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.

Floating Car Data Based Nonparametric Regression Model for Short-Term Travel Speed Prediction

A K-nearest neighbor （K-NN） based nonparametric regression model was proposed to predict travel speed for Beijing expressway. By using the historical traffic data collected from the detectors in Beijing expressways, a specically designed database was developed via the processes including data filtering, wavelet analysis and clustering. The relativity based weighted Euclidean distance was used as the distance metric to identify the K groups of nearest data series. Then, a K-NN nonparametric regression model was built to predict the average travel speeds up to 6 min into the future. Several randomly selected travel speed data series, collected from the floating car data （FCD） system, were used to validate the model. The results indicate that using the FCD, the model can predict average travel speeds with an accuracy of above 90%, and hence is feasible and effective.

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.

Freight Vehicle Routing Optimization for Sporadic Orders Using Floating Car Data

The increasing popularity of e-commerce brings large volumes of sporadic orders from different customers,which have to be handled by freight trucks and distribution centers. To improve the level of service and reduce the total shipping cost as well as traffic congestions in urban area, flexible methods and optimal vehicle routing strategies should be adopted to improve the efficiency of distribution effort. An optimization solution for vehicle routing and scheduling problem with time window for sporadic orders (VRPTW- S) was provided based on time-dependent travel time extracted from floating car data (FCD) with ArcGIS platform. A VRPTW-S model derived from the traditional vehicle routing problem was proposed, in which uncertainty of customer orders and travel time were considered. Based on this model, an advanced vehicle routing algorithm was designed to solve the problem. A case study of Shenzhen, Guangdong province, China, was conducted to demonstrate the vehicle operation flow,in which process of FCD and efficiency of delivery systems under different situations were discussed. The final results demonstrated a good performance of application of time-dependent travel time information using FCD in solving vehicle routing problems.

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.

A Comprehensive Taxi Assessment Index Using Floating Car Data

With the expansion of urban area and development of taxi system,problems arise,such as low operation efficiency,high taxi idling rate,and long passenger waiting-time. Although various studies have been conducted,only limited overview of the factors towards urban taxi system has been provided. Consequently,a comprehensive evaluation of taxi system is essential for the urban planner to analyze the current situation and take effective measures. This paper,by using Floating Car Data( FCD),proposes a Comprehensive Taxi Assessment Index( CTAI) to quantify the quality of existing urban taxi system with the assistance of Geographic Information System( GIS) technology. The proposed index system extracts and classifies key factors,reflecting the taxi system from the perspectives of operation efficiency,customer and taxi-driver satisfaction. The system contributes to improving the organization and operation of urban taxi system. Based on the data obtained from the city of Shenzhen,Guangdong Province,China,for both weekday and weekends( Dec.,2011),the proposed CTAI was illustrated by using the Principal Component Analysis( PCA) with ArcGIS 10. 0 platform. The results indicate that the system provides a good multi-dimensional view to delve into the existing urban taxi operation, thus to point out the most sensitive indices towards the entire system,which consequently provides guidelines for future improvement and management of urban taxi system.

City Routing Velocity Estimation Model under theEnvironment of Lack of Floating Car Data

After introducing the principle of float car data (FCD), this paper gives the primary flow of pre-handing and map- matching of the FCD. After analyzing the percentage of coverage of FCD on the road network, large quantity of heritage database of routing status is used to estimate the routing velocity when lack of FCD on parts road segments. Multi liner regression model is then put forwarded by considering the spatial correlativity among the road network, and some model parameters are deduced when time series is classified in day and week. Besides, error of velocity probability and error of status probability are achieved based on the result from field testing while the feasibility and reliability of the velocity estimation model is obtained as well. Finally, as a case study in Shanghai center area, the whole routing velocity in the road network is estimated and published in real time.

The Relationship between Road Characteristics and Speed Collected from Floating Car Data

Speed is of great importance to the safety level of a road. Speed choice is strongly influenced by the road environment and the drivers＇ assessment of safe speed level at a specific location. This paper presents an analysis of the relationships between speed and road characteristics and speed and driver characteristics. The analysis is based on big data on speed and driver characteristics combined with data on road characteristics on 49 secondary rural two-lane roads in Denmark. Data is modelled using multivariate linear regression. The results show a primarily influence from road and shoulder width, the extent of road markings and the section lengths on speed. Secondly, they also show the presence of woodland and intersections influencing speed as gender, age of vehicle and time of day do.

