Long Term Application of a Vehicle-Based Health Monitoring System to Short and Medium Span Bridges and Damage Detection Sensitivity

Largest portion of the bridge stock in almost any country and bridge owning organisation consists on ordinary bridges that has short or medium spans and are now deteriorating due to aging, etc. Therefore, it is becoming an important social concern to develop and put to practical use simple and efficient health monitoring systems for existing short and medium span (10 - 30 m) bridges. In this paper, one practical solution to the problem for condition assessment of short and medium span bridges was discussed. A vehicle-based measurement with a public bus as part of a public transit system (called “Bus monitoring system”) has been developed to be capable of detecting damage that may affect the structural safety of a bridge from long term vibration measurement data collected while the vehicle (bus) crossed the target bridges. This paper systematically describes how the system has been developed. The bus monitoring system aims to detect the transition from the damage acceleration period, in which the structural safety of an aged bridge declines sharply, to the deterioration period by continually monitoring the bridge of interest. To evaluate the practicality of the newly developed bus monitoring system, it has been field-tested over a period of about four years by using an in-service fixed-route bus operating on a bus route in the city of Ube, Yamaguchi Prefecture, Japan. The verification results thus obtained are also described in this paper. This study also evaluates the sensitivity of “characteristic deflection”, which is a bridge (health) condition indicator used by the bus monitoring system, in damage detection. Sensitivity of “characteristic deflection” is verified by introducing artificial damage into a bridge that has ended its service life and is awaiting removal. As the results, it will be able to make a rational long-term health monitoring system for existing short and mediumspan bridges, and then the system helps bridge administrators to establish the rational maintenance strategies.

Impact of the Community-Based Active Monitoring Program on the Long Term Care Services Use and In-Patient Admissions of the Over-74 Population

Introduction: Social isolation increases in the over-74 population and it is a risk factor for death and Long Term Care (LTC) use. In order to prevent the negative consequences of social isolation on this population community interventions focused on strengthening the social network should be intensified. The aim of this paper is to describe the impact on health care use of a Community-based pro-Active Monitoring Program (CAMP) providing phone monitoring to all the clients and home visits according to the individual’s needs. Methodology: In order to provide an evaluation of the program outcomes, the rates of clients’ hospitalization and admissions to Long Term Care facilities during 2011 have been assessed. The observed rates have been compared with expected ones calculated on available information for similar population. A cost-analysis has been also carried out to analyze the program sustainability. Results: The studied sample is made up by 1408 over-74 citizens followed up during 2011 in Rome (Italy) by CAMP. The cumulative observation time was 1362 p/y;61 individuals died during 2011 (death rate 4.3%). The hospital admission rate observed among CAMP’s clients was 254‰ (357/1408;CL95% ± 91‰), lower than the 282‰ reported for the over-74 population of Rome. This translates into 39 averted hospitalization. The LTC admission rate is also reduced among CAMP’s clients (9/1,408, 6.6‰ CL95% ± 0.8‰ vs. 9.7‰ reported for a comparable sample);it translates into 4 averted LTC admissions. The averted cost ranged between 47,153 € and 220,117 € according to the range of services used by the clients, which translates into a percentage of estimated cost reduction on yearly basis ranged between 3% and 12.5% of the whole cost of services used by the studied population. Discussion: The paper suggests the capacity of CAMP to reduce both the over-74 hospitalization rate and use of LTC. Cost analysis also indicates a cost reduction as a consequence of the CAMP implementation. Further studies including a control group and a detailed cost-benefit analysis are needed to check the program sustainability on larger population.

Adaptive Change Detection for Long-Term Machinery Monitoring Using Incremental Sliding-Window

Detection of structural changes from an opera- tional process is a major goal in machine condition moni- toring. Existing methods for this purpose are mainly based on retrospective analysis, resulting in a large detection delay that limits their usages in real applications. This paper presents a new adaptive real-time change detection algorithm, an extension of the recent research by combin- ing with an incremental sliding-window strategy, to handle the multi-change detection in long-term monitoring of machine operations. In particular, in the framework, Hil- bert space embedding of distribution is used to map the original data into the Re-producing Kernel Hilbert Space （RK_HS） for change detection; then, a new adaptive threshold strategy can be developed when making change decision, in which a global factor （used to control the coarse-to-fine level of detection） is introduced to replace the fixed value of threshold. Through experiments on a range of real testing data which was collected from an experimental rotating machinery system, the excellent detection performances of the algorithm for engineering applications were demonstrated. Compared with state-of- the-art methods, the proposed algorithm can be more suitable for long-term machinery condition monitoring without any manual re-calibration, thus is promising in modern industries.

