Semantics Analytics of Origin-Destination Flows from Crowd Sensed Big Data

Monitoring,understanding and predicting Origin-destination(OD)flows in a city is an important problem for city planning and human activity.Taxi-GPS traces,acted as one kind of typical crowd sensed data,it can be used to mine the semantics of OD flows.In this paper,we firstly construct and analyze a complex network of OD flows based on large-scale GPS taxi traces of a city in China.The spatiotemporal analysis for the OD flows complex network showed that there were distinctive patterns in OD flows.Then based on a novel complex network model,a semantics mining method of OD flows is proposed through compounding Points of Interests(POI)network and public transport network to the OD flows network.The propose method would offer a novel way to predict the location characteristic and future traffic conditions accurately.

Loss-of-Main Monitoring and Detection for Distributed Generations Using Dynamic Principal Component Analysis

In this paper, our previous work on Principal Component Analysis (PCA) based fault detection method is extended to the dynamic monitoring and detection of loss-of-main in power systems using wide-area synchrophasor measurements. In the previous work, a static PCA model was built and verified to be capable of detecting and extracting system faulty events;however the false alarm rate is high. To address this problem, this paper uses a well-known ‘time lag shift’ method to include dynamic behavior of the PCA model based on the synchronized measurements from Phasor Measurement Units (PMU), which is named as the Dynamic Principal Component Analysis (DPCA). Compared with the static PCA approach as well as the traditional passive mechanisms of loss-of-main detection, the proposed DPCA procedure describes how the synchrophasors are linearly auto- and cross-correlated, based on conducting the singular value decomposition on the augmented time lagged synchrophasor matrix. Similar to the static PCA method, two statistics, namely T2 and Q with confidence limits are calculated to form intuitive charts for engineers or operators to monitor the loss-of-main situation in real time. The effectiveness of the proposed methodology is evaluated on the loss-of-main monitoring of a real system, where the historic data are recorded from PMUs installed in several locations in the UK/Ireland power system.

Cryptocurrency Transaction Network Embedding From Static and Dynamic Perspectives: An Overview

Cryptocurrency, as a typical application scene of blockchain, has attracted broad interests from both industrial and academic communities. With its rapid development, the cryptocurrency transaction network embedding(CTNE) has become a hot topic. It embeds transaction nodes into low-dimensional feature space while effectively maintaining a network structure,thereby discovering desired patterns demonstrating involved users' normal and abnormal behaviors. Based on a wide investigation into the state-of-the-art CTNE, this survey has made the following efforts: 1) categorizing recent progress of CTNE methods, 2) summarizing the publicly available cryptocurrency transaction network datasets, 3) evaluating several widely-adopted methods to show their performance in several typical evaluation protocols, and 4) discussing the future trends of CTNE. By doing so, it strives to provide a systematic and comprehensive overview of existing CTNE methods from static to dynamic perspectives,thereby promoting further research into this emerging and important field.

A Service-Based Intelligent Time-Domain and Spectral-Domain Flow Aggregation in IP-over-EON Based on SDON

The rapid growth of IP traffic has contributed to wide deployment of optical devices in elastic optical network.However,the passband shape of wavelength selective switches(WSSs)that are used in reconfigurable optical add-drop multiplexer(ROADM)/optical cross connect(OXC)is not ideal,causing the narrowing of spectrum.Spectral narrowing will lead to signal impairment.Therefore,guard-bands need to be inserted between adjacent paths which will cause the waste of resources.In this paper,we propose a service-based intelligent aggregation node selection and area division(ANS-AD)algorithm.For the rationality of the aggregation node selection,the ANS-AD algorithm chooses the aggregation nodes according to historical traffic information based on big data analysis.Then the ANS-AD algorithm divides the topology into areas according to the result of the aggregation node selection.Based on the ANS-AD algorithm,we propose a time-domain and spectral-domain flow aggregation(TS-FA)algorithm.For the purpose of reducing resources'waste,the TS-FA algorithm attempts to reduce the insertion of guard-bands by time-domain and spectral-domain flow aggregation.Moreover,we design a time-domain and spectral-domain flow aggregation module on software defined optical network(SDON)architecture.Finally,a simulation is designed to evaluate the performance of the proposed algorithms and the results show that our proposed algorithms can effectively reduce the resource waste.

