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
Honeycomb sandwich structures are widely used in lightweight applications.Usually,these structures are subjected to extreme loading conditions,leading to potential failures due to delamination and debonding between th...Honeycomb sandwich structures are widely used in lightweight applications.Usually,these structures are subjected to extreme loading conditions,leading to potential failures due to delamination and debonding between the face sheet and the honeycomb core.Therefore,the present study is focused on the mechanical characterisation of honeycomb sandwich structures fabricated using advanced 3D printing technology.The continuous carbon fibres and ONYX-FR matrix materials have been used as raw materials for 3D printing of the specimens needed for various mechanical characterization testing;ONYX-FR is a commercial trade name for flame retardant short carbon fibre filled nylon filaments,used as a reinforcing material in Morkforged 3D printer.Edgewise and flatwise compression tests have been conducted for different configurations of honeycomb sandwich structures,fabricated by varying the face sheet thickness and core cell size,while keeping the core cell thickness and core height constant.Based on these tests,the proposed structure with face sheet thickness of 3.2 mm and a core cell size of 12.7 mm exhibited the highest energy absorption and prevented delamination and debonding failures.Therefore,3D printing technology can also be considered as an alternative method for sandwich structure fabrication.However,detailed parametric studies still need to be conducted to meet various other structural integrity criteria related to the lightweight applications.展开更多
This paper introduces a lightweight remote sensing image dehazing network called multidimensional weight regulation network(MDWR-Net), which addresses the high computational cost of existing methods. Previous works, o...This paper introduces a lightweight remote sensing image dehazing network called multidimensional weight regulation network(MDWR-Net), which addresses the high computational cost of existing methods. Previous works, often based on the encoder-decoder structure and utilizing multiple upsampling and downsampling layers, are computationally expensive. To improve efficiency, the paper proposes two modules: the efficient spatial resolution recovery module(ESRR) for upsampling and the efficient depth information augmentation module(EDIA) for downsampling.These modules not only reduce model complexity but also enhance performance. Additionally, the partial feature weight learning module(PFWL) is introduced to reduce the computational burden by applying weight learning across partial dimensions, rather than using full-channel convolution.To overcome the limitations of convolutional neural networks(CNN)-based networks, the haze distribution index transformer(HDIT) is integrated into the decoder. We also propose the physicalbased non-adjacent feature fusion module(PNFF), which leverages the atmospheric scattering model to improve generalization of our MDWR-Net. The MDWR-Net achieves superior dehazing performance with a computational cost of just 2.98×10^(9) multiply-accumulate operations(MACs),which is less than one-tenth of previous methods. Experimental results validate its effectiveness in balancing performance and computational efficiency.展开更多
The defence sector is now at an advanced level,catering to the global scenario,and countries also invest heavily in research and development.Countries around the world have spent a lot of money on research and develop...The defence sector is now at an advanced level,catering to the global scenario,and countries also invest heavily in research and development.Countries around the world have spent a lot of money on research and development over the years in order to stay ahead of their competitors.Lightweight materials are critical in defence applications because they allow components to be lighter without sacrificing strength.This review provides an overview of the research related to defence applications.The book provides comprehensive details on current trends in the application of lightweight materials in defence.This review also includes historical and current perspectives on defence technologies.It discusses uses of lightweight materials such as metal matrix composites,polymer composites,ceramic matrix composites,fiber composites in defence sectors Finally,the review paper also emphasizes future military applications of lightweight materials.展开更多
In this paper,a novel method of ultra-lightweight convolution neural network(CNN)design based on neural architecture search(NAS)and knowledge distillation(KD)is proposed.It can realize the automatic construction of th...In this paper,a novel method of ultra-lightweight convolution neural network(CNN)design based on neural architecture search(NAS)and knowledge distillation(KD)is proposed.It can realize the automatic construction of the space target inverse synthetic aperture radar(ISAR)image recognition model with ultra-lightweight and high accuracy.This method introduces the NAS method into the radar image recognition for the first time,which solves the time-consuming and labor-consuming problems in the artificial design of the space target ISAR image automatic recognition model(STIIARM).On this basis,the NAS model’s knowledge is transferred to the student model with lower computational complexity by the flow of the solution procedure(FSP)distillation method.Thus,the decline of recognition accuracy caused by the direct compression of model structural parameters can be effectively avoided,and the ultralightweight STIIARM can be obtained.In the method,the Inverted Linear Bottleneck(ILB)and Inverted Residual Block(IRB)are firstly taken as each block’s basic structure in CNN.And