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
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
Magnesium is at a crossroads,facing significant opportuni-ties and challenges.On one hand,its unique properties-such as low density,high strength-to-weight ratio,and excellent castability-position it as a key material...Magnesium is at a crossroads,facing significant opportuni-ties and challenges.On one hand,its unique properties-such as low density,high strength-to-weight ratio,and excellent castability-position it as a key material for lightweighting in automotive[1,2],aerospace[3,4],and consumer electronics[4,5].On the other hand,challenges such as limited corro-sion resistance,poor formability at room temperature,and a reliance on energy-intensive extraction processes impede its widespread adoption.Despite the steady increase of magne-sium research and production in the last three decades,its growth in recent years has stalled in almost all regions of the world.展开更多
The exploration of titanium alloy applications in railway transportation aims to meet the newly emerged demand for vehicles that are lighter and more efficient.This research focuses on the potential of these materials...The exploration of titanium alloy applications in railway transportation aims to meet the newly emerged demand for vehicles that are lighter and more efficient.This research focuses on the potential of these materials to concurrently reduce vehicle weight and enhance efficiency,sustainability,and safety.Challenges faced include high production and processing costs,durability issues in harsh railway environments,and environmental impacts associated with alloy production.Research findings indicate that innovative alloy design and advanced processing techniques,such as powder metallurgy,additive manufacturing,and surface treatment,significantly improve the applicability of titanium alloys in railway applications.These methods substantially increase energy efficiency and safety.Additionally,advancements in environmentally sustainable practices in the production of titanium alloys address ecological concerns.As research progresses,the study and development of low-cost,high-performance titanium alloys highlight the need for more efficient and environmentally friendly manufacturing processes.Exploring new alloy compositions and applying emerging technologies in processing and manufacturing are key areas for future research.These advancements are expected to enhance the role of titanium alloys in revolutionizing railway transportation,aligning with global trends towards sustainability and performance improvement.This research underscores the significant potential contribution of titanium alloys to future efficient and eco-friendly rail travel.展开更多
Lightweight high/medium-entropy alloys(H/MEAs)possess attractive properties such as high strength-to-weight ratios,however,their limited room-temperature tensile ductility hinders their widespread engi-neering impleme...Lightweight high/medium-entropy alloys(H/MEAs)possess attractive properties such as high strength-to-weight ratios,however,their limited room-temperature tensile ductility hinders their widespread engi-neering implementation,for instance in aerospace structural components.This work achieved a transfor-mative improvement of room-temperature tensile ductility in Ti-V-Zr-Nb MEAs with densities of 5.4-6.5 g/cm3,via ingenious composition modulation.Through the systematic co-adjustment of Ti and V contents,an intrinsic ductility mechanism was unveiled,manifested by a transition from predominant intergranular brittle fracture to pervasive ductile dimpled rupture.Notably,the modulated deformation mechanisms evolved from solitary slip toward collaborative multiple slip modes,without significantly compromising strength.Compared to equimolar Ti-V-Zr-Nb,a(Ti1.5V)3ZrNb composition demonstrated an impressive 360%improvement in elongation while sustaining a high yield strength of around 800 MPa.Increasing Ti and V not only purified the grain boundaries by reducing detrimental phases,but also tai-lored the deformation dislocation configurations.These insights expanded the applicability of lightweight HEAs to areas demanding combined high strength and ductility.展开更多
Tomato plant diseases often first manifest on the leaves,making the detection of tomato leaf diseases particularly crucial for the tomato cultivation industry.However,conventional deep learning models face challenges ...Tomato plant diseases often first manifest on the leaves,making the detection of tomato leaf diseases particularly crucial for the tomato cultivation industry.However,conventional deep learning models face challenges such as large model sizes and slow detection speeds when deployed on resource-constrained platforms and agricultural machinery.This paper proposes a lightweight model for detecting tomato leaf diseases,named LT-YOLO,based on the YOLOv8n architecture.First,we enhance the C2f module into a RepViT Block(RVB)with decoupled token and channel mixers to reduce the cost of feature extraction.Next,we incorporate a novel Efficient Multi-Scale Attention(EMA)mechanism in the deeper layers of the backbone to improve detection of critical disease features.Additionally,we design a lightweight detection head,LT-Detect,using Partial Convolution(PConv)to significantly reduce the classification and localization costs during detection.Finally,we introduce a Receptive Field Block(RFB)in the shallow layers of the backbone to expand the model’s receptive field,enabling effective detection of diseases at various scales.The improved model reduces the number of parameters by 43%and the computational load by 50%.Additionally,it achieves a mean Average Precision(mAP)of 90.9%on a publicly available dataset containing 3641 images of tomato leaf diseases,with only a 0.7%decrease compared to the baseline model.This demonstrates that the model maintains excellent accuracy while being lightweight,making it suitable for rapid detection of tomato leaf diseases.展开更多
