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
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
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
为解决复杂环境下人脸识别模型精度不足的问题,文章提出DynamicFaceNet模型。该模型基于FaceNet改进,通过融合动态注意力机制与人脸嵌入学习,具备更强大的多尺度感知、位置感知及特征通道优化能力。实验结果表明,在LFW(Labeled Faces in...为解决复杂环境下人脸识别模型精度不足的问题,文章提出DynamicFaceNet模型。该模型基于FaceNet改进,通过融合动态注意力机制与人脸嵌入学习,具备更强大的多尺度感知、位置感知及特征通道优化能力。实验结果表明,在LFW(Labeled Faces in the Wild)、CFP-FP(Cross-Pose Frontal-Profile)、AgeDB(Age-in-the-Wild Database)等标准数据集上,其识别精度显著优于对比模型,实际应用价值更高。展开更多
Face recognition has emerged as one of the most prominent applications of image analysis and under-standing,gaining considerable attention in recent years.This growing interest is driven by two key factors:its extensi...Face recognition has emerged as one of the most prominent applications of image analysis and under-standing,gaining considerable attention in recent years.This growing interest is driven by two key factors:its extensive applications in law enforcement and the commercial domain,and the rapid advancement of practical technologies.Despite the significant advancements,modern recognition algorithms still struggle in real-world conditions such as varying lighting conditions,occlusion,and diverse facial postures.In such scenarios,human perception is still well above the capabilities of present technology.Using the systematic mapping study,this paper presents an in-depth review of face detection algorithms and face recognition algorithms,presenting a detailed survey of advancements made between 2015 and 2024.We analyze key methodologies,highlighting their strengths and restrictions in the application context.Additionally,we examine various datasets used for face detection/recognition datasets focusing on the task-specific applications,size,diversity,and complexity.By analyzing these algorithms and datasets,this survey works as a valuable resource for researchers,identifying the research gap in the field of face detection and recognition and outlining potential directions for future research.展开更多
Face liveness detection is essential for securing biometric authentication systems against spoofing attacks,including printed photos,replay videos,and 3D masks.This study systematically evaluates pre-trained CNN model...Face liveness detection is essential for securing biometric authentication systems against spoofing attacks,including printed photos,replay videos,and 3D masks.This study systematically evaluates pre-trained CNN models—DenseNet201,VGG16,InceptionV3,ResNet50,VGG19,MobileNetV2,Xception,and InceptionResNetV2—leveraging transfer learning and fine-tuning to enhance liveness detection performance.The models were trained and tested on NUAA and Replay-Attack datasets,with cross-dataset generalization validated on SiW-MV2 to assess real-world adaptability.Performance was evaluated using accuracy,precision,recall,FAR,FRR,HTER,and specialized spoof detection metrics(APCER,NPCER,ACER).Fine-tuning significantly improved detection accuracy,with DenseNet201 achieving the highest performance(98.5%on NUAA,97.71%on Replay-Attack),while MobileNetV2 proved the most efficient model for real-time applications(latency:15 ms,memory usage:45 MB,energy consumption:30 mJ).A statistical significance analysis(paired t-tests,confidence intervals)validated these improvements.Cross-dataset experiments identified DenseNet201 and MobileNetV2 as the most generalizable architectures,with DenseNet201 achieving 86.4%accuracy on Replay-Attack when trained on NUAA,demonstrating robust feature extraction and adaptability.In contrast,ResNet50 showed lower generalization capabilities,struggling with dataset variability and complex spoofing attacks.These findings suggest that MobileNetV2 is well-suited for low-power applications,while DenseNet201 is ideal for high-security environments requiring superior accuracy.This research provides a framework for improving real-time face liveness detection,enhancing biometric security,and guiding future advancements in AI-driven anti-spoofing techniques.展开更多
As an important sustainable energy source,Li-ion batteries have been widely used in mobile phones,electric vehicles,large-scale energy storage and aerospace.However,due to the inevitable safety risks of traditional li...As an important sustainable energy source,Li-ion batteries have been widely used in mobile phones,electric vehicles,large-scale energy storage and aerospace.However,due to the inevitable safety risks of traditional liquid Li-ion batteries,the use of all-solid-state batteries to replace organic liquid electrolytes has become one of the most effective ways to solve safety problem.Solid-state electrolyte(SSE)is the core part of allsolid-state Li-ion battery,and ideal SSE has the characteristics of high ionic conductivity,wide enough electrochemical stability window,suitable mechanical strength and excellent chemical stability,the first among which is particularly an essential prerequisite.While,so far only a few SSEs exhibit the Li ionic conductivities higher than 10^(-4) S/cm at room temperature.展开更多
The construction of the tunnel face is a critical aspect of tunnel excavation,and its supporting equipment mainly includes drilling jumbos,arch installation trolleys,wet spraying manipulators,and anchor bolt trolleys....The construction of the tunnel face is a critical aspect of tunnel excavation,and its supporting equipment mainly includes drilling jumbos,arch installation trolleys,wet spraying manipulators,and anchor bolt trolleys.To address the issues of high construction costs and the need to replace equipment for different processes,this paper designs an economical and practical multi-functional integrated trolley based on engineering cases.This trolley is suitable for various construction methods such as full-face excavation and benching method,and integrates functions such as drilling and blasting holes,anchor bolt holes,advance grouting holes,pipe roof construction,charging,anchor bolt installation and grouting,and arch mesh installation.It reduces the number of operators,improves the tunnel working environment,lowers construction costs,and enhances construction efficiency.展开更多
All-solid-state lithium ion batteries(ASSLIBs)have attracted much attention due to their high safety and increased energy density,which have become a substitute to conventional liquid electrolyte batteries[1].The deve...All-solid-state lithium ion batteries(ASSLIBs)have attracted much attention due to their high safety and increased energy density,which have become a substitute to conventional liquid electrolyte batteries[1].The development of high-performance solid electrolyte is the key to the development of solid-state battery technology.Solid-state electrolyte(SSE)materials should have high ionic conductivity,poor electronic conductivity,wide electrochemical window,and low electrode and electrolyte interface resistance.展开更多
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(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.
