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
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
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
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
Objective Category-specific recognition and naming deficits have been observed in a variety of patient populations. However, the category-specific cortices for naming famous faces, animals and man-made objects remain ...Objective Category-specific recognition and naming deficits have been observed in a variety of patient populations. However, the category-specific cortices for naming famous faces, animals and man-made objects remain controversial. The present study aimed to study the specific areas involved in naming pictures of these 3 categories using functional magnetic resonance imaging. Methods Functional images were analyzed using statistical parametric mapping and the 3 different contrasts were evaluated using t statistics by comparing the naming tasks to their baselines.The contrast images were entered into a random-effects group level analysis.The results were reported in Montreal Neurological Institute co-ordinates,and anatomical regions were identified using an automated anatomical labeling method with XJview 8.Results Naming famous faces caused more activation in the bilateral head of the hippocampus and amygdala with significant left dominance. Bilateral activation of pars triangularis and pars opercularis in the naming of famous faces was also revealed. Naming animals evoked greater responses in the left supplementary motor area, while naming man-made objects evoked more in the left premotor area,left pars orbitalis and right supplementary motor area. The extent of bilateral fusiform gyri activation by naming man-made objects was much larger than that by naming of famous faces or animals.Even in the overlapping sites of activation,some differences among the categories were found for activation in the fusiform gyri.Conclusion The cortices involved in the naming process vary with the naming of famous faces,animals and man-made objects.This finding suggests that different categories of pictures should be used during intra-operative language mapping to generate a broader map of language function, in order to minimize the incidence of false-negative stimulation and permanent post-operative deficits.展开更多
The multi-principal element characteristic of high-entropy alloys has revolutionized the conventional alloy design concept of single-principal element,endowing them with excellent mechanical properties.However,owing t...The multi-principal element characteristic of high-entropy alloys has revolutionized the conventional alloy design concept of single-principal element,endowing them with excellent mechanical properties.However,owing to this multi-principal element nature,high-entropy alloys exhibit complex deformation behavior dominated by alternating and coupled deformation mechanisms.Therefore,elucidating these intricate deformation mechanisms remains a key challenge in current research.Neutron diffraction(ND)techniques offer distinct advantages over traditional microscopic methods for characterizing such complex deformation behavior.The strong penetration capability of neutrons enables in-situ,real-time,and non-destructive detection of structural evolution in most centimeter-level bulk samples under complex environments,and ND allows precise characterization of lattice site occupations for light elements,such as C and O,and neighboring elements.This review discussed the principles of ND,experiment procedures,and data analysis.Combining with recent advances in the research about face-centered cubic high-entropy alloy,typical examples of using ND to investigate the deformation behavior were summarized,ultimately revealing deformation mechanisms dominated by dislocations,stacking faults,twinning,and phase transformations.展开更多
Post-match interview is a medium for athletes to showcase their impressions.This paper focus on the discourse of a post-match interview by Chinese athletes in the sport of table tennis at the 2024 Paris Olympics using...Post-match interview is a medium for athletes to showcase their impressions.This paper focus on the discourse of a post-match interview by Chinese athletes in the sport of table tennis at the 2024 Paris Olympics using the face-saving theory as the main framework introduced by Brown and Levinson(1987).In addition,theoretical extensions(Gu,1990;Mao,1994;Gao,1996)are also used to explain conceptions of face in the Chinese context.This study adopts a qualitative case study approach to investigating how athletes construct and maintain their face.It specifically analyzes the positive face,negative face,and redressive strategies.The findings indicate that Chinese athletes commonly adopt strategies such as emphasizing collective honor,humor,and indirect expressions to address face issues.These strategies are related to the collectivist values that are embedded in Chinese culture.This study extends the application of face theory to the under-explored domain of sports discourse and offers insights for future studies in sports communication and intercultural pragmatics.展开更多
