967 resultados para Tristan Plume
Resumo:
This paper presents a discussion on the use of MIMO and SISO techniques for identification of the radiation force terms in models for surface vessels. We compare and discuss two techniques recently proposed in literature for this application: time domain identification and frequency domain identification. We compare the methods in terms of estimates model order, accuracy of the fit, use of the available information, and ease of use and implementation.
Resumo:
The development of offshore oil and gas fields require the placement of different equipment on the sea floor. This is done by deploying the equipment from vessels operating in dynamic positioning on the surface. The deployment operation has different phases, and in higher sea states, it may require wave-load synchronization, when the load is going through the splash zone, and heave compensation when the load is close to the sea floor. In this paper, we analyse the performance of a particular type of hardware operating in a heave compensation mode. We derive a comprehensive model, analyse limits of performance and evaluate a control strategy.
Resumo:
In this paper, a method of thrust allocation based on a linearly constrained quadratic cost function capable of handling rotating azimuths is presented. The problem formulation accounts for magnitude and rate constraints on both thruster forces and azimuth angles. The advantage of this formulation is that the solution can be found with a finite number of iterations for each time step. Experiments with a model ship are used to validate the thrust allocation system.
Resumo:
The objective of this work is to formulate a nonlinear, coupled model of a container ship during parametric roll resonance, and to validate the model using experimental data.
Resumo:
The main purpose of the rudder in ships is course keeping. However, the rudder can also be used, in some cases, to reject undesirable wave produced rolling motions. From a fundamental point of view, the main issues associated with this problem are the presence of a nonminimum phase zero and the single input two output nature of the system. In this paper, the limitations imposed on the achievable closed loop performance due to these issues are analyzed. This gives a deeper understanding of the problem and leads to conclusions regarding the inherent design trade-offs which hold regardless of the control strategy used.
Resumo:
This paper addresses the issue of output feedback model predictive control for linear systems with input constraints and stochastic disturbances. We show that the optimal policy uses the Kalman filter for state estimation, but the resultant state estimates are not utilized in a certainty equivalence control law
Resumo:
Speech recognition in car environments has been identified as a valuable means for reducing driver distraction when operating noncritical in-car systems. Under such conditions, however, speech recognition accuracy degrades significantly, and techniques such as speech enhancement are required to improve these accuracies. Likelihood-maximizing (LIMA) frameworks optimize speech enhancement algorithms based on recognized state sequences rather than traditional signal-level criteria such as maximizing signal-to-noise ratio. LIMA frameworks typically require calibration utterances to generate optimized enhancement parameters that are used for all subsequent utterances. Under such a scheme, suboptimal recognition performance occurs in noise conditions that are significantly different from that present during the calibration session – a serious problem in rapidly changing noise environments out on the open road. In this chapter, we propose a dialog-based design that allows regular optimization iterations in order to track the ever-changing noise conditions. Experiments using Mel-filterbank noise subtraction (MFNS) are performed to determine the optimization requirements for vehicular environments and show that minimal optimization is required to improve speech recognition, avoid over-optimization, and ultimately assist with semireal-time operation. It is also shown that the proposed design is able to provide improved recognition performance over frameworks incorporating a calibration session only.
Resumo:
Successful prediction of groundwater flow and solute transport through highly heterogeneous aquifers has remained elusive due to the limitations of methods to characterize hydraulic conductivity (K) and generate realistic stochastic fields from such data. As a result, many studies have suggested that the classical advective-dispersive equation (ADE) cannot reproduce such transport behavior. Here we demonstrate that when high-resolution K data are used with a fractal stochastic method that produces K fields with adequate connectivity, the classical ADE can accurately predict solute transport at the macrodispersion experiment site in Mississippi. This development provides great promise to accurately predict contaminant plume migration, design more effective remediation schemes, and reduce environmental risks. Key Points Non-Gaussian transport behavior at the MADE site is unraveledADE can reproduce tracer transport in heterogeneous aquifers with no calibrationNew fractal method generates heterogeneous K fields with adequate connectivity
Resumo:
This paper describes a lightweight, modular and energy efficient robotic vehicle platform designed for broadacre agriculture - the Small Robotic Farm Vehicle (SRFV). The current trend in farming is towards increasingly large machines that optimise the individual farmer’s productivity. Instead, the SRFV is designed to promote the sustainable intensification of agriculture by allowing farmers to concentrate on more important farm management tasks. The robot has been designed with a user-centred approach which focuses the outcomes of the project on the needs of the key project stakeholders. In this way user and environmental considerations for broadacre farming have informed the vehicle platform configuration, locomotion, power requirements and chassis construction. The resultant design is a lightweight, modular four-wheeled differential steer vehicle incorporating custom twin in-hub electric drives with emergency brakes. The vehicle is designed for a balance between low soil impact, stability, energy efficiency and traction. The paper includes modelling of the robot’s dynamics during an emergency brake in order to determine the potential for tipping. The vehicle is powered by a selection of energy sources including rechargeable lithium batteries and petrol-electric generators.
