A Novel Computationally Efficient Approach to Identify Visually Interpretable Medical Conditions from 2D Skeletal Data

Timely identification and treatment of medical conditions could facilitate faster recovery and better health.Existing systems address this issue using custom-built sensors,which are invasive and difficult to generalize.A low-complexity scalable process is proposed to detect and identify medical conditions from 2D skeletal movements on video feed data.Minimal set of features relevant to distinguish medical conditions:AMF,PVF and GDF are derived from skeletal data on sampled frames across the entire action.The AMF(angular motion features)are derived to capture the angular motion of limbs during a specific action.The relative position of joints is represented by PVF(positional variation features).GDF(global displacement features)identifies the direction of overall skeletal movement.The discriminative capability of these features is illustrated by their variance across time for different actions.The classification of medical conditions is approached in two stages.In the first stage,a low-complexity binary LSTM classifier is trained to distinguish visual medical conditions from general human actions.As part of stage 2,a multi-class LSTM classifier is trained to identify the exact medical condition from a given set of visually interpretable medical conditions.The proposed features are extracted from the 2D skeletal data of NTU RGB+D and then used to train the binary and multi-class LSTM classifiers.The binary and multi-class classifiers observed average F1 scores of 77%and 73%,respectively,while the overall system produced an average F1 score of 69%and a weighted average F1 score of 80%.The multi-class classifier is found to utilize 10 to 100 times fewer parameters than existing 2D CNN-based models while producing similar levels of accuracy.

Effective data sampling strategies and boundary condition constraints of physics-informed neural networks for identifying material properties in solid mechanics

Material identification is critical for understanding the relationship between mechanical properties and the associated mechanical functions.However,material identification is a challenging task,especially when the characteristic of the material is highly nonlinear in nature,as is common in biological tissue.In this work,we identify unknown material properties in continuum solid mechanics via physics-informed neural networks(PINNs).To improve the accuracy and efficiency of PINNs,we develop efficient strategies to nonuniformly sample observational data.We also investigate different approaches to enforce Dirichlet-type boundary conditions(BCs)as soft or hard constraints.Finally,we apply the proposed methods to a diverse set of time-dependent and time-independent solid mechanic examples that span linear elastic and hyperelastic material space.The estimated material parameters achieve relative errors of less than 1%.As such,this work is relevant to diverse applications,including optimizing structural integrity and developing novel materials.

Using Extreme Value Theory Approaches to Estimate High Quantiles for Stroke Data

This paper aims to explore the application of Extreme Value Theory (EVT) in estimating the conditional extreme quantile for time-to-event outcomes by examining the functional relationship between ambulatory blood pressure trajectories and clinical outcomes in stroke patients. The study utilizes EVT to analyze the functional connection between ambulatory blood pressure trajectories and clinical outcomes in a sample of 297 stroke patients. The 24-hour ambulatory blood pressure measurement curves for every 15 minutes are considered, acknowledging a censored rate of 40%. The findings reveal that the sample mean excess function exhibits a positive gradient above a specific threshold, confirming the heavy-tailed distribution of data in stroke patients with a positive extreme value index. Consequently, the estimated conditional extreme quantile indicates that stroke patients with higher blood pressure measurements face an elevated risk of recurrent stroke occurrence at an early stage. This research contributes to the understanding of the relationship between ambulatory blood pressure and recurrent stroke, providing valuable insights for clinical considerations and potential interventions in stroke management.

Real-time rock mass condition prediction with TBM tunneling big data using a novel rock-machine mutual feedback perception method

Real-time perception of rock mass information is of great importance to efficient tunneling and hazard prevention in tunnel boring machines(TBMs).In this study,a TBM-rock mutual feedback perception method based on data mining(DM) is proposed,which takes 10 tunneling parameters related to surrounding rock conditions as input features.For implementation,first,the database of TBM tunneling parameters was established,in which 10,807 tunneling cycles from the Songhua River water conveyance tunnel were accommodated.Then,the spectral clustering(SC) algorithm based on graph theory was introduced to cluster the TBM tunneling data.According to the clustering results and rock mass boreability index,the rock mass conditions were classified into four classes,and the reasonable distribution intervals of the main tunneling parameters corresponding to each class were presented.Meanwhile,based on the deep neural network(DNN),the real-time prediction model regarding different rock conditions was established.Finally,the rationality and adaptability of the proposed method were validated via analyzing the tunneling specific energy,feature importance,and training dataset size.The proposed TBM-rock mutual feedback perception method enables the automatic identification of rock mass conditions and the dynamic adjustment of tunneling parameters during TBM driving.Furthermore,in terms of the prediction performance,the method can predict the rock mass conditions ahead of the tunnel face in real time more accurately than the traditional machine learning prediction methods.

