A Method of Applying Flight Data to Evaluate Landing Operation Performance

Abstract

Pilots’ operation has an important effect on flight safety and performance, particularly in the final landing stage when pilots need to deal with complicated operations. This study aims to determine the potential value of flight data and develop a method of evaluating a pilot’s performance during landing phase based on flight quick access recorder (QAR) data from the perspective of risk assessment. First, a Landing Operation Performance Evaluation Model was developed based on risk evaluation principles. Three landing parameters, which are touchdown distance, touchdown vertical acceleration and touchdown pitch angle, were selected as indicators to evaluate the pilots’ landing operation performance in this model. Second, the flight landing operation performance evaluation system (FLOPES) was set up based on the evaluation model. Test results showed that FLOPES can accomplish all calculation flow of operation performance evaluation. Finally, it concluded that this method is a more accurate and effective way for evaluating the landing operation performance of a flight. It could be as a practical tool for airlines to manage landing risk quantitatively and to provide a more practical support for improving training and design in aviation.Practitioner summary: This study aims to determine the potential value of flight data and to develop a method of evaluating pilot’s landing operation performance from the risk evaluation perspective. Test results showed that this method is effective and could be as a practical tool for airlines to manage landing risk and improve training.Abbreviations:QAR: Quick Access Recorder; FLOPES: Flight Landing Operation Performance Evaluation System; ICAO: International Civil Aviation Organization; IATA: International Air Transport Association; SMS: Safety Management System; CAAC: Civil Aviation Administration of China; FOQA: Flight Operations Quality Assurance; VBA: Visual Basic for Applications     

金沙江水电工程智能建造技术体系研究与实践

运用"全面感知、真实分析、实时控制"的智能闭环控制理论,提出了以工程建设资源要素动态精准管理、业务流程数字化管理、工艺过程智能控制、实物成本精确分析、结构安全与进度耦合分析及联动调控的数字化技术和智能化技术为核心,以水电工程数据模型如大坝全景信息模型DIM为基础,以智能建造管理平台iDam为主体的智能建造技术体系,并对工程数据结构分解与编码体系、工程数据感知传输共享技术、iDam平台的系统架构与业务架构、智能建造工程绩效等关键技术进行了分析。通过统一规范的工程数据结构与编码体系,iDam平台可集成工程建设全过程的基础数据、环境数据、过程数据和监测数据,为各业务模块服务;建立的基于工程技术和管理大数据的机理分析功能,可为工程建设的业主、施工、监理、设计、科研及运行等单位服务。智能建造初步实践表明,本文构建的技术体系是科学可行的,对基础设施智能建造技术与管理体系的研究与实践具有参考价值。     

Two-level intelligent modeling method for the rate of penetration in complex geological drilling process

The rate of penetration (ROP) model is of great importance in achieving a high efficiency in the complex geological drilling process. In this paper, a novel two-level intelligent modeling method is proposed for the ROP considering the drilling characteristics of data incompleteness, couplings, and strong nonlinearities. Firstly, a piecewise cubic Hermite interpolation method is introduced to complete the lost drilling data. Then, a formation drillability (FD) fusion submodel is established by using Nadaboost extreme learning machine (Nadaboost-ELM) algorithm, and the mutual information method is used to obtain the parameters, strongly correlated with the ROP. Finally, a ROP submodel is established by a neural network with radial basis function optimized by the improved particle swarm optimization (RBFNN-IPSO). This two-level ROP model is applied to a real drilling process and the proposed method shows the best performance in ROP prediction as compared with conventional methods. The proposed ROP model provides the basis for intelligent optimization and control in the complex geological drilling process.     

An integrated approach for aircraft turbofan engine fault detection based on data mining techniques

The present study proposes an algorithm for fault detection in terms of condition‐based maintenance with data mining techniques. The proposed algorithm is applied on an aircraft turbofan engine using flight data and consists of two main sections. In the first section, the relationship between engine exhaust gas temperature (EGT) as the main engine health monitoring criterion and other operational and environmental parameters of the engine was modelled using the data‐driven models. In the second section, a data set including EGT residuals, that is, the difference between the actual EGT of the system and the EGT estimated by the developed model in the health conditions of the engine, was created. Finally, faults occurring in each flight were detected based on the identification of abnormal events by a one‐class support vector machine trained by the health condition EGT residual data set. The results indicated that the proposed algorithm was an effective approach for inspecting aircraft engine conditions and detecting faults, with no need for technical knowledge on the interior characteristics of the aircraft engine.     

