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An efficient and balanced feature pyramid network:Shared-FPN 认领 引用
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作者 Sulong TIAN Longfei QIN +2 位作者 Wenchao PANG Ying GAO Dexin ZHAO 《Optoelectronics Letters》 EI 2026年第5期295-301,共7页
In the field of object detection,it is challenging to achieve a balance between neck complexity and accuracy.To address this issue,we propose an efficient and decoupled neck module called shared feature pyramid networ... In the field of object detection,it is challenging to achieve a balance between neck complexity and accuracy.To address this issue,we propose an efficient and decoupled neck module called shared feature pyramid network(Shared-FPN).Not only does Shared-FPN not increase the number of model parameters and floating point operations per second(FLOPs),but it also can be easily ported to any of the detection models.It improves on path aggregation feature pyramid network(PAFPN)by using transposed convolution with a large convolution kernel as the upsampling module,designing spatial pyramidal pooling-fast downsampling(SPPFD)based on shared pooling,and designing shared convolution as the right part module.To evaluate the performance of Shared-FPN in object detection tasks,we conducted experiments on object detection datasets.The results show that Shared-FPN achieved excellent performance across all sizes.In particular,on the VOC 2012 dataset,the Shared-FPN’s mean average precision(mAP)was improved by 11.2%compared to FPN with you only look once extended-s(YOLOX-s)as the detector.On the COCO dataset,the Shared-FPN’s mAP was improved by 7.8%compared to FPN and 7.1%compared to PAFPN with faster region-based convolutional neural network(Faster RCNN)as the detector.The Shared-FPN can be easily inserted into any of the detectors for better performance in various scenarios,such as small,medium or large objects. 展开更多
关键词 efficient feature pyramid network object detectionit shared feature pyramid network shared fpn not path aggregation feature pyramid network pafpn balanced transposed convolution floating point
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Enhanced performance and flexibility of perovskite/silicon tandem solar cells via uniform submicron pyramids 认领 引用
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作者 Qi Liu Junjun Li +9 位作者 Fei Wang Shuangbiao Xia Yuhui Ji Yutao Wang Yunren Luo Jian Yu Fanying Meng Liping Zhang Zhengxin Liu Wenzhu Liu 《ENGINEERING Energy》 SCIE EI CAS CSCD 2026年第3期101-109,共9页
The interfacial stress between silicon bottom cell and perovskite top cell remains a critical challenge for flexible perovskite/silicon tandem solar cells,leading to interfacial delamination and device degradation.In ... The interfacial stress between silicon bottom cell and perovskite top cell remains a critical challenge for flexible perovskite/silicon tandem solar cells,leading to interfacial delamination and device degradation.In this work,the effect of the thickness and pyramid size on mechanical properties of silicon wafers are investigated,demonstrating that thinner wafers and smaller pyramids significantly enhance the flexural strength of thin silicon wafers by mitigating stress concentration effects.Based on these findings,a synergistic optimization strategy is proposed that employs precise wet-etching control to fabricate small-sized,high-density,uniform pyramids on 55μm silicon wafers for efficient and flexible perovskite/silicon tandem solar cells.By optimizing the texturing duration,this approach simultaneously enhances the minority carrier lifetime(τ)and achieves an excellent implied open-circuit voltage(iVoc).Furthermore,the uniform submicron-scale pyramid structure promotes high-quality perovskite film formation and improves interfacial contact properties.As a proof of concept,monolithic flexible perovskite/silicon tandem devices fabricated on such uniformly textured pyramids delivered a power conversion efficiency(PCE)of 30.04%.These devices promise for low-cost,lightweight and flexible photovoltaic applications. 展开更多
关键词 submicron pyramids uniform perovskites silicon flexible tandem solar cells
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Rapid improvement of seed weight and yield by combining QTL pyramiding with speed breeding in Brassica napus L 认领 引用
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作者 Yixian Song Xin Yuan +5 位作者 Pengfei Wang Zhaoyang Wang Lei Kang Jing Yang Guangsheng Yang Dengfeng Hong 《Oil Crop Science》 CAS CSCD 2026年第1期83-91,共9页
