Two-dimensional endoscopic images are susceptible to interferences such as specular reflections and monotonous texture illumination,hindering accurate three-dimensional lesion reconstruction by surgical robots.This st...Two-dimensional endoscopic images are susceptible to interferences such as specular reflections and monotonous texture illumination,hindering accurate three-dimensional lesion reconstruction by surgical robots.This study proposes a novel end-to-end disparity estimation model to address these challenges.Our approach combines a Pseudo-Siamese neural network architecture with pyramid dilated convolutions,integrating multi-scale image information to enhance robustness against lighting interferences.This study introduces a Pseudo-Siamese structure-based disparity regression model that simplifies left-right image comparison,improving accuracy and efficiency.The model was evaluated using a dataset of stereo endoscopic videos captured by the Da Vinci surgical robot,comprising simulated silicone heart sequences and real heart video data.Experimental results demonstrate significant improvement in the network’s resistance to lighting interference without substantially increasing parameters.Moreover,the model exhibited faster convergence during training,contributing to overall performance enhancement.This study advances endoscopic image processing accuracy and has potential implications for surgical robot applications in complex environments.展开更多
Audio-visual scene classification(AVSC)poses a formidable challenge owing to the intricate spatial-temporal relationships exhibited by audio-visual signals,coupled with the complex spatial patterns of objects and text...Audio-visual scene classification(AVSC)poses a formidable challenge owing to the intricate spatial-temporal relationships exhibited by audio-visual signals,coupled with the complex spatial patterns of objects and textures found in visual images.The focus of recent studies has predominantly revolved around extracting features from diverse neural network structures,inadvertently neglecting the acquisition of semantically meaningful regions and crucial components within audio-visual data.The authors present a feature pyramid attention network(FPANet)for audio-visual scene understanding,which extracts semantically significant characteristics from audio-visual data.The authors’approach builds multi-scale hierarchical features of sound spectrograms and visual images using a feature pyramid representation and localises the semantically relevant regions with a feature pyramid attention module(FPAM).A dimension alignment(DA)strategy is employed to align feature maps from multiple layers,a pyramid spatial attention(PSA)to spatially locate essential regions,and a pyramid channel attention(PCA)to pinpoint significant temporal frames.Experiments on visual scene classification(VSC),audio scene classification(ASC),and AVSC tasks demonstrate that FPANet achieves performance on par with state-of-the-art(SOTA)approaches,with a 95.9 F1-score on the ADVANCE dataset and a relative improvement of 28.8%.Visualisation results show that FPANet can prioritise semantically meaningful areas in audio-visual signals.展开更多
Pulmonary nodules represent an early manifestation of lung cancer.However,pulmonary nodules only constitute a small portion of the overall image,posing challenges for physicians in image interpretation and potentially...Pulmonary nodules represent an early manifestation of lung cancer.However,pulmonary nodules only constitute a small portion of the overall image,posing challenges for physicians in image interpretation and potentially leading to false positives or missed detections.To solve these problems,the YOLOv8 network is enhanced by adding deformable convolution and atrous spatial pyramid pooling(ASPP),along with the integration of a coordinate attention(CA)mechanism.This allows the network to focus on small targets while expanding the receptive field without losing resolution.At the same time,context information on the target is gathered and feature expression is enhanced by attention modules in different directions.It effectively improves the positioning accuracy and achieves good results on the LUNA16 dataset.Compared with other detection algorithms,it improves the accuracy of pulmonary nodule detection to a certain extent.展开更多
Drone-based small object detection is of great significance in practical applications such as military actions, disaster rescue, transportation, etc. However, the severe scale differences in objects captured by drones...Drone-based small object detection is of great significance in practical applications such as military actions, disaster rescue, transportation, etc. However, the severe scale differences in objects captured by drones and lack of detail information for small-scale objects make drone-based small object detection a formidable challenge. To address these issues, we first develop a mathematical model to explore how changing receptive fields impacts the polynomial fitting results. Subsequently, based on the obtained conclusions, we propose a simple but effective Hybrid Receptive Field Network (HRFNet), whose modules include Hybrid Feature Augmentation (HFA), Hybrid Feature Pyramid (HFP) and Dual Scale Head (DSH). Specifically, HFA employs parallel dilated convolution kernels of different sizes to extend shallow features with different receptive fields, committed to improving the multi-scale adaptability of the network;HFP enhances the perception of small objects by capturing contextual information across layers, while DSH reconstructs the original prediction head utilizing a set of high-resolution features and ultrahigh-resolution features. In addition, in order to train HRFNet, the corresponding dual-scale loss function is designed. Finally, comprehensive evaluation results on public benchmarks such as VisDrone-DET and TinyPerson demonstrate the robustness of the proposed method. Most impressively, the proposed HRFNet achieves a mAP of 51.0 on VisDrone-DET with 29.3 M parameters, which outperforms the extant state-of-the-art detectors. HRFNet also performs excellently in complex scenarios captured by drones, achieving the best performance on the CS-Drone dataset we built.展开更多
Archaeologists(考古学家)have unearthed the remains of a Mayan city nearly 3,000 years old in northern Guatemala,with pyramids(金字塔)and monuments that showcase its significance as an important ceremonial site.The May...Archaeologists(考古学家)have unearthed the remains of a Mayan city nearly 3,000 years old in northern Guatemala,with pyramids(金字塔)and monuments that showcase its significance as an important ceremonial site.The Mayan civilization arose around 2000 BC,reaching its height between 400 AD and 900 AD in what is present-day southern Mexico and Guatemala,as well as parts of Belize,El Salvador and Honduras.展开更多
Pest detection techniques are helpful in reducing the frequency and scale of pest outbreaks;however,their application in the actual agricultural production process is still challenging owing to the problems of intersp...Pest detection techniques are helpful in reducing the frequency and scale of pest outbreaks;however,their application in the actual agricultural production process is still challenging owing to the problems of interspecies similarity,multi-scale,and background complexity of pests.To address these problems,this study proposes an FD-YOLO pest target detection model.The FD-YOLO model uses a Fully Connected Feature Pyramid Network(FC-FPN)instead of a PANet in the neck,which can adaptively fuse multi-scale information so that the model can retain small-scale target features in the deep layer,enhance large-scale target features in the shallow layer,and enhance the multiplexing of effective features.A dual self-attention module(DSA)is then embedded in the C3 module of the neck,which captures the dependencies between the information in both spatial and channel dimensions,effectively enhancing global features.We selected 16 types of pests that widely damage field crops in the IP102 pest dataset,which were used as our dataset after data supplementation and enhancement.The experimental results showed that FD-YOLO’s mAP@0.5 improved by 6.8%compared to YOLOv5,reaching 82.6%and 19.1%–5%better than other state-of-the-art models.This method provides an effective new approach for detecting similar or multiscale pests in field crops.展开更多
