Magnesium matrix composites(MMCs)combine exceptional low density,high specific strength,and stiffness,positioning them as critical materials for aerospace,automotive,and electronics industries.This review highlights r...Magnesium matrix composites(MMCs)combine exceptional low density,high specific strength,and stiffness,positioning them as critical materials for aerospace,automotive,and electronics industries.This review highlights recent progress in the fabrication of Ti-Mg composites and analyzes the mechanisms behind their enhanced mechanical properties.A key focus is the interfacial deformation incompatibility between Ti and Mg phases,which generates strain gradients and promotes the accumulation of geometrically necessary dislocations(GNDs)at the interface.This process not only improves strain hardening and ductility but also reveals the need for advanced micromechanical models to capture the plastic behavior of both phases.The review critically examines the impact of different Mg matrix types(AZ,AM,VW series)and the role of interfacial product morphology and size on bonding and overall performance.Furthermore,Ti reinforcement endows the composites with superior wear resistance and thermal conductivity,indicating broad application potential.展开更多
The early and precise identification of Alzheimer’s Disease(AD)continues to pose considerable clinical difficulty due to subtle structural alterations and overlapping symptoms across the disease phases.This study pre...The early and precise identification of Alzheimer’s Disease(AD)continues to pose considerable clinical difficulty due to subtle structural alterations and overlapping symptoms across the disease phases.This study presents a novel Deformable Attention Vision Transformer(DA-ViT)architecture that integrates deformable Multi-Head Self-Attention(MHSA)with a Multi-Layer Perceptron(MLP)block for efficient classification of Alzheimer’s disease(AD)using Magnetic resonance imaging(MRI)scans.In contrast to traditional vision transformers,our deformable MHSA module preferentially concentrates on spatially pertinent patches through learned offset predictions,markedly diminishing processing demands while improving localized feature representation.DA-ViT contains only 0.93 million parameters,making it exceptionally suitable for implementation in resource-limited settings.We evaluate the model using a class-imbalanced Alzheimer’s MRI dataset comprising 6400 images across four categories,achieving a test accuracy of 80.31%,a macro F1-score of 0.80,and an area under the receiver operating characteristic curve(AUC)of 1.00 for the Mild Demented category.Thorough ablation studies validate the ideal configuration of transformer depth,headcount,and embedding dimensions.Moreover,comparison research indicates that DA-ViT surpasses state-of-theart pre-trained Convolutional Neural Network(CNN)models in terms of accuracy and parameter efficiency.展开更多
This paper presents CW-HRNet,a high-resolution,lightweight crack segmentation network designed to address challenges in complex scenes with slender,deformable,and blurred crack structures.The model incorporates two ke...This paper presents CW-HRNet,a high-resolution,lightweight crack segmentation network designed to address challenges in complex scenes with slender,deformable,and blurred crack structures.The model incorporates two key modules:Constrained Deformable Convolution(CDC),which stabilizes geometric alignment by applying a tanh limiter and learnable scaling factor to the predicted offsets,and the Wavelet Frequency Enhancement Module(WFEM),which decomposes features using Haar wavelets to preserve low-frequency structures while enhancing high-frequency boundaries and textures.Evaluations on the CrackSeg9k benchmark demonstrate CW-HRNet’s superior performance,achieving 82.39%mIoU with only 7.49M parameters and 10.34 GFLOPs,outperforming HrSegNet-B48 by 1.83% in segmentation accuracy with minimal complexity overhead.The model also shows strong cross-dataset generalization,achieving 60.01%mIoU and 66.22%F1 on Asphalt3k without fine-tuning.These results highlight CW-HRNet’s favorable accuracyefficiency trade-off for real-world crack segmentation tasks.展开更多