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 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.

Spatio-temporal characteristics and influencing factors of urban floating population in China from 2011 to 2015

Although China’s urban floating population is mainly concentrated in developed cities,from the central and western cities to the eastern developed cities,but the characteristics of the floating population in different cities are significantly different.This paper systematically investigates the spatiotemporal characteristics and influencing factors of the floating population in different levels of cities.The results show that the regional imbalance to further strengthen,accumulation and dispersion trend has become increasingly obvious,liquidity is positively correlated and city level scale,and urban agglomeration and the core city is still polarization center of floating population.Flow range is closely related to urban hierarchy:the higher the intra-urban grade,the more tend to inter-provincial flow;the lower the city grade,the more tend to intra-urban mobility.Short-term(1-2 years)and long-term(more than 7 years)flow-time coexist.The short-term liquidity increases with the city grade,and the long-term liquidity decreases with the city level.Farmers are still the main body of the floating population.Younger age,lower education level,low-skilled,high gender ratio employees are the most basic demographic characteristics of the floating population,although there are differences between different cities.The main reason for affecting the floating population is seeking jobs and doing business.

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.

Rapid prediction of floating and sinking components of raw coal

A model that rapidly predicts the density components of raw coal is described.It is based on a threegrade fast float/sink test.The recent comprehensive monthly floating and sinking data are used for comparison.The predicted data are used to draw washability curves and to provide a rapid evaluation of the effect from heavy medium induced separation.Thirty-one production shifts worth of fast float/sink data and the corresponding quick ash data are used to verify the model.The results show a small error with an arithmetic average of 0.53 and an absolute average error of 1.50.This indicates that this model has high precision.The theoretical yield from the washability curves is 76.47% for the monthly comprehensive data and 81.31% using the model data.This is for a desired cleaned coal ash of 9%.The relative error between these two is 6.33%,which is small and indicates that the predicted data can be used to rapidly evaluate the separation effect of gravity separation equipment.

考虑载客状态的改进孤立森林浮动车异常数据检测算法

为提高浮动车数据中异常数据检测能力及不同载客状态下的模型检测分析能力,提出基于S-DTA-IIForest(Summation&Difference Third Order Average&Improvement-Isolation Forest)的浮动车数据异常检测算法。构建由相邻两项求和(S)、三阶求和平均差分(DTA)的二维度空间SDTA特征向量;提出差额累计更新和动态区分辨识的改进孤立森林IIForest算法,通过设置停止阈值参数,避免当出现新样本异常值分数大于停止阈值时,仅更新样本不更新孤立森林模型的问题,设计每个二叉树区分辨识度参数,区分辨识度位于停止区间时停止二叉树生长,提高算法收敛性能,以ROC(Receiver Operating Characteristic)曲线下面积AUC(Area Under ROC Cure)、F1-score为指标对模型精度进行对比分析,并以重庆市中心城区学府大道开展实例验证。结果表明:本文S-DTA-IIForest组合算法AUC、F1-score分别为86.63%、0.89,AUC较传统孤立森林IForest(Isolation Forest)提高32.4%,运行效率提高1.29%,具有收敛速度更快、精度更高的优势,载客条件下模型AUC、F1-score较未载客分别提高7.7%、10.8%,组合算法对载客数据有更高的检测精度,且未载客状态数据异常率较载客状态增加71.4%,未载客数据异常率更高。

Driven By Data

What can occur within a mere second?A bee can flutter its wings 240 times;over 4,000 stars can appear in the vast expanse of the universe;and at Ya’an Big Data Industrial Park,125,000 photos are shared on the Internet and zillions of floating-point operations are completed per second.What can occur within a mere second?A bee can flutter its wings 240 times;over 4,000 stars can appear in the vast expanse of the universe;and at Ya’an Big Data Industrial Park,125,000 photos are shared on the Internet and zillions of floating-point operations are completed per second.

