The use of low-frequency seismic data improves the seismic resolution, and the imaging and inversion quality. Furthermore, low-frequency data are applied in hydrocarbon exploration; thus, we need to better use low-fre...The use of low-frequency seismic data improves the seismic resolution, and the imaging and inversion quality. Furthermore, low-frequency data are applied in hydrocarbon exploration; thus, we need to better use low-frequency data. In seismic wavelets, the loss of low-frequency data decreases the main lobe amplitude and increases the first side lobe amplitude and results in the periodic shocking attenuation of the secondary side lobe. The loss of low frequencies likely produces pseudo-events and the false appearance of higher resolution. We use models to examine the removal of low-frequency data in seismic data processing. The results suggest that the removal of low frequencies create distortions, especially for steep structures and thin layers. We also perform low-frequency expansion using compressed sensing and sparse constraints and develop the corresponding module. Finally, we apply the proposed method to real common image point gathers with good results.展开更多
Passive seismic data contain large amounts of low-frequency information. To effectively extract and compensate active seismic data that lack low frequencies, we propose a multitaper spectral reconstruction method base...Passive seismic data contain large amounts of low-frequency information. To effectively extract and compensate active seismic data that lack low frequencies, we propose a multitaper spectral reconstruction method based on multiple sinusoidal tapers and derive equations for multisource and multitrace conditions. Compared to conventional cross correlation and deconvolution reconstruction methods, the proposed method can more accurately reconstruct the relative amplitude of recordings. Multidomain iterative denoising improves the SNR of retrieved data. By analyzing the spectral characteristics of passive data before and after reconstruction, we found that the data are expressed more clearly after reconstruction and denoising. To compensate for the low-frequency information in active data using passive seismic data, we match the power spectrum, supplement it, and then smooth it in the frequency domain. Finally, we use numerical simulation to verify the proposed method and conduct prestack depth migration using data after low-frequency compensation. The proposed power-matching method adds the losing low frequency information in the active seismic data using the low-frequency information of passive- source seismic data. The imaging of compensated data gives a more detailed information of deep structures.展开更多
AIM:To study the relationships between amplitude of low-frequency fluctuations(ALFF)changes and clinical ophthalmic parameters in patients with primary open angle glaucoma(POAG)and analyze the diagnostic value of ALFF...AIM:To study the relationships between amplitude of low-frequency fluctuations(ALFF)changes and clinical ophthalmic parameters in patients with primary open angle glaucoma(POAG)and analyze the diagnostic value of ALFF.METHODS:Twenty-four POAG patients and 24 healthy controls(HCs)underwent resting-state functional magnetic resonance imaging(rs-fMRI).Nonparametric rank-sum tests were used to compare the ALFF values in the slow-4 and slow-5 bands,and Spearman or Pearson correlation analysis was used to assess the correlation between ALFF changes and clinical ophthalmic parameters in POAG patients.Receiver operating characteristic(ROC)curves were used to evaluate the diagnostic performance of the ALFF.RESULTS:There were 16 males in POAG patients(median age 48y)and 12 males in HCs(median age 39y).Compared with HCs,POAG patients presented increased or decreased ALFF values in different brain regions,and similar changes were observed in mild POAG patients.The ALFF values were correlated with retinal nerve fiber layer(RNFL)thickness,inner limiting membrane-retinal pigment epithelium thickness changes and the degree of visual field defects.Analysis of the diagnostic value of the ALFF via ROC curves revealed that the right medial frontal gyrus[area under the curve(AUC)=0.9063]and superior frontal gyrus(AUC=0.9097)had better diagnostic value than did the optic disc area(AUC=0.8019),visual field index(VFI%,AUC=0.8988)and macular parameters.CONCLUSION:POAG patients present altered cortical function that is significantly correlated with the optic nerve and retinal thickness and had good diagnostic value,which may reflect the underlying neuropathological mechanism of POAG.展开更多
When tracking a unmanned aerial vehicle(UAV)in complex backgrounds,environmen-tal noise and clutter often obscure it.Traditional radar target tracking algorithms face multiple lim-itations when tracking a UAV,includin...When tracking a unmanned aerial vehicle(UAV)in complex backgrounds,environmen-tal noise and clutter often obscure it.Traditional radar target tracking algorithms face multiple lim-itations when tracking a UAV,including high vulnerability to target occlusion and shape variations,as well as pronounced false alarms and missed detections in low signal-to-noise ratio(SNR)envi-ronments.To address these issues,this paper proposes a UAV detection and tracking algorithm based on a low-frequency communication network.The accuracy and effectiveness of the algorithm are validated through simulation experiments using field-measured point cloud data.Additionally,the key parameters of the algorithm are optimized through a process of selection and comparison,thereby improving the algorithm's precision.The experimental results show that the improved algo-rithm can significantly enhance the detection and tracking performance of the UAV under high clutter density conditions,effectively reduce the false alarm rate and markedly improve overall tracking performance metrics.展开更多
In order to obtain a lower frequency band gap,this paper proposes a novel locally resonant meta-beam incorporating a softening nonlinear factor.An improved camroller structure is designed in this meta-beam to achieve ...In order to obtain a lower frequency band gap,this paper proposes a novel locally resonant meta-beam incorporating a softening nonlinear factor.An improved camroller structure is designed in this meta-beam to achieve the softening nonlinear stiffness of the local oscillators.Firstly,based on Hamilton's principle and the Galerkin method,the control equations for the coupled system are established.The theoretical band gap boundary is then derived with the modal analysis method.The theoretical results reveal that the band gap of the meta-beam shifts towards lower frequencies due to the presence of a softening nonlinear factor,distinguishing it from both linear metamaterials and those with hardening nonlinear characteristics.Then,the vibration attenuation characteristics of a finite size meta-beam are investigated through numerical calculation,and are verified by the theoretical results.Furthermore,parameter studies indicate that the reasonable design of the local oscillator parameters based on lightweight principles helps to achieve further broadband and efficient vibration reduction in the low-frequency region.Finally,a prototype of the meta-beam is fabricated and assembled,and the formations of the low-frequency band gap and the amplitude-induced band gap phenomenon are verified through experiments.展开更多
Low-frequency vibroseis acquisition has become a routine operation in land seismic surveys,given the advantages of low-frequency signals in characterizing geological structures and enhancing the imaging of deep explor...Low-frequency vibroseis acquisition has become a routine operation in land seismic surveys,given the advantages of low-frequency signals in characterizing geological structures and enhancing the imaging of deep exploration targets.The two key points of low-frequency sweep design techniques include controlling the distortion and improving the output energy during the low-frequency stage.However,the vibrators are limited by the maximum fl ow provided by the hydraulic systems at the low-frequency stage,causing difficulty in satisfying exploration energy requirements.Initially,a theoretical analysis of the low-frequency acquisition performance of vibrators is conducted.A theoretical maximum output force below 10 Hz is obtained by guiding through theoretical formulas and combining actual vibrator parameters.Then,the signal is optimized according to the surface characteristics of the operation area.Finally,detailed application quality control and operational procedures are established.The new low-frequency sweep design method has overcome the maximum flow limitations of the hydraulic system,increased the low-frequency energy,and achieved broadband acquisition.The designed signal has been tested and applied on various types of ground surfaces in the Middle East desert region,yielding good performance.The proposed low-frequency sweep design method holds considerable value for the application of conventional vibroseis in low-frequency acquisition.展开更多