A PRELIMINARY STUDY ON COMBINING TWO KINDS OF PROXY DATA USING THE CONDITIONAL QUANTILE ADJUSTMENT METHOD

Based on two kinds of proxy data, a tree-ring width chronology at Huashan and the wetness/dryness grade series around Xi'an in north-centralChina, thes presat study demonstrates how different types of proxy climaterecords can be combined to give a more reliable estimate of past climate thaneither record can be done individually. With comparison and correction of thetwo data sets, various statistical models can be developed from individual andcombined senes. Among them, the best combined model produced by theconditional quantile adjustmat method can be selected for reconstruction ofApril-July rainfall at Huashan back to 1600 A.D.

A Geometric Approach to Conditioning and the Search for Minimum Variance Unbiased Estimators

Our purpose is twofold: to present a prototypical example of the conditioning technique to obtain the best estimator of a parameter and to show that this technique resides in the structure of an inner product space. The technique uses conditioning of an unbiased estimator on a sufficient statistic. This procedure is founded upon the conditional variance formula, which leads to an inner product space and a geometric interpretation. The example clearly illustrates the dependence on the sampling methodology. These advantages show the power and centrality of this process.

Some Additional Moment Conditions for a Dynamic Count Panel Data Model with Predetermined Explanatory Variables

This paper proposes some additional moment conditions for the linear feedback model with explanatory variables being predetermined, which is proposed by [1] for the purpose of dealing with count panel data. The newly proposed moment conditions include those associated with the equidispersion, the Negbin I-type model and the stationarity. The GMM estimators are constructed incorporating the additional moment conditions. Some Monte Carlo experiments indicate that the GMM estimators incorporating the additional moment conditions perform well, compared to that using only the conventional moment conditions proposed by [2,3].

Zero Truncated Bivariate Poisson Model: Marginal-Conditional Modeling Approach with an Application to Traffic Accident Data

A new covariate dependent zero-truncated bivariate Poisson model is proposed in this paper employing generalized linear model. A marginal-conditional approach is used to show the bivariate model. The proposed model with estimation procedure and tests for goodness-of-fit and under (or over) dispersion are shown and applied to road safety data. Two correlated outcome variables considered in this study are number of cars involved in an accident and number of casualties for given number of cars.

Minimum MSE Weighted Estimator to Make Inferences for a Common Risk Ratio across Sparse Meta-Analysis Data

The paper aims to discuss three interesting issues of statistical inferences for a common risk ratio (RR) in sparse meta-analysis data. Firstly, the conventional log-risk ratio estimator encounters a number of problems when the number of events in the experimental or control group is zero in sparse data of a 2 × 2 table. The adjusted log-risk ratio estimator with the continuity correction points based upon the minimum Bayes risk with respect to the uniform prior density over (0, 1) and the Euclidean loss function is proposed. Secondly, the interest is to find the optimal weights of the pooled estimate that minimize the mean square error (MSE) of subject to the constraint on where , , . Finally, the performance of this minimum MSE weighted estimator adjusted with various values of points is investigated to compare with other popular estimators, such as the Mantel-Haenszel (MH) estimator and the weighted least squares (WLS) estimator (also equivalently known as the inverse-variance weighted estimator) in senses of point estimation and hypothesis testing via simulation studies. The results of estimation illustrate that regardless of the true values of RR, the MH estimator achieves the best performance with the smallest MSE when the study size is rather large and the sample sizes within each study are small. The MSE of WLS estimator and the proposed-weight estimator adjusted by , or , or are close together and they are the best when the sample sizes are moderate to large (and) while the study size is rather small.

UNIQUENESS OF INVERSE TRANSMISSION SCATTERING WITH A CONDUCTIVE BOUNDARY CONDITION BY PHASELESS FAR FIELD PATTERN

In this paper,we establish the unique determination result for inverse acoustic scattering of a penetrable obstacle with a general conductive boundary condition by using phaseless far field data at a fixed frequency.It is well-known that the modulus of the far field pattern is invariant under translations of the scattering obstacle if only one plane wave is used as the incident field,so it is impossible to reconstruct the location of the underlying scatterers.Based on some new research results on the impenetrable obstacle and inhomogeneous isotropic medium,we consider different types of superpositions of incident waves to break the translation invariance property.