基于迁移学习的GOCI超分辨率重建与海洋漂浮藻类探测

遥感技术是进行海洋漂浮藻类目标识别与变化监测的重要手段。GOCI遥感卫星影像具有高时间分辨率、低空间分辨率的特点,其低空间分辨率影响了海洋漂浮藻类遥感探测的效果。本研究通过对具有较高空间分辨率的Sentinel-2遥感卫星影像结构特征的迁移学习,应用ESRGAN超分辨率重建技术,将GOCI影像的空间分辨率提升至125 m;在此基础上,构建了基于超分辨率重建GOCI遥感影像的U-Net深度学习语义分割网络,实现了海洋漂浮藻类的较高精度探测。实验结果表明:超分辨率重建的GOCI影像显著提升了影像的空间细节清晰度,基于此实现的海洋漂浮藻类探测结果取得了较高的精度,其中面积相对误差下降了51.87%,F1值提高了2.41%。本研究是应用GOCI遥感影像进行海洋漂浮藻类高精度探测的一次成功实践,为实现海洋目标的动态精细化监测提供有益的参考。

基于改进浮动单元的城市职住平衡时间尺度估计——以广州市为例

已有研究对城市职住平衡的测度往往存在可塑性面积单元问题,且忽略了交通网络在职住联系中的媒介作用。提出一种改进的浮动单元法,构建基于道路和城市轨道交通网络的数据集,以时间为职住分析尺度生成浮动单元,运用手机信令数据对职住平衡进行测度。以广州市为例,对就业中心和居住区的职住平衡时间尺度进行估计。研究发现:广州市中心城区职住平衡时间尺度分布呈圈层式空间特征,职住平衡时间尺度从一级就业中心向外围先减小再增大;城市轨道交通缩小了就业中心的职住平衡时间尺度;一级、二级(不处于一级就业中心的辐射范围内)和三级就业中心的职住平衡时间尺度分别为32~40 min、14~28 min和10~20 min,而居住组团的职住平衡时间尺度主要依附于就业中心且受到城市轨道交通的影响。最后,提出在合适的时间尺度下对职住空间结构进行评估和优化住房和就业等资源配置,并重视城市轨道交通对职住空间结构的影响。

基于改进两步移动搜索法的合肥市中心城区综合公园可达性评价

为提高城市综合公园布局均衡性地定量评估的准确性,以合肥市中心城区为例,提出一种基于改进多出行模式的两步移动搜索法,利用高德地图的路径规划模型,结合双变量局部空间自相关、洛伦兹曲线以及基尼系数等方法,在手机信令数据的基础上对不同时间阈值下的城市综合公园的可达性及供需情况进行分析,进一步揭示中心城区综合公园布局的合理性。结果显示:合肥市中心城区综合公园的可达性空间差异显著,可达性较高区域通常分布在新城区以及综合公园周边地区,并且随着时间阈值的提高,可达性水平的空间分布呈现均衡态势;合肥市中心城区在15 min与30 min时间阈值下均只有近5%的居住网格处于供需匹配状态;近7%的居住网格处于供需显著不匹配状态,主要集中于老城区西部以及滨湖区北部区域,居住区密度与综合公园数量是影响供需匹配的重要原因。研究结果表明,通过利用手机信令数据与高德路径规划数据作为数据源对传统可达性的计算方法以及两步移动搜索模型进行改进,建立微观尺度下城市综合公园的可达性评价框架,能够精确地评估城市综合公园的可达性;通过优化城市交通的通达程度以提高居民综合公园游憩出行的阈值,能够显著促进城市综合公园整体的空间分布合理性。

国医大师颜正华治疗呼吸系统疾病用药升降浮沉药性规律分析

目的分析国医大师颜正华教授治疗呼吸系统疾病用药的升降浮沉药性规律,传承其宝贵学术经验。方法以4本颜正华教授弟子为主编的著作为主要来源,系统收集、分析颜正华教授临证治疗呼吸系统疾病的处方,对患者的性别、年龄,处方的辨病、证型,中药的升降浮沉药性、用量及常用药对等进行统计分析。结果共纳入处方208首,涉及中药178味、君药64味,药性多趋向沉降,全方以沉降方居多;处方平均用药13.2味,且大部分用量点在常规用量范围内;后下药中使用频数最高的中药为鱼腥草。常用药对有紫菀-白前、白前-百部、紫菀-百部,沉降方中常用苦杏仁、浙贝母、紫菀等药。结论颜正华教授治疗呼吸系统疾病时喜用沉降药,配以趋向升浮和具有双重趋向的药物调理气机的升降,且具有孟河医派“用药轻灵”的特点。