稳态等离子体推力器磁场设计与数值分析

首先分析了稳态等离子体推力器性能对磁场的要求，据此介绍了磁路系统的工程设计原则和方法；然后，采用ANSYS大型有限元分析软件对一个具体的稳态等离子体推力器在额定工况下的磁场进行了计算，获得了满意的结果，通过对结果的分析获得了对有关现象的直观深入的认识，为所计算具体推力器的改进设计提供了线索；同时，也证实了以ANSYS为软件平台的稳态等离子体推力器磁路系统计算机数值仿真辅助设计的可行性和有效性。     

超声电喷推力器的驻波特性

为研究超声电喷推力器(UAET)驻波尺寸特性的相关变化规律,建立发射极液面的液体振动数值模型,并开展驻波尺寸测量试验对数值模型进行验证。对比驻波间距的测量值与计算值发现,两者在变化趋势上可认为一致,且计算误差在6.55%以内。在此基础上,利用数值模型对不同波源频率、振幅下的驻波相关参数进行计算。实验结果表明,随波源频率升高,驻波间距、高度以及半径均下降,其影响机制在于频率主要对波纹形成时间产生影响;而振幅的升高对间距几乎不产生影响,仅会导致驻波高度和半径升高,其影响机制在于振幅对波纹形成应力产生影响。     

Determination of land surface temperature using precipitable water based Split-Window and Artificial Neural Network in Turkey

Land surface temperature (LST) calculation utilizing satellite thermal images is very difficult due to the great temporal variance of atmospheric water vapor in the atmosphere which strongly affects the thermal radiance incoming to satellite sensors. In this study, Split-Window (SW) and Radial Basis Function (RBF) methods were utilized for prediction of LST using precipitable water for Turkey. Coll 94 Split-Window algorithm was modified using regional precipitable water values estimated from upper-air Radiosond observations for the years 1990–2007 and Local Split-Window (LSW) algorithms were generated for the study area. Using local algorithms and Advanced Very High Resolution Radiometer (AVHRR) data, monthly mean daily sum LST values were calculated. In RBF method latitude, longitude, altitude, surface emissivity, sun shine duration and precipitable water values were used as input variables of the structure. Correlation coefficients between estimated and measured LST values were obtained as 99.23% (for RBF) and 94.48% (for LSW) at 00:00 UTC and 92.77% (for RBF) and 89.98% (for LSW) at 12:00 UTC. These meaningful statistical results suggest that RBF and LSW methods could be used for LST calculation.     

多模式柔索驱动航天员训练机器人力控制

为解决长期在失重环境下生活和工作的航天员的康复训练问题,针对现有的航天员训练设备功能单一、训练效果不理想的现状,研制了多模式柔索驱动航天员训练机器人。基于模块化、可重构的机器人构型,通过机器人模拟重力环境的负载特征,把相应的载荷施加到人体上,实现航天员在失重环境下进行跑步、卧推和负重深蹲等体育训练,帮助航天员减轻或者克服空间适应综合征带来的不利影响;在此基础上提出一种机器人双闭环力控制策略,人机跑步训练实验结果表明,本文研制的多模式航天员训练机器人构型合理,控制策略有效,可以辅助在失重环境下生活和工作的航天员开展体育训练。     

BHM3碳纤维及其氰酸酯基复合材料耐原子氧侵蚀性能

利用原子氧暴露地面模拟实验装置,分别对BHM3型高模量碳纤维及其增强的氰酸酯基复合材料进行了原子氧暴露模拟实验,采用SEM、XRD、XPS技术分析了原子氧对碳纤维及其氰酸酯复合材料的侵蚀行为。结果表明,经过1×10 21 atoms/cm 2剂量的原子氧暴露后,碳纤维及其氰酸酯复合材料质量损失率均低于1%,纤维表面形貌及组成不变,碳纤维/氰酸酯复合材料的层间剪切强度降低16%。     

Tomographic reconstruction of the ionospheric electron density as a function of space and time

Electron density distribution is the major determining parameter of the ionosphere. Computerized Ionospheric Tomography (CIT) is a method to reconstruct ionospheric electron density image by computing Total Electron Content (TEC) values from the recorded Global Positioning Satellite System (GPS) signals. Due to the multi-scale variability of the ionosphere and inherent biases and errors in the computation of TEC, CIT constitutes an underdetermined ill-posed inverse problem. In this study, a novel Singular Value Decomposition (SVD) based CIT reconstruction technique is proposed for the imaging of electron density in both space (latitude, longitude, altitude) and time. The underlying model is obtained from International Reference Ionosphere (IRI) and the necessary measurements are obtained from earth based and satellite based GPS recordings. Based on the IRI-2007 model, a basis is formed by SVD for the required location and the time of interest. Selecting the first few basis vectors corresponding to the most significant singular values, the 3-D CIT is formulated as a weighted least squares estimation problem of the basis coefficients. By providing significant regularization to the tomographic inversion problem with limited projections, the proposed technique provides robust and reliable 3-D reconstructions of ionospheric electron density.     

An ensemble deep learning based shoreline segmentation approach (WaterNet) from Landsat 8 OLI images

Shorelines constantly vary due to natural, urbanization and anthropogenic effects such as global warming, population growth, and environmental pollution. Sustainable monitoring of coastal changes is vital in terms of coastal resource management, environmental preservation and planning. Publicly available Landsat 8 OLI (Operational Land Manager) images provide accurate, reliable, temporal and up-to-date information about coastal areas. Recently, the use of machine learning and deep learning algorithms have become widespread. In this study, we used our public Landsat 8 OLI satellite image dataset to create a majority voting method which is an ensemble automatic shoreline segmentation system (WaterNet) to obtain shorelines automatically. For this purpose, different deep learning architectures have been utilized namely as Standard U-Net, Dilated U-Net, Fractal U-Net, FC-DenseNet, and Pix2Pix. Also, we have suggested a novel framework to create labeling data from OpenStreetMap service to create a unique dataset called YTU-WaterNet. According to the results, IoU and F1 scores have been calculated as 99.59% and 99.79% for the WaterNet. The results indicate that the WaterNet method outperforms other methods in terms of shoreline extraction from Landsat 8 OLI satellite images.     

Non-parametric regional VTEC modeling with Multivariate Adaptive Regression B-Splines

In this work Multivariate Adaptive Regression B-Splines (BMARS) is applied to regional spatio-temporal mapping of the Vertical Total Electron Content (VTEC) using ground based Global Positioning System (GPS) observations. BMARS is a non-parametric regression technique that utilizes compactly supported tensor product B-splines as basis functions, which are automatically obtained from the observations. The algorithm uses a scale-by-scale model building strategy that searches for B-splines at each scale fitting adequately to the data and provides smoother approximations than the original Multivariate Adaptive Regression Splines (MARS). It is capable to process high dimensional problems with large amounts of data and can easily be parallelized. The real test data is collected from 32 ground based GPS stations located in North America. The results are compared numerically and visually with both the regional VTEC modeling generated via original MARS using piecewise-linear basis functions and another regional VTEC modeling based on B-splines.     

稳态等离子体推力器低功率工作模式实验研究

稳态等离子体动力器（SPT）是技术性能和空间适应性良好的电推力器,是小卫星空间动力的理想选择。从实验的角度研究了额定功率为660W的SPT在低于其额定功率下运行,推力器的放电特性、推车特性、推力效率、比冲等性能随推力器工作参数的变化。实验结果表明：660W的SPT推力器在低于其额定功率下工作,具有稳定的放电特性和确定的推力性能,可以将较大尺寸的SPT推力器在低功率模式下运行来替代小尺寸SPT推力器的作用。