Conventional deformable wheel systems in robots and other mechatronic systems face significant challenges in achieving miniaturization,intelligence,and integration.To address these issues,we propose a novel integrated...Conventional deformable wheel systems in robots and other mechatronic systems face significant challenges in achieving miniaturization,intelligence,and integration.To address these issues,we propose a novel integrated structural design method and four-dimensional printing strategy for deformable wheels capable of shaping among multiple programmable direct-driven deformation configurations.The load-bearing capacity of the printed wheel is strengthened by employing deformed components in various locations and actuated states.Additionally,a novel analytical design method is presented to determine the structure,actuation,and deformation parameters of each component under complex coupled deformation.Our findings reveal that the designed wheel can transform into three different configurations,exhibiting desired deformations of 12.5%in the radial direction and 19.6%in the axial direction.It also demonstrates robust deformation behavior and structural stability under multi-directional loads.By integrating a terrain sensing system,the designed wheel exhibits highly adaptive deformation capabilities on various terrains,showing great potential for exploring complex environments.展开更多
Deformable catalytic material with excellent flexible structure is a new type of catalyst that has been applied in various chemical reactions,especially electrocatalytic hydrogen evolution reaction(HER).In recent year...Deformable catalytic material with excellent flexible structure is a new type of catalyst that has been applied in various chemical reactions,especially electrocatalytic hydrogen evolution reaction(HER).In recent years,deformable catalysts for HER have made great progress and would become a research hotspot.The catalytic activities of deformable catalysts could be adjustable by the strain engineering and surface reconfiguration.The surface curvature of flexible catalytic materials is closely related to the electrocatalytic HER properties.Here,firstly,we systematically summarized self-adaptive catalytic performance of deformable catalysts and various micro–nanostructures evolution in catalytic HER process.Secondly,a series of strategies to design highly active catalysts based on the mechanical flexibility of lowdimensional nanomaterials were summarized.Last but not least,we presented the challenges and prospects of the study of flexible and deformable micro–nanostructures of electrocatalysts,which would further deepen the understanding of catalytic mechanisms of deformable HER catalyst.展开更多
Research on adaptive deformable mirror technology for voice coil actuators(VCAs)is an important trend in the development of large ground-based telescopes.A voice coil adaptive deformable mirror contains a large number...Research on adaptive deformable mirror technology for voice coil actuators(VCAs)is an important trend in the development of large ground-based telescopes.A voice coil adaptive deformable mirror contains a large number of actuators,and there are problems with structural coupling and large temperature increases in their internal coils.Additionally,parameters of the traditional proportional integral derivative(PID)control cannot be adjusted in real-time to adapt to system changes.These problems can be addressed by introducing fuzzy control methods.A table lookup method is adopted to replace real-time calculations of the regular fuzzy controller during the control process,and a prototype platform has been established to verify the effectiveness and robustness of this process.Experimental tests compare the control performance of traditional and fuzzy proportional integral derivative(Fuzzy-PID)controllers,showing that,in system step response tests,the fuzzy control system reduces rise time by 20.25%,decreases overshoot by 78.24%,and shortens settling time by 67.59%.In disturbance rejection experiments,fuzzy control achieves a 46.09%reduction in the maximum deviation,indicating stronger robustness.The Fuzzy-PID controller,based on table lookup,outperforms the standard controller significantly,showing excellent potential for enhancing the dynamic performance and disturbance rejection capability of the voice coil motor actuator system.展开更多
Landslide-generated impulsive waves(LGWs)in reservoir areas can seriously threaten waterway safety as well as hu-man life and properties around the two side slopes.The risk reduction and mitigation of such a hazard re...Landslide-generated impulsive waves(LGWs)in reservoir areas can seriously threaten waterway safety as well as hu-man life and properties around the two side slopes.The risk reduction and mitigation of such a hazard require the accurate prediction of near-field wave characteristics,such as wave amplitude and run-up.However,near-field LGW involves complicated fluid-solid interactions.Furthermore,the wave characteristics are closely related to various aspects,including the geometry and physical features of the slide,river,and body of water.However,the empirical