A Real-Time System for Monitoring Distant Dynamic Displacement of Structures

Based on digital image processing technique, a real-time system is developed to monitor and detect the dynamic displacement of engineering structures. By processing pictures with a self-programmed software, the real-time coordinate of an object in a certain coordinate system can be obtained, and further dynamic displacement data and curve of the object can also be achieved. That is, automatic gathering and real-time processing of data can be carried out by this system simultaneously. For this system, first, an untouched monitoring technique is adopted, which can monitor or detect objects several to hundreds of meters apart; second, it has flexible installation condition and good monitoring precision of sub-millimeter degree; third, it is fit for dynamic, quasi-dynamic and static monitoring of large engineering structures. Through several tests and applications in large bridges, good reliability and dominance of the system is proved.

Performance evaluation of near real-time condition monitoring in haul trucks

Strategic maintenance plays a key role in ensuring high availability and utilization of the haul trucks,and as equipment began to grow more complex towards the end of the 20th century,there was a need for a proactive maintenance strategy,which led to the development of condition-based maintenance.Realtime condition monitoring(RTCM)is the ability to perform condition monitoring in real-time and has the ability to alert maintenance and operations of abnormal conditions.These alarms can be used as an indication leading to a problem,and if a suitable corrective action is initiated in time,it could result in significant savings of equipment downtime and repair costs.This study aims to compare some maintenance performance indicators prior to and after implementation of RTCM strategy at a mine site using some tests of statistical significance.The study also indicated the presence of seasonality in the data,and thus the data was deseasonalized and detrended prior to being subjected to the statistical tests.Finally,the results indicated that RTCM strategy has proven to be successful in improving the availability for some of the failure categories chosen in this study.

Research and application of real-time monitoring and early warning thresholds for multi-temporal agricultural products information

Monitoring and early warning is an important means to effectively prevent risks in agricultural production,consumption and price.In particular,with the change of modes of national administration against the background of big data,improving the capacity to monitor agricultural products is of great significance for macroeconomic decision-making.Agricultural product information early warning thresholds are the core of agricultural product monitoring and early warning.How to appropriately determine the early warning thresholds of multi-temporal agricultural product information is a key question to realize real-time and dynamic monitoring and early warning.Based on the theory of abnormal fluctuation of agricultural product information and the research of substantive impact on the society,this paper comprehensively discussed the methods to determine the thresholds of agricultural product information fluctuation in different time dimensions.Based on the data of the National Bureau of Statistics of China(NBSC)and survey data,this paper used a variety of statistical methods to determine the early warning thresholds of the production,consumption and prices of agricultural products.Combined with Delphi expert judgment correction method,it finally determined the early warning thresholds of agricultural product information in multiple time,and carried out early warning analysis on the fluctuation of agricultural product monitoring information in 2018.The results show that:(1)the daily,weekly and monthly monitoring and early warning thresholds of agricultural products play an important early warning role in monitoring abnormal fluctuations with agricultural products;(2)the multitemporal monitoring and early warning thresholds of agricultural product information identified by the research institute can provide effective early warning on current abnormal fluctuation of agricultural product information,provide a benchmarking standard for China's agricultural production,consumption and price monitoring and early warning at the national macro level,and further improve the application of China's agricultural product monitoring and early warning.

A New Material Balance Equation Model for Analyzing Dynamic Performance of C_2 Flooding

Implementing a CO2 flooding scheme successfully requires the capacity to get accurate information of reservoir dynamic performance and fluids injected. Despite some numerical simulation studies, the complicated drive mechanisms and actual reservoir performance have not been fully understood. There is a strong need to develop models from different perspectives to complement current simulators and provide valuable insights into the reservoir performance during CO2 flooding. The aim of this study is to develop a model by using an improved material balance equation （MBE） to analyze quickly the performance of CO2 flooding. After matching the historical field data the proposed model can be used to evaluate, monitor and predict the overall reservoir dynamic performance during CO2 flooding. In order to account accurately for the complex displacement process involving compositional effect and multiphase flow, the PVT properties and flowability of reservoir fluids are incorporated in the model. This study investigates the effects of a number of factors, such as reservoir pressure, the amount of CO2 injected, the CO2 partition ratios in reservoir fluids, the possibility of the existence of a free CO2 gas cap, the proportion of reservoir fluids contacted with CO2, the starting time of CO2 flooding, oil swelling, and oil flowability improvement by mixing with CO2. The model was used to analyze the CO2 flooding project in Weyburn oil field, Saskatchewan, Canada. This study shows that the proposed model is an effective complementary tool to analyze and monitor the overall reservoir performance during CO2 flooding.

