A Modified Principal Component Analysis Method for Honeycomb Sandwich Panel Debonding Recognition Based on Distributed Optical Fiber Sensing Signals

The safety and integrity requirements of aerospace composite structures necessitate real-time health monitoring throughout their service life.To this end,distributed optical fiber sensors utilizing back Rayleigh scattering have been extensively deployed in structural health monitoring due to their advantages,such as lightweight and ease of embedding.However,identifying the precise location of damage from the optical fiber signals remains a critical challenge.In this paper,a novel approach which namely Modified Sliding Window Principal Component Analysis(MSWPCA)was proposed to facilitate automatic damage identification and localization via distributed optical fiber sensors.The proposed method is able to extract signal characteristics interfered by measurement noise to improve the accuracy of damage detection.Specifically,we applied the MSWPCA method to monitor and analyze the debonding propagation process in honeycomb sandwich panel structures.Our findings demonstrate that the training model exhibits high precision in detecting the location and size of honeycomb debonding,thereby facilitating reliable and efficient online assessment of the structural health state.

Tool Health Condition Recognition Method for High Speed Milling of Titanium Alloy Based on Principal Component Analysis (PCA) and Long Short Term Memory (LSTM)

The healthy condition of the milling tool has a very high impact on the machining quality of the titanium components.Therefore,it is important to recognize the healthy condition of the tool and replace the damaged cutter at the right time.In order to recognize the health condition of the milling cutter,a method based on the long short term memory(LSTM)was proposed to recognize tool health state in this paper.The various signals collected in the tool wear experiments were analyzed by time-domain statistics,and then the extracted data were generated by principal component analysis(PCA)method.The preprocessed data extracted by PCA is transmitted to the LSTM model for recognition.Compared with back propagation neural network(BPNN)and support vector machine(SVM),the proposed method can effectively utilize the time-domain regulation in the data to achieve higher recognition speed and accuracy.

Improving autoencoder-based unsupervised damage detection in uncontrolled structural health monitoring under noisy conditions

Structural health monitoring is widely utilized in outdoor environments,especially under harsh conditions,which can introduce noise into the monitoring system.Therefore,designing an effective denoising strategy to enhance the performance of guided wave damage detection in noisy environments is crucial.This paper introduces a local temporal principal component analysis(PCA)reconstruction approach for denoising guided waves prior to implementing unsupervised damage detection,achieved through novel autoencoder-based reconstruction.Experimental results demonstrate that the proposed denoising method significantly enhances damage detection performance when guided waves are contaminated by noise,with SNR values ranging from 10 to-5 dB.Following the implementation of the proposed denoising approach,the AUC score can elevate from 0.65 to 0.96 when dealing with guided waves corrputed by noise at a level of-5 dB.Additionally,the paper provides guidance on selecting the appropriate number of components used in the denoising PCA reconstruction,aiding in the optimization of the damage detection in noisy conditions.

The Impact of Big Five Personality Traits on Older Europeans’ Physical Health

Investigating the role of Big Five personality traits in relation to various health outcomes has been extensively studied. The impact of “Big Five” on physical health is here explored for older Europeans with a focus on examining age groups differences. The study sample included 378,500 respondents derived from the seventh data wave of Survey of Health, Aging and Retirement in Europe (SHARE). The physical health status of older Europeans was estimated by constructing an index considering the combined effect of well-established health indicators such as the number of chronic diseases, mobility limitations, limitations with basic and instrumental activities of daily living, and self-perceived health. This index was used for an overall physical health assessment, for which the higher the score for an individual, the worst health level. Then, through a dichotomization process applied to the retrieved Principal Component Analysis scores, a two-group discrimination (good or bad health status) of SHARE participants was obtained as regards their physical health condition, allowing for further con-structing logistic regression models to assess the predictive significance of “Big Five” and their protective role for physical health. Results showed that neuroti-cism was the most significant predictor of physical health for all age groups un-der consideration, while extraversion, agreeableness and openness were not found to significantly affect the self-reported physical health levels of midlife adults aged 50 up to 64. Older adults aged 65 up to 79 were more prone to open-ness, whereas the oldest old individuals aged 80 up to 105 were mainly affected by openness and conscientiousness. .

