905 resultados para Automatic energy management
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Abraham J. Harris, chairman.
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Bibliography: p. R-1 - R-19, v.3.
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Mode of access: Internet.
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Mode of access: Internet.
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Cover title : Environmental impact statement energy transportation system, inc.
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An automatic email handling system (AutoRouter) was introduced at a national counselling service in Australia. In 2003, counsellors responded to a total of 7421 email messages. Over nine days in early May 2004 the administrator responsible for the management of the manual email counselling service recorded the time spent on managing email messages. The AutoRouter was then introduced. Since the implementation of the AutoRouter the administrator's management role has become redundant, an average of 12 h 5 min per week of staff time has been saved. There have been further savings in supervisor time. Counsellors were taking an average of 6.2 days to respond to email messages (n=4307), with an average delay of 1.2 days from the time counsellors wrote the email to when the email was sent. Thus the response was sent on average 7.4 days after receipt of the original client email message. A significant decrease in response time has been noted since implementation of the AutoRouter, with client responses now taking an average of 5.4 days, a decrease of 2.0 days. Automatic message handling appears to be a promising method of managing the administration of a steadily increasing email counselling service.
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Resting energy expenditure (REE) is lower than predicted in persons taking atypical antipsychotic medication, and weight management is a significant clinical challenge for some of them. However, to date there have been no published guidelines to assist clinicians in choosing appropriate prediction equations to estimate energy expenditure in persons taking atypical antipsychotic medications. The objectives of this study were to measure REE in a group of men taking the atypical antipsychotic clozapine and to determine whether REE can be accurately predicted for this population using previously published regression equations. REE was measured using indirect calorimetry via a ventilated hood on eight men who had completed at least 6 months of treatment with clozapine. Comparisons between measured REE and predicted REE using five different equations were undertaken. The commonly-used Harris-Benedict and Schofield equations systematically overestimated REE. Predictions of REE from other equations were too variable for clinical use. When estimating energy requirements as part of a weight-management program in men who have been taking clozapine for 6 months, predictions of REE from the equations of Harris-Benedict and Schofield should be reduced by 280 kcal/day.
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Texture-segmentation is the crucial initial step for texture-based image retrieval. Texture is the main difficulty faced to a segmentation method. Many image segmentation algorithms either can’t handle texture properly or can’t obtain texture features directly during segmentation which can be used for retrieval purpose. This paper describes an automatic texture segmentation algorithm based on a set of features derived from wavelet domain, which are effective in texture description for retrieval purpose. Simulation results show that the proposed algorithm can efficiently capture the textured regions in arbitrary images, with the features of each region extracted as well. The features of each textured region can be directly used to index image database with applications as texture-based image retrieval.
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Regulation of food intake and body weight involves a complex balance between long-term control of fat mass involving insulin, adrenal steroids and leptin signals to the CNS and short-term, meal-related signals. Cats will normally limit their food intake to their energy requirements. However, in some instances cats appear unable to regulate energy balance. Our research has demonstrated that despite elevated circulating leptin levels in obese cats associated with increased fat mass, they continue to overeat and gain weight. This paradox of increased leptin concentrations in obesity has been observed in other species and is hypothesized to be a consequence of 'leptin resistance'.
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Ontologies have become widely accepted as the main method for representing knowledge in Knowledge Management (KM) applica-tions. Given the continuous and rapid change and dynamic nature of knowledge in all fields, automated methods for construct-ing ontologies are of great importance. All ontologies or taxonomies currently in use have been hand built and require consider-able manpower to keep up to date. Taxono-mies are less logically rigorous than ontolo-gies, and in this paper we consider the re-quirements for a system which automatically constructed taxonomies. There are a number of potentially useful methods for construct-ing hierarchically organised concepts from a collection of texts and there are a number of automatic methods which permit one to as-sociate one word with another. The impor-tant issue for the successful development of this research area is to identify techniques for labelling the relation between two candi-date terms, if one exists. We consider a number of possible approaches and argue that the majority are unsuitable for our re-quirements.
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Automatic ontology building is a vital issue in many fields where they are currently built manually. This paper presents a user-centred methodology for ontology construction based on the use of Machine Learning and Natural Language Processing. In our approach, the user selects a corpus of texts and sketches a preliminary ontology (or selects an existing one) for a domain with a preliminary vocabulary associated to the elements in the ontology (lexicalisations). Examples of sentences involving such lexicalisation (e.g. ISA relation) in the corpus are automatically retrieved by the system. Retrieved examples are validated by the user and used by an adaptive Information Extraction system to generate patterns that discover other lexicalisations of the same objects in the ontology, possibly identifying new concepts or relations. New instances are added to the existing ontology or used to tune it. This process is repeated until a satisfactory ontology is obtained. The methodology largely automates the ontology construction process and the output is an ontology with an associated trained leaner to be used for further ontology modifications.