Controlling low-frequency noise presents a significant challenge for traditional sound absorption materials,such as foams and fibrous substances.Recently developed acoustic absorption metamaterials,which rely on local...Controlling low-frequency noise presents a significant challenge for traditional sound absorption materials,such as foams and fibrous substances.Recently developed acoustic absorption metamaterials,which rely on local resonance can effectively balance the volume occupation and low-frequency absorption performance.However,these materials often exhibit a very narrow and fixed absorption band.Inspired by Helmholtz resonators and bistable structures,we propose bistable reconfigurable acoustic metamaterials(BRAMs)that offer multiband low-frequency absorption.These BRAMs are fabricated using shape-memory polylactic acid(SM-PLA)via four-dimension(4D)printing technology.Consequently,the geometry and absorption performance of the BRAMs can be adjusted by applying thermal stimuli(at 55℃)to switch between two stable states.The BRAMs demonstrate excellent low-frequency absorption with multiband characteristics,achieving an absorption coefficient of 0.981 at 136 Hz and 0.998 at 230 Hz for stable state I,and coefficients of 0.984 at 156 Hz and 0.961 at 542 Hz for stable state II.It was found that the BRAMs with different inclined plate angles had linear recovery stages,and the recovery speeds range from 0.75 mm/s to 1.1 mm/s.By combining a rational structural design and 4D printing,the reported reconfigurable acoustic metamaterials will inspire further studies on the design of dynamic and broadband absorption devices.展开更多
Metamaterials can control and manipulate acoustic/elastic waves on a subwavelength scale using cavities or additional components.However,the large cavity and weak stiffness components of traditional metamaterials may ...Metamaterials can control and manipulate acoustic/elastic waves on a subwavelength scale using cavities or additional components.However,the large cavity and weak stiffness components of traditional metamaterials may cause a conflict between vibroacoustic reduction and load-bearing capacity,and thus limit their application.Here,we propose a lightweight multifunctional metamaterial that can simultaneously achieve low-frequency sound insulation,broadband vibration reduction,and excellent load-bearing performance,named as vibroacoustic isolation and bearing metamaterial(VIBM).The advent of additive manufacturing technology provides a convenient and reliable method for the fabrication of VIBM samples.The results show that the compressive strength of the VIBM is as high as 9.71 MPa,which is nearly 87.81%higher than that of the conventional grid structure(CGS)under the same volume fraction.Moreover,the vibration and sound transmission are significantly reduced over a low and wide frequency range,which agrees well with the experimental data,and the reduction degree is obviously larger than that obtained by the CGS.The design strategy can effectively realize the key components of metamaterials and improve their application scenarios.展开更多
Low-frequency structural vibrations caused by poor rigidity are one of the main obstacles limiting the machining efficiency of robotic milling.Existing vibration suppression strategies primarily focus on passive vibra...Low-frequency structural vibrations caused by poor rigidity are one of the main obstacles limiting the machining efficiency of robotic milling.Existing vibration suppression strategies primarily focus on passive vibration absorption at the robotic end and feedback control at the joint motor.Although these strategies have a certain vibration suppression effect,the limitations of robotic flexibility and the extremely limited applicable speed range remain to be overcome.In this study,a Magnetorheological Joint Damper(MRJD)is developed.The joint-mounted feature ensures machining flexibility of the robot,and the millisecond response time of the Magnetorheological Fluid(MRF)ensures a large effective spindle speed range.More importantly,the evolution law of the damping performance of MRJD was revealed based on a low-frequency chatter mechanism,which guarantees the application of MRJD in robotic milling machining.To analyze the influence of the robotic joint angle on the suppression effect of the MRJD,the joint braking coefficient and end braking coefficient were proposed.Parallel coordinate plots were used to visualize the joint range with the optimal vibration suppression effect.Finally,a combination of different postures and cutting parameters was used to verify the vibration suppression effect and feasibility of the joint angle optimization.The experimental results show that the MRJD,which directly improves the joint vibration resistance,can effectively suppress the low-frequency vibration of robotic milling under a variety of cutting conditions.展开更多
Objective:To study the clinical efficacy of applying the Meridian Flow Low-Frequency Therapy Device combined with Chinese herbal enema for patients with acute and chronic pelvic inflammatory disease(PID).Methods:Sixty...Objective:To study the clinical efficacy of applying the Meridian Flow Low-Frequency Therapy Device combined with Chinese herbal enema for patients with acute and chronic pelvic inflammatory disease(PID).Methods:Sixty-two patients with acute and chronic PID admitted from January 2024 to October 2025 were selected and randomly divided into a standard group(n=31)and an experimental group(n=31).The standard group received conventional medication+Chinese herbal enema treatment.The experimental group received the Meridian Flow Low-Frequency Therapy Device in addition to the standard group’s treatment.The clinical efficacy,changes in inflammatory markers,and pelvic improvement were compared between the two groups.Results:The excellent-good rate of treatment in the experimental group was higher than that in the standard group(p<0.05).The levels of various inflammatory factors in the experimental group were lower than those in the standard group(all p<0.05).The improvement in pelvic mass diameter and pelvic effusion depth in the experimental group was superior to that in the standard group(both p<0.05).Conclusion:The Meridian Flow Low-Frequency Therapy Device combined with Chinese herbal enema has a definite curative effect in treating pelvic inflammatory disease.It can effectively alleviate clinical symptoms,reduce inflammatory response,and promote the improvement of the pelvic environment.展开更多
Earthquakes are highly destructive spatio-temporal phenomena whose analysis is essential for disaster preparedness and risk mitigation.Modern seismological research produces vast volumes of heterogeneous data from sei...Earthquakes are highly destructive spatio-temporal phenomena whose analysis is essential for disaster preparedness and risk mitigation.Modern seismological research produces vast volumes of heterogeneous data from seismic networks,satellite observations,and geospatial repositories,creating the need for scalable infrastructures capable of integrating and analyzing such data to support intelligent decision-making.Data warehousing technologies provide a robust foundation for this purpose;however,existing earthquake-oriented data warehouses remain limited,often relying on simplified schemas,domain-specific analytics,or cataloguing efforts.This paper presents the design and implementation of a spatio-temporal data warehouse for seismic activity.The framework integrates spatial and temporal dimensions in a unified schema and introduces a novel array-based approach for managing many-to-many relationships between facts and dimensions without intermediate bridge tables.A comparative evaluation against a conventional bridge-table schema demonstrates that the array-based design improves fact-centric query performance,while the bridge-table schema remains advantageous for dimension-centric queries.To reconcile these trade-offs,a hybrid schema is proposed that retains both representations,ensuring balanced efficiency across heterogeneous workloads.The proposed framework demonstrates how spatio-temporal data warehousing can address schema complexity,improve query performance,and support multidimensional visualization.In doing so,it provides a foundation for integrating seismic analysis into broader big data-driven intelligent decision systems for disaster resilience,risk mitigation,and emergency management.展开更多
