As an important complement to cloud computing, edge computing can effectively reduce the workload of the backbone network. To reduce latency and energy consumption of edge computing, deep learning is used to learn the...As an important complement to cloud computing, edge computing can effectively reduce the workload of the backbone network. To reduce latency and energy consumption of edge computing, deep learning is used to learn the task offloading strategies by interacting with the entities. In actual application scenarios, users of edge computing are always changing dynamically. However, the existing task offloading strategies cannot be applied to such dynamic scenarios. To solve this problem, we propose a novel dynamic task offloading framework for distributed edge computing, leveraging the potential of meta-reinforcement learning (MRL). Our approach formulates a multi-objective optimization problem aimed at minimizing both delay and energy consumption. We model the task offloading strategy using a directed acyclic graph (DAG). Furthermore, we propose a distributed edge computing adaptive task offloading algorithm rooted in MRL. This algorithm integrates multiple Markov decision processes (MDP) with a sequence-to-sequence (seq2seq) network, enabling it to learn and adapt task offloading strategies responsively across diverse network environments. To achieve joint optimization of delay and energy consumption, we incorporate the non-dominated sorting genetic algorithm II (NSGA-II) into our framework. Simulation results demonstrate the superiority of our proposed solution, achieving a 21% reduction in time delay and a 19% decrease in energy consumption compared to alternative task offloading schemes. Moreover, our scheme exhibits remarkable adaptability, responding swiftly to changes in various network environments.展开更多
A new method based on the iterative adaptive algorithm(IAA)and blocking matrix preprocessing(BMP)is proposed to study the suppression of multi-mainlobe interference.The algorithm is applied to precisely estimate the s...A new method based on the iterative adaptive algorithm(IAA)and blocking matrix preprocessing(BMP)is proposed to study the suppression of multi-mainlobe interference.The algorithm is applied to precisely estimate the spatial spectrum and the directions of arrival(DOA)of interferences to overcome the drawbacks associated with conventional adaptive beamforming(ABF)methods.The mainlobe interferences are identified by calculating the correlation coefficients between direction steering vectors(SVs)and rejected by the BMP pretreatment.Then,IAA is subsequently employed to reconstruct a sidelobe interference-plus-noise covariance matrix for the preferable ABF and residual interference suppression.Simulation results demonstrate the excellence of the proposed method over normal methods based on BMP and eigen-projection matrix perprocessing(EMP)under both uncorrelated and coherent circumstances.展开更多
Covert communication guarantees the security of wireless communications via hiding the existence of the transmission.This paper focuses on the first and second order asymptotics of covert communication in the AWGN cha...Covert communication guarantees the security of wireless communications via hiding the existence of the transmission.This paper focuses on the first and second order asymptotics of covert communication in the AWGN channels.The covertness is measured by the total variation distance between the channel output distributions induced with and without the transmission.We provide the exact expressions of the maximum amount of information that can be transmitted with the maximum error probability and the total variation less than any small numbers.The energy detection and the random coding are employed to prove our results.We further compare our results with those under relative entropy.The results show how many additional amounts of information can be transmitted covertly when changing the covertness constraint to total variation.展开更多
In the field of specific emitter identification(SEI),power amplifiers(PAs)have long been recognized as significant contributors to unintentional modulation characteristics.To enhance signal quality,digital pre-distort...In the field of specific emitter identification(SEI),power amplifiers(PAs)have long been recognized as significant contributors to unintentional modulation characteristics.To enhance signal quality,digital pre-distortion(DPD)techniques are commonly employed in practical applications to mitigate the nonlinear effects of PAs.However,DPD techniques may diminish the distinctive characteristics of individual transmitters,potentially compromising SEI performance.This study investigates the influence of SEI in the presence of DPD applied to PAs.We construct a semi-physical emitter platform using AD9361 and ZYNQ,incorporating memory and non-memory models to emulate an amplification system comprising DPD devices and PAs.Furthermore,we delve into the analysis and evaluation of LMS-based and QRDRLS-based DPD algorithms to ascertain their efficacy in compensating for amplifier nonlinearity.Finally,we conduct a comprehensive set of experiments to demonstrate the adverse impact of DPD techniques on SEI.Our findings demonstrate a direct correlation between the degree of DPD performance and its impact magnitude on SEI,thereby providing a foundational basis for future studies investigating SEI techniques under DPD.展开更多
Hermetically Sealed Electromagnetic Relay(HSER), used in aviation and aerospace,demands high reliability due to its critical applications. Given its complex operating conditions, efficient thermal analysis is essentia...Hermetically Sealed Electromagnetic Relay(HSER), used in aviation and aerospace,demands high reliability due to its critical applications. Given its complex operating conditions, efficient thermal analysis is essential for optimizing reliability. The commonly used Finite Element Method(FEM) is often time-consuming and may not be efficient or adaptable for complex multi-dimensional system calculations and design processes. This paper introduces an analysis method for thermal networks based on matrix perspective technology, encompassing matrix transformation, backpropagation of the heat path model, temperature rise calculation, solution comparison, and product implementation. Using the similarity theory of heat circuits, a basic thermal unit is established. Based on the fundamental connection between key components, a thermal network for a typical HSER is designed. An experimental system is set up, and the thermal network model's accuracy is confirmed using test data. Employing the topology analysis method, the topology of the thermal network is analyzed under both coil-energized and de-energized states. Potential thermal paths are identified, leading to optimized solutions for the HSER. Utilizing these solutions, the thermal path matrix topology model is backpropagated to the thermal path for temperature rise calculations. When compared to prototype HSER test data, the efficiency and accuracy of this matrix topology-based analysis method are confirmed.展开更多
