Augmented reality(AR)is an emerging dynamic technology that effectively supports education across different levels.The increased use of mobile devices has an even greater impact.As the demand for AR applications in ed...Augmented reality(AR)is an emerging dynamic technology that effectively supports education across different levels.The increased use of mobile devices has an even greater impact.As the demand for AR applications in education continues to increase,educators actively seek innovative and immersive methods to engage students in learning.However,exploring these possibilities also entails identifying and overcoming existing barriers to optimal educational integration.Concurrently,this surge in demand has prompted the identification of specific barriers,one of which is three-dimensional(3D)modeling.Creating 3D objects for augmented reality education applications can be challenging and time-consuming for the educators.To address this,we have developed a pipeline that creates realistic 3D objects from the two-dimensional(2D)photograph.Applications for augmented and virtual reality can then utilize these created 3D objects.We evaluated the proposed pipeline based on the usability of the 3D object and performance metrics.Quantitatively,with 117 respondents,the co-creation team was surveyed with openended questions to evaluate the precision of the 3D object created by the proposed photogrammetry pipeline.We analyzed the survey data using descriptive-analytical methods and found that the proposed pipeline produces 3D models that are positively accurate when compared to real-world objects,with an average mean score above 8.This study adds new knowledge in creating 3D objects for augmented reality applications by using the photogrammetry technique;finally,it discusses potential problems and future research directions for 3D objects in the education sector.展开更多
Among their several unique properties,the high electrical conductivity and mechanical strength of carbon nanofibers make them suitable for applications such as catalyst support for fuel cells,flexible electrode materi...Among their several unique properties,the high electrical conductivity and mechanical strength of carbon nanofibers make them suitable for applications such as catalyst support for fuel cells,flexible electrode materials for secondary batteries,and sensors.However,their performance requires improvement for practical applications.Several methods have been pursued to achieve this,such as growing carbon nanotubes from carbon nanofibers;however,the transition metal catalyst used to grow carbon nanotubes causes problems,including side reactions.This study attempts to address this issue by growing numerous branched carbon nanofibers from the main carbon nanofibers using alkali metals.Excellent electrical conductivity is achieved by growing densely branched carbon nanofibers.Consequently,a current collector,binder,and conductive material-free anode material is realized,exhibiting excellent electrochemical performance compared with existing carbon nanofibers.The proposed method is expected to be a powerful tool for secondary batteries and have broad applicability to various fields.展开更多
To investigate the applicability of four commonly used color difference formulas(CIELAB,CIE94,CMC(1:1),and CIEDE2000)in the printing field on 3D objects,as well as the impact of four standard light sources(D65,D50,A,a...To investigate the applicability of four commonly used color difference formulas(CIELAB,CIE94,CMC(1:1),and CIEDE2000)in the printing field on 3D objects,as well as the impact of four standard light sources(D65,D50,A,and TL84)on 3D color difference evaluations,50 glossy spheres with a diameter of 2cm based on the Sailner J4003D color printing device were created.These spheres were centered around the five recommended colors(gray,red,yellow,green,and blue)by CIE.Color difference was calculated according to the four formulas,and 111 pairs of experimental samples meeting the CIELAB gray scale color difference requirements(1.0-14.0)were selected.Ten observers,aged between 22 and 27 with normal color vision,were participated in this study,using the gray scale method from psychophysical experiments to conduct color difference evaluations under the four light sources,with repeated experiments for each observer.The results indicated that the overall effect of the D65 light source on 3D objects color difference was minimal.In contrast,D50 and A light sources had a significant impact within the small color difference range,while the TL84 light source influenced both large and small color difference considerably.Among the four color difference formulas,CIEDE2000 demonstrated the best predictive performance for color difference in 3D objects,followed by CMC(1:1),CIE94,and CIELAB.展开更多
