The demand for extended electric vehicle(EV)range necessitates advanced lightweighting strategies.This study introduces a materials genome approach,augmented by machine learning(ML),for optimizing lightweight composit...The demand for extended electric vehicle(EV)range necessitates advanced lightweighting strategies.This study introduces a materials genome approach,augmented by machine learning(ML),for optimizing lightweight composite designs for EVs.A comprehensive materials genome database was developed,encompassing composites based on carbon,glass,and natural fibers.This database systematically records critical parameters such as mechanical properties,density,cost,and environmental impact.Machine learning models,including Random Forest,Support Vector Machines,and Artificial Neural Networks,were employed to construct a predictive system for material performance.Subsequent material composition optimization was performed using amulti-objective genetic algorithm.Experimental validation demonstrated that an optimized carbon fiber/bio-based resin composite achieved a 45%weight reduction compared to conventional steel,while maintaining equivalent structural strength.The predictive accuracy of the models reached 94.2%.A cost-benefit analysis indicated that despite a 15%increase in material cost,the overall vehicle energy consumption decreased by 12%,leading to an 18%total cost saving over a five-year operational lifecycle,under a representative mid-size battery electric vehicle(BEV)operational scenario.展开更多
The automobile industry is the first to form a typical representative of the global industry in modern industry,with the increase of the national emphasis on the environment,the automobile industry was regarded as an ...The automobile industry is the first to form a typical representative of the global industry in modern industry,with the increase of the national emphasis on the environment,the automobile industry was regarded as an important energy consumption and one of the sources of environmental pollution,the policy of energy conservation and emission reduction requirements for the automobile industry are becoming stricter over the years,energy conservation and emission reduction has becomes the main direction of product optimization in the automobile industry in recent years.Due of a series of excellent properties such as light weight and high strength,composite materials have become the main material for the development of lightweight vehicles.With the development of material technology and the update and iteration of manufacturing technology,composite materials are currently popular being adopted in the automotive field.展开更多
Most of recent research on carbody lightweighting has focused on substitute material and new processing technologies rather than structures. However, new materials and processing techniques inevitably lead to higher c...Most of recent research on carbody lightweighting has focused on substitute material and new processing technologies rather than structures. However, new materials and processing techniques inevitably lead to higher costs. Also, material substitution and processing lightweighting have to be realized through body structural profiles and locations. In the huge conventional workload of lightweight optimization, model modifications involve heavy manual work, and it always leads to a large number of iteration calculations. As a new technique in carbody lightweighting, the implicit parameterization is used to optimize the carbody structure to improve the materials utilization rate in this paper. The implicit parameterized structural modeling enables the use of automatic modification and rapid multidisciplinary design optimization （MDO） in carbody structure, which is impossible in the traditional structure finite element method （FEM） without parameterization. The structural SFE parameterized model is built in accordance with the car structural FE model in concept development stage, and it is validated by some structural performance data. The validated SFE structural parameterized model can be used to generate rapidly and automatically FE model and evaluate different design variables group in the integrated MDO loop. The lightweighting result of body-in-white （BIW） after the optimization rounds reveals that the implicit parameterized model makes automatic MDO feasible and can significantly improve the computational efficiency of carbody structural lightweighting. This paper proposes the integrated method of implicit parameterized model and MDO, which has the obvious practical advantage and industrial significance in the carbody structural lightweighting design.展开更多
Background With the rapid development of Web3D technologies, the online Web3D visualization, particularly for complex models or scenes, has been in a great demand. Owing to the major conflict between the Web3D system ...Background With the rapid development of Web3D technologies, the online Web3D visualization, particularly for complex models or scenes, has been in a great demand. Owing to the major conflict between the Web3D system load and resource consumption in the processing of these huge models, the huge 3D model lightweighting methods for online Web3D visualization are reviewed in this paper. Methods By observing the geometry redundancy introduced by man-made operations in the modeling procedure, several categories of light-weighting related work that aim at reducing the amount of data and resource consumption are elaborated for Web3D visualization. Results By comparing perspectives, the characteristics of each method are summarized, and among the reviewed methods, the geometric redundancy removal that achieves the lightweight goal by detecting and removing the repeated components is an appropriate method for current online Web3D visualization. Meanwhile, the learning algorithm, still in improvement period at present, is our expected future research topic. Conclusions Various aspects should be considered in an efficient lightweight method for online Web3D visualization, such as characteristics of original data, combination or extension of existing methods, scheduling strategy, cache man-agement, and rendering mechanism. Meanwhile, innovation methods, particularly the learning algorithm, are worth exploring.展开更多
Owing to unprecedented climate change issues in recent times, global automotive industry is striving hard in developing novel functional materials to improve vehicle’s fuel efficiency. It is believed that more than a...Owing to unprecedented climate change issues in recent times, global automotive industry is striving hard in developing novel functional materials to improve vehicle’s fuel efficiency. It is believed that more than a quarter of all combined greenhouse gas emissions (GHG) are associated with road transport vehicles. All these facts in association with heightened consumer awareness and energy security issues have led to automotive lightweighting as a major research theme across the globe. Almost all North American and European original equipment manufacturers (OEMs) related to automotive industry have chalked out ambitious weight reduction plans in response to stricter environmental regulations. This review entails main motives and current legislation which has prompted major OEMs to have drastic measures in bringing down vehicle weight to suggested limits. Also discussed are recent advances in developing advanced composites, and cellulose-enabled light weight automotive composites with special focus on research efforts of Center for Biocomposites and Biomaterials Processing (CBBP), University of Toronto, Canada.展开更多
Currently,deep learning is widely used in medical image segmentation and has achieved good results.However,3D medical image segmentation tasks with diverse lesion characters,blurred edges,and unstable positions requir...Currently,deep learning is widely used in medical image segmentation and has achieved good results.However,3D medical image segmentation tasks with diverse lesion characters,blurred edges,and unstable positions require complex networks with a large number of parameters.It is computationally expensive and results in high requirements on equipment,making it hard to deploy the network in hospitals.In this work,we propose a method for network lightweighting and applied it to a 3D CNN based network.We experimented on a COVID-19 lesion segmentation dataset.Specifically,we use three cascaded one-dimensional convolutions to replace a 3D convolution,and integrate instance normalization with the previous layer of one-dimensional convolutions to accelerate network inference.In addition,we simplify test-time augmentation and deep supervision of the network.Experiments show that the lightweight network can reduce the prediction time of each sample and the memory usage by 50%and reduce the number of parameters by 60%compared with the original network.The training time of one epoch is also reduced by 50%with the segmentation accuracy dropped within the acceptable range.展开更多
