In erasure-coded storage systems,updating data requires parity maintenance,which often leads to significant I/O amplification due to“write-after-read”operations.Furthermore,scattered parity placement increases disk ...In erasure-coded storage systems,updating data requires parity maintenance,which often leads to significant I/O amplification due to“write-after-read”operations.Furthermore,scattered parity placement increases disk seek overhead during repair,resulting in degraded system performance.To address these challenges,this paper proposes a Cognitive Update and Repair Method(CURM)that leverages machine learning to classify files into writeonly,read-only,and read-write categories,enabling tailored update and repair strategies.For write-only and read-write files,CURM employs a data-differencemechanism combined with fine-grained I/O scheduling to minimize redundant read operations and mitigate I/O amplification.For read-write files,CURM further reserves adjacent disk space near parity blocks,supporting parallel reads and reducing disk seek overhead during repair.We implement CURM in a prototype system,Cognitive Update and Repair File System(CURFS),and conduct extensive experiments using realworld Network File System(NFS)and Microsoft Research(MSR)workloads on a 25-node cluster.Experimental results demonstrate that CURMimproves data update throughput by up to 82.52%,reduces recovery time by up to 47.47%,and decreases long-term storage overhead by more than 15% compared to state-of-the-art methods including Full Logging(FL),ParityLogging(PL),ParityLoggingwithReservedspace(PLR),andPARIX.These results validate the effectiveness of CURM in enhancing both update and repair performance,providing a scalable and efficient solution for large-scale erasure-coded storage systems.展开更多
The rapid growth of distributed data-centric applications and AI workloads increases demand for low-latency,high-throughput communication,necessitating frequent and flexible updates to network routing configurations.H...The rapid growth of distributed data-centric applications and AI workloads increases demand for low-latency,high-throughput communication,necessitating frequent and flexible updates to network routing configurations.However,maintaining consistent forwarding states during these updates is challenging,particularly when rerouting multiple flows simultaneously.Existing approaches pay little attention to multi-flow update,where improper update sequences across data plane nodes may construct deadlock dependencies.Moreover,these methods typically involve excessive control-data plane interactions,incurring significant resource overhead and performance degradation.This paper presents P4LoF,an efficient loop-free update approach that enables the controller to reroute multiple flows through minimal interactions.P4LoF first utilizes a greedy-based algorithm to generate the shortest update dependency chain for the single-flow update.These chains are then dynamically merged into a dependency graph and resolved as a Shortest Common Super-sequence(SCS)problem to produce the update sequence of multi-flow update.To address deadlock dependencies in multi-flow updates,P4LoF builds a deadlock-fix forwarding model that leverages the flexible packet processing capabilities of the programmable data plane.Experimental results show that P4LoF reduces control-data plane interactions by at least 32.6%with modest overhead,while effectively guaranteeing loop-free consistency.展开更多
Colorectal cancer(CRC)is the most frequently diagnosed malignancy of the digestive system and the second leading cause of cancer-related deaths worldwide(1).In China,CRC ranks as the second most common cancer with inc...Colorectal cancer(CRC)is the most frequently diagnosed malignancy of the digestive system and the second leading cause of cancer-related deaths worldwide(1).In China,CRC ranks as the second most common cancer with incidence and mortality rates continuing to rise(2).The Chinese Society of Clinical Oncology(CSCO)first introduced its guidelines in 2017,and since then,they have been updated annually to incorporate the latest clinical research findings,drug availability,and expert consensus(3-8).This article presents the key updates in the 2025 edition compared to the 2024 version.展开更多
With the rise of remote collaboration,the demand for advanced storage and collaboration tools has rapidly increased.However,traditional collaboration tools primarily rely on access control,leaving data stored on cloud...With the rise of remote collaboration,the demand for advanced storage and collaboration tools has rapidly increased.However,traditional collaboration tools primarily rely on access control,leaving data stored on cloud servers vulnerable due to insufficient encryption.This paper introduces a novel mechanism that encrypts data in‘bundle’units,designed to meet the dual requirements of efficiency and security for frequently updated collaborative data.Each bundle includes updated information,allowing only the updated portions to be reencrypted when changes occur.The encryption method proposed in this paper addresses the inefficiencies of traditional encryption modes,such as Cipher Block Chaining(CBC)and Counter(CTR),which require decrypting and re-encrypting the entire dataset whenever updates occur.The proposed method leverages update-specific information embedded within data bundles and metadata that maps the relationship between these bundles and the plaintext data.By utilizing this information,the method accurately identifies the modified portions and applies algorithms to selectively re-encrypt only those sections.This approach significantly enhances the efficiency of data updates while maintaining high performance,particularly in large-scale data environments.To validate this approach,we conducted experiments measuring execution time as both the size of the modified data and the total dataset size varied.Results show that the proposed method significantly outperforms CBC and CTR modes in execution speed,with greater performance gains as data size increases.Additionally,our security evaluation confirms that this method provides robust protection against both passive and active attacks.展开更多