Recognizing frontal faces from non-frontal or profile images is a major problem due to pose changes,self-occlusions,and the complete loss of important structural and textural components,depressing recognition accuracy...Recognizing frontal faces from non-frontal or profile images is a major problem due to pose changes,self-occlusions,and the complete loss of important structural and textural components,depressing recognition accuracy and visual fidelity.This paper introduces a new deep generative framework,Modified Multi-Scale Fused CycleGAN(MMF-CycleGAN),for robust and photo-realistic profile-to-frontal face synthesis.The MMF-CycleGAN framework utilizes pre-processing and then the generator employs a Deep Dilated DenseNet encoder-based hierarchical feature extraction along with a transformer and decoder.The proposed Multi-Scale Fusion PatchGAN discriminator enforces consistency at multiple spatial resolutions,leading to sharper textures and improved global facial geometry.Also,GAN training stability and identity preservation are improved through the Ranger optimizer,which effectively balances adversarial,identity,and cycle-consistency losses.Experiments on three benchmark datasets show that MMFCycleGAN achieves accuracy of 0.9541,0.9455,and 0.9422,F1-scores of 0.9654,0.9641,and 0.9614,and AUC values of 0.9742,0.9714,and 0.9698,respectively,and the extreme-pose accuracy(yaw>60°)reaches 0.92.Despite its enhanced architecture,the framework maintains an efficient inference time of 0.042 s per image,making it suitable for real-time biometric authentication,surveillance,and security applications in unconstrained environments.展开更多
The airborne diffusion of saliva droplets during respiratory activities is one of the major factors in the spread of infections.During the COVID-19 pandemic,the use of protective face masks was essential to reduce the...The airborne diffusion of saliva droplets during respiratory activities is one of the major factors in the spread of infections.During the COVID-19 pandemic,the use of protective face masks was essential to reduce the risk of infection and spread of SARS-CoV-2.The face mask is able to significantly reduce the saliva droplet emission in front of the person.However,the use of masks also produces a particle leakage towards the back of the person,which could increase the infection risk of people behind the subject.Most of the experimental investigations applied invasive and/or complex experimental techniques to evaluate the face masks leakage.The primary objective of this study is to develop a novel,non-invasive methodology for assessing rearward droplet emission associated with the use of protective face masks.Specifically,a thermographic analysis of the thermal footprint released during ordinary and extraordinary respiratory activities is presented,evaluating the maximum temperature,the detection time,and the spread area of the thermal footprint.Both surgical and FFP2 face masks were tested.Two different subjects were involved in the experimentation to evaluate the influence of face conformation.The findings indicate that the area influenced by droplet dispersion is larger when wearing a surgical mask compared to an FFP2 mask,with the highest recorded temperatures observed for the surgical mask.The thermal footprint was found to be strongly dependent on individual facial morphology and mask fit.Notably,the FFP2 mask also altered the position of the thermal footprint,which was primarily confined to the region near the neck.展开更多
This study develops a contact performance-driven method for skiving face gear drives using a single cutter,eliminating the traditional need for separate cutters to reduce production costs and time.First,the mathematic...This study develops a contact performance-driven method for skiving face gear drives using a single cutter,eliminating the traditional need for separate cutters to reduce production costs and time.First,the mathematical models of the tooth flanks for the face gear drives are established based on the gear skiving processes.Then,load tooth contact analysis(LTCA)model is established to calculate the contact performance data.Next,a two-stage optimization model is employed to determine the optimal parameters of the cutting edge with improved contact performances.The effectiveness of this method is validated through simulations and rolling tests.Compared with the traditional method,the proposed method can machine both the face gear and its mating pinion with a single cutter.Simulation results show that the proposed method avoids tooth surface edge contact,with the maximum tooth surface contact stress reduced by 31.7%,the contact ratio decreases by 21.5%,and the transmission error increases by 22.3%.Rolling tests verify the consistency of tooth surface contact patterns between simulations and experiments.The proposed method provides a reference for the cutting edge design of skiving cutters for face gear pairs.展开更多
