Since the Xingtai （邢台） earthquake in 1966, China Earthquake Administration has carried out a survey campaign along more than thirty deep seismic sounding （DSS） profiles altogether about twenty thousand kilometer...Since the Xingtai （邢台） earthquake in 1966, China Earthquake Administration has carried out a survey campaign along more than thirty deep seismic sounding （DSS） profiles altogether about twenty thousand kilometers long in North China to study the velocity structure of the crust and the upper mantle in this region, and has obtained a great number of research findings. However, these researches have not provided a 3D velocity structure model of the crust of North China and cannot provide seismic evidence for the study of the deep tectonic characteristics of the crust of the whole region. Hence, based on the information from the published data of the DSS profiles, we have chosen 14 profiles to obtain a 3D velocity structure model of North China using the vectorization function of the GIS software （Arc/Info） and the Kriging data gridding method. With this velocity structure model, we have drawn the following conclusions： （1） The P-wave velocity of the uppermost crust of North China changes dramatically, exhibiting a complicated velocity structure in plane view. It can be divided into three velocity zones mainly trending towards north-west. In the research area, the lowest-velocity zones lie in the Haihe （海河） plain and Bohai （渤海） Bay. Although the geological structure of the sedimentary overburden in the study area is somewhat inherited by the upper crust, there are still several differences between them. （2） Generally, the P-wave velocity of the crust increases with depth in the study area, but there still exists local velocity reversion. In the east, low-velocity anomalies of the Haihe plain gradually disappear with increasing depth, and the Shanxi （山西） graben in the west is mainly characterized by relatively low velocity anomalies. Bounded by the Taihang （太行） Mountains, the eastern and western parts differ in structural trend of stratum above the crystalline basement. The structural trend of the Huanghuaihai （黄淮海） block in the east is mainly north-east, while that of the Shanxi block and the eastern edge of the Ordos block is mainly north-west. （3） According to the morphological features of Moho, the crust of the study area can be divided into six blocks. In the Shanxi block, Moho apppears like a nearly south-north trending depression belt with a large crustal thickness. In the southern edge of the Inner Mongolia block and the south of the Yanshan （燕山） block,the Moho exhibits a feature of fold belt, trending nearly towards east-west. In the eastern edge of the Ordos block, the structure of Moho is relatively complex, presenting a pattern of fold trending nearly towards north-west with alternating convexes and concaves. Beneath the Huanghuaihai block, the middle and northern parts of the North China rift zone, the Moho is the shallowest in the entire region, with alternating uplifts and depressions in its shape. For the anteclise zone in the west of Shandong （山东） Province, the Moho is discontinuous for the fault depression extending in the north-west direction along Zaozhuang （枣庄） -Qufu （曲阜）.展开更多
Three-dimensional(3D)imaging with structured light is crucial in diverse scenarios,ranging from intelligent manufacturing and medicine to entertainment.However,current structured light methods rely on projector-camera...Three-dimensional(3D)imaging with structured light is crucial in diverse scenarios,ranging from intelligent manufacturing and medicine to entertainment.However,current structured light methods rely on projector-camera synchronization,limiting the use of affordable imaging devices and their consumer applications.In this work,we introduce an asynchronous structured light imaging approach based on generative deep neural networks to relax the synchronization constraint,accomplishing the challenges of fringe pattern aliasing,without relying on any a priori constraint of the projection system.To overcome this need,we propose a generative deep neural network with U-Net-like encoder-decoder architecture to learn the underlying fringe features directly by exploring the intrinsic prior principles in the fringe pattern aliasing.We train within an adversarial learning framework and supervise the network training via a statisticsinformed loss function.We demonstrate that by evaluating the performance on fields of intensity,phase,and 3D reconstruction.It is shown that the trained network can separate aliased fringe patterns for producing comparable results with the synchronous one:the absolute error is no greater than 8μm,and the standard deviation does not exceed 3μm.Evaluation results on multiple objects and pattern types show it could be generalized for any asynchronous structured light scene.展开更多
The manufacturing of three-dimensional textile preforms used for composites started to re-ceive much attention in the last decade.The major barriers to accelerating the transition from thelamination of two-dimensional...The manufacturing of three-dimensional textile preforms used for composites started to re-ceive much attention in the last decade.The major barriers to accelerating the transition from thelamination of two-dimensional fabrics to manufacturing integral three-dimensional near-netshaped textile preforms are high cost and database deficiency.To reduce the cost of weaving three-dimensional preforms,and make full use of the potential of conventional looms,a rig was designedwhich can convert two-dimensional woven fabric to particular three-dimensional preforms wherethe yarn is orientated in the directions of maximum stress.展开更多
High-performance batteries are poised for electrification of vehicles and therefore mitigate greenhouse gas emissions,which,in turn,promote a sustainable future.However,the design of optimized batteries is challenging...High-performance batteries are poised for electrification of vehicles and therefore mitigate greenhouse gas emissions,which,in turn,promote a sustainable future.However,the design of optimized batteries is challenging due to the nonlinear governing physics and electrochemistry.Recent advancements have demonstrated the potential of deep learning techniques in efficiently designing batteries,particularly in optimizing electrodes and electrolytes.This review provides comprehensive concepts and principles of deep learning and its application in solving battery-related electrochemical problems,which bridges the gap between artificial intelligence and electrochemistry.We also examine the potential challenges and opportunities associated with different deep learning approaches,tailoring them to specific battery requirements.Ultimately,we aim to inspire future advancements in both fundamental scientific understanding and practical engineering in the field of battery technology.Furthermore,we highlight the potential challenges and opportunities for different deep learning methods according to the specific battery demand to inspire future advancement in fundamental science and practical engineering.展开更多
基金This paper is supported by the National Natural Science Foundation of China (No.40434010)the Focused Subject Program of Beijing (No. XK104910589).
