Impact of sampling interval on variance components of epoch-wise residual error in relative GPS positioning: A case study of a 40-km-long baseline

The study presents sampling interval impacts on variance components of the epoch-wise residual errors in relative GPS positioning. In the variance components estimation process, the 2-way nested ANOVA method was used. For that purpose, GPS observation data during four months at two permanent GPS stations, establishing a 40-km-long baseline as a part of the Montenegrin permanent network(Monte Pos), were used. The study results showed that there is no statistically significant impact of sampling interval changes on epoch-wise variance components related to the residual tropospheric and ionospheric delays(effect a) when it comes to such a baseline. However, it is not the case with epoch-wise variance components related to the interstation-distance-independent residual ‘far-field’ multipath effect(effect b). It turned out that the absolute values of relative differences of standard deviations of the effect a on the relative GPS coordinates(e, n and u) had maximum values 11.1%, 10.2% and 8.9%,respectively. Keeping the same order of presentation for the effect b, the values of 5.9%, 9.9% and 12.5%were obtained. In addition, absolute values of relative differences of standard deviations of horizontal and vertical position had maximum values of 3.8% and 7.7%, respectively.

Postoperative Mechanical Axis Alignment and Components Position after Conventional and Patient-Specific Total Knee Arthroplasty

Introduction: Accurate postoperative alignment and implant positioning are determinant factors for successful total knee arthroplasty (TKA). Patient-specific template (PST) is a technique that uses computer technology for the planning, deigning and production of cutting guides. This study aims to compare PST to conventional technique in terms of mechanical axis alignment and component positioning. Patients and method: 109 TKA were performed for 78 patients in 2 groups. Group A included 69 conventional TKA in 55 patients and Group B included 40 patient-specific TKA in 23 patient. Postoperative long-film X-rays were done for all patients to observe the mechanical axis, anatomical axis, lateral distal femoral mechanical angle and medial proximal tibial angle. Results: No statistically significant difference was found between the two groups regarding alignment or component positioning. Conclusion: Both techniques have shown similar results in restoring the mechanical axis and alignment after TKA. However, PST had the advantages of reduced blood loss and shorter operative time.

Effects of an immunosuppressive active component of Periplocae Cortex (Periploca sepium Bge.) on positive selection of thymocytes in vitro

Objective:To determine the effect of an immunosuppressive active component (periploside A) isolated from the stem bark of Periplocae Cortex (Periploca sepium Bge.),a Chinese medicinal herb used in the treatment of rheumatoid arthritis for centuries in China,on positive selection of thymocytes in vitro.Methods:Female C57BL/6 mice at 6 weeks of age were housed in specific pathogen-free conditions.Double-positive thymocytes from C57BL/6 mice were induced into positive selection in vitro with or without periploside A treatment.Cell viability and expression of CD69,CD4,and CD8 were analyzed by flow cytometry.Results:Flow cytometric examination of thymocyte populations revealed that the percentage of CD8+ single-positive thymocytes was decreased by periploside A upon differentiation induced by an anti-CD3 antibody.However,the percentage of CD4+ single-positive thymocytes was decreased by periploside A upon differentiation induced by phorbol 12-myristate 13-acetate/ionomycin.Expression of CD69 plays a major role in prohibiting differentiation of thymocytes.Treatment with periploside A decreased CD69 expression in thymocytes.Conclusion:These results demonstrate that periploside A influences positive selection of thymocytes in vitro.

Beam position monitor troubleshooting by using principal component analysis in Shanghai Synchrotron Radiation Facility

Beam position monitors(BPMs)have been widely used in all kinds of measurement systems,feedback systems and other areas in particle accelerator field these days.The malfunction of a single BPM can cause serious consequences such as the failure of the orbit feedback and the transverse feedback.A troubleshooting has been made to prevent the defective BPMs from affecting the accuracy and stability of the storage ring in Shanghai Synchrotron Radiation Facility(SSRF).Different types of malfunctions have been successfully identified by using the idea of principal component analysis(PCA).