Securing restricted zones such as airports,research facilities,and military bases requires robust and reliable access control mechanisms to prevent unauthorized entry and safeguard critical assets.Face recognition has...Securing restricted zones such as airports,research facilities,and military bases requires robust and reliable access control mechanisms to prevent unauthorized entry and safeguard critical assets.Face recognition has emerged as a key biometric approach for this purpose;however,existing systems are often sensitive to variations in illumination,occlusion,and pose,which degrade their performance in real-world conditions.To address these challenges,this paper proposes a novel hybrid face recognition method that integrates complementary feature descriptors such as Fuzzy-Gabor 2D Fisher Linear Discriminant(FG-2DFLD),Generalized 2D Linear Discriminant Analysis(G2DLDA),andModular-Local Binary Patterns(Modular-LBP)with Dempster–Shafer(DS)evidence theory for decision fusion.The proposed framework extracts global,structural,and local texture features,models them using Gaussian distributions to estimate belief factors,and fuses these belief factors through DS theory to explicitly handle uncertainty and conflict among descriptors.Experimental validation was performed on two widely used benchmark datasets,ORL and Cropped Yale B,achieving recognition rates exceeding 98%,which outperform traditional methods as well as recent deep learning-based approaches.Furthermore,the method demonstrated strong robustness under noisy conditions,maintaining accuracies above 96%with salt-and-pepper and Gaussian noise.These results highlight the effectiveness of the proposed integration strategy in enhancing accuracy,reliability,and resilience compared to single-descriptor and conventional fusion methods.Given its high performance and efficiency,the proposed method shows strong potential for deployment in real-world restricted-zone applications such as smart parking systems,secure facility access,and other high-security domains.展开更多
While neural radiance field(NeRF)methods have shown promising results in generating talking faces,existing studies primarily focus on the correlation between avatars and driving sources.However,these studies often ove...While neural radiance field(NeRF)methods have shown promising results in generating talking faces,existing studies primarily focus on the correlation between avatars and driving sources.However,these studies often overlook emotion modeling,resulting in the generation of emotionless or unnatural facial animations.In response,this paper introduces an audio-driven and emotion-editing dynamic NeRF(AED-NeRF)approach,designed for the real-time generation of expressive talking face avatars driven by audio inputs.Specifically,we integrate audio features into a grid-based NeRF to compensate for the lack of a deformation channel,successfully capturing lip dynamics and enabling end-to-end generation from audio-driven sources to talking face avatars.Emotion labels,comprising emotion categories and intensity levels,guide the proposed NeRF framework to implicitly model visual emotions,allowing for explicit control and editing of facial expressions.Extensive qualitative and quantitative experiments validate the effectiveness and advantages of our proposed method,demonstrating its ability to achieve real-time,photo-realistic talking face avatar generation across different audio and emotion scenarios.展开更多
Shield tunneling in saturated ground poses challenges due to the potential risk of ground collapse resulting from seepage force and inadequate support pressure.This study employed a laboratory model test and a theoret...Shield tunneling in saturated ground poses challenges due to the potential risk of ground collapse resulting from seepage force and inadequate support pressure.This study employed a laboratory model test and a theoretical validation to elucidate the mechanisms of face failure and subsequent ground collapse in saturated ground during slurry pressure-balanced shield(SPBS)tunneling operations.A slurry circulation system was developed to ensure steady shield tunneling and to replicate the phenomena of ground collapse.Investigations into shield tunneling parameters and ground responses,including soil pressure,pore water pressure,and surface subsidence,were conducted to understand the mechanisms of face failure and subsequent ground collapse.The theoretical solution for the critical collapse pressure of the tunnel face,based on the rotational failure mechanism,was validated through the comparison with the experimentally determined critical collapse pressure.The results indicate that:(1)appropriate adjustments of tunneling parameters are crucial for promoting filtercake formation,maintaining chamber pressure,and minimizing ground subsidence;(2)chamber pressure,soil pressure,pore water pressure,and ground subsidence are closely correlated with shield tunneling parameters and the formation of filter cake;(3)ground collapse follows a continuous failure mode due to the destruction of filtercake and the decrease in chamber pressure;(4)the soil pressure at the cutterhead is more sensitive to disturbances from shield tunneling than chamber pressure;and(5)experimentally determined critical collapse pressures is consistent with the theoretical solution of limit analysis.展开更多