Pyramidal dislocations are important for ductility enhancement of magnesium alloys.In this work,molecular dynamics simulations were employed to study the gliding behavior of pyramidal(c+a)dislocations under c-axis com...Pyramidal dislocations are important for ductility enhancement of magnesium alloys.In this work,molecular dynamics simulations were employed to study the gliding behavior of pyramidal(c+a)dislocations under c-axis compressive loading and tensile loading.The Peierls stress of Py-Ⅰ dislocation shows strong tension-compression asymmetry.However,no tension-compression asymmetry is seen on the Py-Ⅱ dislocation and basal dislocation.The tension-compression asymmetry origins from the asymmetry of partial dislocations of Py-Ⅰ dislocation,which leads to the dislocation core contracted under c-axis compressive loading and expanded under tensile loading.By analyzing the forces acting on the partial dislocations,we defined a neutral direction,which deviates from the full dislocation Burgers vector by 70.3°.The neutral direction is dependent on the ratio of lattice stresses of partial dislocations.If the shear stress is applied along the neutral direction,tension-compression asymmetry is eliminated and the dislocation core is un-contracted/un-expanded.The neutral direction of symmetrical dislocations(Py-Ⅱ dislocation and basal dislocation)is just the full dislocation Burgers vector.The tension-compression asymmetry and dislocation core contraction/expansion have an important influence on the dislocation behaviors,such as cross-slip,decomposition,basaltransition and mobility,which can be used to explain the mechanical behaviors of Mg single-crystals compressed along c-axis.展开更多
Dynamic recrystallization(DRX)in inhomogeneous deformation zones,such as grain boundaries,shear bands,and deformation bands,is critical for texture modification in magnesium alloys during deformation at elevated temper...Dynamic recrystallization(DRX)in inhomogeneous deformation zones,such as grain boundaries,shear bands,and deformation bands,is critical for texture modification in magnesium alloys during deformation at elevated temperatures.This study investigates the DRX mechanisms in AZWX3100 magnesium alloy under plane strain compression at 200℃.Microstructural analysis revealed necklace-type DRX accompanied by evidence of local grain boundary bulging.Additionally,ribbons of recrystallized grains were observed withinfine deformation bands,aligned with theoretical pyramidal I and II slip traces derived from the matrix.The distribution of local misorientation within the deformed microstructure demonstrated a clear association between deformation bands and localized strain.Dislocation analysis of lamellar specimens extracted from two pyramidal slip bands revealed<c+a>dislocations,indicating a connection between<c+a>slip activation and the formation of deformation bands.Crystal plasticity simulations suggest that the orientation of deformation bands is responsible for the unique recrystallization texture of the DRX grains within these bands.The texture characteristics imply a progressive,glide-induced DRX mechanism.A fundamental understanding of the role of deformation bands in texture modification can facilitate future alloy and process design.展开更多
The grain-scale tension-compression(T-C)asymmetric slip behavior and geometrically necessary dislocation(GND)density in an aged and twin-free Mg-10Y sheet were statistically studied using slip trace analysis and elect...The grain-scale tension-compression(T-C)asymmetric slip behavior and geometrically necessary dislocation(GND)density in an aged and twin-free Mg-10Y sheet were statistically studied using slip trace analysis and electron backscatter diffraction(EBSD)analysis.A significantly asymmetric slip activity,i.e.,higher tensile slip activity and proportion of non-basal slip,was manifested.Prismatic〈a〉(37.1%)and basal〈a〉(27.6%)slips dominated the tensile deformation,followed by pyramidalⅡ〈c+a〉slip(20.0%).While during compression,basal〈a〉slip(61.9%)was the most active slip mode,and only 6.9% pyramidalⅡ〈c+a〉slip was observed.The critical resolved shear stress(CRSS)ratio was estimated based on~800 sets of the identified slip traces,which suggested that the CRSS_(pyrⅡ)/CRSS_(bas)for compression was~3 times than that of tension.The pyramidalⅡ〈c+a〉slip was more active when the slip plane was under tension than under compression,which was consistent with the calculated asymmetric CRSS_(pyrⅡ)/CRSS_(bas).The activity of multiple slip,cross slip and slip transfer,as well as the GND density were also T-C asymmetric.This work thoughtfully demonstrated the T-C asymmetric slip behavior and plastic heterogeneity in Mg alloys which was believed to be responsible for the macroscopic T-C asymmetry when twinning was absent.The present statistical results are valuable for validating and/or facilitating crystal plasticity simulations.展开更多
Germanium(Ge)-air battery,a new type of semiconductor-air battery,has garnered increasing attention owing to its environmental friendliness,safety,and excellent dynamic performance.However,the flat Ge anode is prone t...Germanium(Ge)-air battery,a new type of semiconductor-air battery,has garnered increasing attention owing to its environmental friendliness,safety,and excellent dynamic performance.However,the flat Ge anode is prone to passivation,owing to GeO_(2) accumulation on its surface,resulting in premature discharge termination.In this study,various nano-Ge pyramid structures(GePS)were prepared using chemical etching(CE)and metal-assisted chemical etching(MACE)methods to enhance the specific surface area of the Ge anode,thereby facilitating the dissolution of the passivation layer.This study revealed that the MACE method significantly accelerated the etching rate of the Ge surface,producing exceptional GePS.Furthermore,Ge-air batteries employing Ge anodes prepared using MACE demonstrated an exceptional discharge life of up to 9240 h(385 days).The peak power density reached 3.03mW/cm^(2),representing improvements of more than 2 times and 1.8 times,respectively,compared with batteries using flat Ge anodes.This study presents a straightforward approach to enhance Ge anode performance,thereby expanding the potential applications of Ge-air batteries.展开更多
The generation of high-quality,realistic face generation has emerged as a key field of research in computer vision.This paper proposes a robust approach that combines a Super-Resolution Generative Adversarial Network(...The generation of high-quality,realistic face generation has emerged as a key field of research in computer vision.This paper proposes a robust approach that combines a Super-Resolution Generative Adversarial Network(SRGAN)with a Pyramid Attention Module(PAM)to enhance the quality of deep face generation.The SRGAN framework is designed to improve the resolution of generated images,addressing common challenges such as blurriness and a lack of intricate details.The Pyramid Attention Module further complements the process by focusing on multi-scale feature extraction,enabling the network to capture finer details and complex facial features more effectively.The proposed method was trained and evaluated over 100 epochs on the CelebA dataset,demonstrating consistent improvements in image quality and a marked decrease in generator and discriminator losses,reflecting the model’s capacity to learn and synthesize high-quality images effectively,given adequate computational resources.Experimental outcome demonstrates that the SRGAN model with PAM module has outperformed,yielding an aggregate discriminator loss of 0.055 for real,0.043 for fake,and a generator loss of 10.58 after training for 100 epochs.The model has yielded an structural similarity index measure of 0.923,that has outperformed the other models that are considered in the current study for analysis.展开更多
