Real-time prediction of mechanical behaviors of underwater shield tunnel structure using machine learning method based on structural health monitoring data

Predicting the mechanical behaviors of structure and perceiving the anomalies in advance are essential to ensuring the safe operation of infrastructures in the long run.In addition to the incomplete consideration of influencing factors,the prediction time scale of existing studies is rough.Therefore,this study focuses on the development of a real-time prediction model by coupling the spatio-temporal correlation with external load through autoencoder network(ATENet)based on structural health monitoring(SHM)data.An autoencoder mechanism is performed to acquire the high-level representation of raw monitoring data at different spatial positions,and the recurrent neural network is applied to understanding the temporal correlation from the time series.Then,the obtained temporal-spatial information is coupled with dynamic loads through a fully connected layer to predict structural performance in next 12 h.As a case study,the proposed model is formulated on the SHM data collected from a representative underwater shield tunnel.The robustness study is carried out to verify the reliability and the prediction capability of the proposed model.Finally,the ATENet model is compared with some typical models,and the results indicate that it has the best performance.ATENet model is of great value to predict the realtime evolution trend of tunnel structure.

Towards Cache-Assisted Hierarchical Detection for Real-Time Health Data Monitoring in IoHT

Real-time health data monitoring is pivotal for bolstering road services’safety,intelligence,and efficiency within the Internet of Health Things(IoHT)framework.Yet,delays in data retrieval can markedly hinder the efficacy of big data awareness detection systems.We advocate for a collaborative caching approach involving edge devices and cloud networks to combat this.This strategy is devised to streamline the data retrieval path,subsequently diminishing network strain.Crafting an adept cache processing scheme poses its own set of challenges,especially given the transient nature of monitoring data and the imperative for swift data transmission,intertwined with resource allocation tactics.This paper unveils a novel mobile healthcare solution that harnesses the power of our collaborative caching approach,facilitating nuanced health monitoring via edge devices.The system capitalizes on cloud computing for intricate health data analytics,especially in pinpointing health anomalies.Given the dynamic locational shifts and possible connection disruptions,we have architected a hierarchical detection system,particularly during crises.This system caches data efficiently and incorporates a detection utility to assess data freshness and potential lag in response times.Furthermore,we introduce the Cache-Assisted Real-Time Detection(CARD)model,crafted to optimize utility.Addressing the inherent complexity of the NP-hard CARD model,we have championed a greedy algorithm as a solution.Simulations reveal that our collaborative caching technique markedly elevates the Cache Hit Ratio(CHR)and data freshness,outshining its contemporaneous benchmark algorithms.The empirical results underscore the strength and efficiency of our innovative IoHT-based health monitoring solution.To encapsulate,this paper tackles the nuances of real-time health data monitoring in the IoHT landscape,presenting a joint edge-cloud caching strategy paired with a hierarchical detection system.Our methodology yields enhanced cache efficiency and data freshness.The corroborative numerical data accentuates the feasibility and relevance of our model,casting a beacon for the future trajectory of real-time health data monitoring systems.

Alternate Data Acquisition and Real-time Monitoring System on HT-7 Tokamak

A new system called alternate data acquisition and real-time monitoring system has been developed for long-time discharge in tokamak operation. It can support continuous on-line data acquisition at a high sampling rate and a graphic display of the plasma parameters during the discharge. Thus operators can monitor and control the plasma state in real time. An application of this system has been demonstrated on the HT-7 tokamak.

Wearable Healthcare and Continuous Vital Sign Monitoring with IoT Integration

Technical and accessibility issues in hospitals often prevent patients from receiving optimal mental and physical health care,which is essential for independent living,especially as societies age and chronic diseases like diabetes and cardiovascular disease become more common.Recent advances in the Internet of Things(IoT)-enabled wearable devices offer potential solutions for remote health monitoring and everyday activity recognition,gaining significant attention in personalized healthcare.This paper comprehensively reviews wearable healthcare technology integrated with the IoT for continuous vital sign monitoring.Relevant papers were extracted and analyzed using a systematic numerical review method,covering various aspects such as sports monitoring,disease detection,patient monitoring,and medical diagnosis.The review highlights the transformative impact of IoTenabled wearable devices in healthcare,facilitating real-time monitoring of vital signs,including blood pressure,temperature,oxygen levels,and heart rate.Results from the reviewed papers demonstrate high accuracy and efficiency in predicting health conditions,improving sports performance,enhancing patient care,and diagnosing diseases.The integration of IoT in wearable healthcare devices enables remote patient monitoring,personalized care,and efficient data transmission,ultimately transcending traditional boundaries of healthcare and leading to better patient outcomes.

