The generation of high-quality,realistic face generation has emerged as a key field of research in computer vision.This paper proposes a robust approach that combines a Super-Resolution Generative Adversarial Network(...The generation of high-quality,realistic face generation has emerged as a key field of research in computer vision.This paper proposes a robust approach that combines a Super-Resolution Generative Adversarial Network(SRGAN)with a Pyramid Attention Module(PAM)to enhance the quality of deep face generation.The SRGAN framework is designed to improve the resolution of generated images,addressing common challenges such as blurriness and a lack of intricate details.The Pyramid Attention Module further complements the process by focusing on multi-scale feature extraction,enabling the network to capture finer details and complex facial features more effectively.The proposed method was trained and evaluated over 100 epochs on the CelebA dataset,demonstrating consistent improvements in image quality and a marked decrease in generator and discriminator losses,reflecting the model’s capacity to learn and synthesize high-quality images effectively,given adequate computational resources.Experimental outcome demonstrates that the SRGAN model with PAM module has outperformed,yielding an aggregate discriminator loss of 0.055 for real,0.043 for fake,and a generator loss of 10.58 after training for 100 epochs.The model has yielded an structural similarity index measure of 0.923,that has outperformed the other models that are considered in the current study for analysis.展开更多
Physical layer authentication(PLA)in the context of the Internet of Things(IoT)has gained significant attention.Compared with traditional encryption and blockchain technologies,PLA provides a more computationally effi...Physical layer authentication(PLA)in the context of the Internet of Things(IoT)has gained significant attention.Compared with traditional encryption and blockchain technologies,PLA provides a more computationally efficient alternative to exploiting the properties of the wireless medium itself.Some existing PLA solutions rely on static mechanisms,which are insufficient to address the authentication challenges in fifth generation(5G)and beyond wireless networks.Additionally,with the massive increase in mobile device access,the communication security of the IoT is vulnerable to spoofing attacks.To overcome the above challenges,this paper proposes a lightweight deep convolutional neural network(CNN)equipped with squeeze and excitation module(SE module)in dynamic wireless environments,namely SE-ConvNet.To be more specific,a convolution factorization is developed to reduce the complexity of PLA models based on deep learning.Moreover,an SE module is designed in the deep CNN to enhance useful features andmaximize authentication accuracy.Compared with the existing solutions,the proposed SE-ConvNet enabled PLA scheme performs excellently in mobile and time-varying wireless environments while maintaining lower computational complexity.展开更多
With the continuous development of artificial intelligence and machine learning techniques,there have been effective methods supporting the work of dermatologist in the field of skin cancer detection.However,object si...With the continuous development of artificial intelligence and machine learning techniques,there have been effective methods supporting the work of dermatologist in the field of skin cancer detection.However,object significant challenges have been presented in accurately segmenting melanomas in dermoscopic images due to the objects that could interfere human observations,such as bubbles and scales.To address these challenges,we propose a dual U-Net network framework for skin melanoma segmentation.In our proposed architecture,we introduce several innovative components that aim to enhance the performance and capabilities of the traditional U-Net.First,we establish a novel framework that links two simplified U-Nets,enabling more comprehensive information exchange and feature integration throughout the network.Second,after cascading the second U-Net,we introduce a skip connection between the decoder and encoder networks,and incorporate a modified receptive field block(MRFB),which is designed to capture multi-scale spatial information.Third,to further enhance the feature representation capabilities,we add a multi-path convolution block attention module(MCBAM)to the first two layers of the first U-Net encoding,and integrate a new squeeze-and-excitation(SE)mechanism with residual connections in the second U-Net.To illustrate the performance of our proposed model,we conducted comprehensive experiments on widely recognized skin datasets.On the ISIC-2017 dataset,the IoU value of our proposed model increased from 0.6406 to 0.6819 and the Dice coefficient increased from 0.7625 to 0.8023.On the ISIC-2018 dataset,the IoU value of proposed model also improved from 0.7138 to 0.7709,while the Dice coefficient increased from 0.8285 to 0.8665.Furthermore,the generalization experiments conducted on the jaw cyst dataset from Quzhou People’s Hospital further verified the outstanding segmentation performance of the proposed model.These findings collectively affirm the potential of our approach as a valuable tool in supporting clinical decision-making in the field of skin cancer detection,as well as advancing research in medical image analysis.展开更多
Electric vehicles(EVs)have garnered significant attention as a vital driver of economic growth and environmental sustainability.Nevertheless,ensuring the safety of high-energy batteries is now a top priority that cann...Electric vehicles(EVs)have garnered significant attention as a vital driver of economic growth and environmental sustainability.Nevertheless,ensuring the safety of high-energy batteries is now a top priority that cannot be overlooked during large-scale applications.This paper proposes an innovative active protection and cooling integrated battery module using smart materials,magneto-sensitive shear thickening fluid(MSTF),which is specifically designed to address safety threats posed by lithium-ion batteries(LIBs)exposed to harsh mechanical and environmental conditions.The theoretical framework introduces a novel approach for harnessing the smoothed-particle hydrodynamics(SPH)methodology that incorporates the intricate interplay of non-Newtonian fluid behavior,capturing the fluid-structure coupling inherent to the MSTF.This approach is further advanced by adopting an enhanced Herschel-Bulkley(H-B)model to encapsulate the intricate rheology of the MSTF under the influence of the magnetorheological effect(MRE)and shear thickening(ST)behavior.Numerical simulation results show that in the case of cooling,the MSTF is an effective cooling medium for rapidly reducing the temperature.In terms of mechanical abuse,the MSTF solidifies through actively applying the magnetic field during mechanical compression and impact within the battery module,resulting in 66%and 61.7%reductions in the maximum stress within the battery jellyroll,and 31.1%and 23%reductions in the reaction force,respectively.This mechanism effectively lowers the risk of short-circuit failure.The groundbreaking concepts unveiled in this paper for active protection battery modules are anticipated to be a valuable technological breakthrough in the areas of EV safety and lightweight/integrated design.展开更多
