Design Optimisation of UAVs Systems achieved on a Framework Environment via Evolution and Game Theory

Over recent years, Unmanned Air Vehicles or UAVs have become a powerful tool for reconnaissance and surveillance tasks. These vehicles are now available in a broad size and capability range and are intended to fly in regions where the presence of onboard human pilots is either too risky or unnecessary. This paper describes the formulation and application of a design framework that supports the complex task of multidisciplinary design optimisation of UAVs systems via evolutionary computation. The framework includes a Graphical User Interface (GUI), a robust Evolutionary Algorithm optimiser named HAPEA, several design modules, mesh generators and post-processing capabilities in an integrated platform. These population –based algorithms such as EAs are good for cases problems where the search space can be multi-modal, non-convex or discontinuous, with multiple local minima and with noise, and also problems where we look for multiple solutions via Game Theory, namely a Nash equilibrium point or a Pareto set of non-dominated solutions. The application of the methodology is illustrated on conceptual and detailed multi-criteria and multidisciplinary shape design problems. Results indicate the practicality and robustness of the framework to find optimal shapes and trade—offs between the disciplinary analyses and to produce a set of non dominated solutions of an optimal Pareto front to the designer.

A cooperative coevolutionary algorithm for the composite SaaS Placement Problem in the Cloud

Cloud computing has become a main medium for Software as a Service (SaaS) hosting as it can provide the scalability a SaaS requires. One of the challenges in hosting the SaaS is the placement process where the placement has to consider SaaS interactions between its components and SaaS interactions with its data components. A previous research has tackled this problem using a classical genetic algorithm (GA) approach. This paper proposes a cooperative coevolutionary algorithm (CCEA) approach. The CCEA has been implemented and evaluated and the result has shown that the CCEA has produced higher quality solutions compared to the GA.

APROMORE : an advanced process model repository

Business process models are becoming available in large numbers due to their popular use in many industrial applications such as enterprise and quality engineering projects. On the one hand, this raises a challenge as to their proper management: How can it be ensured that the proper process model is always available to the interested stakeholder? On the other hand, the richness of a large set of process models also offers opportunities, for example with respect to the re-use of existing model parts for new models. This paper describes the functionalities and architecture of an advanced process model repository, named APROMORE. This tool brings together a rich set of features for the analysis, management and usage of large sets of process models, drawing from state-of-the art research in the field of process modeling. A prototype of the platform is presented in this paper, demonstrating its feasibility, as well as an outlook on the further development of APROMORE.

A method for evaluating lean assembly process at design stage

Lean product design has the potential to reduce the overall product development time and cost and can improve the quality of a product. However, it has been found that no or little work has been carried out to provide an integrated framework of "lean design" and to quantitatively evaluate the effectiveness of lean practices/principles in product development process. This research proposed an integrated framework for lean design process and developed a dynamic decision making tool based on Methods Time Measurement (MTM) approach for assessing the impact of lean design on the assembly process. The proposed integrated lean framework demonstrates the lean processes to be followed in the product design and assembly process in order to achieve overall leanness. The decision tool consists of a central database, the lean design guidelines, and MTM analysis. Microsoft Access and C# are utilized to develop the user interface to use the MTM analysis as decision making tool. MTM based dynamic tool is capable of estimating the assembly time, costs of parts and labour of various alternatives of a design and hence is able to achieve optimum design. A case study is conducted to test and validate the functionality of the MTM Analysis as well as to verify the lean guidelines proposed for product development.

Stepping into cooperative buildings

If we are stepping out of windows, what are we stepping into? We suggest it is into cooperative buildings. For the foreseeable future, at least, we can identify two major characteristics of the cooperative building. The spaces of the building will be augmented in various ways, providing an ambient environment that bridges spatial discontinuities in workgroups and provides a continuous window into the state of the virtual world. Secondly, the ways in which the spaces themselves are used will evolve to be more congruent with the fluid, dynamic and distributed nature of the work taking place in the building. These two characteristics are deeply interconnected. This evolution need not happen entirely in the physical world; the essence of a cooperative building will become the way in which it mixes both physical and virtual affordances to support the workaday activities of its inhabitants.

