Insight into exciton dynamics of two-dimensional(2D)transition metal dichalcogenides(TMDs)is critical for the optimization of their performance in photonic and optoelectronic devices.Although current researches have p...Insight into exciton dynamics of two-dimensional(2D)transition metal dichalcogenides(TMDs)is critical for the optimization of their performance in photonic and optoelectronic devices.Although current researches have primarily concentrated on the near-resonant excitation scenario in 2D TMDs,the case of excitation energies resonating with highenergy excitons or higher energies has yet to be fully elucidated.Here,a comparative analysis is conducted between highenergy excitation(360 nm)and near-resonant excitation(515 nm)utilizing transient absorption spectroscopy to achieve a comprehensive understanding of the exciton dynamics within monolayer WS_(2).It is observed that the high-energy C-exciton can be generated via an up-conversion process under 515 nm excitation,even the energy of which is less than that of the C-exciton.Furthermore,the capacity to efficiently occupy band-edge A-exciton states leads to longer lifetimes for both the C-excitons and the A-excitons under conditions of near-resonant excitation,accompanied by an augmented rate of radiative recombination.This study provides a paradigm for optimizing the performance of 2D TMDs-based devices by offering valuable insights into their exciton dynamics.展开更多
Coulomb drag refers to the phenomenon in which a current driven through one conducting layer induces a voltage nearby,electrically isolated layer sorely through interlayer Coulomb interactions between charge carriers....Coulomb drag refers to the phenomenon in which a current driven through one conducting layer induces a voltage nearby,electrically isolated layer sorely through interlayer Coulomb interactions between charge carriers.It has been extensively studied in various systems,including parallel nanowires,double quantum wells,and double-layer graphene.Here,we report the observation of Coulomb drag in a novel system consisting of two graphene layers separated laterally by a 30 nm gap within the material plane,exhibiting behavior distinct from that in vertical graphene heterostructures.Our experiments reveal pronounced negative drag resistances under an out-of-plane magnetic field at the quantum Hall edges,reaching a maximum when the carrier densities in both graphene layers are tuned to the charge neutrality point via gate voltages.Our work establish two separate and spatially closed quantum Hall edge modes as a new platform to explore electronic interaction physics between one dimensional systems.展开更多
The exponential growth of Internet of Things(IoT)devices,autonomous systems,and digital services is generating massive volumes of big data,projected to exceed 291 zettabytes by 2027.Conventional cloud computing,despit...The exponential growth of Internet of Things(IoT)devices,autonomous systems,and digital services is generating massive volumes of big data,projected to exceed 291 zettabytes by 2027.Conventional cloud computing,despite its high processing and storage capacity,suffers from increased network latency,network congestion,and high operational costs,making it unsuitable for latency-sensitive applications.Edge computing addresses these issues by processing data near the source but faces scalability challenges and elevated Total Cost of Ownership(TCO).Hybrid solutions,such as fog computing,cloudlets,and Mobile Edge Computing(MEC),attempt to balance cost and performance;however,they still struggle with limited resource sharing and high deployment expenses.This paper proposes Public Edge as a Service(PEaaS),a novel paradigm that utilizes idle resources contributed by universities,enterprises,cellular operators,and individuals under a collaborative service model.By decentralizing computation and enabling multi-tenant resource sharing,PEaaS reduces reliance on centralized cloud infrastructure,minimizes communication costs,and enhances scalability.The proposed framework is evaluated using EdgeCloudSim under varying workloads,for keymetrics such as latency,communication cost,server utilization,and task failure rate.Results reveal that while cloud has a task failure rate rising sharply to 12.3%at 2000 devices,PEaaS maintains a low rate of 2.5%,closely matching edge computing.Furthermore,communication costs remain 25% lower than cloud and latency remains below 0.3,even under peak load.These findings demonstrate that PEaaS achieves near-edge performance with reduced costs and enhanced scalability,offering a sustainable and economically viable solution for next-generation computing environments.展开更多
Chitosan(CS),a natural polymer derived from chitin found in the exoskeletons of crustaceans,has garnered significant interest in the pharmaceutical field due to its unique properties,including biocompatibility and bio...Chitosan(CS),a natural polymer derived from chitin found in the exoskeletons of crustaceans,has garnered significant interest in the pharmaceutical field due to its unique properties,including biocompatibility and biodegradability.In recent years,various studies have reported that CS can affect drug bioavailability,and interestingly,it works as an oral absorption enhancer and inhibitor.This review offers an in-depth analysis of the mechanisms underlying such a phenomenon and supports its application as a pharmaceutical excipient.CS enhances oral drug absorption through various mechanisms,such as interaction with the intestinal mucosa,tight junction modulation,inhibition of efflux transporters,enzyme inhibition,solubility and stability enhancement,and complexation.On the other side,CS exhibits the ability to inhibit the absorption of certain drugs by adsorbing to lipids and sterols,modulating bile acids and gut microbiota,altering drug-cell interaction at the polar interface,and mucus-mediated entrapment and interference.Future potential pharmaceutical research in this field includes elucidating the underneath absorption relevant mechanisms,rational use in formulations as excipient,exploring functional CS derivatives,and developing CS-based drug delivery systems.This comprehensive review highlights CS's versatile and significant role in enhancing and inhibiting oral drug absorption,providing insights into the complexities of drug delivery and the potential of CS to improve therapeutic outcomes.展开更多
Using the pulp of Annona squamosa L.as the raw material,polysaccharides were extracted using ultrasonicassisted hot water extraction,with polysaccharide yield as the response variable.The effects of four factors on ex...Using the pulp of Annona squamosa L.as the raw material,polysaccharides were extracted using ultrasonicassisted hot water extraction,with polysaccharide yield as the response variable.The effects of four factors on extraction efficiency were investigated,and the extraction process was optimized using an orthogonal experimental design.The monosaccharide composition of the polysaccharides was analyzed using ion chromatography(IC).The antioxidant activity was evaluated through DPPH radical scavenging,hydroxyl radical(·OH)scavenging,and ferric reducing antioxidant power(FRAP)assays.Additionally,Franz diffusion cells were used to conduct in vitro transdermal absorption experiments,measuring cumulative permeation and calculating permeation kinetics parameters.The results showed that under optimized extraction conditions,the polysaccharide yield was 50.57%±0.02%.Ion chromatography analysis revealed that the main monosaccharide components were arabinose(0.6%),glucose(40.5%),and fructose(58.9%).Antioxidant evaluation indicated that with the addition of 80μL of the extract,the DPPH radical scavenging rate reached 94.13%±0.81%;with 600μL,the hydroxyl radical scavenging rate was 93.33%±0.92%,and the FRAP value was(38.07±1.30)mmol/L.Transdermal absorption experiments demonstrated that the skin retention rates of the polysaccharide aqueous extract at 75%and 100%concentrations were 4.65%±1.52%and 12.37%±2.14%,respectively,with 12 h cumulative permeation rates of 57.43%±1.08%and 74.24%±0.74%.Kinetic analysis of transdermal absorption showed that the process followed zero-order kinetics,indicating a constant release rate under steady-state conditions,consistent with Fick’s diffusion law.This study systematically combined optimization of polysaccharide aqueous extraction,monosaccharide composition analysis,antioxidant activity evaluation,and transdermal permeation performance analysis.It revealed the bioactive properties and potential applications of Annona squamosa L.polysaccharides,particularly in the fields of antioxidant activity and transdermal permeation.The findings provide essential data to support the development of functional products based on Annona squamosa L.polysaccharides.展开更多
