期刊文献+
任意字段
题名或关键词
题名
关键词
摘要
作者
第一作者
机构
刊名
分类号
参考文献
作者简介
基金资助
栏目信息
共找到137,209篇文章
< 1 2 … 250 >
每页显示 20 50 100
已选0条
引用分析
参考文献
引证文献
 统计分析
检索结果
已选文献
显示方式：
Recent advancements and emerging challenges in deep underground engineering 认领 引用
1
作者 Jianguo Wang Chunfai Leung 《Deep Underground Science and Engineering》 EI CAS CSCD 2026年第2期313-314,共2页
Deep Underground Science and Engineering(DUSE)is pleased to present this issue,highlighting recent advancements and emerging challenges in the field of deep underground engineering.This issue comprises 18 highquality ... Deep Underground Science and Engineering(DUSE)is pleased to present this issue,highlighting recent advancements and emerging challenges in the field of deep underground engineering.This issue comprises 18 highquality articles,covering a broad range of topics from deep resource extraction and underground space construction to rock mechanics behavior and disaster prevention.The collection reflects the current depth and diversity of research in deep underground science and engineering. 展开更多
关键词 deep resource extraction underground space construction deep underground science engineering deep underground science deep underground engineeringthis rock mechanics behavior deep underground engineering resource extraction
暂未订购 下载PDF
A Two-Timescale Neurodynamic Approach to Sharpness-Aware Minimization in Deep Learning 认领 引用
2
作者 Dan Su Jun Wang +1 位作者 Chunhua Yang Weihua Gui 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2026年第6期1530-1532,共3页
Dear Editor,This letter presents a two-timescale neurodynamic algorithm for sharpness-aware minimization in deep learning.Deep learning achieves remarkable success in areas such as computer vision,natural language pro... Dear Editor,This letter presents a two-timescale neurodynamic algorithm for sharpness-aware minimization in deep learning.Deep learning achieves remarkable success in areas such as computer vision,natural language processing,robotics and control.In deep learning,it is essential to boost their generalization power[1].Existing deep learning strategies for improving the generalization power include regularization,data augmentation,etc.[2]. 展开更多
关键词 deep learning computer visionnatural language processingrobotics generalization power boost their deep learningit deep learningdeep learning two timescale neurodynamic approach sharpness aware minimization
暂未订购 下载PDF
Determining the Energy Potential of Deep Borehole Heat Exchangers in Croatia and Economic Analysis of Oil&Gas Well Revitalization 认领 引用 被引量：1
3
作者 Marija Macenic Tomislav Kurevija Tin Herbst 《Energy Engineering》 EI 2026年第1期1-26,共26页
The increased interest in geothermal energy is evident,along with the exploitation of traditional hydrothermal systems,in the growing research and projects developing around the reuse of already-drilled oil,gas,and ex... The increased interest in geothermal energy is evident,along with the exploitation of traditional hydrothermal systems,in the growing research and projects developing around the reuse of already-drilled oil,gas,and exploration wells.The Republic of Croatia has around 4000 wells,however,due to a long period since most of these wells were drilled and completed,there is uncertainty about how many are available for retrofitting as deep-borehole heat exchangers.Nevertheless,as hydrocarbon production decreases,it is expected that the number of wells available for the revitalization and exploitation of geothermal energy will increase.The revitalization of wells via deep-borehole heat exchangers involves installing a coaxial heat exchanger and circulating the working fluid in a closed system,during which heat is transferred from the surrounding rock medium to the circulating fluid.Since drilled wells are not of uniformdepth and are located in areas with different thermal rock properties and geothermal gradients,an analysis was conducted to determine available thermal energy as a function of well depth,geothermal gradient,and circulating fluid flow rate.Additionally,an economic analysis was performed to determine the benefits of retrofitting existing assets,such as drilled wells,compared to drilling new wells to obtain the same amount of thermal energy. 展开更多
关键词 Geothermal energy deep coaxial borehole heat exchangers deep BHE heat extraction abandoned wells retrofitted wells
暂未订购 下载PDF
Unveiling ecological risks of deep-sea mining:a focus on collector plumes 认领 引用
4
作者 Xiaolei Liu Zhihao Li +9 位作者 Ying Lai Yihan Liu Cheng Zhang Thorsten Stoesser Dongfang Liang Eckart Meiburg Rita Leal Sousa Yonggang Jia Yunmin Chen Xingsen Guo 《Science Bulletin》 SCIE EI CAS CSCD 2026年第13期3221-3224,共4页
