Weighted Multi-sensor Data Level Fusion Method of Vibration Signal Based on Correlation Function

As the differences of sensor＇s precision and some random factors are difficult to control,the actual measurement signals are far from the target signals that affect the reliability and precision of rotating machinery fault diagnosis.The traditional signal processing methods,such as classical inference and weighted averaging algorithm usually lack dynamic adaptability that is easy for trends to cause the faults to be misjudged or left out.To enhance the measuring veracity and precision of vibration signal in rotary machine multi-sensor vibration signal fault diagnosis,a novel data level fusion approach is presented on the basis of correlation function analysis to fast determine the weighted value of multi-sensor vibration signals.The approach doesn＇t require knowing the prior information about sensors,and the weighted value of sensors can be confirmed depending on the correlation measure of real-time data tested in the data level fusion process.It gives greater weighted value to the greater correlation measure of sensor signals,and vice versa.The approach can effectively suppress large errors and even can still fuse data in the case of sensor failures because it takes full advantage of sensor＇s own-information to determine the weighted value.Moreover,it has good performance of anti-jamming due to the correlation measures between noise and effective signals are usually small.Through the simulation of typical signal collected from multi-sensors,the comparative analysis of dynamic adaptability and fault tolerance between the proposed approach and traditional weighted averaging approach is taken.Finally,the rotor dynamics and integrated fault simulator is taken as an example to verify the feasibility and advantages of the proposed approach,it is shown that the multi-sensor data level fusion based on correlation function weighted approach is better than the traditional weighted average approach with respect to fusion precision and dynamic adaptability.Meantime,the approach is adaptable and easy to use,can be applied to other areas of vibration measurement.

Can a global mean sea-level rise reduce the Last Interglacial model-data mismatch in East Asia?

末次间冰期有着丰富的重建和模拟资料,为研究未来温暖气候提供了一个理想的参考.然而,关于末次间冰期的东亚气候,模拟与重建的结果间长期存在着不匹配的情况,模拟结果普遍较重建结果更为冷干。本研究利用挪威地球系统模式(NorESM1-F),探讨了在末次间冰期模拟试验中纳入全球平均海平面上升能否减少模式-数据的不匹配.该试验结果表明,海平面上升情况下东亚地区会产生一定的增温增湿效应,但不足以消除模式-数据不匹配.基于这些结果,作者探讨了其它可能造成不匹配的因素以供进一步研究.

On the Accuracy Assessment of the Latest Releases of GOCE Satellite-Based Geopotential Models with EGM2008 and Terrestrial GPS/Levelling and Gravity Data over Egypt

The Global Geopotential Models (GGMs) of GOCE (Gravity Recovery and steady- state Ocean Circulation Explorer) differ globally as well as regionally in their accuracy and resolution based on the maximum degree and order (d/o) of the fully normalized spherical harmonic (SH) coefficients, which express each GGM. The main idea of this study is to compare the free-air gravity anomalies and quasi geoid heights determined from several recent GOCE-based GGMs with the corresponding ones from the Earth Gravitational Model 2008 (EGM2008) over Egypt on the one hand and with ground-based measurements on the other hand. The results regarding to the comparison of GOCE-based GGMs with terrestrial gravity and GPS/levelling data provide better improvement with respect to EGM2008. The 4th release GOCE-based GGM developed with the use of space-wise solution strategy (SPW_R4) approximates the gravity field well over the Egyptian region. The SPW_R4 model is accordingly suggested as a reference model for recovering the long wavelength (up to SH d/o 200) components of quasi geoid heights when modelling the gravimetric quasi-geoid over the Egypt. Finally, three types of transformation models: Four-, Five- and Seven-parameter transformations have been applied to reduce the data biases and to provide a better fitting of quasi geoid heights obtained from the studied GOCE-based GGMs to those from GPS/levelling data. These models reveal that the standard deviation of vertical datum over Egypt is at the level of about 32 cm.