For real-time processing of ultra-wide bandwidth low-frequency pulsar baseband data,we designed and implemented an ultra-wide bandwidth low-frequency pulsar data processing pipeline(UWLPIPE)based on the shared ringbuf...For real-time processing of ultra-wide bandwidth low-frequency pulsar baseband data,we designed and implemented an ultra-wide bandwidth low-frequency pulsar data processing pipeline(UWLPIPE)based on the shared ringbuffer and GPU parallel technology.UWLPIPE runs on the GPU cluster and can simultaneously receive multiple 128 MHz dual-polarization VDIF data packets preprocessed by the front-end FPGA.After aligning the dual-polarization data,multiple 128M subband data are packaged into PSRDADA baseband data or multi-channel coherent dispersion filterbank data,and multiple subband filterbank data can be spliced into wideband data after time alignment.We used the Nanshan 26 m radio telescope with the L-band receiver at964~1732 MHz to observe multiple pulsars.Finally,we processed the data using DSPSR software,and the results showed that each subband could correctly fold out the pulse profile,and the wideband pulse profile accumulated by multiple subbands could be correctly aligned.展开更多
Accurately assessing the relationship between tree growth and climatic factors is of great importance in dendrochronology.This study evaluated the consistency between alternative climate datasets(including station and...Accurately assessing the relationship between tree growth and climatic factors is of great importance in dendrochronology.This study evaluated the consistency between alternative climate datasets(including station and gridded data)and actual climate data(fixed-point observations near the sampling sites),in northeastern China’s warm temperate zone and analyzed differences in their correlations with tree-ring width index.The results were:(1)Gridded temperature data,as well as precipitation and relative humidity data from the Huailai meteorological station,was more consistent with the actual climate data;in contrast,gridded soil moisture content data showed significant discrepancies.(2)Horizontal distance had a greater impact on the representativeness of actual climate conditions than vertical elevation differences.(3)Differences in consistency between alternative and actual climate data also affected their correlations with tree-ring width indices.In some growing season months,correlation coefficients,both in magnitude and sign,differed significantly from those based on actual data.The selection of different alternative climate datasets can lead to biased results in assessing forest responses to climate change,which is detrimental to the management of forest ecosystems in harsh environments.Therefore,the scientific and rational selection of alternative climate data is essential for dendroecological and climatological research.展开更多
Photoacoustic-computed tomography is a novel imaging technique that combines high absorption contrast and deep tissue penetration capability,enabling comprehensive three-dimensional imaging of biological targets.Howev...Photoacoustic-computed tomography is a novel imaging technique that combines high absorption contrast and deep tissue penetration capability,enabling comprehensive three-dimensional imaging of biological targets.However,the increasing demand for higher resolution and real-time imaging results in significant data volume,limiting data storage,transmission and processing efficiency of system.Therefore,there is an urgent need for an effective method to compress the raw data without compromising image quality.This paper presents a photoacoustic-computed tomography 3D data compression method and system based on Wavelet-Transformer.This method is based on the cooperative compression framework that integrates wavelet hard coding with deep learning-based soft decoding.It combines the multiscale analysis capability of wavelet transforms with the global feature modeling advantage of Transformers,achieving high-quality data compression and reconstruction.Experimental results using k-wave simulation suggest that the proposed compression system has advantages under extreme compression conditions,achieving a raw data compression ratio of up to 1:40.Furthermore,three-dimensional data compression experiment using in vivo mouse demonstrated that the maximum peak signal-to-noise ratio(PSNR)and structural similarity index(SSIM)values of reconstructed images reached 38.60 and 0.9583,effectively overcoming detail loss and artifacts introduced by raw data compression.All the results suggest that the proposed system can significantly reduce storage requirements and hardware cost,enhancing computational efficiency and image quality.These advantages support the development of photoacoustic-computed tomography toward higher efficiency,real-time performance and intelligent functionality.展开更多
Amid the increasing demand for data sharing,the need for flexible,secure,and auditable access control mechanisms has garnered significant attention in the academic community.However,blockchain-based ciphertextpolicy a...Amid the increasing demand for data sharing,the need for flexible,secure,and auditable access control mechanisms has garnered significant attention in the academic community.However,blockchain-based ciphertextpolicy attribute-based encryption(CP-ABE)schemes still face cumbersome ciphertext re-encryption and insufficient oversight when handling dynamic attribute changes and cross-chain collaboration.To address these issues,we propose a dynamic permission attribute-encryption scheme for multi-chain collaboration.This scheme incorporates a multiauthority architecture for distributed attribute management and integrates an attribute revocation and granting mechanism that eliminates the need for ciphertext re-encryption,effectively reducing both computational and communication overhead.It leverages the InterPlanetary File System(IPFS)for off-chain data storage and constructs a cross-chain regulatory framework—comprising a Hyperledger Fabric business chain and a FISCO BCOS regulatory chain—to record changes in decryption privileges and access behaviors in an auditable manner.Security analysis shows selective indistinguishability under chosen-plaintext attack(sIND-CPA)security under the decisional q-Parallel Bilinear Diffie-Hellman Exponent Assumption(q-PBDHE).In the performance and experimental evaluations,we compared the proposed scheme with several advanced schemes.The results show that,while preserving security,the proposed scheme achieves higher encryption/decryption efficiency and lower storage overhead for ciphertexts and keys.展开更多
With the popularization of new technologies,telephone fraud has become the main means of stealing money and personal identity information.Taking inspiration from the website authentication mechanism,we propose an end-...With the popularization of new technologies,telephone fraud has become the main means of stealing money and personal identity information.Taking inspiration from the website authentication mechanism,we propose an end-to-end datamodem scheme that transmits the caller’s digital certificates through a voice channel for the recipient to verify the caller’s identity.Encoding useful information through voice channels is very difficult without the assistance of telecommunications providers.For example,speech activity detection may quickly classify encoded signals as nonspeech signals and reject input waveforms.To address this issue,we propose a novel modulation method based on linear frequency modulation that encodes 3 bits per symbol by varying its frequency,shape,and phase,alongside a lightweightMobileNetV3-Small-based demodulator for efficient and accurate signal decoding on resource-constrained devices.This method leverages the unique characteristics of linear frequency modulation signals,making them more easily transmitted and decoded in speech channels.To ensure reliable data delivery over unstable voice links,we further introduce a robust framing scheme with delimiter-based synchronization,a sample-level position remedying algorithm,and a feedback-driven retransmission mechanism.We have validated the feasibility and performance of our system through expanded real-world evaluations,demonstrating that it outperforms existing advanced methods in terms of robustness and data transfer rate.This technology establishes the foundational infrastructure for reliable certificate delivery over voice channels,which is crucial for achieving strong caller authentication and preventing telephone fraud at its root cause.展开更多