Design of Low-Power Data Logger of Deep Sea for Long-Term Field Observation

This paper describes the implementation of a data logger for the real-time in-situ monitoring of hydrothermal systems. A compact mechanical structure ensures the security and reliability of data logger when used under deep sea. The data logger is a battery powered instrument, which can connect chemical sensors （pH electrode, H2S electrode, H2 electrode） and temperature sensors. In order to achieve major energy savings, dynamic power management is implemented in hardware design and software design. The working current of the data logger in idle mode and active mode is 15 μA and 1.44 mA respectively, which greatly extends the working time of battery. The data logger has been successftdly tested in the first Sino-American Cooperative Deep Submergence Project from August 13 to September 3, 2005.

Design and Implementation of Long-Term Single-Lead ECG Monitor

Some heart diseases need long-term monitoring to diagnose. In this paper, we present a wearable single lead ECG monitoring device with low power consumption based on MSP430 and single-lead ECG front-end AD8232, which could acquire and store patient’s ECG data for 7 days continuously. This device is available for long-term wearing with a small volume. Also, it could detect user’s motion status with an acceleration sensor and supports Bluetooth 4.0 protocol. So it could be expanded to be a dynamic heart rate monitor and/or sleep quality monitor combined with smart phone. The device has huge potential of application for health care of human daily life.

Swarm-LSTM: Condition Monitoring of Gearbox Fault Diagnosis Based on Hybrid LSTM Deep Neural Network Optimized by Swarm Intelligence Algorithms

Nowadays,renewable energy has been emerging as the major source of energy and is driven by its aggressive expansion and falling costs.Most of the renewable energy sources involve turbines and their operation and maintenance are vital and a difficult task.Condition monitoring and fault diagnosis have seen remarkable and revolutionary up-gradation in approaches,practices and technology during the last decade.Turbines mostly do use a rotating type of machinery and analysis of those signals has been challenging to localize the defect.This paper proposes a new hybrid model wherein multiple swarm intelligence models have been evaluated to optimize the conventional Long Short-Term Memory(LSTM)model in classifying the faults from the vibration signals data acquired from the gearbox.This helps to analyze the performance and behavioral patterns of the system more effectively and efficiently which helps to suggest for replacement of the unit with higher precision.The results have demonstrated that the proposed hybrid modeling approach is effective in classifying the faults of the gearbox from the time series data and achieve higher diagnostic accuracy in comparison to the conventional LSTM methods.

The long-term trend of the sea surface wind speed and the wave height (wind wave, swell, mixed wave) in global ocean during the last 44 a

Utilizing the 45 a European Centre for Medium-Range Weather Forecasts （ECMWF） reanalysis wave da- ta （ERA-40）, the long-term trend of the sea surface wind speed and （wind wave, swell, mixed wave） wave height in the global ocean at grid point 1.5°× 1.5° during the last 44 a is analyzed. It is discovered that a ma- jority of global ocean swell wave height exhibits a significant linear increasing trend （2-8 cm/decade）, the distribution of annual linear trend of the significant wave height （SWH） has good consistency with that of the swell wave height. The sea surface wind speed shows an annually linear increasing trend mainly con- centrated in the most waters of Southern Hemisphere westerlies, high latitude of the North Pacific, Indian Ocean north of 30°S, the waters near the western equatorial Pacific and low latitudes of the Atlantic waters, and the annually linear decreasing mainly in central and eastern equator of the Pacific, Juan. Fernandez Archipelago, the waters near South Georgia Island in the Atlantic waters. The linear variational distribution characteristic of the wind wave height is similar to that of the sea surface wind speed. Another find is that the swell is dominant in the mixed wave, the swell index in the central ocean is generally greater than that in the offshore, and the swell index in the eastern ocean coast is greater than that in the western ocean inshore, and in year-round hemisphere westerlies the swell index is relatively low.