Thousand-seed weight(TSW)is a critical target for genetic improvement in rapeseed(Brassica napus L.).However,phenotypic selection for this trait remains challenging due to its polygenic regulation by multiple quantita... Thousand-seed weight(TSW)is a critical target for genetic improvement in rapeseed(Brassica napus L.).However,phenotypic selection for this trait remains challenging due to its polygenic regulation by multiple quantitative trait loci(QTL).Here,six favorable TSW QTL alleles from two donor parents were introgress into an elite restorer line,621R,using an integrated strategy combining marker-assisted backcrossing and speed breeding protocols.Through six rounds of backcrossing and convergent crossing followed by two generations of selfing strategies,we developed 13 advanced lines with diverse TSW QTL combinations within 24 months.Field evaluations across three environments revealed that all lines exhibited significantly increased TSW in spring conditions(Minle,Gansu)and winter environments(Wuhan and Jiangling,Hubei)except for two lines which only showed increase in the spring environment.Hybridization assays using these lines as male parents crossed with two male-sterile lines(RG430A and 616A)demonstrated transgressive segregation for TSW:For RG430A-derived hybrids,all crosses significantly outperformed the original control(RG430A×621R)in Wuhan,with 8/13 and 9/13 crosses showing significant TSW increases in Minle and Jiangling,respectively.For 616A-derived hybrids,11/13 and 10/13 crosses exhibited significant TSW enhancement in Minle and Jiangling,compared to 3/13 in Wuhan.Notably,two top-performing hybrids achieved 13.0%and 6.8%higher plot yields,respectively.Our results demonstrate that strategic pyramiding of complementary TSW QTL alleles effectively enhances seed weight in rapeseed,and these improved lines represent valuable genetic resources for developing high-yield hybrids. 展开更多
关键词 Brassica napus L. Seed weight QTL pyramiding Speed breeding
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Feature pyramid attention network for audio-visual scene classification 认领 引用 被引量：1
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作者 Liguang Zhou Yuhongze Zhou +3 位作者 Xiaonan Qi Junjie Hu Tin Lun Lam Yangsheng Xu 《CAAI Transactions on Intelligence Technology》 SCIE EI CSCD 2025年第2期359-374,共16页
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
关键词 dimension alignment feature pyramid attention network pyramid channel attention pyramid spatial attention semantic relevant regions
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LP-YOLO:Enhanced Smoke and Fire Detection via Self-Attention and Feature Pyramid Integration 认领 引用
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作者 Qing Long Bing Yi +2 位作者 Haiqiao Liu Zhiling Peng Xiang Liu 《Computers, Materials & Continua》 SCIE EI 2026年第3期1490-1509,共20页
Accurate detection of smoke and fire sources is critical for early fire warning and environmental monitoring.However,conventional detection approaches are highly susceptible to noise,illumination variations,and comple... Accurate detection of smoke and fire sources is critical for early fire warning and environmental monitoring.However,conventional detection approaches are highly susceptible to noise,illumination variations,and complex environmental conditions,which often reduce detection accuracy and real-time performance.To address these limitations,we propose Lightweight and Precise YOLO(LP-YOLO),a high-precision detection framework that integrates a self-attention mechanism with a feature pyramid,built upon YOLOv8.First,to overcome the restricted receptive field and parameter redundancy of conventional Convolutional Neural Networks(CNNs),we design an enhanced backbone based on Wavelet Convolutions(WTConv),which expands the receptive field through multifrequency convolutional processing.Second,a Bidirectional Feature Pyramid Network(BiFPN)is employed to achieve bidirectional feature fusion,enhancing the representation of smoke features across scales.Third,to mitigate the challenge of ambiguous object boundaries,we introduce the Frequency-aware Feature Fusion(FreqFusion)module,in which the Adaptive Low-Pass Filter(ALPF)reduces intra-class inconsistencies,the offset generator refines boundary localization,and the Adaptive High-Pass Filter(AHPF)recovers high-frequency details lost during down-sampling.Experimental evaluations demonstrate that LP-YOLO significantly outperforms the baseline YOLOv8,achieving an improvement of 9.3%in mAP@50 and 9.2%in F1-score.Moreover,the model is 56.6%and 32.4%smaller than YOLOv7-tiny and EfficientDet,respectively,while maintaining real-time inference speed at 238 frames per second(FPS).Validation on multiple benchmark datasets,including D-Fire,FIRESENSE,and BoWFire,further confirms its robustness and generalization ability,with detection accuracy consistently exceeding 82%.These results highlight the potential of LP-YOLO as a practical solution with high accuracy,robustness,and real-time performance for smoke and fire source detection. 展开更多
