Socio-economic and marketing aspects of Laila and Bottom Long Line fisheries in the Kalpitiya peninsula of Sri Lanka

Surrounding Net Fishery (laila) and Bottom Long Line Fishery which operate in the coastal waters of Kalpitiya Peninsula, compete for the same fish resources, resulting in a fishery dispute between the respective fishermen. Both fisheries target demersal as well as mid pelagic fishes, such as travellys (parava), mullets (galmalu) and barracudas (ulava). As the dispute had an adverse impact on the social harmony in the fishing community of the area, a socio-economic survey was conducted to study the underlying factors and to suggest policy measures to resolve the issue. The laila fishermen were resident fishermen in the Kalpitiya Peninsula while bottom long line fishermen were migratory fishermen from Negombo and Chilaw areas in the west coast of Sri Lanka. The Kalpitiya peninsula is located in the North West coast, some 50 km away from the west coast. Although the educational level and literacy rate of the laila community was below that of the bottom long line community, the laila community was economically better off. The net economic returns from laila fishery were superior to that from bottom long line fishery. The boat owner's and crew's share per operation of laila fishery were Rs.3,736 and Rs.947 respectively. The same figures for bottom long line fishery were Rs.588 and Rs.327 respectively. The resource rent from laila fishery was Rs.5,860, however, and much higher than that for bottom long line fishery (Rs.275), showing that the laila fishery exploits the targeted fish resource at a much higher rate compared to bottom long line fishery. This situation badly affects the equitable distribution of resources between the two fishing communities and results in unequal economic gains. Based on the findings of this study, certain input/output controls are proposed to address this problem, among which is the need to increase license fee for laila fishery units to offset the higher exploitation rate of fish resources.

Flux line lattice structure and behavior in antiphase boundary free vicinal YBa2Cu3O7-delta thin films

Field angle dependent critical current, magneto-optical microscopy and high resolution electron microscopy studies have been performed on YBa2Cu3O7-delta thin films grown on miscut substrates. High resolution electron microscopy images show that the films studied exhibited clean epitaxial growth with a low density of antiphase boundaries and stacking faults. Any antiphase boundaries (APBs) formed near the film substrate interface rapidly healed rather than extending through the thickness of the film. Unlike vicinal films grown on annealed substrates, which contain a high density of antiphase boundaries, magneto-optical imaging showed no filamentary flux penetration in the films studied. The flux penetration is, however, asymmetric. This is associated with intrinsic pinning of flux strings by the tilted a-b planes and the dependence of the pinning force on the angle between the local field and the a-b planes. Field angle dependent critical current measurements exhibited the striking vortex channeling effect previously reported in vicinal films. By combining the results of three complementary characterization techniques it is shown that extended APB free films exhibit markedly different critical current behavior compared to APB rich films. This is attributed to the role of APB sites as strong pinning centers for Josephson string vortices between the a-b planes. (C) 2003 American Institute of Physics.

On-line policy optimisation of spoken dialogue systems via live interaction with human subjects

Statistical dialogue models have required a large number of dialogues to optimise the dialogue policy, relying on the use of a simulated user. This results in a mismatch between training and live conditions, and significant development costs for the simulator thereby mitigating many of the claimed benefits of such models. Recent work on Gaussian process reinforcement learning, has shown that learning can be substantially accelerated. This paper reports on an experiment to learn a policy for a real-world task directly from human interaction using rewards provided by users. It shows that a usable policy can be learnt in just a few hundred dialogues without needing a user simulator and, using a learning strategy that reduces the risk of taking bad actions. The paper also investigates adaptation behaviour when the system continues learning for several thousand dialogues and highlights the need for robustness to noisy rewards. © 2011 IEEE.

Effects of initial soil fabric and mode of shearing on quasi-steady state line for monotonic undrained behavior

The effects of initial soil fabric and mode of shearing on quasi-steady state line in void ratiostress space are studied by employing the Distinct Element Method numerical analysis. The results show that the initial soil fabric and the mode of shearing have a profound effect on the location of the quasi-steady state line. The evolution of the soil fabric during the course of undrained shearing shows that the specimens with different initial soil fabrics reach quasi-steady state at various soil fabric conditions. At quasi-steady state, the soil fabric has a significant adjustment to change its behavior from contractive to dilative. As the stress state approaches the steady state, the soil fabrics of different initial conditions become similar. The numerical analysis results are compared qualitatively with the published experimental data and the effects of specimen reconstitution methods and mode of shearing found in the experimental studies canbe systematically explained by the numerical analysis. © 2009 Taylor & Francis Group.

Vehicle trajectory linearisation to enable efficient optimisation of the constant speed racing line

A driver model is presented capable of optimising the trajectory of a simple dynamic nonlinear vehicle, at constant forward speed, so that progression along a predefined track is maximised as a function of time. In doing so, the model is able to continually operate a vehicle at its lateral-handling limit, maximising vehicle performance. The technique used forms a part of the solution to the motor racing objective of minimising lap time. A new approach of formulating the minimum lap time problem is motivated by the need for a more computationally efficient and robust tool-set for understanding on-the-limit driving behaviour. This has been achieved through set point-dependent linearisation of the vehicle model and coupling the vehicle-track system using an intrinsic coordinate description. Through this, the geometric vehicle trajectory had been linearised relative to the track reference, leading to new path optimisation algorithm which can be formed as a computationally efficient convex quadratic programming problem. © 2012 Copyright Taylor and Francis Group, LLC.

