• Toolbox from the EC FP7 HOSANNA project for the reduction of road and rail traffic noise in the outdoor environment

      Forsséna, J.; Hornikx, M.; Van Der Aa, B.; Nilsson, M.; Rådsten-Ekmanc, M.; Defrance, J.; Jean, P.; Koussa, F.; Maillard, J.; Van Maercke, D.; et al. (2014)
      This paper offers a brief overview of innovative methods for road and rail traffic noise reduction between source and receiver. These include using new barrier designs, planting of trees, treatments of ground and road surfaces and greening of building façades and roofs using natural materials, like vegetation, soil and other substrates in combination with recycled materials and artificial elements. The abatements are assessed in terms of numerically predicted sound level reductions, perceptual effects and cost–benefit analysis. Useful reductions of noise from urban roads and tramways are predicted for 1-m-high urban noise barriers and these are increased by adding inter-lane barriers. A 3 m wide 0.3 m high lattice ground treatment, a carefully planted 15-m-wide tree belt and replacing 50 m of paved areas by grassland are predicted to give similar reductions. Tree belts are shown to be very cost-effective and combining tall barriers with a row of trees reduces the negative impact of wind. Green roofs may significantly reduce the noise at the quiet side of buildings.
    • A Toolkit for Multimodal Interface Design: An Empirical Investigation

      Rigas, Dimitrios I.; Alsuraihi, M. (2007)
      This paper introduces a comparative multi-group study carried out to investigate the use of multimodal interaction metaphors (visual, oral, and aural) for improving learnability (or usability from first time use) of interface-design environments. An initial survey was used for taking views about the effectiveness and satisfaction of employing speech and speech-recognition for solving some of the common usability problems. Then, the investigation was done empirically by testing the usability parameters: efficiency, effectiveness, and satisfaction of three design-toolkits (TVOID, OFVOID, and MMID) built especially for the study. TVOID and OFVOID interacted with the user visually only using typical and time-saving interaction metaphors. The third environment MMID added another modality through vocal and aural interaction. The results showed that the use of vocal commands and the mouse concurrently for completing tasks from first time use was more efficient and more effective than the use of visual-only interaction metaphors.
    • “Top-Down-Bottom-Up” Methodology as a Common Approach to Defining Bespoke Sets of Sustainability Assessment Criteria for the Built Environment

      Oltean-Dumbrava, Crina; Watts, Gregory R.; Miah, Abdul H.S. (2014-01)
      The top-down-bottom-up (TDBU) methodology for defining bespoke sets of sustainability criteria for specific civil engineering project types is introduced and discussed. The need to define sustainability criteria for specific civil engineering project types occurs mainly in one or both of the following cases: (1) when a more comprehensive and indicative assessment of the sustainability of the project type in question is required; and/or (2) there is no readily available bespoke sustainability assessment tool, or set of criteria, for assessing the sustainability of the project type. The construction of roads, buildings, airports, tunnels, dams, flood banks, bridges, water supply, and sewage systems and their supporting systems are considered to be unique civil engineering/infrastructure project types. The normative definition of sustainable civil engineering/infrastructure projects and the framework for assessing its sustainability is defined and provided by the authors. An example of the TDBU methodology being applied to define sustainability criteria for transport noise reducing devices is presented and discussed. The end result of applying the methodology is a systematically researched and industry validated set of criteria that denotes assessing the sustainability of the civil engineering/infrastructure project type. The paper concludes that the top-down-bottom-up will support stakeholders and managers involved in assessing sustainability to consider all major research methods to define general and unique sustainability criteria to assess and so maximize sustainability.
    • The torsional response of rotor systems.