With the widespread adoption of hydraulic fracturing technology in oil and gas resource development,improving the accuracy and efficiency of fracturing simulations has become a critical research focus.This paper propo...With the widespread adoption of hydraulic fracturing technology in oil and gas resource development,improving the accuracy and efficiency of fracturing simulations has become a critical research focus.This paper proposes an improved fluid flow algorithm,aiming to enhance the computational efficiency of hydraulic fracturing simulations while ensuring computational accuracy.The algorithm optimizes the aperture law and iteration criteria,focusing on improving the domain volume and crack pressure update strategy,thereby enabling precise capture of dynamic borehole pressure variations during injection tests.The effectiveness of the algorithm is verified through three flow-solid coupling cases.The study also analyzes the effects of borehole size,domain volume,and crack pressure update strategy on fracturing behavior.Furthermore,the performance of the improved algorithm in terms of crack propagation rate,micro-crack formation,and fluid pressure distribution was further evaluated.The results indicate that while large-size boreholes delay crack initiation,the cracks propagate more rapidly once formed.Additionally,the optimized domain volume calculation and crack pressure update strategy significantly shorten the pressure propagation stage,promote crack propagation,and improve computational efficiency.展开更多
The Chinese Society of Clinical Oncology Non-small Cell Lung Cancer(CSCO NSCLC)guidelines were first published in 2016,ranking among the earliest-released guidelines within the CSCO series.In 2020 the CSCO published s...The Chinese Society of Clinical Oncology Non-small Cell Lung Cancer(CSCO NSCLC)guidelines were first published in 2016,ranking among the earliest-released guidelines within the CSCO series.In 2020 the CSCO published separate guidelines for NSCLC and small cell lung cancer(SCLC)for the first time to improve clinical usability.展开更多
Declaration of Competing Interest statements is updated in the published version of the following articles that appeared in issues of Resources Chemicals and Materials.The appropriate updated Declaration of Competing ...Declaration of Competing Interest statements is updated in the published version of the following articles that appeared in issues of Resources Chemicals and Materials.The appropriate updated Declaration of Competing Interest state-ments,provided by the Authors,are included below.展开更多
As vehicular networks become increasingly pervasive,enhancing connectivity and reliability has emerged as a critical objective.Among the enabling technologies for advanced wireless communication,particularly those tar...As vehicular networks become increasingly pervasive,enhancing connectivity and reliability has emerged as a critical objective.Among the enabling technologies for advanced wireless communication,particularly those targeting low latency and high reliability,time synchronization is critical,especially in vehicular networks.However,due to the inherent mobility of vehicular environments,consistently exchanging synchronization packets with a fixed base station or access point is challenging.This issue is further exacerbated in signal shadowed areas such as urban canyons,tunnels,or large-scale indoor hallswhere other technologies,such as global navigation satellite system(GNSS),are unavailable.One-way synchronization techniques offer a feasible approach under such transient connectivity conditions.One-way schemes still suffer from long convergence times to reach the required synchronization accuracy in these circumstances.In this paper,we propose a WLAN-based multi-stage clock synchronization scheme(WMC)tailored for vehicular networks.The proposed method comprises an initial hard update stage to rapidly achieve synchronization,followed by a high-precision stable stage based on Maximum Likelihood Estimation(MLE).By implementing the scheme directly at the network driver,we address key limitations of hard update mechanisms.Our approach significantly reduces the initial period to collect high-quality samples and offset estimation time to reach sub-50μs accuracy,and subsequently transitions to a refined MLE-based synchronization stage,achieving stable accuracy at approximately 30μs.The windowed moving average stabilized(reaching 90%of the baseline)in approximately 35 s,which corresponds to just 5.1%of the baseline time accuracy.Finally,the impact of synchronization performance on the localization model was validated using the Simulation of Urban Mobility(SUMO).The results demonstrate that more accurate conditions for position estimation can be supported,with an improvement about 38.5%in the mean error.展开更多
