非线性地基上桩结构物空间分析

本文提出了桩结构物－非线性地基空间相互作用分析的迭代法和非线性有限元法。其中，桩基分析用ｐ－ｙ曲线法。结合实际结构物进行了大量的计算及比较分析，成果是令人满意的，可以付诸应用。     

高层结构几何非线性分析的QR法

本文利用ＱＲ法分析高层建筑结构几何非线性问题，建立了新的计算格式。该法不仅未知量的数目少，精度高，而且输入简单，计算速度快，可以用微机分析复杂问题。     

江苏省C3本地电话网组网方式探讨

本文结合江苏Ｃ４本地网的实际情况，说明Ｃ４本地网纳入Ｃ３本地电话网的必要性，例举了４种Ｃ３本地网的组网方式。     

Canonical density control

The Sparse Table is a data structure for controlling density in an array which was first proposed in 1981 and has recently reappeared as a component of cache-oblivious data structures. All existing variants of the Sparse Table divide the array into blocks that have a calibrator tree above them. We show that the same amortized complexity can be achieved without this auxiliary structure, obtaining a canonical data structure that can be updated by conceptually simpler algorithms.     

Criteria of dynamic buckling estimation of thin-walled structures

The paper deals with dynamic buckling of thin-walled structures (plates and beam-columns with open cross-section) subjected to compressive rectangular pulse loading. The local, global and interactive dynamic buckling was analysed. Author proposes the new criterion for critical amplitude of pulse loading leading to stability loss. The proposed criterion is a modification of quasi-bifurcation criterion formulated by Kleiber, Kotula and Saran. Results obtained using proposed criterion were compared with other well-known criteria (Volmir (V) and Budiansky–Hutchinson (B–H) criterion).     

Nonlinear black-box modeling in system identification: a unified overview

A nonlinear black-box structure for a dynamical system is a model structure that is prepared to describe virtually any nonlinear dynamics. There has been considerable recent interest in this area, with structures based on neural networks, radial basis networks, wavelet networks and hinging hyperplanes, as well as wavelet-transform-based methods and models based on fuzzy sets and fuzzy rules. This paper describes all these approaches in a common framework, from a user's perspective. It focuses on what are the common features in the different approaches, the choices that have to be made and what considerations are relevant for a successful system-identification application of these techniques. It is pointed out that the nonlinear structures can be seen as a concatenation of a mapping form observed data to a regression vector and a nonlinear mapping from the regressor space to the output space. These mappings are discussed separately. The latter mapping is usually formed as a basis function expansion. The basis functions are typically formed from one simple scalar function, which is modified in terms of scale and location. The expansion from the scalar argument to the regressor space is achieved by a radial- or a ridge-type approach. Basic techniques for estimating the parameters in the structures are criterion minimization, as well as two-step procedures, where first the relevant basis functions are determined, using data, and then a linear least-squares step to determine the coordinates of the function approximation. A particular problem is to deal with the large number of potentially necessary parameters. This is handled by making the number of ‘used’ parameters considerably less than the number of ‘offered’ parameters, by regularization, shrinking, pruning or regressor selection.     

压电主动杆位置和增益在随机智能桁架结构振动控制中的优化

对结构物理参数、几何尺寸、作用荷载和控制力同时具有随机性时 ,随机压电智能桁架结构中主动杆的配置位置和闭环控制系统增益进行了优化 ,基于系统最小储存能 ,构建了具有动应力、动位移可靠性约束的主动杆配置和控制增益的优化模型 ;并对结构动力响应的数字特征进行了推导。通过算例 ,验证了该优化配置模型的合理性和有效性 ,获得了若干有意义的结论。     

Tree-based partitioning of date for association rule mining

The most computationally demanding aspect of Association Rule Mining is the identification and counting of support of the frequent sets of items that occur together sufficiently often to be the basis of potentially interesting rules. The task increases in difficulty with the scale of the data and also with its density. The greatest challenge is posed by data that is too large to be contained in primary memory, especially when high data density and/or low support thresholds give rise to very large numbers of candidates that must be counted. In this paper, we consider strategies for partitioning the data to deal effectively with such cases. We describe a partitioning approach which organises the data into tree structures that can be processed independently. We present experimental results that show the method scales well for increasing dimensions of data and performs significantly better than alternatives, especially when dealing with dense data and low support thresholds. Shakil Ahmed received a first class BSc (Hons) degree from Dhaka University, Bangladesh, in 1990; and an MSc (first class), also Dhaka University, in 1992. He received his PhD from The University of Liverpool, UK, in 2005. From 2000 onwards he is a member of the Data Mining Group at the Department of Computer Science of the University of Liverpool, UK. His research interests include data mining, Association Rule Mining and pattern recognition. Frans Coenen has been working in the field of Data Mining for many years and has written widely on the subject. He received his PhD from Liverpool Polytechnic in 1989, after which he took up a post as a RA within the Department of Computer Science at the University of Liverpool. In 1997, he took up a lecturing post within the same department. His current Data Mining research interests include Association rule Mining, Classification algorithms and text mining. He is on the programme committee for ICDM'05 and was the chair for the UK KDD symposium (UKKDD'05). Paul Leng is professor of e-Learning at the University of Liverpool and director of the e-Learning Unit, which is responsible for overseeing the University's online degree programmes, leading to degrees of MSc in IT and MBA. Along with e-Learning, his main research interests are in Data Mining, especially in methods of discovering Association Rules. In collaboration with Frans Coenen, he has developed efficient new algorithms for finding frequent sets and is exploring applications in text mining and classification.     

Active symbols and internal models: Towards a cognitive connectionism

In the first section of the article, we examine some recent criticisms of the connectionist enterprise: first, that connectionist models are fundamentally behaviorist in nature (and, therefore, non-cognitive), and second that connectionist models are fundamentally associationist in nature (and, therefore, cognitively weak). We argue that, for a limited class of connectionist models (feed-forward, pattern-associator models), the first criticism is unavoidable. With respect to the second criticism, we propose that connectionist modelsare fundamentally associationist but that this is appropriate for building models of human cognition. However, we do accept the point that there are cognitive capacities for which any purely associative model cannot provide a satisfactory account. The implication that we draw from is this is not that associationist models and mechanisms should be scrapped, but rather that they should be enhanced.In the next section of the article, we identify a set of connectionist approaches which are characterized by active symbols — recurrent circuits which are the basis of knowledge representation. We claim that such approaches avoid criticisms of behaviorism and are, in principle, capable of supporting full cognition. In the final section of the article, we speculate at some length about what we believe would be the characteristics of a fully realized active symbol system. This includes both potential problems and possible solutions (for example, mechanisms needed to control activity in a complex recurrent network) as well as the promise of such systems (in particular, the emergence of knowledge structures which would constitute genuine internal models).