Home Monitoring of Estrone-3-Glucuronide (E1-3G) Levels in Two Different Ovarian Stimulation Protocols: A Pilot Study

Background: Studies have shown a strong correlation between the growth of E2 in serum and estrone-3-glucuronide (E1-3G) in urine during ovarian stimulation. Thus, we developed theoretical models for using urinary E1-3G in ovarian stimulation and focused on their experimental verification and analysis. Methods: A prospective, observational pilot study was conducted involving 54 patients who underwent 54 cycles of ovarian stimulation. The goal was to establish the growth rate of urinary E1-3G during the course of stimulation and to determine the daily upper and lower limits of growth rates at which stimulation is appropriate and safe. Controlled ovarian stimulation was performed using two different stimulation protocols—an antagonist protocol in 25 cases and a progestin-primed ovarian stimulation protocol (PPOS) in 29 cases, with fixed doses of gonadotropins. From the second day of stimulation, patients self-measured their daily urine E1-3G levels at home using a portable analyzer. In parallel, a standard ultrasound follow-up protocol accompanied by a determination of E2, LH, and P levels was applied to optimally control stimulation. Results: The average daily growth rates in both groups were about 50%. The daily increase in E1-3G for the antagonist protocol ranged from 14% to 79%, while they were 28% to 79% for the PPOS protocol. Conclusion: This is the first study to analyze the dynamics of E1-3G in two different protocols and to estimate the limits of its increase during the entire course of the stimulation. The results confirm our theoretical model for the viability of using urinary E1-3G for monitoring ovarian stimulation.

Monitoring Bridge Deformation Using Auto-Correlation Adjustment Technique for Total Station Observations

Bridges are omnipresent in every society and they affect its human, social, ecological, economical and cultural aspects. This is why a durable and safe usage of bridges is an imperative goal of structural management. Measurement and monitoring have an essential role in structural management. The benefits of the information obtained by monitoring are apparent in several domains. In deformation analysis, the functional relationship between the acting forces and the resulting deformations should be established. If time depending observations are given, a regression could be used as a functional model. In case of stochastic model uncorrelated observations with identical variance are assumed. Due to the high sampling rate, a small time difference arises between two observations. Thus the assumed stochastic model is not suitable. The calculation has to be effected by means of auto-correlated observations. This paper investigates an integrated monitoring system for the estimation of the deformation (i.e., static, quasi-static) behavior of bridges from total station observations and studies the effect of autocorrelation technique on the accuracy of the estimated parameters and variances. The results have shown that autocorrelation technique is reduced the standard deviation of X&Y-direction about 6.7% to 29.4% and 6.5% to 15.5% of the original value, respectively, but the situation was differ in Z direction;the standard deviation in vertical component Z was increased.

A Multi-Interface Remote Monitoring and Control System Architecture Based on Embedded Server

This paper presents a multi-interface embedded server architecture for remote real-time monitoring system and distributed monitoring applications. In the scheme,an embedded microprocessor( LPC3250 from NXP) is chosen as the CPU of the embedded server with a linux operation system( OS) environment. The embedded server provides multiple interfaces for supporting various application scenarios. The whole network is based on local area network and adopts the Browser / Server( B / S) model. The monitoring and control node is as a browser endpoint and the remote node with an embedded server is as a server endpoint. Users can easily acquire various sensors information through writing Internet protocol address of remote node on the computer browser. Compared with client / server( C / S) mode,B / S model needs less maintain and can be applicable to large user group. In addition,a simple network management protocol( SNMP) is used for management of devices in Internet protocol( IP) networks. The results of the demonstration experiment show that the proposed system gives good support to manage the network from different user terminals and allows the users to better interact with the ambient environment.