We present a gain adaptive tuning method for fiber Raman amplifier(FRA) using two-stage neural networks(NNs) and double weights updates. After training the connection weights of two-stage NNs separately in training ph...We present a gain adaptive tuning method for fiber Raman amplifier(FRA) using two-stage neural networks(NNs) and double weights updates. After training the connection weights of two-stage NNs separately in training phase, the connection weights of the unified NN are updated again in verification phase according to error between the predicted and target gains to eliminate the inherent error of the NNs. The simulation results show that the mean of root mean square error(RMSE) and maximum error of gains are 0.131 d B and 0.281 d B, respectively. It shows that the method can realize adaptive adjustment function of FRA gain with high accuracy.展开更多
To combat packet loss and realize robust video transmission over Intemet and wireless networks, a new multiple description （MD） video coding method is proposed. In the method, two descriptions for each video frame i...To combat packet loss and realize robust video transmission over Intemet and wireless networks, a new multiple description （MD） video coding method is proposed. In the method, two descriptions for each video frame is first created by group of blocks （GOB） alternation. Motion information is then duplicated in both the descriptions and a process called low quality macroblock update is designed to redundantly encode textures in each frame using standard bit stream syntax. In this way, the output bit streams are standard compliant and better trade-offs between redundancy and single charmel reconstruction distortion are achieved. The proposed method has much better performance than the well-known MD transform coding （MDTC） method both in terms of redundancy rate distortion, and in the packet loss scenario.展开更多
Engineering optimization problems are often characterized by high dimensionality,constraints,and complex,multimodal landscapes.Traditional deterministic methods frequently struggle under such conditions,prompting incr...Engineering optimization problems are often characterized by high dimensionality,constraints,and complex,multimodal landscapes.Traditional deterministic methods frequently struggle under such conditions,prompting increased interest in swarm intelligence algorithms.Among these,the Cuckoo Search(CS)algorithm stands out for its promising global search capabilities.However,it often suffers from premature convergence when tackling complex problems.To address this limitation,this paper proposes a Grouped Dynamic Adaptive CS(GDACS)algorithm.Theenhancements incorporated intoGDACS can be summarized into two key aspects.Firstly,a chaotic map is employed to generate initial solutions,leveraging the inherent randomness of chaotic sequences to ensure a more uniform distribution across the search space and enhance population diversity from the outset.Secondly,Cauchy and Levy strategies replace the standard CS population update.This strategy involves evaluating the fitness of candidate solutions to dynamically group the population based on performance.Different step-size adaptation strategies are then applied to distinct groups,enabling an adaptive search mechanism that balances exploration and exploitation.Experiments were conducted on six benchmark functions and four constrained engineering design problems,and the results indicate that the proposed GDACS achieves good search efficiency and produces more accurate optimization results compared with other state-of-the-art algorithms.展开更多
The original online version of this article was revised:The layout update for Article 758 has impacted the page range in the published issue,but did not affect the scholarly content.To ensure consistency with the orig...The original online version of this article was revised:The layout update for Article 758 has impacted the page range in the published issue,but did not affect the scholarly content.To ensure consistency with the originally assigned pages(2595-2614),we will need to publish an erratum to correct the article and restore the original page range.The original article has been corrected.展开更多
This paper investigates the challenges of structural inconsistency,matching accuracy degradation,and trajectory interruptions caused by high-speed motion,frequent occlusions,and appearance variations of unmanned aeria...This paper investigates the challenges of structural inconsistency,matching accuracy degradation,and trajectory interruptions caused by high-speed motion,frequent occlusions,and appearance variations of unmanned aerial vehicle(UAV) targets in low-altitude airspace.A novel UAV visual tracking method is proposed for dynamic structural distortions,with a focus on structural consistency modeling to improve system robustness in complex scenarios.Unlike prior methods such as STARK,which rely on spatio-temporal prediction,and KeepTrack,which emphasize template maintenance,our approach enforces structural-level consistency between historical and current features,thereby addressing UAV-specific issues of rapid maneuvering and environmental complexity.The proposed framework features a structure-aware architecture that incorporates dual complementary mechanisms serving as spatial completion and temporal restoration components.First,a multi-scale structure extraction module with adaptive anchor scheduling is developed to dynamically perceive spatial target shape and generate high-quality proposals.Second,a structural memory module is designed to reconstruct local regions by