74,414 research outputs found

    Simulating California reservoir operation using the classification and regression-tree algorithm combined with a shuffled cross-validation scheme

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    The controlled outflows from a reservoir or dam are highly dependent on the decisions made by the reservoir operators, instead of a natural hydrological process. Difference exists between the natural upstream inflows to reservoirs and the controlled outflows from reservoirs that supply the downstream users. With the decision maker's awareness of changing climate, reservoir management requires adaptable means to incorporate more information into decision making, such as water delivery requirement, environmental constraints, dry/wet conditions, etc. In this paper, a robust reservoir outflow simulation model is presented, which incorporates one of the well-developed data-mining models (Classification and Regression Tree) to predict the complicated human-controlled reservoir outflows and extract the reservoir operation patterns. A shuffled cross-validation approach is further implemented to improve CART's predictive performance. An application study of nine major reservoirs in California is carried out. Results produced by the enhanced CART, original CART, and random forest are compared with observation. The statistical measurements show that the enhanced CART and random forest overperform the CART control run in general, and the enhanced CART algorithm gives a better predictive performance over random forest in simulating the peak flows. The results also show that the proposed model is able to consistently and reasonably predict the expert release decisions. Experiments indicate that the release operation in the Oroville Lake is significantly dominated by SWP allocation amount and reservoirs with low elevation are more sensitive to inflow amount than others

    The anatomy of teleneurosurgery in China

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    With its huge population and vast territory, China faces a great challenge in providing modern advanced health care services to all parts of the country. The advances of information communication technologies (ICTs) and the advent of internet have revolutionised the means in the delivery of healthcare via telemedicine to remote and underserved populations, which to a certain extent has been very well exploited in China, especially where 70% peasants residing in the rural areas. This paper reviews the latest development in telemedicine infrastructure in China with the focus on the development of teleneurosurgery, drawing from the results gained from a 3-year networking project between Europe and China on telemedicine (TIME, 2005–2007) funded by European Commission under Asia ICT programme, with an aim to shape up envisages of future medical care in China. Comparison with its counterparts in Europe is also addressed

    A new evolutionary search strategy for global optimization of high-dimensional problems

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    Global optimization of high-dimensional problems in practical applications remains a major challenge to the research community of evolutionary computation. The weakness of randomization-based evolutionary algorithms in searching high-dimensional spaces is demonstrated in this paper. A new strategy, SP-UCI is developed to treat complexity caused by high dimensionalities. This strategy features a slope-based searching kernel and a scheme of maintaining the particle population's capability of searching over the full search space. Examinations of this strategy on a suite of sophisticated composition benchmark functions demonstrate that SP-UCI surpasses two popular algorithms, particle swarm optimizer (PSO) and differential evolution (DE), on high-dimensional problems. Experimental results also corroborate the argument that, in high-dimensional optimization, only problems with well-formative fitness landscapes are solvable, and slope-based schemes are preferable to randomization-based ones. © 2011 Elsevier Inc. All rights reserved
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