During the 10th Chinese Arctic scientific expedition carried out in the summer of 2019,the surface current in the high-latitude areas of the Arctic Ocean was observed using a self-developed surface drifting buoy,which...During the 10th Chinese Arctic scientific expedition carried out in the summer of 2019,the surface current in the high-latitude areas of the Arctic Ocean was observed using a self-developed surface drifting buoy,which was initially deployed in the Chukchi Sea.The buoy traversed the Chukchi Sea,Chukchi Abyssal Plain,Mendeleev Ridge,Makarov Basin,and Canada Basin over a period of 632 d.After returning to the Mendeleev Ridge,it continued to drift toward the pole.Overall,the track of the buoy reflected the characteristics of the transpolar drift and Chukchi Slope Current,as well as the inertial flow,cross-ridge surface flow,and even the surface disorganized flow for some time intervals.The results showed that:(1)the transpolar drift mainly occurs in the Chukchi Abyssal Plain,Mendeleev Ridge,and western Canada Basin to the east of the ridge where sea ice concentration is high,and the average northward flow velocity in the region between 79.41°N and 86.32°N was 5.1 cm/s;(2)the average surface velocity of the Chukchi Slope Current was 13.5 cm/s,and while this current moves westward along the continental slope,it also extends northwestward across the continental slope and flows to the deep sea;and(3)when sea ice concentration was less than 50%,the inertial flow was more significant(the maximum observed inertial flow was 26 cm/s,and the radius of the inertia circle was 3.6 km).展开更多
The effect of Stokes drift production(SDP),which includes Coriolis-Stokes forcing,Langmuir circulation,and Craik-Lei-bovich vortexes,on the upper ocean during typhoon passage in the Bohai Sea(BS),China,is investigated...The effect of Stokes drift production(SDP),which includes Coriolis-Stokes forcing,Langmuir circulation,and Craik-Lei-bovich vortexes,on the upper ocean during typhoon passage in the Bohai Sea(BS),China,is investigated by using a coupled wave-current model.The role of SDP in turbulent mixing and the further dynamics during the entire typhoon period are comprehensively stud-ied.Experimental results show that SDP greatly increases turbulent mixing at all depths under typhoon conditions by up to seven times that under normal weather conditions.SDP generally strengthens sea surface cooling by more than 0.4℃,with the maximum reduction in sea surface temperature(SST)at the during-typhoon stage exceeding 2℃,which is approximately seven times larger than that under normal weather conditions.The SDP-induced decrease in current speed can exceed 0.2ms^(-1),and the change in current direction is generally opposite the wind direction.These results suggest that Stokes drift depresses the effect of strong winds on currents by intensifying turbulent mixing.Mixed layer depth(MLD)is distinctly increased by O(1)during typhoons due to SDP and can deepen by more than 5m.In addition,the continuous effects of SDP on SST,current,and MLD at the after-typhoon stage indi-cate a hysteretic response between SDP and typhoon actions.展开更多
Pesticide adjuvants,as crop protection products,have been widely used to reduce drift loss and improve utilization efficiency by regulating droplet spectrum.However,the coordinated regulation mechanisms of adjuvants a...Pesticide adjuvants,as crop protection products,have been widely used to reduce drift loss and improve utilization efficiency by regulating droplet spectrum.However,the coordinated regulation mechanisms of adjuvants and nozzles on droplet spectrum remain unclear.Here,we established the relationship between droplet spectrum evolution and liquid atomization by investigating the typical characteristics of droplet diameter distribution near the nozzle.Based on this,the regulation mechanisms of distinctive pesticide adjuvants on droplet spectrum were clarified,and the corresponding drift reduction performances were quantitively evaluated by wind tunnel experiments.It shows that the droplet diameter firstly shifts to the smaller due to the liquid sheet breakup and then prefers to increase caused by droplet interactions.Reducing the surface tension of sprayed liquid facilitates the uniform liquid breakup and increasing the viscosity inhibits the liquid deformation,which prolong the atomization process and effectively improve the droplet spectrum.As a result,the drift losses of flat-fan and hollow cone nozzles are reduced by about 50%after adding organosilicon and vegetable oil adjuvants.By contrast,the air induction nozzle shows a superior anti-drift ability,regardless of distinctive adjuvants.Our findings provide insights into rational adjuvant design and nozzle selection in the field application.展开更多
An integral quality control(QC)procedure that integrates various QC methods and considers the design indexes and operational status of the instruments for the observations of drifting air-sea interface buoy was develo...An integral quality control(QC)procedure that integrates various QC methods and considers the design indexes and operational status of the instruments for the observations of drifting air-sea interface buoy was developed in the order of basic in-spection followed by targeted QC.The innovative method of combining a moving Hampel filter and local anomaly detection com-plies with statistical laws and physical processes,which guarantees the QC performance of meteorological variables.Two sets of observation data were used to verify the applicability and effectiveness of the QC procedure,and the effect was evaluated using the observations of the Kuroshio Extension Observatory buoy as the reference.The results showed that the outliers in the time series can be correctly identified and processed,and the quality of data improved significantly.The linear correlation between the quality-controlled observations and the reference increased,and the difference decreased.The correlation coefficient of wind speed before and after QC increased from 0.77 to 0.82,and the maximum absolute error decreased by approximately 2.8ms^(-1).In addition,air pressure and relative humidity were optimized by 10^(-3)–10^(-2) orders of magnitude.For the sea surface temperature,the weight of coefficients of the continuity test algorithm was optimized based on the sea area of data acquisition,which effectively expanded the applicability of the algorithm.展开更多