leveraging high-confidence historical structural representations,thereby maintaining spatiotemporal coherence across frames.Furthermore,a structural verification mechanism coupled with a meta-learning-driven re-identification strategy is introduced to detect abrupt structural distortions and adaptively update templates,significantly improving resilience against disturbances.Overall,the main contributions of this paper can be summarized as follows:(1) introducing structural consistency modeling into UAV visual tracking for the first time;(2) designing a unified framework that combines adaptive proposal generation,full-image matching,and re-identification under structural constraints;and(3) achieving state-of-the-art performance on the anti-UAV benchmark,highlighting the method's practical value in real-world UAV surveillance applications.展开更多
The operational Tropical Regional Atmospheric Model System(TRAMS)often underestimates initial typhoon intensity when using the global analysis field as the initial condition.The TRAMS tropical cyclone(TC)initializatio...The operational Tropical Regional Atmospheric Model System(TRAMS)often underestimates initial typhoon intensity when using the global analysis field as the initial condition.The TRAMS tropical cyclone(TC)initialization scheme,developed based on the incremental analysis updates(IAU)technique,effectively reduces initial bias.However,the original IAU-based TC initialization scheme only adjusts the wind field at the analysis moment,with other variables adjusted implicitly under the model's constraints according to a gradually inserted wind increment(named“univariate adjustment scheme”hereafter).The univariate adjustment scheme requires approximately 3 h to reach a dynamic equilibrium state,which constrains the assimilation of hourly TC observations and causes excessive dissipation of meaningful short-wave information in adjustment increments.To address this limitation,this study develops a multivariate adjustment IAU-based TC initialization scheme that incorporates gradient wind balance and hydrostatic balance as its largescale constraints.Numerical experiments with TC Hato(2017)demonstrate that the multivariate adjustment scheme reduces the IAU relaxation time to 1 h while marginally improving forecast skill.These findings are consistently replicated across 12 additional TC cases.The development of the IAU-based multivariate adjustment initialization scheme establishes a foundation for 4-D initialization using hourly TC observations.展开更多
The probabilistic stability evolution analysis of reservoir bank slopes is a crucial aspect of risk assessment,with core challenges including the consideration of deformation mechanisms and accurate determination of m...The probabilistic stability evolution analysis of reservoir bank slopes is a crucial aspect of risk assessment,with core challenges including the consideration of deformation mechanisms and accurate determination of mechanical parameters.In this study,a novel time-varying reliability analysis framework based on sequential Bayesian updating of mechanical parameters is proposed.The inverse parameters account for damage time-dependent behavior,incorporating water effect and a strain-driven softening-hardening process that depends on sliding states.The likelihood function is enhanced to simultaneously consider observation error,surrogate model prediction error,and model structural error,with the introduction of physical penalty.Exploration of the high-dimensional parameter space is achieved via the Hamiltonian Monte Carlo(HMC)method and the physics knowledge-based time-dependent deformation surrogate model.The time-varying reliability analysis of the slope is performed using the multi-grid method.Taking a reservoir bank slope as a case study,the sequential updating of 12 mechanical parameters is conducted based on deformation time series from 16 monitoring points,thereby validating the proposed framework.The results indicate that the proposed framework effectively captures the posterior distribution of mechanical parameters,with the case slope remaining in a critically stable state after overall sliding,showing a high failure probability.Introducing model structural error can reduce parameter compensation,and a reasonable sequential updating step size can improve inversion accuracy.展开更多
Aiming at the inaccessible problem of remote embedded devices update and maintenance, this paper presents a method using general packet radio service （GPRS） to achieve update based on the embedded real-time operatin...Aiming at the inaccessible problem of remote embedded devices update and maintenance, this paper presents a method using general packet radio service （GPRS） to achieve update based on the embedded real-time operating system （RTOS） μC/OS-Ⅱ. It introduces architecture of the system first. And then it uses LPC1768 chip as the central processing unit, SIM900A module for data transmission, and SST25VF016B to store the data. To ensure accuracy of the data transmis- sion, cyclic redundancy code （CRC） is adopted. The software uses fixed bootstrap and mutable update program, and thus the embedded devices can still normally start in case of update failure. Finally, high stability and extensive adaptability of the system are verified by experimental data.展开更多
基金supported by the National Natural Science Foundation of China(Grant No.62362019)the Natural Science Foundation of Hainan Province(Grant No.624RC482)the Hainan Provincial Higher Education Teaching Reform Research Project(Grant Hnjg2024-27).