ICT as core business: will we prosper or drown?

Recent changes in IT organisations have resulted in changes to library IT support. Concurrently, new tools and systems for service delivery, have become available, but these require a move away from the traditional ICT model. Many libraries are investigating new models, including Software as a Service (SaaS), cloud computing and open source software. This paper considers whether the adoption of these tools and environments by libraries has occurred as a result of a lack of suitable ICT solutions and support ICT organisations. It also considers what skills library staff need in order to ensure sustainability, supportability, and ultimately, success.

Comparative Studies Simplified in GPFlow

We present a novel, web-accessible scientific workflow system which makes large-scale comparative studies accessible without programming or excessive configuration requirements. GPFlow allows a workflow defined on single input values to be automatically lifted to operate over collections of input values and supports the formation and processing of collections of values without the need for explicit iteration constructs. We introduce a new model for collection processing based on key aggregation and slicing which guarantees processing integrity and facilitates automatic association of inputs, allowing scientific users to manage the combinatorial explosion of data values inherent in large scale comparative studies. The approach is demonstrated using a core task from comparative genomics, and builds upon our previous work in supporting combined interactive and batch operation, through a lightweight web-based user interface.

Stomp : an interactive platform for people with intellectual disabilities

For people with intellectual disabilities there are significant barriers to inclusion in socially cooperative endeavours. This paper investigates the effectiveness of Stomp, a tangible user interface (TUI) designed to provide new participatory experiences for people with intellectual disability. Results from an observational study reveal the extent to which the Stomp system supports social and physical interaction. The tangible, spatial and embodied qualities of Stomp result in an experience that does not rely on the acquisition of specific competencies before interaction and engagement can occur.

Composite SaaS placement and resource optimization in Cloud computing using evolutionary algorithms

Software as a Service (SaaS) is gaining more and more attention from software users and providers recently. This has raised many new challenges to SaaS providers in providing better SaaSes that suit everyone needs at minimum costs. One of the emerging approaches in tackling this challenge is by delivering the SaaS as a composite SaaS. Delivering it in such an approach has a number of benefits, including flexible offering of the SaaS functions and decreased cost of subscription for users. However, this approach also introduces new problems for SaaS resource management in a Cloud data centre. We present the problem of composite SaaS resource management in Cloud data centre, specifically on its initial placement and resource optimization problems aiming at improving the SaaS performance based on its execution time as well as minimizing the resource usage. Our approach differs from existing literature because it addresses the problems resulting from composite SaaS characteristics, where we focus on the SaaS requirements, constraints and interdependencies. The problems are tackled using evolutionary algorithms. Experimental results demonstrate the efficiency and the scalability of the proposed algorithms.

Clustering composite SaaS components in cloud computing using a grouping genetic algorithm

Recently, Software as a Service (SaaS) in Cloud computing, has become more and more significant among software users and providers. To offer a SaaS with flexible functions at a low cost, SaaS providers have focused on the decomposition of the SaaS functionalities, or known as composite SaaS. This approach has introduced new challenges in SaaS resource management in data centres. One of the challenges is managing the resources allocated to the composite SaaS. Due to the dynamic environment of a Cloud data centre, resources that have been initially allocated to SaaS components may be overloaded or wasted. As such, reconfiguration for the components’ placement is triggered to maintain the performance of the composite SaaS. However, existing approaches often ignore the communication or dependencies between SaaS components in their implementation. In a composite SaaS, it is important to include these elements, as they will directly affect the performance of the SaaS. This paper will propose a Grouping Genetic Algorithm (GGA) for multiple composite SaaS application component clustering in Cloud computing that will address this gap. To the best of our knowledge, this is the first attempt to handle multiple composite SaaS reconfiguration placement in a dynamic Cloud environment. The experimental results demonstrate the feasibility and the scalability of the GGA.

