A detailed analysis on the effect of temporal allaying associated with TOPEX/Poseidon, Geosat, and ERS altimeters has been caried out. In the time domain, aliasing occurs when the original period of an oceanographic signal, To, is less than twice the satellite repeat orbit period, Ts. The most striking feature of temporal aliasing is that the alias period, To, appears as a quasi periodic &llke function with respect to Ts, and a non-periodic &-like function with respect to To. Histograms of Ta versus To suggest that signals wltb original periods between 0 and 2Ts are aliased onto periods ranging from 2Ts to with a rapidly decreasing probabilit3z The potential consequences of temporal aliasing in satellite altimetry are discussed in the context of orbit design and sampling strategy for geophysical applications.

[1] More than a dozen of wind speed (U) algorithms have been proposed during the past 2 decades, as a result of a continuing effort to improve altimeter wind measurement. The progress in terms of accuracy, however, is seen to be rather slow. The reported root mean square (RMS) error of prevailing algorithms varies mostly between 1.6 and 2.0m/s for the dominant wind regime. As far as the TOPEX altimeter is concerned, three measured quantities, namely, the radar cross sections from Ku and C band (sKu and sC), as well as the significant wave height (Hs), have been used in previous algorithm developments, resulting in a variety of single-, dual-, and three-parameter model functions. On the basis of the finding of a banded dependency of the U-sKu relationship on sC a new pproach for retrieving altimeter wind speed, termed linear composite method (LCM), is proposed in this study. The LCM model function appears as a set of sC-dependent linear relations between U and sKu. A unique advantage of this approach is that it allows the algorithm to be tuned or expanded for a given range of wind speed without affecting the rest. Over 1.7 million coincident TOPEX/NASA scatterometer (NSCAT) and TOPEX/QuikSCAT data covering a period of 2.5 years are used to adjust the model. Validation against extensive buoy measurements indicates that the LCM algorithm is almost unbiased and has an overall RMS error of 1.56m/s, which is 12% lower compared to the algorithm in operational use [Witter and Chelton, 1991]. In addition, a small (2.5-6%, depending on the reference data set) but significant improvement is found for the LCM when compared to the most recent dual-parameter algorithm [Gourrion et al., 2002].

In this paper, a new method for vibration fault diagnosis of large steam turbine-generation is proposed based on theory of adaptive wavelet network (AWN). A special adaptive wavelet network classifier for arbitrary nonlinear system is developed, algorithm of back propagation type is proposed. By means of computer simulation and with applying to fault diagnosis, and the performance of the AWN are compared with that of the conventional multilayer perception. The results show that this method has better diagnostic, faster speed of learning properties and it is an efficient new method. And then some important related elements, which affect the performance of classification AWN is analyzed, conclusions that are of great importance in real application, are obtained.

本文叙述了面向对象技术构造电力变压器绝缘诊断知识库的原理。采用这种面向对象技术使得整个专家知识呈分布式、层次式特征，从而克服了传统产生式专家系统的固有局限。本文还用“黑板结构”模式改变了基本结构专家系统的构造并用计算机程序实现，取得了较为满意的效果。

Traditionally, the tropical zone is known as the "heat reservoir" of the ocean and the "firebox" of the atmosphere. The western equatorial Pacific has been identified as both the warmest portion of the heat reservoir, named "warm pool" (WP), and the hottest portion of the firebox where a huge amount of precipitation-induced latent-heat release is accumulated. The latter mirrors a fact that the western tropical Pacific is also the wettest area on the globe, termed "rain pool" (RP), where the maximum annual precipitation is observed. The accumulation of continuous satellite data has reached a point that decade-long simultaneous observations of many important geophysical parameters have become available in recent years. One such example is the availability of a concurrent dataset of sea surface temperature, oceanic precipitation, and sea surface wind field for 1993-2002 derived from NOAA/AVHRR (Advanced Very High Resolution Radiometer), TOPEX/TMR (TOPEX Microwave Radiometer), and ERS-1,2/QuikSCAT, respectively. In the present study, this dataset is used to demonstrate and investigate the coupling and covarying effects of the Pacific WP and RP, leading to a number of interesting findings on their structural similarity, locational shift, phase lag, and evolutional coherency in association with the development of and the vacillation between El Nino and La Nina events.