Missing data presents a crucial challenge in data analysis,especially in high-dimensional datasets,where missing data often leads to biased conclusions and degraded model performance.In this study,we present a novel a...Missing data presents a crucial challenge in data analysis,especially in high-dimensional datasets,where missing data often leads to biased conclusions and degraded model performance.In this study,we present a novel autoencoder-based imputation framework that integrates a composite loss function to enhance robustness and precision.The proposed loss combines(i)a guided,masked mean squared error focusing on missing entries;(ii)a noise-aware regularization term to improve resilience against data corruption;and(iii)a variance penalty to encourage expressive yet stable reconstructions.We evaluate the proposed model across four missingness mechanisms,such as Missing Completely at Random,Missing at Random,Missing Not at Random,and Missing Not at Random with quantile censorship,under systematically varied feature counts,sample sizes,and missingness ratios ranging from 5%to 60%.Four publicly available real-world datasets(Stroke Prediction,Pima Indians Diabetes,Cardiovascular Disease,and Framingham Heart Study)were used,and the obtained results show that our proposed model consistently outperforms baseline methods,including traditional and deep learning-based techniques.An ablation study reveals the additive value of each component in the loss function.Additionally,we assessed the downstream utility of imputed data through classification tasks,where datasets imputed by the proposed method yielded the highest receiver operating characteristic area under the curve scores across all scenarios.The model demonstrates strong scalability and robustness,improving performance with larger datasets and higher feature counts.These results underscore the capacity of the proposed method to produce not only numerically accurate but also semantically useful imputations,making it a promising solution for robust data recovery in clinical applications.展开更多
Lightweight nodes are crucial for blockchain scalability,but verifying the availability of complete block data puts significant strain on bandwidth and latency.Existing data availability sampling(DAS)schemes either re...Lightweight nodes are crucial for blockchain scalability,but verifying the availability of complete block data puts significant strain on bandwidth and latency.Existing data availability sampling(DAS)schemes either require trusted setups or suffer from high communication overhead and low verification efficiency.This paper presents ISTIRDA,a DAS scheme that lets light clients certify availability by sampling small random codeword symbols.Built on ISTIR,an improved Reed–Solomon interactive oracle proof of proximity,ISTIRDA combines adaptive folding with dynamic code rate adjustment to preserve soundness while lowering communication.This paper formalizes opening consistency and prove security with bounded error in the random oracle model,giving polylogarithmic verifier queries and no trusted setup.In a prototype compared with FRIDA under equal soundness,ISTIRDA reduces communication by 40.65%to 80%.For data larger than 16 MB,ISTIRDA verifies faster and the advantage widens;at 128 MB,proofs are about 60%smaller and verification time is roughly 25%shorter,while prover overhead remains modest.In peer-to-peer emulation under injected latency and loss,ISTIRDA reaches confidence more quickly and is less sensitive to packet loss and load.These results indicate that ISTIRDA is a scalable and provably secure DAS scheme suitable for high-throughput,large-block public blockchains,substantially easing bandwidth and latency pressure on lightweight nodes.展开更多
With the accelerating aging process of China’s population,the demand for community elderly care services has shown diversified and personalized characteristics.However,problems such as insufficient total care service...With the accelerating aging process of China’s population,the demand for community elderly care services has shown diversified and personalized characteristics.However,problems such as insufficient total care service resources,uneven distribution,and prominent supply-demand contradictions have seriously affected service quality.Big data technology,with core advantages including data collection,analysis and mining,and accurate prediction,provides a new solution for the allocation of community elderly care service resources.This paper systematically studies the application value of big data technology in the allocation of community elderly care service resources from three aspects:resource allocation efficiency,service accuracy,and management intelligence.Combined with practical needs,it proposes optimal allocation strategies such as building a big data analysis platform and accurately grasping the elderly’s care needs,striving to provide operable path references for the construction of community elderly care service systems,promoting the early realization of the elderly care service goal of“adequate support and proper care for the elderly”,and boosting the high-quality development of China’s elderly care service industry.展开更多
Multivariate anomaly detection plays a critical role in maintaining the stable operation of information systems.However,in existing research,multivariate data are often influenced by various factors during the data co...Multivariate anomaly detection plays a critical role in maintaining the stable operation of information systems.However,in existing research,multivariate data are often influenced by various factors during the data collection process,resulting in temporal misalignment or displacement.Due to these factors,the node representations carry substantial noise,which reduces the adaptability of the multivariate coupled network structure and subsequently degrades anomaly detection performance.Accordingly,this study proposes a novel multivariate anomaly detection model grounded in graph structure learning.Firstly,a recommendation strategy is employed to identify strongly coupled variable pairs,which are then used to construct a recommendation-driven multivariate coupling network.Secondly,a multi-channel graph encoding layer is used to dynamically optimize the structural properties of the multivariate coupling network,while a multi-head attention mechanism enhances the spatial characteristics of the multivariate data.Finally,unsupervised anomaly detection is conducted using a dynamic threshold selection algorithm.Experimental results demonstrate that effectively integrating the structural and spatial features of multivariate data significantly mitigates anomalies caused by temporal dependency misalignment.展开更多
基金supported by the National Science and Technology Major Project(No.2011ZX05051)Science and Technology Project of Shengli Oilfi eld(No.YKW1301)
文摘The use of low-frequency seismic data improves the seismic resolution, and the imaging and inversion quality. Furthermore, low-frequency data are applied in hydrocarbon exploration; thus, we need to better use low-frequency data. In seismic wavelets, the loss of low-frequency data decreases the main lobe amplitude and increases the first side lobe amplitude and results in the periodic shocking attenuation of the secondary side lobe. The loss of low frequencies likely produces pseudo-events and the false appearance of higher resolution. We use models to examine the removal of low-frequency data in seismic data processing. The results suggest that the removal of low frequencies create distortions, especially for steep structures and thin layers. We also perform low-frequency expansion using compressed sensing and sparse constraints and develop the corresponding module. Finally, we apply the proposed method to real common image point gathers with good results.
