To solve the radio frequency identification problem,an anti-collision algorithm based on the Gray code(BSGC) was developed. The proposed algorithm reduces the effort required to search for tag prefixes in accordance w...
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To solve the radio frequency identification problem,an anti-collision algorithm based on the Gray code(BSGC) was developed. The proposed algorithm reduces the effort required to search for tag prefixes in accordance with Gray code regulations and enhances the identification speed through efficient division and branching of the collision tags. The results of simulation and subsequent analysis of the proposed algorithm confirm that it effectively reduces the number of collisions and transmission delays,enhances the throughput rate,and increases the tag identification efficiency.
Based on analysis of basic cubic spline interpolation, the clamped cubic spline interpolation is generalized in this paper. The methods are presented on the condition that the first derivative and second derivative of...
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Based on analysis of cubic spline interpolation, the differentiation formulas of the cubic spline interpolation on the three boundary conditions are put up forward in this paper. At last, this calculation method is il...
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In this paper, an efficient approach for extracting semantic object using artificial bee colony algorithm(ABCA) has been proposed. First, we reduce speckle noise in the image. Then fitness function of ABC algorithm is...
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In this paper, an efficient approach for extracting semantic object using artificial bee colony algorithm(ABCA) has been proposed. First, we reduce speckle noise in the image. Then fitness function of ABC algorithm is constructed, and image pixels are classified into different regions. Further semantic objects are extracted in terms of color information. The simulation results show that the color clustering via bee colony algorithm gives superior results in enhancing cluster compactness.
The grey system forecasting model, neural network forecasting model and support vector machine forecasting model are proposed in this paper. Taking the road goods traffic volume from year of 1996 to 2003 in the whole ...
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By use of the properties of ant colony algorithm and genetic algorithm, a novel ant colony genetic hybrid algorithm, whose framework of hybrid algorithm is genetic algorithm, is proposed to solve the traveling salesma...
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Fuzzy entropy clustering (FEC) is sensitive to noises the same as fuzzy c-means (FCM) clustering because the probabilistic constraints in their memberships. To solve this noise sensitive problem of FCM, Krishnapuram a...
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Fuzzy entropy clustering (FEC) is sensitive to noises the same as fuzzy c-means (FCM) clustering because the probabilistic constraints in their memberships. To solve this noise sensitive problem of FCM, Krishnapuram and Keller have presented the possibilistic c-means (PCM) clustering by abandoning the constraints of FCM. A possibilistic type of fuzzy entropy clustering is proposed based on fuzzy entropy clustering and possibilistic c-means clustering. The proposed algorithm deals with noisy data better than FEC. Furthermore, the parameters of PCM is optimized using possibilistic clustering trick. Our experiments show that FEC is sensitive to noises while our proposed algorithm is insensitive to noises and has better clustering accuracy than FEC.
The user often retrieves few anticipated results when searching information in Internet via generalized search engine due that the web pages also exist the synonymous and ambiguous characteristic. How to classify the ...
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The user often retrieves few anticipated results when searching information in Internet via generalized search engine due that the web pages also exist the synonymous and ambiguous characteristic. How to classify the webpage is the key point to construct vertical search engine, which is known as solution for the problems. This paper proposes a conceptual framework based on Learning Vector Quantization (LVQ) network and Ontology to automatic classification for the webpage from agricultural website. System test shows that the framework can automatically classify Chinese agricultural products web pages and perfectly resolve the synonymous problem.
Feature selection algorithm plays a crucial role in intrusion detection, data mining and pattern recognition. According to some evaluation criteria, it gets optimal feature subset by deleting unrelated and redundant f...
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In the domain of historical Mongolian document image retrieval (HMDIR), word spotting poses a inherent challenge due to the frequent appearance of out-of-vocabulary (OOV) words. Existing methods have mainly focused on...
In the domain of historical Mongolian document image retrieval (HMDIR), word spotting poses a inherent challenge due to the frequent appearance of out-of-vocabulary (OOV) words. Existing methods have mainly focused on query-by-example (QBE), neglecting the query-by-string (QBS) approach. Meanwhile, the hierarchical structure of word makes Euclidean space not the optimal choice for representing complex structured data. To address the aforementioned problems, we propose a novel method that leverages a shared hyperbolic space to effectively align text strings and word images. Specifically, we use the Pyramidal Histogram of Characters (PHOC) for text string embeding, and a robust encoder-decoder architecture for word image embedding, then map their embeddings in the shared hyperbolic space. Moreover, we propose a new dataset of historical Mongolian documents called Geser, which includes 143,508 word images and 10,951 vocabularies. Extensive experiments conducted on two datasets of historical Mongolian documents with an OOV partitioning scheme (Kanjur and Geser), demonstrate that our proposed method surpasses state-of-the-art methods and achieves outstanding results on Geser.
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