Environmental fate and health exposures of the geogenic and anthropogenic contaminants in potable groundwater of Lower Ganga Basin,India

The study evaluated the sources and controlling factors of the groundwater contaminants in an agroeconomic region of Lower Ganga Basin using principal component analysis(PCA),multivariable linear regressions(MLR),correlation analysis,and hierarchical cluster analysis,and evaluated the public health risks using the Latin Hypercube Sampling,goodness-of-fit statistics,Monte Carlo simulation and Sobol sensitivity analysis based on the 1000 samples collected in two sampling cycles(N=1000).The study reveals that the dissolution of fluoride-bearing minerals and semi-arid climate regulate the fluoride concentrations(0.10–18.25 mg/L)in groundwater.Extensive application of inorganic nitrogenous fertilizers and livestock manure mainly contributed to elevated nitrate levels(up to 435.0 mg/L)in groundwater.The health risks analysis indicates that fluoride exposure is more prevalent in the residents of each age group than the nitrate and both contaminants exhibited higher non-carcinogenic health risks on the infant and child(minor)age groups compared to adolescents and adults.Based on the cokriging interpolation mapping,the minor residents of 17.88%–23.15%of the total area(4545.0 km^(2))are vulnerable to methemoglobinemia whereas the residents of all age-groups in 38.47%–44.45%of the total area are susceptible to mild to severe dental/skeletal fluorosis owing to consumption of untreated nitrate and fluoride enriched groundwater.The Sobol sensitivity indices revealed contaminant levels,groundwater intake rate and their collective effects are the most influential factors to pose potential health risks on the residents.Artificial recharge and rainwater harvesting practices should be adopted to improve the groundwater quality and the residents are advised to drink purified groundwater.

芜湖内河水环境重金属分布特征及健康风险评价

为明确芜湖内河水体中重金属健康危险程度,以芜湖内河为研究对象,选取芜湖3条典型城市河道设置采样断面,对样品中Cr、Cu、Mn、Ni、Pb、Zn这6种重金属进行测试分析,使用健康风险模型评价重金属对人体健康风险。水样分析结果表明:研究区重金属Mn、Ni超过《地表水环境质量标准》(GB3838—2002)Ⅲ类水标准,Cr、Pb有部分点位超过Ⅲ类水标准。主成分分析结果表明:Cu、Ni、Mn和Zn由工业和生活源控制,Pb属于交通源因子,Cr是自然地质背景和人类活动因子。健康风险评价结果表明:6种重金属不存在非致癌风险,汇成河道和中山南路河道存在非致癌风险,直接摄入途径下,重金属Cr和Ni存在致癌风险。该研究结果可为芜湖城市内河水体重金属污染防治和居民生活安全用水提供措施和建议。

四种保健黄酒中的挥发性组分分析

采用顶空固相微萃取结合全二维气相色谱-质谱(Headspace solid-phase microextraction-comprehensive two dimensional gas chromatography/mass spectrometry HS-SPME-GC×GC-MS)技术,对4种保健黄酒(黄精酒、黄米酒、藜麦酒和苦荞酒)中挥发性物质的种类、含量分进行分析,并且通过主成分分析法很好地区分不同原料的保健黄酒,找出重要的组分差异特征,探究其风味成分。结果表明,GC×GC-MS检测到4种保健黄酒中挥发性组分156种,选取匹配度大于800的挥发性组分,4种保健黄酒中共鉴定出140种挥发性组分,其中包括酯类、醇类、醛酮类、酸类、烃类、含氮化合物、苯系芳烃及其它化合物等。该方法可以通过鉴定黄酒挥发性组分,寻找挥发性组分与黄酒品质之间的关系,为保健黄酒的生产优化提供一定的理论依据。

