Taking TM images, SPOT photos and DEM images as the basic information, this paper had not only put forward a kind of manual controlled computer-automatic extraction method, but also completed the task of extracting th...Taking TM images, SPOT photos and DEM images as the basic information, this paper had not only put forward a kind of manual controlled computer-automatic extraction method, but also completed the task of extracting the main types of ground objects in the Three Gorges Reservoir area under relatively high accuracy, after finishing such preprocessing tasks as correcting the topographical spectrum and synthesizing the data. Taking the specialized image analysis software-eCognition as the platform, the research achieved the goal of classifying through choosing samples, picking out the best wave bands, and producing the identifying functions. At the same time the extraction process partly dispelled the influence of such phenomena as the same thing with different spectrums, different things with the same spectrum, border transitions, etc. The research did certain exploration in the aspect of technological route and method of using automatic extraction of the remote sensing image to obtain the information of land cover for the regions whose ground objects have complicated spectrums.展开更多
A Support Vector Machine is used as a classifier to the automatic detection and recognition of underwater still objects. Discrimination between the objects can be transferred into different projection spaces by the pr...A Support Vector Machine is used as a classifier to the automatic detection and recognition of underwater still objects. Discrimination between the objects can be transferred into different projection spaces by the process of multi-field feature extraction. The multi-field feature vector includes time-domain, spectral, time-frequency distribution and bi-spectral features. Underwater target recognition can be considered as a problem of small sample recognition. SVM algorithm is appropriate to this kind of problems because of its outstanding generalizability. The SVM is contrasted with a Gaussian classifier and a k-nearest classifier in some experiments using real data of lake or sea trial. The experimental results indicate that SVM is better than the others two.展开更多
We have collected a sample of 70 BL Lacs （33 radio-selected BL Lacs and 37 X-ray selected BL Lacs） with multi-waveband data for investigating the classifying criteria of BL Lacertae Objects. For each source, we esti...We have collected a sample of 70 BL Lacs （33 radio-selected BL Lacs and 37 X-ray selected BL Lacs） with multi-waveband data for investigating the classifying criteria of BL Lacertae Objects. For each source, we estimate its luminosities in radio, optical and X-ray, the broad-band spectral index from radio to X-ray and the peak frequency of the synchrotron emission, and make a statistical analysis of the data obtained. Our main results are as follows： （1） The broad-band spectral index and the peak frequency have no correlation with the redshift, while they are inversely correlated with each other and they could be regarded as equivalent classifying criteria of BL Lac objects. （2） There are significant effects of the luminosity/redshift relation on the observed luminosity distribution in our sample, hence, if the radio luminosity is to be used as a classifying criterion of BL Lac objects, it should not be regarded as equivalent to the broad-band spectral index or the peak frequency. （3） Our resuits supply a specific piece of evidence for the suggestion that the use of luminosities always introduces a redshift bias to the data and show that the location of the peak frequency is not always linked to the luminosity of any wave band.展开更多
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
In this study we review the occurrence of different types (A, B, C, M, and X classes) of solar flares during different solar cycle phases from 1996 to 2019 covering the solar cycles 23 and 24. During this period, a to...In this study we review the occurrence of different types (A, B, C, M, and X classes) of solar flares during different solar cycle phases from 1996 to 2019 covering the solar cycles 23 and 24. During this period, a total of 19,126 solar flares were observed regardless the class: 3548 flares in solar cycle 23 (SC23) and 15,668 flares in solar cycle 24 (SC24). Our findings show that the cycle 23 has observed the highest occurrences of M-class and X-class flares, whereas cycle 24 has pointed out a predominance of B-class and C-class flares throughout its different phases. The results indicate that the cycle 23 was magnetically more intense than cycle 24, leading to more powerful solar flares and more frequent geomagnetic storms, capable of generating significant electromagnetic emissions that can affect satellites and GPS signals. The decrease in intense solar flares during cycle 24 compared to cycle 23 reflects an evolution in solar activity patterns over time.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