Transorbital craniocerebral injury is a relatively rare type of penetrating head injury that poses a significant threat to the ocular and cerebral structures.^([1])The clinical prognosis of transorbital craniocerebral...Transorbital craniocerebral injury is a relatively rare type of penetrating head injury that poses a significant threat to the ocular and cerebral structures.^([1])The clinical prognosis of transorbital craniocerebral injury is closely related to the size,shape,speed,nature,and trajectory of the foreign object,as well as the incidence of central nervous system damage and secondary complications.The foreign objects reported to have caused these injuries are categorized into wooden items,metallic items,^([2-8])and other materials,which penetrate the intracranial region via fi ve major pathways,including the orbital roof (OR),superior orbital fissure (SOF),inferior orbital fissure(IOF),optic canal (OC),and sphenoid wing.Herein,we present eight cases of transorbital craniocerebral injury caused by an unusual metallic foreign body.展开更多
With the rapid development of technology,artificial intelligence(AI)is increasingly being applied in various fields.In today’s context of resource scarcity,pursuit of sustainable development and resource reuse,the tr...With the rapid development of technology,artificial intelligence(AI)is increasingly being applied in various fields.In today’s context of resource scarcity,pursuit of sustainable development and resource reuse,the transformation of old objects is particularly important.This article analyzes the current status of old object transformation and the opportunities brought by the internet to old objects and delves into the application of artificial intelligence in old object transformation.The focus is on five aspects:intelligent identification and classification,intelligent evaluation and prediction,automation integration,intelligent design and optimization,and integration of 3D printing technology.Finally,the process of“redesigning an old furniture,such as a wooden desk,through AI technology”is described,including the recycling,identification,detection,design,transformation,and final user feedback of the old wooden desk.This illustrates the unlimited potential of the“AI+old object transformation”approach,advocates for people to strengthen green environmental protection,and drives sustainable development.展开更多
Shape prediction of deformable linear objects(DLO)plays critical roles in robotics,medical devices,aerospace,and manufacturing,especially in manipulating objects such as cables,wires,and fibers.Due to the inherent fle...Shape prediction of deformable linear objects(DLO)plays critical roles in robotics,medical devices,aerospace,and manufacturing,especially in manipulating objects such as cables,wires,and fibers.Due to the inherent flexibility of DLO and their complex deformation behaviors,such as bending and torsion,it is challenging to predict their dynamic characteristics accurately.Although the traditional physical modeling method can simulate the complex deformation behavior of DLO,the calculation cost is high and it is difficult to meet the demand of real-time prediction.In addition,the scarcity of data resources also limits the prediction accuracy of existing models.To solve these problems,a method of fiber shape prediction based on a physical information graph neural network(PIGNN)is proposed in this paper.This method cleverly combines the powerful expressive power of graph neural networks with the strict constraints of physical laws.Specifically,we learn the initial deformation model of the fiber through graph neural networks(GNN)to provide a good initial estimate for the model,which helps alleviate the problem of data resource scarcity.During the training process,we incorporate the physical prior knowledge of the dynamic deformation of the fiber optics into the loss function as a constraint,which is then fed back to the network model.This ensures that the shape of the fiber optics gradually approaches the true target shape,effectively solving the complex nonlinear behavior prediction problem of deformable linear objects.Experimental results demonstrate that,compared to traditional methods,the proposed method significantly reduces execution time and prediction error when handling the complex deformations of deformable fibers.This showcases its potential application value and superiority in fiber manipulation.展开更多
