Uncertainties of landslide susceptibility prediction: Influences of random errors in landslide conditioning factors and errors reduction by low pass filter method

In the existing landslide susceptibility prediction(LSP)models,the influences of random errors in landslide conditioning factors on LSP are not considered,instead the original conditioning factors are directly taken as the model inputs,which brings uncertainties to LSP results.This study aims to reveal the influence rules of the different proportional random errors in conditioning factors on the LSP un-certainties,and further explore a method which can effectively reduce the random errors in conditioning factors.The original conditioning factors are firstly used to construct original factors-based LSP models,and then different random errors of 5%,10%,15% and 20%are added to these original factors for con-structing relevant errors-based LSP models.Secondly,low-pass filter-based LSP models are constructed by eliminating the random errors using low-pass filter method.Thirdly,the Ruijin County of China with 370 landslides and 16 conditioning factors are used as study case.Three typical machine learning models,i.e.multilayer perceptron(MLP),support vector machine(SVM)and random forest(RF),are selected as LSP models.Finally,the LSP uncertainties are discussed and results show that:(1)The low-pass filter can effectively reduce the random errors in conditioning factors to decrease the LSP uncertainties.(2)With the proportions of random errors increasing from 5%to 20%,the LSP uncertainty increases continuously.(3)The original factors-based models are feasible for LSP in the absence of more accurate conditioning factors.(4)The influence degrees of two uncertainty issues,machine learning models and different proportions of random errors,on the LSP modeling are large and basically the same.(5)The Shapley values effectively explain the internal mechanism of machine learning model predicting landslide sus-ceptibility.In conclusion,greater proportion of random errors in conditioning factors results in higher LSP uncertainty,and low-pass filter can effectively reduce these random errors.

Estimation of random errors for lidar based on noise scale factor

Estimation of random errors, which are due to shot noise of photomultiplier tube(PMT) or avalanche photodiode(APD) detectors, is very necessary in lidar observation. Due to the Poisson distribution of incident electrons, there still exists a proportional relationship between standard deviation and square root of its mean value. Based on this relationship,noise scale factor(NSF) is introduced into the estimation, which only needs a single data sample. This method overcomes the distractions of atmospheric fluctuations during calculation of random errors. The results show that this method is feasible and reliable.

BLU Factorization for Block Tridiagonal Matrices and Its Error Analysis

A block representation of the BLU factorization for block tridiagonal matrices is presented. Some properties on the factors obtained in the course of the factorization are studied. Simpler expressions for errors incurred at the process of the factorization for block tridiagonal matrices are considered.

Human Factor Based Leadership: Critical Leadership Tools to Reduce Burnout and Latent Error in a Time of Accelerating Change

The majority of errors in healthcare are from systems factors that create the latent conditions for error to occur. The majority of occupational stressors causing burnout are also the result of systemic factors. Advances in technology create new levels of stress and expectations on healthcare workers (HCW) with an endless infusion of requirements from multiple authoritative sources that are tracked and monitored. The quality of care and safety of patients is affected by the wellbeing of HCWs who now practice in an environment that has become more complex to navigate, often expending limited neural resource (brainpower) on classifying, organizing, constantly making decisions on how and when they can accomplish what is required(extraneous cognitive load) in addition to direct patient care. New information demonstrates profound biological impact on the brains of those who have burnout in areas that affect the quality and safety of the decisions they make-which affects risk to patients in healthcare. Healthcare administration curriculum currently does not include ways to address these stress-induced problems in healthcare delivery. The science of human factors and ergonomics (HFE) promotes system performance and worker wellbeing. Patient safety is one component of system performance. Since many requirements come without resource to accomplish them, it becomes incumbent upon health system leadership to organize the means for completion of these to minimize the needless loss of brain power diverted away from the delivery of patient care. Human Factor-Based Leadership (HFBL) is an interactive, problem solving seminar series designed for healthcare leaders. The purpose is to provide relevant human factor science to integrate into their leadership and management decisions to make HCWs occupational environment more manageable and sustainable-which makes safer conditions for clinician wellbeing and patient care. After learning the content, a cohort of healthcare leaders believed that adequately addressing HFE in healthcare delivery would significantly reduce clinician burnout and risk of latent errors from upstream leadership decisions. An overview of the content of the seminars is described. Leadership feedback on usability of these seminars is reported. Three HFBL seminars described are Human Factor Relevance in Leadership, Biopsychosocial Approach to Wellness and Burnout, Human Factor Based Leadership: Examples and Applications.

