Titanium alloy has the advantages of high strength,strong corrosion resistance,excellent high and low temperature mechanical properties,etc.,and is widely used in aerospace,shipbuilding,weapons and equipment,and other...Titanium alloy has the advantages of high strength,strong corrosion resistance,excellent high and low temperature mechanical properties,etc.,and is widely used in aerospace,shipbuilding,weapons and equipment,and other fields.In recent years,with the continuous increase in demand for medium-thick plate titanium alloys,corresponding welding technologies have also continued to develop.Therefore,this article reviews the research progress of deep penetration welding technology for medium-thick plate titanium alloys,mainly covering traditional arc welding,high-energy beam welding,and other welding technologies.Among many methods,narrow gap welding,hybrid welding,and external energy field assistance welding all contribute to improving the welding efficiency and quality of medium-thick plate titanium alloys.Finally,the development trend of deep penetration welding technology for mediumthick plate titanium alloys is prospected.展开更多
The fifth-generation (5G) communication requires a highly accurate estimation of the channel state information (CSI)to take advantage of the massive multiple-input multiple-output(MIMO) system. However, traditional ch...The fifth-generation (5G) communication requires a highly accurate estimation of the channel state information (CSI)to take advantage of the massive multiple-input multiple-output(MIMO) system. However, traditional channel estimation methods do not always yield reliable estimates. The methodology of this paper consists of deep residual shrinkage network (DRSN)neural network-based method that is used to solve this problem.Thus, the channel estimation approach, based on DRSN with its learning ability of noise-containing data, is first introduced. Then,the DRSN is used to train the noise reduction process based on the results of the least square (LS) channel estimation while applying the pilot frequency subcarriers, where the initially estimated subcarrier channel matrix is considered as a three-dimensional tensor of the DRSN input. Afterward, a mixed signal to noise ratio (SNR) training data strategy is proposed based on the learning ability of DRSN under different SNRs. Moreover, a joint mixed scenario training strategy is carried out to test the multi scenarios robustness of DRSN. As for the findings, the numerical results indicate that the DRSN method outperforms the spatial-frequency-temporal convolutional neural networks (SF-CNN)with similar computational complexity and achieves better advantages in the full SNR range than the minimum mean squared error (MMSE) estimator with a limited dataset. Moreover, the DRSN approach shows robustness in different propagation environments.展开更多
In the present study,two-layered stainless steel-copper composites with a thickness of 50μm were initially subjected to annealing at 800,900 and 1000℃for 5 min,respectively,to achieve diverse microstructural feature...In the present study,two-layered stainless steel-copper composites with a thickness of 50μm were initially subjected to annealing at 800,900 and 1000℃for 5 min,respectively,to achieve diverse microstructural features.Then the influence of annealing temperature on the formability of stainless steel-copper composites and the quality of micro composite cups manufactured by micro deep drawing(MDD)were investigated,and the underlying mechanism was analyzed.Three finite element(FE)models,including basic FE model,Voronoi FE model and surface morphological FE model,were developed to analyze the forming performance of stainless steel-copper composites during MDD.The results show that the stainless steel-copper composites annealed at 900℃possess the best plasticity owing to the homogeneous and refined microstructure in both stainless steel and copper matrixes,and the micro composite cup with specimen annealed at 900℃exhibits a uniform wall thickness as well as high surface quality with the fewest wrinkles.The results obtained from the surface morphological FE model considering material inhomogeneity and surface morphology of the composites are the closest to the experimental results compared to the basic and Voronoi FE model.During MDD process,the drawing forces decrease with increasing annealing temperature as a consequence of the strength reduction.展开更多
