On June 26, 2019, the Geological Exploration Association of China Mining Federation published a news in China Mining News: The drilling project in southwest Yun'nan, Institute of Mineral Resources, Chinese Academy...On June 26, 2019, the Geological Exploration Association of China Mining Federation published a news in China Mining News: The drilling project in southwest Yun'nan, Institute of Mineral Resources, Chinese Academy of Geological Sciences, undertaken by the Fourth Branch of China Coal Huasheng Hydrogeological Exploration Co., Ltd. has recently received good news, and its rope coring drilling depth has reached the designed hole depth of 2,700 m, creating the deepest record of large-diameter (drilling diameter φ 127mm, core diameter φ80mm) rope coring drilling in China.展开更多
Aimed at the long and narrow geometric features and poor generalization ability of the damage detection in conveyor belts with steel rope cores using the X-ray image,a detection method of damage X-ray image is propose...Aimed at the long and narrow geometric features and poor generalization ability of the damage detection in conveyor belts with steel rope cores using the X-ray image,a detection method of damage X-ray image is proposed based on the improved fully convolutional one-stage object detection(FCOS)algorithm.The regression performance of bounding boxes was optimized by introducing the complete intersection over union loss function into the improved algorithm.The feature fusion network structure is modified by adding adaptive fusion paths to the feature fusion network structure,which makes full use of the features of accurate localization and semantics of multi-scale feature fusion networks.Finally,the network structure was trained and validated by using the X-ray image dataset of damages in conveyor belts with steel rope cores provided by a flaw detection equipment manufacturer.In addition,the data enhancement methods such as rotating,mirroring,and scaling,were employed to enrich the image dataset so that the model is adequately trained.Experimental results showed that the improved FCOS algorithm promoted the precision rate and the recall rate by 20.9%and 14.8%respectively,compared with the original algorithm.Meanwhile,compared with Fast R-CNN,Faster R-CNN,SSD,and YOLOv3,the improved FCOS algorithm has obvious advantages;detection precision rate and recall rate of the modified network reached 95.8%and 97.0%respectively.Furthermore,it demonstrated a higher detection accuracy without affecting the speed.The results of this work have some reference significance for the automatic identification and detection of steel core conveyor belt damage.展开更多
During the construction of Mengyejing Potassium Salt Drilling Project in southwest Yunnan, according to the previous construction experience and the formation situation, improve the drilling efficiency and heart rate....During the construction of Mengyejing Potassium Salt Drilling Project in southwest Yunnan, according to the previous construction experience and the formation situation, improve the drilling efficiency and heart rate. During the construction, we adopt new drilling technology and newly developed coring equipment, and the drilling efficiency was increased by 30% on average. The heart rate increased by about 10%-20%.展开更多
文摘On June 26, 2019, the Geological Exploration Association of China Mining Federation published a news in China Mining News: The drilling project in southwest Yun'nan, Institute of Mineral Resources, Chinese Academy of Geological Sciences, undertaken by the Fourth Branch of China Coal Huasheng Hydrogeological Exploration Co., Ltd. has recently received good news, and its rope coring drilling depth has reached the designed hole depth of 2,700 m, creating the deepest record of large-diameter (drilling diameter φ 127mm, core diameter φ80mm) rope coring drilling in China.
文摘Aimed at the long and narrow geometric features and poor generalization ability of the damage detection in conveyor belts with steel rope cores using the X-ray image,a detection method of damage X-ray image is proposed based on the improved fully convolutional one-stage object detection(FCOS)algorithm.The regression performance of bounding boxes was optimized by introducing the complete intersection over union loss function into the improved algorithm.The feature fusion network structure is modified by adding adaptive fusion paths to the feature fusion network structure,which makes full use of the features of accurate localization and semantics of multi-scale feature fusion networks.Finally,the network structure was trained and validated by using the X-ray image dataset of damages in conveyor belts with steel rope cores provided by a flaw detection equipment manufacturer.In addition,the data enhancement methods such as rotating,mirroring,and scaling,were employed to enrich the image dataset so that the model is adequately trained.Experimental results showed that the improved FCOS algorithm promoted the precision rate and the recall rate by 20.9%and 14.8%respectively,compared with the original algorithm.Meanwhile,compared with Fast R-CNN,Faster R-CNN,SSD,and YOLOv3,the improved FCOS algorithm has obvious advantages;detection precision rate and recall rate of the modified network reached 95.8%and 97.0%respectively.Furthermore,it demonstrated a higher detection accuracy without affecting the speed.The results of this work have some reference significance for the automatic identification and detection of steel core conveyor belt damage.
文摘During the construction of Mengyejing Potassium Salt Drilling Project in southwest Yunnan, according to the previous construction experience and the formation situation, improve the drilling efficiency and heart rate. During the construction, we adopt new drilling technology and newly developed coring equipment, and the drilling efficiency was increased by 30% on average. The heart rate increased by about 10%-20%.