the expansion ratio,output filter size,number of IRBs,and convolution kernel size are set as the search parameters to construct a hierarchical decomposition search space.Then,the recognition accuracy and computational complexity are taken as the objective function and constraint conditions,respectively,and the global optimization model of the CNN architecture search is established.Next,the simulated annealing(SA)algorithm is used as the search strategy to search out the lightweight and high accuracy STIIARM directly.After that,based on the three principles of similar block structure,the same corresponding channel number,and the minimum computational complexity,the more lightweight student model is designed,and the FSP matrix pairing between the NAS model and student model is completed.Finally,by minimizing the loss between the FSP matrix pairs of the NAS model and student model,the student model’s weight adjustment is completed.Thus the ultra-lightweight and high accuracy STIIARM is obtained.The proposed method’s effectiveness is verified by the simulation experiments on the ISAR image dataset of five types of space targets.展开更多
Vehicle ad-hoc networks have developed rapidly these years,whose security and privacy issues are always concerned widely.In spite of a remarkable research on their security solutions,but in which there still lacks con...Vehicle ad-hoc networks have developed rapidly these years,whose security and privacy issues are always concerned widely.In spite of a remarkable research on their security solutions,but in which there still lacks considerations on how to secure vehicleto-vehicle communications,particularly when infrastructure is unavailable.In this paper,we propose a lightweight certificateless and oneround key agreement scheme without pairing,and further prove the security of the proposed scheme in the random oracle model.The proposed scheme is expected to not only resist known attacks with less computation cost,but also as an efficient way to relieve the workload of vehicle-to-vehicle authentication,especially in no available infrastructure circumstance.A comprehensive evaluation,including security analysis,efficiency analysis and simulation evaluation,is presented to confirm the security and feasibility of the proposed scheme.展开更多
The lightweight shielding design of small reactors is a popular research topic.Based on a small helium-xenon-cooled solid reactor,the effects of neutron and photon shielding sequence and the number of shielding layers...The lightweight shielding design of small reactors is a popular research topic.Based on a small helium-xenon-cooled solid reactor,the effects of neutron and photon shielding sequence and the number of shielding layers on the radiation dose were first studied.It was found that when photons were shielded first and the number of shielding layers was odd,the radiation dose could be significantly reduced.To reduce the weight of the shielding body,the relative thickness of the shielding layers was optimized using the genetic algorithm.The optimized scheme can reduce the radiation dose by up to 57%and reduce the weight by 11.84%.To determine the total thickness of the shielding layers and avoid the local optimal solution of the genetic algorithm,a series of formulas that describes the relationship between the total thickness and the radiation dose was developed through large-scale calculations.A semi-empirical and semi-quantitative lightweight shielding design method is proposed to integrate the above shielding optimization method that verified by the Monte Carlo method.Finally,a code,SDIC1.0,was developed to achieve the optimized lightweight shielding design for small reactors.It was verified that the difference between the SDIC1.0 and the RMC code is approximately 10%and that the computation time is shortened by 6.3 times.展开更多
This paper investigates the snowdrifts caused by lightweight fences along the lines on the flatland through the computational fluid dynamics method. The characteristic ambient flows around the solid fences and the por...This paper investigates the snowdrifts caused by lightweight fences along the lines on the flatland through the computational fluid dynamics method. The characteristic ambient flows around the solid fences and the porous fences with varied heights and bottom wind gaps are simulated in the numerical model, and the working mechanism of ＂interception＂ and ＂scouring＂ of the lightweight fences are analyzed. Based on the friction velocities near the ground, two sets of criteria are proposed to evaluate the deposition and erosion effects of different fences. According to flow separation and reattachment, the simplified relationships between the most likely positions for snow accumulations and fence parameters are devel- oped. The study indicates that the capabilities for snow interception by the solid fence without wind gap and the distance from which to the second snow coverage center both increase with the fence height. Furthermore, it is found that the scouring range for snow surface increases significantly with the size of wind gap, and the snow accumulation rate on the leeward side decreases with the increasing fence porosity.展开更多
