Accurate and rapid recognition of weathering degree(WD)and groundwater condition(GC)is essential for evaluating rock mass quality and conducting stability analyses in underground engineering.Conventional WD and GC rec...Accurate and rapid recognition of weathering degree(WD)and groundwater condition(GC)is essential for evaluating rock mass quality and conducting stability analyses in underground engineering.Conventional WD and GC recognition methods often rely on subjective evaluation by field experts,supplemented by field sampling and laboratory testing.These methods are frequently complex and timeconsuming,making it challenging to meet the rapidly evolving demands of underground engineering.Therefore,this study proposes a rock non-geometric parameter classification network(RNPC-net)to rapidly achieve the recognition and mapping ofWD and GC of tunnel faces.The hybrid feature extraction module(HFEM)in RNPC-net can fully extract,fuse,and utilize multi-scale features of images,enhancing the network's classification performance.Moreover,the designed adaptive weighting auxiliary classifier(AC)helps the network learn features more efficiently.Experimental results show that RNPC-net achieved classification accuracies of 0.8756 and 0.8710 for WD and GC,respectively,representing an improvement of approximately 2%e10%compared to other methods.Both quantitative and qualitative experiments confirm the effectiveness and superiority of RNPC-net.Furthermore,for WD and GC mapping,RNPC-net outperformed other methods by achieving the highest mean intersection over union(mIOU)across most tunnel faces.The mapping results closely align with measurements provided by field experts.The application of WD and GC mapping results to the rock mass rating(RMR)system achieved a transition from conventional qualitative to quantitative evaluation.This advancement enables more accurate and reliable rock mass quality evaluations,particularly under critical conditions of RMR.展开更多
In deep coal mining,skip mining techniques are increasingly adopted,yet their discontinuous extraction sequences and unique coal pillar support mechanisms create complex overburden failure patterns.This complexity giv...In deep coal mining,skip mining techniques are increasingly adopted,yet their discontinuous extraction sequences and unique coal pillar support mechanisms create complex overburden failure patterns.This complexity gives rise to severe multi-source water hazards,including persistent threats from bed-separation water,goaf water accumulation,and structural water ingress.The intricate hydro-geological conditions,characterized by variable resistivity and significant electromagnetic interference,often render single geophysical detection methods inadequate,leading to interpretive ambiguities and potential oversight of critical risks.To address these challenges,this study innovatively proposes and demonstrates an integrated detection methodology that synergistically combines the Audio Frequency Electric Penetration(AFEP)method and the Radio Wave Penetration(RWP)method.The core innovation of this research is the design of a coordinated observation system meticulously tailored to the spatial distribution of coal pillars.Beyond data acquisition,a systematic,graded classification framework was established for the comprehensive analysis and fusion of the dual-method results.Crucially,these classification outcomes directly inform the formulation of targeted and tiered governance recommendations,translating detection data into actionable mitigation strategies.Practical application at the 22213 face yielded highly positive results.The integrated approach successfully delineated the spatial distribution of water-bearing anomalies and their connecting channels with a clarity unattainable by either method alone.This not only significantly enhanced the accuracy and reliability of the hydrological threat assessment but also provided a robust scientific foundation for implementing effective water hazard prevention and control measures,thereby ensuring the safe and efficient extraction of the skip mining face.展开更多