基于最小数据集的集约化葡萄园土壤健康评价

健康土壤是生产高产优质葡萄的基础,目前葡萄园不合理管理导致果园土壤生产力下降和生态失衡。为摸清集约化葡萄园土壤健康状况,该研究以河北省曲周县典型葡萄园为研究对象,通过测定20项土壤物理、化学和生物学指标,利用主成分分析法构建最小数据集,开展土壤健康评价并揭示葡萄园存在的主要障碍因子。结果表明,集约化葡萄园土壤健康评价最小数据集由有机碳、亚表层土壤硬度、交换性钠、容重、含水率和水稳性团聚体6个指标构成。利用线性和非线性评分函数,基于全数据集和最小数据集计算的土壤健康指数间呈显著正相关(P<0.01),这说明最小数据集可以代替全数据集用于葡萄园土壤健康评价。基于最小数据集,利用线性和非线性评分函数获得的葡萄园土壤健康指数范围分别为0.39~0.59和0.36~0.66,平均值分别为0.52和0.51,处于中等水平。不同树龄葡萄土壤健康指数差异不显著(P>0.05)。集约化葡萄园土壤障碍因子主要有土壤压实、养分不平衡和有机碳含量低等问题。通过适当减少田间管理频率,结合增施(生物)有机肥、种植覆盖作物和养分综合管理能有效消减土壤障碍因子,提升葡萄园土壤健康水平,促进当地葡萄产业可持续发展。

基于自适应多尺度注意力机制的CNN-GRU矿用电动机健康状态评估

利用多传感器信息融合技术进行电动机健康状态评估时,矿用电动机监测数据中存在异常值和缺失值,而卷积神经网络和循环神经网络等深度学习模型在数据质量下降严重的情况下难以有效提取数据特征和更新网络权重,导致梯度消失或爆炸等问题。针对上述问题,提出了一种基于自适应多尺度注意力机制的CNN-GRU(CNN-GRU-AMSA)模型,用于评估矿用电动机健康状态。首先,对传感器采集的电动机运行数据进行填补、剔除和标准化处理,并以环境温度变化作为依据对矿用电动机运行数据进行工况划分。然后,根据马氏距离计算出电动机电流、电动机三相绕组温度、电动机前端轴承温度和电动机后端轴承温度等健康评估指标的健康指数(HI),采用Savitzky–Golay滤波器对指标HI进行降噪、平滑、归一化处理,并结合主成分分析法计算的不同指标对矿用电动机的贡献度,对指标HI进行加权融合得到矿用电动机HI。最后,将矿用电动机HI输入CNN-GRU-AMSA模型中,该模型通过动态调整注意力权重,实现对不同尺度特征的信息融合,从而准确输出电动机健康状态评估结果。实验结果表明,与其他常见的深度学习模型CNN,CNN-GRU,CNN-LSTM,CNN-LSTM-Attention相比,CNN-GRU-AMSA模型在均方根误差、平均绝对误差、准确率、Macro F1及Micro F1等评价指标上更优,且预测残差的波动范围更小,稳定性更优。

高山地区夏季大气PM_(2.5)中元素的污染特征、生态风险及健康风险评估——以武当山为例

为了解华中高山地区夏季大气PM_(2.5)中元素的污染特征,于2018年6月在湖北省十堰市武当山国家空气质量监测站采集PM_(2.5)样品,利用电感耦合等离子体质谱仪(ICP-MS)测定样品中18种元素(Na、K、Ca、Mg、Al、Fe、V、Cr、Mo、Cu、Zn、Mn、Ni、As、Se、Cd、Ba和Pb)的浓度,并探讨了其来源、生态风险和健康风险.结果表明,武当山PM_(2.5)的日均浓度范围为5.00—33.65μg·m^(-3),平均浓度为(16.84±7.07)μg·m^(-3);元素K、Na、Fe、Ca、Al、Mg和Zn的浓度较高,7种元素占所分析元素的97.68%以上;富集因子结果表明,Mo、Zn、Pb、Cd和Se的EF值高于100,可能受周边人为活动排放污染物的区域或长距离传输影响;主成分-多元线性回归(PCA-MLR)结果表明,PM_(2.5)中元素主要来自于燃煤和机动车(57.57%)、工业源(22.52%)和地壳(19.91%);武当山PM_(2.5)重金属的生态风险指数极高,其中Cd、Se和Mo的潜在生态危害程度极强;健康风险评估显示,综合非致癌风险(HI)在儿童和成人中分别为2.28×10-2和3.04×10-2,均在可接受水平内,综合致癌风险(CRT)在儿童和成人中分别为4.45×10-7和2.37×10-6,说明成人存在潜在的致癌风险;Cr在成人中的致癌风险为1.88×10-6,说明Cr在成人中存在潜在的致癌风险,同种金属对人体的非致癌风险和致癌风险均表现为成人>儿童.

