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.

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.

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.

稠油开采注汽锅炉在线综合预警关键技术

为了对工作在高温高压下注汽锅炉的各项运行参数进行在线准确监测和异常预警,本文在对稠油开采注汽锅炉工况参数进行采集、处理、分析的基础上,提出对注汽锅炉显性故障和隐性故障进行检测的方案。采用长期短期记忆神经网络,利用锅炉的时序数据对系统进行分析和建模,完成锅炉显性故障检测和预警,并通过预测数据的方式缓解锅炉大时滞的特性;利用深度异常检测技术,将无故障判别标准的数据进行隐性故障分析和预警。本文提出的综合预警方案对克拉玛依油田注汽锅炉进行了实验验证,预测误差仅有0.08%,同时异常检测范围也在设定值范围内。

企业基础信息设备的运行风险控制和具体应用

通过Zabbix监控系统,运维人员可以集中监控机房各类设备和系统软件的运行情况。一旦出現异常指标,运维人员可以及时进行针对性处理,大幅提高了运维效率。Zabbix监控系统还具备定制化设置监控项和数据可视化模板的功能,对特定需求有更好的针对性和扩展性。通过有效监控运雏中的凤险项,Zabbix监控系統为企业的科研和生产提供了强大的保障。

基于EMD-ABOD的大坝异常监测数据识别方法研究

大坝监测数据普遍存在异常值,对异常数据进行识别和剔除,可保持模型的稳定性和可靠性,并提高模型的预测或分类性能;同时,可及时发现异常情况,以保证系统的安全运行。因此,将基于角度的异常值检测算法(ABOD)引入大坝监测异常数据识别,首先通过经验模态分解(EMD)提取监测数据的高频本征函数,然后对由高频本征函数构成的新数据进行异常数据识别。对长河坝沉降监测数据的验证结果表明,与其他方法相比,EMD-ABOD可有效提升异常数据识别的准确性。

基于Prophet-GMM的大坝监测数据异常检测算法

大坝监测数据受环境等因素影响,往往存在异常数据,异常数据的检测对于大坝的正常运行起着不可或缺的作用,但是传统异常检测算法对于大坝监测数据往往达不到精度要求。提出了一种基于Prophet-GMM的异常检测算法,利用Prophet算法较好的拟合性能对大坝数据进行拟合,由拟合数据与实测数据求残差序列,再利用GMM算法对残差序列进行聚类,从而准确识别出异常值。结果表明:Prophet-GMM法对于不同类型的大坝监测数据都能准确识别出异常值,与传统检测算法相比,在查准率、查全率及准确率3个检测指标上,均有较为明显的提升。

柔性直流换流站异常监控数据实时预警研究

为了提高对异常数据的预警性能、维护柔性直流换流站的运行稳定性,提出了柔性直流换流站异常监控数据实时预警方法。首先,利用经验规则消除数据碰撞问题,根据数据信息哈希函数的异常值,准确检测出其中的异常数据。然后,利用小波变换对异常监控数据进行预处理,并从异常数据中提取关键变量信息。最后,根据监控过程中出现异常数据的概率,设定预警阈值,再利用预警函数构建实时预警模型。试验结果表明,应用该方法后,误报率被控制在20%以下,平均绝对误差被控制在0.12以下。相比于传统方法,该方法的预警结果更有效、准确率更高。

动态网络舆情异常数据准确监测算法仿真

网络舆情信息的形式多样,既有文本也有图片、视频等,复杂度较高,且包含很多谣言、恶意攻击、虚假信息等。如何从众多信息中排除无价值和虚假信息是动态网络舆情监测的难点之一。为此,改进贝叶斯算法,提出一种新的动态网络舆情异常数据监测方法。利用网络爬虫技术采集动态网络舆情数据,通过改进多小波变换系数相关去噪算法,滤除数据中的噪声。利用TF-IDF算法提取动态网络舆情异常数据关键特征,引入LSI算法获取高频数据。基于此,应用改进的贝叶斯算法构建贝叶斯模型,将全部特征输入模型中,实现动态网络舆情异常数据监测。实验结果表明,所提方法可以精准获取动态网络舆情异常数据,且监测时间更短,监测相对误差低于0.04%。

基于胶囊网络的异常多分类模型

国网公司日益庞大的服务器集群产生的大量生产运行数据,以及实时分析各类设备、系统产生的海量监控数据成为电力IT运维工作的新挑战。异常检测技术作为智能电网信息运维工作的关键技术,可以有效检测运维故障并及时告警,避免损坏敏感设备。目前一些传统异常检测方法检测的异常种类少且精度低,导致故障发现不及时。为了应对这一挑战,提出了基于胶囊网络的多维时间序列异常多分类模型NNCapsNet。首先,应用无监督算法结合专家知识对电网营销业务应用服务器性能监控数据进行预处理和标注。其次,引入胶囊网络进行分类和异常检测。五折交叉验证的实验结果表明,NNCapsNet在包含15类异常的数据集上实现了91.21%的平均分类准确度。还在包含2万条监控数据的数据集上与4个基准模型进行了对比,NNCapsNet在关键评估指标上均取得了较好的结果。