Magnesium alloy is one of the most widely used lightweight structural materials,and the development of high strength-toughness magnesium alloy is an important research field at present and even in the future.The prepa...Magnesium alloy is one of the most widely used lightweight structural materials,and the development of high strength-toughness magnesium alloy is an important research field at present and even in the future.The preparation process parameters of magnesium alloy directly affect the microstructure of the magnesium alloy,and then determine the properties of the magnesium alloy.The cooling rate has important effects on the microstructure and properties of the magnesium alloy,and is an important preparation process parameter that cannot be ignored.Both the cooling rate from liquid phase to solid phase and the cooling rate of the magnesium alloy after heat treatment will change the microstructure of the magnesium alloy.Furthermore,the properties of magnesium alloy will be affected.In this paper,the effects of cooling rate on the solidification behavior,the rheological behavior,the change of microstructure(the solid solution of alloying elements in matrix,the composition,size,distribution and morphology of second phase,the diffusion and segregation of alloying elements,the grain size,the formation and morphology of dendrite,etc.),and the effects of cooling rate of magnesium alloy after heat treatment on the microstructure and stress distribution are reviewed.The reasons for the divergence about the influence of cooling rate on the microstructure of magnesium alloy are analyzed in detail.The effects of cooling rate on the mechanical properties,corrosion resistance and oxidation resistance of magnesium alloy are also analyzed and discussed deeply.Finally,the new methods and approaches to study the effects of cooling rate on the microstructure and properties of magnesium alloy are prospected.展开更多
In mobile edge computing,unmanned aerial vehicles(UAVs)equipped with computing servers have emerged as a promising solution due to their exceptional attributes of high mobility,flexibility,rapid deployment,and terrain...In mobile edge computing,unmanned aerial vehicles(UAVs)equipped with computing servers have emerged as a promising solution due to their exceptional attributes of high mobility,flexibility,rapid deployment,and terrain agnosticism.These attributes enable UAVs to reach designated areas,thereby addressing temporary computing swiftly in scenarios where ground-based servers are overloaded or unavailable.However,the inherent broadcast nature of line-of-sight transmission methods employed by UAVs renders them vulnerable to eavesdropping attacks.Meanwhile,there are often obstacles that affect flight safety in real UAV operation areas,and collisions between UAVs may also occur.To solve these problems,we propose an innovative A*SAC deep reinforcement learning algorithm,which seamlessly integrates the benefits of Soft Actor-Critic(SAC)and A*(A-Star)algorithms.This algorithm jointly optimizes the hovering position and task offloading proportion of the UAV through a task offloading function.Furthermore,our algorithm incorporates a path-planning function that identifies the most energy-efficient route for the UAV to reach its optimal hovering point.This approach not only reduces the flight energy consumption of the UAV but also lowers overall energy consumption,thereby optimizing system-level energy efficiency.Extensive simulation results demonstrate that,compared to other algorithms,our approach achieves superior system benefits.Specifically,it exhibits an average improvement of 13.18%in terms of different computing task sizes,25.61%higher on average in terms of the power of electromagnetic wave interference intrusion into UAVs emitted by different auxiliary UAVs,and 35.78%higher on average in terms of the maximum computing frequency of different auxiliary UAVs.As for path planning,the simulation results indicate that our algorithm is capable of determining the optimal collision-avoidance path for each auxiliary UAV,enabling them to safely reach their designated endpoints in diverse obstacle-ridden environments.展开更多
Thermal analysis plays a key role in the online inspection of molten iron quality.Different solidification process of molten iron can be reflected by thermal analysis curves,and silicon is one of important elements af...Thermal analysis plays a key role in the online inspection of molten iron quality.Different solidification process of molten iron can be reflected by thermal analysis curves,and silicon is one of important elements affecting the solidification of molten iron.In this study,FeSi75 was added in one chamber of the dual-chamber sample cup,and the influences of FeSi75 additive on the characteristic values of thermal analysis curves and vermiculating rate were investigated.The results show that with the increase of FeSi75,the start temperature of austenite formation TALfirstly decreases and then increases,but the start temperature of eutectic growth TSEF,the lowest eutectic temperature TEU,temperature at maximum eutectic reaction rate TEM,and highest eutectic temperature TERkeep always an increase.The temperature at final solidification point TEShas little change.The FeSi75 additive has different influences on the vermiculating rate of molten iron with different vermiculation,and the vermiculating rate increases for lower vermiculation molten iron while decreases for higher one.According to the thermal analysis curves obtained by a dual-chamber sample cup with 0.30wt.%FeSi75 additive in one chamber,the vermiculating rate of molten iron can be evaluated by comparing the characteristic values of these curves.The time differenceΔtERcorresponding to the highest eutectic temperature TERhas a closer relationship with the vermiculating rate,and a parabolic regression curve between the time differenceΔtERand vermiculating rateηhas been obtained within the range of 65%to 95%,which is suitable for the qualified melt.展开更多
Utilizing lightweight Al alloys in various industrial applications requires achieving precise pressure tightness and leak requirements.Vacuum pressure impregnation(VPI)with thermosetting polymers is commonly used to a...Utilizing lightweight Al alloys in various industrial applications requires achieving precise pressure tightness and leak requirements.Vacuum pressure impregnation(VPI)with thermosetting polymers is commonly used to address leakage defects in die-cast Al alloys.In this study,the efficacy of the VPI technique in sealing alloy parts was investigated using a combination of nondestructive micro X-ray computed tomography(micro XCT)and a standard leak test.The results demonstrate that the commonly used water leak test is insufficient for determining the sealing performance.Instead,micro XCT shows distinct advantages by enabling more comprehensive analysis.It reveals the presence of a low atomic number impregnates sealant within casting defects,which has low grey contrast and allows for visualizing primary leakage paths in 3D.The effective atomic number of impregnated resin is 6.75 and that of Al alloy is 13.69 by dual-energy X-ray CT.This research findings will contribute to enhancing the standard VPI process parameters and the properties of impregnating sealants to improve quality assurance for impregnation in industrial metals.展开更多
Federated learning(FL)is a distributed machine learning paradigm for edge cloud computing.FL can facilitate data-driven decision-making in tactical scenarios,effectively addressing both data volume and infrastructure ...Federated learning(FL)is a distributed machine learning paradigm for edge cloud computing.FL can facilitate data-driven decision-making in tactical scenarios,effectively addressing both data volume and infrastructure challenges in edge environments.However,the diversity of clients in edge cloud computing presents significant challenges for FL.Personalized federated learning(pFL)received considerable attention in recent years.One example of pFL involves exploiting the global and local information in the local model.Current pFL algorithms experience limitations such as slow convergence speed,catastrophic forgetting,and poor performance in complex tasks,which still have significant shortcomings compared to the centralized learning.To achieve high pFL performance,we propose FedCLCC:Federated Contrastive Learning and Conditional Computing.The core of FedCLCC is the use of contrastive learning and conditional computing.Contrastive learning determines the feature representation similarity to adjust the local model.Conditional computing separates the global and local information and feeds it to their corresponding heads for global and local handling.Our comprehensive experiments demonstrate that FedCLCC outperforms other state-of-the-art FL algorithms.展开更多