Automatic modulation classification(AMC) technology is one of the cutting-edge technologies in cognitive radio communications. AMC based on deep learning has recently attracted much attention due to its superior perfo...Automatic modulation classification(AMC) technology is one of the cutting-edge technologies in cognitive radio communications. AMC based on deep learning has recently attracted much attention due to its superior performances in classification accuracy and robustness. In this paper, we propose a novel, high resolution and multi-scale feature fusion convolutional neural network model with a squeeze-excitation block, referred to as HRSENet,to classify different kinds of modulation signals.The proposed model establishes a parallel computing mechanism of multi-resolution feature maps through the multi-layer convolution operation, which effectively reduces the information loss caused by downsampling convolution. Moreover, through dense skipconnecting at the same resolution and up-sampling or down-sampling connection at different resolutions, the low resolution representation of the deep feature maps and the high resolution representation of the shallow feature maps are simultaneously extracted and fully integrated, which is benificial to mine signal multilevel features. Finally, the feature squeeze and excitation module embedded in the decoder is used to adjust the response weights between channels, further improving classification accuracy of proposed model.The proposed HRSENet significantly outperforms existing methods in terms of classification accuracy on the public dataset “Over the Air” in signal-to-noise(SNR) ranging from-2dB to 20dB. The classification accuracy in the proposed model achieves 85.36% and97.30% at 4dB and 10dB, respectively, with the improvement by 9.71% and 5.82% compared to LWNet.Furthermore, the model also has a moderate computation complexity compared with several state-of-the-art methods.展开更多
Photovoltaic (PV) modules, as essential components of solar power generation systems, significantly influence unitpower generation costs.The service life of these modules directly affects these costs. Over time, the p...Photovoltaic (PV) modules, as essential components of solar power generation systems, significantly influence unitpower generation costs.The service life of these modules directly affects these costs. Over time, the performanceof PV modules gradually declines due to internal degradation and external environmental factors.This cumulativedegradation impacts the overall reliability of photovoltaic power generation. This study addresses the complexdegradation process of PV modules by developing a two-stage Wiener process model. This approach accountsfor the distinct phases of degradation resulting from module aging and environmental influences. A powerdegradation model based on the two-stage Wiener process is constructed to describe individual differences inmodule degradation processes. To estimate the model parameters, a combination of the Expectation-Maximization(EM) algorithm and the Bayesian method is employed. Furthermore, the Schwarz Information Criterion (SIC) isutilized to identify critical change points in PV module degradation trajectories. To validate the universality andeffectiveness of the proposed method, a comparative analysis is conducted against other established life predictiontechniques for PV modules.展开更多
A two-way K/Ka-band series-Doherty PA(SDPA)with a distributed impedance inverting network(IIN)for millimeter wave applications is presented in this article.The proposed distributed IIN contributes to achieve wideband ...A two-way K/Ka-band series-Doherty PA(SDPA)with a distributed impedance inverting network(IIN)for millimeter wave applications is presented in this article.The proposed distributed IIN contributes to achieve wideband linear and power back-off(PBO)efficiency enhancement.Implemented in 65 nm bulk CMOS technology,this work realizes a measured 3 dB band-width of 15.5 GHz with 21.2 dB peak small-signal gain at 34.2 GHz.Under 1-V power supply,it achieves OP1dB over 13.4 dBm and Psat over 16 dBm between 21 to 30 GHz.The measured maximum Psat,OP1dB,peak/OP1dB/6dBPBO PAE results are 17.5,14.7 dBm,and 28.2%/23.2%/13.2%.Without digital pre-distortion(DPD)and equalization,EVMs are lower than-25.2 dB for 200 MHz 64-QAM signals.Besides,this work achieves-33.35,-23.52,and-20 dB EVMs for 100 MHz 256-QAM,600 MHz 64-QAM and 2 GHz 16-QAM signals at 27 GHz without DPD and equalization.展开更多
Self-assembled prodrug nanomedicine has emerged as an advanced platform for antitumor therapy,mainly comprise drug modules,response modules and modification modules.However,existing studies usually compare the differe...Self-assembled prodrug nanomedicine has emerged as an advanced platform for antitumor therapy,mainly comprise drug modules,response modules and modification modules.However,existing studies usually compare the differences between single types of modification modules,neglecting the impact of steric-hindrance effect caused by chemical structure.Herein,single-tailed modification module with low-steric-hindrance effect and two-tailed modification module with high-steric-hindrance effect were selected to construct paclitaxel prodrugs(P-LA_(C18)and P-BAC18),and the in-depth insights of the sterichindrance effect on prodrug nanoassemblies were explored.Notably,the size stability of the two-tailed prodrugs was enhanced due to improved intermolecular interactions and steric hindrance.Single-tailed prodrug nanoassemblies were more susceptible to attack by redox agents,showing faster drug release and stronger antitumor efficacy,but with poorer safety.In contrast,two-tailed prodrug nanoassemblies exhibited significant advantages in terms of pharmacokinetics,tumor accumulation and safety due to the good size stability,thus ensuring equivalent antitumor efficacy at tolerance dose.These findings highlighted the critical role of steric-hindrance effect of the modification module in regulating the structureactivity relationship of prodrug nanoassemblies and proposed new perspectives into the precise design of self-assembled prodrugs for high-performance cancer therapeutics.展开更多
Pest detection techniques are helpful in reducing the frequency and scale of pest outbreaks;however,their application in the actual agricultural production process is still challenging owing to the problems of intersp...Pest detection techniques are helpful in reducing the frequency and scale of pest outbreaks;however,their application in the actual agricultural production process is still challenging owing to the problems of interspecies similarity,multi-scale,and background complexity of pests.To address these problems,this study proposes an FD-YOLO pest target detection model.The FD-YOLO model uses a Fully Connected Feature Pyramid Network(FC-FPN)instead of a PANet in the neck,which can adaptively fuse multi-scale information so that the model can retain small-scale target features in the deep layer,enhance large-scale target features in the shallow layer,and enhance the multiplexing of effective features.A dual self-attention module(DSA)is then embedded in the C3 module of the neck,which captures the dependencies between the information in both spatial and channel dimensions,effectively enhancing global features.We selected 16 types of pests that widely damage field crops in the IP102 pest dataset,which were used as our dataset after data supplementation and enhancement.The experimental results showed that FD-YOLO’s mAP@0.5 improved by 6.8%compared to YOLOv5,reaching 82.6%and 19.1%–5%better than other state-of-the-art models.This method provides an effective new approach for detecting similar or multiscale pests in field crops.展开更多
With the rapid development of the new energy automotive industry,the enhancement of lithium battery performance and production efficiency has become critical.This article explores the application of artificial intelli...With the rapid development of the new energy automotive industry,the enhancement of lithium battery performance and production efficiency has become critical.This article explores the application of artificial intelligence technology in the lithium battery module PACK line,analyzing how it optimizes the production process and improves production efficiency,and predicts future development trends.The PACK line is an important link in battery manufacturing,involving complex processes such as cell sorting,welding,assembly and testing.The application of AI technology in image recognition,data analysis and predictive maintenance provides new solutions for the intelligent upgrading of the PACK line.This article describes the process of the PACK line in detail,analyzes the challenges under current technological levels,and reviews the application cases of AI technology in the manufacturing industry.The study aims to provide theoretical and practical guidance for the intelligent development of lithium battery module PACK lines,discussing the integration of AI technology,its actual performance,technical challenges,and solutions.It is expected that AI technology will play a greater role in the PACK line,and future research will focus on improving the adaptability of models,developing efficient algorithms,and further integrating into the production line.展开更多
