基于任务分析的飞行机组失误预测研究

在过去的20年内有多起严重事故由机组失误引起,采取措施防止或减少飞行机组失误成为降低航空事故率的关键。通过使用人误预测技术预测机组在执行飞行任务时的失误,寻找失误原因,进一步改进驾驶舱设计或增加培训来降低航空事故率是可行的方法。本研究以民航领域飞行安全需求为背景,在原有人误预测技术基础上,开发了基于任务分析的飞行机组失误预测模型,以某型飞机起飞任务为例,对飞行机组在操纵飞机起飞过程中可能发生的人误进行分析,实现了飞行机组失误的定性和定量预测。

视觉SLAM中基于误匹配风险预测的特征选择

针对视觉SLAM(同时定位与建图)问题,提出了一种预测误匹配风险最小化的特征选择方法.该方法采用预测误匹配风险来衡量新检测到的特征对未来特征匹配过程的影响,然后采用多级排序的方法优先选择误匹配风险小且重现率高的候选特征进行初始化.该方法能根据系统状态估计不确定度的强弱自适应地选择不易被误匹配的特征,从而保证了SLAM算法的收敛性和一致性.在实际单目视觉SLAM系统上的对比实验表明,本文方法在降低特征误匹配率和保证SLAM结果的正确性等方面具有明显优势.

人误率预测技术方法的几个问题及应用时的建议

本文首先提出ＴＨＥＲＰ方法几个值得注意的问题：人误概率遵从对数正态分布假定的欠合理性、ＴＨＥＲＰ方法中设计出的条件人误概率不确定性边界的计算缺乏连贯性等．针对这些问题，提出了修正建议——假定人误概率Ｐ遵从贝塔分布，并阐述了这一建议的理由．但为了尽量保持ＴＨＥＲＰ方法定量分析的体系及便于计算。

超声测量胃液量在急诊全麻患者拔管后发生呕吐误吸的预测价值

目的:运用超声扫描方式对胃窦部横截面积进行测量,并计算出胃液量,以预测呕吐误吸的发生情况。方法:收集急诊手术患者200例作为研究对象,取患者平卧位,利用超声系统,按照体内标志物(肝左叶与腹主动脉),旋转探头获得胃窦图像,通过超声图像测量胃窦部最大直径(D1与D2),计算出胃窦部横截面积,参考Bouvet回归方程进行胃液量的计算。将200例急诊手术患者分成三组,拔管前胃窦部横截面积小于340mm^2者70例为A组,再将拔管前胃窦部横截面积在340mm^2及以上者例为B、C组各65例,其中B组给予常规清醒拔管处理,C组将胃管放置吸引至胃窦部横截面积小于340mm^2后将胃管拔除,清醒后拔管。运用超声扫描方式得到胃窦部横截面积,计算出胃液量后预测呕吐误吸发生率。结果:急诊手术麻醉拔管后测量患者胃窦部横截面积与胃液体积之间为正相关,胃窦部CSA可作为判断围术期呕吐误吸发生率的判断。运用超声方式测量胃窦部横截面积是可行的。结论:运用超声方式测量胃窦部横截面积,并对胃液量进行计算,可用于对呕吐误吸情况的预测之中,有望在以后的急诊手术中用于呕吐误吸情况的预测。

一种精确的分支预测微处理器模型

在当今深流水宽发射的微处理器中 ,为实现高性能 ,精确的分支预测是不可缺少的关键技术 分支预测失效将浪费大量的时钟周期 ,无法发挥乱序执行的效能 宽发射微处理器的有效性能同时还依赖指令窗口的大小和指令预取宽度 提出了一种新的更精确的支持分支预测和分支误预测周期损失的微处理器模型 根据指令的执行带宽为指令窗口中可用指令数的平方根统计规律 ,给出了一个更为精确的描述微处理器取指带宽、分支预测精度、分支误预测周期损失、指令窗口大小和IPC之间关系的算法 ,并讨论了这些参数的综合权衡以及这些参数对程序IPC的影响