Reed membrane,a natural cellulosic material traditionally used in musical instruments,holds promise in flexible electronics due to its abundance,low cost,and excellent biocompatibility.However,its native form contains...Reed membrane,a natural cellulosic material traditionally used in musical instruments,holds promise in flexible electronics due to its abundance,low cost,and excellent biocompatibility.However,its native form contains water-soluble ions and lipid-soluble waxes that hinder performance in acoustic and electronics by compromising electrical insulation and mechanical stability.Here,supercritical fluid superposition purification(SCSP-WA)is introduced,which utilizes supercritical CO_(2)with water and acetone as bipolar co-solvents to selectively remove these impurities.Post-SCSP-WA treatment,the reed membrane exhibits significant enhancements in mechanical strength and electrical insulation,achieving a 4-fold increase in elongation at break,improved tensile strength and Young’s modulus,and a 98.5%reduction in leakage current,all while maintaining low and stable capacitance.These improvements stem from the restructuring of the fibrous network into a porous,interconnected microstructure.Material characterization(X-ray photoelectron spectroscopy(XPS),Fourier-transform infrared spectroscopy(FTIR),and scanning electron microscopy(SEM))confirmed the effective removal of magnesium and waxy functional groups,along with enhanced fiber crosslinking.Cytotoxicity tests further validated the biocompatibility of the SCSP-WA-treated membranes.This environmentally sustainable approach expands the potential of reed membranes in flexible bioelectronics and bio-integrated acoustic systems.展开更多
Due to the urgent demand for lightweight and high-strength materials in rail transportation,this study proposed foamed polylactic acid(PLA)composites reinforced with continuous basalt fibres using a 3D printing techni...Due to the urgent demand for lightweight and high-strength materials in rail transportation,this study proposed foamed polylactic acid(PLA)composites reinforced with continuous basalt fibres using a 3D printing technique to address the limitations posed by foaming-induced strength reduction in foam.Through a combination of parametric calculations,microscopic observations and compression experiments,the effects of printing parameters on the expansion ratio and print accuracy of foamed composite were investigated.It was found that adding fibres to foamed PLA reduced the expansion ratio of PLA by up to 9.52%at lower printing temperatures and layer heights but increased it at higher settings.The expansion ratio of the composite significantly increased with high printing temperatures and layer heights.When the composites were fabricated at low print temperatures and high layer heights,noticeable interlayer gaps and exposed fibres leading to poor impregnation were observed at cross-section.This phenomenon was improved as the expansion ratio increased.In addition,specimens with optimal print accuracy were prepared at specific combinations of printing temperature and layer height.In light of this discovery,a predictive function based on combined printing parameters was established to design composites with excellent print accuracy and specific densities.Finally,compression test results showed that with the same density of 0.5 g/cm^(3),the foamed composite exhibited substantial improvements in compressive strength,modulus and strain energy density compared to the foamed PLA,with increases of 44.44%,57.02%and 24.19%,respectively.展开更多
In this study,an inverse design framework was established to find lightweight honeycomb structures(HCSs)with high impact resistance.The hybrid HCS,composed of re-entrant(RE)and elliptical annular re-entrant(EARE)honey...In this study,an inverse design framework was established to find lightweight honeycomb structures(HCSs)with high impact resistance.The hybrid HCS,composed of re-entrant(RE)and elliptical annular re-entrant(EARE)honeycomb cells,was created by constructing arrangement matrices to achieve structural lightweight.The machine learning(ML)framework consisted of a neural network(NN)forward regression model for predicting impact resistance and a multi-objective optimization algorithm for generating high-performance designs.The surrogate of the local design space was initially realized by establishing the NN in the small sample dataset,and the active learning strategy was used to continuously extended the local optimal design until the model converged in the global space.The results indicated that the active learning strategy significantly improved the inference capability of the NN model in unknown design domains.By guiding the iteration direction of the optimization algorithm,lightweight designs with high impact resistance were identified.The energy absorption capacity of the optimal design reached 94.98%of the EARE honeycomb,while the initial peak stress and mass decreased by 28.85%and 19.91%,respectively.Furthermore,Shapley Additive Explanations(SHAP)for global explanation of the NN indicated a strong correlation between the arrangement mode of HCS and its impact resistance.By reducing the stiffness of the cells at the top boundary of the structure,the initial impact damage sustained by the structure can be significantly improved.Overall,this study proposed a general lightweight design method for array structures under impact loads,which is beneficial for the widespread application of honeycomb-based protective structures.展开更多
Attribute-Based Encryption(ABE)has emerged as a fundamental access control mechanism in data sharing,enabling data owners to define flexible access policies.A critical aspect of ABE is key revocation,which plays a piv...Attribute-Based Encryption(ABE)has emerged as a fundamental access control mechanism in data sharing,enabling data owners to define flexible access policies.A critical aspect of ABE is key revocation,which plays a pivotal role in maintaining security.However,existing key revocation mechanisms face two major challenges:(1)High overhead due to ciphertext and key updates,primarily stemming from the reliance on revocation lists during attribute revocation,which increases computation and communication costs.(2)Limited universality,as many attribute revocation mechanisms are tailored to specific ABE constructions,restricting their broader applicability.To address these challenges,we propose LUAR(Lightweight and Universal Attribute Revocation),a novel revocation mechanism that leverages Intel Software Guard Extensions(SGX)while minimizing its inherent limitations.Given SGX’s constrained memory(≈90 MB in a personal computer)and susceptibility to side-channel attacks,we carefully manage its usage to reduce reliance while mitigating potential collusion risks between cloud service providers and users.To evaluate LUAR’s lightweight and universality,we integrate it with the classic BSW07 scheme,which can be seamlessly replaced with other ABE constructions.Experimental results demonstrate that LUAR enables secure attribute revocation with low computation and communication overhead.The processing time within the SGX environment remains stable at approximately 55 ms,regardless of the complexity of access policies,ensuring no additional storage or computational burden on SGX.Compared to the Hardware-based Revocable Attribute-Based Encryption(HR-ABE)scheme(IEEE S&P 2024),LUAR incurs a slightly higher computational cost within SGX;however,the overall time from initiating a data request to obtaining plaintext is shorter.As access policies grow more complex,LUAR’s advantages become increasingly evident,showcasing its superior efficiency and broader applicability.展开更多