The work aims to investigate the formation and transformation mechanism of non-basal texture in the extruded Mg alloys.With this purpose a pure Mg as reference and eight Mg-Gd binary alloys with the Gd concentration r...The work aims to investigate the formation and transformation mechanism of non-basal texture in the extruded Mg alloys.With this purpose a pure Mg as reference and eight Mg-Gd binary alloys with the Gd concentration ranging from 0.5 wt.%to 18 wt.%were prepared for extrusion.This study shows that the basal fiber texture in pure Mg transited into RE(rare earth)texture in diluted Mg-Gd alloys and into the abnormal C-texture in high-concentration Mg-Gd alloys.In pure Mg,discontinuous dynamic recrystallization plays a predominant role during the extrusion process,resulting in the formation of a typical basal fiber texture.Alloying with high concentration of Gd impedes the dynamic recrystallization process,facilitating the heterogeneous nucleation of shear bands as well as the dynamic recrystallization within shear bands.Dynamic recrystallized grains within shear bands nucleate with a similar orientation to the host deformed parent grains and gradually tilt their c-axis to the extrusion direction during growth by absorbing dislocations,leading to the formation of either the REtexture orientation or the C-texture orientation,depending on the stored energy within shear bands.The analysis aided by IGMA and TEM characterization reveals that the shear bands originate from the extensive but heterogeneous activation of pyramidal I slip.Tensile tests illustrate a close correlation between the fracture elongation and texture types.A comprehensive understanding of the formation and transformation mechanism of different texture components in Mg alloys holds significant importance for the design of high-performance Mg alloys by texture engineering.展开更多
To overcome the challenges of poor real-time performance,limited scalability,and low intelligence in conventional jamming pattern recognition methods,this paper proposes a method based on Wavelet Packet Decomposition(...To overcome the challenges of poor real-time performance,limited scalability,and low intelligence in conventional jamming pattern recognition methods,this paper proposes a method based on Wavelet Packet Decomposition(WPD)and enhanced deep learning techniques.In the proposed method,an agent at the receiver processes the received signal using WPD to generate an initial Spectrogram Waterfall(SW),which is subsequently segmented using a sliding window to serve as the input for the jamming recognition network.The network employs a bilateral filter to preprocess the input SW,thereby enhancing the edge features of the jamming signals.To extract abstract features,depthwise separable convolution is utilized instead of traditional convolution,thereby reducing the network’s parameter count and enhancing real-time performance.A pyramid pooling layer is integrated before the fully connected layer to enable the network to process input SW of varying sizes,thus enhancing scalability.During network training,adaptive moment estimation is employed as the optimizer,allowing the network to dynamically adjust the learning rate and accelerate convergence.A comprehensive comparison between the proposed jamming recognition network and six other models is conducted,along with Ablation Experiments(AE)based on numerical simulations.Simulation results demonstrate that the proposed method based on WPD and enhanced deep learning achieves high-precision recognition of various jamming patterns while maintaining a favorable balance among prediction accuracy,network complexity,and prediction time.展开更多
Zn vapour is easily generated on the surface by fusion welding galvanized steel sheet,resulting in the formation of defects.Rapidly developing computer vision sensing technology collects weld images in the welding pro...Zn vapour is easily generated on the surface by fusion welding galvanized steel sheet,resulting in the formation of defects.Rapidly developing computer vision sensing technology collects weld images in the welding process,then obtains laser fringe information through digital image processing,identifies welding defects,and finally realizes online control of weld defects.The performance of a convolutional neural network is related to its structure and the quality of the input image.The acquired original images are labeled with LabelMe,and repeated attempts are made to determine the appropriate filtering and edge detection image preprocessing methods.Two-stage convolutional neural networks with different structures are built on the Tensorflow deep learning framework,different thresholds of intersection over union are set,and deep learning methods are used to evaluate the collected original images and the preprocessed images separately.Compared with the test results,the comprehensive performance of the improved feature pyramid networks algorithm based on the basic network VGG16 is lower than that of the basic network Resnet101.Edge detection of the image will significantly improve the accuracy of the model.Adding blur will reduce the accuracy of the model slightly;however,the overall performance of the improved algorithm is still relatively good,which proves the stability of the algorithm.The self-developed software inspection system can be used for image preprocessing and defect recognition,which can be used to record the number and location of typical defects in continuous welds.展开更多
Rail surface damage is a critical component of high-speed railway infrastructure,directly affecting train operational stability and safety.Existing methods face limitations in accuracy and speed for small-sample,multi...Rail surface damage is a critical component of high-speed railway infrastructure,directly affecting train operational stability and safety.Existing methods face limitations in accuracy and speed for small-sample,multi-category,and multi-scale target segmentation tasks.To address these challenges,this paper proposes Pyramid-MixNet,an intelligent segmentation model for high-speed rail surface damage,leveraging dataset construction and expansion alongside a feature pyramid-based encoder-decoder network with multi-attention mechanisms.The encoding net-work integrates Spatial Reduction Masked Multi-Head Attention(SRMMHA)to enhance global feature extraction while reducing trainable parameters.The decoding network incorporates Mix-Attention(MA),enabling multi-scale structural understanding and cross-scale token group correlation learning.Experimental results demonstrate that the proposed method achieves 62.17%average segmentation accuracy,80.28%Damage Dice Coefficient,and 56.83 FPS,meeting real-time detection requirements.The model’s high accuracy and scene adaptability significantly improve the detection of small-scale and complex multi-scale rail damage,offering practical value for real-time monitoring in high-speed railway maintenance systems.展开更多
Techniques in deep learning have significantly boosted the accuracy and productivity of computer vision segmentation tasks.This article offers an intriguing architecture for semantic,instance,and panoptic segmentation...Techniques in deep learning have significantly boosted the accuracy and productivity of computer vision segmentation tasks.This article offers an intriguing architecture for semantic,instance,and panoptic segmentation using EfficientNet-B7 and Bidirectional Feature Pyramid Networks(Bi-FPN).When implemented in place of the EfficientNet-B5 backbone,EfficientNet-B7 strengthens the model’s feature extraction capabilities and is far more appropriate for real-world applications.By ensuring superior multi-scale feature fusion,Bi-FPN integration enhances the segmentation of complex objects across various urban environments.The design suggested is examined on rigorous datasets,encompassing Cityscapes,Common Objects in Context,KITTI Karlsruhe Institute of Technology and Toyota Technological Institute,and Indian Driving Dataset,which replicate numerous real-world driving conditions.During extensive training,validation,and testing,the model showcases major gains in segmentation accuracy and surpasses state-of-the-art performance in semantic,instance,and panoptic segmentation tasks.Outperforming present methods,the recommended approach generates noteworthy gains in Panoptic Quality:+0.4%on Cityscapes,+0.2%on COCO,+1.7%on KITTI,and+0.4%on IDD.These changes show just how efficient it is in various driving circumstances and datasets.This study emphasizes the potential of EfficientNet-B7 and Bi-FPN to provide dependable,high-precision segmentation in computer vision applications,primarily autonomous driving.The research results suggest that this framework efficiently tackles the constraints of practical situations while delivering a robust solution for high-performance tasks involving segmentation.展开更多
