Applying the Hough transform pseudo-linearity property to improve computing speed

This work describes a general method of acceleration of the convergence of the Hough transform based, on the one hand, on an improvement of the image analysis speed, and, on the other hand, on the space undersampling of the image. This method is used in image processing to extract lines, circles, ellipses or arbitrary shapes. The results presented are applied to the detection of straight-line segments and ellipses, but can be extended to any type of transform.     

IrBurst Modelling and Performance Analysis in the Presence of Transmission Errors

IrBurst is a session/presentation layer protocol dedicated for high speed large volume information transfer over IrDA links. The protocol is developed by IrDA in order to complement the existing protocol OBEX which is best suited for small files. This paper presents a mathematical model for IrBurst over the IrDA protocol stacks taking into account the presence of bit errors and multiple simultaneously applications. The performance of IrBurst is examined considering TinyTP buffer sizes and the number of IrBurst application connections as parameters. The contribution of this work is to develop a mathematical model for IrBurst, investigate the compatibility of IrBurst to low layer protocols, as well as providing suitable design guidelines for IrDA devices for high performance of IrBurst. Pi Huang received the B.Sc. degree in Electrical and Electronic Engineering from University of Central Lancashire, U.K., in 2001, the M.Sc. degree in Telecommunications from University College London, U.K., in 2002 and the Ph.D degree in the Personal Wireless Networks and Outdoor Optical Links from Bournemouth University, U.K., in 2006. He is currently working in wireless solution division of British Telecom. His research focuses on performance modelling and analysis as well as discrete-event simulation of wireless communication protocols and wireless communication networks. He has published over 20 papers in the areas of wireless communications. Anthony C. Boucouvalas has worked at GEC Hirst Research Centre, and became Group Leader and Divisional Chief Scientist until 1987, when he joined Hewlett Packard (HP) Laboratories as Project Manager. He joined Bournemouth University in 1994 and became a Professor in Multimedia Communications in 1996, and in 1999 became Director of the Microelectronics and Multimedia Research Centre. His current research interests span the fields of wireless communications, optical fibre communications and components, multimedia communications, and human-computer interfaces, where he has published over 200 papers. He has contributed to the formation of IrDA as an industry standard and he is now a Member of the IrDA Architectures Council. He is a Fellow of Fellow of the Royal Society for the encouragement of Arts, Manufacturers and Commerce, (FRSA) and a Fellow of IEE, (FIEE). In 2002 he became a Fellow of the Institute of Electrical and Electronic Engineers (FIEEE), for contributions to optical fibre components and optical wireless communications. He is an Editor of numerous Journals and in the Organising committee of many conferences.     

A robust optimisation model and cutting planes for the planning of energy-efficient wireless networks

In this paper, we present an optimisation model for the energy-efficient planning of future wireless networks. By applying robust optimisation, we extend this model to a robust formulation which considers demand uncertainties. The computability of the resulting model is moderate. Hence, we apply three different cutting plane approaches for an improvement. Furthermore, an extensive case study is performed to examine the price of robustness, to compare the robust solution to conventional planning, and to explore the performance of the cutting planes.     

Recurrent neural network approach for cyclic job shop scheduling problem

While cyclic scheduling is involved in numerous real-world applications, solving the derived problem is still of exponential complexity. This paper focuses specifically on modelling the manufacturing application as a cyclic job shop problem and we have developed an efficient neural network approach to minimise the cycle time of a schedule. Our approach introduces an interesting model for a manufacturing production, and it is also very efficient, adaptive and flexible enough to work with other techniques. Experimental results validated the approach and confirmed our hypotheses about the system model and the efficiency of neural networks for such a class of problems.     

Selective disassembly planning for waste electrical and electronic equipment with case studies on liquid crystaldisplays

Waste Electrical and Electronic Equipments (WEEEs) are one of the most significant waste streams in modern societies. In the past decade, disassembly of WEEE to support remanufacturing and recycling has been growingly adopted by industries. With the increasing customisation and diversity of Electrical and Electronic Equipment (EEE) and more complex assembly processes, full disassembly of WEEE is rarely an ideal solution due to high disassembly cost. Selective disassembly, which prioritises operations for partial disassembly according to the legislative and economic considerations of specific stakeholders, is becoming an important but still a challenging research topic in recent years. In order to address the issue effectively, in this paper, a Particle Swarm Optimisation (PSO)-based selective disassembly planning method embedded with customisable decision making models and a novel generic constraint handling algorithm has been developed. With multi-criteria and adaptive decision making models, the developed method is flexible to handle WEEE to meet the various requirements of stakeholders. Based on the generic constraint handling and intelligent optimisation algorithms, the developed research is capable to process complex constraints and achieve optimised selective disassembly plans. Industrial cases on Liquid Crystal Display (LCD) televisions have been used to verify and demonstrate the effectiveness and robustness of the research in different application scenarios.     