Extensive usage of highly conductive carbon materials with large specific surface area(e.g.,carbon nanotubes,CNTs)in lithium ion batteries(LIBs),especially as current collector of anodes,suffers from low initial coulo...Extensive usage of highly conductive carbon materials with large specific surface area(e.g.,carbon nanotubes,CNTs)in lithium ion batteries(LIBs),especially as current collector of anodes,suffers from low initial coulombic efficiency(ICE),large interfacial resistance,and severe embrittlement,as the large specific surface area often results in severe interfacial decomposition of the electrolyte and the formation of thick and fluffy solid electrolyte interphase(SEI)during cycling of LIBs.Herein,we demonstrate that when the CNT-based current collector and Na foil(which are being stacked intimately upon each other)are being placed in Na+-based organic electrolyte,local redox reaction between the Na foil and the electrolyte would occur spontaneously,generating a thin and homogeneous NaF-based passivating layer on the CNTs.More importantly,we found that owing to the weak solvation behaviors of Na+in the organic electrolyte,the resulting passivation layer,which is rich in NaF,is thin and dense;when used as the anode current collector in LIBs,the pre-existing passivating layer can function effectively in isolating the anode from the solvated Li+,thus suppressing the formation of bulky SEI and the destructive intercalation of solvated Li+.The relevant half-cell(graphite as anode)exhibits a high ICE of 92.1%;the relevant pouch cell with thus passivated CNT film as current collectors for both electrodes(LiCoO_(2)as cathode,graphite as anode)displays a high energy density of 255 Wh kg^(-1),spelling an increase of 50%compared with that using the conventional metal current collectors.展开更多
The global trend towards carbon reduction,energy conservation,and sustainable use of resources has led to an increased focus on the use of waste sludge in construction.We used waste sludge from a reservoir to produce ...The global trend towards carbon reduction,energy conservation,and sustainable use of resources has led to an increased focus on the use of waste sludge in construction.We used waste sludge from a reservoir to produce high-strength sintered lightweight aggregate,and then used the densified mixture design algorithm to create high-performance concrete from the sintered aggregate with only small amounts of mixing water and cement.Ultrasonic,electrical resistance and concrete strength efficiency tests were perfo...展开更多
Rolling stock manufacturers are finding structural solutions to reduce power required by the vehicles,and the lightweight design of the car body represents a possible solution.Optimization processes and innovative mat...Rolling stock manufacturers are finding structural solutions to reduce power required by the vehicles,and the lightweight design of the car body represents a possible solution.Optimization processes and innovative materials can be combined in order to achieve this goal.In this framework,we propose the redesign and optimization process of the car body roof for a light rail vehicle,introducing a sandwich structure.Bonded joint was used as a fastening system.The project was carried out on a single car of a modern tram platform.This preliminary numerical work was developed in two main steps:redesign of the car body structure and optimization of the innovated system.Objective of the process was the mass reduction of the whole metallic structure,while the constraint condition was imposed on the first frequency of vibration of the system.The effect of introducing a sandwich panel within the roof assembly was evaluated,focusing on the mechanical and dynamic performances of the whole car body.A mass saving of 63%on the optimized components was achieved,corresponding to a 7.6%if compared to the complete car body shell.In addition,a positive increasing of 17.7%on the first frequency of vibration was observed.Encouraging results have been achieved in terms of weight reduction and mechanical behaviour of the innovated car body.展开更多
Congestion control(CC)is always an important issue in the field of networking,and the enthusiasm for its research has never diminished in both academia and industry.In current years,due to the rapid development of mac...Congestion control(CC)is always an important issue in the field of networking,and the enthusiasm for its research has never diminished in both academia and industry.In current years,due to the rapid development of machine learning(ML),the combination of reinforcement learning(RL)and CC has a striking effect.However,These complicated schemes lack generalization and are too heavyweight in storage and computing to be directly implemented in mobile devices.In order to address these problems,we propose Plume,a high-performance,lightweight and generalized RL-CC scheme.Plume proposes a lightweight framework to reduce the overheads while preserving the original performance.Besides,Plume innovatively modifies the framework parameters of the reward function during the retraining process,so that the algorithm can be applied to a variety of scenarios.Evaluation results show that Plume can retain almost all the performance of the original model but the size and decision latency can be reduced by more than 50%and 20%,respectively.Moreover,Plume has better performances in some special scenes.展开更多
Significant progress has been made in computational imaging(CI),in which deep convolutional neural networks(CNNs)have demonstrated that sparse speckle patterns can be reconstructed.However,due to the limited“local”k...Significant progress has been made in computational imaging(CI),in which deep convolutional neural networks(CNNs)have demonstrated that sparse speckle patterns can be reconstructed.However,due to the limited“local”kernel size of the convolutional operator,for the spatially dense patterns,such as the generic face images,the performance of CNNs is limited.Here,we propose a“non-local”model,termed the Speckle-Transformer(SpT)UNet,for speckle feature extraction of generic face images.It is worth noting that the lightweight SpT UNet reveals a high efficiency and strong comparative performance with Pearson Correlation Coefficient(PCC),and structural similarity measure(SSIM)exceeding 0.989,and 0.950,respectively.展开更多