In this paper,we investigate covert communications under multi-antenna detection,and explore the impacts of the warden’s channel state information(CSI)availability and the noise uncertainty on system covert capabilit...In this paper,we investigate covert communications under multi-antenna detection,and explore the impacts of the warden’s channel state information(CSI)availability and the noise uncertainty on system covert capability.The detection performance at warden is analyzed in two cases under the perfect and statistical CSI at warden,respectively.In particular,for the former one,the warden utilizes the likelihood ratio(LR)detector,while for the latter one,the generalized likelihood ratio(GLR)detector is adopted.We first consider the scenario where the blocklength is finite,and demonstrate that the covert rate under both cases asymptotically goes to zero as the blocklength goes to infinity.Subsequently,we take the noise uncertainty at the warden into account which leads to positive covert rate,and characterize the covert rate for infinite blocklength.Specially,we derive the optimal transmit power for the legitimate transmitter that maximizes the covert rate.Besides,the rate gap under two cases,with different CSI availability at the warden,can be presented in closed form.Finally,numerical results validate the effectiveness of our theoretical analysis and also demonstrate the impacts of the factors studied on the system covertness.展开更多
This paper investigates how to achieve integrated sensing and communication(ISAC)based on a cell-free radio access network(CF-RAN)architecture with a minimum footprint of communication resources.We propose a new passi...This paper investigates how to achieve integrated sensing and communication(ISAC)based on a cell-free radio access network(CF-RAN)architecture with a minimum footprint of communication resources.We propose a new passive sensing scheme.The scheme is based on the radio frequency(RF)fingerprint learning of the RF radio unit(RRU)to build an RF fingerprint library of RRUs.The source RRU is identified by comparing the RF fingerprints carried by the signal at the receiver side.The receiver extracts the channel parameters from the signal and estimates the channel environment,thus locating the reflectors in the environment.The proposed scheme can effectively solve the problem of interference between signals in the same time-frequency domain but in different spatial domains when multiple RRUs jointly serve users in CF-RAN architecture.Simulation results show that the proposed passive ISAC scheme can effectively detect reflector location information in the environment without degrading the communication performance.展开更多
Heterogeneous federated learning(HtFL)has gained significant attention due to its ability to accommodate diverse models and data from distributed combat units.The prototype-based HtFL methods were proposed to reduce t...Heterogeneous federated learning(HtFL)has gained significant attention due to its ability to accommodate diverse models and data from distributed combat units.The prototype-based HtFL methods were proposed to reduce the high communication cost of transmitting model parameters.These methods allow for the sharing of only class representatives between heterogeneous clients while maintaining privacy.However,existing prototype learning approaches fail to take the data distribution of clients into consideration,which results in suboptimal global prototype learning and insufficient client model personalization capabilities.To address these issues,we propose a fair trainable prototype federated learning(FedFTP)algorithm,which employs a fair sampling training prototype(FSTP)mechanism and a hyperbolic space constraints(HSC)mechanism to enhance the fairness and effectiveness of prototype learning on the server in heterogeneous environments.Furthermore,a local prototype stable update(LPSU)mechanism is proposed as a means of maintaining personalization while promoting global consistency,based on contrastive learning.Comprehensive experimental results demonstrate that FedFTP achieves state-of-the-art performance in HtFL scenarios.展开更多
Variable-fidelity(VF)surrogate models have received increasing attention in engineering design optimization as they can approximate expensive high-fidelity(HF)simulations with reduced computational power.A key challen...Variable-fidelity(VF)surrogate models have received increasing attention in engineering design optimization as they can approximate expensive high-fidelity(HF)simulations with reduced computational power.A key challenge to building a VF model is devising an adaptive model updating strategy that jointly selects additional low-fidelity(LF)and/or HF samples.The additional samples must enhance the model accuracy while maximizing the computational efficiency.We propose ISMA-VFEEI,a global optimization framework that integrates an Improved Slime-Mould Algorithm(ISMA)and a Variable-Fidelity Expected Extension Improvement(VFEEI)learning function to construct a VF surrogate model efficiently.First,A cost-aware VFEEI function guides the adaptive LF/HF sampling by explicitly incorporating evaluation cost and existing sample proximity.Second,ISMA is employed to solve the resulting non-convex optimization problem and identify global optimal infill points for model enhancement.The efficacy of ISMA-VFEEI is demonstrated through six numerical benchmarks and one real-world engineering case study.The engineering case study of a high-speed railway Electric Multiple Unit(EMU),the optimization objective of a sanding device attained a minimum value of 1.546 using only 20 HF evaluations,outperforming all the compared methods.展开更多
Visual Place Recognition(VPR)technology aims to use visual information to judge the location of agents,which plays an irreplaceable role in tasks such as loop closure detection and relocation.It is well known that pre...Visual Place Recognition(VPR)technology aims to use visual information to judge the location of agents,which plays an irreplaceable role in tasks such as loop closure detection and relocation.It is well known that previous VPR algorithms emphasize the extraction and integration of general image features,while ignoring the mining of salient features that play a key role in the discrimination of VPR tasks.To this end,this paper proposes a Domain-invariant Information Extraction and Optimization Network(DIEONet)for VPR.The core of the algorithm is a newly designed Domain-invariant Information Mining Module(DIMM)and a Multi-sample Joint Triplet Loss(MJT Loss).Specifically,DIMM incorporates the interdependence between different spatial regions of the feature map in the cascaded convolutional unit group,which enhances the model’s attention to the domain-invariant static object class.MJT Loss introduces the“joint processing of multiple samples”mechanism into the original triplet loss,and adds a new distance constraint term for“positive and negative”samples,so that the model can avoid falling into local optimum during training.We demonstrate the effectiveness of our algorithm by conducting extensive experiments on several authoritative benchmarks.In particular,the proposed method achieves the best performance on the TokyoTM dataset with a Recall@1 metric of 92.89%.展开更多