      Whalley, R.; Ebrahimi, Kambiz M.; Jamil, Z.M. (2005)
      The torsional response of rotor systems comprising bearings, inertia discs, and relatively long, slim shafts is considered. Lumped, finite element and hybrid, distributed-lumped parameter procedures are employed to represent the rotor systems of concern in efforts aimed at increasing accuracy, integrity, and computational efficiency. Rotor, shaft, and bearing elements of arbitrary dimensions, constructed from materials with differing mechanical properties, can be accommodated within the system models formulated. General results for multiple rotor assemblies are derived. Simple computational techniques are employed to obtain the frequency response and time domain characteristics for the models proposed. Analytical validation of the resonance conditions identified is provided. Application studies are presented for purposes of comparison.
    • Tough bio-based elastomer nanocomposites with high performance for engineering applications

      Wei, T.; Lei, L.; Kang, H.; Qiao, B.; Wang, Z.; Zhang, L.; Coates, Philip D.; Hua, K-C.; Kulig, J. (2012)
      Biomass feedstock is a viable alternative to finite fossil fuel resources to provide many of the same—plus others that petrochemicals cannot—chemical building blocks required to fabricate durable and high-performance materials. We demonstrate here for the first time a new generation of synthesized elastomers, namely bio-based engineering elastomers (BEE). These are of particular significance because they are synthesized from monomers derived from biomass, by routes which are suitable for large scale production, and they exhibit thermo-mechanical properties at least equivalent to current commercial petrochemical-derived elastomers. Bio-based monomers in large scale production, such as sebacic acid, itaconic acid, succinate acid, 1,3-propanediol, and 1,4 butanediol are chosen to generate the first synthetic BEE matrix through melting polycondensation—a comparatively simple reaction scheme offering good control and the potential for low cost, large-scale production. A novel linear BEE, an almost non-crystalline copolyester elastomer with low glass transition temperature (Tg) containing double bonds is designed and synthesized using multiple monomers (to help suppress crystallization). Silica nanoparticles are then introduced into the BEE matrix to achieve significant strengthening and improved environmental stability. Chemical crosslinks formed by peroxide and the pendant double bonds in the copolyester macromolecules endow the BEE with both the necessary high elasticity and required environmental stability. The BEE nanocomposites obtained exhibit excellent thermomechanical properties, such as an ultimate tensile strength of 20 MPa.
    • Toward full-stack in silico synthetic biology: integrating model specification, simulation, verification, and biological compilation

      Konur, Savas; Mierla, L.M.; Fellermann, H.; Ladroue, C.; Brown, B.; Wipat, A.; Twycross, J.; Dun, B.P.; Kalvala, S.; Gheorghe, Marian; et al. (2021-08-02)
      We present the Infobiotics Workbench (IBW), a user-friendly, scalable, and integrated computational environment for the computer-aided design of synthetic biological systems. It supports an iterative workflow that begins with specification of the desired synthetic system, followed by simulation and verification of the system in high- performance environments and ending with the eventual compilation of the system specification into suitable genetic constructs. IBW integrates modelling, simulation, verification and bicompilation features into a single software suite. This integration is achieved through a new domain-specific biological programming language, the Infobiotics Language (IBL), which tightly combines these different aspects of in silico synthetic biology into a full-stack integrated development environment. Unlike existing synthetic biology modelling or specification languages, IBL uniquely blends modelling, verification and biocompilation statements into a single file. This allows biologists to incorporate design constraints within the specification file rather than using decoupled and independent formalisms for different in silico analyses. This novel approach offers seamless interoperability across different tools as well as compatibility with SBOL and SBML frameworks and removes the burden of doing manual translations for standalone applications. We demonstrate the features, usability, and effectiveness of IBW and IBL using well-established synthetic biological circuits.
    • Toward Improving Confidence in Autonomous Vehicle Software: A Study on Traffic Sign Recognition Systems

