Classification of aviation incident causes using LGBM with improved cross-validation

Aviation accidents are currently one of the leading causes of significant injuries and deaths worldwide. This entices researchers to investigate aircraft safety using data analysis approaches based on an advanced machine learning algorithm.To assess aviation safety and identify the causes of incidents, a classification model with light gradient boosting machine (LGBM)based on the aviation safety reporting system (ASRS) has been developed. It is improved by k-fold cross-validation with hybrid sampling model (HSCV), which may boost classification performance and maintain data balance. The results show that employing the LGBM-HSCV model can significantly improve accuracy while alleviating data imbalance. Vertical comparison with other cross-validation (CV) methods and lateral comparison with different fold times comprise the comparative approach. Aside from the comparison, two further CV approaches based on the improved method in this study are discussed:one with a different sampling and folding order, and the other with more CV. According to the assessment indices with different methods, the LGBMHSCV model proposed here is effective at detecting incident causes. The improved model for imbalanced data categorization proposed may serve as a point of reference for similar data processing, and the model’s accurate identification of civil aviation incident causes can assist to improve civil aviation safety.

Robust U-type test for high dimensional regression coefficients using refitted cross-validation variance estimation

This paper aims to develop a new robust U-type test for high dimensional regression coefficients using the estimated U-statistic of order two and refitted cross-validation error variance estimation. It is proved that the limiting null distribution of the proposed new test is normal under two kinds of ordinary models.We further study the local power of the proposed test and compare with other competitive tests for high dimensional data. The idea of refitted cross-validation approach is utilized to reduce the bias of sample variance in the estimation of the test statistic. Our theoretical results indicate that the proposed test can have even more substantial power gain than the test by Zhong and Chen(2011) when testing a hypothesis with outlying observations and heavy tailed distributions. We assess the finite-sample performance of the proposed test by examining its size and power via Monte Carlo studies. We also illustrate the application of the proposed test by an empirical analysis of a real data example.

基于Cross-Validation的小波自适应去噪方法

小波去噪算法中,阈值的选择非常关键.提出一种自适应阈值选择算法.该算法先通过Cross-Validation方法将噪声干扰信号分成两个子信号,一个用于阈值处理,一个用作参考信号;再采用最深梯度法来寻求一个最优去噪阈值.仿真和实验结果表明:在均方误差意义上,所提算法去噪效果优于Donoho等提出的VisuShrink和SureShrink两种去噪算法,且不需要带噪信号的任何'先验信息',适应于实际信号去噪处理.

Cross-Validation, Shrinkage and Variable Selection in Linear Regression Revisited

In deriving a regression model analysts often have to use variable selection, despite of problems introduced by data- dependent model building. Resampling approaches are proposed to handle some of the critical issues. In order to assess and compare several strategies, we will conduct a simulation study with 15 predictors and a complex correlation structure in the linear regression model. Using sample sizes of 100 and 400 and estimates of the residual variance corresponding to R2 of 0.50 and 0.71, we consider 4 scenarios with varying amount of information. We also consider two examples with 24 and 13 predictors, respectively. We will discuss the value of cross-validation, shrinkage and backward elimination (BE) with varying significance level. We will assess whether 2-step approaches using global or parameterwise shrinkage (PWSF) can improve selected models and will compare results to models derived with the LASSO procedure. Beside of MSE we will use model sparsity and further criteria for model assessment. The amount of information in the data has an influence on the selected models and the comparison of the procedures. None of the approaches was best in all scenarios. The performance of backward elimination with a suitably chosen significance level was not worse compared to the LASSO and BE models selected were much sparser, an important advantage for interpretation and transportability. Compared to global shrinkage, PWSF had better performance. Provided that the amount of information is not too small, we conclude that BE followed by PWSF is a suitable approach when variable selection is a key part of data analysis.

ON THE CONSISTENCY OF CROSS-VALIDATIONIN NONLINEAR WAVELET REGRESSION ESTIMATION

For the nonparametric regression model Y-ni = g(x(ni)) + epsilon(ni)i = 1, ..., n, with regularly spaced nonrandom design, the authors study the behavior of the nonlinear wavelet estimator of g(x). When the threshold and truncation parameters are chosen by cross-validation on the everage squared error, strong consistency for the case of dyadic sample size and moment consistency for arbitrary sample size are established under some regular conditions.