Semantic segmentation of remote sensing images is a critical research area in the field of remote sensing.Despite the success of Convolutional Neural Networks(CNNs),they often fail to capture inter-layer feature relat...Semantic segmentation of remote sensing images is a critical research area in the field of remote sensing.Despite the success of Convolutional Neural Networks(CNNs),they often fail to capture inter-layer feature relationships and fully leverage contextual information,leading to the loss of important details.Additionally,due to significant intraclass variation and small inter-class differences in remote sensing images,CNNs may experience class confusion.To address these issues,we propose a novel Category-Guided Feature Collaborative Learning Network(CG-FCLNet),which enables fine-grained feature extraction and adaptive fusion.Specifically,we design a Feature Collaborative Learning Module(FCLM)to facilitate the tight interaction of multi-scale features.We also introduce a Scale-Aware Fusion Module(SAFM),which iteratively fuses features from different layers using a spatial attention mechanism,enabling deeper feature fusion.Furthermore,we design a Category-Guided Module(CGM)to extract category-aware information that guides feature fusion,ensuring that the fused featuresmore accurately reflect the semantic information of each category,thereby improving detailed segmentation.The experimental results show that CG-FCLNet achieves a Mean Intersection over Union(mIoU)of 83.46%,an mF1 of 90.87%,and an Overall Accuracy(OA)of 91.34% on the Vaihingen dataset.On the Potsdam dataset,it achieves a mIoU of 86.54%,an mF1 of 92.65%,and an OA of 91.29%.These results highlight the superior performance of CG-FCLNet compared to existing state-of-the-art methods.展开更多
Following publication of the original article[1],the authors found that they pasted the same data when drawing XRD for sample NCO-1 and NCO-2 in Fig.2a,however,the XRD of all four samples in the manuscript was tested,...Following publication of the original article[1],the authors found that they pasted the same data when drawing XRD for sample NCO-1 and NCO-2 in Fig.2a,however,the XRD of all four samples in the manuscript was tested,and XRD raw data were kept and can be offered.The correct Fig.2 has been provided in this Correction.展开更多
Modules enable students to engage with content at their own pace,fostering autonomy and deeper understanding.The modular approach ensures clarity in presenting objectives,instructions,and concepts,while having illustr...Modules enable students to engage with content at their own pace,fostering autonomy and deeper understanding.The modular approach ensures clarity in presenting objectives,instructions,and concepts,while having illustrations,activities,and assessments could enhance comprehension and retention.This paper was a developmental study on STS module for college students using the ADDIE Model(Analysis,Design,Development,Implementation,and Evaluation).Sampled 673 first-year students from Northwest Samar State University participated in the study,with 299 participating in a test try-out and 374 in the students’performance evaluation.Three expert evaluators with backgrounds in science,English,and psychology,each with over four years of experience,assessed the modules to ensure alignment with the study’s constructivist learning goals and instructional integrity.The findings revealed that both students and experts had rated the instructional module positively,indicating its effectiveness in facilitating learning and completing lessons.Key aspects such as the style of illustrations and written expressions,the usefulness of learning activities,and the guidance provided by illustrations and captions were especially well-received.The module was praised for its clear objectives,understandable instructions,and engaging tasks like trivia and puzzles.Expert evaluations highlighted relevance,simplicity,and balanced emphasis on topics in the module content.Furthermore,students in test group demonstrated significant improvement in performance,with post-test scores notably higher than pre-test scores,confirming the module’s effectiveness in enhancing learning outcomes.Consequently,this paper provides an opportunity to integrate science learning with initiatives aimed at promoting environmental preservation and driving social change.展开更多
This paper proposes an automated detection framework for transmission facilities using a featureattention multi-scale robustness network(FAMSR-Net)with high-fidelity virtual images.The proposed framework exhibits thre...This paper proposes an automated detection framework for transmission facilities using a featureattention multi-scale robustness network(FAMSR-Net)with high-fidelity virtual images.The proposed framework exhibits three key characteristics.First,virtual images of the transmission facilities generated using StyleGAN2-ADA are co-trained with real images.This enables the neural network to learn various features of transmission facilities to improve the detection performance.Second,the convolutional block attention module is deployed in FAMSR-Net to effectively extract features from images and construct multi-dimensional feature maps,enabling the neural network to perform precise object detection in various environments.Third,an effective bounding box optimization method called Scylla-IoU is deployed on FAMSR-Net,considering the intersection over union,center point distance,angle,and shape of the bounding box.This enables the detection of power facilities of various sizes accurately.Extensive experiments demonstrated that FAMSRNet outperforms other neural networks in detecting power facilities.FAMSR-Net also achieved the highest detection accuracy when virtual images of the transmission facilities were co-trained in the training phase.The proposed framework is effective for the scheduled operation and maintenance of transmission facilities because an optical camera is currently the most promising tool for unmanned aerial vehicles.This ultimately contributes to improved inspection efficiency,reduced maintenance risks,and more reliable power delivery across extensive transmission facilities.展开更多
Transcription factors(TFs)play key roles in the regulatory network of leaf senescence.However,many nodes in this network remain unclear.To elucidate the mechanism of leaf senescence mediated by a rice TF,WRKY10,the ex...Transcription factors(TFs)play key roles in the regulatory network of leaf senescence.However,many nodes in this network remain unclear.To elucidate the mechanism of leaf senescence mediated by a rice TF,WRKY10,the expression of multiple senescence-related genes and physiological phenotypes were monitored in WRKY10-and VQ MOTIF-CONTAINING PROTEIN8(VQ8)-overexpressing plants and the wrky10 and vq8 mutants.Our results showed that WRKY10 positively regulates abscisic acid(ABA)-and dark-induced senescence(DIS)by directly regulating the expression of multiple senescence-related genes.The VQ8 protein,a repressor of WRKY10,negatively regulates WRKY10-mediated DIS.The WRKY10-VQ8 module fine-tunes the progression of DIS.ABA,methyl jasmonate,and H_(2)O_(2) accelerate WRKY10-mediated DIS,whereas ammonium nitrate and dithiothreitol delay WRKY10-mediated DIS.Further analysis revealed that WRKY10 and VQ8 interact with ABA RESPONSIVE ELEMENT BINDING FACTOR1(ABF1)or ABF2.VQ8 represses the transcriptional activity of ABF1 and ABF2.Overexpression of ABF1 or ABF2 accelerates ABA-and dark-induced senescence and H_(2)O_(2) accumulation in N.benthamiana leaves,and WRKY10 and VQ8 can inhibit either ABF1-or ABF2-induced cell necrosis.Taken together,WRKY10 integrates multiple senescence signals to establish an orderly progression of leaf senescence.The VQ8 protein acts as a brake on WRKY10-induced senescence and ABF1/2-induced cell death,preventing uncontrolled cell death.展开更多
