High-plateau flight safety is an important research hotspot in the field of civil aviation transportation safety science. Complete and accurate high-plateau flight data are beneficial for effectively assessing and improving the flight status of civil aviation aircrafts, and can play an important role in carrying out high-plateau operation safety risk analysis. Due to various reasons, such as low temperature and low pressure in the harsh environment of high-plateau flights, the abnormality or loss of the quick access recorder (QAR) data affects the flight data processing and analysis results to a certain extent. In order to effectively solve this problem, an improved least squares support vector machines method is proposed. Firstly, the entropy weight method is used to obtain the index weights. Secondly, the principal component analysis method is used for dimensionality reduction. Finally, the data are fitted and repaired by selecting appropriate eigenvalues through multiple tests based on the LS-SVM. In order to verify the effectiveness of this method, the QAR data related to multiple real plateau flights are used for testing and comparing with the improved method for verification. The fitting results show that the error measurement index mean absolute error of the average error accuracy is more than 90%, and the error index value equal coefficient reaches a high fit degree of 0.99, which proves that the improved least squares support vector machines machine learning model can fit and supplement the missing QAR data in the plateau area through historical flight data to effectively meet application needs.

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Improved LS-SVM Method for Flight Data Fitting of Civil Aircraft Flying at High Plateau

There are different missing flight data due to various reasons in the process of acquisition and storage, especially in general aviation, which cause inconvenience for flight data analysis. Effectively explaining the relationship between flight data parameters and selecting a simple and effective method for fitting and correcting flight data suitable for engineering applications are the main points of the paper. Herein, a convenient and applicable approach of missing data correction and fitting based on the least squares polynomial method is introduced in this work. Firstly, the polynomial fitting model based on the least squares method is used to establish multi-order polynomial by existing flight data since the order of the least squares polynomial has a direct impact on the fitting effect. The order is too high or too small, over-fitting or deviation will occur, resulting in improper data. Therefore, the optimization and selection of the model order are significant for flight data correction and fitting. Because the flight data of the aircraft engine exhaust gas temperature (EGT) are often lost because of the immature detection technology, a series of the multi-order polynomial are established by the relationship of aircraft engine exhaust gas temperature and Revolutions Per Minute (RPM). Case study results confirm the optimal model order is four for the fitting and correction of aircraft engine exhaust temperature, and the least squares polynomial method is applicable and effective for EGT flight data correction and fitting based on RPM data.

/pdf/correction-and-fitting-civil-aviation-flight-data-egt-based-2lpowpq8.pdf

Correction and Fitting Civil Aviation Flight Data EGT Based on RPM: Polynomial Least Squares Analysis

Resumen es: El desarrollo de Internet ha puesto a disposicion de las organizaciones una nueva y atractiva via para comercializar sus productos. No obstante, muchas...

Cómo usan Internetlas líneas aéreas paradesarrollar relacionesestables con los clientes

Mining association rules is essential in the discovery of knowledge hidden in datasets. There are many efficient association rule mining algorithms. The problem is with the large number of rules they often discover. Large number of rules makes the discovery of knowledge very challenging because too many rules are difficult to understand, interpret, or visualize. To reduce the number of discovered rules, researchers proposed a number of solutions. However, these solutions are limited to the rules generated from traditional datasets and are incapable of handling rules generated from big datasets. To solve this problem, this paper proposes a Hadoop MapReduce-based parallel association rule pruning algorithm, named PPrune. Experimental results show that PPrune to be efficient and has good speedup, scaleup, and sizeup.

Parallel Association Rules Pruning Algorithm on Hadoop MapReduce

Reliability of loosely coupled services through the paradigm of service-oriented computing and observing their fault tolerance against massive load in clustered load balancing web server plays an important role while evaluating the quality aspects of software-as-a-service (SaaS), grid, and distributed systems. This chapter shows some aspects of service execution while observing their failure records against massive execution of server-side instruction. A novel reliability estimation framework is proposed that can be deployed for evaluating the reliability of service execution over clustered load balancing web server. A load generating tool is used to generate massive load over the service execution. In this study we will discuss an experimental system and its architecture by using clustered load balancing web server, the reliability estimation framework along with the goodness of fit study through statistical analysis. The overall assessment of the work will validate the applicability of the proposed framework for the loosely coupled service in clustered load balancing web server.

Francisco Javier Villar Martín

Papers

Using Data Mining Techniques to Discover Patterns in an Airline's Flight Hours Assignments