Data inversion of multi-dimensional magnetic resonance in porous media

Since its inception in the 1970s,multi-dimensional magnetic resonance(MR)has emerged as a powerful tool for non-invasive investigations of structures and molecular interactions.MR spectroscopy beyond one dimension allows the study of the correlation,exchange processes,and separation of overlapping spectral information.The multi-dimensional concept has been re-implemented over the last two decades to explore molecular motion and spin dynamics in porous media.Apart from Fourier transform,methods have been developed for processing the multi-dimensional time-domain data,identifying the fluid components,and estimating pore surface permeability via joint relaxation and diffusion spectra.Through the resolution of spectroscopic signals with spatial encoding gradients,multi-dimensional MR imaging has been widely used to investigate the microscopic environment of living tissues and distinguish diseases.Signals in each voxel are usually expressed as multi-exponential decay,representing microstructures or environments along multiple pore scales.The separation of contributions from different environments is a common ill-posed problem,which can be resolved numerically.Moreover,the inversion methods and experimental parameters determine the resolution of multi-dimensional spectra.This paper reviews the algorithms that have been proposed to process multidimensional MR datasets in different scenarios.Detailed information at the microscopic level,such as tissue components,fluid types and food structures in multi-disciplinary sciences,could be revealed through multi-dimensional MR.

基于改进Marching Cubes算法的雷达气象数据三维重建

在气象领域中,多普勒天气雷达探测到的气象数据采用以雷达站点为原点的三维极坐标系进行存储,具有形状不规则、数据量大等特征。Marching Cubes(MC)算法是三维重建中的经典算法,但应用于气象领域时存在重建效率低下和不能直接处理气象数据的缺点。为了实现气象数据的三维重建,基于MC算法提出雷达数据归一化处理与状态标记判别算法NBV-MC。该算法根据雷达基数据文件的特点对其进行归一化预处理,使用雷达基数据构建拟梯形六面体体素,对每一个六面体体素进行状态标记,在遍历六面体体素时动态判别其是否需要处理。实验结果表明,NBV-MC算法不仅解决了由于气象数据具有不规则性而不能直接用于MC算法的问题,而且可以在保证数据真实性和重建效果的情况下有效减少绘制等值面所需要的三角面片数量,提高重建速度。与MC算法相比,NBV-MC算法的重建效率提升了77.70%以上,有利于实时场景交互,便于气象研究人员直接分析雷达数据。

ST-Map:an Interactive Map for Discovering Spatial and Temporal Patterns in Bibliographic Data

Getting insight into the spatiotemporal distribution patterns of knowledge innovation is receiving increasing attention from policymakers and economic research organizations.Many studies use bibliometric data to analyze the popularity of certain research topics,well-adopted methodologies,influential authors,and the interrelationships among research disciplines.However,the visual exploration of the patterns of research topics with an emphasis on their spatial and temporal distribution remains challenging.This study combined a Space-Time Cube(STC)and a 3D glyph to represent the complex multivariate bibliographic data.We further implemented a visual design by developing an interactive interface.The effectiveness,understandability,and engagement of ST-Map are evaluated by seven experts in geovisualization.The results suggest that it is promising to use three-dimensional visualization to show the overview and on-demand details on a single screen.

Hierarchical Datacubes

Many approaches have been proposed to pre-compute data cubes in order to efficiently respond to OLAP queries in data warehouses. However, few have proposed solutions integrating all of the possible outcomes, and it is this idea that leads the integration of hierarchical dimensions into these responses. To meet this need, we propose, in this paper, a complete redefinition of the framework and the formal definition of traditional database analysis through the prism of hierarchical dimensions. After characterizing the hierarchical data cube lattice, we introduce the hierarchical data cube and its most concise reduced representation, the closed hierarchical data cube. It offers compact replication so as to optimize storage space by removing redundancies of strongly correlated data. Such data are typical of data warehouses, and in particular in video games, our field of study and experimentation, where hierarchical dimension attributes are widely represented.

