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
Satellite image segmentation plays a crucial role in remote sensing,supporting applications such as environmental monitoring,land use analysis,and disaster management.However,traditional segmentation methods often rel...Satellite image segmentation plays a crucial role in remote sensing,supporting applications such as environmental monitoring,land use analysis,and disaster management.However,traditional segmentation methods often rely on large amounts of labeled data,which are costly and time-consuming to obtain,especially in largescale or dynamic environments.To address this challenge,we propose the Semi-Supervised Multi-View Picture Fuzzy Clustering(SS-MPFC)algorithm,which improves segmentation accuracy and robustness,particularly in complex and uncertain remote sensing scenarios.SS-MPFC unifies three paradigms:semi-supervised learning,multi-view clustering,and picture fuzzy set theory.This integration allows the model to effectively utilize a small number of labeled samples,fuse complementary information from multiple data views,and handle the ambiguity and uncertainty inherent in satellite imagery.We design a novel objective function that jointly incorporates picture fuzzy membership functions across multiple views of the data,and embeds pairwise semi-supervised constraints(must-link and cannot-link)directly into the clustering process to enhance segmentation accuracy.Experiments conducted on several benchmark satellite datasets demonstrate that SS-MPFC significantly outperforms existing state-of-the-art methods in segmentation accuracy,noise robustness,and semantic interpretability.On the Augsburg dataset,SS-MPFC achieves a Purity of 0.8158 and an Accuracy of 0.6860,highlighting its outstanding robustness and efficiency.These results demonstrate that SSMPFC offers a scalable and effective solution for real-world satellite-based monitoring systems,particularly in scenarios where rapid annotation is infeasible,such as wildfire tracking,agricultural monitoring,and dynamic urban mapping.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
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
Locating multi-view faces in images with a complex background remains a challenging problem. In this paper, an integrated method for real-time multi-view face detection and pose estimation is presented. A simple-to-...Locating multi-view faces in images with a complex background remains a challenging problem. In this paper, an integrated method for real-time multi-view face detection and pose estimation is presented. A simple-to-complex and coarse-to-fine view-based detector architecture has been designed to detect multi- view faces and estimate their poses efficiently. Both the pose estimators and the view-based face/nonface detectors are trained by a cost-sensitive AdaBoost algorithm to improve the generalization ability. Experi- mental results show that the proposed multi-view face detector, which can be constructed easily, gives more robust face detection and pose estimation and has a faster real-time detection speed compared with other conventional methods.展开更多
Realistic personalized face animation mainly depends on a picture-perfect appearance and natural head rotation. This paper describes a face model for generation of novel view facial textures with various realistic exp...Realistic personalized face animation mainly depends on a picture-perfect appearance and natural head rotation. This paper describes a face model for generation of novel view facial textures with various realistic expressions and poses. The model is achieved from corpora of a talking person using machine learning techniques. In face modeling, the facial texture variation is expressed by a multi-view facial texture space model, with the facial shape variation represented by a compact 3-D point distribution model （PDM）. The facial texture space and the shape space are connected by bridging 2-D mesh structures. Levenberg-Marquardt optimization is employed for fine model fitting. Animation trajectory is trained for smooth and continuous image sequences. The test results show that this approach can achieve a vivid talking face sequence in various views. Moreover, the animation complexity is significantly reduced by the vector representation.展开更多