Research on mass gathering events is critical for ensuring public security and maintaining social order.However,most of the existing works focus on crowd behavior analysis areas such as anomaly detection and crowd cou...Research on mass gathering events is critical for ensuring public security and maintaining social order.However,most of the existing works focus on crowd behavior analysis areas such as anomaly detection and crowd counting,and there is a relative lack of research on mass gathering behaviors.We believe real-time detection and monitoring of mass gathering behaviors are essential formigrating potential security risks and emergencies.Therefore,it is imperative to develop a method capable of accurately identifying and localizing mass gatherings before disasters occur,enabling prompt and effective responses.To address this problem,we propose an innovative Event-Driven Attention Network(EDAN),which achieves image-text matching in the scenario of mass gathering events with good results for the first time.Traditional image-text retrieval methods based on global alignment are difficult to capture the local details within complex scenes,limiting retrieval accuracy.While local alignment-based methods aremore effective at extracting detailed features,they frequently process raw textual features directly,which often contain ambiguities and redundant information that can diminish retrieval efficiency and degrade model performance.To overcome these challenges,EDAN introduces an Event-Driven AttentionModule that adaptively focuses attention on image regions or textual words relevant to the event type.By calculating the semantic distance between event labels and textual content,this module effectively significantly reduces computational complexity and enhances retrieval efficiency.To validate the effectiveness of EDAN,we construct a dedicated multimodal dataset tailored for the analysis of mass gathering events,providing a reliable foundation for subsequent studies.We conduct comparative experiments with other methods on our dataset,the experimental results demonstrate the effectiveness of EDAN.In the image-to-text retrieval task,EDAN achieved the best performance on the R@5 metric,while in the text-to-image retrieval task,it showed superior results on both R@10 and R@5 metrics.Additionally,EDAN excelled in the overall Rsummetric,achieving the best performance.Finally,ablation studies further verified the effectiveness of event-driven attention module.展开更多
The gap between the projected urban areas in the current trend(UAC)and those in the sustainable scenario(UAS)is a critical factor in understanding whether cities can fulfill the requirements of sustainable development...The gap between the projected urban areas in the current trend(UAC)and those in the sustainable scenario(UAS)is a critical factor in understanding whether cities can fulfill the requirements of sustainable development.However,there is a paucity of knowledge on this cutting-edge topic.Given the extensive and rapid urbanization in the United States(U.S.)over the past two centuries,accurately measuring this gap between UAS and UAC is of critical importance for advancing future sustainable urban development,as well as having significant global implications.This study finds that although the 740 U.S.cities have a large UAC in 2100,these cities will encom pass a significant gap from UAC to UAS(approximately 165,000 km2),accounting for 30%UAC at that time.The study also reveals the spatio-temporal heterogeneity of the gap.The gap initially increases before reaching a inflection point in 2090,and it disparates greatly from−100%to 240%at city level.While cities in the Northwestern U.S.maintain UAC that exceeds UAS from 2020 to 2100,cities in other regions shift from UAC that exceeds UAS to UAC that falls short of UAS.Filling the gap without additional urban growth planning could lead to a reduction of crop production ranging from 0.3%to 3%and a 0.68%loss of biomass.Hence,dynamic and forward-looking urban planning is essential for addressing the challenges of sustainable development posed by urbanization,both within the U.S.and globally.展开更多
Several acid compounds have been employed as additives of the V(V) electrolyte for vanadium redox flow battery(VRB) to improve its stability and electrochemical activity. Stability of the V(V) electrolyte with and wit...Several acid compounds have been employed as additives of the V(V) electrolyte for vanadium redox flow battery(VRB) to improve its stability and electrochemical activity. Stability of the V(V) electrolyte with and without additives was investigated with ex-situ heating/cooling treatment at a wide temperature range of-5 ?C to 60 ?C. It was observed that methanesulfonic acid, boric acid, hydrochloric acid, trifluoroacetic acid,polyacrylic acid, oxalic acid, methacrylic acid and phosphotungstic acid could improve the stability of the V(V) electrolyte at a certain range of temperature. Their electrochemical behaviors in the V(V) electrolyte were further studied by cyclic voltammetry(CV), steady state polarization and electrochemical impedance spectroscopy(EIS). The results showed that the electrochemical activity, including the reversibility of electrode reaction, the diffusivity of V(V) species, the polarization resistance and the flexibility of charge transfer for the V(V) electrolyte with these additives were all improved compared with the pristine solution.展开更多
In this paper, the performance of HG70D welded joint of ultra-high strength steel plates is presented, and the performance of HG70D and Q345B welded joints is studied. The high strength steel plate HG70D showed excell...In this paper, the performance of HG70D welded joint of ultra-high strength steel plates is presented, and the performance of HG70D and Q345B welded joints is studied. The high strength steel plate HG70D showed excellent weldability. Through the X-ray inspection of welded joints, tensile strength, impact test and bending performance test, the comprehensive performance of the joint was excellent. The macroscopic and microscopic metallographic analysises of the welded joints show that the welding seams included pearlite, sorbite, ferrite, etc. The influence of stress annealing temperature on HG70D of high strength steel plate was analyzed by heat treatment.展开更多
Radio frequency interference（RFI） is becoming more and more frequently, which makes it an important issue in SAR imaging.RFI presented in synthetic aperture radar either on purpose or inadvertent will distort the us...Radio frequency interference（RFI） is becoming more and more frequently, which makes it an important issue in SAR imaging.RFI presented in synthetic aperture radar either on purpose or inadvertent will distort the useful SAR echoes, thus degrade the SAR image quality.To resolve this issue, a long time study was carried out to study the characteristic of the RFI through the RFIaffected spaceborne and airborne SAR data.Based on the narrow band nature of RFI, this paper proposes a new process which contains both RFI detection and RFI suppression.A useful subband spectral kurtosis detector is first used to detect RFI, and then its results are used for RFI suppression.The proposed process has two advantages： one is the economization on the compute time for unnecessary interference suppression when no RFI existed; the other is improving the performance of the suppression method with knowing the exact position where RFI is.Moreover, the previous RFI suppression method––subband spectral cancelation（SSC） is supplemented and perfected.The subband division step is also elaborated detail in this paper.The experiment results show that the subband spectral kurtosis detector exhibits good performance in recognizing both weak and narrow-band RFI.In addition, the validity of the SSC method with subband spectral kurtosis detector is also validated on the real SAR echoes.展开更多
基金funded by the Fundamental Research Funds for the Central Universities(J2023-024,J2023-027).