Real-Time Grouting Monitoring and Visualization Analysis System for Dam Foundation Curtain Grouting

A real-time monitoring and 3D visualization analysis system is proposed for dam foundation curtain grouting. Based on the real-time control technology, the optimization method and the set theory, a mathematical model of the system is established. The real-time collection and transmission technology of the grouting data provides a data foundation for the system. The real-time grouting monitoring and dynamic alarming method helps the system control the grouting quality during the grouting process, thus, the abnormalities of grouting, such as jacking and hydraulic uplift, can be effectively controlled. In addition, the 3D grouting visualization analysis technology is proposed to establish the grouting information model(GIM). The GIM provides a platform to visualize and analyze the grouting process and results. The system has been applied to a hydraulic project of China as a case study, and the application results indicate that the real-time grouting monitoring and 3D visualization analysis for the grouting process can help engineers control the grouting quality more efficiently.

Real-Time Monitoring of Dynamic Chemical Processes in Microbial Metabolism with Optical Sensors

Monitoring microbial metabolism is vital for revealing the mechanism of disease related to microbial metabolism and providing guidance for biomanufacturing processes optimization.However,it remains a grand challenge to offer real-time insights into microbial metabolism owing to the complex and dynamic process.In this paper,the recent advances and prospects of optical biosensors including the organic,genetic coding and inorganic optical biosensors are briefly described for real-time monitoring of dynamic microbial metabolism.This paper points out that challenges remain in microbial heterogeneity.We believe that this work will inspire the application of developing new methods for single cell real-time analysis.

Dynamic time-cost-quality tradeoff of rockfill dam construction based on real-time monitoring

Time, cost, and quality are three key control factors in rockfill dam construction, and the tradeoff among them is important. Research has focused on the construction time-cost-quality tradeoff for the planning or design phase, built on static empirical data. However, due to its intrinsic uncertainties, rockfill dam construction is a dynamic process which requires the tradeoffto adjust dynamically to changes in construction conditions. In this study, a dynamic time-cost-quality tradeoff （DTCQT） method is proposed to balance time, cost, and quality at any stage of the construction process. A time-cost-quality tradeoff model is established that considers time cost and quality cost. Time, cost, and quality are dynamically estimated based on real-time monitoring. The analytic hierarchy process （AHP） method is applied to quantify the decision preferences among time, cost, and quality as objective weights. In addition, an improved non-dominated sorting genetic algorithm （NSGA-II） coupled with the technique for order preference by similarity to ideal solution （TOPSIS） method is used to search for the optimal compromise solution. A case study project is analyzed to demonstrate the applicability of the method, and the efficiency of the proposed optimization method is compared with that of the linear weighted sum （LWS） and NSGA-II.

滑道不平顺对大跨度桥梁转体动力性能影响研究

滑道是转体桥梁的关键部件,滑道不平顺将直接影响转体过程中桥梁的安全与稳定。为研究滑道不平顺对大跨度转体桥梁的动力性能影响规律及其合理取值问题,基于某大跨度跨线桥梁转体施工现场实时监测并结合数值模拟分析方法,开展不同程度条件下滑道不平顺对转体桥梁关键部位受力、变形、振动等动力响应影响研究。研究结果表明:实测转体桥梁滑道不平顺差异性较大,其数值介于0~15 mm之间,滑道不平顺的存在会导致转体桥梁单侧发生较小程度的倾斜;转体过程中,滑道不平顺差异变化速率与转体箱梁梁端振动响应成正比关系,其变化速率越大,梁端振动越剧烈,且环形滑道不平顺的数值差异也引起转体桥梁端振动响应不一致;0,5,10,15,20 mm和22 mm六种滑道不平顺条件下,转体桥梁主梁线形、撑脚应力、梁端竖向加速度和梁端动挠度整体表现为随着滑道不平顺数值增加而逐渐增大的趋势;滑道不平顺数值超过15 mm后,撑脚应力和桥梁振动响应明显增大,桥梁安全与稳定性降低,建议将转体桥梁滑道不平顺安全控制值确定为≤15 mm,以供类似转体桥梁结构参考使用。

