Performance validation of High Mountain Asia 8-meter Digital Elevation Model using ICESat-2 geolocated photons

High Mountain Asia(HMA),recognized as a third pole,needs regular and intense studies as it is susceptible to climate change.An accurate and high-resolution Digital Elevation Model(DEM)for this region enables us to analyze it in a 3D environment and understand its intricate role as the Water Tower of Asia.The science teams of NASA realized an 8-m DEM using satellite stereo imagery for HMA,termed HMA 8-m DEM.In this research,we assessed the vertical accuracy of HMA 8-m DEM using reference elevations from ICESat-2 geolocated photons at three test sites of varied topography and land covers.Inferences were made from statistical quantifiers and elevation profiles.For the world’s highest mountain,Mount Everest,and its surroundings,Root Mean Squared Error(RMSE)and Mean Absolute Error(MAE)resulted in 1.94 m and 1.66 m,respectively;however,a uniform positive bias observed in the elevation profiles indicates the seasonal snow cover change will dent the accurate estimation of the elevation in this sort of test sites.The second test site containing gentle slopes with forest patches has exhibited the Digital Surface Model(DSM)features with RMSE and MAE of 0.58 m and 0.52 m,respectively.The third test site,situated in the Zanda County of the Qinghai-Tibet,is a relatively flat terrain bed,mostly bare earth with sudden river cuts,and has minimal errors with RMSE and MAE of 0.32 m and 0.29 m,respectively,and with a negligible bias.Additionally,in one more test site,the feasibility of detecting the glacial lakes was tested,which resulted in exhibiting a flat surface over the surface of the lakes,indicating the potential of HMA 8-m DEM for deriving the hydrological parameters.The results accrued in this investigation confirm that the HMA 8-m DEM has the best vertical accuracy and should be of high use for analyzing natural hazards and monitoring glacier surfaces.

Intellicise Model Transmission for Semantic Communication in Intelligence-Native 6G Networks

To facilitate emerging applications and demands of edge intelligence(EI)-empowered 6G networks,model-driven semantic communications have been proposed to reduce transmission volume by deploying artificial intelligence(AI)models that provide abilities of semantic extraction and recovery.Nevertheless,it is not feasible to preload all AI models on resource-constrained terminals.Thus,in-time model transmission becomes a crucial problem.This paper proposes an intellicise model transmission architecture to guarantee the reliable transmission of models for semantic communication.The mathematical relationship between model size and performance is formulated by employing a recognition error function supported with experimental data.We consider the characteristics of wireless channels and derive the closed-form expression of model transmission outage probability(MTOP)over the Rayleigh channel.Besides,we define the effective model accuracy(EMA)to evaluate the model transmission performance of both communication and intelligence.Then we propose a joint model selection and resource allocation(JMSRA)algorithm to maximize the average EMA of all users.Simulation results demonstrate that the average EMA of the JMSRA algorithm outperforms baseline algorithms by about 22%.

A predictive model for regional zenith tropospheric delay correction

The conventional zenith tropospheric delay(ZTD)model(known as the Saastamoinen model)does not consider seasonal variations affecting the delay,giving it low accuracy and stability.This may be improved with adjustments to account for annual and semi-annual variations.This method uses ZTD data provided by the Global Geodetic Observing System to analyze seasonal variations in the bias of the Saastamoinen model in Asia,and then constructs a model with seasonal variation corrections,denoted as SSA.To overcome the dependence of the model on in-situ meteorological parameters,the SSA+GPT3 model is formed by combining the SSA and GPT3(global pressure-temperature)models.The results show that the introduction of annual and semi-annual variations can substantially improve the Saastamoinen model,yielding small and time-stable variations in bias and root mean square(RMS).In summer and autumn,the bias and RMS are noticeably smaller than those from the Saastamoinen model.In addition,the SSA model performs better in low-latitude and low-altitude areas,and bias and RMS decease with the increase of latitude or altitude.The prediction accuracy of the SSA model is also evaluated for external consistency.The results show that the accuracy of the SSA model(bias:-0.38 cm,RMS:4.43 cm)is better than that of the Saastamoinen model(bias:1.45 cm,RMS:5.16 cm).The proposed method has strong applicability and can therefore be used for predictive ZTD correction across Asia.