Many hydropower projects have been constructed in Southwest China with the strategic goal of achieving carbon neutrality.Most of these hydropower projects utilize concrete face rockfilldams(CFRDs)built on a deep overb...Many hydropower projects have been constructed in Southwest China with the strategic goal of achieving carbon neutrality.Most of these hydropower projects utilize concrete face rockfilldams(CFRDs)built on a deep overburden layer.The deep overburden layer causes uneven settlement between the overburden layer and the dam,which poses a serious threat to the safety of both the construction and operation of the dam.In this study,microseismic(MS)monitoring technology was employed for the firsttime in the fieldof dam fillingengineering,allowing for the real-time monitoring of microfracture in the bedrock during dam construction.The time-frequency analysis method was used to summarize the MS waveform characteristics induced by dam filling.The fracture mechanism of bedrock was revealed,and the relationships among slope deformation,dam settlement,and MS activity were analyzed.The following research results have been obtained.The MS signal induced by dam fillinghas low energy and amplitude,short duration,and high frequency.The fracture of the bedrock was mainly shear failure.MS monitoring can predict deformation during blasting excavation and capture the large settlement that may occur during dam fillingin advance.Research findingshave demonstrated the significantapplication value of MS monitoring technology in predicting the risk of dam settlement and provide a reference for similar projects.展开更多
In order to investigate the stability problem of shield tunnel faces subjected to seismic loading,the pseudodynamic method(P-DM)was employed to analyze the seismic effect on the face.Two kinds of failure mechanisms of...In order to investigate the stability problem of shield tunnel faces subjected to seismic loading,the pseudodynamic method(P-DM)was employed to analyze the seismic effect on the face.Two kinds of failure mechanisms of active collapse and passive extrusion were considered,and a seismic reliability model of shield tunnel faces under multifailure mode was established.The limit analysis method and the response surface method(RSM)were used together to solve the reliability of shield tunnel faces subjected to seismic action.Comparing with existing results,the results of this work are effective.The effects of seismic load and rock mass strength on the collapse pressure,extrusion pressure and reliability index were discussed,and reasonable ranges of support pressure of shield tunnel faces under seismic action were presented.This method can provide a new idea for solving the shield thrust parameter under the seismic loading.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.
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.
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.
基金supported bythe Foundation of Science and Technology Program of Guangdong Province,China(No.2008A030201021)the Natural Science Foundation of Guangdong Province,China(No.10151001002000010)
文摘Objective Category-specific recognition and naming deficits have been observed in a variety of patient populations. However, the category-specific cortices for naming famous faces, animals and man-made objects remain controversial. The present study aimed to study the specific areas involved in naming pictures of these 3 categories using functional magnetic resonance imaging. Methods Functional images were analyzed using statistical parametric mapping and the 3 different contrasts were evaluated using t statistics by comparing the naming tasks to their baselines.The contrast images were entered into a random-effects group level analysis.The results were reported in Montreal Neurological Institute co-ordinates,and anatomical regions were identified using an automated anatomical labeling method with XJview 8.Results Naming famous faces caused more activation in the bilateral head of the hippocampus and amygdala with significant left dominance. Bilateral activation of pars triangularis and pars opercularis in the naming of famous faces was also revealed. Naming animals evoked greater responses in the left supplementary motor area, while naming man-made objects evoked more in the left premotor area,left pars orbitalis and right supplementary motor area. The extent of bilateral fusiform gyri activation by naming man-made objects was much larger than that by naming of famous faces or animals.Even in the overlapping sites of activation,some differences among the categories were found for activation in the fusiform gyri.Conclusion The cortices involved in the naming process vary with the naming of famous faces,animals and man-made objects.This finding suggests that different categories of pictures should be used during intra-operative language mapping to generate a broader map of language function, in order to minimize the incidence of false-negative stimulation and permanent post-operative deficits.