Unlike traditional video cameras,event cameras capture asynchronous event streams in which each event encodes pixel location,triggers’timestamps,and the polarity of brightness changes.In this paper,we introduce a nov...Unlike traditional video cameras,event cameras capture asynchronous event streams in which each event encodes pixel location,triggers’timestamps,and the polarity of brightness changes.In this paper,we introduce a novel hypergraph-based framework for moving object classification.Specifically,we capture moving objects with an event camera,to perceive and collect asynchronous event streams in a high temporal resolution.Unlike stacked event frames,we encode asynchronous event data into a hypergraph,fully mining the high-order correlation of event data,and designing a mixed convolutional hypergraph neural network for training to achieve a more efficient and accurate motion target recognition.The experimental results show that our method has a good performance in moving object classification(e.g.,gait identification).展开更多
Abs As a crucial vehicle for young children’s artistic enlightenment,music appreciation holds an irreplaceable value in cognitive development,emotional edification,and the cultivation of aesthetic abilities.Currently...Abs As a crucial vehicle for young children’s artistic enlightenment,music appreciation holds an irreplaceable value in cognitive development,emotional edification,and the cultivation of aesthetic abilities.Currently,in music appreciation activities for senior kindergarten classes,there is a widespread phenomenon of homogenized teaching content and mechanized teaching methods,which results in insufficient enthusiasm for participation among young children and a superficial understanding of music.The situational teaching method,by constructing concrete and immersive learning scenarios,can effectively activate young children’s multi-dimensional sensory experiences.Its characteristics of intuitiveness and interactivity are highly consistent with the traits of young children’s concrete thinking,thus providing a new approach to resolving the current predicament.The research focuses on the practical pain points in music appreciation activities for senior kindergarten classes and proposes targeted solutions from four dimensions:content design,method innovation,resource integration,and teacher training,aiming to reconstruct a child-centered,in-depth music learning model.Practice has shown that the situational teaching method can not only enhance young children’s perceptual sensitivity to musical elements but also guide them to achieve emotional resonance through role-playing and life-related associations,laying a foundation for the sustainable development of young children’s musical literacy.展开更多
Dialogue and fusion of horizons are two important concepts of Gadamer’s philosophical hermeneutics,which falls into the pedagogical category of teaching English News Listening Classes.The course of English News Liste...Dialogue and fusion of horizons are two important concepts of Gadamer’s philosophical hermeneutics,which falls into the pedagogical category of teaching English News Listening Classes.The course of English News Listening is one of the most fundamental and difficult courses in the curriculum for college students who are English majors.The simultaneous interpreting training method of shadowing is used in English News Listening Classes in helping students improve their language skill of listening and speaking.In fulfilling a teacher’s pedagogical performance of dialoguing and fusion of horizons,still one thing is important,i.e.,solidarity triggered between students and teacher,which is the good or the ethical choice between students and teacher.In English News Listening Classes,“道”or“the way(Dao)”is shadowing.In teaching English News Listening,a“dialogue”of shadowing could be achieved between students and teacher is even more significant than that of other courses.This paper intends to present the dialogic ethical triggering of fusion of horizons in class.In another word,students’knowing could be guided by teacher’s dialogic ethical triggering in English News classes.In voicing out the language,knowing in listening and speaking could help students have confidence in not only their language skills but in conquering their difficulties in their life.Teaching English News Listening at Northeastern University(NEU)in this way since 2013 has turned out to be good for students’growth and maturation in life.展开更多
Letϕbe a smooth radial weight that decays faster than the class Gaussian ones.We obtain certain estimates for the reproducing kernels and the Lp-estimates for solutions of theδ-equation on the weighted Fock spaces F_...Letϕbe a smooth radial weight that decays faster than the class Gaussian ones.We obtain certain estimates for the reproducing kernels and the Lp-estimates for solutions of theδ-equation on the weighted Fock spaces F_(ϕ)^(p)(1≤p≤∞),which extends the classical Hörmander Theorem.Furthermore,for a suitable f,we completely characterize the boundedness and compactness of the Hankel operator H_(f):F_(ϕ)^(p)→L^(q)(C,e^(qϕ(·))dm)for all possible 1≤p,q<∞and also characterize the Schatten-p class Hankel operator Hf from F_(ϕ)^(2)to L^(2)(C,E^(-2ϕ)dm) for all 0<p<∞. As an application, we give a complete characterization of the simultaneously bounded, compact and Schatten-p classes Hankel operators H_(f) and h_(f)^(-) on F_(ϕ)^(2).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