Semi-implantable device based on multiplexed microfilament electrode cluster for continuous monitoring of physiological ions

Modern medicine is increasingly interested in advanced sensors to detect and analyze biochemical indicators.Ion sensors based on potentiometric methods are a promising platform for monitoring physiological ions in biological subjects.Current semi-implantable devices are mainly based on single-parameter detection.Miniaturized semi-implantable electrodes for multiparameter sensing have more restrictions on the electrode size due to biocompatibility considerations,but reducing the electrode surface area could potentially limit electrode sensitivity.This study developed a semi-implantable device system comprising a multiplexed microfilament electrode cluster(MMEC)and a printed circuit board for real-time monitoring of intra-tissue K^(+),Ca^(2+),and Na^(+)concentrations.The electrode surface area was less important for the potentiometric sensing mechanism,suggesting the feasibility of using a tiny fiber-like electrode for potentiometric sensing.The MMEC device exhibited a broad linear response(K^(+):2–32 mmol/L;Ca^(2+):0.5–4 mmol/L;Na^(+):10–160 mmol/L),high sensitivity(about 20–45 mV/decade),temporal stability(>2weeks),and good selectivity(>80%)for the above ions.In vitro detection and in vivo subcutaneous and brain experiment results showed that the MMEC system exhibits good multi-ion monitoring performance in several complex environments.This work provides a platform for the continuous real-time monitoring of ion fluctuations in different situations and has implications for developing smart sensors to monitor human health.

A real-time AI-assisted seismic monitoring system based on new nodal stations with 4G telemetry and its application in the Yangbi M_(S) 6.4 aftershock monitoring in southwest China

A rapidly deployable dense seismic monitoring system which is capable of transmitting acquired data in real time and analyzing data automatically is crucial in seismic hazard mitigation after a major earthquake.However,it is rather difficult for current seismic nodal stations to transmit data in real time for an extended period of time,and it usually takes a great amount of time to process the acquired data manually.To monitor earthquakes in real time flexibly,we develop a mobile integrated seismic monitoring system consisting of newly developed nodal units with 4G telemetry and a real-time AI-assisted automatic data processing workflow.The integrated system is convenient for deployment and has been successfully applied in monitoring the aftershocks of the Yangbi M_(S) 6.4 earthquake occurred on May 21,2021 in Yangbi County,Dali,Yunnan in southwest China.The acquired seismic data are transmitted almost in real time through the 4G cellular network,and then processed automat-ically for event detection,positioning,magnitude calculation and source mechanism inversion.From tens of seconds to a couple of minutes at most,the final seismic attributes can be presented remotely to the end users through the integrated system.From May 27 to June 17,the real-time system has detected and located 7905 aftershocks in the Yangbi area before the internal batteries exhausted,far more than the catalog provided by China Earthquake Networks Center using the regional permanent stations.The initial application of this inte-grated real-time monitoring system is promising,and we anticipate the advent of a new era for Real-time Intelligent Array Seismology(RIAS),for better monitoring and understanding the subsurface dynamic pro-cesses caused by Earth's internal forces as well as anthropogenic activities.

Development and Application of Real-time Bridge Scour Monitoring System

Because of the complex nature of the changes in the current and movement of the riverbeds by bridge scouring, it is impossible to understand or predict these changes. In order to have a reliable data, it is critical to have the current methods and equipment for measuring bridge scouring replaced with technology that could acquire real-time bridge scouring data. Despite the critical need for real-time data acquisition, the harsh environmental conditions have prevented the scientific community from acquiring real-time data. Harsh environmental conditions were addressed by the developmental of an automated, remote data collection system, allowing real-time data such as scour movement, scour depth, and scour trend to be viewed in a safe location. As a result, accurate sea-floor movements were seen for the first time, aiding the direction and future of bridge scour research, ultimately contributing greatly to the safety of bridges.

Patient Centered Real-Time Mobile Health Monitoring System

In this paper, we introduce a system architecture for a patient centered mobile health monitoring (PCMHM) system that deploys different sensors to determine patients’ activities, medical conditions, and the cause of an emergency event. This system combines and analyzes sensor data to produce the patients’ detailed health information in real-time. A central computational node with data analyzing capability is used for sensor data integration and analysis. In addition to medical sensors, surrounding environmental sensors are also utilized to enhance the interpretation of the data and to improve medical diagnosis. The PCMHM system has the ability to provide on-demand health information of patients via the Internet, track real-time daily activities and patients’ health condition. This system also includes the capability for assessing patients’ posture and fall detection.

