A generative model for online depth fusion

We present a probabilistic, online, depth map fusion framework, whose generative model for the sensor measurement process accurately incorporates both long-range visibility constraints and a spatially varying, probabilistic outlier model. In addition, we propose an inference algorithm that updates the state variables of this model in linear time each frame. Our detailed evaluation compares our approach against several others, demonstrating and explaining the improvements that this model offers, as well as highlighting a problem with all current methods: systemic bias. © 2012 Springer-Verlag.

Using QFD for e-business planning and analysis in a micro-sized enterprise

Purpose - The purpose of this paper is to show how QFD can be used as part of a structured planning and analysis framework for micro-sized enterprises to build-up their e-business capabilities. Design/methodology/approach - This case study has been produced using a new framework which integrates the balanced scorecard, value chain and quality function deployment techniques into an integrated framework known as the E-Business Planning and Analysis Framework (E-PAF). It has been produced using an action research approach. Findings - A new framework with a supporting case study is provided. This case study has demonstrated that the framework can be applied successfully to micro-sized enterprises (those with less than ten employees) to successfully plan new strategic and technical developments. This will enhance the online service that the company is able to provide. Research limitations/implications - This paper presents a single case study. The technical recommendations are currently being implemented. Originality/value - Such analytical techniques are most commonly associated with large organisations, and are not specifically associated with e-business planning. This paper provides a new framework that will be of general applicability to other similarly sized enterprises that are looking to improve e-business capabilities. © Emerald Group Publishing Limited.

Developing a framework to evaluate business networks:the case of Ireland's industry-led network initiative

Business networks have been described as cooperative arrangements between independent business organisations that vary from contractual joint ventures to informal exchanges of information. This collaboration has become recognised as an innovative and efficient tool for organising interdependent activities, with benefits accruing to both firms and the local economy. For a number of years, resources have been devoted to supporting Irish networking policies. One recent example of such support is the Irish government's target of €20 million per annum for five years to support the creation of enterprise-led networks. It is imperative that a clear rationale for such interventions is established, as the opportunity cost of public funds is high. This article, therefore, develops an evaluation framework for such networking interventions. This framework will facilitate effective programme planning, implementation and evaluation. It will potentially show how a chain of cause-and-effect at both micro and macro-levels for networking interventions can be established.

High level data fusion

We address the question of how to obtain effective fusion of identification information such that it is robust to the quality of this information. As well as technical issues data fusion is encumbered with a collection of (potentially confusing) practical considerations. These considerations are described during the early chapters in which a framework for data fusion is developed. Following this process of diversification it becomes clear that the original question is not well posed and requires more precise specification. We use the framework to focus on some of the technical issues relevant to the question being addressed. We show that fusion of hard decisions through use of an adaptive version of the maximum a posteriori decision rule yields acceptable performance. Better performance is possible using probability level fusion as long as the probabilities are accurate. Of particular interest is the prevalence of overconfidence and the effect it has on fused performance. The production of accurate probabilities from poor quality data forms the latter part of the thesis. Two approaches are taken. Firstly the probabilities may be moderated at source (either analytically or numerically). Secondly, the probabilities may be transformed at the fusion centre. In each case an improvement in fused performance is demonstrated. We therefore conclude that in order to obtain robust fusion care should be taken to model the probabilities accurately; either at the source or centrally.

An advanced real-time predictive maintenance framework for large scale machine systems

This thesis introduces and develops a novel real-time predictive maintenance system to estimate the machine system parameters using the motion current signature. Recently, motion current signature analysis has been addressed as an alternative to the use of sensors for monitoring internal faults of a motor. A maintenance system based upon the analysis of motion current signature avoids the need for the implementation and maintenance of expensive motion sensing technology. By developing nonlinear dynamical analysis for motion current signature, the research described in this thesis implements a novel real-time predictive maintenance system for current and future manufacturing machine systems. A crucial concept underpinning this project is that the motion current signature contains infor­mation relating to the machine system parameters and that this information can be extracted using nonlinear mapping techniques, such as neural networks. Towards this end, a proof of con­cept procedure is performed, which substantiates this concept. A simulation model, TuneLearn, is developed to simulate the large amount of training data required by the neural network ap­proach. Statistical validation and verification of the model is performed to ascertain confidence in the simulated motion current signature. Validation experiment concludes that, although, the simulation model generates a good macro-dynamical mapping of the motion current signature, it fails to accurately map the micro-dynamical structure due to the lack of knowledge regarding performance of higher order and nonlinear factors, such as backlash and compliance. Failure of the simulation model to determine the micro-dynamical structure suggests the pres­ence of nonlinearity in the motion current signature. This motivated us to perform surrogate data testing for nonlinearity in the motion current signature. Results confirm the presence of nonlinearity in the motion current signature, thereby, motivating the use of nonlinear tech­niques for further analysis. Outcomes of the experiment show that nonlinear noise reduction combined with the linear reverse algorithm offers precise machine system parameter estimation using the motion current signature for the implementation of the real-time predictive maintenance system. Finally, a linear reverse algorithm, BJEST, is developed and applied to the motion current signature to estimate the machine system parameters.