Current research focuses on the performance degradation of photovoltaic(PV)modules,examining both crystalline silicon(p-Si and m-Si)and thin-film technologies,including a-Si/μc-Si,HIT,CdTe and CIGS.These modules were...Current research focuses on the performance degradation of photovoltaic(PV)modules,examining both crystalline silicon(p-Si and m-Si)and thin-film technologies,including a-Si/μc-Si,HIT,CdTe and CIGS.These modules were operated outdoors in two distinct climatic zones in the United States(US)over a period of three years.The degradation analysis includes the study of various quantities,such as the decrease in peak power,the reduction in current and voltage,and the variation in the fill factor.The annual degradation rate(DR)of PV modules is obtained by a linear fit of the effective maximum power evolution over time.The results indicate that m-Si and p-Si modules experienced a slight decrease in performance,with DRs of−0.83%and−1.07%,respectively.Subsequently,the HIT module exhibited a DR of−1.75%,while CdTe and CIGS modules demonstrated DRs of−2.03%and−2.45%,respectively.The a-Si/μc-Si module showed the highest DR at−3.26%.Using the Single Diode Model(SDM),we monitored the temporal evolution of physical parameters as well as changes in the shape of the I-V and P-V curves over time.We found that the key points of the I-V curve degrade over time,as do the I-V and P-V characteristics between two days approximately 30 months apart.展开更多
To extract and display the significant information of combat systems,this paper introduces the methodology of functional cartography into combat networks and proposes an integrated framework named“functional cartogra...To extract and display the significant information of combat systems,this paper introduces the methodology of functional cartography into combat networks and proposes an integrated framework named“functional cartography of heterogeneous combat networks based on the operational chain”(FCBOC).In this framework,a functional module detection algorithm named operational chain-based label propagation algorithm(OCLPA),which considers the cooperation and interactions among combat entities and can thus naturally tackle network heterogeneity,is proposed to identify the functional modules of the network.Then,the nodes and their modules are classified into different roles according to their properties.A case study shows that FCBOC can provide a simplified description of disorderly information of combat networks and enable us to identify their functional and structural network characteristics.The results provide useful information to help commanders make precise and accurate decisions regarding the protection,disintegration or optimization of combat networks.Three algorithms are also compared with OCLPA to show that FCBOC can most effectively find functional modules with practical meaning.展开更多
This study proposes a novel visual maintenance method for photovoltaic(PV)modules based on a two-stage Wiener degradation model,addressing the limitations of traditional PV maintenance strategies that often result in ...This study proposes a novel visual maintenance method for photovoltaic(PV)modules based on a two-stage Wiener degradation model,addressing the limitations of traditional PV maintenance strategies that often result in insufficient or excessive maintenance.The approach begins by constructing a two-stage Wiener process performance degradation model and a remaining life prediction model under perfect maintenance conditions using historical degradation data of PV modules.This enables accurate determination of the optimal timing for postfailure corrective maintenance.To optimize the maintenance strategy,the study establishes a comprehensive cost model aimed at minimizing the long-term average cost rate.The model considers multiple cost factors,including inspection costs,preventive maintenance costs,restorative maintenance costs,and penalty costs associated with delayed fault detection.Through this optimization framework,the method determines both the optimal maintenance threshold and the ideal timing for predictive maintenance actions.Comparative analysis demonstrates that the twostage Wiener model provides superior fitting performance compared to conventional linear and nonlinear degradation models.When evaluated against traditional maintenance approaches,including Wiener process-based corrective maintenance strategies and static periodic maintenance strategies,the proposed method demonstrates significant advantages in reducing overall operational costs while extending the effective service life of PV components.The method achieves these improvements through effective coordination between reliability optimization and economic benefit maximization,leading to enhanced power generation performance.These results indicate that the proposed approach offers a more balanced and efficient solution for PV system maintenance.展开更多
The intelligent vehicle corner module system,which integrates four-wheel independent drive,independent steering,independent braking and active suspension,can accurately and efficiently perform vehicle driving tasks an...The intelligent vehicle corner module system,which integrates four-wheel independent drive,independent steering,independent braking and active suspension,can accurately and efficiently perform vehicle driving tasks and is the best carrier of intelligent vehicles.Nevertheless,too many angle/torque control inputs make control difficult and non-real-time.In this paper,a hierarchical real-time motion control framework for corner module configuration intelligent electric vehicles is proposed.In the trajectory planning module,an improved driving risk field is designed to describe the surrounding environment’s driving risk.Combined with the kinematic vehicle-road model,model predictive control(MPC)method,spline curve method,the local reference trajectory of safety,comfort and smoothness is planned in real time.The optimal steering angle is determined using MPC method in path tracking module.In the motion control module,a feedforward-feedback controller assigns the optimal steering angle to the front/rear axles,and an angle allocation controller distributes the target angles of the front/rear axles to four steered wheels.Finally,the PreScan-Simulink-CarSim joint simulation environment is established for conducting the human-in-the-loop emergency obstacle avoidance experiment.It took only 0.005 s for the hierarchical motion control system to determine its average solution time.This proves the effectiveness of the hierarchical motion control system.展开更多
Vehicle re-identification involves matching images of vehicles across varying camera views.The diversity of camera locations along different roadways leads to significant intra-class variation and only minimal inter-c...Vehicle re-identification involves matching images of vehicles across varying camera views.The diversity of camera locations along different roadways leads to significant intra-class variation and only minimal inter-class similarity in the collected vehicle images,which increases the complexity of re-identification tasks.To tackle these challenges,this study proposes AG-GCN(Attention-Guided Graph Convolutional Network),a novel framework integrating several pivotal components.Initially,AG-GCN embeds a lightweight attention module within the ResNet-50 structure to learn feature weights automatically,thereby improving the representation of vehicle features globally by highlighting salient features and suppressing extraneous ones.Moreover,AG-GCN adopts a graph-based structure to encapsulate deep local features.A graph convolutional network then amalgamates these features to understand the relationships among vehicle-related characteristics.Subsequently,we amalgamate feature maps from both the attention and graph-based branches for a more comprehensive representation of vehicle features.The framework then gauges feature similarities and ranks them,thus enhancing the accuracy of vehicle re-identification.Comprehensive qualitative and quantitative analyses on two publicly available datasets verify the efficacy of AG-GCN in addressing intra-class and inter-class variability issues.展开更多
基金supported by the National Research Foundation of Korea(NRF)grant funded by the Korea government(*MSIT)(No.2018R1A5A7059549).