Big Data Capability,Knowledge Dynamic Capability,and Business Model Innovation:The Moderating Effect of Innovation Legitimacy

Based on dynamic capability theory and legitimacy theory,a theoretical model is constructed to examine how big data capability,through the mediation of knowledge dynamic capability,drive business model innovation under the moderation effect of innovation legitimacy.The eanalys is isconducted using regression analysis and fuzzy set qualitative comparative analysis(fsQCA)on survey data from 302 enterprises that have already implemented big data application practices.The study finds the following four conclusions.(1)Big data capability has a significant positive impact on business model innovation.(2)Dynamic knowledge capability partially mediates the relationship between big data capability and business model innovation.(3)Innovation legitimacy positively influences business model innovation and positively moderates the relationship between big data capability and businessmodel innovation.(4)Through further qualitative comparative analysis,two causal paths that influence business model innovation are identified.

Three-stage approach for dynamic traffic temporal-spatial model

In order to describe the characteristics of dynamic traffic flow and improve the robustness of its multiple applications, a dynamic traffic temporal-spatial model(DTTS) is established. With consideration of the temporal correlation, spatial correlation and historical correlation, a basic DTTS model is built. And a three-stage approach is put forward for the simplification and calibration of the basic DTTS model. Through critical sections pre-selection and critical time pre-selection, the first stage reduces the variable number of the basic DTTS model. In the second stage, variable coefficient calibration is implemented based on basic model simplification and stepwise regression analysis. Aimed at dynamic noise estimation, the characteristics of noise are summarized and an extreme learning machine is presented in the third stage. A case study based on a real-world road network in Beijing, China, is carried out to test the efficiency and applicability of proposed DTTS model and the three-stage approach.

大数据分析在中医教学评价中的应用

在大数据和数字化转型的大背景下,文章分析、阐述了目前中医教学评价的困难以及如何将大数据技术应用在中医教学评价过程中,总结了大数据在中医教学评价过程中的应用方法。首先需要更加广泛地采集数据,包括静态数据、实时数据、非结构化数据等;然后通过系统化的数据标准体系构建,利用数据处理技术,建立全面的教学评价数据仓库;最后结合教学评价业务目标,构建动态数据分析模型,持续不断地以可视化方式直观输出评价结果,真正帮助教师、学生、教学管理者提高教学评价效率,为教学改革提供支撑。

大数据分析能力对供应链绩效的影响研究

竞争加剧和市场环境的快速变化使得制造业商业竞争模式从企业间的竞争转变为供应链间的竞争,而大数据分析能力对于供应链管理具有重要的作用,探寻大数据分析能力对供应链绩效产生影响的作用机理可以提高供应链竞争力。该文基于协同创新视角,引入环境动态性这一变量建立调节效应模型,探究了大数据分析能力对供应链协同创新和绩效之间深层次的作用机制,以制造业供应链上企业为研究对象通过问卷调查进行实证分析,结果表明:大数据分析能力(资源整合能力、深度分析能力、应用能力)对供应链绩效具有显著的正向影响,供应链协同创新在大数据分析能力各维度与供应链绩效之间起中介作用,环境动态性对深度分析能力与供应链协同创新的关系具有正向调节作用,但对资源整合能力、应用能力与供应链协同创新的调节作用不显著。

大数据分析能力、动态能力与制造企业商业模式创新——来自资源编排视角

商业模式创新是企业竞争优势的重要来源。制造业是国民经济的主体,是立国之本、兴国之器、强国之基。因此,探讨制造企业商业模式创新路径具有重要理论和现实意义。从资源编排视角与能力层级观视角出发,构建大数据分析能力—动态能力—商业模式创新理论模型,基于中国286家制造企业一手调研数据,采用PLS结构方程进行实证检验,结果显示:大数据技术能力、大数据应用能力对新颖型和效率型商业模式创新发挥促进作用;动态能力在大数据技术能力、大数据应用能力与新颖型商业模式创新间发挥部分中介作用,在大数据技术能力、大数据应用能力与效率型商业模式创新间发挥完全中介作用。结论可为数字经济背景下我国制造企业商业模式创新提供管理启示。