文摘In erasure-coded storage systems,updating data requires parity maintenance,which often leads to significant I/O amplification due to“write-after-read”operations.Furthermore,scattered parity placement increases disk seek overhead during repair,resulting in degraded system performance.To address these challenges,this paper proposes a Cognitive Update and Repair Method(CURM)that leverages machine learning to classify files into writeonly,read-only,and read-write categories,enabling tailored update and repair strategies.For write-only and read-write files,CURM employs a data-differencemechanism combined with fine-grained I/O scheduling to minimize redundant read operations and mitigate I/O amplification.For read-write files,CURM further reserves adjacent disk space near parity blocks,supporting parallel reads and reducing disk seek overhead during repair.We implement CURM in a prototype system,Cognitive Update and Repair File System(CURFS),and conduct extensive experiments using realworld Network File System(NFS)and Microsoft Research(MSR)workloads on a 25-node cluster.Experimental results demonstrate that CURMimproves data update throughput by up to 82.52%,reduces recovery time by up to 47.47%,and decreases long-term storage overhead by more than 15% compared to state-of-the-art methods including Full Logging(FL),ParityLogging(PL),ParityLoggingwithReservedspace(PLR),andPARIX.These results validate the effectiveness of CURM in enhancing both update and repair performance,providing a scalable and efficient solution for large-scale erasure-coded storage systems.
基金supported by the National Key Research and Development Program of China under Grant 2022YFB2901501in part by the Science and Technology Innovation leading Talents Subsidy Project of Central Plains under Grant 244200510038.
文摘The rapid growth of distributed data-centric applications and AI workloads increases demand for low-latency,high-throughput communication,necessitating frequent and flexible updates to network routing configurations.However,maintaining consistent forwarding states during these updates is challenging,particularly when rerouting multiple flows simultaneously.Existing approaches pay little attention to multi-flow update,where improper update sequences across data plane nodes may construct deadlock dependencies.Moreover,these methods typically involve excessive control-data plane interactions,incurring significant resource overhead and performance degradation.This paper presents P4LoF,an efficient loop-free update approach that enables the controller to reroute multiple flows through minimal interactions.P4LoF first utilizes a greedy-based algorithm to generate the shortest update dependency chain for the single-flow update.These chains are then dynamically merged into a dependency graph and resolved as a Shortest Common Super-sequence(SCS)problem to produce the update sequence of multi-flow update.To address deadlock dependencies in multi-flow updates,P4LoF builds a deadlock-fix forwarding model that leverages the flexible packet processing capabilities of the programmable data plane.Experimental results show that P4LoF reduces control-data plane interactions by at least 32.6%with modest overhead,while effectively guaranteeing loop-free consistency.
基金supported by the National Natural Science Foundation of China(No.82373415)Beijing Xisike Clinical Oncology Research Foundation(No.Ytongshu2021/ms-0003)。
文摘Colorectal cancer(CRC)is the most frequently diagnosed malignancy of the digestive system and the second leading cause of cancer-related deaths worldwide(1).In China,CRC ranks as the second most common cancer with incidence and mortality rates continuing to rise(2).The Chinese Society of Clinical Oncology(CSCO)first introduced its guidelines in 2017,and since then,they have been updated annually to incorporate the latest clinical research findings,drug availability,and expert consensus(3-8).This article presents the key updates in the 2025 edition compared to the 2024 version.
基金supported by the Institute of Information&communications Technology Planning&Evaluation(IITP)grant funded by the Korea government(MSIT)(RS-2024-00399401,Development of Quantum-Safe Infrastructure Migration and Quantum Security Verification Technologies).
