A novel 3 D digital image correlation(DIC) system based on a single three charge-couple device(3 CCD) color camera is proposed in this paper. Images from three different perspectives are captured by a 3 CCD camera usi...A novel 3 D digital image correlation(DIC) system based on a single three charge-couple device(3 CCD) color camera is proposed in this paper. Images from three different perspectives are captured by a 3 CCD camera using a reflective-based pseudo-vision system. These images are then separated by the different CCD channels, and the correlation algorithm for the multi-camera DIC system is adopted to evaluate the images. Compared to the conventional multi-camera DIC system, the proposed system is much more compact. In addition, the proposed system has no loss of spatial resolution, compares to the traditional single camera DIC system. The complex surface measurement ability and the measurement accuracy is significantly improved through the use of the multi-camera DIC algorithm. The principle of the proposed system is described in detail as well as the experimental setup. A series of validation tests are performed, and the results are verified with the commercial 3 D-DIC system.展开更多
Predictive Business Process Monitoring(PBPM)is a significant research area in Business Process Management(BPM)aimed at accurately forecasting future behavioral events.At present,deep learning methods are widely cited ...Predictive Business Process Monitoring(PBPM)is a significant research area in Business Process Management(BPM)aimed at accurately forecasting future behavioral events.At present,deep learning methods are widely cited in PBPM research,but no method has been effective in fusing data information into the control flow for multi-perspective process prediction.Therefore,this paper proposes a process prediction method based on the hierarchical BERT and multi-perspective data fusion.Firstly,the first layer BERT network learns the correlations between different category attribute data.Then,the attribute data is integrated into a weighted event-level feature vector and input into the second layer BERT network to learn the impact and priority relationship of each event on future predicted events.Next,the multi-head attention mechanism within the framework is visualized for analysis,helping to understand the decision-making logic of the framework and providing visual predictions.Finally,experimental results show that the predictive accuracy of the framework surpasses the current state-of-the-art research methods and significantly enhances the predictive performance of BPM.展开更多
基金supported by the National Natural Science Foundation of China(Grant Nos.51375136&11672045)
文摘A novel 3 D digital image correlation(DIC) system based on a single three charge-couple device(3 CCD) color camera is proposed in this paper. Images from three different perspectives are captured by a 3 CCD camera using a reflective-based pseudo-vision system. These images are then separated by the different CCD channels, and the correlation algorithm for the multi-camera DIC system is adopted to evaluate the images. Compared to the conventional multi-camera DIC system, the proposed system is much more compact. In addition, the proposed system has no loss of spatial resolution, compares to the traditional single camera DIC system. The complex surface measurement ability and the measurement accuracy is significantly improved through the use of the multi-camera DIC algorithm. The principle of the proposed system is described in detail as well as the experimental setup. A series of validation tests are performed, and the results are verified with the commercial 3 D-DIC system.
基金Supported by the National Natural Science Foundation,China(No.61402011)the Open Project Program of the Key Laboratory of Embedded System and Service Computing of Ministry of Education(No.ESSCKF2021-05).
文摘Predictive Business Process Monitoring(PBPM)is a significant research area in Business Process Management(BPM)aimed at accurately forecasting future behavioral events.At present,deep learning methods are widely cited in PBPM research,but no method has been effective in fusing data information into the control flow for multi-perspective process prediction.Therefore,this paper proposes a process prediction method based on the hierarchical BERT and multi-perspective data fusion.Firstly,the first layer BERT network learns the correlations between different category attribute data.Then,the attribute data is integrated into a weighted event-level feature vector and input into the second layer BERT network to learn the impact and priority relationship of each event on future predicted events.Next,the multi-head attention mechanism within the framework is visualized for analysis,helping to understand the decision-making logic of the framework and providing visual predictions.Finally,experimental results show that the predictive accuracy of the framework surpasses the current state-of-the-art research methods and significantly enhances the predictive performance of BPM.
