Fuzzy concepts and formal methods

It has been recognised that formal methods are useful as a modelling tool in requirements engineering. Specification languages such as Z permit the precise and unambiguous modelling of system properties and behaviour. However some system problems, particularly those drawn from the information systems problem domain, may be difficult to model in crisp or precise terms. It may also be desirable that formal modelling should commence as early as possible, even when our understanding of parts of the problem domain is only approximate. This thesis suggests fuzzy set theory as a possible representation scheme for this imprecision or approximation. A fuzzy logic toolkit that defines the operators, measures and modifiers necessary for the manipulation of fuzzy sets and relations is developed. The toolkit contains a detailed set of laws that demonstrate the properties of the definitions when applied to partial set membership. It also provides a set of laws that establishes an isomorphism between the toolkit notation and that of conventional Z when applied to boolean sets and relations. The thesis also illustrates how the fuzzy logic toolkit can be applied in the problem domains of interest. Several examples are presented and discussed including the representation of imprecise concepts as fuzzy sets and relations, system requirements as a series of linguistically quantified propositions, the modelling of conflict and agreement in terms of fuzzy sets and the partial specification of a fuzzy expert system. The thesis concludes with a consideration of potential areas for future research arising from the work presented here.

Extending robust industrial optimisation using simulation and reinforcement learning

Traditional optimisation methods are incapable of capturing the complexity of today's dynamic manufacturing systems. A new methodology, integrating simulation models and intelligent learning agents, was successfully applied to identify solutions to a fundamental scheduling problem. The robustness of this approach was then demonstrated through a series of real-world industrial applications.

Parallelization of dissipative particle dynamics simulation

The dissipative particle dynamics simulation is usually used to study polymer in mesoscopic space. The traditional methods are resource intensive, especially when the scale of research is large. Therefore, improving computing efficiency is a key point in this research area. Two major issues are addressed in this paper. First, the DPD methods are analysed and the most time-consuming parts are identified: conservative force, dissipative force and random force. Second, we describe how to parallelize the existing serial application in the Windows Compute Cluster Server (WCCS) platform. The results show that the parallel algorithm not only effectively shortens the computing time, but also improves the resource utilization rate.

Effects of replication on measured S-N curves of metals

Replication, or repeated tests at the same stress amplitude, is used to provide statistical confidence in life data during the development of S-N curves. This paper discusses the effects of replication on the measurement of S-N curves and presents an alternative to traditional replication methods for the determination of S-N curves, particularly for the development of preliminary S-N curves. Using specimens made out of the extruded bars of a magnesium alloy, it is demonstrated that the S-N curve estimated using the data from non-replication tests is almost same as that from replication tests. The advantage of using non-replication fatigue tests is that it uses fewer specimens, in this instance, only half of that required for 50% replication fatigue test, to achieve the same estimation as that of the replication fatigue tests. Another advantage of using non-replication fatigue tests is that it can detect the non-linearity using limited specimens.

Recent progress on biosorption of heavy metals from liquids using low cost biosorbents : characterization, biosorption parameters and mechanism studies

A significant number of biosorption studies on the removal of heavy metal from aqueous solutions have been conducted worldwide. Nearly all of them have been directed towards optimizing biosorption parameters to obtain the highest removal efficiency while the rest of them are concerned with the biosorption mechanism. Combinations of FTIR, SEM-EDX, TEM as well as classical methods such as titrations are extremely useful in determining the main processes on the surfaces of biosorbents. Diverse functional groups represented by carboxyl, hydroxyl, sulfate and amino groups play significant roles in the biosorption process. Solution pH normally has a large impact on biosorption performance. In brief, ion exchange and complexation can be pointed out as the most prevalent mechanisms for the biosorption of most heavy metals.

A comparative study of supervised learning techniques for data-driven haptic simulation

This paper focuses on the choice of a supervised learning algorithm and possible data preprocessing in the domain of data-driven haptic simulation. This is done through a comparison of the performance of different supervised learning techniques with and without data preprocessing. The simulation of haptic interactions with deformable objects using data-driven methods has emerged as an alternative to parametric methods. The accuracy of the simulation depends on the empirical data and the learning method. Several methods were suggested in the literature and here we provide a comparison between their performance and applicability to this domain. We selected four examples to be compared: singular learning mechanism which is artificial neural networks (ANN), attribute selection followed by ANN learning process, ensemble of multiple learning techniques, and attribute selection followed by the learning ensemble. These methods performance was compared in the domain of simulating multiple interactions with a deformable object with nonlinear material behavior.

Physically based simulation of heterogeneous deformable models using XFEM

This paper addresses the problem of heterogeneous deformable model accuracy using the finite element methods (FEM). Classic FEM uses predefined shape functions for interpolation and does not account easily for regions of discontinuities. Extended finite element methods (XFEM) use enrichment functions to compensate for the change in an element degrees of freedom (DoFs) in deformable objects. The XFEM is an accurate and fast method as no remeshing is required. In this study we investigate the performance of XFEM and demonstrate how it may be applied to discontinuities of materials that exist in heterogeneous (piece-wise homogeneous) models. The results show realistic stress prediction compared to modeling the same objects with classic FEM.

