某型教练机起落架信号电路改进设计

针对某型教练机存外场使用时出现起落架未放到位，而飞行员却没能及时发现的问题，本文对飞机起落架信号电路进行了分析，并提出了相应的改进措施。     

应用六西格玛提高钛合金超塑成形/扩散连接产品合格率

应用六西格玛提供的质量改进方法和统计分析工具,按照界定、测量、分析、改进、控制的步骤,运用Pareto图、因果图、过程能力分析、多元回归分析等多种工具,深入分析钛合金超塑成形/扩散连接产品制造过程中产生缺陷的根本原因并采取改进措施,有效提高了制造过程能力和产品合格率。     

基于AMESim的某型客机舵面液压系统仿真分析

当某型客机飞控舵面作为液压能源系统的用户时,应用AMESim仿真软件,在相应的载荷作用下,对液压系统所需压力、流量以及相关液压附件参数的设置进行分析,并对舵面的仿真运动结果进行动画演示。     

运用GPS空中校正飞机无线电罗差误差分析

针对某些特种飞机无线电罗差无法沿用传统校正方法在地面进行校正的状况 ,首次提出了运用 GPS接收机在空中校正飞机无线电罗差的方法。本文对影响该校正方法校正精度的各项误差因素进行了分析。     

重量的一种遥测式监测系统

本文提出一种重量信号的无线传输和测量方法。重量信号经传感顺转换成电信号，再经Ｖ／Ｆ变换后调频发射，接收信号解调后单片机处理。单片机的信号处理采用电子计数器测频方法，经过计算送显示单元，实现重量信号的遥测。     

Beam-Park-Experiment-1/2000 with TIRA

A monostatic 24-h debris observation campaign (BPE-1/2000) has been prepared and conducted using FGAN's TIRA L-Band system. Based on experiences from previous Beam-park experiments a similar largely automated data processing is applied on an extended range window of 300–2000km. More than 1500 detections are encountered, 471 of them are verified as being real objects in Low-Earth-Orbit (LEO). PROOF's observation forecasting of catalogued objects is evaluated against the observed objects, and the difficulties obtaining radar cross-sections (RCSs) and object sizes from Beam-park experiments are discussed. Sidelobe detections are identified by using background information like two-line element (TLE) sets and/or catalogued RCSs.In comparison with previous experiments, the statistics show similarities confirming the concept of Beam-park experiments for space debris observations, despite the snapshot character of 24-h experiments. The comparison with MASTER/PROOF'99 and ORDEM2000 leads to a reasonable agreement between models and observations.     

Vertical motions in Thailand after the 2004 Sumatra–Andaman Earthquake from GPS observations and its geophysical modelling

Following previous findings from ongoing GPS research in Thailand since 2004 we continue to exploit the GPS technique to monitor and model land motions induced by the Sumatra–Andaman Earthquake. Our latest results show that up to the end of 2010, Thailand has been co-seismically displaced and is subsequently undergoing a post-seismic horizontal deformation with total displacements (co-seismic plus post-seismic) ranging from 10.5 to 74.7 cm. We observed the largest horizontal displacements in the southern part of Thailand and moderate and small displacements in the central and northern parts. In addition to horizontal displacements throughout Thailand, continuous GPS measurements show that large parts of Thailand are subsiding at rates up to 1 cm/yr. It is the first time that such vertical post-seismic deformations at large distances (650–1500 km away from the Earthquake’s epicentre) have been recorded. We have investigated the physical processes leading to the observed subsidence. While after-slip on the subduction interface induces negligible or even slightly positive vertical motions, relaxation in the asthenosphere is associated with a sizable subsidence. Predictions from a 3D finite element model feature an asthenosphere with an effective viscosity of the order of 3 * 10¹⁸ Pas, fit the horizontal post-seismic data and the observed subsidence well. This model is then used to predict the subsidence over the whole seismic cycle. The subsidence should go on with a diminishing rate through the next two decades and its final magnitude should not exceed 10 cm in the Bangkok area.     

Application of SVM on satellite images to detect hotspots in Jharia coal field region of India

The present paper deals with the application of Support Vector Machine (SVM) and image analysis techniques on NOAA/AVHRR satellite image to detect hotspots on the Jharia coal field region of India. One of the major advantages of using these satellite data is that the data are free with very good temporal resolution; while, one drawback is that these have low spatial resolution (i.e., approximately 1.1 km at nadir). Therefore, it is important to do research by applying some efficient optimization techniques along with the image analysis techniques to rectify these drawbacks and use satellite images for efficient hotspot detection and monitoring. For this purpose, SVM and multi-threshold techniques are explored for hotspot detection. The multi-threshold algorithm is developed to remove the cloud coverage from the land coverage. This algorithm also highlights the hotspots or fire spots in the suspected regions. SVM has the advantage over multi-thresholding technique that it can learn patterns from the examples and therefore is used to optimize the performance by removing the false points which are highlighted in the threshold technique. Both approaches can be used separately or in combination depending on the size of the image. The RBF (Radial Basis Function) kernel is used in training of three sets of inputs: brightness temperature of channel 3, Normalized Difference Vegetation Index (NDVI) and Global Environment Monitoring Index (GEMI), respectively. This makes a classified image in the output that highlights the hotspot and non-hotspot pixels. The performance of the SVM is also compared with the performance obtained from the neural networks and SVM appears to detect hotspots more accurately (greater than 91% classification accuracy) with lesser false alarm rate. The results obtained are found to be in good agreement with the ground based observations of the hotspots. This type of work will be quite helpful in the near future to develop a hotspots monitoring system using these operational satellites data.