文摘As an important complement to cloud computing, edge computing can effectively reduce the workload of the backbone network. To reduce latency and energy consumption of edge computing, deep learning is used to learn the task offloading strategies by interacting with the entities. In actual application scenarios, users of edge computing are always changing dynamically. However, the existing task offloading strategies cannot be applied to such dynamic scenarios. To solve this problem, we propose a novel dynamic task offloading framework for distributed edge computing, leveraging the potential of meta-reinforcement learning (MRL). Our approach formulates a multi-objective optimization problem aimed at minimizing both delay and energy consumption. We model the task offloading strategy using a directed acyclic graph (DAG). Furthermore, we propose a distributed edge computing adaptive task offloading algorithm rooted in MRL. This algorithm integrates multiple Markov decision processes (MDP) with a sequence-to-sequence (seq2seq) network, enabling it to learn and adapt task offloading strategies responsively across diverse network environments. To achieve joint optimization of delay and energy consumption, we incorporate the non-dominated sorting genetic algorithm II (NSGA-II) into our framework. Simulation results demonstrate the superiority of our proposed solution, achieving a 21% reduction in time delay and a 19% decrease in energy consumption compared to alternative task offloading schemes. Moreover, our scheme exhibits remarkable adaptability, responding swiftly to changes in various network environments.
基金The National Natural Science Foundation of China(No.U19B2031).
文摘A new method based on the iterative adaptive algorithm(IAA)and blocking matrix preprocessing(BMP)is proposed to study the suppression of multi-mainlobe interference.The algorithm is applied to precisely estimate the spatial spectrum and the directions of arrival(DOA)of interferences to overcome the drawbacks associated with conventional adaptive beamforming(ABF)methods.The mainlobe interferences are identified by calculating the correlation coefficients between direction steering vectors(SVs)and rejected by the BMP pretreatment.Then,IAA is subsequently employed to reconstruct a sidelobe interference-plus-noise covariance matrix for the preferable ABF and residual interference suppression.Simulation results demonstrate the excellence of the proposed method over normal methods based on BMP and eigen-projection matrix perprocessing(EMP)under both uncorrelated and coherent circumstances.
基金supported in part by the Natural Science Foundation of Xinjiang Uygur Autonomous Region under Grant 2022D01B184the National Natural Science Foundation of China under Grant 62301117,62131005.
文摘Covert communication guarantees the security of wireless communications via hiding the existence of the transmission.This paper focuses on the first and second order asymptotics of covert communication in the AWGN channels.The covertness is measured by the total variation distance between the channel output distributions induced with and without the transmission.We provide the exact expressions of the maximum amount of information that can be transmitted with the maximum error probability and the total variation less than any small numbers.The energy detection and the random coding are employed to prove our results.We further compare our results with those under relative entropy.The results show how many additional amounts of information can be transmitted covertly when changing the covertness constraint to total variation.
基金supported by the National Natural Science Foundation of China under Grant No.61671185 and 62071153.
文摘In the field of specific emitter identification(SEI),power amplifiers(PAs)have long been recognized as significant contributors to unintentional modulation characteristics.To enhance signal quality,digital pre-distortion(DPD)techniques are commonly employed in practical applications to mitigate the nonlinear effects of PAs.However,DPD techniques may diminish the distinctive characteristics of individual transmitters,potentially compromising SEI performance.This study investigates the influence of SEI in the presence of DPD applied to PAs.We construct a semi-physical emitter platform using AD9361 and ZYNQ,incorporating memory and non-memory models to emulate an amplification system comprising DPD devices and PAs.Furthermore,we delve into the analysis and evaluation of LMS-based and QRDRLS-based DPD algorithms to ascertain their efficacy in compensating for amplifier nonlinearity.Finally,we conduct a comprehensive set of experiments to demonstrate the adverse impact of DPD techniques on SEI.Our findings demonstrate a direct correlation between the degree of DPD performance and its impact magnitude on SEI,thereby providing a foundational basis for future studies investigating SEI techniques under DPD.
基金supported by the National Natural Science Foundation of China (No. 52177134)。
文摘Hermetically Sealed Electromagnetic Relay(HSER), used in aviation and aerospace,demands high reliability due to its critical applications. Given its complex operating conditions, efficient thermal analysis is essential for optimizing reliability. The commonly used Finite Element Method(FEM) is often time-consuming and may not be efficient or adaptable for complex multi-dimensional system calculations and design processes. This paper introduces an analysis method for thermal networks based on matrix perspective technology, encompassing matrix transformation, backpropagation of the heat path model, temperature rise calculation, solution comparison, and product implementation. Using the similarity theory of heat circuits, a basic thermal unit is established. Based on the fundamental connection between key components, a thermal network for a typical HSER is designed. An experimental system is set up, and the thermal network model's accuracy is confirmed using test data. Employing the topology analysis method, the topology of the thermal network is analyzed under both coil-energized and de-energized states. Potential thermal paths are identified, leading to optimized solutions for the HSER. Utilizing these solutions, the thermal path matrix topology model is backpropagated to the thermal path for temperature rise calculations. When compared to prototype HSER test data, the efficiency and accuracy of this matrix topology-based analysis method are confirmed.
基金supports from the Natural Science Foundation of Inner Mongolia Autonomous Region of china(2024MS05009)National Natural Science Foundation of China(51661025)+1 种基金Research Program of science and technology at Universities of Inner Mongolia Autonomous Region(NJZY21315)Scientific research project of Inner Mongolia University of Technology(ZY202001 and BS2020003).