In order to overcome the shortcoming of space-borne rigid antenna reflector made of carbon fiber reinforced plastic(CFRP)skins with aluminum honeycomb sandwich(SAHS)structure,a new type of full CFRP skin plus rib(SPR)...In order to overcome the shortcoming of space-borne rigid antenna reflector made of carbon fiber reinforced plastic(CFRP)skins with aluminum honeycomb sandwich(SAHS)structure,a new type of full CFRP skin plus rib(SPR)structure ring-focused parabolic surface antenna reflector with the size of 2.5 m×1.9 m is designed.Under the condition that the original caliber,surface type,and interface remain unchanged,the main influence factors are designed and controlled.First,from the perspective of high stiffness,lightweight,and easy to form,a finite element simulation software is used to analyze and optimize the layout of the rib,the cross-sectional shape of the rib,the size of the rib,and the matching of the size and the coefficients of thermal expansion(CTEs)of the rib and the skin.Second,two structures are prepared by the autoclave molding process.Third,the weight and the surface precision root mean square(RMS)value are measured.The results show that the fundamental frequency of the SPR structure is 142.2 Hz,which is 3.5 Hz higher;the number of the new structural parts is reduced by 40%,and the forming process is greatly simplified.The total weight of the new structure is 11.9 kg,lighter 42.5%,indicating that the weight loss is obvious.The RMS value is 0.15 mm,which is slightly lower 0.01 mm but satisfies the accuracy requirement not greater than 0.3 mm.It is proved that the SPR structure reflector is a superior structure of the lightweight spaceborne antenna reflector.展开更多
To solve the problems in online target detection on the embedded platform,such as high missed detection rate,low accuracy,and slow speed,a lightweight target recognition method of MobileNetV3-CenterNet is proposed.Thi...To solve the problems in online target detection on the embedded platform,such as high missed detection rate,low accuracy,and slow speed,a lightweight target recognition method of MobileNetV3-CenterNet is proposed.This method combines the anchor-free Centernet network with the MobileNetV3 small network and is trained on the University at Albany Detection and Tracking(UA-DETRAC)and the Pattern Analysis,Statical Modeling and Computational Learn-ing Visual Object Classes(PASCAL VOC)07+12 standard datasets.While reducing the scale of the network model,the MobileNetV3-CenterNet model shows a good balance in the accuracy and speed of target recognition and effectively solves the problems of missing detection of dense and small targets in online detection.To verify the recognition performance of the model,it is tested on 2683 images of the UA-DETRAC and PASCAL VOC 07+12 datasets,and compared with the analysis results of CenterNet-Deep Layer Aggregation(DLA)34,CenterNet-Residual Network(ResNet)18,CenterNet-MobileNetV3-large,You Only Look Once vision 3(YOLOv3),MobileNetV2-YOLOv3,Single Shot Multibox Detector(SSD),MobileNetV2-SSD and Faster region convolutional neural network(RCNN)models.The results show that the MobileNetV3-CenterNet model accurately rec-ognized the dense targets and small targets missed by other methods,and obtained a recognition accuracy of 99.4%with a running speed at 53(on a server)and 14(on an ipad)frame/s,respectively.The MobileNetV3-CenterNet lightweight target recognition method will provide effective technical support for the target recognition of deep learning networks in embedded platforms and online detection.展开更多
A finite element model of one-arm planet carrier was built, and influence of structural parameters on strength and stiffness for one-arm planet carrier was analyzed. The stress and deformation of the round structure a...A finite element model of one-arm planet carrier was built, and influence of structural parameters on strength and stiffness for one-arm planet carrier was analyzed. The stress and deformation of the round structure and the triangle structure for one-arm planet carrier were analyzed and compared. The finite element model of the same specifications arms planet carrier was established, and influence of the connecting slab thickness and input side plate thickness on strength and stiffness for arms planet carrier was analyzed. Strength, stiffness and mass for one-arm and arms planet carrier in the same specifications were analyzed and compared.展开更多
Memristor-based neuromorphic computing shows great potential for high-speed and high-throughput signal processing applications,such as electroencephalogram(EEG)signal processing.Nonetheless,the size of one-transistor ...Memristor-based neuromorphic computing shows great potential for high-speed and high-throughput signal processing applications,such as electroencephalogram(EEG)signal processing.Nonetheless,the size of one-transistor one-resistor(1T1R)memristor arrays is limited by the non-ideality of the devices,which prevents the hardware implementation of large and complex networks.In this work,we propose the depthwise separable convolution and bidirectional gate recurrent unit(DSC-BiGRU)network,a lightweight and highly robust hybrid neural network based on 1T1R arrays that enables efficient processing of EEG signals in the temporal,frequency and spatial domains by hybridizing DSC and BiGRU blocks.The network size is reduced and the network robustness is improved while ensuring the network classification accuracy.In the simulation,the measured non-idealities of the 1T1R array are brought into the network through statistical analysis.Compared with traditional convolutional networks,the network parameters are reduced by 95%and the network classification accuracy is improved by 21%at a 95%array yield rate and 5%tolerable error.This work demonstrates that lightweight and highly robust networks based on memristor arrays hold great promise for applications that rely on low consumption and high efficiency.展开更多
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.