The existence of a significant electron drift instability(EDI) in the Hall thruster is considered as one of the possible causes of the abnormal increase in axial electron mobility near the outlet of the channel. In re...The existence of a significant electron drift instability(EDI) in the Hall thruster is considered as one of the possible causes of the abnormal increase in axial electron mobility near the outlet of the channel. In recent years, extensive simulation research on the characteristics of EDI has been conducted, but the excitation mechanism and growth mechanism of EDI in linear stage and nonlinear stage remain unclear. In this work, a one-dimensional PIC model in the azimuthal direction of the thruster near-exit region is established to gain further insights into the mechanism of the EDI in detail, and the effects of different types of propellants on EDI characteristics are discussed. The changes in axial electron transport caused by EDI under different types of propellants and electromagnetic field strengths are also examined. The results indicate that EDI undergoes a short linear growth phase before transitioning to the nonlinear phase and finally reaching saturation through the ion Landau damping. The EDI drives a significant ion heating in the azimuthal direction through electron–ion friction before entering the quasi-steady state, which increases the axial mobility of the electrons. Using lighter atomic weight propellant can effectively suppress the oscillation amplitude of EDI, but it will increase the linear growth rate, frequency, and phase velocity of EDI. Compared with the classical mobility, the axial electron mobility under the EDI increases by three orders of magnitude, which is consistent with experimental phenomena. The change of propellant type is insufficient to significantly change the axial electron mobility. It is also found that the collisions between electrons and neutral gasescan significantly affect the axial electron mobility under the influence of EDI, and lead the strength of the electric field to increase and the strength of the magnetic field to decrease, thereby both effectively suppressing the axial transport of electrons.展开更多
A reliable multiphase flow simulator is an important tool to improve wellbore integrity and production decision-making.To develop a multiphase flow model with high adaptability and high accuracy,we first build a multi...A reliable multiphase flow simulator is an important tool to improve wellbore integrity and production decision-making.To develop a multiphase flow model with high adaptability and high accuracy,we first build a multiphase flow database with 3561 groups of data and developed a drift closure relationship with stable continuity and high adaptability.Second,a high-order numerical scheme with strong fault capture ability is constructed by effectively combining MUSCL technology,van Albada slope limiter and AUSMV numerical scheme.Finally,the energy equation is coupled into the AUSMV numerical scheme of the drift flow model in the form of finite difference.A transient non-isothermal wellbore multiphase flow model with wide applicability is formed by integrating the three technologies,and the effects of various factors on the calculation accuracy are studied.The accuracy of the simulator is verified by comparing the measurement results with the blowout experiment of a full-scale experimental well.展开更多
The outbreak of Ulva in the Yellow Sea has seriously affected marine ecology and economic activities.Therefore,effective prediction of the distribution of Ulva is of great significance for disaster prevention and redu...The outbreak of Ulva in the Yellow Sea has seriously affected marine ecology and economic activities.Therefore,effective prediction of the distribution of Ulva is of great significance for disaster prevention and reduction.However,the prediction method of Ulva is mainly based on numerical simulation.There are two problems with these methods.First is that the initial distribution of Ulva is simulated using independent pixel-level particles.Besides,the influence of Ulva growth and diffusion on the drift is not considered.Therefore,this paper proposes a multi-module with a two-way feedback method(MTF)to solve the above problems.The main contributions of our approach are summarized as follows.First,the initialization module,the generation and elimination module,and the drive module are composed in our way.Second,we proposed an initialization method using rectangle objects to simulate the Ulva distribution extracted from remote sensing images.Thirdly,the drift and diffusion mechanism of the Ulva is considered to realize the two-way feedback between the generation and elimination module and the drive module.The results of our experiments show that the MTF performs better than the traditional method in predicting the drift and diffusion of Ulva.The code is already publicly available at https://github.com/UPCGIT/A-multi-module-with-a-two-way-feedback-method-for-Ulva-drift-diffusion.展开更多
To better understand divertor detachment and asymmetry in the Experimental Advanced Superconducting Tokamak(EAST),drift modeling via the comprehensive edge plasma code SOLPS-ITER of neon impurity seeded plasmas in fav...To better understand divertor detachment and asymmetry in the Experimental Advanced Superconducting Tokamak(EAST),drift modeling via the comprehensive edge plasma code SOLPS-ITER of neon impurity seeded plasmas in favorable/unfavorable toroidal magnetic field(BT)has been performed.Firstly,electrostatic potential/field(f/E)distribution has been analyzed,to make sure that f and E are correctly described and to better understand drift-driven processes.After that,drift effects on divertor detachment and asymmetry have been focused on.In accordance with the corresponding experimental observations,simulation results demonstrate that in favorable BTthe onset of detachment is highly asymmetric between the inner and outer divertors;and reversing BT can significantly decrease the magnitude of in-out asymmetry in the onset of detachment,physics reasons for which have been explored.It is found that,apart from the well-known E×B drift particle flow from one divertor to the other through the private flux region,scrape-off layer(SOL)heat flow,which is much more asymmetrically distributed between the high field side and low field side for favorable BTthan that for unfavorable B_T,is also a critical parameter affecting divertor detachment and asymmetry.During detachment,upstream pressure(P_u)reduction occurs and tends to be more dramatical in the colder side than that in the hotter side.The convective SOL heat flow,emerging due to in-out asymmetry in P_u reduction,is found to be critical for understanding divertor detachment and asymmetry observed in EAST.To better understand the calculated drastic power radiation in the core and upstream SOL,drift effects on divertor leakage/retention of neon in EAST with both BTdirections have been addressed for the first time,by analyzing profile of poloidal neon velocity and that of neon ionization source from atoms.This work can be a reference for future numeric simulations performed more closely related to experimental regimes.展开更多
Concept drift is a main security issue that has to be resolved since it presents a significant barrier to the deployment of machine learning(ML)models.Due to attackers’(and/or benign equivalents’)dynamic behavior ch...Concept drift is a main security issue that has to be resolved since it presents a significant barrier to the deployment of machine learning(ML)models.Due to attackers’(and/or benign equivalents’)dynamic behavior changes,testing data distribution frequently diverges from original training data over time,resulting in substantial model failures.Due to their dispersed and dynamic nature,distributed denial-of-service attacks pose a danger to cybersecurity,resulting in attacks with serious consequences for users and businesses.This paper proposes a novel design for concept drift analysis and detection of malware attacks like Distributed Denial of Service(DDOS)in the network.The goal of this architecture combination is to accurately represent data and create an effective cyber security prediction agent.The intrusion detection system and concept drift of the network has been analyzed using secure adaptive windowing with website data authentication protocol(SAW_WDA).The network has been analyzed by authentication protocol to avoid malware attacks.The data of network users will be collected and classified using multilayer perceptron gradient decision tree(MLPGDT)classifiers.Based on the classification output,the decision for the detection of attackers and authorized users will be identified.The experimental results show output based on intrusion detection and concept drift analysis systems in terms of throughput,end-end delay,network security,network concept drift,and results based on classification with regard to accuracy,memory,and precision and F-1 score.展开更多