National system of innovation and technological learning:an integrated framework for understanding technological capability development in the Russian forestry sector

One of the issues in the innovation system literature is examination of technological learning strategies of laggard nations. Two distinct bodies of literature have contributed to our insight into forces driving learning and innovation, National Systems of Innovation (NSI) and technological learning literature. Although both literatures yield insights on catch-up strategies of 'latecomer' nations, the explanatory powers of each literature by itself is limited. In this paper, a possible way of linking the macro- and the micro-level approaches by incorporating enterprises as active learning entities into the learning and innovation system is proposed. The proposed model has been used to develop research hypotheses and indicate research directions and is relevant for investigating the learning strategies of firms in less technologically intensive industries outside East Asia.

Evidence-based nanoscopic and molecular framework for excipient functionality in compressed orally disintegrating tablets

The work investigates the adhesive/cohesive molecular and physical interactions together with nanoscopic features of commonly used orally disintegrating tablet (ODT) excipients microcrystalline cellulose (MCC) and D-mannitol. This helps to elucidate the underlying physico-chemical and mechanical mechanisms responsible for powder densification and optimum product functionality. Atomic force microscopy (AFM) contact mode analysis was performed to measure nano-adhesion forces and surface energies between excipient-drug particles (6-10 different particles per each pair). Moreover, surface topography images (100 nm2-10 μm2) and roughness data were acquired from AFM tapping mode. AFM data were related to ODT macro/microscopic properties obtained from SEM, FTIR, XRD, thermal analysis using DSC and TGA, disintegration testing, Heckel and tabletability profiles. The study results showed a good association between the adhesive molecular and physical forces of paired particles and the resultant densification mechanisms responsible for mechanical strength of tablets. MCC micro roughness was 3 times that of D-mannitol which explains the high hardness of MCC ODTs due to mechanical interlocking. Hydrogen bonding between MCC particles could not be established from both AFM and FTIR solid state investigation. On the contrary, D-mannitol produced fragile ODTs due to fragmentation of surface crystallites during compression attained from its weak crystal structure. Furthermore, AFM analysis has shown the presence of extensive micro fibril structures inhabiting nano pores which further supports the use of MCC as a disintegrant. Overall, excipients (and model drugs) showed mechanistic behaviour on the nano/micro scale that could be related to the functionality of materials on the macro scale. © 2014 Al-khattawi et al.

Bridging large and small scales of water models using hybrid Molecular Dynamics/Fluctuating Hydrodynamics framework

This thesis presents a two-dimensional water model investigation and development of a multiscale method for the modelling of large systems, such as virus in water or peptide immersed in the solvent. We have implemented a two-dimensional ‘Mercedes Benz’ (MB) or BN2D water model using Molecular Dynamics. We have studied its dynamical and structural properties dependence on the model’s parameters. For the first time we derived formulas to calculate thermodynamic properties of the MB model in the microcanonical (NVE) ensemble. We also derived equations of motion in the isothermal–isobaric (NPT) ensemble. We have analysed the rotational degree of freedom of the model in both ensembles. We have developed and implemented a self-consistent multiscale method, which is able to communicate micro- and macro- scales. This multiscale method assumes, that matter consists of the two phases. One phase is related to micro- and the other to macroscale. We simulate the macro scale using Landau Lifshitz-Fluctuating Hydrodynamics, while we describe the microscale using Molecular Dynamics. We have demonstrated that the communication between the disparate scales is possible without introduction of fictitious interface or approximations which reduce the accuracy of the information exchange between the scales. We have investigated control parameters, which were introduced to control the contribution of each phases to the matter behaviour. We have shown, that microscales inherit dynamical properties of the macroscales and vice versa, depending on the concentration of each phase. We have shown, that Radial Distribution Function is not altered and velocity autocorrelation functions are gradually transformed, from Molecular Dynamics to Fluctuating Hydrodynamics description, when phase balance is changed. In this work we test our multiscale method for the liquid argon, BN2D and SPC/E water models. For the SPC/E water model we investigate microscale fluctuations which are computed using advanced mapping technique of the small scales to the large scales, which was developed by Voulgarakisand et. al.

Energy-based damage and fracture framework for viscoelastic asphalt concrete

A framework based on the continuum damage mechanics and thermodynamics of irreversible processes using internal state variables is used to characterize the distributed damage in viscoelastic asphalt materials in the form of micro-crack initiation and accumulation. At low temperatures and high deformation rates, micro-cracking is considered as the source of nonlinearity and thus the cause of deviation from linear viscoelastic response. Using a non-associated damage evolution law, the proposed model shows the ability to describe the temperature-dependent processes of micro-crack initiation, evolution and macro-crack formation with good comparison to the material response in the Superpave indirect tensile (IDT) strength test.