基金sponsored by the Natural Science Foundation of China(No.41374115)National High Technology Research and Development Program of China(863 project)(No.2014AA06A605)
文摘Passive seismic data contain large amounts of low-frequency information. To effectively extract and compensate active seismic data that lack low frequencies, we propose a multitaper spectral reconstruction method based on multiple sinusoidal tapers and derive equations for multisource and multitrace conditions. Compared to conventional cross correlation and deconvolution reconstruction methods, the proposed method can more accurately reconstruct the relative amplitude of recordings. Multidomain iterative denoising improves the SNR of retrieved data. By analyzing the spectral characteristics of passive data before and after reconstruction, we found that the data are expressed more clearly after reconstruction and denoising. To compensate for the low-frequency information in active data using passive seismic data, we match the power spectrum, supplement it, and then smooth it in the frequency domain. Finally, we use numerical simulation to verify the proposed method and conduct prestack depth migration using data after low-frequency compensation. The proposed power-matching method adds the losing low frequency information in the active seismic data using the low-frequency information of passive- source seismic data. The imaging of compensated data gives a more detailed information of deep structures.
基金Supported by National Natural Science Foundation of China(No.82260203).
文摘AIM:To study the relationships between amplitude of low-frequency fluctuations(ALFF)changes and clinical ophthalmic parameters in patients with primary open angle glaucoma(POAG)and analyze the diagnostic value of ALFF.METHODS:Twenty-four POAG patients and 24 healthy controls(HCs)underwent resting-state functional magnetic resonance imaging(rs-fMRI).Nonparametric rank-sum tests were used to compare the ALFF values in the slow-4 and slow-5 bands,and Spearman or Pearson correlation analysis was used to assess the correlation between ALFF changes and clinical ophthalmic parameters in POAG patients.Receiver operating characteristic(ROC)curves were used to evaluate the diagnostic performance of the ALFF.RESULTS:There were 16 males in POAG patients(median age 48y)and 12 males in HCs(median age 39y).Compared with HCs,POAG patients presented increased or decreased ALFF values in different brain regions,and similar changes were observed in mild POAG patients.The ALFF values were correlated with retinal nerve fiber layer(RNFL)thickness,inner limiting membrane-retinal pigment epithelium thickness changes and the degree of visual field defects.Analysis of the diagnostic value of the ALFF via ROC curves revealed that the right medial frontal gyrus[area under the curve(AUC)=0.9063]and superior frontal gyrus(AUC=0.9097)had better diagnostic value than did the optic disc area(AUC=0.8019),visual field index(VFI%,AUC=0.8988)and macular parameters.CONCLUSION:POAG patients present altered cortical function that is significantly correlated with the optic nerve and retinal thickness and had good diagnostic value,which may reflect the underlying neuropathological mechanism of POAG.
基金supported in part by National Natural Science Founda-tion of China(No.62372284)in part by Shanghai Nat-ural Science Foundation(No.24ZR1421800).
文摘When tracking a unmanned aerial vehicle(UAV)in complex backgrounds,environmen-tal noise and clutter often obscure it.Traditional radar target tracking algorithms face multiple lim-itations when tracking a UAV,including high vulnerability to target occlusion and shape variations,as well as pronounced false alarms and missed detections in low signal-to-noise ratio(SNR)envi-ronments.To address these issues,this paper proposes a UAV detection and tracking algorithm based on a low-frequency communication network.The accuracy and effectiveness of the algorithm are validated through simulation experiments using field-measured point cloud data.Additionally,the key parameters of the algorithm are optimized through a process of selection and comparison,thereby improving the algorithm's precision.The experimental results show that the improved algo-rithm can significantly enhance the detection and tracking performance of the UAV under high clutter density conditions,effectively reduce the false alarm rate and markedly improve overall tracking performance metrics.
基金supported by the National Natural Science Foundation of China(Nos.12172014,U224126412332001)。
文摘In order to obtain a lower frequency band gap,this paper proposes a novel locally resonant meta-beam incorporating a softening nonlinear factor.An improved camroller structure is designed in this meta-beam to achieve the softening nonlinear stiffness of the local oscillators.Firstly,based on Hamilton's principle and the Galerkin method,the control equations for the coupled system are established.The theoretical band gap boundary is then derived with the modal analysis method.The theoretical results reveal that the band gap of the meta-beam shifts towards lower frequencies due to the presence of a softening nonlinear factor,distinguishing it from both linear metamaterials and those with hardening nonlinear characteristics.Then,the vibration attenuation characteristics of a finite size meta-beam are investigated through numerical calculation,and are verified by the theoretical results.Furthermore,parameter studies indicate that the reasonable design of the local oscillator parameters based on lightweight principles helps to achieve further broadband and efficient vibration reduction in the low-frequency region.Finally,a prototype of the meta-beam is fabricated and assembled,and the formations of the low-frequency band gap and the amplitude-induced band gap phenomenon are verified through experiments.
基金The authors would like to express their sincere appreciation to the research project of CNPC Geophysical Key Lab(2022DQ0604-4)National Natural Science Foundation of China(Grant No.42074141).
