In this paper,we present local functional law of the iterated logarithm for Cs?rg?-Révész type increments of fractional Brownian motion.The results obtained extend works of Gantert[Ann.Probab.,1993,21(2):104...In this paper,we present local functional law of the iterated logarithm for Cs?rg?-Révész type increments of fractional Brownian motion.The results obtained extend works of Gantert[Ann.Probab.,1993,21(2):1045-1049]and Monrad and Rootzén[Probab.Theory Related Fields,1995,101(2):173-192].展开更多
A new analytical model for geometric size and forming force prediction in incremental flanging(IF)is presented in this work.The complex deformation characteristics of IF are considered in the modeling process,which ca...A new analytical model for geometric size and forming force prediction in incremental flanging(IF)is presented in this work.The complex deformation characteristics of IF are considered in the modeling process,which can accurately describe the strain and stress states in IF.Based on strain analysis,the model can predict the material thickness distribution and neck height after IF.By considering contact area,strain characteristics,material thickness changes,and friction,the model can predict specific moments and corresponding values of maximum axial forming force and maximum horizontal forming force during IF.In addition,an IF experiment involving different tool diameters,flanging diameters,and opening hole diameters is conducted.On the basis of the experimental strain paths,the strain characteristics of different deformation zones are studied,and the stable strain ratio is quantitatively described through two dimensionless parameters:relative tool diameter and relative hole diameter.Then,the changing of material thickness and forming force in IF,and the variation of minimum material thickness,neck height,maximum axial forming force,and maximum horizontal forming force with flanging parameters are studied,and the reliability of the analytical model is verified in this process.Finally,the influence of the horizontal forming force on the tool design and the fluctuation of the forming force are explained.展开更多
Severe ground-level ozone(O_(3))pollution over major Chinese cities has become one of the most challenging problems,which have deleterious effects on human health and the sustainability of society.This study explored ...Severe ground-level ozone(O_(3))pollution over major Chinese cities has become one of the most challenging problems,which have deleterious effects on human health and the sustainability of society.This study explored the spatiotemporal distribution characteristics of ground-level O_(3) and its precursors based on conventional pollutant and meteorological monitoring data in Zhejiang Province from 2016 to 2021.Then,a high-performance convolutional neural network(CNN)model was established by expanding the moment and the concentration variations to general factors.Finally,the response mechanism of O_(3) to the variation with crucial influencing factors is explored by controlling variables and interpolating target variables.The results indicated that the annual average MDA8-90th concentrations in Zhejiang Province are higher in the northern and lower in the southern.When the wind direction(WD)ranges from east to southwest and the wind speed(WS)ranges between 2 and 3 m/sec,higher O_(3) concentration prone to occur.At different temperatures(T),the O_(3) concentration showed a trend of first increasing and subsequently decreasing with increasing NO_(2) concentration,peaks at the NO_(2) concentration around 0.02mg/m^(3).The sensitivity of NO_(2) to O_(3) formation is not easily affected by temperature,barometric pressure and dew point temperature.Additionally,there is a minimum IRNO_(2) at each temperature when the NO_(2) concentration is 0.03 mg/m^(3),and this minimum IRNO_(2) decreases with increasing temperature.The study explores the response mechanism of O_(3) with the change of driving variables,which can provide a scientific foundation and methodological support for the targeted management of O_(3) pollution.展开更多
This paper presents the design of an asymmetrically variable wingtip anhedral angles morphing aircraft,inspired by biomimetic mechanisms,to enhance lateral maneuver capability.Firstly,we establish a lateral dynamic mo...This paper presents the design of an asymmetrically variable wingtip anhedral angles morphing aircraft,inspired by biomimetic mechanisms,to enhance lateral maneuver capability.Firstly,we establish a lateral dynamic model considering additional forces and moments resulting during the morphing process,and convert it into a Multiple Input Multiple Output(MIMO)virtual control system by importing virtual inputs.Secondly,a classical dynamics inversion controller is designed for the outer-loop system.A new Global Fast Terminal Incremental Sliding Mode Controller(NDO-GFTISMC)is proposed for the inner-loop system,in which an adaptive law is implemented to weaken control surface chattering,and a Nonlinear Disturbance Observer(NDO)is integrated to compensate for unknown disturbances.The whole control system is proven semiglobally uniformly ultimately bounded based on the multi-Lyapunov function method.Furthermore,we consider tracking errors and self-characteristics of actuators,a quadratic programmingbased dynamic control allocation law is designed,which allocates virtual control inputs to the asymmetrically deformed wingtip and rudder.Actuator dynamic models are incorporated to ensure physical realizability of designed allocation law.Finally,comparative experimental results validate the effectiveness of the designed control system and control allocation law.The NDO-GFTISMC features faster convergence,stronger robustness,and 81.25%and 75.0%reduction in maximum state tracking error under uncertainty compared to the Incremental Nonlinear Dynamic Inversion Controller based on NDO(NDO-INDI)and Incremental Sliding Mode Controller based on NDO(NDO-ISMC),respectively.The design of the morphing aircraft significantly enhances lateral maneuver capability,maintaining a substantial control margin during lateral maneuvering,reducing the burden of the rudder surface,and effectively solving the actuator saturation problem of traditional aircraft during lateral maneuvering.展开更多
Soil organic carbon(SOC)from different sources and with distinct chemical properties exhibit variations in their accumulation mechanisms.Exploring the effects of different litter treatments on SOC storage is of great ...Soil organic carbon(SOC)from different sources and with distinct chemical properties exhibit variations in their accumulation mechanisms.Exploring the effects of different litter treatments on SOC storage is of great significance for understanding the formation and accumulation mechanisms of the SOC pool.The feedback mechanisms of new and old SOC in response to tree species and litter treatments were quantitatively analyzed by the C3 plant/C4 soil replacement method.The litter treatments included no litter,aboveground litter,belowground forest litter,and aboveground+belowground litter,totaling four treatments.The results showed that in the first year,cork oak(Quercusvariabilis)exhibited the highest net SOC content increment and net new SOC increment,but the values declined rapidly from the second year onward.The net increment in SOC content was positive at all sample sites,while the priming effect was not significant at any site.Litter treatments had a significant impact on both SOC content and net SOC increment.Compared with aboveground litter,belowground litter was more effective in increasing SOC Content and net SOC increment.展开更多
By using the Chinese stock market data from 2018 to 2024,the weak association between structural trends stocks and market index under investors’preference effect in trading cause the market is lack of liquidity and m...By using the Chinese stock market data from 2018 to 2024,the weak association between structural trends stocks and market index under investors’preference effect in trading cause the market is lack of liquidity and more likely to be dominated by structural trends,as in this market,the willingness to engage in passive trading exceeds that for active trading and investors’preference easy to reverse toward market volatility.The lack of incremental capital in the market often leads to sector-specific rallies rather than broad-based increases,which is one of the key reasons why the Chinese stock market has struggled to achieve overall growth over the long-term period.展开更多