Due to the necessity for lightweight and efficient network models, deploying semantic segmentation models on mobile robots (MRs) is a formidable task. The fundamental limitation of the problem lies in the training per...Due to the necessity for lightweight and efficient network models, deploying semantic segmentation models on mobile robots (MRs) is a formidable task. The fundamental limitation of the problem lies in the training performance, the ability to effectively exploit the dataset, and the ability to adapt to complex environments when deploying the model. By utilizing the knowledge distillation techniques, the article strives to overcome the above challenges with the inheritance of the advantages of both the teacher model and the student model. More precisely, the ResNet152-PSP-Net model’s characteristics are utilized to train the ResNet18-PSP-Net model. Pyramid pooling blocks are utilized to decode multi-scale feature maps, creating a complete semantic map inference. The student model not only preserves the strong segmentation performance from the teacher model but also improves the inference speed of the prediction results. The proposed method exhibits a clear advantage over conventional convolutional neural network (CNN) models, as evident from the conducted experiments. Furthermore, the proposed model also shows remarkable improvement in processing speed when compared with light-weight models such as MobileNetV2 and EfficientNet based on latency and throughput parameters. The proposed KD-SegNet model obtains an accuracy of 96.3% and a mIoU (mean Intersection over Union) of 77%, outperforming the performance of existing models by more than 15% on the same training dataset. The suggested method has an average training time that is only 0.51 times less than same field models, while still achieving comparable segmentation performance. Hence, the semantic segmentation frames are collected, forming the motion trajectory for the system in the environment. Overall, this architecture shows great promise for the development of knowledge-based systems for MR’s navigation.展开更多
This study focuses on tool condition recognition through data-driven approaches to enhance the intelligence level of computerized numerical control(CNC)machining processes and improve tool utilization efficiency.Tradi...This study focuses on tool condition recognition through data-driven approaches to enhance the intelligence level of computerized numerical control(CNC)machining processes and improve tool utilization efficiency.Traditional tool monitoring methods that rely on empirical knowledge or limited mathematical models struggle to adapt to complex and dynamic machining environments.To address this,we implement real-time tool condition recognition by introducing deep learning technology.Aiming to the insufficient recognition accuracy,we propose a pyramid pooling-based vision Transformer network(P2ViT-Net)method for tool condition recognition.Using images as input effectively mitigates the issue of low-dimensional signal features.We enhance the vision Transformer(ViT)framework for image classification by developing the P2ViT model and adapt it to tool condition recognition.Experimental results demonstrate that our improved P2ViT model achieves 94.4%recognition accuracy,showing a 10%improvement over conventional ViT and outperforming all comparative convolutional neural network models.展开更多
In remote sensing imagery,approximately 67%of the data are affected by cloud cover,significantly increasing the difficulty of image classification,recognition,and other downstream interpretation tasks.To effectively a...In remote sensing imagery,approximately 67%of the data are affected by cloud cover,significantly increasing the difficulty of image classification,recognition,and other downstream interpretation tasks.To effectively address the randomness of cloud distribution and the non-uniformity of cloud thickness,we propose a coarse-to-fine thin cloud removal architecture based on the observations of the random distribution and uneven thickness of cloud.In the coarse-level declouding network,we innovatively introduce a multi-scale attention mechanism,i.e.,pyramid nonlocal attention(PNA).By integrating global context with local detail information,it specifically addresses image quality degradation caused by the uncertainty in cloud distribution.During the fine-level declouding stage,we focus on the impact of cloud thickness on declouding results(primarily manifested as insufficient detail information).Through a carefully designed residual dense module,we significantly enhance the extraction and utilization of feature details.Thus,our approach precisely restores lost local texture features on top of coarse-level results,achieving a substantial leap in declouding quality.To evaluate the effectiveness of our cloud removal technology and attention mechanism,we conducted comprehensive analyses on publicly available datasets.Results demonstrate that our method achieves state-of-the-art performance across a wide range of techniques.展开更多
This paper mainly explores the architectural form of the interior of Maiji Mountain’s Cave 4 and the spiritual realm it establishes.Previous research holds the view that the niche within Cave 4 is architecturally mod...This paper mainly explores the architectural form of the interior of Maiji Mountain’s Cave 4 and the spiritual realm it establishes.Previous research holds the view that the niche within Cave 4 is architecturally modeled after a tent-like structure.However,after in-depth research,this paper suggests that the niche in Cave 4 is more of an imitation of an embedded stupa,or rather,it reconstructs the stupa in an embedded form within the cave.In prior studies,analysis historical background and architectural details of Cave 4 remains inadequate,thus,this paper aims to take a thorough investigation on this point,and further clarify the significance of the form of the Buddha niche in Cave 4,as well as its construction design origins.Specifically,the octagonal wooden-imitating column,lotus-shaped clamp,inverted lotus pedestal,five lotus petals,and shadow sculptures within the niche,the shallow-relief ceiling and small platform outside the niche,as well as the banana leaf decorations,all suggest that the architectural form Cave 4 imitates is likely a single-story wooden stupa.In this way,the connection that this embedded stupa creates between the Budda and the faithful,is more direct and intimate,especially compared to the one built in traditional Buddhist activities.Moreover,by coordinating with various visual materials inside the cave,such as the layout of the Dharma assembly and the exquisite details of the Buddha images,it reconstructs the“realm of the mind”in Buddhism,reinforcing the faithful’s dual experience both in physical and spiritual fields.The Master Teacher Studio of basic education came into being with the new curriculum reform,which has become a new mechanism for the construction of teaching staff in social situations in China.As a brand-new way in the construction of teaching staff in the new era,through reviewing the relevant research,it is found that the focus of academic circles on Master Teacher Studio is mainly in four aspects:clarifying the conceptual boundary,seeking theoretical support,defining the functional orientation,and exploring status quo of development.The exploration of the research process is not only a process of summary but also a process of reflection.By reviewing relevant research,reflecting on the problems that have appeared in the process of building the Master Teacher Studio in basic education,clarify the development path of the Master Teacher Studio and further affirm its advantages to the construction of teaching staff in the Chinese context.展开更多
基金Supported by Sichuan Science and Technology Program(2023YFSY0026,2023YFH0004)Supported by the Institute of Information&Communications Technology Planning&Evaluation(IITP)grant funded by the Korean government(MSIT)(No.RS-2022-00155885,Artificial Intelligence Convergence Innovation Human Resources Development(Hanyang University ERICA)).