Real-time horizontality adjusting and control system of a large platform applied to satellite experiment

In order to satisfy a satellite horizontality requirement in an experiment, it is indispensable to monitor and adjust the horizontality of a large platform loading the satellite under the condition of ultra-low temperature with real time. So the control system design and control strategy are described in detail to accomplish the horizontality monitoring and adjusting. The system adopts the industry control computer as the upper computer and the SIEMENS S7-300 PLC as the lower computer. The upper computer that bases on industry configuration software IFIX takes charge of monitoring the platform and puts forward the control strategy. PLC takes charge of receiving the adjusting instructions and controlling the legs moving to accomplish the horizontality adjusting. The horizontality adjusting strategy is emphasized and the concept of grads is introduced to establish a mathematics model of the platform inclined state, so the adjusting method is obtained. Accordingly the key question of the automatic horizontality adjusting is solved in this control system.

Opportunities and Challenges of College Mental Health Education from the Perspective of Big Data

This paper explores the opportunities and challenges of college mental health education from the perspective of big data.Firstly,through literature review,the importance of mental health education and the current issues are elucidated.Then,from the perspective of big data,the potential opportunities of big data in college mental health education are analyzed,including data-driven personalized education,real-time monitoring and warning systems,and interdisciplinary research and collaboration.At the same time,the challenges faced by college mental health education under the perspective of big data are also pointed out,such as data privacy and security issues,insufficient data analysis and interpretation capabilities,and inadequate technical facilities and talent support.Lastly,the research content of this paper is summarized,and directions and suggestions for future research are proposed.

Development and Field Evaluation of a Low-Cost Wireless Sensor Network System for Hydrological Monitoring of a Small Agricultural Watershed

Hydrological monitoring and real-time access to data are valuable for hydrological research and water resources management. In the recent decades, rapid developments in digital technology, micro-electromechanical systems, low power micro-sensing technologies and improved industrial manufacturing processes have resulted in retrieving real-time data through Wireless Sensor Networks (WSNs) systems. In this study, a remotely operated low-cost and robust WSN system was developed to monitor and collect real-time hydrologic data from a small agricultural watershed in harsh weather conditions and upland rolling topography of Southern Ontario, Canada. The WSN system was assembled using off-the-shelf hardware components, and an open source operating system was used to minimize the cost. The developed system was rigorously tested in the laboratory and the field and found to be accurate and reliable for monitoring climatic and hydrologic parameters. The soil moisture and runoff data for 7 springs, 19 summer, and 19 fall season rainfall events over the period of more than two years were successfully collected in a small experimental agricultural watershed situated near Elora, Ontario, Canada. The developed WSN system can be readily extended for the purpose of most hydrological monitoring applications, although it was explicitly tailored for a project focused on mapping the Variable Source Areas (VSAs) in a small agricultural watershed.

基于GIS与大数据分析的矿压监测预警平台

为响应国家关于加快煤矿智能化发展的号召,针对煤矿GIS“一张图”中巷道路径不能实现精确计算,矿压数据不能实现高效甄别、有效提取的问题,以及矿压预警需要实现在线推演、可视化展示的需求,提出建立基于GIS与大数据分析的矿压监测预警平台。采用GIS“一张图”、巷道结点-路径拓扑算法、多源异构监测数据处理技术、矿压大数据分析技术,搭建成能够实现矿井下复杂情况切实反映、矿压监测数据有效提取、采掘风险在线预测、可视化程度高的矿压监测预警平台。通过在李雅庄矿的实际应用,表明该系统运行稳定可靠,可有效提升煤矿的矿压风险监测预警。

基于数字孪生的工业机器人建模及监测方法

针对工业机器人数据不透明、监测存在死角等问题,通过对实体工业机器人分析和研究,构建数字孪生机器人监测系统。首先从几何、物理、逻辑3个维度完成虚拟机器人的建模;其次基于开放平台通信联合架构(open platform communications united architecture,OPC UA),实现机器人的数据采集和通信,设计用户显示界面,达到数据实时可视化的效果;最后,通过机器人仿真实验得出机器人末端执行器X、Y、Z坐标的相对位置误差分别为0.48%、0.32%、0.27%,通过数字孪生机器人监测同步实验验证机器人关节角度误差最大值为0.31°。实验结果表明:该方法能够实现数字孪生工业机器人的运动数据监测,减少工业机器人在生产过程中发生意外和故障的风险,为智能工业机器人的发展提供思路和方向。