Biochar and biochar composites are versatile materials that can be used in many applications.In this study,biochar was prepared from sawdust and combined with the yttrium iron garnet(YIG)nanocrystal to investigate the...Biochar and biochar composites are versatile materials that can be used in many applications.In this study,biochar was prepared from sawdust and combined with the yttrium iron garnet(YIG)nanocrystal to investigate the shielding effectiveness of the composite structure.Firstly,the effect of the pyrolysis temperature on the shielding effectiveness of biochar was investigated.Secondly,biochars combined with YIG nanocrystals with different contents and shielding effectiveness of the composites were investigated.The electromagnetic effectiveness of the samples was investigated within the X band(8-12 GHz).The findings indicate that biochar demonstrates enhanced absorption properties with elevated pyrolysis temperatures.Biochars demonstrated an approximate 40 d B shielding effectiveness,while YIG exhibited approximately 7 d B,corresponding to absorption at 8 GHz.However,the combination of biochar and YIG exhibited exceptional absorption,reaching 67.12 d B at 8 GHz.展开更多
As a fundamental component in computer vision,edges can be categorized into four types based on discontinuities in reflectance,illumination,surface normal,or depth.While deep CNNs have significantly advanced generic e...As a fundamental component in computer vision,edges can be categorized into four types based on discontinuities in reflectance,illumination,surface normal,or depth.While deep CNNs have significantly advanced generic edge detection,real-time multi-class semantic edge detection under resource constraints remains challenging.To address this,we propose a lightweight framework based on PiDiNet that enables fine-grained semantic edge detection.Our model simultaneously predicts background and four edge categories from full-resolution inputs,balancing accuracy and efficiency.Key contributions include:a multi-channel output structure expanding binary edge prediction to five classes,supported by a deep supervision mechanism;a dynamic class-balancing strategy combining adaptive weighting with physical priors to handle extreme class imbalance;and maintained architectural efficiency enabling real-time inference.Extensive evaluations on BSDS-RIND show our approach achieves accuracy competitive with state-of-the-art methods while operating in real time.展开更多
This work presents a systematic analysis of proton-induced total ionizing dose(TID)effects in 1.2 k V silicon carbide(SiC)power devices with various edge termination structures.Three edge terminations including ring-a...This work presents a systematic analysis of proton-induced total ionizing dose(TID)effects in 1.2 k V silicon carbide(SiC)power devices with various edge termination structures.Three edge terminations including ring-assisted junction termination extension(RA-JTE),multiple floating zone JTE(MFZ-JTE),and field limiting rings(FLR)were fabricated and irradiated with45 Me V protons at fluences ranging from 1×10^(12) to 1×10^(14) cm^(-2).Experimental results,supported by TCAD simulations,show that the RA-JTE structure maintained stable breakdown performance with less than 1%variation due to its effective electric field redistribution by multiple P+rings.In contrast,MFZ-JTE and FLR exhibit breakdown voltage shifts of 6.1%and 15.2%,respectively,under the highest fluence.These results demonstrate the superior radiation tolerance of the RA-JTE structure under TID conditions and provide practical design guidance for radiation-hardened Si C power devices in space and other highradiation environments.展开更多
Given the growing number of vehicle accidents caused by unintended acceleration and braking failure,verifying Sudden Unintended Acceleration(SUA)incidents has become a persistent challenge.A central issue of debate is...Given the growing number of vehicle accidents caused by unintended acceleration and braking failure,verifying Sudden Unintended Acceleration(SUA)incidents has become a persistent challenge.A central issue of debate is whether such events stem frommechanical malfunctions or driver pedalmisapplications.However,existing verification procedures implemented by vehiclemanufacturers often involve closed tests after vehicle recalls;thus raising ongoing concerns about reliability and transparency.Consequently,there is a growing need for a user-driven framework that enables independent data acquisition and verification.Although previous studies have addressed SUA detection using deep learning,few have explored howclass granularity optimization affects power efficiency and inference performance in real-time Edge AI systems.To address this problem,this work presents a cloud-assisted artificial intelligence(AI)solution for the reliable verification of SUA occurrences.The proposed system integrates multimodal sensor streams including camera-based foot images,On-Board Diagnostics II(OBD-II)signals,and six-axismeasurements to determine whether the brake pedal was actually engaged at themoment of a suspected SUA.Beyond image acquisition,convolutional neural network(CNN)models perform real-time inference to classify the driver’s pedal operation states with the resulting outputs transmitted and archived in the cloud.A dedicated dataset of brake and accelerator pedal images was collected from 15 vehicles produced by 6 domestic and international manufacturers.Using this dataset,transfer learning techniques were applied to compare and analyze model performance and generalization as the CNN class granularity varied from coarse to fine levels.Furthermore,classification performance was evaluated in terms of latency and power efficiency under different class configurations.The experimental results demonstrated that the proposed solution identified the driver’s pedal behavior accurately and promptly,with the two-class model achieving the highest F1-score and accuracy among all granularity settings.展开更多
We present a solid 226 nm deep ultraviolet laser system pumped by a Nd:YAG laser.A diamond Raman laser with a 1485 nm wavelength was generated up to 2.53 mJ pumped by a 9.7 mJ 1064 nm laser,which is the highest pulse ...We present a solid 226 nm deep ultraviolet laser system pumped by a Nd:YAG laser.A diamond Raman laser with a 1485 nm wavelength was generated up to 2.53 mJ pumped by a 9.7 mJ 1064 nm laser,which is the highest pulse energy of a second Stokes diamond Raman laser pumped by a 1064 nm laser as we know.Then,the Raman laser is mixed with the frequency-quadrupled 1064 nm laser to produce the 226 nm laser.The maximum output pulse energy at 226 nm reaches 0.49 mJ.The overall conversion efficiency from1064 to 226 nm is up to 1.14%,which is significantly higher than conventional optical parametric oscillator technology for the generation of 226 nm laser.The 226 nm laser system has been used in a laser-induced fluorescence(LIF)experiment of oxygen two-photon to demonstrate its potential for LIF measurements.展开更多
Traffic sign detection is a critical component of driving systems.Single-stage network-based traffic sign detection algorithms,renowned for their fast detection speeds and high accuracy,have become the dominant approa...Traffic sign detection is a critical component of driving systems.Single-stage network-based traffic sign detection algorithms,renowned for their fast detection speeds and high accuracy,have become the dominant approach in current practices.However,in complex and dynamic traffic scenes,particularly with smaller traffic sign objects,challenges such as missed and false detections can lead to reduced overall detection accuracy.To address this issue,this paper proposes a detection algorithm that integrates edge and shape information.Recognizing that traffic signs have specific shapes and distinct edge contours,this paper introduces an edge feature extraction branch within the backbone network,enabling adaptive fusion with features of the same hierarchical level.Additionally,a shape prior convolution module is designed to replaces the first two convolutional modules of the backbone network,aimed at enhancing the model's perception ability for specific shape objects and reducing its sensitivity to background noise.The algorithm was evaluated on the CCTSDB and TT100k datasets,and compared to YOLOv8s,the mAP50 values increased by 3.0%and 10.4%,respectively,demonstrating the effectiveness of the proposed method in improving the accuracy of traffic sign detection.展开更多