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.

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.

Multi-dimension and multi-modal rolling mill vibration prediction model based on multi-level network fusion

Mill vibration is a common problem in rolling production,which directly affects the thickness accuracy of the strip and may even lead to strip fracture accidents in serious cases.The existing vibration prediction models do not consider the features contained in the data,resulting in limited improvement of model accuracy.To address these challenges,this paper proposes a multi-dimensional multi-modal cold rolling vibration time series prediction model(MDMMVPM)based on the deep fusion of multi-level networks.In the model,the long-term and short-term modal features of multi-dimensional data are considered,and the appropriate prediction algorithms are selected for different data features.Based on the established prediction model,the effects of tension and rolling force on mill vibration are analyzed.Taking the 5th stand of a cold mill in a steel mill as the research object,the innovative model is applied to predict the mill vibration for the first time.The experimental results show that the correlation coefficient(R^(2))of the model proposed in this paper is 92.5%,and the root-mean-square error(RMSE)is 0.0011,which significantly improves the modeling accuracy compared with the existing models.The proposed model is also suitable for the hot rolling process,which provides a new method for the prediction of strip rolling vibration.

CUBE自动滤波在远海多波束测深中的应用

为提升远海多波束测深数据标准化处理能力,验证CUBE自动滤波在远海海域的实际应用效果,选取远海海域3个典型深度测区,使用优选的CUBE自动处理参数组,对多波束测深数据进行自动滤波处理,并从处理用时、逐点标记以及交叉点水深差值3个方面,将处理结果与多名作业人员的手工处理结果进行对比分析。实验结果表明,使用CUBE自动处理与人工处理结果基本相同,且少量处理不同之处产生的水深成果差值均在测量误差允许范围之内,且CUBE自动处理在处理效率、成果标准一致性方面均优于人工处理。本文成果对于提升远海多波束测深数据标准化处理能力具有较强的实践指导意义。

PC Cluster环境下基于多维数组的Data Cube算法

因为需要存取大量的数据,计算datacube要花费大量的时间和存储空间。本文研究了使用便宜的PC机群计算datacube的方法。我们使用多维数组作为存储结构,并将数组划分成若干个分片,对每个分片进行压缩以节约存储空间、减少存取时间和增量维护时间,分片被分布到不同的处理机。我们提出了一个新的流水线组织方法以及对分片建立索引的思想,大大减少了外排序的代价和存取磁盘的次数。实验结果表明我们的算法具有一定的伸缩性。

基于时态层次链的Data Cube多版本维护方案

针对Data Cube的模式变动造成的多版本问题,对时态聚集关系与时态层次链进行了形式化描述,并基于这些关系实现了多个版本的Data Cube的统一生成算法,不仅可以高效地进行Data Cube多个版本的维护,而且在OLAP查询也可以基于时态层次链来执行,从而提高系统的整体效率。

XML Based Data Cube and X-OLAP

Data warehouse provides storage and management for mass data, but data schema evolves with time on. When data schema is changed, added or deleted, the data in data warehouse must comply with the changed data schema, so data warehouse must be re organized or re constructed, but this process is exhausting and wasteful. In order to cope with these problems, this paper develops an approach to model data cube with XML, which emerges as a universal format for data exchange on the Web and which can make data warehouse flexible and scalable. This paper also extends OLAP algebra for XML based data cube, which is called X OLAP.

基于语义的Data Cube数字水印技术

数字水印技术可以有效地保护版权,数据仓库中用于OLAP(OnLine Analyti-cal Processing,联机分析处理)的Data Cube(数据立方体,亦称多维数据集)不仅包含有价值的数据,而且其设计模式与分析模式也体现了Data Cube拥有者的知识产权.将数字水印技术引入Data Cube中,并充分利用Data Cube的语义信息,从而提供一个通用、实用的Da-ta Cube数字水印技术解决方案.