Recovering 3D Structure of Poorly Textured Infrastructure Scenes Using Point and Line Features

Most of the existing automated machine vision-based techniques for as-built documentation of civil infrastructure utilize only point features to recover the 3D structure of a scene. However it is often the case in man-made structures that not enough point features can be reliably detected (e.g. buildings and roofs); this can potentially lead to the failure of these techniques. To address the problem, this paper utilizes the prominence of straight lines in infrastructure scenes. It presents a hybrid approach that benefits from both point and line features. A calibrated stereo set of video cameras is used to collect data. Point and line features are then detected and matched across video frames. Finally, the 3D structure of the scene is recovered by finding 3D coordinates of the matched features. The proposed approach has been tested on realistic outdoor environments and preliminary results indicate its capability to deal with a variety of scenes.

Blade loading and its application in the mean-line design of low pressure turbines

In order to minimize the number of iterations to a turbine design, reasonable choices of the key parameters must be made at the earliest possible opportunity. The choice of blade loading is of particular concern in the low pressure (LP) turbine of civil aero engines, where the use of high-lift blades is widespread. This paper presents an analytical mean-line design study for a repeating-stage, axial-flow Low Pressure (LP) turbine. The problem of how to measure blade loading is first addressed. The analysis demonstrates that the Zweifel coefficient [1] is not a reasonable gauge of blade loading because it inherently depends on the flow angles. A more appropriate coefficient based on blade circulation is proposed. Without a large set of turbine test data it is not possible to directly evaluate the accuracy of a particular loss correlation. The analysis therefore focuses on the efficiency trends with respect to flow coefficient, stage loading, lift coefficient and Reynolds number. Of the various loss correlations examined, those based on Ainley and Mathieson ([2], [3], [4]) do not produce realistic trends. The profile loss model of Coull and Hodson [5] and the secondary loss models of Craig and Cox [6] and Traupel [7] gave the most reasonable results. The analysis suggests that designs with the highest flow turning are the least sensitive to increases in blade loading. The increase in Reynolds number lapse with loading is also captured, achieving reasonable agreement with experiments. Copyright © 2011 by ASME.

Uncertainty management for on-line optimisation of a POMDP-based large-scale spoken dialogue system

The optimization of dialogue policies using reinforcement learning (RL) is now an accepted part of the state of the art in spoken dialogue systems (SDS). Yet, it is still the case that the commonly used training algorithms for SDS require a large number of dialogues and hence most systems still rely on artificial data generated by a user simulator. Optimization is therefore performed off-line before releasing the system to real users. Gaussian Processes (GP) for RL have recently been applied to dialogue systems. One advantage of GP is that they compute an explicit measure of uncertainty in the value function estimates computed during learning. In this paper, a class of novel learning strategies is described which use uncertainty to control exploration on-line. Comparisons between several exploration schemes show that significant improvements to learning speed can be obtained and that rapid and safe online optimisation is possible, even on a complex task. Copyright © 2011 ISCA.

Sensor graphs for guaranteed cooperative localization performance

A group of mobile robots can localize cooperatively, using relative position and absolute orientation measurements, fused through an extended Kalman filter (ekf). The topology of the graph of relative measurements is known to affect the steady-state value of the position error covariance matrix. Classes of sensor graphs are identified, for which tight bounds for the trace of the covariance matrix can be obtained based on the algebraic properties of the underlying relative measurement graph. The string and the star graph topologies are considered, and the explicit form of the eigenvalues of error covariance matrix is given. More general sensor graph topologies are considered as combinations of the string and star topologies, when additional edges are added. It is demonstrated how the addition of edges increases the trace of the steady-state value of the position error covariance matrix, and the theoretical predictions are verified through simulation analysis.

The most persistent soft-clique in a set of sampled graphs

When searching for characteristic subpatterns in potentially noisy graph data, it appears self-evident that having multiple observations would be better than having just one. However, it turns out that the inconsistencies introduced when different graph instances have different edge sets pose a serious challenge. In this work we address this challenge for the problem of finding maximum weighted cliques. We introduce the concept of most persistent soft-clique. This is subset of vertices, that 1) is almost fully or at least densely connected, 2) occurs in all or almost all graph instances, and 3) has the maximum weight. We present a measure of clique-ness, that essentially counts the number of edge missing to make a subset of vertices into a clique. With this measure, we show that the problem of finding the most persistent soft-clique problem can be cast either as: a) a max-min two person game optimization problem, or b) a min-min soft margin optimization problem. Both formulations lead to the same solution when using a partial Lagrangian method to solve the optimization problems. By experiments on synthetic data and on real social network data we show that the proposed method is able to reliably find soft cliques in graph data, even if that is distorted by random noise or unreliable observations. Copyright 2012 by the author(s)/owner(s).