文摘为解决复杂环境下人脸识别模型精度不足的问题,文章提出DynamicFaceNet模型。该模型基于FaceNet改进,通过融合动态注意力机制与人脸嵌入学习,具备更强大的多尺度感知、位置感知及特征通道优化能力。实验结果表明,在LFW(Labeled Faces in the Wild)、CFP-FP(Cross-Pose Frontal-Profile)、AgeDB(Age-in-the-Wild Database)等标准数据集上,其识别精度显著优于对比模型,实际应用价值更高。
文摘Face recognition has emerged as one of the most prominent applications of image analysis and under-standing,gaining considerable attention in recent years.This growing interest is driven by two key factors:its extensive applications in law enforcement and the commercial domain,and the rapid advancement of practical technologies.Despite the significant advancements,modern recognition algorithms still struggle in real-world conditions such as varying lighting conditions,occlusion,and diverse facial postures.In such scenarios,human perception is still well above the capabilities of present technology.Using the systematic mapping study,this paper presents an in-depth review of face detection algorithms and face recognition algorithms,presenting a detailed survey of advancements made between 2015 and 2024.We analyze key methodologies,highlighting their strengths and restrictions in the application context.Additionally,we examine various datasets used for face detection/recognition datasets focusing on the task-specific applications,size,diversity,and complexity.By analyzing these algorithms and datasets,this survey works as a valuable resource for researchers,identifying the research gap in the field of face detection and recognition and outlining potential directions for future research.
基金funded by Centre for Advanced Modelling and Geospatial Information Systems(CAMGIS),Faculty of Engineering and IT,University of Technology Sydney.Moreover,Ongoing Research Funding Program(ORF-2025-14)King Saud University,Riyadh,Saudi Arabia,under Project ORF-2025-。
文摘Face liveness detection is essential for securing biometric authentication systems against spoofing attacks,including printed photos,replay videos,and 3D masks.This study systematically evaluates pre-trained CNN models—DenseNet201,VGG16,InceptionV3,ResNet50,VGG19,MobileNetV2,Xception,and InceptionResNetV2—leveraging transfer learning and fine-tuning to enhance liveness detection performance.The models were trained and tested on NUAA and Replay-Attack datasets,with cross-dataset generalization validated on SiW-MV2 to assess real-world adaptability.Performance was evaluated using accuracy,precision,recall,FAR,FRR,HTER,and specialized spoof detection metrics(APCER,NPCER,ACER).Fine-tuning significantly improved detection accuracy,with DenseNet201 achieving the highest performance(98.5%on NUAA,97.71%on Replay-Attack),while MobileNetV2 proved the most efficient model for real-time applications(latency:15 ms,memory usage:45 MB,energy consumption:30 mJ).A statistical significance analysis(paired t-tests,confidence intervals)validated these improvements.Cross-dataset experiments identified DenseNet201 and MobileNetV2 as the most generalizable architectures,with DenseNet201 achieving 86.4%accuracy on Replay-Attack when trained on NUAA,demonstrating robust feature extraction and adaptability.In contrast,ResNet50 showed lower generalization capabilities,struggling with dataset variability and complex spoofing attacks.These findings suggest that MobileNetV2 is well-suited for low-power applications,while DenseNet201 is ideal for high-security environments requiring superior accuracy.This research provides a framework for improving real-time face liveness detection,enhancing biometric security,and guiding future advancements in AI-driven anti-spoofing techniques.
基金supported by the Natural Science Foundation of Shandong Province(No.ZR2020MB049)the Shandong Laboratory of Advanced Materials and Green Manufacturing at Yantai(No.AMGM2023A07)。
文摘As an important sustainable energy source,Li-ion batteries have been widely used in mobile phones,electric vehicles,large-scale energy storage and aerospace.However,due to the inevitable safety risks of traditional liquid Li-ion batteries,the use of all-solid-state batteries to replace organic liquid electrolytes has become one of the most effective ways to solve safety problem.Solid-state electrolyte(SSE)is the core part of allsolid-state Li-ion battery,and ideal SSE has the characteristics of high ionic conductivity,wide enough electrochemical stability window,suitable mechanical strength and excellent chemical stability,the first among which is particularly an essential prerequisite.While,so far only a few SSEs exhibit the Li ionic conductivities higher than 10^(-4) S/cm at room temperature.
文摘The construction of the tunnel face is a critical aspect of tunnel excavation,and its supporting equipment mainly includes drilling jumbos,arch installation trolleys,wet spraying manipulators,and anchor bolt trolleys.To address the issues of high construction costs and the need to replace equipment for different processes,this paper designs an economical and practical multi-functional integrated trolley based on engineering cases.This trolley is suitable for various construction methods such as full-face excavation and benching method,and integrates functions such as drilling and blasting holes,anchor bolt holes,advance grouting holes,pipe roof construction,charging,anchor bolt installation and grouting,and arch mesh installation.It reduces the number of operators,improves the tunnel working environment,lowers construction costs,and enhances construction efficiency.
文摘All-solid-state lithium ion batteries(ASSLIBs)have attracted much attention due to their high safety and increased energy density,which have become a substitute to conventional liquid electrolyte batteries[1].The development of high-performance solid electrolyte is the key to the development of solid-state battery technology.Solid-state electrolyte(SSE)materials should have high ionic conductivity,poor electronic conductivity,wide electrochemical window,and low electrode and electrolyte interface resistance.