Evidences of the expanding Earth from space-geodetic data over solid land and sea level rise in recent two decades

According to the space-geodetic data recorded at globally distributed stations over solid land spanning a period of more than 20-years under the International Terrestrial Reference Frame 2008,our previous estimate of the average-weighted vertical variation of the Earth＇s solid surface suggests that the Earth＇s solid part is expanding at a rate of 0.24 ± 0.05 mm/a in recent two decades.In another aspect,the satellite altimetry observations spanning recent two decades demonstrate the sea level rise（SLR） rate 3.2 ± 0.4 mm/a,of which1.8 ± 0.5 mm/a is contributed by the ice melting over land.This study shows that the oceanic thermal expansion is 1.0 ± 0.1 mm/a due to the temperature increase in recent half century,which coincides with the estimate provided by previous authors.The SLR observation by altimetry is not balanced by the ice melting and thermal expansion,which is an open problem before this study.However,in this study we infer that the oceanic part of the Earth is expanding at a rate about 0.4 mm/a.Combining the expansion rates of land part and oceanic part,we conclude that the Earth is expanding at a rate of 0.35 ± 0.47 mm/a in recent two decades.If the Earth expands at this rate,then the altimetry-observed SLR can be well explained.

Deformation Evolution Characteristics Revealed by GPS and Cross-fault Leveling Data before the M_S8. 0 Wenchuan Earthquake

Based on GPS velocity during 1999-2007,GPS baseline time series on large scale during1999-2008 and cross-fault leveling data during 1985-2008,the paper makes some analysis and discussion to study and summarize the movement,tectonic deformation and strain accumulation evolution characteristics of the Longmenshan fault and the surrounding area before the MS8. 0 Wenchuan earthquake,as well as the possible physical mechanism late in the seismic cycle of the Wenchuan earthquake. Multiple results indicate that:GPS velocity profiles show that obvious continuous deformation across the eastern Qinghai-Tibetan Plateau before the earthquake was distributed across a zone at least 500 km wide,while there was little deformation in Sichuan Basin and Longmenshan fault zone,which means that the eastern Qinghai-Tibetan Plateau provides energy accumulation for locked Longmenshan fault zone continuously. GPS strain rates show that the east-west compression deformation was larger in the northwest of the mid-northern segment of the Longmenshan fault zone,and deformation amplitude decreased gradually from far field to near fault zone,and there was little deformation in fault zone. The east-west compression deformation was significant surrounding the southwestern segment of the Longmenshan fault zone,and strain accumulation rate was larger than that of mid-northern segment.Fault locking indicates nearly whole Longmenshan fault was locked before the earthquake except the source of the earthquake which was weakly locked,and a 20 km width patch in southwestern segment between 12 km to 22. 5 km depth was in creeping state. GPS baseline time series in northeast direction on large scale became compressive generally from 2005 in the North-South Seismic Belt,which reflects that relative compression deformation enhances. The cross-fault leveling data show that annual vertical change rate and deformation trend accumulation rate in the Longmenshan fault zone were little,which indicates that vertical activity near the fault was very weak and the fault was tightly locked. According to analyses of GPS and cross-fault leveling data before the Wenchuan earthquake,we consider that the Longmenshan fault is tightly locked from the surface to the deep,and the horizontal and vertical deformation are weak surrounding the fault in relatively small-scale crustal deformation. The process of weak deformation may be slow,and weak deformation area may be larger when large earthquake is coming. Continuous and slow compression deformation across eastern Qinghai-Tibetan Plateau before the earthquake provides dynamic support for strain accumulation in the Longmenshan fault zone in relative large-scale crustal deformation.

Multilevel Modeling of Binary Outcomes with Three-Level Complex Health Survey Data

Complex survey designs often involve unequal selection probabilities of clus-ters or units within clusters. When estimating models for complex survey data, scaled weights are incorporated into the likelihood, producing a pseudo likeli-hood. In a 3-level weighted analysis for a binary outcome, we implemented two methods for scaling the sampling weights in the National Health Survey of Pa-kistan (NHSP). For NHSP with health care utilization as a binary outcome we found age, gender, household (HH) goods, urban/rural status, community de-velopment index, province and marital status as significant predictors of health care utilization (p-value < 0.05). The variance of the random intercepts using scaling method 1 is estimated as 0.0961 (standard error 0.0339) for PSU level, and 0.2726 (standard error 0.0995) for household level respectively. Both esti-mates are significantly different from zero (p-value < 0.05) and indicate consid-erable heterogeneity in health care utilization with respect to households and PSUs. The results of the NHSP data analysis showed that all three analyses, weighted (two scaling methods) and un-weighted, converged to almost identical results with few exceptions. This may have occurred because of the large num-ber of 3rd and 2nd level clusters and relatively small ICC. We performed a sim-ulation study to assess the effect of varying prevalence and intra-class correla-tion coefficients (ICCs) on bias of fixed effect parameters and variance components of a multilevel pseudo maximum likelihood (weighted) analysis. The simulation results showed that the performance of the scaled weighted estimators is satisfactory for both scaling methods. Incorporating simulation into the analysis of complex multilevel surveys allows the integrity of the results to be tested and is recommended as good practice.