Accurate detection of small objects is critically important in high-stakes applications such as military reconnaissance and emergency rescue.However,low resolution,occlusion,and background interference make small obje...Accurate detection of small objects is critically important in high-stakes applications such as military reconnaissance and emergency rescue.However,low resolution,occlusion,and background interference make small object detection a complex and demanding task.One effective approach to overcome these issues is the integration of multimodal image data to enhance detection capabilities.This paper proposes a novel small object detection method that utilizes three types of multimodal image combinations,such as Hyperspectral-Multispectral(HSMS),Hyperspectral-Synthetic Aperture Radar(HS-SAR),and HS-SAR-Digital Surface Model(HS-SAR-DSM).The detection process is done by the proposed Jaccard Deep Q-Net(JDQN),which integrates the Jaccard similarity measure with a Deep Q-Network(DQN)using regression modeling.To produce the final output,a Deep Maxout Network(DMN)is employed to fuse the detection results obtained from each modality.The effectiveness of the proposed JDQN is validated using performance metrics,such as accuracy,Mean Squared Error(MSE),precision,and Root Mean Squared Error(RMSE).Experimental results demonstrate that the proposed JDQN method outperforms existing approaches,achieving the highest accuracy of 0.907,a precision of 0.904,the lowest normalized MSE of 0.279,and a normalized RMSE of 0.528.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
The increasing prevalence of violent incidents in public spaces has created an urgent need for intelligent surveillance systems capable of detecting dangerous objects in real time.While traditional video surveillance ...The increasing prevalence of violent incidents in public spaces has created an urgent need for intelligent surveillance systems capable of detecting dangerous objects in real time.While traditional video surveillance relies on human monitoring,this approach suffers from limitations such as fatigue and delayed response times.This study addresses these challenges by developing an automated detection system using advanced deep learning techniques to enhance public safety.Our approach leverages state-of-the-art convolutional neural networks(CNNs),specifically You Only Look Once version 4(YOLOv4)and EfficientDet,for real-time object detection.The system was trained on a comprehensive dataset of over 50,000 images,enhanced through data augmentation techniques to improve robustness across varying lighting conditions and viewing angles.Cloud-based deployment on Amazon Web Services(AWS)ensured scalability and efficient processing.Experimental evaluations demonstrated high performance,with YOLOv4 achieving 92%accuracy and processing images in 0.45 s,while EfficientDet reached 93%accuracy with a slightly longer processing time of 0.55 s per image.Field tests in high-traffic environments such as train stations and shopping malls confirmed the system’s reliability,with a false alarm rate of only 4.5%.The integration of automatic alerts enabled rapid security responses to potential threats.The proposed CNN-based system provides an effective solution for real-time detection of dangerous objects in video surveillance,significantly improving response times and public safety.While YOLOv4 proved more suitable for speed-critical applications,EfficientDet offered marginally better accuracy.Future work will focus on optimizing the system for low-light conditions and further reducing false positives.This research contributes to the advancement of AI-driven surveillance technologies,offering a scalable framework adaptable to various security scenarios.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
In this paper,the class of starlike functions of complex order γ(γ∈ℂ−{0})is extended from the case on unit disk U=(z∈C:|z|<1)to the case on the unit ball B in a complex Banach space or the unit polydisk U^(n) i...In this paper,the class of starlike functions of complex order γ(γ∈ℂ−{0})is extended from the case on unit disk U=(z∈C:|z|<1)to the case on the unit ball B in a complex Banach space or the unit polydisk U^(n) in C^(n).Let g be a convex function in U. We mainly establish the sharp bounds of all terms of homogeneous polynomial expansions for a subclass of g-parametric starlike mappings of complex order γ on B (resp.U^(n))when the mappings f are k-fold symmetric, k ∈ N. Our results partly solve the Bieberbach conjecture in several complex variables and generalize some prior works.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