The evolution of cracks in shale directly affects the efficient production of shale gas.However,there is a lack of research on the characteristics of crack initiation in deep dense shale under different stress conditi...The evolution of cracks in shale directly affects the efficient production of shale gas.However,there is a lack of research on the characteristics of crack initiation in deep dense shale under different stress conditions.In this work,considering the different combinations of confining pressure and bedding plane inclination angle(α),biaxial mechanical loading experiments were conducted on shale containing circular holes.The research results indicate that the confining pressure and inclination angle of the bedding planes significantly influence the failure patterns of shale containing circular holes.The instability of shale containing circular holes can be classified into five types:tensile failure along the bedding planes,tensile failure through the bedding planes,shear slip along the bedding planes,shear failure through the bedding planes,and block instability failure.Furthermore,the evolution of strain and stress fields around the circular holes was found to be the fundamental cause of variations in the initiation characteristics and locations of shale cracks.The crack initiation criterion for shale containing circular hole was established,providing a new method for evaluating the trajectory of shale hole wall fractures.This study holds significant importance for evaluating the evolution and stability of fracture networks within shale reservoirs.展开更多
Rock residual strength,as an important input parameter,plays an indispensable role in proposing the reasonable and scientific scheme about stope design,underground tunnel excavation and stability evaluation of deep ch...Rock residual strength,as an important input parameter,plays an indispensable role in proposing the reasonable and scientific scheme about stope design,underground tunnel excavation and stability evaluation of deep chambers.Therefore,previous residual strength models of rocks established were reviewed.And corresponding related problems were stated.Subsequently,starting from the effects of bedding and whole life-cycle evolution process,series of triaxial mechanical tests of deep bedded sandstone with five bedding angles were conducted under different confining pressures.Then,six residual strength models considering the effects of bedding and whole life-cycle evolution process were established and evaluated.Finally,a cohesion loss model for determining residual strength of deep bedded sandstone was verified.The results showed that the effects of bedding and whole life-cycle evolution process had both significant influences on the evolution characteristic of residual strength of deep bedded sandstone.Additionally,residual strength parameters:residual cohesion and residual internal friction angle of deep bedded sandstone were not constant,which both significantly changed with increasing bedding angle.Besides,the cohesion loss model was the most suitable for determining and estimating the residual strength of bedded rocks,which could provide more accurate theoretical guidance for the stability control of deep chambers.展开更多
To tackle the challenges of intractable parameter tun-ing,significant computational expenditure and imprecise model-driven sparse-based direction of arrival(DOA)estimation with array error(AE),this paper proposes a de...To tackle the challenges of intractable parameter tun-ing,significant computational expenditure and imprecise model-driven sparse-based direction of arrival(DOA)estimation with array error(AE),this paper proposes a deep unfolded amplitude-phase error self-calibration network.Firstly,a sparse-based DOA model with an array convex error restriction is established,which gets resolved via an alternating iterative minimization(AIM)algo-rithm.The algorithm is then unrolled to a deep network known as AE-AIM Network(AE-AIM-Net),where all parameters are opti-mized through multi-task learning using the constructed com-plete dataset.The results of the simulation and theoretical analy-sis suggest that the proposed unfolded network achieves lower computational costs compared to typical sparse recovery meth-ods.Furthermore,it maintains excellent estimation performance even in the presence of array magnitude-phase errors.展开更多
分析了常见的实体识别方法,提出了一种基于语义及统计分析的实体识别机制(deep Web entity identification mechanism based on semantics and statistical analysis,简称SS-EIM),能够有效解决Deep Web数据集成中数据纠错、消重及整合...分析了常见的实体识别方法,提出了一种基于语义及统计分析的实体识别机制(deep Web entity identification mechanism based on semantics and statistical analysis,简称SS-EIM),能够有效解决Deep Web数据集成中数据纠错、消重及整合等问题.SS-EIM主要由文本匹配模型、语义分析模型和分组统计模型组成,采用文本粗略匹配、表象关联关系获取以及分组统计分析的三段式逐步求精策略,基于文本特征、语义信息及约束规则来不断精化识别结果;根据可获取的有限的实例信息,采用静态分析、动态协调相结合的自适应知识维护策略,构建和完善表象关联知识库,以适应Web数据的动态性并保证表象关联知识的完备性.通过实验验证了SS-EIM中所采用的关键技术的可行性和有效性.展开更多
针对复杂电子对抗场景中的非理想信道环境,该文提出了一种基于深度学习的异常检测(anomaly detection,AD)方法。首先,分析了利用时频同相/正交(in-phase/quadrature,I/Q)采样数据进行AD的可行性;然后,设计了深度学习网络架构,并提出基...针对复杂电子对抗场景中的非理想信道环境,该文提出了一种基于深度学习的异常检测(anomaly detection,AD)方法。首先,分析了利用时频同相/正交(in-phase/quadrature,I/Q)采样数据进行AD的可行性;然后,设计了深度学习网络架构,并提出基于深度支持向量描述(deep support vector data description,Deep SVDD)和调制识别的AD方法。仿真及实验结果表明:相比于经典的单分类检测算法,该方法检测性能和实时性明显提升,且在非理想信道环境下表现鲁棒。该方法已在某型号项目原理样机上得到验证,具有很高应用价值。展开更多
基金financially supported by the Key Research and Development Program of Ningbo(Grant No.2023Z098)Natural Science Foundation of Inner Mongolia(Grant No.2023MS05040)+1 种基金Shenyang Collaborative Innovation Center Project for Multiple Energy Fields Composite Processing of Special Materials(Grant No.JG210027)Shenyang Key Technology Special Project of The Open Competition Mechanism to Select the Best Solution(Grant Nos.2022210101000827,2022-0-43-048).