As batteries become increasingly essential for energy storage technologies,battery prognosis,and diagnosis remain central to ensure reliable operation and effective management,as well as to aid the in-depth investigat...As batteries become increasingly essential for energy storage technologies,battery prognosis,and diagnosis remain central to ensure reliable operation and effective management,as well as to aid the in-depth investigation of degradation mechanisms.However,dynamic operating conditions,cell-to-cell inconsistencies,and limited availability of labeled data have posed significant challenges to accurate and robust prognosis and diagnosis.Herein,we introduce a time-series-decomposition-based ensembled lightweight learning model(TELL-Me),which employs a synergistic dual-module framework to facilitate accurate and reliable forecasting.The feature module formulates features with physical implications and sheds light on battery aging mechanisms,while the gradient module monitors capacity degradation rates and captures aging trend.TELL-Me achieves high accuracy in end-of-life prediction using minimal historical data from a single battery without requiring offline training dataset,and demonstrates impressive generality and robustness across various operating conditions and battery types.Additionally,by correlating feature contributions with degradation mechanisms across different datasets,TELL-Me is endowed with the diagnostic ability that not only enhances prediction reliability but also provides critical insights into the design and optimization of next-generation batteries.展开更多
The Internet of Things(IoT)has gained substantial attention in both academic research and real-world applications.The proliferation of interconnected devices across various domains promises to deliver intelligent and ...The Internet of Things(IoT)has gained substantial attention in both academic research and real-world applications.The proliferation of interconnected devices across various domains promises to deliver intelligent and advanced services.However,this rapid expansion also heightens the vulnerability of the IoT ecosystem to security threats.Consequently,innovative solutions capable of effectively mitigating risks while accommodating the unique constraints of IoT environments are urgently needed.Recently,the convergence of Blockchain technology and IoT has introduced a decentralized and robust framework for securing data and interactions,commonly referred to as the Internet of Blockchained Things(IoBT).Extensive research efforts have been devoted to adapting Blockchain technology to meet the specific requirements of IoT deployments.Within this context,consensus algorithms play a critical role in assessing the feasibility of integrating Blockchain into IoT ecosystems.The adoption of efficient and lightweight consensus mechanisms for block validation has become increasingly essential.This paper presents a comprehensive examination of lightweight,constraint-aware consensus algorithms tailored for IoBT.The study categorizes these consensus mechanisms based on their core operations,the security of the block validation process,the incorporation of AI techniques,and the specific applications they are designed to support.展开更多
Introducing B2 ordering can effectively improve the mechanical properties of lightweight refractory high-entropy alloys(LRHEAs).However,(Zr,Al)-enriched B2 precipitates generally reduce the ductility because their ord...Introducing B2 ordering can effectively improve the mechanical properties of lightweight refractory high-entropy alloys(LRHEAs).However,(Zr,Al)-enriched B2 precipitates generally reduce the ductility because their ordering characteristic is destroyed after dislocation shearing.Meanwhile,the local chemical order(LCO)cannot provide an adequate strengthening effect due to its small size.展开更多
