This paper presents a cascaded Hidden Markov Model （HMM）, which allows state＇s transition, skip and duration. The cascaded HMM extends the way of HMM pattern description of Handwritten Chinese Character （HCC） and...This paper presents a cascaded Hidden Markov Model （HMM）, which allows state＇s transition, skip and duration. The cascaded HMM extends the way of HMM pattern description of Handwritten Chinese Character （HCC） and depicts the behavior of handwritten curve more reliably in terms of the statistic probability. Hence character segmentation and labeling are unnecessary. Viterbi algorithm is integrated in the cascaded HMM after the whole sample sequence of a HCC is input. More than 26,000 component samples are used tor training 407 handwritten component HMMs. At the improved training stage 94 models of 94 Chinese characters are gained by 32,000 samples, Compared with the Segment HMMs approach, the recognition rate of this model tier the tirst candidate is 87.89% and the error rate could be reduced by 12.4%.展开更多
To investigate the targets and mechanism of Hedysarum Multijugum Maxim(HMM)in treatment of bladder cancer(BC).Based on Traditional Chinese Medicine Systems Pharmacology(TCMSP)and gene databases,active substances and p...To investigate the targets and mechanism of Hedysarum Multijugum Maxim(HMM)in treatment of bladder cancer(BC).Based on Traditional Chinese Medicine Systems Pharmacology(TCMSP)and gene databases,active substances and potential targets of HMM were screened,and the HMM-active substances-targets-BC(HATB)regulatory network and PPI network were constructed.Hub targets were screened by Cytoscape.The main active substances and Hub targets were molecularly docked with AutoDock and visualized by PyMOL.12 Hub targets were screened.Molecular docking showed that active substances mainly acted on MAPK14,MAPK1 and CCND1.The bindings of calycosin to MAPK14,formononetin to MAPK14,and calycosin to CCND1 were stable.展开更多
Despite the gradual transformation of traditional manufacturing by the Human-Robot Collaboration Assembly(HRCA),challenges remain in the robot’s ability to understand and predict human assembly intentions.This study ...Despite the gradual transformation of traditional manufacturing by the Human-Robot Collaboration Assembly(HRCA),challenges remain in the robot’s ability to understand and predict human assembly intentions.This study aims to enhance the robot’s comprehension and prediction capabilities of operator assembly intentions by capturing and analyzing operator behavior and movements.We propose a video feature extraction method based on the Temporal Shift Module Network(TSM-ResNet50)to extract spatiotemporal features from assembly videos and differentiate various assembly actions using feature differences between video frames.Furthermore,we construct an action recognition and segmentation model based on the Refined-Multi-Scale Temporal Convolutional Network(Refined-MS-TCN)to identify assembly action intervals and accurately acquire action categories.Experiments on our self-built reducer assembly action dataset demonstrate that our network can classify assembly actions frame by frame,achieving an accuracy rate of 83%.Additionally,we develop a HiddenMarkovModel(HMM)integrated with assembly task constraints to predict operator assembly intentions based on the probability transition matrix and assembly task constraints.The experimental results show that our method for predicting operator assembly intentions can achieve an accuracy of 90.6%,which is a 13.3%improvement over the HMM without task constraints.展开更多
文摘This paper presents a cascaded Hidden Markov Model （HMM）, which allows state＇s transition, skip and duration. The cascaded HMM extends the way of HMM pattern description of Handwritten Chinese Character （HCC） and depicts the behavior of handwritten curve more reliably in terms of the statistic probability. Hence character segmentation and labeling are unnecessary. Viterbi algorithm is integrated in the cascaded HMM after the whole sample sequence of a HCC is input. More than 26,000 component samples are used tor training 407 handwritten component HMMs. At the improved training stage 94 models of 94 Chinese characters are gained by 32,000 samples, Compared with the Segment HMMs approach, the recognition rate of this model tier the tirst candidate is 87.89% and the error rate could be reduced by 12.4%.
基金2025 Open Experimental Special Fund of Beijing Institute of Technology, “Applications and Practices of R Language in Bioinformatics”。
文摘To investigate the targets and mechanism of Hedysarum Multijugum Maxim(HMM)in treatment of bladder cancer(BC).Based on Traditional Chinese Medicine Systems Pharmacology(TCMSP)and gene databases,active substances and potential targets of HMM were screened,and the HMM-active substances-targets-BC(HATB)regulatory network and PPI network were constructed.Hub targets were screened by Cytoscape.The main active substances and Hub targets were molecularly docked with AutoDock and visualized by PyMOL.12 Hub targets were screened.Molecular docking showed that active substances mainly acted on MAPK14,MAPK1 and CCND1.The bindings of calycosin to MAPK14,formononetin to MAPK14,and calycosin to CCND1 were stable.
文摘Despite the gradual transformation of traditional manufacturing by the Human-Robot Collaboration Assembly(HRCA),challenges remain in the robot’s ability to understand and predict human assembly intentions.This study aims to enhance the robot’s comprehension and prediction capabilities of operator assembly intentions by capturing and analyzing operator behavior and movements.We propose a video feature extraction method based on the Temporal Shift Module Network(TSM-ResNet50)to extract spatiotemporal features from assembly videos and differentiate various assembly actions using feature differences between video frames.Furthermore,we construct an action recognition and segmentation model based on the Refined-Multi-Scale Temporal Convolutional Network(Refined-MS-TCN)to identify assembly action intervals and accurately acquire action categories.Experiments on our self-built reducer assembly action dataset demonstrate that our network can classify assembly actions frame by frame,achieving an accuracy rate of 83%.Additionally,we develop a HiddenMarkovModel(HMM)integrated with assembly task constraints to predict operator assembly intentions based on the probability transition matrix and assembly task constraints.The experimental results show that our method for predicting operator assembly intentions can achieve an accuracy of 90.6%,which is a 13.3%improvement over the HMM without task constraints.