自激非平稳振动数据驱动的拉索动力性能表征

跨海斜拉桥的拉索在非极端风环境下会产生自激振动,基于现代监测与数据分析技术可即时把握拉索自激振动的数字特征,并以此反映拉索的实时动力性能。根据某跨海大桥主航道斜拉桥拉索振动加速度长期监测数据中的共性特征,提出基于振动加速度时间序列信号上包络线高斯混合模型和频域功率谱的拉索风致自激振动非平稳段自动提取方法。提出基于各自激振动非平稳时间序列进行主导频率识别,并采用带通滤波后的最后1个下降段数据进行阻尼比识别的策略,排除了振动幅值上升段环境风自然激励带入能量对阻尼比识别的干扰。基于各自激振动非平稳段主导频率-阻尼比的识别结果,通过对频率值进行聚类,得到各阶模态对应的模态频率-阻尼比数据簇。根据阻尼比数据的离散、偏态特征提出采用对数正态分布模型的e^(μ)和其累积分布函数的分位值来描述拉索阻尼比统计规律。采用各数据簇的频率质心以及阻尼比概率特征参数作为表征拉索动力性能当前状态的指标。针对背景工程的主要结论包括:桥梁拉索振动加速度信号噪声强、干扰大,在动力特征分析时须排除类似干扰;桥梁拉索的自激振动加速度幅值最高已超过3000 mm/s^(2),振动幅度较大;桥梁拉索的阻尼比平均水平约为0.03%,与设计规范推荐值相比偏低。

UPLC-MS/MS法同时测定蛋白饮料和液体调味品中36种防腐剂

采用提取和净化相结合的前处理方式,建立并优化了同时测定蛋白饮料、液体调味品中36种防腐剂的超高效液相色谱-串联质谱检测方法。样品采用饱和NaCl溶液(用磷酸调节pH=3)净化和乙腈-甲醇(9∶1,体积比)(含体积分数为0.2%的甲酸)提取。采用C18反向色谱柱进行分离,以甲醇和5 mmol/L乙酸铵为流动相梯度洗脱,采用电喷雾离子源正负离子同时扫描,动态多反应监测模式,基质匹配标准曲线,外标法定量。结果表明,36种防腐剂在1~250 ng/mL线性良好(相关系数≥0.999),方法定量限为0.04~0.2 mg/kg;空白样品不同加标水平下的平均加标回收率为75%~119%;相对标准偏差为0.90%~9.8%。建立的高通量检测方法灵敏、快速,准确度高,操作简便且能有效减少基质干扰、降低检测成本,极大提高多种防腐剂的定性定量检测效率。

蜂窝梁一体化架桥机力学性能研究

蜂窝梁一体化架桥机作为全预制装配式桥梁的重要施工机械,使用期间结构的静动力性能是影响一体化安装施工的重要因素.为揭示其静动力性能,分别采用了应变传感器,基于数字图像相关(DIC)技术的光电图像测量仪对关键截面的应变、挠度进行监测,并对结构的强度、刚度进行分析评估.采用简单移动平均法提取结构的动位移,并进行动力特性分析.此外,建立了结构主梁的有限元模型,并将其分析结果与监测结果进行对比.研究结果表明:有限元分析结果与实测结果一致,验证了有限元分析的准确性,两者均证明了其强度及刚度满足规范要求.基于动位移数据采用峰值拾取法能够有效识别架桥机的自振频率,有限元计算的一阶自振频率为4.08 Hz,相对于实测分析结果的最大误差仅为0.73%.