气象应用业务融入气象大数据云平台的设计

针对现有气象应用不能直接融入气象大数据平台的现状,该文采用“云+端”融入的方法,对气象应用的数据存储、产品加工、业务监控、应用接口以及应用端进行改造设计,使现有气象应用业务能够快速接入气象大数据云平台。该设计在湖北气象业务进行应用,避免了因气象业务智能化建设而造成的重复建设和资源浪费,有利于推动气象业务智能化发展,为今后气象应用改造提供了参考。

基于Prometheus的高校数据中心云计算平台监控系统研究

高校数据中心云计算平台的使用频率在教学和科研中越来越密集。通过研究在现有云计算平台中利用cAdvisor和Pushgateway进行容器与节点的系统资源监控数据的采集方法,设计适用于高校数据中心的Prometheus监控系统。系统利用Grafana对接Prometheus的数据源,动态展示数据采集结果,从而监控各种业务、工作节点、网络及中间件的运行。在资源失效的第一时间,系统能及时向管理员告警,由管理员或自动化运维组件做出相应处理,确保高校教学和科研的顺利进行。

基于WebGL的高铁路基监测展示平台研究与应用

随着铁路行业加速向数字化、智能化、信息化转型,传统路基建、运、维监测中的数据管理方法和可视化水平已无法满足未来发展的需求。为解决这一困境,提出1套基于3层浏览器/服务器(Browser/Server,B/S)架构的高铁路基监测展示平台总体构建方案,以实现监测数据和工程数据的高效集成;使用Revit软件建立传感器实体模型,基于工业基础类(Industry Foundation Classes,IFC)标准实现传感器属性信息扩展,以Java语言为中介实现IFC文件与MySQL数据库表的精准映射,并通过设置约束关联其他工程信息;利用建筑信息模型(Building Information Modeling,BIM)软件创建集成传感器的高铁路基模型,通过轻量化处理并利用Three.js引擎实现该模型在网页端的可视化展示;通过进一步关联传感器模型和数据库中的监测信息,实现监测数据的实时展示和超限示警定位功能。结果表明:该展示平台的建立可帮助相关人员快速掌握监测项目资料,动态精准评估建、运、维过程中路基的状态,从而达到促进各方交流协作和辅助决策实施的目的。

基于VISIO组件的配电网监测系统设计与实现

针对中低压配电网监测技术无法实现对各类电网运行信息的有效共享以及可视化效果差等问题,设计出一个能够将配电网多种运行数据集成实现信息共享并直观显示配电网架构的实时数据监测系统。基于.NET平台结合VISIO开发技术以下位机设备端作为远程测量设备设计配电网实时数据的监测系统,其中主机端与下位机设备端采用C/S架构以及Modbus协议进行通信。该系统具有配电网设备数据监测、VISIO组件主界面数据显示、数据存储、数据管理等功能。测试运行结果表明,该系统可以较好地实现远程监测与控制,对于提升中低压配电网的分析和管理水平具有一定参考价值。

基于机器学习的大数据平台管理系统设计

伴随互联网技术的快速发展,网络业务和互联网流量数据呈现爆发式增长,进而影响互联网大数据分析平台,产生数据量和运维环境的变化,冲击传统的大数据平台运维模式和数据技术方案。该文在总结机器学习在大数据分析平台运维观众的应用的基础上,分析聚类方法和回归算法的特征,提出基于机器学习的大数据平台智能化运维系统设计和监控系统设计方法,为平台运维人员监控、排查和修复等问题提供便利。

基于物联网技术的智慧健康监测系统设计与实现

传统的医疗模式很难及时获取人的健康数据,不能实现对老年人健康及姿态信息的实时监控。文中以STM32F103单片机为核心控制器,采用心率血氧传感器、姿态传感器等采集身体健康数据,通过MQTT物联网协议将其上传至阿里云平台,并应用云端数据分析处理平台对身体健康数据进行实时分析和预警。系统经过测试,当用户跌倒或心率血压异常时,设备会发出警报声,提醒周围人进行救护,同时监护人手机会收到异常预警。

大气环境智能监测平台的设计

针对大气环境监测海量数据处理困难、处理效率低等问题,设计了一种基于物联网技术的大气环境智能化监测平台,应用关系性数据库,实现数据的实时分级储存,通过Sqoop工具使数据在异构数据库间实时迁移,备份历史冗余数据,强化数据库储存能力,增强系统运行效率。平台集成了HBase协处理器与分布式搜索器(ElasticSearch),不仅能实现多条件数据检索,还可提供WebService数据接口,实现监测数据共享。经测试,该监测平台的安全性、兼容性及可靠性均满足要求。