一类典型模糊阵及其在传递闭包化简中的应用

本文研究模糊矩阵传递闭包的化简问题。通过定义 1类典型阵 ,给出传递闭包能够进一步化简的充要条件 ,为网络最大流分析提供了 1种新的模式。     

Filters for linear sea-wave prediction

Deterministic sea-wave prediction (DSWP) models are appearing in the literature designed for quiescent interval prediction in marine applications dominated by large swell seas. The approach has focused upon spectral methods which are straightforward and intuitively attractive. However, such methods have the disadvantage that while the sea is aperiodic in nature, the standard discrete spectral processing techniques force an absolutely periodic structure onto the resulting sea surface prediction models. As it is the shape of the sea surface that is important in such applications, particularly near the end of the domain which is important, the standard windowing techniques used in signal processing work to reduce leakage artifacts cannot be employed. This has necessitated the use of end matching methods that can be both inconvenient and may reduce the fraction of the time for which legitimate predictions are available. As a result, an investigation has been undertaken of the use of finite impulse response prediction filters to provide the necessary dispersive phase shifting required in DSWP systems. The present work examines the theoretical basis for such filters and explores their properties together with their application to both long and short crested swell seas. It is shown that wide band forms of such filters are only convergent in the sense of distributions having both infinite duration impulse responses and asymptotically divergent first derivatives. However, appropriate band limitation can produce useful finite impulse responses allowing implementation via standard discrete convolution methods. It is demonstrated that despite the prediction filters having a non-causal impulse response such filters can be used in practice due to a combination of the asymmetric nature of the impulse response and the fundamental nature of the prediction process. The findings are confirmed against actual sea-wave data.     

黄海陆架表层水温的低频振动

本文在对位于黄海南部陆架上的朝连岛站30年水温资料进行分析时发现,该站的表层水温有周期大于20个月的低频振动,其中以准两年周期和6年周期最显著。陆架水温的这种低频振动是对东亚季风异常的响应。这种低频振动在冬季与渤海冰情的变化同步;在夏季与黄海底层冷水团的强弱相关。同时,这种低频振动在对马暖流上也有一定的反映。     

Deep ocean fluxes and their link to surface ocean processes and the biological pump

Intense studies of upper and deep ocean processes were carried out in the Northwestern Indian Ocean (Arabian Sea) within the framework of JGOFS and related projects in order to improve our understanding of the marine carbon cycle and the ocean’s role as a reservoir for atmospheric CO₂. The results show a pronounced monsoon-driven seasonality with enhanced organic carbon fluxes into the deep-sea during the SW Monsoon and during the early and late NE Monsoon north of 10°N. The productivity is mainly regulated by inputs of nutrients from subsurface waters into the euphotic zone via upwelling and mixed layer-deepening. Deep mixing introduces light limitation by carrying photoautotrophic organisms below the euphotic zone during the peak of the NE Monsoon. Nevertheless, deep mixing and strong upwelling during the SW Monsoon provide an ecological advantage for diatoms over other photoautotrophic organisms by increasing the silica concentrations in the euphotic zone. When silica concentrations fall below 2 μmol l⁻¹, diatoms lose their dominance in the plankton community. During diatom-dominated blooms, the biological pathway of uptake of CO₂ (the biological pump) appears to be more efficient than during blooms of other organisms, as indicated by organic carbon to carbonate carbon (rain) ratios. Due to the seasonal alternation of diatom and non-diatom dominated exports, spatial variations of the annual mean rain ratios are hardly discernible along the main JGOFS transect.Data-based estimates of the annual mean impact of the biological pump on the fCO₂ in the surface water suggest that the biological pump reduces the increase of fCO₂ in the surface water caused by intrusion of CO₂-enriched subsurface water by 50–70%. The remaining 30 to 50% are attributed to CO₂ emissions into the atmosphere. Rain ratios up to 60% higher in river-influenced areas off Pakistan and in the Bay of Bengal than in the open Arabian Sea imply that riverine silica inputs can further enhance the impact of the biological pump on the fCO₂ in the surface water by supporting diatom blooms. Consequently, it is assumed that reduced river discharges caused by the damming of major rivers increase CO₂ emission by lowering silica inputs to the Arabian Sea; this mechanism probably operates in other regions of the world ocean also.     