Oil spill prediction is critical for reducing the detrimental impact of oil spills on marine ecosystems,and the wind strong-ly influences the performance of oil spill models.However,the wind drift factor is assumed to...Oil spill prediction is critical for reducing the detrimental impact of oil spills on marine ecosystems,and the wind strong-ly influences the performance of oil spill models.However,the wind drift factor is assumed to be constant or parameterized by linear regression and other methods in existing studies,which may limit the accuracy of the oil spill simulation.A parameterization method for wind drift factor(PMOWDF)based on deep learning,which can effectively extract the time-varying characteristics on a regional scale,is proposed in this paper.The method was adopted to forecast the oil spill in the East China Sea.The discrepancies between predicted positions and actual measurement locations of the drifters are obtained using seasonal statistical analysis.Results reveal that PMOWDF can improve the accuracy of oil spill simulation compared with the traditional method.Furthermore,the parameteriza-tion method is validated with satellite observations of the Sanchi oil spill in 2018.展开更多
Every application in a smart city environment like the smart grid,health monitoring, security, and surveillance generates non-stationary datastreams. Due to such nature, the statistical properties of data changes over...Every application in a smart city environment like the smart grid,health monitoring, security, and surveillance generates non-stationary datastreams. Due to such nature, the statistical properties of data changes overtime, leading to class imbalance and concept drift issues. Both these issuescause model performance degradation. Most of the current work has beenfocused on developing an ensemble strategy by training a new classifier on thelatest data to resolve the issue. These techniques suffer while training the newclassifier if the data is imbalanced. Also, the class imbalance ratio may changegreatly from one input stream to another, making the problem more complex.The existing solutions proposed for addressing the combined issue of classimbalance and concept drift are lacking in understating of correlation of oneproblem with the other. This work studies the association between conceptdrift and class imbalance ratio and then demonstrates how changes in classimbalance ratio along with concept drift affect the classifier’s performance.We analyzed the effect of both the issues on minority and majority classesindividually. To do this, we conducted experiments on benchmark datasetsusing state-of-the-art classifiers especially designed for data stream classification.Precision, recall, F1 score, and geometric mean were used to measure theperformance. Our findings show that when both class imbalance and conceptdrift problems occur together the performance can decrease up to 15%. Ourresults also show that the increase in the imbalance ratio can cause a 10% to15% decrease in the precision scores of both minority and majority classes.The study findings may help in designing intelligent and adaptive solutionsthat can cope with the challenges of non-stationary data streams like conceptdrift and class imbalance.展开更多
We find that the perturbed Lagrangian derived from the drift-kinetic equation in[Porcelli F et al 1994 Phys.Plasmas 1470]is inconsistent with the ordering for the low-frequency large-scale magnetohydrodynamic(MHD).Her...We find that the perturbed Lagrangian derived from the drift-kinetic equation in[Porcelli F et al 1994 Phys.Plasmas 1470]is inconsistent with the ordering for the low-frequency large-scale magnetohydrodynamic(MHD).Here,we rederive the expression for the perturbed Lagrangian within the framework of nonideal MHD using the ordering system for the low-frequency largescale MHD in a low-beta plasma.The obtained perturbed Lagrangian is consistent with Chen's gyrokinetic theory[Chen L and Zonca F 2016 Rev.Mod.Phys.88015008],where the terms related to the field curvature and gradient are small quantities of higher order and thus negligible.As the perturbed Lagrangian has been widely used in the literature to calculate the plasma nonadiabatic response in low-frequency MHD applications,this finding may have a significant impact on the understanding of the kinetic driving and dissipative mechanisms of MHD instabilities and the plasma response to electromagnetic perturbations in fusion plasmas.展开更多
The fault caused by a pantograph-catenary arc is the main factor that threatens the stability of high-speed railway energy transmission.Pantograph-catenary arc vertical drift is more severe than the case under normal ...The fault caused by a pantograph-catenary arc is the main factor that threatens the stability of high-speed railway energy transmission.Pantograph-catenary arc vertical drift is more severe than the case under normal pressure,as it is easy to develop the rigid busbar,which may lead to the flashover occurring around the support insulators.We establish a pantograph-catenary arc experiment and diagnosis platform to simulate low pressure and strong airflow environment.Meanwhile,the variation law of arc drift height with time under different air pressures and airflow velocities is analyzed.Moreover,arc drift characteristics and influencing factors are explored.The physical process of the arc column drifting to the rigid busbar with the jumping mechanism of the arc root on the rigid busbar is summarized.In order to further explore the mechanism of the above physical process,a multi-field stress coupling model is built,as the multi-stress variation law of arc is quantitatively evaluated.The dynamic action mechanism of multi-field stress on arc drifting characteristics is explored,as the physical mechanism of arc drifting under low pressure is theoretically explained.The research results provide theoretical support for arc suppression in high-altitude areas.展开更多