基于LabVIEW的发电机组动态性能监测系统设计

针对船舶电力系统电力品质评估试验研发了一款交直流发电机组动态性能监测系统。系统采用C/S架构。基于虚拟仪器技术、TCP/IP通信协议,以PCIe-6343板卡为核心搭建硬件系统,以LabVIEW为平台开发软件系统,实现对发电机组转速、电压、电流等动态信息长时间、多通道的实时同步监测。现场试验结果表明该系统可以快速、高精度的完成对发电机组动态信息的采集分析,具有极高的实用价值。

轨道车辆车体气密疲劳性能试验研究

为验证车体结构、型材、焊缝等在气动载荷作用下的疲劳性能,在新车型研发设计阶段有必要开展车体气密疲劳强度试验。本文在实验室搭建了车体气动载荷加载系统,可实现静态加载和连续10万次典型压力波动态加载,基于设计需求和仿真计算结果布置应力、位移测点,并对关键位置进行动态监测和渗透探伤,实现了评估车体气密疲劳性能及可靠性的目的,同时为车体优化设计提供了强有力的数据支撑。

A dynamic risk-based early warning monitoring system for population-based management of cardiovascular disease

Risk prediction tools are crucial for population-based management of cardiovascular disease(CVD).However,most prediction models are currently used to assess long-term risk instead of the risk of short-term CVD onset.We developed a Dynamic Risk-based Early wAming Monitoring(DREAM)system using large-scale,real-time electronic health record data from 2010 to 2020 from the CHinese Electronic health Records Research in Yinzhou study.The dynamic risk scores were derived from a 1:5 matched nested case-control set comprising 70,470 individuals(11,745 CVD events)and then validated in a cohort of 81,205 individuals(5950 CVD events).The individuals were Chinese adults aged 40-79 years without a history of CVD at baseline.Eleven predictors related to vital signs,laboratory tests,and health service utilization were selected to establish the dynamic scores.The proposed scores were significantly associated with the subsequent CVD onset(adjusted odds ratio,1.21;95%confidence interval,1.20-1.23).The area under the receiver operating characteristic curves(AUCs)was 0.6010(0.5929-0.6092)and 0.6021(0.5937-0.6105)for the long-term 10-year CVD risk<10%and≥10%groups in the derivation set,respectively.In the long-term 10-year CVD risk>10%group in the validation set,the change in AUC in addition to the long-term risk was 0.0235(0.0155-0.0315).By increasing the risk threshold from 7 to 16 points,the proportion of true subsequent CVD cases among those given alerts increased from 40.61%to 85.31%.In terms of management efficiency,the number needed to manage per CVD case ranged from 2.46 to 1.17 using the risk scores.With the increasing popularity and integration of EHR systems with wearable technology,the DREAM scores can be incorporated into an early-warning system and applied in dynamic,real-time,EHR-based,automated management to support healthcare decision making for individuals,general practitioners,and policymakers.

Probabilistic Automata-Based Method for Enhancing Performance of Deep Reinforcement Learning Systems

Deep reinforcement learning(DRL) has demonstrated significant potential in industrial manufacturing domains such as workshop scheduling and energy system management.However, due to the model's inherent uncertainty, rigorous validation is requisite for its application in real-world tasks. Specific tests may reveal inadequacies in the performance of pre-trained DRL models, while the “black-box” nature of DRL poses a challenge for testing model behavior. We propose a novel performance improvement framework based on probabilistic automata,which aims to proactively identify and correct critical vulnerabilities of DRL systems, so that the performance of DRL models in real tasks can be improved with minimal model modifications.First, a probabilistic automaton is constructed from the historical trajectory of the DRL system by abstracting the state to generate probabilistic decision-making units(PDMUs), and a reverse breadth-first search(BFS) method is used to identify the key PDMU-action pairs that have the greatest impact on adverse outcomes. This process relies only on the state-action sequence and final result of each trajectory. Then, under the key PDMU, we search for the new action that has the greatest impact on favorable results. Finally, the key PDMU, undesirable action and new action are encapsulated as monitors to guide the DRL system to obtain more favorable results through real-time monitoring and correction mechanisms. Evaluations in two standard reinforcement learning environments and three actual job scheduling scenarios confirmed the effectiveness of the method, providing certain guarantees for the deployment of DRL models in real-world applications.

