Activation-Based Recursive Self-Organising Maps: A General Formulation and Empirical Results

We generalize a class of neural network models that extend the Kohonen Self-Organising Map (SOM) algorithm into the sequential and temporal domain using recurrent connections. Behaviour of the class of Activation-based Recursive Self-Organising Maps (ARSOM) is discussed with respect to the choice of transfer function and parameter settings. By comparing performances to existing benchmarks we demonstrate the robustness and systematicity of the ARSOM models, thus opening the door to practical applications.     

Choosing Feature Sets for Training and Testing Self-Organising Maps: A Case Study

Statistical pattern recognition techniques, supervised and unsupervised classification techniques being two good examples here, rely on the computations of similarity and distance metrics. The   distances are computed in a multi-dimensional space. The axes of this space in principle relate to the features inherent in the input data. Usually, such features are chosen by neural network developers, thereby introducing a possible bias. A method of automatically generating feature sets is discussed, with specific reference to the categorisation of streams of free-text news items. The feature sets were generated by a procedure that automatically selects a group of keywords based on a lexico-semantic analysis. Three different types of text streams – headlines only, news summaries and full news items including the body of the text –have been categorised using Self-Organising Feature Maps (SOFM). A method for assessing the discrimination ability of a SOFM, based on Fisher’s Linear Discriminant Rule suggests that the maps trained on vectors related to summaries only provides a fairly accurate cluster when compared with vectors related to full text. The use of summaries as document surrogates for document categorisation is suggested.     

A Visualised Software Library: Nested Self-Organising Maps for Retrieving and Browsing Reusable Software Assets

This paper presents an approach to self-structuring software libraries. The authors developed a representation scheme to construct a feature space over a collection of software assets. The feature space is represented and classified by a variety of the self-organising map, called the Nested Software Self-Organising Map (NSSOM), consisting of a top map and a set of sub-maps nested in the top map. The clustering on the top map provides general improvements in retrieval recall, while the lower-level nested maps further elaborate the clusters into more specific groups enhancing retrieval precision. The results of preliminary evaluation showed that NSSOM is capable of enhancing precision without sacrificing recall. In addition, a user-friendly browsing facility has also been developed which helps users predict the desired components by providing an intelligible search space. The present approach attempts to achieve an optimal combination of efficiency, accuracy and user-friendliness, which is not offered by the existing software retrieval systems.     

TCAM中规则的优化表示

在高速分组分类中,基于TCAM的硬件实现方法是一种重要的解决方案.但TCAM存储空间价格昂贵,并且要求区间匹配转换成前缀匹配,大区间报文匹配的直接转化通常会引起规则空间的膨胀.本文提出一种规则的优化表示方案,提高了区间规则表示的效率,使区间匹配转化的最坏情况由2w-2减少到w,有效节省了空间.     

A new way to query GISs on the Web

We created a distributed open spatial query mechanism (Geo) that gives users access to cross-platform, cross-vendor geographic information systems via the Internet. A Java implementation provides maximum flexibilty for Web based clients     

A new approach to partial constraint satisfaction problems

A finite binary Constraint Satisfaction Problem (CSPs) is defined as consisting of a set of n problem variables, a domain of d potential values for each variable and a set of m binary constraints involving only two variables at a time. A solution to such a CSP is specified by assignment of a value to each variable that does not violate any of the constraints. The CSPs belong to the class of NP-Complete Problems. Backtracking and its variants have been generally used for solving CSPs. The class of Partial Constraint Satisfaction Problems (PCSPs) is a subclass of CSPs that are either too difficult to solve or are unsolvable. Near optimal solutions are always desired to these problems.

In this article, we have considered only finite binary CSPs or PCSPs and developed a method of time complexity O(n ² d ²) to obtain a near optimal solution for them. The performance of the method in terms of the average number of consistency checks and the average number of constraint violations is measured on various randomly generated binary CSPs and compared with the Branch and Bound (BB) method used to obtain the same solution. The BB method is a widely used optimization technique that may be viewed as a variation of backtracking. Thus, it was a natural choice in seeking an analog of backtracking to find optimal partial solutions for PCSPs. The proposed method moves much faster to the solution. The performance results indicate that in terms of the number of consistency checks, the proposed method has much less consistency checks than BB whereas in terms of average number of constraint violations both methods are same. An upper bound on the distance of the solution from the optimal solution is obtained analytically as ?n(n???2)(d???2)/(d???1)?.     

A new way to represent the relative position between areal objects

The fuzzy qualitative evaluation of directional spatial relationships (such as “to the right of”, “to the south of...”) between areal objects often relies on the computation of a histogram of angles, which is considered to provide a good representation of the relative position of an object with regard to another. In this paper, the notion of the histogram of forces is introduced. It generalizes and may supersede the histogram of angles. The objects (2D entities) are handled as longitudinal sections (1D entities), not as points (OD entities). It is thus possible to fully benefit from the power of integral calculus and, so, ensure rapid processing of raster data, as well as of vector data, explicitly considering both angular and metric information     

A new way to detect arrows in line drawings

A new way of detecting arrows in line drawings is proposed in this paper. We provide a set of criteria which are aggregated using the Choquet integral. These criteria are defined from the geometric properties of an arrow. Experimental results on two kinds of line-drawing documents show the interest of our approach.     

