Preliminary Study on the Differences between Automatic-observed and Manual-observed Wind Speed and Its Correction

Comparing and analyzing the difference between automatic-observed and manual-observed wind speed based on the wind speed parallel observations in two methods, we find that many elements can influence the difference between automatic-observed and manual-observed wind speed, including the levels of speed wind, observation instruments and different regions. According to these elements, correction has been conducted, and find that the correction according to the level of wind speed has the best correction effect.

Comparison of Manual and Automatic Methods for Measurement of Methane Emission from Rice Paddy Fields

S The methane emission flux from rice paddies was simultaneously measured with automatic and manual methods in the suburban of Suzhou. Both methods were based on the static chamber/GC-FID techniques. Detail analysis of the experimental results indicates: a) The data of methane emission measured with the automatic method is reliable. b) About 11 or 19 o′clock of local time is recommended as the optimum sampling time for the manual spot measurement of methane emission from rice paddies. The methane emission fluxes measured by manual sampling at local time other than the optimum time have to be corrected. The correction coefficient may be determined by automatic and continuous measurement. c) In order to get a more accurate result, an empirical correction factor, such as 18%, is recommended to correct the seasonally total amount of measured methane emission by enlarging the automatically measured data or reducing the manually measured ones.

Enhancing manual P-phase arrival detection and automatic onset time picking in a noisy microseismic data in underground mines

Accurate detection and picking of the P-phase onset time in noisy microseismic data from underground mines remains a big challenge. Reliable P-phase onset time picking is necessary for accurate source location needed for planning and rescue operations in the event of failures. In this paper, a new technique based on the discrete stationary wavelet transform （DSWT）and higher order statist!cs, is proposed for processing noisy data from underground mines. The objectives of this method are to （1） Improve manual detection and tPicking of P-phase onset; and （ii） provide an automatic means of detecting and picking P-phase onset me accurately. The DSWT is first used to filter the signal over several scales. The manual P-phase onset detection and picking are then obtained by computing the signal energy across selected scales with frequency bands that capture the signal of interest. The automatic P-phase onset, on the other hand, is achieved by using skewness- and kurtosis-based criterion applied to selected scales in a time-frequency domain. The method was tested using synthetic and field data from an underground limestone mine. Results were compared with results obtained by using the short-term to long-term average （STA/LTA） ratio and that by Reference Ge et al. （2009）. The results show that the me!hod provides a more reliable estimate of the P-phase onset arrival than the STA]LTA method when the signal to noise ratio is very low. Also, the results obtained from the field data matched accurately with the results from Reference Ge et al. （2009）.

Towards Increasing Data Availability for Meteorological Services: Inter-Comparison of Meteorological Data from a Synoptic Weather Station and Two Automatic Weather Stations in Kenya

Meteorological data is useful for varied applications and sectors ranging from weather and climate forecasting, landscape planning to disaster management among others. However, the availability of these data requires a good network of manual meteorological stations and other support systems for its collection, recording, processing, archiving, communication and dissemination. In sub-Saharan Africa, such networks are limited due to low investment and capacity. To bridge this gap, the National Meteorological Services in Kenya and few others from African countries have moved to install a number of Automatic Weather Stations (AWSs) in the past decade including a few additions from private institutions and individuals. Although these AWSs have the potential to improve the existing observation network and the early warning systems in the region, the quality and capacity of the data collected from the stations are not well exploited. This is mainly due to low confidence, by data users, in electronically observed data. In this study, we set out to confirm that electronically observed data is of comparable quality to a human observer recorded data, and can thus be used to bridge data gaps at temporal and spatial scales. To assess this potential, we applied the simple Pearson correlation method and other statistical tests and approaches by conducting inter-comparison analysis of weather observations from the manual synoptic station and data from two Automatic Weather Stations (TAHMO and 3D-PAWS) co-located at KMD Headquarters to establish existing consistencies and variances in several weather parameters. Results show there is comparable consistency in most of the weather parameters between the three stations. Strong associations were noted between the TAHMO and manual station data for minimum (r = 0.65) and maximum temperatures (r = 0.86) and the maximum temperature between TAHMO and 3DPAWS (r = 0.56). Similar associations were indicated for surface pressure (r = 0.99) and RH (r > 0.6) with the weakest correlations occurring in wind direction and speed. The Shapiro test for normality assumption indicated that the distribution of several parameters compared between the 3 stations were normally distributed (p > 0.05). We conclude that these findings can be used as a basis for wider use of data sets from Automatic Weather Stations in Kenya and elsewhere. This can inform various applications in weather and climate related decisions.