The Concept and Design of Safety Management Innovation in the Use of Medical Equipment:Design of Real-time Monitoring System for Temperature and Humidity of Infant Incubator Based on Internet of Things in China

Ensuring safety in the use of medical equipment is an important guarantee for clinical diagnosis and treatment.Because medical equipment is mostly electrical and electronic equipment,the suddenness of its failure is inevitable.Although some equipment has regular preventive maintenance and metrological calibration,it is patient-free static calibrated.In China,because of its large population,large patient base,and large use of medical equipment,nurses are too busy taking care of patients and cannot pay attention to the safety of the use of each device.Therefore,we propose using the Internet of Things and IT technology to carry out real-time monitoring and alarming of important parameters of some special,high-risk,and large-used medical equipment,so as to strengthen the safety management of equipment use.Through the management innovation,this article successfully implemented real-time monitoring of the temperature and humidity of the neonatal incubator;and greatly improved the safety in the use of such equipment.

步履式架桥机静动力性能测试与评估研究

步履式架桥机作为装配式桥梁的主要施工机械,为揭示其服役期间的静动力性能对桥梁施工安全的影响,本文以无锡高架桥施工中所用步履式架桥机为研究对象,进行了简化力学分析并确定该结构的危险控制截面。采用振弦式应变计对控制截面的应变进行实测并对结构强度进行评估。基于目标跟踪算法编制了相应的计算机视觉动态监测程序,结合全站仪对控制截面的挠度进行实测,并对结构刚度进行评估。基于动态监测程序提取了结构动位移,进行了动力特性分析,并将数值模拟与实测的结果进行相互验证。研究结果表明:简化力学分析能够较好地预测加载过程中结构内力变化,并能对其刚度进行预先评估。实测结果与数值模拟的结果吻合较好,证明了该架桥机的强度和刚度符合要求,同时验证了本文建模与实测方法的合理性与有效性。

新工科背景下农林院校土木与水利专业研究生培养质量的监控与提升

随着“新工科”理念的提出,我国研究生教育迎来了结构调整的关键时期,然而,当前土木与水利工程专业研究生的培养因为受到各类因素的影响而出现了质量下降的问题。立足于农林院校的办学特点和基础条件,通过问卷调查的方式收集福建农林大学土木与水利工程专业研究生的发展现状,引入灰度预测模型预测问卷数据的未来发展趋势。研究结果表明:土木水利专业研究生创新能力质量系数与实践水平质量系数均呈现出明显的下滑趋势,外语能力也未得到有效提升。为此,从学校和教师层面出发,详细阐述了提升创新能力、实践水平及外语能力的改进措施和建议。研究成果在一定程度上揭示了当今土木与水利工程专业研究生培养过程中存在的主要问题,提出了符合“新工科”理念和农林院校特点的研究生质量提升方法,这将有助于高校有针对性地培养适应未来发展格局的高素质土木与水利工程专业人才。

基于Prometheus的云计算资源全链路监控系统

在云计算环境中,为将所有动态资源全部统筹于平台主机之上,并对其进行全方位的分配与监控,为提升云计算资源全链路监控精度,提出基于Prometheus的云计算资源全链路监控系统。利用云平台整体架构,分析监控系统的功能需求与性能,设计云计算资源全链路操控系统总体架构。在此基础上,设计资源数据库对资源数据进行收集处理,通过已连接的Prometheus数据库主机,完成监控信息的获取与传输,结合中间件设备架构,实现基于Prometheus云计算资源全链路监控系统的设计。实验结果表明,与云端网络型监控系统相比,全链路监控系统在单位运行时间内的动态资源统筹能力更强,能够更好地适应云计算环境的全方位调节与分配需求。

认知神经科学视角下冬奥心理科技攻关服务新体系的构建与应用

在“科技冬奥”理念指导下,通过文献梳理、理论推理、实践应用探究认知神经科学视角下的冬奥心理科技攻关服务。认为:①冬奥心理科技攻关服务以“提升赛时运动表现稳定性”为目标,融合认知神经科学技术和心理服务,具有量化运动表现神经活动、预警赛前心理风险、调控赛时心理能力等特征;②其基于动作控制的基本原理,利用认知神经科学技术,大数据、人工智能等算法,实现对心理服务效益的量化、迭代和实时监测;③其以移动脑电和无线传输为载体,通过编码接口和服务平台拟合多维度、多模态的生理、心理数据,动态化监控和调整运动员心理状态。冬奥心理科技攻关服务正在实现从实践经验向数据驱动导向的迭代发展,为提升心理服务的精准性和高效性,助力运动员赛时的稳定发挥开辟新思路。