一种基于多层次链的Data Cube维层次编码

研究了Data Cube的索引技术,提出一种能够处理复杂维层次结构情况的维层次编码.维层次编码充分利用了Data Cube中维的层次性及其语义特性,结合二进制编码与维层次结构编码对维成员值进行编码,通过二进制操作,可以快速检索出与查询关键字相匹配的维层次编码.同时,基于维层次编码定义的层次链掩码,层次掩码及检索函数能够充分利用多维数据中的语义信息,实现基于语义的检索,减少了I/O开销,提高了OLAP查询效率.

基于data cube的成绩分析系统的设计

现有成绩分析系统只是将成绩作为一个数据利用查询语句加以简单统计和表示,并没有真正的实现分析功能,针对这一状况设计了一种基于数据立方体(data cube)的成绩分析系统。该系统能实现多维、多层次地分析,为教学效果的评估提供定量的数据支持,有助于提高评估体系的客观性和公正性,并为教育教学改革提供可行的策略和方法。

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.

Fast Computation of Sparse Data Cubes with Constraints

For a data cube there are always constraints between dimensions or among attributes in a dimension, such as functional dependencies. We introduce the problem that when there are functional dependencies, how to use them to speed up the computation of sparse data cubes. A new algorithm CFD (Computation by Functional Dependencies) is presented to satisfy this demand. CFD determines the order of dimensions by considering cardinalities of dimensions and functional dependencies between dimensions together, thus reduce the number of partitions for such dimensions. CFD also combines partitioning from bottom to up and aggregate computation from top to bottom to speed up the computation further. CFD can efficiently compute a data cube with hierarchies in a dimension from the smallest granularity to the coarsest one. Key words sparse data cube - functional dependency - dimension - partition - CFD CLC number TP 311 Foundation item: Supported by the E-Government Project of the Ministry of Science and Technology of China (2001BA110B01)Biography: Feng Yu-cai (1945-), male, Professor, research direction: database system.

Design of similarity measure for discrete data and application to multi-dimension

Similarity measure design for discrete data group was proposed. Similarity measure design for continuous membership function was also carried out. Proposed similarity measures were designed based on fuzzy number and distance measure, and were proved. To calculate the degree of similarity of discrete data, relative degree between data and total distribution was obtained. Discrete data similarity measure was completed with combination of mentioned relative degrees. Power interconnected system with multi characteristics was considered to apply discrete similarity measure. Naturally, similarity measure was extended to multi-dimensional similarity measure case, and applied to bus clustering problem.

一种特殊DATA CUBE的技术研究

封闭数据立方体利用元组间的关联,除去冗余信息,在减小数据立方体体积的同时,避免了查询时的解压缩。从源数据的分组角度对封闭数据立方体概念进行了解释,并在此基础上详细分析了由于源数据的更新而导致的对应封闭数据立方体的更新过程,给出了更新算法的框架。

Web Data Cube Construction in Multidimensional On-line Analytical Processing Environment

This paper investigates how to integrate Web data into a multidimensional data warehouse （cube） for comprehensive on-line analytical processing （OLAP） and decision making. An approach for Web data-based cube construction is proposed, which includes Web data modeling based on MIX （ Metadam based Integration model for data X-change ）, generic and specific mapping rules design, and a transformation algorithm for mapping Web data to a multidimensional array. Besides, the structure and implementation of the prototype of a Web data base cube are discussed.

Outlier detection based on multi-dimensional clustering and local density

Outlier detection is an important task in data mining. In fact, it is difficult to find the clustering centers in some sophisticated multidimensional datasets and to measure the deviation degree of each potential outlier. In this work, an effective outlier detection method based on multi-dimensional clustering and local density(ODBMCLD) is proposed. ODBMCLD firstly identifies the center objects by the local density peak of data objects, and clusters the whole dataset based on the center objects. Then, outlier objects belonging to different clusters will be marked as candidates of abnormal data. Finally, the top N points among these abnormal candidates are chosen as final anomaly objects with high outlier factors. The feasibility and effectiveness of the method are verified by experiments.