Design and Simulation of an Audio Signal Alerting and Automatic Control System

A large part of our daily lives is spent with audio information. Massive obstacles are frequently presented by the colossal amounts of acoustic information and the incredibly quick processing times. This results in the need for applications and methodologies that are capable of automatically analyzing these contents. These technologies can be applied in automatic contentanalysis and emergency response systems. Breaks in manual communication usually occur in emergencies leading to accidents and equipment damage. The audio signal does a good job by sending a signal underground, which warrants action from an emergency management team at the surface. This paper, therefore, seeks to design and simulate an audio signal alerting and automatic control system using Unity Pro XL to substitute manual communication of emergencies and manual control of equipment. Sound data were trained using the neural network technique of machine learning. The metrics used are Fast Fourier transform magnitude, zero crossing rate, root mean square, and percentage error. Sounds were detected with an error of approximately 17%;thus, the system can detect sounds with an accuracy of 83%. With more data training, the system can detect sounds with minimal or no error. The paper, therefore, has critical policy implications about communication, safety, and health for underground mine.

全自动免疫组织化学染色与手工染色在胃肠道肿瘤患者中的应用价值比较

目的比较全自动免疫组织化学染色与手工染色在胃肠道肿瘤患者中的应用价值。方法选取2022年1月至12月宝鸡市中医医院收治的300例胃肠道肿瘤患者为研究对象。所有患者各采集病灶组织2份,分别采用全自动免疫组化仪染色(自动组)和手工染色(手工组)。比较不同染色方式的染色质量、切片背景着色、边缘着色发生率、阳性细胞检出率、染色时间及染色方式满意度。结果自动组的染色质量优良率高于手工组,差异具有统计学意义(P<0.05)。自动组的切片背景着色、边缘着色发生率低于手工组,阳性细胞检出率高于手工组,差异具有统计学意义(P<0.05)。自动组的人力工作时间、步骤完成时间短于手工组,差异具有统计学意义(P<0.05)。自动组的显色情况、染色结果、诊断结果及应用价值满意度评分高于手工组,差异具有统计学意义(P<0.05)。结论全自动免疫组织化学染色在胃肠道肿瘤患者中应用的价值优于手工染色,其可提高染色质量、阳性细胞检出率、染色效率与染色满意度。

基于半自动分割技术的膝关节有限元建模

背景:膝关节有限元建模可以深入了解膝关节力学,但其复杂的图像分割工作对于研究人员来说较为困难。随着深度学习技术的发展,深度学习技术已经广泛应用于膝关节有限元建模中。目的:使用3D Swin UNETR结合统计形状模型半自动分割技术来代替膝关节有限元建模中人工分割的步骤。方法:基于MR建立手动(人工)膝关节有限元模型和3D Swin UNETR+统计形状模型分割的半自动膝关节有限元模型,对2个模型施加相同的载荷与边界条件,通过计算Dice相似系数、平均距离以及比较2个模型的等效应力峰值、最大主应力和最大剪切应力来进行验证。结果与结论:①半自动分割模型股骨和胫骨的Dice相似系数均超过98%,平均距离均≤(0.35±0.08)mm;②对手动和半自动有限元模型股骨顶端施加纵向荷载750 N及10 Nm内翻力矩,手动有限元模型半月板等效应力峰值、最大主应力、最大剪切应力为14.12,18.54,7.35 MPa;股骨软骨等效应力峰值、最大主应力、最大剪切应力为2.22,2.15,1.18 MPa;胫骨软骨等效应力峰值、最大主应力、最大剪切应力为2.50,1.91,1.41 MPa;半自动有限元模型半月板等效应力峰值、最大主应力、最大剪切应力为14.93,18.53,7.75 MPa;股骨软骨等效应力峰值、最大主应力、最大剪切应力为2.26,2.18,1.20 MPa;胫骨软骨等效应力峰值、最大主应力、最大剪切应力为2.60,1.91,1.46 MPa;手动和半自动有限元模型等效应力峰值、最大主应力和最大剪切应力之间基本一致,差异无显著性意义(P>0.05);③此次研究所提出的半自动分割技术在创建准确的膝关节有限元模型方面可代替人工分割。

