In social bookmark tools users are setting up lightweight conceptual structures called folksonomies. Currently, the information retrieval support is limited. We present a formal model and a new search algorithm for fo...
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In social bookmark tools users are setting up lightweight conceptual structures called folksonomies. Currently, the information retrieval support is limited. We present a formal model and a new search algorithm for folksonomies, called FolkRank, that exploits the structure of the folksonomy. The proposed algorithm is also applied to find communities within the folksonomy and is used to structure search results. All findings are demonstrated on a large scale dataset. A long version of this paper has been published at the European Semantic Web Conference 2006 [3].
The detection of the types of local surface form deviations is a major step in the automated quality assessment of car body parts during the manufacturing process. In previous studies we compared the performance of di...
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The detection of the types of local surface form deviations is a major step in the automated quality assessment of car body parts during the manufacturing process. In previous studies we compared the performance of different soft computing techniques for this purpose. We achieved promising results with regard to classification accuracy and interpretability of rule bases, even though the dataset was rather small, high dimensional and unbalanced. In this paper we reconsider the collection of training examples and their assignment to defect types by the quality experts. We attempt to minimize the uncertainty of the quality experts' subjective and error-prone labelling in order to achieve a higher reliability of the defect detection. We show that refined and more accurate classification models can be built on the basis of a preprocessed training set that is more consistent. Using a partially supervised learning strategy we can report improvements in classification accuracy.
The main objective of this paper is to construct a distributed environment through which the capacitance requirements of self excited induction generators can be monitored and controlled. A single-server/ multi-client...
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The main objective of this paper is to construct a distributed environment through which the capacitance requirements of self excited induction generators can be monitored and controlled. A single-server/ multi-client architecture has been proposed which enables the self excited induction generators to access the remote server at any time, being able with their respective data to get the minimum capacitance requirements. An RMI (Remote Method Invocation) based distributed model has been developed in such a way that for every specific period of time, the remote server obtains the system data simultaneously from the neighbouring self excited induction generators with which the clients are registered and the server sends back the capacitance requirements as a response to the respective clients. The server creates a new thread of control for every client request and hence a complete distributed environment has been exploited.
Becausemining complete set of frequent patterns from dense database could be impractical, an interesting alternative has been proposed recently. Instead of mining the complete set of frequent patterns, the new model o...
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Becausemining complete set of frequent patterns from dense database could be impractical, an interesting alternative has been proposed recently. Instead of mining the complete set of frequent patterns, the new model only finds out the maximal frequent patterns, which can generate all frequent patterns. FP-growth algorithm is one of the most efficient frequent-pattern mining methods published so far. However,because FP-tree and conditional FP-trees must be two-way traversable, a great deal memory is needed in process of mining. This paper proposes an efficient algorithm Unid_FP-Max for mining maximal frequent patterns based on unidirectional FP-tree. Because of generation method of unidirectional FP-tree and conditional unidirectional FP-trees, the algorithm reduces the space consumption to the fullest extent. With the development of two techniques:single path pruning and header table pruning which can cut down many conditional unidirectional FP-trees generated recursively in mining process, Unid_ FP-Max further lowers the expense of time and space.
In order to reduce the complexity of the state space in Order-k Markov predictor, a new Step-2 Markov predictor is proposed to make path prediction over WLAN. The feasibility of the Step-2 Markov predictor is proved b...
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In order to reduce the complexity of the state space in Order-k Markov predictor, a new Step-2 Markov predictor is proposed to make path prediction over WLAN. The feasibility of the Step-2 Markov predictor is proved by calculating and comparing conditional entropy of Step-2 and Order-k Markov predictors. And the paper also analyzes and compares the prediction accuracy of the two kinds of Markov predictors using actual Wi-Fi trace data. The work shows that the Step-2 Markov predictor is more stable than Order-1 Markov predictor for different length trace files and it also reduces the complexity of the Markov state space dramatically and gets approximately the same prediction accuracy with Order-2 Markov predictor and higher accuracy than Order-k (k≠2) Markov predictors.
The algorithmic form of GAs conforms well to SIMD computing environments with relatively minor adjustments to the operators. In this paper we consider in detail a GA implementation on a MasPar machine. The question of...
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Over recent years a huge library of data mining algorithms has been developed to tackle a variety of problems in fields such as medical imaging and network traffic analysis. Many of these techniques are far more flexi...
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ISBN:
(纸本)1424308526
Over recent years a huge library of data mining algorithms has been developed to tackle a variety of problems in fields such as medical imaging and network traffic analysis. Many of these techniques are far more flexible than more classical modelling approaches and could be usefully applied to data-rich environmental problems. Certain techniques such as Artificial Neural Networks, Clustering, Case-Based Reasoning and more recently Bayesian Decision Networks have found application in environmental modelling while other methods, for example classification and association rule extraction, have not yet been taken up on any wide scale. We propose that these and other data mining techniques could be usefully applied to difficult problems in the field. This paper introduces several data mining concepts and briefly discusses their application to environmental modelling, where data may be sparse, incomplete, or heterogenous.
The escape time algorithm cannot render the convergence region of mapping, so there are some black regions in escape time fractal. In this paper, a novel method is presented to construct fractal image, which is named ...
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The escape time algorithm cannot render the convergence region of mapping, so there are some black regions in escape time fractal. In this paper, a novel method is presented to construct fractal image, which is named the distance ratio iteration method. This method performs iteration on two points and render fractal image by using their distance ratio convergence times. Taking complex mapping z←zα+c as example, the generalized Mandelbrot and Julia sets are constructed based on distance ratio and their visual properties are analyzed. The result fractal image has complex and self-similarity structure in inner convergence region. It is proved that the boundary of distance ratio fractal is the same as M-J set when α>0, and some visual structure of it with various exponent α are discussed. When α<0, the generalized Mandelbrot and Julia set based on distance ratio have some complex structures which M-J set does not have.
Some recent trends in manufacturing in particular and business in general, lead to new approaches regarding the organisation and software architecture, mainly adopting distributed solutions. Such organisations imply o...
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Some recent trends in manufacturing in particular and business in general, lead to new approaches regarding the organisation and software architecture, mainly adopting distributed solutions. Such organisations imply organisational and technological evolution through agility, distribution, decentralisation, reactivity and flexibility. New organisational and technological paradigms are needed in order to reply to the modern manufacturing systems challenges. The Multi-Agent paradigm represents one of the most promising approaches to build complex, flexible, and cost-effective scheduling systems because of its distributed and dynamic nature. Modelling the Scheduling of Manufacturing Systems by means of two technologies like Meta-Heuristics and Multi-Agent Systems seems to be an interesting way to see Industrial Systems in the future. A multi-agent based model for support dynamic scheduling in manufacturing environments is proposed.
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