Binary segmentation tasks in computer vision exhibit diverse appearance distributions and complex boundary characteristics.To address the limited generalization and adaptability of existing models across heterogeneous...Binary segmentation tasks in computer vision exhibit diverse appearance distributions and complex boundary characteristics.To address the limited generalization and adaptability of existing models across heterogeneous tasks,we propose Abel-Net,an Aggregated Bilateral Edge Localization Network designed as a universal framework formulti-task binary segmentation.Abel-Net integrates global and local contextual cues to enhance feature learning and edge precision.Specifically,amulti-scale feature pyramid fusion strategy is implemented via anAggregated Skip Connection(ASC)module to strengthen feature adaptability,while the Edge Dual Localization(EDL)mechanism performs coarse-to-fine refinement through edge-aware supervision.Additionally,Edge Attention(EA)and Edge Fusion Attention(EFA)modules prioritize edge-critical regions and facilitate accurate boundary alignment.Extensive experiments on nine diverse binary segmentation tasks demonstrate thatAbel-Net performs comparably to or surpasses state-of-the-art task-specific networks,exhibiting strong adaptability to a wide range of visual perception challenges.展开更多
Due to the growth of smart cities,many real-time systems have been developed to support smart cities using Internet of Things(IoT)and emerging technologies.They are formulated to collect the data for environment monit...Due to the growth of smart cities,many real-time systems have been developed to support smart cities using Internet of Things(IoT)and emerging technologies.They are formulated to collect the data for environment monitoring and automate the communication process.In recent decades,researchers have made many efforts to propose autonomous systems for manipulating network data and providing on-time responses in critical operations.However,the widespread use of IoT devices in resource-constrained applications and mobile sensor networks introduces significant research challenges for cybersecurity.These systems are vulnerable to a variety of cyberattacks,including unauthorized access,denial-of-service attacks,and data leakage,which compromise the network’s security.Additionally,uneven load balancing between mobile IoT devices,which frequently experience link interferences,compromises the trustworthiness of the system.This paper introduces a Multi-Agent secured framework using lightweight edge computing to enhance cybersecurity for sensor networks,aiming to leverage artificial intelligence for adaptive routing and multi-metric trust evaluation to achieve data privacy and mitigate potential threats.Moreover,it enhances the efficiency of distributed sensors for energy consumption through intelligent data analytics techniques,resulting in highly consistent and low-latency network communication.Using simulations,the proposed framework reveals its significant performance compared to state-of-the-art approaches for energy consumption by 43%,latency by 46%,network throughput by 51%,packet loss rate by 40%,and denial of service attacks by 42%.展开更多
Underwater images often affect the effectiveness of underwater visual tasks due to problems such as light scattering,color distortion,and detail blurring,limiting their application performance.Existing underwater imag...Underwater images often affect the effectiveness of underwater visual tasks due to problems such as light scattering,color distortion,and detail blurring,limiting their application performance.Existing underwater image enhancement methods,although they can improve the image quality to some extent,often lead to problems such as detail loss and edge blurring.To address these problems,we propose FENet,an efficient underwater image enhancement method.FENet first obtains three different scales of images by image downsampling and then transforms them into the frequency domain to extract the low-frequency and high-frequency spectra,respectively.Then,a distance mask and a mean mask are constructed based on the distance and magnitude mean for enhancing the high-frequency part,thus improving the image details and enhancing the effect by suppressing the noise in the low-frequency part.Affected by the light scattering of underwater images and the fact that some details are lost if they are directly reduced to the spatial domain after the frequency domain operation.For this reason,we propose a multi-stage residual feature aggregation module,which focuses on detail extraction and effectively avoids information loss caused by global enhancement.Finally,we combine the edge guidance strategy to further enhance the edge details of the image.Experimental results indicate that FENet outperforms current state-of-the-art underwater image enhancement methods in quantitative and qualitative evaluations on multiple publicly available datasets.展开更多
For hypersonic air-breathing vehicles,the V-shaped leading edges(VSLEs)of supersonic combustion ramjet(scramjet)inlets experience complex shock interactions and intense aerodynamic loads.This paper provides a comprehe...For hypersonic air-breathing vehicles,the V-shaped leading edges(VSLEs)of supersonic combustion ramjet(scramjet)inlets experience complex shock interactions and intense aerodynamic loads.This paper provides a comprehensive review of flow characteristics at the crotch of VSLEs,with particular focus on the transition of shock interaction types and the variation of wall heat flux under different freestream Mach numbers and geometric configurations.The mechanisms governing shock transition,unsteady oscillations,hysteresis,and three-dimensional effects in VSLE flows are first examined.Subsequently,thermal protection strategies aimed at mitigating extreme heating loads are reviewed,emphasizing their relevance to practical engineering applications.Special attention is given to recent studies addressing thermochemical nonequilibrium effects on VSLE shock interactions,and the limitations of current research are critically assessed.Finally,perspectives for future investigations into hypersonic VSLE shock interactions are outlined,highlighting opportunities for advancing design and thermal management strategies.展开更多
With the large-scale deployment of the Internet ofThings(IoT)devices,their weak securitymechanisms make them prime targets for malware attacks.Attackers often use Domain Generation Algorithm(DGA)to generate random dom...With the large-scale deployment of the Internet ofThings(IoT)devices,their weak securitymechanisms make them prime targets for malware attacks.Attackers often use Domain Generation Algorithm(DGA)to generate random domain names,hiding the real IP of Command and Control(C&C)servers to build botnets.Due to the randomness and dynamics of DGA,traditional methods struggle to detect them accurately,increasing the difficulty of network defense.This paper proposes a lightweight DGA detection model based on knowledge distillation for resource-constrained IoT environments.Specifically,a teacher model combining CharacterBERT,a bidirectional long short-term memory(BiLSTM)network,and attention mechanism(ATT)is constructed:it extracts character-level semantic features viaCharacterBERT,captures sequence dependencieswith the BiLSTM,and integrates theATT for key feature weighting,formingmulti-granularity feature fusion.An improved knowledge distillation approach transfers the teacher model’s learned knowledge to the simplified DistilBERT student model.Experimental results show the teacher model achieves 98.68%detection accuracy.The student modelmaintains slightly improved accuracy while significantly compressing parameters to approximately 38.4%of the teacher model’s scale,greatly reducing computational overhead for IoT deployment.展开更多