四川九寨沟M_(S)7.0地震余震序列自动处理结果探讨

为了检测人工智能方法对地质构造复杂的川西高原地区强震后余震序列的自动处理能力,采用RISP系统对2017年四川九寨沟M_(S)7.0地震序列进行自动处理,并将获得的自动目录结果与人工目录结果进行对比分析。统计结果显示:震后10天内,自动处理产出的目录数为8071个,其数量是人工目录总数的2.9倍;以四川台网产出的2782个人工目录结果为准,采用发震时刻偏差位于-3.0~3.0s和震中位置偏差小于15.0km的原则进行匹配,自动目录能成功匹配2646个,事件匹配率为95.11%;人工目录中的136个地震事件匹配失败,其中,97个地震事件在自动目录中有产出,39个地震事件属于漏检事件,漏检率为1.40%;在成功匹配的地震事件中,发震时刻偏差为-1.0~1.0s的地震占比为85.9%,震中位置偏差小于5.0km的地震占比为83.18%,震级偏差为-0.3~0.3的地震占比为87.26%。结论表明:RISP系统具有一定的地震目录自动化处理能力,检测地震事件的匹配率较高,但仍可以通过进一步改进地震定位方法和区域速度模型来提升系统的自动化处理能力。

不同核酸提取方法对HBV-DNA检测性能验证情况分析

目的评估2种乙型肝炎病毒(HBV)-DNA提取方法及2家检测试剂的性能,有助于选择优化提取试剂和检测试剂。方法2023年4月采用达安全自动核酸提取仪提取法(磁珠法)和手工提取法(一步法),并用达安和圣湘2种HBV-DNA试剂检测,对其进行精密度、正确度、线性范围、检出限及抗干扰能力等性能进行验证和评价。结果达安全自动核酸提取仪提取达安试剂检测、手工提取达安试剂检测和手工提取圣湘试剂检测在精密度、正确度、线性范围、检出限方面验证结果均达标;达安全自动核酸提取仪提取圣湘试剂检测在低值检测中变异系数大于5%,最低检测限验证不合格;抗干扰能力方面,全自动核酸提取仪提取的2.0 g/dL血红蛋白浓度的样本用达安和圣湘试剂检测结果均不受影响。手工提取甘油三酯浓度达3000 mg/dL的样本用达安和圣湘试剂检测的结果均不受影响。结论不同厂家的提取和检测试剂避免混用,达安和圣湘试剂对HBV-DNA定量检测的结果均符合要求。

自动滴定仪测定土壤有机碳及其组分的方法优化

土壤有机碳及其组分(颗粒有机碳、矿物结合态有机碳等)是反映土壤质量的关键性指标,开展准确高效地测定这些指标对相关研究具有重要意义。自动滴定仪与传统人工滴定相比,人员工作强度低,检测准确,但检测效率不及人工滴定的30%。为解决此问题,本文探讨了4种氧化剂加入量对测定有机碳的影响,研究了提前预加滴定液对检测效率提高的效果。最终优选氧化剂加入量为2mL,预加滴定液量以空白样滴定量的1/3计,建立了用于测定土壤有机碳的自动滴定仪法。采用有机碳不同水平的土壤样品和标准物质对方法进行验证,并与人工滴定进行比较,结果表明,自动滴定仪法与人工滴定法无显著性差异,方法的相对标准偏差(RSD,n=6)在2.10%~12.96%,加标回收率在93.37%~98.06%,标准物质相对误差为4.31%~4.79%;优化后的自动滴定法单个试样滴定用时由11min缩短到3.5min,整体检测效率高于人工滴定,而试剂耗用量仅是人工滴定法的40%。自动滴定仪法显著提升了有机碳的检测能力。

基于LabVIEW的继电保护测试仪自动检测系统设计

针对现有的继电保护测试仪检测程序繁琐,自动化程度低,点对点调试效率低下等问题,提出基于LabVIEW的继电保护测试仪自动检测系统;系统硬件以STM32F407ZGT6为主控芯片研制了继电保护测试仪输出转换装置,自动控制测试仪与标准器连成试验回路,代替人工拆接线行为;系统软件以LabVIEW为开发平台,设计了仪器通信、自动检测、生成报告、强制选择测点等模块,实现了继电保护测试仪的全自动检测;通过对某型继电保护测试仪进行测试,结果表明该系统工作状态稳定,能够实现一键式全自动检测,用户界面友好,可以替代繁琐的人工检测方法,具有一定的工程应用前景。