or analytical methods used for rough estimation cannot derive accurate results,especially for deformable landslides,due to their significant geometry changes during the sliding process.In this study,the near-field waves generated by deformable landslides were simulated by smoothed particle hydrodynamics(SPH)based on multi-phase flow.The deformable landslides were generalized as a kind of viscous flow by adopting the Herschel-Bulkley-Papanastasiou(HBP)-based nonNewtonian rheology model.The HBP model is capable of producing deformable landslide dynamics even though the high-speed sliding process is involved.In this study,an idealized experiment case originating from Lituya LGW and a practical case of Gongjiafang LGW were reproduced for verification and demonstration.The simulation results of both cases show satisfactory consistency with the experiment/investigation data in terms of landslide movement and near-field impulsive wave characteristics,thus indicating the applicability and accuracy of the proposed method.Finally,the effects of the HBP model’s rheological parameters on the landslide dynamics and near-field wave characteristics are discussed,providing a parameter calibration method along with sug-gestions for further applications.展开更多
Deformable gel particles(DGPs) possess the capability of deep profile control and flooding. However, the deep migration behavior and plugging mechanism along their path remain unclear. Breakage, an inevitable phenomen...Deformable gel particles(DGPs) possess the capability of deep profile control and flooding. However, the deep migration behavior and plugging mechanism along their path remain unclear. Breakage, an inevitable phenomenon during particle migration, significantly impacts the deep plugging effect. Due to the complexity of the process, few studies have been conducted on this subject. In this paper, we conducted DGP flow experiments using a physical model of a multi-point sandpack under various injection rates and particle sizes. Particle size and concentration tests were performed at each measurement point to investigate the transportation behavior of particles in the deep part of the reservoir. The residual resistance coefficient and concentration changes along the porous media were combined to analyze the plugging performance of DGPs. Furthermore, the particle breakage along their path was revealed by analyzing the changes in particle size along the way. A mathematical model of breakage and concentration changes along the path was established. The results showed that the passage after breakage is a significant migration behavior of particles in porous media. The particles were reduced to less than half of their initial size at the front of the porous media. Breakage is an essential reason for the continuous decreases in particle concentration, size, and residual resistance coefficient. However, the particles can remain in porous media after breakage and play a significant role in deep plugging. Higher injection rates or larger particle sizes resulted in faster breakage along the injection direction, higher degrees of breakage, and faster decreases in residual resistance coefficient along the path. These conditions also led to a weaker deep plugging ability. Smaller particles were more evenly retained along the path, but more particles flowed out of the porous media, resulting in a poor deep plugging effect. The particle size is a function of particle size before injection, transport distance, and different injection parameters(injection rate or the diameter ratio of DGP to throat). Likewise, the particle concentration is a function of initial concentration, transport distance, and different injection parameters. These models can be utilized to optimize particle injection parameters, thereby achieving the goal of fine-tuning oil displacement.展开更多
The intensive application of deep learning in medical image processing has facilitated the advancement of automatic retinal vessel segmentation research.To overcome the limitation that traditional U-shaped vessel segm...The intensive application of deep learning in medical image processing has facilitated the advancement of automatic retinal vessel segmentation research.To overcome the limitation that traditional U-shaped vessel segmentation networks fail to extract features in fundus image sufficiently,we propose a novel network(DSeU-net)based on deformable convolution and squeeze excitation residual module.The deformable convolution is utilized to dynamically adjust the receptive field for the feature extraction of retinal vessel.And the squeeze excitation residual module is used to scale the weights of the low-level features so that the network learns the complex relationships of the different feature layers efficiently.We validate the DSeU-net on three public retinal vessel segmentation datasets including DRIVE,CHASEDB1,and STARE,and the experimental results demonstrate the satisfactory segmentation performance of the network.展开更多
