基于FPGA的四轴飞行控制器的硬件系统设计

为提高四轴飞行器的数据采集与数据处理能力,降低四轴飞行器的功耗,研制了一种基于FPGA的四轴飞行控制器。飞行控制器以NIOS II处理器为控制核心,结合嵌入的SPI、I2C、UART等IP核实现了数据的实时采集与快速处理,并提出并行处理PPM解码和编码、超声波检测与控制、蜂鸣器控制的设计方案,利用VerilogHDL语言在FPGA上设计了这些并行处理功能模块,这些功能模块通过PIO核与NIOS II处理器连接,能够自主完成所规定的处理功能。经过多次飞行测试,四轴飞行器能够稳定的起飞和降落,快速的飞行,转弯,上升和下降,也能够避开障碍物,验证了四轴飞行控制器功能稳定,功耗较低,已达到设计的要求。     

基于心音窗函数的心音图形化处理方法的研究

心音分析与识别目前主要局限在一维信号处理方面, 为了获得心音信号更直观特征表现形式, 提高分类识别效果, 拓展心音识别研究领域, 提出了一种将心音与图像处理技术相结合、基于心音窗函数的心音纹理图特征提取与识别算法. 本文首先给出心音的模型, 定义心音时频图和心音纹理图, 然后讨论如何利用心音窗函数和短时傅里叶变换获取二维心音时频图, 并且针对心音的特点, 重点研究了心音窗函数的构造原则和实现方法, 最后通过改进的脉冲耦合神经网络模型实现了对心音纹理图的特征提取与身份识别. 仿真实验表明, 心音窗函数与传统窗函数相比较, 所获得的心音时频图具有第一、第二心音纹理更加清晰, 噪声纹理得到较好抑制的优点, 并且改进的脉冲耦合神经网络模型具有更低的计算成本, 与3种典型识别方法相比较, 呈现更高的识别率, 因而基于图像处理技术对心音进行特征提取与身份识别是一种行之有效的方法.     

A real-time infrared radiation imaging simulation method of aircraft skin with aerodynamic heating effect

Real-time infrared radiation simulation technology can provide effective support for rapid design and evaluation of a system which integrates infrared imaging technology. Considering the aerodynamic heating effect, this paper presents a real-time infrared radiation characteristic simulation method of aircraft skin based on the panel element method, which can help to assess the infrared radiation impacts of different environment factors and materials. A 3-D model of an aircraft was established and its surface was divided into different parts and panel element meshes to attach material properties. For each mesh, its heat exchange equation is solved so as to obtain the whole skin’s temperature and infrared radiation distribution. The simulation results reveal the influence of different factors on the skin surface radiation, including environmental radiation, aerodynamic heating and material properties. And the credibility and efficiency of the proposed aerodynamic heating simulation method were confirmed by comparing to the CFD simulation results.     

Vibrations’ influence on Dieselness perception

Up to now, different studies dealing with vibrations’ influence on acoustics have been, in most cases, realized on global annoyance. In our case, the present study examines the vibrations’ influence on the auditory perception of Diesel character (called Dieselness in this article) of a vehicle. In addition, cultural experience is evaluated by testing two groups of Diesel owners from two European countries (respectively France and Germany). During the experiment, each population was exposed to sound only, and sound and vibrations simultaneously. This perceptual test was realized on a vibration bench (driver seat and steering wheel) with headphones. Three kinds of vehicules and six different driving situations have been tested. Results reveal no differences between French and German. Nevertheless, the adding of vibrations influences the Dieselness evaluation. The participants give slightly higher scores (more Diesel) or equal (as Diesel) with vibrations than without. However, this vibration effect is slightly dependent on the type of vehicles and on the driving situations and it appears less important for German people. In addition, for each group of participants, the other factors vehicle and driving situation have an effect on Dieselness assessment. The effect of vehicle allows to show that 3 cylinders car is significantly different from 4 cylinders and 6 cylinders cars. Finally, the interaction between driving situation and vehicle shows the strongest effect on Dieselness evaluation, among all interactions tested. The vehicle effect is dependent on the driving situation. All results and conclusions have to be taken with care in order not to generalize for all similar classification cars.     

