Seismic hazard assessment and risk mitigation depend critically on rapid analysis and characterization of earthquake sequences.Increasing seismicity in shale gas blocks of the Sichuan Basin,China,has presented a serio...Seismic hazard assessment and risk mitigation depend critically on rapid analysis and characterization of earthquake sequences.Increasing seismicity in shale gas blocks of the Sichuan Basin,China,has presented a serious challenge to monitoring and managing the seismicity itself.In this study,to detect events we apply a machine-learning-based phase picker(PhaseNet)to continuous seismic data collected between November 2015 and November 2016 from a temporary network covering the Weiyuan Shale Gas Blocks(SGB).Both P-and S-phases are picked and associated for location.We refine the velocity model by using detected explosions and earthquakes and then relocate the detected events using our new velocity model.Our detections and absolute relocations provide the basis for building a high-precision earthquake catalog.Our primary catalog contains about 60 times as many earthquakes as those in the catalog of the Chinese Earthquake Network Center(CENC),which used only the sparsely distributed permanent stations.We also measure the local magnitude and achieve magnitude completeness of ML0.We relocate clusters of events,showing sequential migration patterns overlapping with horizontal well branches around several well pads in the Wei202 and Wei204 blocks.Our results demonstrate the applicability of a machine-learning phase picker to a dense seismic network.The algorithms can facilitate rapid characterization of earthquake sequences.展开更多
Energy consumption of block-cutting machines represents a major cost item in the processing of travertines and other natural stones.Therefore,determining the optimum sawing conditions for a particular stone is of majo...Energy consumption of block-cutting machines represents a major cost item in the processing of travertines and other natural stones.Therefore,determining the optimum sawing conditions for a particular stone is of major importance in the natural stone-processing industry.An experimental study was carried out utilizing a fully instrumented block-cutter to investigate the sawing performances of five different types of travertine blocks during cutting with a circular diamond saw,The sawing tests were performed in the down-cutting mode,Performance measurements were determined by measuring the cutting speed and energy consumption.Then,specific energy was determined.The one main cutting parameter,cutting speed,was varied in the investigation of optimum cutting performance.Furthermore,some physico-mechanical properties of the travertine blocks were determined in the laboratory.As a result,it is found that the energy consumption(specific energy) of block cutting machines is highly affected by cutting speed.It is determined that specific energy value usually decreases when cutting speed increases.When the cutting speed is higher than the determined value,the diamond saw can become stuck in the travertine block;this situation can be a problem for the block-cutting machine,As a result,the optimum cutting speed obtained for the travertine mines examined is approximately 1.5-2.0 m/min.展开更多
由于文档纸张的几何形变、拍摄场景的干扰及拍摄角度不理想导致的透视失真,移动设备获取的文档图像的光学字符识别(Optical character recognition,OCR)性能受到很大挑战。针对折叠和扭曲的畸变文档图像预处理问题,设计了两种基于自编...由于文档纸张的几何形变、拍摄场景的干扰及拍摄角度不理想导致的透视失真,移动设备获取的文档图像的光学字符识别(Optical character recognition,OCR)性能受到很大挑战。针对折叠和扭曲的畸变文档图像预处理问题,设计了两种基于自编码器的网络结构,以实现自适应性图像矫正并提高文字识别正确率。首先提出空洞残差块和非对称卷积残差块两种残差块,然后将残差块与自编码器相结合,设计了一种非对称空洞自编码器网络;同时利用空间金字塔池化代替全连接层,并用非对称卷积残差块实现特征提取,设计了另一种空间金字塔自编码器网络。实验结果表明,与畸变图像相比,经非对称空洞自编码器网络矫正后的图像在OCR正确率、OCR召回率和文本相似度上分别提高了26.3%、20.4%和12.3%,而经空间金字塔自编码器网络矫正后的图像在正确率、召回率和文本相似度上分别提高了27.7%、22.0%和15.5%。与RectiNet等其他图像矫正网络相比,这两种网络可以自适应矫正多种类型的畸变文档图像,且矫正后的图像在文字识别上表现更为优异。本文提出的两种矫正网络能有效提高图像文字识别正确率、召回率和文本相似度,同时在鲁棒性、泛化性等方面与现有矫正网络相比具有明显的优势。展开更多
An improved block matching approach to fast disparity estimation in machine vision applications is proposed, where the matching criterion is the sum of the absolute difference（SAD）.By evaluating the lower bounds, wh...An improved block matching approach to fast disparity estimation in machine vision applications is proposed, where the matching criterion is the sum of the absolute difference（SAD）.By evaluating the lower bounds, which become increasingly tighter for the matching criteria, the method tries to successively terminate unnecessary computations of the matching criteria between the reference block in one image and the ineligible candidate blocks in another image.It also eliminates the ineligible blocks as early as possible, while ensuring the optimal disparity of each pixel.Also, the proposed method can further speed up the elimination of ineligible candidate blocks by efficiently using the continuous constraint of disparity to predict the initial disparity of each pixel.The performance of the new algorithm is evaluated by carrying out a theoretical analysis, and by comparing its performance with the disparity estimation method based on the standard block matching.Simulated results demonstrate that the proposed algorithm achieves a computational cost reduction of over 50.5% in comparision with the standard block matching method.展开更多