Influence of initial check, information exchange, final accuracy check, reaction information nursing on the psychology of elderly with lung cancer

BACKGROUND As one of the fatal diseases with high incidence,lung cancer has seriously endangered public health and safety.Elderly patients usually have poor self-care and are more likely to show a series of psychological problems.AIM To investigate the effectiveness of the initial check,information exchange,final accuracy check,reaction(IIFAR)information care model on the mental health status of elderly patients with lung cancer.METHODS This study is a single-centre study.We randomly recruited 60 elderly patients with lung cancer who attended our hospital from January 2021 to January 2022.These elderly patients with lung cancer were randomly divided into two groups,with the control group taking the conventional propaganda and education and the observation group taking the IIFAR information care model based on the conventional care protocol.The differences in psychological distress,anxiety and depression,life quality,fatigue,and the locus of control in psychology were compared between these two groups,and the causes of psychological distress were analyzed.RESULTS After the intervention,Distress Thermometer,Hospital Anxiety and Depression Scale(HADS)for anxiety and the HADS for depression,Revised Piper’s Fatigue Scale,and Chance Health Locus of Control scores were lower in the observation group compared to the pre-intervention period in the same group and were significantly lower in the observation group compared to those of the control group(P<0.05).After the intervention,Quality of Life Questionnaire Core 30(QLQ-C30),Internal Health Locus of Control,and Powerful Others Health Locus of Control scores were significantly higher in the observation and the control groups compared to the pre-intervention period in their same group,and QLQ-C30 scores were significantly higher in the observation group compared to those of the control group(P<0.05).CONCLUSION The IIFAR information care model can help elderly patients with lung cancer by reducing their anxiety and depression,psychological distress,and fatigue,improving their tendencies on the locus of control in psychology,and enhancing their life qualities.

Objective Model Selection in Physics: Exploring the Finite Information Quantity Approach

Traditional methods for selecting models in experimental data analysis are susceptible to researcher bias, hindering exploration of alternative explanations and potentially leading to overfitting. The Finite Information Quantity (FIQ) approach offers a novel solution by acknowledging the inherent limitations in information processing capacity of physical systems. This framework facilitates the development of objective criteria for model selection (comparative uncertainty) and paves the way for a more comprehensive understanding of phenomena through exploring diverse explanations. This work presents a detailed comparison of the FIQ approach with ten established model selection methods, highlighting the advantages and limitations of each. We demonstrate the potential of FIQ to enhance the objectivity and robustness of scientific inquiry through three practical examples: selecting appropriate models for measuring fundamental constants, sound velocity, and underwater electrical discharges. Further research is warranted to explore the full applicability of FIQ across various scientific disciplines.

Accuracy assessment of global ocean tide models in the South China Sea using satellite altimeter and tide gauge data

In this study,to meet the need for accurate tidal prediction,the accuracy of global ocean tide models was assessed in the South China Sea(0°–26°N,99°–121°E).Seven tide models,namely,DTU10,EOT11 a,FES2014,GOT4.8,HAMTIDE12,OSU12 and TPXO8,were considered.The accuracy of eight major tidal constituents(i.e.,Q1,O1,P1,K1,N2,M2,S2 and K2)were assessed for the shallow water and coastal areas based on the tidal constants derived from multi-mission satellite altimetry(TOPEX and Jason series)and tide gauge observations.The root mean square values of each constituent between satellite-derived tidal constants and tide models were found in the range of 0.72–1.90 cm in the deep ocean(depth>200 m)and 1.18–5.63 cm in shallow water area(depth<200 m).Large inter-model discrepancies were noted in the Strait of Malacca and the Taiwan Strait,which could be attributable to the complicated hydrodynamic systems and the paucity of high-quality satellite altimetry data.In coastal regions,an accuracy performance was investigated using tidal results from 37 tide gauge stations.The root sum square values were in the range of 9.35–19.11 cm,with the FES2014 model exhibiting slightly superior performance.

Dynamic Accuracy Analysis of a 5PSS/UPU Parallel Mechanism Based on Rigid-Flexible Coupled Modeling

In order to improve the low output accuracy caused by the elastic deformations of the branch chains,a finite element-based dynamic accuracy analysis method for parallel mechanisms is proposed in this paper.First,taking a 5-prismatic-spherical-spherical(PSS)/universal-prismatic-universal(UPU)parallel mechanism as an example,the error model is established by a closed vector chain method,while its influence on the dynamic accuracy of the parallel mechanism is analyzed through numerical simulation.According to the structural and error characteristics of the parallel mechanism,a vector calibration algorithm is proposed to reduce the position and pose errors along the whole motion trajectory.Then,considering the elastic deformation of the rod,the rigid-flexible coupling dynamic equations of each component are established by combining the finite element method with the Lagrange method.The elastodynamic model of the whole machine is obtained based on the constraint condition of each moving part,and the correctness of the model is verified by simulation.Moreover,the effect of component flexibility on the dimensionless root mean square error of the displacement,velocity and acceleration of the moving platform is investigated by using a Newmark method,and the mapping relationship of these dimensionless root mean square errors to dynamic accuracy is further studied.The research work provides a theoretical basis for the design of the parameter size of the prototype.