Detumbling operation toward a rotating target with nutation is meaningful for debris removal but challenging. In this study, a deformable end-effector is first designed based on the requirements for contacting the nut...Detumbling operation toward a rotating target with nutation is meaningful for debris removal but challenging. In this study, a deformable end-effector is first designed based on the requirements for contacting the nutating target. A dual-arm robotic system installed with the deformable end-effectors is modeled and the movement of the end-tips is analyzed. The complex operation of the contact toward a nutating target places strict requirements on control accuracy and controller robustness. Thus, an improvement of the tracking error transformation is proposed and an adaptive sliding mode controller with prescribed performance is designed to guarantee the fast and precise motion of the effector during the contact detumbling.Finally, by employing the proposed effector and the controller,numerical simulations are carried out to verify the effectiveness and efficiency of the contact detumbling toward a nutating target.展开更多
Multi-modal histological image registration tasks pose significant challenges due to tissue staining operations causing partial loss and folding of tissue.Convolutional neural network(CNN)and generative adversarial ne...Multi-modal histological image registration tasks pose significant challenges due to tissue staining operations causing partial loss and folding of tissue.Convolutional neural network(CNN)and generative adversarial network(GAN)are pivotal inmedical image registration.However,existing methods often struggle with severe interference and deformation,as seen in histological images of conditions like Cushing’s disease.We argue that the failure of current approaches lies in underutilizing the feature extraction capability of the discriminator inGAN.In this study,we propose a novel multi-modal registration approach GAN-DIRNet based on GAN for deformable histological image registration.To begin with,the discriminators of two GANs are embedded as a new dual parallel feature extraction module into the unsupervised registration networks,characterized by implicitly extracting feature descriptors of specific modalities.Additionally,modal feature description layers and registration layers collaborate in unsupervised optimization,facilitating faster convergence and more precise results.Lastly,experiments and evaluations were conducted on the registration of the Mixed National Institute of Standards and Technology database(MNIST),eight publicly available datasets of histological sections and the Clustering-Registration-Classification-Segmentation(CRCS)dataset on the Cushing’s disease.Experimental results demonstrate that our proposed GAN-DIRNet method surpasses existing approaches like DIRNet in terms of both registration accuracy and time efficiency,while also exhibiting robustness across different image types.展开更多
基金the financial support from the National Key R&D Program of China(No.2022YFB3708400)National Natural Science Foundation of China(No.52171133,52225101)Basic and Applied Basic Research Foundation of Guangdong(No.2020B0301030006)。
文摘Magnesium matrix composites(MMCs)combine exceptional low density,high specific strength,and stiffness,positioning them as critical materials for aerospace,automotive,and electronics industries.This review highlights recent progress in the fabrication of Ti-Mg composites and analyzes the mechanisms behind their enhanced mechanical properties.A key focus is the interfacial deformation incompatibility between Ti and Mg phases,which generates strain gradients and promotes the accumulation of geometrically necessary dislocations(GNDs)at the interface.This process not only improves strain hardening and ductility but also reveals the need for advanced micromechanical models to capture the plastic behavior of both phases.The review critically examines the impact of different Mg matrix types(AZ,AM,VW series)and the role of interfacial product morphology and size on bonding and overall performance.Furthermore,Ti reinforcement endows the composites with superior wear resistance and thermal conductivity,indicating broad application potential.
基金Prince Sattambin Abdulaziz University for funding this research work through the project number(PSAU/2025/R/1446).