A singular evolutive extended Kalman filter to assimilate ocean color data in a coupled physical–biochemical model of the North Atlantic ocean

Within the European DIADEM project, a data assimilation system for coupled ocean circulation and marine ecosystem models has been implemented for the North Atlantic and the Nordic Seas. One objective of this project is to demonstrate the relevance of sophisticated methods to assimilate satellite data such as altimetry, surface temperature and ocean color, into realistic ocean models. In this paper, the singular evolutive extended Kalman (SEEK) filter, which is an advanced assimilation scheme where three-dimensional, multivariate error statistics are taken into account, is used to assimilate ocean color data into the biological component of the coupled system. The marine ecosystem model, derived from the FDM model [J. Mar. Res. 48 (1990) 591], includes 11 nitrogen and carbon compartments and describes the synthesis of organic matter in the euphotic zone, its consumption by animals of upper trophic levels, and the recycling of detritic material in the deep ocean. The circulation model coupled to the ecosystem is the Miami isopycnic coordinate ocean model (MICOM), which covers the Atlantic and the Arctic Oceans with an enhanced resolution in the North Atlantic basin. The model is forced with realistic ECMWF ocean/atmosphere fluxes, which permits to resolve the seasonal variability of the circulation and mixed layer properties. In the twin assimimation experiments reported here, the predictions of the coupled model are corrected every 10 days using pseudo-measurements of surface phytoplankton as a substitute to chlorophyll concentrations measured from space. The diagnostics of these experiments indicate that the assimilation is feasible with a reduced-order Kalman filter of small rank (of order 10) as long as a sufficiently good identification of the error structure is available. In addition, the control of non-observed quantities such as zooplankton and nitrate concentrations is made possible, owing to the multivariate nature of the analysis scheme. However, a too severe truncation of the error sub-space downgrades the propagation of surface information below the mixed layer. The reduction of the actual state vector to the surface layers is therefore investigated to improve the estimation process in the perspective of sea-viewing wide field-of-view sensor (SeaWiFS) data assimilation experiments.     

Bringing GPS into harsh environments for fully automated deformation monitoring

Engineering projects that require deformation monitoring frequently utilize geodetic sensors to measure displacements of target points located in the deformation zone. In situations where control stations and targets are separated by a kilometer or more, GPS can offer higher precision position updates at more frequent intervals than can normally be achieved using total station technology. For large-scale deformation projects requiring the highest precision, it is therefore advisable to use a combination of the two sensors. In response to the need for high precision, continuous GPS position updates in harsh deformation monitoring environments, a software has been developed that employs triple-differenced carrier-phase measurements in a delayed-state Kalman filter. Two data sets were analyzed to test the capabilities of the software. In the first test, a GPS antenna was displaced using a translation stage to mimic slow deformation. In the second test, data collected at a large open pit mine were processed. It was shown that the delayed-state Kalman filter developed could detect millimeter-level displacements of a GPS antenna. The actual precision attained depends upon the amount of process noise infused at each epoch to accommodate the antenna displacements. Higher process noise values result in quicker detection times, but at the same time increase the noise in the solutions. A slow, 25 mm displacement was detected within 30 min of the full displacement with sigma values in E, N and U of ±10 mm or better. The same displacement could also be detected in less than 5 h with sigma values in E, N and U of ±5 mm or better. The software works best for detecting long period deformations (e.g., 20 mm per day or less) for which sigma values of 1–2 mm are attained in all three solution components. It was also shown that the triple-differenced carrier-phase observation can be used to significantly reduce the effects of residual tropospheric delay that would normally plague double-differenced observations in harsh GPS environments.

An approach to aid INS using time-differenced GPS carrier phase (TDCP) measurements

This paper presents a simple and effective approach that incorporates single-frequency, L1 time-differenced GPS carrier phase (TDCP) measurements without the need of ambiguity resolution techniques and the complexity to accommodate the delayed-state terms. Static trial results are included to illustrate the stochastic characteristics and effectiveness of the TDCP measurements in controlling position error growth. The formulation of the TDCP observation model is also described in a 17-state tightly-coupled GPS/INS iterative, extended Kalman filter (IEKF) approach. Preliminary land vehicle trial results are also presented to illustrate the effectiveness of the TDCP which provides sub-meter positional accuracies when operating for more than 10 min.     

经验模式分解的尺度阈值滤波在变形分析中的应用

本文将信号处理领域的经验模式分解算法应用于变形信息的提取中,通过引入阈值函数,建立了基于经验模式分解的尺度阈值滤波模型,采用优化模型确定了经验模式分解的次数。分别通过模拟试验和实测数据与小波阈值法和多项式拟合法进行了比对,分析表明:在低噪声情况下,三种方法都有一定的滤波效果;在高噪声情况下,经验模式分解的尺度阈值滤波法具有与小波阈值去噪法相等的精度,而且瞬时强噪声识别能力更好,优于多项式拟合法。     

基于卡尔曼滤波的时间序列影像背景估计

文章将卡尔曼滤波理论应用于时间序列影像的背景估计,引入衰减记忆因子解决滤波的发散问题,最终实现了对背景信号的估计。