This paper presents a new class of surfaces that give two quite different appearances when they are seen from two special viewpoints.The inconsistent appearances can be perceived by simultaneously viewing them directl...This paper presents a new class of surfaces that give two quite different appearances when they are seen from two special viewpoints.The inconsistent appearances can be perceived by simultaneously viewing them directly and in a mirror.This phenomenon is a new type of optical illusion,and we have named it the"ambiguous cylinder illusion",because it is typically generated by cylindrical surfaces.We consider why this illusion arises,and we present a mathematical method for designing ambiguous cylinders.展开更多
基金Under the auspices of the Construction Committeeof Three GorgesR eservoirProject(No .SX [2002]00401) andChineseAcademy ofSciences(No .KZCX2-SW-319-01 )
文摘Taking TM images, SPOT photos and DEM images as the basic information, this paper had not only put forward a kind of manual controlled computer-automatic extraction method, but also completed the task of extracting the main types of ground objects in the Three Gorges Reservoir area under relatively high accuracy, after finishing such preprocessing tasks as correcting the topographical spectrum and synthesizing the data. Taking the specialized image analysis software-eCognition as the platform, the research achieved the goal of classifying through choosing samples, picking out the best wave bands, and producing the identifying functions. At the same time the extraction process partly dispelled the influence of such phenomena as the same thing with different spectrums, different things with the same spectrum, border transitions, etc. The research did certain exploration in the aspect of technological route and method of using automatic extraction of the remote sensing image to obtain the information of land cover for the regions whose ground objects have complicated spectrums.
基金Supported by the Major State Basic Research Development Program of China under Grant No. 5132103ZZT32.
文摘A Support Vector Machine is used as a classifier to the automatic detection and recognition of underwater still objects. Discrimination between the objects can be transferred into different projection spaces by the process of multi-field feature extraction. The multi-field feature vector includes time-domain, spectral, time-frequency distribution and bi-spectral features. Underwater target recognition can be considered as a problem of small sample recognition. SVM algorithm is appropriate to this kind of problems because of its outstanding generalizability. The SVM is contrasted with a Gaussian classifier and a k-nearest classifier in some experiments using real data of lake or sea trial. The experimental results indicate that SVM is better than the others two.
基金the National Natural Science Foundation of China
文摘We have collected a sample of 70 BL Lacs （33 radio-selected BL Lacs and 37 X-ray selected BL Lacs） with multi-waveband data for investigating the classifying criteria of BL Lacertae Objects. For each source, we estimate its luminosities in radio, optical and X-ray, the broad-band spectral index from radio to X-ray and the peak frequency of the synchrotron emission, and make a statistical analysis of the data obtained. Our main results are as follows： （1） The broad-band spectral index and the peak frequency have no correlation with the redshift, while they are inversely correlated with each other and they could be regarded as equivalent classifying criteria of BL Lac objects. （2） There are significant effects of the luminosity/redshift relation on the observed luminosity distribution in our sample, hence, if the radio luminosity is to be used as a classifying criterion of BL Lac objects, it should not be regarded as equivalent to the broad-band spectral index or the peak frequency. （3） Our resuits supply a specific piece of evidence for the suggestion that the use of luminosities always introduces a redshift bias to the data and show that the location of the peak frequency is not always linked to the luminosity of any wave band.
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.
文摘In this study we review the occurrence of different types (A, B, C, M, and X classes) of solar flares during different solar cycle phases from 1996 to 2019 covering the solar cycles 23 and 24. During this period, a total of 19,126 solar flares were observed regardless the class: 3548 flares in solar cycle 23 (SC23) and 15,668 flares in solar cycle 24 (SC24). Our findings show that the cycle 23 has observed the highest occurrences of M-class and X-class flares, whereas cycle 24 has pointed out a predominance of B-class and C-class flares throughout its different phases. The results indicate that the cycle 23 was magnetically more intense than cycle 24, leading to more powerful solar flares and more frequent geomagnetic storms, capable of generating significant electromagnetic emissions that can affect satellites and GPS signals. The decrease in intense solar flares during cycle 24 compared to cycle 23 reflects an evolution in solar activity patterns over time.