基于PSD-BPA的无功潮流收敛性二阶段自动调整方法

潮流计算是电网规划与运行分析的基础,目前一些系统分析工作需要收敛的潮流工况。针对电力系统研究所潮流及暂态稳定程序(power system department,Bonneville Power Administration,PSD-BPA)中运行方式安排时出现的无功潮流无解问题,提出一种无功潮流收敛性的二阶段自动调整方法。对于不收敛的潮流,首先通过节点类型转换将500 kV输电网上含无功补偿的节点转为PV节点,获取量化无功不收敛的指标。其次,按电网分区将转换类型的节点逐步还原,以还原节点的电压幅值及各分区的电压水平为参考制定第一阶段调整策略;通过Floyd算法确定输电网无功调整路径且由节点补偿量进行修正,获得第二阶段调整策略并使潮流恢复收敛。最后,以某省实际电网验证算法在无功潮流收敛性调整方面的有效性。

光伏出力序列场景生成新方法

面向更精准的中长期电力电量平衡校验,提出横纵向融合及人为指定持续极端日的光伏出力场景生成新方法。首先,依据日辐照度特征指标及聚类把历史数据划分为多个极端日和多个典型日;然后,利用马氏链生成下一个月横向辐照度日类型序列及特征指标序列的基础上,提出基于日类型出现概率及日类型允许转换矩阵的辐照度日类型序列生成方法,生成下一个月纵向日类型序列及特征指标序列,并将横纵向数据进行融合;最后,基于优化方法及光电转化模型生成月度光伏出力场景,并在此基础上提出人为指定持续极端日光伏出力序列场景生成方法。算例表明所提算法能更好地为运行调度提供参考。

自动粪便分析仪与人工复核法检测粪便结果的比较分析

目的比较自动粪便分析仪与人工复核法两种粪便检测方法的检验效果。方法收集2022年9月—2023年8月三明市第二医院就诊患者的9402份新鲜粪便标本,分别使用FA160自动粪便分析仪和人工复核法检测粪便有形成分(白细胞、红细胞、脂肪球、真菌)和隐血,记录两种方法的检测结果并进行统计学分析。结果自动粪便分析仪检测白细胞、红细胞、脂肪球、真菌和隐血的阳性检出率分别为4.90%、2.67%、6.31%、4.54%、25.34%,人工复核法阳性检出率分别为4.17%、2.22%、5.55%、5.29%、25.23%,自动粪便分析仪对粪便有形成分的阳性检出率除真菌略低于人工复核外,其他各项指标均高于人工复核,且差异均有统计学意义(均P<0.05),而粪便隐血试验的阳性检出率比较差异无统计学意义(P>0.05)。以人工复核法为标准,自动粪便分析仪检测白细胞、红细胞、脂肪球、真菌、隐血的阳性符合率分别为91.07%、96.17%、94.83%、78.07%、98.19%;阴性符合率分别为98.85%、99.46%、98.90%、99.56%、99.25%;阳性预测值分别为77.44%、80.08%、83.47%、90.87%、97.77%;阴性预测值分别为99.61%、99.91%、99.69%、98.79%、99.39%。结论自动粪便分析仪与人工复核法的检测结果虽然符合率较高,但还存在漏检和误检。因此对自动分析结果必须进行人工复核,提高检测结果的准确性,为临床提供可靠的参考依据。

软式内镜管道自动清洗装置与手工操作清洗在消化内镜管道刷洗中的消毒质量对比

目的:观察软式内镜管道自动清洗装置与手工操作清洗在消化内镜管道刷洗中的消毒质量。方法:选取2022年10月—2024年2月江苏省中医院消化内镜中心70条次消化内镜为研究对象,以随机数表法分成观察组(n=35)与对照组(n=35)。观察组采用软式内镜管道自动清洗装置清洗消化内镜,对照组采用手工操作清洗消化内镜。比较两组内镜清洗质量、清洗前后内镜微生物与芽孢水平。结果:两组内镜表面及阀门目测法清洗合格率均为100%。两组三磷酸腺苷(ATP)生物荧光测定法表面、阀门清洗合格率比较,差异无统计学意义(P>0.05);观察组ATP生物荧光测定法管道清洗合格率高于对照组(P=0.046)。清洗前,两组菌落单位(CFU)、相对光单位(RLU)、芽孢水平比较,差异无统计学意义(P>0.05);清洗后,观察组CFU、RLU、芽孢水平低于对照组(P<0.05)。结论:软式内镜管道自动清洗装置与手工操作清洗对消化内镜表面及阀门的清洗效果相当,但软式内镜管道自动清洗装置对消化内镜管道的清洗质量更佳,能有效清除内镜微生物与芽孢。