Application of deep autoencoder model for structural condition monitoring

Damage detection in structures is performed via vibra-tion based structural identification. Modal information, such as fre-quencies and mode shapes, are widely used for structural dama-ge detection to indicate the health conditions of civil structures.The deep learning algorithm that works on a multiple layer neuralnetwork model termed as deep autoencoder is proposed to learnthe relationship between the modal information and structural stiff-ness parameters. This is achieved via dimension reduction of themodal information feature and a non-linear regression against thestructural stiffness parameters. Numerical tests on a symmetri-cal steel frame model are conducted to generate the data for thetraining and validation, and to demonstrate the efficiency of theproposed approach for vibration based structural damage detec-tion.

Comprehensive Overview on Computational Intelligence Techniques for Machinery Condition Monitoring and Fault Diagnosis

Computational intelligence is one of the most powerful data processing tools to solve complex nonlinear problems, and thus plays a significant role in intelligent fault diagnosis and prediction. However, only few com- prehensive reviews have summarized the ongoing efforts of computational intelligence in machinery condition moni- toring and fault diagnosis. The recent research and devel- opment of computational intelligence techniques in fault diagnosis, prediction and optimal sensor placement are reviewed. The advantages and limitations of computational intelligence techniques in practical applications are dis- cussed. The characteristics of different algorithms are compared, and application situations of these methods are summarized. Computational intelligence methods need to be further studied in deep understanding algorithm mech- anism, improving algorithm efficiency and enhancing engineering application. This review may be considered as a useful guidance for researchers in selecting a suit- able method for a specific situation and pointing out potential research directions.

Auto Identification of Rolling Layers in Roller-Compacted Concrete Dam

A scheme for identifying rolling layers in roller-compacted concrete (RCC) dam automatically was presented. First, a conceptual model was developed. Second, by using a computational geometry method, the auto identification of rolling layers and auto matching between rolling compaction machines and rolling layers were realized based on spatial control points. An application to the construction of Guandi RCC dam showed that the auto identification of rolling layers played an important role in ensuring the engineering quality.

Online Condition Monitoring of Gripper Cylinder in TBM Based on EMD Method

The gripper cylinder that provides braced force for Tunnel Boring Machine （TBM） might fail due to severe vibration when the TBM excavates in the tunnel. Early fault diagnosis of the gripper cylinder is important for the safety and efficiency of the whole tunneling project. In this paper, an online condition monitoring system based on the Empirical Mode Decomposition （EMD） method is estab- lished for fault diagnosis of the gripper cylinder while TBM is working. Firstly, the lumped mass parameter model of the gripper cylinder is established considering the influence of the variable stiffness at the rock interface, the equivalent stiffness of the oil, the seals, and the copper guide sleeve. The dynamic performance of the gripper cylinder is investigated to provide basis for its health condition evaluation. Then, the EMD method is applied to identify the characteristic frequencies of the gripper cylinder for fault diagnosis and a field test is used to verify the accuracy of the EMD method for detection of the characteristic frequencies. Furthermore, the contact stiff- ness at the interface between the barrel and the rod is calculated with Hertz theory and the relationship between the natural frequency and the stiffness varying with the health condition of the cylinder is simulated based on the dynamic model. The simulation shows that the character- istic frequencies decrease with the increasing clearance between the barrel and the rod, thus the defects could be indicated by monitoring the natural frequency. Finally, a health condition management system of the gripper cylin- der based on the vibration signal and the EMD method is established, which could ensure the safety of TBM.

Wireless sensing system for environmental humidity and temperature monitoring

Aiming at the actual demand for monitoring environmental information,a wireless sensing system for temperature and relative humidity(RH)monitoring based on radio frequency(RF)technology and mobile network was designed.This paper introduces the architecture of the system.The system uses AVR micro controller unit(MCU),KYL-1020U RF module and SHT71 to complete real-time temperature and humidity monitoring,and uses SIM900A module to realize remote alarming and monitoring with short message system(SMS)through global system for mobile communication(GSM).Experimental results show that the designed system has good stability of measurement and real-time performance,and it can be used in some small temperature and humidity monitoring occasions.