关键词 Deep learning smoke detection feature pyramid boundary refinement
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Thyroid carcinoma of the pyramidal lobe: A narrative review of reported cases 认领 引用
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作者 Eleni Evdoxia Tziona Angeliki Emmanouilidou +7 位作者 Ioannis Stouras Chrysanthi Tzenin Omar Marina Koloka Artemis Savva Konstantina Dimitrakopoulou Grigoris Iosifidis Kalliopi Pazaitou-Panayiotou Nickos Michalopoulos 《World Journal of Clinical Cases》 2026年第10期1-12,共12页
Papillary thyroid carcinoma arising from the pyramidal lobe is a rare entity that poses diagnostic and therapeutic challenges,as this anatomical structure is frequently overlooked during preoperative assessment and th... Papillary thyroid carcinoma arising from the pyramidal lobe is a rare entity that poses diagnostic and therapeutic challenges,as this anatomical structure is frequently overlooked during preoperative assessment and thyroid surgery,potentially compromising oncological outcomes.A narrative review of the literature identified 38 reported cases of pyramidal lobe thyroid carcinoma,of which 29 cases,including one additional illustrative case,met predefined inclusion criteria and were analyzed.Extracted data included demographic characteristics,clinical presentation,thyroid function,imaging and biopsy methods,operative management,histopathological findings,tumor multifocality,extrathyroidal extension,tumor-node-metastasis staging,adjuvant treatment,and follow-up.Most tumors were papillary carcinomas occurring predominantly in female patients,commonly presenting as anterior neck masses with preserved thyroid function.Ultrasound and computed tomography were the most frequently utilized imaging modalities,while fine-needle aspiration was the primary diagnostic tool.Surgery most often involved total thyroidectomy with central or combined central and lateral neck dissections.Multifocality and extrathyroidal extension were reported in a substantial proportion of cases.Follow-up data were limited,with no documented recurrences among reported cases.Awareness of the pyramidal lobe as a potential primary site of thyroid carcinoma and its complete surgical excision are essential for optimal oncological management. 展开更多
关键词 Pyramidal lobe Thyroid cancer Cervical lymphadenopathy Thyroidectomy Papillary thyroid carcinoma
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Double Self-Attention Based Fully Connected Feature Pyramid Network for Field Crop Pest Detection 认领 引用 被引量：2
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作者 Zijun Gao Zheyi Li +2 位作者 Chunqi Zhang Ying Wang Jingwen Su 《Computers, Materials & Continua》 SCIE EI 2025年第6期4353-4371,共19页
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
关键词 Pest detection YOLOv5 feature pyramid network transformer attention module
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Understanding pyramidal slip-induced deformation bands and dynamic recrystallization in AZWX3100 magnesium alloy 认领 引用 被引量：1
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作者 Risheng Pei Fatim-Zahra Mouhib +3 位作者 Mattis Seehaus Simon Arnoldi Pei-Ling Sun Talal Al-Samman 《Journal of Magnesium and Alloys》 SCIE EI CAS CSCD 2025年第3期1088-1098,共11页
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 revealeddislocations,indicating a connection betweenslip 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
关键词 Magnesium Channel die Dynamic recrystallization Texture modification Pyramidal slip
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Super-Resolution Generative Adversarial Network with Pyramid Attention Module for Face Generation 认领 引用 被引量：1
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作者 Parvathaneni Naga Srinivasu G.JayaLakshmi +4 位作者 Sujatha Canavoy Narahari Victor Hugo C.de Albuquerque Muhammad Attique Khan Hee-Chan Cho Byoungchol Chang 《Computers, Materials & Continua》 SCIE EI 2025年第10期2117-2139,共23页
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
关键词 Artificial intelligence generative adversarial network pyramid attention module face generation deep learning
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Pyramid Pooling-Based Vision Transformer for Tool Condition Recognition 认领 引用
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作者 ZHENG Kun LI Yonglin +2 位作者 GU Xinyan DING Zhiying ZHU Haihua 《Transactions of Nanjing University of Aeronautics and Astronautics》 EI CSCD 2025年第3期322-336,共15页
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
