869 resultados para Concept hierarchy


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Concept mapping is a technique for visualizing the relationships between different concepts, and collaborative concept mapping is used to model knowledge and transfer expert knowledge. Because of lacking some features,existing systems can’t support collaborative concept mapping effectively. In this paper, we analysis the collaborative concept mapping process according to the theory of distributed cognition, and argue the functions effective systems ought to include. A collaborative concept mapping system should have the following features: visualization of concept map, flexible collaboration style,supporting natural interaction, knowledge management and history management. Furthermore, we describe every feature in details. Finally,a prototype system has been built to fully explore the above technologies.

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Concept maps are an important tool to knowledge organization,representation, and sharing. Most current concept map tools do not provide full support for hand-drawn concept map creation and manipulation, largely due to the lack of methods to recognize hand-drawn concept maps. This paper proposes a structure recognition method. Our algorithm can extract node blocks and link blocks of a hand-drawn concept map by combining dynamic programming and graph partitioning and then build a concept-map structure by relating extracted nodes and links. We also introduce structure-based intelligent manipulation technique of hand-drawn concept maps. Evaluation shows that our method has high structure recognition accuracy in real time, and the intelligent manipulation technique is efficient and effective.

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We analyze in this paper the general covariant energy-momentum tensor of the gravitational system in general five-dimensional cosmological brane-world models. Then through calculating this energy-momentum for the cosmological generalization of the Randall-Sundrum model, which includes the original RS model as the static limit, we are able to show that the weakness of the gravitation on the "visible" brane is a general feature of this model. This is the origin of the gauge hierarchy from a gravitational point of view. Our results are also consistent with the fact that a gravitational system has vanishing total energy.

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Forage selection plays a prominent role in the process of returning cultivated lands back into grasslands. The conventional method of selecting forage species can only provide attempts for problem-solving without considering the relationships among the decision factors globally. Therefore, this study is dedicated to developing a decision support system to help farmers correctly select suitable forage species for the target sites. After collecting data through a field study, we developed this decision support system. It consists of three steps: (1) the analytic hierarchy process (AHP), (2) weights determination, and (3) decision making. In the first step, six factors influencing forage growth were selected by reviewing the related references and by interviewing experts. Then a fuzzy matrix was devised to determine the weight of each factor in the second step. Finally, a gradual alternative decision support system was created to help farmers choose suitable forage species for their lands in the third step. The results showed that the AHP and fuzzy logic are useful for forage selection decision making, and the proposed system can provide accurate results in a certain area (Gansu Province) of China.

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本文首先论述了体系结构研究的重要性 ,简要综述了体系结构研究的 4个发展阶段 ,并提出了研究体系结构的谱系概念 .在空间结构的研究中 ,提出了 5个视图与 9种过程流的描述方法和以扁平化递阶 -分散混合集成为特征的柔性体系结构 .本文还提出了体系结构中时间结构的概念及其应用 .

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分布、自主、协调与合作是多机器人系统的发展趋势。本文作者在研究易于协调合作的多机器人系统的基础上,采用分层递阶和多Agent概念,构造了一个装配系统-MROCAS系统。该系统具有任务自动建模分解,快速重组、良好柔性、友好人机界面,各机器人具有一定自主能力等特点,它实现了在较复杂环境下快速完成装配作业。

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Classification is a kind of basic cognitive process, and category is an important way for human beings to define the world. At the same time, categories are organized in a hierarchical way, which makes it possible for human beings to process information efficiency. For those reasons, the development of classification ability is always one of the foci in developmental psychology. By using the methods of spontaneous and trained classification of both familiar stimuli materials and artificial concepts, this research explored the 4-6 year old children's classification criteria. And by the artificial concept system formed in these classification criteria experiments, the mastery degree of class hierarchy in these young children was analyzed. The main results and conclusions are: 1) The classification ability increases quickly among kindergarteners from 4 to 6 year old: the 4 year old children seemed unable to classify objects by classificatory criteria, however, the 6 year ones had shown the ability in many experimental conditions. But the main basis of classificatory criteria in these young children, including 6 year old ones, was the functional relation of the objects but the conceptual relations, and their classification criteria was not consistent because they seem to be easily affected by experimental conditions. 2) The age of 5 is a more sensitive period of classification ability development: for the children of 5 year old, it was found that their classification ability was easily enhanced by training. The zone of proximal development in classification ability by category standard could probably lie in this period of age. 3) Knowledge is an important factor that affects young children's classification ability, meanwhile, their classification activity are affected by cognitive processing ability: young children exhibited different classification ability as they had different understanding of stimuli materials. Kindergarteners of different age were significantly different in their classification ability as the difference in cognitive processing ability, even if they had the same knowledge about the stimuli materials. 4) Different properties of class hierarchy are different in difficulty for young children: the 5-6 year old children showed that the could master the transitivity of the class hierarchy. No matter under what learning condition, they could answer most of the transitivity questions correctly and infer the property of the sub-class according to that of the super-class. The young children at 5-6 years old had mastered the branching property of class hierarchy at a relative high level, but their answers were easily affected by the hints in the questions. However, it seemed that the asymmetry of class hierarchy was difficult for young children to learn. Because young children could not understand the class inclusion relation, they always drew wrong conclusions about super-class from sub-class in their classification.

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For a very large network deployed in space with only nearby nodes able to talk to each other, we want to do tasks like robust routing and data storage. One way to organize the network is via a hierarchy, but hierarchies often have a few critical nodes whose death can disrupt organization over long distances. I address this with a system of distributed aggregates called Persistent Nodes, such that spatially local failures disrupt the hierarchy in an area proportional to the diameter of the failure. I describe and analyze this system, which has been implemented in simulation.

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Trees are a common way of organizing large amounts of information by placing items with similar characteristics near one another in the tree. We introduce a classification problem where a given tree structure gives us information on the best way to label nearby elements. We suggest there are many practical problems that fall under this domain. We propose a way to map the classification problem onto a standard Bayesian inference problem. We also give a fast, specialized inference algorithm that incrementally updates relevant probabilities. We apply this algorithm to web-classification problems and show that our algorithm empirically works well.

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M J Neal, A hardware proof of concept of a sailing robot for ocean observation, IEEE Journal of Oceanic Engineering, 2006 accepted for publication RAE2008

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McArdle disease is a metabolic disorder caused by pathogenic mutations in the PYGM gene. Timely diagnosis can sometimes be difficult with direct genomic analysis, which requires additional studies of cDNA from muscle transcripts. Although the "nonsense-mediated mRNA decay" (NMD) eliminates tissue-specific aberrant transcripts, there is some residual transcription of tissue-specific genes in virtually all cells, such as peripheral blood mononuclear cells (PBMCs).We studied a subset of the main types of PYGM mutations (deletions, missense, nonsense, silent, or splicing mutations) in cDNA from easily accessible cells (PBMCs) in 12 McArdle patients.Analysis of cDNA from PBMCs allowed detection of all mutations. Importantly, the effects of mutations with unknown pathogenicity (silent and splicing mutations) were characterized in PBMCs. Because the NMD mechanism does not seem to operate in nonspecific cells, PBMCs were more suitable than muscle biopsies for detecting the pathogenicity of some PYGM mutations, notably the silent mutation c.645G>A (p.K215=), whose effect in the splicing of intron 6 was unnoticed in previous muscle transcriptomic studies.We propose considering the use of PBMCs for detecting mutations that are thought to cause McArdle disease, particularly for studying their actual pathogenicity.