文摘Honeycomb sandwich structures are widely used in lightweight applications.Usually,these structures are subjected to extreme loading conditions,leading to potential failures due to delamination and debonding between the face sheet and the honeycomb core.Therefore,the present study is focused on the mechanical characterisation of honeycomb sandwich structures fabricated using advanced 3D printing technology.The continuous carbon fibres and ONYX-FR matrix materials have been used as raw materials for 3D printing of the specimens needed for various mechanical characterization testing;ONYX-FR is a commercial trade name for flame retardant short carbon fibre filled nylon filaments,used as a reinforcing material in Morkforged 3D printer.Edgewise and flatwise compression tests have been conducted for different configurations of honeycomb sandwich structures,fabricated by varying the face sheet thickness and core cell size,while keeping the core cell thickness and core height constant.Based on these tests,the proposed structure with face sheet thickness of 3.2 mm and a core cell size of 12.7 mm exhibited the highest energy absorption and prevented delamination and debonding failures.Therefore,3D printing technology can also be considered as an alternative method for sandwich structure fabrication.However,detailed parametric studies still need to be conducted to meet various other structural integrity criteria related to the lightweight applications.
文摘This paper introduces a lightweight remote sensing image dehazing network called multidimensional weight regulation network(MDWR-Net), which addresses the high computational cost of existing methods. Previous works, often based on the encoder-decoder structure and utilizing multiple upsampling and downsampling layers, are computationally expensive. To improve efficiency, the paper proposes two modules: the efficient spatial resolution recovery module(ESRR) for upsampling and the efficient depth information augmentation module(EDIA) for downsampling.These modules not only reduce model complexity but also enhance performance. Additionally, the partial feature weight learning module(PFWL) is introduced to reduce the computational burden by applying weight learning across partial dimensions, rather than using full-channel convolution.To overcome the limitations of convolutional neural networks(CNN)-based networks, the haze distribution index transformer(HDIT) is integrated into the decoder. We also propose the physicalbased non-adjacent feature fusion module(PNFF), which leverages the atmospheric scattering model to improve generalization of our MDWR-Net. The MDWR-Net achieves superior dehazing performance with a computational cost of just 2.98×10^(9) multiply-accumulate operations(MACs),which is less than one-tenth of previous methods. Experimental results validate its effectiveness in balancing performance and computational efficiency.
文摘The defence sector is now at an advanced level,catering to the global scenario,and countries also invest heavily in research and development.Countries around the world have spent a lot of money on research and development over the years in order to stay ahead of their competitors.Lightweight materials are critical in defence applications because they allow components to be lighter without sacrificing strength.This review provides an overview of the research related to defence applications.The book provides comprehensive details on current trends in the application of lightweight materials in defence.This review also includes historical and current perspectives on defence technologies.It discusses uses of lightweight materials such as metal matrix composites,polymer composites,ceramic matrix composites,fiber composites in defence sectors Finally,the review paper also emphasizes future military applications of lightweight materials.
文摘In this paper,a novel method of ultra-lightweight convolution neural network(CNN)design based on neural architecture search(NAS)and knowledge distillation(KD)is proposed.It can realize the automatic construction of the space target inverse synthetic aperture radar(ISAR)image recognition model with ultra-lightweight and high accuracy.This method introduces the NAS method into the radar image recognition for the first time,which solves the time-consuming and labor-consuming problems in the artificial design of the space target ISAR image automatic recognition model(STIIARM).On this basis,the NAS model’s knowledge is transferred to the student model with lower computational complexity by the flow of the solution procedure(FSP)distillation method.Thus,the decline of recognition accuracy caused by the direct compression of model structural parameters can be effectively avoided,and the ultralightweight STIIARM can be obtained.In the method,the Inverted Linear Bottleneck(ILB)and Inverted Residual Block(IRB)are firstly taken as each block’s basic structure in CNN.And the expansion ratio,output filter size,number of IRBs,and convolution kernel size are set as the search parameters to construct a hierarchical decomposition search space.Then,the recognition accuracy and computational complexity are taken as the objective function and constraint conditions,respectively,and the global optimization model of the CNN architecture search is established.Next,the simulated annealing(SA)algorithm is used as the search strategy to search out the lightweight and high accuracy STIIARM directly.After that,based on the three principles of similar block structure,the same corresponding channel number,and the minimum computational complexity,the more lightweight student model is designed,and the FSP matrix pairing between the NAS model and student model is completed.Finally,by minimizing the loss between the FSP matrix pairs of the NAS model and student model,the student model’s weight adjustment is completed.Thus the ultra-lightweight and high accuracy STIIARM is obtained.The proposed method’s effectiveness is verified by the simulation experiments on the ISAR image dataset of five types of space targets.