R-Factor Analysis of Data Based on Population Models Comprising R- and Q-Factors Leads to Biased Loading Estimates

Effects of performing an R-factor analysis of observed variables based on population models comprising R- and Q-factors were investigated. Although R-factor analysis of data based on a population model comprising R- and Q-factors is possible, this may lead to model error. Accordingly, loading estimates resulting from R-factor analysis of sample data drawn from a population based on a combination of R- and Q-factors will be biased. It was shown in a simulation study that a large amount of Q-factor variance induces an increase in the variation of R-factor loading estimates beyond the chance level. Tests of the multivariate kurtosis of observed variables are proposed as an indicator of possible Q-factor variance in observed variables as a prerequisite for R-factor analysis.

Online Capacitor Voltage Transformer Measurement Error State Evaluation Method Based on In-Phase Relationship and Abnormal Point Detection

The assessment of the measurement error status of online Capacitor Voltage Transformers (CVT) within the power grid is of profound significance to the equitable trade of electric energy and the secure operation of the power grid. This paper advances an online CVT error state evaluation method, anchored in the in-phase relationship and outlier detection. Initially, this method leverages the in-phase relationship to obviate the influence of primary side fluctuations in the grid on assessment accuracy. Subsequently, Principal Component Analysis (PCA) is employed to meticulously disentangle the error change information inherent in the CVT from the measured values and to compute statistics that delineate the error state. Finally, the Local Outlier Factor (LOF) is deployed to discern outliers in the statistics, with thresholds serving to appraise the CVT error state. Experimental results incontrovertibly demonstrate the efficacy of this method, showcasing its prowess in effecting online tracking of CVT error changes and conducting error state assessments. The discernible enhancements in reliability, accuracy, and sensitivity are manifest, with the assessment accuracy reaching an exemplary 0.01%.

3D打印技术的精度提升与误差控制策略研究

3D打印技术以其柔性制造、个性化定制等优势,在工业制造、医疗、航空航天等领域得到广泛应用。本研究在分析3D打印精度影响因素的基础上,系统阐述了3D打印过程中的误差来源与表现,重点探讨了高精度设备研发、优质材料应用、智能化工艺优化、精密检测补偿等提升打印精度的关键技术。针对3D打印误差控制难题,研究从数据驱动、实时监测、多源融合等角度提出了相应优化策略,为提高3D打印精度、推动3D打印技术进步提供参考。

具有GARCH-normal-error时序的贝叶斯单位根检验

针对时间序列呈现尖峰厚尾、条件方差时变特征时,DF检验功效性较低的问题,文章基于贝叶斯理论设计了MCMC抽样算法,检验具有GARCH-normal-error时序的平稳性,并用Monte Carlo模拟仿真验证贝叶斯单位根检验的可行性与有效性,结果表明:贝叶斯单位根检验的稳健性较好,样本容量、ρ的大小及波动参数和单整程度的差异对单位根检验势的影响均不大;当ρ<0.7时,DF检验的势略高于贝叶斯方法,但当ρ> 0.7时,贝叶斯方法的势远高于DF检验;DF k检验与DFτ检验的可靠性受样本容量的影响较大。

Excitation Prediction by Dynamic Transmission Error under Sliding Friction in Helical Gear System

Monte Carlo method was adopted to calculate the meshing error considering the manufacture error and assembly error of the meshing point along the time-varying contact line for helical gear pair. The flexural-torsion-axis dynamic model coupled was established under the tooth friction force and solved by the perturbation method to compute real dynamic tooth load. The change laws of the friction force and friction torque were obtained in a meshing period. The transmission error formulation was analyzed to introduce meshing excitations. The maximum dynamic transmission error, the maximum meshing force and the maximum dynamic factor were calculated under different speeds, external loads and damping factors. The conclusions can provide theoretical basis for the gear design especially in tooth profile correction.

