With the increasing adoption of intelligent operation and maintenance technologies in urban rail transit,most maintenance systems have been equipped with fault diagnosis modules targeting key components of metro vehic...With the increasing adoption of intelligent operation and maintenance technologies in urban rail transit,most maintenance systems have been equipped with fault diagnosis modules targeting key components of metro vehicles.However,the integration between engineering-level diagnostic algorithms and advanced academic research remains limited.Two major challenges hinder vibration-based fault diagnosis under real-world operating conditions:the complex noise and interference caused by wheel-rail coupling and the typically weak expression of fault features.Considering the widespread application of wavelet transform in noise reduction and the maturity of ensemble empirical mode decomposition(EEMD)in handling nonlinear and non-stationary signals without parameter tuning,this study proposes a diagnostic method that combines wavelet threshold denoising with EEMD.The method was applied to bearing vibration signals collected from an operational subway line.The diagnostic results were consistent with actual disassembly findings,demonstrating the effectiveness and practical value of the proposed approach.展开更多
The Savitzky-Golay(SG)filter,which employs polynomial least-squares approximations to smooth data and estimate derivatives,is widely used for processing noisy data.However,noise suppression by the SG filter is recogni...The Savitzky-Golay(SG)filter,which employs polynomial least-squares approximations to smooth data and estimate derivatives,is widely used for processing noisy data.However,noise suppression by the SG filter is recognized to be limited at data boundaries and high frequencies,which can significantly reduce the signal-to-noise ratio(SNR).To solve this problem,a novel method synergistically integrating Principal Component Analysis(PCA)with SG filtering is proposed in this paper.This approach avoids the is-sue of excessive smoothing associated with larger window sizes.The proposed PCA-SG filtering algorithm was applied to a CO gas sensing system based on Cavity Ring-Down Spectroscopy(CRDS).The perform-ance of the PCA-SG filtering algorithm is demonstrated through comparison with Moving Average Filtering(MAF),Wavelet Transformation(WT),Kalman Filtering(KF),and the SG filter.The results demonstrate that the proposed algorithm exhibits superior noise reduction capabilities compared to the other algorithms evaluated.The SNR of the ring-down signal was improved from 11.8612 dB to 29.0913 dB,and the stand-ard deviation of the extracted ring-down time constant was reduced from 0.037μs to 0.018μs.These results confirm that the proposed PCA-SG filtering algorithm effectively improves the smoothness of the ring-down curve data,demonstrating its feasibility.展开更多
[Objectives]This study was conducted to isolate and identify the components from stems of Polyalthia plagioneura.[Methods]The compounds were isolated and purified by silica gel column,Sephadex LH-20,and C_(18) chromat...[Objectives]This study was conducted to isolate and identify the components from stems of Polyalthia plagioneura.[Methods]The compounds were isolated and purified by silica gel column,Sephadex LH-20,and C_(18) chromatography.Their chemical structures were elucidated on the basis of physicochemical properties and spectral data.[Results]Five compounds were isolated and identified as:di(2-ethylhexyl)phthalate(1),cinnamic anhydride(2),phthalic acid(3),citric acid(4),and syringaldehyde(5).[Conclusions]All compounds were isolated from this plant for the first time.展开更多
Accurate and efficient detection of building changes in remote sensing imagery is crucial for urban planning,disaster emergency response,and resource management.However,existing methods face challenges such as spectra...Accurate and efficient detection of building changes in remote sensing imagery is crucial for urban planning,disaster emergency response,and resource management.However,existing methods face challenges such as spectral similarity between buildings and backgrounds,sensor variations,and insufficient computational efficiency.To address these challenges,this paper proposes a novel Multi-scale Efficient Wavelet-based Change Detection Network(MewCDNet),which integrates the advantages of Convolutional Neural Networks and Transformers,balances computational costs,and achieves high-performance building change detection.The network employs EfficientNet-B4 as the backbone for hierarchical feature extraction,integrates multi-level feature maps through a multi-scale fusion strategy,and incorporates two key modules:Cross-temporal Difference Detection(CTDD)and Cross-scale Wavelet Refinement(CSWR).CTDD adopts a dual-branch architecture that combines pixel-wise differencing with semanticaware Euclidean distance weighting to enhance the distinction between true changes and background noise.CSWR integrates Haar-based Discrete Wavelet Transform with multi-head cross-attention mechanisms,enabling cross-scale feature fusion while significantly improving edge localization and suppressing spurious changes.Extensive experiments on four benchmark datasets demonstrate MewCDNet’s superiority over comparison methods:achieving F1 scores of 91.54%on LEVIR,93.70%on WHUCD,and 64.96%on S2Looking for building change detection.Furthermore,MewCDNet exhibits optimal performance on the multi-class⋅SYSU dataset(F1:82.71%),highlighting its exceptional generalization capability.展开更多
Image captioning,a pivotal research area at the intersection of image understanding,artificial intelligence,and linguistics,aims to generate natural language descriptions for images.This paper proposes an efficient im...Image captioning,a pivotal research area at the intersection of image understanding,artificial intelligence,and linguistics,aims to generate natural language descriptions for images.This paper proposes an efficient image captioning model named Mob-IMWTC,which integrates improved wavelet convolution(IMWTC)with an enhanced MobileNet V3 architecture.The enhanced MobileNet V3 integrates a transformer encoder as its encoding module and a transformer decoder as its decoding module.This innovative neural network significantly reduces the memory space required and model training time,while maintaining a high level of accuracy in generating image descriptions.IMWTC facilitates large receptive fields without significantly increasing the number of parameters or computational overhead.The improvedMobileNet V3 model has its classifier removed,and simultaneously,it employs IMWTC layers to replace the original convolutional layers.This makes Mob-IMWTC exceptionally well-suited for deployment on lowresource devices.Experimental results,based on objective evaluation metrics such as BLEU,ROUGE,CIDEr,METEOR,and SPICE,demonstrate that Mob-IMWTC outperforms state-of-the-art models,including three CNN architectures(CNN-LSTM,CNN-Att-LSTM,CNN-Tran),two mainstream methods(LCM-Captioner,ClipCap),and our previous work(Mob-Tran).Subjective evaluations further validate the model’s superiority in terms of grammaticality,adequacy,logic,readability,and humanness.Mob-IMWTC offers a lightweight yet effective solution for image captioning,making it suitable for deployment on resource-constrained devices.展开更多