文摘With the rise of remote collaboration,the demand for advanced storage and collaboration tools has rapidly increased.However,traditional collaboration tools primarily rely on access control,leaving data stored on cloud servers vulnerable due to insufficient encryption.This paper introduces a novel mechanism that encrypts data in‘bundle’units,designed to meet the dual requirements of efficiency and security for frequently updated collaborative data.Each bundle includes updated information,allowing only the updated portions to be reencrypted when changes occur.The encryption method proposed in this paper addresses the inefficiencies of traditional encryption modes,such as Cipher Block Chaining(CBC)and Counter(CTR),which require decrypting and re-encrypting the entire dataset whenever updates occur.The proposed method leverages update-specific information embedded within data bundles and metadata that maps the relationship between these bundles and the plaintext data.By utilizing this information,the method accurately identifies the modified portions and applies algorithms to selectively re-encrypt only those sections.This approach significantly enhances the efficiency of data updates while maintaining high performance,particularly in large-scale data environments.To validate this approach,we conducted experiments measuring execution time as both the size of the modified data and the total dataset size varied.Results show that the proposed method significantly outperforms CBC and CTR modes in execution speed,with greater performance gains as data size increases.Additionally,our security evaluation confirms that this method provides robust protection against both passive and active attacks.
基金supported by the National Natural Science Foundation of China(Nos.52164001,52064006,52004072 and 52364004)the Science and Technology Support Project of Guizhou(Nos.[2020]4Y044,[2021]N404 and[2021]N511)+1 种基金the Guizhou Provincial Science and Technology Foundation(No.GCC[2022]005-1),Talents of Guizhou University(No.201901)the Special Research Funds of Guizhou University(Nos.201903,202011,and 202012).
文摘With the widespread adoption of hydraulic fracturing technology in oil and gas resource development,improving the accuracy and efficiency of fracturing simulations has become a critical research focus.This paper proposes an improved fluid flow algorithm,aiming to enhance the computational efficiency of hydraulic fracturing simulations while ensuring computational accuracy.The algorithm optimizes the aperture law and iteration criteria,focusing on improving the domain volume and crack pressure update strategy,thereby enabling precise capture of dynamic borehole pressure variations during injection tests.The effectiveness of the algorithm is verified through three flow-solid coupling cases.The study also analyzes the effects of borehole size,domain volume,and crack pressure update strategy on fracturing behavior.Furthermore,the performance of the improved algorithm in terms of crack propagation rate,micro-crack formation,and fluid pressure distribution was further evaluated.The results indicate that while large-size boreholes delay crack initiation,the cracks propagate more rapidly once formed.Additionally,the optimized domain volume calculation and crack pressure update strategy significantly shorten the pressure propagation stage,promote crack propagation,and improve computational efficiency.
文摘The Chinese Society of Clinical Oncology Non-small Cell Lung Cancer(CSCO NSCLC)guidelines were first published in 2016,ranking among the earliest-released guidelines within the CSCO series.In 2020 the CSCO published separate guidelines for NSCLC and small cell lung cancer(SCLC)for the first time to improve clinical usability.
文摘Declaration of Competing Interest statements is updated in the published version of the following articles that appeared in issues of Resources Chemicals and Materials.The appropriate updated Declaration of Competing Interest state-ments,provided by the Authors,are included below.
基金supported by Korea Institute of Energy Technology Evaluation and Planning(KETEP)grant funded by the Korea government(MOTIE)(No.20224B10300090)supported by the MSIT(Ministry of Science and ICT),Republic of Korea,under the ITRC(Information Technology Research Center)support program(IITP-2025-RS-2021-II211835)supervised by the IITP(Institute of Information&Communications Technology Planning&Evaluation).
文摘As vehicular networks become increasingly pervasive,enhancing connectivity and reliability has emerged as a critical objective.Among the enabling technologies for advanced wireless communication,particularly those targeting low latency and high reliability,time synchronization is critical,especially in vehicular networks.However,due to the inherent mobility of vehicular environments,consistently exchanging synchronization packets with a fixed base station or access point is challenging.This issue is further exacerbated in signal shadowed areas such as urban canyons,tunnels,or large-scale indoor hallswhere other technologies,such as global navigation satellite system(GNSS),are unavailable.One-way synchronization techniques offer a feasible approach under such transient connectivity conditions.One-way schemes still suffer from long convergence times to reach the required synchronization accuracy in these circumstances.In this paper,we propose a WLAN-based multi-stage clock synchronization scheme(WMC)tailored for vehicular networks.The proposed method comprises an initial hard update stage to rapidly achieve synchronization,followed by a high-precision stable stage based on Maximum Likelihood Estimation(MLE).By implementing the scheme directly at the network driver,we address key limitations of hard update mechanisms.Our approach significantly reduces the initial period to collect high-quality samples and offset estimation time to reach sub-50μs accuracy,and subsequently transitions to a refined MLE-based synchronization stage,achieving stable accuracy at approximately 30μs.The windowed moving average stabilized(reaching 90%of the baseline)in approximately 35 s,which corresponds to just 5.1%of the baseline time accuracy.Finally,the impact of synchronization performance on the localization model was validated using the Simulation of Urban Mobility(SUMO).The results demonstrate that more accurate conditions for position estimation can be supported,with an improvement about 38.5%in the mean error.