Conventional lightweight refractory materials with low bulk density and more pores suffer from harsh corrosion and erosion in actual applications.A type of lightweight Al_(2)O_(3)-MgAl_(2)O_(4)aggregates with a core-s...Conventional lightweight refractory materials with low bulk density and more pores suffer from harsh corrosion and erosion in actual applications.A type of lightweight Al_(2)O_(3)-MgAl_(2)O_(4)aggregates with a core-shell structure was synthesized at 1750℃using a rolling granulation method.Microstructural evolution and properties of the spherical aggregates were systematically studied.Scanning electron microscope and X-ray computed tomography results confirmed that a continuous and dense MgAl_(2)O_(4)spinel shell structure with a thickness of 200-300μm was formed on the surface.The corrosion results indicated that the corrosion index of the core-shell aggregates exhibited a 60%enhancement when compared to Al_(2)O_(3)spherical.Moreover,Al_(2)O_(3)-MgAl_(2)O_(4)refractory materials,which are based on the lightweight Al_(2)O_(3)-MgAl_(2)O_(4)spherical aggregates,possessed a higher temperature modulus of rupture of 9.19 MPa,and the retention rate of residual flexural strength reached 70%after thermal shock testing.The above results showed an improvement of 129.75 and 44.28%compared with pure Al_(2)O_(3)aggregate samples,respectively.In addition,the MgAl_(2)O_(4)spinel shell could trap the Mn,Fe elements from infiltrated slag and transfer into(Mg,Fe,Mn)Al_(2)O_(4)spinel,infiltrated CaO reacts with Sample Al_(2)O_(3)matrix to form a calcium hexaluminate(CA6)isolation layer,and the above two reasons enhance the corrosion resistance of the material.The corrosion mechanism was elaborated in detail.展开更多
With the large-scale deployment of the Internet ofThings(IoT)devices,their weak securitymechanisms make them prime targets for malware attacks.Attackers often use Domain Generation Algorithm(DGA)to generate random dom...With the large-scale deployment of the Internet ofThings(IoT)devices,their weak securitymechanisms make them prime targets for malware attacks.Attackers often use Domain Generation Algorithm(DGA)to generate random domain names,hiding the real IP of Command and Control(C&C)servers to build botnets.Due to the randomness and dynamics of DGA,traditional methods struggle to detect them accurately,increasing the difficulty of network defense.This paper proposes a lightweight DGA detection model based on knowledge distillation for resource-constrained IoT environments.Specifically,a teacher model combining CharacterBERT,a bidirectional long short-term memory(BiLSTM)network,and attention mechanism(ATT)is constructed:it extracts character-level semantic features viaCharacterBERT,captures sequence dependencieswith the BiLSTM,and integrates theATT for key feature weighting,formingmulti-granularity feature fusion.An improved knowledge distillation approach transfers the teacher model’s learned knowledge to the simplified DistilBERT student model.Experimental results show the teacher model achieves 98.68%detection accuracy.The student modelmaintains slightly improved accuracy while significantly compressing parameters to approximately 38.4%of the teacher model’s scale,greatly reducing computational overhead for IoT deployment.展开更多
The strength-ductility trade-off in low-Mn lightweight steels is a significant challenge due to the low thermal stability of austenite and the presence ofδ-ferrite.Two types of low-Mn lightweight steels containing V ...The strength-ductility trade-off in low-Mn lightweight steels is a significant challenge due to the low thermal stability of austenite and the presence ofδ-ferrite.Two types of low-Mn lightweight steels containing V and NbVMo microalloying elements were developed by warm rolling.Among these,NbVMo steel demonstrated superior properties,achieving a tensile strength of~1.2 GPa and a product of strength and elongation exceeding 45 GPa%.In-depth mechanism analysis by atom probe tomography and quasi-in-situ electron backscatter diffraction revealed that different microalloying compositions influence the mechanical properties by strengtheningδ-ferrite,refining retained austenite and homogenizing matrix strain.In NbVMo steel,δ-ferrite strengthening is attributed to the synergistic effects of(V,Mo,Cr,Nb)C composite precipitation,fine NbC and MoC precipitates,and the solid solution strengthening of Mo.These mechanisms collectively contribute to a higher yield strength andδ-ferrite microhardness compared to V steel.Consequently,δ-ferrite and the surrounding matrix in NbVMo steel exhibit coordinated elongation during deformation,enhancing the ductility.The improved microstructural and strain uniformity in NbVMo steel mitigates stress concentration effects onδ-ferrite deformation and serves as a barrier that delays the transformation of retained austenite.In contrast,the retained austenite in V steel exhibits a blocky morphology with larger grain sizes,resulting in lower stability.Combined with localized stress concentrations due to non-uniform strain distribution,this leads to premature transformation of retained austenite to alleviate stress,ultimately impairing elongation and the continuity of strain hardening.Furthermore,the precipitation mechanisms of(V,Mo,Cr,Nb)C composite precipitates are elucidated.展开更多
As a fundamental component in computer vision,edges can be categorized into four types based on discontinuities in reflectance,illumination,surface normal,or depth.While deep CNNs have significantly advanced generic e...As a fundamental component in computer vision,edges can be categorized into four types based on discontinuities in reflectance,illumination,surface normal,or depth.While deep CNNs have significantly advanced generic edge detection,real-time multi-class semantic edge detection under resource constraints remains challenging.To address this,we propose a lightweight framework based on PiDiNet that enables fine-grained semantic edge detection.Our model simultaneously predicts background and four edge categories from full-resolution inputs,balancing accuracy and efficiency.Key contributions include:a multi-channel output structure expanding binary edge prediction to five classes,supported by a deep supervision mechanism;a dynamic class-balancing strategy combining adaptive weighting with physical priors to handle extreme class imbalance;and maintained architectural efficiency enabling real-time inference.Extensive evaluations on BSDS-RIND show our approach achieves accuracy competitive with state-of-the-art methods while operating in real time.展开更多
The Internet of Things(IoT)ecosystem is inherently heterogeneous,comprising diverse devices that must interoperate seamlessly to enable federated message and data exchange.However,as the number of service requests gro...The Internet of Things(IoT)ecosystem is inherently heterogeneous,comprising diverse devices that must interoperate seamlessly to enable federated message and data exchange.However,as the number of service requests grows,existing approaches suffer from increased discovery time and degraded Quality of Service(QoS).Moreover,the massive data generated by heterogeneous IoT devices often contain redundancy and noise,posing challenges to efficient data management.To address these issues,this paper proposes a lightweight ontology-based architecture that enhances service discovery and QoS-aware semantic data management.The architecture employs Modified-Ordered Points to Identify theClustering Structure(M-OPTICS)to cluster and eliminate redundant IoT data.The clustered data are then modelled into a lightweight ontology,enabling semantic relationship inference and rule generation through an embedded inference engine.User requests,transmitted via theConstrainedApplication Protocol(CoAP),are semantically enriched and matched to QoS parameters using Dynamic Shannon Entropy optimized with the Salp Swarm Algorithm.Semantic matching is further refined using a bidirectional recurrent neural network(Bi-RNN),while a State–Action–Reward–State–Action(SARSA)reinforcement learning model dynamically defines and updates semantic rules to retrieve themost recent and relevant data across heterogeneous devices.Experimental results demonstrate that the proposed architecture outperforms existing methods in terms of response time,service delay,execution time,precision,recall,and F-score under varying CoAP request loads and communication overheads.The results confirm the effectiveness of the proposed lightweight ontology architecture for service discovery and data management in heterogeneous IoT environments.展开更多