Nitrogen doping has significant effects on the photocatalytic performance of ceria(CeO_(2)),and the possible synergistic effect with the inevitably introduced abundant oxygen vacancies(OVs)is of great significance for...Nitrogen doping has significant effects on the photocatalytic performance of ceria(CeO_(2)),and the possible synergistic effect with the inevitably introduced abundant oxygen vacancies(OVs)is of great significance for further investigation,and the specifically exposed crystal faces of CeO_(2)may have an impact on the performance of nitrogen doped CeO_(2).Herein,nitrogen-doped CeO_(2)with different morphologies and exposed crystal faces was prepared,and its performances in the photocatalytic degradation of tetracycline(TC)or hydrogen production via water splitting were evaluated.Density functional theory(DFT)was used to simulate the band structures,density of states,and oxygen defect properties of different CeO_(2)structures.It was found that nitrogen doping and OVs synergistically promoted the catalytic activity of nitrogen-doped CeO_(2).In addition,the exposed crystal faces of CeO_(2)have significant effects on the introduction of nitrogen and the ease of OV generation,as well as the synergistic effect of nitrogen doping with OVs.Among them,the rod-like nitrogen-doped CeO_(2)with exposed(110)face(R-CeO_(2)-NH_(3))showed a photocatalytic degradation ratio of 73.59%for TC and hydrogen production of 156.89μmol/g,outperforming other prepared photocatalysts.展开更多
Detecting faces under occlusion remains a significant challenge in computer vision due to variations caused by masks,sunglasses,and other obstructions.Addressing this issue is crucial for applications such as surveill...Detecting faces under occlusion remains a significant challenge in computer vision due to variations caused by masks,sunglasses,and other obstructions.Addressing this issue is crucial for applications such as surveillance,biometric authentication,and human-computer interaction.This paper provides a comprehensive review of face detection techniques developed to handle occluded faces.Studies are categorized into four main approaches:feature-based,machine learning-based,deep learning-based,and hybrid methods.We analyzed state-of-the-art studies within each category,examining their methodologies,strengths,and limitations based on widely used benchmark datasets,highlighting their adaptability to partial and severe occlusions.The review also identifies key challenges,including dataset diversity,model generalization,and computational efficiency.Our findings reveal that deep learning methods dominate recent studies,benefiting from their ability to extract hierarchical features and handle complex occlusion patterns.More recently,researchers have increasingly explored Transformer-based architectures,such as Vision Transformer(ViT)and Swin Transformer,to further improve detection robustness under challenging occlusion scenarios.In addition,hybrid approaches,which aim to combine traditional andmodern techniques,are emerging as a promising direction for improving robustness.This review provides valuable insights for researchers aiming to develop more robust face detection systems and for practitioners seeking to deploy reliable solutions in real-world,occlusionprone environments.Further improvements and the proposal of broader datasets are required to developmore scalable,robust,and efficient models that can handle complex occlusions in real-world scenarios.展开更多
基金supported by the National Natural Science Foundation of China(Grant Nos.42077242 and 42171407)the Graduate Innovation Fund of Jilin University.
文摘Accurate and rapid recognition of weathering degree(WD)and groundwater condition(GC)is essential for evaluating rock mass quality and conducting stability analyses in underground engineering.Conventional WD and GC recognition methods often rely on subjective evaluation by field experts,supplemented by field sampling and laboratory testing.These methods are frequently complex and timeconsuming,making it challenging to meet the rapidly evolving demands of underground engineering.Therefore,this study proposes a rock non-geometric parameter classification network(RNPC-net)to rapidly achieve the recognition and mapping ofWD and GC of tunnel faces.The hybrid feature extraction module(HFEM)in RNPC-net can fully extract,fuse,and utilize multi-scale features of images,enhancing the network's classification performance.Moreover,the designed adaptive weighting auxiliary classifier(AC)helps the network learn features more efficiently.Experimental results show that RNPC-net achieved classification accuracies of 0.8756 and 0.8710 for WD and GC,respectively,representing an improvement of approximately 2%e10%compared to other methods.Both quantitative and qualitative experiments confirm the effectiveness and superiority of RNPC-net.Furthermore,for WD and GC mapping,RNPC-net outperformed other methods by achieving the highest mean intersection over union(mIOU)across most tunnel faces.The mapping results closely align with measurements provided by field experts.The application of WD and GC mapping results to the rock mass rating(RMR)system achieved a transition from conventional qualitative to quantitative evaluation.This advancement enables more accurate and reliable rock mass quality evaluations,particularly under critical conditions of RMR.
