In this paper, a new recursive structure based on the convolution model of discrete cosine transform (DCT) for designing of a finite impulse response (FIR) digital filter is proposed. In our derivation, we start with the convolution model of DCT-II to use its Z-transform for the proposed filter structure perspective. Moreover, using the same algorithm, a filter base implementation of the inverse DCT (IDCT) for image reconstruction is developed. The computational time experiments of the proposed DCT/IDCT filter(s) demonstrate that the proposed filters achieve faster elapsed CPU time compared to the others. The image filtering and reconstruction performance of the proposed approach on ultrasound images are presented to validate the theoretical framework.

Remote diagnosis procedures are prone to communication errors due to varying levels of experience and knowledge between expert maintainers and technicians. These result in inefficiencies that delay the diagnosis process. The aim of the paper is to develop a Structured-Message Authoring framework for Augmented Reality (AR) Remote Communication (SMAARRC) and to evaluate its ability to enhance the efficiency of remote diagnosis services. The framework proposes a message structure and automatic AR content creation rules for it that enable data capture and sharing within a remote context. Laboratory experiments present an average time reduction of 56% for remote calls while maintaining same quality compared to traditional remote communication methods (phone calls and emails). Remote experts feedback evidence the usability and feasibility of this framework to work in real-life conditions.

Nondestructive testing (NDT), including active thermography, has become an inevitable part of composite process and product verification, post manufacturing. However, there is no reliable NDT technique available to ensure the interlaminar bond integrity during composite laminate integration, bonding or repair where the presence of thin air gaps in the interface of dissimilar polymer composite materials would be detrimental to structural integrity. This paper introduces a novel approach attempting to quantify the damage thickness of composites (the thickness of air gaps inside composites) through a single-side inspection of pulsed thermography. The potential of this method is demonstrated by testing on three specimens with different types of defect, where the Pearson correlation coefficients of the thickness estimation for block defects and flat-bottom holes are 0.75 and 0.85, respectively. This approach will considerably enhance the degradation assessment performance of active thermography by extending damage measurement from currently two dimensions to three dimensions, and become an enabling technology on quality assurance of structural integrity.

This paper presents the development of an ontology for component service degradation. In this paper, degradation mechanisms in gas turbine metallic components are used for a case study to explain how a taxonomy within an ontology can be validated. The validation method used in this paper uses an iterative process and sanity checks. Data extracted from on-demand textual information are filtered and grouped into classes of degradation mechanisms. Various concepts are systematically and hierarchically arranged for use in the service maintenance ontology. The allocation of the mechanisms to the AS-IS ontology presents a robust data collection hub. Data integrity is guaranteed when the TO-BE ontology is introduced to analyse processes relative to various failure events. The initial evaluation reveals improvement in the performance of the TO-BE domain ontology based on iterations and updates with recognised mechanisms. The information extracted and collected is required to improve service k nowledge and performance feedback which are important for service engineers. Existing research areas such as natural language processing, knowledge management, and information extraction were also examined.

The design of a rolling system is a multistage process optimization problem involving sequential relationship between consecutive stages. This relationship is peculiar to sequential processes in which the output stock of one stage serves as the input stock into the deforming tool of the other stage. This paper describes the optimization of a real-life rolling system design using a genetic algorithm (GA)-based approach capable of dealing with the sequential nature of this problem. It presents a mathematical model of a real-life rolling system design and explains the proposed optimization approach. Even in the presence of multiple stages, the proposed approach identifies a variety of near-optimal design solutions from which one could be finally chosen based on designer's preferences. It is also shown that the obtained solutions dominate the designs reported in literature.

ICSPEA is a novel multi-objective evolutionary algorithm which integrates aspects from the powerful variation operators of the Covariance Matrix Adaptation Evolution Strategy (CMA-ES) and the well proven multi-objective Strength Pareto Evaluation Scheme of the SPEA 2. The CMA-ES has already shown excellent performance on various kinds of complex single-objective problems. The evaluation scheme of the SPEA 2 selects individuals with respect to their current position in the objective space using a scalar index in order to form proper Pareto front approximations. These indices can be used by the CMA-part of ICSPEA for learning and guiding the search towards better Pareto front approximations. The ICSPEA is applied to complex benchmark functions such as an extended n-dimensional Schaffer's function or Quagliarella's problem. The results show that the CMA operator allows ICSPEA to find the Pareto set of the generalised Schaffer's function faster than SPEA 2. Furthermore, this concept is tested on the complex real-world application of the multi-objective optimization of five-axis milling NC-paths. An application of ICSPEA to the milling-path optimisation problem yielded efficient sets of five-axis NC-paths.