Investigation on the Structure of Chute-feed and Card Auto-leveller and Their Selection of Technological Processes

Based on the structure of chute - feed and autoleveHer, an analysis of their working principle and the verification of their practical production results have been carried out. Finally, the future investigation direetiom of chute - feed and card autuleveller are put forward.

自动固相微萃取(SPM E)GC-MS、GC-MS-MS法检测环境水中有机磷杀虫剂的研究

固相微萃取(solid-phase microextraction,SPME)是20世纪90年代发展起来的一种样品前处理技术,与传统的液-液提取、液-固提取相比,SPME更适用于提取、浓缩液态或气态的挥发性和半挥发性物质,SPME技术可将采样、萃取、浓缩和样本引入集中于一个步骤完成,尤其随着自动SPME与GC-MS等联用技术的日益完善,使SPME技术优点得到更充分的发挥.……

基于EMD与LS-SVM的刀具磨损识别方法

针对刀具磨损声发射信号的非平稳特征和BP神经网络学习算法收敛速度慢、易陷入局部极小值等问题,提出了基于经验模态分解和最小二乘支持向量机的刀具磨损状态识别方法.首先对声发射信号进行经验模态分解,将其分解为若干个固有模态函数之和,然后分别对每一个固有模态函数进行自回归建模,最后提取每一个自回归模型的系数组成特征向量,特征向量被分为两组,一组用于对最小二乘支持向量机训练,另一组用于识别刀具磨损状态.试验结果表明:该方法能很好地识别刀具磨损状态,与BP神经网络相比具有更高的识别率.

基于动态矩阵与特征相似度的AAKR风电机组状态监测

针对传统自组织核回归(AAKR)模型所选记忆矩阵冗余度较高、无法根据在线数据实时更新、计算相似度时未考虑特征参数权值不一的问题,提出一种基于动态矩阵与特征相似度的自组织核回归(DM-FS-AAKR)风电机组状态监测方法。首先基于样本间距离对原始数据集去冗余以降低运算复杂度,形成待选数据集;其次基于k-最近邻算法选取最符合当前运行条件的历史数据构建动态矩阵;为克服相似度计算时不良参数的偏差污染,提出一种特征相似度计算方法为不同参数分配相应权值进一步提高预测精度;最后以河北某风电场SCADA数据为例,对机组故障停机前工况进行验证实验。结果表明,相比于传统AAKR模型,所提算法平均绝对误差降低约15.6%,故障预警时能够提前35天实现预警,具有较高精度和实时性。

基于WIA-PA工业无线技术的循环水自动加药监测系统

针对石化企业自动加药装置分布广、跨域大、布线及调试维护成本高等突出问题,提出一种基于WIA-PA工业无线技术的循环水自动加药监测系统。通过建立基于WIA-PA的工业无线网络,将循环水自动加药装置的关键数据进行采集、上传和存储,经云终端进行实时显示,并通过雷达图等技术手段实现了数据预警、合格率等数据的综合分析。

基于STC89C52的汽车灯具诊断电压监控装置

文章设计并制作基于STC89C52单片机的汽车灯具诊断引脚输出电压监控装置,其系统使用LMV331SE-7电压比较器,并结合单片机外部中断及定时器功能,实时监控汽车灯具可靠性试验中诊断引脚的输出电压情况,从而确认灯具的工作状态,保证汽车灯具试验的有效性。该装置结构简单,且成本低廉,可有效降低汽车灯具可靠性试验中的人工及设备成本。

基于GTS-901A全站仪的自动变形监测软件设计与实现

基于GTS-901A智能型全站仪,开发了用于自动变形监测系统中的机载变形数据采集软件,通过数据后处理,实现了原始观测数据的手簿输出、改正计算、数据入库、成果输出及图形显示等功能。通过模拟试验,表明该软件能够实现变形监测系统中的自动多测回长期观测和数据采集,具有精度较高、操作简单、可靠性强等特点。

