A statistical index of growth condition in an aquaculture experiment

A simple statistical index, for evaluating the condition of growth in an aquaculture experiment and indicating the extent of effect of any plausible rival hypothesis, is presented.

Gender effect on the right-left discrimination task in a sample of heroin-dependent patients

Rationale: Discriminating right from left is an everyday cognitive ability. Repeated exposure to certain drugs, such as heroin, can produce poor performance on many cognitive tasks. However, it is yet unclear whether drug abuse impairs the ability of right-left discrimination. Objectives: The aim of the present study is to examine whether the spatial ability measured by the right-left discrimination task can be affected by heroin abuse and whether such drug effect, if it exists, is gender related. Methods: A paper-and-pen test was used. The test consists of line drawings of a person with no arm, one arm, or both arms crossing the vertical body axis of the figure. The line drawings are viewed from the back, from the front, or randomly alternating between the back and front drawings. The subjects task is to mark which is the right or left hand in the figure as fast as possible. Results: A main finding in this study was that the ability to discriminate between left and right in visual space was impaired in heroin-dependent patients. Especially, heroin-dependent females performed poorer than control females in all conditions but heroin-dependent males only performed poorly in part of conditions. Conclusions: Recent heroin abuse impairs the ability of right-left discrimination and such impairment is gender related: heroin-dependent females demonstrated greater performance deficits than males.

Statistical gear health analysis which is robust to fluctuating loads and operating speeds

Condition-based maintenance is concerned with the collection and interpretation of data to support maintenance decisions. The non-intrusive nature of vibration data enables the monitoring of enclosed systems such as gearboxes. It remains a significant challenge to analyze vibration data that are generated under fluctuating operating conditions. This is especially true for situations where relatively little prior knowledge regarding the specific gearbox is available. It is therefore investigated how an adaptive time series model, which is based on Bayesian model selection, may be used to remove the non-fault related components in the structural response of a gear assembly to obtain a residual signal which is robust to fluctuating operating conditions. A statistical framework is subsequently proposed which may be used to interpret the structure of the residual signal in order to facilitate an intuitive understanding of the condition of the gear system. The proposed methodology is investigated on both simulated and experimental data from a single stage gearbox. © 2011 Elsevier Ltd. All rights reserved.

Statistical image modelling using interscale phase relationships of complex wavelet coefficients

A novel method for modelling the statistics of 2D photographic images useful in image restoration is defined. The new method is based on the Dual Tree Complex Wavelet Transform (DT-CWT) but a phase rotation is applied to the coefficients to create complex coefficients whose phase is shift-invariant at multiscale edge and ridge features. This is in addition to the magnitude shift invariance achieved by the DT-CWT. The increased correlation between coefficients adjacent in space and scale provides an improved mechanism for signal estimation. © 2006 IEEE.

Reinforcement learning for parameter estimation in statistical spoken dialogue systems

Reinforcement techniques have been successfully used to maximise the expected cumulative reward of statistical dialogue systems. Typically, reinforcement learning is used to estimate the parameters of a dialogue policy which selects the system's responses based on the inferred dialogue state. However, the inference of the dialogue state itself depends on a dialogue model which describes the expected behaviour of a user when interacting with the system. Ideally the parameters of this dialogue model should be also optimised to maximise the expected cumulative reward. This article presents two novel reinforcement algorithms for learning the parameters of a dialogue model. First, the Natural Belief Critic algorithm is designed to optimise the model parameters while the policy is kept fixed. This algorithm is suitable, for example, in systems using a handcrafted policy, perhaps prescribed by other design considerations. Second, the Natural Actor and Belief Critic algorithm jointly optimises both the model and the policy parameters. The algorithms are evaluated on a statistical dialogue system modelled as a Partially Observable Markov Decision Process in a tourist information domain. The evaluation is performed with a user simulator and with real users. The experiments indicate that model parameters estimated to maximise the expected reward function provide improved performance compared to the baseline handcrafted parameters. © 2011 Elsevier Ltd. All rights reserved.