文摘The generation of high-quality,realistic face generation has emerged as a key field of research in computer vision.This paper proposes a robust approach that combines a Super-Resolution Generative Adversarial Network(SRGAN)with a Pyramid Attention Module(PAM)to enhance the quality of deep face generation.The SRGAN framework is designed to improve the resolution of generated images,addressing common challenges such as blurriness and a lack of intricate details.The Pyramid Attention Module further complements the process by focusing on multi-scale feature extraction,enabling the network to capture finer details and complex facial features more effectively.The proposed method was trained and evaluated over 100 epochs on the CelebA dataset,demonstrating consistent improvements in image quality and a marked decrease in generator and discriminator losses,reflecting the model’s capacity to learn and synthesize high-quality images effectively,given adequate computational resources.Experimental outcome demonstrates that the SRGAN model with PAM module has outperformed,yielding an aggregate discriminator loss of 0.055 for real,0.043 for fake,and a generator loss of 10.58 after training for 100 epochs.The model has yielded an structural similarity index measure of 0.923,that has outperformed the other models that are considered in the current study for analysis.
基金supported in part by the National Key R&D Program of China under grant no.2022YFB2703000in part by the Young Backbone Teachers Support Plan of BISTU under grant no.YBT202437+1 种基金in part by the R&D Program of Beijing Municipal Education Commission under grant no.KM202211232012in part by the Educational Innovation Program of BISTU under grant no.2025JGYB19。
文摘Physical layer authentication(PLA)in the context of the Internet of Things(IoT)has gained significant attention.Compared with traditional encryption and blockchain technologies,PLA provides a more computationally efficient alternative to exploiting the properties of the wireless medium itself.Some existing PLA solutions rely on static mechanisms,which are insufficient to address the authentication challenges in fifth generation(5G)and beyond wireless networks.Additionally,with the massive increase in mobile device access,the communication security of the IoT is vulnerable to spoofing attacks.To overcome the above challenges,this paper proposes a lightweight deep convolutional neural network(CNN)equipped with squeeze and excitation module(SE module)in dynamic wireless environments,namely SE-ConvNet.To be more specific,a convolution factorization is developed to reduce the complexity of PLA models based on deep learning.Moreover,an SE module is designed in the deep CNN to enhance useful features andmaximize authentication accuracy.Compared with the existing solutions,the proposed SE-ConvNet enabled PLA scheme performs excellently in mobile and time-varying wireless environments while maintaining lower computational complexity.
基金funded by Zhejiang Basic Public Welfare Research Project,grant number LZY24E060001supported by Guangzhou Development Zone Science and Technology(2021GH10,2020GH10,2023GH02)+1 种基金the University of Macao(MYRG2022-00271-FST)the Science and Technology Development Fund(FDCT)of Macao(0032/2022/A).
文摘With the continuous development of artificial intelligence and machine learning techniques,there have been effective methods supporting the work of dermatologist in the field of skin cancer detection.However,object significant challenges have been presented in accurately segmenting melanomas in dermoscopic images due to the objects that could interfere human observations,such as bubbles and scales.To address these challenges,we propose a dual U-Net network framework for skin melanoma segmentation.In our proposed architecture,we introduce several innovative components that aim to enhance the performance and capabilities of the traditional U-Net.First,we establish a novel framework that links two simplified U-Nets,enabling more comprehensive information exchange and feature integration throughout the network.Second,after cascading the second U-Net,we introduce a skip connection between the decoder and encoder networks,and incorporate a modified receptive field block(MRFB),which is designed to capture multi-scale spatial information.Third,to further enhance the feature representation capabilities,we add a multi-path convolution block attention module(MCBAM)to the first two layers of the first U-Net encoding,and integrate a new squeeze-and-excitation(SE)mechanism with residual connections in the second U-Net.To illustrate the performance of our proposed model,we conducted comprehensive experiments on widely recognized skin datasets.On the ISIC-2017 dataset,the IoU value of our proposed model increased from 0.6406 to 0.6819 and the Dice coefficient increased from 0.7625 to 0.8023.On the ISIC-2018 dataset,the IoU value of proposed model also improved from 0.7138 to 0.7709,while the Dice coefficient increased from 0.8285 to 0.8665.Furthermore,the generalization experiments conducted on the jaw cyst dataset from Quzhou People’s Hospital further verified the outstanding segmentation performance of the proposed model.These findings collectively affirm the potential of our approach as a valuable tool in supporting clinical decision-making in the field of skin cancer detection,as well as advancing research in medical image analysis.
基金Project supported by the National Natural Science Foundation of China(Nos.12072183 and11872236)the Key Research Project of Zhejiang Laboratory(No.2021PE0AC02)。
文摘Electric vehicles(EVs)have garnered significant attention as a vital driver of economic growth and environmental sustainability.Nevertheless,ensuring the safety of high-energy batteries is now a top priority that cannot be overlooked during large-scale applications.This paper proposes an innovative active protection and cooling integrated battery module using smart materials,magneto-sensitive shear thickening fluid(MSTF),which is specifically designed to address safety threats posed by lithium-ion batteries(LIBs)exposed to harsh mechanical and environmental conditions.The theoretical framework introduces a novel approach for harnessing the smoothed-particle hydrodynamics(SPH)methodology that incorporates the intricate interplay of non-Newtonian fluid behavior,capturing the fluid-structure coupling inherent to the MSTF.This approach is further advanced by adopting an enhanced Herschel-Bulkley(H-B)model to encapsulate the intricate rheology of the MSTF under the influence of the magnetorheological effect(MRE)and shear thickening(ST)behavior.Numerical simulation results show that in the case of cooling,the MSTF is an effective cooling medium for rapidly reducing the temperature.In terms of mechanical abuse,the MSTF solidifies through actively applying the magnetic field during mechanical compression and impact within the battery module,resulting in 66%and 61.7%reductions in the maximum stress within the battery jellyroll,and 31.1%and 23%reductions in the reaction force,respectively.This mechanism effectively lowers the risk of short-circuit failure.The groundbreaking concepts unveiled in this paper for active protection battery modules are anticipated to be a valuable technological breakthrough in the areas of EV safety and lightweight/integrated design.