Missing Data Imputations for Upper Air Temperature at 24 Standard Pressure Levels over Pakistan Collected from Aqua Satellite

This research was an effort to select best imputation method for missing upper air temperature data over 24 standard pressure levels. We have implemented four imputation techniques like inverse distance weighting, Bilinear, Natural and Nearest interpolation for missing data imputations. Performance indicators for these techniques were the root mean square error (RMSE), absolute mean error (AME), correlation coefficient and coefficient of determination ( R2 ) adopted in this research. We randomly make 30% of total samples (total samples was 324) predictable from 70% remaining data. Although four interpolation methods seem good (producing <1 RMSE, AME) for imputations of air temperature data, but bilinear method was the most accurate with least errors for missing data imputations. RMSE for bilinear method remains <0.01 on all pressure levels except 1000 hPa where this value was 0.6. The low value of AME (<0.1) came at all pressure levels through bilinear imputations. Very strong correlation (>0.99) found between actual and predicted air temperature data through this method. The high value of the coefficient of determination (0.99) through bilinear interpolation method, tells us best fit to the surface. We have also found similar results for imputation with natural interpolation method in this research, but after investigating scatter plots over each month, imputations with this method seem to little obtuse in certain months than bilinear method.

Imagery Data Quality of ZY Satellite Reached International Level

The in-orbit commissioning of ZY-1 02C satellite is proceeding smoothly. According to the relevant experts in this field, the imagery quality of the satellite has reached or nearly reached the level of international satellites of the same kind. ZY-1 02C satellite and ZY-3 satellite were successfully launched on December 22, 2011 and January 9, 2012 respectively. China Centre for Resources Satellite Data andApplication (CRSDA) was responsible for the building of a ground

基层政府数据治理缘何艰难?——基于多重堕距视角的分析

当前,基层政府数据治理的理想愿景与现实之间存在明显差距,产生“堕距”现象。究其根源,技术运行与公共管理的价值诉求和价值实现存在矛盾、数据治理成本—收益不明确造成“文化堕距”;“数据”与“治理”关系模糊、制度执行偏离制度旨趣、技术变化与制度稳定存在张力导致“制度堕距”;数据共享动力不足、权责关系不明晰催生“职能堕距”等。政府应立足于基层治理的实际和协同发展的需要,从强调以人为本、构建数字创新氛围和可持续的技术支撑体系、健全配套制度体系等多重维度出发整体推动数字化转型,改善数据在多元治理情境中的创新应用,从而有效弥合多元“堕距”并走向技术善治。

A Water Level Forecast of Pattani River in the Southern of Thailand by Deep Learning

Nowadays, the deep learning methods are widely applied to analyze and predict the trend of various disaster events and offer the alternatives to make the appropriate decisions. These support the water resource management and the short-term planning. In this paper, the water levels of the Pattani River in the Southern of Thailand have been predicted every hour of 7 days forecast. Time Series Transformer and Linear Regression were applied in this work. The results of both were the water levels forecast that had the high accuracy. Moreover, the water levels forecasting dashboard was developed for using to monitor the water levels at the Pattani River as well.

考虑大规模风光分层接入的配电网多层协调无功优化方法

大量风光新能源分层、多点接入给配电网运行带来了电压越限等负面影响,增加了不同电压等级无功协同优化的难度。兼顾不同电压等级调节需求,从模型降维等值的角度,提出了基于等值模型的配电网多层协调无功优化方法。该方法分别针对含有无功调节设备及没有无功调节设备的两种台区系统,利用神经网络对大量台区相关运行数据进行训练得到台区拟合模型,形成不可控和可控两种类型的台区等值模型,并用这两类拟合模型分别代替两类台区系统的物理模型,使台区以数据驱动的方式参与配电网无功优化,形成馈线物理模型和台区拟合模型组成的单一电压等级物数混合优化调度模型。将原多层无功优化问题转换为单层系统优化问题,再对该单层系统无功优化调度问题进行求解。该方法可降低配电网系统优化计算规模,从而减小计算量,以无功优化数学模型与数据驱动方法相结合的方式,实现配电网馈线-台区多层协调无功优化。通过算例验证了所提方法对于解决分布式风光分层接入所导致电压越限问题的可行性、有效性和优越性,能够保障配电网的经济安全稳定运行。