In many automatic face recognition systems, posture constraining is a key factor preventin g them from application. In thi5.paper, a series of strategles. will be described to achieve a system which enables face recog...In many automatic face recognition systems, posture constraining is a key factor preventin g them from application. In thi5.paper, a series of strategles. will be described to achieve a system which enables face recognition under varying pose. These approaches include the multi-view face modeling, the threshold image based face feature detection, the affine transformation based face posture normalization and the template matching based face idelltification. Combining all of these strategies, a face recognition system with the pose invariance is designed successfully. Using a 75MHZ Pentium PC and with a database of 75 individuals, 15 images for each person, and 225 test images with various postures, a very good recognition rate of 96.89% is obtained.展开更多
Rapidly and accurately assessing the geometric characteristics of coarse aggregate particles is crucial for ensuring pavement performance in highway engineering.This article introduces an innovative system for the thr...Rapidly and accurately assessing the geometric characteristics of coarse aggregate particles is crucial for ensuring pavement performance in highway engineering.This article introduces an innovative system for the three-dimensional(3D)surface reconstruction of coarse aggregate particles using occlusion-free multi-view imaging.The system captures synchronized images of particles in free fall,employing a matte sphere and a nonlinear optimization approach to estimate the camera projection matrices.A pre-trained segmentation model is utilized to eliminate the background of the images.The Shape from Silhouettes(SfS)algorithm is then applied to generate 3D voxel data,followed by the Marching Cubes algorithm to construct the 3D surface contour.Validation against standard parts and diverse coarse aggregate particles confirms the method's high accuracy,with an average measurement precision of 0.434 mm and a significant increase in scanning and reconstruction efficiency.展开更多
Multi-view clustering is a critical research area in computer science aimed at effectively extracting meaningful patterns from complex,high-dimensional data that single-view methods cannot capture.Traditional fuzzy cl...Multi-view clustering is a critical research area in computer science aimed at effectively extracting meaningful patterns from complex,high-dimensional data that single-view methods cannot capture.Traditional fuzzy clustering techniques,such as Fuzzy C-Means(FCM),face significant challenges in handling uncertainty and the dependencies between different views.To overcome these limitations,we introduce a new multi-view fuzzy clustering approach that integrates picture fuzzy sets with a dual-anchor graph method for multi-view data,aiming to enhance clustering accuracy and robustness,termed Multi-view Picture Fuzzy Clustering(MPFC).In particular,the picture fuzzy set theory extends the capability to represent uncertainty by modeling three membership levels:membership degrees,neutral degrees,and refusal degrees.This allows for a more flexible representation of uncertain and conflicting data than traditional fuzzy models.Meanwhile,dual-anchor graphs exploit the similarity relationships between data points and integrate information across views.This combination improves stability,scalability,and robustness when handling noisy and heterogeneous data.Experimental results on several benchmark datasets demonstrate significant improvements in clustering accuracy and efficiency,outperforming traditional methods.Specifically,the MPFC algorithm demonstrates outstanding clustering performance on a variety of datasets,attaining a Purity(PUR)score of 0.6440 and an Accuracy(ACC)score of 0.6213 for the 3 Sources dataset,underscoring its robustness and efficiency.The proposed approach significantly contributes to fields such as pattern recognition,multi-view relational data analysis,and large-scale clustering problems.Future work will focus on extending the method for semi-supervised multi-view clustering,aiming to enhance adaptability,scalability,and performance in real-world applications.展开更多