太行山南麓3种典型人工林健康评价

[目的]森林健康评价是进行森林管理的依据,开展森林健康评价能够为实现森林可持续经营目标提供一定理论基础。[方法]以太行山南麓广泛分布的侧柏人工林、栓皮栎人工林和刺槐人工林为研究对象,综合使用主成分分析法、聚类分析法和判别分析法3种多元统计分析方法,选择林分生产力、立地条件、群落结构、稳定性与可持续能力4个一级指标和21个二级指标构成森林健康评价体系,对其森林健康状态进行评价。[结果]树高、胸径、土壤全K含量、森林火险指数是影响研究区森林健康的主要因子,其主成分因子载荷均大于0.6;在所调查的17块样地中,8个样地森林健康等级为“健康”或“亚健康”(占比和为47.0%),侧柏、栓皮栎和刺槐人工林相应森林健康等级占比和分别是60.0%,33.4%和50.0%,9块样地森林等级为“中健康”或“不健康”(占比和为53.0%),侧柏、栓皮栎和刺槐人工林相应森林健康等级占比和分别是40.0%,66.7%和50.0%。[结论]整体上看,研究区侧柏和刺槐人工林林分健康状况良好,栓皮栎人工林健康状况较差。

常州市大气PM_(2.5)中金属元素浓度变化、来源及健康风险-以武进区监测点为例

目的探讨常州市武进区大气细颗粒物(PM_(2.5))中金属元素浓度变化、来源及健康风险。方法2016-2022年,每月定期采集常州市武进区大气PM_(2.5)样品,采用电感耦合等离子体质谱(ICP-MS)法对PM_(2.5)中的金属元素质量浓度进行检测,采用富集因子(EF)法和主成分分析法(PCA)对金属来源进行分析,并评估金属元素可能带来的健康风险。结果2016-2022年共采集610份有效样品,常州市武进区大气PM_(2.5)质量浓度的M(P_(25),P_(75))依次为70(40,110)、54(41,79)、56(38,85)、54(41,76)、41(28,58)、40(28,60)和39(28,54)μg/m^(3),整体上呈递减趋势且差异具有统计学意义(P<0.05)。各金属元素浓度整体上表现为:铝>锰>铅>铬>镍>锑>镉,除铬元素外,其他元素浓度均表现为冬春季高的特点;除锰的EF值在10~100之间,其他元素的EF值在不同年份和季节均有大于100的情况。PCA结果显示,因子1由锑、铝、镉、铅和锰组成,因子2由铬组成,因子3由镍组成,方差贡献率分别为52.91%、15.46%和10.95%。各元素的危害商(hazard quotient,HQ)整体上从高到低依次为:锰>镍>镉>铝>铬>锑,按中位数浓度计算,各元素HQ值均小于1,铬的致癌风险(carcinogenic risk,CR)均大于10^(-6),镉在2016和2018年的CR值大于10^(-6);按第95百分位数浓度计算,锰的HQ值均大于1,铬、镉和镍的CR值均大于10^(-6)。结论2016-2022年常州市武进区大气PM_(2.5)中多数金属元素浓度水平总体上呈下降趋势,其主要来源有工业排放、交通排放、煤炭燃烧、钢铁工业和航运排放,高浓度锰存在非致癌风险,而铬、镉和高浓度镍存在潜在致癌风险。