文摘The early and precise identification of Alzheimer’s Disease(AD)continues to pose considerable clinical difficulty due to subtle structural alterations and overlapping symptoms across the disease phases.This study presents a novel Deformable Attention Vision Transformer(DA-ViT)architecture that integrates deformable Multi-Head Self-Attention(MHSA)with a Multi-Layer Perceptron(MLP)block for efficient classification of Alzheimer’s disease(AD)using Magnetic resonance imaging(MRI)scans.In contrast to traditional vision transformers,our deformable MHSA module preferentially concentrates on spatially pertinent patches through learned offset predictions,markedly diminishing processing demands while improving localized feature representation.DA-ViT contains only 0.93 million parameters,making it exceptionally suitable for implementation in resource-limited settings.We evaluate the model using a class-imbalanced Alzheimer’s MRI dataset comprising 6400 images across four categories,achieving a test accuracy of 80.31%,a macro F1-score of 0.80,and an area under the receiver operating characteristic curve(AUC)of 1.00 for the Mild Demented category.Thorough ablation studies validate the ideal configuration of transformer depth,headcount,and embedding dimensions.Moreover,comparison research indicates that DA-ViT surpasses state-of-theart pre-trained Convolutional Neural Network(CNN)models in terms of accuracy and parameter efficiency.
文摘This paper presents CW-HRNet,a high-resolution,lightweight crack segmentation network designed to address challenges in complex scenes with slender,deformable,and blurred crack structures.The model incorporates two key modules:Constrained Deformable Convolution(CDC),which stabilizes geometric alignment by applying a tanh limiter and learnable scaling factor to the predicted offsets,and the Wavelet Frequency Enhancement Module(WFEM),which decomposes features using Haar wavelets to preserve low-frequency structures while enhancing high-frequency boundaries and textures.Evaluations on the CrackSeg9k benchmark demonstrate CW-HRNet’s superior performance,achieving 82.39%mIoU with only 7.49M parameters and 10.34 GFLOPs,outperforming HrSegNet-B48 by 1.83% in segmentation accuracy with minimal complexity overhead.The model also shows strong cross-dataset generalization,achieving 60.01%mIoU and 66.22%F1 on Asphalt3k without fine-tuning.These results highlight CW-HRNet’s favorable accuracyefficiency trade-off for real-world crack segmentation tasks.
基金supported by the National Key Research and Development Program of China(Grant No 2022YFB4600102)the National Natural Science Foundation of China(Grant No.U23A20637 and Grant No 52275561)。
文摘Conventional deformable wheel systems in robots and other mechatronic systems face significant challenges in achieving miniaturization,intelligence,and integration.To address these issues,we propose a novel integrated structural design method and four-dimensional printing strategy for deformable wheels capable of shaping among multiple programmable direct-driven deformation configurations.The load-bearing capacity of the printed wheel is strengthened by employing deformed components in various locations and actuated states.Additionally,a novel analytical design method is presented to determine the structure,actuation,and deformation parameters of each component under complex coupled deformation.Our findings reveal that the designed wheel can transform into three different configurations,exhibiting desired deformations of 12.5%in the radial direction and 19.6%in the axial direction.It also demonstrates robust deformation behavior and structural stability under multi-directional loads.By integrating a terrain sensing system,the designed wheel exhibits highly adaptive deformation capabilities on various terrains,showing great potential for exploring complex environments.
基金This work was financially supported by the National Natural Science Foundation of China(Nos.51902101 and 21875203)the Natural Science Foundation of Hunan Province(Nos.2021JJ40044 and 2023JJ50287)Natural Science Foundation of Jiangsu Province(No.BK20201381).
文摘Deformable catalytic material with excellent flexible structure is a new type of catalyst that has been applied in various chemical reactions,especially electrocatalytic hydrogen evolution reaction(HER).In recent years,deformable catalysts for HER have made great progress and would become a research hotspot.The catalytic activities of deformable catalysts could be adjustable by the strain engineering and surface reconfiguration.The surface curvature of flexible catalytic materials is closely related to the electrocatalytic HER properties.Here,firstly,we systematically summarized self-adaptive catalytic performance of deformable catalysts and various micro–nanostructures evolution in catalytic HER process.Secondly,a series of strategies to design highly active catalysts based on the mechanical flexibility of lowdimensional nanomaterials were summarized.Last but not least,we presented the challenges and prospects of the study of flexible and deformable micro–nanostructures of electrocatalysts,which would further deepen the understanding of catalytic mechanisms of deformable HER catalyst.