巨噬细胞样细胞(macrophage-like cells, MLC)指起源、功能与巨噬细胞类似的免疫细胞,包括小胶质细胞、玻璃体细胞及巨噬细胞。将en face OCT显示层面设置在视网膜表明即可观测到视网膜表明的MLC(epiretinal MLC, eMLC),随后利用Image ...巨噬细胞样细胞(macrophage-like cells, MLC)指起源、功能与巨噬细胞类似的免疫细胞,包括小胶质细胞、玻璃体细胞及巨噬细胞。将en face OCT显示层面设置在视网膜表明即可观测到视网膜表明的MLC(epiretinal MLC, eMLC),随后利用Image J软件即可对细胞进行提取和量化。研究表明,eMLC在炎症情况下均可出现细胞募集及活化现象,但在不同眼底病中各具特点。在糖尿病视网膜病变、视网膜静脉阻塞等视网膜缺血缺氧性疾病中,eMLC密度越高,黄斑水肿可能越严重。此外,eMLC密度更高的视网膜静脉阻塞患者抗VEGF疗效更差,视力预后不佳,提示基于en face OCT的eMLC不仅可用于评估视网膜炎症情况,而且还能充当提示疾病疗效及预后的标志物。在葡萄膜炎等免疫炎症性疾病中,en face OCT亦可观测到eMLC密度、形态等改变。白塞病葡萄膜炎患者视网膜血管渗漏程度与eMLC密度相关性强,故eMLC密度可充当无创评估视网膜血管渗漏程度的新指标。然而,目前提取和量化eMLC的方法及标准不统一,降低了各研究间的可比性。因此,亟需制定统一的操作规范和评估标准。此外eMLC所代表的具体细胞类型及功能仍需进一步探究。未来,研究者可以利用en face OCT对眼底炎症地进行无创评估。基于en face OCT的eMLC还能作为基础研究与临床研究之间的桥梁,为揭示疾病的致病机制提供重要参考。展开更多
The accurate prediction of drug absorption,distribution,metabolism,excretion,and toxicity(ADMET)properties represents a crucial step in early drug development for reducing failure risk.Current deep learning approaches...The accurate prediction of drug absorption,distribution,metabolism,excretion,and toxicity(ADMET)properties represents a crucial step in early drug development for reducing failure risk.Current deep learning approaches face challenges with data sparsity and information loss due to single-molecule representation limitations and isolated predictive tasks.This research proposes molecular properties prediction with parallel-view and collaborative learning(MolP-PC),a multi-view fusion and multi-task deep learning framework that integrates 1D molecular fingerprints(MFs),2D molecular graphs,and 3D geometric representations,incorporating an attention-gated fusion mechanism and multi-task adaptive learning strategy for precise ADMET property predictions.Experimental results demonstrate that MolP-PC achieves optimal performance in 27 of 54 tasks,with its multi-task learning(MTL)mechanism significantly enhancing predictive performance on small-scale datasets and surpassing single-task models in 41 of 54 tasks.Additional ablation studies and interpretability analyses confirm the significance of multi-view fusion in capturing multi-dimensional molecular information and enhancing model generalization.A case study examining the anticancer compound Oroxylin A demonstrates MolP-PC’s effective generalization in predicting key pharmacokinetic parameters such as half-life(T0.5)and clearance(CL),indicating its practical utility in drug modeling.However,the model exhibits a tendency to underestimate volume of distribution(VD),indicating potential for improvement in analyzing compounds with high tissue distribution.This study presents an efficient and interpretable approach for ADMET property prediction,establishing a novel framework for molecular optimization and risk assessment in drug development.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.
基金funded by the Research Project:THTETN.05/24-25,VietnamAcademy of Science and Technology.
文摘Satellite image segmentation plays a crucial role in remote sensing,supporting applications such as environmental monitoring,land use analysis,and disaster management.However,traditional segmentation methods often rely on large amounts of labeled data,which are costly and time-consuming to obtain,especially in largescale or dynamic environments.To address this challenge,we propose the Semi-Supervised Multi-View Picture Fuzzy Clustering(SS-MPFC)algorithm,which improves segmentation accuracy and robustness,particularly in complex and uncertain remote sensing scenarios.SS-MPFC unifies three paradigms:semi-supervised learning,multi-view clustering,and picture fuzzy set theory.This integration allows the model to effectively utilize a small number of labeled samples,fuse complementary information from multiple data views,and handle the ambiguity and uncertainty inherent in satellite imagery.We design a novel objective function that jointly incorporates picture fuzzy membership functions across multiple views of the data,and embeds pairwise semi-supervised constraints(must-link and cannot-link)directly into the clustering process to enhance segmentation accuracy.Experiments conducted on several benchmark satellite datasets demonstrate that SS-MPFC significantly outperforms existing state-of-the-art methods in segmentation accuracy,noise robustness,and semantic interpretability.On the Augsburg dataset,SS-MPFC achieves a Purity of 0.8158 and an Accuracy of 0.6860,highlighting its outstanding robustness and efficiency.These results demonstrate that SSMPFC offers a scalable and effective solution for real-world satellite-based monitoring systems,particularly in scenarios where rapid annotation is infeasible,such as wildfire tracking,agricultural monitoring,and dynamic urban mapping.