文摘In deep coal mining,skip mining techniques are increasingly adopted,yet their discontinuous extraction sequences and unique coal pillar support mechanisms create complex overburden failure patterns.This complexity gives rise to severe multi-source water hazards,including persistent threats from bed-separation water,goaf water accumulation,and structural water ingress.The intricate hydro-geological conditions,characterized by variable resistivity and significant electromagnetic interference,often render single geophysical detection methods inadequate,leading to interpretive ambiguities and potential oversight of critical risks.To address these challenges,this study innovatively proposes and demonstrates an integrated detection methodology that synergistically combines the Audio Frequency Electric Penetration(AFEP)method and the Radio Wave Penetration(RWP)method.The core innovation of this research is the design of a coordinated observation system meticulously tailored to the spatial distribution of coal pillars.Beyond data acquisition,a systematic,graded classification framework was established for the comprehensive analysis and fusion of the dual-method results.Crucially,these classification outcomes directly inform the formulation of targeted and tiered governance recommendations,translating detection data into actionable mitigation strategies.Practical application at the 22213 face yielded highly positive results.The integrated approach successfully delineated the spatial distribution of water-bearing anomalies and their connecting channels with a clarity unattainable by either method alone.This not only significantly enhanced the accuracy and reliability of the hydrological threat assessment but also provided a robust scientific foundation for implementing effective water hazard prevention and control measures,thereby ensuring the safe and efficient extraction of the skip mining face.
基金Project(52164025)supported by the National Natural Science Foundation of ChinaProject([2020]1Y219)supported by the Basic Research Program from the Science&Technology Department of Guizhou Province,China+2 种基金Project([2019]30)supported by the Training Project from Guizhou University,ChinaProject([2023]04)supported by the Guizhou University Innovation Talent Team Project,ChinaProject([2022]041)supported by the Natural Science Research Project of Guizhou Provincial Department of Education,China。
文摘Nitrogen doping has significant effects on the photocatalytic performance of ceria(CeO_(2)),and the possible synergistic effect with the inevitably introduced abundant oxygen vacancies(OVs)is of great significance for further investigation,and the specifically exposed crystal faces of CeO_(2)may have an impact on the performance of nitrogen doped CeO_(2).Herein,nitrogen-doped CeO_(2)with different morphologies and exposed crystal faces was prepared,and its performances in the photocatalytic degradation of tetracycline(TC)or hydrogen production via water splitting were evaluated.Density functional theory(DFT)was used to simulate the band structures,density of states,and oxygen defect properties of different CeO_(2)structures.It was found that nitrogen doping and OVs synergistically promoted the catalytic activity of nitrogen-doped CeO_(2).In addition,the exposed crystal faces of CeO_(2)have significant effects on the introduction of nitrogen and the ease of OV generation,as well as the synergistic effect of nitrogen doping with OVs.Among them,the rod-like nitrogen-doped CeO_(2)with exposed(110)face(R-CeO_(2)-NH_(3))showed a photocatalytic degradation ratio of 73.59%for TC and hydrogen production of 156.89μmol/g,outperforming other prepared photocatalysts.
基金funded by A’Sharqiyah University,Sultanate of Oman,under Research Project grant number(BFP/RGP/ICT/22/490).
文摘Detecting faces under occlusion remains a significant challenge in computer vision due to variations caused by masks,sunglasses,and other obstructions.Addressing this issue is crucial for applications such as surveillance,biometric authentication,and human-computer interaction.This paper provides a comprehensive review of face detection techniques developed to handle occluded faces.Studies are categorized into four main approaches:feature-based,machine learning-based,deep learning-based,and hybrid methods.We analyzed state-of-the-art studies within each category,examining their methodologies,strengths,and limitations based on widely used benchmark datasets,highlighting their adaptability to partial and severe occlusions.The review also identifies key challenges,including dataset diversity,model generalization,and computational efficiency.Our findings reveal that deep learning methods dominate recent studies,benefiting from their ability to extract hierarchical features and handle complex occlusion patterns.More recently,researchers have increasingly explored Transformer-based architectures,such as Vision Transformer(ViT)and Swin Transformer,to further improve detection robustness under challenging occlusion scenarios.In addition,hybrid approaches,which aim to combine traditional andmodern techniques,are emerging as a promising direction for improving robustness.This review provides valuable insights for researchers aiming to develop more robust face detection systems and for practitioners seeking to deploy reliable solutions in real-world,occlusionprone environments.Further improvements and the proposal of broader datasets are required to developmore scalable,robust,and efficient models that can handle complex occlusions in real-world scenarios.