基于小波变换和AR-GM(1.1)的发动机状态监控

一般情况下,润滑油对发动机起着非常重要的作用,它直接关系着发动机能否安全可靠工作以及工作性能的优劣。由于润滑油中带有大量的发动机磨损情况的有用信息,需要对润滑油进行油液监控分析,从而可以得到发动机的磨损情况,可提前发现发动机的潜在故障,做到预防性维修。本文提出一种基于小波变换和AR-GM(1.1)方法对滑油金属含量时间序列值的变化情况进行预测。通过与GM(1.1)预测方法相比较,该方法具有很好的预测效果。

基于体积测量的病人尿量自动监测装置设计

目的:设计基于体积测量的病人尿量自动监测装置,以实现对患者尿量的准确、稳定、便捷监测。方法:该监测装置采用分治法设计,由单片机控制模块、尿液分段模块、液位检测模块、电磁阀驱动模块、数据显示模块、传输模块、存储模块、电源管理模块等组成。其中,单片机控制模块由STM32F103C8T6单片机及其外围电路构成,负责控制所有外围模块的工作流程和尿量计算处理;尿液分段模块由2个电磁阀、2个液位检测电极、1个集液管组成,主要功能是将患者排出的尿液划分为固定体积;液位检测模块由2个电阻和1个电容构成,用以判断集液管内的尿液充盈与排空状况;电磁阀驱动模块的芯片采用DRV8870全桥驱动芯片。结果:该监测装置可实现对患者尿量的准确、自动监测,可克服尿液密度及尿袋方位等因素变化对尿量监测的影响。结论:该监测装置使用方便、测量准确、稳定可靠,可为医生判断病情及调整治疗方案提供准确的尿量数据,具有一定的实用价值。

基于Map-Reduce并行构架和AE的大数据指纹监控识别算法

传统的指纹识别监控方法在应用到以海量、在线和增量为特征的大数据环境中时,不能自动高效地进行识别;由此设计了一种基于Map-Reduce并行框架和AE的大数据智能监控指纹识别算法;首先,设计了基于AE和BP神经网络的识别模型,采用AE进行指纹图像自动特征提取,然后提出了一种基于比较差异算法对AE进行参数初始化的训练算法,采用BP神经网络进行具体识别;最后,将识别过程分解为Map函数和Reduce函数;在Map-Reduce并行框架下对FVC2004中的指纹数据库进行实验,实验结果表明了文中方法能自动、高效地进行指纹识别。

2021—2022年洱海水环境现状研究

为了研究洱海水环境现状,应用2021年1月—2022年4月大理洱海金河国家水质自动监测站水质监测资料及洱海水质监测数据,采用综合评价法、单因子评价法分析了洱海金河水质现状及运动变化趋势,结果显示,4个月达到Ⅱ类水质标准,12个月达到Ⅲ类标准,自2016年洱海实施抢救模式以来,T-P、T-N得到有效遏制。

气相色谱-负化学源质谱联用法测定水产品及食用油中氟乐灵的残留量

建立了一种可用于水产品及食用油中氟乐灵残留量分析的分散型固相萃取-气相色谱-负化学离子源质谱方法。水产品及食用油经乙腈提取，4℃冷藏后，采用分散型固相萃取法净化，由气相色谱-负化学离子源质谱选择离子监测技术进行测定与确证，同位素内标法定量。在1～40μg / L 范围内氟乐灵农药的线性关系良好；方法定量限（LOQ）为0.02μg / kg；对鳗鱼、烤鳗、梭子蟹、小龙虾、猪油和橄榄油等6种复杂基质进行1.0、2.0和3.0μg / kg 等3个水平的添加回收试验，平均回收率均处于80％～100％之间，RSD≤10.3％；无干扰现象出现。该方法可作为水产品及食用油中氟乐灵残留检测的确证方法。