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.
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Locating multi-view faces in images with a complex background remains a challenging problem. In this paper, an integrated method for real-time multi-view face detection and pose estimation is presented. A simple-to-complex and coarse-to-fine view-based detector architecture has been designed to detect multi- view faces and estimate their poses efficiently. Both the pose estimators and the view-based face/nonface detectors are trained by a cost-sensitive AdaBoost algorithm to improve the generalization ability. Experi- mental results show that the proposed multi-view face detector, which can be constructed easily, gives more robust face detection and pose estimation and has a faster real-time detection speed compared with other conventional methods.
基金the National Natural Science Foundation of China (No. 60673189)
文摘Realistic personalized face animation mainly depends on a picture-perfect appearance and natural head rotation. This paper describes a face model for generation of novel view facial textures with various realistic expressions and poses. The model is achieved from corpora of a talking person using machine learning techniques. In face modeling, the facial texture variation is expressed by a multi-view facial texture space model, with the facial shape variation represented by a compact 3-D point distribution model （PDM）. The facial texture space and the shape space are connected by bridging 2-D mesh structures. Levenberg-Marquardt optimization is employed for fine model fitting. Animation trajectory is trained for smooth and continuous image sequences. The test results show that this approach can achieve a vivid talking face sequence in various views. Moreover, the animation complexity is significantly reduced by the vector representation.
文摘In many automatic face recognition systems, posture constraining is a key factor preventin g them from application. In thi5.paper, a series of strategles. will be described to achieve a system which enables face recognition under varying pose. These approaches include the multi-view face modeling, the threshold image based face feature detection, the affine transformation based face posture normalization and the template matching based face idelltification. Combining all of these strategies, a face recognition system with the pose invariance is designed successfully. Using a 75MHZ Pentium PC and with a database of 75 individuals, 15 images for each person, and 225 test images with various postures, a very good recognition rate of 96.89% is obtained.
基金Supported by the Key R&D Projects in Shaanxi Province(2022JBGS3-08)。
文摘Rapidly and accurately assessing the geometric characteristics of coarse aggregate particles is crucial for ensuring pavement performance in highway engineering.This article introduces an innovative system for the three-dimensional(3D)surface reconstruction of coarse aggregate particles using occlusion-free multi-view imaging.The system captures synchronized images of particles in free fall,employing a matte sphere and a nonlinear optimization approach to estimate the camera projection matrices.A pre-trained segmentation model is utilized to eliminate the background of the images.The Shape from Silhouettes(SfS)algorithm is then applied to generate 3D voxel data,followed by the Marching Cubes algorithm to construct the 3D surface contour.Validation against standard parts and diverse coarse aggregate particles confirms the method's high accuracy,with an average measurement precision of 0.434 mm and a significant increase in scanning and reconstruction efficiency.
基金funded by the Research Project:THTETN.05/24-25,VietnamAcademy of Science and Technology.
文摘Multi-view clustering is a critical research area in computer science aimed at effectively extracting meaningful patterns from complex,high-dimensional data that single-view methods cannot capture.Traditional fuzzy clustering techniques,such as Fuzzy C-Means(FCM),face significant challenges in handling uncertainty and the dependencies between different views.To overcome these limitations,we introduce a new multi-view fuzzy clustering approach that integrates picture fuzzy sets with a dual-anchor graph method for multi-view data,aiming to enhance clustering accuracy and robustness,termed Multi-view Picture Fuzzy Clustering(MPFC).In particular,the picture fuzzy set theory extends the capability to represent uncertainty by modeling three membership levels:membership degrees,neutral degrees,and refusal degrees.This allows for a more flexible representation of uncertain and conflicting data than traditional fuzzy models.Meanwhile,dual-anchor graphs exploit the similarity relationships between data points and integrate information across views.This combination improves stability,scalability,and robustness when handling noisy and heterogeneous data.Experimental results on several benchmark datasets demonstrate significant improvements in clustering accuracy and efficiency,outperforming traditional methods.Specifically,the MPFC algorithm demonstrates outstanding clustering performance on a variety of datasets,attaining a Purity(PUR)score of 0.6440 and an Accuracy(ACC)score of 0.6213 for the 3 Sources dataset,underscoring its robustness and efficiency.The proposed approach significantly contributes to fields such as pattern recognition,multi-view relational data analysis,and large-scale clustering problems.Future work will focus on extending the method for semi-supervised multi-view clustering,aiming to enhance adaptability,scalability,and performance in real-world applications.