Sensitivity of the FAA Integrated Noise Model to input parameters

The standard method for computing noise contours around civil airports is SAE-AIR-1845 (FAA Integrated Noise Model, European ECAC-CEAC Doc. 29). It is subject to the inaccuracies implicit in the model as well as those caused by erroneous or imprecise input data. Regarding the latter, the existing errors and/or uncertainties, may be amplified in the output results, to a greater or lower extent, in some cases offering unreliable predictions.In order to study this phenomenon, the Institute of Sound and Vibration Research (ISVR - Southampton) carried out a theoretical sensitivity analysis based on the segmentation technique, regarding the input parameters of the SAE-AIR-1845, and obtaining the input variables of the model, the variation of which implied greater changes in the output variables. The results were validated by using the FAA Integrated Noise Model Version 6.0 software, based on the aforementioned document. It has been revealed that the model has a greater sensitivity to factors that modify the flight path, and a lower sensitivity to the other parameters. Thus, an error greater than 10% in the variable “gross weight” offers an additional error of between 3 and 7 dB. However, parameters such as the ID of the flaps hardly modify the results obtained for the least favourable case by 1 dB.As a result of this research, the sensitivity of the model was quantified for each of the input parameters (taken alone and also in interaction with other parameters), and criteria for the minimisation of global error, resulting from uncertainties in the input parameters, were stated.     

Integrated decision support for aviation safety inspectors

This paper presents a systems viewpoint for developing an advanced decision support system for aircraft safety inspectors. Research results from a Federal Aviation Administration (FAA) sponsored project to use neural network and expert systems technology to analyze aircraft maintenance databases are summarized. One of the main objectives of this research is to define more refined “alert” indicators for national comparison purposes that can signal potential problem areas by aircraft type for safety inspector consideration.

Integration aspects are addressed on two levels: (1) integration of the various technical components of the decision support system, and (2) integration of the decision support system with individual behavior, management systems and organizational structure, as well as corporate culture across both formal and informal dimensions. The paper summarizes the creation of strategic “inspection profiles” for aging aircraft and reliability curve fitting for structural components both based upon using neural network technology. Also, the potential use of a model-based expert system to facilitate field inspection diagnostics is presented. Finally, a framework for developing an intelligent decision system to support aircraft safety inspections is proposed that links expert systems, neural networks, as well as a paradigm of the decision making process typically used in unstructured situations.     

Network flow-based approaches for integrated aircraft fleeting and routing

Given a schedule of flights to be flown, the aircraft fleeting and routing problem (AFRP) consists of determining a minimum-cost route assignment for each aircraft so as to cover each flight by exactly one aircraft while satisfying maintenance requirements and other activity constraints. We investigate network flow-based heuristic approaches for this problem. Computational experiments conducted on real-data given by TunisAir show that the proposed heuristic consistently yields very near-optimal solutions while requiring modest CPU effort.     

Strategic planning in fractional aircraft ownership programs

In the fractional ownership model, the partial owner of an aircraft is entitled to certain flight hours per year, and the management company is responsible for all the operational considerations of the aircraft and for making an aircraft available to the owner at the requested time and place. In the recent years although the industry as a whole has experienced significant growth, most of the major fractional jet management companies have been unprofitable. To increase profitability a management company must minimize its operating costs and increase its crew and aircraft utilization. In this paper, we present a methodology for efficiently scheduling the available resources of a fractional jet management company that takes into consideration the details in real world situations. We then discuss several strategic planning issues, including aircraft maintenance, crew swapping, demand increase and differentiation, and analyze their effects on the resource utilization and profitability.     

Aircraft take-off noises classification based on human auditory’s matched features extraction

Air transportation is one of the most important services in the world, contributing greatly to the advancement of modern society. However, it has a local and a global impact on the environment making aircraft take-off noise an important environmental public health concern near airports, and this is a significant subject for monitoring and research. In this work an experimentally validated computational model for aircraft classification is presented. In addition, potentially harmful effects to human health and comfort associated with noise exposure are discussed. The feature extraction of aircraft take-off signals is conducted through a 1/24 octave analysis and Mel frequency cepstral coefficients (MFCC). The aircraft classification is made by using two parallel feed forward neural networks. The aircraft are clustered into classes depending on the installed engine type. This model has 13 aircraft classes and a classification level above 83% with measurements in real time environment.