文摘Low-frequency vibroseis acquisition has become a routine operation in land seismic surveys,given the advantages of low-frequency signals in characterizing geological structures and enhancing the imaging of deep exploration targets.The two key points of low-frequency sweep design techniques include controlling the distortion and improving the output energy during the low-frequency stage.However,the vibrators are limited by the maximum fl ow provided by the hydraulic systems at the low-frequency stage,causing difficulty in satisfying exploration energy requirements.Initially,a theoretical analysis of the low-frequency acquisition performance of vibrators is conducted.A theoretical maximum output force below 10 Hz is obtained by guiding through theoretical formulas and combining actual vibrator parameters.Then,the signal is optimized according to the surface characteristics of the operation area.Finally,detailed application quality control and operational procedures are established.The new low-frequency sweep design method has overcome the maximum flow limitations of the hydraulic system,increased the low-frequency energy,and achieved broadband acquisition.The designed signal has been tested and applied on various types of ground surfaces in the Middle East desert region,yielding good performance.The proposed low-frequency sweep design method holds considerable value for the application of conventional vibroseis in low-frequency acquisition.
基金financially supported by National Key Research and Development Program of China(Grant No.2023YFB4604800)National Natural Science Foundation of China(Grant No.52275331)financial support from the Hong Kong Scholars Program(Grant No.XJ2022014).
文摘Controlling low-frequency noise presents a significant challenge for traditional sound absorption materials,such as foams and fibrous substances.Recently developed acoustic absorption metamaterials,which rely on local resonance can effectively balance the volume occupation and low-frequency absorption performance.However,these materials often exhibit a very narrow and fixed absorption band.Inspired by Helmholtz resonators and bistable structures,we propose bistable reconfigurable acoustic metamaterials(BRAMs)that offer multiband low-frequency absorption.These BRAMs are fabricated using shape-memory polylactic acid(SM-PLA)via four-dimension(4D)printing technology.Consequently,the geometry and absorption performance of the BRAMs can be adjusted by applying thermal stimuli(at 55℃)to switch between two stable states.The BRAMs demonstrate excellent low-frequency absorption with multiband characteristics,achieving an absorption coefficient of 0.981 at 136 Hz and 0.998 at 230 Hz for stable state I,and coefficients of 0.984 at 156 Hz and 0.961 at 542 Hz for stable state II.It was found that the BRAMs with different inclined plate angles had linear recovery stages,and the recovery speeds range from 0.75 mm/s to 1.1 mm/s.By combining a rational structural design and 4D printing,the reported reconfigurable acoustic metamaterials will inspire further studies on the design of dynamic and broadband absorption devices.
基金Project supported by the National Natural Science Foundation of China(Nos.11991032 and 52241103)the Hunan Province Graduate Research Innovation Project of China(No.KY0409052440)。
文摘Metamaterials can control and manipulate acoustic/elastic waves on a subwavelength scale using cavities or additional components.However,the large cavity and weak stiffness components of traditional metamaterials may cause a conflict between vibroacoustic reduction and load-bearing capacity,and thus limit their application.Here,we propose a lightweight multifunctional metamaterial that can simultaneously achieve low-frequency sound insulation,broadband vibration reduction,and excellent load-bearing performance,named as vibroacoustic isolation and bearing metamaterial(VIBM).The advent of additive manufacturing technology provides a convenient and reliable method for the fabrication of VIBM samples.The results show that the compressive strength of the VIBM is as high as 9.71 MPa,which is nearly 87.81%higher than that of the conventional grid structure(CGS)under the same volume fraction.Moreover,the vibration and sound transmission are significantly reduced over a low and wide frequency range,which agrees well with the experimental data,and the reduction degree is obviously larger than that obtained by the CGS.The design strategy can effectively realize the key components of metamaterials and improve their application scenarios.
基金supported by the National Natural Science Foundation of China(No.U20A20294)the National Natural Science Foundation of China(No.52322511)the National Natural Science Foundation of China(No.52188102).
文摘Low-frequency structural vibrations caused by poor rigidity are one of the main obstacles limiting the machining efficiency of robotic milling.Existing vibration suppression strategies primarily focus on passive vibration absorption at the robotic end and feedback control at the joint motor.Although these strategies have a certain vibration suppression effect,the limitations of robotic flexibility and the extremely limited applicable speed range remain to be overcome.In this study,a Magnetorheological Joint Damper(MRJD)is developed.The joint-mounted feature ensures machining flexibility of the robot,and the millisecond response time of the Magnetorheological Fluid(MRF)ensures a large effective spindle speed range.More importantly,the evolution law of the damping performance of MRJD was revealed based on a low-frequency chatter mechanism,which guarantees the application of MRJD in robotic milling machining.To analyze the influence of the robotic joint angle on the suppression effect of the MRJD,the joint braking coefficient and end braking coefficient were proposed.Parallel coordinate plots were used to visualize the joint range with the optimal vibration suppression effect.Finally,a combination of different postures and cutting parameters was used to verify the vibration suppression effect and feasibility of the joint angle optimization.The experimental results show that the MRJD,which directly improves the joint vibration resistance,can effectively suppress the low-frequency vibration of robotic milling under a variety of cutting conditions.
文摘Objective:To study the clinical efficacy of applying the Meridian Flow Low-Frequency Therapy Device combined with Chinese herbal enema for patients with acute and chronic pelvic inflammatory disease(PID).Methods:Sixty-two patients with acute and chronic PID admitted from January 2024 to October 2025 were selected and randomly divided into a standard group(n=31)and an experimental group(n=31).The standard group received conventional medication+Chinese herbal enema treatment.The experimental group received the Meridian Flow Low-Frequency Therapy Device in addition to the standard group’s treatment.The clinical efficacy,changes in inflammatory markers,and pelvic improvement were compared between the two groups.Results:The excellent-good rate of treatment in the experimental group was higher than that in the standard group(p<0.05).The levels of various inflammatory factors in the experimental group were lower than those in the standard group(all p<0.05).The improvement in pelvic mass diameter and pelvic effusion depth in the experimental group was superior to that in the standard group(both p<0.05).Conclusion:The Meridian Flow Low-Frequency Therapy Device combined with Chinese herbal enema has a definite curative effect in treating pelvic inflammatory disease.It can effectively alleviate clinical symptoms,reduce inflammatory response,and promote the improvement of the pelvic environment.
文摘Earthquakes are highly destructive spatio-temporal phenomena whose analysis is essential for disaster preparedness and risk mitigation.Modern seismological research produces vast volumes of heterogeneous data from seismic networks,satellite observations,and geospatial repositories,creating the need for scalable infrastructures capable of integrating and analyzing such data to support intelligent decision-making.Data warehousing technologies provide a robust foundation for this purpose;however,existing earthquake-oriented data warehouses remain limited,often relying on simplified schemas,domain-specific analytics,or cataloguing efforts.This paper presents the design and implementation of a spatio-temporal data warehouse for seismic activity.The framework integrates spatial and temporal dimensions in a unified schema and introduces a novel array-based approach for managing many-to-many relationships between facts and dimensions without intermediate bridge tables.A comparative evaluation against a conventional bridge-table schema demonstrates that the array-based design improves fact-centric query performance,while the bridge-table schema remains advantageous for dimension-centric queries.To reconcile these trade-offs,a hybrid schema is proposed that retains both representations,ensuring balanced efficiency across heterogeneous workloads.The proposed framework demonstrates how spatio-temporal data warehousing can address schema complexity,improve query performance,and support multidimensional visualization.In doing so,it provides a foundation for integrating seismic analysis into broader big data-driven intelligent decision systems for disaster resilience,risk mitigation,and emergency management.