文摘Since the Xingtai （邢台） earthquake in 1966, China Earthquake Administration has carried out a survey campaign along more than thirty deep seismic sounding （DSS） profiles altogether about twenty thousand kilometers long in North China to study the velocity structure of the crust and the upper mantle in this region, and has obtained a great number of research findings. However, these researches have not provided a 3D velocity structure model of the crust of North China and cannot provide seismic evidence for the study of the deep tectonic characteristics of the crust of the whole region. Hence, based on the information from the published data of the DSS profiles, we have chosen 14 profiles to obtain a 3D velocity structure model of North China using the vectorization function of the GIS software （Arc/Info） and the Kriging data gridding method. With this velocity structure model, we have drawn the following conclusions： （1） The P-wave velocity of the uppermost crust of North China changes dramatically, exhibiting a complicated velocity structure in plane view. It can be divided into three velocity zones mainly trending towards north-west. In the research area, the lowest-velocity zones lie in the Haihe （海河） plain and Bohai （渤海） Bay. Although the geological structure of the sedimentary overburden in the study area is somewhat inherited by the upper crust, there are still several differences between them. （2） Generally, the P-wave velocity of the crust increases with depth in the study area, but there still exists local velocity reversion. In the east, low-velocity anomalies of the Haihe plain gradually disappear with increasing depth, and the Shanxi （山西） graben in the west is mainly characterized by relatively low velocity anomalies. Bounded by the Taihang （太行） Mountains, the eastern and western parts differ in structural trend of stratum above the crystalline basement. The structural trend of the Huanghuaihai （黄淮海） block in the east is mainly north-east, while that of the Shanxi block and the eastern edge of the Ordos block is mainly north-west. （3） According to the morphological features of Moho, the crust of the study area can be divided into six blocks. In the Shanxi block, Moho apppears like a nearly south-north trending depression belt with a large crustal thickness. In the southern edge of the Inner Mongolia block and the south of the Yanshan （燕山） block,the Moho exhibits a feature of fold belt, trending nearly towards east-west. In the eastern edge of the Ordos block, the structure of Moho is relatively complex, presenting a pattern of fold trending nearly towards north-west with alternating convexes and concaves. Beneath the Huanghuaihai block, the middle and northern parts of the North China rift zone, the Moho is the shallowest in the entire region, with alternating uplifts and depressions in its shape. For the anteclise zone in the west of Shandong （山东） Province, the Moho is discontinuous for the fault depression extending in the north-west direction along Zaozhuang （枣庄） -Qufu （曲阜）.
基金funding from the National Natural Science Foundation of China(Grant Nos.62375078 and 12002197)the Youth Talent Launching Program of Shanghai University+2 种基金the General Science Foundation of Henan Province(Grant No.222300420427)the Key Research Project Plan for Higher Education Institutions in Henan Province(Grant No.24ZX011)the National Key Laboratory of Ship Structural Safety
文摘Three-dimensional(3D)imaging with structured light is crucial in diverse scenarios,ranging from intelligent manufacturing and medicine to entertainment.However,current structured light methods rely on projector-camera synchronization,limiting the use of affordable imaging devices and their consumer applications.In this work,we introduce an asynchronous structured light imaging approach based on generative deep neural networks to relax the synchronization constraint,accomplishing the challenges of fringe pattern aliasing,without relying on any a priori constraint of the projection system.To overcome this need,we propose a generative deep neural network with U-Net-like encoder-decoder architecture to learn the underlying fringe features directly by exploring the intrinsic prior principles in the fringe pattern aliasing.We train within an adversarial learning framework and supervise the network training via a statisticsinformed loss function.We demonstrate that by evaluating the performance on fields of intensity,phase,and 3D reconstruction.It is shown that the trained network can separate aliased fringe patterns for producing comparable results with the synchronous one:the absolute error is no greater than 8μm,and the standard deviation does not exceed 3μm.Evaluation results on multiple objects and pattern types show it could be generalized for any asynchronous structured light scene.
文摘The manufacturing of three-dimensional textile preforms used for composites started to re-ceive much attention in the last decade.The major barriers to accelerating the transition from thelamination of two-dimensional fabrics to manufacturing integral three-dimensional near-netshaped textile preforms are high cost and database deficiency.To reduce the cost of weaving three-dimensional preforms,and make full use of the potential of conventional looms,a rig was designedwhich can convert two-dimensional woven fabric to particular three-dimensional preforms wherethe yarn is orientated in the directions of maximum stress.
文摘High-performance batteries are poised for electrification of vehicles and therefore mitigate greenhouse gas emissions,which,in turn,promote a sustainable future.However,the design of optimized batteries is challenging due to the nonlinear governing physics and electrochemistry.Recent advancements have demonstrated the potential of deep learning techniques in efficiently designing batteries,particularly in optimizing electrodes and electrolytes.This review provides comprehensive concepts and principles of deep learning and its application in solving battery-related electrochemical problems,which bridges the gap between artificial intelligence and electrochemistry.We also examine the potential challenges and opportunities associated with different deep learning approaches,tailoring them to specific battery requirements.Ultimately,we aim to inspire future advancements in both fundamental scientific understanding and practical engineering in the field of battery technology.Furthermore,we highlight the potential challenges and opportunities for different deep learning methods according to the specific battery demand to inspire future advancement in fundamental science and practical engineering.