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the National Key Research and Development Program of China(No.2021ZD0112400)。
文摘Unlike traditional video cameras,event cameras capture asynchronous event streams in which each event encodes pixel location,triggers’timestamps,and the polarity of brightness changes.In this paper,we introduce a novel hypergraph-based framework for moving object classification.Specifically,we capture moving objects with an event camera,to perceive and collect asynchronous event streams in a high temporal resolution.Unlike stacked event frames,we encode asynchronous event data into a hypergraph,fully mining the high-order correlation of event data,and designing a mixed convolutional hypergraph neural network for training to achieve a more efficient and accurate motion target recognition.The experimental results show that our method has a good performance in moving object classification(e.g.,gait identification).
文摘Abs As a crucial vehicle for young children’s artistic enlightenment,music appreciation holds an irreplaceable value in cognitive development,emotional edification,and the cultivation of aesthetic abilities.Currently,in music appreciation activities for senior kindergarten classes,there is a widespread phenomenon of homogenized teaching content and mechanized teaching methods,which results in insufficient enthusiasm for participation among young children and a superficial understanding of music.The situational teaching method,by constructing concrete and immersive learning scenarios,can effectively activate young children’s multi-dimensional sensory experiences.Its characteristics of intuitiveness and interactivity are highly consistent with the traits of young children’s concrete thinking,thus providing a new approach to resolving the current predicament.The research focuses on the practical pain points in music appreciation activities for senior kindergarten classes and proposes targeted solutions from four dimensions:content design,method innovation,resource integration,and teacher training,aiming to reconstruct a child-centered,in-depth music learning model.Practice has shown that the situational teaching method can not only enhance young children’s perceptual sensitivity to musical elements but also guide them to achieve emotional resonance through role-playing and life-related associations,laying a foundation for the sustainable development of young children’s musical literacy.
基金Liaoning Province Education and Scientific Research Young and Middle-Aged Teachers Special Project:Innovative Research and Practice of Chunk-Based Interpreting Teaching for IP’s Curriculum(Fund No.:JG24QGA06).
文摘Dialogue and fusion of horizons are two important concepts of Gadamer’s philosophical hermeneutics,which falls into the pedagogical category of teaching English News Listening Classes.The course of English News Listening is one of the most fundamental and difficult courses in the curriculum for college students who are English majors.The simultaneous interpreting training method of shadowing is used in English News Listening Classes in helping students improve their language skill of listening and speaking.In fulfilling a teacher’s pedagogical performance of dialoguing and fusion of horizons,still one thing is important,i.e.,solidarity triggered between students and teacher,which is the good or the ethical choice between students and teacher.In English News Listening Classes,“道”or“the way(Dao)”is shadowing.In teaching English News Listening,a“dialogue”of shadowing could be achieved between students and teacher is even more significant than that of other courses.This paper intends to present the dialogic ethical triggering of fusion of horizons in class.In another word,students’knowing could be guided by teacher’s dialogic ethical triggering in English News classes.In voicing out the language,knowing in listening and speaking could help students have confidence in not only their language skills but in conquering their difficulties in their life.Teaching English News Listening at Northeastern University(NEU)in this way since 2013 has turned out to be good for students’growth and maturation in life.
文摘Letϕbe a smooth radial weight that decays faster than the class Gaussian ones.We obtain certain estimates for the reproducing kernels and the Lp-estimates for solutions of theδ-equation on the weighted Fock spaces F_(ϕ)^(p)(1≤p≤∞),which extends the classical Hörmander Theorem.Furthermore,for a suitable f,we completely characterize the boundedness and compactness of the Hankel operator H_(f):F_(ϕ)^(p)→L^(q)(C,e^(qϕ(·))dm)for all possible 1≤p,q<∞and also characterize the Schatten-p class Hankel operator Hf from F_(ϕ)^(2)to L^(2)(C,E^(-2ϕ)dm) for all 0<p<∞. As an application, we give a complete characterization of the simultaneously bounded, compact and Schatten-p classes Hankel operators H_(f) and h_(f)^(-) on F_(ϕ)^(2).
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.