Long-term survival of early-stage non-small cell lung cancer patients who underwent robotic procedure:a propensity score-matched study

Background:In the past decade,many researchers focused on to robot-assisted surgery.However,on long-term outcomes for patients with early-stage non-small cell lung cancer(NSCLC),whether the robotic procedure is superior to video-assisted thoracic surgery(VATS) and thoracotomy is unclear.Nonetheless,in the article titled "Long-term survival based on the surgical approach to lobectomy for clinical stage I non-small cell lung cancer:comparison of robotic,video assisted thoracic surgery,and thoracotomy lobectomy" by Yang et al.that was recently published in Annals of Surgery,the authors provided convincing evidence that the robotic procedure results in similar long-term survival as compared with VATS and thoracotomy.Minimally invasive procedures typically result in shorter lengths of hospital stay,and the robotic procedure in particular results in superior lymph node assessment.Main body:Our propensity score-matched study generated high-quality data.Based on our findings,we see promise in expanding patient access to robotic lung resections.In this study,propensity score matching minimized the bias involved between groups.Nevertheless,due to its retrospective nature,bias may still exist.Currently,the concept of rapid rehabilitation is widely accepted,and it is very difficult to set up a randomized controlled trial to compare robotic,VATS,and thoracotomy procedures for the treatment of NSCLC.Therefore,to overcome this limitation and to minimize bias,the best approach is to use a registry and prospectively collected,propensity score-matched data.Conclusions:Robotic lung resections result in similar long-term survival as compared with VATS and thoracotomy.Robot-assisted and VATS procedures are associated with short lengths of hospital stay,and the robotic procedure in particular results in superior lymph node assessment.Considering the alarming increase in the incidence of lung cancer in China,a nationwide database of prospectively collected data available for clinical research would be especially important.

Long-Term Trends in Photosynthetically Active Radiation in Beijing

A long-term dataset of photosynthetically active radiation （Qp） is reconstructed from a broadband global solar radiation （Rs） dataset through an all-weather reconstruction model. This method is based on four years＇ worth of data collected in Beijing. Observation data of Rs and Qp from 2005-2008 are used to investigate the temporal variability of Qp and its dependence on the clearness index and solar zenith angle. A simple and effcient all-weather empirically derived reconstruction model is proposed to reconstruct Qp from Rs. This reconstruction method is found to estimate instantaneous Qp with high accuracy. The annual mean of the daily values of Qp during the period 1958-2005 period is 25.06 mol m-2 d-1. The magnitude of the long-term trend for the annual averaged Qp is presented （-0.19 mol m-2 yr-1 from 1958-1997 and -0.12 mol m-2 yr-1 from 1958-2005）. The trend in Qp exhibits sharp decreases in the spring and summer and more gentle decreases in the autumn and winter.

GPS monitoring and analysis of ground movement and deformation induced by transition from open-pit to underground mining

To trace the potential hazards of open-pit slope in Longshou mine,global positioning system（GPS） is applied to monitoring ground movement and deformation induced by transition from open-pit to underground mining.Through long-term monitoring from 2003 to 2008,huge amounts of data were acquired.Monitoring results show that large-scale ground movement and deformation have occurred in mining area,and the movement area is ellipse-shaped.The displacement boundary of settlement trough is 2.0 km long along the exploratory line,and 1.5 km long along the strike of ore body.GPS monitoring results basically agree with the practical deformation state of open-pit slope.It is indicated that the long-term GPS monitoring is an effective way to understand the mechanism of ground movement and deformation in mine area. 更多

Thermomechanical analysis of long-term global modal and local deformation measurements of the Kishwaukee Bridge using the bootstrap

In this paper we present a comparative analysis of global frequency and local deformation data for a large concrete bridge. The asymptotic probability distributions of the central statistics are presented, and compared with empirical bootstrap estimates. Bootstrapped distributions are calculated from reference data obtained during 1999–2000 and used to develop change-point alarm criteria for the structure, using reasonable sensitivity measures developed from FEM simulations and structural analysis. The implications of the frequency data are discussed in conjunction with the strain and displacement measurements in order to discern if the load carrying capacity of the bridge has been affected. The critical need for more advanced temperature compensation models for large structures continually in thermal disequilibrium is discussed.

A Study of Temperature and Aging Effects on Eigenfrequencies of Concrete Bridges for Health Monitoring

This paper discusses the influence of environmental factors and of normal material aging on the eigenfrequencies of concrete bridges based on monitoring data registered during 4 years of a specific bridge. It is a new composite steel-concrete bridge built in 2006 in Luxembourg. The measurements are analyzed and compared to literature data. The final objective is the use of real monitored eigenfrequencies for structural health monitoring and damage detection based on identification of stiffness losses in practical applications. Therefore, it is very important to identify and compensate for outdoor influences namely temperature, excitation force level and normal aging effects, like creep and shrinkage of concrete and their impact on material properties. The present paper aims at describing these effects in order to separate them from damage effects. It is shown that temperature change rates and temperature gradients within the bridge have an influence on the eigenfrequencies. Hence the key idea for assessment from the full database is to select only measurements with small temperature differences and slow temperature change rates.

