Multi-Perspective Data Fusion Framework Based on Hierarchical BERT: Provide Visual Predictions of Business Processes

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.

Multi-perspective digital image correlation method using a single color camera

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.

An ellipse detection method for 3D head image fusion based on color-coded mark points

In this paper, we proposed a new kind of mark points coded by color and a new quasi-ellipse detector on pixel level. This method is especially applicable to three- dimensional （3D） head panoramic reconstruction. Images of adjacent perspectives can be stitched by matching pasted color-coded mark points in overlap area to calculate the transformation matrix. This paper focuses on how the color-coded mark points work and how to detect and match corresponding points from different perspectives. Tests are performed to show the efficiency and accuracy of this method based on the original data obtained by structured light projection.