The deep sea holds vast mineral resources,including polymetallic nodules,cobalt-rich crusts,polymetallic sulfides,and deep-sea rare earth elements,which offer substantial commercial potential[1].In the eastern Pacific... The deep sea holds vast mineral resources,including polymetallic nodules,cobalt-rich crusts,polymetallic sulfides,and deep-sea rare earth elements,which offer substantial commercial potential[1].In the eastern Pacific Ocean’s Clarion-Clipperton Zone(CCZ)alone,nickel and cobalt reserves are estimated at 270 million tons and 44 million tons,respectively,three to five times the terrestrial reserves of these metals[2].As global mineral consumption exceeds 24 billion tons per year and terrestrial resources become increasingly constrained,many countries are turning to deep-sea mining[3].However,achieving high extraction efficiency while minimizing environmental impacts remains a critical challenge.After decades of research and technological advances,deep-sea mining techniques have been refined,and the pipeline hydraulic lifting system is the most promising.Among this system,seabed collection and tailings discharge both generate plumes:collector plumes and tailing plumes,respectively[4].These plumes’adverse environmental impacts are now a major barrier to commercial deep-sea mining,and their ecological effects have drawn substantial scrutiny during the current trial stage. 展开更多
关键词 mineral resourcesincluding terrestrial resources polymetallic nodulescobalt rich ecological risks deep sea collector plumes deep sea mining cobalt
暂未订购 下载PDF
In-situ pressure-preserved coring for deep resources exploration:A novel pressure sealing subsystem and its service performance 认领 引用
5
作者 Da Guo Ling Chen +7 位作者 Jia-Nan Li Le Zhao Yi-Wei Zhang Ding-Ming Wang Xin Fang Zhi-Qiang He Lin Dai He-Ping Xie 《Petroleum Science》 SCIE EI CAS CSCD 2026年第1期262-277,共16页
In-situ pressure coring technology is a responsible exploration technique for enhancing the efficiency and capacity of deep resources development.However,reliability issues in pressure sealing introduce significant un... In-situ pressure coring technology is a responsible exploration technique for enhancing the efficiency and capacity of deep resources development.However,reliability issues in pressure sealing introduce significant uncertainty in field applications of this technology.This work presents a novel pressure sealing subsystem within the in-situ pressure-preserved coring system to overcome the inherent problem.The design concept and structure composition of the pressure sealing subsystem are described.To enhance pressure sealing reliability in real downhole conditions,the subsystem incorporates a dynamic sealing structure between the inner tube and the pressure bearing tube,and a close-fitting sealing face between the pressure controller and the bottom of the inner tube.Theoretical calculations and computational fluid dynamics(CFD)simulations were conducted to evaluate the mechanical behavior and fluid flow characteristics within the pressure sealing subsystem,determining the structural effects on performance.A smaller pump displacement during inner tube lifting and a moderate overflow hole diameter of 7 mm enhance the success rate of a sequence of mechanical actions required for the in-situ pressure sealing.Numerical,laboratory,and field tests were conducted to verify the service performance.Numerical analysis indicates that the particle settlement ratio in the novel structure is only 32%of that in the original design.In laboratory downhole circulation and drilling tests,the pressure sealing subsystem successfully maintained an in-situ pressure of 0.2 MPa at a depth of approximately 9-10 m.In field applications,a 1.95 m in-situ core sample was retrieved at 22 MPa from a depth of approximately 1970 m. 展开更多
关键词 Deep mining In-situ rock mechanics Pressure coring Deep resources exploration Onshore drilling
暂未订购 下载PDF
Deep-learning-aided spectroscopic detection and fusion imaging using a micro-tapered fiber and multimodal speckles 认领 引用
6
作者 HAORAN ZHANG LELE WANG +7 位作者 JUNYI LIAO YIJIE ZHANG ZHAOFAN HE HAIDI GENG DAN LI PING YAN QIANG LIU QIRONG XIAO 《Photonics Research》 SCIE EI CAS CSCD 2026年第5期1737-1748,共12页
Multimode fibers are promising for compact imaging and spectroscopy.However,current implementations are typically limited to a single function and often lack robustness against environmental disturbances.Unlike approa... Multimode fibers are promising for compact imaging and spectroscopy.However,current implementations are typically limited to a single function and often lack robustness against environmental disturbances.Unlike approaches that solely analyze the fiber end-face,we exploit the high information density of the leaky field from a fiber taper.A lensless system with a deep learning framework is developed,simultaneously capturing multi-modal data from the taper leaky field and the end-face speckle.This approach achieves spectral reconstruction with a resolution of 0.05 nm and enables high-quality image recovery.By fusing both light fields,we significantly enhance image quality(MNIST SSIM up to 0.99),demonstrating a robust,all-fiber platform for integrated spectroscopic and imaging applications. 展开更多
