Detection of hydrothermal alteration zones using ASTER data in Nimu porphyry copper deposit,south Tibet,China

Advanced space-borne thermal emission and reflection radiometer (ASTER) data were evaluated for the hydrothermal alteration mapping of the Nimu porphyry copper deposit, southern Tibet. According to the metallogenic model for porphyry copper, we chose hydrothermal alteration mineral association and then established a remote sensing model. Relative absorption-band depth (RBD) and simple band combination methods in visible near infrared (VNIR) and shortwave infrared (SWIR) bands were used to retrieve information about lithological distributions. Principal component analyses (PCA) were applied to extract the prospecting information based on the spectral information of argillization mineral association, propylitization mineral association, and iron stained (limonite) effects. Results of the study match up well with known copper occurrences and a circular structure in study area, while circular structures usually have close relationship with mineralization. Combined with field validation, ASTER data are proved able to characterise the alteration zone of these porphyry deposits. With VNIR and SWIR bands, argillic, propylitic and ferritization alterations in a single deposit can be effectively discriminated.     

Application of a PCA-DBN-based surrogate model to robust aerodynamic design optimization

An efficient method employing a Principal Component Analysis (PCA)-Deep Belief Network (DBN)-based surrogate model is developed for robust aerodynamic design optimization in this study. In order to reduce the number of design variables for aerodynamic optimizations, the PCA technique is implemented to the geometric parameters obtained by parameterization method. For the purpose of predicting aerodynamic parameters, the DBN model is established with the reduced design variables as input and the aerodynamic parameters as output, and it is trained using the k-step contrastive divergence algorithm. The established PCA-DBN-based surrogate model is validated through predicting lift-to-drag ratios of a set of airfoils, and the results indicate that the PCA-DBN-based surrogate model is reliable and obtains more accurate predictions than three other surrogate models. Then the efficient optimization method is established by embedding the PCA-DBN-based surrogate model into an improved Particle Swarm Optimization (PSO) framework, and applied to the robust aerodynamic design optimizations of Natural Laminar Flow (NLF) airfoil and transonic wing. The optimization results indicate that the PCA-DBN-based surrogate model works very well as a prediction model in the robust optimization processes of both NLF airfoil and transonic wing. By employing the PCA-DBN-based surrogate model, the developed efficient method improves the optimization efficiency obviously.     

半个太阳活动周期内我国中纬地区上空电离层等效板厚的变化特征

本文用新乡(电离层400km对下点是:32.4°N,115.6°E)、重庆(电离层400km对下点是:27.2°N,108.7°E)接收日本同步卫星ETS-Ⅱ的信标资料,研究了我国中纬地区上空半个太阳活动周期内(1981—1985)电离层等效板厚的变化特征,得到了等效板厚日变化、季变化的二维和三维等值图。由付里叶分析和回归方法得出等效板厚日变化各谐波分量与太阳黑子数12个月滑动平均值之间存在弱的线性关系。同时指出在等效板厚日变化中,于当地时0400—0700LT存在一明显的黎明峰。并对出现这种峰的原因作了讨论。      

SPOT遥感图象的信息保持型压缩

对SPOT遥感图象的信息保持型压缩编码进行了研究。对几种不同的预测器及A_2、A_3线性码进行了比较,得出如下结论:在SPOT 遥感图象的信息保持型压缩编码中,采用文献[1]提出的三点预测器较好,线性码采用A_3码较好;两者结合,在信息保持率大于99%时,可使压缩比达2倍以上。文内还对在压缩图象的传输与恢复中碰到的问题进行了探讨。     

一种手持设备的条形码技术

主要讨论条形码（BARCODE）技术中的解码技术,文中详细地讲述了条形码解码过程中可能出现的问题以及解决的方法、解码系统的识别性能问题,最后列出了一个条形码解码的具体算法。     

飞机外挂物及其部件同时测力试验技术研究

介绍了气动中心高速所开展飞机外挂物及其部件同时测力试验技术研究的概况,指出了研究的主要技术难点并给出了相应的解决措施。应用该项技术,在1.2m×1.2m风洞中成功进行了××飞机外挂测力试验,获得了该机翼下外挂物及其部件可靠的气动特性。该项试验技术研究的成功及其在型号试验中的应用,拓宽了风洞试验能力,丰富和发展了外挂测力试验技术的内容和手段,具有广阔的应用前景。     

异步COM技术在串口通信组件的应用

异步COM是客户和服务器组件之间相互通信中的异步传输模式。基于它设计相应的串口通信组件,更能合理利用各种硬件资源。     

涡扇发动机低压部件通流耦合计算

为了研究各部件匹配与过渡段几何参数对发动机总体性能的影响,针对某涡扇发动机风扇、低压涡轮、内外涵道、掺混段和尾喷管等部件,基于周向平均Navier-St okes通流控制方程,采用时间推进有限体积法进行了部件耦合计算求解,将沿程各部件性能计算参数与试验做了对比,结果表明:在设计状态下发动机沿程各截面计算特征参数与试验值最大误差不超过5.0%,说明该通流耦合计算方法能够快速预估发动机总体性能,完善各部件匹配关系,在发动机总体设计初期有一定工程应用价值。     

Framework and development of data-driven physics based model with application in dimensional accuracy prediction in pocket milling

In the manufacturing of thin wall components for aerospace industry, apart from the side wall contour error, the Remaining Bottom Thickness Error (RBTE) for the thin-wall pocket component (e.g. rocket shell) is of the same importance but overlooked in current research. If the RBTE reduces by 30%, the weight reduction of the entire component will reach up to tens of kilograms while improving the dynamic balance performance of the large component. Current RBTE control requires the off-process measurement of limited discrete points on the component bottom to provide the reference value for compensation. This leads to incompleteness in the remaining bottom thickness control and redundant measurement in manufacturing. In this paper, the framework of data-driven physics based model is proposed and developed for the real-time prediction of critical quality for large components, which enables accurate prediction and compensation of RBTE value for the thin wall components. The physics based model considers the primary root cause, in terms of tool deflection and clamping stiffness induced Axial Material Removal Thickness (AMRT) variation, for the RBTE formation. And to incorporate the dynamic and inherent coupling of the complicated manufacturing system, the multi-feature fusion and machine learning algorithm, i.e. kernel Principal Component Analysis (kPCA) and kernel Support Vector Regression (kSVR), are incorporated with the physics based model. Therefore, the proposed data-driven physics based model combines both process mechanism and the system disturbance to achieve better prediction accuracy. The final verification experiment is implemented to validate the effectiveness of the proposed method for dimensional accuracy prediction in pocket milling, and the prediction accuracy of AMRT achieves 0.014 mm and 0.019 mm for straight and corner milling, respectively.