      Aslansefat, K.; Kabir, Sohag; Abdullatif, Amr R.A.; Vasudevan, Vinod; Papadopoulos, Y. (IEEE, 2021-08)
      This article proposes an approach named SafeML II, which applies empirical cumulative distribution function-based statistical distance measures in a designed human-in-the loop procedure to ensure the safety of machine learning-based classifiers in autonomous vehicle software. The application of artificial intelligence (AI) and data-driven decision-making systems in autonomous vehicles is growing rapidly. As autonomous vehicles operate in dynamic environments, the risk that they can face an unknown observation is relatively high due to insufficient training data, distributional shift, or cyber-security attack. Thus, AI-based algorithms should make dependable decisions to improve their interpretation of the environment, lower the risk of autonomous driving, and avoid catastrophic accidents. This paper proposes an approach named SafeML II, which applies empirical cumulative distribution function (ECDF)-based statistical distance measures in a designed human-in-the-loop procedure to ensure the safety of machine learning-based classifiers in autonomous vehicle software. The approach is model-agnostic and it can cover various machine learning and deep learning classifiers. The German Traffic Sign Recognition Benchmark (GTSRB) is used to illustrate the capabilities of the proposed approach.
    • Towards a Data Quality Framework for Heterogeneous Data

      Micic, Natasha; Neagu, Daniel; Campean, I. Felician; Habib Zadeh, Esmaeil (2017)
      Every industry has significant data output as a product of their working process, and with the recent advent of big data mining and integrated data warehousing it is the case for a robust methodology for assessing the quality for sustainable and consistent processing. In this paper a review is conducted on Data Quality (DQ) in multiple domains in order to propose connections between their methodologies. This critical review suggests that within the process of DQ assessment of heterogeneous data sets, not often are they treated as separate types of data in need of an alternate data quality assessment framework. We discuss the need for such a directed DQ framework and the opportunities that are foreseen in this research area and propose to address it through degrees of heterogeneity.
    • Towards a definition of virtual objects with Partial Differential Equations

      Ugail, Hassan; Sourin, A.; Sourina, O.; Gonzalez Castro, Gabriela (2009)
      We propose an efficient alternative to commonly used parametric surfaces such as NURBS surfaces for definition of complex geometry in shared virtual spaces. Our mathematical model allows to define objects by only providing coordinates of the section curves in 3-space. The resulting parametric functions allow fast calculation of the coordinates of the points on the surface of the objects. We devise an algorithm which evaluates the coefficients of these functions in real time. Given the small size of the resulting formulas and interactive rates for their calculation, we are able to efficiently use such PDE-based models for making virtual objects in shared virtual spaces. We describe the modeling framework and illustrate the proposed theoretical concepts with our function-based extension of VRML and X3D.
    • Towards a framework for engineering big data: An automotive systems perspective

      Byrne, Thomas J.; Campean, I. Felician; Neagu, Daniel (2018-05)
      Demand for more sophisticated models to meet big data expectations require significant data repository obligations, operating concurrently in higher-level applications. Current models provide only disjointed modelling paradigms. The proposed framework addresses the need for higher-level abstraction, using low-level logic in the form of axioms, from which higher-level functionality is logically derived. The framework facilitates definition and usage of subjective structures across the cyber-physical system domain, and is intended to converge the range of heterogeneous data-driven objects.
    • Towards a Fuzzy Expert System on Toxicological Data Quality Assessment