基金supported by the National Key R&D Program of China Nos.2021YFC2203502 and 2022YFF0711502the National Natural Science Foundation of China(NSFC)(12173077)+4 种基金the Tianshan Talent Project of Xinjiang Uygur Autonomous Region(2022TSYCCX0095 and2023TSYCCX0112)the Scientific Instrument Developing Project of the Chinese Academy of Sciences,grant No.PTYQ2022YZZD01China National Astronomical Data Center(NADC)the Operation,Maintenance and Upgrading Fund for Astronomical Telescopes and Facility Instruments,budgeted from the Ministry of Finance of China(MOF)and administrated by the Chinese Academy of Sciences(CAS)Natural Science Foundation of Xinjiang Uygur Autonomous Region(2022D01A360)。
文摘For real-time processing of ultra-wide bandwidth low-frequency pulsar baseband data,we designed and implemented an ultra-wide bandwidth low-frequency pulsar data processing pipeline(UWLPIPE)based on the shared ringbuffer and GPU parallel technology.UWLPIPE runs on the GPU cluster and can simultaneously receive multiple 128 MHz dual-polarization VDIF data packets preprocessed by the front-end FPGA.After aligning the dual-polarization data,multiple 128M subband data are packaged into PSRDADA baseband data or multi-channel coherent dispersion filterbank data,and multiple subband filterbank data can be spliced into wideband data after time alignment.We used the Nanshan 26 m radio telescope with the L-band receiver at964~1732 MHz to observe multiple pulsars.Finally,we processed the data using DSPSR software,and the results showed that each subband could correctly fold out the pulse profile,and the wideband pulse profile accumulated by multiple subbands could be correctly aligned.
基金supported by the International Partnership program of the Chinese Academy of Sciences(170GJHZ2023074GC)National Natural Science Foundation of China(42425706 and 42488201)+1 种基金National Key Research and Development Program of China(2024YFF0807902)Beijing Natural Science Foundation(8242041),and China Postdoctoral Science Foundation(2025M770353).
文摘Accurately assessing the relationship between tree growth and climatic factors is of great importance in dendrochronology.This study evaluated the consistency between alternative climate datasets(including station and gridded data)and actual climate data(fixed-point observations near the sampling sites),in northeastern China’s warm temperate zone and analyzed differences in their correlations with tree-ring width index.The results were:(1)Gridded temperature data,as well as precipitation and relative humidity data from the Huailai meteorological station,was more consistent with the actual climate data;in contrast,gridded soil moisture content data showed significant discrepancies.(2)Horizontal distance had a greater impact on the representativeness of actual climate conditions than vertical elevation differences.(3)Differences in consistency between alternative and actual climate data also affected their correlations with tree-ring width indices.In some growing season months,correlation coefficients,both in magnitude and sign,differed significantly from those based on actual data.The selection of different alternative climate datasets can lead to biased results in assessing forest responses to climate change,which is detrimental to the management of forest ecosystems in harsh environments.Therefore,the scientific and rational selection of alternative climate data is essential for dendroecological and climatological research.
基金supported by the National Key R&D Program of China[Grant No.2023YFF0713600]the National Natural Science Foundation of China[Grant No.62275062]+3 种基金Project of Shandong Innovation and Startup Community of High-end Medical Apparatus and Instruments[Grant No.2023-SGTTXM-002 and 2024-SGTTXM-005]the Shandong Province Technology Innovation Guidance Plan(Central Leading Local Science and Technology Development Fund)[Grant No.YDZX2023115]the Taishan Scholar Special Funding Project of Shandong Provincethe Shandong Laboratory of Advanced Biomaterials and Medical Devices in Weihai[Grant No.ZL202402].
文摘Photoacoustic-computed tomography is a novel imaging technique that combines high absorption contrast and deep tissue penetration capability,enabling comprehensive three-dimensional imaging of biological targets.However,the increasing demand for higher resolution and real-time imaging results in significant data volume,limiting data storage,transmission and processing efficiency of system.Therefore,there is an urgent need for an effective method to compress the raw data without compromising image quality.This paper presents a photoacoustic-computed tomography 3D data compression method and system based on Wavelet-Transformer.This method is based on the cooperative compression framework that integrates wavelet hard coding with deep learning-based soft decoding.It combines the multiscale analysis capability of wavelet transforms with the global feature modeling advantage of Transformers,achieving high-quality data compression and reconstruction.Experimental results using k-wave simulation suggest that the proposed compression system has advantages under extreme compression conditions,achieving a raw data compression ratio of up to 1:40.Furthermore,three-dimensional data compression experiment using in vivo mouse demonstrated that the maximum peak signal-to-noise ratio(PSNR)and structural similarity index(SSIM)values of reconstructed images reached 38.60 and 0.9583,effectively overcoming detail loss and artifacts introduced by raw data compression.All the results suggest that the proposed system can significantly reduce storage requirements and hardware cost,enhancing computational efficiency and image quality.These advantages support the development of photoacoustic-computed tomography toward higher efficiency,real-time performance and intelligent functionality.
文摘Amid the increasing demand for data sharing,the need for flexible,secure,and auditable access control mechanisms has garnered significant attention in the academic community.However,blockchain-based ciphertextpolicy attribute-based encryption(CP-ABE)schemes still face cumbersome ciphertext re-encryption and insufficient oversight when handling dynamic attribute changes and cross-chain collaboration.To address these issues,we propose a dynamic permission attribute-encryption scheme for multi-chain collaboration.This scheme incorporates a multiauthority architecture for distributed attribute management and integrates an attribute revocation and granting mechanism that eliminates the need for ciphertext re-encryption,effectively reducing both computational and communication overhead.It leverages the InterPlanetary File System(IPFS)for off-chain data storage and constructs a cross-chain regulatory framework—comprising a Hyperledger Fabric business chain and a FISCO BCOS regulatory chain—to record changes in decryption privileges and access behaviors in an auditable manner.Security analysis shows selective indistinguishability under chosen-plaintext attack(sIND-CPA)security under the decisional q-Parallel Bilinear Diffie-Hellman Exponent Assumption(q-PBDHE).In the performance and experimental evaluations,we compared the proposed scheme with several advanced schemes.The results show that,while preserving security,the proposed scheme achieves higher encryption/decryption efficiency and lower storage overhead for ciphertexts and keys.