Sheet metal spinning is an incremental forming process for producing axisymmetric thinwalled parts through continuous local deformation under the action of rollers.While studying the spinning process by finite element...Sheet metal spinning is an incremental forming process for producing axisymmetric thinwalled parts through continuous local deformation under the action of rollers.While studying the spinning process by finite element(FE)method,a critical bottleneck is the enormous simulation time.For beating off this challenge,a novel multi-mesh method is developed.The method can dynamically track the movement of rollers and adaptively refine the mesh.Thus,a locally refined quadrilateral computation mesh can be generated in the locally-deforming zone and reduce the unnecessary fine elements outside the locally-deforming zone.In the multi-mesh system,the fine elements and coarse elements are extracted from a storage mesh and a background mesh,respectively.Meanwhile,the hanging nodes in the locally refined mesh are removed by designing 4-refinement templates.Between computation mesh and storage mesh,a bi-cubic parametric surface fitting algorithm and accurate remapping methods are conducted to transmit geometric information and physical fields.The proposed method has been verified by two spinning processes.The results suggest that the method can save time by up to about 67%with satisfactory accuracy,especially for distributions of thickness and strain compared with the fully refined mesh.展开更多
In permanently uneven-aged forests, continuous ingrowth of recruitment into higher stand layers is a critical process for the formation and maintenance of differentiated stand structures. This study analyses the abund...In permanently uneven-aged forests, continuous ingrowth of recruitment into higher stand layers is a critical process for the formation and maintenance of differentiated stand structures. This study analyses the abundance and diversity of recruitment(diameter at breast high(DBH) 0.1–4 cm) across 241 research plots in 11 structurally differentiated Norway spruce(silver fir)-dominated forest stands distributed at altitudes between 500 and 1,440 m a.s.l. The influence of light conditions and lateral competition characteristics on the height increment and crown architecture of recruitment was investigated in detail for 352 Norway spruce and 361 silver fir trees. Light-related variables were confirmed to directly affect the recruitment distribution only to a limited extent. Under relatively low light conditions in montane stands, silver fir reached higher height increments than Norway spruce. The better adaptation of silver fir to shaded conditions was reflected also in its higher apical dominance ratio compared to Norway spruce. The height increment and apical dominance ratio of Norway spruce and silver fir recruitment were positively correlated with indirect radiation, DBH, and relative crown length(RCL), but not with lateral competition. These results confirm that the regulation of light conditions in permanently unevenaged stands is crucial for the growth dynamics of recruitment, as well as for the future proportions of Norway spruce and silver fir in mixed, structurally diverse stands.展开更多
Objective:Diabetic retinopathy(DR)is a top leading cause of blindness worldwide,requiring early detection for timely intervention.Artificial intelligence(AI)has emerged as a promising tool to improve DR screening effi...Objective:Diabetic retinopathy(DR)is a top leading cause of blindness worldwide,requiring early detection for timely intervention.Artificial intelligence(AI)has emerged as a promising tool to improve DR screening efficiency,accessibility,and cost-effectiveness.This study conducted a systematic review of literature and meta-analysis on the economic outcomes of AI-based DR screening.Methods:A systematic review of studies published before September 2024 was conducted throughout PubMed,Scopus,Embase,the Cochrane Library,the National Health Service Economic Evaluation Database,and the Cost-Effectiveness Analysis Registry.Eligible studies were included if they were(1)conducted among type 1 diabetes mellitus or type 2 diabetes mellitus adult diabetic population;(2)studies compared AI-based DR screening strategy to non-AI screening;and(3)performed a cost-effectiveness analysis.Meta-analysis was applied to pool incremental net benefit(INB)across studies stratified by country income and study perspective using a random-effects model.Statistical heterogeneity among studies was assessed using the I2 statistic,Cochrane Q statistics,and meta regression.Results:Nine studies were included in the analysis.From a healthcare system/payer perspective,AI-based DR screening was significantly cost-effective compared to non-AI-based screening,with a pooled INB of 615.77(95%confidence interval[CI]:558.27-673.27).Subgroup analysis showed robust cost-effectiveness of AI-based DR screening in high-income countries(INB=613.62,95%CI:556.06-671.18)and upper-/lower-middle income countries(INB=1,739.97,95%CI:423.13-3,056.82)with low heterogeneity.From a societal perspective,AI-based DR screening was generally cost-effective(INB=5,102.33,95%CI:-815.47-11,020.13),though the result lacked statistical significance and showed high heterogeneity.Conclusions:AI-based DR screening is generally cost-effective from a healthcare system perspective,particularly in high-income countries.Heterogeneity in cost-effectiveness across different perspectives highlights the importance of context-specific evaluations,to accurately evaluate the potential of AI-based DR screening in reducing global healthcare disparities.展开更多
Clustered heavy precipitation(CHP)events can severely impact human society,infrastructure,and natural ecosystems.Consequently,short-term climate prediction of CHP events is vital for the prevention and mitigation of a...Clustered heavy precipitation(CHP)events can severely impact human society,infrastructure,and natural ecosystems.Consequently,short-term climate prediction of CHP events is vital for the prevention and mitigation of associated hazards.Employing year-to-year increment(DY)and multiple linear regression approaches,this study developed a seasonal prediction model for pre-summer(i.e.,May and June)CHP frequency in South China(SC)during 1981–2022.Three robust predictor factors were identified:March sea surface temperature in Southwestern Atlantic,early-winter snow depth in East Europe,and winter soil moisture in Central Asia.Three predictors exert substantial impacts on presummer precipitation in SC via modulation of an anomalous anticyclone(cyclone)over the(subtropical)western North Pacific.In leave-one-out cross-validation test during 1981–2022,the prediction model exhibited reasonable performance in predicting the interannual and interdecadal variations and trends of CHP days.The temporal correlation coefficient(TCC)was 0.66 between the observations and predictions.In the independent hindcast for 2013–2022,the TCC was as high as 0.85.Moreover,coherent covariations were observed between the frequency and the amounts of CHP,with a TCC of 0.99 for 1981–2022.Those three predictors show good performance in forecasting CHP amounts over SC,with a TCC of 0.68 between the predictions and observations in the cross-validation test during 1981–2022 and of 0.86 in the independent hindcasts during 2013–2022.Notably,the predictors also showed good predictive skill for years with high CHP occurrence(e.g.,1998 and 2019).The predicted high-incidence areas of heavy precipitation days were highly consistent with observations,with a pattern correlation coefficient of 0.44(0.55)for 1998(2019).This study provides valuable insights to improve seasonal prediction of pre-summer CHP frequency in SC.展开更多