基金supported by the National Key R&D Program of China (2022YFA1603001,2021YFC2801402)the National Nature Science Foundation of China (12073053)the Science and Technology Plan of Inner Mongolia (2021GG0245).
文摘Research on adaptive deformable mirror technology for voice coil actuators(VCAs)is an important trend in the development of large ground-based telescopes.A voice coil adaptive deformable mirror contains a large number of actuators,and there are problems with structural coupling and large temperature increases in their internal coils.Additionally,parameters of the traditional proportional integral derivative(PID)control cannot be adjusted in real-time to adapt to system changes.These problems can be addressed by introducing fuzzy control methods.A table lookup method is adopted to replace real-time calculations of the regular fuzzy controller during the control process,and a prototype platform has been established to verify the effectiveness and robustness of this process.Experimental tests compare the control performance of traditional and fuzzy proportional integral derivative(Fuzzy-PID)controllers,showing that,in system step response tests,the fuzzy control system reduces rise time by 20.25%,decreases overshoot by 78.24%,and shortens settling time by 67.59%.In disturbance rejection experiments,fuzzy control achieves a 46.09%reduction in the maximum deviation,indicating stronger robustness.The Fuzzy-PID controller,based on table lookup,outperforms the standard controller significantly,showing excellent potential for enhancing the dynamic performance and disturbance rejection capability of the voice coil motor actuator system.
基金support from the National Natural Sciences Foundation of China(Nos.42177159,42077277,41877253)the Fundamental Research Funds for the Central Universities,China University of Geosciences(Wuhan)(No.CUG2106304).
文摘Landslide-generated impulsive waves(LGWs)in reservoir areas can seriously threaten waterway safety as well as hu-man life and properties around the two side slopes.The risk reduction and mitigation of such a hazard require the accurate prediction of near-field wave characteristics,such as wave amplitude and run-up.However,near-field LGW involves complicated fluid-solid interactions.Furthermore,the wave characteristics are closely related to various aspects,including the geometry and physical features of the slide,river,and body of water.However,the empirical or analytical methods used for rough estimation cannot derive accurate results,especially for deformable landslides,due to their significant geometry changes during the sliding process.In this study,the near-field waves generated by deformable landslides were simulated by smoothed particle hydrodynamics(SPH)based on multi-phase flow.The deformable landslides were generalized as a kind of viscous flow by adopting the Herschel-Bulkley-Papanastasiou(HBP)-based nonNewtonian rheology model.The HBP model is capable of producing deformable landslide dynamics even though the high-speed sliding process is involved.In this study,an idealized experiment case originating from Lituya LGW and a practical case of Gongjiafang LGW were reproduced for verification and demonstration.The simulation results of both cases show satisfactory consistency with the experiment/investigation data in terms of landslide movement and near-field impulsive wave characteristics,thus indicating the applicability and accuracy of the proposed method.Finally,the effects of the HBP model’s rheological parameters on the landslide dynamics and near-field wave characteristics are discussed,providing a parameter calibration method along with sug-gestions for further applications.