Real-time multi-person pose estimation(MPE)built upon neural network architectures aims to simultaneously detect multiple human instances and regress joint coordinates in dynamic scenes.However,due to factors such as ...Real-time multi-person pose estimation(MPE)built upon neural network architectures aims to simultaneously detect multiple human instances and regress joint coordinates in dynamic scenes.However,due to factors such as high model complexity and limited expression of keypoint information,both the efficiency and accuracy of real-time MPE remain to be improved.To mitigate the adverse impacts caused by the aforementioned issues,this work develops FSEM-Pose,a real-time MPE model rooted in the YOLOv10 framework.In detail,first,FSEM-Pose upgrades the backbone module of the baseline network by introducing the Feature Shuffling-Convolution(FS-Conv),which effectively reduces the backbone size while maximizing the retention of spatial information from the input image.Second,FSEM-Pose incorporates a Feature Saliency Enhancement Module(FSEM)to strengthen the feature encoding of human keypoints,thereby improving the accuracy of pose estimation.Finally,FSEM-Pose further enhances inference efficiency via a lightweight optimization of the head using shared convolutional layers.Our method achieves competitive results across multiple accuracy and efficiency metrics on the MS COCO 2017 and CrowdPose datasets.While being lightweight in design,it improves average precision(AP)by 2.1%and 2.5%,respectively.展开更多
To address critical challenges in nighttime ship detection—high small-target missed detection(over 20%),insufficient lightweighting,and limited generalization due to scarce,low-quality datasets—this study proposes a...To address critical challenges in nighttime ship detection—high small-target missed detection(over 20%),insufficient lightweighting,and limited generalization due to scarce,low-quality datasets—this study proposes a systematic solution.First,a high-quality Night-Ships dataset is constructed via CycleGAN-based day-night transfer,combined with a dual-threshold cleaning strategy(Laplacian variance sharpness filtering and brightness-color deviation screening).Second,a Cross-stage Lightweight Fusion-You Only Look Once version 8(CLF-YOLOv8)is proposed with key improvements:the Neck network is reconstructed by replacing Cross Stage Partial(CSP)structure with the Cross Stage Partial Multi-Scale Convolutional Block(CSP-MSCB)and integrating Bidirectional Feature Pyramid Network(BiFPN)for weighted multi-scale fusion to enhance small-target detection;a Lightweight Shared Convolutional and Separated Batch Normalization Detection-Head(LSCSBD-Head)with shared convolutions and layer-wise Batch Normalization(BN)reduces parameters to 1.8M(42% fewer than YOLOv8n);and the FocalMinimum Point Distance Intersection over Union(Focal-MPDIoU)loss combines Minimum Point Distance Intersection over Union(MPDIoU)geometric constraints and Focal weighting to optimize low-overlap targets.Experiments show CLFYOLOv8 achieves 97.6%mAP@0.5(0.7% higher than YOLOv8n)with 1.8 M parameters,outperforming mainstream models in small-target detection,overlapping target discrimination,and adaptability to complex lighting.展开更多
Fabric defect detection plays a vital role in ensuring textile quality.However,traditional manual inspection methods are often inefficient and inaccurate.To overcome these limitations,we propose FD-YOLO,an enhanced li...Fabric defect detection plays a vital role in ensuring textile quality.However,traditional manual inspection methods are often inefficient and inaccurate.To overcome these limitations,we propose FD-YOLO,an enhanced lightweight detection model based on the YOLOv11n framework.The proposed model introduces the Bi-level Routing Attention(BRAttention)mechanism to enhance defect feature extraction,enabling more detailed feature representation.It proposes Deep Progressive Cross-Scale Fusion Neck(DPCSFNeck)to better capture smallscale defects and incorporates a Multi-Scale Dilated Residual(MSDR)module to strengthen multi-scale feature representation.Furthermore,a Shared Detail-Enhanced Lightweight Head(SDELHead)is employed to reduce the risk of gradient explosion during training.Experimental results demonstrate that FD-YOLO achieves superior detection accuracy and Lightweight performance compared to the baseline YOLOv11n.展开更多
Tomato is a major economic crop worldwide,and diseases on tomato leaves can significantly reduce both yield and quality.Traditional manual inspection is inefficient and highly subjective,making it difficult to meet th...Tomato is a major economic crop worldwide,and diseases on tomato leaves can significantly reduce both yield and quality.Traditional manual inspection is inefficient and highly subjective,making it difficult to meet the requirements of early disease identification in complex natural environments.To address this issue,this study proposes an improved YOLO11-based model,YOLO-SPDNet(Scale Sequence Fusion,Position-Channel Attention,and Dual Enhancement Network).The model integrates the SEAM(Self-Ensembling Attention Mechanism)semantic enhancement module,the MLCA(Mixed Local Channel Attention)lightweight attention mechanism,and the SPA(Scale-Position-Detail Awareness)module composed of SSFF(Scale Sequence Feature Fusion),TFE(Triple Feature Encoding),and CPAM(Channel and Position Attention Mechanism).These enhancements strengthen fine-grained lesion detection while maintaining model lightweightness.Experimental results show that YOLO-SPDNet achieves an accuracy of 91.8%,a recall of 86.5%,and an mAP@0.5 of 90.6%on the test set,with a computational complexity of 12.5 GFLOPs.Furthermore,the model reaches a real-time inference speed of 987 FPS,making it suitable for deployment on mobile agricultural terminals and online monitoring systems.Comparative analysis and ablation studies further validate the reliability and practical applicability of the proposed model in complex natural scenes.展开更多
Aiming at the problem of potential information noise introduced during the generation of ghost feature maps in GhostNet,this paper proposes a novel lightweight neural network model called ResghostNet.This model constr...Aiming at the problem of potential information noise introduced during the generation of ghost feature maps in GhostNet,this paper proposes a novel lightweight neural network model called ResghostNet.This model constructs the Resghost Module by combining residual connections and Adaptive-SE Blocks,which enhances the quality of generated feature maps through direct propagation of original input information and selection of important channels before cheap operations.Specifically,ResghostNet introduces residual connections on the basis of the Ghost Module to optimize the information flow,and designs a weight self-attention mechanism combined with SE blocks to enhance feature expression capabilities in cheap operations.Experimental results on the ImageNet dataset show that,compared to GhostNet,ResghostNet achieves higher accuracy while reducing the number of parameters by 52%.Although the computational complexity increases,by optimizing the usage strategy of GPU cachememory,themodel’s inference speed becomes faster.The ResghostNet is optimized in terms of classification accuracy and the number of model parameters,and shows great potential in edge computing devices.展开更多
文摘The demand for extended electric vehicle(EV)range necessitates advanced lightweighting strategies.This study introduces a materials genome approach,augmented by machine learning(ML),for optimizing lightweight composite designs for EVs.A comprehensive materials genome database was developed,encompassing composites based on carbon,glass,and natural fibers.This database systematically records critical parameters such as mechanical properties,density,cost,and environmental impact.Machine learning models,including Random Forest,Support Vector Machines,and Artificial Neural Networks,were employed to construct a predictive system for material performance.Subsequent material composition optimization was performed using amulti-objective genetic algorithm.Experimental validation demonstrated that an optimized carbon fiber/bio-based resin composite achieved a 45%weight reduction compared to conventional steel,while maintaining equivalent structural strength.The predictive accuracy of the models reached 94.2%.A cost-benefit analysis indicated that despite a 15%increase in material cost,the overall vehicle energy consumption decreased by 12%,leading to an 18%total cost saving over a five-year operational lifecycle,under a representative mid-size battery electric vehicle(BEV)operational scenario.