The field of biometric identification has seen significant advancements over the years,with research focusing on enhancing the accuracy and security of these systems.One of the key developments is the integration of d...The field of biometric identification has seen significant advancements over the years,with research focusing on enhancing the accuracy and security of these systems.One of the key developments is the integration of deep learning techniques in biometric systems.However,despite these advancements,certain challenges persist.One of the most significant challenges is scalability over growing complexity.Traditional methods either require maintaining and securing a growing database,introducing serious security challenges,or relying on retraining the entiremodelwhen new data is introduced-a process that can be computationally expensive and complex.This challenge underscores the need for more efficient methods to scale securely.To this end,we introduce a novel approach that addresses these challenges by integrating multimodal biometrics,cancelable biometrics,and incremental learning techniques.This work is among the first attempts to seamlessly incorporate deep cancelable biometrics with dynamic architectural updates,applied incrementally to the deep learning model as new users are enrolled,achieving high performance with minimal catastrophic forgetting.By leveraging a One-Dimensional Convolutional Neural Network(1D-CNN)architecture combined with a hybrid incremental learning approach,our system achieves high recognition accuracy,averaging 98.98% over incrementing datasets,while ensuring user privacy through cancelable templates generated via a pre-trained CNN model and random projection.The approach demonstrates remarkable adaptability,utilizing the least intrusive biometric traits like facial features and fingerprints,ensuring not only robust performance but also long-term serviceability.展开更多
This paper adopts the Global Workspace Theory as a neuro-scientifically plausible theory for developing conscious cognitive architecture.The Global Workspace Theory’s compatibility with the working mechanisms underne...This paper adopts the Global Workspace Theory as a neuro-scientifically plausible theory for developing conscious cognitive architecture.The Global Workspace Theory’s compatibility with the working mechanisms underneath human brains is enhanced by the implementation of different cognitive features based on this framework.Amongst the topics in the literature for intelligent systems,we start with attention,memory and learning mechanisms,and corresponding experiments are summarized here.We also discuss how other topics of cognitive robotics could be developed based on these three basic components,and their correlations.This provides a foundation for future long-term development of cognitive architectures of cognitive robots.The research in this paper follows the incremental research pathway for the architecture implementation,which is consistent with the Biologically Inspired Cognitive Architecture roadmap.展开更多
Under global warming,extreme precipitation in the Yellow River Basin(YRB)is increasing,threatening regional development and the environment.This trend highlights the need to improve our ability to predict extreme prec...Under global warming,extreme precipitation in the Yellow River Basin(YRB)is increasing,threatening regional development and the environment.This trend highlights the need to improve our ability to predict extreme precipitation.We used an empirical orthogonal function(EOF)analysis and a method based on year-to-year increments(DY)method to extract the spatial patterns and principal components(PCs)of the DY of total precipition exceeding 95th percentile threshold in summer(R95p TOT-DY)in the YRB from 1962 to 2010.Prediction models of PCs during 2011–2022 were autonomously established using two sets of machine learning(ML)models(Light Gradient Boosting Machine(Light GBM)and Categorical Boosting(Cat Boost)).Without human intervention,these models independently identified significant climatic factors from 114 ocean and circulation indices advanced by 1–6 months.Light GBM predicted time correlation coefficients(TCCs)of 0.53,0.63,and 0.60 for the PCs,while Cat Boost yielded TCCs of 0.50,0.69,and 0.57,respectively.Notably,the PC3 prediction significantly outperformed traditional linear regression(LR).Improved R95p TOT-DY prediction was observed in the YRB's source area and its middle reaches.The ensemble models of Light GBM and Cat Boost showed the best R95p TOT-DY prediction(regional average TCC of 0.51),with a reasonable performance in 12years of forecasting R95p TOT(a multiyear average Pattern Correlation Coefficient(PCC)of 0.36),surpassing the traditional LR method.The SHapley Additive ex Planations(SHAP)analysis attributed PC3 enhancement to the North American Polar Vortex Area Index(NAPVAI).Overall,this ML-based incremental model enhances extreme precipitation prediction,aiding risk assessment and warnings in the YRB.展开更多
In this paper,an incremental contact model is developed for the elastic self-affine fractal rough surfaces under plane strain condition.The contact between a rough surface and a rigid plane is simplified by the accumu...In this paper,an incremental contact model is developed for the elastic self-affine fractal rough surfaces under plane strain condition.The contact between a rough surface and a rigid plane is simplified by the accumulation of identical line contacts with half-width given by the truncated area divided by the contact patch number at varying heights.Based on the contact stiffness of two-dimensional flat punch,the total stiffness of rough surface is estimated,and then the normal load is calculated by an incremental method.For various rough surfaces,the approximately linear load-area relationships predicted by the proposed model agree well with the results of finite element simulations.It is found that the real average contact pressure depends significantly on profile properties.展开更多
The incremental capacity analysis(ICA)technique is notably limited by its sensitivity to variations in charging conditions,which constrains its practical applicability in real-world scenarios.This paper introduces an ...The incremental capacity analysis(ICA)technique is notably limited by its sensitivity to variations in charging conditions,which constrains its practical applicability in real-world scenarios.This paper introduces an ICA-compensation technique to address this limitation and propose a generalized framework for assessing the state of health(SOH)of batteries based on ICA that is applicable under differing charging conditions.This novel approach calculates the voltage profile under quasi-static conditions by subtracting the voltage increase attributable to the additional polarization effects at high currents from the measured voltage profile.This approach's efficacy is contingent upon precisely acquiring the equivalent impedance.To obtain the equivalent impedance throughout the batteries'lifespan while minimizing testing costs,this study employs a current interrupt technique in conjunction with a long short-term memory(LSTM)network to develop a predictive model for equivalent impedance.Following the derivation of ICA curves using voltage profiles under quasi-static conditions,the research explores two scenarios for SOH estimation:one utilizing only incremental capacity(IC)features and the other incorporating both IC features and IC sampling.A genetic algorithm-optimized backpropagation neural network(GABPNN)is employed for the SOH estimation.The proposed generalized framework is validated using independent training and test datasets.Variable test conditions are applied for the test set to rigorously evaluate the methodology under challenging conditions.These evaluation results demonstrate that the proposed framework achieves an estimation accuracy of 1.04%for RMSE and 0.90%for MAPE across a spectrum of charging rates ranging from 0.1 C to 1 C and starting SOCs between 0%and 70%,which constitutes a major advancement compared to established ICA methods.It also significantly enhances the applicability of conventional ICA techniques in varying charging conditions and negates the necessity for separate testing protocols for each charging scenario.展开更多