昆明市重点饮用水源地水体抗生素的赋存及其风险评估

饮水是抗生素进入人体的主要途径,为探明昆明市饮用水源地水体中抗生素的污染现状、来源及潜在风险,于2023年枯水期(4月)和丰水期(8月)采集并分析了云龙水库及其前置库双化水库水体中7类共38种抗生素浓度及部分常规理化参数,利用PCA分析解释水体抗生素的主要来源,同时评估了生态风险及人体健康风险。结果表明:①昆明市重点饮用水源地2023年枯水期(4月)和丰水期(8月)共检出31种抗生素,所有采样点抗生素总浓度呈丰水期(1109.0 ng/L)远高于枯水期(414.7 ng/L)的特征,枯水期和丰水期单体抗生素检出浓度范围分别为ND(低于检出限)~37.5 ng/L和ND~139.3 ng/L。主要污染物为磺胺类、四环素类和氟喹诺酮类抗生素(检出率均达50%)。②PCA分析表明,畜禽养殖废水、医疗废水与生活污水均为昆明市重点饮用水源地水体抗生素污染的主要来源。③昆明市重点饮用水源地枯水期和丰水期所有抗生素的生态风险熵值(RQ)均小于0.1,健康风险熵值(RQH)均小于0.01。研究显示,昆明市重点饮用水源地水体单体抗生素对生态环境造成的风险可忽略不计,对不同年龄、性别人体健康造成的风险可忽略不计。

卫生健康服务水平统计测度及影响因素分析

新医改背景下,提升卫生健康服务水平是推进健康中国建设的有效途径之一。依据卫生健康服务水平统计测度指标体系和中国31个省级面板数据,研究中国卫生健康服务水平及其影响因素,结果显示:中国卫生健康服务水平呈阶段式“上升—下降—上升”的“N”型波动趋势,截面差异显著,其系统聚类为五个类别,2010年以来,中国卫生健康服务水平均有不同程度的波动;不同地区卫生健康服务水平高低次序为:东部>中部>东北>西部,区域非均衡性特征明显,东部、中部、东北和西部分别呈现平稳提升、波浪式上升、波浪式下降和平稳发展的波动特征;仅妇幼保健水平呈现平稳提升的发展态势,各省市区妇幼保健水平均高于其他子系统;医疗卫生投入占比、财政支出、财政收入、医疗保障、城镇化水平和电信业务总量等均能促进卫生健康服务水平的提升,而医疗消费指数、人均地区生产总值和人口密度的作用相反。该结果可为优化卫生健康服务管理提供经验依据。

结合逆非线性主成分分析和极值理论的桥梁损伤检测

为提高环境和运营变化(environmental and operational variations,EOV)影响下的桥梁损伤检测可靠性,结合逆非线性主成分分析(inverse nonlinear principal component analysis,INLPCA)和极值理论,提出一种新的桥梁损伤检测方法.该方法采用INLPCA对桥梁损伤特征进行建模,利用不完备健康监测数据的估计均方误差和添加神经网络训练惩罚项控制INLPCA的非线性程度.采用INLPCA对损伤特征的重构误差和马氏平方距离(Mahalanobis squared distance,MSD)建立损伤指标(ID),最后基于ID的广义极值(generalized extreme value,GEV)分布建立损伤检测阈值.以比利时KW51铁路桥和天津永和斜拉桥为例,验证所提方法的有效性.结果表明,所提方法能准确检测EOV影响下的桥梁损伤,且对不同桥型和不同损伤特征均有良好的适用性.

西藏羊卓雍措水体重金属空间分布特征与健康风险评估

为探究羊卓雍措水体重金属污染特征及其对人体的危害程度,于2018,2019年内分5次采集26个采样点的130个水样,对水样中的Cr,Co,Cu,Fe,Cd,Sb,Ba,Pb,As等9种重金属进行检测分析,并采用美国环境保护署推荐的健康风险评价模型评价羊卓雍措的潜在健康风险。研究结果可以为当地水资源管理部门今后在水资源有效利用与饮用水安全管理方面提供参考。

Construction of project quality health monitoring system based on life-cycle theory

In order to more effectively assess the health status of a project, the monitoring indices in a project＇s life cycle are divided into quality index, cost index, time index, satisfaction index, and sustainable development index. Based on the feature of qualitative and quantitative indices combining, the PCA-PR （principal component analysis and pattern recognition） model is constructed. The model first analyzes the principal components of the life-cycle indices system constructed above, and picks up those principal component indices that can reflect the health status of a project at any time. Then the pattern recognition model is used to study these principal components, which means that the real time health status of the project can be divided into five lamps from a green lamp to a red one and the health status lamp of the project can be recognized by using the PR model and those principal components. Finally, the process is shown with a real example and a conclusion consistent with the actual situation is drawn. So the validity of the index system and the PCA-PR model can be confirmed.