An Automatic Text Region Positioning Method for the Low-Contrast Image

Text extraction is the key step in the character recognition;its accuracy highly relies on the location of the text region. In this paper, we propose a new method which can find the text location automatically to solve some regional problems such as incomplete, false position or orientation deviation occurred in the low-contrast image text extraction. Firstly, we make some pre-processing for the original image, including color space transform, contrast-limited adaptive histogram equalization, Sobel edge detector, morphological method and eight neighborhood processing method (ENPM) etc., to provide some results to compare the different methods. Secondly, we use the connected component analysis (CCA) method to get several connected parts and non-connected parts, then use the morphology method and CCA again for the non-connected part to erode some noises, obtain another connected and non-connected parts. Thirdly, we compute the edge feature for all connected areas, combine Support Vector Machine (SVM) to classify the real text region, obtain the text location coordinates. Finally, we use the text region coordinate to extract the block including the text, then binarize, cluster and recognize all text information. At last, we calculate the precision rate and recall rate to evaluate the method for more than 200 images. The experiments show that the method we proposed is robust for low-contrast text images with the variations in font size and font color, different language, gloomy environment, etc.

Dependence of epoch-wise two-way nested ANOVA estimates of variances of unmodeled effects present in relative GPS positioning on satellite elevation cutoff angle and PDOP mask

Impact of satellite elevation cutoff angle and position dilution of precision(PDOP)mask change on epoch-wise variance components of unmodeled effects that accompany relative Global Positioning System(GPS)positioning is presented herein.Data used for this study refer to the winter and summer periods of the years with minimal(2008)and maximal(2013)solar activity.These data were collected every 30 s in static mode,at two permanent GPS stations located in Montenegro,establishing a mediumdistance(116-km-long)baseline with a height difference of approximately 760 m between its endpoints.The study showed that changing satellite elevation cutoff angle,with a fixed PDOP mask,affects epochwise two-way nested ANOVA estimates of variances related to the‘far-field’multipath(considered as the nested factor herein)and the combined unmodeled effect of tropospheric and ionospheric refraction(considered as the nesting factor herein).However,changing of PDOP mask,with a fixed satellite elevation cutoff angle,doesn’t affect epoch-wise two-way nested ANOVA estimate of variance of the combined unmodeled effect of tropospheric and ionospheric refraction,but,generally,affects the estimate of variance of the‘far-field’multipath(possibly mixed with a part of a‘shorter-term’ionospheric refraction),which is especially pronounced for the summer period.It should also be noted that there is a significant influence of satellite elevation cutoff angle change on both epoch-wise horizontal and vertical position accuracy,only for the summer period,especially in the presence of maximal solar activity,while there is no significant impact of PDOP mask change on epoch-wise positional accuracy.

Lung Cancer Prediction from Elvira Biomedical Dataset Using Ensemble Classifier with Principal Component Analysis

Machine learning algorithms (MLs) can potentially improve disease diagnostics, leading to early detection and treatment of these diseases. As a malignant tumor whose primary focus is located in the bronchial mucosal epithelium, lung cancer has the highest mortality and morbidity among cancer types, threatening health and life of patients suffering from the disease. Machine learning algorithms such as Random Forest (RF), Support Vector Machine (SVM), K-Nearest Neighbor (KNN) and Naïve Bayes (NB) have been used for lung cancer prediction. However they still face challenges such as high dimensionality of the feature space, over-fitting, high computational complexity, noise and missing data, low accuracies, low precision and high error rates. Ensemble learning, which combines classifiers, may be helpful to boost prediction on new data. However, current ensemble ML techniques rarely consider comprehensive evaluation metrics to evaluate the performance of individual classifiers. The main purpose of this study was to develop an ensemble classifier that improves lung cancer prediction. An ensemble machine learning algorithm is developed based on RF, SVM, NB, and KNN. Feature selection is done based on Principal Component Analysis (PCA) and Analysis of Variance (ANOVA). This algorithm is then executed on lung cancer data and evaluated using execution time, true positives (TP), true negatives (TN), false positives (FP), false negatives (FN), false positive rate (FPR), recall (R), precision (P) and F-measure (FM). Experimental results show that the proposed ensemble classifier has the best classification of 0.9825% with the lowest error rate of 0.0193. This is followed by SVM in which the probability of having the best classification is 0.9652% at an error rate of 0.0206. On the other hand, NB had the worst performance of 0.8475% classification at 0.0738 error rate.