文摘The automobile industry is the first to form a typical representative of the global industry in modern industry,with the increase of the national emphasis on the environment,the automobile industry was regarded as an important energy consumption and one of the sources of environmental pollution,the policy of energy conservation and emission reduction requirements for the automobile industry are becoming stricter over the years,energy conservation and emission reduction has becomes the main direction of product optimization in the automobile industry in recent years.Due of a series of excellent properties such as light weight and high strength,composite materials have become the main material for the development of lightweight vehicles.With the development of material technology and the update and iteration of manufacturing technology,composite materials are currently popular being adopted in the automotive field.
基金Supported by National Natural Science Foundation of China(Grant No.51175214)Scientific and Technological Planning Project of China(Grant No.2011BAG03B02-1)
文摘Most of recent research on carbody lightweighting has focused on substitute material and new processing technologies rather than structures. However, new materials and processing techniques inevitably lead to higher costs. Also, material substitution and processing lightweighting have to be realized through body structural profiles and locations. In the huge conventional workload of lightweight optimization, model modifications involve heavy manual work, and it always leads to a large number of iteration calculations. As a new technique in carbody lightweighting, the implicit parameterization is used to optimize the carbody structure to improve the materials utilization rate in this paper. The implicit parameterized structural modeling enables the use of automatic modification and rapid multidisciplinary design optimization （MDO） in carbody structure, which is impossible in the traditional structure finite element method （FEM） without parameterization. The structural SFE parameterized model is built in accordance with the car structural FE model in concept development stage, and it is validated by some structural performance data. The validated SFE structural parameterized model can be used to generate rapidly and automatically FE model and evaluate different design variables group in the integrated MDO loop. The lightweighting result of body-in-white （BIW） after the optimization rounds reveals that the implicit parameterized model makes automatic MDO feasible and can significantly improve the computational efficiency of carbody structural lightweighting. This paper proposes the integrated method of implicit parameterized model and MDO, which has the obvious practical advantage and industrial significance in the carbody structural lightweighting design.
文摘Background With the rapid development of Web3D technologies, the online Web3D visualization, particularly for complex models or scenes, has been in a great demand. Owing to the major conflict between the Web3D system load and resource consumption in the processing of these huge models, the huge 3D model lightweighting methods for online Web3D visualization are reviewed in this paper. Methods By observing the geometry redundancy introduced by man-made operations in the modeling procedure, several categories of light-weighting related work that aim at reducing the amount of data and resource consumption are elaborated for Web3D visualization. Results By comparing perspectives, the characteristics of each method are summarized, and among the reviewed methods, the geometric redundancy removal that achieves the lightweight goal by detecting and removing the repeated components is an appropriate method for current online Web3D visualization. Meanwhile, the learning algorithm, still in improvement period at present, is our expected future research topic. Conclusions Various aspects should be considered in an efficient lightweight method for online Web3D visualization, such as characteristics of original data, combination or extension of existing methods, scheduling strategy, cache man-agement, and rendering mechanism. Meanwhile, innovation methods, particularly the learning algorithm, are worth exploring.
文摘Owing to unprecedented climate change issues in recent times, global automotive industry is striving hard in developing novel functional materials to improve vehicle’s fuel efficiency. It is believed that more than a quarter of all combined greenhouse gas emissions (GHG) are associated with road transport vehicles. All these facts in association with heightened consumer awareness and energy security issues have led to automotive lightweighting as a major research theme across the globe. Almost all North American and European original equipment manufacturers (OEMs) related to automotive industry have chalked out ambitious weight reduction plans in response to stricter environmental regulations. This review entails main motives and current legislation which has prompted major OEMs to have drastic measures in bringing down vehicle weight to suggested limits. Also discussed are recent advances in developing advanced composites, and cellulose-enabled light weight automotive composites with special focus on research efforts of Center for Biocomposites and Biomaterials Processing (CBBP), University of Toronto, Canada.
文摘Currently,deep learning is widely used in medical image segmentation and has achieved good results.However,3D medical image segmentation tasks with diverse lesion characters,blurred edges,and unstable positions require complex networks with a large number of parameters.It is computationally expensive and results in high requirements on equipment,making it hard to deploy the network in hospitals.In this work,we propose a method for network lightweighting and applied it to a 3D CNN based network.We experimented on a COVID-19 lesion segmentation dataset.Specifically,we use three cascaded one-dimensional convolutions to replace a 3D convolution,and integrate instance normalization with the previous layer of one-dimensional convolutions to accelerate network inference.In addition,we simplify test-time augmentation and deep supervision of the network.Experiments show that the lightweight network can reduce the prediction time of each sample and the memory usage by 50%and reduce the number of parameters by 60%compared with the original network.The training time of one epoch is also reduced by 50%with the segmentation accuracy dropped within the acceptable range.