Using Multiple Risk Factors and Generalized Linear Mixed Models with 5-Fold Cross-Validation Strategy for Optimal Carotid Plaque Progression Prediction

Background Cardiovascular diseases are closely linked to atherosclerotic plaque development and rupture.Plaque progression prediction is of fundamental significance to cardiovascular research and disease diagnosis,prevention,and treatment.Generalized linear mixed models(GLMM)is an extension of linear model for categorical responses while considering the correlation among observations.Methods Magnetic resonance image(MRI)data of carotid atheroscleroticplaques were acquired from 20 patients with consent obtained and 3D thin-layer models were constructed to calculate plaque stress and strain for plaque progression prediction.Data for ten morphological and biomechanical risk factors included wall thickness(WT),lipid percent(LP),minimum cap thickness(MinCT),plaque area(PA),plaque burden(PB),lumen area(LA),maximum plaque wall stress(MPWS),maximum plaque wall strain(MPWSn),average plaque wall stress(APWS),and average plaque wall strain(APWSn)were extracted from all slices for analysis.Wall thickness increase(WTI),plaque burden increase(PBI)and plaque area increase(PAI) were chosen as three measures for plaque progression.Generalized linear mixed models(GLMM)with 5-fold cross-validation strategy were used to calculate prediction accuracy for each predictor and identify optimal predictor with the highest prediction accuracy defined as sum of sensitivity and specificity.All 201 MRI slices were randomly divided into 4 training subgroups and 1 verification subgroup.The training subgroups were used for model fitting,and the verification subgroup was used to estimate the model.All combinations(total1023)of 10 risk factors were feed to GLMM and the prediction accuracy of each predictor were selected from the point on the ROC(receiver operating characteristic)curve with the highest sum of specificity and sensitivity.Results LA was the best single predictor for PBI with the highest prediction accuracy(1.360 1),and the area under of the ROC curve(AUC)is0.654 0,followed by APWSn(1.336 3)with AUC=0.6342.The optimal predictor among all possible combinations for PBI was the combination of LA,PA,LP,WT,MPWS and MPWSn with prediction accuracy=1.414 6(AUC=0.715 8).LA was once again the best single predictor for PAI with the highest prediction accuracy(1.184 6)with AUC=0.606 4,followed by MPWSn(1. 183 2)with AUC=0.6084.The combination of PA,PB,WT,MPWS,MPWSn and APWSn gave the best prediction accuracy(1.302 5)for PAI,and the AUC value is 0.6657.PA was the best single predictor for WTI with highest prediction accuracy(1.288 7)with AUC=0.641 5,followed by WT(1.254 0),with AUC=0.6097.The combination of PA,PB,WT,LP,MinCT,MPWS and MPWS was the best predictor for WTI with prediction accuracy as 1.314 0,with AUC=0.6552.This indicated that PBI was a more predictable measure than WTI and PAI. The combinational predictors improved prediction accuracy by 9.95%,4.01%and 1.96%over the best single predictors for PAI,PBI and WTI(AUC values improved by9.78%,9.45%,and 2.14%),respectively.Conclusions The use of GLMM with 5-fold cross-validation strategy combining both morphological and biomechanical risk factors could potentially improve the accuracy of carotid plaque progression prediction.This study suggests that a linear combination of multiple predictors can provide potential improvement to existing plaque assessment schemes.

一种靶弹多功能控制器设计与实现

靶弹是模拟敌方来袭导弹技战术特性的实体空中靶标;针对退役导弹改装靶弹中面临的改装线路复杂、对动目标供靶精度不足等实际问题,对靶弹飞行控制规律和改装设计方法进行了研究探索,创新了靶弹制导控制方法,综合采用靶弹飞行控制回路状态转换、动目标供靶航路精确控制、靶弹功能扩展等关键技术,设计研制了集电气改装、接口转换、目标导引、安全控制等功能于一体的靶弹多功能控制器,实现了退役飞航导弹到多用途靶弹的通用化、标准化改装,探索了一种经济而有效的靶弹开发方法;试验结果表明,该多功能控制器具备简便可靠、工作量小、通用性好、弹道控制灵活等突出特点,较好地解决了靶弹改装难题,满足了防空武器系统试验训练的需要。

替代法测量改装表内阻的误差分析

本文旨在探讨电表改装实验中,用替代法测量表头内阻时,电源电压的取值对改装表内阻测量的影响。通过理论计算和实验探究的方法,发现较大电源电压导致测量误差增加的本质原因是保护电阻在整个电路的电阻中占比太高。本文的研究结果为提高替代法测量电阻的精度提供改进方向。

Multi-environment BSA-seq using large F3 populations is able to achieve reliable QTL mapping with high power and resolution: An experimental demonstration in rice