基金This work was supported in part by the National Natural Science Foundation of China under Grant No.61170217,61272469,61303212,61332019,and Grant No.U1135004,and by the Fundamental Research Founds for National University,China University of Geosciences
文摘Vehicle ad-hoc networks have developed rapidly these years,whose security and privacy issues are always concerned widely.In spite of a remarkable research on their security solutions,but in which there still lacks considerations on how to secure vehicleto-vehicle communications,particularly when infrastructure is unavailable.In this paper,we propose a lightweight certificateless and oneround key agreement scheme without pairing,and further prove the security of the proposed scheme in the random oracle model.The proposed scheme is expected to not only resist known attacks with less computation cost,but also as an efficient way to relieve the workload of vehicle-to-vehicle authentication,especially in no available infrastructure circumstance.A comprehensive evaluation,including security analysis,efficiency analysis and simulation evaluation,is presented to confirm the security and feasibility of the proposed scheme.
基金sponsored by Natural Science Foundation of Shanghai (No.22ZR1431900)the Young Elite Scientist Sponsorship Program of the China National Nuclear Corporation (CNNC).
文摘The lightweight shielding design of small reactors is a popular research topic.Based on a small helium-xenon-cooled solid reactor,the effects of neutron and photon shielding sequence and the number of shielding layers on the radiation dose were first studied.It was found that when photons were shielded first and the number of shielding layers was odd,the radiation dose could be significantly reduced.To reduce the weight of the shielding body,the relative thickness of the shielding layers was optimized using the genetic algorithm.The optimized scheme can reduce the radiation dose by up to 57%and reduce the weight by 11.84%.To determine the total thickness of the shielding layers and avoid the local optimal solution of the genetic algorithm,a series of formulas that describes the relationship between the total thickness and the radiation dose was developed through large-scale calculations.A semi-empirical and semi-quantitative lightweight shielding design method is proposed to integrate the above shielding optimization method that verified by the Monte Carlo method.Finally,a code,SDIC1.0,was developed to achieve the optimized lightweight shielding design for small reactors.It was verified that the difference between the SDIC1.0 and the RMC code is approximately 10%and that the computation time is shortened by 6.3 times.
基金the supports of the National Natural Science Foundation of China(No.51525804)the Sichuan Province Youth Science and Technology Innovation Team(No.2015TD0004)the Construction Technology Project of China Transport Ministry(No.2014318800240)
文摘This paper investigates the snowdrifts caused by lightweight fences along the lines on the flatland through the computational fluid dynamics method. The characteristic ambient flows around the solid fences and the porous fences with varied heights and bottom wind gaps are simulated in the numerical model, and the working mechanism of ＂interception＂ and ＂scouring＂ of the lightweight fences are analyzed. Based on the friction velocities near the ground, two sets of criteria are proposed to evaluate the deposition and erosion effects of different fences. According to flow separation and reattachment, the simplified relationships between the most likely positions for snow accumulations and fence parameters are devel- oped. The study indicates that the capabilities for snow interception by the solid fence without wind gap and the distance from which to the second snow coverage center both increase with the fence height. Furthermore, it is found that the scouring range for snow surface increases significantly with the size of wind gap, and the snow accumulation rate on the leeward side decreases with the increasing fence porosity.
基金financially supported by the National Key Research and Development Program of China(2022YFB4002103)the National Natural Science Foundation of China(22279107)。
文摘Extensive usage of highly conductive carbon materials with large specific surface area(e.g.,carbon nanotubes,CNTs)in lithium ion batteries(LIBs),especially as current collector of anodes,suffers from low initial coulombic efficiency(ICE),large interfacial resistance,and severe embrittlement,as the large specific surface area often results in severe interfacial decomposition of the electrolyte and the formation of thick and fluffy solid electrolyte interphase(SEI)during cycling of LIBs.Herein,we demonstrate that when the CNT-based current collector and Na foil(which are being stacked intimately upon each other)are being placed in Na+-based organic electrolyte,local redox reaction between the Na foil and the electrolyte would occur spontaneously,generating a thin and homogeneous NaF-based passivating layer on the CNTs.More importantly,we found that owing to the weak solvation behaviors of Na+in the organic electrolyte,the resulting passivation layer,which is rich in NaF,is thin and dense;when used as the anode current collector in LIBs,the pre-existing passivating layer can function effectively in isolating the anode from the solvated Li+,thus suppressing the formation of bulky SEI and the destructive intercalation of solvated Li+.The relevant half-cell(graphite as anode)exhibits a high ICE of 92.1%;the relevant pouch cell with thus passivated CNT film as current collectors for both electrodes(LiCoO_(2)as cathode,graphite as anode)displays a high energy density of 255 Wh kg^(-1),spelling an increase of 50%compared with that using the conventional metal current collectors.
