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811.
简要介绍了有关数据流分析的基本概念之后,较为着重地讨论了一种基于静态分析的Ada软件数据流分析方法,同时也讨论了分析过程中一些实现原理。实践结果表明,文中讨论的数据结构,实现机制与处理方法是行之有效的。 相似文献
812.
分析讨论了快速傅里叶变换(FFT)的算法结构,基于FFT运算特点,给出了一种采用现场可编程门阵列(FPGA)实现FFT运算的新方案。该方案采用基2算法及单元结构的设计思路,计算单元采用流水与并行结合的结构,加快了运算速度,内部接收单元采用乒乓RAM结构,扩大了数据吞吐量。MAX plusⅡ环境下的时序分析结果与基于Matlab的理论计算相一致,说明了方案设计的正确性。FPGA与FFT的结合将大幅度提高FFT的处理速度,扩大了FFT的应用领域。 相似文献
813.
根据载人航天器人-机界面宜人设计的必要性,详细论述了人-机界面的发展趋势和未来的研究方向,在此基础上,提出宜人设计的具体措施,最后,形成几点看法以供讨论。 相似文献
814.
简述了产品编号在航天型号研制、生产过程中的重要作用,思考了编制企业标准《产品编号规定》的必要性,并对其企业标准应编写的内容、要求及实施效果作了介绍。 相似文献
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Hideo Hanada Takahiro Iwata Qinghui Liu Fuyuhiko Kikuchi Koji Matsumoto Sander Goossens Yuji Harada Kazuyoshi Asari Toshiaki Ishikawa Yoshiaki Ishihara Hirotomo Noda Seiitsu Tsuruta Natalia Petrova Nobuyuki Kawano Sho Sasaki Kaori Sato Noriyuki Namiki Yusuke Kono Kenzaburo Iwadate Osamu Kameya Katsunori M. Shibata Yoshiaki Tamura Shunichi Kamata Yukihiro Yahagi Wataru Masui Koji Tanaka Hironori Maejima Xiaoyu Hong Jinsong Ping Xian Shi Qian Huang Yusufu Aili Simon Ellingsen Wolfgang Schlüter 《Space Science Reviews》2010,154(1-4):123-144
The Japanese lunar explorer SELENE (Kaguya), which was launched on September 14th, 2007, was the target of VLBI observations over the period November 2007 to June 2009. These observations were made in order to improve the lunar gravity field model, in particular the lower degree coefficients and the model near the limb. Differential VLBI Radio sources, called VRAD instruments, were on-board the subsatellites, Rstar (Okina) and Vstar (Ouna), and the radio signals were observed by the Japanese VERA (VLBI Exploration of Radio Astrometry) network, and an international VLBI network. Multi-frequency and same-beam VLBI techniques were utilized and were essential aspects of the successful observing program. Multi-frequency VLBI was employed in order to improve the accuracy of the orbit determination obtained from the phase delay from the narrow-band satellite signals, while the same-beam VLBI method was used to resolve the cycle ambiguity which is inherent in the multi-frequency VLBI method. The observations were made at three S-band frequencies (2212, 2218 and 2287 MHz), and one X-band frequency (8456 MHz). We have succeeded in correlating the recorded signals from Okina/Ouna, and we obtained phase delays with an accuracy of several pico-seconds at S-band. 相似文献
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819.
Joint time-frequency transform for radar range-Doppler imaging 总被引:5,自引:0,他引:5
Conventional radar imaging uses the Fourier transform to retrieve Doppler information. However, due to the complex motion of a target, the Doppler frequency shifts are actually time-varying. By using the Fourier transform, the Doppler spectrum becomes smeared and the image is blurred. Without resorting to sophisticated motion compensation algorithms, the image blurring problem can be resolved with the joint time-frequency transform. High-resolution time-frequency transforms are investigated, and examples of applications to radar imaging of single and multiple targets with complex motion are given 相似文献
820.
Unsteady aerodynamic modeling at high angles of attack using support vector machines 总被引:2,自引:5,他引:2
Accurate aerodynamic models are the basis of flight simulation and control law design.Mathematically modeling unsteady aerodynamics at high angles of attack bears great difficulties in model structure determination and parameter estimation due to little understanding of the flow mechanism.Support vector machines(SVMs)based on statistical learning theory provide a novel tool for nonlinear system modeling.The work presented here examines the feasibility of applying SVMs to high angle-of-attack unsteady aerodynamic modeling field.Mainly,after a review of SVMs,several issues associated with unsteady aerodynamic modeling by use of SVMs are discussed in detail,such as selection of input variables,selection of output variables and determination of SVM parameters.The least squares SVM(LS-SVM)models are set up from certain dynamic wind tunnel test data of a delta wing and an aircraft configuration,and then used to predict the aerodynamic responses in other tests.The predictions are in good agreement with the test data,which indicates the satisfying learning and generalization performance of LS-SVMs. 相似文献