Vehicle-induced response separation is a crucial issue in structural health monitoring(SHM).This paper proposes a block-wise sliding recursive wavelet transform algorithm to meet the real-time processing requirements ...Vehicle-induced response separation is a crucial issue in structural health monitoring(SHM).This paper proposes a block-wise sliding recursive wavelet transform algorithm to meet the real-time processing requirements of monitoring data.To extend the separation target from a fixed dataset to a continuously updating data stream,a block-wise sliding framework is first developed.This framework is further optimized considering the characteristics of real-time data streams,and its advantage in computational efficiency is theoretically demonstrated.During the decomposition and reconstruction processes,information from neighboring data blocks is fully utilized to reduce algorithmic complexity.In addition,a delay-setting strategy is introduced for each processing window to mitigate boundary effects,thereby balancing accuracy and efficiency.Simulated signal experiments are conducted to determine the optimal delay configuration and to verify the algorithm’s superior performance,achieving a lower Root Mean Square Error(RMSE)and only 0.0249 times the average computational time compared with the original algorithm.Furthermore,strain signals from the Lieshi River Bridge are employed to validate the method.The proposed algorithm successfully separates the static trend from vehicle-induced responses in real time across different sampling frequencies,demonstrating its effectiveness and applicability in real-time bridge monitoring.展开更多
Low-light image enhancement aims to improve the visibility of severely degraded images captured under insufficient illumination,alleviating the adverse effects of illumination degradation on image quality.Traditional ...Low-light image enhancement aims to improve the visibility of severely degraded images captured under insufficient illumination,alleviating the adverse effects of illumination degradation on image quality.Traditional Retinex-based approaches,inspired by human visual perception of brightness and color,decompose an image into illumination and reflectance components to restore fine details.However,their limited capacity for handling noise and complex lighting conditions often leads to distortions and artifacts in the enhanced results,particularly under extreme low-light scenarios.Although deep learning methods built upon Retinex theory have recently advanced the field,most still suffer frominsufficient interpretability and sub-optimal enhancement performance.This paper presents RetinexWT,a novel framework that tightly integrates classical Retinex theory with modern deep learning.Following Retinex principles,RetinexWT employs wavelet transforms to estimate illumination maps for brightness adjustment.A detail-recovery module that synergistically combines Vision Transformer(ViT)and wavelet transforms is then introduced to guide the restoration of lost details,thereby improving overall image quality.Within the framework,wavelet decomposition splits input features into high-frequency and low-frequency components,enabling scale-specific processing of global illumination/color cues and fine textures.Furthermore,a gating mechanism selectively fuses down-sampled and up-sampled features,while an attention-based fusion strategy enhances model interpretability.Extensive experiments on the LOL dataset demonstrate that RetinexWT surpasses existing Retinex-oriented deeplearning methods,achieving an average Peak Signal-to-Noise Ratio(PSNR)improvement of 0.22 dB over the current StateOfTheArt(SOTA),thereby confirming its superiority in low-light image enhancement.Code is available at https://github.com/CHEN-hJ516/RetinexWT(accessed on 14 October 2025).展开更多
The commercial AM60(Mg−6Al−0.3Mn)die-casting alloy was modified through Mn,Ce,and La micro-alloying,each at a content below 0.2 wt.%.SEM,TEM,and Micro-CT were employed to characterize the microstructures and propertie...The commercial AM60(Mg−6Al−0.3Mn)die-casting alloy was modified through Mn,Ce,and La micro-alloying,each at a content below 0.2 wt.%.SEM,TEM,and Micro-CT were employed to characterize the microstructures and properties of AM60 based alloys.AM60-0.2La alloy showed excellent mechanical properties.The ultimate tensile strength,yield strength,and elongation of(288.0±1.7)MPa,(158.0±1.0)MPa,and(22.0±3.0)%were achieved in AM60-0.2La alloy.Besides,AM60-0.2La alloy exhibited the best corrosion resistance(0.29 mm/a)and fluidity among the investigated four alloys.The excellent mechanical properties and corrosion resistance are mainly attributed to the grain refinement strengthening,low porosity,and low content of large shrinkage porosity,promising for super-sized integrated automotive components.展开更多
Crassostrea gigas has good taste and high nutritional value;however,there are few assessments of comprehensive and panoramic analyses of the nutritional quality of the northern oyster.To study the nutritional characte...Crassostrea gigas has good taste and high nutritional value;however,there are few assessments of comprehensive and panoramic analyses of the nutritional quality of the northern oyster.To study the nutritional characteristics of C.gigas from different sources(ploidy,region,size,and culture mode),C.gigas from various ploidy(diploid and triploid),regions(Rushan,Off-site fattening,and Rongcheng),sizes(small,medium,and large)and culture modes(nearshore and offshore)were selected for comparative analyses.The nutritional components(moisture,protein,fat,and mineral),flavor substances(taste amino acids,nucleotides,and succinic acid),and functional indices(eicosapentaenoic acid(EPA),docosahexaenoic acid(DHA),and taurine)of C.gigas were determined.Principal component analysis(PCA)was used to comprehensively evaluate the oysters and investigate the variations in nutritional quality.The PCA results indicate that protein,essential fatty acids,selenium,zinc,taste amino acids,taurine,EPA,and DHA were core components contributing to 82.25%of the cumulative variance,providing a more comprehensive reflection of the nutrient composition of C.gigas.The extensive quality rankings for the C.gigas were as follows:diploid>triploid,Rushan>fattening>Rongcheng,medium>large>small,and offshore>nearshore.The score rank revealed that diploid oysters of medium-size from Rushan demonstrated superior nutritional quality compared to other tested samples.This is the first comprehensive and systematic investigation of C.gigas in northern China to reveal the feature of nutrients,flavor,and functional components.The study provided data support for the culture,consumption,processing,research,and nutritional quality improvement of oyster industry.展开更多
Enhancing the resilience of critical infrastructure(CI)systems has become a focal point of national and inter-national policies.However,the formulation of resilience enhancement strategies often requires component-(i....Enhancing the resilience of critical infrastructure(CI)systems has become a focal point of national and inter-national policies.However,the formulation of resilience enhancement strategies often requires component-(i.e.asset-)level prioritization,which entails many complexities.Acknowledging the complex and interdependent nature of infrastructure systems,this paper aims to aid researchers,practitioners and policy-makers by pre-senting a review of the relative literature and current state-of-the-art,and by identifying future research op-portunities to improve the applicability and operationalizability of CI component identification and prioritization methods.Theoretical and practical applications are reviewed for definitions,analysis and modelling approaches regarding the resilience of interdependent infrastructure systems.A detailed review of infrastructure criticality definitions,component criticality assessment and prioritization frameworks,from scientific,policy and other documents,is presented.A discussion on social justice and equity dimensions therein is included,which have the potential to greatly influence decisions and should always be incorporated in infrastructure planning and in-vestment discussions.The findings of this review are discussed in terms of applicability and operationalizability.Key recommendations for future research include:(i)developing quantification frameworks for CI component criticality based on formal definitions and multiple criteria,(ii)incorporating the entire resilience cycle of CI in component prioritization,(iii)accounting for the socio-technical nature of CI systems by integrating social di-mensions and their wider operating environment and(iv)developing comprehensive model validation,cali-bration and uncertainty analysis frameworks.展开更多