Accurate segmentation of camouflage objects in aerial imagery is vital for improving the efficiency of UAV-based reconnaissance and rescue missions.However,camouflage object segmentation is increasingly challenging du...Accurate segmentation of camouflage objects in aerial imagery is vital for improving the efficiency of UAV-based reconnaissance and rescue missions.However,camouflage object segmentation is increasingly challenging due to advances in both camouflage materials and biological mimicry.Although multispectral-RGB based technology shows promise,conventional dual-aperture multispectral-RGB imaging systems are constrained by imprecise and time-consuming registration and fusion across different modalities,limiting their performance.Here,we propose the Reconstructed Multispectral-RGB Fusion Network(RMRF-Net),which reconstructs RGB images into multispectral ones,enabling efficient multimodal segmentation using only an RGB camera.Specifically,RMRF-Net employs a divergentsimilarity feature correction strategy to minimize reconstruction errors and includes an efficient boundary-aware decoder to enhance object contours.Notably,we establish the first real-world aerial multispectral-RGB semantic segmentation of camouflage objects dataset,including 11 object categories.Experimental results demonstrate that RMRF-Net outperforms existing methods,achieving 17.38 FPS on the NVIDIA Jetson AGX Orin,with only a 0.96%drop in mIoU compared to the RTX 3090,showing its practical applicability in multimodal remote sensing.展开更多
Most image-based object detection methods employ horizontal bounding boxes(HBBs)to capture objects in tunnel images.However,these bounding boxes often fail to effectively enclose objects oriented in arbitrary directio...Most image-based object detection methods employ horizontal bounding boxes(HBBs)to capture objects in tunnel images.However,these bounding boxes often fail to effectively enclose objects oriented in arbitrary directions,resulting in reduced accuracy and suboptimal detection performance.Moreover,HBBs cannot provide directional information for rotated objects.This study proposes a rotated detection method for identifying apparent defects in shield tunnels.Specifically,the oriented region-convolutional neural network(oriented R-CNN)is utilized to detect rotated objects in tunnel images.To enhance feature extraction,a novel hybrid backbone combining CNN-based networks with Swin Transformers is proposed.A feature fusion strategy is employed to integrate features extracted from both networks.Additionally,a neck network based on the bidirectional-feature pyramid network(Bi-FPN)is designed to combine multi-scale object features.The bolt hole dataset is curated to evaluate the efficacyof the proposed method.In addition,a dedicated pre-processing approach is developed for large-sized images to accommodate the rotated,dense,and small-scale characteristics of objects in tunnel images.Experimental results demonstrate that the proposed method achieves a more than 4%improvement in mAP_(50-95)compared to other rotated detectors and a 6.6%-12.7%improvement over mainstream horizontal detectors.Furthermore,the proposed method outperforms mainstream methods by 6.5%-14.7%in detecting leakage bolt holes,underscoring its significant engineering applicability.展开更多
Electrophoretic deposition in conjunction with electrochemical reduction was used to make flexible free-standing graphene-like films. Firstly, graphene oxide （GO） film was deposited on graphite substrate by electrop...Electrophoretic deposition in conjunction with electrochemical reduction was used to make flexible free-standing graphene-like films. Firstly, graphene oxide （GO） film was deposited on graphite substrate by electrophoretic deposition method, and then reduced by subsequent electrochemical reduction of GO to obtain reduced GO （ERGO） film with high electrochemical performance. The morphology, structure and electrochemical performance of the prepared graphene-like film were confirmed by SEM, XRD and FT-IR. These unique materials were found to provide high specific capacitance and good cycling stability. The high specific capacitance of 254 F/g was obtained from cyclic voltammetry measurement at a scan rate of 10 mV/s. When the current density increased to 83.3 A/g, the specific capacitance values still remained 132 F/g. Meanwhile, the high powder density of 39.1 kW/kg was measured at energy density of 11.8 W-h/kg in 1 mol/L H2SO4 solution. Furthermore, at a constant scan rate of 50 mV/s, 97.02% of its capacitance was retained for 1000 cycles. These promising results were attributed to the unique assembly structure of graphene film and low contact resistance, which indicated their potential application to electrochemical capacitors.展开更多
Fuzziness is an internal property of spatial objects.How to model fuzziness of a spatial object is a main task of next generation GIS.This paper proposes basic fuzzy spatial object types based on fuzzy topology.These ...Fuzziness is an internal property of spatial objects.How to model fuzziness of a spatial object is a main task of next generation GIS.This paper proposes basic fuzzy spatial object types based on fuzzy topology.These object types are the natural extension of current nonfuzzy spatial object types.A fuzzy cell complex structure is defined for modeling fuzzy regions,lines and points.Furthermore,fuzzy topological relations between these fuzzy spatial objects are formalized based on the 9intersection approach.This model can be implemented for GIS applications due to its scientific theory basis.展开更多
