Salwa K. Abd-El-Hafiz

Using neural networks in the identification of Preisach-type hysteresis models

The identification process of the classical Preisach-type hysteresis model reduces to the determination of the weight function of elementary hysteresis operators upon which the model is built. It is well known that the classical Preisach model can exactly represent hysteretic nonlinearities which exhibit wiping-out and congruency properties. In that case, the model identification can be analytically and systematically accomplished by using first-order reversal curves. If the congruency property is not exactly valid, the Preisach model can only be used as an approximation. It is possible to improve the model accuracy in this situation by incorporating more appropriate experimental data during the identification stage. However, performing this process using the traditional systematic techniques becomes almost impossible. In this paper, the machinery of neural networks is proposed as a tool to accomplish this identification task. The suggested identification approach has been numerically implemented and carried out for a magnetic tape sample that does not possess the congruency property. A comparison between measured data and model predictions suggests that the proposed identification approach yields more accurate results.

A knowledge-based approach to the analysis of loops

The paper presents a knowledge-based analysis approach that generates first order predicate logic annotations of loops. A classification of loops according to their complexity levels is presented. Based on this taxonomy, variations on the basic analysis approach that best fit each of the different classes are described. In general, mechanical annotation of loops is performed by first decomposing them using data flow analysis. This decomposition encapsulates closely related statements in events, that can be analyzed individually. Specifications of the resulting loop events are then obtained by utilizing patterns, called plans, stored in a knowledge base. Finally, a consistent and rigorous functional abstraction of the whole loop is synthesized from the specifications of its individual events. To test the analysis techniques and to assess their effectiveness, a case study was performed on an existing program of reasonable size. Results concerning the analyzed loops and the plans designed for them are given.

Entropies as measures of software information

This paper investigates the use of entropies as measures of software information content. Several entropies, including the well-known Shannon entropy, are characterized by their mathematical properties. Based on these characterizations, the entropies, which are suitable for measuring software systems, are rigorously chosen. By treating a software system as an information source, the function calls in procedural systems or method invocations in object-oriented systems resemble the emission of symbols from an information source. Thus, the probabilities, required for computing the entropies, are obtained using an empirical distribution of function calls or method invocations. Application of the suggested measures on procedural and object-oriented programs is further explained using two small examples. Because a. rigorous definition of information measures does not guarantee their usefulness in practice, an evaluation case study is performed In particular, the aim of this study is to practically evaluate the intuitiveness and scalability of the measures on a real software system totaling about 460000 lines of code. In addition to being intuitive and meaningful, the case study results highlight differences between the information measures. Thus, the family of measures presented can satisfy different measurement requirements.

Towards automated support for extraction of reusable components

The problems related to the analysis and reengineering of existing software in order to reuse it are discussed. The authors introduce a process model for component extraction and focus on the problem of analyzing and qualifying software components which are candidates for reuse. A prototype tool for supporting the extraction of reusable components is presented. One of the components of this tool aids in understanding programs and is based on the functional model of correctness. It can assist software engineers in the process of finding correct formal specifications for programs. A detailed description of this component and an example to demonstrate a possible operational scenario are given.<<ETX>>

A fractal-based image encryption system

This study introduces a novel image encryption system based on diffusion and confusion processes in which the image information is hidden inside the complex details of fractal images. A simplified encryption technique is, first, presented using a single-fractal image and statistical analysis is performed. A general encryption system utilising multiple fractal images is, then, introduced to improve the performance and increase the encryption key up to hundreds of bits. This improvement is achieved through several parameters: feedback delay, multiplexing and independent horizontal or vertical shifts. The effect of each parameter is studied separately and, then, they are combined to illustrate their influence on the encryption quality. The encryption quality is evaluated using different analysis techniques such as correlation coefficients, differential attack measures, histogram distributions, key sensitivity analysis and the National Institute of Standards and Technology (NIST) statistical test suite. The obtained results show great potential compared to other techniques.

Identification and testing of an efficient hopfield neural network magnetostriction model

Abstract Magnetostriction models are indispensable to different crucial computational activities such as those dealing with active vibration damping devices and optimum clamping stresses for transformer sheets. This paper presents an efficient magnetostriction Preisach-type model based on the effective field approach. According to this approach, the total applied field may be regarded as a super position of the actual magnetic field and a stress-dependent feedback term. Construction and identification of the model is carried out by providing experimental training data to a modular discrete Hopfield neural network–linear neural network combination. Experimental testing suggests that this model can lead to good qualitative and quantitative simulation results.

Symmetric encryption algorithms using chaotic and non-chaotic generators: A review

Graphical abstract

A performance-oriented power transformer design methodology using multi-objective evolutionary optimization

Transformers are regarded as crucial components in power systems. Due to market globalization, power transformer manufacturers are facing an increasingly competitive environment that mandates the adoption of design strategies yielding better performance at lower costs. In this paper, a power transformer design methodology using multi-objective evolutionary optimization is proposed. Using this methodology, which is tailored to be target performance design-oriented, quick rough estimation of transformer design specifics may be inferred. Testing of the suggested approach revealed significant qualitative and quantitative match with measured design and performance values. Details of the proposed methodology as well as sample design results are reported in the paper.

Image encryption using generalized tent map

This paper introduces two generalized tent maps where the conventional map is a special case. Although the output of the conventional tent map shows different responses, it has only one control parameter that limits its behavior and applications. The proposed generalized tent maps increase the degrees of freedom and produce a versatile response that can fit many applications. The characteristics of each generalization are discussed such as: fixed points, bifurcation diagrams, and Lyapunov exponents. Finally, a simple image encryption application, based on the generalized tent maps, is presented for the design of long encryption key using the added parameters. Moreover, statistical and sensitivity analysis are presented to demonstrate the benefits of the generalized maps.

A Metrics-Based Data Mining Approach for Software Clone Detection

The detection of function clones in software systems is valuable for the code adaptation and error checking maintenance activities. This paper presents an efficient metrics-based data mining clone detection approach. First, metrics are collected for all functions in the software system. A data mining algorithm, fractal clustering, is then utilized to partition the software system into a relatively small number of clusters. Each of the resulting clusters encapsulates functions that are within a specific proximity of each other in the metrics space. Finally, clone classes, rather than pairs, are easily extracted from the resulting clusters. For large software systems, the approach is very space efficient and linear in the size of the data set. Evaluation is performed using medium and large open source software systems. In this evaluation, the effect of the chosen metrics on the detection precision is investigated.