A benchmark data set for two-phase Coriolis metering

For more than a decade there has been growing interest in the use of Coriolis mass flow metering applied to two-phase (gas/liquid) and multiphase (oil/water/gas) conditions. It is well-established that the mass flow and density measurements generated from multiphase flows are subject to large errors, and a variety of physical models and correction techniques have been proposed to explain and/or to compensate for these errors. One difficulty is the absence of a common basis for comparing correction techniques, because different flowtube designs and configurations, as well as liquid and gas properties, may result in quite different error curves. Furthermore, some researchers with interests in the modelling aspects of the field may not have suitable multiphase laboratory facilities to generate their own data sets. This paper offers a small data set that may be used by researchers as a benchmark i.e. a common data set for comparing correction techniques. The data set was collected at the UK National Flow Laboratory TUV-NEL, using air and a viscous oil, and provides experimental points over a wide flow range (8:1 turndown) and with Gas Volume Fraction (GVF) values up to 60%. As a first investigation using the benchmark data set, we consider how data sparsity (i.e. the flow rate and GVF spacing in the experimental grid) affects the accuracy of a correction model. A range of neural network models are evaluated, based on different subsets of the benchmark data set. The data set and some exemplary code are provided with the paper. Additional data sets are available on a web site created to support this initiative.     

Smart firefighting construction in China: Status,problems, and reflections

“Smart firefighting” construction as a part of the “smart city” has been a concern of the public security and fire agencies at all levels. In this study, the status, problems, and reflections of “smart firefighting” construction in China are discussed. A recent survey indicates that China has launched its smart firefighting construction and initially created a new perspective on its regional smart firefighting work based on three main aspects: intelligent disaster perception by Internet of Things (IoT) construction, intelligent disaster prevention by big data construction, and intelligent disaster disposal by emergency rescue platform construction. However, the current smart firefighting construction in China still has some prominent problems such as the data interconnectivity and normalized management of various platforms, the extensibility of smart firefighting platforms, and the intelligent level of smart firefighting researches, which need to be solved urgently. Therefore, we argue that smart firefighting construction in China should establish data interconnectivity, industrial normalized management, 2D/3D geographic information interaction and extension, high-integration fire protection theory, and many other aspects in the near future and truly realize firefighting visualization and efficient data applications for 4D time space. This study could provide valuable reference for smart firefighting and smart city construction.     

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。     

Seismic life-cycle cost analysis of ageing highway bridges under chloride exposure conditions: modelling and recommendations

Chloride-induced corrosion of highway bridges constitutes a critical form of environmental deterioration and may result in significant escalation of seismic life-cycle costs due to increased fragility during earthquake events. Most of existing literature tends to adopt simplistic uniform area loss assumptions in lieu of potentially complex, yet realistic and more detrimental, pitting corrosion models for seismic vulnerability analysis. Since the degree of deterioration depends on the severity and duration of exposure, there exists a need to investigate the influence of uniform vs. pitting corrosion assumption on seismic life-cycle costs for varied chloride exposure conditions. A case-study example of a highway bridge in Central and Southeastern US reveals consideration of pitting corrosion as critical for extreme exposures compared to relatively minor settings. Subsequently this study provides recommendations to aid bridge engineers and stakeholders to balance between computational cost and accuracy of results to aid prompt decisions on rehabilitation of ageing bridges in different exposure conditions. A framework is also included to compute seismic life-cycle costs from generic measures of corrosion, independent of assumed exposure scenario. This framework is particularly helpful for seismic loss assessment of highway bridges in chloride exposure zones with periodic field measurements to estimate the extent of structural deterioration.     

Identifying cooperation for innovation―a comparison of data sources

ABSTRACT

The value of social network analysis is critically dependent on the comprehensive and reliable identification of actors and their relationships. We compare regional knowledge networks based on different types of data sources, namely, co-patents, co-publications, and publicly subsidized collaborative R&D projects. Moreover, by combining these three data sources, we construct a multilayer network that provides a comprehensive picture of intraregional interactions. By comparing the networks based on the data sources, we address the problems of coverage and selection bias. We observe that using only one data source leads to a severe underestimation of regional knowledge interactions, especially those of private sector firms and independent researchers.     

A Differentially Private Data Aggregation Method Based on Worker Partition and Location Obfuscation for Mobile Crowdsensing

With the popularity of sensor-rich mobile devices, mobile crowdsensing (MCS) has emerged as an effective method for data collection and processing. However, MCS platform usually need workers’ precise locations for optimal task execution and collect sensing data from workers, which raises severe concerns of privacy leakage. Trying to preserve workers’ location and sensing data from the untrusted MCS platform, a differentially private data aggregation method based on worker partition and location obfuscation (DP-DAWL method) is proposed in the paper. DP-DAWL method firstly use an improved K-means algorithm to divide workers into groups and assign different privacy budget to the group according to group size (the number of workers). Then each worker’s location is obfuscated and his/her sensing data is perturbed by adding Laplace noise before uploading to the platform. In the stage of data aggregation, DP-DAWL method adopts an improved Kalman filter algorithm to filter out the added noise (including both added noise of sensing data and the system noise in the sensing process). Through using optimal estimation of noisy aggregated sensing data, the platform can finally gain better utility of aggregated data while preserving workers’ privacy. Extensive experiments on the synthetic datasets demonstrate the effectiveness of the proposed method.     

大数据在公共卫生应急管理中的应用--以内蒙古自治区为例

在此次新冠肺炎防控中,以大数据为代表的新一代信息技术,广泛应用于疫情管控、追踪溯源、模型预测、资源调配、舆情管控等多个方面,发挥了重大作用。通过对大数据在内蒙古自治区疫情防控中的应用分析,从应用情况、存在问题及对策建议三个方面,探讨如何合理有效地利用丰富的数据资源、大数据应用技术,对完善重大疫情防控体制机制提供强有力的支撑,从而推动自治区,乃至国家公共卫生应急管理体系的健全与完善。     

美国大数据战略及对我国启示

美国当前在大数据方面全球领先。2010年奥巴马将发展大数据上升为国家战略,建立了一系列负责大数据的专门机构,发布大量相关战略性文件和法令。大数据在美国各个领域得到广泛应用,并基本成体系。当前我国大数据发展取得进步但仍存在问题,借鉴美国大数据战略的具体做法可为完善我国大数据战略提供较好的参考价值。     

汽车路况网的进一步研究

汽车路况网的概念源于数字城市的概念,通过研究网络、实时数据库和汽车智能,实现安全的交通网络。研究的语言一般应用Lisp语言,数据库为实时数据库。     

Local weather prediction system for a heating plant using cognitive approaches

Present-day requirements emphasize the need of saving energy. It relates mainly to industrial companies, where the minimization of energy consumption is one of their most important tasks they face. In our paper, we deal with the design of the so-called weather prediction system (WPS) for the needs of a heating plant. The primary task of such a WPS is timely predicting expected heat consumption to prepare the technology characterized by long delays in advance. Heat prediction depends primarily on weather so the crucial part of WPS is the weather, especially temperature, prediction. However, a prediction system needs a variety of further data, too. Therefore, WPS must be regarded as a complex system, including data collection, its processing, own prediction and eventual decision support. This paper gives the overview about existing data processing systems and prediction methods and then it describes a concrete design of a WPS with distributed data measuring points (stations), which are processed using a structure of neural networks based on multilayer perceptrons (MLP) with a combination of fuzzy logic. Based on real experiments we show that also such simple means as MLPs are able to solve complex problems. The paper contains a basic methodology for designing similar WPS, too.