In wave-equation migration and demigration,the cross-correlation imaging/forwarding step implicitly injects an additional copy of the source wavelet,so that the amplitude spectrum of the wavelet is applied redundantly...In wave-equation migration and demigration,the cross-correlation imaging/forwarding step implicitly injects an additional copy of the source wavelet,so that the amplitude spectrum of the wavelet is applied redundantly(effectively imposing a wavelet-spectrum weighting,often akin to an amplitude-squared bias).This redundancy degrades structural fidelity and amplitude balance yet is frequently overlooked.We(i)formalize the mechanism by which cross-correlation duplicates the source-wavelet amplitude effect in both migration and demigration,and(ii)introduce a source-equalized operator that removes the redundancy by deconvolving(or dividing by)the wavelet amplitude spectrum in the imaging condition and its demigration counterpart,while leaving phase/kinematics intact.Using a band-limited Ricker wavelet on a two-layer model and on Marmousi,we show that,if unmanaged,the redundant wavelet spectrum broadens main lobes,introduces ringing,and suppresses vertical resolution in migrated images,and inflates spectrum mismatches between demigrated and observed data even when peak times agree.With our correction,images recover observed-data-consistent bandwidth and sharpened interfaces,and demigrated data also exhibit improved spectrum conformity and reduced amplitude misfit.The results clarify when source amplitudes matter,why cross-correlation makes them redundantly matter,and how a lightweight spectral correction restores physically meaningful amplitude behavior in wave-equation migration/demigration.展开更多
The multi-pass intermittent local loading process,which features a more flexible processing path,can further enhance the second material distribution during local loading,improve the formability of components,and redu...The multi-pass intermittent local loading process,which features a more flexible processing path,can further enhance the second material distribution during local loading,improve the formability of components,and reduce forming loads.However,the absence of compatible forming equipment makes it difficult to control the constraint in the unloaded zones during the forming process.This difficulty complicates coordination and control of deformation,particularly for asymmetric rib-web components.Additionally,the current implementation involves multi-fire heating,a long process flow,and high energy consumption,which limits the popularization and application of the local loading process.In this study,a new multi-pass local loading hydraulic forming apparatus that can quickly and reliably switch between heavy-load deformation and low-load constraint for different local loading sub-dies was developed.A 10-tonne laboratory prototype was developed,and the forming characteristics during the forming process as well as the response characteristics of the hydraulic system during the multi-pass intermittent local loading of rib-web component were investigated using numerical simulations and physical experiments.Results indicated that,compared to a whole loading process with the same initial geometry of billet,the total forming load(i.e.,the sum of loaded and restrained loads)is reduced by more than 40%with the local loading process,and by nearly 50%with multi-pass local loading.The multi-pass local loading process allows for more effective control of material flow compared to single-pass local loading,leading to improved cavity filling and reduced flow line disturbance.For a large-scale,complex titanium alloy bulkhead,the cavity filling problem was addressed by optimizing the multi-pass local loading path with an unequal thickness billet.The dynamic performance of the multi-pass local loading hydraulic system was found to be robust,with stable pressure transitions during motion and load switching for the sub-die(s).The dynamic characteristic of the hydraulic cylinder when switching from non-moving/unloaded state to a moving/loading state are consistent whether a load is present or not.However,the dynamic characteristics differ when switching from a moving/loading state to non-moving/unloaded state,showing opposite behavior.The developed hydraulic drive mechanism provides a way for implementation of multi-pass local loading without auxiliary operation and extra heating.The results of the study provide a foundation for the industrial production of large-scale,complex components with reduced force requirement and low-energy consumption.展开更多
Lignocellulosic biomass is the most abundant re-newable resource on Earth,boasting advan-tages such as wide avail-ability and negative car-bon emissions.Especial-ly,efficient separation of lignocellulose into cellu-lo...Lignocellulosic biomass is the most abundant re-newable resource on Earth,boasting advan-tages such as wide avail-ability and negative car-bon emissions.Especial-ly,efficient separation of lignocellulose into cellu-lose,hemicellulose and lignin,and realizing val-orization of these compo-nents are more responsive to the development needs of biomass refinery and the green chem-istry era.This review outlines the main components of lignocellulose and briefly summerizes their utilization in chemical raw materials and energy production.It mainly focused on cur-rent advances in component separation methods of lignocellulose by organic solvents,ionic liquids and deep eutectic solvents.The design of separation methods,understanding of sepa-ration mechanisms,and optimization of reaction systems in each method are highlighted in detail.Furthermore,the ongoing challenges and future directions based on mechanism and in-dustrialization are critically discussed.Our goal is to elucidate the separation mechanisms and principles of method design,providing guidance for the development of highly efficient com-ponent separation methods of lignocellulose.展开更多
Predictive maintenance(PdM)is vital for ensuring the reliability,safety,and cost efficiency of heavyduty vehicle fleets.However,real-world sensor data are often highly imbalanced,noisy,and temporally irregular,posing ...Predictive maintenance(PdM)is vital for ensuring the reliability,safety,and cost efficiency of heavyduty vehicle fleets.However,real-world sensor data are often highly imbalanced,noisy,and temporally irregular,posing significant challenges to model robustness and deployment.Using multivariate time-series data from Scania trucks,this study proposes a novel PdM framework that integrates efficient feature summarization with cost-sensitive hierarchical classification.First,the proposed last_k_summary method transforms recent operational records into compact statistical and trend-based descriptors while preserving missingness,allowing LightGBM to leverage its inherent split rules without ad-hoc imputation.Then,a two-stage LightGBM framework is developed for fault detection and severity classification:Stage A performs safety-prioritized fault screening(normal vs.fault)with a false-negativeweighted objective,and Stage B refines the detected faults into four severity levels through a cascaded hierarchy of binary classifiers.Under the official cost matrix of the IDA Industrial Challenge,the framework achieves total misclassification costs of 36,113(validation)and 36,314(test),outperforming XGBoost and Bi-LSTM by 3.8%-13.5%while maintaining high recall for the safety-critical class(0.83 validation,0.77 test).These results demonstrate that the proposed approach not only improves predictive accuracy but also provides a practical and deployable PdM solution that reduces maintenance cost,enhances fleet safety,and supports data-driven decision-making in industrial environments.展开更多
