Multi-dimensional database design and implementation of dam safety monitoring system

To improve the effectiveness of dam safety monitoring database systems, the development process of a multi-dimensional conceptual data model was analyzed and a logic design wasachieved in multi-dimensional database mode. The optimal data model was confirmed by identifying data objects, defining relations and reviewing entities. The conversion of relations among entities to external keys and entities and physical attributes to tables and fields was interpreted completely. On this basis, a multi-dimensional database that reflects the management and analysis of a dam safety monitoring system on monitoring data information has been established, for which factual tables and dimensional tables have been designed. Finally, based on service design and user interface design, the dam safety monitoring system has been developed with Delphi as the development tool. This development project shows that the multi-dimensional database can simplify the development process and minimize hidden dangers in the database structure design. It is superior to other dam safety monitoring system development models and can provide a new research direction for system developers.

Multidimensional Data Querying on Tree-Structured Overlay

Multidimensional data query has been gaining much interest in database research communities in recent years, yet many of the existing studies focus mainly on ten tralized systems. A solution to querying in Peer-to-Peer（P2P） environment was proposed to achieve both low processing cost in terms of the number of peers accessed and search messages and balanced query loads among peers. The system is based on a balanced tree structured P2P network. By partitioning the query space intelligently, the amount of query forwarding is effectively controlled, and the number of peers involved and search messages are also limited. Dynamic load balancing can be achieved during space partitioning and query resolving. Extensive experiments confirm the effectiveness and scalability of our algorithms on P2P networks.

Goodness-of-fit tests for multi-dimensional copulas:Expanding application to historical drought data

The question of how to choose a copula model that best fits a given dataset is a predominant limitation of the copula approach, and the present study aims to investigate the techniques of goodness-of-fit tests for multi-dimensional copulas. A goodness-of-fit test based on Rosenblatt＇s transformation was mathematically expanded from two dimensions to three dimensions and procedures of a bootstrap version of the test were provided. Through stochastic copula simulation, an empirical application of historical drought data at the Lintong Gauge Station shows that the goodness-of-fit tests perform well, revealing that both trivariate Gaussian and Student t copulas are acceptable for modeling the dependence structures of the observed drought duration, severity, and peak. The goodness-of-fit tests for multi-dimensional copulas can provide further support and help a lot in the potential applications of a wider range of copulas to describe the associations of correlated hydrological variables. However, for the application of copulas with the number of dimensions larger than three, more complicated computational efforts as well as exploration and parameterization of corresponding copulas are required.

Dimensionality Reduction of High-Dimensional Highly Correlated Multivariate Grapevine Dataset

Viticulturists traditionally have a keen interest in studying the relationship between the biochemistry of grapevines’ leaves/petioles and their associated spectral reflectance in order to understand the fruit ripening rate, water status, nutrient levels, and disease risk. In this paper, we implement imaging spectroscopy (hyperspectral) reflectance data, for the reflective 330 - 2510 nm wavelength region (986 total spectral bands), to assess vineyard nutrient status;this constitutes a high dimensional dataset with a covariance matrix that is ill-conditioned. The identification of the variables (wavelength bands) that contribute useful information for nutrient assessment and prediction, plays a pivotal role in multivariate statistical modeling. In recent years, researchers have successfully developed many continuous, nearly unbiased, sparse and accurate variable selection methods to overcome this problem. This paper compares four regularized and one functional regression methods: Elastic Net, Multi-Step Adaptive Elastic Net, Minimax Concave Penalty, iterative Sure Independence Screening, and Functional Data Analysis for wavelength variable selection. Thereafter, the predictive performance of these regularized sparse models is enhanced using the stepwise regression. This comparative study of regression methods using a high-dimensional and highly correlated grapevine hyperspectral dataset revealed that the performance of Elastic Net for variable selection yields the best predictive ability.

Finding Main Causes of Elevator Accidents via Multi-Dimensional Association Rule in Edge Computing Environment

In order to discover the main causes of elevator group accidents in edge computing environment, a multi-dimensional data model of elevator accident data is established by using data cube technology, proposing and implementing a method by combining classical Apriori algorithm with the model, digging out frequent items of elevator accident data to explore the main reasons for the occurrence of elevator accidents. In addition, a collaborative edge model of elevator accidents is set to achieve data sharing, making it possible to check the detail of each cause to confirm the causes of elevator accidents. Lastly the association rules are applied to find the law of elevator Accidents.

Design and Realization of Block Level Augmented Reality Three-Dimensional Map

In response to the construction needs of “Real 3D China”, the system structure, functional framework, application direction and product form of block level augmented reality three-dimensional map is designed. Those provide references and ideas for the later large-scale production of augmented reality three-dimensional map. The augmented reality three-dimensional map is produced based on skyline software. Including the map browsing, measurement and analysis and so on, the basic function of three-dimensional map is realized. The special functional module including housing management, pipeline management and so on is developed combining the need of residential quarters development, that expands the application fields of augmented reality three-dimensional map. Those lay the groundwork for the application of augmented reality three-dimensional map. .