医院资源计划系统成本原子级数据治理及实践

探讨面对医院成本管理复杂性高、难度大的挑战,如何借助医院资源计划(HRP)系统建立统一高效的成本管理体系。结合医院实践,通过构建面向成本管理的原子级数据治理体系和实践路径,逐步迭代优化HRP系统,最终实现对全院成本的精细化管理,辅助管理决策,为各医疗机构建立HRP系统提供相关技术参考和行业思考。

噪声与危险水平混合条件下危险识别变化特征——基于多模态数据的证据

为探讨噪声和危险水平混合环境下矿工危险识别内在加工过程各指标的变化特征,模拟了有噪声高危险、有噪声低危险、无噪声高危险、无噪声低危险4种不同的环境,召集23名矿工,通过便携式脑电采集系统、人机功效分析系统、无线传感生理仪及E-prime程序采集被试试验中的多模态数据并进行分析。结果表明:噪声环境下被试正确率降低、反应时间变长;噪声环境中P300成分额区波幅最大,噪声影响大脑的高级认知加工,噪声环境下被试心率变异性(Heart Rate Variability,HRV)3个指标都显著升高。噪声对高危险场景的影响更大,无噪声环境下低危险场景往往更难识别,在安全管理中要重视安全隐患的排查。从认知神经科学层面研究危险识别的内在加工机制,为噪声和危险水平对危险识别的影响提供了直接的证据,可为噪声防治以及矿山和煤矿企业安全隐患管理提供一定的依据。

基于AIS数据的船舶风险领域模型

为构建船舶风险领域及分析其特征,基于船舶自动识别系统(Automatic Identification System,AIS)数据提出一种具有风险级别的船舶领域模型。首先,根据预处理后的AIS数据,获取他船相对于本船的位置。然后,采用椭圆领域边界对船舶相对位置数据进行筛选,同时获取到代表风险级别的临界点,并使用最小二乘法对其进行拟合,从而得到船舶风险领域。最后,利用老铁山水道中149 m、190 m、229 m、300 m船舶的AIS数据对所提方法进行验证,并分析船舶风险领域的特征。结果表明,该方法可以较好地反映船舶的风险级别;在同一风险级别时,不同尺度船舶间的风险领域长、短半轴与船长之比差异较小;风险级别为1的船舶领域边界接近于圆形;他船在本船周围分布的密集程度不同。本研究所提模型对航行安全保障、航行风险研究有一定的参考意义。

随机误差传递的高空气象秒级数据快速校验方法

【目的】为保证气象预报结果的准确性。【方法】提出了考虑随机误差传递的高空气象秒级数据快速校验方法。对高空气象秒级数据进行预处理,包括数据中心化处理、特征点提取。数据中心化的处理方式为实施各组数据的加权平均处理,获得新的数据组作为基准数据;特征点提取使用的处理方法为遍历法,需要遍历全部数据点。考虑数据获取过程中的随机误差传递情况,对预处理后的高空气象秒级数据实施空间一致性检查、内部一致性检查。应用集合经验模态分解EEMD(Ensemble Empirical Mode Decomposition)算法与对比源反演CSI(Contrast Source Inversion)算法构建基于CSI-EEMD的高空气象秒级数据快速校验模型,实施数据得到快速校验。【结果】测试结果表明,该方法能够实现4个气象站数据的快速校验,校验结果的均方根误差与平均绝对误差均低于0.1,实现了数据的质量控制。【结论】将该方法应用于气象站数据校验中,可以实现精准度的数据校验,具有一定的实用价值。