关键词 lensless system deep learning taper leaky field fiber tapera spectroscopic detection leaky field multimode fibers deep learning framework
暂未订购 下载PDF
A multi-scale fracture prediction method based on improved deep embedded clustering 认领 引用
7
作者 Yu-jia Lu Chao Chen +2 位作者 Zhu-jiang Liu Fu-bin Wei Zhe-ge Liu 《Applied Geophysics》 SCIE CSCD 2026年第2期571-590,867,共20页
Accurately characterizing the distribution and scale characteristics of fractures in subsurface media is a crucial step for quantitatively evaluating shale gas sweet spots and guiding reservoir fracturing stimulation.... Accurately characterizing the distribution and scale characteristics of fractures in subsurface media is a crucial step for quantitatively evaluating shale gas sweet spots and guiding reservoir fracturing stimulation.However,a single seismic attribute is often used to identify fracture features of a specifi c scale,making it diffi cult to achieve detailed characterization of fractures across multiple scales simultaneously.Multi-attribute fusion algorithms often focus on statistical correlations,lacking in-depth exploration of the spatial topological relationships and intrinsic physical connections among fractures of diff erent scales,resulting in reduced accuracy in complex structural areas.To address this challenge,we propose a multi-scale integrated fracture prediction method based on an improved deep embedded clustering(DEC)framework,using the marine shale reservoir of the Wufeng–Longmaxi Formation in southeastern Sichuan Basin as a case study.Specifically,(1)an improved DEC objective function integrating fracture topology constraints and cluster-balancing mechanisms is developed to enhance the model’s adaptability to complex geological structures;(2)an“expand–then–contract”stacked autoencoder architecture is designed to better capture nonlinear relationships among multi-attribute data and decouple multi-scale fracture features;and(3)an integrated workfl ow from multi-attribute optimization,intelligent fusion clustering to geological interpretation is established,enabling diff erentiated and high-precision characterization of multi-scale fractures.Furthermore,based on the geological characteristics of the study area,we systematically analyze the spatial mapping relationships of the autoencoder’s multi-layer features and elucidate their implicit geophysical signifi cance.This analysis reveals the intrinsic processes through which the proposed model performs fracture attribute optimization,noise separation,and multi-scale feature extraction.Finally,by integrating intelligent fault identifi cation,micro-fracture amplitude variation with azimuth(AVAZ)inversion,and conventional geometric attributes,high-precision spatial characterization of the fracture system is achieved,spanning from large-scale faults to micro-fractures.The prediction results show strong agreement with geological understanding. 展开更多
关键词 Multi-scale fractures Deep embedded clustering Deep learning Autoencoding Marine shale
暂未订购 下载PDF
Noise-driven enhancement for exploration:Deep reinforcement learning for UAV autonomous navigation in complex environments 认领 引用
8
作者 Haotian ZHANG Yiyang LI +1 位作者 Lingquan CHENG Jianliang AI 《Chinese Journal of Aeronautics》 SCIE EI CAS CSCD 2026年第1期454-471,共18页
Unmanned Aerial Vehicle(UAV)plays a prominent role in various fields,and autonomous navigation is a crucial component of UAV intelligence.Deep Reinforcement Learning(DRL)has expanded the research avenues for addressin... Unmanned Aerial Vehicle(UAV)plays a prominent role in various fields,and autonomous navigation is a crucial component of UAV intelligence.Deep Reinforcement Learning(DRL)has expanded the research avenues for addressing challenges in autonomous navigation.Nonetheless,challenges persist,including getting stuck in local optima,consuming excessive computations during action space exploration,and neglecting deterministic experience.This paper proposes a noise-driven enhancement strategy.In accordance with the overall learning phases,a global noise control method is designed,while a differentiated local noise control method is developed by analyzing the exploration demands of four typical situations encountered by UAV during navigation.Both methods are integrated into a dual-model for noise control to regulate action space exploration.Furthermore,noise dual experience replay buffers are designed to optimize the rational utilization of both deterministic and noisy experience.In uncertain environments,based on the Twin Delay Deep Deterministic Policy Gradient(TD3)algorithm with Long Short-Term Memory(LSTM)network and Priority Experience Replay(PER),a Noise-Driven Enhancement Priority Memory TD3(NDE-PMTD3)is developed.We established a simulation environment to compare different algorithms,and the performance of the algorithms is analyzed in various scenarios.The training results indicate that the proposed algorithm accelerates the convergence speed and enhances the convergence stability.In test experiments,the proposed algorithm successfully and efficiently performs autonomous navigation tasks in diverse environments,demonstrating superior generalization results. 展开更多