基于OBE的数据结构多维融合教学模式实践

在工程教育专业认证背景下,为了开展“以学生为中心、以产出为导向”的数据结构教学,借助现代信息技术构建线上线下、课内课外以及理论实践多维融合的教学模式。该教学模式以OBE理念为指导进行混合式教学及考核评价方法设计,并基于翻转课堂有效整合不同维度的学习活动,激发学生自主学习的积极性,拓展学生的知识体系。目前,该教学模式已面向河南大学计算机科学与技术专业进行了3个学期的教学实践,从学生、专家的反馈情况及课程目标达成情况来看,教学效果明显,多维融合教学模式有效提升了学生的自主学习能力、分析与解决实际问题能力以及拓展创新能力。

大型锻造液压机锻造成形数智化应用研究

随着工业4.0时代的到来,智能制造已成为制造业转型升级的关键方向。在此背景下,大型锻造液压机作为重要的锻造设备,对生产效率和产品质量的提升尤为重要。研究基于人工智能的大型锻造压机锻造成形全过程多维度数据采集及关键参数智能感知技术,构建一套完整的数据采集系统,实时监控锻造过程中的温度、压力、速度等关键参数,借助云计算平台的强大数据处理能力,对采集到的数据进行深度分析和处理,实现锻造过程参数的实时监测和智能调控,达到锻造过程的数智化应用,为智能制造领域提供理论支持和技术参考。

基于运营商网络数据的数字乡村场景赋能研究

电信运营商在数字乡村建设中充分利用大数据技术,结合网络数据的深度挖掘和外部数据集成,构建了一个以O域、B域、M域数据为核心,辅以外部数据(如全国人口普查数据)的融合数据赋能体系。该体系包括数据引入层、多维度融合分析层和场景应用层3个层级结构,旨在从人口分布、网络资源利用率、营服体系效能、渠道布局优化以及产业行业发展等多个视角全面透视数字乡村现状,制定出因地制宜、因需而变的差异化优先聚焦策略。

基于点云的多源数据融合技术

三维激光扫描技术是一门逆向工程应用技术,凭借其高速、全面且高自动化的特点,显示出了强大的不可替代性。三维点云数据有着其多样化、精度高、数据完整的优点。多源数据融合技术使不同设备获取的点云数据、影像数据、空载数据能够相互弥补,提高不同场景环境下的作业效率,完善数据的结构和精度质量,使空、地、水下三维数据的一体化成为可能。通过对不同射程多台三维激光扫描仪的数据进行相对精度分析、调整和融合,并对不同融合方法获取的多源融合数据进行精度验证,探索一种多源数据融合的方法,形成多源数据融合的通用技术流程。为多种载体三维激光扫描技术的数据和影像数据的融合方法研究奠定基础。

ZFT索引:基于分段线性回归的学习型多维索引

传统索引方式一般是一种通用的数据结构,不特别针对数据分布和特征设计或优化其索引方式,随着数据空间维度或数据量的增加,可能会导致存储消耗大且查询效率急剧下降。针对这些问题提出ZFT索引(Z-order Fiting-tree Index),它主要分为离线和在线两个部分。离线构造部分使用Z-order曲线将多维空间中的数据点映射到一维空间中,构建线性回归模型学习映射后数据的分布与特征;在线部分完成点查询和范围查询。实验结果表明,ZFT索引的空间效率和查询效率明显优于传统的R树以及UB树,并且在范围查询和模型训练速度上都优于ZM索引。

智能化多源多维大数据融合设计在卫生应急中的应用

目的/意义针对来源复杂、标准多样的多源多维大数据,提出不同业务应用场景的数据治理和融合技术。方法/过程以多源多维大数据融合在应对重大突发公共卫生事件的实践为例,重点阐述多源多维大数据融合面临的挑战、应用架构设计、技术集成、实施路径、应用场景等内容,分析应用效果和存在的不足。结果/结论通过智能化多源多维大数据融合技术的设计,实现对千亿级别高频次更新的实时监听、读取、治理和交互关联,探索有效应对突发公共应急事件的智慧化路径。

区块链环境下多源大数据访问加密防护仿真

为了提高多源大数据在网络中传输和存储的安全性,提出区块链技术下多源大数据加密防护算法。对多源大数据展开降维处理,减少数据计算量,提高算法的整体效率;结合Henon映射与Logistic映射设计一种双混沌加密算法,实现多源大数据初步加密;利用区块链技术设计多源大数据加密防护方案,在智能合约的基础上,大数据所有者可以将双混沌加密算法加密后的密钥上传至区块链中,同时对数据的有效访问时间展开设定,在有效访问时间内只有满足访问策略的用户才可以访问多源大数据,以此实现多源大数据的加密防护。实验结果表明,所提算法加密后的数据不容易被攻击,且具有较高的运行效率和良好的加密效果。