Textual data streams have been extensively used in practical applications where consumers of online products have expressed their views regarding online products.Due to changes in data distribution,commonly referred t...Textual data streams have been extensively used in practical applications where consumers of online products have expressed their views regarding online products.Due to changes in data distribution,commonly referred to as concept drift,mining this data stream is a challenging problem for researchers.The majority of the existing drift detection techniques are based on classification errors,which have higher probabilities of false-positive or missed detections.To improve classification accuracy,there is a need to develop more intuitive detection techniques that can identify a great number of drifts in the data streams.This paper presents an adaptive unsupervised learning technique,an ensemble classifier based on drift detection for opinion mining and sentiment classification.To improve classification performance,this approach uses four different dissimilarity measures to determine the degree of concept drifts in the data stream.Whenever a drift is detected,the proposed method builds and adds a new classifier to the ensemble.To add a new classifier,the total number of classifiers in the ensemble is first checked if the limit is exceeded before the classifier with the least weight is removed from the ensemble.To this end,a weighting mechanism is used to calculate the weight of each classifier,which decides the contribution of each classifier in the final classification results.Several experiments were conducted on real-world datasets and the resultswere evaluated on the false positive rate,miss detection rate,and accuracy measures.The proposed method is also compared with the state-of-the-art methods,which include DDM,EDDM,and PageHinkley with support vector machine(SVM)and Naive Bayes classifiers that are frequently used in concept drift detection studies.In all cases,the results show the efficiency of our proposed method.展开更多
To correct spectral peak drift and obtain more reliable net counts,this study proposes a long short-term memory(LSTM)model fused with a convolutional neural network(CNN)to accurately estimate the relevant parameters o...To correct spectral peak drift and obtain more reliable net counts,this study proposes a long short-term memory(LSTM)model fused with a convolutional neural network(CNN)to accurately estimate the relevant parameters of a nuclear pulse signal by learning of samples.A predefined mathematical model was used to train the CNN-LSTM model and generate a dataset composed of distorted pulse sequences.The trained model was validated using simulated pulses.The relative errors in the amplitude estimation of pulse sequences with different degrees of distortion were obtained using triangular shaping,CNN-LSTM,and LSTM models.As a result,for severely distorted pulses,the relative error of the CNN-LSTM model in estimating the pulse parameters was reduced by 14.35%compared with that of the triangular shaping algorithm.For slightly distorted pulses,the relative error of the CNN-LSTM model was reduced by 0.33%compared with that of the triangular shaping algorithm.The model was then evaluated considering two performance indicators,the correction ratio and the efficiency ratio,which represent the proportion of the increase in peak area of the two characteristic peak regions of interest(ROIs)to the peak area of the corrected characteristic peak ROI and the proportion of the increase in peak area of the two characteristic peak ROIs to the peak areas of the two shadow peak ROI,respectively.Ten measurement results of the iron ore samples indicate that approximately 86.27%of the decreased peak area of the shadow peak ROI was corrected to the characteristic peak ROI,and the proportion of the corrected peak area to the peak area of the characteristic peak ROI was approximately 1.72%.The proposed CNN-LSTM model can be applied to X-ray energy spectrum correction,which is of great significance for X-ray spectroscopy and elemental content analyses.展开更多
Handling sentiment drifts in real time twitter data streams are a challen-ging task while performing sentiment classifications,because of the changes that occur in the sentiments of twitter users,with respect to time....Handling sentiment drifts in real time twitter data streams are a challen-ging task while performing sentiment classifications,because of the changes that occur in the sentiments of twitter users,with respect to time.The growing volume of tweets with sentiment drifts has led to the need for devising an adaptive approach to detect and handle this drift in real time.This work proposes an adap-tive learning algorithm-based framework,Twitter Sentiment Drift Analysis-Bidir-ectional Encoder Representations from Transformers(TSDA-BERT),which introduces a sentiment drift measure to detect drifts and a domain impact score to adaptively retrain the classification model with domain relevant data in real time.The framework also works on static data by converting them to data streams using the Kafka tool.The experiments conducted on real time and simulated tweets of sports,health care andfinancial topics show that the proposed system is able to detect sentiment drifts and maintain the performance of the classification model,with accuracies of 91%,87%and 90%,respectively.Though the results have been provided only for a few topics,as a proof of concept,this framework can be applied to detect sentiment drifts and perform sentiment classification on real time data streams of any topic.展开更多
Cybersecurity increasingly relies on machine learning(ML)models to respond to and detect attacks.However,the rapidly changing data environment makes model life-cycle management after deployment essential.Real-time det...Cybersecurity increasingly relies on machine learning(ML)models to respond to and detect attacks.However,the rapidly changing data environment makes model life-cycle management after deployment essential.Real-time detection of drift signals from various threats is fundamental for effectively managing deployed models.However,detecting drift in unsupervised environments can be challenging.This study introduces a novel approach leveraging Shapley additive explanations(SHAP),a widely recognized explainability technique in ML,to address drift detection in unsupervised settings.The proposed method incorporates a range of plots and statistical techniques to enhance drift detection reliability and introduces a drift suspicion metric that considers the explanatory aspects absent in the current approaches.To validate the effectiveness of the proposed approach in a real-world scenario,we applied it to an environment designed to detect domain generation algorithms(DGAs).The dataset was obtained from various types of DGAs provided by NetLab.Based on this dataset composition,we sought to validate the proposed SHAP-based approach through drift scenarios that occur when a previously deployed model detects new data types in an environment that detects real-world DGAs.The results revealed that more than 90%of the drift data exceeded the threshold,demonstrating the high reliability of the approach to detect drift in an unsupervised environment.The proposed method distinguishes itself fromexisting approaches by employing explainable artificial intelligence(XAI)-based detection,which is not limited by model or system environment constraints.In conclusion,this paper proposes a novel approach to detect drift in unsupervised ML settings for cybersecurity.The proposed method employs SHAP-based XAI and a drift suspicion metric to improve drift detection reliability.It is versatile and suitable for various realtime data analysis contexts beyond DGA detection environments.This study significantly contributes to theMLcommunity by addressing the critical issue of managing ML models in real-world cybersecurity settings.Our approach is distinguishable from existing techniques by employing XAI-based detection,which is not limited by model or system environment constraints.As a result,our method can be applied in critical domains that require adaptation to continuous changes,such as cybersecurity.Through extensive validation across diverse settings beyond DGA detection environments,the proposed method will emerge as a versatile drift detection technique suitable for a wide range of real-time data analysis contexts.It is also anticipated to emerge as a new approach to protect essential systems and infrastructures from attacks.展开更多