Resumo:
This paper presents a trajectory-tracking control strategy for a class of mechanical systems in Hamiltonian form. The class is characterised by a simplectic interconnection arising from the use of generalised coordinates and full actuation. The tracking error dynamic is modelled as a port-Hamiltonian Systems (PHS). The control action is designed to take the error dynamics into a desired closed-loop PHS characterised by a constant mass matrix and a potential energy with a minimum at the origin. A transformation of the momentum and a feedback control is exploited to obtain a constant generalised mass matrix in closed loop. The stability of the close-loop system is shown using the close-loop Hamiltonian as a Lyapunov function. The paper also considers the addition of integral action to design a robust controller that ensures tracking in spite of disturbances. As a case study, the proposed control design methodology is applied to a fully actuated robotic manipulator.
Resumo:
We present an overview of the QUT plant classification system submitted to LifeCLEF 2014. This system uses generic features extracted from a convolutional neural network previously used to perform general object classification. We examine the effectiveness of these features to perform plant classification when used in combination with an extremely randomised forest. Using this system, with minimal tuning, we obtained relatively good results with a score of 0:249 on the test set of LifeCLEF 2014.
Resumo:
The research reported in this paper explores autonomous technologies for agricultural farming application and is focused on the development of multiple-cooperative agricultural robots (AgBots). These are highly autonomous, small, lightweight, and unmanned machines that operate cooperatively (as opposed to a traditional single heavy machine) and are suited to work on broadacre land (large-scale crop operations on land parcels greater than 4,000m2). Since this is a new, and potentially disruptive technology, little is yet known about farmer attitudes towards robots, how robots might be incorporated into current farming practice, and how best to marry the capability of the robot with the work of the farmer. This paper reports preliminary insights (with a focus on farmer-robot control) gathered from field visits and contextual interviews with farmers, and contributes knowledge that will enable further work toward the design and application of agricultural robotics.
Bayesian networks as a complex system tool in the context of a major industry and university project
Resumo:
A long-period magnetotelluric (MT) survey, with 39 sites covering an area of 270 by 150 km, has identified melt within the thinned lithosphere of Pleistocene-Holocene Newer Volcanics Province (NVP) in southeast Australia, which has been variously attributed to mantle plume activity or edge-driven mantle convection. Two-dimensional inversions from the MT array imaged a low-resistivity anomaly (10-30Ωm) beneath the NVP at ∼40-80 km depth, which is consistent with the presence of ∼1.5-4% partial melt in the lithosphere, but inconsistent with elevated iron content, metasomatism products or a hot spot. The conductive zone is located within thin juvenile oceanic mantle lithosphere, which was accreted onto thicker Proterozoic continental mantle lithosphere. We propose that the NVP owes its origin to decompression melting within the asthenosphere, promoted by lithospheric thickness variations in conjunction with rapid shear, where asthenospheric material is drawn by shear flow at a "step" at the base of the lithosphere.
Resumo:
The morphology, colour, fluorescence, cathodoluminescence, nitrogen content and aggregation state, internal structure and mineral inclusions have been studied for 69 alluvial diamonds from the Rio Soriso (Juina area, Mato Grosso State, Brazil). Nitrogen in most diamonds (53%) is fully aggregated as B centres, but there is also a large proportion of N-free stones (38%). A strong positive correlation between nitrogen and IR-active hydrogen concentrations is observed. The diamonds contain (in order of decreasing abundance) ferropericlase, CaSi-perovskite, magnetite, MgSi-perovskite, pyrrhotite, 'olivine', SiO2, perovskite, tetragonal almandine-pyrope phase and some other minerals represented by single grains. The Rio Soriso diamond suite is subdivided into several subpopulations that originated in upper and lower mantle of ultramafic and mafic compositions, with the largest subgroup forming in the ultramafic lower mantle. Analysed ferropericlase grains are enriched in Fe (Mg#=0.43-0.89), which is ascribed to their origin in the lowermost mantle. The Juina kimberlites may be unique in sampling the material from depths below 1,700 km that ascended in a plume formed at the core-mantle boundary.