关键词 tool condition recognition Transformer pyramid pooling deep convolutional neural network
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Hyperspectral Satellite Image Classification Based on Feature Pyramid Networks With 3D Convolution 认领 引用
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作者 CHEN Cheng PENG Pan +1 位作者 TAO Wei ZHAO Hui 《Journal of Shanghai Jiaotong university(Science)》 EI 2025年第6期1073-1084,共12页
Recent advances in convolution neural network (CNN) have fostered the progress in object recognition and semantic segmentation, which in turn has improved the performance of hyperspectral image (HSI) classification. N... Recent advances in convolution neural network (CNN) have fostered the progress in object recognition and semantic segmentation, which in turn has improved the performance of hyperspectral image (HSI) classification. Nevertheless, the difficulty of high dimensional feature extraction and the shortage of small training samples seriously hinder the future development of HSI classification. In this paper, we propose a novel algorithm for HSI classification based on three-dimensional (3D) CNN and a feature pyramid network (FPN), called 3D-FPN. The framework contains a principle component analysis, a feature extraction structure and a logistic regression. Specifically, the FPN built with 3D convolutions not only retains the advantages of 3D convolution to fully extract the spectral-spatial feature maps, but also concentrates on more detailed information and performs multi-scale feature fusion. This method avoids the excessive complexity of the model and is suitable for small sample hyperspectral classification with varying categories and spatial resolutions. In order to test the performance of our proposed 3D-FPN method, rigorous experimental analysis was performed on three public hyperspectral data sets and hyperspectral data of GF-5 satellite. Quantitative and qualitative results indicated that our proposed method attained the best performance among other current state-of-the-art end-to-end deep learning-based methods. 展开更多
关键词 hyperspectral image(HSI) deep learning feature pyramid network(FPN) spectral-spatial feature extraction
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Pyramid–MixNet: Integrate Attention into Encoder-Decoder Transformer Framework for Automatic Railway Surface Damage Segmentation 认领 引用
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作者 Hui Luo Wenqing Li Wei Zeng 《Computers, Materials & Continua》 SCIE EI 2025年第7期1567-1580,共14页
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
关键词 Pyramid vision transformer encoder–decoder architecture railway damage segmentation masked multi-head attention mix-attention
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Tension-compression asymmetry of pyramidal dislocations in magnesium 认领 引用 被引量：1
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作者 Zikun Li Chuanlong Xu +3 位作者 Xiaobao Tian Wentao Jiang Qingyuan Wang Haidong Fan 《Journal of Magnesium and Alloys》 SCIE EI CAS CSCD 2025年第7期3198-3208,共11页
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
关键词 Tension-compression asymmetry Pyramidal dislocation Magnesium Molecular dynamics
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Optimized Convolutional Neural Networks with Multi-Scale Pyramid Feature Integration for Efficient Traffic Light Detection in Intelligent Transportation Systems 认领 引用 被引量：1
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作者 Yahia Said Yahya Alassaf +2 位作者 Refka Ghodhbani Taoufik Saidani Olfa Ben Rhaiem 《Computers, Materials & Continua》 SCIE EI 2025年第2期3005-3018,共14页
Transportation systems are experiencing a significant transformation due to the integration of advanced technologies, including artificial intelligence and machine learning. In the context of intelligent transportatio... Transportation systems are experiencing a significant transformation due to the integration of advanced technologies, including artificial intelligence and machine learning. In the context of intelligent transportation systems (ITS) and Advanced Driver Assistance Systems (ADAS), the development of efficient and reliable traffic light detection mechanisms is crucial for enhancing road safety and traffic management. This paper presents an optimized convolutional neural network (CNN) framework designed to detect traffic lights in real-time within complex urban environments. Leveraging multi-scale pyramid feature maps, the proposed model addresses key challenges such as the detection of small, occluded, and low-resolution traffic lights amidst complex backgrounds. The integration of dilated convolutions, Region of Interest (ROI) alignment, and Soft Non-Maximum