文摘Two-dimensional endoscopic images are susceptible to interferences such as specular reflections and monotonous texture illumination,hindering accurate three-dimensional lesion reconstruction by surgical robots.This study proposes a novel end-to-end disparity estimation model to address these challenges.Our approach combines a Pseudo-Siamese neural network architecture with pyramid dilated convolutions,integrating multi-scale image information to enhance robustness against lighting interferences.This study introduces a Pseudo-Siamese structure-based disparity regression model that simplifies left-right image comparison,improving accuracy and efficiency.The model was evaluated using a dataset of stereo endoscopic videos captured by the Da Vinci surgical robot,comprising simulated silicone heart sequences and real heart video data.Experimental results demonstrate significant improvement in the network’s resistance to lighting interference without substantially increasing parameters.Moreover,the model exhibited faster convergence during training,contributing to overall performance enhancement.This study advances endoscopic image processing accuracy and has potential implications for surgical robot applications in complex environments.
基金Shenzhen Institute of Artificial Intelligence and Robotics for Society,Grant/Award Number:AC01202201003-02GuangDong Basic and Applied Basic Research Foundation,Grant/Award Number:2024A1515010252Longgang District Shenzhen's“Ten Action Plan”for Supporting Innovation Projects,Grant/Award Number:LGKCSDPT2024002。
文摘Audio-visual scene classification(AVSC)poses a formidable challenge owing to the intricate spatial-temporal relationships exhibited by audio-visual signals,coupled with the complex spatial patterns of objects and textures found in visual images.The focus of recent studies has predominantly revolved around extracting features from diverse neural network structures,inadvertently neglecting the acquisition of semantically meaningful regions and crucial components within audio-visual data.The authors present a feature pyramid attention network(FPANet)for audio-visual scene understanding,which extracts semantically significant characteristics from audio-visual data.The authors’approach builds multi-scale hierarchical features of sound spectrograms and visual images using a feature pyramid representation and localises the semantically relevant regions with a feature pyramid attention module(FPAM).A dimension alignment(DA)strategy is employed to align feature maps from multiple layers,a pyramid spatial attention(PSA)to spatially locate essential regions,and a pyramid channel attention(PCA)to pinpoint significant temporal frames.Experiments on visual scene classification(VSC),audio scene classification(ASC),and AVSC tasks demonstrate that FPANet achieves performance on par with state-of-the-art(SOTA)approaches,with a 95.9 F1-score on the ADVANCE dataset and a relative improvement of 28.8%.Visualisation results show that FPANet can prioritise semantically meaningful areas in audio-visual signals.
文摘Pulmonary nodules represent an early manifestation of lung cancer.However,pulmonary nodules only constitute a small portion of the overall image,posing challenges for physicians in image interpretation and potentially leading to false positives or missed detections.To solve these problems,the YOLOv8 network is enhanced by adding deformable convolution and atrous spatial pyramid pooling(ASPP),along with the integration of a coordinate attention(CA)mechanism.This allows the network to focus on small targets while expanding the receptive field without losing resolution.At the same time,context information on the target is gathered and feature expression is enhanced by attention modules in different directions.It effectively improves the positioning accuracy and achieves good results on the LUNA16 dataset.Compared with other detection algorithms,it improves the accuracy of pulmonary nodule detection to a certain extent.
基金supported by the National Natural Science Foundation of China(Nos.62276204 and 62203343)the Fundamental Research Funds for the Central Universities(No.YJSJ24011)+1 种基金the Natural Science Basic Research Program of Shanxi,China(Nos.2022JM-340 and 2023-JC-QN-0710)the China Postdoctoral Science Foundation(Nos.2020T130494 and 2018M633470).
文摘Drone-based small object detection is of great significance in practical applications such as military actions, disaster rescue, transportation, etc. However, the severe scale differences in objects captured by drones and lack of detail information for small-scale objects make drone-based small object detection a formidable challenge. To address these issues, we first develop a mathematical model to explore how changing receptive fields impacts the polynomial fitting results. Subsequently, based on the obtained conclusions, we propose a simple but effective Hybrid Receptive Field Network (HRFNet), whose modules include Hybrid Feature Augmentation (HFA), Hybrid Feature Pyramid (HFP) and Dual Scale Head (DSH). Specifically, HFA employs parallel dilated convolution kernels of different sizes to extend shallow features with different receptive fields, committed to improving the multi-scale adaptability of the network;HFP enhances the perception of small objects by capturing contextual information across layers, while DSH reconstructs the original prediction head utilizing a set of high-resolution features and ultrahigh-resolution features. In addition, in order to train HRFNet, the corresponding dual-scale loss function is designed. Finally, comprehensive evaluation results on public benchmarks such as VisDrone-DET and TinyPerson demonstrate the robustness of the proposed method. Most impressively, the proposed HRFNet achieves a mAP of 51.0 on VisDrone-DET with 29.3 M parameters, which outperforms the extant state-of-the-art detectors. HRFNet also performs excellently in complex scenarios captured by drones, achieving the best performance on the CS-Drone dataset we built.
文摘Archaeologists(考古学家)have unearthed the remains of a Mayan city nearly 3,000 years old in northern Guatemala,with pyramids(金字塔)and monuments that showcase its significance as an important ceremonial site.The Mayan civilization arose around 2000 BC,reaching its height between 400 AD and 900 AD in what is present-day southern Mexico and Guatemala,as well as parts of Belize,El Salvador and Honduras.
基金funded by Liaoning Provincial Department of Education Project,Award number JYTMS20230418.
文摘Pest detection techniques are helpful in reducing the frequency and scale of pest outbreaks;however,their application in the actual agricultural production process is still challenging owing to the problems of interspecies similarity,multi-scale,and background complexity of pests.To address these problems,this study proposes an FD-YOLO pest target detection model.The FD-YOLO model uses a Fully Connected Feature Pyramid Network(FC-FPN)instead of a PANet in the neck,which can adaptively fuse multi-scale information so that the model can retain small-scale target features in the deep layer,enhance large-scale target features in the shallow layer,and enhance the multiplexing of effective features.A dual self-attention module(DSA)is then embedded in the C3 module of the neck,which captures the dependencies between the information in both spatial and channel dimensions,effectively enhancing global features.We selected 16 types of pests that widely damage field crops in the IP102 pest dataset,which were used as our dataset after data supplementation and enhancement.The experimental results showed that FD-YOLO’s mAP@0.5 improved by 6.8%compared to YOLOv5,reaching 82.6%and 19.1%–5%better than other state-of-the-art models.This method provides an effective new approach for detecting similar or multiscale pests in field crops.