高技术企业利用大数据能力实现创新双元性的多元路径——基于知识动态能力的组态分析

立足于数字经济背景,研究采用fsQCA方法,以236家高技术企业为对象,探讨多维度大数据分析能力与内部动态能力联动实现创新双元性的组态路径。研究发现:(1)大数据人员能力是创新双元性的核心条件。(2)存在两类驱动路径,即基础决策型、管理优化型的时间双元模式与分析协同型的结构双元模式。(3)知识协同辅助下,大数据管理及人员能力是实现创新双元性的关键;大数据基础设施与管理能力存在驱动与抑制效应的替代关系。研究结论有助于厘清如何在数字化背景下整合利用不同类型数字能力,帮助高技术企业兼顾探索式创新与利用式创新。

工作面矿压大数据动态分析与预测系统

为实现矿井工作面矿压自动分析与预测,以大柳矿为研究对象,使用GUI编程,采用HTML5、Python语言联合编写,运用长短时记忆(LSTM)与ARIMA算法模型,搭建多源数据可视化分析展示平台与预测分析系统。系统程序设计划分为基础信息、监测参量、支架数据、分析预警、报表输出等主模块;通过导入矿压监测数据(支架压力、钻孔应力、巷道变形、顶板离层、锚杆锚索),借助人工智能和大数据技术,可实现监测数据的快速清洗、多参量融合,以及对矿压风险的智能识别与预警。

基于分簇聚类的光网络流量大数据跨源调度方法

光网络凭借其容量大、传输速率高、业务透明、损耗低等优势得到了广泛的应用,并且随着用户规模与用户传输需求的增加,导致光网络流量不足现象发生的频率增加,制约了光网络的发展。因此,提出基于分簇聚类的光网络流量大数据跨源调度方法研究。首先,采用分簇聚类算法处理光网络流量大数据。其次,通过XGBoost模型预测下一时刻的光网络流量。以成本最小化为目标,构建流量跨源调度数学模型,并确定所构建模型的约束条件。最后,以遗传算法为工具,获取流量跨源调度最佳方案,执行最佳方案即可实现光网络流量大数据的跨源调度。实验结果显示:应用提出方法获得的光网络流量大数据聚类结果与期望光网络流量大数据聚类结果一致,流量跨源调度成本最小值为16万元,表明提出方法具备更好的流量跨源调度性能。

基于历史大数据分析的电力负荷区间组合预测系统

为避免电量信号过大导致电力负荷区间内电流暂态载流量出现频繁波动的情况,设计基于历史大数据分析的电力负荷区间组合预测系统。根据电力大数据样本集合定义条件,求解传输电流的暂态负载量与动态负载量,完成基于历史大数据分析的电力负荷统计。优化区域电路连接结构,借助大数据统计模块确定Model终端对于电量信号传输行为的预测能力,实现硬件运行环境搭建。测试结果表明,设计系统可将电流暂态载流量的最大值控制在30.3 A以下,可以解决电流信号频繁波动的问题,满足准确预测电力负荷区间内实时耗电量的应用需求。

一种基于深度学习的时序病变数据段分类方法

针对在大规模时序医疗数据的分析中现有检测方法检测精度低、检测速度慢等问题,文中提出了一种基于深度学习的时序病变数据段分类方法。该方法在TSTKS(Ternary Search Trees and modified Kolmogorov-Smirnov)算法和滑动窗口理论的基础上,利用深度学习技术实现了对病变数据段的快速准确分类。文中以利用该方法对病变数据段进行分类的结果作为依据,实现了滑动窗口大小的动态调整。通过对真实癫痫脑电信号(Electroencephalogram,EEG)进行分析,证明了所提病变数据段分类方法和基于该分类方法的滑动窗口动态调整机制具有检测速度快、精度较高等优点,可以为大规模时序数据的快速分析研究提供一种新选择。

基于Spark计算的大数据终端潜在异常识别仿真

本文主要针对大数据终端在实时处理过程中潜在的异常情况进行识别和仿真研究。基于Spark计算框架,提出了一种新的方法监测和识别大数据终端中可能存在的异常行为。通过使用实时数据流分析和机器学习技术,及时发现并处理潜在的异常情况,提高数据处理效率和质量,并最终提升系统的可靠性和稳定性。