UHMWPE(Ultra-High Molecular Weight Polyethylene)plain-weave fabric,characterized by its lightweight and high-strength properties,is widely used in protective equipment such as bulletproof vests and stab-resistant vest...UHMWPE(Ultra-High Molecular Weight Polyethylene)plain-weave fabric,characterized by its lightweight and high-strength properties,is widely used in protective equipment such as bulletproof vests and stab-resistant vests,serving as a key material for enhancing protective performance.This study systematically investigates the influence mechanism of interfacial properties on the energy absorption characteristics of UHMWPE-based protective structures through multi-scale experiments and numerical simulations,and establishes a cross-scale design methodology.Innovatively,an orthotropic constitutive model incorporating dynamic friction coefficients is constructed,combined with a modified Johnson-Cook failure criterion,to achieve high-precision simulation of the entire ballistic impact process(error<3.5%).Additionally,a friction field prediction model considering strain rate effects is developed,and the friction evolution laws of UHMWPE and Para-aramid(Kevlar)fabrics under strain rates of 10^(−3) and 10^(−4) s^(−1) are obtained through MTS pull-out tests.The results show that:(1)there exists a critical yarn-yarn friction coefficient(μ=0.2);exceeding this value leads to a 19%reduction in energy absorption capacity,while viscous interfaces increase the energy dissipation peak by 16%;(2)UHMWPE shows kinetically-dominated absorption(58%)with high rate but high load,increasing damage risk.Para-aramid has friction-dominated absorption(53%)with a lower rate but stable load.Hybrid fabrics use potential-dominated absorption(49%)at a moderate rate,balancing stability and protection.(3)3–5 layers of UHMWPE fabric yield optimal cost-effectiveness,with the unit cost reduction rate of the HS+5U scheme reaching 2.74 m/(s·$),which is 91%higher than that of the hybrid scheme.(4)For HS+5U(5-ply UHMWPE),V50 is 520 m/s,meeting primary protection requirement.For hybrid solutions with U/K≥3(e.g.,HS+6U+2K),V50 reaches 580 m/s(≥540 m/s),satisfying advanced protection requirement.This research provides critical references for the design of flexible protective structures and their engineering applications.展开更多
With the rapid development of power Internet of Things(IoT)scenarios such as smart factories and smart homes,numerous intelligent terminal devices and real-time interactive applications impose higher demands on comput...With the rapid development of power Internet of Things(IoT)scenarios such as smart factories and smart homes,numerous intelligent terminal devices and real-time interactive applications impose higher demands on computing latency and resource supply efficiency.Multi-access edge computing technology deploys cloud computing capabilities at the network edge;constructs distributed computing nodes and multi-access systems and offers infrastructure support for services with low latency and high reliability.Existing research relies on a strong assumption that the environmental state is fully observable and fails to thoroughly consider the continuous time-varying features of edge server load fluctuations,leading to insufficient adaptability of the model in a heterogeneous dynamic environment.Thus,this paper establishes a framework for end-edge collaborative task offloading based on a partially observable Markov decision-making process(POMDP)and proposes a method for end-edge collaborative task offloading in heterogeneous scenarios.It achieves time-series modeling of the historical load characteristics of edge servers and endows the agent with the ability to be aware of the load in dynamic environmental states.Moreover,by dynamically assessing the exploration value of historical trajectories in the central trajectory pool and adjusting the sample weight distribution,directional exploration and strategy optimization of high-value trajectories are realized.Experimental results indicate that the proposed method exhibits distinct advantages compared with existing methods in terms of average delay and task failure rate and also verifies the method’s robustness in a dynamic environment.展开更多
There has been an increasing emphasis on performing deep neural network(DNN)inference locally on edge devices due to challenges such as network congestion and security concerns.However,as DRAM process technology conti...There has been an increasing emphasis on performing deep neural network(DNN)inference locally on edge devices due to challenges such as network congestion and security concerns.However,as DRAM process technology continues to scale down,the bit-flip errors in the memory of edge devices become more frequent,thereby leading to substantial DNN inference accuracy loss.Though several techniques have been proposed to alleviate the accuracy loss in edge environments,they require complex computations and additional parity bits for error correction,thus resulting in significant performance and storage overheads.In this paper,we propose FeatherGuard,a data-driven lightweight error protection scheme for DNN inference on edge devices.FeatherGuard selectively protects critical bit positions(that have a significant impact on DNN inference accuracy)against bit-flip errors,by considering various DNN characteristics(e.g.,data format,layer-wise weight distribution,actually stored logical values).Thus,it achieves high error tolerability during DNN inference.Since FeatherGuard reduces the bit-flip errors based on only a few simple arithmetic operations(e.g.,NOT operations)without parity bits,it causes negligible performance overhead and no storage overhead.Our experimental results show that FeatherGuard improves the error tolerability by up to 6667×and 4000×,compared to the conventional systems and the state-of-the-art error protection technique for edge environments,respectively.展开更多
Industrial operators need reliable communication in high-noise,safety-critical environments where speech or touch input is often impractical.Existing gesture systems either miss real-time deadlines on resourceconstrai...Industrial operators need reliable communication in high-noise,safety-critical environments where speech or touch input is often impractical.Existing gesture systems either miss real-time deadlines on resourceconstrained hardware or lose accuracy under occlusion,vibration,and lighting changes.We introduce Industrial EdgeSign,a dual-path framework that combines hardware-aware neural architecture search(NAS)with large multimodalmodel(LMM)guided semantics to deliver robust,low-latency gesture recognition on edge devices.The searched model uses a truncated ResNet50 front end,a dimensional-reduction network that preserves spatiotemporal structure for tubelet-based attention,and localized Transformer layers tuned for on-device inference.To reduce reliance on gloss annotations and mitigate domain shift,we distill semantics from factory-tuned vision-language models and pre-train with masked language modeling and video-text contrastive objectives,aligning visual features with a shared text space.OnML2HP and SHREC’17,theNAS-derived architecture attains 94.7% accuracywith 86ms inference latency and about 5.9W power on Jetson Nano.Under occlusion,lighting shifts,andmotion blur,accuracy remains above 82%.For safetycritical commands,the emergency-stop gesture achieves 72 ms 99th percentile latency with 99.7% fail-safe triggering.Ablation studies confirm the contribution of the spatiotemporal tubelet extractor and text-side pre-training,and we observe gains in translation quality(BLEU-422.33).These results show that Industrial EdgeSign provides accurate,resource-aware,and safety-aligned gesture recognition suitable for deployment in smart factory settings.展开更多
基金supported by the National Natural Science Foundation of China(Grant Nos.12474421 and 12104066)the Fund from Education Department of Jilin Province(Grant Nos.JJKH20250473KJ and JJKH20241413KJ)the Fund from Department of Science and Technology of Jilin Province(Grant No.YDZJ202101ZYTS041)。
文摘Insight into exciton dynamics of two-dimensional(2D)transition metal dichalcogenides(TMDs)is critical for the optimization of their performance in photonic and optoelectronic devices.Although current researches have primarily concentrated on the near-resonant excitation scenario in 2D TMDs,the case of excitation energies resonating with highenergy excitons or higher energies has yet to be fully elucidated.Here,a comparative analysis is conducted between highenergy excitation(360 nm)and near-resonant excitation(515 nm)utilizing transient absorption spectroscopy to achieve a comprehensive understanding of the exciton dynamics within monolayer WS_(2).It is observed that the high-energy C-exciton can be generated via an up-conversion process under 515 nm excitation,even the energy of which is less than that of the C-exciton.Furthermore,the capacity to efficiently occupy band-edge A-exciton states leads to longer lifetimes for both the C-excitons and the A-excitons under conditions of near-resonant excitation,accompanied by an augmented rate of radiative recombination.This study provides a paradigm for optimizing the performance of 2D TMDs-based devices by offering valuable insights into their exciton dynamics.