Significant advancements have been achieved in the field of Single Image Super-Resolution(SISR)through the utilization of Convolutional Neural Networks(CNNs)to attain state-of-the-art performance.Recent efforts have e...Significant advancements have been achieved in the field of Single Image Super-Resolution(SISR)through the utilization of Convolutional Neural Networks(CNNs)to attain state-of-the-art performance.Recent efforts have explored the incorporation of Transformers to augment network performance in SISR.However,the high computational cost of Transformers makes them less suitable for deployment on lightweight devices.Moreover,the majority of enhancements for CNNs rely predominantly on small spatial convolutions,thereby neglecting the potential advantages of large kernel convolution.In this paper,the authors propose a Multi-Perception Large Kernel convNet(MPLKN)which delves into the exploration of large kernel convolution.Specifically,the authors have architected a Multi-Perception Large Kernel(MPLK)module aimed at extracting multi-scale features and employ a stepwise feature fusion strategy to seamlessly integrate these features.In addition,to enhance the network's capacity for nonlinear spatial information processing,the authors have designed a Spatial-Channel Gated Feed-forward Network(SCGFN)that is capable of adapting to feature interactions across both spatial and channel dimensions.Experimental results demonstrate that MPLKN outperforms other lightweight image super-resolution models while maintaining a minimal number of parameters and FLOPs.展开更多
In recent years,with the development of synthetic aperture radar(SAR)technology and the widespread application of deep learning,lightweight detection of SAR images has emerged as a research direction.The ultimate goal...In recent years,with the development of synthetic aperture radar(SAR)technology and the widespread application of deep learning,lightweight detection of SAR images has emerged as a research direction.The ultimate goal is to reduce computational and storage requirements while ensuring detection accuracy and reliability,making it an ideal choice for achieving rapid response and efficient processing.In this regard,a lightweight SAR ship target detection algorithm based on YOLOv8 was proposed in this study.Firstly,the C2f-Sc module was designed by fusing the C2f in the backbone network with the ScConv to reduce spatial redundancy and channel redundancy between features in convolutional neural networks.At the same time,the Ghost module was introduced into the neck network to effectively reduce model parameters and computational complexity.A relatively lightweight EMA attention mechanism was added to the neck network to promote the effective fusion of features at different levels.Experimental results showed that the Parameters and GFLOPs of the improved model are reduced by 8.5%and 7.0%when mAP@0.5 and mAP@0.5:0.95 are increased by 0.7%and 1.8%,respectively.It makes the model lightweight and improves the detection accuracy,which has certain application value.展开更多
There is a problem of real-time detection difficulty in road surface damage detection. This paper proposes an improved lightweight model based on you only look once version 5(YOLOv5). Firstly, this paper fully utilize...There is a problem of real-time detection difficulty in road surface damage detection. This paper proposes an improved lightweight model based on you only look once version 5(YOLOv5). Firstly, this paper fully utilized the convolutional neural network(CNN) + ghosting bottleneck(G_bneck) architecture to reduce redundant feature maps. Afterwards, we upgraded the original upsampling algorithm to content-aware reassembly of features(CARAFE) and increased the receptive field. Finally, we replaced the spatial pyramid pooling fast(SPPF) module with the basic receptive field block(Basic RFB) pooling module and added dilated convolution. After comparative experiments, we can see that the number of parameters and model size of the improved algorithm in this paper have been reduced by nearly half compared to the YOLOv5s. The frame rate per second(FPS) has been increased by 3.25 times. The mean average precision(m AP@0.5: 0.95) has increased by 8%—17% compared to other lightweight algorithms.展开更多
Aiming at the problems of low detection efficiency and difficult positioning of traditional steel surface defect detection methods,a lightweight steel surface defect detection model based on you only look once version...Aiming at the problems of low detection efficiency and difficult positioning of traditional steel surface defect detection methods,a lightweight steel surface defect detection model based on you only look once version 7(YOLOv7)is proposed.First,a cascading style sheets(CSS)block module is proposed,which uses more lightweight operations to obtain redundant information in the feature map,reduces the amount of computation,and effectively improves the detection speed.Secondly,the improved spatial pyramid pooling with cross stage partial convolutions(SPPCSPC)structure is adopted to ensure that the model can also pay attention to the defect location information while predicting the defect category information,obtain richer defect features.In addition,the convolution operation in the original model is simplified,which significantly reduces the size of the model and helps to improve the detection speed.Finally,using efficient intersection over union(EIOU)loss to focus on high-quality anchors,speed up convergence and improve positioning accuracy.Experiments were carried out on the Northeastern University-defect(NEU-DET)steel surface defect dataset.Compared with the original YOLOv7 model,the number of parameters of this model was reduced by 40%,the frames per second(FPS)reached 112,and the average accuracy reached 79.1%,the detection accuracy and speed have been improved,which can meet the needs of steel surface defect detection.展开更多