The pharmaceutical industry is facing an ever-increasing demand to discover novel drugs that are more effective and safer than existing ones. The industry faces huge problem in improving its drug discovery and development processes since formerly used methods have shown their limits. Additionally, tests for safety of drugs are performed at the later end of the drug discovery pipeline instead of earlier. Therefore, the industry is looking for predictive tools that would be useful in testing the behaviour of a drug candidate earlier on in the pipeline before performing the large scale clinical tests. This paper explores the application of evolutionary multi-objective optimisation techniques for achieving such predictive work in protein-ligand docking. The paper reviews the literature of multi-objective optimisation and the drug discovery process and proposes a framework as a predictive tool to calculate good docking configuration for a given target protein and its binding compound. Finally existing models for drug evaluation are used for framework validation.

In this paper, we propose a generic library of problem-solving methods for scheduling applications. Although some attempts have been made in the past at developing the libraries of scheduling problem-solvers, these only provide limited coverage. Many lack generality, as they subscribe to a particular scheduling domain. Others simply implement a particular problem-solving technique, which may be applicable only to a subset of the space of scheduling problems. In addition, most of these libraries fail to provide the required degree of depth and precision. In our approach, we subscribe to the task-method-domain-application knowledge modeling framework which provides a structured organization for the different components of the library. At the task level, we construct a generic scheduling task ontology to formalize the space of scheduling problems. At the method level, we construct a generic problem-solving model of scheduling that generalizes from the variety of approaches to scheduling problem-solving, which can be found in the literature. The generic nature of this model is demonstrated by constructing seven methods for scheduling as an alternative specialization of the model. Finally, we validated our library on a number of applications to demonstrate its generic nature and effective support for developing scheduling applications

This paper describes the application of two multi-objective optimisation techniques to the high efficiency deep grinding process. The process is modelled using a fuzzy expert system. This allows understanding gained through theoretical analysis to be combined with empirical data in a solitary model. The objective is to simultaneously minimise the surface temperature and specific grinding energy. A problem constraint is represented within the fuzzy model. It forms an objective representing the degree of feasibility of the solution.

Since information in engineering design problems can be both quantitative (Q/sup T/) and qualitative (Q/sup L/) in nature, combining both types of information can result in more realistic solution for real world optimisation problems. However, most of the approaches reported in literature are incapable of conducting optimisation search in such mixed environment. Therefore we propose a mathematically proven methodology for handling integrated Q/sup T/ and Q/sup L/ search space in real world optimisation problems. We begin by presenting the definition of these optimisation problems an analysis of the challenges that they pose for existing optimisation strategies and related research. We then present the proposed solution strategy and the mathematical proof. Furthermore, a case study on rod rolling problem is presented to validate the effectiveness of the proposed metholodology. We conclude with a brief outline of limitations and future research activities.

In this paper we describe a generic library of problem-solving methods (PSMs) for scheduling applications. Although, some attempts have been made in the past at developing libraries of scheduling methods, these only provide limited coverage: in some cases they are specific to a particular scheduling domain; in other cases they simply implement a particular scheduling technique; in other cases they fail to provide the required degree of depth and precision. Our library is based on a structured approach, whereby we first develop a scheduling task ontology, and then construct a task-specific but domain independent model of scheduling problem-solving, which generalises from specific approaches to scheduling problem-solving. Different PSMs are then constructed uniformly by specialising the generic model of scheduling problem-solving. Our library has been evaluated on a number of real-life and benchmark applications to demonstrate its generic and comprehensive nature.

The design of control systems for large-scale and complex industrial plants involves numerous trade-off problems, such as costs, quality, environmental impact, safety, reliability, accuracy, and robustness. Some of these parameters are even conflicting. Thus, the use of a multidiscipline approach is suggested to satisfy these requirements in an acceptable and well-balanced manner, and a fusion of soft computing and hard computing appears to be a natural and practical choice. Although the state-of-the-art soft computing technology has distinguished features, the use of soft computing technology would be ineffective, if it is improperly fused with conventional hard computing technology and control processes. Proper fusion is key to success, and a general model of fusion is worth examining. In this paper, through a survey of published literature, a general fusion model and fusion topologies are shown at the system level as well as at the algorithm level.