Accuracy analysis of a single-fault Markov model for FADEC system

Time-limited dispatching(TLD)analysis of the full authority digital engine control(FADEC)systems is an important part of the aircraft system safety analysis and a necessary task for the certification of commercial aircraft and aeroengines.In the time limited dispatch guidance document ARP5107B,a single-fault Markov model(MM)approach is proposed for TLD analysis.However,ARP5107B also requires that the loss of thrust control(LOTC)rate error calculated by applying the single-fault MM must be less than 5%when performing airworthiness certification.Firstly,the sources of accuracy errors in three kinds of MM are analyzed and specified through a case study of the general FADEC system,and secondly a two-fault MM considering maintenance policy is established through analyzing and calculating the expected repair time when two related faults happen.Finally,a specific FADEC system is given to study on the influence factors of accuracy error in the single-fault MM,and the results show that the accuracy error of the single-fault MM decreases with the increase of short or long prescribed dispatch time,and the range values of short time(ST)and long time(LT)are determined to satisfy the requirement of accuracy error within 5%.

Influencing factors on the accuracy of local geoid model

Different modification methods and software programs were developed to obtain accurate local geoid models in the past two decades.The quantitative effect of the main factors on the accuracy of local geoid modeling is still ambiguous and has not been clearly diagnosed yet.This study presents efforts to find the most influential factors on the accuracy of the local geoid model,as well as the amount of each factor’s effect quantitatively.The methodology covers extracting the quantitative characteristics of 16 articles regarding local geoid models of different countries.The Statistical Package of Social Sciences(SPSS)software formulated a strong multiple regression model of correlation coefficient r = 0.999 with a high significance coefficient of determination R^2 = 0.997 and adjusted R^2 = 0,98 for the required effective factors.Then,factor analysis is utilized to extract the dominant factors which include:accuracy of gravity data(40%),the density of gravity data(25%)(total gravity factors is 65%),the Digital Elevation Model(DEM)resolution(16%),the accuracy of GPS/leveling points(10%)and the area of the terrain of the country/state under the study(9%).These results of this study will assist in developing more accurate local geoid models.

Development of basic technique to improve seismic response accuracy of tributary area-based lumped-mass stick models

Although a detailed finite element(FE) model provides more precise results, a lumped-mass stick(LMS) model is preferred because of its simplicity and rapid computational time. However, the reliability of LMS models has been questioned especially for structures dominated by higher modes and seismic inputs. Normally, the natural frequencies and dynamic responses of a LMS model based on tributary area mass consideration are different from the results of the FE model. This study proposes a basic updating technique to overcome these discrepancies; the technique employs the identical modal response, D(t), to the detailed FE model. The parameter D(t) is a time variable function in the dynamic response composition and it depends on frequency and damping ratio for each mode, independent of the structure's mode shapes. The identical response D(t) for each mode is obtained from the frequency adaptive LMS model; the adaptive LMS model which can provide identical modal frequencies as the detailed FE model. Theoretical backgrounds and formulations of the updating technique are proposed. To validate the updating technique, two types of structures(a symmetric straight column and an unsymmetric T-shaped structure) are considered. From the seismic response results including base shear and base moment, the updating technique considerably improves the seismic response accuracy of the tributary area-based LMS model.

Prognostic Kalman Filter Based Bayesian Learning Model for Data Accuracy Prediction

Data is always a crucial issue of concern especially during its prediction and computation in digital revolution.This paper exactly helps in providing efficient learning mechanism for accurate predictability and reducing redundant data communication.It also discusses the Bayesian analysis that finds the conditional probability of at least two parametric based predictions for the data.The paper presents a method for improving the performance of Bayesian classification using the combination of Kalman Filter and K-means.The method is applied on a small dataset just for establishing the fact that the proposed algorithm can reduce the time for computing the clusters from data.The proposed Bayesian learning probabilistic model is used to check the statistical noise and other inaccuracies using unknown variables.This scenario is being implemented using efficient machine learning algorithm to perpetuate the Bayesian probabilistic approach.It also demonstrates the generative function forKalman-filer based prediction model and its observations.This paper implements the algorithm using open source platform of Python and efficiently integrates all different modules to piece of code via Common Platform Enumeration(CPE)for Python.

