This research,grounded in the theory of discourse intonation,investigates Chinese EFL learners' intonation features in interactive context.Its research findings reveal that Chinese EFL learners differ from British...This research,grounded in the theory of discourse intonation,investigates Chinese EFL learners' intonation features in interactive context.Its research findings reveal that Chinese EFL learners differ from British native speakers in their application of tone units,prominence selection and tone choice.It further points out that these intonation features are caused on the one hand by Chinese EFL learners' relatively low English proficiency level and on the other hand by their lack of awareness of the communicative and discourse function of intonation.Based on these findings,this research proposes that a discourse-based awareness-raising approach to intonation teaching in the input-poor Chinese EFL context might be of pedagogical necessity and effectiveness.展开更多
It is vital to recognize the intention of finger motions for human-machine interaction(HMI).The latest research focuses on fine myoelectric control through the decoding of neural motor unit action potential trains(MUA...It is vital to recognize the intention of finger motions for human-machine interaction(HMI).The latest research focuses on fine myoelectric control through the decoding of neural motor unit action potential trains(MUAPt) from high-density surface electromyographic(sEMG) signals.However,the existing EMG decoding algorithms rarely obtain the spatial matching relationship between decoded motion units(MU) and designated muscles,and the control interface can only recognize the trained hand gestures.In this study,a semi-supervised HMI based on MU-muscle matching(MMM) is proposed to recognize individual finger motions and even the untrained combined multi-finger actions.Through automatic channel selection from high-density s EMG signals,the optimal spatial positions to monitor the MU activation of finger muscles are determined.Finger tapping experiment is carried out on ten subjects,and the experimental results show that the proposed s EMG decomposition algorithm based on MMM can accurately identify single finger motions with an accuracy of 93.1%±1.4%,which is comparable to that of state-of-the-art pattern recognition methods.Furthermore,the MMM allows unsupervised recognizing the untrained combined multi-finger motions with an accuracy of 73%±3.8%.The outcomes of this study benefit the practical applications of HMI,such as controlling prosthetic hand and virtual keyboard.展开更多
The data fusion in tracking the same trajectory by multi-measurement unit (MMU) is considered. Firstly, the reduced parameter model (RPM) of trajectory parameter (TP), system error and random error are presented, and ...The data fusion in tracking the same trajectory by multi-measurement unit (MMU) is considered. Firstly, the reduced parameter model (RPM) of trajectory parameter (TP), system error and random error are presented, and then the RPM on trajectory tracking data (TTD) is obtained, a weighted method on measuring elements (ME) is studied and criteria on selection of ME based on residual and accuracy estimation are put forward. According to RPM, the problem about selection of ME and self-calibration of TTD is thoroughly investigated. The method improves data accuracy in trajectory tracking obviously and gives accuracy evaluation of trajectory tracking system simultaneously.展开更多
文摘This research,grounded in the theory of discourse intonation,investigates Chinese EFL learners' intonation features in interactive context.Its research findings reveal that Chinese EFL learners differ from British native speakers in their application of tone units,prominence selection and tone choice.It further points out that these intonation features are caused on the one hand by Chinese EFL learners' relatively low English proficiency level and on the other hand by their lack of awareness of the communicative and discourse function of intonation.Based on these findings,this research proposes that a discourse-based awareness-raising approach to intonation teaching in the input-poor Chinese EFL context might be of pedagogical necessity and effectiveness.
基金supported in part by the China National Key R&D Program(Grant No.2018YFB1307200)the National Natural Science Foundation of China (Grant Nos.51905339&91948302)。
文摘It is vital to recognize the intention of finger motions for human-machine interaction(HMI).The latest research focuses on fine myoelectric control through the decoding of neural motor unit action potential trains(MUAPt) from high-density surface electromyographic(sEMG) signals.However,the existing EMG decoding algorithms rarely obtain the spatial matching relationship between decoded motion units(MU) and designated muscles,and the control interface can only recognize the trained hand gestures.In this study,a semi-supervised HMI based on MU-muscle matching(MMM) is proposed to recognize individual finger motions and even the untrained combined multi-finger actions.Through automatic channel selection from high-density s EMG signals,the optimal spatial positions to monitor the MU activation of finger muscles are determined.Finger tapping experiment is carried out on ten subjects,and the experimental results show that the proposed s EMG decomposition algorithm based on MMM can accurately identify single finger motions with an accuracy of 93.1%±1.4%,which is comparable to that of state-of-the-art pattern recognition methods.Furthermore,the MMM allows unsupervised recognizing the untrained combined multi-finger motions with an accuracy of 73%±3.8%.The outcomes of this study benefit the practical applications of HMI,such as controlling prosthetic hand and virtual keyboard.
基金Project supported by the National Natural Science Foundation of China (Grant No. 69872039) and the National High-Tech Program of China.
文摘The data fusion in tracking the same trajectory by multi-measurement unit (MMU) is considered. Firstly, the reduced parameter model (RPM) of trajectory parameter (TP), system error and random error are presented, and then the RPM on trajectory tracking data (TTD) is obtained, a weighted method on measuring elements (ME) is studied and criteria on selection of ME based on residual and accuracy estimation are put forward. According to RPM, the problem about selection of ME and self-calibration of TTD is thoroughly investigated. The method improves data accuracy in trajectory tracking obviously and gives accuracy evaluation of trajectory tracking system simultaneously.