I-AHSDT: intrusion detection using adaptive dynamic directive operative fractional lion clustering and hyperbolic secant-based decision tree classifier

ABSTRACT

This paper proposes an effective intrusion detection method, named I-AHSDT, which is the combination of the Adaptive Dynamic Directive Operative Fractional Lion clustering (ADDOFL) and Hyperbolic Secant-based Decision Tree classifier (HSDT). The proposed HSDT classifier is based on the inverse hyperbolic secant function and it performs the two level classification to detect the intrusion, which offers robust classification performance. The experimentation is performed using the KDD Cup 1999 data, and the HCR Lab data set, and the experimental results prove that the proposed method outperforms the existing system in terms of the accuracy, which is 0.95.     

Quantifying the exploration performed by metaheuristics

In this article we propose a formalisation of the concept of exploration performed by metaheuristics. In particular, we define and test a method for studying this aspect regardless of the specific approach implemented. Understanding the behaviour of metaheuristics is important for being able to boost their results. Measuring the exploration performed may help increase this understanding. We propose an experimental analysis to show how the measure of exploration defined may be used to this aim. We quantify the different level of exploration implied by different parameter settings in an ant colony optimisation and in a genetic algorithm for the travelling salesman problem. The results suggest that it may be possible to establish a relation between exploration and performance of the algorithm.     

Non-linear system control using a recurrent fuzzy neural network based on improved particle swarm optimisation

This article introduces a recurrent fuzzy neural network based on improved particle swarm optimisation (IPSO) for non-linear system control. An IPSO method which consists of the modified evolutionary direction operator (MEDO) and the Particle Swarm Optimisation (PSO) is proposed in this article. A MEDO combining the evolutionary direction operator and the migration operation is also proposed. The MEDO will improve the global search solution. Experimental results have shown that the proposed IPSO method controls the magnetic levitation system and the planetary train type inverted pendulum system better than the traditional PSO and the genetic algorithm methods.     

Multi-criteria decision-making methods with optimism and pessimism based on Atanassov's intuitionistic fuzzy sets

The theory of Atanassov's intuitionistic fuzzy sets (A-IFSs) developed over the last several decades has found useful application in fields requiring multiple-criteria decision analysis. Since the membership–nonmembership pair in A-IFSs belongs to the bivariate unipolarity type, this article describes an approach that relates optimism and pessimism to multi-criteria decision analysis in an intuitionistic fuzzy-decision environment. First, several optimistic and pessimistic point operators were defined to alter the estimation of decision outcomes. Next, based on the core of the estimations, optimistic and pessimistic score functions were developed to evaluate each alternative with respect to each criterion. The suitability function was then established to determine the degree to which each an alternative satisfies the decision maker's requirement. Because the information on multiple criteria corresponding to decision importance is often incomplete, this study included suitability functions in the optimisation models to account for poorly known membership grades. Using a linear equal-weighted summation method, these models were transformed into a single objective optimisation model to generate the optimal weights for criteria. The feasibility and effectiveness of the proposed methods were illustrated through a practical example. Finally, computational experiments with enormous amounts of simulation data were designed to conduct a comparative analysis on the ranking orders yielded by different optimistic/pessimistic point operators.     

Control of dynamic microsystems

This paper formulates and solves control problems for nonlinear microsystems which comprise micro-electromechanical devices, micromachined transducers and microelectronics. We perform a consistent dynamic analysis and coherent designs with a minimum level of simplifications using high-fidelity mathematical models. The proposed methodology enables practical implementation for multi-input/multi-output systems due to overall conceptual consistency, design coherence, computational efficiency, algorithmic effectiveness and hardware simplicity. Various issues in nonlinear analysis and control are examined and experimentally verified substantiating design concepts for high-performance microsystems. The reported findings are demonstrated for a proof-of-concept closed-loop electrostatic microactuator.