基金support from the National Key Projects for Research and Development of China(Grant Nos.2022YFA1204700,2021YFA1400400)National Natural Science Foundation of China(Grant No.12525403)+3 种基金Natural Science Foundation of Jiangsu Province(Grant Nos.BK20220066,BK20233001)Program for Innovative Talents and Entrepreneur in Jiangsu(Grant No.JSSCTD202101)support from the JSPS KAKENHI(Grant Numbers 21H05233 and 23H02052)World Premier International Research Center Initiative(WPI),MEXT,Japan.
文摘Coulomb drag refers to the phenomenon in which a current driven through one conducting layer induces a voltage nearby,electrically isolated layer sorely through interlayer Coulomb interactions between charge carriers.It has been extensively studied in various systems,including parallel nanowires,double quantum wells,and double-layer graphene.Here,we report the observation of Coulomb drag in a novel system consisting of two graphene layers separated laterally by a 30 nm gap within the material plane,exhibiting behavior distinct from that in vertical graphene heterostructures.Our experiments reveal pronounced negative drag resistances under an out-of-plane magnetic field at the quantum Hall edges,reaching a maximum when the carrier densities in both graphene layers are tuned to the charge neutrality point via gate voltages.Our work establish two separate and spatially closed quantum Hall edge modes as a new platform to explore electronic interaction physics between one dimensional systems.
文摘The exponential growth of Internet of Things(IoT)devices,autonomous systems,and digital services is generating massive volumes of big data,projected to exceed 291 zettabytes by 2027.Conventional cloud computing,despite its high processing and storage capacity,suffers from increased network latency,network congestion,and high operational costs,making it unsuitable for latency-sensitive applications.Edge computing addresses these issues by processing data near the source but faces scalability challenges and elevated Total Cost of Ownership(TCO).Hybrid solutions,such as fog computing,cloudlets,and Mobile Edge Computing(MEC),attempt to balance cost and performance;however,they still struggle with limited resource sharing and high deployment expenses.This paper proposes Public Edge as a Service(PEaaS),a novel paradigm that utilizes idle resources contributed by universities,enterprises,cellular operators,and individuals under a collaborative service model.By decentralizing computation and enabling multi-tenant resource sharing,PEaaS reduces reliance on centralized cloud infrastructure,minimizes communication costs,and enhances scalability.The proposed framework is evaluated using EdgeCloudSim under varying workloads,for keymetrics such as latency,communication cost,server utilization,and task failure rate.Results reveal that while cloud has a task failure rate rising sharply to 12.3%at 2000 devices,PEaaS maintains a low rate of 2.5%,closely matching edge computing.Furthermore,communication costs remain 25% lower than cloud and latency remains below 0.3,even under peak load.These findings demonstrate that PEaaS achieves near-edge performance with reduced costs and enhanced scalability,offering a sustainable and economically viable solution for next-generation computing environments.
基金financially supported by National Key Research and Development Program of China (No.2021YFD1800900)National Natural Science Foundation of China (No.82073790)+2 种基金Special Fund for Youth Team of Southwest University (No.SWUXJLJ202306)Chongqing Science and Technology Commission (Nos.CSTB2022TIAD-LUX0001,CSTB2023NSCQ-JQX0002)Innovation Research 2035 Pilot Plan of Southwest University (No.SWUXDPY22007)。
文摘Chitosan(CS),a natural polymer derived from chitin found in the exoskeletons of crustaceans,has garnered significant interest in the pharmaceutical field due to its unique properties,including biocompatibility and biodegradability.In recent years,various studies have reported that CS can affect drug bioavailability,and interestingly,it works as an oral absorption enhancer and inhibitor.This review offers an in-depth analysis of the mechanisms underlying such a phenomenon and supports its application as a pharmaceutical excipient.CS enhances oral drug absorption through various mechanisms,such as interaction with the intestinal mucosa,tight junction modulation,inhibition of efflux transporters,enzyme inhibition,solubility and stability enhancement,and complexation.On the other side,CS exhibits the ability to inhibit the absorption of certain drugs by adsorbing to lipids and sterols,modulating bile acids and gut microbiota,altering drug-cell interaction at the polar interface,and mucus-mediated entrapment and interference.Future potential pharmaceutical research in this field includes elucidating the underneath absorption relevant mechanisms,rational use in formulations as excipient,exploring functional CS derivatives,and developing CS-based drug delivery systems.This comprehensive review highlights CS's versatile and significant role in enhancing and inhibiting oral drug absorption,providing insights into the complexities of drug delivery and the potential of CS to improve therapeutic outcomes.
文摘Using the pulp of Annona squamosa L.as the raw material,polysaccharides were extracted using ultrasonicassisted hot water extraction,with polysaccharide yield as the response variable.The effects of four factors on extraction efficiency were investigated,and the extraction process was optimized using an orthogonal experimental design.The monosaccharide composition of the polysaccharides was analyzed using ion chromatography(IC).The antioxidant activity was evaluated through DPPH radical scavenging,hydroxyl radical(·OH)scavenging,and ferric reducing antioxidant power(FRAP)assays.Additionally,Franz diffusion cells were used to conduct in vitro transdermal absorption experiments,measuring cumulative permeation and calculating permeation kinetics parameters.The results showed that under optimized extraction conditions,the polysaccharide yield was 50.57%±0.02%.Ion chromatography analysis revealed that the main monosaccharide components were arabinose(0.6%),glucose(40.5%),and fructose(58.9%).Antioxidant evaluation indicated that with the addition of 80μL of the extract,the DPPH radical scavenging rate reached 94.13%±0.81%;with 600μL,the hydroxyl radical scavenging rate was 93.33%±0.92%,and the FRAP value was(38.07±1.30)mmol/L.Transdermal absorption experiments demonstrated that the skin retention rates of the polysaccharide aqueous extract at 75%and 100%concentrations were 4.65%±1.52%and 12.37%±2.14%,respectively,with 12 h cumulative permeation rates of 57.43%±1.08%and 74.24%±0.74%.Kinetic analysis of transdermal absorption showed that the process followed zero-order kinetics,indicating a constant release rate under steady-state conditions,consistent with Fick’s diffusion law.This study systematically combined optimization of polysaccharide aqueous extraction,monosaccharide composition analysis,antioxidant activity evaluation,and transdermal permeation performance analysis.It revealed the bioactive properties and potential applications of Annona squamosa L.polysaccharides,particularly in the fields of antioxidant activity and transdermal permeation.The findings provide essential data to support the development of functional products based on Annona squamosa L.polysaccharides.