Based on a normalized difference vegetation index(NDVI)dataset for 1982-2021,this work investigates the principal modes of interannual variability in summer NDVI over eastern Siberia using the year-to-year increment m...Based on a normalized difference vegetation index(NDVI)dataset for 1982-2021,this work investigates the principal modes of interannual variability in summer NDVI over eastern Siberia using the year-to-year increment method and empirical orthogonal function(EOF)analysis.The first three principal modes(EOF1-3)of the year-to-year increment of summer NDVI(NDVI_DY)exhibit a regionally consistent mode,a western-eastern dipole mode,and a northern-southern dipole mode,respectively.Further analysis shows that sea surface temperature(SST)in the tropical Indian Ocean in February-March and western Siberian soil moisture in April could influence EOF1.EOF2 is modulated by April Northwest Pacific SST and western Siberian soil moisture in May.May North Atlantic SST and sea ice in the Kara Sea in the preceding October significantly affect EOF3.Using the year-to-year increment method and multiple linear regression analysis,prediction schemes for EOF1-3 are developed based on these predictors.To assess the predictive skill of these schemes,one-year-out cross-validation and independent hindcast methods are employed.The temporal correlation coefficients between observed EOF1-3 and the cross-validation results are 0.62,0.46,and 0.37,respectively,exceeding the 95%confidence level.In addition,reconstructed schemes for summer NDVI are developed using predicted NDVI_DY and the observed principal modes of NDVI_DY.Independent hindcasts of NDVI anomalies during 2019-2021 also present consistent distributions with the observed results.展开更多
Semantic communication(SemCom)aims to achieve high-fidelity information delivery under low communication consumption by only guaranteeing semantic accuracy.Nevertheless,semantic communication still suffers from unexpe...Semantic communication(SemCom)aims to achieve high-fidelity information delivery under low communication consumption by only guaranteeing semantic accuracy.Nevertheless,semantic communication still suffers from unexpected channel volatility and thus developing a re-transmission mechanism(e.g.,hybrid automatic repeat request[HARQ])becomes indispensable.In that regard,instead of discarding previously transmitted information,the incremental knowledge-based HARQ(IK-HARQ)is deemed as a more effective mechanism that could sufficiently utilize the information semantics.However,considering the possible existence of semantic ambiguity in image transmission,a simple bit-level cyclic redundancy check(CRC)might compromise the performance of IK-HARQ.Therefore,there emerges a strong incentive to revolutionize the CRC mechanism,thus more effectively reaping the benefits of both SemCom and HARQ.In this paper,built on top of swin transformer-based joint source-channel coding(JSCC)and IK-HARQ,we propose a semantic image transmission framework SC-TDA-HARQ.In particular,different from the conventional CRC,we introduce a topological data analysis(TDA)-based error detection method,which capably digs out the inner topological and geometric information of images,to capture semantic information and determine the necessity for re-transmission.Extensive numerical results validate the effectiveness and efficiency of the proposed SC-TDA-HARQ framework,especially under the limited bandwidth condition,and manifest the superiority of TDA-based error detection method in image transmission.展开更多
Net primary productivity(NPP)is the net accumulation of organic matter by vegetation through photosynthesis and serves as a key indicator for exploring vegetation responses to climate change.Considering the remote and...Net primary productivity(NPP)is the net accumulation of organic matter by vegetation through photosynthesis and serves as a key indicator for exploring vegetation responses to climate change.Considering the remote and local impacts of soil heat capacities on vegetation growth through pathways of atmospheric circulation and land–atmosphere interaction,this paper develops a statistical prediction model for NPP from April to June(AMJ)across the middle-to-high latitudes of Eurasia.The model introduces two physically meaningful predictors:the snow water equivalent(SWE)from February to March(FM)over central Europe and the FM local soil temperature(ST).The positive phase of FM SWE triggers anomalous eastward-propagating Rossby waves,leading to an anomalous low-pressure system and cooling in the middle-to-high latitudes of Eurasia.This effect persists into spring through snow feedback to the atmosphere and affects subsequent NPP changes.The ST is closely related to the AMJ temperature and precipitation.With positive ST anomalies,the AMJ temperature and precipitation exhibit an east–west dipole anomaly distribution in this region.The single-factor prediction scheme using ST as the predictor is much better than using SWE as the predictor.Independent validation results from 2009 to 2014 demonstrate that the ST scheme alone has good predictive performance for the spatial distribution and interannual variability of NPP.The predictive skills of the multi-factor prediction schemes can be improved by about 13%if the ST predictor is included.The findings confirm that local ST is a predictor that must be included for NPP prediction.展开更多
Ever since the research in machine learning gained traction in recent years,it has been employed to address challenges in a wide variety of domains,including mechanical devices.Most of the machine learning models are ...Ever since the research in machine learning gained traction in recent years,it has been employed to address challenges in a wide variety of domains,including mechanical devices.Most of the machine learning models are built on the assumption of a static learning environment,but in practical situations,the data generated by the process is dynamic.This evolution of the data is termed concept drift.This research paper presents an approach for predictingmechanical failure in real-time using incremental learning based on the statistically calculated parameters of mechanical equipment.The method proposed here is applicable to allmechanical devices that are susceptible to failure or operational degradation.The proposed method in this paper is equipped with the capacity to detect the drift in data generation and adaptation.The proposed approach evaluates the machine learning and deep learning models for their efficacy in handling the errors related to industrial machines due to their dynamic nature.It is observed that,in the settings without concept drift in the data,methods like SVM and Random Forest performed better compared to deep neural networks.However,this resulted in poor sensitivity for the smallest drift in the machine data reported as a drift.In this perspective,DNN generated the stable drift detection method;it reported an accuracy of 84%and an AUC of 0.87 while detecting only a single drift point,indicating the stability to performbetter in detecting and adapting to new data in the drifting environments under industrial measurement settings.展开更多
The influence of geometric configuration on the friction characteristics during incremental sheet forming of AA5052 was analyzed by integrating surface morphology and its characteristic parameters,along with plastic s...The influence of geometric configuration on the friction characteristics during incremental sheet forming of AA5052 was analyzed by integrating surface morphology and its characteristic parameters,along with plastic strain,contact pressure,and area.The interface promotes lubrication and support when wall angles were≤40°,a 0.5 mm-thin sheet was used,and a 10 mm-large tool radius was employed.This mainly results in micro-plowing and plastic extrusion flow,leading to lower friction coefficient.However,when wall angles exceed 40°,significant plastic strain roughening occurs,leading to inadequate lubrication on the newly formed surface.Increased sheet thickness and decreased tool radius elevate contact pressure.These actions trigger micro-cutting and adhesion,potentially leading to localized scuffing and dimple tears,and higher friction coefficient.The friction mechanisms remain unaffected by the part’s plane curve features.As the forming process progresses,abrasive wear intensifies,and surface morphology evolves unfavorably for lubrication and friction reduction.展开更多
基金Supported by NSFC(Nos.11661025,12161024)Natural Science Foundation of Guangxi(Nos.2020GXNSFAA159118,2021GXNSFAA196045)+2 种基金Guangxi Science and Technology Project(No.Guike AD20297006)Training Program for 1000 Young and Middle-aged Cadre Teachers in Universities of GuangxiNational College Student's Innovation and Entrepreneurship Training Program(No.202110595049)。
文摘In this paper,we present local functional law of the iterated logarithm for Cs?rg?-Révész type increments of fractional Brownian motion.The results obtained extend works of Gantert[Ann.Probab.,1993,21(2):1045-1049]and Monrad and Rootzén[Probab.Theory Related Fields,1995,101(2):173-192].