大跨度斜拉桥模态参数长期追踪及其变异性分析

为了丰富大跨度斜拉桥模态参数的实测数据库,依托苏通大桥结构健康监测系统采集的数据,采用所建立的模态自动识别和追踪方法,获取了2010年期间该桥的模态参数值,并据此分析了在温度和风速作用下桥梁模态参数的变异性。研究结果表明,大桥主梁模态频率的变化受温度和风速共同影响,随着温度的升高而降低,随着风速的升高而增加。大桥主梁模态阻尼比的变异性显著大于模态频率,低风速下主梁一阶侧弯阻尼比在0.5%~15%之间波动,风速大于9 m/s时逐渐降低并稳定在2%左右。大桥主梁前四阶竖弯阻尼比受气动阻尼的影响较大,在风速较小时随着风速的增加略有增加。研究结果可为大桥的服役性能评估与运营管理提供参考。

融合多源异构气象数据的光伏功率预测模型

高精度光伏功率预测对提高电力系统运行效率具有重要意义。光伏功率受多种因素影响,其中云层的变化是最主要的不确定因素。传统光伏功率预测方法没有充分考虑云的3维结构和气象要素对光伏功率的影响。因此,该文提出一种融合多源异构气象数据的多源变量光伏功率预测模型(MPPM)。MPPM的核心包括时空条件扩散模型(STCDM)、注意力堆叠LSTM网络(ASLSTM)和多维特征融合模块(MFFM)。STCDM模型通过对2维卫星云图进行精确预测,消除了云层边界处的模糊现象。ASLSTM模型则提取了3维天气研究与预报模式(WRF)气象要素特征。MFFM模块将2维卫星云图特征和3维WRF气象要素特征进行融合,以得到未来1 h光伏功率预测结果。该文分别利用STCDM模型和MPPM模型开展卫星云图预测实验和光伏功率预测实验。实验结果显示,STCDM模型预测1 h内卫星云图的结构相似性指数(SSIM)达到0.914,MPPM模型预测1 h内光伏功率的相关系数(CORR)达到0.949,优于所有对比算法。

城市生态学长期监测的几个关键问题

伴随城市人口快速聚集及人类活动加剧,城市生态环境质量及其对人类社会经济发展的支撑能力正在受到严重挑战,城市生态系统及其变化的研究越来越受到重视,国内外涌现出大量城市生态学长期监测工作,但相关监测方法的讨论还很少。在分析城市生态学长期监测类型和实践经验基础上,从城市面临的环境胁迫和建设管理需求出发,构建了城市生态学长期监测的“胁迫+需求-生态系统响应”总体思路框架;从科学上最基本和社会上最关心的城市生态系统要素、格局和过程着眼,提出城市生态学长期监测的7项内容(土地覆盖与利用、人居环境质量、生物多样性、生物质生产、水文过程、生物地球化学循环和物质代谢);并分析了城市生态学长期监测的4种方法(遥感监测、仪器监测、调查监测、公众科学监测),分析了城市生态学长期监测单元及其空间布局及监测频率。这将为我国城市生态学长期监测工作开展提供方法论基础和实践参考。

基于神经网络模型的水平井破裂压力预测方法

破裂压力是井身结构设计的基础依据,也是水力压裂设备选型和方案设计的基础参数,通常采用测井解释获取破裂压力剖面,但其存在参数准确获取难、计算过程繁琐、普适性较差、计算精度低等问题,机器学习提供了一种解决这些问题的新方法。为此,以测井数据作为输入参数,采用4种不同的神经网络模型,建立水平井测井数据与破裂压力间的非线性关系,通过测试集预测结果的对比分析,优选出最佳的神经网络模型,并优化模型网络结构和超参数,实现水平井破裂压力的直接预测。研究结果表明:1)破裂压力与井斜角、横波时差和纵波时差表现为极强相关性,与井深、岩性密度和补偿中子表现为强相关性,与井径和自然伽马表现为弱相关性;2)不同组合的测井参数对模型预测结果具有显著影响,最优输入参数为井斜角、横波时差、纵波时差、井深、岩性密度和补偿中子;3)对比多层感知机、深度神经网络、循环神经网络和长短期记忆神经网络(LSTM)模型,发现LSTM模型的预测效果最佳;4)优化了LSTM模型的网络结构及超参数,优化后破裂压力预测的平均绝对百分比误差为0.106%、决定系数为0.996。LSTM模型能够有效构建水平井测井参数与破裂压力之间的非线性关系,可以实现水平井破裂压力的准确预测,对于准确预测破裂压力、简化破裂压力计算过程、推广机器学习在石油工程领域的应用具有重要的作用。