Temporary seismic network deployments often suffer from incorrect timing records and thus pose a challenge to fully utilize the valuable data.To inspect and fix such time problems,the ambient noise cross-correlation f...Temporary seismic network deployments often suffer from incorrect timing records and thus pose a challenge to fully utilize the valuable data.To inspect and fix such time problems,the ambient noise cross-correlation function(NCCF)has been widely adopted by using daily waveforms.However,it is still challenging to detect the shortterm clock drift and overcome the influence of local noise on NCCF.To address these challenges,we conduct a study on two temporary datasets,including an ocean-bottom-seismometer(OBS)dataset from the southern Mariana subduction zone and a dataset from a temporary dense network from the Weiyuan shale gas field,Sichuan,China.We first inspect the teleseismic and local event waveforms to evaluate the overall clock drift and data quality for both datasets.For the OBS dataset,NCCF using different time segments(3,6,and 12-h)beside daily waveforms data is computed to select the data length with optimal detection capability.Eventually,the 6-h segment is the preferred choice with high detection efficiency and low noise level.For the land dataset,higher drift detection is achieved by NCCF using the daily long waveforms.Meanwhile,we find that NCCF symmetry on the dense array is highly influenced by localized intense noise for large interstation distances(>1 km)but is well preserved for short interstation distances.The results have shown that the use of different segments of daily waveform data in the OBS dataset,and the careful selection of interstation distances in the land dataset substantially improved the NCCF results.All the clock drifts in both datasets are successfully corrected and verified with waveforms and NCCF.The newly developed strategies using short-segment NCCF help to overcome the existing issues to correct the clock drift of seismic data.展开更多
Multilevel phase-change memory is an attractive technology to increase storage capacity and density owing to its high-speed,scalable and non-volatile characteristics.However,the contradiction between thermal stability...Multilevel phase-change memory is an attractive technology to increase storage capacity and density owing to its high-speed,scalable and non-volatile characteristics.However,the contradiction between thermal stability and operation speed is one of key factors to restrain the development of phase-change memory.Here,N-doped Ge_(2)Sb_(2)Te_(5)-based optoelectronic hybrid memory is proposed to simultaneously implement high thermal stability and ultrafast operation speed.The picosecond laser is adopted to write/erase information based on reversible phase transition characteristics whereas the resistance is detected to perform information readout.Results show that when N content is 27.4 at.%,N-doped Ge_(2)Sb_(2)Te_(5)film possesses high ten-year data retention temperature of 175℃and low resistance drift coefficient of 0.00024 at 85℃,0.00170 at 120℃,and 0.00249 at 150℃,respectively,owing to the formation of Ge–N,Sb–N,and Te–N bonds.The SET/RESET operation speeds of the film reach 520 ps/13 ps.In parallel,the reversible switching cycle of the corresponding device is realized with the resistance ratio of three orders of magnitude.Four-level reversible resistance states induced by various crystallization degrees are also obtained together with low resistance drift coefficients.Therefore,the N-doped Ge_(2)Sb_(2)Te_(5)thin film is a promising phase-change material for ultrafast multilevel optoelectronic hybrid storage.展开更多
According to the drive of planetary-scale upper magma fluid motions associated with the core-magma angular momentum exchange in the early Earth’s interior, this paper reviewed the results of continental drift studied...According to the drive of planetary-scale upper magma fluid motions associated with the core-magma angular momentum exchange in the early Earth’s interior, this paper reviewed the results of continental drift studied over the last three decades. The theoretical speculation is in good fit to the traces of geological events left on the Earth’s surface. A northeastward drift directionality of the Australian, African, and South American continents relative to the Antarctica Continent in the Southern Hemisphere is reanalyzed according to the slowing down of the early Earth’s rotation. Six traces of significant back-and-forth drifts of the Australian and Asian continents left respectively on the Southwest and Northwest Pacific seafloors are reidentified according to the gradually decreasing amplitude of core-magma angular momentum exchange during early geological evolution. Finally, the thickening and shortening of different continents during the early drift processes are re-simulated by using a simple magma fluid dynamical model.展开更多
基金The Fundamental Research Fund Project of the First Institute of OceanographyMinistry of Natural Resources+1 种基金under contract No.GY022Y07the National Natural Science Foundation of China under contract No.42106232。
文摘During the 10th Chinese Arctic scientific expedition carried out in the summer of 2019,the surface current in the high-latitude areas of the Arctic Ocean was observed using a self-developed surface drifting buoy,which was initially deployed in the Chukchi Sea.The buoy traversed the Chukchi Sea,Chukchi Abyssal Plain,Mendeleev Ridge,Makarov Basin,and Canada Basin over a period of 632 d.After returning to the Mendeleev Ridge,it continued to drift toward the pole.Overall,the track of the buoy reflected the characteristics of the transpolar drift and Chukchi Slope Current,as well as the inertial flow,cross-ridge surface flow,and even the surface disorganized flow for some time intervals.The results showed that:(1)the transpolar drift mainly occurs in the Chukchi Abyssal Plain,Mendeleev Ridge,and western Canada Basin to the east of the ridge where sea ice concentration is high,and the average northward flow velocity in the region between 79.41°N and 86.32°N was 5.1 cm/s;(2)the average surface velocity of the Chukchi Slope Current was 13.5 cm/s,and while this current moves westward along the continental slope,it also extends northwestward across the continental slope and flows to the deep sea;and(3)when sea ice concentration was less than 50%,the inertial flow was more significant(the maximum observed inertial flow was 26 cm/s,and the radius of the inertia circle was 3.6 km).
基金supported by the National Natural Science Foundation of China(No.42176020)the National Key Research and Development Program(No.2022 YFC3105002).