文摘With the popularization of new technologies,telephone fraud has become the main means of stealing money and personal identity information.Taking inspiration from the website authentication mechanism,we propose an end-to-end datamodem scheme that transmits the caller’s digital certificates through a voice channel for the recipient to verify the caller’s identity.Encoding useful information through voice channels is very difficult without the assistance of telecommunications providers.For example,speech activity detection may quickly classify encoded signals as nonspeech signals and reject input waveforms.To address this issue,we propose a novel modulation method based on linear frequency modulation that encodes 3 bits per symbol by varying its frequency,shape,and phase,alongside a lightweightMobileNetV3-Small-based demodulator for efficient and accurate signal decoding on resource-constrained devices.This method leverages the unique characteristics of linear frequency modulation signals,making them more easily transmitted and decoded in speech channels.To ensure reliable data delivery over unstable voice links,we further introduce a robust framing scheme with delimiter-based synchronization,a sample-level position remedying algorithm,and a feedback-driven retransmission mechanism.We have validated the feasibility and performance of our system through expanded real-world evaluations,demonstrating that it outperforms existing advanced methods in terms of robustness and data transfer rate.This technology establishes the foundational infrastructure for reliable certificate delivery over voice channels,which is crucial for achieving strong caller authentication and preventing telephone fraud at its root cause.
文摘Missing data presents a crucial challenge in data analysis,especially in high-dimensional datasets,where missing data often leads to biased conclusions and degraded model performance.In this study,we present a novel autoencoder-based imputation framework that integrates a composite loss function to enhance robustness and precision.The proposed loss combines(i)a guided,masked mean squared error focusing on missing entries;(ii)a noise-aware regularization term to improve resilience against data corruption;and(iii)a variance penalty to encourage expressive yet stable reconstructions.We evaluate the proposed model across four missingness mechanisms,such as Missing Completely at Random,Missing at Random,Missing Not at Random,and Missing Not at Random with quantile censorship,under systematically varied feature counts,sample sizes,and missingness ratios ranging from 5%to 60%.Four publicly available real-world datasets(Stroke Prediction,Pima Indians Diabetes,Cardiovascular Disease,and Framingham Heart Study)were used,and the obtained results show that our proposed model consistently outperforms baseline methods,including traditional and deep learning-based techniques.An ablation study reveals the additive value of each component in the loss function.Additionally,we assessed the downstream utility of imputed data through classification tasks,where datasets imputed by the proposed method yielded the highest receiver operating characteristic area under the curve scores across all scenarios.The model demonstrates strong scalability and robustness,improving performance with larger datasets and higher feature counts.These results underscore the capacity of the proposed method to produce not only numerically accurate but also semantically useful imputations,making it a promising solution for robust data recovery in clinical applications.
基金supported in part by the Research Fund of Key Lab of Education Blockchain and Intelligent Technology,Ministry of Education(EBME25-F-08).
文摘Lightweight nodes are crucial for blockchain scalability,but verifying the availability of complete block data puts significant strain on bandwidth and latency.Existing data availability sampling(DAS)schemes either require trusted setups or suffer from high communication overhead and low verification efficiency.This paper presents ISTIRDA,a DAS scheme that lets light clients certify availability by sampling small random codeword symbols.Built on ISTIR,an improved Reed–Solomon interactive oracle proof of proximity,ISTIRDA combines adaptive folding with dynamic code rate adjustment to preserve soundness while lowering communication.This paper formalizes opening consistency and prove security with bounded error in the random oracle model,giving polylogarithmic verifier queries and no trusted setup.In a prototype compared with FRIDA under equal soundness,ISTIRDA reduces communication by 40.65%to 80%.For data larger than 16 MB,ISTIRDA verifies faster and the advantage widens;at 128 MB,proofs are about 60%smaller and verification time is roughly 25%shorter,while prover overhead remains modest.In peer-to-peer emulation under injected latency and loss,ISTIRDA reaches confidence more quickly and is less sensitive to packet loss and load.These results indicate that ISTIRDA is a scalable and provably secure DAS scheme suitable for high-throughput,large-block public blockchains,substantially easing bandwidth and latency pressure on lightweight nodes.
文摘With the accelerating aging process of China’s population,the demand for community elderly care services has shown diversified and personalized characteristics.However,problems such as insufficient total care service resources,uneven distribution,and prominent supply-demand contradictions have seriously affected service quality.Big data technology,with core advantages including data collection,analysis and mining,and accurate prediction,provides a new solution for the allocation of community elderly care service resources.This paper systematically studies the application value of big data technology in the allocation of community elderly care service resources from three aspects:resource allocation efficiency,service accuracy,and management intelligence.Combined with practical needs,it proposes optimal allocation strategies such as building a big data analysis platform and accurately grasping the elderly’s care needs,striving to provide operable path references for the construction of community elderly care service systems,promoting the early realization of the elderly care service goal of“adequate support and proper care for the elderly”,and boosting the high-quality development of China’s elderly care service industry.
基金supported by Natural Science Foundation of Qinghai Province(2025-ZJ-994M)Scientific Research Innovation Capability Support Project for Young Faculty(SRICSPYF-BS2025007)National Natural Science Foundation of China(62566050).
文摘Multivariate anomaly detection plays a critical role in maintaining the stable operation of information systems.However,in existing research,multivariate data are often influenced by various factors during the data collection process,resulting in temporal misalignment or displacement.Due to these factors,the node representations carry substantial noise,which reduces the adaptability of the multivariate coupled network structure and subsequently degrades anomaly detection performance.Accordingly,this study proposes a novel multivariate anomaly detection model grounded in graph structure learning.Firstly,a recommendation strategy is employed to identify strongly coupled variable pairs,which are then used to construct a recommendation-driven multivariate coupling network.Secondly,a multi-channel graph encoding layer is used to dynamically optimize the structural properties of the multivariate coupling network,while a multi-head attention mechanism enhances the spatial characteristics of the multivariate data.Finally,unsupervised anomaly detection is conducted using a dynamic threshold selection algorithm.Experimental results demonstrate that effectively integrating the structural and spatial features of multivariate data significantly mitigates anomalies caused by temporal dependency misalignment.
