In order to alleviate urban traffic congestion and provide fast vehicle paths,a hidden Markov model(HMM)based on multi-feature data of urban regional roads is constructed to solve the problems of low recognition rate ...In order to alleviate urban traffic congestion and provide fast vehicle paths,a hidden Markov model(HMM)based on multi-feature data of urban regional roads is constructed to solve the problems of low recognition rate and poor instability of traditional model algorithms.At first,the HHM is obtained by training.Then according to dynamic planning principle,the traffic states of intersections are obtained by the Viterbi algorithm.Finally,the optimal path is selected based on the obtained traffic states of intersections.The experiment results show that the proposed method is superior to other algorithms in road unobstruction rate and recognition rate under complex road conditions.展开更多
Hidden Maxkov models （HMMs） have been used to model burst error sources of wireless channels. This paper proposes a hybrid method of using genetic algorithm （GA） and simulated annealing （SA） to train HMM for dis...Hidden Maxkov models （HMMs） have been used to model burst error sources of wireless channels. This paper proposes a hybrid method of using genetic algorithm （GA） and simulated annealing （SA） to train HMM for discrete channel modelling. The proposed method is compared with pure GA, and experimental results show that the HMMs trained by the hybrid method can better describe the error sequences due to SA＇s ability of facilitating hill-climbing at the later stage of the search. The burst error statistics of the HMMs trained by the proposed method and the corresponding error sequences are also presented to validate the proposed method.展开更多
A multi-character recognition method based on hidden Markov model （HMM） was presented. The method can reduce the calculation load of correlation and improve recognition accuracy compared with singlecharacter recogni...A multi-character recognition method based on hidden Markov model （HMM） was presented. The method can reduce the calculation load of correlation and improve recognition accuracy compared with singlecharacter recognition in video. The characteristics used for recognizing include the shape character, the color character, the texture character and so on. Even our human being generally uses these characteristics to recognize objects in practice..4, recognition experiment of 17 fishes was carried out in the paper. The experimental results demonstrate the high veracity of the multi-character recognition algorithm. Together with the tracking process, it can handle dynamic objects, so the multi-character recognition is more like the human recognition, and has great application value.展开更多
基金Natural Science Foundation of Gansu Provincial Science&Technology Department(No.1504GKCA018)。
文摘In order to alleviate urban traffic congestion and provide fast vehicle paths,a hidden Markov model(HMM)based on multi-feature data of urban regional roads is constructed to solve the problems of low recognition rate and poor instability of traditional model algorithms.At first,the HHM is obtained by training.Then according to dynamic planning principle,the traffic states of intersections are obtained by the Viterbi algorithm.Finally,the optimal path is selected based on the obtained traffic states of intersections.The experiment results show that the proposed method is superior to other algorithms in road unobstruction rate and recognition rate under complex road conditions.
文摘Hidden Maxkov models （HMMs） have been used to model burst error sources of wireless channels. This paper proposes a hybrid method of using genetic algorithm （GA） and simulated annealing （SA） to train HMM for discrete channel modelling. The proposed method is compared with pure GA, and experimental results show that the HMMs trained by the hybrid method can better describe the error sequences due to SA＇s ability of facilitating hill-climbing at the later stage of the search. The burst error statistics of the HMMs trained by the proposed method and the corresponding error sequences are also presented to validate the proposed method.
基金the National Natural Science Foundation of China (No.60675024)
文摘A multi-character recognition method based on hidden Markov model （HMM） was presented. The method can reduce the calculation load of correlation and improve recognition accuracy compared with singlecharacter recognition in video. The characteristics used for recognizing include the shape character, the color character, the texture character and so on. Even our human being generally uses these characteristics to recognize objects in practice..4, recognition experiment of 17 fishes was carried out in the paper. The experimental results demonstrate the high veracity of the multi-character recognition algorithm. Together with the tracking process, it can handle dynamic objects, so the multi-character recognition is more like the human recognition, and has great application value.