Suppression (Soft-NMS) further improves detection accuracy and reduces false positives. By optimizing computational efficiency and parameter complexity, the framework is designed to operate seamlessly on embedded systems, ensuring robust performance in real-world applications. Extensive experiments using real-world datasets demonstrate that our model significantly outperforms existing methods, providing a scalable solution for ITS and ADAS applications. This research contributes to the advancement of Artificial Intelligence-driven (AI-driven) pattern recognition in transportation systems and offers a mathematical approach to improving efficiency and safety in logistics and transportation networks. 展开更多
关键词 Intelligent transportation systems(ITS) traffic light detection multi-scale pyramid feature maps advanced driver assistance systems(ADAS) real-time detection AI in transportation
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A Coarse to Fine Thin Cloud Removal Network with Pyramid Non-local Attention 认领 引用
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作者 GUAN Wang TIAN Zhenkai +5 位作者 MA Tao ZHAO Lingyuan XIE Shizhe YAN Jin DU Yang ZOU Yunkun 《Transactions of Nanjing University of Aeronautics and Astronautics》 EI CSCD 2025年第5期589-600,共12页
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
关键词 channel attention thin cloud removal network pyramid non-local attention(PNA) remote sensing image residual dense connection
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Direct Hippocampal and Thalamic Inputs to Layer 3 Pyramidal Cells in the Medial Entorhinal Cortex Revealed by Monosynaptic Rabies Tracing 认领 引用
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作者 Ze Chen Dietmar Schmitz John J.Tukker 《Neuroscience Bulletin》 SCIE CAS CSCD 2025年第4期707-712,共6页
Dear Editor,The importance of the medial entorhinal cortex(MEC)for memory and spatial navigation has been shown repeatedly in many species,including mice and humans[1,2].It is,therefore,not surprising that the connect... Dear Editor,The importance of the medial entorhinal cortex(MEC)for memory and spatial navigation has been shown repeatedly in many species,including mice and humans[1,2].It is,therefore,not surprising that the connectivity of this structure has been studied extensively over the past century,mainly using a range of anterograde and retrograde anatomical tracers[3]. 展开更多
关键词 medial entorhinal cortex mec hippocampal thalamic layer pyramidal cells connectivity structure spatial navigation anterograde retrograde anatomical tracers medial entorhinal cortex
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Scalable and Passive Concentrator Photovoltaics Using a Multi-Focal Pyramidal Array:A Multi-Physics Modeling Approach 认领 引用
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作者 Mussad Mohammed Al-Zahrani Taher Maatallah 《Frontiers in Heat and Mass Transfer》 EI CAS 2025年第6期1883-1905,共23页
Conventional concentrator photovoltaics(CPV)face a persistent trade-off between high efficiency and high cost,driven by expensive multi-junction solar cells and complex active cooling systems.This study presents a com... Conventional concentrator photovoltaics(CPV)face a persistent trade-off between high efficiency and high cost,driven by expensive multi-junction solar cells and complex active cooling systems.This study presents a computational investigation of a novel Multi-Focal Pyramidal Array(MFPA)-based CPV system designed to overcome this limitation.The MFPA architecture employs a geometrically optimized pyramidal concentrator to distribute concen-trated sunlight onto strategically placed,low-cost monocrystalline silicon cells,enabling high efficiency energy capture while passively managing thermal loads.Coupled optical thermal electrical simulations in COMSOL Multiphysics demonstrate a geometric concentration ratio of 120×,with system temperatures maintained below 110℃ under standard 1000 W/m2 Direct Normal Irradiance(DNI).Ray tracing confirms 95%optical efficiency and a concentrated light spot radius of 2.48 mm.Compared with conventional CPV designs,the MFPA improves power-per-cost by 25%and reduces tracking requirements by 50%owing to its wide±15°acceptance angle.These results highlight the MFPA’s potential as a scalable,low-cost,and energy-efficient pathway for expanding solar power generation. 展开更多
关键词 Concentrating photovoltaic(CPV) multi-focal pyramidal array(MFPA) multi-physics simulation optical-thermal coupling geometric concentration solar energy conversion
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Motor recovery via aberrant pyramidal tract in a patient with traumatic brain injury A diffusion tensor tractography study 认领 引用 被引量：1
