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).     

Corrosion behaviour of Ni-Zr-Ti-Si-Sn amorphous plasma spray coating

Ni₅₉Zr₂₀Ti₁₆Si₂Sn₃ amorphous material was deposited by a vacuum plasma spraying technique onto steel and copper substrates in order to investigate their behaviour in a corrosive environment. For comparison, the same alloy was prepared as amorphous ribbons by melt spinning. The amorphous nature of the coatings and ribbons was characterized by XRD, DSC and TEM, while XPS and AES analyses were performed to understand the origin of passivation and mode of corrosion. The corrosion behaviour of the coating was studied in H₂SO₄ and HCl solutions open to air at room temperature. Potentiodynamic polarisation and galvanic coupling tests were carried out on the substrate and the coating. It was found that the formation of Zr-, Ti- and Si-rich passive oxide layers provide a high corrosion resistance in H₂SO₄ solution while the breakdown of the passive layer by chloride ion adsorption was responsible for pitting corrosion of the Ni₅₉Zr₂₀Ti₁₆Si₂Sn₃ amorphous ribbons in HCl solution. Galvanic corrosion was the dominant corrosion mechanism for the coating/copper hybrid structure, in contrast to the Ni₅₉Zr₂₀Ti₁₆Si₂Sn₃ amorphous coating, which efficiently protected the steel substrate in the corrosive environment.     

Coaxial Alginate Hydrogels: From Self-Assembled 3D Cellular Constructs to Long-Term Storage

Alginate as a versatile naturally occurring biomaterial has found widespread use in the biomedical field due to its unique features such as biocompatibility and biodegradability. The ability of its semipermeable hydrogels to provide a favourable microenvironment for clinically relevant cells made alginate encapsulation a leading technology for immunoisolation, 3D culture, cryopreservation as well as cell and drug delivery. The aim of this work is the evaluation of structural properties and swelling behaviour of the core-shell capsules for the encapsulation of multipotent stromal cells (MSCs), their 3D culture and cryopreservation using slow freezing. The cells were encapsulated in core-shell capsules using coaxial electrospraying, cultured for 35 days and cryopreserved. Cell viability, metabolic activity and cell–cell interactions were analysed. Cryopreservation of MSCs-laden core-shell capsules was performed according to parameters pre-selected on cell-free capsules. The results suggest that core-shell capsules produced from the low viscosity high-G alginate are superior to high-M ones in terms of stability during in vitro culture, as well as to solid beads in terms of promoting formation of viable self-assembled cellular structures and maintenance of MSCs functionality on a long-term basis. The application of 0.3 M sucrose demonstrated a beneficial effect on the integrity of capsules and viability of formed 3D cell assemblies, as compared to 10% dimethyl sulfoxide (DMSO) alone. The proposed workflow from the preparation of core-shell capsules with self-assembled cellular structures to the cryopreservation appears to be a promising strategy for their off-the-shelf availability.     

面向工业平稳/非平稳复杂系统的在线故障监测技术

针对复杂系统中关键性能指标（KPI）相关故障检测方法检测精度低的问题,提出基于双层改进潜结构投影（DL-IPLS）的KPI相关故障检测方法. 利用协整分析和主元分析建立底层模型,对非平稳和平稳变量进行特征提取. 将提取的信息进行融合,建立改进潜结构投影的上层模型,根据融合信息对KPI的贡献进行空间分解. 在2个正交子空间中设计统计量,实现KPI相关故障的在线监测. 田纳西-伊斯曼过程和青霉素发酵过程的仿真结果表明,在面向工业平稳和非平稳复杂工业系统检测时,所提方法有效提高了KPI相关故障的检测率,降低了KPI无关故障的误报率.