基金National Key R&D Program of China(2023YFB3711904,2022YFA1603801)National Natural Science Foundation of China(12404230,52471181,52301213,52130108,52471005)+2 种基金National Nature Science Foundation of Zhejiang Province(LY23E010002)Open Fund of the China Spallation Neutron Source,Songshan Lake Science City(KFKT2023B11)Guangdong Basic and Applied Basic Research Foundation(2022A1515110805,2024A1515010878)。
文摘The multi-principal element characteristic of high-entropy alloys has revolutionized the conventional alloy design concept of single-principal element,endowing them with excellent mechanical properties.However,owing to this multi-principal element nature,high-entropy alloys exhibit complex deformation behavior dominated by alternating and coupled deformation mechanisms.Therefore,elucidating these intricate deformation mechanisms remains a key challenge in current research.Neutron diffraction(ND)techniques offer distinct advantages over traditional microscopic methods for characterizing such complex deformation behavior.The strong penetration capability of neutrons enables in-situ,real-time,and non-destructive detection of structural evolution in most centimeter-level bulk samples under complex environments,and ND allows precise characterization of lattice site occupations for light elements,such as C and O,and neighboring elements.This review discussed the principles of ND,experiment procedures,and data analysis.Combining with recent advances in the research about face-centered cubic high-entropy alloy,typical examples of using ND to investigate the deformation behavior were summarized,ultimately revealing deformation mechanisms dominated by dislocations,stacking faults,twinning,and phase transformations.
文摘Post-match interview is a medium for athletes to showcase their impressions.This paper focus on the discourse of a post-match interview by Chinese athletes in the sport of table tennis at the 2024 Paris Olympics using the face-saving theory as the main framework introduced by Brown and Levinson(1987).In addition,theoretical extensions(Gu,1990;Mao,1994;Gao,1996)are also used to explain conceptions of face in the Chinese context.This study adopts a qualitative case study approach to investigating how athletes construct and maintain their face.It specifically analyzes the positive face,negative face,and redressive strategies.The findings indicate that Chinese athletes commonly adopt strategies such as emphasizing collective honor,humor,and indirect expressions to address face issues.These strategies are related to the collectivist values that are embedded in Chinese culture.This study extends the application of face theory to the under-explored domain of sports discourse and offers insights for future studies in sports communication and intercultural pragmatics.
文摘Recognizing frontal faces from non-frontal or profile images is a major problem due to pose changes,self-occlusions,and the complete loss of important structural and textural components,depressing recognition accuracy and visual fidelity.This paper introduces a new deep generative framework,Modified Multi-Scale Fused CycleGAN(MMF-CycleGAN),for robust and photo-realistic profile-to-frontal face synthesis.The MMF-CycleGAN framework utilizes pre-processing and then the generator employs a Deep Dilated DenseNet encoder-based hierarchical feature extraction along with a transformer and decoder.The proposed Multi-Scale Fusion PatchGAN discriminator enforces consistency at multiple spatial resolutions,leading to sharper textures and improved global facial geometry.Also,GAN training stability and identity preservation are improved through the Ranger optimizer,which effectively balances adversarial,identity,and cycle-consistency losses.Experiments on three benchmark datasets show that MMFCycleGAN achieves accuracy of 0.9541,0.9455,and 0.9422,F1-scores of 0.9654,0.9641,and 0.9614,and AUC values of 0.9742,0.9714,and 0.9698,respectively,and the extreme-pose accuracy(yaw>60°)reaches 0.92.Despite its enhanced architecture,the framework maintains an efficient inference time of 0.042 s per image,making it suitable for real-time biometric authentication,surveillance,and security applications in unconstrained environments.
文摘The airborne diffusion of saliva droplets during respiratory activities is one of the major factors in the spread of infections.During the COVID-19 pandemic,the use of protective face masks was essential to reduce the risk of infection and spread of SARS-CoV-2.The face mask is able to significantly reduce the saliva droplet emission in front of the person.However,the use of masks also produces a particle leakage towards the back of the person,which could increase the infection risk of people behind the subject.Most of the experimental investigations applied invasive and/or complex experimental techniques to evaluate the face masks leakage.The primary objective of this study is to develop a novel,non-invasive methodology for assessing rearward droplet emission associated with the use of protective face masks.Specifically,a thermographic analysis of the thermal footprint released during ordinary and extraordinary respiratory activities is presented,evaluating the maximum temperature,the detection time,and the spread area of the thermal footprint.Both surgical and FFP2 face masks were tested.Two different subjects were involved in the experimentation to evaluate the influence of face conformation.The findings indicate that the area influenced by droplet dispersion is larger when wearing a surgical mask compared to an FFP2 mask,with the highest recorded temperatures observed for the surgical mask.The thermal footprint was found to be strongly dependent on individual facial morphology and mask fit.Notably,the FFP2 mask also altered the position of the thermal footprint,which was primarily confined to the region near the neck.