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作者 Sang Seok Yeo Sung Ho Jang 《Neural Regeneration Research》 SCIE CAS CSCD 2013年第1期90-94,共5页
The aberrant pyramidal tract is the collateral pathway of the pyramidal tract through the medial lemniscus in the brainstem. A 21-year-old man presented with right hemiparesis due to a traumatic intracerebral hemorrha... The aberrant pyramidal tract is the collateral pathway of the pyramidal tract through the medial lemniscus in the brainstem. A 21-year-old man presented with right hemiparesis due to a traumatic intracerebral hemorrhage in the left corona radiata. His motor function recovered almost to the normal state at 10 months after onset. Through diffusion tensor tractography, the pyramidal tract in the affected （left） hemisphere showed discontinuation at the pontine level at 13 months after onset. An aberrant pyramidal tract was observed, which originated from the primary motor cortex and the supplementary motor area and descended through the corona radiata, then through the posterior limb of the internal capsule and the medial lemniscus pathway from the midbrain to the pons, finally entered into the pyramidal tract area at the pontomedullary junction, it suggests that the motor functions of the right extremities in this patient had recovered by this aberrant pyramidal tract. 展开更多
关键词 neural regeneration neuroimaging diffusion tensor imaging diffusion tensor tractography transcranial magnetic stimulation pyramidal tract aberrant pyramidal tract motor paralysis motorrecovery traumatic brain injury head trauma intracerebral hemorrhage grant-supported paper photographs-containing paper neuroregeneration
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Mechanical Response of All-composite Pyramidal Lattice Truss Core Sandwich Structures 认领 引用 被引量：13
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作者 Ming Li Linzhi Wu +2 位作者 Li Ma Bing Wang Zhengxi Guan 《Journal of Materials Science & Technology》 SCIE EI CAS CSCD 2011年第6期570-576,共7页
The mechanical performance of an all-composite pyramidal lattice truss core sandwich structure was investigated both theoretically and experimentally.Sandwich structures were fabricated with a hot compression molding ... The mechanical performance of an all-composite pyramidal lattice truss core sandwich structure was investigated both theoretically and experimentally.Sandwich structures were fabricated with a hot compression molding method using carbon fiber reinforced composite T700/3234.The out-of-plane compression and shear tests were conducted.Experimental results showed that the all-composite pyramidal lattice truss core sandwich structures were more weight efficient than other metallic lattice truss core sandwich structures.Failure modes revealed that node rupture dominated the mechanical behavior of sandwich structures. 展开更多
关键词 Sandwich structures Pyramidal truss Composite Mechanical properties
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A Pyramid Breeding of Eight Grain-yield Related Quantitative Trait Loci Based on Marker-assistant and Phenotype Selection in Rice(Oryza sativa L.) 认领 引用 被引量：11
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作者 Guo Zong Ahong Wang +4 位作者 Lu Wang Guohua Liang Minghong Gu Tao Sang Bin Han 《Journal of Genetics and Genomics》 SCIE CAS CSCD 2012年第7期335-350,共16页
1000-Grain weight and spikelet number per panicle are two important components for rice grain yield. In our previous study, eight quantitative trait loci （QTLs） conferring spikelet number per panicle and 1000-grain ... 1000-Grain weight and spikelet number per panicle are two important components for rice grain yield. In our previous study, eight quantitative trait loci （QTLs） conferring spikelet number per panicle and 1000-grain weight were mapped through sequencing-based genotyping of 150 rice recombinant inbred lines （RILs）. In this study, we validated the effects of four QTLs from Nipponbare using chromosome segment substitution lines （CSSLs）, and pyramided eight grain yield related QTLs. The new lines containing the eight QTLs with positive effects showed increased panicle and spikelet size as compared with the parent variety 93-11. We further proposed a novel pyramid breeding scheme based on marker-assistant and phenotype selection （MAPS）. This scheme allowed pyramiding of as many as 24 QTLs at a single hybridization without massive cross work. This study provided insights into the molecular basis of rice grain yield for direct wealth for high-yielding rice breeding. 展开更多
关键词 Rice Spikelet number 1000-Grain weight Quantitative trait loci Pyramid breeding Marker assisted and phenotype selection
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