In aerospace,nuclear power,and new energy vehicles industries,utilizing integrated metal components with extreme sizes and/or structures is crucial for achieving significant weight-saving,performance-improvement,and e...In aerospace,nuclear power,and new energy vehicles industries,utilizing integrated metal components with extreme sizes and/or structures is crucial for achieving significant weight-saving,performance-improvement,and excellent reliability.These components,made from metal sheets,rings,or tubes,exhibit characteristics like ultra-thin,ultra-thick,ultra-large,ultra-long,ultra-high ribs,and large variable diameters.During plastic de-formation in metal forming processes,defects such as ruptures,wrinkles,excessive strain differences,and un-expected weak performance areas are likely to occur due to the intersection of multiple effects in different research disciplines,including materials science,processes,and mechanics of materials.Consequently,the smooth forming of integrated parts is difficult.It is the first time to review,summarize,and analyze the ad-vancement of forming methods for producing integrated parts with extreme sizes and structures.The general academic ideas to change the process conditions and sequences to optimize stress state and improve plastic deformation ability for forming the components with extreme sizes/structures are introduced.Practical ex-amples,discussed in detail in the paper,include the forming of(i)integrated ultra-thin and ultra-thick sheet components;(ii)integrated ultra-large size ring components with thin wall and high ribs;and(iii)integrated ultra-long tube components with large perimeter difference.Various plasticity technologies and process se-quences have been developed.The key processes and applications of the technologies are discussed in detail,which achieve successful plastic forming of integrated components.This paper provides state-of-the-art and perspectives for the rapidly advancing material forming fields of key metal components for the next generation of equipment.展开更多
It is an important precondition for machine fault diagnosis that vibrationsignal can be extracted effectively. Based on the characteristic of noise interfused during thecourse of sampling vibration signal, independent...It is an important precondition for machine fault diagnosis that vibrationsignal can be extracted effectively. Based on the characteristic of noise interfused during thecourse of sampling vibration signal, independent component analysis (ICA) method is combined withwavelet to de-noise. Firstly, The sampled signal can be separated with ICA, then the function offrequency band chosen with multi-resolution wavelet transform can be used to judge whether thestochastic disturbance singular signal is interfused. By these ways, the vibration signals can beextracted effectively, which provides favorable condition for subsequent feature detection ofvibration signal and fault diagnosis.展开更多
Speech signals in frequency domain were separated based on discrete wavelet transform (DWT) and independent component analysis (ICA). First, mixed speech signals were decomposed into different frequency domains by DWT...Speech signals in frequency domain were separated based on discrete wavelet transform (DWT) and independent component analysis (ICA). First, mixed speech signals were decomposed into different frequency domains by DWT and the subbands of speech signals were separated using ICA in each wavelet domain; then, the permutation and scaling problems of frequency domain blind source separation (BSS) were solved by utilizing the correlation between adjacent bins in speech signals; at last, source signals were reconstructed from single branches. Experiments were carried out with 2 sources and 6 microphones using speech signals at sampling rate of 40 kHz. The microphones were aligned with 2 sources in front of them, on the left and right. The separation of one male and one female speeches lasted 2.5 s. It is proved that the new method is better than single ICA method and the signal to noise ratio is improved by 1 dB approximately.展开更多
This paper puts forward wavelet transform method to identify P and S phases in three component seismograms using polarization information contained in the wavelet transform coefficients of signal. The P and S wave loc...This paper puts forward wavelet transform method to identify P and S phases in three component seismograms using polarization information contained in the wavelet transform coefficients of signal. The P and S wave locator functions are constructed by using eigenvalue analysis method to wavelet transform coefficient across several scales. Locator functions formed by wavelet transform have stated noise resistance capability, and is proved to be very effective in identifying the P and S arrivals of the test data and actual earthquake data.展开更多
This paper covers a novel method named wavelet packet transform based Elman recurrent neural network(WPTERNN) for the simultaneous kinetic determination of periodate and iodate. The wavelet packet representations of s...This paper covers a novel method named wavelet packet transform based Elman recurrent neural network(WPTERNN) for the simultaneous kinetic determination of periodate and iodate. The wavelet packet representations of signals provide a local time-frequency description, thus in the wavelet packet domain, the quality of the noise removal can be improved. The Elman recurrent network was applied to non-linear multivariate calibration. In this case, by means of optimization, the wavelet function, decomposition level and number of hidden nodes for WPTERNN method were selected as D4, 5 and 5 respectively. A program PWPTERNN was designed to perform multicomponent kinetic determination. The relative standard error of prediction(RSEP) for all the components with WPTERNN, Elman RNN and PLS were 3.23%, 11.8% and 10.9% respectively. The experimental results show that the method is better than the others.展开更多
The discrete time wavelet transform has been used to develop software that detects seismic P and S-phases. The detection algorithm is based on the enhanced amplitude and polarization information provided by the wavele...The discrete time wavelet transform has been used to develop software that detects seismic P and S-phases. The detection algorithm is based on the enhanced amplitude and polarization information provided by the wavelet transform coefficients of the raw seismic data. The algorithm detects phases, determines arrival times and indicates the seismic event direction from three component seismic data that represents the ground displacement in three orthogonal directions. The essential concept is that strong features of the seismic signal are present in the wavelet coefficients across several scales of time and direction. The P-phase is detected by generating a function using polarization information while S-phase is detected by generating a function based on the transverse to radial amplitude ratio. These functions are shown to be very effective metrics in detecting P and S-phases and for determining their arrival times for low signal-to-noise arrivals. Results are compared with arrival times obtained by a human analyst as well as with a standard STA/LTA algorithm from local and regional earthquakes and found to be consistent.展开更多
文摘With the increasing adoption of intelligent operation and maintenance technologies in urban rail transit,most maintenance systems have been equipped with fault diagnosis modules targeting key components of metro vehicles.However,the integration between engineering-level diagnostic algorithms and advanced academic research remains limited.Two major challenges hinder vibration-based fault diagnosis under real-world operating conditions:the complex noise and interference caused by wheel-rail coupling and the typically weak expression of fault features.Considering the widespread application of wavelet transform in noise reduction and the maturity of ensemble empirical mode decomposition(EEMD)in handling nonlinear and non-stationary signals without parameter tuning,this study proposes a diagnostic method that combines wavelet threshold denoising with EEMD.The method was applied to bearing vibration signals collected from an operational subway line.The diagnostic results were consistent with actual disassembly findings,demonstrating the effectiveness and practical value of the proposed approach.