基金supported in part by financial support from the National Key R&D Program of China(No.2023YFB3407003)the National Natural Science Foundation of China(No.52375378).
文摘A new analytical model for geometric size and forming force prediction in incremental flanging(IF)is presented in this work.The complex deformation characteristics of IF are considered in the modeling process,which can accurately describe the strain and stress states in IF.Based on strain analysis,the model can predict the material thickness distribution and neck height after IF.By considering contact area,strain characteristics,material thickness changes,and friction,the model can predict specific moments and corresponding values of maximum axial forming force and maximum horizontal forming force during IF.In addition,an IF experiment involving different tool diameters,flanging diameters,and opening hole diameters is conducted.On the basis of the experimental strain paths,the strain characteristics of different deformation zones are studied,and the stable strain ratio is quantitatively described through two dimensionless parameters:relative tool diameter and relative hole diameter.Then,the changing of material thickness and forming force in IF,and the variation of minimum material thickness,neck height,maximum axial forming force,and maximum horizontal forming force with flanging parameters are studied,and the reliability of the analytical model is verified in this process.Finally,the influence of the horizontal forming force on the tool design and the fluctuation of the forming force are explained.
基金supported by the National Key Research and Development Program of China (Nos.2022YFC3702000 and 2022YFC3703500)the Key R&D Project of Zhejiang Province (No.2022C03146).
文摘Severe ground-level ozone(O_(3))pollution over major Chinese cities has become one of the most challenging problems,which have deleterious effects on human health and the sustainability of society.This study explored the spatiotemporal distribution characteristics of ground-level O_(3) and its precursors based on conventional pollutant and meteorological monitoring data in Zhejiang Province from 2016 to 2021.Then,a high-performance convolutional neural network(CNN)model was established by expanding the moment and the concentration variations to general factors.Finally,the response mechanism of O_(3) to the variation with crucial influencing factors is explored by controlling variables and interpolating target variables.The results indicated that the annual average MDA8-90th concentrations in Zhejiang Province are higher in the northern and lower in the southern.When the wind direction(WD)ranges from east to southwest and the wind speed(WS)ranges between 2 and 3 m/sec,higher O_(3) concentration prone to occur.At different temperatures(T),the O_(3) concentration showed a trend of first increasing and subsequently decreasing with increasing NO_(2) concentration,peaks at the NO_(2) concentration around 0.02mg/m^(3).The sensitivity of NO_(2) to O_(3) formation is not easily affected by temperature,barometric pressure and dew point temperature.Additionally,there is a minimum IRNO_(2) at each temperature when the NO_(2) concentration is 0.03 mg/m^(3),and this minimum IRNO_(2) decreases with increasing temperature.The study explores the response mechanism of O_(3) with the change of driving variables,which can provide a scientific foundation and methodological support for the targeted management of O_(3) pollution.
基金supported by the National Natural Science Foundation of China(Nos.62103052 and No.52175214)。
文摘This paper presents the design of an asymmetrically variable wingtip anhedral angles morphing aircraft,inspired by biomimetic mechanisms,to enhance lateral maneuver capability.Firstly,we establish a lateral dynamic model considering additional forces and moments resulting during the morphing process,and convert it into a Multiple Input Multiple Output(MIMO)virtual control system by importing virtual inputs.Secondly,a classical dynamics inversion controller is designed for the outer-loop system.A new Global Fast Terminal Incremental Sliding Mode Controller(NDO-GFTISMC)is proposed for the inner-loop system,in which an adaptive law is implemented to weaken control surface chattering,and a Nonlinear Disturbance Observer(NDO)is integrated to compensate for unknown disturbances.The whole control system is proven semiglobally uniformly ultimately bounded based on the multi-Lyapunov function method.Furthermore,we consider tracking errors and self-characteristics of actuators,a quadratic programmingbased dynamic control allocation law is designed,which allocates virtual control inputs to the asymmetrically deformed wingtip and rudder.Actuator dynamic models are incorporated to ensure physical realizability of designed allocation law.Finally,comparative experimental results validate the effectiveness of the designed control system and control allocation law.The NDO-GFTISMC features faster convergence,stronger robustness,and 81.25%and 75.0%reduction in maximum state tracking error under uncertainty compared to the Incremental Nonlinear Dynamic Inversion Controller based on NDO(NDO-INDI)and Incremental Sliding Mode Controller based on NDO(NDO-ISMC),respectively.The design of the morphing aircraft significantly enhances lateral maneuver capability,maintaining a substantial control margin during lateral maneuvering,reducing the burden of the rudder surface,and effectively solving the actuator saturation problem of traditional aircraft during lateral maneuvering.
基金Sponsored by The National Natural Science Foundation of China(32271848).
文摘Soil organic carbon(SOC)from different sources and with distinct chemical properties exhibit variations in their accumulation mechanisms.Exploring the effects of different litter treatments on SOC storage is of great significance for understanding the formation and accumulation mechanisms of the SOC pool.The feedback mechanisms of new and old SOC in response to tree species and litter treatments were quantitatively analyzed by the C3 plant/C4 soil replacement method.The litter treatments included no litter,aboveground litter,belowground forest litter,and aboveground+belowground litter,totaling four treatments.The results showed that in the first year,cork oak(Quercusvariabilis)exhibited the highest net SOC content increment and net new SOC increment,but the values declined rapidly from the second year onward.The net increment in SOC content was positive at all sample sites,while the priming effect was not significant at any site.Litter treatments had a significant impact on both SOC content and net SOC increment.Compared with aboveground litter,belowground litter was more effective in increasing SOC Content and net SOC increment.
文摘By using the Chinese stock market data from 2018 to 2024,the weak association between structural trends stocks and market index under investors’preference effect in trading cause the market is lack of liquidity and more likely to be dominated by structural trends,as in this market,the willingness to engage in passive trading exceeds that for active trading and investors’preference easy to reverse toward market volatility.The lack of incremental capital in the market often leads to sector-specific rallies rather than broad-based increases,which is one of the key reasons why the Chinese stock market has struggled to achieve overall growth over the long-term period.