文摘Titanium alloy has the advantages of high strength,strong corrosion resistance,excellent high and low temperature mechanical properties,etc.,and is widely used in aerospace,shipbuilding,weapons and equipment,and other fields.In recent years,with the continuous increase in demand for medium-thick plate titanium alloys,corresponding welding technologies have also continued to develop.Therefore,this article reviews the research progress of deep penetration welding technology for medium-thick plate titanium alloys,mainly covering traditional arc welding,high-energy beam welding,and other welding technologies.Among many methods,narrow gap welding,hybrid welding,and external energy field assistance welding all contribute to improving the welding efficiency and quality of medium-thick plate titanium alloys.Finally,the development trend of deep penetration welding technology for mediumthick plate titanium alloys is prospected.
基金supported by the National Key Scientific Instrument and Equipment Development Project(61827801).
文摘The fifth-generation (5G) communication requires a highly accurate estimation of the channel state information (CSI)to take advantage of the massive multiple-input multiple-output(MIMO) system. However, traditional channel estimation methods do not always yield reliable estimates. The methodology of this paper consists of deep residual shrinkage network (DRSN)neural network-based method that is used to solve this problem.Thus, the channel estimation approach, based on DRSN with its learning ability of noise-containing data, is first introduced. Then,the DRSN is used to train the noise reduction process based on the results of the least square (LS) channel estimation while applying the pilot frequency subcarriers, where the initially estimated subcarrier channel matrix is considered as a three-dimensional tensor of the DRSN input. Afterward, a mixed signal to noise ratio (SNR) training data strategy is proposed based on the learning ability of DRSN under different SNRs. Moreover, a joint mixed scenario training strategy is carried out to test the multi scenarios robustness of DRSN. As for the findings, the numerical results indicate that the DRSN method outperforms the spatial-frequency-temporal convolutional neural networks (SF-CNN)with similar computational complexity and achieves better advantages in the full SNR range than the minimum mean squared error (MMSE) estimator with a limited dataset. Moreover, the DRSN approach shows robustness in different propagation environments.
基金Projects(51975398,52105392)supported by the National Natural Science Foundation of ChinaProject(YDZJSX2021A006)supported by the Central Government Guided Local Science and Technology Development Fund Project,China+1 种基金Project(20210035)supported by the Scientific Activities of Selected Returned Overseas Professionals in Shanxi Province,ChinaProject(2020-037)supported by the Fund Program for the Research Project Supported by Shanxi Scholarship Council,China。
文摘In the present study,two-layered stainless steel-copper composites with a thickness of 50μm were initially subjected to annealing at 800,900 and 1000℃for 5 min,respectively,to achieve diverse microstructural features.Then the influence of annealing temperature on the formability of stainless steel-copper composites and the quality of micro composite cups manufactured by micro deep drawing(MDD)were investigated,and the underlying mechanism was analyzed.Three finite element(FE)models,including basic FE model,Voronoi FE model and surface morphological FE model,were developed to analyze the forming performance of stainless steel-copper composites during MDD.The results show that the stainless steel-copper composites annealed at 900℃possess the best plasticity owing to the homogeneous and refined microstructure in both stainless steel and copper matrixes,and the micro composite cup with specimen annealed at 900℃exhibits a uniform wall thickness as well as high surface quality with the fewest wrinkles.The results obtained from the surface morphological FE model considering material inhomogeneity and surface morphology of the composites are the closest to the experimental results compared to the basic and Voronoi FE model.During MDD process,the drawing forces decrease with increasing annealing temperature as a consequence of the strength reduction.
基金Projects(52104143,52109135,52374099)supported by the National Natural Science Foundation of ChinaProject(2025YFHZ0323)supported by the Natural Science Foundation of Sichuan Province,China。
文摘The evolution of cracks in shale directly affects the efficient production of shale gas.However,there is a lack of research on the characteristics of crack initiation in deep dense shale under different stress conditions.In this work,considering the different combinations of confining pressure and bedding plane inclination angle(α),biaxial mechanical loading experiments were conducted on shale containing circular holes.The research results indicate that the confining pressure and inclination angle of the bedding planes significantly influence the failure patterns of shale containing circular holes.The instability of shale containing circular holes can be classified into five types:tensile failure along the bedding planes,tensile failure through the bedding planes,shear slip along the bedding planes,shear failure through the bedding planes,and block instability failure.Furthermore,the evolution of strain and stress fields around the circular holes was found to be the fundamental cause of variations in the initiation characteristics and locations of shale cracks.The crack initiation criterion for shale containing circular hole was established,providing a new method for evaluating the trajectory of shale hole wall fractures.This study holds significant importance for evaluating the evolution and stability of fracture networks within shale reservoirs.