Transport Map sampling with PGD model reduction for fast dynamical Bayesian data assimilation

The motivation of this work is to address real-time sequential inference of parameters with a full Bayesian formulation. First, the proper generalized decomposition (PGD) is used to reduce the computational evaluation of the posterior density in the online phase. Second, Transport Map sampling is used to build a deterministic coupling between a reference measure and the posterior measure. The determination of the transport maps involves the solution of a minimization problem. As the PGD model is quasi-analytical and under a variable separation form, the use of gradient and Hessian information speeds up the minimization algorithm. Eventually, uncertainty quantification on outputs of interest of the model can be easily performed due to the global feature of the PGD solution over all coordinate domains. Numerical examples highlight the performance of the method.     

Optimal data-based binning for histograms and histogram-based probability density models

Histograms are convenient non-parametric density estimators, which continue to be used ubiquitously. Summary quantities estimated from histogram-based probability density models depend on the choice of the number of bins. We introduce a straightforward data-based method of determining the optimal number of bins in a uniform bin-width histogram. By assigning a multinomial likelihood and a non-informative prior, we derive the posterior probability for the number of bins in a piecewise-constant density model given the data. In addition, we estimate the mean and standard deviations of the resulting bin heights, examine the effects of small sample sizes and digitized data, and demonstrate the application to multi-dimensional histograms.     

基于改进粗糙集理论的SAR图像目标增强

图像增强在提高SAR图像舰船目标检测精度方面具有十分重要的意义。由于传统算法不能很好地对SAR图像进行目标增强，提出了基于改进粗糙集理论和引力场强度的目标增强算法。通过借鉴引力场相关理论知识，将粗糙集条件属性集中的梯度属性改进为引力场强度属性，从而实现对原图像的目标增强。与其他算法进行了实验比较，结果表明提出的改进算法相比于其他算法更适用于SAR图像特性，能更好地对舰船目标像素进行针对性增强，具备一定的工程应用价值。     

无失效数据机载多余度EWIS可靠性研究

针对高可靠度机载多余度EWIS各组成部分寿命服从指数分布但参数未知的情况，提出采用无失效数据可靠度分析方法评估EWIS的可靠度水平。通过Monte-Carlo仿真方法对连接形式为“先并联、后串联”EWIS各组成部分寿命进行抽样，利用“最小最大值”方法获得系统寿命的抽样值，用概率纸检验法初步判断EWIS寿命是否服从威布尔分布，再用Pearson拟合优度检验法判断EWIS寿命是否服从威布尔分布。结合无故障飞行时间的样本值与EWIS寿命服从威布尔分布的假设，采用无失效数据分析方法评估EWIS的可靠度水平。研究方法对机载多余度EWIS无失效数据可靠度分析有一定的贡献。     

OWL‐based acquisition and editing of computer‐interpretable guidelines with the CompGuide editor

Computer‐Interpretable Guidelines (CIGs) are the dominant medium for the delivery of clinical decision support, given the evidence‐based nature of their source material. Therefore, these machine‐readable versions have the ability to improve practitioner performance and conformance to standards, with availability at the point and time of care. The formalisation of Clinical Practice Guideline knowledge in a machine‐readable format is a crucial task to make it suitable for the integration in Clinical Decision Support Systems. However, the current tools for this purpose reveal shortcomings with respect to their ease of use and the support offered during CIG acquisition and editing. In this work, we characterise the current landscape of CIG acquisition tools based on the properties of guideline visualisation, organisation, simplicity, automation, manipulation of knowledge elements, and guideline storage and dissemination. Additionally, we describe the CompGuide Editor, a tool for the acquisition of CIGs in the CompGuide model for Clinical Practice Guidelines that also allows the editing of previously encoded guidelines. The Editor guides the users throughout the process of guideline encoding and does not require proficiency in any programming language. The features of the CIG encoding process are revealed through a comparison with already established tools for CIG acquisition.     

Stream-Based Data Sampling Mechanism for Process Object

Process object is the instance of process. Vertexes and edges are in the graph of process object. There are different types of the object itself and the associations between object. For the large-scale data, there are many changes reflected. Recently, how to find appropriate real-time data for process object becomes a hot research topic. Data sampling is a kind of finding c hanges o f p rocess o bjects. There i s r equirements f or s ampling to be adaptive to underlying distribution of data stream. In this paper, we have proposed a adaptive data sampling mechanism to find a ppropriate d ata t o m odeling. F irst o f all, we use concept drift to make the partition of the life cycle of process object. Then, entity community detection is proposed to find changes. Finally, we propose stream-based real-time optimization of data sampling. Contributions of this paper are concept drift, community detection, and stream-based real-time computing. Experiments show the effectiveness and feasibility of our proposed adaptive data sampling mechanism for process object.