文摘The Savitzky-Golay(SG)filter,which employs polynomial least-squares approximations to smooth data and estimate derivatives,is widely used for processing noisy data.However,noise suppression by the SG filter is recognized to be limited at data boundaries and high frequencies,which can significantly reduce the signal-to-noise ratio(SNR).To solve this problem,a novel method synergistically integrating Principal Component Analysis(PCA)with SG filtering is proposed in this paper.This approach avoids the is-sue of excessive smoothing associated with larger window sizes.The proposed PCA-SG filtering algorithm was applied to a CO gas sensing system based on Cavity Ring-Down Spectroscopy(CRDS).The perform-ance of the PCA-SG filtering algorithm is demonstrated through comparison with Moving Average Filtering(MAF),Wavelet Transformation(WT),Kalman Filtering(KF),and the SG filter.The results demonstrate that the proposed algorithm exhibits superior noise reduction capabilities compared to the other algorithms evaluated.The SNR of the ring-down signal was improved from 11.8612 dB to 29.0913 dB,and the stand-ard deviation of the extracted ring-down time constant was reduced from 0.037μs to 0.018μs.These results confirm that the proposed PCA-SG filtering algorithm effectively improves the smoothness of the ring-down curve data,demonstrating its feasibility.
基金Supported by Jiangxi Education Department Project(GJJ201533)University-level Project of Gannan Medical University(YB201902).
文摘[Objectives]This study was conducted to isolate and identify the components from stems of Polyalthia plagioneura.[Methods]The compounds were isolated and purified by silica gel column,Sephadex LH-20,and C_(18) chromatography.Their chemical structures were elucidated on the basis of physicochemical properties and spectral data.[Results]Five compounds were isolated and identified as:di(2-ethylhexyl)phthalate(1),cinnamic anhydride(2),phthalic acid(3),citric acid(4),and syringaldehyde(5).[Conclusions]All compounds were isolated from this plant for the first time.
基金supported by the Henan Province Key R&D Project under Grant 241111210400the Henan Provincial Science and Technology Research Project under Grants 252102211047,252102211062,252102211055 and 232102210069+2 种基金the Jiangsu Provincial Scheme Double Initiative Plan JSS-CBS20230474,the XJTLU RDF-21-02-008the Science and Technology Innovation Project of Zhengzhou University of Light Industry under Grant 23XNKJTD0205the Higher Education Teaching Reform Research and Practice Project of Henan Province under Grant 2024SJGLX0126。
文摘Accurate and efficient detection of building changes in remote sensing imagery is crucial for urban planning,disaster emergency response,and resource management.However,existing methods face challenges such as spectral similarity between buildings and backgrounds,sensor variations,and insufficient computational efficiency.To address these challenges,this paper proposes a novel Multi-scale Efficient Wavelet-based Change Detection Network(MewCDNet),which integrates the advantages of Convolutional Neural Networks and Transformers,balances computational costs,and achieves high-performance building change detection.The network employs EfficientNet-B4 as the backbone for hierarchical feature extraction,integrates multi-level feature maps through a multi-scale fusion strategy,and incorporates two key modules:Cross-temporal Difference Detection(CTDD)and Cross-scale Wavelet Refinement(CSWR).CTDD adopts a dual-branch architecture that combines pixel-wise differencing with semanticaware Euclidean distance weighting to enhance the distinction between true changes and background noise.CSWR integrates Haar-based Discrete Wavelet Transform with multi-head cross-attention mechanisms,enabling cross-scale feature fusion while significantly improving edge localization and suppressing spurious changes.Extensive experiments on four benchmark datasets demonstrate MewCDNet’s superiority over comparison methods:achieving F1 scores of 91.54%on LEVIR,93.70%on WHUCD,and 64.96%on S2Looking for building change detection.Furthermore,MewCDNet exhibits optimal performance on the multi-class⋅SYSU dataset(F1:82.71%),highlighting its exceptional generalization capability.
基金funded by National Social Science Fund of China,grant number 23BYY197.
文摘Image captioning,a pivotal research area at the intersection of image understanding,artificial intelligence,and linguistics,aims to generate natural language descriptions for images.This paper proposes an efficient image captioning model named Mob-IMWTC,which integrates improved wavelet convolution(IMWTC)with an enhanced MobileNet V3 architecture.The enhanced MobileNet V3 integrates a transformer encoder as its encoding module and a transformer decoder as its decoding module.This innovative neural network significantly reduces the memory space required and model training time,while maintaining a high level of accuracy in generating image descriptions.IMWTC facilitates large receptive fields without significantly increasing the number of parameters or computational overhead.The improvedMobileNet V3 model has its classifier removed,and simultaneously,it employs IMWTC layers to replace the original convolutional layers.This makes Mob-IMWTC exceptionally well-suited for deployment on lowresource devices.Experimental results,based on objective evaluation metrics such as BLEU,ROUGE,CIDEr,METEOR,and SPICE,demonstrate that Mob-IMWTC outperforms state-of-the-art models,including three CNN architectures(CNN-LSTM,CNN-Att-LSTM,CNN-Tran),two mainstream methods(LCM-Captioner,ClipCap),and our previous work(Mob-Tran).Subjective evaluations further validate the model’s superiority in terms of grammaticality,adequacy,logic,readability,and humanness.Mob-IMWTC offers a lightweight yet effective solution for image captioning,making it suitable for deployment on resource-constrained devices.