通信干扰信号分类识别方法综述

通信干扰信号的分类识别作为通信抗干扰的前提和基础,被广泛运用于无线通信和通信电子战等诸多领域。从通信干扰信号分类识别的基本原理出发,将分类识别方法分为基于人工特征提取(Manual Feature Extraction,MFE)和自动特征学习(Automatic Feature Learning,AFL)两大类进行介绍。系统阐述了两类方法的区别,并对比分析了不同方法的特点和性能。最后,分析了该领域面临的挑战,并对未来的研究方向进行了展望。

继电保护测试仪自动检测系统设计

继电保护测试仪是对继电保护装置调试和检验的重要仪器。为了进一步提高继电保护测试仪的检测精度、稳定性和检测效率,设计了基于LabVIEW的继电保护测试仪自动检测系统。该系统采用LabVIEW模块化的编程思想,实现继电保护测试仪的自动检测、手动检测、保存报告等功能。为了解决继电保护测试仪的输出为三相,而万用表和示波器仅支持单相测试的问题,设计了输出转换装置。基于转换装置,可以在测试开始前一次性完成所有接线,并在测试过程中根据不同校准项目进行测试回路自动切换。功能测试结果表明,该系统能满足检验标准化、报告标准化和闭环自动测试要求。该系统对其他二次测量设备的自动检测校准有一定的借鉴意义。

基于截割顶底板高度预测模型的采煤机自动调高技术

传统的煤层截割路径规划通过几何控制、规划计算等方式对采煤机滚筒高度进行预测,但存在预测的数据误差较大、无法适应地质条件变化的问题。针对上述问题,提出了一种基于截割顶底板高度预测模型的采煤机自动调高技术。首先,分析了影响截割顶底板高度的因素,指出影响顶底板高度的主要因素包括煤层的起伏变化数据、历史截割数据、刮板输送机的高程数据及人工操作记录,将上述4类数据融合处理,建立以长短期记忆(LSTM)模型和灰色马尔可夫模型为基础的截割顶底板高度预测模型,通过算法模型预测出截割顶底板的高度。然后,以截割顶底板的高度数据为基础,结合采煤机位姿和空间坐标,建立计算滚筒高度的几何模型,同时依据刮板输送机上窜下滑量及是否执行加减刀工艺等因素进行修正,最终将顶底板高度序列转换为滚筒高度序列,即将截割顶底板高度转换为采煤机滚筒的目标高度,由采煤机执行到目标高度,实现滚筒自动调高工业性试验结果表明:(1)在自动调高技术的控制下,顶滚筒和底滚筒的预测高度与实际高度偏差值有90%的数据量均在10 cm以内,滚筒的预测高度和实际高度具有明显的一致性。(2)与传统手动控制方式相比,中部截割一刀煤的人工干预调高次数由49次下降为21次,说明截割顶底板的高度预测模型和计算滚筒高度的几何模型是准确合理的,采煤机滚筒的自动调高技术是可行的。

基于潜蛟P100的渔网缝补机械臂设计

为解决传统手工修补渔网效率低下且成本较高的问题,设计了一种可自动缝补渔网的机械臂。该机械臂以STM32F103C8T6为主控制器,通过红外传感器、电机执行器和通信模块实现其功能。红外传感器模块负责检测渔网破损位置,电机执行器模块驱动6个轴上的电机使机械臂能灵活地将缝补装置送到预设节点处,通信模块负责将检测的渔网破损信息传输给控制器进行处理。实验测试结果表明,所设计的机械臂通过六自由度的旋转,实现每分钟4~5个简单破损网孔的修补。与传统的手工修补方法相比,提高了渔网修补效率和质量、降低了人工成本,具有明显优势.

仪器法和手工法测定水源水和生活饮用水中高锰酸盐指数的对比研究

利用高锰酸盐指数全自动分析仪和手工滴定法对国家标准物质、水源水和生活饮用水中实际样品进行比对,比较了两种方法的检出限、精密度、准确度、加标回收率等,且分别考察了氯离子对高锰酸盐指数测定的干扰。结果表明,两种方法的标准样品测定值均在标准值允许范围内,且精密度好、准确度高,均能满足水源水和生活饮用水中高锰酸盐指数的检测需要。