A new approach to averaging level control

In this paper, a new simple algorithm for averaging level control is presented. The proposed algorithm is based on the use of a static nonlinear controller to improve the input flow filtering by reducing the slope of the controller around the average input flow. In order to adapt the controller to changes in the average input flow, a low-pass filtered value of the estimated input flow is used to update the nonlinear function that defines the static controller. The stability of the closed loop is demonstrated, and the validity of the proposed strategy is tested in simulations and on a real oil refinery process.     

A new way to treat material discontinuities in the numerical manifold method

A new technique to describe the material discontinuities within the numerical manifold method framework is presented. A new concept of ‘weak-discontinuous physical covers’ is introduced. Special local approximations, which are continuous in the approximation but discontinuous in its first derivative, are tailored for weak-discontinuous physical covers. This new method takes full advantage of the partition of unity. Several typical examples are presented. Results have shown the accuracy and convenience of the proposed method in modeling material interfaces, especially composite materials with multiple holes/inclusions.     

A new hybrid method using evolutionary algorithms to train Fuzzy Cognitive Maps

A novel hybrid method based on evolutionary computation techniques is presented in this paper for training Fuzzy Cognitive Maps. Fuzzy Cognitive Maps is a soft computing technique for modeling complex systems, which combines the synergistic theories of neural networks and fuzzy logic. The methodology of developing Fuzzy Cognitive Maps relies on human expert experience and knowledge, but still exhibits weaknesses in utilization of learning methods and algorithmic background. For this purpose, we investigate a coupling of differential evolution algorithm and unsupervised Hebbian learning algorithm, using both the global search capabilities of Evolutionary strategies and the effectiveness of the nonlinear Hebbian learning rule. The use of differential evolution algorithm is related to the concept of evolution of a number of individuals from generation to generation and that of nonlinear Hebbian rule to the concept of adaptation to the environment by learning. The hybrid algorithm is introduced, presented and applied successfully in real-world problems, from chemical industry and medicine. Experimental results suggest that the hybrid strategy is capable to train FCM effectively leading the system to desired states and determining an appropriate weight matrix for each specific problem.     

A new way to stabilize discrete-time plants under compact uncertainty

A method is proposed for stabilization of uncertain discrete-time plants of which only compact sets are known to which plant parameters and exogenous perturbations belong. The parametric family of feedbacks is quantified and subsequently a stabilizing feedback is localized on the resultant finite discrete set by using on-line measurements of the state path. This method is termed locatization. Its salient features are quantification and the localization algorithm. Three localization procedures are described which are used under various assumptions on the form of the uncertainty. Solvability conditions are provided for the stabilization problem and estimates of transient processes are reported.     

改进水印图片里文字识别效果的新方法

现今,OCR文字识别技术已经飞快发展,如一般的车牌识别、扫描文本识别等技术的识别率已达到非常高的水平,但这些识别技术在带水印图片的文字识别这一方面表现一般.其原因主要在识别前后的处理还不够.主要讲述了文字识别的新预处理的方法和后期处理的实现.     

Structure-Measure: A New Way to Evaluate Foreground Maps

International Journal of Computer Vision - Foreground map evaluation is crucial for gauging the progress of object segmentation algorithms, in particular in the field of salient object detection...     

A new tractable class of constraint satisfaction problems

In this paper we consider constraint satisfaction problems where the set of constraint relations is fixed. Feder and Vardi (1998) identified three families of constraint satisfaction problems containing all known polynomially solvable problems. We introduce a new class of problems called para-primal problems, incomparable with the families identified by Feder and Vardi (1998) and we prove that any constraint problem in this class is decidable in polynomial time. As an application of this result we prove a complete classification for the complexity of constraint satisfaction problems under the assumption that the basis contains all the permutation relations. In the proofs, we make an intensive use of algebraic results from clone theory about the structure of para-primal and homogeneous algebras. AMS subject classification 08A70     

A new way of counting n

In this paper, we shall give a combinatorial proof of the following equation:

,

where m and n are positive integers, m ≤ n, and k₁, k₂, …, k_n-1 are nonnegative integers.     

Feature‐oriented programming: A new way of object composition

We propose a new model for flexible composition of objects from a set of features. Features are services of an object and are similar to classes in object‐oriented languages. In many cases, features have to be adapted in the presence of other features, which is also called the feature interaction problem. We introduce explicit interaction handlers which can adapt features to other features by overriding methods. When features are composed, the appropriate interaction handling is added in a way which generalizes inheritance and aggregation. For a set of features, an exponential number of different feature combinations is possible, based on a quadratic number of interaction resolutions. We present the feature model as an extension of Java and give two translations to Java, one via inheritance and the other via aggregation. We show that the feature model interacts nicely with several common language extensions such as type parameters, exceptions, and higher‐order functions. Copyright © 2001 John Wiley & Sons, Ltd.