      Yang, Longzhi; Neagu, Daniel; Cronin, M.T.D.; Hewitt, M.; Enoch, S.J.; Madden, J.C.; Przybylak, K. (2013-01)
      Quality assessment (QA) requires high levels of domain-specific experience and knowledge. QA tasks for toxicological data are usually performed by human experts manually, although a number of quality evaluation schemes have been proposed in the literature. For instance, the most widely utilised Klimisch scheme1 defines four data quality categories in order to tag data instances with respect to their qualities; ToxRTool2 is an extension of the Klimisch approach aiming to increase the transparency and harmonisation of the approach. Note that the processes of QA in many other areas have been automatised by employing expert systems. Briefly, an expert system is a computer program that uses a knowledge base built upon human expertise, and an inference engine that mimics the reasoning processes of human experts to infer new statements from incoming data. In particular, expert systems have been extended to deal with the uncertainty of information by representing uncertain information (such as linguistic terms) as fuzzy sets under the framework of fuzzy set theory and performing inferences upon fuzzy sets according to fuzzy arithmetic. This paper presents an experimental fuzzy expert system for toxicological data QA which is developed on the basis of the Klimisch approach and the ToxRTool in an effort to illustrate the power of expert systems to toxicologists, and to examine if fuzzy expert systems are a viable solution for QA of toxicological data. Such direction still faces great difficulties due to the well-known common challenge of toxicological data QA that "five toxicologists may have six opinions". In the meantime, this challenge may offer an opportunity for expert systems because the construction and refinement of the knowledge base could be a converging process of different opinions which is of significant importance for regulatory policy making under the regulation of REACH, though a consensus may never be reached. Also, in order to facilitate the implementation of Weight of Evidence approaches and in silico modelling proposed by REACH, there is a higher appeal of numerical quality values than nominal (categorical) ones, where the proposed fuzzy expert system could help. Most importantly, the deriving processes of quality values generated in this way are fully transparent, and thus comprehensible, for final users, which is another vital point for policy making specified in REACH. Case studies have been conducted and this report not only shows the promise of the approach, but also demonstrates the difficulties of the approach and thus indicates areas for future development.
    • Towards a more sustainable surface transport infrastructure: a case study of applying multi criteria analysis techniques to assess the sustainability of transport noise reducing devices.

      Oltean-Dumbrava, Crina; Watts, Gregory R.; Miah, Abdul H.S. (2015)
      The surface transport infrastructure (i.e. road and rail) has seen increasing pressure in recent years to achieve better sustainability performance. Transport Noise Reducing Devices (NRDs) form a major part of the surface transport infrastructure system in mitigating undesirable surface noise pollution to impacted communities. Their sustainability is a growing interest for practitioners and policy makers in this area as NRDs projects now have to balance integrating and assessing social, environmental, and economic objectives besides meeting key technical requirements. This paper presents an account of the first study carried out to assess the absolute sustainability of NRDs via the application of multi criteria analysis (MCA) techniques. The general procedure, selection of criteria, data gathering, and the use of three MCA techniques, SAW (Simple Additive Weighting), PROMETHEE (Preference Ranking Organisation MeTHod for Enrichment Evaluations), and ELECTRE III (Elimination et Choice Translating Reality), to assess the absolute sustainability of two built and operating European NRDs projects (one in Spain, and one in Italy) is presented. The novel concept of defining an Optimal Hypothetic Ideal Solution (OHIS) to assess the sustainability of NRDs in absolute terms to achieve this end is also introduced and discussed. The presented case studies will thus provide a useful model for practitioners to adopt or amend to conduct their own assessments of NRDs' sustainability. The paper further concludes that the generation of index values by the three MCA techniques to denote the overall absolute sustainability of solutions is a useful feature for communicating the sustainability of NRDs across a broad range of stakeholders, and for conducting “what-if” analyses. The presented research could also support broader aims of developing harmonized sustainability standards for the NRDs industry to adopt and so forward the sustainability transport agenda.
    • Towards a resilience assurance model for robotic autonomous systems

      Campean, I. Felician; Kabir, Sohag; Dao, Cuong D.; Zhang, Qichun; Eckert, C. (2021-01)
      Applications of autonomous systems are becoming increasingly common across the field of engineered systems from cars, drones, manufacturing systems and medical devices, addressing prevailing societal changes, and, increasingly, consumer demand. Autonomous systems are expected to self-manage and self-certify against risks affecting the mission, safety and asset integrity. While significant progress has been achieved in relation to the modelling of safety and safety assurance of autonomous systems, no similar approach is available for resilience that integrates coherently across the cyber and physical parts. This paper presents a comprehensive discussion of resilience in the context of robotic autonomous systems, covering both resilience by design and resilience by reaction, and proposes a conceptual model of a system of learning for resilience assurance in a continuous product development framework. The resilience assurance model is proposed as a composable digital artefact, underpinned by a rigorous model-based resilience analysis at the system design stage, and dynamically monitored and continuously updated at run time in the system operation stage, with machine learning based knowledge extraction and validation.
    • Towards a Seamless Future Generation Network for High Speed Wireless Communications