基金the support of the Major Science and Technology Project of Yunnan Province,China(Grant No.202502AD080007)the National Natural Science Foundation of China(Grant No.52378288)。
文摘Vehicle-induced response separation is a crucial issue in structural health monitoring(SHM).This paper proposes a block-wise sliding recursive wavelet transform algorithm to meet the real-time processing requirements of monitoring data.To extend the separation target from a fixed dataset to a continuously updating data stream,a block-wise sliding framework is first developed.This framework is further optimized considering the characteristics of real-time data streams,and its advantage in computational efficiency is theoretically demonstrated.During the decomposition and reconstruction processes,information from neighboring data blocks is fully utilized to reduce algorithmic complexity.In addition,a delay-setting strategy is introduced for each processing window to mitigate boundary effects,thereby balancing accuracy and efficiency.Simulated signal experiments are conducted to determine the optimal delay configuration and to verify the algorithm’s superior performance,achieving a lower Root Mean Square Error(RMSE)and only 0.0249 times the average computational time compared with the original algorithm.Furthermore,strain signals from the Lieshi River Bridge are employed to validate the method.The proposed algorithm successfully separates the static trend from vehicle-induced responses in real time across different sampling frequencies,demonstrating its effectiveness and applicability in real-time bridge monitoring.
基金supported in part by the National Natural Science Foundation of China[Grant number 62471075]the Major Science and Technology Project Grant of the Chongqing Municipal Education Commission[Grant number KJZD-M202301901].
文摘Low-light image enhancement aims to improve the visibility of severely degraded images captured under insufficient illumination,alleviating the adverse effects of illumination degradation on image quality.Traditional Retinex-based approaches,inspired by human visual perception of brightness and color,decompose an image into illumination and reflectance components to restore fine details.However,their limited capacity for handling noise and complex lighting conditions often leads to distortions and artifacts in the enhanced results,particularly under extreme low-light scenarios.Although deep learning methods built upon Retinex theory have recently advanced the field,most still suffer frominsufficient interpretability and sub-optimal enhancement performance.This paper presents RetinexWT,a novel framework that tightly integrates classical Retinex theory with modern deep learning.Following Retinex principles,RetinexWT employs wavelet transforms to estimate illumination maps for brightness adjustment.A detail-recovery module that synergistically combines Vision Transformer(ViT)and wavelet transforms is then introduced to guide the restoration of lost details,thereby improving overall image quality.Within the framework,wavelet decomposition splits input features into high-frequency and low-frequency components,enabling scale-specific processing of global illumination/color cues and fine textures.Furthermore,a gating mechanism selectively fuses down-sampled and up-sampled features,while an attention-based fusion strategy enhances model interpretability.Extensive experiments on the LOL dataset demonstrate that RetinexWT surpasses existing Retinex-oriented deeplearning methods,achieving an average Peak Signal-to-Noise Ratio(PSNR)improvement of 0.22 dB over the current StateOfTheArt(SOTA),thereby confirming its superiority in low-light image enhancement.Code is available at https://github.com/CHEN-hJ516/RetinexWT(accessed on 14 October 2025).
基金financially supported by the National Key Research and Development Program of China(Nos.2022YFB3709300,2021YFB3701000)the National Natural Science Foundation of China(Nos.52271090,52071036,U2037601,U21A2048)+1 种基金Chongqing Science and Technology Commission,China(Nos.CSTB2022TIAD-KPX0021,CSTC2024YCJHBGZXM0164,CSTB2024TIAD-KPX0001)the Fundamental Research Funds for the Central Universities,China(No.2022CDJDX-002)。
文摘The commercial AM60(Mg−6Al−0.3Mn)die-casting alloy was modified through Mn,Ce,and La micro-alloying,each at a content below 0.2 wt.%.SEM,TEM,and Micro-CT were employed to characterize the microstructures and properties of AM60 based alloys.AM60-0.2La alloy showed excellent mechanical properties.The ultimate tensile strength,yield strength,and elongation of(288.0±1.7)MPa,(158.0±1.0)MPa,and(22.0±3.0)%were achieved in AM60-0.2La alloy.Besides,AM60-0.2La alloy exhibited the best corrosion resistance(0.29 mm/a)and fluidity among the investigated four alloys.The excellent mechanical properties and corrosion resistance are mainly attributed to the grain refinement strengthening,low porosity,and low content of large shrinkage porosity,promising for super-sized integrated automotive components.
基金Supported by the Central Public-interest Scientific Institution Basal Research Fund,YSFRI,CAFS(No.20603022024016)the Central Public-interest Scientific Institution Basal Research Fund,CAFS(Nos.2023TD52,2023TD76)the earmarked fund for CARS(No.CARS-49)。
文摘Crassostrea gigas has good taste and high nutritional value;however,there are few assessments of comprehensive and panoramic analyses of the nutritional quality of the northern oyster.To study the nutritional characteristics of C.gigas from different sources(ploidy,region,size,and culture mode),C.gigas from various ploidy(diploid and triploid),regions(Rushan,Off-site fattening,and Rongcheng),sizes(small,medium,and large)and culture modes(nearshore and offshore)were selected for comparative analyses.The nutritional components(moisture,protein,fat,and mineral),flavor substances(taste amino acids,nucleotides,and succinic acid),and functional indices(eicosapentaenoic acid(EPA),docosahexaenoic acid(DHA),and taurine)of C.gigas were determined.Principal component analysis(PCA)was used to comprehensively evaluate the oysters and investigate the variations in nutritional quality.The PCA results indicate that protein,essential fatty acids,selenium,zinc,taste amino acids,taurine,EPA,and DHA were core components contributing to 82.25%of the cumulative variance,providing a more comprehensive reflection of the nutrient composition of C.gigas.The extensive quality rankings for the C.gigas were as follows:diploid>triploid,Rushan>fattening>Rongcheng,medium>large>small,and offshore>nearshore.The score rank revealed that diploid oysters of medium-size from Rushan demonstrated superior nutritional quality compared to other tested samples.This is the first comprehensive and systematic investigation of C.gigas in northern China to reveal the feature of nutrients,flavor,and functional components.The study provided data support for the culture,consumption,processing,research,and nutritional quality improvement of oyster industry.
基金supported by the European Union’s Horizon 2020 research and innovation programme under grant agreement No.101037424.