Engineering approach for human error probability quantification

A novel approach for engineering application to human error probability quantification is presented based on an overview of the existing human reliability analysis methods. The set of performance shaping factors is classified as two subsets of dominant factors and adjusting factors respectively. Firstly, the dominant factors are used to determine the probabilities of three behavior modes. The basic probability and its interval of human error for each behavior mode are given. Secondly, the basic probability and its interval are modified by the adjusting factors, and the total probability of human error is calculated by a total probability formula. Finally, a simple example is introduced, and the consistency and validity of the presented approach are illustrated.

耙吸挖泥动态分层定深施工工艺应用

本文主要介绍了一种适用于耙吸挖泥船疏浚施工的新型施工工艺,即动态分层定深施工工艺。首先采用敏感性因素分析法对影响疏浚工程扫浅时间的各个因素进行重要性排序,然后逐个分析每个因素对平整度影响的机理,最后根据分析结果构建动态分层定深计算公式,计算得出分阶段施工的不同定深深度。整个工艺通过加强对施工过程中平整度的阶段控制,从而降低乃至消除后期扫浅时间,减少前期抢工程量造成的垄沟,最终减少施工工期,提高项目收益。通过在洋山四期基建项目中的应用,体现出该施工工艺的可行性和优势。

An improved four-dimensional variation source term inversion model with observation error regularization

Aiming at the Four-Dimensional Variation source term inversion algorithm proposed earlier,the observation error regularization factor is introduced to improve the prediction accuracy of the diffusion model,and an improved Four-Dimensional Variation source term inversion algorithm with observation error regularization(OER-4DVAR STI model)is formed.Firstly,by constructing the inversion process and basic model of OER-4DVAR STI model,its basic principle and logical structure are studied.Secondly,the observation error regularization factor estimation method based on Bayesian optimization is proposed,and the error factor is separated and optimized by two parameters:error statistical time and deviation degree.Finally,the scientific,feasible and advanced nature of the OER-4DVAR STI model are verified by numerical simulation and tracer test data.The experimental results show that OER-4DVAR STI model can better reverse calculate the hazard source term information under the conditions of high atmospheric stability and flat underlying surface.Compared with the previous inversion algorithm,the source intensity estimation accuracy of OER-4DVAR STI model is improved by about 46.97%,and the source location estimation accuracy is improved by about 26.72%.

Iterative identification of output error model for industrial processes with time delay subject to colored noise

To deal with colored noise and unexpected load disturbance in identification of industrial processes with time delay, a bias-eliminated iterative least-squares(ILS) identification method is proposed in this paper to estimate the output error model parameters and time delay simultaneously. An extended observation vector is constructed to establish an ILS identification algorithm. Moreover, a variable forgetting factor is introduced to enhance the convergence rate of parameter estimation. For consistent estimation, an instrumental variable method is given to deal with the colored noise. The convergence and upper bound error of parameter estimation are analyzed. Two illustrative examples are used to show the effectiveness and merits of the proposed method.

Identify gaps between local and international measures to avoid administration error on 1-year review in United Christian Hospital, Hong Kong

Many medication errors could be avoided if administration was more closely linked with structured monitoring. The contributory factors to administration errors occurred in one recent year were reviewed and identified contributory factors to errors as six domains of administration principles according to the report from the Quality and Safety website. The current measures including guidelines, policy, and practices to prevent the administration errors identified in the previous step were searched from the United Christian Hospital (UCH) homepage. Meanwhile, the international measures suggested in literature were identified to address the administration errors identified. 41 cases were identified as medication errors related to administration error events, with total twenty contributory factors identified according to the incident report which identified five contributory factors as common causes. Measures to prevent interruption of medication round and measures to improve individual knowledge and skills, and personal responsebility were suggested to fill the gaps. The medication administration errors should be avoided through both education reinforcing programme and preventive interventions of distraction or interruption to the procedure after comparing the existing measures to the suggested measures from literature. This study was so important to improve the current measures to prevent medication administration errors.

