710 resultados para Learning through life
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This paper presents the design and implementation of a learning controller for the Automatic Generation Control (AGC) in power systems based on a reinforcement learning (RL) framework. In contrast to the recent RL scheme for AGC proposed by us, the present method permits handling of power system variables such as Area Control Error (ACE) and deviations from scheduled frequency and tie-line flows as continuous variables. (In the earlier scheme, these variables have to be quantized into finitely many levels). The optimal control law is arrived at in the RL framework by making use of Q-learning strategy. Since the state variables are continuous, we propose the use of Radial Basis Function (RBF) neural networks to compute the Q-values for a given input state. Since, in this application we cannot provide training data appropriate for the standard supervised learning framework, a reinforcement learning algorithm is employed to train the RBF network. We also employ a novel exploration strategy, based on a Learning Automata algorithm,for generating training samples during Q-learning. The proposed scheme, in addition to being simple to implement, inherits all the attractive features of an RL scheme such as model independent design, flexibility in control objective specification, robustness etc. Two implementations of the proposed approach are presented. Through simulation studies the attractiveness of this approach is demonstrated.
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Over the past six years Lowestoft College has embraced the revolution in mobile learning by welcoming Web 2.0, social media, cloud computing and Bring Your Own Device (BYOD). This open attitude to new technologies has led to a marked improvement in student achievement rates, has increased staff and student satisfaction and has resulted in a variety of cost savings for senior management during the current economic downturn.
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This report draws together outcomes from the JISC Curriculum Delivery Programme on behalf of JISC and includes recommendations for further investigation.
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Therapy employing epidural electrostimulation holds great potential for improving therapy for patients with spinal cord injury (SCI) (Harkema et al., 2011). Further promising results from combined therapies using electrostimulation have also been recently obtained (e.g., van den Brand et al., 2012). The devices being developed to deliver the stimulation are highly flexible, capable of delivering any individual stimulus among a combinatorially large set of stimuli (Gad et al., 2013). While this extreme flexibility is very useful for ensuring that the device can deliver an appropriate stimulus, the challenge of choosing good stimuli is quite substantial, even for expert human experimenters. To develop a fully implantable, autonomous device which can provide useful therapy, it is necessary to design an algorithmic method for choosing the stimulus parameters. Such a method can be used in a clinical setting, by caregivers who are not experts in the neurostimulator's use, and to allow the system to adapt autonomously between visits to the clinic. To create such an algorithm, this dissertation pursues the general class of active learning algorithms that includes Gaussian Process Upper Confidence Bound (GP-UCB, Srinivas et al., 2010), developing the Gaussian Process Batch Upper Confidence Bound (GP-BUCB, Desautels et al., 2012) and Gaussian Process Adaptive Upper Confidence Bound (GP-AUCB) algorithms. This dissertation develops new theoretical bounds for the performance of these and similar algorithms, empirically assesses these algorithms against a number of competitors in simulation, and applies a variant of the GP-BUCB algorithm in closed-loop to control SCI therapy via epidural electrostimulation in four live rats. The algorithm was tasked with maximizing the amplitude of evoked potentials in the rats' left tibialis anterior muscle. These experiments show that the algorithm is capable of directing these experiments sensibly, finding effective stimuli in all four animals. Further, in direct competition with an expert human experimenter, the algorithm produced superior performance in terms of average reward and comparable or superior performance in terms of maximum reward. These results indicate that variants of GP-BUCB may be suitable for autonomously directing SCI therapy.
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This short paper summarises findings of experiments that were carried out using copepod eggs taken from guts of Coregonus which breeds in various Ural lakes. The study showed that copepod eggs can pass through the gut of Coregonus unharmed.
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Background: The integrated treatment of first episode psychosis has been shown to improve functionality and negative symptoms in previous studies. In this paper, we describe a study of integrated treatment (individual psychoeducation complementary to pharmacotherapy) versus treatment as usual, comparing results at baseline with those at 6-month re-assessment (at the end of the study) for these patients, and online training of professionals to provide this complementary treatment, with the following objectives: 1) to compare the efficacy of individual psychoeducation as add-on treatment versus treatment as usual in improving psychotic and mood symptoms; 2) to compare adherence to medication, functioning, insight, social response, quality of life, and brain-derived neurotrophic factor, between both groups; and 3) to analyse the efficacy of online training of psychotherapists. Methods/design: This is a single-blind randomised clinical trial including patients with first episode psychosis from hospitals across Spain, randomly assigned to either a control group with pharmacotherapy and regular sessions with their psychiatrist (treatment as usual) or an intervention group with integrated care including treatment as usual plus a psychoeducational intervention (14 sessions). Training for professionals involved at each participating centre was provided by the coordinating centre (University Hospital of Alava) through video conferences. Patients are evaluated with an extensive battery of tests assessing clinical and sociodemographic characteristics (Positive and Negative Syndrome Scale, State-Trait Anxiety Inventory, Liebowitz Social Anxiety Scale, Hamilton Rating Scale for Depression, Scale to Assess Unawareness of Mental Disorders, Strauss and Carpenter Prognostic Scale, Global Assessment of Functioning Scale, Morisky Green Adherence Scale, Functioning Assessment Short Test, World Health Organization Quality of Life instrument WHOQOL-BREF (an abbreviated version of the WHOQOL-100), and EuroQoL questionnaire), and brain-derived neurotrophic factor levels are measured in peripheral blood at baseline and at 6 months. The statistical analysis, including bivariate analysis, linear and logistic regression models, will be performed using SPSS. Discussion: This is an innovative study that includes the assessment of an integrated intervention for patients with first episode psychosis provided by professionals who are trained online, potentially making it possible to offer the intervention to more patients.
Enhancing learning effectiveness through connectance diagrams: a new tool for learning organisations
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Many aerospace companies are currently making the transition to providing fully-integrated product-service offerings in which their products are designed from the outset with life-cycle considerations in mind. Based on a case study at Rolls-Royce, Civil Aerospace, this paper demonstrates how an interactive approach to process simulation can be used to support the redesign of existing design processes in order to incorporate life-cycle engineering (LCE) considerations. The case study provides insights into the problems of redesigning the conceptual stages of a complex, concurrent engineering design process and the practical value of process simulation as a tool to support the specification of process changes in the context of engineering design. The paper also illustrates how development of a simulation model can provide significant benefit to companies through the understanding of process behaviour that is gained through validating the behaviour of the model using different design and iteration scenarios. Keywords: jet engine design; life-cycle engineering; LCE; process change; design process simulation; applied signposting model; ASM. Copyright © 2011 Inderscience Enterprises Ltd.
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Numerous observations in clinical and preclinical studies indicate that the developing brain is particular sensitive to lead (Pb)'s pernicious effects. However, the effect of gestation-only Pb exposure on cognitive functions at maturation has not been studied. We investigated the potential effects of three levels of Pb exposure (low, middle, and high Pb: 0.03%, 0.09%, and 0.27% of lead acetate-containing diets) at the gestational period on the spatial memory of young adult offspring by Morris water maze spatial learning and fixed location/visible platform tasks. Our results revealed that three levels of Pb exposure significantly impaired memory retrieval in male offspring, but only female offspring at low levels of Pb exposure showed impairment of memory retrieval. These impairments were not due to the gross disturbances in motor performance and in vision because these animals performed the fixed location/visible platform task as well as controls, indicating that the specific aspects of spatial learning/memory were impaired. These results suggest that exposure to Pb during the gestational period is sufficient to cause long-term learning/memory deficits in young adult offspring. (C) 2003 Elsevier Inc. All rights reserved.