面向多目标优化的航空发动机装配特征选择

由于航空发动机装配工艺和试车工艺的复杂性,收集到的航空发动机装配数据的装配特征非常庞大,严重干扰了对航空发动机装配质量的准确预测,如何选择航空发动机装配的关键质量特征实现质量预测成为极具挑战性的问题。因此,针对航空发动机装配特征选择难题,本文提出面向多目标优化的航空发动机装配数据的两阶段特征选择方法。明确特征选择的优化目标,在第1阶段,基于最大相关最小冗余算法的相关特征选择过程,计算装配特征与试车指标的互信息值,筛选出与试车指标相关性最大的相关特征,并剔除有干扰影响的冗余特征。在第2阶段,通过引入种群初始化策略和自适应遗传算子,提出基于改进的二代非支配排序遗传算法的关键质量特征选择过程,得到航空发动机装配的关键质量特征子集的帕累托前沿。实验表明,本文所提出的两阶段特征选择方法比传统的方法有更好的适用性和有效性,实现了对航空发动机装配特征选择,提高了对航空发动机装配质量的预测准确率。

融合多维注意力机制与动态尺度的数据校核算法

为实现对医疗机构中设备、药品及耗材等数据的智能监控与校核,文中提出了一种基于多维注意力机制的动态尺度数据校核算法。该算法引入了通道注意力和空间注意力两种机制来挖掘出数据的深度特征及空间位置,并将其送入主干网络Transformer中进行准确地数据校核。同时在算法模型中加入了动态尺度,且通过设置阈值来确定是否需要利用潜补偿来缓解不平衡网络。在自建数据集上进行的实验结果表明,所提算法模型的预测精确率可达96%,优于传统模型。证明了该算法的有效性,能够实现对数据的自动校核。

基于分布式多关联属性的高维数据差分隐私保护方法

针对高维数据发布的过程中存在由多关联属性引发的隐私信息泄露风险问题,在分布式环境下提出一种满足差分隐私保护的多关联属性高维数据发布方法(HDMPDP)。根据数据维度,提出一种基于分布式划分的粗糙集高效降维方法,完成对高维复杂数据特征属性的划分,降低数据维度的同时提高处理效率;设计属性分类准则,利用属性信息熵改进关联分析方法;对得到的属性分别进行加噪,优化噪声添加的方式,减轻关联属性带来的隐私问题。在Spark分布式框架下实现隐私保护数据发布,通过高维数据实验验证了该方法的有效性和隐私保护的安全性。

基于堆叠降噪自编码器的肝癌亚型分类

肝癌是威胁人类健康的常见恶性肿瘤之一。通过对基因数据使用深度学习方法进行整合来系统地获取对肝癌的认知,使用多组学的疾病分析方法来探究各组学之间的相互关系,有助于更准确的临床决策。然而,由于多组学数据具有高维稀疏性,存在大量的冗余特征和较少的可用临床标签样本。堆叠降噪编码器(SDAE)是能够从海量数据中获取有效特征的高效模型,因此基于SDAE模型提出一种层次式堆叠降噪编码器,来学习肝癌的RNA表达、miRNA表达和DNA甲基化数据的特征并进行整合和识别。实验结果表明:Hi-SDAE方法提高了对肝癌亚型分类的准确度,为肝癌针对性治疗提供了更有价值的参考依据。

基于情境实验的红色旅游恢复性效益及其影响因素分析——以延安革命纪念馆为例

红色旅游在我国近年来发展迅速,吸引了众多游客。社会效益是红色旅游的重要功能之一,如何拓展红色旅游的社会功能属性,推动红色旅游体验转型升级,是红色旅游发展中的重要问题。文章以注意力恢复理论为基础,采用情境实验法,运用调查问卷和便携式生理设备获取心理生理多维数据,探究红色旅游目的地环境恢复性潜力以及红色旅游对游客恢复性体验的影响。研究发现如下。1)和其他旅游相比较,反思是红色旅游为游客带来的更深刻的恢复性效益。2)红色旅游和其他旅游都可以给游客带来心理和生理层面的恢复性效益。心理层面具体表现为舒缓压力、情感体验中愉悦度的提升与唤醒度的降低、注意力恢复等;生理层面具体表现为脉率值、皮肤电导水平、肌电均方根的降低。3)个人偏好在红色旅游情境和游客自我反思之间发挥显著的调节作用。该研究探索了红色旅游情境下的旅游恢复性,丰富了注意力恢复理论,为红色旅游社会效益提升提供了新思路和新方向。

基于多维关联规则的用电负荷智能预测方法

用电负荷预测受到冗余数据影响,负荷预测值与实际值相差较大,因此提出基于多维关联规则的用电负荷智能预测方法。使用多维关联规则挖掘用电负荷频繁项集,获取全部用电负荷待预测数据,根据挖掘结果划分用电负荷种类。计算多维关联规则提升度,预处理冗余数据,生成待预测目标集。根据获取的用电序列,整合全部频繁项集,构建预测模型,并进行强关联学习。通过调整负荷数据训练收敛程度,获取用电负荷的最大、最小值。在用电设备节点中注入用电负荷预测多维关联规则修正数值,避免噪声数据影响预测结果。实验结果表明,该方法最大、最小负荷与实际数据,分别在9月30日和6月15日存在5 MW和0.3 MW的误差,说明该方法预测结果精准。