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
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
This paper is concerned with a non-intrusive anomaly detection method for carving machine systems with variant working conditions,and a novel unsupervised detection framework that integrates convolutional autoencoder(...This paper is concerned with a non-intrusive anomaly detection method for carving machine systems with variant working conditions,and a novel unsupervised detection framework that integrates convolutional autoencoder(CAE)and Gaussian mixture hidden Markov model(GMHMM)is proposed.Firstly,the built-in sensor information under normal conditions is recorded,and a 1D convolutional autoencoder is employed to compress high-dimensional time series,thereby transforming the anomaly detection problem in high-dimensional space into a density estimation problem in a latent low-dimensional space.Then,two separate estimation networks are utilized to predict the mixture memberships and state transition probabilities for each sample,enabling GMHMM to handle low-dimensional representations and multi-condition information.Furthermore,a cost function comprising CAE reconstruction and GMHMM probability assessment is constructed for the low-dimensional representation generation and subsequent density estimation in an end-to-end fashion,and the joint optimization effectively enhances the anomaly detection performance.Finally,experiments are carried out on a self-developed multi-axis carving machine platform to validate the effectiveness and superiority of the proposed method.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%.
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.
基金Supported by the National Natural Science Foundation of China(No.62203390).
文摘This paper is concerned with a non-intrusive anomaly detection method for carving machine systems with variant working conditions,and a novel unsupervised detection framework that integrates convolutional autoencoder(CAE)and Gaussian mixture hidden Markov model(GMHMM)is proposed.Firstly,the built-in sensor information under normal conditions is recorded,and a 1D convolutional autoencoder is employed to compress high-dimensional time series,thereby transforming the anomaly detection problem in high-dimensional space into a density estimation problem in a latent low-dimensional space.Then,two separate estimation networks are utilized to predict the mixture memberships and state transition probabilities for each sample,enabling GMHMM to handle low-dimensional representations and multi-condition information.Furthermore,a cost function comprising CAE reconstruction and GMHMM probability assessment is constructed for the low-dimensional representation generation and subsequent density estimation in an end-to-end fashion,and the joint optimization effectively enhances the anomaly detection performance.Finally,experiments are carried out on a self-developed multi-axis carving machine platform to validate the effectiveness and superiority of the proposed method.