      Anoh, Kelvin O.O.; Abd-Alhameed, Raed A.; Chukwu, M.C.; Buhari, M.; Jones, Steven M.R. (2013)
      The MIMO technology towards achieving future generation broadband networks design criteria is presented. Typical next generation scenarios are investigated. The MIMO technology is integrated with the OFDM technology for effective space, time and frequency diversity exploitations for high speed outdoor environment. Two different OFDM design kernels (fast Fourier transform (FFT) and wavelet packet transform (WPT)) are used at the baseband for OFDM system travelling at terrestrial high speed for 800MHz and 2.6GHz operating frequencies. Results show that the wavelet kernel for designing OFDM systems can withstand doubly selective channel fading for mobiles speeds up to 280Km/hr at the expense of the traditional OFDM design kernel, the fast Fourier transform.
    • Towards An Enterprise Self-healing System against Botnets Attacks

      Alhomoud, Adeeb M.; Awan, Irfan U.; Pagna Disso, Jules F. (2013-05)
      Protecting against cyber attacks is no longer a problem of organizations and home users only. Cyber security programs are now a priority of most governments. Cyber criminals have been using botnets to gain control over millions of computer, steel information and commit other malicious activities. In this paper we propose a self-healing architecture that was originally inspired from a nature paradigm and applied in the computer field. Our solution is designed to work within a network domain. We present the initial design of our solution based on the principles of self healing systems and the analysis of botnet behaviour. We discuss how to either neutralize or reverse (correct) their actions ensuring that network operations continue without disruption.
    • Towards an Integrated Approach to Verification and Model-Based Testing in System Engineering

      Lefticaru, Raluca; Konur, Savas; Yildirim, Unal; Uddin, Amad; Campean, I. Felician; Gheorghe, Marian (2017)
      Engineering design in general and system design of embedded software have a direct impact on the final engineering product and the software implementation, respectively. Guaranteeing that the models utilised meet the specified requirements is beneficial in detecting misbehaviour and software flaws. This requires an integrated approach, combining verification and model-based testing methodology and notations and methods from system engineering and software engineering. In this paper, we propose a model-based approach integrating various notations utilised in the functional design of complex systems with formal verification and testing. We illustrate our approach on the cruise control system of an e-Bike case study.
    • Towards Autonomous Health Monitoring of Rails Using a FEA-ANN Based Approach

      Brown, L.; Afazov, S.; Scrimieri, Daniele (Springer, 2021-11-18)
      The current UK rail network is managed by Network Rail, which requires an investment of £5.2bn per year to cover operational costs [1]. These expenses include the maintenance and repairs of the railway rails. This paper aims to create a proof of concept for an autonomous health monitoring system of the rails using an integrated finite element analysis (FEA) and artificial neural network (ANN) approach. The FEA is used to model worn profiles of a standard rail and predict the stress field considering the material of the rail and the loading condition representing a train travelling on a straight line. The generated FEA data is used to train an ANN model which is utilised to predict the stress field of a worn rail using optically scanned data. The results showed that the stress levels in a rail predicted with the ANN model are in an agreement with the FEA predictions for a worn rail profile. These initial results indicate that the ANN can be used for the rapid prediction of stresses in worn rails and the FEA-ANN based approach has the potential to be applied to autonomous health monitoring of rails using fast scanners and validated ANN models. However, further development of this technology would be required before it could be used in the railway industry, including: real time data processing of scanned rails; improved scanning rates to enhance the inspection efficiency; development of fast computational methods for the ANN model; and training the ANN model with a large set of representative data representing application specific scenarios.
    • Towards design and implementation of Industry 4.0 for food manufacturing