文摘Magnesium alloy is one of the most widely used lightweight structural materials,and the development of high strength-toughness magnesium alloy is an important research field at present and even in the future.The preparation process parameters of magnesium alloy directly affect the microstructure of the magnesium alloy,and then determine the properties of the magnesium alloy.The cooling rate has important effects on the microstructure and properties of the magnesium alloy,and is an important preparation process parameter that cannot be ignored.Both the cooling rate from liquid phase to solid phase and the cooling rate of the magnesium alloy after heat treatment will change the microstructure of the magnesium alloy.Furthermore,the properties of magnesium alloy will be affected.In this paper,the effects of cooling rate on the solidification behavior,the rheological behavior,the change of microstructure(the solid solution of alloying elements in matrix,the composition,size,distribution and morphology of second phase,the diffusion and segregation of alloying elements,the grain size,the formation and morphology of dendrite,etc.),and the effects of cooling rate of magnesium alloy after heat treatment on the microstructure and stress distribution are reviewed.The reasons for the divergence about the influence of cooling rate on the microstructure of magnesium alloy are analyzed in detail.The effects of cooling rate on the mechanical properties,corrosion resistance and oxidation resistance of magnesium alloy are also analyzed and discussed deeply.Finally,the new methods and approaches to study the effects of cooling rate on the microstructure and properties of magnesium alloy are prospected.
基金supported by the Central University Basic Research Business Fee Fund Project(J2023-027)Open Fund of Key Laboratory of Flight Techniques and Flight Safety,CAAC(No.FZ2022KF06)China Postdoctoral Science Foundation(No.2022M722248).
文摘In mobile edge computing,unmanned aerial vehicles(UAVs)equipped with computing servers have emerged as a promising solution due to their exceptional attributes of high mobility,flexibility,rapid deployment,and terrain agnosticism.These attributes enable UAVs to reach designated areas,thereby addressing temporary computing swiftly in scenarios where ground-based servers are overloaded or unavailable.However,the inherent broadcast nature of line-of-sight transmission methods employed by UAVs renders them vulnerable to eavesdropping attacks.Meanwhile,there are often obstacles that affect flight safety in real UAV operation areas,and collisions between UAVs may also occur.To solve these problems,we propose an innovative A*SAC deep reinforcement learning algorithm,which seamlessly integrates the benefits of Soft Actor-Critic(SAC)and A*(A-Star)algorithms.This algorithm jointly optimizes the hovering position and task offloading proportion of the UAV through a task offloading function.Furthermore,our algorithm incorporates a path-planning function that identifies the most energy-efficient route for the UAV to reach its optimal hovering point.This approach not only reduces the flight energy consumption of the UAV but also lowers overall energy consumption,thereby optimizing system-level energy efficiency.Extensive simulation results demonstrate that,compared to other algorithms,our approach achieves superior system benefits.Specifically,it exhibits an average improvement of 13.18%in terms of different computing task sizes,25.61%higher on average in terms of the power of electromagnetic wave interference intrusion into UAVs emitted by different auxiliary UAVs,and 35.78%higher on average in terms of the maximum computing frequency of different auxiliary UAVs.As for path planning,the simulation results indicate that our algorithm is capable of determining the optimal collision-avoidance path for each auxiliary UAV,enabling them to safely reach their designated endpoints in diverse obstacle-ridden environments.
基金the financial support of the State Key Laboratory of Engine Reliability(skler-202105)。
文摘Thermal analysis plays a key role in the online inspection of molten iron quality.Different solidification process of molten iron can be reflected by thermal analysis curves,and silicon is one of important elements affecting the solidification of molten iron.In this study,FeSi75 was added in one chamber of the dual-chamber sample cup,and the influences of FeSi75 additive on the characteristic values of thermal analysis curves and vermiculating rate were investigated.The results show that with the increase of FeSi75,the start temperature of austenite formation TALfirstly decreases and then increases,but the start temperature of eutectic growth TSEF,the lowest eutectic temperature TEU,temperature at maximum eutectic reaction rate TEM,and highest eutectic temperature TERkeep always an increase.The temperature at final solidification point TEShas little change.The FeSi75 additive has different influences on the vermiculating rate of molten iron with different vermiculation,and the vermiculating rate increases for lower vermiculation molten iron while decreases for higher one.According to the thermal analysis curves obtained by a dual-chamber sample cup with 0.30wt.%FeSi75 additive in one chamber,the vermiculating rate of molten iron can be evaluated by comparing the characteristic values of these curves.The time differenceΔtERcorresponding to the highest eutectic temperature TERhas a closer relationship with the vermiculating rate,and a parabolic regression curve between the time differenceΔtERand vermiculating rateηhas been obtained within the range of 65%to 95%,which is suitable for the qualified melt.
文摘Utilizing lightweight Al alloys in various industrial applications requires achieving precise pressure tightness and leak requirements.Vacuum pressure impregnation(VPI)with thermosetting polymers is commonly used to address leakage defects in die-cast Al alloys.In this study,the efficacy of the VPI technique in sealing alloy parts was investigated using a combination of nondestructive micro X-ray computed tomography(micro XCT)and a standard leak test.The results demonstrate that the commonly used water leak test is insufficient for determining the sealing performance.Instead,micro XCT shows distinct advantages by enabling more comprehensive analysis.It reveals the presence of a low atomic number impregnates sealant within casting defects,which has low grey contrast and allows for visualizing primary leakage paths in 3D.The effective atomic number of impregnated resin is 6.75 and that of Al alloy is 13.69 by dual-energy X-ray CT.This research findings will contribute to enhancing the standard VPI process parameters and the properties of impregnating sealants to improve quality assurance for impregnation in industrial metals.
基金supported by the Natural Science Foundation of Xinjiang Uygur Autonomous Region(Grant No.2022D01B 187)。
文摘Federated learning(FL)is a distributed machine learning paradigm for edge cloud computing.FL can facilitate data-driven decision-making in tactical scenarios,effectively addressing both data volume and infrastructure challenges in edge environments.However,the diversity of clients in edge cloud computing presents significant challenges for FL.Personalized federated learning(pFL)received considerable attention in recent years.One example of pFL involves exploiting the global and local information in the local model.Current pFL algorithms experience limitations such as slow convergence speed,catastrophic forgetting,and poor performance in complex tasks,which still have significant shortcomings compared to the centralized learning.To achieve high pFL performance,we propose FedCLCC:Federated Contrastive Learning and Conditional Computing.The core of FedCLCC is the use of contrastive learning and conditional computing.Contrastive learning determines the feature representation similarity to adjust the local model.Conditional computing separates the global and local information and feeds it to their corresponding heads for global and local handling.Our comprehensive experiments demonstrate that FedCLCC outperforms other state-of-the-art FL algorithms.