基金supported by the Hong Kong Research Grants Council(No.14303721 and N_CUHK430/16)Faculty of Science,CUHK,National Natural Science Foundation of China(Grants No.41804015,41661164035)+1 种基金National Key R&D Program of China(2018YFC1504501-02)by the Stanford Center for Induced and Triggered Seismicity。
文摘Seismic hazard assessment and risk mitigation depend critically on rapid analysis and characterization of earthquake sequences.Increasing seismicity in shale gas blocks of the Sichuan Basin,China,has presented a serious challenge to monitoring and managing the seismicity itself.In this study,to detect events we apply a machine-learning-based phase picker(PhaseNet)to continuous seismic data collected between November 2015 and November 2016 from a temporary network covering the Weiyuan Shale Gas Blocks(SGB).Both P-and S-phases are picked and associated for location.We refine the velocity model by using detected explosions and earthquakes and then relocate the detected events using our new velocity model.Our detections and absolute relocations provide the basis for building a high-precision earthquake catalog.Our primary catalog contains about 60 times as many earthquakes as those in the catalog of the Chinese Earthquake Network Center(CENC),which used only the sparsely distributed permanent stations.We also measure the local magnitude and achieve magnitude completeness of ML0.We relocate clusters of events,showing sequential migration patterns overlapping with horizontal well branches around several well pads in the Wei202 and Wei204 blocks.Our results demonstrate the applicability of a machine-learning phase picker to a dense seismic network.The algorithms can facilitate rapid characterization of earthquake sequences.
文摘Energy consumption of block-cutting machines represents a major cost item in the processing of travertines and other natural stones.Therefore,determining the optimum sawing conditions for a particular stone is of major importance in the natural stone-processing industry.An experimental study was carried out utilizing a fully instrumented block-cutter to investigate the sawing performances of five different types of travertine blocks during cutting with a circular diamond saw,The sawing tests were performed in the down-cutting mode,Performance measurements were determined by measuring the cutting speed and energy consumption.Then,specific energy was determined.The one main cutting parameter,cutting speed,was varied in the investigation of optimum cutting performance.Furthermore,some physico-mechanical properties of the travertine blocks were determined in the laboratory.As a result,it is found that the energy consumption(specific energy) of block cutting machines is highly affected by cutting speed.It is determined that specific energy value usually decreases when cutting speed increases.When the cutting speed is higher than the determined value,the diamond saw can become stuck in the travertine block;this situation can be a problem for the block-cutting machine,As a result,the optimum cutting speed obtained for the travertine mines examined is approximately 1.5-2.0 m/min.
文摘由于文档纸张的几何形变、拍摄场景的干扰及拍摄角度不理想导致的透视失真,移动设备获取的文档图像的光学字符识别(Optical character recognition,OCR)性能受到很大挑战。针对折叠和扭曲的畸变文档图像预处理问题,设计了两种基于自编码器的网络结构,以实现自适应性图像矫正并提高文字识别正确率。首先提出空洞残差块和非对称卷积残差块两种残差块,然后将残差块与自编码器相结合,设计了一种非对称空洞自编码器网络;同时利用空间金字塔池化代替全连接层,并用非对称卷积残差块实现特征提取,设计了另一种空间金字塔自编码器网络。实验结果表明,与畸变图像相比,经非对称空洞自编码器网络矫正后的图像在OCR正确率、OCR召回率和文本相似度上分别提高了26.3%、20.4%和12.3%,而经空间金字塔自编码器网络矫正后的图像在正确率、召回率和文本相似度上分别提高了27.7%、22.0%和15.5%。与RectiNet等其他图像矫正网络相比,这两种网络可以自适应矫正多种类型的畸变文档图像,且矫正后的图像在文字识别上表现更为优异。本文提出的两种矫正网络能有效提高图像文字识别正确率、召回率和文本相似度,同时在鲁棒性、泛化性等方面与现有矫正网络相比具有明显的优势。
基金supported by the Opening Project of State Key Laboratory for Manufacturing Systems EngineeringFoundation for Youth Teacher of School of Mechanical Engineering, Xi’an Jiaotong University Brain Korea 21(BK21) Program of Ministry of Education and Human Resources Development
文摘An improved block matching approach to fast disparity estimation in machine vision applications is proposed, where the matching criterion is the sum of the absolute difference（SAD）.By evaluating the lower bounds, which become increasingly tighter for the matching criteria, the method tries to successively terminate unnecessary computations of the matching criteria between the reference block in one image and the ineligible candidate blocks in another image.It also eliminates the ineligible blocks as early as possible, while ensuring the optimal disparity of each pixel.Also, the proposed method can further speed up the elimination of ineligible candidate blocks by efficiently using the continuous constraint of disparity to predict the initial disparity of each pixel.The performance of the new algorithm is evaluated by carrying out a theoretical analysis, and by comparing its performance with the disparity estimation method based on the standard block matching.Simulated results demonstrate that the proposed algorithm achieves a computational cost reduction of over 50.5% in comparision with the standard block matching method.