文摘The effect of Stokes drift production(SDP),which includes Coriolis-Stokes forcing,Langmuir circulation,and Craik-Lei-bovich vortexes,on the upper ocean during typhoon passage in the Bohai Sea(BS),China,is investigated by using a coupled wave-current model.The role of SDP in turbulent mixing and the further dynamics during the entire typhoon period are comprehensively stud-ied.Experimental results show that SDP greatly increases turbulent mixing at all depths under typhoon conditions by up to seven times that under normal weather conditions.SDP generally strengthens sea surface cooling by more than 0.4℃,with the maximum reduction in sea surface temperature(SST)at the during-typhoon stage exceeding 2℃,which is approximately seven times larger than that under normal weather conditions.The SDP-induced decrease in current speed can exceed 0.2ms^(-1),and the change in current direction is generally opposite the wind direction.These results suggest that Stokes drift depresses the effect of strong winds on currents by intensifying turbulent mixing.Mixed layer depth(MLD)is distinctly increased by O(1)during typhoons due to SDP and can deepen by more than 5m.In addition,the continuous effects of SDP on SST,current,and MLD at the after-typhoon stage indi-cate a hysteretic response between SDP and typhoon actions.
基金financially supported by the National Key Research and Development Program of China(2017YFD0200304)。
文摘Pesticide adjuvants,as crop protection products,have been widely used to reduce drift loss and improve utilization efficiency by regulating droplet spectrum.However,the coordinated regulation mechanisms of adjuvants and nozzles on droplet spectrum remain unclear.Here,we established the relationship between droplet spectrum evolution and liquid atomization by investigating the typical characteristics of droplet diameter distribution near the nozzle.Based on this,the regulation mechanisms of distinctive pesticide adjuvants on droplet spectrum were clarified,and the corresponding drift reduction performances were quantitively evaluated by wind tunnel experiments.It shows that the droplet diameter firstly shifts to the smaller due to the liquid sheet breakup and then prefers to increase caused by droplet interactions.Reducing the surface tension of sprayed liquid facilitates the uniform liquid breakup and increasing the viscosity inhibits the liquid deformation,which prolong the atomization process and effectively improve the droplet spectrum.As a result,the drift losses of flat-fan and hollow cone nozzles are reduced by about 50%after adding organosilicon and vegetable oil adjuvants.By contrast,the air induction nozzle shows a superior anti-drift ability,regardless of distinctive adjuvants.Our findings provide insights into rational adjuvant design and nozzle selection in the field application.
基金supported by the Natural Resources Development Special Fund Project of Jiangsu Province(No.JSZRHYKJ202009)the Taishan Scholar Funds(No.tsqn 201812022)+2 种基金the Fundamental Research Funds for the Central Universities(No.202072001)the Guangxi Key Laboratory of Marine Disaster in the Beibu Gulf,Beibu Gulf University(No.2021KF03)the National Natural Science Foundation of China(No.42176020).
文摘An integral quality control(QC)procedure that integrates various QC methods and considers the design indexes and operational status of the instruments for the observations of drifting air-sea interface buoy was developed in the order of basic in-spection followed by targeted QC.The innovative method of combining a moving Hampel filter and local anomaly detection com-plies with statistical laws and physical processes,which guarantees the QC performance of meteorological variables.Two sets of observation data were used to verify the applicability and effectiveness of the QC procedure,and the effect was evaluated using the observations of the Kuroshio Extension Observatory buoy as the reference.The results showed that the outliers in the time series can be correctly identified and processed,and the quality of data improved significantly.The linear correlation between the quality-controlled observations and the reference increased,and the difference decreased.The correlation coefficient of wind speed before and after QC increased from 0.77 to 0.82,and the maximum absolute error decreased by approximately 2.8ms^(-1).In addition,air pressure and relative humidity were optimized by 10^(-3)–10^(-2) orders of magnitude.For the sea surface temperature,the weight of coefficients of the continuity test algorithm was optimized based on the sea area of data acquisition,which effectively expanded the applicability of the algorithm.
基金Project supported by the National Natural Science Foundation of China (Grant Nos.11975062 and 11605021)the Fundamental Research Funds for the Central Universities (Grant No.3132023192)。
文摘The existence of a significant electron drift instability(EDI) in the Hall thruster is considered as one of the possible causes of the abnormal increase in axial electron mobility near the outlet of the channel. In recent years, extensive simulation research on the characteristics of EDI has been conducted, but the excitation mechanism and growth mechanism of EDI in linear stage and nonlinear stage remain unclear. In this work, a one-dimensional PIC model in the azimuthal direction of the thruster near-exit region is established to gain further insights into the mechanism of the EDI in detail, and the effects of different types of propellants on EDI characteristics are discussed. The changes in axial electron transport caused by EDI under different types of propellants and electromagnetic field strengths are also examined. The results indicate that EDI undergoes a short linear growth phase before transitioning to the nonlinear phase and finally reaching saturation through the ion Landau damping. The EDI drives a significant ion heating in the azimuthal direction through electron–ion friction before entering the quasi-steady state, which increases the axial mobility of the electrons. Using lighter atomic weight propellant can effectively suppress the oscillation amplitude of EDI, but it will increase the linear growth rate, frequency, and phase velocity of EDI. Compared with the classical mobility, the axial electron mobility under the EDI increases by three orders of magnitude, which is consistent with experimental phenomena. The change of propellant type is insufficient to significantly change the axial electron mobility. It is also found that the collisions between electrons and neutral gasescan significantly affect the axial electron mobility under the influence of EDI, and lead the strength of the electric field to increase and the strength of the magnetic field to decrease, thereby both effectively suppressing the axial transport of electrons.
基金The work was supported by the National Natural Science Foundation of China(No.51874045)National Natural Science Foundation-Youth Foundation(52104056)+2 种基金Department of Natural Resources of Guangdong Province(GDNRC[2021]56)Postdoctoral innovative talents support program in China(BX2021374)Scientific Research Program of Hubei Provincial Department of Education(T2021004).