Superiority of Bayesian Imputation to Mice in Logit Panel Data Models

Non-responses leading to missing data are common in most studies and causes inefficient and biased statistical inferences if ignored. When faced with missing data, many studies choose to employ complete case analysis approach to estimate the parameters of the model. This however compromises on the susceptibility of the estimates to reduced bias and minimum variance as expected. Several classical and model based techniques of imputing the missing values have been mentioned in literature. Bayesian approach to missingness is deemed superior amongst the other techniques through its natural self-lending to missing data settings where the missing values are treated as unobserved random variables that have a distribution which depends on the observed data. This paper digs up the superiority of Bayesian imputation to Multiple Imputation with Chained Equations (MICE) when estimating logistic panel data models with single fixed effects. The study validates the superiority of conditional maximum likelihood estimates for nonlinear binary choice logit panel model in the presence of missing observations. A Monte Carlo simulation was designed to determine the magnitude of bias and root mean square errors (RMSE) arising from MICE and Full Bayesian imputation. The simulation results show that the conditional maximum likelihood (ML) logit estimator presented in this paper is less biased and more efficient when Bayesian imputation is performed to curb non-responses.

论公共数据授权运营的立法路径

当前,为了释放公共数据价值、培育数据要素市场,公共数据授权运营成为重要的破局之策。然而,公共数据授权运营立法远落后于实践需求,是否立法以及如何立法仍成为悬而未决的难题。在立法条件方面,理论依据的充分、经济条件的具备为授权运营立法提供了可行性,而数据增值、企业利用、政府治理、国家愿景等需求,均证实了立法的必要性。在立法模式方面,权衡中央立法模式和地方立法模式的利弊,应推行“地方先行先试”的立法模式。在具体制度实现方面,地方立法应秉持效率与安全并重的立法理念,采用实用性立法结构,授权主体内容应设计为统一授权模式,运营主体内容应将基本业务能力和安全保障能力设置为准入条件,以法定情形和约定情形设置退出条件,授权运营的公共数据内容应遵循以混合分类法分类、以敏感程度分级的标准,安全监督与管理内容应以安全和效益为监督考核标准。

免除伦理审查制度适法性与可操作性探讨

结合我国现行法律和基本伦理原则,深入分析了免除伦理审查的前置条件和适用情形在适法性与可操作性上可能存在的问题,并在此基础上提出了可行性的建议:第一,明确监管范围,免除伦理审查是一种特殊的审查方式,而非不进行伦理审查;第二,全面评估风险,对研究的风险判断不仅要考虑因研究给个人带来的生理风险,还应考虑其心理、经济、社会以及法律等方面的风险,尤其是对最小风险应明确界定;第三,界定前置条件中的具体概念,以及在此基础上适用情形的判断,制定具体、合理、合法的实施细则,为免除伦理审查的实施提供参考。

大功率履带越野车用液力变矩器循环工况构建

为解决液力变矩器的传统稳态试验工况同实车工况契合度不高、无法反应实车上运行状态的问题,提出了一种大功率履带越野车用液力变矩器循环工况。在某型履带车辆实车工况数据基础上,统计分析液力变矩器在实车运行中的工况特征,选取了12个统计特征参数和10个比例特征参数,利用主成分分析法对工况数据进行了降维处理,通过无监督学习中的K均值聚类算法完成了数据片段的聚类分析,使用动态规划方法整合闭锁工况,获取典型工况片段用于循环工况重构,利用汉宁窗对片段进行平滑连接。以循环工况与总体数据主要特征值平均误差、连接处转速差值总和及斜率差值总和为目标,借助模拟退火和多目标粒子群算法进行优化,构建基于实车数据、契合液力变矩器实车运行特征的循环工况。结果表明,最终所得工况由叶轮转速——时间和闭锁信号——时间组成,主要特征参数平均相对误差为2.92%,与实车数据的工况特征表现一致。本研究成果为设计液力变矩器的可靠性试验工况提供了一种新的思路和途径。

