patternrecognition under fuzzy environments is an interesting and important research topic which has been receiving more and more attention in recent years. Aiming at this kind of patternrecognition problems, fuzzy ...
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ISBN:
(纸本)9781424447541
patternrecognition under fuzzy environments is an interesting and important research topic which has been receiving more and more attention in recent years. Aiming at this kind of patternrecognition problems, fuzzy theories have been applied to the field widely and effectively. Especially interval-valued intuitionistic fuzzy sets (IVIFSs) can give not only a membership degree, but also a non-membership degree, which is more or less independent. Meanwhile the membership degree and non-membership degree are denoted by an interval which makes the IVIFSs can represent the dynamic character of features. Therefore in this paper, depending on IVIFSs and corresponding similarity degree (or distance measure) we construct a kind of novel patternrecognition approach. This approach chooses different weight for each feature according to its dissimilarity with other features. Thus the approach can show the corresponding influence and importance of different features. Finally, we utilize concrete examples to validate the proposed approach.
To develop effective learning algorithms for online cursive word recognition is still a challenge research issue. In this paper, we propose a probabilistic framework to model the inherent ambiguity of cursive handwrit...
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In this paper we investigate a non-parametric classification of English phonemes in speaker-independent continuous speech. We employ the "voting" k-Nearest Neighbour (k-NN) classifier, a powerful technique i...
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ISBN:
(纸本)9781424423538
In this paper we investigate a non-parametric classification of English phonemes in speaker-independent continuous speech. We employ the "voting" k-Nearest Neighbour (k-NN) classifier, a powerful technique in patternrecognition problems, along with a new representation of phonemes for the speech recognition task. We also exploit the idea behind "approximate" k-NN that results in a very fast way of computing the k approximate closest neighbours of each data point. Comparing the recognition performance of the proposed method with the HMM-based recognizer of HTK toolkit reveals that the k-NN-based recognizer outperforms its counterpart. In addition, incorporating the "approximate" nearest neighbour search instead of the "exact" one results in completing the training step much faster than the HMM-based system, and the testing step with a comparable computational time. We also reduced the amount of the training data by applying a patternrecognition technique, called "thinning" algorithm. The outcome was a considerable reduction in the k-NN search space and hence the execution time, and also a slight increase in the recognition performance.
softcomputing is an emerging field that consists of complementary elements of fuzzy logic, neural computing and evolutionary computation. softcomputing techniques have found wide applications. One of the most import...
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The capability to support plethora of new diverse applications has placed Wireless Sensor Network (WSN) technology at threshold of an era of significant potential growth. In this regard, patternrecognition especially...
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ISBN:
(纸本)9781424449620
The capability to support plethora of new diverse applications has placed Wireless Sensor Network (WSN) technology at threshold of an era of significant potential growth. In this regard, patternrecognition especially in real-nine applications plays a paramount role in securing the network against malicious activity. In this paper, an attempt is made to introduce a novel method using a highly scalable and distributed associative memory technique, called Hierarchical Graph Neuron (HGN), while its effectiveness is analyzed from different points of view. The proposed approach not only enjoys front conserving the limited power resources of resource-constrained sensor nodes. but also can be scaled effectively to address scalability issues, which are of primary concern in wireless sensor networks In addition, the algorithm overcomes the issue of crosstalk available in the original GN algorithm, and thus not only promises to deliver accurate results, but also can be deployed for diverse types of applications in a multidimensional domain
For the automatic inspection for printed labels, which are covered with rubber-like coatings and Curl, we have developed a camera-based portable inspection system. In this paper, we explained the developed system, and...
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ISBN:
(纸本)9783642024801
For the automatic inspection for printed labels, which are covered with rubber-like coatings and Curl, we have developed a camera-based portable inspection system. In this paper, we explained the developed system, and especially discuss the inspection method of the spread and chip of the printed labels using neural networks. The experimental results confirm the validity of the proposed method for the spread and chip of alphanumerics.
The grading of woods is mainly determined by the defects on wood surfaces and determines the potential uses and values for the Sawmills. However, the dimensions of wood images are high, which is difficult to deal with...
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ISBN:
(纸本)9780769537399
The grading of woods is mainly determined by the defects on wood surfaces and determines the potential uses and values for the Sawmills. However, the dimensions of wood images are high, which is difficult to deal with. Dimensionality reduction is one of key interests in processing the higher-dimensional image data without losing intrinsic information. The problem of sub-pattern based discriminative non-linear dimensionality reduction called Sp-DNDR is considered for wood image recognition. This setting uses the sub-pattern of the original samples data and within-class and between-class scatters are used to specify whether pairs of instances belong to the same class or not. Sp-DNDR can project the data onto a set of 'useful' features and preserve the structure of the data as well as the scatters defined in the feature spaces. We demonstrate the practical usefulness and high scalability of the Sp-DNDR for wood knot defects recognition tasks by extensive simulation experiments. Experimental results show Sp-DNDR based recognition method can achieve a higher accuracy. For dimensionality reduction, Sp-DNDR method outperforms some established typical dimensionality reduction methods. Besides, the proposed method has better robust to the interferences on wood surfaces.
The intelligent computing annual conference primarily aims to promote the research,development and application of advanced intelligent computing techniques by providing a vibrant and effective forum across a variety o...
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The intelligent computing annual conference primarily aims to promote the research,development and application of advanced intelligent computing techniques by providing a vibrant and effective forum across a variety of *** conference has a further aim of increasing the awareness of industry of advanced intelligent computing techniques and the economic benefits that can be gained by implementing them.
As humans, we have innate faculties that allow us to efficiently segment groups of objects. Computers, to some degree, can be programmed with similar categorical capabilities, which stem from exploratory data analysis...
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As humans, we have innate faculties that allow us to efficiently segment groups of objects. Computers, to some degree, can be programmed with similar categorical capabilities, which stem from exploratory data analysis. Out of the various subsets of data reasoning, clustering provides insight into the structure and relationships of input samples situated in a number of distributions. To determine these relationships, many clustering methods rely on one or more human inputs;the most important being the number of distributions, c, to seek. This work investigates a technique for estimating the number of clusters from a general type of data called relational data. Several numerical examples are presented to illustrate the effectiveness of the proposed method.
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