Real-time estimation of the structural utilization level of segmental tunnel lining

Over the last decades,an expansion of the underground network has been taking place to cope with the increasing amount of moving people and freight.As a consequence,it is of vital importance to guarantee the full functionality of the tunnel network by means of preventive maintenance and the monitoring of the tunnel lining state over time.A new method has been developed for the real-time prediction of the utilization level in tunnel segmental linings based on input monitoring data.The new concept is founded on a framework,which encompasses an offline and an online stage.In the former,the generation of feedforward neural networks is accomplished by employing synthetically produced data.Finite element simulations of the lining structure are conducted to analyze the structural response under multiple loading conditions.The scenarios are generated by assuming ranges of variation of the model input parameters to account for the uncertainty due to the not fully determined in situ conditions.Input and target quantities are identified to better assess the structural utilization of the lining.The latter phase consists in the application of the methodological framework on input monitored data,which allows for a real-time prediction of the physical quantities deployed for the estimation of the lining utilization.The approach is validated on a full-scale test of segmental lining,where the predicted quantities are compared with the actual measurements.Finally,it is investigated the influence of artificial noise added to the training data on the overall prediction performances and the benefits along with the limits of the concept are set out.

基于船载测控雷达的关键信息实时监测软件设计与实现

在航天测控中,关键信息的实时监测十分重要,有利于岗位人员及时掌握设备状态,并针对异常情况做出应急处置。针对现阶段海上测控任务中岗位人员设备监视状态多、监视压力大的问题,基于Qt开发了一款关键信息实时监测软件,软件从航天业务网中提取出关键信息,采用数据、图表等形式将所需信息进行显示,对异常的数据进行语音提醒,以辅助岗位人员实时监测。

低速增压风洞测力试验异常数据检测专家系统设计研究

针对低速增压风洞测力试验,分析气动特性曲线的原始数据源,以天平信号、流场状态和模型姿态为主要对象,结合试验控制流程,从单点数据向量、单车次数据矩阵和同期多车次数据集等维度,研究试验数据的异常检测方法策略,并以此为核心知识库,完成异常数据检测专家系统设计开发。试验过程中系统推理机自动在线执行,经过数据识别、规则推理、逻辑推理和知识迭代,实现原始数据的预检测和预诊断。试验应用结果表明,专家系统对天平桥压异常、线性段跳点和零点检测等异常类型检测敏感度高,为异常数据分析指引方向,提升问题数据排查效率。

多判据融合的换流站监控数据异常甄别系统

换流站监控数据中存在部分异常数据,如果无法有效融合全部异常数据,会造成异常数据维度过大,导致甄别结果不精准,为此设计了基于多判据融合的换流站监控数据异常甄别系统。以MSP430单片机为核心,通过双存储器传输传感信号。设计可视甄别模块,计算两个图像圆点中心坐标距离,以此为依据,判断甄别点与设定阈值之间的关系。检测和筛选换流站监控异常数据,融合多判据数据特征,对数据进行降维处理,降低异常数据维度。构建LSTM分位数回归多判据融合模型,识别电流、电压、功率时间序列,获取置信区间范围,结合曲线斜率甄别异常数据,避免人为设置阈值给甄别结果带来的不确定性。由实验结果可知,该系统甄别出的电压、电流异常节点与实际异常节点数量一致,分别是3个和2个,说明使用该系统能够精准甄别异常数据。

基于卫星遥感监测极端气象预报数据异常值检测方法

在遥感数据采集过程中,由于传感器故障、气象条件等原因,可能会导致少量的异常点出现在采集的数据中,这些异常点可能会对极端天气预报的准确性产生负面影响;为此,需要研究一种基于卫星遥感监测极端气象预报数据异常值检测方法;基于改进K-均值聚类算法对缺失的卫星遥感监测极端气象预报数据进行插补,还原数据完整性;划分卫星遥感监测极端气象预报数据的区段,提取每个区段的裕度指标、偏斜度、频率歪度、重心频率4个特征参数,以此为输入,利用蝙蝠算法优化BP神经网络识别异常区段;计算异常区段中每个卫星遥感监测极端气象预报数据的局部离群因子,局部离群因子大于1.0数据为气象预报数据异常值,以此完成气象预报数据异常值检测;结果表明:所提方法插补误差小于±1.0,可以准确识别异常区段中的异常值,且在不同样本中的协调指数高于0.8,检测效果更好。