Current you only look once(YOLO)-based algorithm model is facing the challenge of overwhelming parameters and calculation complexity under the printed circuit board(PCB)defect detection application scenario.In order t...Current you only look once(YOLO)-based algorithm model is facing the challenge of overwhelming parameters and calculation complexity under the printed circuit board(PCB)defect detection application scenario.In order to solve this problem,we propose a new method,which combined the lightweight network mobile vision transformer(Mobile Vi T)with the convolutional block attention module(CBAM)mechanism and the new regression loss function.This method needed less computation resources,making it more suitable for embedded edge detection devices.Meanwhile,the new loss function improved the positioning accuracy of the bounding box and enhanced the robustness of the model.In addition,experiments on public datasets demonstrate that the improved model achieves an average accuracy of 87.9%across six typical defect detection tasks,while reducing computational costs by nearly 90%.It significantly reduces the model's computational requirements while maintaining accuracy,ensuring reliable performance for edge deployment.展开更多
The exponential expansion of the Internet of Things(IoT),Industrial Internet of Things(IIoT),and Transportation Management of Things(TMoT)produces vast amounts of real-time streaming data.Ensuring system dependability...The exponential expansion of the Internet of Things(IoT),Industrial Internet of Things(IIoT),and Transportation Management of Things(TMoT)produces vast amounts of real-time streaming data.Ensuring system dependability,operational efficiency,and security depends on the identification of anomalies in these dynamic and resource-constrained systems.Due to their high computational requirements and inability to efficiently process continuous data streams,traditional anomaly detection techniques often fail in IoT systems.This work presents a resource-efficient adaptive anomaly detection model for real-time streaming data in IoT systems.Extensive experiments were carried out on multiple real-world datasets,achieving an average accuracy score of 96.06%with an execution time close to 7.5 milliseconds for each individual streaming data point,demonstrating its potential for real-time,resourceconstrained applications.The model uses Principal Component Analysis(PCA)for dimensionality reduction and a Z-score technique for anomaly detection.It maintains a low computational footprint with a sliding window mechanism,enabling incremental data processing and identification of both transient and sustained anomalies without storing historical data.The system uses a Multivariate Linear Regression(MLR)based imputation technique that estimates missing or corrupted sensor values,preserving data integrity prior to anomaly detection.The suggested solution is appropriate for many uses in smart cities,industrial automation,environmental monitoring,IoT security,and intelligent transportation systems,and is particularly well-suited for resource-constrained edge devices.展开更多
In the field of Weakly Supervised Semantic Segmentation(WSSS),methods based on image-level annotation face challenges in accurately capturing objects of varying sizes,lacking sensitivity to image details,and having hi...In the field of Weakly Supervised Semantic Segmentation(WSSS),methods based on image-level annotation face challenges in accurately capturing objects of varying sizes,lacking sensitivity to image details,and having high computational costs.To address these issues,we improve the dual-branch architecture of the Conformer as the fundamental network for generating class activation graphs,proposing a multi-scale efficient weakly-supervised semantic segmentation method based on the improved Conformer.In the Convolution Neural Network(CNN)branch,a cross-scale feature integration convolution module is designed,incorporating multi-receptive field convolution layers to enhance the model’s ability to capture long-range dependencies and improve sensitivity to multi-scale objects.In the Vision Transformer(ViT)branch,an efficient multi-head self-attention module is developed,reducing unnecessary computation through spatial compression and feature partitioning,thereby improving overall network efficiency.Finally,a multi-feature coupling module is introduced to complement the features generated by both branches.This design retains the strength of Convolution Neural Network in extracting local details while harnessing the strength of Vision Transformer to capture comprehensive global features.Experimental results show that the mean Intersection over Union of the image segmentation results of the proposed method on the validation and test sets of the PASCAL VOC 2012 datasets are improved by 2.9%and 3.6%,respectively,over the TransCAM algorithm.Besides,the improved model demonstrates a 1.3%increase of the mean Intersections over Union on the COCO 2014 datasets.Additionally,the number of parameters and the floating-point operations are reduced by 16.2%and 12.9%.However,the proposed method still has limitations of poor performance when dealing with complex scenarios.There is a need for further enhancing the performance of this method to address this issue.展开更多
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.