基金supported in part by the National Natural Science Foundation of China under Grants 62301117,62001094,and U19B2014in part by the National Key Laboratory of Wireless Communications Foundation under Grant 2023KP01602in part by the Natural Science Foundation of Xinjiang Uygur Autonomous Region under Grant 2022D01B184 and 2022D01A297.
文摘In this paper,we investigate covert communications under multi-antenna detection,and explore the impacts of the warden’s channel state information(CSI)availability and the noise uncertainty on system covert capability.The detection performance at warden is analyzed in two cases under the perfect and statistical CSI at warden,respectively.In particular,for the former one,the warden utilizes the likelihood ratio(LR)detector,while for the latter one,the generalized likelihood ratio(GLR)detector is adopted.We first consider the scenario where the blocklength is finite,and demonstrate that the covert rate under both cases asymptotically goes to zero as the blocklength goes to infinity.Subsequently,we take the noise uncertainty at the warden into account which leads to positive covert rate,and characterize the covert rate for infinite blocklength.Specially,we derive the optimal transmit power for the legitimate transmitter that maximizes the covert rate.Besides,the rate gap under two cases,with different CSI availability at the warden,can be presented in closed form.Finally,numerical results validate the effectiveness of our theoretical analysis and also demonstrate the impacts of the factors studied on the system covertness.
基金supported in part by the National Key Research and Development Program under Grant(2021YFB2900300)by the National Natural Science Foundation of China(NSFC)under Grants 61971127,61871122by the Southeast University-China Mobile Research Institute Joint Innovation Center,and by the Major Key Project of PCL(PCL2021A01-2).
文摘This paper investigates how to achieve integrated sensing and communication(ISAC)based on a cell-free radio access network(CF-RAN)architecture with a minimum footprint of communication resources.We propose a new passive sensing scheme.The scheme is based on the radio frequency(RF)fingerprint learning of the RF radio unit(RRU)to build an RF fingerprint library of RRUs.The source RRU is identified by comparing the RF fingerprints carried by the signal at the receiver side.The receiver extracts the channel parameters from the signal and estimates the channel environment,thus locating the reflectors in the environment.The proposed scheme can effectively solve the problem of interference between signals in the same time-frequency domain but in different spatial domains when multiple RRUs jointly serve users in CF-RAN architecture.Simulation results show that the proposed passive ISAC scheme can effectively detect reflector location information in the environment without degrading the communication performance.
基金supported by the Natural Science Foundation of Xinjiang Uygur Autonomous Region(No.2022D01B187).
文摘Heterogeneous federated learning(HtFL)has gained significant attention due to its ability to accommodate diverse models and data from distributed combat units.The prototype-based HtFL methods were proposed to reduce the high communication cost of transmitting model parameters.These methods allow for the sharing of only class representatives between heterogeneous clients while maintaining privacy.However,existing prototype learning approaches fail to take the data distribution of clients into consideration,which results in suboptimal global prototype learning and insufficient client model personalization capabilities.To address these issues,we propose a fair trainable prototype federated learning(FedFTP)algorithm,which employs a fair sampling training prototype(FSTP)mechanism and a hyperbolic space constraints(HSC)mechanism to enhance the fairness and effectiveness of prototype learning on the server in heterogeneous environments.Furthermore,a local prototype stable update(LPSU)mechanism is proposed as a means of maintaining personalization while promoting global consistency,based on contrastive learning.Comprehensive experimental results demonstrate that FedFTP achieves state-of-the-art performance in HtFL scenarios.
基金funded by National Natural Science Foundation of China(grant No.52405255)Special Program of Huzhou(grant No.2023GZ05)+1 种基金Projects of Huzhou Science and Technology Correspondent(grant No.2023KT76)Guangdong Basic and Applied Basic Research Foundation(grant No.2025A1515010487)。
文摘Variable-fidelity(VF)surrogate models have received increasing attention in engineering design optimization as they can approximate expensive high-fidelity(HF)simulations with reduced computational power.A key challenge to building a VF model is devising an adaptive model updating strategy that jointly selects additional low-fidelity(LF)and/or HF samples.The additional samples must enhance the model accuracy while maximizing the computational efficiency.We propose ISMA-VFEEI,a global optimization framework that integrates an Improved Slime-Mould Algorithm(ISMA)and a Variable-Fidelity Expected Extension Improvement(VFEEI)learning function to construct a VF surrogate model efficiently.First,A cost-aware VFEEI function guides the adaptive LF/HF sampling by explicitly incorporating evaluation cost and existing sample proximity.Second,ISMA is employed to solve the resulting non-convex optimization problem and identify global optimal infill points for model enhancement.The efficacy of ISMA-VFEEI is demonstrated through six numerical benchmarks and one real-world engineering case study.The engineering case study of a high-speed railway Electric Multiple Unit(EMU),the optimization objective of a sanding device attained a minimum value of 1.546 using only 20 HF evaluations,outperforming all the compared methods.
基金supported by the Natural Science Foundation of Xinjiang Uygur Autonomous Region under grant number 2022D01B186.
文摘Visual Place Recognition(VPR)technology aims to use visual information to judge the location of agents,which plays an irreplaceable role in tasks such as loop closure detection and relocation.It is well known that previous VPR algorithms emphasize the extraction and integration of general image features,while ignoring the mining of salient features that play a key role in the discrimination of VPR tasks.To this end,this paper proposes a Domain-invariant Information Extraction and Optimization Network(DIEONet)for VPR.The core of the algorithm is a newly designed Domain-invariant Information Mining Module(DIMM)and a Multi-sample Joint Triplet Loss(MJT Loss).Specifically,DIMM incorporates the interdependence between different spatial regions of the feature map in the cascaded convolutional unit group,which enhances the model’s attention to the domain-invariant static object class.MJT Loss introduces the“joint processing of multiple samples”mechanism into the original triplet loss,and adds a new distance constraint term for“positive and negative”samples,so that the model can avoid falling into local optimum during training.We demonstrate the effectiveness of our algorithm by conducting extensive experiments on several authoritative benchmarks.In particular,the proposed method achieves the best performance on the TokyoTM dataset with a Recall@1 metric of 92.89%.