基金support provided by the Center for Fabrication and Application of Electronic Materials at Dokuz Eylül University,Türkiye。
文摘Biochar and biochar composites are versatile materials that can be used in many applications.In this study,biochar was prepared from sawdust and combined with the yttrium iron garnet(YIG)nanocrystal to investigate the shielding effectiveness of the composite structure.Firstly,the effect of the pyrolysis temperature on the shielding effectiveness of biochar was investigated.Secondly,biochars combined with YIG nanocrystals with different contents and shielding effectiveness of the composites were investigated.The electromagnetic effectiveness of the samples was investigated within the X band(8-12 GHz).The findings indicate that biochar demonstrates enhanced absorption properties with elevated pyrolysis temperatures.Biochars demonstrated an approximate 40 d B shielding effectiveness,while YIG exhibited approximately 7 d B,corresponding to absorption at 8 GHz.However,the combination of biochar and YIG exhibited exceptional absorption,reaching 67.12 d B at 8 GHz.
基金supported by the National Natural Science Foundation of China 62402171.
文摘As a fundamental component in computer vision,edges can be categorized into four types based on discontinuities in reflectance,illumination,surface normal,or depth.While deep CNNs have significantly advanced generic edge detection,real-time multi-class semantic edge detection under resource constraints remains challenging.To address this,we propose a lightweight framework based on PiDiNet that enables fine-grained semantic edge detection.Our model simultaneously predicts background and four edge categories from full-resolution inputs,balancing accuracy and efficiency.Key contributions include:a multi-channel output structure expanding binary edge prediction to five classes,supported by a deep supervision mechanism;a dynamic class-balancing strategy combining adaptive weighting with physical priors to handle extreme class imbalance;and maintained architectural efficiency enabling real-time inference.Extensive evaluations on BSDS-RIND show our approach achieves accuracy competitive with state-of-the-art methods while operating in real time.
基金supported by the IITP(Institute for Information&Communications Technology Planning&Evaluation)under the ITRC(Information Technology Research Center)support program(IITP-2025-RS-2024-00438288)grant funded by the Korea government(MSIT)+1 种基金National Research Council of Science&Technology(NST)grant by the MSIT(Aerospace Semiconductor Strategy Research Project No.GTL25051-000)supported by the IC Design Education Center(IDEC),Korea。
文摘This work presents a systematic analysis of proton-induced total ionizing dose(TID)effects in 1.2 k V silicon carbide(SiC)power devices with various edge termination structures.Three edge terminations including ring-assisted junction termination extension(RA-JTE),multiple floating zone JTE(MFZ-JTE),and field limiting rings(FLR)were fabricated and irradiated with45 Me V protons at fluences ranging from 1×10^(12) to 1×10^(14) cm^(-2).Experimental results,supported by TCAD simulations,show that the RA-JTE structure maintained stable breakdown performance with less than 1%variation due to its effective electric field redistribution by multiple P+rings.In contrast,MFZ-JTE and FLR exhibit breakdown voltage shifts of 6.1%and 15.2%,respectively,under the highest fluence.These results demonstrate the superior radiation tolerance of the RA-JTE structure under TID conditions and provide practical design guidance for radiation-hardened Si C power devices in space and other highradiation environments.
基金supported by Basic Science Research Program to Research Institute for Basic Sciences(RIBS)of Jeju National University through the National Research Foundation of Korea(NRF)funded by the Ministry of Education(RS-2019-NR040080)This research was also carried out with the support of the Jeju RISE Center,funded by the Ministry of Education and Jeju Special Self-Governing Province in 2025,as part of the“Regional Innovation System&Education(RISE):Glocal University 30”initiative.
文摘Given the growing number of vehicle accidents caused by unintended acceleration and braking failure,verifying Sudden Unintended Acceleration(SUA)incidents has become a persistent challenge.A central issue of debate is whether such events stem frommechanical malfunctions or driver pedalmisapplications.However,existing verification procedures implemented by vehiclemanufacturers often involve closed tests after vehicle recalls;thus raising ongoing concerns about reliability and transparency.Consequently,there is a growing need for a user-driven framework that enables independent data acquisition and verification.Although previous studies have addressed SUA detection using deep learning,few have explored howclass granularity optimization affects power efficiency and inference performance in real-time Edge AI systems.To address this problem,this work presents a cloud-assisted artificial intelligence(AI)solution for the reliable verification of SUA occurrences.The proposed system integrates multimodal sensor streams including camera-based foot images,On-Board Diagnostics II(OBD-II)signals,and six-axismeasurements to determine whether the brake pedal was actually engaged at themoment of a suspected SUA.Beyond image acquisition,convolutional neural network(CNN)models perform real-time inference to classify the driver’s pedal operation states with the resulting outputs transmitted and archived in the cloud.A dedicated dataset of brake and accelerator pedal images was collected from 15 vehicles produced by 6 domestic and international manufacturers.Using this dataset,transfer learning techniques were applied to compare and analyze model performance and generalization as the CNN class granularity varied from coarse to fine levels.Furthermore,classification performance was evaluated in terms of latency and power efficiency under different class configurations.The experimental results demonstrated that the proposed solution identified the driver’s pedal behavior accurately and promptly,with the two-class model achieving the highest F1-score and accuracy among all granularity settings.
基金supported by the National Natural Science Foundation of China(Grant Nos.U2032136 and U2241288)the Shaanxi Fundamental Science Research Project for Mathematics and Physics(Grant No.23JSY012)。
文摘We present a solid 226 nm deep ultraviolet laser system pumped by a Nd:YAG laser.A diamond Raman laser with a 1485 nm wavelength was generated up to 2.53 mJ pumped by a 9.7 mJ 1064 nm laser,which is the highest pulse energy of a second Stokes diamond Raman laser pumped by a 1064 nm laser as we know.Then,the Raman laser is mixed with the frequency-quadrupled 1064 nm laser to produce the 226 nm laser.The maximum output pulse energy at 226 nm reaches 0.49 mJ.The overall conversion efficiency from1064 to 226 nm is up to 1.14%,which is significantly higher than conventional optical parametric oscillator technology for the generation of 226 nm laser.The 226 nm laser system has been used in a laser-induced fluorescence(LIF)experiment of oxygen two-photon to demonstrate its potential for LIF measurements.