基金supported by the Beijing Natural Science Foundation (L202003)National Natural Science Foundation of China (No. 31700479)。
文摘Automatic modulation classification(AMC) technology is one of the cutting-edge technologies in cognitive radio communications. AMC based on deep learning has recently attracted much attention due to its superior performances in classification accuracy and robustness. In this paper, we propose a novel, high resolution and multi-scale feature fusion convolutional neural network model with a squeeze-excitation block, referred to as HRSENet,to classify different kinds of modulation signals.The proposed model establishes a parallel computing mechanism of multi-resolution feature maps through the multi-layer convolution operation, which effectively reduces the information loss caused by downsampling convolution. Moreover, through dense skipconnecting at the same resolution and up-sampling or down-sampling connection at different resolutions, the low resolution representation of the deep feature maps and the high resolution representation of the shallow feature maps are simultaneously extracted and fully integrated, which is benificial to mine signal multilevel features. Finally, the feature squeeze and excitation module embedded in the decoder is used to adjust the response weights between channels, further improving classification accuracy of proposed model.The proposed HRSENet significantly outperforms existing methods in terms of classification accuracy on the public dataset “Over the Air” in signal-to-noise(SNR) ranging from-2dB to 20dB. The classification accuracy in the proposed model achieves 85.36% and97.30% at 4dB and 10dB, respectively, with the improvement by 9.71% and 5.82% compared to LWNet.Furthermore, the model also has a moderate computation complexity compared with several state-of-the-art methods.
基金supported by the National Natural Science Foundation of China(51767017)the Basic Research Innovation Group Project of Gansu Province(18JR3RA133)the Industrial Support and Guidance Project of Universities in Gansu Province(2022CYZC-22).
文摘Photovoltaic (PV) modules, as essential components of solar power generation systems, significantly influence unitpower generation costs.The service life of these modules directly affects these costs. Over time, the performanceof PV modules gradually declines due to internal degradation and external environmental factors.This cumulativedegradation impacts the overall reliability of photovoltaic power generation. This study addresses the complexdegradation process of PV modules by developing a two-stage Wiener process model. This approach accountsfor the distinct phases of degradation resulting from module aging and environmental influences. A powerdegradation model based on the two-stage Wiener process is constructed to describe individual differences inmodule degradation processes. To estimate the model parameters, a combination of the Expectation-Maximization(EM) algorithm and the Bayesian method is employed. Furthermore, the Schwarz Information Criterion (SIC) isutilized to identify critical change points in PV module degradation trajectories. To validate the universality andeffectiveness of the proposed method, a comparative analysis is conducted against other established life predictiontechniques for PV modules.
基金supported in part by the National Key Research and Development Program of China under Grant 2020YFB1807300the Beijing Advanced Innovation Center for Integrated Circuits。
文摘A two-way K/Ka-band series-Doherty PA(SDPA)with a distributed impedance inverting network(IIN)for millimeter wave applications is presented in this article.The proposed distributed IIN contributes to achieve wideband linear and power back-off(PBO)efficiency enhancement.Implemented in 65 nm bulk CMOS technology,this work realizes a measured 3 dB band-width of 15.5 GHz with 21.2 dB peak small-signal gain at 34.2 GHz.Under 1-V power supply,it achieves OP1dB over 13.4 dBm and Psat over 16 dBm between 21 to 30 GHz.The measured maximum Psat,OP1dB,peak/OP1dB/6dBPBO PAE results are 17.5,14.7 dBm,and 28.2%/23.2%/13.2%.Without digital pre-distortion(DPD)and equalization,EVMs are lower than-25.2 dB for 200 MHz 64-QAM signals.Besides,this work achieves-33.35,-23.52,and-20 dB EVMs for 100 MHz 256-QAM,600 MHz 64-QAM and 2 GHz 16-QAM signals at 27 GHz without DPD and equalization.
基金supported by the National Natural Science Foundation of China,(Nos.82272151,82204318)Liaoning Revitalization Talents Program(No.XLYC2203083)+2 种基金Shenyang Young and Middle-aged Science and Technology Innovation Talent Support Program(No.RC220389)Postdoctoral Fellowship Program of CPSF(No.GZC20231732)China Postdoctoral Science Foundation(Nos.2023TQ0222,2023MD744229).
文摘Self-assembled prodrug nanomedicine has emerged as an advanced platform for antitumor therapy,mainly comprise drug modules,response modules and modification modules.However,existing studies usually compare the differences between single types of modification modules,neglecting the impact of steric-hindrance effect caused by chemical structure.Herein,single-tailed modification module with low-steric-hindrance effect and two-tailed modification module with high-steric-hindrance effect were selected to construct paclitaxel prodrugs(P-LA_(C18)and P-BAC18),and the in-depth insights of the sterichindrance effect on prodrug nanoassemblies were explored.Notably,the size stability of the two-tailed prodrugs was enhanced due to improved intermolecular interactions and steric hindrance.Single-tailed prodrug nanoassemblies were more susceptible to attack by redox agents,showing faster drug release and stronger antitumor efficacy,but with poorer safety.In contrast,two-tailed prodrug nanoassemblies exhibited significant advantages in terms of pharmacokinetics,tumor accumulation and safety due to the good size stability,thus ensuring equivalent antitumor efficacy at tolerance dose.These findings highlighted the critical role of steric-hindrance effect of the modification module in regulating the structureactivity relationship of prodrug nanoassemblies and proposed new perspectives into the precise design of self-assembled prodrugs for high-performance cancer therapeutics.
基金funded by Liaoning Provincial Department of Education Project,Award number JYTMS20230418.
文摘Pest detection techniques are helpful in reducing the frequency and scale of pest outbreaks;however,their application in the actual agricultural production process is still challenging owing to the problems of interspecies similarity,multi-scale,and background complexity of pests.To address these problems,this study proposes an FD-YOLO pest target detection model.The FD-YOLO model uses a Fully Connected Feature Pyramid Network(FC-FPN)instead of a PANet in the neck,which can adaptively fuse multi-scale information so that the model can retain small-scale target features in the deep layer,enhance large-scale target features in the shallow layer,and enhance the multiplexing of effective features.A dual self-attention module(DSA)is then embedded in the C3 module of the neck,which captures the dependencies between the information in both spatial and channel dimensions,effectively enhancing global features.We selected 16 types of pests that widely damage field crops in the IP102 pest dataset,which were used as our dataset after data supplementation and enhancement.The experimental results showed that FD-YOLO’s mAP@0.5 improved by 6.8%compared to YOLOv5,reaching 82.6%and 19.1%–5%better than other state-of-the-art models.This method provides an effective new approach for detecting similar or multiscale pests in field crops.