2011-2015年东洞庭湖洲滩典型植物群落样方调查数据集

固定样地调查通过长期数据的积累和精准的时空对比获取生态系统动态特征,为长期的生态系统研究提供了坚实的基础。洞庭湖湿地生态系统观测研究站按中国生态系统研究网络(Chinese Ecosystem Research Network,CERN)统一的监测规范,对洞庭湖水文情势变化下,湿地生态系统中典型洲滩植被的物种组成和群落特征等指标进行长期定位监测。通过东洞庭湖三种典型湿地植物群落(苔草,南荻和水蓼)长期监测样地的数据进行加工处理,获得2011-2015年洞庭湖洲滩植物群落长期监测数据集。本数据集包含有植物种名、拉丁名、株(丛)数(株或丛/样方)、叶层平均高度(cm)、生殖枝平均高度(cm)、盖度(%)、物候期、优势种、植物种数、密度(株或丛/m~2)、优势种叶层高度(cm)、优势种生殖枝高度(cm)、总盖度(%)、地上绿色部分总干重(g/m~2),共14个指标,同时附有完整的背景信息。本数据集实行全过程数据质量控制,并由专家审核验证,确保数据时空上的相对一致和准确可靠。本数据集可以为探究洞庭湖水文情势下,洲滩湿地生态系统过程和演替趋势提供本底资料,为洞庭湖植被的遥感监测、生物多样性保护和湿地生态修复及适应性管理等提供数据支撑。

基于长短时记忆的农作物生长环境数据预测

针对传统温室农作物生长监控系统控制灵活性差且精确度低等问题,设计了一个面向智慧农业的农作物生长闭环监控系统.引入单变量长短时记忆(long short-term memory,LSTM)网络模型,对土壤含水率、土壤温度和土壤电导率3个农作物生长环境数据进行预测研究.在优化时间步长参数的基础上,分析不同预测步长对单变量LSTM模型预测准确性的影响,采用不同时间段的测试集数据对模型的预测性能和稳定性进行验证.分别采用单变量LSTM模型、最小绝对值收敛和选择算法、随机森林回归、双向LSTM模型和编解码LSTM模型进行预测对比,结果表明,单变量LSTM模型预测的平均绝对误差值和均方根误差值均为最小,模型具有更好的准确性和稳定性.本研究设计的农作物生长闭环监控系统能有效预测农作物的生长环境数据,为农作物监控系统的智能控制提供有效数据支撑.

基于CNN-LSTM和卷复制方法的高可用系统设计方法

针对单机服务器存在的单点故障问题,以及主备双机中存在的逻辑故障导致数据丢失的问题,设计了一种基于卷积和长短期记忆神经网络(CNN-LSTM)和卷复制方法的HA(High Availability)系统。系统至少包含两个节点,一个主节点以及一个或多个备用节点,主节点和备节点之间支持主备切换。每个服务器节点上包含4个模块,分别是负责接收配置信息与读写请求的代理模块;进行磁盘读写操作和重定向读写的磁盘I/O(输入输出)模块;负责主备节点间备份快照、映射表、数据块复制的卷复制模块以及基于CNN-LSTM进行状态检测的高可用模块。实验表明,该系统不仅可以解决单点故障问题,也可以解决主备双机集群中无法解决的逻辑错误问题;同时基于CNN-LSTM方法,自动针对服务器的运行健康状态进行分析和预测,可以根据预测结果自动通知管理员进行处理或自动进行主备切换。

护航战略目标落地:澳大利亚国家教育监测评估的经验与启示

开展针对国家中长期教育政策性宣言的教育监测评估是澳大利亚推进教育改革发展的有效经验,包括:以法律和政策坚实监测评估“合法性”基础,依据政策性宣言设计监测指标体系,打造支撑监测评估的多源异构数据基座,以“见林见树”的全面观呈现现状与发展趋势,强化监测评估服务行政决策和公共监督。澳大利亚的经验对我国开展国家教育监测评估有如下启示:一是政府委托专门机构对国家教育中长期战略落实进展监测评估,二是监测评估坚持纵向发展与横向国际比较相结合,三是强化监测评估服务政府决策和舆论引导效应,四是推动人工智能等新一代信息技术与教育监测评估深度融合。