文摘Magnesium is at a crossroads,facing significant opportuni-ties and challenges.On one hand,its unique properties-such as low density,high strength-to-weight ratio,and excellent castability-position it as a key material for lightweighting in automotive[1,2],aerospace[3,4],and consumer electronics[4,5].On the other hand,challenges such as limited corro-sion resistance,poor formability at room temperature,and a reliance on energy-intensive extraction processes impede its widespread adoption.Despite the steady increase of magne-sium research and production in the last three decades,its growth in recent years has stalled in almost all regions of the world.
基金Supported by National Natural Science Foundation of China(Grant No.52375159)Independent Project of State Key Laboratory of Rail Transit Vehicle System(Grant No.2025RVL-T14).
文摘The exploration of titanium alloy applications in railway transportation aims to meet the newly emerged demand for vehicles that are lighter and more efficient.This research focuses on the potential of these materials to concurrently reduce vehicle weight and enhance efficiency,sustainability,and safety.Challenges faced include high production and processing costs,durability issues in harsh railway environments,and environmental impacts associated with alloy production.Research findings indicate that innovative alloy design and advanced processing techniques,such as powder metallurgy,additive manufacturing,and surface treatment,significantly improve the applicability of titanium alloys in railway applications.These methods substantially increase energy efficiency and safety.Additionally,advancements in environmentally sustainable practices in the production of titanium alloys address ecological concerns.As research progresses,the study and development of low-cost,high-performance titanium alloys highlight the need for more efficient and environmentally friendly manufacturing processes.Exploring new alloy compositions and applying emerging technologies in processing and manufacturing are key areas for future research.These advancements are expected to enhance the role of titanium alloys in revolutionizing railway transportation,aligning with global trends towards sustainability and performance improvement.This research underscores the significant potential contribution of titanium alloys to future efficient and eco-friendly rail travel.
基金supported by the National Natural Science Foundation of China(Nos.51925103,52271149,52171159)the Innovation Program of Shanghai Municipal Education Commission(No.2021-01-07-00-09-E00114)+5 种基金the Natural Science Foundation of Shanghai(22ZR1422500)the Innovation Program of Shanghai Science and Technology(No.23520760700)the Aviation Foundation(No.2023Z0530S6004)the Fund of the State Key Laboratory of Solidification Processing in NWPU(No.SKLSP202221)the financial support from Program 173(No.2020-JCIQ-ZD-186-01)the Space Utilization System of China Manned Space Engineering(No.KJZ-YY-NCL08).
文摘Lightweight high/medium-entropy alloys(H/MEAs)possess attractive properties such as high strength-to-weight ratios,however,their limited room-temperature tensile ductility hinders their widespread engi-neering implementation,for instance in aerospace structural components.This work achieved a transfor-mative improvement of room-temperature tensile ductility in Ti-V-Zr-Nb MEAs with densities of 5.4-6.5 g/cm3,via ingenious composition modulation.Through the systematic co-adjustment of Ti and V contents,an intrinsic ductility mechanism was unveiled,manifested by a transition from predominant intergranular brittle fracture to pervasive ductile dimpled rupture.Notably,the modulated deformation mechanisms evolved from solitary slip toward collaborative multiple slip modes,without significantly compromising strength.Compared to equimolar Ti-V-Zr-Nb,a(Ti1.5V)3ZrNb composition demonstrated an impressive 360%improvement in elongation while sustaining a high yield strength of around 800 MPa.Increasing Ti and V not only purified the grain boundaries by reducing detrimental phases,but also tai-lored the deformation dislocation configurations.These insights expanded the applicability of lightweight HEAs to areas demanding combined high strength and ductility.