基金financially supported by National Natural Science Foundation of China(12072211,12232008)Foundation of Key laboratory(2022JCJQLB05703)Sichuan Province Science and Technology Project(2023NSFSC0914)。
文摘Pyramidal dislocations are important for ductility enhancement of magnesium alloys.In this work,molecular dynamics simulations were employed to study the gliding behavior of pyramidal(c+a)dislocations under c-axis compressive loading and tensile loading.The Peierls stress of Py-Ⅰ dislocation shows strong tension-compression asymmetry.However,no tension-compression asymmetry is seen on the Py-Ⅱ dislocation and basal dislocation.The tension-compression asymmetry origins from the asymmetry of partial dislocations of Py-Ⅰ dislocation,which leads to the dislocation core contracted under c-axis compressive loading and expanded under tensile loading.By analyzing the forces acting on the partial dislocations,we defined a neutral direction,which deviates from the full dislocation Burgers vector by 70.3°.The neutral direction is dependent on the ratio of lattice stresses of partial dislocations.If the shear stress is applied along the neutral direction,tension-compression asymmetry is eliminated and the dislocation core is un-contracted/un-expanded.The neutral direction of symmetrical dislocations(Py-Ⅱ dislocation and basal dislocation)is just the full dislocation Burgers vector.The tension-compression asymmetry and dislocation core contraction/expansion have an important influence on the dislocation behaviors,such as cross-slip,decomposition,basaltransition and mobility,which can be used to explain the mechanical behaviors of Mg single-crystals compressed along c-axis.
基金by the Deutsche Forschungsgemeinschaft(DFG)through projects 420149269,394480829as part of the CRC1394“Structural and Chemical Atomic Complexity-From Defect Phase Diagrams to Material Properties”(project 409476157).
文摘Dynamic recrystallization(DRX)in inhomogeneous deformation zones,such as grain boundaries,shear bands,and deformation bands,is critical for texture modification in magnesium alloys during deformation at elevated temperatures.This study investigates the DRX mechanisms in AZWX3100 magnesium alloy under plane strain compression at 200℃.Microstructural analysis revealed necklace-type DRX accompanied by evidence of local grain boundary bulging.Additionally,ribbons of recrystallized grains were observed withinfine deformation bands,aligned with theoretical pyramidal I and II slip traces derived from the matrix.The distribution of local misorientation within the deformed microstructure demonstrated a clear association between deformation bands and localized strain.Dislocation analysis of lamellar specimens extracted from two pyramidal slip bands revealed<c+a>dislocations,indicating a connection between<c+a>slip activation and the formation of deformation bands.Crystal plasticity simulations suggest that the orientation of deformation bands is responsible for the unique recrystallization texture of the DRX grains within these bands.The texture characteristics imply a progressive,glide-induced DRX mechanism.A fundamental understanding of the role of deformation bands in texture modification can facilitate future alloy and process design.
基金supported by the National Natural Science Foundation of China(No.52171125)the Sichuan Science and Technology Program(No.2024NSFSC0193)。
文摘The grain-scale tension-compression(T-C)asymmetric slip behavior and geometrically necessary dislocation(GND)density in an aged and twin-free Mg-10Y sheet were statistically studied using slip trace analysis and electron backscatter diffraction(EBSD)analysis.A significantly asymmetric slip activity,i.e.,higher tensile slip activity and proportion of non-basal slip,was manifested.Prismatic〈a〉(37.1%)and basal〈a〉(27.6%)slips dominated the tensile deformation,followed by pyramidalⅡ〈c+a〉slip(20.0%).While during compression,basal〈a〉slip(61.9%)was the most active slip mode,and only 6.9% pyramidalⅡ〈c+a〉slip was observed.The critical resolved shear stress(CRSS)ratio was estimated based on~800 sets of the identified slip traces,which suggested that the CRSS_(pyrⅡ)/CRSS_(bas)for compression was~3 times than that of tension.The pyramidalⅡ〈c+a〉slip was more active when the slip plane was under tension than under compression,which was consistent with the calculated asymmetric CRSS_(pyrⅡ)/CRSS_(bas).The activity of multiple slip,cross slip and slip transfer,as well as the GND density were also T-C asymmetric.This work thoughtfully demonstrated the T-C asymmetric slip behavior and plastic heterogeneity in Mg alloys which was believed to be responsible for the macroscopic T-C asymmetry when twinning was absent.The present statistical results are valuable for validating and/or facilitating crystal plasticity simulations.
基金financially supported by the National Natural Science Foundation of China(No.61904073)Spring City Plan-Special Program for Young Talents(No.K202005007)+2 种基金Yunnan Talents Support Plan for Yong Talents(No.XDYC-QNRC-2022-0482)Yunnan Local Colleges Applied Basic Research Projects(No.202101BA070001-138)Frontier Research Team of Kunming University 2023.
文摘Germanium(Ge)-air battery,a new type of semiconductor-air battery,has garnered increasing attention owing to its environmental friendliness,safety,and excellent dynamic performance.However,the flat Ge anode is prone to passivation,owing to GeO_(2) accumulation on its surface,resulting in premature discharge termination.In this study,various nano-Ge pyramid structures(GePS)were prepared using chemical etching(CE)and metal-assisted chemical etching(MACE)methods to enhance the specific surface area of the Ge anode,thereby facilitating the dissolution of the passivation layer.This study revealed that the MACE method significantly accelerated the etching rate of the Ge surface,producing exceptional GePS.Furthermore,Ge-air batteries employing Ge anodes prepared using MACE demonstrated an exceptional discharge life of up to 9240 h(385 days).The peak power density reached 3.03mW/cm^(2),representing improvements of more than 2 times and 1.8 times,respectively,compared with batteries using flat Ge anodes.This study presents a straightforward approach to enhance Ge anode performance,thereby expanding the potential applications of Ge-air batteries.
基金supported by the National Research Foundation of Korea(NRF)grant funded by the Korea government(*MSIT)(No.2018R1A5A7059549).