文摘A reliable multiphase flow simulator is an important tool to improve wellbore integrity and production decision-making.To develop a multiphase flow model with high adaptability and high accuracy,we first build a multiphase flow database with 3561 groups of data and developed a drift closure relationship with stable continuity and high adaptability.Second,a high-order numerical scheme with strong fault capture ability is constructed by effectively combining MUSCL technology,van Albada slope limiter and AUSMV numerical scheme.Finally,the energy equation is coupled into the AUSMV numerical scheme of the drift flow model in the form of finite difference.A transient non-isothermal wellbore multiphase flow model with wide applicability is formed by integrating the three technologies,and the effects of various factors on the calculation accuracy are studied.The accuracy of the simulator is verified by comparing the measurement results with the blowout experiment of a full-scale experimental well.
基金The Shandong Provincial Natural Science Foundation of China under contract No.ZR2019MD023the National Natural Science Foundation of China under contract No.41776182.
文摘The outbreak of Ulva in the Yellow Sea has seriously affected marine ecology and economic activities.Therefore,effective prediction of the distribution of Ulva is of great significance for disaster prevention and reduction.However,the prediction method of Ulva is mainly based on numerical simulation.There are two problems with these methods.First is that the initial distribution of Ulva is simulated using independent pixel-level particles.Besides,the influence of Ulva growth and diffusion on the drift is not considered.Therefore,this paper proposes a multi-module with a two-way feedback method(MTF)to solve the above problems.The main contributions of our approach are summarized as follows.First,the initialization module,the generation and elimination module,and the drive module are composed in our way.Second,we proposed an initialization method using rectangle objects to simulate the Ulva distribution extracted from remote sensing images.Thirdly,the drift and diffusion mechanism of the Ulva is considered to realize the two-way feedback between the generation and elimination module and the drive module.The results of our experiments show that the MTF performs better than the traditional method in predicting the drift and diffusion of Ulva.The code is already publicly available at https://github.com/UPCGIT/A-multi-module-with-a-two-way-feedback-method-for-Ulva-drift-diffusion.
基金supported by National Natural Sciences Foundation of China(Nos.12075052,12175034 and 12275098)National Key R&D Program of China(Nos.2018YFE0309103,2017YFE0301100 and 2017YFE0301104)。
文摘To better understand divertor detachment and asymmetry in the Experimental Advanced Superconducting Tokamak(EAST),drift modeling via the comprehensive edge plasma code SOLPS-ITER of neon impurity seeded plasmas in favorable/unfavorable toroidal magnetic field(BT)has been performed.Firstly,electrostatic potential/field(f/E)distribution has been analyzed,to make sure that f and E are correctly described and to better understand drift-driven processes.After that,drift effects on divertor detachment and asymmetry have been focused on.In accordance with the corresponding experimental observations,simulation results demonstrate that in favorable BTthe onset of detachment is highly asymmetric between the inner and outer divertors;and reversing BT can significantly decrease the magnitude of in-out asymmetry in the onset of detachment,physics reasons for which have been explored.It is found that,apart from the well-known E×B drift particle flow from one divertor to the other through the private flux region,scrape-off layer(SOL)heat flow,which is much more asymmetrically distributed between the high field side and low field side for favorable BTthan that for unfavorable B_T,is also a critical parameter affecting divertor detachment and asymmetry.During detachment,upstream pressure(P_u)reduction occurs and tends to be more dramatical in the colder side than that in the hotter side.The convective SOL heat flow,emerging due to in-out asymmetry in P_u reduction,is found to be critical for understanding divertor detachment and asymmetry observed in EAST.To better understand the calculated drastic power radiation in the core and upstream SOL,drift effects on divertor leakage/retention of neon in EAST with both BTdirections have been addressed for the first time,by analyzing profile of poloidal neon velocity and that of neon ionization source from atoms.This work can be a reference for future numeric simulations performed more closely related to experimental regimes.
基金The Taif University Deanship of Scientific Research supported this endeavor(Project Number:1-443-4)for which the authors are grateful to Taif University for their kind support.
文摘Concept drift is a main security issue that has to be resolved since it presents a significant barrier to the deployment of machine learning(ML)models.Due to attackers’(and/or benign equivalents’)dynamic behavior changes,testing data distribution frequently diverges from original training data over time,resulting in substantial model failures.Due to their dispersed and dynamic nature,distributed denial-of-service attacks pose a danger to cybersecurity,resulting in attacks with serious consequences for users and businesses.This paper proposes a novel design for concept drift analysis and detection of malware attacks like Distributed Denial of Service(DDOS)in the network.The goal of this architecture combination is to accurately represent data and create an effective cyber security prediction agent.The intrusion detection system and concept drift of the network has been analyzed using secure adaptive windowing with website data authentication protocol(SAW_WDA).The network has been analyzed by authentication protocol to avoid malware attacks.The data of network users will be collected and classified using multilayer perceptron gradient decision tree(MLPGDT)classifiers.Based on the classification output,the decision for the detection of attackers and authorized users will be identified.The experimental results show output based on intrusion detection and concept drift analysis systems in terms of throughput,end-end delay,network security,network concept drift,and results based on classification with regard to accuracy,memory,and precision and F-1 score.
基金funded by the Social Science Foundation of Shandong(No.20CXWJ08).
文摘Oil spill prediction is critical for reducing the detrimental impact of oil spills on marine ecosystems,and the wind strong-ly influences the performance of oil spill models.However,the wind drift factor is assumed to be constant or parameterized by linear regression and other methods in existing studies,which may limit the accuracy of the oil spill simulation.A parameterization method for wind drift factor(PMOWDF)based on deep learning,which can effectively extract the time-varying characteristics on a regional scale,is proposed in this paper.The method was adopted to forecast the oil spill in the East China Sea.The discrepancies between predicted positions and actual measurement locations of the drifters are obtained using seasonal statistical analysis.Results reveal that PMOWDF can improve the accuracy of oil spill simulation compared with the traditional method.Furthermore,the parameteriza-tion method is validated with satellite observations of the Sanchi oil spill in 2018.