基金supported by the Natural Science Research Project of Colleges and Universities in Anhui Province (No.KJ2021A0479)the Science Research Program of Anhui University of Finance and Economics (No.ACKYC22082)。
文摘We present a gain adaptive tuning method for fiber Raman amplifier(FRA) using two-stage neural networks(NNs) and double weights updates. After training the connection weights of two-stage NNs separately in training phase, the connection weights of the unified NN are updated again in verification phase according to error between the predicted and target gains to eliminate the inherent error of the NNs. The simulation results show that the mean of root mean square error(RMSE) and maximum error of gains are 0.131 d B and 0.281 d B, respectively. It shows that the method can realize adaptive adjustment function of FRA gain with high accuracy.
文摘To combat packet loss and realize robust video transmission over Intemet and wireless networks, a new multiple description （MD） video coding method is proposed. In the method, two descriptions for each video frame is first created by group of blocks （GOB） alternation. Motion information is then duplicated in both the descriptions and a process called low quality macroblock update is designed to redundantly encode textures in each frame using standard bit stream syntax. In this way, the output bit streams are standard compliant and better trade-offs between redundancy and single charmel reconstruction distortion are achieved. The proposed method has much better performance than the well-known MD transform coding （MDTC） method both in terms of redundancy rate distortion, and in the packet loss scenario.
基金supported in part by the Ministry of Higher Education Malaysia(MOHE)through Fundamental Research Grant Scheme(FRGS)Ref:FRGS/1/2024/ICT02/UTM/02/10,Vot.No:R.J130000.7828.5F748the Scientific Research Project of Education Department of Hunan Province(Nos.22B1046 and 24A0771).
文摘Engineering optimization problems are often characterized by high dimensionality,constraints,and complex,multimodal landscapes.Traditional deterministic methods frequently struggle under such conditions,prompting increased interest in swarm intelligence algorithms.Among these,the Cuckoo Search(CS)algorithm stands out for its promising global search capabilities.However,it often suffers from premature convergence when tackling complex problems.To address this limitation,this paper proposes a Grouped Dynamic Adaptive CS(GDACS)algorithm.Theenhancements incorporated intoGDACS can be summarized into two key aspects.Firstly,a chaotic map is employed to generate initial solutions,leveraging the inherent randomness of chaotic sequences to ensure a more uniform distribution across the search space and enhance population diversity from the outset.Secondly,Cauchy and Levy strategies replace the standard CS population update.This strategy involves evaluating the fitness of candidate solutions to dynamically group the population based on performance.Different step-size adaptation strategies are then applied to distinct groups,enabling an adaptive search mechanism that balances exploration and exploitation.Experiments were conducted on six benchmark functions and four constrained engineering design problems,and the results indicate that the proposed GDACS achieves good search efficiency and produces more accurate optimization results compared with other state-of-the-art algorithms.
文摘The original online version of this article was revised:The layout update for Article 758 has impacted the page range in the published issue,but did not affect the scholarly content.To ensure consistency with the originally assigned pages(2595-2614),we will need to publish an erratum to correct the article and restore the original page range.The original article has been corrected.
基金Supported by the National Science Foundation of China (No.62571164)the Natural Science Foundation of Heilongjiang Province (No.PL2024F025)the Fundamental Scientific Research Funds of Heilongjiang Province (No.2022-KYYWF-1050)。
文摘This paper investigates the challenges of structural inconsistency,matching accuracy degradation,and trajectory interruptions caused by high-speed motion,frequent occlusions,and appearance variations of unmanned aerial vehicle(UAV) targets in low-altitude airspace.A novel UAV visual tracking method is proposed for dynamic structural distortions,with a focus on structural consistency modeling to improve system robustness in complex scenarios.Unlike prior methods such as STARK,which rely on spatio-temporal prediction,and KeepTrack,which emphasize template maintenance,our approach enforces structural-level consistency between historical and current features,thereby addressing UAV-specific issues of rapid maneuvering and environmental complexity.The proposed framework features a structure-aware architecture that incorporates dual complementary mechanisms serving as spatial completion and temporal restoration components.First,a multi-scale structure extraction module with adaptive anchor scheduling is developed to dynamically perceive spatial target shape and generate high-quality proposals.Second,a structural memory module is designed to reconstruct local regions by leveraging high-confidence historical structural representations,thereby maintaining spatiotemporal coherence across frames.Furthermore,a structural verification mechanism coupled with a meta-learning-driven re-identification strategy is introduced to detect abrupt structural distortions and adaptively update templates,significantly improving resilience against disturbances.Overall,the main contributions of this paper can be summarized as follows:(1) introducing structural consistency modeling into UAV visual tracking for the first time;(2) designing a unified framework that combines adaptive proposal generation,full-image matching,and re-identification under structural constraints;and(3) achieving state-of-the-art performance on the anti-UAV benchmark,highlighting the method's practical value in real-world UAV surveillance applications.