文摘The generation of high-quality,realistic face generation has emerged as a key field of research in computer vision.This paper proposes a robust approach that combines a Super-Resolution Generative Adversarial Network(SRGAN)with a Pyramid Attention Module(PAM)to enhance the quality of deep face generation.The SRGAN framework is designed to improve the resolution of generated images,addressing common challenges such as blurriness and a lack of intricate details.The Pyramid Attention Module further complements the process by focusing on multi-scale feature extraction,enabling the network to capture finer details and complex facial features more effectively.The proposed method was trained and evaluated over 100 epochs on the CelebA dataset,demonstrating consistent improvements in image quality and a marked decrease in generator and discriminator losses,reflecting the model’s capacity to learn and synthesize high-quality images effectively,given adequate computational resources.Experimental outcome demonstrates that the SRGAN model with PAM module has outperformed,yielding an aggregate discriminator loss of 0.055 for real,0.043 for fake,and a generator loss of 10.58 after training for 100 epochs.The model has yielded an structural similarity index measure of 0.923,that has outperformed the other models that are considered in the current study for analysis.
基金funding from the National Natural Science Foundation of China under Grant No 52275327。
文摘The work aims to investigate the formation and transformation mechanism of non-basal texture in the extruded Mg alloys.With this purpose a pure Mg as reference and eight Mg-Gd binary alloys with the Gd concentration ranging from 0.5 wt.%to 18 wt.%were prepared for extrusion.This study shows that the basal fiber texture in pure Mg transited into RE(rare earth)texture in diluted Mg-Gd alloys and into the abnormal C-texture in high-concentration Mg-Gd alloys.In pure Mg,discontinuous dynamic recrystallization plays a predominant role during the extrusion process,resulting in the formation of a typical basal fiber texture.Alloying with high concentration of Gd impedes the dynamic recrystallization process,facilitating the heterogeneous nucleation of shear bands as well as the dynamic recrystallization within shear bands.Dynamic recrystallized grains within shear bands nucleate with a similar orientation to the host deformed parent grains and gradually tilt their c-axis to the extrusion direction during growth by absorbing dislocations,leading to the formation of either the REtexture orientation or the C-texture orientation,depending on the stored energy within shear bands.The analysis aided by IGMA and TEM characterization reveals that the shear bands originate from the extensive but heterogeneous activation of pyramidal I slip.Tensile tests illustrate a close correlation between the fracture elongation and texture types.A comprehensive understanding of the formation and transformation mechanism of different texture components in Mg alloys holds significant importance for the design of high-performance Mg alloys by texture engineering.
基金supported by National Natural Science Foundation of China under Grant U23A20279China Electronics Tian’ao Innovation Theory and Technology Group Fund under Grand 20221193-04-04.
文摘To overcome the challenges of poor real-time performance,limited scalability,and low intelligence in conventional jamming pattern recognition methods,this paper proposes a method based on Wavelet Packet Decomposition(WPD)and enhanced deep learning techniques.In the proposed method,an agent at the receiver processes the received signal using WPD to generate an initial Spectrogram Waterfall(SW),which is subsequently segmented using a sliding window to serve as the input for the jamming recognition network.The network employs a bilateral filter to preprocess the input SW,thereby enhancing the edge features of the jamming signals.To extract abstract features,depthwise separable convolution is utilized instead of traditional convolution,thereby reducing the network’s parameter count and enhancing real-time performance.A pyramid pooling layer is integrated before the fully connected layer to enable the network to process input SW of varying sizes,thus enhancing scalability.During network training,adaptive moment estimation is employed as the optimizer,allowing the network to dynamically adjust the learning rate and accelerate convergence.A comprehensive comparison between the proposed jamming recognition network and six other models is conducted,along with Ablation Experiments(AE)based on numerical simulations.Simulation results demonstrate that the proposed method based on WPD and enhanced deep learning achieves high-precision recognition of various jamming patterns while maintaining a favorable balance among prediction accuracy,network complexity,and prediction time.
基金the National Natural Science Foundation of China(No.12064027)。
文摘Zn vapour is easily generated on the surface by fusion welding galvanized steel sheet,resulting in the formation of defects.Rapidly developing computer vision sensing technology collects weld images in the welding process,then obtains laser fringe information through digital image processing,identifies welding defects,and finally realizes online control of weld defects.The performance of a convolutional neural network is related to its structure and the quality of the input image.The acquired original images are labeled with LabelMe,and repeated attempts are made to determine the appropriate filtering and edge detection image preprocessing methods.Two-stage convolutional neural networks with different structures are built on the Tensorflow deep learning framework,different thresholds of intersection over union are set,and deep learning methods are used to evaluate the collected original images and the preprocessed images separately.Compared with the test results,the comprehensive performance of the improved feature pyramid networks algorithm based on the basic network VGG16 is lower than that of the basic network Resnet101.Edge detection of the image will significantly improve the accuracy of the model.Adding blur will reduce the accuracy of the model slightly;however,the overall performance of the improved algorithm is still relatively good,which proves the stability of the algorithm.The self-developed software inspection system can be used for image preprocessing and defect recognition,which can be used to record the number and location of typical defects in continuous welds.
基金supported in part by the National Natural Science Foundation of China under Grant 6226070954Jiangxi Provincial Key R&D Programme under Grant 20244BBG73002.
文摘Rail surface damage is a critical component of high-speed railway infrastructure,directly affecting train operational stability and safety.Existing methods face limitations in accuracy and speed for small-sample,multi-category,and multi-scale target segmentation tasks.To address these challenges,this paper proposes Pyramid-MixNet,an intelligent segmentation model for high-speed rail surface damage,leveraging dataset construction and expansion alongside a feature pyramid-based encoder-decoder network with multi-attention mechanisms.The encoding net-work integrates Spatial Reduction Masked Multi-Head Attention(SRMMHA)to enhance global feature extraction while reducing trainable parameters.The decoding network incorporates Mix-Attention(MA),enabling multi-scale structural understanding and cross-scale token group correlation learning.Experimental results demonstrate that the proposed method achieves 62.17%average segmentation accuracy,80.28%Damage Dice Coefficient,and 56.83 FPS,meeting real-time detection requirements.The model’s high accuracy and scene adaptability significantly improve the detection of small-scale and complex multi-scale rail damage,offering practical value for real-time monitoring in high-speed railway maintenance systems.
文摘Techniques in deep learning have significantly boosted the accuracy and productivity of computer vision segmentation tasks.This article offers an intriguing architecture for semantic,instance,and panoptic segmentation using EfficientNet-B7 and Bidirectional Feature Pyramid Networks(Bi-FPN).When implemented in place of the EfficientNet-B5 backbone,EfficientNet-B7 strengthens the model’s feature extraction capabilities and is far more appropriate for real-world applications.By ensuring superior multi-scale feature fusion,Bi-FPN integration enhances the segmentation of complex objects across various urban environments.The design suggested is examined on rigorous datasets,encompassing Cityscapes,Common Objects in Context,KITTI Karlsruhe Institute of Technology and Toyota Technological Institute,and Indian Driving Dataset,which replicate numerous real-world driving conditions.During extensive training,validation,and testing,the model showcases major gains in segmentation accuracy and surpasses state-of-the-art performance in semantic,instance,and panoptic segmentation tasks.Outperforming present methods,the recommended approach generates noteworthy gains in Panoptic Quality:+0.4%on Cityscapes,+0.2%on COCO,+1.7%on KITTI,and+0.4%on IDD.These changes show just how efficient it is in various driving circumstances and datasets.This study emphasizes the potential of EfficientNet-B7 and Bi-FPN to provide dependable,high-precision segmentation in computer vision applications,primarily autonomous driving.The research results suggest that this framework efficiently tackles the constraints of practical situations while delivering a robust solution for high-performance tasks involving segmentation.