基金supported by the National Natural Science Foundation of China(Grant Nos.62572057,62272049,U24A20331)Beijing Natural Science Foundation(Grant Nos.4232026,4242020)Academic Research Projects of Beijing Union University(Grant No.ZK10202404).
文摘Traffic sign detection is a critical component of driving systems.Single-stage network-based traffic sign detection algorithms,renowned for their fast detection speeds and high accuracy,have become the dominant approach in current practices.However,in complex and dynamic traffic scenes,particularly with smaller traffic sign objects,challenges such as missed and false detections can lead to reduced overall detection accuracy.To address this issue,this paper proposes a detection algorithm that integrates edge and shape information.Recognizing that traffic signs have specific shapes and distinct edge contours,this paper introduces an edge feature extraction branch within the backbone network,enabling adaptive fusion with features of the same hierarchical level.Additionally,a shape prior convolution module is designed to replaces the first two convolutional modules of the backbone network,aimed at enhancing the model's perception ability for specific shape objects and reducing its sensitivity to background noise.The algorithm was evaluated on the CCTSDB and TT100k datasets,and compared to YOLOv8s,the mAP50 values increased by 3.0%and 10.4%,respectively,demonstrating the effectiveness of the proposed method in improving the accuracy of traffic sign detection.
基金supported by the National Natural Science Foundation of China under Grant 62206083.
文摘Binary segmentation tasks in computer vision exhibit diverse appearance distributions and complex boundary characteristics.To address the limited generalization and adaptability of existing models across heterogeneous tasks,we propose Abel-Net,an Aggregated Bilateral Edge Localization Network designed as a universal framework formulti-task binary segmentation.Abel-Net integrates global and local contextual cues to enhance feature learning and edge precision.Specifically,amulti-scale feature pyramid fusion strategy is implemented via anAggregated Skip Connection(ASC)module to strengthen feature adaptability,while the Edge Dual Localization(EDL)mechanism performs coarse-to-fine refinement through edge-aware supervision.Additionally,Edge Attention(EA)and Edge Fusion Attention(EFA)modules prioritize edge-critical regions and facilitate accurate boundary alignment.Extensive experiments on nine diverse binary segmentation tasks demonstrate thatAbel-Net performs comparably to or surpasses state-of-the-art task-specific networks,exhibiting strong adaptability to a wide range of visual perception challenges.
基金supported by the Deanship of Graduate Studies and Scientific Research at Jouf University.
文摘Due to the growth of smart cities,many real-time systems have been developed to support smart cities using Internet of Things(IoT)and emerging technologies.They are formulated to collect the data for environment monitoring and automate the communication process.In recent decades,researchers have made many efforts to propose autonomous systems for manipulating network data and providing on-time responses in critical operations.However,the widespread use of IoT devices in resource-constrained applications and mobile sensor networks introduces significant research challenges for cybersecurity.These systems are vulnerable to a variety of cyberattacks,including unauthorized access,denial-of-service attacks,and data leakage,which compromise the network’s security.Additionally,uneven load balancing between mobile IoT devices,which frequently experience link interferences,compromises the trustworthiness of the system.This paper introduces a Multi-Agent secured framework using lightweight edge computing to enhance cybersecurity for sensor networks,aiming to leverage artificial intelligence for adaptive routing and multi-metric trust evaluation to achieve data privacy and mitigate potential threats.Moreover,it enhances the efficiency of distributed sensors for energy consumption through intelligent data analytics techniques,resulting in highly consistent and low-latency network communication.Using simulations,the proposed framework reveals its significant performance compared to state-of-the-art approaches for energy consumption by 43%,latency by 46%,network throughput by 51%,packet loss rate by 40%,and denial of service attacks by 42%.
基金supported in part by the National Natural Science Foundation of China[Grant number 62471075]the Major Science and Technology Project Grant of the Chongqing Municipal Education Commission[Grant number KJZD-M202301901].
文摘Underwater images often affect the effectiveness of underwater visual tasks due to problems such as light scattering,color distortion,and detail blurring,limiting their application performance.Existing underwater image enhancement methods,although they can improve the image quality to some extent,often lead to problems such as detail loss and edge blurring.To address these problems,we propose FENet,an efficient underwater image enhancement method.FENet first obtains three different scales of images by image downsampling and then transforms them into the frequency domain to extract the low-frequency and high-frequency spectra,respectively.Then,a distance mask and a mean mask are constructed based on the distance and magnitude mean for enhancing the high-frequency part,thus improving the image details and enhancing the effect by suppressing the noise in the low-frequency part.Affected by the light scattering of underwater images and the fact that some details are lost if they are directly reduced to the spatial domain after the frequency domain operation.For this reason,we propose a multi-stage residual feature aggregation module,which focuses on detail extraction and effectively avoids information loss caused by global enhancement.Finally,we combine the edge guidance strategy to further enhance the edge details of the image.Experimental results indicate that FENet outperforms current state-of-the-art underwater image enhancement methods in quantitative and qualitative evaluations on multiple publicly available datasets.
基金funded by the Research Fund of National Key Laboratory of Aerospace Physics in Fluids,grant number 2024-APF-KFZD-01Guangdong Basic and Applied Basic Research Foundation,grant number 2025A1515012081+1 种基金National Natural Science Foundation of China,grant number 12002193Shandong Provincial Natural Science Foundation,China,grant number ZR2019QA018.
文摘For hypersonic air-breathing vehicles,the V-shaped leading edges(VSLEs)of supersonic combustion ramjet(scramjet)inlets experience complex shock interactions and intense aerodynamic loads.This paper provides a comprehensive review of flow characteristics at the crotch of VSLEs,with particular focus on the transition of shock interaction types and the variation of wall heat flux under different freestream Mach numbers and geometric configurations.The mechanisms governing shock transition,unsteady oscillations,hysteresis,and three-dimensional effects in VSLE flows are first examined.Subsequently,thermal protection strategies aimed at mitigating extreme heating loads are reviewed,emphasizing their relevance to practical engineering applications.Special attention is given to recent studies addressing thermochemical nonequilibrium effects on VSLE shock interactions,and the limitations of current research are critically assessed.Finally,perspectives for future investigations into hypersonic VSLE shock interactions are outlined,highlighting opportunities for advancing design and thermal management strategies.
基金supported by the following projects:National Natural Science Foundation of China(62461041)Natural Science Foundation of Jiangxi Province China(20242BAB25068).