文摘With the rapid development of the new energy automotive industry,the enhancement of lithium battery performance and production efficiency has become critical.This article explores the application of artificial intelligence technology in the lithium battery module PACK line,analyzing how it optimizes the production process and improves production efficiency,and predicts future development trends.The PACK line is an important link in battery manufacturing,involving complex processes such as cell sorting,welding,assembly and testing.The application of AI technology in image recognition,data analysis and predictive maintenance provides new solutions for the intelligent upgrading of the PACK line.This article describes the process of the PACK line in detail,analyzes the challenges under current technological levels,and reviews the application cases of AI technology in the manufacturing industry.The study aims to provide theoretical and practical guidance for the intelligent development of lithium battery module PACK lines,discussing the integration of AI technology,its actual performance,technical challenges,and solutions.It is expected that AI technology will play a greater role in the PACK line,and future research will focus on improving the adaptability of models,developing efficient algorithms,and further integrating into the production line.
基金funded by National Natural Science Foundation of China(61603245).
文摘Semantic segmentation of remote sensing images is a critical research area in the field of remote sensing.Despite the success of Convolutional Neural Networks(CNNs),they often fail to capture inter-layer feature relationships and fully leverage contextual information,leading to the loss of important details.Additionally,due to significant intraclass variation and small inter-class differences in remote sensing images,CNNs may experience class confusion.To address these issues,we propose a novel Category-Guided Feature Collaborative Learning Network(CG-FCLNet),which enables fine-grained feature extraction and adaptive fusion.Specifically,we design a Feature Collaborative Learning Module(FCLM)to facilitate the tight interaction of multi-scale features.We also introduce a Scale-Aware Fusion Module(SAFM),which iteratively fuses features from different layers using a spatial attention mechanism,enabling deeper feature fusion.Furthermore,we design a Category-Guided Module(CGM)to extract category-aware information that guides feature fusion,ensuring that the fused featuresmore accurately reflect the semantic information of each category,thereby improving detailed segmentation.The experimental results show that CG-FCLNet achieves a Mean Intersection over Union(mIoU)of 83.46%,an mF1 of 90.87%,and an Overall Accuracy(OA)of 91.34% on the Vaihingen dataset.On the Potsdam dataset,it achieves a mIoU of 86.54%,an mF1 of 92.65%,and an OA of 91.29%.These results highlight the superior performance of CG-FCLNet compared to existing state-of-the-art methods.
文摘Following publication of the original article[1],the authors found that they pasted the same data when drawing XRD for sample NCO-1 and NCO-2 in Fig.2a,however,the XRD of all four samples in the manuscript was tested,and XRD raw data were kept and can be offered.The correct Fig.2 has been provided in this Correction.
文摘Modules enable students to engage with content at their own pace,fostering autonomy and deeper understanding.The modular approach ensures clarity in presenting objectives,instructions,and concepts,while having illustrations,activities,and assessments could enhance comprehension and retention.This paper was a developmental study on STS module for college students using the ADDIE Model(Analysis,Design,Development,Implementation,and Evaluation).Sampled 673 first-year students from Northwest Samar State University participated in the study,with 299 participating in a test try-out and 374 in the students’performance evaluation.Three expert evaluators with backgrounds in science,English,and psychology,each with over four years of experience,assessed the modules to ensure alignment with the study’s constructivist learning goals and instructional integrity.The findings revealed that both students and experts had rated the instructional module positively,indicating its effectiveness in facilitating learning and completing lessons.Key aspects such as the style of illustrations and written expressions,the usefulness of learning activities,and the guidance provided by illustrations and captions were especially well-received.The module was praised for its clear objectives,understandable instructions,and engaging tasks like trivia and puzzles.Expert evaluations highlighted relevance,simplicity,and balanced emphasis on topics in the module content.Furthermore,students in test group demonstrated significant improvement in performance,with post-test scores notably higher than pre-test scores,confirming the module’s effectiveness in enhancing learning outcomes.Consequently,this paper provides an opportunity to integrate science learning with initiatives aimed at promoting environmental preservation and driving social change.
基金supported by the Korea Electric Power Corporation(R22TA14,Development of Drone Systemfor Diagnosis of Porcelain Insulators in Overhead Transmission Lines)the National Fire Agency of Korea(RS-2024-00408270,Fire Hazard Analysis and Fire Safety Standards Development for Transportation and Storage Stage of Reuse Battery)the Ministry of the Interior and Safety of Korea(RS-2024-00408982,Development of Intelligent Fire Detection and Sprinkler Facility Technology Reflecting the Characteristics of Logistics Facilities).
文摘This paper proposes an automated detection framework for transmission facilities using a featureattention multi-scale robustness network(FAMSR-Net)with high-fidelity virtual images.The proposed framework exhibits three key characteristics.First,virtual images of the transmission facilities generated using StyleGAN2-ADA are co-trained with real images.This enables the neural network to learn various features of transmission facilities to improve the detection performance.Second,the convolutional block attention module is deployed in FAMSR-Net to effectively extract features from images and construct multi-dimensional feature maps,enabling the neural network to perform precise object detection in various environments.Third,an effective bounding box optimization method called Scylla-IoU is deployed on FAMSR-Net,considering the intersection over union,center point distance,angle,and shape of the bounding box.This enables the detection of power facilities of various sizes accurately.Extensive experiments demonstrated that FAMSRNet outperforms other neural networks in detecting power facilities.FAMSR-Net also achieved the highest detection accuracy when virtual images of the transmission facilities were co-trained in the training phase.The proposed framework is effective for the scheduled operation and maintenance of transmission facilities because an optical camera is currently the most promising tool for unmanned aerial vehicles.This ultimately contributes to improved inspection efficiency,reduced maintenance risks,and more reliable power delivery across extensive transmission facilities.
基金supported by the National Natural Science Foundation of China (31371557 and 31571574)Wenzhou Basic Scientific Research Project (N20240009)。
文摘Transcription factors(TFs)play key roles in the regulatory network of leaf senescence.However,many nodes in this network remain unclear.To elucidate the mechanism of leaf senescence mediated by a rice TF,WRKY10,the expression of multiple senescence-related genes and physiological phenotypes were monitored in WRKY10-and VQ MOTIF-CONTAINING PROTEIN8(VQ8)-overexpressing plants and the wrky10 and vq8 mutants.Our results showed that WRKY10 positively regulates abscisic acid(ABA)-and dark-induced senescence(DIS)by directly regulating the expression of multiple senescence-related genes.The VQ8 protein,a repressor of WRKY10,negatively regulates WRKY10-mediated DIS.The WRKY10-VQ8 module fine-tunes the progression of DIS.ABA,methyl jasmonate,and H_(2)O_(2) accelerate WRKY10-mediated DIS,whereas ammonium nitrate and dithiothreitol delay WRKY10-mediated DIS.Further analysis revealed that WRKY10 and VQ8 interact with ABA RESPONSIVE ELEMENT BINDING FACTOR1(ABF1)or ABF2.VQ8 represses the transcriptional activity of ABF1 and ABF2.Overexpression of ABF1 or ABF2 accelerates ABA-and dark-induced senescence and H_(2)O_(2) accumulation in N.benthamiana leaves,and WRKY10 and VQ8 can inhibit either ABF1-or ABF2-induced cell necrosis.Taken together,WRKY10 integrates multiple senescence signals to establish an orderly progression of leaf senescence.The VQ8 protein acts as a brake on WRKY10-induced senescence and ABF1/2-induced cell death,preventing uncontrolled cell death.