文摘Augmented reality(AR)is an emerging dynamic technology that effectively supports education across different levels.The increased use of mobile devices has an even greater impact.As the demand for AR applications in education continues to increase,educators actively seek innovative and immersive methods to engage students in learning.However,exploring these possibilities also entails identifying and overcoming existing barriers to optimal educational integration.Concurrently,this surge in demand has prompted the identification of specific barriers,one of which is three-dimensional(3D)modeling.Creating 3D objects for augmented reality education applications can be challenging and time-consuming for the educators.To address this,we have developed a pipeline that creates realistic 3D objects from the two-dimensional(2D)photograph.Applications for augmented and virtual reality can then utilize these created 3D objects.We evaluated the proposed pipeline based on the usability of the 3D object and performance metrics.Quantitatively,with 117 respondents,the co-creation team was surveyed with openended questions to evaluate the precision of the 3D object created by the proposed photogrammetry pipeline.We analyzed the survey data using descriptive-analytical methods and found that the proposed pipeline produces 3D models that are positively accurate when compared to real-world objects,with an average mean score above 8.This study adds new knowledge in creating 3D objects for augmented reality applications by using the photogrammetry technique;finally,it discusses potential problems and future research directions for 3D objects in the education sector.
基金supported by the Ministry of Education of the Republic of Korea and the National Research Foundation of Korea(NRF2023R1A2C2004191)supported by Korea Basic Science Institute(National research Facilities and Equipment Center)grant funded by Ministry of Education(grant No.2022R1A6C101B738).
文摘Among their several unique properties,the high electrical conductivity and mechanical strength of carbon nanofibers make them suitable for applications such as catalyst support for fuel cells,flexible electrode materials for secondary batteries,and sensors.However,their performance requires improvement for practical applications.Several methods have been pursued to achieve this,such as growing carbon nanotubes from carbon nanofibers;however,the transition metal catalyst used to grow carbon nanotubes causes problems,including side reactions.This study attempts to address this issue by growing numerous branched carbon nanofibers from the main carbon nanofibers using alkali metals.Excellent electrical conductivity is achieved by growing densely branched carbon nanofibers.Consequently,a current collector,binder,and conductive material-free anode material is realized,exhibiting excellent electrochemical performance compared with existing carbon nanofibers.The proposed method is expected to be a powerful tool for secondary batteries and have broad applicability to various fields.
文摘To investigate the applicability of four commonly used color difference formulas(CIELAB,CIE94,CMC(1:1),and CIEDE2000)in the printing field on 3D objects,as well as the impact of four standard light sources(D65,D50,A,and TL84)on 3D color difference evaluations,50 glossy spheres with a diameter of 2cm based on the Sailner J4003D color printing device were created.These spheres were centered around the five recommended colors(gray,red,yellow,green,and blue)by CIE.Color difference was calculated according to the four formulas,and 111 pairs of experimental samples meeting the CIELAB gray scale color difference requirements(1.0-14.0)were selected.Ten observers,aged between 22 and 27 with normal color vision,were participated in this study,using the gray scale method from psychophysical experiments to conduct color difference evaluations under the four light sources,with repeated experiments for each observer.The results indicated that the overall effect of the D65 light source on 3D objects color difference was minimal.In contrast,D50 and A light sources had a significant impact within the small color difference range,while the TL84 light source influenced both large and small color difference considerably.Among the four color difference formulas,CIEDE2000 demonstrated the best predictive performance for color difference in 3D objects,followed by CMC(1:1),CIE94,and CIELAB.