基金Project(2024YFB3410402)supported by the National Key R&D Program of ChinaProject(52075558)supported by the National Natural Science Foundation of China+2 种基金Project(2021RC3012)supported by the Science and Technology Innovation Program of Hunan Province,ChinaProject(2023CXQD050)supported by the Central South University Innovation-Driven Research Program,ChinaProject(CX20230255)supported by the Fundamental Research Funds for the Central Universities,China。
文摘This study develops a contact performance-driven method for skiving face gear drives using a single cutter,eliminating the traditional need for separate cutters to reduce production costs and time.First,the mathematical models of the tooth flanks for the face gear drives are established based on the gear skiving processes.Then,load tooth contact analysis(LTCA)model is established to calculate the contact performance data.Next,a two-stage optimization model is employed to determine the optimal parameters of the cutting edge with improved contact performances.The effectiveness of this method is validated through simulations and rolling tests.Compared with the traditional method,the proposed method can machine both the face gear and its mating pinion with a single cutter.Simulation results show that the proposed method avoids tooth surface edge contact,with the maximum tooth surface contact stress reduced by 31.7%,the contact ratio decreases by 21.5%,and the transmission error increases by 22.3%.Rolling tests verify the consistency of tooth surface contact patterns between simulations and experiments.The proposed method provides a reference for the cutting edge design of skiving cutters for face gear pairs.
文摘Securing restricted zones such as airports,research facilities,and military bases requires robust and reliable access control mechanisms to prevent unauthorized entry and safeguard critical assets.Face recognition has emerged as a key biometric approach for this purpose;however,existing systems are often sensitive to variations in illumination,occlusion,and pose,which degrade their performance in real-world conditions.To address these challenges,this paper proposes a novel hybrid face recognition method that integrates complementary feature descriptors such as Fuzzy-Gabor 2D Fisher Linear Discriminant(FG-2DFLD),Generalized 2D Linear Discriminant Analysis(G2DLDA),andModular-Local Binary Patterns(Modular-LBP)with Dempster–Shafer(DS)evidence theory for decision fusion.The proposed framework extracts global,structural,and local texture features,models them using Gaussian distributions to estimate belief factors,and fuses these belief factors through DS theory to explicitly handle uncertainty and conflict among descriptors.Experimental validation was performed on two widely used benchmark datasets,ORL and Cropped Yale B,achieving recognition rates exceeding 98%,which outperform traditional methods as well as recent deep learning-based approaches.Furthermore,the method demonstrated strong robustness under noisy conditions,maintaining accuracies above 96%with salt-and-pepper and Gaussian noise.These results highlight the effectiveness of the proposed integration strategy in enhancing accuracy,reliability,and resilience compared to single-descriptor and conventional fusion methods.Given its high performance and efficiency,the proposed method shows strong potential for deployment in real-world restricted-zone applications such as smart parking systems,secure facility access,and other high-security domains.
基金supported by ZTE Industry-University-Institute Cooperation Funds under Grant No.IA20230921015。
文摘While neural radiance field(NeRF)methods have shown promising results in generating talking faces,existing studies primarily focus on the correlation between avatars and driving sources.However,these studies often overlook emotion modeling,resulting in the generation of emotionless or unnatural facial animations.In response,this paper introduces an audio-driven and emotion-editing dynamic NeRF(AED-NeRF)approach,designed for the real-time generation of expressive talking face avatars driven by audio inputs.Specifically,we integrate audio features into a grid-based NeRF to compensate for the lack of a deformation channel,successfully capturing lip dynamics and enabling end-to-end generation from audio-driven sources to talking face avatars.Emotion labels,comprising emotion categories and intensity levels,guide the proposed NeRF framework to implicitly model visual emotions,allowing for explicit control and editing of facial expressions.Extensive qualitative and quantitative experiments validate the effectiveness and advantages of our proposed method,demonstrating its ability to achieve real-time,photo-realistic talking face avatar generation across different audio and emotion scenarios.