Bulked-segregant analysis by deep sequencing(BSA-seq) is a widely used method for mapping QTL(quantitative trait loci) due to its simplicity, speed, cost-effectiveness, and efficiency. However, the ability of BSA-seq to detect QTL is often limited by inappropriate experimental designs, as evidenced by numerous practical studies. Most BSA-seq studies have utilized small to medium-sized populations, with F2populations being the most common choice. Nevertheless, theoretical studies have shown that using a large population with an appropriate pool size can significantly enhance the power and resolution of QTL detection in BSA-seq, with F_(3)populations offering notable advantages over F2populations. To provide an experimental demonstration, we tested the power of BSA-seq to identify QTL controlling days from sowing to heading(DTH) in a 7200-plant rice F_(3)population in two environments, with a pool size of approximately 500. Each experiment identified 34 QTL, an order of magnitude greater than reported in most BSA-seq experiments, of which 23 were detected in both experiments, with 17 of these located near41 previously reported QTL and eight cloned genes known to control DTH in rice. These results indicate that QTL mapping by BSA-seq in large F_(3)populations and multi-environment experiments can achieve high power, resolution, and reliability.

Height-diameter models for King Boris fir(Abies borisii regis Mattf.) and Scots pine(Pinus sylvestris L.) in Olympus and Pieria Mountains, Greece

In forest science and practice, the total tree height is one of the basic morphometric attributes at the tree level and it has been closely linked with important stand attributes. In the current research, sixteen nonlinear functions for height prediction were tested in terms of their fitting ability against samples of Abies borisii regis and Pinus sylvestris trees from mountainous forests in central Greece. The fitting procedure was based on generalized nonlinear weighted regression. At the final stage, a five-quantile nonlinear height-diameter model was developed for both species through a quantile regression approach, to estimate the entire conditional distribution of tree height, enabling the evaluation of the diameter impact at various quantiles and providing a comprehensive understanding of the proposed relationship across the distribution. The results clearly showed that employing the diameter as the sole independent variable, the 3-parameter Hossfeld function and the 2-parameter N?slund function managed to explain approximately 84.0% and 81.7% of the total height variance in the case of King Boris fir and Scots pine species, respectively. Furthermore, the models exhibited low levels of error in both cases(2.310m for the fir and 3.004m for the pine), yielding unbiased predictions for both fir(-0.002m) and pine(-0.004m). Notably, all the required assumptions for homogeneity and normality of the associated residuals were achieved through the weighting procedure, while the quantile regression approach provided additional insights into the height-diameter allometry of the specific species. The proposed models can turn into valuable tools for operational forest management planning, particularly for wood production and conservation of mountainous forest ecosystems.

Adaptive Random Effects/Coefficients Modeling

Adaptive fractional polynomial modeling of general correlated outcomes is formulated to address nonlinearity in means, variances/dispersions, and correlations. Means and variances/dispersions are modeled using generalized linear models in fixed effects/coefficients. Correlations are modeled using random effects/coefficients. Nonlinearity is addressed using power transforms of primary (untransformed) predictors. Parameter estimation is based on extended linear mixed modeling generalizing both generalized estimating equations and linear mixed modeling. Models are evaluated using likelihood cross-validation (LCV) scores and are generated adaptively using a heuristic search controlled by LCV scores. Cases covered include linear, Poisson, logistic, exponential, and discrete regression of correlated continuous, count/rate, dichotomous, positive continuous, and discrete numeric outcomes treated as normally, Poisson, Bernoulli, exponentially, and discrete numerically distributed, respectively. Example analyses are also generated for these five cases to compare adaptive random effects/coefficients modeling of correlated outcomes to previously developed adaptive modeling based on directly specified covariance structures. Adaptive random effects/coefficients modeling substantially outperforms direct covariance modeling in the linear, exponential, and discrete regression example analyses. It generates equivalent results in the logistic regression example analyses and it is substantially outperformed in the Poisson regression case. Random effects/coefficients modeling of correlated outcomes can provide substantial improvements in model selection compared to directly specified covariance modeling. However, directly specified covariance modeling can generate competitive or substantially better results in some cases while usually requiring less computation time.

基于Unity3D的电流表改装虚拟实验系统

为了帮助线上物理实验课程的开展,同时进一步实现实验操作的上机考试,本文在Unity3D平台上设计开发了“电表改装”的虚拟仿真实验软件,使用软件可以进行电路连线和实际所有可控器件的调节,实现了实验过程的完全仿真,虚拟仪器在外观、操作步骤和精度上与教学使用的实验仪器一致,同时虚拟仪器配有简洁的UI界面,能够帮助学生在电脑上进行实验原理和实验过程的预习和复习.本文详述了虚拟仪器的设计方法和参数的选择依据.