关键词 Action space exploration Autonomous navigation Deep reinforcement learning Twin delay deep deterministic policy gradient Unmanned aerial vehicle
暂未订购 下载PDF
Thermo-mechanical analysis of wellbore stability in ultra-deep formations 认领 引用
9
作者 Yang Yang Qian Wang +3 位作者 Fujian Yang Dawei Hu Yunhu Lu Hui Zhou 《Journal of Rock Mechanics and Geotechnical Engineering》 SCIE CSCD 2026年第3期2015-2032,共18页
Wellbore stability is crucial for ultra-deep wells operating under high temperature and high stress conditions.To analyze the effects of thermal shock and horizontal stress on wellbore stability,a series of true triax... Wellbore stability is crucial for ultra-deep wells operating under high temperature and high stress conditions.To analyze the effects of thermal shock and horizontal stress on wellbore stability,a series of true triaxial compression experiments and wellbore stability experiments were conducted on dolomite specimens under high temperature and high triaxial stress.In the true triaxial compression experiments,as the horizontal stress increases,the peak strength of the specimen exhibits a nonlinear growth trend.As the intermediate principal stress increases,the peak strength of the specimen first increases and then decreases,with the failure mode transitioning from shear failure to a mixed tensile-shear failure.Thermal shock stimulates the formation of microcracks within the specimen,reducing its peak strength,cohesion,and internal friction angle.In the wellbore stability experiments,the maximum horizontal principal stress that causes wellbore instability increases nonlinearly with the increase in minimum horizontal principal stress.Under the same minimum horizontal principal stress conditions,the specimen after thermal shock exhibits a lower maximum horizontal principal stress that causes wellbore instability.Based on the experimental result,Mohr-Coulomb(M-C)and Mogi-Coulomb(MG-C)criteria incorporating the effects of thermal shock were established to evaluate wellbore stability.Comparing the M-C criterion,the MG-C criterion considers the effect of the intermediate principal stress,thus providing a more accurate prediction of wellbore stability in ultra-deep wells.This study enhances the understanding of the mechanisms underlying wellbore instability and offers valuable insights for managing stability challenges in ultra-deep well environments. 展开更多
关键词 High temperature High stress Ultra-deep wellbore stability Thermal shock Mogi-coulomb(MG-C)criterion Mohr-coulomb(M-C)criterion Deep earth science and engineering
暂未订购 下载PDF
Hybrid Deep Learning for Hydraulic Cylinder Fault Diagnosis under Complex Conditions via Multi-Source Signal Fusion 认领 引用
10
作者 Chen Yang Jianwen Yan +2 位作者 Yixiong Feng Lei Li Jianrong Tan 《Instrumentation》 2026年第1期40-56,共17页
Hydraulic presses are indispensable in automotive and aerospace manufacturing,with hydraulic cylinders serving as key components for operational safety and product quality.Internal leakage faults in hydraulic cylinder... Hydraulic presses are indispensable in automotive and aerospace manufacturing,with hydraulic cylinders serving as key components for operational safety and product quality.Internal leakage faults in hydraulic cylinders are difficult to diagnose due to the scarcity of labeled data,the complexity of fault mechanisms,and the limited representation capability of single-signal methods under variable operating conditions.To address these issues,a hybrid deep learning feature fusion model based on displacement error and pressure signal,including convolutional autoencoder,multi-head attention mechanism,residual network and bidirectional long short time series neural network(CAEMRAB),is proposed for the diagnosis and classification of leakage faults in hydraulic cylinders.A hydraulic cylinder test system simulates heavy load,variable speed,and nonlinear motion under actual operating conditions.Through the all-round deep feature decoupling of the proposed model,the multi-source signal representation ability in complex and multi-noise environments is enhanced,effectively extracting the local and global features of displacement error and pressure signal fault data and achieving efficient classification.Experimental results indicate that the proposed model achieves at least a 3.95%improvement in diagnostic accuracy compared with ablation models.In addition,it exhibits high diagnostic stability across other models,single-signal diagnosis,varying sample sizes,and complex noise conditions.These experiments fully validate the superior performance of the proposed method in terms of diagnostic accuracy,reliability,and robustness. 展开更多