基金support of the National Natural Science Foundation of China(Grant Nos.52179116 and 51991392)the support of Key Deployment Projects of Chinese Academy of Sciences(Grant No.ZDRW-ZS-2021-3).
文摘Shield tunneling in saturated ground poses challenges due to the potential risk of ground collapse resulting from seepage force and inadequate support pressure.This study employed a laboratory model test and a theoretical validation to elucidate the mechanisms of face failure and subsequent ground collapse in saturated ground during slurry pressure-balanced shield(SPBS)tunneling operations.A slurry circulation system was developed to ensure steady shield tunneling and to replicate the phenomena of ground collapse.Investigations into shield tunneling parameters and ground responses,including soil pressure,pore water pressure,and surface subsidence,were conducted to understand the mechanisms of face failure and subsequent ground collapse.The theoretical solution for the critical collapse pressure of the tunnel face,based on the rotational failure mechanism,was validated through the comparison with the experimentally determined critical collapse pressure.The results indicate that:(1)appropriate adjustments of tunneling parameters are crucial for promoting filtercake formation,maintaining chamber pressure,and minimizing ground subsidence;(2)chamber pressure,soil pressure,pore water pressure,and ground subsidence are closely correlated with shield tunneling parameters and the formation of filter cake;(3)ground collapse follows a continuous failure mode due to the destruction of filtercake and the decrease in chamber pressure;(4)the soil pressure at the cutterhead is more sensitive to disturbances from shield tunneling than chamber pressure;and(5)experimentally determined critical collapse pressures is consistent with the theoretical solution of limit analysis.
基金support from the Joint Funds of the National Natural Science Foundation of China(Grant No.42177143)the National Natural Science Foundation of China(Grant No.U23A2060).
文摘Many hydropower projects have been constructed in Southwest China with the strategic goal of achieving carbon neutrality.Most of these hydropower projects utilize concrete face rockfilldams(CFRDs)built on a deep overburden layer.The deep overburden layer causes uneven settlement between the overburden layer and the dam,which poses a serious threat to the safety of both the construction and operation of the dam.In this study,microseismic(MS)monitoring technology was employed for the firsttime in the fieldof dam fillingengineering,allowing for the real-time monitoring of microfracture in the bedrock during dam construction.The time-frequency analysis method was used to summarize the MS waveform characteristics induced by dam filling.The fracture mechanism of bedrock was revealed,and the relationships among slope deformation,dam settlement,and MS activity were analyzed.The following research results have been obtained.The MS signal induced by dam fillinghas low energy and amplitude,short duration,and high frequency.The fracture of the bedrock was mainly shear failure.MS monitoring can predict deformation during blasting excavation and capture the large settlement that may occur during dam fillingin advance.Research findingshave demonstrated the significantapplication value of MS monitoring technology in predicting the risk of dam settlement and provide a reference for similar projects.
基金Projects(51804113,52074116)supported by the National Natural Science Foundation of ChinaProject(2020M682563)supported by the China Postdoctoral Science Foundation+1 种基金Project(19C0743)supported by the Scientific Research Foundation of Hunan Provincial Education Department,ChinaProject(E52076)supported by the Science Foundation of Hunan University of Science and Technology,China。
文摘In order to investigate the stability problem of shield tunnel faces subjected to seismic loading,the pseudodynamic method(P-DM)was employed to analyze the seismic effect on the face.Two kinds of failure mechanisms of active collapse and passive extrusion were considered,and a seismic reliability model of shield tunnel faces under multifailure mode was established.The limit analysis method and the response surface method(RSM)were used together to solve the reliability of shield tunnel faces subjected to seismic action.Comparing with existing results,the results of this work are effective.The effects of seismic load and rock mass strength on the collapse pressure,extrusion pressure and reliability index were discussed,and reasonable ranges of support pressure of shield tunnel faces under seismic action were presented.This method can provide a new idea for solving the shield thrust parameter under the seismic loading.
