898 resultados para Adaptive and Intelligent Technologies
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Libraries are caught in the middle—between static or shrinking budgets on one hand and ever-expanding user needs on the other. How did we get here, and where do we go from here? This paper will offer two perspectives: Part I will present survey results about changing Library purchasing habits in light of changing formats, access, business models and user demands. Data from a previous survey on this topic will be compared and updated. Pricing trends and possible futures will be discussed. Part II will briefly trace the history of libraries’ roles in scholarly communication and connecting learners with knowledge. From there, we show an example of phasing in a patron-driven / demand-driven and short-term loan e-book program, complete with incorporating these tools in library instruction, research, and portable device loadability for field work.
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Cutting analysis is a important and crucial task task to detect and prevent problems during the petroleum well drilling process. Several studies have been developed for drilling inspection, but none of them takes care about analysing the generated cutting at the vibrating shale shakers. Here we proposed a system to analyse the cutting's concentration at the vibrating shale shakers, which can indicate problems during the petroleum well drilling process, such that the collapse of the well borehole walls. Cutting's images are acquired and sent to the data analysis module, which has as the main goal to extract features and to classify frames according to one of three previously classes of cutting's volume. A collection of supervised classifiers were applied in order to allow comparisons about their accuracy and efficiency. We used the Optimum-Path Forest (OPF), Artificial Neural Network using Multi layer Perceptrons (ANN-MLP), Support Vector Machines (SVM) and a Bayesian Classifier (BC) for this task. The first one outperformed all the remaining classifiers. Recall that we are also the first to introduce the OPF classifier in this field of knowledge. Very good results show the robustness of the proposed system, which can be also integrated with other commonly system (Mud-Logging) in order to improve the last one's efficiency.
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This paper presents an approach to integrate an artificial intelligence (AI) technique, concretely rule-based processing, into mobile agents. In particular, it focuses on the aspects of designing and implementing an appropriate inference engine of small size to reduce migration costs. The main goal is combine two lines of agent research, First, the engineering oriented approach on mobile agent architectures, and, second, the AI related approach on inference engines driven by rules expressed in a restricted subset of first-order predicate logic (FOPL). In addition to size reduction, the main functions of this type of engine were isolated, generalized and implemented as dynamic components, making possible not only their migration with the agent, but also their dynamic migration and loading on demand. A set of classes for representing and exchanging knowledge between rule-based systems was also proposed.
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Includes bibliography
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Incluye Bibliografía
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Incluye Bibliografía
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Spanish version available
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Incluye Bibliografía
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Includes bibliography