关键词 hydraulic cylinder feature fusion multi-source signal hybrid deep learning deep feature decoupling
暂未订购 下载PDF
Integrable Deep Learning 认领 引用
11
作者 Yong Chen 《Chinese Physics Letters》 SCIE EI CAS CSCD 2026年第6期1-9,共9页
We explain the motivation for proposing the concept and framework of integrable deep learning(IDL),and focuse on a series of advances we have made in IDL algorithms.1.Two-stage PINN methods based on conservation laws,... We explain the motivation for proposing the concept and framework of integrable deep learning(IDL),and focuse on a series of advances we have made in IDL algorithms.1.Two-stage PINN methods based on conservation laws,and PINN methods based on the Miura transformation;2.Lax pair-informed neural networks(LPNNs)and DT-LPNN combined with the Darboux transformation;3.Novel convolutional neural network architectures for integrable systems,including pseudo grid-based physics-informed convolutional-recurrent network(PG-PhyCRNet)and polynomial extractor for rogue wave patterns(PE-RWP). 展开更多
关键词 miura transformation PINN miura transformation lax convolutional neural network darboux transformation novel integrable deep learning idl conservation laws integrable deep learning
暂未订购 下载PDF
Retraction: A Lightweight Multimodal Deep Fusion Network for Face Antis Poofing with Cross-Axial Attention and Deep Reinforcement Learning Technique 认领 引用
12
作者 无 《Computers, Materials & Continua》 SCIE EI 2026年第7期1981-1981,共1页
The published article titled“A Lightweight Multimodal Deep Fusion Network for Face Antis Poofing with Cross-Axial Attention and Deep Reinforcement Learning Technique”has been retracted from Computers,Materials&C... The published article titled“A Lightweight Multimodal Deep Fusion Network for Face Antis Poofing with Cross-Axial Attention and Deep Reinforcement Learning Technique”has been retracted from Computers,Materials&Continua,Vol.85,No.3,2025,pp.5671-5702. 展开更多
关键词 face antis poofing deep reinforcement learning technique deep reinforcement learning face antispoofing lightweight multimodal deep fusion network cross axial attention
暂未订购 下载PDF
Transfer Learning for Deep Reinforcement Learning-Based Path Following of Autonomous Surface Vessels 认领 引用 被引量：1
13
作者 Aniket Malviya Suresh Rajendran Xueqian Zhou 《哈尔滨工程大学学报（英文版）》 CSCD 2026年第3期728-744,共17页
Deep Reinforcement Learning(DRL)offers a powerful,model-free,and data-driven approach for the navigation and control of Autonomous Surface Vessels(ASVs).The primary challenge,however,lies in the extensive training req... Deep Reinforcement Learning(DRL)offers a powerful,model-free,and data-driven approach for the navigation and control of Autonomous Surface Vessels(ASVs).The primary challenge,however,lies in the extensive training required for an agent to converge to an effective policy within a complex simulation,leading to significant computational overhead.This paper presents a multi-stage training framework that uses Transfer Learning to pass knowledge between different simulation models,resulting in a highly robust DRL controller for ASVs.The proposed framework utilizes the Deep Deterministic Policy Gradient(DDPG)algorithm to develop the data-driven controller.First,a foundational policy is efficiently learned using a simplified first-order Nomoto dynamics and second-order Nomoto dynamics,which captures the fundamental vessel dynamics.This pre-trained policy is then transferred to a complex,nonlinear Manoeuvring Modelling Group(MMG)model,significantly accelerating training convergence.Subsequently,the agent is fine-tuned within the MMG simulation with environmental disturbances.The models are evaluated on various trajectories during testing to ensure robust performance.The accuracy of the DRL controller is assessed by measuring heading error(eψ)and cross-track error(ye).A traditional Proportional-Integral-Derivative(PID)controller is implemented and compared to benchmark the DRL controller's effectiveness,to highlight the relative advantages and limitations of each approach. 展开更多
关键词 Deep reinforcement learning(DRL) Autonomous surface vessels(ASVs) Deep deterministic policy gradient(DDPG) Transfer learning Proportional-integral-derivative(PID)controller Line of sight(LOS)guidance algorithm
暂未订购 下载PDF
A dual attention-based deep learning model for lithology identificationwhile drilling 认领 引用 被引量：2
14
作者 Jie Chen Zhen Gui +6 位作者 Yichao Rui Xusheng Zhao Xiaokang Pan Qingfeng Wang Yuanyuan Pu Zheng Li Maoyi Liu 《Journal of Rock Mechanics and Geotechnical Engineering》 SCIE CSCD 2026年第2期1177-1192,共16页