基金co-supported by the supports of Guangdong Basic and Applied Basic Research Foundation(No.2019B1515120047)the National Natural Science Foundation of China(No.52130507)。
文摘Sheet metal spinning is an incremental forming process for producing axisymmetric thinwalled parts through continuous local deformation under the action of rollers.While studying the spinning process by finite element(FE)method,a critical bottleneck is the enormous simulation time.For beating off this challenge,a novel multi-mesh method is developed.The method can dynamically track the movement of rollers and adaptively refine the mesh.Thus,a locally refined quadrilateral computation mesh can be generated in the locally-deforming zone and reduce the unnecessary fine elements outside the locally-deforming zone.In the multi-mesh system,the fine elements and coarse elements are extracted from a storage mesh and a background mesh,respectively.Meanwhile,the hanging nodes in the locally refined mesh are removed by designing 4-refinement templates.Between computation mesh and storage mesh,a bi-cubic parametric surface fitting algorithm and accurate remapping methods are conducted to transmit geometric information and physical fields.The proposed method has been verified by two spinning processes.The results suggest that the method can save time by up to about 67%with satisfactory accuracy,especially for distributions of thickness and strain compared with the fully refined mesh.
基金supported by the Scientific Grant Agency of Ministry of Education,Science,Research and Sport of the Slovak Republic (project VEGA 1/0183/25)by the Slovak Research and Development Agency under the Contract No. APVV-21-0199 and APVV-19-0183
文摘In permanently uneven-aged forests, continuous ingrowth of recruitment into higher stand layers is a critical process for the formation and maintenance of differentiated stand structures. This study analyses the abundance and diversity of recruitment(diameter at breast high(DBH) 0.1–4 cm) across 241 research plots in 11 structurally differentiated Norway spruce(silver fir)-dominated forest stands distributed at altitudes between 500 and 1,440 m a.s.l. The influence of light conditions and lateral competition characteristics on the height increment and crown architecture of recruitment was investigated in detail for 352 Norway spruce and 361 silver fir trees. Light-related variables were confirmed to directly affect the recruitment distribution only to a limited extent. Under relatively low light conditions in montane stands, silver fir reached higher height increments than Norway spruce. The better adaptation of silver fir to shaded conditions was reflected also in its higher apical dominance ratio compared to Norway spruce. The height increment and apical dominance ratio of Norway spruce and silver fir recruitment were positively correlated with indirect radiation, DBH, and relative crown length(RCL), but not with lateral competition. These results confirm that the regulation of light conditions in permanently unevenaged stands is crucial for the growth dynamics of recruitment, as well as for the future proportions of Norway spruce and silver fir in mixed, structurally diverse stands.
基金supported by the Global STEM Professorship Scheme(P0046113)Henry G.Leong Endowed Professorship in Elderly Vision Health.
文摘Objective:Diabetic retinopathy(DR)is a top leading cause of blindness worldwide,requiring early detection for timely intervention.Artificial intelligence(AI)has emerged as a promising tool to improve DR screening efficiency,accessibility,and cost-effectiveness.This study conducted a systematic review of literature and meta-analysis on the economic outcomes of AI-based DR screening.Methods:A systematic review of studies published before September 2024 was conducted throughout PubMed,Scopus,Embase,the Cochrane Library,the National Health Service Economic Evaluation Database,and the Cost-Effectiveness Analysis Registry.Eligible studies were included if they were(1)conducted among type 1 diabetes mellitus or type 2 diabetes mellitus adult diabetic population;(2)studies compared AI-based DR screening strategy to non-AI screening;and(3)performed a cost-effectiveness analysis.Meta-analysis was applied to pool incremental net benefit(INB)across studies stratified by country income and study perspective using a random-effects model.Statistical heterogeneity among studies was assessed using the I2 statistic,Cochrane Q statistics,and meta regression.Results:Nine studies were included in the analysis.From a healthcare system/payer perspective,AI-based DR screening was significantly cost-effective compared to non-AI-based screening,with a pooled INB of 615.77(95%confidence interval[CI]:558.27-673.27).Subgroup analysis showed robust cost-effectiveness of AI-based DR screening in high-income countries(INB=613.62,95%CI:556.06-671.18)and upper-/lower-middle income countries(INB=1,739.97,95%CI:423.13-3,056.82)with low heterogeneity.From a societal perspective,AI-based DR screening was generally cost-effective(INB=5,102.33,95%CI:-815.47-11,020.13),though the result lacked statistical significance and showed high heterogeneity.Conclusions:AI-based DR screening is generally cost-effective from a healthcare system perspective,particularly in high-income countries.Heterogeneity in cost-effectiveness across different perspectives highlights the importance of context-specific evaluations,to accurately evaluate the potential of AI-based DR screening in reducing global healthcare disparities.
基金Guangdong Major Project of Basic and Applied Basic Research(2020B0301030004)Science and Technology Development Plan in Jilin Province of China(20230203135SF)+1 种基金National Natural Science Foundation of China(41875119)Special Fund for Innovative Development of China Meteorological Administration(CXFZ2022J007)。
文摘Clustered heavy precipitation(CHP)events can severely impact human society,infrastructure,and natural ecosystems.Consequently,short-term climate prediction of CHP events is vital for the prevention and mitigation of associated hazards.Employing year-to-year increment(DY)and multiple linear regression approaches,this study developed a seasonal prediction model for pre-summer(i.e.,May and June)CHP frequency in South China(SC)during 1981–2022.Three robust predictor factors were identified:March sea surface temperature in Southwestern Atlantic,early-winter snow depth in East Europe,and winter soil moisture in Central Asia.Three predictors exert substantial impacts on presummer precipitation in SC via modulation of an anomalous anticyclone(cyclone)over the(subtropical)western North Pacific.In leave-one-out cross-validation test during 1981–2022,the prediction model exhibited reasonable performance in predicting the interannual and interdecadal variations and trends of CHP days.The temporal correlation coefficient(TCC)was 0.66 between the observations and predictions.In the independent hindcast for 2013–2022,the TCC was as high as 0.85.Moreover,coherent covariations were observed between the frequency and the amounts of CHP,with a TCC of 0.99 for 1981–2022.Those three predictors show good performance in forecasting CHP amounts over SC,with a TCC of 0.68 between the predictions and observations in the cross-validation test during 1981–2022 and of 0.86 in the independent hindcasts during 2013–2022.Notably,the predictors also showed good predictive skill for years with high CHP occurrence(e.g.,1998 and 2019).The predicted high-incidence areas of heavy precipitation days were highly consistent with observations,with a pattern correlation coefficient of 0.44(0.55)for 1998(2019).This study provides valuable insights to improve seasonal prediction of pre-summer CHP frequency in SC.
基金the Deputyship for Research&Innovation,Ministry of Education in Saudi Arabia for funding this research work through project number RI-44-0833.