文摘The global trend towards carbon reduction,energy conservation,and sustainable use of resources has led to an increased focus on the use of waste sludge in construction.We used waste sludge from a reservoir to produce high-strength sintered lightweight aggregate,and then used the densified mixture design algorithm to create high-performance concrete from the sintered aggregate with only small amounts of mixing water and cement.Ultrasonic,electrical resistance and concrete strength efficiency tests were perfo...
文摘Rolling stock manufacturers are finding structural solutions to reduce power required by the vehicles,and the lightweight design of the car body represents a possible solution.Optimization processes and innovative materials can be combined in order to achieve this goal.In this framework,we propose the redesign and optimization process of the car body roof for a light rail vehicle,introducing a sandwich structure.Bonded joint was used as a fastening system.The project was carried out on a single car of a modern tram platform.This preliminary numerical work was developed in two main steps:redesign of the car body structure and optimization of the innovated system.Objective of the process was the mass reduction of the whole metallic structure,while the constraint condition was imposed on the first frequency of vibration of the system.The effect of introducing a sandwich panel within the roof assembly was evaluated,focusing on the mechanical and dynamic performances of the whole car body.A mass saving of 63%on the optimized components was achieved,corresponding to a 7.6%if compared to the complete car body shell.In addition,a positive increasing of 17.7%on the first frequency of vibration was observed.Encouraging results have been achieved in terms of weight reduction and mechanical behaviour of the innovated car body.
基金supported by National Natural Science Foundation of China (NSFC) under Grant (No.61872401)National Natural Science Foundation of China (NSFC) under Grant (No.62132022)+1 种基金Fok Ying Tung Education Foundation (No.171059)BUPT Excellent Ph.D.Students Foundation (No. CX2021102)
文摘Congestion control(CC)is always an important issue in the field of networking,and the enthusiasm for its research has never diminished in both academia and industry.In current years,due to the rapid development of machine learning(ML),the combination of reinforcement learning(RL)and CC has a striking effect.However,These complicated schemes lack generalization and are too heavyweight in storage and computing to be directly implemented in mobile devices.In order to address these problems,we propose Plume,a high-performance,lightweight and generalized RL-CC scheme.Plume proposes a lightweight framework to reduce the overheads while preserving the original performance.Besides,Plume innovatively modifies the framework parameters of the reward function during the retraining process,so that the algorithm can be applied to a variety of scenarios.Evaluation results show that Plume can retain almost all the performance of the original model but the size and decision latency can be reduced by more than 50%and 20%,respectively.Moreover,Plume has better performances in some special scenes.
基金funding support from the Science and Technology Commission of Shanghai Municipality(Grant No.21DZ1100500)the Shanghai Frontiers Science Center Program(2021-2025 No.20)+2 种基金the Zhangjiang National Innovation Demonstration Zone(Grant No.ZJ2019ZD-005)supported by a fellowship from the China Postdoctoral Science Foundation(2020M671169)the International Postdoctoral Exchange Program from the Administrative Committee of Post-Doctoral Researchers of China([2020]33)。
文摘Significant progress has been made in computational imaging(CI),in which deep convolutional neural networks(CNNs)have demonstrated that sparse speckle patterns can be reconstructed.However,due to the limited“local”kernel size of the convolutional operator,for the spatially dense patterns,such as the generic face images,the performance of CNNs is limited.Here,we propose a“non-local”model,termed the Speckle-Transformer(SpT)UNet,for speckle feature extraction of generic face images.It is worth noting that the lightweight SpT UNet reveals a high efficiency and strong comparative performance with Pearson Correlation Coefficient(PCC),and structural similarity measure(SSIM)exceeding 0.989,and 0.950,respectively.