文摘Tomato plant diseases often first manifest on the leaves,making the detection of tomato leaf diseases particularly crucial for the tomato cultivation industry.However,conventional deep learning models face challenges such as large model sizes and slow detection speeds when deployed on resource-constrained platforms and agricultural machinery.This paper proposes a lightweight model for detecting tomato leaf diseases,named LT-YOLO,based on the YOLOv8n architecture.First,we enhance the C2f module into a RepViT Block(RVB)with decoupled token and channel mixers to reduce the cost of feature extraction.Next,we incorporate a novel Efficient Multi-Scale Attention(EMA)mechanism in the deeper layers of the backbone to improve detection of critical disease features.Additionally,we design a lightweight detection head,LT-Detect,using Partial Convolution(PConv)to significantly reduce the classification and localization costs during detection.Finally,we introduce a Receptive Field Block(RFB)in the shallow layers of the backbone to expand the model’s receptive field,enabling effective detection of diseases at various scales.The improved model reduces the number of parameters by 43%and the computational load by 50%.Additionally,it achieves a mean Average Precision(mAP)of 90.9%on a publicly available dataset containing 3641 images of tomato leaf diseases,with only a 0.7%decrease compared to the baseline model.This demonstrates that the model maintains excellent accuracy while being lightweight,making it suitable for rapid detection of tomato leaf diseases.
基金supported by the National Natural Science Foundation of China(22379021 and 22479021)。
文摘As batteries become increasingly essential for energy storage technologies,battery prognosis,and diagnosis remain central to ensure reliable operation and effective management,as well as to aid the in-depth investigation of degradation mechanisms.However,dynamic operating conditions,cell-to-cell inconsistencies,and limited availability of labeled data have posed significant challenges to accurate and robust prognosis and diagnosis.Herein,we introduce a time-series-decomposition-based ensembled lightweight learning model(TELL-Me),which employs a synergistic dual-module framework to facilitate accurate and reliable forecasting.The feature module formulates features with physical implications and sheds light on battery aging mechanisms,while the gradient module monitors capacity degradation rates and captures aging trend.TELL-Me achieves high accuracy in end-of-life prediction using minimal historical data from a single battery without requiring offline training dataset,and demonstrates impressive generality and robustness across various operating conditions and battery types.Additionally,by correlating feature contributions with degradation mechanisms across different datasets,TELL-Me is endowed with the diagnostic ability that not only enhances prediction reliability but also provides critical insights into the design and optimization of next-generation batteries.
文摘The Internet of Things(IoT)has gained substantial attention in both academic research and real-world applications.The proliferation of interconnected devices across various domains promises to deliver intelligent and advanced services.However,this rapid expansion also heightens the vulnerability of the IoT ecosystem to security threats.Consequently,innovative solutions capable of effectively mitigating risks while accommodating the unique constraints of IoT environments are urgently needed.Recently,the convergence of Blockchain technology and IoT has introduced a decentralized and robust framework for securing data and interactions,commonly referred to as the Internet of Blockchained Things(IoBT).Extensive research efforts have been devoted to adapting Blockchain technology to meet the specific requirements of IoT deployments.Within this context,consensus algorithms play a critical role in assessing the feasibility of integrating Blockchain into IoT ecosystems.The adoption of efficient and lightweight consensus mechanisms for block validation has become increasingly essential.This paper presents a comprehensive examination of lightweight,constraint-aware consensus algorithms tailored for IoBT.The study categorizes these consensus mechanisms based on their core operations,the security of the block validation process,the incorporation of AI techniques,and the specific applications they are designed to support.
基金supported by the National Natural Science Foundation of China(Nos.52171166 and U20A20231)the Natural Science Foundation of Hunan Province,China(Nos.2024JJ2060 and 2024JJ5406)+1 种基金the Key Laboratory of Materials in Dynamic Extremes of Sichuan Province(No.2023SCKT1102)the Postgraduate Scientific Research Innovation Project of National University of Defense Technology(No.XJJC2024065).
文摘Introducing B2 ordering can effectively improve the mechanical properties of lightweight refractory high-entropy alloys(LRHEAs).However,(Zr,Al)-enriched B2 precipitates generally reduce the ductility because their ordering characteristic is destroyed after dislocation shearing.Meanwhile,the local chemical order(LCO)cannot provide an adequate strengthening effect due to its small size.