基于一种新型正弦幅值积分器的谐波检测方法

针对基于瞬时无功理论的ip-iq谐波检测法结构复杂、响应速度慢等问题,提出了一种采用改进正弦幅值积分器结构的谐波电流检测方法。首先,利用二阶广义积分器的输出滤波特性及正弦幅值积分器的极性选择特性,使其相结合构造出了一种滤波性能更强,同时具有频率及极性选择作用的新型积分器。然后,利用该积分器快速准确地提取基波正序分量,并有效应用于谐波检测环节中。当电网电压存在直流偏置问题时,该积分器所构造的锁相环也能准确锁定电网频率及相位信息。最后,理论分析及仿真实验结果表明:该方法无需进行瞬时对称分量分离,就可提取出电网电压及负载电流基波的正负序分量,计算量小,谐波检测更精确。

含逆变型分布式电源和分支负荷的配电网自适应电流差动保护

电流差动保护具有良好的选择性和灵敏性。但是,逆变型分布式电源(inverter-interfaced distributed generator,IIDG)和分支负荷接入改变了配电网的故障特性,现有配电网的电流差动保护面临拒动或误动的风险。为解决以上问题,该文在计及P/Q控制的IIDG和分支负荷的故障等值建模基础上,从故障点、IIDG和分支负荷的相对位置出发,挖掘配电网线路两端正序电流差的幅值特征以及正序电流故障分量的相位特征。在此基础上,提出一种正序电流幅值与故障分量相角阈值配合的双比例制动系数自适应差动保护方案,并且利用幅相平面分析所提保护判据的可靠性和灵敏性。最后通过PSCAD/EMTDC验证了所提保护方案的有效性,仿真结果表明该方案适应不同故障类型、位置和过渡电阻,且不受IIDG以及分支负荷接入的影响。

HL-3装置测量基准网的建立及部件定位测量

根据HL-3装置总装集成设计安装精度的要求,需要建立一个高精度的测量基准网,在总装过程中采用激光跟踪仪等先进测量设备对安装部件的空间位置进行测量。建立的基准网实现了网内基准点空间坐标最大不确定度为0.133mm。特别在对真空室、临时第一壁/限制器等部件的安装中,进行定位测量和数据反馈,然后再进行安装调整,实现了真空室∅1.84mm的同轴度精度,满足同轴度≤∅3mm的要求;标高偏差为-0.08~+0.136mm,满足标高偏差≤±1mm的要求。临时第一壁/限制器安装最大偏差值为+1.9351mm,最小偏差值为-1.8337mm,均满足各模块表面位置误差不超过±2mm的技术要求。测量基准网的建立以及安装过程中对部件高精度的定位测量,保证了HL-3装置高质量的建造。

含分布式电源配电网的自适应距离保护

针对逆变型分布式电源接入的配电网系统,对分布式电源采用低电压穿越及P/Q控制策略,分析其故障输出特性及对传统距离保护方案产生的影响;针对逆变型分布式电源接入造成传统距离保护易发生拒动或误动的问题,结合不同故障类型下的复合序网分析,提出一种基于逆变型分布式电源正序分量的自适应距离保护方案,通过检测逆变型分布式电源其输出的故障电流构造助增分支系数对原有分支系数进行自适应修正,讨论在相间短路故障和接地短路故障情况下该方案的适用性。结果表明,该自适应距离保护方案在逆变型分布式电源出力变化时具备优异可靠性和灵敏性。