基金supported by the Shenzhen Scientific and Technological Foundation(RCYX20231211090332037 and JCYJ20240813160211015)the National Natural Science Foundation of China(62474008 and 62204007)+2 种基金the Guangdong Provincial Natural Science Foundation(2024A1515030044)the Guangdong Provincial Key Laboratory of In-Memory Computing Chips(2024B1212020002)the Shenzhen Science and Technology Program(KJZD20230923115005009)。
文摘Reed membrane,a natural cellulosic material traditionally used in musical instruments,holds promise in flexible electronics due to its abundance,low cost,and excellent biocompatibility.However,its native form contains water-soluble ions and lipid-soluble waxes that hinder performance in acoustic and electronics by compromising electrical insulation and mechanical stability.Here,supercritical fluid superposition purification(SCSP-WA)is introduced,which utilizes supercritical CO_(2)with water and acetone as bipolar co-solvents to selectively remove these impurities.Post-SCSP-WA treatment,the reed membrane exhibits significant enhancements in mechanical strength and electrical insulation,achieving a 4-fold increase in elongation at break,improved tensile strength and Young’s modulus,and a 98.5%reduction in leakage current,all while maintaining low and stable capacitance.These improvements stem from the restructuring of the fibrous network into a porous,interconnected microstructure.Material characterization(X-ray photoelectron spectroscopy(XPS),Fourier-transform infrared spectroscopy(FTIR),and scanning electron microscopy(SEM))confirmed the effective removal of magnesium and waxy functional groups,along with enhanced fiber crosslinking.Cytotoxicity tests further validated the biocompatibility of the SCSP-WA-treated membranes.This environmentally sustainable approach expands the potential of reed membranes in flexible bioelectronics and bio-integrated acoustic systems.
基金financial support of the Natural Science Foundation of Hunan(Grant No.2024JJ5434)the Postdoctoral Fellowship Program of CPSF(Grant No.GZC20240646).
文摘Due to the urgent demand for lightweight and high-strength materials in rail transportation,this study proposed foamed polylactic acid(PLA)composites reinforced with continuous basalt fibres using a 3D printing technique to address the limitations posed by foaming-induced strength reduction in foam.Through a combination of parametric calculations,microscopic observations and compression experiments,the effects of printing parameters on the expansion ratio and print accuracy of foamed composite were investigated.It was found that adding fibres to foamed PLA reduced the expansion ratio of PLA by up to 9.52%at lower printing temperatures and layer heights but increased it at higher settings.The expansion ratio of the composite significantly increased with high printing temperatures and layer heights.When the composites were fabricated at low print temperatures and high layer heights,noticeable interlayer gaps and exposed fibres leading to poor impregnation were observed at cross-section.This phenomenon was improved as the expansion ratio increased.In addition,specimens with optimal print accuracy were prepared at specific combinations of printing temperature and layer height.In light of this discovery,a predictive function based on combined printing parameters was established to design composites with excellent print accuracy and specific densities.Finally,compression test results showed that with the same density of 0.5 g/cm^(3),the foamed composite exhibited substantial improvements in compressive strength,modulus and strain energy density compared to the foamed PLA,with increases of 44.44%,57.02%and 24.19%,respectively.
基金the financial supports from National Key R&D Program for Young Scientists of China(Grant No.2022YFC3080900)National Natural Science Foundation of China(Grant No.52374181)+1 种基金BIT Research and Innovation Promoting Project(Grant No.2024YCXZ017)supported by Science and Technology Innovation Program of Beijing institute of technology under Grant No.2022CX01025。
文摘In this study,an inverse design framework was established to find lightweight honeycomb structures(HCSs)with high impact resistance.The hybrid HCS,composed of re-entrant(RE)and elliptical annular re-entrant(EARE)honeycomb cells,was created by constructing arrangement matrices to achieve structural lightweight.The machine learning(ML)framework consisted of a neural network(NN)forward regression model for predicting impact resistance and a multi-objective optimization algorithm for generating high-performance designs.The surrogate of the local design space was initially realized by establishing the NN in the small sample dataset,and the active learning strategy was used to continuously extended the local optimal design until the model converged in the global space.The results indicated that the active learning strategy significantly improved the inference capability of the NN model in unknown design domains.By guiding the iteration direction of the optimization algorithm,lightweight designs with high impact resistance were identified.The energy absorption capacity of the optimal design reached 94.98%of the EARE honeycomb,while the initial peak stress and mass decreased by 28.85%and 19.91%,respectively.Furthermore,Shapley Additive Explanations(SHAP)for global explanation of the NN indicated a strong correlation between the arrangement mode of HCS and its impact resistance.By reducing the stiffness of the cells at the top boundary of the structure,the initial impact damage sustained by the structure can be significantly improved.Overall,this study proposed a general lightweight design method for array structures under impact loads,which is beneficial for the widespread application of honeycomb-based protective structures.
基金support from the National Key Research and Development Program of China(Grant No.2021YFF0704102)the Chongqing Education Commission Key Project of Science and Technology Research(Grant No.KJZD-K202400610)the Chongqing Natural Science Foundation General Project(Grant No.CSTB2025NSCQ-GPX1263).
文摘Attribute-Based Encryption(ABE)has emerged as a fundamental access control mechanism in data sharing,enabling data owners to define flexible access policies.A critical aspect of ABE is key revocation,which plays a pivotal role in maintaining security.However,existing key revocation mechanisms face two major challenges:(1)High overhead due to ciphertext and key updates,primarily stemming from the reliance on revocation lists during attribute revocation,which increases computation and communication costs.(2)Limited universality,as many attribute revocation mechanisms are tailored to specific ABE constructions,restricting their broader applicability.To address these challenges,we propose LUAR(Lightweight and Universal Attribute Revocation),a novel revocation mechanism that leverages Intel Software Guard Extensions(SGX)while minimizing its inherent limitations.Given SGX’s constrained memory(≈90 MB in a personal computer)and susceptibility to side-channel attacks,we carefully manage its usage to reduce reliance while mitigating potential collusion risks between cloud service providers and users.To evaluate LUAR’s lightweight and universality,we integrate it with the classic BSW07 scheme,which can be seamlessly replaced with other ABE constructions.Experimental results demonstrate that LUAR enables secure attribute revocation with low computation and communication overhead.The processing time within the SGX environment remains stable at approximately 55 ms,regardless of the complexity of access policies,ensuring no additional storage or computational burden on SGX.Compared to the Hardware-based Revocable Attribute-Based Encryption(HR-ABE)scheme(IEEE S&P 2024),LUAR incurs a slightly higher computational cost within SGX;however,the overall time from initiating a data request to obtaining plaintext is shorter.As access policies grow more complex,LUAR’s advantages become increasingly evident,showcasing its superior efficiency and broader applicability.
基金funded by the Key Project of the National Natural Science Foundation of China(Grant No.U21A2058)Research Project of Hubei Provincial Department of Science and Technology(2024CSA075)support from the Taizhou Fengcheng Talent Program(2024).
