747 resultados para Adaptive clustering


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We present some additions to a fuzzy variable radius niche technique called Dynamic Niche Clustering (DNC) (Gan and Warwick, 1999; 2000; 2001) that enable the identification and creation of niches of arbitrary shape through a mechanism called Niche Linkage. We show that by using this mechanism it is possible to attain better feature extraction from the underlying population.

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This paper describes the recent developments and improvements made to the variable radius niching technique called Dynamic Niche Clustering (DNC). DNC is fitness sharing based technique that employs a separate population of overlapping fuzzy niches with independent radii which operate in the decoded parameter space, and are maintained alongside the normal GA population. We describe a speedup process that can be applied to the initial generation which greatly reduces the complexity of the initial stages. A split operator is also introduced that is designed to counteract the excessive growth of niches, and it is shown that this improves the overall robustness of the technique. Finally, the effect of local elitism is documented and compared to the performance of the basic DNC technique on a selection of 2D test functions. The paper is concluded with a view to future work to be undertaken on the technique.

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In the coming decades, the Mediterranean region is expected to experience various climate impacts with negative consequences on agricultural systems and which will cause uneven reductions in agricultural production. By and large, the impacts of climate change on Mediterranean agriculture will be heavier for southern areas of the region. This unbalanced distribution of negative impacts underscores the significance and role of ethics in such a context of analysis. Consequently, the aim of this article is to justify and develop an ethical approach to agricultural adaptation in the Mediterranean and to derive the consequent implications for adaptation policy in the region. In particular, we define an index of adaptive capacity for the agricultural systems of the Mediterranean region on whose basis it is possible to group its different sub-regions, and we provide an overview of the suitable adaptation actions and policies for the sub-regions identified. We then vindicate and put forward an ethical approach to agricultural adaptation, highlighting the implications for the Mediterranean region and the limitations of such an ethical framework. Finally, we emphasize the broader potential of ethics for agricultural adaptation policy.

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The adaptive thermal comfort theory considers people as active rather than passive recipients in response to ambient physical thermal stimuli, in contrast with conventional, heat-balance-based, thermal comfort theory. Occupants actively interact with the environments they occupy by means of utilizing adaptations in terms of physiological, behavioural and psychological dimensions to achieve ‘real world’ thermal comfort. This paper introduces a method of quantifying the physiological, behavioural and psychological portions of the adaptation process by using the analytic hierarchy process (AHP) based on the case studies conducted in the UK and China. Apart from three categories of adaptations which are viewed as criteria, six possible alternatives are considered: physiological indices/health status, the indoor environment, the outdoor environment, personal physical factors, environmental control and thermal expectation. With the AHP technique, all the above-mentioned criteria, factors and corresponding elements are arranged in a hierarchy tree and quantified by using a series of pair-wise judgements. A sensitivity analysis is carried out to improve the quality of these results. The proposed quantitative weighting method provides researchers with opportunities to better understand the adaptive mechanisms and reveal the significance of each category for the achievement of adaptive thermal comfort.

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This paper fully describes a nation-wide field study on building thermal environment and thermal comfort of occupant, which was carried out in summer 2005 and in winter 2006 respectively in China, illustrating the adaptive strategies adopted by occupants in domestic buildings in China. According to the climate division in China, the buildings in Beijing (BJ), Shanghai (SH), Wuhan (WH) and Chongqing (CQ), Guangzhou (GZ), Kunming (KM), were selected as targets which are corresponding to cold zone, hot summer and cold winter zone (SWC-SH, WH, CQ), hot summer and warm winter zone and temperate zone, respectively. The methodology used in the field study is the combination of subjective questionnaire regarding thermal sensation and adaptive approaches and physical environmental monitoring including indoor air temperature and relative humidity. A total of 1671 subjects participate in this investigation with more than 80% response rate in all surveyed cities. Both physiological and non-physiological factors (behavioural and psychological adaptations) have been analysed.

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Sirens’ used by police, fire and paramedic vehicles generate noise that propagates inside the vehicle cab that subsequently corrupts intelligibility of voice communications from the emergency vehicle to the control room. It is even common for the siren to be turned off to enable the control room to hear what is being said. Both fixed filter and adaptive filter systems have previously been developed to help cancel the transmission of the siren noise over the radio. Previous cancellation systems have only concentrated on the traditional 2-tone, wail and yelp sirens. This paper discusses an improvement to a previous adaptive filter system and presents the cancellation results to three new types of sirens; being chirp pulsar and localiser. A siren noise filter system has the capability to improve the response time for an emergency vehicle and thus help save lives. To date, this system has been tested using live recordings taken from a nonemergency situation with good results.

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The K-Means algorithm for cluster analysis is one of the most influential and popular data mining methods. Its straightforward parallel formulation is well suited for distributed memory systems with reliable interconnection networks. However, in large-scale geographically distributed systems the straightforward parallel algorithm can be rendered useless by a single communication failure or high latency in communication paths. This work proposes a fully decentralised algorithm (Epidemic K-Means) which does not require global communication and is intrinsically fault tolerant. The proposed distributed K-Means algorithm provides a clustering solution which can approximate the solution of an ideal centralised algorithm over the aggregated data as closely as desired. A comparative performance analysis is carried out against the state of the art distributed K-Means algorithms based on sampling methods. The experimental analysis confirms that the proposed algorithm is a practical and accurate distributed K-Means implementation for networked systems of very large and extreme scale.