基于强震记录的建筑结构楼层设计谱研究

非结构部件地震损失占比远超结构部件已成为近年震害新现象,然而国内对非结构部件进行抗震设计所必需的楼层设计谱的研究尚不充分。通过选取工程强震数据中心(CESMD)中81栋结构所记录的1300条楼层加速度时程数据,研究了各类结构楼层峰值加速度放大系数沿结构高度的变化规律。研究结果表明:针对各类结构,GB 50011−2010中的位置系数严重低估了楼面加速度响应;GB 50011−2010及ASCE 7-16规范中部件放大系数取值存在对短周期非结构部件严重偏小,对于长周期非结构部件则过于保守的问题。与此同时,鉴于现行规范中相关参数并不能很好反映非结构部件的加速度放大特性,提出一种可考虑主体结构及不同输入地震动影响的楼层设计谱,可更好地用于非结构部件的抗震及分析。

融合组件位置信息特征的少样本字体生成

局部组件方法实现少样本字体生成任务,通常会忽略组件位置信息对文字生成的作用,造成生成文字整体结构布局存有偏差。为了能够有效捕获组件位置信息,提出一种融合组件位置信息特征的少样本字体生成方法(CPI-Font)。提出的CPI-Font模型以MX-Font为基本框架,设计一种新的全局位置信息提取器。以坐标注意力提取全局位置信息,并通过多头组件注意力关注每个组件在全局位置信息中的不同重要程度,以捕获局部组件在整个字形中的全局位置关系特征,避免生成的字形结构产生偏差。采用公开构建的39种字体作为汉字数据集,与目前主流模型进行大量实验。实验结果显示,提出模型的LPIPS值达到0.112,FID值达到88.5,在内容和风格上的准确率分别达到83.1%、70.4%,在三个评价指标上模型均优于其他算法。结果表明,CPI-Font模型能够有效捕获组件位置信息,并具有较为先进的少样本字体生成性能。

基于正序电流故障分量的有源配电网线路差动保护

传统配电网在大量分布式电源接入后变为有源配电网,拓扑结构与故障特征发生改变,现有电流差动保护无法同时满足可靠性与灵敏性要求。为此分析了逆变型分布式电源故障输出特性,建立有源配电网正序电流故障分量网络,在此基础上提出一种基于正序电流故障分量的电流差动保护方案。该方案以线路两侧正序电流故障分量为特征量对常规电流差动保护进行改造,解决了在分布式电源接入后线路发生区内高过渡电阻故障引起的电流差动保护无法准确识别故障的问题。最后使用PSCAD/EMTDC对该方案在不同工况下的动作情况进行仿真,仿真结果表明该方案能够适应有源配电网保护需求。

基于正序突变量相轨迹辨识的可再生能源配电网电流保护

【目的】随着可再生能源的大量接入,中性点经小电阻接地的配电网潮流不确定性使得传统三段式电流保护的整定变得日趋复杂,其灵敏性和可靠性都有所降低。为了解决上述问题,提出了一种基于正序突变量电流相轨迹辨识的新型电流保护方法予以应对。【方法】首先,分析了故障点上游、下游的正序突变量电流大小及其分布特征;其次,利用亲和力聚类算法,根据聚类结果识别定义的主故障路径;最后,提出一种搜索方法,将搜索得到的正序突变量产生源头,即位于主故障路径上的最后一级馈线视为故障馈线。【结果】基于PSCAD的仿真研究表明,所提出的保护方法能够适应各种故障工况,不受分布式电源加入的影响,具有较高的灵敏度和可靠性。【结论】研究结果有助于提升中性点经小电阻接地的配电网运行安全性,同时提升故障排查效率。

Detection Scheme of Positive Sequence Components Based on Operation Period Delay Filter