文摘Conventional lightweight refractory materials with low bulk density and more pores suffer from harsh corrosion and erosion in actual applications.A type of lightweight Al_(2)O_(3)-MgAl_(2)O_(4)aggregates with a core-shell structure was synthesized at 1750℃using a rolling granulation method.Microstructural evolution and properties of the spherical aggregates were systematically studied.Scanning electron microscope and X-ray computed tomography results confirmed that a continuous and dense MgAl_(2)O_(4)spinel shell structure with a thickness of 200-300μm was formed on the surface.The corrosion results indicated that the corrosion index of the core-shell aggregates exhibited a 60%enhancement when compared to Al_(2)O_(3)spherical.Moreover,Al_(2)O_(3)-MgAl_(2)O_(4)refractory materials,which are based on the lightweight Al_(2)O_(3)-MgAl_(2)O_(4)spherical aggregates,possessed a higher temperature modulus of rupture of 9.19 MPa,and the retention rate of residual flexural strength reached 70%after thermal shock testing.The above results showed an improvement of 129.75 and 44.28%compared with pure Al_(2)O_(3)aggregate samples,respectively.In addition,the MgAl_(2)O_(4)spinel shell could trap the Mn,Fe elements from infiltrated slag and transfer into(Mg,Fe,Mn)Al_(2)O_(4)spinel,infiltrated CaO reacts with Sample Al_(2)O_(3)matrix to form a calcium hexaluminate(CA6)isolation layer,and the above two reasons enhance the corrosion resistance of the material.The corrosion mechanism was elaborated in detail.
基金supported by the following projects:National Natural Science Foundation of China(62461041)Natural Science Foundation of Jiangxi Province China(20242BAB25068).
文摘With the large-scale deployment of the Internet ofThings(IoT)devices,their weak securitymechanisms make them prime targets for malware attacks.Attackers often use Domain Generation Algorithm(DGA)to generate random domain names,hiding the real IP of Command and Control(C&C)servers to build botnets.Due to the randomness and dynamics of DGA,traditional methods struggle to detect them accurately,increasing the difficulty of network defense.This paper proposes a lightweight DGA detection model based on knowledge distillation for resource-constrained IoT environments.Specifically,a teacher model combining CharacterBERT,a bidirectional long short-term memory(BiLSTM)network,and attention mechanism(ATT)is constructed:it extracts character-level semantic features viaCharacterBERT,captures sequence dependencieswith the BiLSTM,and integrates theATT for key feature weighting,formingmulti-granularity feature fusion.An improved knowledge distillation approach transfers the teacher model’s learned knowledge to the simplified DistilBERT student model.Experimental results show the teacher model achieves 98.68%detection accuracy.The student modelmaintains slightly improved accuracy while significantly compressing parameters to approximately 38.4%of the teacher model’s scale,greatly reducing computational overhead for IoT deployment.
基金supported by the Fundamental Research Funds for the Central Universities(Grant No.FRF-BD-25-001)Development and Application of Ultra-High Strength Hot Stamping Steel Strip for Automobiles(Grant No.20232BCJ22030)Manufacturing and Application Innovation and Integration of High-Safety Automotive Steel(Grant No.24431002D).
文摘The strength-ductility trade-off in low-Mn lightweight steels is a significant challenge due to the low thermal stability of austenite and the presence ofδ-ferrite.Two types of low-Mn lightweight steels containing V and NbVMo microalloying elements were developed by warm rolling.Among these,NbVMo steel demonstrated superior properties,achieving a tensile strength of~1.2 GPa and a product of strength and elongation exceeding 45 GPa%.In-depth mechanism analysis by atom probe tomography and quasi-in-situ electron backscatter diffraction revealed that different microalloying compositions influence the mechanical properties by strengtheningδ-ferrite,refining retained austenite and homogenizing matrix strain.In NbVMo steel,δ-ferrite strengthening is attributed to the synergistic effects of(V,Mo,Cr,Nb)C composite precipitation,fine NbC and MoC precipitates,and the solid solution strengthening of Mo.These mechanisms collectively contribute to a higher yield strength andδ-ferrite microhardness compared to V steel.Consequently,δ-ferrite and the surrounding matrix in NbVMo steel exhibit coordinated elongation during deformation,enhancing the ductility.The improved microstructural and strain uniformity in NbVMo steel mitigates stress concentration effects onδ-ferrite deformation and serves as a barrier that delays the transformation of retained austenite.In contrast,the retained austenite in V steel exhibits a blocky morphology with larger grain sizes,resulting in lower stability.Combined with localized stress concentrations due to non-uniform strain distribution,this leads to premature transformation of retained austenite to alleviate stress,ultimately impairing elongation and the continuity of strain hardening.Furthermore,the precipitation mechanisms of(V,Mo,Cr,Nb)C composite precipitates are elucidated.
基金supported by the National Natural Science Foundation of China 62402171.
文摘As a fundamental component in computer vision,edges can be categorized into four types based on discontinuities in reflectance,illumination,surface normal,or depth.While deep CNNs have significantly advanced generic edge detection,real-time multi-class semantic edge detection under resource constraints remains challenging.To address this,we propose a lightweight framework based on PiDiNet that enables fine-grained semantic edge detection.Our model simultaneously predicts background and four edge categories from full-resolution inputs,balancing accuracy and efficiency.Key contributions include:a multi-channel output structure expanding binary edge prediction to five classes,supported by a deep supervision mechanism;a dynamic class-balancing strategy combining adaptive weighting with physical priors to handle extreme class imbalance;and maintained architectural efficiency enabling real-time inference.Extensive evaluations on BSDS-RIND show our approach achieves accuracy competitive with state-of-the-art methods while operating in real time.
文摘The Internet of Things(IoT)ecosystem is inherently heterogeneous,comprising diverse devices that must interoperate seamlessly to enable federated message and data exchange.However,as the number of service requests grows,existing approaches suffer from increased discovery time and degraded Quality of Service(QoS).Moreover,the massive data generated by heterogeneous IoT devices often contain redundancy and noise,posing challenges to efficient data management.To address these issues,this paper proposes a lightweight ontology-based architecture that enhances service discovery and QoS-aware semantic data management.The architecture employs Modified-Ordered Points to Identify theClustering Structure(M-OPTICS)to cluster and eliminate redundant IoT data.The clustered data are then modelled into a lightweight ontology,enabling semantic relationship inference and rule generation through an embedded inference engine.User requests,transmitted via theConstrainedApplication Protocol(CoAP),are semantically enriched and matched to QoS parameters using Dynamic Shannon Entropy optimized with the Salp Swarm Algorithm.Semantic matching is further refined using a bidirectional recurrent neural network(Bi-RNN),while a State–Action–Reward–State–Action(SARSA)reinforcement learning model dynamically defines and updates semantic rules to retrieve themost recent and relevant data across heterogeneous devices.Experimental results demonstrate that the proposed architecture outperforms existing methods in terms of response time,service delay,execution time,precision,recall,and F-score under varying CoAP request loads and communication overheads.The results confirm the effectiveness of the proposed lightweight ontology architecture for service discovery and data management in heterogeneous IoT environments.