文摘Accurate detection of small objects is critically important in high-stakes applications such as military reconnaissance and emergency rescue.However,low resolution,occlusion,and background interference make small object detection a complex and demanding task.One effective approach to overcome these issues is the integration of multimodal image data to enhance detection capabilities.This paper proposes a novel small object detection method that utilizes three types of multimodal image combinations,such as Hyperspectral-Multispectral(HSMS),Hyperspectral-Synthetic Aperture Radar(HS-SAR),and HS-SAR-Digital Surface Model(HS-SAR-DSM).The detection process is done by the proposed Jaccard Deep Q-Net(JDQN),which integrates the Jaccard similarity measure with a Deep Q-Network(DQN)using regression modeling.To produce the final output,a Deep Maxout Network(DMN)is employed to fuse the detection results obtained from each modality.The effectiveness of the proposed JDQN is validated using performance metrics,such as accuracy,Mean Squared Error(MSE),precision,and Root Mean Squared Error(RMSE).Experimental results demonstrate that the proposed JDQN method outperforms existing approaches,achieving the highest accuracy of 0.907,a precision of 0.904,the lowest normalized MSE of 0.279,and a normalized RMSE of 0.528.
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.
文摘The increasing prevalence of violent incidents in public spaces has created an urgent need for intelligent surveillance systems capable of detecting dangerous objects in real time.While traditional video surveillance relies on human monitoring,this approach suffers from limitations such as fatigue and delayed response times.This study addresses these challenges by developing an automated detection system using advanced deep learning techniques to enhance public safety.Our approach leverages state-of-the-art convolutional neural networks(CNNs),specifically You Only Look Once version 4(YOLOv4)and EfficientDet,for real-time object detection.The system was trained on a comprehensive dataset of over 50,000 images,enhanced through data augmentation techniques to improve robustness across varying lighting conditions and viewing angles.Cloud-based deployment on Amazon Web Services(AWS)ensured scalability and efficient processing.Experimental evaluations demonstrated high performance,with YOLOv4 achieving 92%accuracy and processing images in 0.45 s,while EfficientDet reached 93%accuracy with a slightly longer processing time of 0.55 s per image.Field tests in high-traffic environments such as train stations and shopping malls confirmed the system’s reliability,with a false alarm rate of only 4.5%.The integration of automatic alerts enabled rapid security responses to potential threats.The proposed CNN-based system provides an effective solution for real-time detection of dangerous objects in video surveillance,significantly improving response times and public safety.While YOLOv4 proved more suitable for speed-critical applications,EfficientDet offered marginally better accuracy.Future work will focus on optimizing the system for low-light conditions and further reducing false positives.This research contributes to the advancement of AI-driven surveillance technologies,offering a scalable framework adaptable to various security scenarios.
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supported by the National Natural Science Foundation of China(12061035)the Research Foundation of Jiangxi Science and Technology Normal University of China(2021QNBJRC003)supported by the Graduate Innovation Fund of Jiangxi Science and Technology Normal University(YC2024-X10).
文摘In this paper,the class of starlike functions of complex order γ(γ∈ℂ−{0})is extended from the case on unit disk U=(z∈C:|z|<1)to the case on the unit ball B in a complex Banach space or the unit polydisk U^(n) in C^(n).Let g be a convex function in U. We mainly establish the sharp bounds of all terms of homogeneous polynomial expansions for a subclass of g-parametric starlike mappings of complex order γ on B (resp.U^(n))when the mappings f are k-fold symmetric, k ∈ N. Our results partly solve the Bieberbach conjecture in several complex variables and generalize some prior works.
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Supported the Grant-in-Aid for Basic Scientific Research(No.24360039)Challenging Exploratory Research(No.15K12067)
文摘This paper presents a new class of surfaces that give two quite different appearances when they are seen from two special viewpoints.The inconsistent appearances can be perceived by simultaneously viewing them directly and in a mirror.This phenomenon is a new type of optical illusion,and we have named it the"ambiguous cylinder illusion",because it is typically generated by cylindrical surfaces.We consider why this illusion arises,and we present a mathematical method for designing ambiguous cylinders.