文摘The field of biometric identification has seen significant advancements over the years,with research focusing on enhancing the accuracy and security of these systems.One of the key developments is the integration of deep learning techniques in biometric systems.However,despite these advancements,certain challenges persist.One of the most significant challenges is scalability over growing complexity.Traditional methods either require maintaining and securing a growing database,introducing serious security challenges,or relying on retraining the entiremodelwhen new data is introduced-a process that can be computationally expensive and complex.This challenge underscores the need for more efficient methods to scale securely.To this end,we introduce a novel approach that addresses these challenges by integrating multimodal biometrics,cancelable biometrics,and incremental learning techniques.This work is among the first attempts to seamlessly incorporate deep cancelable biometrics with dynamic architectural updates,applied incrementally to the deep learning model as new users are enrolled,achieving high performance with minimal catastrophic forgetting.By leveraging a One-Dimensional Convolutional Neural Network(1D-CNN)architecture combined with a hybrid incremental learning approach,our system achieves high recognition accuracy,averaging 98.98% over incrementing datasets,while ensuring user privacy through cancelable templates generated via a pre-trained CNN model and random projection.The approach demonstrates remarkable adaptability,utilizing the least intrusive biometric traits like facial features and fingerprints,ensuring not only robust performance but also long-term serviceability.
基金Supported by the European Union’s Horizon Europe research and innovation program(101120727-PRIMI).
文摘This paper adopts the Global Workspace Theory as a neuro-scientifically plausible theory for developing conscious cognitive architecture.The Global Workspace Theory’s compatibility with the working mechanisms underneath human brains is enhanced by the implementation of different cognitive features based on this framework.Amongst the topics in the literature for intelligent systems,we start with attention,memory and learning mechanisms,and corresponding experiments are summarized here.We also discuss how other topics of cognitive robotics could be developed based on these three basic components,and their correlations.This provides a foundation for future long-term development of cognitive architectures of cognitive robots.The research in this paper follows the incremental research pathway for the architecture implementation,which is consistent with the Biologically Inspired Cognitive Architecture roadmap.
基金supported by the National Science Foundation of China(Grant No.42041004)。
文摘Under global warming,extreme precipitation in the Yellow River Basin(YRB)is increasing,threatening regional development and the environment.This trend highlights the need to improve our ability to predict extreme precipitation.We used an empirical orthogonal function(EOF)analysis and a method based on year-to-year increments(DY)method to extract the spatial patterns and principal components(PCs)of the DY of total precipition exceeding 95th percentile threshold in summer(R95p TOT-DY)in the YRB from 1962 to 2010.Prediction models of PCs during 2011–2022 were autonomously established using two sets of machine learning(ML)models(Light Gradient Boosting Machine(Light GBM)and Categorical Boosting(Cat Boost)).Without human intervention,these models independently identified significant climatic factors from 114 ocean and circulation indices advanced by 1–6 months.Light GBM predicted time correlation coefficients(TCCs)of 0.53,0.63,and 0.60 for the PCs,while Cat Boost yielded TCCs of 0.50,0.69,and 0.57,respectively.Notably,the PC3 prediction significantly outperformed traditional linear regression(LR).Improved R95p TOT-DY prediction was observed in the YRB's source area and its middle reaches.The ensemble models of Light GBM and Cat Boost showed the best R95p TOT-DY prediction(regional average TCC of 0.51),with a reasonable performance in 12years of forecasting R95p TOT(a multiyear average Pattern Correlation Coefficient(PCC)of 0.36),surpassing the traditional LR method.The SHapley Additive ex Planations(SHAP)analysis attributed PC3 enhancement to the North American Polar Vortex Area Index(NAPVAI).Overall,this ML-based incremental model enhances extreme precipitation prediction,aiding risk assessment and warnings in the YRB.
基金supported by the National Natural Science Foundation of China(Grant Nos.12372100,12302126,and 12302141)the China Postdoctoral Science Foundation(Grant No.2023M732799)+1 种基金the Fundamental Research Funds for the Central Universities(Grant No.xzy012024020)Sihe Wang also thanks the support from the China Scholarship Council(CSC).
文摘In this paper,an incremental contact model is developed for the elastic self-affine fractal rough surfaces under plane strain condition.The contact between a rough surface and a rigid plane is simplified by the accumulation of identical line contacts with half-width given by the truncated area divided by the contact patch number at varying heights.Based on the contact stiffness of two-dimensional flat punch,the total stiffness of rough surface is estimated,and then the normal load is calculated by an incremental method.For various rough surfaces,the approximately linear load-area relationships predicted by the proposed model agree well with the results of finite element simulations.It is found that the real average contact pressure depends significantly on profile properties.
基金funded by the Bavarian State Ministry of ScienceResearch and Art(Grant number:H.2-F1116.WE/52/2)。
文摘The incremental capacity analysis(ICA)technique is notably limited by its sensitivity to variations in charging conditions,which constrains its practical applicability in real-world scenarios.This paper introduces an ICA-compensation technique to address this limitation and propose a generalized framework for assessing the state of health(SOH)of batteries based on ICA that is applicable under differing charging conditions.This novel approach calculates the voltage profile under quasi-static conditions by subtracting the voltage increase attributable to the additional polarization effects at high currents from the measured voltage profile.This approach's efficacy is contingent upon precisely acquiring the equivalent impedance.To obtain the equivalent impedance throughout the batteries'lifespan while minimizing testing costs,this study employs a current interrupt technique in conjunction with a long short-term memory(LSTM)network to develop a predictive model for equivalent impedance.Following the derivation of ICA curves using voltage profiles under quasi-static conditions,the research explores two scenarios for SOH estimation:one utilizing only incremental capacity(IC)features and the other incorporating both IC features and IC sampling.A genetic algorithm-optimized backpropagation neural network(GABPNN)is employed for the SOH estimation.The proposed generalized framework is validated using independent training and test datasets.Variable test conditions are applied for the test set to rigorously evaluate the methodology under challenging conditions.These evaluation results demonstrate that the proposed framework achieves an estimation accuracy of 1.04%for RMSE and 0.90%for MAPE across a spectrum of charging rates ranging from 0.1 C to 1 C and starting SOCs between 0%and 70%,which constitutes a major advancement compared to established ICA methods.It also significantly enhances the applicability of conventional ICA techniques in varying charging conditions and negates the necessity for separate testing protocols for each charging scenario.
基金supported by the National Key Research and Development Program of China[grant number 2022YFE0106800]the National Natural Science Foundation of China[grant number 42230603]+1 种基金the Innovation Group Project of Southern Marine Science and Engineering Guangdong Laboratory(Zhuhai)[grant number 311024001]supported by Southern Marine Science and Engineering Guangdong Laboratory(Zhuhai)[grant number SML2023SP209].
