An algebraic approach to modeling distributed multiphysics problems: The case of a DRI reactor

© 2015, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.This paper deals with the problem of modelling a chemical reactor for the Direct Reduction of Iron ore (DRI). Such a process is being increasingly promoted as a more viable alternative to the classic Blast Furnace for the production of iron from raw minerals. Due to the inherent complexity of the process and the reactor itself, its effective monitoring and control requires advanced mathematical models containing distributed-parameter components. While classical approaches such as Finite Element or Finite Differences are still reasonable options, for accuracy and computational efficiency reasons, an algebraic approach is proposed. A full multi-physical, albeit one-dimensional model is addressed and its accuracy is analysed

A distributed networked approach for fault detection of large-scale systems

Networked systems present some key new challenges in the development of fault diagnosis architectures. This paper proposes a novel distributed networked fault detection methodology for large-scale interconnected systems. The proposed formulation incorporates a synchronization methodology with a filtering approach in order to reduce the effect of measurement noise and time delays on the fault detection performance. The proposed approach allows the monitoring of multi-rate systems, where asynchronous and delayed measurements are available. This is achieved through the development of a virtual sensor scheme with a model-based re-synchronization algorithm and a delay compensation strategy for distributed fault diagnostic units. The monitoring architecture exploits an adaptive approximator with learning capabilities for handling uncertainties in the interconnection dynamics. A consensus-based estimator with timevarying weights is introduced, for improving fault detectability in the case of variables shared among more than one subsystem. Furthermore, time-varying threshold functions are designed to prevent false-positive alarms. Analytical fault detectability sufficient conditions are derived and extensive simulation results are presented to illustrate the effectiveness of the distributed fault detection technique

Optimal topology for distributed fault detection of large-scale systems

© 2015, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.The paper deals with the problem of defining the optimal topology for a distributed fault detection architecture for non-linear large-scale systems. A stochastic modelbased framework for diagnosis is formulated. The system structural graph is decomposed into subsystems and each subsystem is monitored by one local diagnoser. It is shown that overlapping of subsystems allows to improve the detectability properties of the monitoring architecture. Based on this theoretical result, an optimal decomposition design method is proposed, able to define the minimum number of detection units needed to guarantee the detectability of certain faults while minimizing the communication costs subject to some computation cost constraints. An algorithmic procedure is presented to solve the proposed optimal decomposition problem. Preliminary simulation results show the potential of the proposed approach

In Touch with the Wild: Exploring Real-time Feedback for Learning to Play the Violin

Real-time feedback has great potential for enhancing learning complex motor-skills by enabling people to correct their mistakes as they go. Multimodal real-time cues could provide reinforcement to inform players whether they are making the correct or incorrect movements at a given time. However, little is known about how best to communicate information in real-time so that people can readily perceive and apply it to improving their movement while learning complex motor-skills. This thesis addresses this gap in knowledge by investigating how real-time feedback can enhance learning to play the violin. It explores how haptic and visual feedback are perceived, understood and acted upon in real-time when engaged in the primary task of playing the violin. Prototypes were built with sensors to measure movement and either vibrations on the body or visual signals as feedback. Three in-the-wild user studies were conducted: one comparing visual and vibrotactile feedback for individual practice; one investigating shared feedback at a musical summer school; and one examining real-time feedback as part of a programme of learning at a high school. In-the-wild studies investigate users interacting with technology in a naturalistic setting, with all the demands that this entails. The findings show real-time feedback is effective at improving violin technique and can support learning in other ways such as encouraging mutual support between learners. The positive learning outcomes, however, need to be understood with respect to the complex interplay between the technology, demands of the setting and characteristics of individual learners. A conceptual framework is provided that outlines these interdependent factors. The findings are discussed regarding their applicability to learning other physical skills and the challenges and insights of using an in-the-wild methodology. The contribution of this thesis is to demonstrate empirically and theoretically how real-time vibrotactile and visual feedback can enhance learning a complex motor-skill

Ecological and Behavioural Correlates of Intracellular Buffering Capacity in the Muscles of Antarctic Fishes

Five species of antarctic fishes can be arranged in order of increasing anaerobic capacity of the white muscles for burst swimming: Rhigophila dearborni (Zoarcidae), icefish (Channichthyidae), Dissostichus mawsoni, Trematomus centronotus, and Pagothenia borchgrevinki (Nototheniidae). This order reflects in-creasing dependence on anaerobic work done during short bursts of speed during prey capture or predator avoidance. Buffer capacity (beta) for white muscle was lower than that of behaviourally equivalent fish from lower latitudes and beta is itself temperature-dependent

Optic flow speed modulates guidance level control: New insights into two-level steering

© 2016 American Psychological Association. Responding to changes in the road ahead is essential for successful driving. Steering control can be modeled using 2 complementary mechanisms: guidance control (to anticipate future steering requirements) and compensatory control (to stabilize position-in-lane). Drivers seem to rapidly sample the visual information needed for steering using active gaze patterns, but the way in which this perceptual information is combined remains unclear. Influential models of steering capture many steering behaviors using just 'far' and 'near' road regions to inform guidance and compensatory control respectively (Salvucci & Gray, 2004). However, optic flow can influence steering even when road-edges are visible (Kountouriotis, Mole, Merat, & Wilkie, 2016). Two experiments assessed whether flow selectively interacted with compensatory and/or guidance levels of steering control, under either unconstrained gaze or constrained gaze conditions. Optic flow speed was manipulated independent of the veridical roadedges so that use of flow would lead to predictable understeering or oversteering. Steering was found to systematically vary according to flow speed, but crucially the Flow-Induced Steering Bias (FISB) magnitude depended on which road-edge components were visible. The presence of a guidance signal increased the influence of flow, with the largest FISB in 'Far' and 'Complete' road conditions, whereas the smallest FISB was observed when only 'Near' road-edges were visible. Gaze behaviors influenced steering to some degree, but did not fully explain the interaction between flow and road-edges. Overall the experiments demonstrate that optic flow can act indirectly upon steering control by modulating the guidance signal provided by a demarcated path