基金Projects(2024YFC3013801,2022YFC3004602)supported by the National Key R&D Program of ChinaProjects(U23B2093,52034009)supported by the National Natural Science Foundation of China。
文摘Rock residual strength,as an important input parameter,plays an indispensable role in proposing the reasonable and scientific scheme about stope design,underground tunnel excavation and stability evaluation of deep chambers.Therefore,previous residual strength models of rocks established were reviewed.And corresponding related problems were stated.Subsequently,starting from the effects of bedding and whole life-cycle evolution process,series of triaxial mechanical tests of deep bedded sandstone with five bedding angles were conducted under different confining pressures.Then,six residual strength models considering the effects of bedding and whole life-cycle evolution process were established and evaluated.Finally,a cohesion loss model for determining residual strength of deep bedded sandstone was verified.The results showed that the effects of bedding and whole life-cycle evolution process had both significant influences on the evolution characteristic of residual strength of deep bedded sandstone.Additionally,residual strength parameters:residual cohesion and residual internal friction angle of deep bedded sandstone were not constant,which both significantly changed with increasing bedding angle.Besides,the cohesion loss model was the most suitable for determining and estimating the residual strength of bedded rocks,which could provide more accurate theoretical guidance for the stability control of deep chambers.
基金supported by the National Natural Science Foundation of China(62301598).
文摘To tackle the challenges of intractable parameter tun-ing,significant computational expenditure and imprecise model-driven sparse-based direction of arrival(DOA)estimation with array error(AE),this paper proposes a deep unfolded amplitude-phase error self-calibration network.Firstly,a sparse-based DOA model with an array convex error restriction is established,which gets resolved via an alternating iterative minimization(AIM)algo-rithm.The algorithm is then unrolled to a deep network known as AE-AIM Network(AE-AIM-Net),where all parameters are opti-mized through multi-task learning using the constructed com-plete dataset.The results of the simulation and theoretical analy-sis suggest that the proposed unfolded network achieves lower computational costs compared to typical sparse recovery meth-ods.Furthermore,it maintains excellent estimation performance even in the presence of array magnitude-phase errors.
基金Supported by the Specialized Research Fund for the Doctoral Program of Higher Education of China under Grant No.20070422107 (高等学校博士学科点专项科研基金)the Key Science-Technology Project of Shandong Province of China under Grant No.2007GG10001002 (山东省科技攻关项目)
基金Supported by the National Natural Science Foundation of China under Grant No.60673139 (国家自然科学基金)
文摘分析了常见的实体识别方法,提出了一种基于语义及统计分析的实体识别机制(deep Web entity identification mechanism based on semantics and statistical analysis,简称SS-EIM),能够有效解决Deep Web数据集成中数据纠错、消重及整合等问题.SS-EIM主要由文本匹配模型、语义分析模型和分组统计模型组成,采用文本粗略匹配、表象关联关系获取以及分组统计分析的三段式逐步求精策略,基于文本特征、语义信息及约束规则来不断精化识别结果;根据可获取的有限的实例信息,采用静态分析、动态协调相结合的自适应知识维护策略,构建和完善表象关联知识库,以适应Web数据的动态性并保证表象关联知识的完备性.通过实验验证了SS-EIM中所采用的关键技术的可行性和有效性.
基金Supported by the National Natural Science Foundation of China under Grant No.60573096 (国家自然科学基金)the NSFC-JST Major International (Regional) Joint Research Project under Grant No.60720106001 (NSFC-JST 重大国际(地区)合作项目)
文摘针对复杂电子对抗场景中的非理想信道环境,该文提出了一种基于深度学习的异常检测(anomaly detection,AD)方法。首先,分析了利用时频同相/正交(in-phase/quadrature,I/Q)采样数据进行AD的可行性;然后,设计了深度学习网络架构,并提出基于深度支持向量描述(deep support vector data description,Deep SVDD)和调制识别的AD方法。仿真及实验结果表明:相比于经典的单分类检测算法,该方法检测性能和实时性明显提升,且在非理想信道环境下表现鲁棒。该方法已在某型号项目原理样机上得到验证,具有很高应用价值。