文摘Significant advancements have been achieved in the field of Single Image Super-Resolution(SISR)through the utilization of Convolutional Neural Networks(CNNs)to attain state-of-the-art performance.Recent efforts have explored the incorporation of Transformers to augment network performance in SISR.However,the high computational cost of Transformers makes them less suitable for deployment on lightweight devices.Moreover,the majority of enhancements for CNNs rely predominantly on small spatial convolutions,thereby neglecting the potential advantages of large kernel convolution.In this paper,the authors propose a Multi-Perception Large Kernel convNet(MPLKN)which delves into the exploration of large kernel convolution.Specifically,the authors have architected a Multi-Perception Large Kernel(MPLK)module aimed at extracting multi-scale features and employ a stepwise feature fusion strategy to seamlessly integrate these features.In addition,to enhance the network's capacity for nonlinear spatial information processing,the authors have designed a Spatial-Channel Gated Feed-forward Network(SCGFN)that is capable of adapting to feature interactions across both spatial and channel dimensions.Experimental results demonstrate that MPLKN outperforms other lightweight image super-resolution models while maintaining a minimal number of parameters and FLOPs.
文摘In recent years,with the development of synthetic aperture radar(SAR)technology and the widespread application of deep learning,lightweight detection of SAR images has emerged as a research direction.The ultimate goal is to reduce computational and storage requirements while ensuring detection accuracy and reliability,making it an ideal choice for achieving rapid response and efficient processing.In this regard,a lightweight SAR ship target detection algorithm based on YOLOv8 was proposed in this study.Firstly,the C2f-Sc module was designed by fusing the C2f in the backbone network with the ScConv to reduce spatial redundancy and channel redundancy between features in convolutional neural networks.At the same time,the Ghost module was introduced into the neck network to effectively reduce model parameters and computational complexity.A relatively lightweight EMA attention mechanism was added to the neck network to promote the effective fusion of features at different levels.Experimental results showed that the Parameters and GFLOPs of the improved model are reduced by 8.5%and 7.0%when mAP@0.5 and mAP@0.5:0.95 are increased by 0.7%and 1.8%,respectively.It makes the model lightweight and improves the detection accuracy,which has certain application value.
基金supported by the Shanghai Sailing Program,China (No.20YF1447600)the Research Start-Up Project of Shanghai Institute of Technology (No.YJ2021-60)+1 种基金the Collaborative Innovation Project of Shanghai Institute of Technology (No.XTCX2020-12)the Science and Technology Talent Development Fund for Young and Middle-Aged Teachers at Shanghai Institute of Technology (No.ZQ2022-6)。
文摘There is a problem of real-time detection difficulty in road surface damage detection. This paper proposes an improved lightweight model based on you only look once version 5(YOLOv5). Firstly, this paper fully utilized the convolutional neural network(CNN) + ghosting bottleneck(G_bneck) architecture to reduce redundant feature maps. Afterwards, we upgraded the original upsampling algorithm to content-aware reassembly of features(CARAFE) and increased the receptive field. Finally, we replaced the spatial pyramid pooling fast(SPPF) module with the basic receptive field block(Basic RFB) pooling module and added dilated convolution. After comparative experiments, we can see that the number of parameters and model size of the improved algorithm in this paper have been reduced by nearly half compared to the YOLOv5s. The frame rate per second(FPS) has been increased by 3.25 times. The mean average precision(m AP@0.5: 0.95) has increased by 8%—17% compared to other lightweight algorithms.
基金supported by the National Natural Science Foundation of China(No.62103298)the Natural Science Foundation of Hebei Province(No.F2018209289)。
文摘Aiming at the problems of low detection efficiency and difficult positioning of traditional steel surface defect detection methods,a lightweight steel surface defect detection model based on you only look once version 7(YOLOv7)is proposed.First,a cascading style sheets(CSS)block module is proposed,which uses more lightweight operations to obtain redundant information in the feature map,reduces the amount of computation,and effectively improves the detection speed.Secondly,the improved spatial pyramid pooling with cross stage partial convolutions(SPPCSPC)structure is adopted to ensure that the model can also pay attention to the defect location information while predicting the defect category information,obtain richer defect features.In addition,the convolution operation in the original model is simplified,which significantly reduces the size of the model and helps to improve the detection speed.Finally,using efficient intersection over union(EIOU)loss to focus on high-quality anchors,speed up convergence and improve positioning accuracy.Experiments were carried out on the Northeastern University-defect(NEU-DET)steel surface defect dataset.Compared with the original YOLOv7 model,the number of parameters of this model was reduced by 40%,the frames per second(FPS)reached 112,and the average accuracy reached 79.1%,the detection accuracy and speed have been improved,which can meet the needs of steel surface defect detection.