基金supported by the Major National Science and Technology Project(No.2016ZX05054011)。
文摘Deformable gel particles(DGPs) possess the capability of deep profile control and flooding. However, the deep migration behavior and plugging mechanism along their path remain unclear. Breakage, an inevitable phenomenon during particle migration, significantly impacts the deep plugging effect. Due to the complexity of the process, few studies have been conducted on this subject. In this paper, we conducted DGP flow experiments using a physical model of a multi-point sandpack under various injection rates and particle sizes. Particle size and concentration tests were performed at each measurement point to investigate the transportation behavior of particles in the deep part of the reservoir. The residual resistance coefficient and concentration changes along the porous media were combined to analyze the plugging performance of DGPs. Furthermore, the particle breakage along their path was revealed by analyzing the changes in particle size along the way. A mathematical model of breakage and concentration changes along the path was established. The results showed that the passage after breakage is a significant migration behavior of particles in porous media. The particles were reduced to less than half of their initial size at the front of the porous media. Breakage is an essential reason for the continuous decreases in particle concentration, size, and residual resistance coefficient. However, the particles can remain in porous media after breakage and play a significant role in deep plugging. Higher injection rates or larger particle sizes resulted in faster breakage along the injection direction, higher degrees of breakage, and faster decreases in residual resistance coefficient along the path. These conditions also led to a weaker deep plugging ability. Smaller particles were more evenly retained along the path, but more particles flowed out of the porous media, resulting in a poor deep plugging effect. The particle size is a function of particle size before injection, transport distance, and different injection parameters(injection rate or the diameter ratio of DGP to throat). Likewise, the particle concentration is a function of initial concentration, transport distance, and different injection parameters. These models can be utilized to optimize particle injection parameters, thereby achieving the goal of fine-tuning oil displacement.
基金Beijing Natural Science Foundation(No.IS23112)Beijing Institute of Technology Research Fund Program for Young Scholars(No.6120220236)。
文摘The intensive application of deep learning in medical image processing has facilitated the advancement of automatic retinal vessel segmentation research.To overcome the limitation that traditional U-shaped vessel segmentation networks fail to extract features in fundus image sufficiently,we propose a novel network(DSeU-net)based on deformable convolution and squeeze excitation residual module.The deformable convolution is utilized to dynamically adjust the receptive field for the feature extraction of retinal vessel.And the squeeze excitation residual module is used to scale the weights of the low-level features so that the network learns the complex relationships of the different feature layers efficiently.We validate the DSeU-net on three public retinal vessel segmentation datasets including DRIVE,CHASEDB1,and STARE,and the experimental results demonstrate the satisfactory segmentation performance of the network.
基金supported by the National Natural Science Foundation of China(11972077,11672035)。
文摘Detumbling operation toward a rotating target with nutation is meaningful for debris removal but challenging. In this study, a deformable end-effector is first designed based on the requirements for contacting the nutating target. A dual-arm robotic system installed with the deformable end-effectors is modeled and the movement of the end-tips is analyzed. The complex operation of the contact toward a nutating target places strict requirements on control accuracy and controller robustness. Thus, an improvement of the tracking error transformation is proposed and an adaptive sliding mode controller with prescribed performance is designed to guarantee the fast and precise motion of the effector during the contact detumbling.Finally, by employing the proposed effector and the controller,numerical simulations are carried out to verify the effectiveness and efficiency of the contact detumbling toward a nutating target.
文摘Multi-modal histological image registration tasks pose significant challenges due to tissue staining operations causing partial loss and folding of tissue.Convolutional neural network(CNN)and generative adversarial network(GAN)are pivotal inmedical image registration.However,existing methods often struggle with severe interference and deformation,as seen in histological images of conditions like Cushing’s disease.We argue that the failure of current approaches lies in underutilizing the feature extraction capability of the discriminator inGAN.In this study,we propose a novel multi-modal registration approach GAN-DIRNet based on GAN for deformable histological image registration.To begin with,the discriminators of two GANs are embedded as a new dual parallel feature extraction module into the unsupervised registration networks,characterized by implicitly extracting feature descriptors of specific modalities.Additionally,modal feature description layers and registration layers collaborate in unsupervised optimization,facilitating faster convergence and more precise results.Lastly,experiments and evaluations were conducted on the registration of the Mixed National Institute of Standards and Technology database(MNIST),eight publicly available datasets of histological sections and the Clustering-Registration-Classification-Segmentation(CRCS)dataset on the Cushing’s disease.Experimental results demonstrate that our proposed GAN-DIRNet method surpasses existing approaches like DIRNet in terms of both registration accuracy and time efficiency,while also exhibiting robustness across different image types.