GNSS/MEMS IMU车载组合导航中IMU比例因子误差的影响分析

从系统状态模型出发,分析比例因子误差对组合导航精度和计算量的影响,同时基于车载运动的特点分析比例因子误差的观测性,提出一种仅保留航向陀螺仪和水平加速度计比例因子误差的降维状态模型。实验结果表明,当比例因子误差大于6×10^(-3)时,增广比例因子误差有助于提高导航精度,但计算量增加约170%;降维模型能够达到高维模型的导航精度,与不增广比例因子误差相比,计算量仅增加约70%。

Dynamic Factor Method of Computing Dynamic Mathematical Model for System Simulation

The computational methods of a typical dynamic mathematical model that can describe the differential element and the inertial element for the system simulation are researched. The stability of numerical solutions of the dynamic mathematical model is researched. By means of theoretical analysis, the error formulas, the error sign criteria and the error relationship criterion of the implicit Euler method and the trapezoidal method are given, the dynamic factor affecting the computational accuracy has been found, the formula and the methods of computing the dynamic factor are given. The computational accuracy of the dynamic mathematical model like this can be improved by use of the dynamic factor.

Human factors in anaesthetic crisis

This paper discusses some of the key aspects of human factors in anaesthesia for the improvement of patient safety. Medical errors have emerged as a serious issue in healthcare delivery. There has been new interest in human factors as a means of reducing these errors. Human factors are important contributors to critical incidents and crises in anaesthesia. It has been shown that the prevalence of human factors in anaesthesia can be as high as 83%. Cognitive thinking process and biases involved are important in understanding human factors. Errors of cognition linked with human factors lead to anaesthetic errors and crisis. Multiple errors in the cognitive thinking process, known as "Cognitive dispositions to respond" have been identified leading to errors. These errors classified into latent or active can be easily identified in the clinical vignettes of serious medical errors. Application of the knowledge on human factors and use of cognitive de-biasing strategies can avoid human errors. These strategies could involve use of checklists, strategies to cope with stress and fatigue and the use of standard operating procedures. A safety culture and health care model designed to promote patient safety can compliment this further. Incorporation of these strategies strengthens the defence layers against the "Swiss Cheese" models, which exist in the health care industry.

SEDSR: Soft Error Detection Using Software Redundancy

This paper presents a new method for soft error detection using software redundancy (SEDSR) that is able to detect transient faults. Soft errors damage the control flow and data of programs and designers usually use hardware-based solutions to handle them. Software-based techniques for soft error detection force less cost and delay to systems and do not change their configuration. Therefore, these kinds of methods are appropriate alternatives for hardware-based techniques. SEDSR has two separate parts for data and control flow errors detection. Fault injection method is used to compare SEDSR with previous methods of this field based on the new parameter of “Evaluation Factor” that takes in account fault coverage, memory and performance overheads. These parameters are important in real time safety critical applications. Experimental results on SPEC2000 and some traditional benchmarks of this field show that SEDSR is much better than previous methods of this field. SEDSR’s evaluation factor is about 50% better than other methods of this field. These results show its success in satisfaction of the existing tradeoff between fault coverage, performance and memory overheads.

利用星载激光雷达数据校正森林区DEM误差

在植被区,通过光学遥感或InSAR技术生产的DEM产品不能反映真实林下地形高度。森林区DEM误差主要是植被高引起的系统偏差,植被覆盖度和地形是森林区DEM的主要误差来源。新一代星载激光雷达可以提供大量高精度林下地形控制点产品,为森林区DEM误差的纠正提供了新的契机。鉴于此,文章提出基于机器学习框架下顾及植被覆盖及地形因素的林区DEM误差校正方法。首先,获取高精度星载激光雷达地形控制点与DEM的地形残差;其次,利用光学遥感数据、SAR遥感数据及DEM产品数据计算与植被覆盖和地形有关的特征参数;最后,联合这些特征参数与获取的地形残差点分别建立不同类型DEM产品误差校正模型。选取位于美国田纳西州和北卡罗来纳州交界处的山地林区作为本研究的实验区。研究结果表明,相对原始DEM,校正高程误差后的DEM精度提升超过40%,有效校正了林区DEM误差。