文摘Transorbital craniocerebral injury is a relatively rare type of penetrating head injury that poses a significant threat to the ocular and cerebral structures.^([1])The clinical prognosis of transorbital craniocerebral injury is closely related to the size,shape,speed,nature,and trajectory of the foreign object,as well as the incidence of central nervous system damage and secondary complications.The foreign objects reported to have caused these injuries are categorized into wooden items,metallic items,^([2-8])and other materials,which penetrate the intracranial region via fi ve major pathways,including the orbital roof (OR),superior orbital fissure (SOF),inferior orbital fissure(IOF),optic canal (OC),and sphenoid wing.Herein,we present eight cases of transorbital craniocerebral injury caused by an unusual metallic foreign body.
基金2023 College Student Innovation and Entrepreneurship Training Program-Provincial and Ministerial Level(Chongqing City):Jiangjiang-A DIY Old Object Transformation Platform Integrating AI Technology(Project No.:S202312608036)。
文摘With the rapid development of technology,artificial intelligence(AI)is increasingly being applied in various fields.In today’s context of resource scarcity,pursuit of sustainable development and resource reuse,the transformation of old objects is particularly important.This article analyzes the current status of old object transformation and the opportunities brought by the internet to old objects and delves into the application of artificial intelligence in old object transformation.The focus is on five aspects:intelligent identification and classification,intelligent evaluation and prediction,automation integration,intelligent design and optimization,and integration of 3D printing technology.Finally,the process of“redesigning an old furniture,such as a wooden desk,through AI technology”is described,including the recycling,identification,detection,design,transformation,and final user feedback of the old wooden desk.This illustrates the unlimited potential of the“AI+old object transformation”approach,advocates for people to strengthen green environmental protection,and drives sustainable development.
基金Supported by the Fundamental Research Funds for the Central Universities(Grant Nos.2232024Y-01,LZB2023001)DHU Distinguished Young Professor Program+1 种基金National Natural Science Foundation of China(Grant No.52275478)AI-Enhanced Research Program of Shanghai Municipal Education Commission(Grant No.SMEC-AI-DHUY-05)。
文摘Shape prediction of deformable linear objects(DLO)plays critical roles in robotics,medical devices,aerospace,and manufacturing,especially in manipulating objects such as cables,wires,and fibers.Due to the inherent flexibility of DLO and their complex deformation behaviors,such as bending and torsion,it is challenging to predict their dynamic characteristics accurately.Although the traditional physical modeling method can simulate the complex deformation behavior of DLO,the calculation cost is high and it is difficult to meet the demand of real-time prediction.In addition,the scarcity of data resources also limits the prediction accuracy of existing models.To solve these problems,a method of fiber shape prediction based on a physical information graph neural network(PIGNN)is proposed in this paper.This method cleverly combines the powerful expressive power of graph neural networks with the strict constraints of physical laws.Specifically,we learn the initial deformation model of the fiber through graph neural networks(GNN)to provide a good initial estimate for the model,which helps alleviate the problem of data resource scarcity.During the training process,we incorporate the physical prior knowledge of the dynamic deformation of the fiber optics into the loss function as a constraint,which is then fed back to the network model.This ensures that the shape of the fiber optics gradually approaches the true target shape,effectively solving the complex nonlinear behavior prediction problem of deformable linear objects.Experimental results demonstrate that,compared to traditional methods,the proposed method significantly reduces execution time and prediction error when handling the complex deformations of deformable fibers.This showcases its potential application value and superiority in fiber manipulation.
基金National Natural Science Foundation of China(Grant Nos.62005049 and 62072110)Natural Science Foundation of Fujian Province(Grant No.2020J01451).
文摘Accurate segmentation of camouflage objects in aerial imagery is vital for improving the efficiency of UAV-based reconnaissance and rescue missions.However,camouflage object segmentation is increasingly challenging due to advances in both camouflage materials and biological mimicry.Although multispectral-RGB based technology shows promise,conventional dual-aperture multispectral-RGB imaging systems are constrained by imprecise and time-consuming registration and fusion across different modalities,limiting their performance.Here,we propose the Reconstructed Multispectral-RGB Fusion Network(RMRF-Net),which reconstructs RGB images into multispectral ones,enabling efficient multimodal segmentation using only an RGB camera.Specifically,RMRF-Net employs a divergentsimilarity feature correction strategy to minimize reconstruction errors and includes an efficient boundary-aware decoder to enhance object contours.Notably,we establish the first real-world aerial multispectral-RGB semantic segmentation of camouflage objects dataset,including 11 object categories.Experimental results demonstrate that RMRF-Net outperforms existing methods,achieving 17.38 FPS on the NVIDIA Jetson AGX Orin,with only a 0.96%drop in mIoU compared to the RTX 3090,showing its practical applicability in multimodal remote sensing.