Fast and accurate acquisition of positive sequence components of unbalanced grid voltage is an essential requirement to ensure the safety operation of the grid-connected inverter.To improve the extraction speed of positive sequence components of unbalanced voltage,this study proposes a sampling period delay filter(SPDF)to quickly separate positive and negative sequence components by delaying two sampling periods of grid voltage in dq frame.With the SPDF method,only one coordinate transformation is required and the computational burden can be reduced apparently.Then,the noise immunity performance of the proposed SPDF algorithm is investigated;and the corresponding solution,operation period delay filter(OPDF),can guarantee the desired fast response performance under the premise of limiting the amplified noise within the acceptable range.Finally,the feasibility and priority of the above two algorithms have been verified by the simulation and experimental results.

新型3UPRR并联机构的运动学分析与多目标优化

根据基于方位特征方程的并联机构拓扑设计理论和方法,提出一种三平移3UPRR并联机构,通过矢量法建立运动学方程并计算得到位置正解和逆解。分别研究工作空间、灵巧度、全域同向系数、全域最小奇异值等性能指标。建立多维性能指标优化目标,选择具有降维和多重信息有效融合特点的主成分分析法(PCA),得到不依赖主观选择的机构评价函数,通过给定运动轨迹仿真验证支链驱动副位移连续且无明显突变跳跃。最后通过SolidWorks软件模拟零件抓取过程进行仿真验证,研究可为该类机器人的设计应用奠定理论基础。

浙江庆元东部匠派传统木构建筑的大木构件组织体系

空间与构件命名体系是匠师认识与营造一栋建筑的前提。而传统木构建筑由大量木构件通过榫卯连接而成,因此工匠又建立了一套构件定位系统用以确定每个构件的位置,依此设计构件尺寸、榫卯做法,并在制作与组装时进行区分。文章通过对庆元东部匠派营造现场的实地调研,总结了该匠派针对当地建筑特征形成的地域性的空间、构件命名体系与构件定位方法。

边坡安全监测GPS-RTK信号的降噪算法研究

全球定位系统实时动态差分技术(global positioning system-real time kinematic, GPS-RTK)是解决路基边坡安全监测问题的重要手段,但GPS-RTK信号易受到多路径误差和共模误差的影响。基于小波变换(wavelet transform, WT)和主成分分析(principal component analysis, PCA)分别可以有效去除多路径误差和共模误差,提出WT-PCA算法去除信号误差。首先设置仿真信号,通过参数调优进一步提高单一算法的降噪效果。其次提出组合算法WT-PCA改进单一算法的缺陷,并与其他组合算法进行对比分析。最后,对十天高速路基边坡的GPS-RTK监测数据进行实例分析。结果表明,WT-PCA算法的信噪比和均方根误差较于WT-VMD优于66%和50%左右,算法可以有效地消除GPS-RTK信号的多路径误差和共模误差影响。提高边坡位移监测信号处理精度,进一步评估边坡结构形变及安全状态。

基于模型跟随自适应控制的电液位置伺服系统重构控制

航空机电系统中电液位置伺服系统(EHPSS)组件故障易引发连锁反应,对此,本文提出了一种基于模型跟随自适应控制的重构控制器。首先,根据电液位置伺服系统的组成结构、工作原理以及典型组件故障机理,分别建立其标称模型与故障模型;然后,结合模型跟随理论与Lyapunov稳定性理论,设计了模型跟随自适应重构控制器,该控制器不需要故障先验信息,仅根据故障系统的状态与参考模型状态之间的广义误差,在线调整自适应控制器参数,使得当时间趋向无穷大时,广义误差信号逐渐收敛于零,不仅可以保证系统的稳定性,还具有自适应速度快的优点;最后,通过多种故障情况下的位移跟踪试验,验证了该重构控制方法的重构控制能力和鲁棒性。本文所提出的建模与控制方法对于机电系统重构控制的未来研究具有参考价值。