文摘With the large-scale deployment of the Internet ofThings(IoT)devices,their weak securitymechanisms make them prime targets for malware attacks.Attackers often use Domain Generation Algorithm(DGA)to generate random domain names,hiding the real IP of Command and Control(C&C)servers to build botnets.Due to the randomness and dynamics of DGA,traditional methods struggle to detect them accurately,increasing the difficulty of network defense.This paper proposes a lightweight DGA detection model based on knowledge distillation for resource-constrained IoT environments.Specifically,a teacher model combining CharacterBERT,a bidirectional long short-term memory(BiLSTM)network,and attention mechanism(ATT)is constructed:it extracts character-level semantic features viaCharacterBERT,captures sequence dependencieswith the BiLSTM,and integrates theATT for key feature weighting,formingmulti-granularity feature fusion.An improved knowledge distillation approach transfers the teacher model’s learned knowledge to the simplified DistilBERT student model.Experimental results show the teacher model achieves 98.68%detection accuracy.The student modelmaintains slightly improved accuracy while significantly compressing parameters to approximately 38.4%of the teacher model’s scale,greatly reducing computational overhead for IoT deployment.
基金the Postdoctoral Science Foundation Funded Project of China with grant No.2021M701687Introduction and Education Plan for Young Innovative Talents in Colleges and Universities of Shandong Province.
文摘UHMWPE(Ultra-High Molecular Weight Polyethylene)plain-weave fabric,characterized by its lightweight and high-strength properties,is widely used in protective equipment such as bulletproof vests and stab-resistant vests,serving as a key material for enhancing protective performance.This study systematically investigates the influence mechanism of interfacial properties on the energy absorption characteristics of UHMWPE-based protective structures through multi-scale experiments and numerical simulations,and establishes a cross-scale design methodology.Innovatively,an orthotropic constitutive model incorporating dynamic friction coefficients is constructed,combined with a modified Johnson-Cook failure criterion,to achieve high-precision simulation of the entire ballistic impact process(error<3.5%).Additionally,a friction field prediction model considering strain rate effects is developed,and the friction evolution laws of UHMWPE and Para-aramid(Kevlar)fabrics under strain rates of 10^(−3) and 10^(−4) s^(−1) are obtained through MTS pull-out tests.The results show that:(1)there exists a critical yarn-yarn friction coefficient(μ=0.2);exceeding this value leads to a 19%reduction in energy absorption capacity,while viscous interfaces increase the energy dissipation peak by 16%;(2)UHMWPE shows kinetically-dominated absorption(58%)with high rate but high load,increasing damage risk.Para-aramid has friction-dominated absorption(53%)with a lower rate but stable load.Hybrid fabrics use potential-dominated absorption(49%)at a moderate rate,balancing stability and protection.(3)3–5 layers of UHMWPE fabric yield optimal cost-effectiveness,with the unit cost reduction rate of the HS+5U scheme reaching 2.74 m/(s·$),which is 91%higher than that of the hybrid scheme.(4)For HS+5U(5-ply UHMWPE),V50 is 520 m/s,meeting primary protection requirement.For hybrid solutions with U/K≥3(e.g.,HS+6U+2K),V50 reaches 580 m/s(≥540 m/s),satisfying advanced protection requirement.This research provides critical references for the design of flexible protective structures and their engineering applications.
基金funded by the State Grid Corporation Science and Technology Project“Research and Application of Key Technologies for Integrated Sensing and Computing for Intelligent Operation of Power Grid”(Grant No.5700-202318596A-3-2-ZN).
文摘With the rapid development of power Internet of Things(IoT)scenarios such as smart factories and smart homes,numerous intelligent terminal devices and real-time interactive applications impose higher demands on computing latency and resource supply efficiency.Multi-access edge computing technology deploys cloud computing capabilities at the network edge;constructs distributed computing nodes and multi-access systems and offers infrastructure support for services with low latency and high reliability.Existing research relies on a strong assumption that the environmental state is fully observable and fails to thoroughly consider the continuous time-varying features of edge server load fluctuations,leading to insufficient adaptability of the model in a heterogeneous dynamic environment.Thus,this paper establishes a framework for end-edge collaborative task offloading based on a partially observable Markov decision-making process(POMDP)and proposes a method for end-edge collaborative task offloading in heterogeneous scenarios.It achieves time-series modeling of the historical load characteristics of edge servers and endows the agent with the ability to be aware of the load in dynamic environmental states.Moreover,by dynamically assessing the exploration value of historical trajectories in the central trajectory pool and adjusting the sample weight distribution,directional exploration and strategy optimization of high-value trajectories are realized.Experimental results indicate that the proposed method exhibits distinct advantages compared with existing methods in terms of average delay and task failure rate and also verifies the method’s robustness in a dynamic environment.
基金the“Convergence and Open sharing System”Project,supported by the Ministry of Education and National Research Foundation of Korea.
文摘There has been an increasing emphasis on performing deep neural network(DNN)inference locally on edge devices due to challenges such as network congestion and security concerns.However,as DRAM process technology continues to scale down,the bit-flip errors in the memory of edge devices become more frequent,thereby leading to substantial DNN inference accuracy loss.Though several techniques have been proposed to alleviate the accuracy loss in edge environments,they require complex computations and additional parity bits for error correction,thus resulting in significant performance and storage overheads.In this paper,we propose FeatherGuard,a data-driven lightweight error protection scheme for DNN inference on edge devices.FeatherGuard selectively protects critical bit positions(that have a significant impact on DNN inference accuracy)against bit-flip errors,by considering various DNN characteristics(e.g.,data format,layer-wise weight distribution,actually stored logical values).Thus,it achieves high error tolerability during DNN inference.Since FeatherGuard reduces the bit-flip errors based on only a few simple arithmetic operations(e.g.,NOT operations)without parity bits,it causes negligible performance overhead and no storage overhead.Our experimental results show that FeatherGuard improves the error tolerability by up to 6667×and 4000×,compared to the conventional systems and the state-of-the-art error protection technique for edge environments,respectively.
文摘Industrial operators need reliable communication in high-noise,safety-critical environments where speech or touch input is often impractical.Existing gesture systems either miss real-time deadlines on resourceconstrained hardware or lose accuracy under occlusion,vibration,and lighting changes.We introduce Industrial EdgeSign,a dual-path framework that combines hardware-aware neural architecture search(NAS)with large multimodalmodel(LMM)guided semantics to deliver robust,low-latency gesture recognition on edge devices.The searched model uses a truncated ResNet50 front end,a dimensional-reduction network that preserves spatiotemporal structure for tubelet-based attention,and localized Transformer layers tuned for on-device inference.To reduce reliance on gloss annotations and mitigate domain shift,we distill semantics from factory-tuned vision-language models and pre-train with masked language modeling and video-text contrastive objectives,aligning visual features with a shared text space.OnML2HP and SHREC’17,theNAS-derived architecture attains 94.7% accuracywith 86ms inference latency and about 5.9W power on Jetson Nano.Under occlusion,lighting shifts,andmotion blur,accuracy remains above 82%.For safetycritical commands,the emergency-stop gesture achieves 72 ms 99th percentile latency with 99.7% fail-safe triggering.Ablation studies confirm the contribution of the spatiotemporal tubelet extractor and text-side pre-training,and we observe gains in translation quality(BLEU-422.33).These results show that Industrial EdgeSign provides accurate,resource-aware,and safety-aligned gesture recognition suitable for deployment in smart factory settings.
