The MIG welding of in-situ generated nano-Al_(2)O_(3)powder metallurgy 7A52(PM 7A52)aluminum alloy was investigated.The microstructure was characterized using EBSD and TEM,while macrotexture and internal residual stre...The MIG welding of in-situ generated nano-Al_(2)O_(3)powder metallurgy 7A52(PM 7A52)aluminum alloy was investigated.The microstructure was characterized using EBSD and TEM,while macrotexture and internal residual stresses were analyzed with a self-developed SWXRD technique.The results revealed that PM 7A52 aluminum alloy effectively reduced the grain size,dislocation density,and texture strength in the post-weld microstructure.Furthermore,the residual stress in the weld zone(WZ)of PM 7A52 aluminum alloy was reduced by 38 MPa compared to that of the conventional melt-cast 7A52(CM 7A52)aluminum alloy.Notably,the tensile strength and elongation of welded joints in PM 7A52 aluminum alloy were increased by approximately 15%and 26%,respectively.The improvement in joint tensile strength was primarily attributed to grain boundary strengthening and dispersion strengthening caused byγ-Al_(2)O_(3)particles entering the WZ.展开更多
Abstract—Focused crawlers （also known as subjectoriented crawlers）, as the core part of vertical search engine, collect topic-specific web pages as many as they can to form a subject-oriented corpus for the latter ...Abstract—Focused crawlers （also known as subjectoriented crawlers）, as the core part of vertical search engine, collect topic-specific web pages as many as they can to form a subject-oriented corpus for the latter data analyzing or user querying. This paper demonstrates that the popular algorithms utilized at the process of focused web crawling, basically refer to webpage analyzing algorithms and crawling strategies （prioritize the uniform resource locator （URLs） in the queue）. Advantages and disadvantages of three crawling strategies are shown in the first experiment, which indicates that the best-first search with an appropriate heuristics is a smart choice for topic-oriented crawlingwhile the depth-first search is helpless in focused crawling. Besides, another experiment on comparison of improved ones （with a webpage analyzing algorithm added） is carried out to verify that crawling strategies alone are not quite efficient for focused crawling and in most cases their mutual efforts are taken into consideration. In light of the experiment results and recent researches, some points on the research tendency of focused crawler algorithms are suggested.展开更多
Recently, deep learning processors have become one of the most promising solutions of accelerating deep learning algorithms. Currently, the only method of programming the deep learning processors is through writing as...Recently, deep learning processors have become one of the most promising solutions of accelerating deep learning algorithms. Currently, the only method of programming the deep learning processors is through writing assembly instructions by bare hands, which costs a lot of programming efforts and causes very low efficiency. One solution is to integrate the deep learning processors as a new back-end into one prevalent high-level deep learning framework （e.g., TPU （tensor processing unit） is integrated into Tensorflow directly）. However, this will obstruct other frameworks to profit from the programming interface, The alternative approach is to design a framework-independent low-level library for deep learning processors （e.g., the deep learning library for GPU, cuDNN）. In this fashion, the library could be conveniently invoked in high-level programming frameworks and provides more generality. In order to allow more deep learning frameworks to gain benefits from this environment, we envision it as a low-level library which could be easily embedded into current high-level frameworks and provide high performance. Three major issues of designing such a library are discussed. The first one is the design of data structures. Data structures should be as few as possible while being able to support all possible operations. This will allow us to optimize the data structures easier without compromising the generality. The second one is the selection of operations, which should provide a rather wide range of operations to support various types of networks with high efficiency. The third is the design of the API, which should provide a flexible and user-friendly programming model and should be easy to be embedded into existing deep learning frameworks. Considering all the above issues, we propose DLPIib, a tensor-filter based library designed specific for deep learning processors. It contains two major data structures, tensor and filter, and a set of operators including basic neural network primitives and matrix/vector operations. It provides a descriptor-based API exposed as a C＋＋ interface. The library achieves a speedup of 0.79x compared with the performance of hand-written assembly instructions.展开更多
Main observation and conclusion A redox-neutral Fe-catalyzed intramolecular C-H amidation of N-benzoyloxyureas is described.This methodology employs a simple iron complex in situ generated from Fe(OTf)2 and bipyridine...Main observation and conclusion A redox-neutral Fe-catalyzed intramolecular C-H amidation of N-benzoyloxyureas is described.This methodology employs a simple iron complex in situ generated from Fe(OTf)2 and bipyridine as the catalyst and N-benzoyloxyureas as the nitrene precursors without using exogenous oxidants.An array of cyclic ureas were synthesized via aliphatic C(sp^(3))-H amidation in excellent yields.In addition,this catalytic system is also amenable to aryl C(sp^(2))-H nitrene insertion to provide benzimidazolones in moderate yields.展开更多
基金supported by the National Key Research and Development Program of China(No.SQ2021YFF0600011)。
文摘The MIG welding of in-situ generated nano-Al_(2)O_(3)powder metallurgy 7A52(PM 7A52)aluminum alloy was investigated.The microstructure was characterized using EBSD and TEM,while macrotexture and internal residual stresses were analyzed with a self-developed SWXRD technique.The results revealed that PM 7A52 aluminum alloy effectively reduced the grain size,dislocation density,and texture strength in the post-weld microstructure.Furthermore,the residual stress in the weld zone(WZ)of PM 7A52 aluminum alloy was reduced by 38 MPa compared to that of the conventional melt-cast 7A52(CM 7A52)aluminum alloy.Notably,the tensile strength and elongation of welded joints in PM 7A52 aluminum alloy were increased by approximately 15%and 26%,respectively.The improvement in joint tensile strength was primarily attributed to grain boundary strengthening and dispersion strengthening caused byγ-Al_(2)O_(3)particles entering the WZ.
