春秋时期吴国都城遗迹位置的遥感调查及预测

利用遥感技术对苏州及太湖地区做了大面积的地学调查与分析,并在进一步研究了史料和前人工作成果后,进行了春秋吴国都城遗迹位置的遥感调查及预测,它将为考古界进一步有计划地探查和发掘提供参考。     

A new method for indentifying the model error of adjustment system

Some theory problems affecting parameter estimation are discussed in this paper. Influence and transformation between errors of stochastic and functional models is pointed out as well. For choosing the best adjustment model, a formula, which is different from the literatures existing methods, for estimating and identifying the model error, is proposed. On the basis of the proposed formula, an effective approach of selecting the best model of adjustment system is given. Project supported by the Open Research Fund Program of the Key Laboratory of Geospace Environment and Geodesy, Ministry of Education, Wuhan University (No. 905276031-04-10).     

CAPPS多箱模式中光化学模式的嵌套与城市大气臭氧数值预报

为解决城市边界层中大气O₃浓度的预报问题，选用一种简明有效的光化学反应方案建立光化学模式, 并与大气平流扩散的箱格预报模型进行了嵌套，从而将大气化学过程引入到城市空气污染数值预报系统 (CAPPS) 中。同时，根据实际需要研究了VOC (Volatile Organic Compounds) 浓度及NO_x源排放量等参数缺测时，根据前期监测浓度采用遗传算法反演未知参数的具体方法。利用2001年9月8~17日在北京舞蹈学院监测的O₃、NO_x资料及相应的气象数据，用改进后的CAPPS模式进行了单箱预报试验。预报试验结果表明，嵌套后的模式对O₃浓度的变化具备一定的预报能力。     

一种短期气候预测集成技术的应用研究

以递归正权综合决策方案为基础，结合实际情况，首先对多种预报方法进行筛选，选取预报效果较好并且较为稳定的几种方法参与集成，并将集成结果写成Micaps数据格式，最终以图形方式显示出来。评分结果表明：该集成方法对温度预报和灾害指数预报较权重平均集成法效果好；而对月雨量预报和季度雨量、温度预报则与权重平均方法相当；但是对季度雨量、温度预报来说，集成后预测精度有明显提高。同时，将集成结果通过Micaps工作平台以图形方式显示，不仅客观、定量，而且更一目了然．     

NWP在颠簸预报中的应用

颠簸是航空气上重要预告内容之一，历史上由于颠簸引起的飞行事故及征候占有一定的比例，颠簸预报也是气象预报的难点，颠簸对飞行的影响至关重要，轻则引起航行中的飞机摆动偏航，重则造成飞机解体，近年来随着太原机场卫星广播传真数值预报产品的使用，我们也在逐步尝试使用数值产品分析颠簸，使颠簸的预报有了明显提高。     

Structural damage detection using the optimal weights of the approximating artificial neural networks

This work presents a novel neural network‐based approach to detect structural damage. The proposed approach comprises two steps. The first step, system identification, involves using neural system identification networks (NSINs) to identify the undamaged and damaged states of a structural system. The partial derivatives of the outputs with respect to the inputs of the NSIN, which identifies the system in a certain undamaged or damaged state, have a negligible variation with different system errors. This loosely defined unique property enables these partial derivatives to quantitatively indicate system damage from the model parameters. The second step, structural damage detection, involves using the neural damage detection network (NDDN) to detect the location and extent of the structural damage. The input to the NDDN is taken as the aforementioned partial derivatives of NSIN, and the output of the NDDN identifies the damage level for each member in the structure. Moreover, SDOF and MDOF examples are presented to demonstrate the feasibility of using the proposed method for damage detection of linear structures. Copyright © 2001 John Wiley & Sons, Ltd.     

Parameter identification of inelastic structures under dynamic loads

The generalized model of differential hysteresis contains 13 control parameters with which it can curve‐fit practically any hysteretic trace. Three identification algorithms are developed to estimate the control parameters of hysteresis for different classes of inelastic structures. These algorithms are based upon the simplex, extended Kalman filter, and generalized reduced gradient methods. Novel techniques such as global search and internal constraints are incorporated to facilitate convergence and stability. Effectiveness of the devised algorithms is demonstrated through simulations of two inelastic systems with both pinching and degradation characteristics in their hysteretic traces. Owing to very modest computing requirements, these identification algorithms may become acceptable as a design tool for mapping the hysteretic traces of inelastic structures. Copyright © 2002 John Wiley & Sons, Ltd.     

Damaging properties of ground motions and prediction of maximum response of structures based on momentary energy response

Dynamic damaging potential of ground motions must be evaluated by the response behaviour of structures, and it is necessary to indicate what properties of ground motions are most appropriate for evaluation. For that purpose, the behaviour of energy input process and hysteretic energy dissipation are investigated in this study. It is found that the momentary input energy that is an index for the intensity of input energy is related to the characteristics of earthquakes such as cyclic or impulsive, and to the response displacement of structures immediately. On the basis of these results, a procedure is proposed to predict inelastic response displacement of structures by corresponding earthquake input energy to structural dissipated damping and hysteretic energy. In this procedure the earthquake response of structures is recognized as an input and dissipation process of energy, and therefore structural properties and damaging properties of ground motions can be taken into account more generally. Lastly, the studies of the pseudodynamic loading test of reinforced concrete structure specimens subjected to ground motions with different time duration are shown. The purpose of this test is to estimate the damaging properties of ground motions and the accuracy of the proposed prediction procedure. Copyright © 2002 John Wiley & Sons, Ltd.     

神头泉流量变化规律研究--灰色系统理论的具体应用

在分析神头泉流量衰减特征的前提下，利用灰色系统理论建立了描述神头泉流量变化规律的数学模型。以1985年为界，神头泉的流量变化过程可分为两个阶段，GM（1，1）模型与灰色预测校正模型则分别用于对这两个阶段的流量变化特征进行刻画。神头泉2000－2004年的流量预测值表明，在神头泉域保证90年代中期的高降水量和朔州市各地下水用户对泉域内的地下水不进行超采的基础上，神头泉的流量在未来的一段时间内会维持1993年以来的增长趋势。     

应用CP网络进行岩性识别

为通过测井解决岩性识别问题，引入了具有分类准确、算法简练等优点的CP（Counter-Propagation)网络。在详细介绍CP网络的网络模型和算法的基础上，结合某油田的实际测井资料，进行了CP网络识别研究。应用结果表明：CP网络训练周期短、识别准确率高、不存在收敛问题。通过试验研究得出结论：CP网络完全可以用于解决岩性识别等问题，具有广阔的应用前景。