介休井水位异常变化特征分析

根据山西介休井水位2007年数字化观测以来的数据变化,梳理归纳该井水位观测曲线的正常变化形态、同震变化以及影响介休井水位变化的人为干扰和自然环境干扰因素;同时对2023年7月27日介休井水位出现的突降异常,从井区构造环境、周边地震活动、地球物理观测异常变化等方面,与典型震例前的异常变化进行对比分析,认为该异常与山西中南部中等地震前的异常在时、空、强特征方面具有较高的相似性,表明山西南部区域的构造活动、应力水平在逐步增强。

基于多模态数据关联分析的学习成效达成度研究

课堂是教学的重要环节,科学的数据依据是保证评价结果正确性的关键.文中介绍了研究背景和国内外研究现状,基于课堂达成度分析理论,深度融合新一代信息化技术,采集多模态数据进行分析,搭建了课堂多模态关联评价框架,并基于该框架开展实证研究.以某高职院校某班级为例,通过虚拟空间、物理空间采集课堂教学过程中产生的多模态学习数据,采用人工智能方法处理并分析学习数据,实现课堂行为识别与分析评价.结果显示,与教学活动设计关联的评价框架能更加可靠地为课堂学习达成度分析提供依据,改善课堂教学质量,助力智慧课堂建设.

基于气象因素的玉米单产堆栈集成学习建模与预测

【目的】在世界气候变化加剧和气象灾害频发的背景下,探究气象因素对玉米单产的重要性并准确预测玉米单产对于促进农业生产和田间管理具有重要意义。本文旨在量化分析玉米各生育阶段气象因素对单产的重要性并建立高精度、高可靠性的玉米气象单产堆栈集成学习估测模型来预测单产。【方法】利用HP滤波法和移动平均法确定各县域趋势单产模型并分离出各县气象单产。采用轻量级梯度提升机(LightGBM)、Bagging和Stacking 3种集成学习方法,通过对中国12个省份596个县级行政区域和气象观测站跨度34年的日度气象数据和玉米产量数据进行分析,建立3种基于不同集成学习框架(LightGBM、Bagging和Stacking)的玉米气象单产预测模型。【结果】适用HP滤波法作为趋势单产模型的县域主要集中在陕西、河南、江苏和安徽地区。相较于HP滤波法,更多县域适用于移动平均法,且多数县域R 2分布于0.8以上。基于5年滑动预测和模型精度评价指标,3种模型对玉米单产的平均绝对百分比误差(MAPE)指标均低于6%。Stacking模型MAPE值达到4.60%,预测精度高,泛化性强。结果表明玉米气象单产堆栈集成学习预测模型(Stacking)具有更高精度和更强鲁棒性,并能有效利用各基学习器特点与优势,提升预测精度,是根据气象因素预测玉米单产的最优模型。此外,基于12省玉米生育阶段27个气象因素的随机森林特征重要性评分对玉米单产的定量分析,对作物监测和田间管理有借鉴和参考意义。【结论】3种集成学习方法,尤其是堆栈集成学习模型(Stacking)预测效果能够详细反映出玉米单产的时空分布变化情况。基于气象因素的玉米单产堆栈集成学习模型可为田间管理和精准预测玉米单产提供新方法。

数据要素赋能对人力资本升级的作用机制分析--基于国家级大数据综合试验区数据

以国家级大数据综合试验区作为实证场域,基于2011—2021年沪深A股上市公司数据,采用基准回归分析平行趋势检验方法检验了数据要素赋能对于企业人力资本升级的促进作用并进行相关的稳健性与异质性分析,在此基础上,对数据赋能效果的创新激励效应和规模扩张效应两者作用机制以及对企业低技能劳动力组别、雇佣规模以及人力资本相对结构的影响进行进一步探讨。研究结果表明,国家级大数据综合试验区建设的数据要素赋能功效有力推动了企业人力资本升级与人才队伍扩张,一系列的稳健性检验有力支撑了上述结论;企业层面产权性质与创新属性的不同、区域层面的地理区位与行政层级的区别导致了经营策略与资源禀赋差异,对企业人力资本升级产生了异质性影响;国家级大数据综合试验区建设畅通了企业数字化转型渠道,通过创新激励效应和规模扩张效应作用于人力资本升级进程;扩展性讨论表明,数据要素赋能也会增加部分低技能劳动力岗位,带来企业雇佣规模的扩张。因此,需要充分发挥数据要素的赋能功效,推动数据要素与企业生产经营过程的深度耦合,最终实现人力资本的优化升级,为培育壮大企业发展的新动能注入人才动力。