AUTOMATED HIGH-SPEED MONITORING OF METAL TRANSFER FOR REAL-TIME CONTROL

In the novel Double Electrode Gas Metal Arc Welding (DE-GMAW), the transfer of the liquid metal from the wire to the work-piece determines the weld quality and for applications where the precision is critical, the metal transfer process needs to be monitored and controlled to control the diameter, trajectory, and transfer rate of the droplet of liquid metal. In this doctoral research work, the traditional methods of tracking, Correlation, Least Square Matching (LSM) and Kalman Filtering (KF), are tried first. All of them failed due to the poor quality of the metal transfer image and the variety of the droplet. Then several novel image processing algorithms, Brightness Based Separation Algorithm (BBSA), Brightness and Subtraction Based Separation Algorithm (BSBSA) and Brightness Based Selection and Edge Detection Based Enhancement Separation Algorithm (BBSEDBESA), are proposed to compute the size and locate the position of the droplet. Experimental results verified that the proposed algorithms can automatically locate the droplets and compute the droplet size with an adequate accuracy. Since the final objective is to automatically process the metal transfer in real time, a real time processing system is implemented and the details are described. In traditional Gas Metal Arc Welding (GMAW), the famous laser back-lighting technique has been widely used to image the metal transfer process. Due to laser imaging systems complexity, it is too inconvenient for practical applications. In this doctoral research work, a simplified laser imaging system is proposed and two effective image algorithms, Probability Based Double Thresholds Separation Algorithm and Edge Based Separation Algorithm, are proposed to process the corresponding captured metal transfer images. Experimental results verified that the proposed simplified laser back-light imaging system and image processing algorithms can be used for real time processing of metal transfer images

Fully Automated Segmentation of the Left Ventricle in Magnetic Resonance Images

Automatic and robust segmentation of the left ventricle (LV) in magnetic resonance images (MRI) has remained challenging for many decades. With the great success of deep learning in object detection and classification, the research focus of LV segmentation has changed to convolutional neural network (CNN) in recent years. However, LV segmentation is a pixel-level classification problem and its categories are intractable compared to object detection and classification. Although lots of CNN based methods have been proposed for LV segmentation, no robust and reproducible results are achieved yet. In this paper, we try to reproduce the CNN based LV segmentation methods with their disclosed codes and trained CNN models. Not surprisingly, the reproduced results are significantly worse than their claimed accuracies. We also proposed a fully automated LV segmentation method based on slope difference distribution (SDD) threshold selection to compare with the reproduced CNN methods. The proposed method achieved 95.44% DICE score on the test set of automated cardiac diagnosis challenge (ACDC) while the two compared CNN methods achieved 90.28% and 87.13% DICE scores. Our achieved accuracy is also higher than the best accuracy reported in the published literatures. The MATLAB codes of our proposed method are freely available on line

Radical-Enhanced Chinese Character Embedding

We present a method to leverage radical for learning Chinese character embedding. Radical is a semantic and phonetic component of Chinese character. It plays an important role as characters with the same radical usually have similar semantic meaning and grammatical usage. However, existing Chinese processing algorithms typically regard word or character as the basic unit but ignore the crucial radical information. In this paper, we fill this gap by leveraging radical for learning continuous representation of Chinese character. We develop a dedicated neural architecture to effectively learn character embedding and apply it on Chinese character similarity judgement and Chinese word segmentation. Experiment results show that our radical-enhanced method outperforms existing embedding learning algorithms on both tasks.Comment: 8 pages, 4 figure