基金funded by Hanoi University of Science and Technology(HUST)under project number T2023-PC-008.
文摘Due to the necessity for lightweight and efficient network models, deploying semantic segmentation models on mobile robots (MRs) is a formidable task. The fundamental limitation of the problem lies in the training performance, the ability to effectively exploit the dataset, and the ability to adapt to complex environments when deploying the model. By utilizing the knowledge distillation techniques, the article strives to overcome the above challenges with the inheritance of the advantages of both the teacher model and the student model. More precisely, the ResNet152-PSP-Net model’s characteristics are utilized to train the ResNet18-PSP-Net model. Pyramid pooling blocks are utilized to decode multi-scale feature maps, creating a complete semantic map inference. The student model not only preserves the strong segmentation performance from the teacher model but also improves the inference speed of the prediction results. The proposed method exhibits a clear advantage over conventional convolutional neural network (CNN) models, as evident from the conducted experiments. Furthermore, the proposed model also shows remarkable improvement in processing speed when compared with light-weight models such as MobileNetV2 and EfficientNet based on latency and throughput parameters. The proposed KD-SegNet model obtains an accuracy of 96.3% and a mIoU (mean Intersection over Union) of 77%, outperforming the performance of existing models by more than 15% on the same training dataset. The suggested method has an average training time that is only 0.51 times less than same field models, while still achieving comparable segmentation performance. Hence, the semantic segmentation frames are collected, forming the motion trajectory for the system in the environment. Overall, this architecture shows great promise for the development of knowledge-based systems for MR’s navigation.
基金supported by China Postdoctoral Science Foundation(No.2024M754122)the Postdoctoral Fellowship Programof CPSF(No.GZB20240972)+3 种基金the Jiangsu Funding Program for Excellent Postdoctoral Talent(No.2024ZB194)Natural Science Foundation of Jiangsu Province(No.BK20241389)Basic Science ResearchFund of China(No.JCKY2023203C026)2024 Jiangsu Province Talent Programme Qinglan Project.
文摘This study focuses on tool condition recognition through data-driven approaches to enhance the intelligence level of computerized numerical control(CNC)machining processes and improve tool utilization efficiency.Traditional tool monitoring methods that rely on empirical knowledge or limited mathematical models struggle to adapt to complex and dynamic machining environments.To address this,we implement real-time tool condition recognition by introducing deep learning technology.Aiming to the insufficient recognition accuracy,we propose a pyramid pooling-based vision Transformer network(P2ViT-Net)method for tool condition recognition.Using images as input effectively mitigates the issue of low-dimensional signal features.We enhance the vision Transformer(ViT)framework for image classification by developing the P2ViT model and adapt it to tool condition recognition.Experimental results demonstrate that our improved P2ViT model achieves 94.4%recognition accuracy,showing a 10%improvement over conventional ViT and outperforming all comparative convolutional neural network models.
基金supported by the Fundamental Research Funds for the Central Universities(No.2572025BR14)the China Energy Digital Intelligence Technology Development(Beijing)Co.,Ltd.Science and Technology Innovation Project(No.YA2024001500).
文摘In remote sensing imagery,approximately 67%of the data are affected by cloud cover,significantly increasing the difficulty of image classification,recognition,and other downstream interpretation tasks.To effectively address the randomness of cloud distribution and the non-uniformity of cloud thickness,we propose a coarse-to-fine thin cloud removal architecture based on the observations of the random distribution and uneven thickness of cloud.In the coarse-level declouding network,we innovatively introduce a multi-scale attention mechanism,i.e.,pyramid nonlocal attention(PNA).By integrating global context with local detail information,it specifically addresses image quality degradation caused by the uncertainty in cloud distribution.During the fine-level declouding stage,we focus on the impact of cloud thickness on declouding results(primarily manifested as insufficient detail information).Through a carefully designed residual dense module,we significantly enhance the extraction and utilization of feature details.Thus,our approach precisely restores lost local texture features on top of coarse-level results,achieving a substantial leap in declouding quality.To evaluate the effectiveness of our cloud removal technology and attention mechanism,we conducted comprehensive analyses on publicly available datasets.Results demonstrate that our method achieves state-of-the-art performance across a wide range of techniques.
文摘This paper mainly explores the architectural form of the interior of Maiji Mountain’s Cave 4 and the spiritual realm it establishes.Previous research holds the view that the niche within Cave 4 is architecturally modeled after a tent-like structure.However,after in-depth research,this paper suggests that the niche in Cave 4 is more of an imitation of an embedded stupa,or rather,it reconstructs the stupa in an embedded form within the cave.In prior studies,analysis historical background and architectural details of Cave 4 remains inadequate,thus,this paper aims to take a thorough investigation on this point,and further clarify the significance of the form of the Buddha niche in Cave 4,as well as its construction design origins.Specifically,the octagonal wooden-imitating column,lotus-shaped clamp,inverted lotus pedestal,five lotus petals,and shadow sculptures within the niche,the shallow-relief ceiling and small platform outside the niche,as well as the banana leaf decorations,all suggest that the architectural form Cave 4 imitates is likely a single-story wooden stupa.In this way,the connection that this embedded stupa creates between the Budda and the faithful,is more direct and intimate,especially compared to the one built in traditional Buddhist activities.Moreover,by coordinating with various visual materials inside the cave,such as the layout of the Dharma assembly and the exquisite details of the Buddha images,it reconstructs the“realm of the mind”in Buddhism,reinforcing the faithful’s dual experience both in physical and spiritual fields.The Master Teacher Studio of basic education came into being with the new curriculum reform,which has become a new mechanism for the construction of teaching staff in social situations in China.As a brand-new way in the construction of teaching staff in the new era,through reviewing the relevant research,it is found that the focus of academic circles on Master Teacher Studio is mainly in four aspects:clarifying the conceptual boundary,seeking theoretical support,defining the functional orientation,and exploring status quo of development.The exploration of the research process is not only a process of summary but also a process of reflection.By reviewing relevant research,reflecting on the problems that have appeared in the process of building the Master Teacher Studio in basic education,clarify the development path of the Master Teacher Studio and further affirm its advantages to the construction of teaching staff in the Chinese context.