基金sponsored by Natural Science Foundation of Xinjiang Uygur Autonomous Region(2024D01A19).
文摘Research on mass gathering events is critical for ensuring public security and maintaining social order.However,most of the existing works focus on crowd behavior analysis areas such as anomaly detection and crowd counting,and there is a relative lack of research on mass gathering behaviors.We believe real-time detection and monitoring of mass gathering behaviors are essential formigrating potential security risks and emergencies.Therefore,it is imperative to develop a method capable of accurately identifying and localizing mass gatherings before disasters occur,enabling prompt and effective responses.To address this problem,we propose an innovative Event-Driven Attention Network(EDAN),which achieves image-text matching in the scenario of mass gathering events with good results for the first time.Traditional image-text retrieval methods based on global alignment are difficult to capture the local details within complex scenes,limiting retrieval accuracy.While local alignment-based methods aremore effective at extracting detailed features,they frequently process raw textual features directly,which often contain ambiguities and redundant information that can diminish retrieval efficiency and degrade model performance.To overcome these challenges,EDAN introduces an Event-Driven AttentionModule that adaptively focuses attention on image regions or textual words relevant to the event type.By calculating the semantic distance between event labels and textual content,this module effectively significantly reduces computational complexity and enhances retrieval efficiency.To validate the effectiveness of EDAN,we construct a dedicated multimodal dataset tailored for the analysis of mass gathering events,providing a reliable foundation for subsequent studies.We conduct comparative experiments with other methods on our dataset,the experimental results demonstrate the effectiveness of EDAN.In the image-to-text retrieval task,EDAN achieved the best performance on the R@5 metric,while in the text-to-image retrieval task,it showed superior results on both R@10 and R@5 metrics.Additionally,EDAN excelled in the overall Rsummetric,achieving the best performance.Finally,ablation studies further verified the effectiveness of event-driven attention module.
基金supported by the National Natural Science Foun-dation of China(Grants No.42330103,42271469)the Ningbo Science and Technology Bureau(Grant No.2022Z081).
文摘The gap between the projected urban areas in the current trend(UAC)and those in the sustainable scenario(UAS)is a critical factor in understanding whether cities can fulfill the requirements of sustainable development.However,there is a paucity of knowledge on this cutting-edge topic.Given the extensive and rapid urbanization in the United States(U.S.)over the past two centuries,accurately measuring this gap between UAS and UAC is of critical importance for advancing future sustainable urban development,as well as having significant global implications.This study finds that although the 740 U.S.cities have a large UAC in 2100,these cities will encom pass a significant gap from UAC to UAS(approximately 165,000 km2),accounting for 30%UAC at that time.The study also reveals the spatio-temporal heterogeneity of the gap.The gap initially increases before reaching a inflection point in 2090,and it disparates greatly from−100%to 240%at city level.While cities in the Northwestern U.S.maintain UAC that exceeds UAS from 2020 to 2100,cities in other regions shift from UAC that exceeds UAS to UAC that falls short of UAS.Filling the gap without additional urban growth planning could lead to a reduction of crop production ranging from 0.3%to 3%and a 0.68%loss of biomass.Hence,dynamic and forward-looking urban planning is essential for addressing the challenges of sustainable development posed by urbanization,both within the U.S.and globally.
基金supported by the Doctoral Program of Higher Education(No.20110181110003)the Collaborative innovation fund by China Academyof Engineering Physics and Sichuan University(No.XTCX2011001)the Sichuan Provincial Department of Science and Technology R&D Program(No.2013FZ0034)
文摘Several acid compounds have been employed as additives of the V(V) electrolyte for vanadium redox flow battery(VRB) to improve its stability and electrochemical activity. Stability of the V(V) electrolyte with and without additives was investigated with ex-situ heating/cooling treatment at a wide temperature range of-5 ?C to 60 ?C. It was observed that methanesulfonic acid, boric acid, hydrochloric acid, trifluoroacetic acid,polyacrylic acid, oxalic acid, methacrylic acid and phosphotungstic acid could improve the stability of the V(V) electrolyte at a certain range of temperature. Their electrochemical behaviors in the V(V) electrolyte were further studied by cyclic voltammetry(CV), steady state polarization and electrochemical impedance spectroscopy(EIS). The results showed that the electrochemical activity, including the reversibility of electrode reaction, the diffusivity of V(V) species, the polarization resistance and the flexibility of charge transfer for the V(V) electrolyte with these additives were all improved compared with the pristine solution.
文摘In this paper, the performance of HG70D welded joint of ultra-high strength steel plates is presented, and the performance of HG70D and Q345B welded joints is studied. The high strength steel plate HG70D showed excellent weldability. Through the X-ray inspection of welded joints, tensile strength, impact test and bending performance test, the comprehensive performance of the joint was excellent. The macroscopic and microscopic metallographic analysises of the welded joints show that the welding seams included pearlite, sorbite, ferrite, etc. The influence of stress annealing temperature on HG70D of high strength steel plate was analyzed by heat treatment.
基金co-supported by the China Postdoctoral Science Foundation (No.2013M541035)the National Natural Science Foundation of China (No.61301025)
文摘Radio frequency interference（RFI） is becoming more and more frequently, which makes it an important issue in SAR imaging.RFI presented in synthetic aperture radar either on purpose or inadvertent will distort the useful SAR echoes, thus degrade the SAR image quality.To resolve this issue, a long time study was carried out to study the characteristic of the RFI through the RFIaffected spaceborne and airborne SAR data.Based on the narrow band nature of RFI, this paper proposes a new process which contains both RFI detection and RFI suppression.A useful subband spectral kurtosis detector is first used to detect RFI, and then its results are used for RFI suppression.The proposed process has two advantages： one is the economization on the compute time for unnecessary interference suppression when no RFI existed; the other is improving the performance of the suppression method with knowing the exact position where RFI is.Moreover, the previous RFI suppression method––subband spectral cancelation（SSC） is supplemented and perfected.The subband division step is also elaborated detail in this paper.The experiment results show that the subband spectral kurtosis detector exhibits good performance in recognizing both weak and narrow-band RFI.In addition, the validity of the SSC method with subband spectral kurtosis detector is also validated on the real SAR echoes.