Lithology identificationwhile drilling technology can obtain rock information in real-time.However,traditional lithology identificationmodels often face limitations in feature extraction and adaptability to complex ge... Lithology identificationwhile drilling technology can obtain rock information in real-time.However,traditional lithology identificationmodels often face limitations in feature extraction and adaptability to complex geological conditions,limiting their accuracy in challenging environments.To address these challenges,a deep learning model for lithology identificationwhile drilling is proposed.The proposed model introduces a dual attention mechanism in the long short-term memory(LSTM)network,effectively enhancing the ability to capture spatial and channel dimension information.Subsequently,the crayfishoptimization algorithm(COA)is applied to optimize the model network structure,thereby enhancing its lithology identificationcapability.Laboratory test results demonstrate that the proposed model achieves 97.15%accuracy on the testing set,significantlyoutperforming the traditional support vector machine(SVM)method(81.77%).Field tests under actual drilling conditions demonstrate an average accuracy of 91.96%for the proposed model,representing a 14.31%improvement over the LSTM model alone.The proposed model demonstrates robust adaptability and generalization ability across diverse operational scenarios.This research offers reliable technical support for lithology identification while drilling. 展开更多
关键词 Lithology identificationwhile drilling Deep learning Dual attention mechanism Metaheuristic algorithm Field applications
暂未订购 下载PDF
Effect of dynamic disturbance frequency on brittle failure of granite in deep excavation 认领 引用 被引量：2
15
作者 Ben-Guo He Hanyi Liu +1 位作者 Xia-Ting Feng Hongyuan Fu 《Journal of Rock Mechanics and Geotechnical Engineering》 SCIE CSCD 2026年第2期1002-1015,共14页
Dynamic disturbances with various frequencies could trigger different failure modes of deep excavations.Superimposed on this static stress are dynamic disturbances due to various dynamic vibrations,e.g.excavation blas... Dynamic disturbances with various frequencies could trigger different failure modes of deep excavations.Superimposed on this static stress are dynamic disturbances due to various dynamic vibrations,e.g.excavation blasting,blasting,tunnel boring machine(TBM)vibration,rockburst wave,earthquakes.Specifically,these dynamic sources are characterized by a wide range of wave frequencies f,resulting in differences in failure modes.A series of true-triaxial compression tests were conducted on granite to simulate the excavation-induced stress path in three-dimensional(3D)stresses.Subsequently,a dynamic disturbance with various frequencies was applied to a cuboid specimen,to reveal the behavior associated with brittle failure.The dynamic disturbance with frequencies f of 5 Hz,10 Hz,and 40 Hz generates less disturbed energy components in the granite together with higher peak strength.However,dynamic disturbances with f of 20 Hz and 30 Hz resulted in a lower peak strength;the peak strength of the rock increases sp albeit it decreases at first,then increases.This U-shaped phenomenon relates to the natural frequency of the granite under such stress conditions.Different rock lithologies consisting of diverse mineral composition,respond differently to each sensitive resonance frequency.Interestingly,the weak disturbance stress with a high frequency f and low amplitude A increases the ratio of crack damage to peak strength(scd/sp)in the granite.This leads to the inhibition of the expansion of the granite during the dynamic disturbance process.Multiple penetrating tensileeshear cracks appear in the s3-direction as the disturbance frequency f increases. 展开更多
关键词 True-triaxial compression Disturbance frequency Brittle failure Characteristic strength Deep excavation
暂未订购 下载PDF
基于DeepSeek智能算法的财务概念框架演进研究——数据资产确认、计量与报告的三维重构 认领 引用 被引量：3
16
作者 赵雪艳 孟令云 耿华 《会计之友》 北大核心 2026年第3期114-121,共8页
基于DeepSeek智能算法,探讨了数据资产在财务会计概念框架中的确认、计量与报告问题,提出了“三维重构”理论。文章创新性地引入DeepSeek技术构建“场景—时间—质量”标准,重新定义了数据资产的确认逻辑、计量模式和报告体系,认为数据... 基于DeepSeek智能算法,探讨了数据资产在财务会计概念框架中的确认、计量与报告问题,提出了“三维重构”理论。文章创新性地引入DeepSeek技术构建“场景—时间—质量”标准,重新定义了数据资产的确认逻辑、计量模式和报告体系,认为数据资产的价值实现依赖于算法中介的有效性,会计确认标准应从“控制观”转向“治理观”,财务报告周期需与算法迭代周期同步化,会计信息质量特征体系应纳入算法伦理维度。建议数据资产要素尽快融入相应的财务概念框架体系,相关会计理论需要接入DeepSeek算法构建数据资产的多维度计量,政府也需要加强DeepSeek等智能技术算法的伦理监管。 展开更多
关键词 Deep Seek 新质生产力 数据资产 智能算法 财务概念框架
暂未订购 下载PDF
Landslide susceptibility assessment integrating deep transfer learning and physical models in the Baihetan reservoir area,China 认领 引用 被引量：1
17
作者 Ming Peng Yue Wang +5 位作者 Chenyi Ma Haojie Wang Shaoqiang Meng Zhenming Shi Weijiang Chu Jianrong Xu 《Journal of Rock Mechanics and Geotechnical Engineering》 SCIE CSCD 2026年第6期4382-4401,共20页