基于非线性PCA的有效载荷健康参数提取

随着卫星有效载荷功能和结构日趋复杂,对设备的稳定性、可靠性提出更高要求,文章针对载荷设备健康表征参数庞大,导致健康诊断困难的问题,提出了非线性的PCA分析法。该方法首先对有效载荷设备自顶向下构建一个健康表征参数合并集,其次在传统PCA的基础上,采用非线性数据融合技术获取关键健康表征参数,并对所产生的协方差矩阵中的特征值、特征向量以及各特征值比率做了深入研究,结果显示非线性的PCA算法能带来更好的降维效果以及特征值累计占比率,不仅能保持数据之间的差异度信息,还能保存样本自身信息,有效降低了数据处理导致的信息损耗以及对星上载荷计算及存储能力的需求,说明改进方法在提取有效载荷设备健康表征参数中表现出显著优势。

天津市农田土壤重金属来源及健康风险评价

为揭示城市周边蔬菜粮食种植区土壤重金属风险,同时针对目前研究中缺少经手-口摄入、呼吸途径和皮肤接触暴露途径引起的人体健康风险及估算不同来源下量化的成人和儿童的健康风险情况,以天津市西青区农田表层土壤为研究对象,分析了Cr、Cu、Ni、Pb、Zn、As、Hg、Cd共8种重金属的浓度,采用主成分分析(PCA)、正定矩阵分解(PMF)、健康风险评价模型(PMF-HRA)分别评价土壤重金属污染程度、来源及健康风险,厘定主要的重金属污染物及其暴露途径。结果表明:天津市农用地土壤8种重金属中Cd、Hg污染最为严重,均值分别超出背景值151.9%、324.1%,约有15%的点位处于中度至重度污染等级。研究区土壤重金属污染的来源主要包括自然成土过程、农业生产和交通运输、燃煤、农药使用。Cd(农药源)和Hg(燃煤源)是研究区农用地土壤污染的重要元素。在摄食农作物、手-口摄入和皮肤接触3种暴露途径下,均不存在显著的非致癌健康风险。自然源的总致癌风险指数(TCR)最高,Cr元素是源贡献中儿童非致癌风险总指数(HI)和儿童TCR的主要贡献者;燃煤源中Hg对HI的贡献最大,Cu和Zn对HI的贡献来自于农药和交通混合源。以上重金属元素的污染来源均与周围居民的工业、农业及交通活动存在密切的联系,应引起重视。

融合多项式特征扩展与CNN-Transformer模型的锂电池SOH估计

为了提高锂离子电池健康状态(state of health,SOH)估计的精确度,本研究结合卷积神经网络(convolutional neural networks,CNN)强大的局部特征提取能力和Transformer的序列处理能力,提出了基于多项式特征扩展的CNN-Transformer融合模型。该方法提取了与电池容量高度相关的增量容量(incremental capacity,IC)曲线峰值、IC曲线对应电压、面积及充电时间作为健康因子,然后将其进行多项式扩展,增加融合模型对输入特征的非线性处理能力。引入主成分分析法(principal component analysis,PCA)对特征空间进行降维,有利于捕获数据有效信息,减少模型训练时间。采用美国国家宇航局(National Aeronautics and Space Administration,NASA)数据集和马里兰大学数据集,通过加入多项式特征前后的CNN-Transformer模型对比、加入多项式特征的CNN-Transformer模型和单一模型算法对比,验证了加入多项式特征的CNN-Transformer融合算法的有效性和精确度,结果表明提出模型的SOH估计精度相较于未加入多项式特征的CNN-Transformer模型,对于B0005、B0006、B0007、B0018数据集分别提高了38.71%、50.28%、4.71%、17.58%。