基金supported by the National University of Defense Technology(NUDT)Research Initiation Funding for High-Level Scientific and Technological Innovative Talents(202402-YJRC-LJ-001)the National Natural Science Foundation of China(Grant No.U2142213)+1 种基金the Basic and Applied Basic Research Foundation of Guangdong Province(Grants 2025A1515011835,2022A1515011870)the National Natural Science Foundation of China(Grant No.42305167)。
文摘The operational Tropical Regional Atmospheric Model System(TRAMS)often underestimates initial typhoon intensity when using the global analysis field as the initial condition.The TRAMS tropical cyclone(TC)initialization scheme,developed based on the incremental analysis updates(IAU)technique,effectively reduces initial bias.However,the original IAU-based TC initialization scheme only adjusts the wind field at the analysis moment,with other variables adjusted implicitly under the model's constraints according to a gradually inserted wind increment(named“univariate adjustment scheme”hereafter).The univariate adjustment scheme requires approximately 3 h to reach a dynamic equilibrium state,which constrains the assimilation of hourly TC observations and causes excessive dissipation of meaningful short-wave information in adjustment increments.To address this limitation,this study develops a multivariate adjustment IAU-based TC initialization scheme that incorporates gradient wind balance and hydrostatic balance as its largescale constraints.Numerical experiments with TC Hato(2017)demonstrate that the multivariate adjustment scheme reduces the IAU relaxation time to 1 h while marginally improving forecast skill.These findings are consistently replicated across 12 additional TC cases.The development of the IAU-based multivariate adjustment initialization scheme establishes a foundation for 4-D initialization using hourly TC observations.
基金supported by the National Natural Science Foundation of China(Grant No.41961134032).
文摘The probabilistic stability evolution analysis of reservoir bank slopes is a crucial aspect of risk assessment,with core challenges including the consideration of deformation mechanisms and accurate determination of mechanical parameters.In this study,a novel time-varying reliability analysis framework based on sequential Bayesian updating of mechanical parameters is proposed.The inverse parameters account for damage time-dependent behavior,incorporating water effect and a strain-driven softening-hardening process that depends on sliding states.The likelihood function is enhanced to simultaneously consider observation error,surrogate model prediction error,and model structural error,with the introduction of physical penalty.Exploration of the high-dimensional parameter space is achieved via the Hamiltonian Monte Carlo(HMC)method and the physics knowledge-based time-dependent deformation surrogate model.The time-varying reliability analysis of the slope is performed using the multi-grid method.Taking a reservoir bank slope as a case study,the sequential updating of 12 mechanical parameters is conducted based on deformation time series from 16 monitoring points,thereby validating the proposed framework.The results indicate that the proposed framework effectively captures the posterior distribution of mechanical parameters,with the case slope remaining in a critically stable state after overall sliding,showing a high failure probability.Introducing model structural error can reduce parameter compensation,and a reasonable sequential updating step size can improve inversion accuracy.
文摘Aiming at the inaccessible problem of remote embedded devices update and maintenance, this paper presents a method using general packet radio service （GPRS） to achieve update based on the embedded real-time operating system （RTOS） μC/OS-Ⅱ. It introduces architecture of the system first. And then it uses LPC1768 chip as the central processing unit, SIM900A module for data transmission, and SST25VF016B to store the data. To ensure accuracy of the data transmis- sion, cyclic redundancy code （CRC） is adopted. The software uses fixed bootstrap and mutable update program, and thus the embedded devices can still normally start in case of update failure. Finally, high stability and extensive adaptability of the system are verified by experimental data.