文摘Enhancing the resilience of critical infrastructure(CI)systems has become a focal point of national and inter-national policies.However,the formulation of resilience enhancement strategies often requires component-(i.e.asset-)level prioritization,which entails many complexities.Acknowledging the complex and interdependent nature of infrastructure systems,this paper aims to aid researchers,practitioners and policy-makers by pre-senting a review of the relative literature and current state-of-the-art,and by identifying future research op-portunities to improve the applicability and operationalizability of CI component identification and prioritization methods.Theoretical and practical applications are reviewed for definitions,analysis and modelling approaches regarding the resilience of interdependent infrastructure systems.A detailed review of infrastructure criticality definitions,component criticality assessment and prioritization frameworks,from scientific,policy and other documents,is presented.A discussion on social justice and equity dimensions therein is included,which have the potential to greatly influence decisions and should always be incorporated in infrastructure planning and in-vestment discussions.The findings of this review are discussed in terms of applicability and operationalizability.Key recommendations for future research include:(i)developing quantification frameworks for CI component criticality based on formal definitions and multiple criteria,(ii)incorporating the entire resilience cycle of CI in component prioritization,(iii)accounting for the socio-technical nature of CI systems by integrating social di-mensions and their wider operating environment and(iv)developing comprehensive model validation,cali-bration and uncertainty analysis frameworks.
基金supported by the National Natural Science Foundation of China(42430303)Strategy Priority Research Program(Category B)of the Chinese Academy of Sciences(XDB0710000)+2 种基金National Natural Science Foundation of China(42288201)the National Key R&D Program of China(2023YFF0803203)the IGGCAS start-up funding(Grant No.E251510101).
文摘In wave-equation migration and demigration,the cross-correlation imaging/forwarding step implicitly injects an additional copy of the source wavelet,so that the amplitude spectrum of the wavelet is applied redundantly(effectively imposing a wavelet-spectrum weighting,often akin to an amplitude-squared bias).This redundancy degrades structural fidelity and amplitude balance yet is frequently overlooked.We(i)formalize the mechanism by which cross-correlation duplicates the source-wavelet amplitude effect in both migration and demigration,and(ii)introduce a source-equalized operator that removes the redundancy by deconvolving(or dividing by)the wavelet amplitude spectrum in the imaging condition and its demigration counterpart,while leaving phase/kinematics intact.Using a band-limited Ricker wavelet on a two-layer model and on Marmousi,we show that,if unmanaged,the redundant wavelet spectrum broadens main lobes,introduces ringing,and suppresses vertical resolution in migrated images,and inflates spectrum mismatches between demigrated and observed data even when peak times agree.With our correction,images recover observed-data-consistent bandwidth and sharpened interfaces,and demigrated data also exhibit improved spectrum conformity and reduced amplitude misfit.The results clarify when source amplitudes matter,why cross-correlation makes them redundantly matter,and how a lightweight spectral correction restores physically meaningful amplitude behavior in wave-equation migration/demigration.
基金the supports of the National Natural Science Foundation of China(Grant No.52375378)。
文摘The multi-pass intermittent local loading process,which features a more flexible processing path,can further enhance the second material distribution during local loading,improve the formability of components,and reduce forming loads.However,the absence of compatible forming equipment makes it difficult to control the constraint in the unloaded zones during the forming process.This difficulty complicates coordination and control of deformation,particularly for asymmetric rib-web components.Additionally,the current implementation involves multi-fire heating,a long process flow,and high energy consumption,which limits the popularization and application of the local loading process.In this study,a new multi-pass local loading hydraulic forming apparatus that can quickly and reliably switch between heavy-load deformation and low-load constraint for different local loading sub-dies was developed.A 10-tonne laboratory prototype was developed,and the forming characteristics during the forming process as well as the response characteristics of the hydraulic system during the multi-pass intermittent local loading of rib-web component were investigated using numerical simulations and physical experiments.Results indicated that,compared to a whole loading process with the same initial geometry of billet,the total forming load(i.e.,the sum of loaded and restrained loads)is reduced by more than 40%with the local loading process,and by nearly 50%with multi-pass local loading.The multi-pass local loading process allows for more effective control of material flow compared to single-pass local loading,leading to improved cavity filling and reduced flow line disturbance.For a large-scale,complex titanium alloy bulkhead,the cavity filling problem was addressed by optimizing the multi-pass local loading path with an unequal thickness billet.The dynamic performance of the multi-pass local loading hydraulic system was found to be robust,with stable pressure transitions during motion and load switching for the sub-die(s).The dynamic characteristic of the hydraulic cylinder when switching from non-moving/unloaded state to a moving/loading state are consistent whether a load is present or not.However,the dynamic characteristics differ when switching from a moving/loading state to non-moving/unloaded state,showing opposite behavior.The developed hydraulic drive mechanism provides a way for implementation of multi-pass local loading without auxiliary operation and extra heating.The results of the study provide a foundation for the industrial production of large-scale,complex components with reduced force requirement and low-energy consumption.
基金supported by National Key Technolo-gy R&D Program of China(2023YFD1701505)De-velopment Projects in Anhui Province(2022107020013).
文摘Lignocellulosic biomass is the most abundant re-newable resource on Earth,boasting advan-tages such as wide avail-ability and negative car-bon emissions.Especial-ly,efficient separation of lignocellulose into cellu-lose,hemicellulose and lignin,and realizing val-orization of these compo-nents are more responsive to the development needs of biomass refinery and the green chem-istry era.This review outlines the main components of lignocellulose and briefly summerizes their utilization in chemical raw materials and energy production.It mainly focused on cur-rent advances in component separation methods of lignocellulose by organic solvents,ionic liquids and deep eutectic solvents.The design of separation methods,understanding of sepa-ration mechanisms,and optimization of reaction systems in each method are highlighted in detail.Furthermore,the ongoing challenges and future directions based on mechanism and in-dustrialization are critically discussed.Our goal is to elucidate the separation mechanisms and principles of method design,providing guidance for the development of highly efficient com-ponent separation methods of lignocellulose.
基金supported by the GRRC program of Gyeonggi province[GRRC KGU 2023-B01,Research on Intelligent Industrial Data Analytics].