高性能代价比的两层关联间接转移预测器设计

随着面向对象语言程序、动态链接库(DLL)等的普遍应用,间接转移指令的使用越来越频繁.两层关联间接转移预测器预测准确度高,但实现硬件代价较高,因此并不实用.文中深入分析了两层关联间接转移预测器中产生误预测的原因,通过改进索引方法、压缩存储等实用方法减小硬件实现代价.实验结果表明,通过这些方法的改进,在133K比特硬件存储代价下,使用一组SPEC CPU2000测试程序进行评估,间接转移误预测率为9.6%,仅比两层关联预测器理想误预测率高2.3%,而4路组相联BTB预测器的误预测率为31%.

空管人因失效概率的THERP-CREAM预测方法

管制员的人为差错是导致航空器空中相撞的主要原因之一,对空管人因失效概率进行有效预测是一个难题。为此,提出一种基于人误率预测技术(THERP)与认知可靠性和失误率分析模型(CREAM)的组合空管人因失效概率预测方法,基于THERP技术二叉树模型构建事故发展基本脉络,而后基于CREAM方法对各个人误行为触发概率进行修正,从而得出更为准确的概率预测结果。通过对相似事故进行对比分析,结果表明:本文提出的预测方法可识别关键性的事故影响因子,以便制定有针对性的管控措施,从而为研究预测空管人因失效概率提供了一种有效的分析手段。

通过地铁突发事件的行车调度系统人误预防分析

本文主要研究当前基于地铁突发事件的行车调度系统人误预防相关问题,随着国家高技术研究发展计划的逐渐落实,对预防地铁行车调度系统人误预测的研究不断深入,通过对人误行为风险和人误诱发因素风险进行预测研究能够有效提升地铁突发事件的管理质量和效率,保障人们乘坐地铁出行的便捷与安全。

基于HTA方法的飞行机组任务分析

人误预测首先需要对任务进行分析,将任务进行层次化分解,使分析者理解任务并识别可能导致人误的重要操作。根据飞行机组任务特点,确立了飞行任务中止原则,建立了基于HTA的飞行机组任务分析流程,提出关键步骤的选取准则。通过对A320飞机起飞任务进行案例分析,识别出对安全性有重大影响的操作任务,为改进驾驶舱设计及飞行机组培训提供支持。

FLIGHT DELAY STATE-SPACE MODEL BASED ON GENETIC EM ALGORITHM

Flight delay prediction remains an important research topic due to dynamic nature in flight operation and numerous delay factors.Dynamic data-driven application system in the control area can provide a solution to this problem.However,in order to apply the approach,a state-space flight delay model needs to be established to represent the relationship among system states,as well as the relationship between system states and input/output variables.Based on the analysis of delay event sequence in a single flight,a state-space mixture model is established and input variables in the model are studied.Case study is also carried out on historical flight delay data.In addition,the genetic expectation-maximization（EM）algorithm is used to obtain the global optimal estimates of parameters in the mixture model,and results fit the historical data.At last,the model is validated in Kolmogorov-Smirnov tests.Results show that the model has reasonable goodness of fitting the data,and the search performance of traditional EM algorithm can be improved by using the genetic algorithm.

空中交通管制员可靠性认知模型的应用

阐述了人的认知模型中几种重要的、与人的可靠性模型相关的概念,描述了通用失误模型系统(GEMS)及其在空中交通管制事故分析中的应用,并指出了该GEMS模型在管制员失误预测中的作用,对几个管制案例作了具体的理论分析.