Modelling an Efficient URL Phishing Detection Approach Based on a Dense Network Model

The social engineering cyber-attack is where culprits mislead the users by getting the login details which provides the information to the evil server called phishing.The deep learning approaches and the machine learning are compared in the proposed system for presenting the methodology that can detect phishing websites via Uniform Resource Locator(URLs)analysis.The legal class is composed of the home pages with no inclusion of login forms in most of the present modern solutions,which deals with the detection of phishing.Contrarily,the URLs in both classes from the login page due,considering the representation of a real case scenario and the demonstration for obtaining the rate of false-positive with the existing approaches during the legal login pages provides the test having URLs.In addition,some model reduces the accuracy rather than training the base model and testing the latest URLs.In addition,a feature analysis is performed on the present phishing domains to identify various approaches to using the phishers in the campaign.A new dataset called the MUPD dataset is used for evaluation.Lastly,a prediction model,the Dense forward-backwards Long Short Term Memory(LSTM)model(d−FBLSTM),is presented for combining the forward and backward propagation of LSMT to obtain the accuracy of 98.5%on the initiated login URL dataset.

Machine learning models for stroke detection by observing the eye-movement features under five-color visual stimuli in traditional Chinese medicine

Objective:To develop a novel diagnostic modality to identify and diagnose stroke in daily life scenarios for improving the therapeutic effects and prognoses of patients.Methods:In this study,16 stroke patients and 24 age-matched healthy participants as controls were recruited for comparative analysis.Leveraging a portable eye-tracking device and integrating traditional Chinese medicine theory with modern color psychology principles,we recorded the eye movement signals and calculated eye movement features.Meanwhile,the stroke recognition models based on eye movement features were further trained by using random forest(RF),k-nearest neighbors(KNN),decision tree(DT),gradient boosting classifier(GBC),XGBoost,and CatBoost.Results:The stroke group and the healthy group showed significant differences in some eye movement features(P<.05).The models trained based on eye movement characteristics had good performances in recognizing stroke individuals,with accuracies ranging from 77.40%to 88.45%.Under the red stimulus,the eye movement model trained by RF became the best machine learning model with a recall of 84.65%,a precision of 86.48%,a F1 score of 85.47%.Among the six algorithms,RF and CatBoost performed better in classification.Conclusion:This study pioneers the application of traditional Chinese medicine's five-color stimuli to visual observation tasks.On the basis of the combined design,the eye-movement models can accurately identify stroke,and the developed high-performance models may be used in daily life scenarios.

智能手机GNSS数据质量分析与随机模型建立

为提高移动智能终端定位精度,利用小米8和华为Mate20手机采集多个卫星系统的观测数据并进行质量分析,通过不同卫星系统的伪距残差分别对信噪比模型参数进行估计,最后进行静态伪距单点定位实验。结果表明,手机端观测的BDS和GPS伪距精度接近,且高于GLONASS;相较于高度角,伪距残差与信噪比的相关性更强;在信噪比模型参数估计中,小米8拟合效果优于华为Mate20,BDS拟合效果差于其他卫星系统;参数拟合后的信噪比模型在水平和高程方向的定位精度分别为2.37 m和8.38 m,相较于高度角模型,定位精度提升约10%。

考虑摇头角的非赫兹滚动接触算法对比研究

为研究轮对摇头角对轮轨接触行为的影响以及评估简化非赫兹轮轨接触算法的精度,选取EKP、MKP与MSHM共3种考虑摇头角的轮轨非赫兹简化算法分析轮轨法向与切向接触行为。以S1002CN车轮踏面和CHN60钢轨为研究对象,以精确理论CONTACT算法的结果为参考,对比各简化接触算法得到的接触斑形状、接触应力、切应力等微观接触结果的准确性。利用UM软件建立车辆-轨道耦合动力学模型进行仿真分析得到轮轨运动参数,然后输入到接触算法计算轮轨接触解,基于统计学累积误差方法评估不同接触算法在实际车辆运行工况下的计算精度与稳定性。计算结果表明:摇头角使轮轨接触斑与接触应力呈三维非对称分布;对于法向接触,在大部分工况下,MKP算法相比EKP、MSHM算法精度较高,但接触斑内轮轨曲率变化较大时,如横移量为6 mm,MKP算法所得到的最大接触应力误差为47.2%,此时更适合采用MSHM算法,其误差为27%;对于切向问题,采用EKP+FaStrip、MKP+FaStrip算法比MSHM+FASTSIM算法得到的结果更准确;对于动力学结果,MKP+FaStrip算法比EKP+FaStrip、MSHM+FASTSIM算法得到的接触结果更加精确和稳定。