文摘In order to overcome the shortcoming of space-borne rigid antenna reflector made of carbon fiber reinforced plastic(CFRP)skins with aluminum honeycomb sandwich(SAHS)structure,a new type of full CFRP skin plus rib(SPR)structure ring-focused parabolic surface antenna reflector with the size of 2.5 m×1.9 m is designed.Under the condition that the original caliber,surface type,and interface remain unchanged,the main influence factors are designed and controlled.First,from the perspective of high stiffness,lightweight,and easy to form,a finite element simulation software is used to analyze and optimize the layout of the rib,the cross-sectional shape of the rib,the size of the rib,and the matching of the size and the coefficients of thermal expansion(CTEs)of the rib and the skin.Second,two structures are prepared by the autoclave molding process.Third,the weight and the surface precision root mean square(RMS)value are measured.The results show that the fundamental frequency of the SPR structure is 142.2 Hz,which is 3.5 Hz higher;the number of the new structural parts is reduced by 40%,and the forming process is greatly simplified.The total weight of the new structure is 11.9 kg,lighter 42.5%,indicating that the weight loss is obvious.The RMS value is 0.15 mm,which is slightly lower 0.01 mm but satisfies the accuracy requirement not greater than 0.3 mm.It is proved that the SPR structure reflector is a superior structure of the lightweight spaceborne antenna reflector.
基金supported by Research and Development Project of Key Core Technology and Common Technology in Shanxi Province(No.2020XXX009).
文摘To solve the problems in online target detection on the embedded platform,such as high missed detection rate,low accuracy,and slow speed,a lightweight target recognition method of MobileNetV3-CenterNet is proposed.This method combines the anchor-free Centernet network with the MobileNetV3 small network and is trained on the University at Albany Detection and Tracking(UA-DETRAC)and the Pattern Analysis,Statical Modeling and Computational Learn-ing Visual Object Classes(PASCAL VOC)07+12 standard datasets.While reducing the scale of the network model,the MobileNetV3-CenterNet model shows a good balance in the accuracy and speed of target recognition and effectively solves the problems of missing detection of dense and small targets in online detection.To verify the recognition performance of the model,it is tested on 2683 images of the UA-DETRAC and PASCAL VOC 07+12 datasets,and compared with the analysis results of CenterNet-Deep Layer Aggregation(DLA)34,CenterNet-Residual Network(ResNet)18,CenterNet-MobileNetV3-large,You Only Look Once vision 3(YOLOv3),MobileNetV2-YOLOv3,Single Shot Multibox Detector(SSD),MobileNetV2-SSD and Faster region convolutional neural network(RCNN)models.The results show that the MobileNetV3-CenterNet model accurately rec-ognized the dense targets and small targets missed by other methods,and obtained a recognition accuracy of 99.4%with a running speed at 53(on a server)and 14(on an ipad)frame/s,respectively.The MobileNetV3-CenterNet lightweight target recognition method will provide effective technical support for the target recognition of deep learning networks in embedded platforms and online detection.
基金Funded by National Science and Technology Support Program(Grant No.2013BAF01B05)Zhengzhou Science and Technology Project(Grant No.121PZDGG255)
文摘A finite element model of one-arm planet carrier was built, and influence of structural parameters on strength and stiffness for one-arm planet carrier was analyzed. The stress and deformation of the round structure and the triangle structure for one-arm planet carrier were analyzed and compared. The finite element model of the same specifications arms planet carrier was established, and influence of the connecting slab thickness and input side plate thickness on strength and stiffness for arms planet carrier was analyzed. Strength, stiffness and mass for one-arm and arms planet carrier in the same specifications were analyzed and compared.
基金Project supported by the National Key Research and Development Program of China(Grant No.2019YFB2205102)the National Natural Science Foundation of China(Grant Nos.61974164,62074166,61804181,62004219,62004220,and 62104256).
文摘Memristor-based neuromorphic computing shows great potential for high-speed and high-throughput signal processing applications,such as electroencephalogram(EEG)signal processing.Nonetheless,the size of one-transistor one-resistor(1T1R)memristor arrays is limited by the non-ideality of the devices,which prevents the hardware implementation of large and complex networks.In this work,we propose the depthwise separable convolution and bidirectional gate recurrent unit(DSC-BiGRU)network,a lightweight and highly robust hybrid neural network based on 1T1R arrays that enables efficient processing of EEG signals in the temporal,frequency and spatial domains by hybridizing DSC and BiGRU blocks.The network size is reduced and the network robustness is improved while ensuring the network classification accuracy.In the simulation,the measured non-idealities of the 1T1R array are brought into the network through statistical analysis.Compared with traditional convolutional networks,the network parameters are reduced by 95%and the network classification accuracy is improved by 21%at a 95%array yield rate and 5%tolerable error.This work demonstrates that lightweight and highly robust networks based on memristor arrays hold great promise for applications that rely on low consumption and high efficiency.