Reservoir landslides pose significant risks to hydropower projects,potentially leading to catastrophic disasters that threaten downstream lives and properties.Landslide susceptibility assessments are critical for effe... Reservoir landslides pose significant risks to hydropower projects,potentially leading to catastrophic disasters that threaten downstream lives and properties.Landslide susceptibility assessments are critical for effective regional disaster prevention and mitigation.However,the complexity,model uninterpretability,and data scarcity related to reservoir landslides,particularly when adapting models across diverse geographic regions,present significant challenges.This study proposes an interpretable Deep Transfer Learning model coupled with multi-source data and Physical methods(DTLP).The model is trained on multi-source data from the Three Gorges Reservoir Area(TGRA)and Lower Jinsha River Basin(LJRB),tested in Baihetan Reservoir Area(BHT),addressing the issues of limited data and cross-regional generalization.The physical method captures the effect of dynamic water level changes on slope stability.SHAP values are used to interpret the model,providing clear insights into its internal mechanisms.Results demonstrate that DTLP outperforms TrAdaBoost in data-scarce regions,achieving higher accuracy(AUC=0.953,Accuracy=0.941)with better feature generalization and susceptibility zone identification.Incorporating dynamic water level changes into the physical model enhances identification of high-susceptibility areas and reduces misclassifications.SHAP analysis indicates that elevation,lithology,and distance to river significantly influence the model decisions.Using TGRA as the source domain further validates the superiority of DTLP framework.However,due to the initial discrepancies between TGRA and the target domain,the transferability is constrained to some extent,resulting in models trained on LJRB data outperforming those trained on TGRA data. 展开更多
关键词 Landslide susceptibility assessment Deep transfer learning Reservoir landslides Infiniteslope model SHAP values
暂未订购 下载PDF
Microseismic signal processing and rockburst disaster identification:A multi-task deep learning and machine learning approach 认领 引用 被引量：1
18
作者 Chunchi Ma Weihao Xu +3 位作者 Xuefeng Ran Tianbin Li Hang Zhang Dongwei Xing 《Journal of Rock Mechanics and Geotechnical Engineering》 SCIE CSCD 2026年第1期441-456,共16页
Underground engineering projects such as deep tunnel excavation often encounter rockburst disasters accompanied by numerous microseismic events.Rapid interpretation of microseismic signals is crucial for the timely id... Underground engineering projects such as deep tunnel excavation often encounter rockburst disasters accompanied by numerous microseismic events.Rapid interpretation of microseismic signals is crucial for the timely identification of rockbursts.However,conventional processing encompasses multi-step workflows,including classification,denoising,picking,locating,and computational analysis,coupled with manual intervention,which collectively compromise the reliability of early warnings.To address these challenges,this study innovatively proposes the“microseismic stethoscope"-a multi-task machine learning and deep learning model designed for the automated processing of massive microseismic signals.This model efficiently extracts three key parameters that are necessary for recognizing rockburst disasters:rupture location,microseismic energy,and moment magnitude.Specifically,the model extracts raw waveform features from three dedicated sub-networks:a classifier for source zone classification,and two regressors for microseismic energy and moment magnitude estimation.This model demonstrates superior efficiency compared to traditional processing and semi-automated processing,reducing per-event processing time from 0.71 s to 0.49 s to merely 0.036 s.It concurrently achieves 98%accuracy in source zone classification,with microseismic energy and moment magnitude estimation errors of 0.13 and 0.05,respectively.This model has been well applied and validated in the Daxiagu Tunnel case in Sichuan,China.The application results indicate that the model is as accurate as traditional methods in determining source parameters,and thus can be used to identify potential geomechanical processes of rockburst disasters.By enhancing the signal processing reliability of microseismic events,the proposed model in this study presents a significant advancement in the identification of rockburst disasters. 展开更多
关键词 Underground engineering Microseismic signal processing Deep learning Multi-task Rockburst identification
暂未订购 下载PDF
Achieving strength-ductility synergy of an additively manufactured metastable high-entropy alloy via deep cryogenic treatment followed by laser shock peening 认领 引用 被引量：1
19
作者 Zhaopeng Tong Yuze Zhang +4 位作者 Yichen Wang YunXia Ye Jie Yin Yucheng Bao Xudong Ren 《International Journal of Extreme Manufacturing》 SCIE EI CAS CSCD 2026年第1期554-572,共19页