基金support from the National Natural Science Foundation of China(Grant Nos.52025084 and 52408420)the Beijing Natural Science Foundation(Grant No.8244058).
文摘Most image-based object detection methods employ horizontal bounding boxes(HBBs)to capture objects in tunnel images.However,these bounding boxes often fail to effectively enclose objects oriented in arbitrary directions,resulting in reduced accuracy and suboptimal detection performance.Moreover,HBBs cannot provide directional information for rotated objects.This study proposes a rotated detection method for identifying apparent defects in shield tunnels.Specifically,the oriented region-convolutional neural network(oriented R-CNN)is utilized to detect rotated objects in tunnel images.To enhance feature extraction,a novel hybrid backbone combining CNN-based networks with Swin Transformers is proposed.A feature fusion strategy is employed to integrate features extracted from both networks.Additionally,a neck network based on the bidirectional-feature pyramid network(Bi-FPN)is designed to combine multi-scale object features.The bolt hole dataset is curated to evaluate the efficacyof the proposed method.In addition,a dedicated pre-processing approach is developed for large-sized images to accommodate the rotated,dense,and small-scale characteristics of objects in tunnel images.Experimental results demonstrate that the proposed method achieves a more than 4%improvement in mAP_(50-95)compared to other rotated detectors and a 6.6%-12.7%improvement over mainstream horizontal detectors.Furthermore,the proposed method outperforms mainstream methods by 6.5%-14.7%in detecting leakage bolt holes,underscoring its significant engineering applicability.
基金Projects(21361020,21061012)supported by the National Natural Science Foundation of ChinaProject(NZ12156)supported by the Natural Science Foundation of Ningxia,ChinaProject(N-09-13)supported by Project of State Key Laboratory of Catalysis,Dalian Institute of Chemical Physics of the Chinese Academy of Sciences
文摘Electrophoretic deposition in conjunction with electrochemical reduction was used to make flexible free-standing graphene-like films. Firstly, graphene oxide （GO） film was deposited on graphite substrate by electrophoretic deposition method, and then reduced by subsequent electrochemical reduction of GO to obtain reduced GO （ERGO） film with high electrochemical performance. The morphology, structure and electrochemical performance of the prepared graphene-like film were confirmed by SEM, XRD and FT-IR. These unique materials were found to provide high specific capacitance and good cycling stability. The high specific capacitance of 254 F/g was obtained from cyclic voltammetry measurement at a scan rate of 10 mV/s. When the current density increased to 83.3 A/g, the specific capacitance values still remained 132 F/g. Meanwhile, the high powder density of 39.1 kW/kg was measured at energy density of 11.8 W-h/kg in 1 mol/L H2SO4 solution. Furthermore, at a constant scan rate of 50 mV/s, 97.02% of its capacitance was retained for 1000 cycles. These promising results were attributed to the unique assembly structure of graphene film and low contact resistance, which indicated their potential application to electrochemical capacitors.
文摘Fuzziness is an internal property of spatial objects.How to model fuzziness of a spatial object is a main task of next generation GIS.This paper proposes basic fuzzy spatial object types based on fuzzy topology.These object types are the natural extension of current nonfuzzy spatial object types.A fuzzy cell complex structure is defined for modeling fuzzy regions,lines and points.Furthermore,fuzzy topological relations between these fuzzy spatial objects are formalized based on the 9intersection approach.This model can be implemented for GIS applications due to its scientific theory basis.