认知物联网中继传感节点最小功耗布置方法研究

物联网数据传输过程中,中继传感节点能量不足或者能量消耗过多将导致部分节点失效,降低物联网的使用寿命。为解决这一问题,提出认知物联网中继传感节点最小功耗布置方法。根据中继节点结构特征、有向加权图,建立数据传输功耗的数学模型;以最小功耗布置为目的,制定数据流向、通信容量、数据最大传输次数的约束条件,阻止中继节点逆向传输,使节点满足通信容量范围的同时,避免出现逐条传输数据的事件;引入贪婪算法布置中继传感节点,实现中继传感节点最小功耗布置。仿真结果表明,所提方法的中继传感节点布置最大功耗为17.58 J;在传感节点分布密度为370个/m^(3)时,中继节点布置数量为126个;在11.9 s内即可完成150个中继节点的布置。

砂-粉混合料颗粒接触状态的临界条件确定

砂-粉混合料被广泛应用于高速铁路路基、人工筑岛及海底沉管隧道垫层等重大工程,如何科学地表征砂-粉混合料复杂的颗粒接触状态及连续演变的力学行为特征,并深入揭示其对混合料静/动力学特性的影响已是一项紧迫的基础性科学研究任务。本文基于代表性砂-粉混合料的基本物理性能指标及力学特性指标试验数据,验证Rahman等提出的区分“细粒填充砂粒”和“砂粒悬浮细粒”的阈值细粒含量FCth半经验公式预测能力。基于理想二元介质材料的理论最小孔隙比计算方法,提出确定中间性态土颗粒接触状态临界条件参量FC_(in-min)和FC_(in-max)的方法,分析各参数对FC_(in-min)和FC_(in-max)的影响规律,建立基于基本物理性能指标的FC_(in-min)和FC_(in-max)预测方法,最终提出全FC范围(FC=0~100%)的砂-粉混合料各颗粒接触状态临界条件参量的有效评价方法。

基于QAR着陆数据的跑道状况等级评估方法

跑道状况等级是飞机安全着陆的一个关键信息。为了进一步提高跑道状况评估的准确性和及时性,提出一种利用飞行快速存取记录器(Quick Access Recorder, QAR)数据快速评估跑道状况等级的方法。首先,根据飞行数据特征从繁多的数据中提取模型所需输入参数;其次,建立基于QAR数据建立飞机采用自动刹车着陆的动力学模型,并计算不同跑道状况等级对应的理论着陆距离;最后,将实际着陆距离与不同的跑道状况等级对应的理论着陆距离作对比,确定跑道状况等级。利用波音738和空客A320的QAR着陆数据分析某机场的跑道状况等级,并与该机场的雪情通告进行对比,验证了模型的准确性。研究成果可用于未来航班的着陆距离预测,为飞行员选择合适的刹车等级提供参考。

基于条件生成对抗网络的无线传感网络多节点失效修复研究

当前主流的传感节点失效修复主要通过纠删码完成,修复后节点具有更高的空间利用率,但无法有效提升网络寿命。为此,提出基于条件生成对抗网络的无线传感网络多节点数据重构方法,完成失效修复。感知无线传感网络节点,对失效节点展开裁决,确定失效节点位置,并重构节点内数据;将获取的失效节点用于条件生成对抗网络(CGAN)框架中生成器与节点替换网络的训练,通过训练好的生成器,以失效节点为条件,生成未失效节点;为提升修复性能,使用粒子群算法寻优节点替换网络参数,完成节点重构数据置换,实现失效节点的有效修复。结果表明:利用所提方法进行修复时,能耗最高仅为17 J,剩余寿命最低可达到300 h,连通度最高可达到99.2%,具有较好的修复效果。

基于网络开放数据的区域消防救援总体有效覆盖率评估

高效准确地掌握消防救援有效覆盖率是优化消防站点资源配置的前提。将判断是否有效覆盖的消防救援行程时间阈值定为240 s,建立了基于时间加权的区域消防救援总体有效覆盖率评估模型。基于网络开放数据收集了长沙的50座消防站作为消防救援供给点、5746家被抽查到的社会单位作为消防救援需求点,并调用网络地图应用程序编程接口基于实时路况来仿真消防救援行程时间。在连续7 d内设置197个评估场景,共获得了1131962个有效样本,进而得出:长沙消防救援总体有效覆盖率为21.22%,结果等级为“C”,消防救援水平一般,需加强消防站建设。这种基于网络开放数据的区域消防救援有效覆盖率评估,具备高效和准确的特点,可为优化消防站点资源配置、提升公共安全水平提供关键理论和方法支撑。