OPT-BAG Model for Predicting Student Employability

The use of machine learning to predict student employability is important in order to analyse a student’s capability to get a job.Based on the results of this type of analysis,university managers can improve the employability of their students,which can help in attracting students in the future.In addition,learners can focus on the essential skills identified through this analysis during their studies,to increase their employability.An effectivemethod calledOPT-BAG(OPTimisation of BAGging classifiers)was therefore developed to model the problem of predicting the employability of students.This model can help predict the employability of students based on their competencies and can reveal weaknesses that need to be improved.First,we analyse the relationships between several variables and the outcome variable using a correlation heatmap for a student employability dataset.Next,a standard scaler function is applied in the preprocessing module to normalise the variables in the student employability dataset.The training set is then input to our model to identify the optimal parameters for the bagging classifier using a grid search cross-validation technique.Finally,the OPT-BAG model,based on a bagging classifier with optimal parameters found in the previous step,is trained on the training dataset to predict student employability.The empirical outcomes in terms of accuracy,precision,recall,and F1 indicate that the OPT-BAG approach outperforms other cutting-edge machine learning models in terms of predicting student employability.In this study,we also analyse the factors affecting the recruitment process of employers,and find that general appearance,mental alertness,and communication skills are the most important.This indicates that educational institutions should focus on these factors during the learning process to improve student employability.

SCADA Data-Based Support Vector Machine for False Alarm Identification for Wind Turbine Management

Maintenance operations have a critical influence on power gen-eration by wind turbines(WT).Advanced algorithms must analyze large volume of data from condition monitoring systems(CMS)to determine the actual working conditions and avoid false alarms.This paper proposes different support vector machine(SVM)algorithms for the prediction and detection of false alarms.K-Fold cross-validation(CV)is applied to evaluate the classification reliability of these algorithms.Supervisory Control and Data Acquisition(SCADA)data from an operating WT are applied to test the proposed approach.The results from the quadratic SVM showed an accuracy rate of 98.6%.Misclassifications from the confusion matrix,alarm log and maintenance records are analyzed to obtain quantitative information and determine if it is a false alarm.The classifier reduces the number of false alarms called misclassifications by 25%.These results demonstrate that the proposed approach presents high reliability and accuracy in false alarm identification.

Functional magnetic resonance imaging study of group independent components underpinning item responses to paranoid-depressive scale

BACKGROUND Our study expand upon a large body of evidence in the field of neuropsychiatric imaging with cognitive,affective and behavioral tasks,adapted for the functional magnetic resonance imaging(MRI)(fMRI)experimental environment.There is sufficient evidence that common networks underpin activations in task-based fMRI across different mental disorders.AIM To investigate whether there exist specific neural circuits which underpin differ-ential item responses to depressive,paranoid and neutral items(DN)in patients respectively with schizophrenia(SCZ)and major depressive disorder(MDD).METHODS 60 patients were recruited with SCZ and MDD.All patients have been scanned on 3T magnetic resonance tomography platform with functional MRI paradigm,comprised of block design,including blocks with items from diagnostic paranoid(DP),depression specific(DS)and DN from general interest scale.We performed a two-sample t-test between the two groups-SCZ patients and depressive patients.Our purpose was to observe different brain networks which were activated during a specific condition of the task,respectively DS,DP,DN.RESULTS Several significant results are demonstrated in the comparison between SCZ and depressive groups while performing this task.We identified one component that is task-related and independent of condition(shared between all three conditions),composed by regions within the temporal(right superior and middle temporal gyri),frontal(left middle and inferior frontal gyri)and limbic/salience system(right anterior insula).Another com-ponent is related to both diagnostic specific conditions(DS and DP)e.g.It is shared between DEP and SCZ,and includes frontal motor/language and parietal areas.One specific component is modulated preferentially by to the DP condition,and is related mainly to prefrontal regions,whereas other two components are significantly modulated with the DS condition and include clusters within the default mode network such as posterior cingulate and precuneus,several occipital areas,including lingual and fusiform gyrus,as well as parahippocampal gyrus.Finally,component 12 appeared to be unique for the neutral condition.In addition,there have been determined circuits across components,which are either common,or distinct in the preferential processing of the sub-scales of the task.CONCLUSION This study has delivers further evidence in support of the model of trans-disciplinary cross-validation in psychiatry.

