Data quality and uncertainty assessment methodology for pavement LCA

In performing pavement life cycle assessment (LCA), users are facing various reports of energy intensity coefficient (EIC) of pavement materials which differ considerably among data sources and therefore alter the LCA results significantly. Instead of selecting a certain EIC without or of little explanation for the current pavement LCA practices, this study proposed a methodology to build probability density function (PDF) for EIC based on available data-set and their qualities. Each data was first evaluated about the data quality indicator (DQI) through data quality pedigree matrix and converted to PDF in modified Beta distribution form. Three weighting methods, the DQI one, coefficient of variation (COV) one and analytical hierarchy process (AHP) one, were developed to assign weightings for different data. Monte Carlo simulation was run with the weighted PDF of each data as input to obtain the ultimate PDF for EIC. A case study to estimate the bitumen’s EIC with eight data samples were performed using the proposed methodology. It is found (1): the estimates by the proposed methodology is of higher reliability (lower COV) compared to any single data due to utilisation of information of the overall data samples; (2) the AHP weighting method is most favoured despite the results of the three weighting methods are close; (3) the central estimates of bitumen’s EIC are between5.4~5.8 MJ/kg. The proposed methodology is helpful in aiding calculating EICs for pavement materials and capturing uncertainties in LCA results in a statistical sense.     

机翼极限环颤振的CFD/CSD全隐式紧耦合方法

针对轴承振动实时状态监控的需要，从符号动力学角度提出了符号聚合近似与Lempel-ziv复杂度（SAX-LZC）融合的振动监控参数。首先，以Logistic映射和Duffing方程为对象，从理论角度验证了SAX-LZC对动力学结构表征的准确性，并验证了该指标的抗噪能力和计算效率；其次，将SAX-LZC指标与信息熵、样本熵、多分段Lempel-ziv复杂度等动力学参数性能进行了综合对比；最后，从实验角度对轴承早期微弱异常进行了监测，并对轴承典型故障进行了特征提取。理论研究结果表明，SAX-LZC具有动力学结构表征准确、抗噪能力好、计算高效简洁等优点，克服了常规动力学参数工程应用能力弱的问题。实验研究结果表明，SAX-LZC对早期微弱异常有准确的监测，对不同种类故障具有较好的区分度，弥补了时域和频域对轴承微弱故障表征能力不足的缺陷，是一种轴承振动实时状态监控与故障特征提取的有效参数。     

智能变电站设备管控大数据分析系统研究

结合数据挖掘、大数据技术在电力系统中的应用现状，分析和设计了一种智能变电站设备管控大数据分析系统。对变电站设备运行状态的大数据特征及基本框架进行了分析，并重点阐述了变电站设备运行状态大数据分析系统的数据集成与预处理、数据存储与处理、设备状态评估模型以及数据可视化展示。实验结果表明，所设计的大数据分析系统能够大幅度提升变电站设备数据分析智能化水平，为变电站管控稳定运行提供更加有效和实用性的技术支撑。     

Learning Association Rules and Tracking the Changing Concepts on Webpages: An Effective Pornographic Websites Filtering Approach

We applied the decision tree algorithm to learn association rules between webpage’s category (pornographic or normal) and the critical features. Based on these rules, we proposed an efficient method of filtering pornographic webpages with the following major advantages: 1) a weighted window-based technique was proposed to estimate for the condition of concept drift for the keywords found recently in pornographic webpages; 2) checking only contexts of webpages without scanning pictures; 3) an incremental learning mechanism was designed to incrementally update the pornographic keyword database.     

基于大数据挖掘的用电行为分析

得益于智能电网的发展,电力大数据的价值与作用逐渐被挖掘。通过对电力大数据的深入挖掘与研究,可促进电网企业管理水平的进一步提升,为我国智能电网的发展提供助力。而依托于大数据挖掘的用户用电行为分析,可实现当前企业电网调控与需求侧的充分融合,为电力系统的稳定性运行提供保障。基于此,文章针对大数据挖掘的用电行为分析进行深入探析,以期为智能电网发展提供帮助。     

Applied data science in patient-centric healthcare: Adaptive analytic systems for empowering physicians and patients

We define the emerging research field of applied data science as the knowledge discovery process in which analytic systems are designed and evaluated to improve the daily practices of domain experts. We investigate adaptive analytic systems as a novel research perspective of the three intertwining aspects within the knowledge discovery process in healthcare: domain and data understanding for physician- and patient-centric healthcare, data preprocessing and modelling using natural language processing and (big) data analytic techniques, and model evaluation and knowledge deployment through information infrastructures. We align these knowledge discovery aspects with the design science research steps of problem investigation, treatment design, and treatment validation, respectively. We note that the adaptive component in healthcare system prototypes may translate to data-driven personalisation aspects including personalised medicine. We explore how applied data science for patient-centric healthcare can thus empower physicians and patients to more effectively and efficiently improve healthcare. We propose meta-algorithmic modelling as a solution-oriented design science research framework in alignment with the knowledge discovery process to address the three key dilemmas in the emerging “post-algorithmic era” of data science: depth versus breadth, selection versus configuration, and accuracy versus transparency.     

基于大数据的C语言程序设计

C语言作为一种程序设计语言,在大数据组成的物联网和云计算中应用广泛,具有强大的适应性和兼容性,是大数据时代下,在IT行业中起到关键作用的程序设计语言。文章分析了基于大数据的C语言程序设计的必然性和应用策略,为C语言在大数据上的应用方向提供了参考依据。以汉字的应用为例,提出了解决C语言应用问题的思路。     

数据驱动的机床等待过程节能方法研究

为了降低机床等待过程中的能耗，提出了一种实时数据驱动的机床等待时间预测与节能控制方法。首先，建立了射频识别驱动的生产进度评估方法，并以生产进度数据作为输入，构建了基于堆栈降噪自编码的机床等待时间预测模型；其次，依据预测的机床等待时间，提出了机床状态切换方法，以降低机床能耗；最后，通过一个电梯零部件制造车间的案例分析，表明该方法的预测误差仅为4.1%，同时将机床等待过程能耗降低了57%，实现了制造车间的节能减排。     

The effect of traffic data source on deterioration rates of heavy-duty flexible pavements

The purpose of this study is to assess the effect of traffic data source (estimated vs. actual) on predicted progression rates of roughness and rutting for heavy-duty flexible pavements of rural freeways. Progression rates are predicted using calibrated HDM-4 models. The assessment is performed in terms of variations in maintenance intervention timing associated with the variations in progression rates. Time series pavement condition data (covering 3–5 years) have been collected for 7 sections of rural freeways for use in calibrating HDM-4 deterioration models. They range in length from 10 to 60.8 km and cover different traffic volumes, climate zones and subgrade soil types. For these sections, estimated annual average daily traffic (AADT), growth factors and assumed loading have been extracted from relevant database. Only six segments of these sections have Weigh-in-Motion (WIM) sites so relevant actual AADT, growth factors and axle load distributions have been extracted from WIM reports. The results of running the calibrated HDM-4 deterioration models using different traffic data show that actual traffic data from WIM sites result in higher rates of deterioration to that of estimated data for four sites, resulting in earlier intervention timing and higher present value agency cost. The other two sites have lower rates with actual data due to lower traffic loading than estimated.