Laser powder bed fusion(LPBF)is an attractive additive manufacturing technology for preparing high-performance high-entropy alloys(HEAs)engineering components.Unfortunately,the existence of inherent thermal residual s... Laser powder bed fusion(LPBF)is an attractive additive manufacturing technology for preparing high-performance high-entropy alloys(HEAs)engineering components.Unfortunately,the existence of inherent thermal residual stress and non-equilibrium microstructures in the additively manufactured components results in unsatisfactory mechanical properties.Herein,we propose a novel strengthening strategy,namely deep cryogenic treatment(DCT)followed by laser shock peening(LSP),to tailor the microstructures and enhance performances of an LPBF additively manufactured metastable HEA.The post-treatment effects of DCT+LSP on the LPBF-fabricated Fe50Mn30Co10Cr10HEA are evaluated in terms of microstructural modifications,residual stress,and microhardness redistribution,as well as tensile properties.Results indicate that a gradient heterogeneous structure is formed on the as-built sample surface,featuring gradient variations in grain size,martensitic phase content,and dislocation density,due to the grain refinement and martensitic phase transformation under DCT+LSP.The initial tensile residual stress on the surface is fully transformed into compressive stress,achieving a peak of-289 MPa,and the surface microhardness attains a maximum of 380.8 HV.The various strengthening mechanisms of gradient heterogeneous structures,as well as the multiple effects of heterodeformation-induced(HDI)hardening,transformation-induced plasticity(TRIP),and twinning-induced plasticity(TWIP),are responsible for achieving strength-ductility synergy.This work provides a practical pathway and valuable scientific insights for enhancing the mechanical behaviors of additively manufactured metastable HEAs via microstructural engineering. 展开更多
关键词 metastable high-entropy alloys laser shock peening deep cryogenic treatment gradient heterogeneous structure strengthening mechanisms
暂未订购 下载PDF
RCTUnet:a deep learning model for crop-residue-soil image segmentation and crop residue cover extraction 认领 引用 被引量：1
20
作者 Ting LI Yang LIU +10 位作者 Haikuan FENG Meiyan SHU Hao YANG Yuanyuan FU Xin XU Yinghao LIN Hongbo QIAO Wei GUO Xinming MA Lei SHI Jibo YUE 《Journal of Zhejiang University-SCIENCE B》 SCIE CAS CSCD 2026年第5期517-536,共20页
Accurate quantification of crop residue cover(CRC)is crucial for monitoring and evaluating conservation tillage practices,yet it poses a significant image segmentation challenge.The subtle visual distinctions between ... Accurate quantification of crop residue cover(CRC)is crucial for monitoring and evaluating conservation tillage practices,yet it poses a significant image segmentation challenge.The subtle visual distinctions between fragmented residue and soil,compounded by variable illumination and shadows in field imagery,often lead to poor segmentation performance.To overcome these limitations,we introduce RCTUnet,a novel deep learning architecture designed for robust crop-residue-soil segmentation and precise CRC estimation.RCTUnet’s architecture synergistically integrates three key components:(1)a ResNet50 backbone for deep,multi-scale feature extraction;(2)a convolutional block attention module(CBAM)to adaptively focus on salient residue features across both channel and spatial dimensions;and(3)a transformer-based global context fusion module(GCFM)to model long-range spatial dependencies,which is critical for interpreting heterogeneous residue patterns.We evaluated RCTUnet on a dataset of 1220 field-acquired images spanning four typical crop rotations.Experimental results show that,compared to traditional models:(1)RCTUnet achieves significantly higher crop-residue-soil segmentation accuracy than classic models including Unet,Unet++,DeepLabV3,segmentation network(SegNet),and fully convolutional network(FCN),with improvements of 3.24%,3.42%,4.88%,8.28%,and 6.05%in overall accuracy,respectively;(2)RCTUnet yields superior residue-soil segmentation performance,with increases in residue recall of 7.67%,7.37%,14.09%,27.05%,and 16.91%,respectively;(3)RCTUnet shows enhanced CRC estimation accuracy,achieving a root mean square error(RMSE)of 4.875,representing a 45.5%improvement over Unet(RMSE=8.941).These results demonstrate the efficacy of our hybrid approach,which combines deep hierarchical features,dual-domain attention,and global context modeling.RCTUnet provides a robust and reliable tool for automated CRC assessment,advancing the capabilities of in-field agricultural monitoring. 展开更多
关键词 Deep learning Crop residue cover Image segmentation Conservation tillage
暂未订购 下载PDF
已选0条
引用分析
参考文献
引证文献
 统计分析
检索结果
已选文献
上一页 1 2 … 250 下一页 到第 页
在线咨询 使用帮助 返回顶部 意见反馈