文摘Current research focuses on the performance degradation of photovoltaic(PV)modules,examining both crystalline silicon(p-Si and m-Si)and thin-film technologies,including a-Si/μc-Si,HIT,CdTe and CIGS.These modules were operated outdoors in two distinct climatic zones in the United States(US)over a period of three years.The degradation analysis includes the study of various quantities,such as the decrease in peak power,the reduction in current and voltage,and the variation in the fill factor.The annual degradation rate(DR)of PV modules is obtained by a linear fit of the effective maximum power evolution over time.The results indicate that m-Si and p-Si modules experienced a slight decrease in performance,with DRs of−0.83%and−1.07%,respectively.Subsequently,the HIT module exhibited a DR of−1.75%,while CdTe and CIGS modules demonstrated DRs of−2.03%and−2.45%,respectively.The a-Si/μc-Si module showed the highest DR at−3.26%.Using the Single Diode Model(SDM),we monitored the temporal evolution of physical parameters as well as changes in the shape of the I-V and P-V curves over time.We found that the key points of the I-V curve degrade over time,as do the I-V and P-V characteristics between two days approximately 30 months apart.
文摘To extract and display the significant information of combat systems,this paper introduces the methodology of functional cartography into combat networks and proposes an integrated framework named“functional cartography of heterogeneous combat networks based on the operational chain”(FCBOC).In this framework,a functional module detection algorithm named operational chain-based label propagation algorithm(OCLPA),which considers the cooperation and interactions among combat entities and can thus naturally tackle network heterogeneity,is proposed to identify the functional modules of the network.Then,the nodes and their modules are classified into different roles according to their properties.A case study shows that FCBOC can provide a simplified description of disorderly information of combat networks and enable us to identify their functional and structural network characteristics.The results provide useful information to help commanders make precise and accurate decisions regarding the protection,disintegration or optimization of combat networks.Three algorithms are also compared with OCLPA to show that FCBOC can most effectively find functional modules with practical meaning.
基金supported by the National Natural Science Foundation of China(51767017)the Basic Research Innovation Group Project of Gansu Province(18JR3RA133)the Industrial Support and Guidance Project of Universities in Gansu Province(2022CYZC-22).
文摘This study proposes a novel visual maintenance method for photovoltaic(PV)modules based on a two-stage Wiener degradation model,addressing the limitations of traditional PV maintenance strategies that often result in insufficient or excessive maintenance.The approach begins by constructing a two-stage Wiener process performance degradation model and a remaining life prediction model under perfect maintenance conditions using historical degradation data of PV modules.This enables accurate determination of the optimal timing for postfailure corrective maintenance.To optimize the maintenance strategy,the study establishes a comprehensive cost model aimed at minimizing the long-term average cost rate.The model considers multiple cost factors,including inspection costs,preventive maintenance costs,restorative maintenance costs,and penalty costs associated with delayed fault detection.Through this optimization framework,the method determines both the optimal maintenance threshold and the ideal timing for predictive maintenance actions.Comparative analysis demonstrates that the twostage Wiener model provides superior fitting performance compared to conventional linear and nonlinear degradation models.When evaluated against traditional maintenance approaches,including Wiener process-based corrective maintenance strategies and static periodic maintenance strategies,the proposed method demonstrates significant advantages in reducing overall operational costs while extending the effective service life of PV components.The method achieves these improvements through effective coordination between reliability optimization and economic benefit maximization,leading to enhanced power generation performance.These results indicate that the proposed approach offers a more balanced and efficient solution for PV system maintenance.
基金Supported by National Natural Science Foundation of China(Grant No.52332013)。
文摘The intelligent vehicle corner module system,which integrates four-wheel independent drive,independent steering,independent braking and active suspension,can accurately and efficiently perform vehicle driving tasks and is the best carrier of intelligent vehicles.Nevertheless,too many angle/torque control inputs make control difficult and non-real-time.In this paper,a hierarchical real-time motion control framework for corner module configuration intelligent electric vehicles is proposed.In the trajectory planning module,an improved driving risk field is designed to describe the surrounding environment’s driving risk.Combined with the kinematic vehicle-road model,model predictive control(MPC)method,spline curve method,the local reference trajectory of safety,comfort and smoothness is planned in real time.The optimal steering angle is determined using MPC method in path tracking module.In the motion control module,a feedforward-feedback controller assigns the optimal steering angle to the front/rear axles,and an angle allocation controller distributes the target angles of the front/rear axles to four steered wheels.Finally,the PreScan-Simulink-CarSim joint simulation environment is established for conducting the human-in-the-loop emergency obstacle avoidance experiment.It took only 0.005 s for the hierarchical motion control system to determine its average solution time.This proves the effectiveness of the hierarchical motion control system.
基金funded by the National Natural Science Foundation of China(grant number:62172292).
文摘Vehicle re-identification involves matching images of vehicles across varying camera views.The diversity of camera locations along different roadways leads to significant intra-class variation and only minimal inter-class similarity in the collected vehicle images,which increases the complexity of re-identification tasks.To tackle these challenges,this study proposes AG-GCN(Attention-Guided Graph Convolutional Network),a novel framework integrating several pivotal components.Initially,AG-GCN embeds a lightweight attention module within the ResNet-50 structure to learn feature weights automatically,thereby improving the representation of vehicle features globally by highlighting salient features and suppressing extraneous ones.Moreover,AG-GCN adopts a graph-based structure to encapsulate deep local features.A graph convolutional network then amalgamates these features to understand the relationships among vehicle-related characteristics.Subsequently,we amalgamate feature maps from both the attention and graph-based branches for a more comprehensive representation of vehicle features.The framework then gauges feature similarities and ranks them,thus enhancing the accuracy of vehicle re-identification.Comprehensive qualitative and quantitative analyses on two publicly available datasets verify the efficacy of AG-GCN in addressing intra-class and inter-class variability issues.
