensure such coherency we pose the problem of maximizing the likelihood function as a k-dimensional matching and use an approximation to find the optimal assignment of association. Using the proposed error function, canonical trajectories of each object and optimal estimates of inter-camera transformations (in a maximum likelihood sense) are computed. Finally, we show that as a result of associating objects across the cameras, a concurrent visualization of multiple aerial video streams is possible and that, under special conditions, trajectories interrupted due to occlusion or missing detections can be repaired. Results are shown on a number of real and controlled scenarios with multiple objects observed by multiple cameras, validating our qualitative models, and through simulation quantitative performance is also reported.     

扩展AL log挖掘日志本体的ILP方法*

为发现Web使用记录中所蕴涵的用户访问模式,在深入分析日志本体中事件间的抽象关系后,提出适用于原子事件和复合事件间整分关系推理的ALC传播规则扩展已有的推理模式,并在此基础上提出一种挖掘日志本体的ILP方法。该方法结合描述逻辑和Horn规则在知识表示和推理过程中互补的特点,采用ALlog混合系统构建知识库,利用约束SLD反驳消解和扩展ALC传播规则从日志本体中学习用户访问模式,达到站点商业智能和个性化的目的。最后给出验证该方法的实例,实验结果表明了该方法的可行性和有效性。     

Semantic association enhancement transformer with relative position for image captioning

Transformer-based architectures have shown encouraging results in image captioning. They usually utilize self-attention based methods to establish the semantic association between objects in an image for predicting caption. However, when appearance features between the candidate object and query object show weak dependence, the self-attention based methods are hard to capture the semantic association between them. In this paper, a Semantic Association Enhancement Transformer model is proposed to address the above challenge. First, an Appearance-Geometry Multi-Head Attention is introduced to model a visual relationship by integrating the geometry features and appearance features of the objects. The visual relationship characterizes the semantic association and relative position among the objects. Secondly, a Visual Relationship Improving module is presented to weigh the importance of appearance feature and geometry feature of query object to the modeled visual relationship. Then, the visual relationship among different objects is adaptively improved according to the constructed importance, especially the objects with weak dependence on appearance features, thereby enhancing their semantic association. Extensive experiments on MS COCO dataset demonstrate that the proposed method outperforms the state-of-the-art methods.

     

Layered data association using graph-theoretic formulation with applications to tennis ball tracking in monocular sequences

In this paper, we propose a multi-layered data association scheme with graph-theoretic formulation for tracking multiple objects that undergo switching dynamics in clutter. The proposed scheme takes as input object candidates detected in each frame. At the object candidate level, "tracklets' are "grown' from sets of candidates that have high probabilities of containing only true positives. At the tracklet level, a directed and weighted graph is constructed, where each node is a tracklet, and the edge weight between two nodes is defined according to the "compatibility' of the two tracklets. The association problem is then formulated as an all-pairs shortest path (APSP) problem in this graph. Finally, at the path level, by analyzing the all-pairs shortest paths, all object trajectories are identified, and track initiation and track termination are automatically dealt with. By exploiting a special topological property of the graph, we have also developed a more efficient APSP algorithm than the general-purpose ones. The proposed data association scheme is applied to tennis sequences to track tennis balls. Experiments show that it works well on sequences where other data association methods perform poorly or fail completely.     

面向对象的顺控输送系统编程方法

将面向对象方法引入复杂逻辑顺序控制系统，提出将逻辑关系分成两部分，设备及相关的自有逻辑经抽象与封装成为设备对象，设备之间的逻辑则被封装到虚拟的流程对象中，通过对象之间的消息交互实现顺序控制功能。采用面向对象方法，顺序控制系统编程可以从实现复杂的逻辑运算转变成创建两类对象和建立对象之间的消息传递机制以一个输送系统为例阐明面向对象方法可以显著提高编程效率，增强系统的可扩充性，面向对象方法为顺控系统的建模、优化等进一步研究提供了一个新思路。     

信息管理系统中实体bean问题之解决方案研究

设计企业bean应用时,实体bean的目的不是代表对象模型中的每个持久性对象。实体bean更适合于粗粒度的持久性业务对象,使用复合实体模式可以设计粗粒度的持久性业务对象。本文首先简单介绍了目前流行的MIS系统整体架构设计,然后分析了某学院MIS系统B端学生信息管理模块中的数据库模型,接着说明将复杂的对象模型直接映射到实体bean模型时产生的细粒度实体bean问题。最后介绍了在MIS系统中细粒度实体bean问题的解决方案——复合实体设计模式及相关的会话外观和值对象设计模式。     

Efficient and privacy-enhanced object traceability based on unified and linked EPCIS events

EPC Information Service (EPCIS) is a de-facto standard for visibility event/master data in supply chains, and the standard is widely known as a key solution to achieve object traceability. Despite its expressive power, this information service is prone to make application developers write queries redundantly and recursively, especially in distributed environments; additionally, it is hard for business partners to protect sensitive business contexts in each object level due to its event-oriented persistence approach. Therefore, we propose a graph-oriented persistence approach for the visibility data to achieve efficient and privacy-enhanced object traceability based on unified and linked EPCIS events.