Detection and mapping vegetation cover based on the Spectral Angle Mapper algorithm using NOAA AVHRR data

Satellite data, taken from the National Oceanic and Atmospheric Administration (NOAA) have been proposed and used for the detection and the cartography of vegetation cover in North Africa. The data used were acquired at the Analysis and Application of Radiation Laboratory (LAAR) from the Advanced Very High Resolution Radiometer (AVHRR) sensor of 1 km spatial resolution. The Spectral Angle Mapper Algorithm (SAM) is used for the classification of many studies using high resolution satellite data. In the present paper, we propose to apply the SAM algorithm to the moderate resolution of the NOAA AVHRR sensor data for classifying the vegetation cover. This study allows also exploiting other classification methods for the low resolution. First, the normalized difference vegetation index (NDVI) is extracted from two channels 1 and 2 of the AVHRR sensor. In order to obtain an initial density representation of vegetal formation distribution, a methodology, based on the combination between the threshold method and the decision tree, is used. This combination is carried out due to the lack of accurate data related to the thresholds that delimit each class. In a second time, and based on spectral behavior, a vegetation cover map is developed using SAM algorithm. Finally, with the use of low resolution satellite images (NOAA AVHRR) and with only two channels, it is possible to identify the most dominant species in North Africa such as: forests of the Liege oaks, other forests, cereal’s cultivation, steppes and bar soil.     

故障树分析在可靠性、安全性分析中的作用与运用

故障树分析是实施可靠性、安全性分析的有效手段之一。本文介绍了故障树分析的方法、其在可靠性、安全性分析中的不同作用以及目前运用中的遇到的问题和解决思路。     

某型涡桨发动机减速器负拉力试验不合格故障分析

针对某型涡桨发动机减速器负拉力试验不合格故障，分析了负拉力传感器的结构特点和影响因素，建立了故障树开展故障排查，明确了故障原因，提出了改进措施。     

故障树分析应用于某型照射器质量改进

在收集已发生故障数量和故障模式的基础上．应用故障树分析的原理，对某型照射器建立故障树模型和最小割集分析，根据计算概率重要度，确定照射器的质量改进内容。     

An integrated graphic–taxonomic–associative approach to analyze human factors in aviation accidents

Human factors are critical causes of modern aviation accidents. However, existing accident analysis methods encounter limitations in addressing aviation human factors, especially in complex accident scenarios. The existing graphic approaches are effective for describing accident mechanisms within various categories of human factors, but cannot simultaneously describe inadequate human–aircraft–environment interactions and organizational deficiencies effectively, and highly depend on analysts' skills and experiences. Moreover, the existing methods do not emphasize latent unsafe factors outside accidents. This paper focuses on the above three limitations and proposes an integrated graphic–taxonomic–associative approach. A new graphic model named accident tree(AcciTree), with a two-mode structure and a reaction-based concept, is developed for accident modeling and safety defense identification. The AcciTree model is then integrated with the well-established human factors analysis and classification system(HFACS) to enhance both reliability of the graphic part and logicality of the taxonomic part for improving completeness of analysis. An associative hazard analysis technique is further put forward to extend analysis to factors outside accidents, to form extended safety requirements for proactive accident prevention. Two crash examples, a research flight demonstrator by our team and an industrial unmanned aircraft, illustrate that the integrated approach is effective for identifying more unsafe factors and safety requirements.     

基于小波包能量谱的航空发动机故障特征提取方法

使用成熟的旋转机械振动信号频谱分析方法,对采集到的多台航空发动机试车振动信号进行分析,找出能反应出发动机转子不对中、转子不平衡和转静件碰磨3种故障频谱特征的振动信号.再使用小波包将信号分解为不同的频段,之后分别计算能反应出故障信息的特征频段的能量,将它们组成用来区分上述3种故障的特征向量,为以后的航空发动机故障的模式识别做准备.     

Multi-Criteria Decision Analysis integrated with GIS and remote sensing for astronomical observatory site selection in Antalya province,Turkey

Astronomical observatory site selection is a complex problem that involves evaluation of multiple factors from different sources. The aim of this study is to select the best possible candidates for astronomical observations sites using Multi-Criteria Decision Analysis integrated with Geographical Information Systems and remote sensing technologies. The study was implemented in the Antalya province of Turkey, which is convenient for astronomical site observatory facilities with its appropriate climate properties and weather conditions. Eleven factors (cloud cover, precipitable water, earthquake zones, geology, landslide inventory, active fault lines, Digital Elevation Model, city lights, mining activities, settlement areas, roads) were determined, splitting into three categories; meteorological, geographical and anthropogenic criteria. These factors were evaluated using Analytical Hierarchy Process method and the weights of criteria layers were determined. As a result, the most suitable areas were located extensively in western and eastern part of Antalya. This study offers a robust, accurate, cost and time effective procedure for preliminary site selection for astronomical observatory. However, for a final decision of the best location of astronomical observatory, site testing measurements and atmospheric seeing observations will be further required in these preliminary areas.     

基于故障树的智能型故障诊断系统

本文在故障树定性和定量分析基础上，提出了基于故障树的智能型故障诊断方法。该方法是建立在故障模式按发生概率大小排序和底事件按发生概率大小排序基础上，因此较好地反映了故障诊断过程中各个底事件和中间事件对系统故障的影响。     

航材应急保障决策支持系统的设计与实现

结合航材保障决策支持系统的数据需求,详细地研究数据库系统的结构;根据航材应急保障的决策问题,对模型库系统所应包含的模型进行分析;设计的系统的实现架构,综合人和计算机两者优势,能有效地辅助航材保障决策机关对航材的保障进行决策。