改造起重船临时锚泊允许海况评估

对长大海升号起重船进行改造,以提高其海上风电施工能力。为确定改造后起重船可临时锚泊的允许海况,介绍一种基于时域模拟和概率统计的海上作业允许海况评估方法,并将其应用至改造后的起重船临时锚泊作业中。选取合适的波浪种子数,采用时域耦合分析方法对锚泊起重船的运动、锚链张力和卧底锚链长度进行计算,提取时域响应最值,采用经验分布拟合方法评估指定不超越概率下每种海况的极值响应,并与规范规定的安全准则进行比较,从而获得以有义波高和谱峰周期描述的可作业允许海况。结果表明20个波浪种子是足够的,得到的允许海况可为临时锚泊作业决策提供参考。

民用客机运送伤病员安置器材的研制与应用

目的:针对目前国内民用飞机运送伤病员机上安置存在的问题,研制一种加装于民用客机的伤病员安置器材。方法:伤病员安置器材主要包括双层型和单元护理型2种类型,主要由铺架总成、支撑架总成、导轨锁总成等组成,主体材料均为航空铝材。该器材通过民用客机客舱内用于安装旅客座椅的地面导轨与飞机实现连接固定,无需破坏飞机结构和舱内设施,只需拆除客舱原有的部分座椅即可安装。通过抗冲击、抗振动、阻燃试验以及实装实飞验证等方法验证该器材在实际作业环境使用的可靠性。结果:该器材能够承受向前9.0g、向下8.6g、向上5.4g和侧向4.0g的极限惯性载荷,且抗振动及非金属材料阻燃性均满足适航规定的要求。经多机型实装实飞及实际伤病员接运验证,该器材保障效果良好。结论:该器材安装使用操作便捷、通用化程度高、安全可靠性强,为战时和应急情况下利用民用航空运力实施伤病员批量运送提供了技术手段。

泥河湾盆地南山根地点发掘简报

南山根地点位于河北省张家口市阳原县大田洼乡岑家湾村西南,考古文化层埋藏于泥河湾湖相层中,地质年代为早更新世。2017年5~7月,河北师范大学考古队对该地点进行了抢救性发掘,在面积为22 m2的范围内共出土编号遗物519件,主要为石制品,出土化石17件。石制品类型包括石核、石片、断块、碎屑、石器等。石制品原料可能为就地取材,取自附近基岩及河滩砾石;剥片方法主要为锤击法;石制品整体形制较小,属于石片石器工业;石制品类型以石片、断块和碎屑居多;共发现25个拼合组,包括57件可拼合石制品,应为原地埋藏。通过与邻近的岑家湾遗址地层对比,南山根地点文化层与岑家湾文化层处于同一自然堆积层内,但较岑家湾遗址文化层稍高,据此推断其年代可能接近并略晚于岑家湾遗址(1.1 MaBP)。

An Adaptive Approach for Hazard Regression Modeling

Regression models for survival time data involve estimation of the hazard rate as a function of predictor variables and associated slope parameters. An adaptive approach is formulated for such hazard regression modeling. The hazard rate is modeled using fractional polynomials, that is, linear combinations of products of power transforms of time together with other available predictors. These fractional polynomial models are restricted to generating positive-valued hazard rates and decreasing survival times. Exponentially distributed survival times are a special case. Parameters are estimated using maximum likelihood estimation allowing for right censored survival times. Models are evaluated and compared using likelihood cross-validation (LCV) scores. LCV scores and tolerance parameters are used to control an adaptive search through alternative fractional polynomial hazard rate models to identify effective models for the underlying survival time data. These methods are demonstrated using two different survival time data sets including survival times for lung cancer patients and for multiple myeloma patients. For the lung cancer data, the hazard rate depends distinctly on time. However, controlling for cell type provides a distinct improvement while the hazard rate depends only on cell type and no longer on time. Furthermore, Cox regression is unable to identify a cell type effect. For the multiple myeloma data, the hazard rate also depends distinctly on time. Moreover, consideration of hemoglobin at diagnosis provides a distinct improvement, the hazard rate still depends distinctly on time, and hemoglobin distinctly moderates the effect of time on the hazard rate. These results indicate that adaptive hazard rate modeling can provide unique insights into survival time data.

飞机加油车加装电子附属设备对其安全产生的影响

现有的加油车并没有针对航空油料作业形式进行本地化特征化改装,需要航空油料企业进行后续改装,但是其改装存在部分问题,改装未标准化、改装未模块化、改装后缺少安全检测的问题。同时因为地域性限制导致部分飞机加油特种车品牌车型不同,致使每次改装的车辆及线路不确定,就造成改装的随意性,同时加装的附属设备存在改装深度大,耗电量大,工程量大等特点且后续没有相应的安全检测,极易造成加装的附属设备失控造成安全事故的发生。