The constructing of pore structure factor in carbonate rocks and the inversion of reservoir parameters

With a more complex pore structure system compared with clastic rocks, carbonate rocks have not yet been well described by existing conventional rock physical models concerning the pore structure vagary as well as the influence on elastic rock properties. We start with a discussion and an analysis about carbonate rock pore structure utilizing rock slices. Then, given appropriate assumptions, we introduce a new approach to modeling carbonate rocks and construct a pore structure algorithm to identify pore structure mutation with a basis on the Gassmann equation and the Eshelby-Walsh ellipsoid inclusion crack theory. Finally, we compute a single well＇s porosity using this new approach with full wave log data and make a comparison with the predicted result of traditional method and simultaneously invert for reservoir parameters. The study results reveal that the rock pore structure can significantly influence the rocks＇ elastic properties and the predicted porosity error of the new modeling approach is merely 0.74%. Therefore, the approach we introduce can effectively decrease the predicted error of reservoir parameters.

学生锻炼标准的力量项目评价方法探讨

对目前国家体育锻炼标准中单杠引体向上和双杠双臂屈伸的评分标准提出了修改建议。用实验研究方法,提出了应以体重分级,按学生拉引次数进行评分。

Assimilation of High Frequency Radar Data into a Shelf Sea Circulation Model

High Frequency(HF) radar current data is assimilated into a shelf sea circulation model based on optimal interpolation(OI) method. The purpose of this work is to develop a real-time computationally highly efficient assimilation method to improve the forecast of shelf current. Since the true state of the ocean is not known, the specification of background error covariance is arduous. Usually, it is assumed or calculated from an ensemble of model states and is kept in constant. In our method, the spatial covariances of model forecast errors are derived from differences between the adjacent model forecast fields, which serve as the forecast tendencies. The assumption behind this is that forecast errors can resemble forecast tendencies, since variances are large when fields change quickly and small when fields change slowly. The implementation of HF radar data assimilation is found to yield good information for analyses. After assimilation, the root-mean-square error of model decreases significantly. Besides, three assimilation runs with variational observation density are implemented. The comparison of them indicates that the pattern described by observations is much more important than the amount of observations. It is more useful to expand the scope of observations than to increase the spatial interval. From our tests, the spatial interval of observation can be 5 times bigger than that of model grid.

Adaptive multiple subtraction using a constrained L1-norm method with lateral continuity

The Lt-norm method is one of the widely used matching filters for adaptive multiple subtraction. When the primaries and multiples are mixed together, the L1-norm method might damage the primaries, leading to poor lateral continuity. In this paper, we propose a constrained L1-norm method for adaptive multiple subtraction by introducing the lateral continuity constraint for the estimated primaries. We measure the lateral continuity using prediction-error filters （PEF）. We illustrate our method with the synthetic Pluto dataset. The results show that the constrained L1-norm method can simultaneously attenuate the multiples and preserve the primaries.

Energy-absorption forecast of thin-walled structure by GA-BP hybrid algorithm

In order to analyze the influence rule of experimental parameters on the energy-absorption characteristics and effectively forecast energy-absorption characteristic of thin-walled structure, the forecast model of GA-BP hybrid algorithm was presented by uniting respective applicability of back-propagation artificial neural network （BP-ANN） and genetic algorithm （GA）. The detailed process was as follows. Firstly, the GA trained the best weights and thresholds as the initial values of BP-ANN to initialize the neural network. Then, the BP-ANN after initialization was trained until the errors converged to the required precision. Finally, the network model, which met the requirements after being examined by the test samples, was applied to energy-absorption forecast of thin-walled cylindrical structure impacting. After example analysis, the GA-BP network model was trained until getting the desired network error only by 46 steps, while the single BP-ANN model achieved the same network error by 992 steps, which obviously shows that the GA-BP hybrid algorithm has faster convergence rate. The average relative forecast error （ARE） of the SEA predictive results obtained by GA-BP hybrid algorithm is 1.543%, while the ARE of the SEA predictive results obtained by BP-ANN is 2.950%, which clearly indicates that the forecast precision of the GA-BP hybrid algorithm is higher than that of the BP-ANN.