A parallel cooperative co-evolutionary genetic algorithm for the composite SaaS placement problem in Cloud computing

A composite SaaS (Software as a Service) is a software that is comprised of several software components and data components. The composite SaaS placement problem is to determine where each of the components should be deployed in a cloud computing environment such that the performance of the composite SaaS is optimal. From the computational point of view, the composite SaaS placement problem is a large-scale combinatorial optimization problem. Thus, an Iterative Cooperative Co-evolutionary Genetic Algorithm (ICCGA) was proposed. The ICCGA can find reasonable quality of solutions. However, its computation time is noticeably slow. Aiming at improving the computation time, we propose an unsynchronized Parallel Cooperative Co-evolutionary Genetic Algorithm (PCCGA) in this paper. Experimental results have shown that the PCCGA not only has quicker computation time, but also generates better quality of solutions than the ICCGA.

A penalty-based grouping genetic algorithm for multiple composite SaaS components clustering in Cloud

Software as a Service (SaaS) in Cloud is getting more and more significant among software users and providers recently. A SaaS that is delivered as composite application has many benefits including reduced delivery costs, flexible offers of the SaaS functions and decreased subscription cost for users. However, this approach has introduced a new problem in managing the resources allocated to the composite SaaS. The resource allocation that has been done at the initial stage may be overloaded or wasted due to the dynamic environment of a Cloud. A typical data center resource management usually triggers a placement reconfiguration for the SaaS in order to maintain its performance as well as to minimize the resource used. Existing approaches for this problem often ignore the underlying dependencies between SaaS components. In addition, the reconfiguration also has to comply with SaaS constraints in terms of its resource requirements, placement requirement as well as its SLA. To tackle the problem, this paper proposes a penalty-based Grouping Genetic Algorithm for multiple composite SaaS components clustering in Cloud. The main objective is to minimize the resource used by the SaaS by clustering its component without violating any constraint. Experimental results demonstrate the feasibility and the scalability of the proposed algorithm.

Low cost railway level crossings

The Cooperative Research Centre (CRC) for Rail Innovation is conducting a tranche of industry-led research projects looking into safer rail level crossings. This paper will provide an overview of the Affordable Level Crossings project, a project that is performing research in both engineering and human factors aspects of low-cost level crossing warning devices (LCLCWDs), and is facilitating a comparative trial of these devices over a period of 12 months in several jurisdictions. Low-cost level crossing warning devices (LCLCWDs) are characterised by the use of alternative technologies for high cost components including train detection and connectivity (e.g. radar, acoustic, magnetic induction train detection systems and wireless connectivity replacing traditional track circuits and wiring). These devices often make use of solar power where mains power is not available, and aim to make substantial savings in lifecycle costs. The project involves trialling low-cost level crossing warning devices in shadow-mode, where devices are installed without the road-user interface at a number of existing level crossing sites that are already equipped with conventional active warning systems. It may be possible that the deployment of lower-cost devices can provide a significantly larger safety benefit over the network than a deployment of expensive conventional devices, as the lower cost would allow more passive level crossing sites to be upgraded with the same capital investment. The project will investigate reliability and safety integrity issues of the low-cost devices, as well as evaluate lifecycle costs and investigate human factors issues related to warning reliability. This paper will focus on the requirements and safety issues of LCLCWDs, and will provide an overview of the Rail CRC projects.

Cross-cultural language learning and web design complexity

Accepting the fact that culture and language are interrelated in second language learning (SLL), the web sites should be designed to integrate with the cultural aspects. Yet many SLL web sites fail to integrate with the cultural aspects and/or focus on language acquisition only. This study identified three issues: (1) anthropologists’ cultural models mostly adopted in cross-cultural web user interface have been superficially used; (2) web designers deal with culture as a fixed one which needs to be modeled into interface design elements, so (3) there is a need for a communication framework between educators and design practitioners, which can be utilized in web design processes. This paper discusses what anthropology can contribute to language learning, mediated through web design processes and suggests a cultural user experience framework for web-based SLL by presenting an exemplary matrix. To evaluate the effectiveness of the framework, the key stakeholders (learners, teachers, and designers) participated in a case scenario-based evaluation. The result shows a high possibility that the framework can enhance the effective communication and collaboration for the cultural integration.