大数据分析在通信系统联调联试中的运用

为了更好地完成通信系统联调联试,基于接口监测数据的GSM-R网络运行数据综合分析系统,对通信系统测试数据进行补充,在综合检测列车联调联试的基础上,能够补充发现无线通信网络存在的隐患并进行克缺;通过加强GSM-R网络优化和精调,无线通信网络在联调联试结束时不仅能够实现检测达标,而且能够完成深度网络优化。以话音质量优化为例,介绍基于GSM-R网络运行数据综合分析系统大数据分析的具体应用情况,得出该系统方法能够提高通信系统联调联试过程中解决问题的效率,并为高铁开通提供更高质量的通信网络。

基于大数据分析技术的景区短期客流量预测研究

景区短期客流量受多种因素影响,如天气、节假日等,导致预测难度上升,为此提出基于大数据分析技术的景区短期客流量预测方法。该方法通过多种途径采集并整合景区客流量历史数据,利用大数据分析技术从整合后的数据中高效筛选和提取与客流量预测最相关的特征。将筛选出的关键特征输入到景区短期客流量预测模型中得到相关的预测结果。实验结果表明,提出方法的客流量预测值与实际客流量值之间的差异较小,F1分数较高。

大数据环境下道路场景高时空分辨率众包感知方法

道路场景作为人类发展演变中最重要、最复杂、最典型的载体之一,是道路基础设施与活动行为共同构成的综合体,链接和构建“人地关系”。道路场景感知从二维抽象简略到三维精细丰富、从静态过去式向动态现在时发展,成为智慧城市、智能交通、无人驾驶的关键技术支撑,是我国新型城镇化战略、交通强国战略的核心技术保障。本文立足于时空交通大数据,提出基于道路场景静态基础设施“形”和动态活动行为“流”的高时空分辨率道路场景感知方法。该方法从静态路网“形”角度,以“点-线-面-体”等要素为研究脉络,构建高精度道路地图众包感知的理论体系;在活动行为“流”感知上,突破传统的点模式分析局限,发展了道路网络空间活动流的时空建模与多尺度分析方法。本文揭示了静态基础设施“形”结构与动态活动行为“流”模式交互作用下的道路场景演化规律,形成以“形”控“流”、借“流”定“形”、“形”“流”叠置的高时空精度道路场景众包感知理论体系,为智慧城市、智能交通的发展提供核心技术与数据支撑。

协议逆向工程研究进展

首先给出了协议逆向工程的形式化定义,并探讨了主要应用领域的特定需求;然后从报文序列分析和指令执行序列分析两个方面介绍了协议逆向技术的研究现状,并对两类技术的优劣进行了比较;最后结合当前方案的缺陷和实际应用的需求,对协议逆向技术的发展趋势进行了展望。

1999年夏季白令海陆坡区海流动力分析

利用中国首次北极科学考察白令海调查资料计算了白令海东部陆坡区 58— 60°N范围内海流的流幅、流速和流量 ,并获得如下结论 :(1 )白令海东陆坡区陆坡流由东南向西北变化 ,流速由东南向西北逐渐增加 ,在 58°N处 ,最大流速达到 1 0cm/s,到 60°N处 ,最大流速达到 30cm/s,是历年调查中最高的 ;流幅由东南向西北不断增宽 ,在 58°N处 ,最大流幅约 1 0 0km ,到 60°N处 ,最大流幅达到 2 1 0km ,流量由东南向西北也不断增加 ,在 58°N处 ,最大流量 5 .3Sv ,到 60°N处 ,最大流量达到 1 0 .2Sv ;(2 )陆坡流的近陆架一侧陆坡逆流的北界位置在 58°N处 ,其表层流幅宽度为 70km ,流速达到 5cm/s以上 ,在 59°N处其影响深度可以直达海底 ,,陆坡逆流的流速降低 ,流幅增宽到 1 1 0km ,60°N附近为陆坡逆流的北界 ,未观测到陆坡逆流 ;(3)陆架坡折锋强化了陆坡流。