ReTweeting Analysis and Prediction in Microblogs: An Epidemic Inspired Approach

Microblogs currently play an important role in social communication. Hot topics currently being tweeted can quickly become popular within a very short time as a result of retweeting. Gaining an understanding of the retweeting behavior is desirable for a number of tasks such as topic detection, personalized message recommendation, and fake information monitoring and prevention. Interestingly, the propagation of tweets bears some similarity to the spread of infectious diseases. We present a method to model the tweets' spread behavior in microblogs based on the classic Susceptible-Infectious-Susceptible (SIS) epidemic model that was developed in the medical field for the spread of infectious diseases. On the basis of this model, future retweeting trends can be predicted. Our experiments on data obtained from the Chinese micro-blogging website Sina Weibo show that the proposed model has lower predictive error compared to the four commonly used prediction methods.

Bias-Corrected Short-Range Ensemble Forecasts for Near-Surface Variables during the Summer Season of 2010 in Northern China

A running mean bias （RMB） correction ap- proach was applied to the forecasts of near-surface variables in a seasonal short-range ensemble forecasting experiment with 57 consecutive cases during summer 2010 in the northern China region. To determine a proper training window length for calculating RMB, window lengths from 2 to 20 days were evaluated, and 16 days was taken as an optimal window length, since it receives most of the benefit from extending the window length. The raw and 16-day RMB corrected ensembles were then evaluated for their ensemble mean forecast skills. The results show that the raw ensemble has obvious bias in all near-surface variables. The RMB correction can remove the bias reasonably well, and generate an unbiased ensemble. The bias correction not only reduces the ensemble mean forecast error, but also results in a better spreaderror relationship. Moreover, two methods for computing calibrated probabilistic forecast （PF） were also evaluated through the 57 case dates： 1） using the relative frequency from the RMB-eorrected ensemble; 2） computing the forecasting probabilities based on a historical rank histogram. The first method outperforms the second one, as it can improve both the reliability and the resolution of the PFs, while the second method only has a small effect on the reliability, indicating the necessity and importance of removing the systematic errors from the ensemble.

Prediction of XRF analyzers error for elements on-line assaying using Kalman Filter

Determination of chemical elements assay plays an important role in mineral processing operations.This factor is used to control process accuracy,recovery calculation and plant profitability.The new assaying methods including chemical methods,X-ray fluorescence and atomic absorption spectrometry are advanced and accurate.However,in some applications,such as on-line assaying process,high accuracy is required.In this paper,an algorithm based on Kalman Filter is presented to predict on-line XRF errors.This research has been carried out on the basis of based the industrial real data collection for evaluating the performance of the presented algorithm.The measurements and analysis for this study were conducted at the Sarcheshmeh Copper Concentrator Plant located in Iran.The quality of the obtained results was very satisfied;so that the RMS errors of prediction obtained for Cu and Mo grade assaying errors in rougher feed were less than 0.039 and 0.002 and in final flotation concentration less than 0.58 and 0.074,respectively.The results indicate that the mentioned method is quite accurate to reduce the on-line XRF errors measurement.

A Potential High-Score Scheme for the Prediction of Atlantic Storm Activity

A new empirical approach for the seasonal prediction of annual Atlantic tropical storm number (ATSN) was developed using precipitation and 500 hPa geopotential height data from the preceding January February and April May.The 2.5°×2.5° resolution reanalysis data from both the US National Center for Environmental Prediction/the National Center for Atmospheric Research (NCEP/NCAR) and the European Center for Medium-Range Weather Forecasting (ECMWF) were applied.The model was cross-validated using data from 1979 2002.The ATSN predictions from the two reanalysis models were correlated with the observations with the anomaly correlation coefficients (ACC) of 0.79 (NCEP/NCAR) and 0.78 (ECMWF) and the multi-year mean absolute prediction errors (MAE) of 1.85 and 1.76,respectively.When the predictions of the two models were averaged,the ACC increased to 0.90 and the MAE decreased to 1.18,an exceptionally high score.Therefore,this new empirical approach has the potential to improve the operational prediction of the annual tropical Atlantic storm frequency.