Effects of replication on measured S-N curves of metals

Replication, or repeated tests at the same stress amplitude, is used to provide statistical confidence in life data during the development of S-N curves. This paper discusses the effects of replication on the measurement of S-N curves and presents an alternative to traditional replication methods for the determination of S-N curves, particularly for the development of preliminary S-N curves. Using specimens made out of the extruded bars of a magnesium alloy, it is demonstrated that the S-N curve estimated using the data from non-replication tests is almost same as that from replication tests. The advantage of using non-replication fatigue tests is that it uses fewer specimens, in this instance, only half of that required for 50% replication fatigue test, to achieve the same estimation as that of the replication fatigue tests. Another advantage of using non-replication fatigue tests is that it can detect the non-linearity using limited specimens.

Novel watermarking methods for copyright protection of audio signals

This research presented improved watermarking methods for mono and stereo audio signals. To enhance the performance, novel methods are developed using echo hiding techniques and patchwork-based algorithms. The superior performances of the proposed methods are demonstrated by theoretical analysis and simulation examples, in comparison with the existing methods.

Managing patient deterioration : a protocol for enhancing undergraduate nursing students’ competence through web-based simulation and feedback techniques

Aims: To describe a funded proposal for the development of an on-line evidence based educational program for the management of deteriorating patients.
Background: There are international concerns regarding the management of deteriorating patients with issues around the ‘failure to rescue’. The primary response to these issues has been the development of medical emergency teams with little focus on the education of primary first responders.
Design/Methods: A mixed methods triangulated convergent design. In this four phase proposal we plan to 1. examine nursing student team ability to manage deteriorating patients and based upon these findings 2. develop web based educational material, including interactive scenarios. This educational material will be tested and refined in the third Phase 3, prior to evaluation and dissemination in the final phase.
Conclusion: This project aims to enhance knowledge development for the management of deteriorating patients through rigorous assessment of team performance and to produce a contemporary evidence-based online training program.

A generalised data analysis approach for baggage handling systems simulation

Airport baggage handling systems are a critical infrastructure component within major airports, and essential to ensure smooth luggage transfer while preventing dangerous material being loaded onto aircraft. This paper proposes a standard set of measures to assess the expected performance of a baggage handling system through discrete event simulation. These evaluation methods also have application in the study of general network systems. Results from the application of these methods reveal operational characteristics of the studied BHS, in terms of metrics such as peak throughput, in-system time and system recovery time.

Electrochemical methods applied in innovative corrosion inhibitor experiments : case studies

Electrochemical methods can be used more effectively if innovative experiments are designed and employed. This is especially true if a corrosion system to be tested involves inhibitors that could change the mechanism and pattern of corrosion in complex ways. This paper discusses corrosion inhibitor test design using several practical cases as examples. Particular attention is on difficult issues such as the simulation of localized corrosion phenomena and the influence of corrosion mechanism on inhibitor test results. ©2012 by NACE International.

Intelligent traffic light control of isolated intersections using machine learning methods

Traffic congestion is one of the major problems in modern cities. This study applies machine learning methods to determine green times in order to minimize in an isolated intersection. Q-learning and neural networks are applied here to set signal light times and minimize total delays. It is assumed that an intersection behaves in a similar fashion to an intelligent agent learning how to set green times in each cycle based on traffic information. Here, a comparison between Q-learning and neural network is presented. In Q-learning, considering continuous green time requires a large state space, making the learning process practically impossible. In contrast to Q-learning methods, the neural network model can easily set the appropriate green time to fit the traffic demand. The performance of the proposed neural network is compared with two traditional alternatives for controlling traffic lights. Simulation results indicate that the application of the proposed method greatly reduces the total delay in the network compared to the alternative methods.

An overview of methods for simulating and evaluating pipeline corrosion

Steel pipelines, buried under the soil and protected by the combination of protective coatings and cathodic protection (CP), are used for oil and gas transportation. These pipelines are one of the critical infrastructures for energy transportation and therefore became lifelines of modern society. The deterioration of the external surfaces of transmission pipelines is a serious problem and is caused mainly by coating and/or CP failure leading to the loss of integrity of pipelines. To avoid such damage, there is a need of techniques which are able to locate active corrosion sites, monitor corrosion, and evaluate corrosion damage. Fundamental understanding of such processes occurring on coated pipelines (with various types of defects in coatings as well as pipe) in complex soil environment is necessary for the development of such techniques. Numerous laboratory techniques, i.e., electrochemical impedance spectroscopy based, polarisation measurements based, mathematical simulations, direct observation etc. have been used to develop fundamental understanding, simulate and evaluate corrosion occurring in oil and gas pipelines under various operating conditions. Given the complex nature of the pipeline corrosion, application of these laboratory techniques in field measurements as well as in understanding the corrosion mechanisms is lacking. This paper presents an overview of investigations, based on electrochemical techniques, for simulation and evaluation of pipeline corrosion in laboratory.