基金supported by the Research Fund for International Young Scientists of National Natural Science Foundation of China under Grant No.61550110248Tibet Autonomous Region Key Scientific Research Projects under Grant No.Z2014A18G2-13
文摘Abstract—Focused crawlers （also known as subjectoriented crawlers）, as the core part of vertical search engine, collect topic-specific web pages as many as they can to form a subject-oriented corpus for the latter data analyzing or user querying. This paper demonstrates that the popular algorithms utilized at the process of focused web crawling, basically refer to webpage analyzing algorithms and crawling strategies （prioritize the uniform resource locator （URLs） in the queue）. Advantages and disadvantages of three crawling strategies are shown in the first experiment, which indicates that the best-first search with an appropriate heuristics is a smart choice for topic-oriented crawlingwhile the depth-first search is helpless in focused crawling. Besides, another experiment on comparison of improved ones （with a webpage analyzing algorithm added） is carried out to verify that crawling strategies alone are not quite efficient for focused crawling and in most cases their mutual efforts are taken into consideration. In light of the experiment results and recent researches, some points on the research tendency of focused crawler algorithms are suggested.
基金This work is partially supported by the National Natural Science Foundation of China under Grant Nos. 61432016, 61472396, 61473275, 61522211, 61532016, 61521092, 61502446, 61672491, 61602441, and 61602446, the National Basic Research 973 Program of China under Grant No. 2015CB358800, and the Strategic Priority Research Program of the Chinese Academy of Sciences under Grant No. XDB02040009.
文摘Recently, deep learning processors have become one of the most promising solutions of accelerating deep learning algorithms. Currently, the only method of programming the deep learning processors is through writing assembly instructions by bare hands, which costs a lot of programming efforts and causes very low efficiency. One solution is to integrate the deep learning processors as a new back-end into one prevalent high-level deep learning framework （e.g., TPU （tensor processing unit） is integrated into Tensorflow directly）. However, this will obstruct other frameworks to profit from the programming interface, The alternative approach is to design a framework-independent low-level library for deep learning processors （e.g., the deep learning library for GPU, cuDNN）. In this fashion, the library could be conveniently invoked in high-level programming frameworks and provides more generality. In order to allow more deep learning frameworks to gain benefits from this environment, we envision it as a low-level library which could be easily embedded into current high-level frameworks and provide high performance. Three major issues of designing such a library are discussed. The first one is the design of data structures. Data structures should be as few as possible while being able to support all possible operations. This will allow us to optimize the data structures easier without compromising the generality. The second one is the selection of operations, which should provide a rather wide range of operations to support various types of networks with high efficiency. The third is the design of the API, which should provide a flexible and user-friendly programming model and should be easy to be embedded into existing deep learning frameworks. Considering all the above issues, we propose DLPIib, a tensor-filter based library designed specific for deep learning processors. It contains two major data structures, tensor and filter, and a set of operators including basic neural network primitives and matrix/vector operations. It provides a descriptor-based API exposed as a C＋＋ interface. The library achieves a speedup of 0.79x compared with the performance of hand-written assembly instructions.
基金the financial support from NSFC(Nos.21971198 and 21772148)Large-scale Instrument and Equipment Sharing Foundation of Wuhan University and the Natural Science Foundation of Hubei Province(Grant No.2020CFA036)。
文摘Main observation and conclusion A redox-neutral Fe-catalyzed intramolecular C-H amidation of N-benzoyloxyureas is described.This methodology employs a simple iron complex in situ generated from Fe(OTf)2 and bipyridine as the catalyst and N-benzoyloxyureas as the nitrene precursors without using exogenous oxidants.An array of cyclic ureas were synthesized via aliphatic C(sp^(3))-H amidation in excellent yields.In addition,this catalytic system is also amenable to aryl C(sp^(2))-H nitrene insertion to provide benzimidazolones in moderate yields.
