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介绍了一种采用FPGA技术实现MPEG-4 ASP级视频DCT量化模块的设计方案。该模块包括二维DCT/IDCT、量化/反量化和帧内直流/交流(DC/AC)预测。用VHDL进行描述并通过模拟试验表明,该模块可在880个时钟周期内处理完一个宏块的数据,工作频率达到40MHZ。文章采用全硬件实现方法,提出了各模块的硬件电路结构设计,减少了电路规模。 相似文献
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Han He Huaning Wang Zhanle Du Rong Li Yanmei Cui Liyun Zhang Yulin He 《Advances in Space Research (includes Cospar's Information Bulletin, Space Research Today)》2008,42(9):1450-1456
Solar activity prediction services started in 1960’s in National Astronomical Observatories, Chinese Academy of Sciences (NAOC). As one of the members of the International Space Environment Service (ISES), Regional Warning Center of China (RWC-China) was set up in 1990’s. Solar Activity Prediction Center (SAPC), as one of the four sub-centers of RWC-China, is located in NAOC. Solar activity prediction studies and services in NAOC cover short-term, medium-term, and long-term forecast of solar activities. Nowadays, certain prediction models, such as solar X-ray flare model, solar proton event model, solar 10 cm radio flux model, have been established for the practical prediction services. Recently, more and more physical analyses are introduced in the studies of solar activity prediction, such as the magnetic properties of solar active regions and magnetic structure of solar atmosphere. Besides traditional statistics algorithms, Machine Learning and Artificial Intelligence techniques, such as Support Vector Machine (SVM) method, are employed in the establishment of forecast models. A Web-based integrated platform for solar activity data sharing and forecast distribution is under construction. 相似文献
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《中国航空学报》2021,34(2):343-357
Tip clearances of multistage rotors and stators greatly affect aero-engines’ aerodynamic efficiency, stability and safety. The inevitable machining and assembly errors, as well as the complicated error propagation mechanism, cause overproof or non-uniform tip clearances. However, it is generally accepted that tip clearances are difficult to predict, even under assembly state. In this paper, a tip clearance prediction model is proposed based on measured error data. Some 3D error propagation sub-models, regarding rotors, supports and casings, are built and combined. The complex error coupling relationship is uncovered using mathematical methods. Rotor and stator tip clearances are predicted and analyzed in different phase angles. The maximum, minimum and average tip clearances can be calculated. The proposed model is implemented by a computer program, and a case study illustrates its performance and verifies its feasibility. The results can be referred by engineers in assembly quality judgement and decision-making. 相似文献
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Considerable progress for the study of solar corona physics has been achieved by China's space physics community. It involves the theoretical study of coronal process of solar active phenomena, solar wind origin, acceleration of solar wind and coronal mass ejections, observational and numerical study of these problems and prediction methods of solar eruptive activities (such as flares/CMEs). Here is a brief summary of the progress in this area. Main progress is put upon the following three topics: corona and solar wind, numerical method, prediction method. 相似文献
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Application of support vector machine combined with K-nearest neighbors in solar flare and solar proton events forecasting 总被引:3,自引:1,他引:2
Rong Li Yanmei Cui Han He Huaning Wang 《Advances in Space Research (includes Cospar's Information Bulletin, Space Research Today)》2008,42(9):1469-1474
The support vector machine (SVM) combined with K-nearest neighbors (KNN), called the SVM-KNN method, is new classing algorithm that take the advantages of the SVM and KNN. This method is applied to the forecasting models for solar flares and proton events. For the solar flare forecasting model, the sunspot area, the sunspot magnetic class, and the McIntosh class of sunspot group and 10 cm solar radio flux are chosen as inputs; for the solar proton event forecasting model, the inputs include the longitude of active regions, the flux of soft X-ray, and those for the solar flare forecasting model. Detailed tests are implemented for both of the proposed forecasting models, in which the SVM-KNN and the SVM methods are compared. The testing results demonstrate that the SVM-KNN method provide a higher forecasting accuracy in contrast to the SVM. It also gives an increased rate of ‘Low’ prediction at the same time. The ‘Low’ prediction means occurrence of solar flares or proton events with predictions of non-occurrence. This method show promise for forecasting models of solar flare and proton events. 相似文献