      Konur, Savas; Lan, Yang; Thakker, Dhaval; Mokryani, Geev; Polovina, N.; Sharp, J. (2021)
      Today’s factories are considered as smart ecosystems with humans, machines and devices interacting with each other for efficient manufacturing of products. Industry 4.0 is a suite of enabler technologies for such smart ecosystems that allow transformation of industrial processes. When implemented, Industry 4.0 technologies have a huge impact on efficiency, productivity and profitability of businesses. The adoption and implementation of Industry 4.0, however, require to overcome a number of practical challenges, in most cases, due to the lack of modernisation and automation in place with traditional manufacturers. This paper presents a first of its kind case study for moving a traditional food manufacturer, still using the machinery more than one hundred years old, a common occurrence for small- and medium-sized businesses, to adopt the Industry 4.0 technologies. The paper reports the challenges we have encountered during the transformation process and in the development stage. The paper also presents a smart production control system that we have developed by utilising AI, machine learning, Internet of things, big data analytics, cyber-physical systems and cloud computing technologies. The system provides novel data collection, information extraction and intelligent monitoring services, enabling improved efficiency and consistency as well as reduced operational cost. The platform has been developed in real-world settings offered by an Innovate UK-funded project and has been integrated into the company’s existing production facilities. In this way, the company has not been required to replace old machinery outright, but rather adapted the existing machinery to an entirely new way of operating. The proposed approach and the lessons outlined can benefit similar food manufacturing industries and other SME industries.
    • Towards early diagnosis of dementia using a virtual environment

      Shamsuddin, Syadiah Nor Wan; Ugail, Hassan; Lesk, Valerie E.; Walters, Elizabeth R. (2013)
      Dementia is one of the biggest fears in the process of ageing and the most common cause is Alzheimer’s Disease(AD). Topographic disorientation is an early manifestation of AD and threatens activities of their daily lives. Finding solutions are essential in the early diagnosis of dementia if medical treatment and healthcare services to be deployed in time. Recent studies have shown that people with mild cognitive impairment (MCI) may convert to Alzheimer’s disease (AD) over time although not all MCI cases progress to dementia. The diagnosis of MCI is important to allow prompt treatment and disease management before the neurons degenerate to a stage beyond repair. Hence, the ability to obtain a method of identifying MCI is of great importance. This work presents a virtual environment which can be utilized as a quick, easy and friendly tool for early diagnosis of dementia. This tool was developed with an aim to investigate cognitive functioning in a group of healthy elderly and those with MCI. It focuses on the task of following a route, since Topographical Disorientation (TD) is common in AD. The results shows that this novel simulation was able to predict with about 90% overall accuracy using weighting function proposed to discriminate between MCI and healthy elderly.
    • Towards Lattice-Boltzmann modelling of unconfined gas mixing in anaerobic digestion

      Dapelo, Davide; Trunk, R.; Krause, M.J.; Bridgeman, John (2019-02-15)
      A novel Lattice-Boltzmann model to simulate gas mixing in anaerobic digestion is developed and described. For the first time, Euler–Lagrange multiphase, non-Newtonian and turbulence modelling are applied jontly with a novel hybrid boundary condition. The model is validated in a laboratory-scale framework and flow patterns are assessed through Particle Imaging Velocimetry (PIV) and innovative Positron-Emission Particle Tracking (PEPT). The model is shown to reproduce the experimental flow patterns with fidelity in both qualitative and quantitative terms. The model opens up a new approach to computational modelling of the complex multiphase flow in anaerobic digesters and offers specific advantages, such as computational efficiency, over an analogous Euler-Lagrange finite-volume computational fluid dynamics approach.