GNSS基准站信噪比联合卫星系统综合定权的手机RTK定位

随着智能手机芯片和天线性能的不断提升,基于手机的高精度定位逐渐引起学术界和工业界的广泛关注.相较于单点定位,实时动态(real-time kinematic,RTK)定位通常表现出更高的定位性能.然而,目前对于手机RTK定位的随机模型的研究仍相对较少.因此,文中以小米8为例,重点探讨了智能手机在多系统RTK定位情境下的随机模型.研究结果表明:不同系统的卫星伪距噪声存在显著差异,而相位噪声的差异则较小.基于此,本文提出了一种基站信噪比联合卫星系统间综合定权的随机模型.相对于传统的信噪比模型更准确地考虑了不同系统之间的权重分配.并在开阔和遮挡条件下进行了静态和动态实验.结果表明:相比于传统的信噪比模型,新模型在静态开阔、静态遮挡、动态开阔和动态遮挡下三维方向的RTK定位精度分别提升了18.7%、18.3%、3.3%和4.6%.

影响山西台网地震定位因素的定量分析

基于山西地震台网现有的台站布局,利用数值模拟方法定量分析地壳速度模型、台网布局及震相拾取精度等因素对震源位置测定的影响,结果显示,3种因素对准确测定震源位置的影响不同。地壳速度模型中上地壳速度对震中位置的影响最大,而震中位置对莫霍面速度变化的反映更为灵敏。单独改变地壳速度模型引起的震中位置偏差通常不超过5 km,震源深度对地壳速度模型的变化十分敏感,尤其是莫霍面深度,最大偏差可能超过20 km。在不考虑其他因素的条件下,P波拾取精度在4 s以内测定的震中位置和震源深度最大偏差不超过2 km和5 km。当地壳速度模型适宜且震相拾取精度较高时,最大空隙角在0°~180°的台站布局对震源位置的约束较好,震中偏差基本小于1 km,震源深度偏差略大(约为2 km)。

环形桁架式空间薄膜展开机构运动精度可靠性建模与分析

空间薄膜展开机构的姿态和位置精度会对薄膜索网的工作形态产生影响。文章首先构建环形桁架式空间薄膜展开机构运动精度可靠性理论模型,然后基于ADAMS建立考虑展开机构杆长误差的参数化仿真模型,并采用蒙特卡罗法对展开机构进行运动精度可靠性仿真分析,求解运动精度可靠度。结果表明,以机构展开到位时折展杆支撑点所拟合的空间圆心坐标位置误差及半径误差作为其运动精度可靠性量化评价指标是正确的。该研究可为空间薄膜展开机构的可靠性设计提供参考。

面向切削力修正的机床主轴回转精度预测实验分析

为了提高机床控制精度,设计一种可以精确预测面向切削力修正的主轴回转精度分析方法。为验证回转精度预测准确性,建立一套不需要通过标准球实现的主轴回转精度分析系统,可以针对具体切削工况开展主轴回转精度测试。研究结果表明:随着进给速率和切削深度改变,形成了具有规律性的同步误差,切深受到同步误差因素的影响程度最大,而进给速度次之。受切削载荷影响,主轴回转精度显著改变,同步误差和切削载荷具有正相关关系。在变进给及变切宽条件下,测试结果与仿真结果相近,最大误差为0.2μm。设定切宽12 mm与进给速度1200 mm/min时,分离得到的圆度误差与100 r/min空转时圆度误差相比相吻合。

水下控制模块安装工具结构设计与对接精度分析

水下控制模块是水下生产系统的关键部件,针对水下控制模块在水下如何精准、高效、安全地实现回收和再安装工作的问题,本文设计了一种水下控制模块安装工具。对此安装工具进行了机械结构的设计,将安装工具各部分结构在工作过程中的作用进行了介绍。针对安装工具能够顺利完成水下控制模块的安装,基于安装工具各部分结构对接精度关系分析,通过数学建模与仿真分析,得到影响水下控制模块安装精度的原因,设计合理的安装精度参数。将仿真分析得到的数据与设计参数相比较,可证明水下控制模块的结构设计能满足水下控制模块的对接定位精度要求。结果表明:各精度参数满足水下控制模块安装工具设计要求,在此设计参数下安装工具能够完成水下控制模块的精确安装。