Liu L.,Jilin University |
Liu L.,University of Illinois at Urbana - Champaign |
Peng T.,Jilin University |
Peng T.,University of Illinois at Urbana - Champaign |
Peng T.,Key Laboratory of Symbol Computation
Advances in Electrical and Computer Engineering | Year: 2013
Many methods are utilized to extract and process query results in deep Web, which rely on the different structures of Web pages and various designing modes of databases. However, some semantic meanings and relations are ignored. So, in this paper, we present an approach for postprocessing deep Web query results based on domain ontology which can utilize the semantic meanings and relations. A block identification model (BIM) based on node similarity is defined to extract data blocks that are relevant to specific domain after reducing noisy nodes. Feature vector of domain books is obtained by result set extraction model (RSEM) based on vector space model (VSM). RSEM, in combination with BIM, builds the domain ontology on books which can not only remove the limit of Web page structures when extracting data information, but also make use of semantic meanings of domain ontology. After extracting basic information of Web pages, a ranking algorithm is adopted to offer an ordered list of data records to users. Experimental results show that BIM and RSEM extract data blocks and build domain ontology accurately. In addition, relevant data records and basic information are extracted and ranked. The performances precision and recall show that our proposed method is feasible and efficient. © 2013 AECE.
Zhang J.,Jilin University |
Zhang J.,Key Laboratory of Symbol Computation |
Qin G.,Jilin University |
Qin G.,Key Laboratory of Symbol Computation |
And 2 more authors.
Journal of Multimedia | Year: 2012
The speech interaction in-vehicle was mainly realized by the speech recognition. The human-machine interaction around was usually disturbed by the noise, and the speech received by the receiver was not the original pure speech, so compared to the pure environment, the accuracy of the speech recognition declined so sharply that it could not meet the demand of the practical application of human- machine interaction. So the speech recognition was required to have the strong adaptability and processing capacity to the speech with noise. In this paper, the FastICA algorithm in signal process and statistics was studied and used to separate the driver's speech in the vehicle environment, and realizes the pretreatment in recognizing driver's speech. The effectiveness of the method had been validated in actual vehicle experimental configuration. © 2012 Academy Publisher.
Zhang C.,Northeastern University China |
Zhang C.,Key Laboratory of Symbol Computation |
Ning J.,Northeast Normal University |
Ouyang D.,Key Laboratory of Symbol Computation
Computers and Industrial Engineering | Year: 2010
A hybrid alternate two phases particle swarm optimization (PSO) algorithm called ATPPSO is proposed to solve the flow shop scheduling problem (FSSP) with the objective of minimizing makespan which combines the PSO with genetic operators and annealing strategy. In the ATPPSO algorithm, each particle contains two states, the attractive state and the repulsive state. In order to refrain from the shortcoming of premature convergence, a two point reversal crossover operator is defined and in the repulsive process each particle is repelled away from some inferior solution in the current tabu list to fly towards some promising areas which can introduce some new information to guide the swarm searching process. To preserve the swarm diversity, an annealing criterion is used to update the personal best of each particle. Moreover an easy understanding makespan computation method based on matrix is designed. Finally, the proposed algorithm is tested on different scale benchmarks and compared with the recently proposed efficient algorithms. The results show that both the solution quality and the convergence speed of the ATPPSO algorithm precede the other two recently proposed algorithms. It can be used to solve large scale flow shop scheduling problem effectively. © 2009 Elsevier Ltd. All rights reserved.