文摘Predictive maintenance(PdM)is vital for ensuring the reliability,safety,and cost efficiency of heavyduty vehicle fleets.However,real-world sensor data are often highly imbalanced,noisy,and temporally irregular,posing significant challenges to model robustness and deployment.Using multivariate time-series data from Scania trucks,this study proposes a novel PdM framework that integrates efficient feature summarization with cost-sensitive hierarchical classification.First,the proposed last_k_summary method transforms recent operational records into compact statistical and trend-based descriptors while preserving missingness,allowing LightGBM to leverage its inherent split rules without ad-hoc imputation.Then,a two-stage LightGBM framework is developed for fault detection and severity classification:Stage A performs safety-prioritized fault screening(normal vs.fault)with a false-negativeweighted objective,and Stage B refines the detected faults into four severity levels through a cascaded hierarchy of binary classifiers.Under the official cost matrix of the IDA Industrial Challenge,the framework achieves total misclassification costs of 36,113(validation)and 36,314(test),outperforming XGBoost and Bi-LSTM by 3.8%-13.5%while maintaining high recall for the safety-critical class(0.83 validation,0.77 test).These results demonstrate that the proposed approach not only improves predictive accuracy but also provides a practical and deployable PdM solution that reduces maintenance cost,enhances fleet safety,and supports data-driven decision-making in industrial environments.
基金Supported by National Natural Science Foundation of China(Grant Nos.52422510,52373320,52175360,50725517)the Young Elite Scientists Sponsorship Program by China Association for Science and Technology(Grant No.2021QNRC001)+1 种基金the Key R&D Program of Hubei Province(Grant No.2024BAB080)Natural Science Foundation of Wuhan(Grant No.2024040801020257).
文摘In aerospace,nuclear power,and new energy vehicles industries,utilizing integrated metal components with extreme sizes and/or structures is crucial for achieving significant weight-saving,performance-improvement,and excellent reliability.These components,made from metal sheets,rings,or tubes,exhibit characteristics like ultra-thin,ultra-thick,ultra-large,ultra-long,ultra-high ribs,and large variable diameters.During plastic de-formation in metal forming processes,defects such as ruptures,wrinkles,excessive strain differences,and un-expected weak performance areas are likely to occur due to the intersection of multiple effects in different research disciplines,including materials science,processes,and mechanics of materials.Consequently,the smooth forming of integrated parts is difficult.It is the first time to review,summarize,and analyze the ad-vancement of forming methods for producing integrated parts with extreme sizes and structures.The general academic ideas to change the process conditions and sequences to optimize stress state and improve plastic deformation ability for forming the components with extreme sizes/structures are introduced.Practical ex-amples,discussed in detail in the paper,include the forming of(i)integrated ultra-thin and ultra-thick sheet components;(ii)integrated ultra-large size ring components with thin wall and high ribs;and(iii)integrated ultra-long tube components with large perimeter difference.Various plasticity technologies and process se-quences have been developed.The key processes and applications of the technologies are discussed in detail,which achieve successful plastic forming of integrated components.This paper provides state-of-the-art and perspectives for the rapidly advancing material forming fields of key metal components for the next generation of equipment.
基金This project is supported by National Natural Science Foundation of China (No.50275154) Municipal Natural Science Foundation of Chongqing, China (No.8773).
文摘It is an important precondition for machine fault diagnosis that vibrationsignal can be extracted effectively. Based on the characteristic of noise interfused during thecourse of sampling vibration signal, independent component analysis (ICA) method is combined withwavelet to de-noise. Firstly, The sampled signal can be separated with ICA, then the function offrequency band chosen with multi-resolution wavelet transform can be used to judge whether thestochastic disturbance singular signal is interfused. By these ways, the vibration signals can beextracted effectively, which provides favorable condition for subsequent feature detection ofvibration signal and fault diagnosis.
基金Supported by Tianjin Municipal Science and Technology Commission (No.09JCYBJC02200)
文摘Speech signals in frequency domain were separated based on discrete wavelet transform (DWT) and independent component analysis (ICA). First, mixed speech signals were decomposed into different frequency domains by DWT and the subbands of speech signals were separated using ICA in each wavelet domain; then, the permutation and scaling problems of frequency domain blind source separation (BSS) were solved by utilizing the correlation between adjacent bins in speech signals; at last, source signals were reconstructed from single branches. Experiments were carried out with 2 sources and 6 microphones using speech signals at sampling rate of 40 kHz. The microphones were aligned with 2 sources in front of them, on the left and right. The separation of one male and one female speeches lasted 2.5 s. It is proved that the new method is better than single ICA method and the signal to noise ratio is improved by 1 dB approximately.
文摘This paper puts forward wavelet transform method to identify P and S phases in three component seismograms using polarization information contained in the wavelet transform coefficients of signal. The P and S wave locator functions are constructed by using eigenvalue analysis method to wavelet transform coefficient across several scales. Locator functions formed by wavelet transform have stated noise resistance capability, and is proved to be very effective in identifying the P and S arrivals of the test data and actual earthquake data.
基金National Natural Science Foundation of China(No.2 996 5 0 0 1) and Natural Science Foundation of InnerMongolia(No.2 0 0 2 2 0 80 2 0 115 )
文摘This paper covers a novel method named wavelet packet transform based Elman recurrent neural network(WPTERNN) for the simultaneous kinetic determination of periodate and iodate. The wavelet packet representations of signals provide a local time-frequency description, thus in the wavelet packet domain, the quality of the noise removal can be improved. The Elman recurrent network was applied to non-linear multivariate calibration. In this case, by means of optimization, the wavelet function, decomposition level and number of hidden nodes for WPTERNN method were selected as D4, 5 and 5 respectively. A program PWPTERNN was designed to perform multicomponent kinetic determination. The relative standard error of prediction(RSEP) for all the components with WPTERNN, Elman RNN and PLS were 3.23%, 11.8% and 10.9% respectively. The experimental results show that the method is better than the others.
文摘The discrete time wavelet transform has been used to develop software that detects seismic P and S-phases. The detection algorithm is based on the enhanced amplitude and polarization information provided by the wavelet transform coefficients of the raw seismic data. The algorithm detects phases, determines arrival times and indicates the seismic event direction from three component seismic data that represents the ground displacement in three orthogonal directions. The essential concept is that strong features of the seismic signal are present in the wavelet coefficients across several scales of time and direction. The P-phase is detected by generating a function using polarization information while S-phase is detected by generating a function based on the transverse to radial amplitude ratio. These functions are shown to be very effective metrics in detecting P and S-phases and for determining their arrival times for low signal-to-noise arrivals. Results are compared with arrival times obtained by a human analyst as well as with a standard STA/LTA algorithm from local and regional earthquakes and found to be consistent.