基金supported by the Talent Startup Program of Huangshan University under Grant No.2025xkjq003Additional partial funding was gratefully received from the Scientific Research Project of the Anhui Provincial Department of Education under Grant No.2025AHGXZK40303.
文摘Real-time multi-person pose estimation(MPE)built upon neural network architectures aims to simultaneously detect multiple human instances and regress joint coordinates in dynamic scenes.However,due to factors such as high model complexity and limited expression of keypoint information,both the efficiency and accuracy of real-time MPE remain to be improved.To mitigate the adverse impacts caused by the aforementioned issues,this work develops FSEM-Pose,a real-time MPE model rooted in the YOLOv10 framework.In detail,first,FSEM-Pose upgrades the backbone module of the baseline network by introducing the Feature Shuffling-Convolution(FS-Conv),which effectively reduces the backbone size while maximizing the retention of spatial information from the input image.Second,FSEM-Pose incorporates a Feature Saliency Enhancement Module(FSEM)to strengthen the feature encoding of human keypoints,thereby improving the accuracy of pose estimation.Finally,FSEM-Pose further enhances inference efficiency via a lightweight optimization of the head using shared convolutional layers.Our method achieves competitive results across multiple accuracy and efficiency metrics on the MS COCO 2017 and CrowdPose datasets.While being lightweight in design,it improves average precision(AP)by 2.1%and 2.5%,respectively.
基金the Shandong Provincial Key Research and Development Program(Grant No.2024SFGC0201).
文摘To address critical challenges in nighttime ship detection—high small-target missed detection(over 20%),insufficient lightweighting,and limited generalization due to scarce,low-quality datasets—this study proposes a systematic solution.First,a high-quality Night-Ships dataset is constructed via CycleGAN-based day-night transfer,combined with a dual-threshold cleaning strategy(Laplacian variance sharpness filtering and brightness-color deviation screening).Second,a Cross-stage Lightweight Fusion-You Only Look Once version 8(CLF-YOLOv8)is proposed with key improvements:the Neck network is reconstructed by replacing Cross Stage Partial(CSP)structure with the Cross Stage Partial Multi-Scale Convolutional Block(CSP-MSCB)and integrating Bidirectional Feature Pyramid Network(BiFPN)for weighted multi-scale fusion to enhance small-target detection;a Lightweight Shared Convolutional and Separated Batch Normalization Detection-Head(LSCSBD-Head)with shared convolutions and layer-wise Batch Normalization(BN)reduces parameters to 1.8M(42% fewer than YOLOv8n);and the FocalMinimum Point Distance Intersection over Union(Focal-MPDIoU)loss combines Minimum Point Distance Intersection over Union(MPDIoU)geometric constraints and Focal weighting to optimize low-overlap targets.Experiments show CLFYOLOv8 achieves 97.6%mAP@0.5(0.7% higher than YOLOv8n)with 1.8 M parameters,outperforming mainstream models in small-target detection,overlapping target discrimination,and adaptability to complex lighting.
基金financially supported by the Fujian Provincial Department of Science and Technology,the Collaborative Innovation Platform Project for Key Technologies of Smart Warehousing and Logistics Systems in the Fuzhou-Xiamen-Quanzhou National Independent Innovation Demonstration Zone(No.2025E3024).
文摘Fabric defect detection plays a vital role in ensuring textile quality.However,traditional manual inspection methods are often inefficient and inaccurate.To overcome these limitations,we propose FD-YOLO,an enhanced lightweight detection model based on the YOLOv11n framework.The proposed model introduces the Bi-level Routing Attention(BRAttention)mechanism to enhance defect feature extraction,enabling more detailed feature representation.It proposes Deep Progressive Cross-Scale Fusion Neck(DPCSFNeck)to better capture smallscale defects and incorporates a Multi-Scale Dilated Residual(MSDR)module to strengthen multi-scale feature representation.Furthermore,a Shared Detail-Enhanced Lightweight Head(SDELHead)is employed to reduce the risk of gradient explosion during training.Experimental results demonstrate that FD-YOLO achieves superior detection accuracy and Lightweight performance compared to the baseline YOLOv11n.
基金Tianmin Tianyuan Boutique Vegetable Industry Technology Service Station(Grant No.2024120011003081)Development of Environmental Monitoring and Traceability System for Wuqing Agricultural Production Areas(Grant No.2024120011001866)。
文摘Tomato is a major economic crop worldwide,and diseases on tomato leaves can significantly reduce both yield and quality.Traditional manual inspection is inefficient and highly subjective,making it difficult to meet the requirements of early disease identification in complex natural environments.To address this issue,this study proposes an improved YOLO11-based model,YOLO-SPDNet(Scale Sequence Fusion,Position-Channel Attention,and Dual Enhancement Network).The model integrates the SEAM(Self-Ensembling Attention Mechanism)semantic enhancement module,the MLCA(Mixed Local Channel Attention)lightweight attention mechanism,and the SPA(Scale-Position-Detail Awareness)module composed of SSFF(Scale Sequence Feature Fusion),TFE(Triple Feature Encoding),and CPAM(Channel and Position Attention Mechanism).These enhancements strengthen fine-grained lesion detection while maintaining model lightweightness.Experimental results show that YOLO-SPDNet achieves an accuracy of 91.8%,a recall of 86.5%,and an mAP@0.5 of 90.6%on the test set,with a computational complexity of 12.5 GFLOPs.Furthermore,the model reaches a real-time inference speed of 987 FPS,making it suitable for deployment on mobile agricultural terminals and online monitoring systems.Comparative analysis and ablation studies further validate the reliability and practical applicability of the proposed model in complex natural scenes.
基金funded by Science and Technology Innovation Project grant No.ZZKY20222304.
文摘Aiming at the problem of potential information noise introduced during the generation of ghost feature maps in GhostNet,this paper proposes a novel lightweight neural network model called ResghostNet.This model constructs the Resghost Module by combining residual connections and Adaptive-SE Blocks,which enhances the quality of generated feature maps through direct propagation of original input information and selection of important channels before cheap operations.Specifically,ResghostNet introduces residual connections on the basis of the Ghost Module to optimize the information flow,and designs a weight self-attention mechanism combined with SE blocks to enhance feature expression capabilities in cheap operations.Experimental results on the ImageNet dataset show that,compared to GhostNet,ResghostNet achieves higher accuracy while reducing the number of parameters by 52%.Although the computational complexity increases,by optimizing the usage strategy of GPU cachememory,themodel’s inference speed becomes faster.The ResghostNet is optimized in terms of classification accuracy and the number of model parameters,and shows great potential in edge computing devices.