文摘巨噬细胞样细胞(macrophage-like cells, MLC)指起源、功能与巨噬细胞类似的免疫细胞,包括小胶质细胞、玻璃体细胞及巨噬细胞。将en face OCT显示层面设置在视网膜表明即可观测到视网膜表明的MLC(epiretinal MLC, eMLC),随后利用Image J软件即可对细胞进行提取和量化。研究表明,eMLC在炎症情况下均可出现细胞募集及活化现象,但在不同眼底病中各具特点。在糖尿病视网膜病变、视网膜静脉阻塞等视网膜缺血缺氧性疾病中,eMLC密度越高,黄斑水肿可能越严重。此外,eMLC密度更高的视网膜静脉阻塞患者抗VEGF疗效更差,视力预后不佳,提示基于en face OCT的eMLC不仅可用于评估视网膜炎症情况,而且还能充当提示疾病疗效及预后的标志物。在葡萄膜炎等免疫炎症性疾病中,en face OCT亦可观测到eMLC密度、形态等改变。白塞病葡萄膜炎患者视网膜血管渗漏程度与eMLC密度相关性强,故eMLC密度可充当无创评估视网膜血管渗漏程度的新指标。然而,目前提取和量化eMLC的方法及标准不统一,降低了各研究间的可比性。因此,亟需制定统一的操作规范和评估标准。此外eMLC所代表的具体细胞类型及功能仍需进一步探究。未来,研究者可以利用en face OCT对眼底炎症地进行无创评估。基于en face OCT的eMLC还能作为基础研究与临床研究之间的桥梁,为揭示疾病的致病机制提供重要参考。
基金supported by the research on key technologies for monitoring and identifying drug abuse of anesthetic drugs and psychotropic drugs,and intervention for addiction(No.2023YFC3304200)the program of a study on the diagnosis of addiction to synthetic cannabinoids and methods of assessing the risk of abuse(No.2022YFC3300905)+1 种基金the program of Ab initio design and generation of AI models for small molecule ligands based on target structures(No.2022PE0AC03)ZHIJIANG LAB.
文摘The accurate prediction of drug absorption,distribution,metabolism,excretion,and toxicity(ADMET)properties represents a crucial step in early drug development for reducing failure risk.Current deep learning approaches face challenges with data sparsity and information loss due to single-molecule representation limitations and isolated predictive tasks.This research proposes molecular properties prediction with parallel-view and collaborative learning(MolP-PC),a multi-view fusion and multi-task deep learning framework that integrates 1D molecular fingerprints(MFs),2D molecular graphs,and 3D geometric representations,incorporating an attention-gated fusion mechanism and multi-task adaptive learning strategy for precise ADMET property predictions.Experimental results demonstrate that MolP-PC achieves optimal performance in 27 of 54 tasks,with its multi-task learning(MTL)mechanism significantly enhancing predictive performance on small-scale datasets and surpassing single-task models in 41 of 54 tasks.Additional ablation studies and interpretability analyses confirm the significance of multi-view fusion in capturing multi-dimensional molecular information and enhancing model generalization.A case study examining the anticancer compound Oroxylin A demonstrates MolP-PC’s effective generalization in predicting key pharmacokinetic parameters such as half-life(T0.5)and clearance(CL),indicating its practical utility in drug modeling.However,the model exhibits a tendency to underestimate volume of distribution(VD),indicating potential for improvement in analyzing compounds with high tissue distribution.This study presents an efficient and interpretable approach for ADMET property prediction,establishing a novel framework for molecular optimization and risk assessment in drug development.