基金supported by the National Natural Science Foundation of China(Nos.62373215,62373219 and 62073193)the Natural Science Foundation of Shandong Province(No.ZR2023MF100)+1 种基金the Key Projects of the Ministry of Industry and Information Technology(No.TC220H057-2022)the Independently Developed Instrument Funds of Shandong University(No.zy20240201)。
文摘Current you only look once(YOLO)-based algorithm model is facing the challenge of overwhelming parameters and calculation complexity under the printed circuit board(PCB)defect detection application scenario.In order to solve this problem,we propose a new method,which combined the lightweight network mobile vision transformer(Mobile Vi T)with the convolutional block attention module(CBAM)mechanism and the new regression loss function.This method needed less computation resources,making it more suitable for embedded edge detection devices.Meanwhile,the new loss function improved the positioning accuracy of the bounding box and enhanced the robustness of the model.In addition,experiments on public datasets demonstrate that the improved model achieves an average accuracy of 87.9%across six typical defect detection tasks,while reducing computational costs by nearly 90%.It significantly reduces the model's computational requirements while maintaining accuracy,ensuring reliable performance for edge deployment.
基金funded by the Ongoing Research Funding Program(ORF-2025-890)King Saud University,Riyadh,Saudi Arabia and was supported by the Competitive Research Fund of theUniversity of Aizu,Japan.
文摘The exponential expansion of the Internet of Things(IoT),Industrial Internet of Things(IIoT),and Transportation Management of Things(TMoT)produces vast amounts of real-time streaming data.Ensuring system dependability,operational efficiency,and security depends on the identification of anomalies in these dynamic and resource-constrained systems.Due to their high computational requirements and inability to efficiently process continuous data streams,traditional anomaly detection techniques often fail in IoT systems.This work presents a resource-efficient adaptive anomaly detection model for real-time streaming data in IoT systems.Extensive experiments were carried out on multiple real-world datasets,achieving an average accuracy score of 96.06%with an execution time close to 7.5 milliseconds for each individual streaming data point,demonstrating its potential for real-time,resourceconstrained applications.The model uses Principal Component Analysis(PCA)for dimensionality reduction and a Z-score technique for anomaly detection.It maintains a low computational footprint with a sliding window mechanism,enabling incremental data processing and identification of both transient and sustained anomalies without storing historical data.The system uses a Multivariate Linear Regression(MLR)based imputation technique that estimates missing or corrupted sensor values,preserving data integrity prior to anomaly detection.The suggested solution is appropriate for many uses in smart cities,industrial automation,environmental monitoring,IoT security,and intelligent transportation systems,and is particularly well-suited for resource-constrained edge devices.
文摘In the field of Weakly Supervised Semantic Segmentation(WSSS),methods based on image-level annotation face challenges in accurately capturing objects of varying sizes,lacking sensitivity to image details,and having high computational costs.To address these issues,we improve the dual-branch architecture of the Conformer as the fundamental network for generating class activation graphs,proposing a multi-scale efficient weakly-supervised semantic segmentation method based on the improved Conformer.In the Convolution Neural Network(CNN)branch,a cross-scale feature integration convolution module is designed,incorporating multi-receptive field convolution layers to enhance the model’s ability to capture long-range dependencies and improve sensitivity to multi-scale objects.In the Vision Transformer(ViT)branch,an efficient multi-head self-attention module is developed,reducing unnecessary computation through spatial compression and feature partitioning,thereby improving overall network efficiency.Finally,a multi-feature coupling module is introduced to complement the features generated by both branches.This design retains the strength of Convolution Neural Network in extracting local details while harnessing the strength of Vision Transformer to capture comprehensive global features.Experimental results show that the mean Intersection over Union of the image segmentation results of the proposed method on the validation and test sets of the PASCAL VOC 2012 datasets are improved by 2.9%and 3.6%,respectively,over the TransCAM algorithm.Besides,the improved model demonstrates a 1.3%increase of the mean Intersections over Union on the COCO 2014 datasets.Additionally,the number of parameters and the floating-point operations are reduced by 16.2%and 12.9%.However,the proposed method still has limitations of poor performance when dealing with complex scenarios.There is a need for further enhancing the performance of this method to address this issue.
