Based on the loose medium flow field theory, the loose top-coal drawing law of longwall top-coal caving（LTCC） mining technology is studied by using self-developed three-dimensional（3D） test device. The loose top-c...Based on the loose medium flow field theory, the loose top-coal drawing law of longwall top-coal caving（LTCC） mining technology is studied by using self-developed three-dimensional（3D） test device. The loose top-coal drawing test with shields and the controlled test without shields are performed in the condition without any boundary effect. Test results show that shields will cause reduction in drawing volume of coal in the LTCC mining. The deflection phenomenon of drawing body is also observed in the controlled test, which is verified that the deflection of drawing body is caused by shield. It is found that the deflection angle decreases with increasing caving height, with the maximum value of atailand the minimum value of 0. In addition, the formula to calculate the drawing volume is proposed subsequently.The deflection of drawing body is numerically simulated using particle flow code PFC3 Dand the proposed formula to calculate drawing volume in LTCC is also verified.展开更多
Landmarks on human body models are of great significance for applications such as digital anthropometry and clothing design.The diversity of pose and shape of human body models and the semantic gap make landmarking a ...Landmarks on human body models are of great significance for applications such as digital anthropometry and clothing design.The diversity of pose and shape of human body models and the semantic gap make landmarking a challenging problem.Inthis paper,a learning-based method is proposed to locate landmarks on human body models by analyzing the relationship between geometric descriptors and semantic labels of landmarks.A shape alignmentalgorithm is proposed to align human body models to break symmetric ambiguity.A symmetry-awaredescriptor is proposed based on the structure of the human body models,which is robust to both pose and shape variations in human body models.AnAdaBoost regression algorithm is adopted to establish the correspondence between several descriptors and semantic labels of the landmarks.Quantitative and qualitative analyses and comparisons show that the proposed method can obtain more accurate landmarks and distinguish symmetrical landmarks semantically.Additionally,a dataset of landmarked human body models is also provided,containing 271 human body models collected from current human body datasets;each model has 17 landmarks labeled manually.展开更多
基金financially supported by the Coal Joint Funds of the National Natural Science Foundation of China(No.U1361209)the National Basic Research Program of China(973 Program)(No.2013CB227903)
文摘Based on the loose medium flow field theory, the loose top-coal drawing law of longwall top-coal caving（LTCC） mining technology is studied by using self-developed three-dimensional（3D） test device. The loose top-coal drawing test with shields and the controlled test without shields are performed in the condition without any boundary effect. Test results show that shields will cause reduction in drawing volume of coal in the LTCC mining. The deflection phenomenon of drawing body is also observed in the controlled test, which is verified that the deflection of drawing body is caused by shield. It is found that the deflection angle decreases with increasing caving height, with the maximum value of atailand the minimum value of 0. In addition, the formula to calculate the drawing volume is proposed subsequently.The deflection of drawing body is numerically simulated using particle flow code PFC3 Dand the proposed formula to calculate drawing volume in LTCC is also verified.
基金jointly supported by the National Natural Science Foundation of China under Grant Nos.61732015,61932018,and 61472349.
文摘Landmarks on human body models are of great significance for applications such as digital anthropometry and clothing design.The diversity of pose and shape of human body models and the semantic gap make landmarking a challenging problem.Inthis paper,a learning-based method is proposed to locate landmarks on human body models by analyzing the relationship between geometric descriptors and semantic labels of landmarks.A shape alignmentalgorithm is proposed to align human body models to break symmetric ambiguity.A symmetry-awaredescriptor is proposed based on the structure of the human body models,which is robust to both pose and shape variations in human body models.AnAdaBoost regression algorithm is adopted to establish the correspondence between several descriptors and semantic labels of the landmarks.Quantitative and qualitative analyses and comparisons show that the proposed method can obtain more accurate landmarks and distinguish symmetrical landmarks semantically.Additionally,a dataset of landmarked human body models is also provided,containing 271 human body models collected from current human body datasets;each model has 17 landmarks labeled manually.
