DocumentCode :
3334700
Title :
Web-based search system of pattern recognition for the pattern of industrial components by an innovate technology
Author :
Fan, Kuo-Chin ; Hsiao, Sung-Jung ; Sung, Wen-Tsai
Author_Institution :
Dept. of Comput. Sci. & Inf. Eng., Nat. Central Univ., Keelung, Taiwan
Volume :
1
fYear :
2002
fDate :
2002
Firstpage :
82
Abstract :
This work utilizes the pattern recognition (PR) technologies by Client-Server network structure into a web-based recognizing system. The system uses a recurrent neural network (RNN) with associative memory to perform the action of training and recognition. Client-end engineer is able to draw directly the shape of engineering components by the browser, and the recognition system will proceed with search for the component database of company by the structure of Internet. In this paper, these component patterns are stored in the database system. Their properties and specifications are also attached to the data field of each component pattern except the pattern of engineering component. These Component patterns with the approach of database system will be able to improve the capacity of recognition system effectively. In our approach, the recognition system adopts parallel computing, and it will raise the recognition rate of system. Our recognition system is a client-server network structure by Internet. The last phase joins the technology of database matching in process of the recognition, and it will solve the problem of spurious state. In this paper, our study will be carried out in the Yang-Fen Automation Electrical Engineering Company. Therefore, the cooperative plan of above context will be analyzed and discussed in this paper.
Keywords :
client-server systems; content-addressable storage; engineering computing; image retrieval; pattern recognition; query processing; recurrent neural nets; associative memory; client-server network; component database; component patterns; database matching; engineering components; pattern recognition; recognition; recurrent neural network; training; Associative memory; Data engineering; Database systems; IP networks; Industrial training; Internet; Parallel processing; Pattern recognition; Recurrent neural networks; Shape;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Industrial Technology, 2002. IEEE ICIT '02. 2002 IEEE International Conference on
Print_ISBN :
0-7803-7657-9
Type :
conf
DOI :
10.1109/ICIT.2002.1189867
Filename :
1189867
Link To Document :
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