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14. Cutting control of boom-type roadheader considering coal rock hardness

Author

XU Xiangqian, JIAN Kuo, WANG Ning, and LI Shengli

Subjects
boom-type roadheader, automatic cutting control, coal rock hardness, automatic control parameters, weighted balance, pid, Mining engineering. Metallurgy, TN1-997
Abstract

The coal rock hardness significantly affects the spatial operation status of boom-type roadheader. Analyzing the correlation between the spatial operation status of roadheader and changes in coal rock hardness can help better achieve automatic cutting control of boom-type roadheader. To improve the cutting control precision, a boom-type roadheader cutting control method considering coal rock hardness is proposed. Based on the principles of dynamics, the relationship between the spatial operation status of the boom-type roadheader and the changes of coal rock hardness is obtained. It is found that as the distance between the cutting head and the target point, the radius of the motion range, and the dynamic angle increase, the operational stability of the cutting head will correspondingly improve. The automation control parameters are determined using a weighted balance method, and PID control and closed-loop fuzzy control methods are used to achieve automatic cutting control of the roadheader. The experimental results show that the method exhibits good performance in both horizontal and vertical control. The cutting head swing speed of the roadheader reaches a stable value within 2 seconds, and the dynamic working stability is good. The alignment between the trajectory of the rotation and lifting angle change of the boom-type roadheader's cutting head and the expected trajectory is high, and the overall angle deviation is small, resulting in high control precision.

Published
2024
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32. Hybrid Attention for Automatic Segmentation of Whole Fetal Head in Prenatal Ultrasound Volumes

Author

Yang, Xin, Wang, Xu, Wang, Yi, Dou, Haoran, Li, Shengli, Wen, Huaxuan, Lin, Yi, Heng, Pheng-Ann, and Ni, Dong

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition
Abstract

Background and Objective: Biometric measurements of fetal head are important indicators for maternal and fetal health monitoring during pregnancy. 3D ultrasound (US) has unique advantages over 2D scan in covering the whole fetal head and may promote the diagnoses. However, automatically segmenting the whole fetal head in US volumes still pends as an emerging and unsolved problem. The challenges that automated solutions need to tackle include the poor image quality, boundary ambiguity, long-span occlusion, and the appearance variability across different fetal poses and gestational ages. In this paper, we propose the first fully-automated solution to segment the whole fetal head in US volumes. Methods: The segmentation task is firstly formulated as an end-to-end volumetric mapping under an encoder-decoder deep architecture. We then combine the segmentor with a proposed hybrid attention scheme (HAS) to select discriminative features and suppress the non-informative volumetric features in a composite and hierarchical way. With little computation overhead, HAS proves to be effective in addressing boundary ambiguity and deficiency. To enhance the spatial consistency in segmentation, we further organize multiple segmentors in a cascaded fashion to refine the results by revisiting context in the prediction of predecessors. Results: Validated on a large dataset collected from 100 healthy volunteers, our method presents superior segmentation performance (DSC (Dice Similarity Coefficient), 96.05%), remarkable agreements with experts. With another 156 volumes collected from 52 volunteers, we ahieve high reproducibilities (mean standard deviation 11.524 mL) against scan variations. Conclusion: This is the first investigation about whole fetal head segmentation in 3D US. Our method is promising to be a feasible solution in assisting the volumetric US-based prenatal studies., Comment: Accepted by Computer Methods and Programs in Biomedicine

Published
2020

46. FetusMap: Fetal Pose Estimation in 3D Ultrasound

Author

Yang, Xin, Shi, Wenlong, Dou, Haoran, Qian, Jikuan, Wang, Yi, Xue, Wufeng, Li, Shengli, Ni, Dong, and Heng, Pheng-Ann

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning, Electrical Engineering and Systems Science - Image and Video Processing
Abstract

The 3D ultrasound (US) entrance inspires a multitude of automated prenatal examinations. However, studies about the structuralized description of the whole fetus in 3D US are still rare. In this paper, we propose to estimate the 3D pose of fetus in US volumes to facilitate its quantitative analyses in global and local scales. Given the great challenges in 3D US, including the high volume dimension, poor image quality, symmetric ambiguity in anatomical structures and large variations of fetal pose, our contribution is three-fold. (i) This is the first work about 3D pose estimation of fetus in the literature. We aim to extract the skeleton of whole fetus and assign different segments/joints with correct torso/limb labels. (ii) We propose a self-supervised learning (SSL) framework to finetune the deep network to form visually plausible pose predictions. Specifically, we leverage the landmark-based registration to effectively encode case-adaptive anatomical priors and generate evolving label proxy for supervision. (iii) To enable our 3D network perceive better contextual cues with higher resolution input under limited computing resource, we further adopt the gradient check-pointing (GCP) strategy to save GPU memory and improve the prediction. Extensively validated on a large 3D US dataset, our method tackles varying fetal poses and achieves promising results. 3D pose estimation of fetus has potentials in serving as a map to provide navigation for many advanced studies., Comment: 9 pages, 6 figures, 2 tables. Accepted by MICCAI 2019

Published
2019

49. Joint Segmentation and Landmark Localization of Fetal Femur in Ultrasound Volumes

Author

Wang, Xu, Yang, Xin, Dou, Haoran, Li, Shengli, Heng, Pheng-Ann, and Ni, Dong

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition
Abstract

Volumetric ultrasound has great potentials in promoting prenatal examinations. Automated solutions are highly desired to efficiently and effectively analyze the massive volumes. Segmentation and landmark localization are two key techniques in making the quantitative evaluation of prenatal ultrasound volumes available in clinic. However, both tasks are non-trivial when considering the poor image quality, boundary ambiguity and anatomical variations in volumetric ultrasound. In this paper, we propose an effective framework for simultaneous segmentation and landmark localization in prenatal ultrasound volumes. The proposed framework has two branches where informative cues of segmentation and landmark localization can be propagated bidirectionally to benefit both tasks. As landmark localization tends to suffer from false positives, we propose a distance based loss to suppress the noise and thus enhance the localization map and in turn the segmentation. Finally, we further leverage an adversarial module to emphasize the correspondence between segmentation and landmark localization. Extensively validated on a volumetric ultrasound dataset of fetal femur, our proposed framework proves to be a promising solution to facilitate the interpretation of prenatal ultrasound volumes., Comment: Accepted by IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI), 2019

Published
2019

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