文摘Based on a normalized difference vegetation index(NDVI)dataset for 1982-2021,this work investigates the principal modes of interannual variability in summer NDVI over eastern Siberia using the year-to-year increment method and empirical orthogonal function(EOF)analysis.The first three principal modes(EOF1-3)of the year-to-year increment of summer NDVI(NDVI_DY)exhibit a regionally consistent mode,a western-eastern dipole mode,and a northern-southern dipole mode,respectively.Further analysis shows that sea surface temperature(SST)in the tropical Indian Ocean in February-March and western Siberian soil moisture in April could influence EOF1.EOF2 is modulated by April Northwest Pacific SST and western Siberian soil moisture in May.May North Atlantic SST and sea ice in the Kara Sea in the preceding October significantly affect EOF3.Using the year-to-year increment method and multiple linear regression analysis,prediction schemes for EOF1-3 are developed based on these predictors.To assess the predictive skill of these schemes,one-year-out cross-validation and independent hindcast methods are employed.The temporal correlation coefficients between observed EOF1-3 and the cross-validation results are 0.62,0.46,and 0.37,respectively,exceeding the 95%confidence level.In addition,reconstructed schemes for summer NDVI are developed using predicted NDVI_DY and the observed principal modes of NDVI_DY.Independent hindcasts of NDVI anomalies during 2019-2021 also present consistent distributions with the observed results.
基金supported in part by the National Key Research and Development Program of China under Grant 2024YFE0200600in part by the National Natural Science Foundation of China under Grant 62071425+3 种基金in part by the Zhejiang Key Research and Development Plan under Grant 2022C01093in part by the Zhejiang Provincial Natural Science Foundation of China under Grant LR23F010005in part by the National Key Laboratory of Wireless Communications Foundation under Grant 2023KP01601in part by the Big Data and Intelligent Computing Key Lab of CQUPT under Grant BDIC-2023-B-001.
文摘Semantic communication(SemCom)aims to achieve high-fidelity information delivery under low communication consumption by only guaranteeing semantic accuracy.Nevertheless,semantic communication still suffers from unexpected channel volatility and thus developing a re-transmission mechanism(e.g.,hybrid automatic repeat request[HARQ])becomes indispensable.In that regard,instead of discarding previously transmitted information,the incremental knowledge-based HARQ(IK-HARQ)is deemed as a more effective mechanism that could sufficiently utilize the information semantics.However,considering the possible existence of semantic ambiguity in image transmission,a simple bit-level cyclic redundancy check(CRC)might compromise the performance of IK-HARQ.Therefore,there emerges a strong incentive to revolutionize the CRC mechanism,thus more effectively reaping the benefits of both SemCom and HARQ.In this paper,built on top of swin transformer-based joint source-channel coding(JSCC)and IK-HARQ,we propose a semantic image transmission framework SC-TDA-HARQ.In particular,different from the conventional CRC,we introduce a topological data analysis(TDA)-based error detection method,which capably digs out the inner topological and geometric information of images,to capture semantic information and determine the necessity for re-transmission.Extensive numerical results validate the effectiveness and efficiency of the proposed SC-TDA-HARQ framework,especially under the limited bandwidth condition,and manifest the superiority of TDA-based error detection method in image transmission.
基金funded by the National Natural Science Foundation of China[grant numbers 42075115 and 41991285]the Joint Open Project of KLME&CIC-FEMD[grant number KLME201901]。
文摘Net primary productivity(NPP)is the net accumulation of organic matter by vegetation through photosynthesis and serves as a key indicator for exploring vegetation responses to climate change.Considering the remote and local impacts of soil heat capacities on vegetation growth through pathways of atmospheric circulation and land–atmosphere interaction,this paper develops a statistical prediction model for NPP from April to June(AMJ)across the middle-to-high latitudes of Eurasia.The model introduces two physically meaningful predictors:the snow water equivalent(SWE)from February to March(FM)over central Europe and the FM local soil temperature(ST).The positive phase of FM SWE triggers anomalous eastward-propagating Rossby waves,leading to an anomalous low-pressure system and cooling in the middle-to-high latitudes of Eurasia.This effect persists into spring through snow feedback to the atmosphere and affects subsequent NPP changes.The ST is closely related to the AMJ temperature and precipitation.With positive ST anomalies,the AMJ temperature and precipitation exhibit an east–west dipole anomaly distribution in this region.The single-factor prediction scheme using ST as the predictor is much better than using SWE as the predictor.Independent validation results from 2009 to 2014 demonstrate that the ST scheme alone has good predictive performance for the spatial distribution and interannual variability of NPP.The predictive skills of the multi-factor prediction schemes can be improved by about 13%if the ST predictor is included.The findings confirm that local ST is a predictor that must be included for NPP prediction.
文摘Ever since the research in machine learning gained traction in recent years,it has been employed to address challenges in a wide variety of domains,including mechanical devices.Most of the machine learning models are built on the assumption of a static learning environment,but in practical situations,the data generated by the process is dynamic.This evolution of the data is termed concept drift.This research paper presents an approach for predictingmechanical failure in real-time using incremental learning based on the statistically calculated parameters of mechanical equipment.The method proposed here is applicable to allmechanical devices that are susceptible to failure or operational degradation.The proposed method in this paper is equipped with the capacity to detect the drift in data generation and adaptation.The proposed approach evaluates the machine learning and deep learning models for their efficacy in handling the errors related to industrial machines due to their dynamic nature.It is observed that,in the settings without concept drift in the data,methods like SVM and Random Forest performed better compared to deep neural networks.However,this resulted in poor sensitivity for the smallest drift in the machine data reported as a drift.In this perspective,DNN generated the stable drift detection method;it reported an accuracy of 84%and an AUC of 0.87 while detecting only a single drift point,indicating the stability to performbetter in detecting and adapting to new data in the drifting environments under industrial measurement settings.
基金the support of the Key Research and Development Program of Shaanxi Province,China(No.2021GXLH-Z-049)。
文摘The influence of geometric configuration on the friction characteristics during incremental sheet forming of AA5052 was analyzed by integrating surface morphology and its characteristic parameters,along with plastic strain,contact pressure,and area.The interface promotes lubrication and support when wall angles were≤40°,a 0.5 mm-thin sheet was used,and a 10 mm-large tool radius was employed.This mainly results in micro-plowing and plastic extrusion flow,leading to lower friction coefficient.However,when wall angles exceed 40°,significant plastic strain roughening occurs,leading to inadequate lubrication on the newly formed surface.Increased sheet thickness and decreased tool radius elevate contact pressure.These actions trigger micro-cutting and adhesion,potentially leading to localized scuffing and dimple tears,and higher friction coefficient.The friction mechanisms remain unaffected by the part’s plane curve features.As the forming process progresses,abrasive wear intensifies,and surface morphology evolves unfavorably for lubrication and friction reduction.