基金The authors would like to extend their gratitude to Universiti Teknologi PETRONAS (Malaysia)for funding this research through grant number (015LA0-037).
文摘Every application in a smart city environment like the smart grid,health monitoring, security, and surveillance generates non-stationary datastreams. Due to such nature, the statistical properties of data changes overtime, leading to class imbalance and concept drift issues. Both these issuescause model performance degradation. Most of the current work has beenfocused on developing an ensemble strategy by training a new classifier on thelatest data to resolve the issue. These techniques suffer while training the newclassifier if the data is imbalanced. Also, the class imbalance ratio may changegreatly from one input stream to another, making the problem more complex.The existing solutions proposed for addressing the combined issue of classimbalance and concept drift are lacking in understating of correlation of oneproblem with the other. This work studies the association between conceptdrift and class imbalance ratio and then demonstrates how changes in classimbalance ratio along with concept drift affect the classifier’s performance.We analyzed the effect of both the issues on minority and majority classesindividually. To do this, we conducted experiments on benchmark datasetsusing state-of-the-art classifiers especially designed for data stream classification.Precision, recall, F1 score, and geometric mean were used to measure theperformance. Our findings show that when both class imbalance and conceptdrift problems occur together the performance can decrease up to 15%. Ourresults also show that the increase in the imbalance ratio can cause a 10% to15% decrease in the precision scores of both minority and majority classes.The study findings may help in designing intelligent and adaptive solutionsthat can cope with the challenges of non-stationary data streams like conceptdrift and class imbalance.
基金supported by the National Magnetic Confinement Fusion Energy Program of China(No.2019YFE03030000)National Natural Science Foundation of China(Nos.11905253 and U19A20113)。
文摘We find that the perturbed Lagrangian derived from the drift-kinetic equation in[Porcelli F et al 1994 Phys.Plasmas 1470]is inconsistent with the ordering for the low-frequency large-scale magnetohydrodynamic(MHD).Here,we rederive the expression for the perturbed Lagrangian within the framework of nonideal MHD using the ordering system for the low-frequency largescale MHD in a low-beta plasma.The obtained perturbed Lagrangian is consistent with Chen's gyrokinetic theory[Chen L and Zonca F 2016 Rev.Mod.Phys.88015008],where the terms related to the field curvature and gradient are small quantities of higher order and thus negligible.As the perturbed Lagrangian has been widely used in the literature to calculate the plasma nonadiabatic response in low-frequency MHD applications,this finding may have a significant impact on the understanding of the kinetic driving and dissipative mechanisms of MHD instabilities and the plasma response to electromagnetic perturbations in fusion plasmas.
基金Project supported by the National Natural Science Foundation of China(Grant Nos.51707166,51922090,U1966602,and U19A20105)the Sichuan Science and Technology General Project(Grant Nos.2019YJ0213 and2019JDJQ0019)。
文摘The fault caused by a pantograph-catenary arc is the main factor that threatens the stability of high-speed railway energy transmission.Pantograph-catenary arc vertical drift is more severe than the case under normal pressure,as it is easy to develop the rigid busbar,which may lead to the flashover occurring around the support insulators.We establish a pantograph-catenary arc experiment and diagnosis platform to simulate low pressure and strong airflow environment.Meanwhile,the variation law of arc drift height with time under different air pressures and airflow velocities is analyzed.Moreover,arc drift characteristics and influencing factors are explored.The physical process of the arc column drifting to the rigid busbar with the jumping mechanism of the arc root on the rigid busbar is summarized.In order to further explore the mechanism of the above physical process,a multi-field stress coupling model is built,as the multi-stress variation law of arc is quantitatively evaluated.The dynamic action mechanism of multi-field stress on arc drifting characteristics is explored,as the physical mechanism of arc drifting under low pressure is theoretically explained.The research results provide theoretical support for arc suppression in high-altitude areas.
基金The authors extend their appreciation to the Deanship of Scientific Research at King Khalid University for funding this work through Large Groups(Project under Grant Number(RGP.2/49/43)).
文摘Textual data streams have been extensively used in practical applications where consumers of online products have expressed their views regarding online products.Due to changes in data distribution,commonly referred to as concept drift,mining this data stream is a challenging problem for researchers.The majority of the existing drift detection techniques are based on classification errors,which have higher probabilities of false-positive or missed detections.To improve classification accuracy,there is a need to develop more intuitive detection techniques that can identify a great number of drifts in the data streams.This paper presents an adaptive unsupervised learning technique,an ensemble classifier based on drift detection for opinion mining and sentiment classification.To improve classification performance,this approach uses four different dissimilarity measures to determine the degree of concept drifts in the data stream.Whenever a drift is detected,the proposed method builds and adds a new classifier to the ensemble.To add a new classifier,the total number of classifiers in the ensemble is first checked if the limit is exceeded before the classifier with the least weight is removed from the ensemble.To this end,a weighting mechanism is used to calculate the weight of each classifier,which decides the contribution of each classifier in the final classification results.Several experiments were conducted on real-world datasets and the resultswere evaluated on the false positive rate,miss detection rate,and accuracy measures.The proposed method is also compared with the state-of-the-art methods,which include DDM,EDDM,and PageHinkley with support vector machine(SVM)and Naive Bayes classifiers that are frequently used in concept drift detection studies.In all cases,the results show the efficiency of our proposed method.
基金This work was supported by the Open Project of the Guangxi Key Laboratory of Nuclear Physics and Nuclear Technology(No.NLK2022-05)Central Government Guidance Funds for Local Scientific and Technological Development,China(No.Guike ZY22096024)+3 种基金Sichuan Natural Science Youth Fund Project(No.2023NSFSC1366)Open Research Fund of the National Engineering Research Center for Agro-Ecological Big Data Analysis&Application,Anhui University(No.AE202209)Research Fund of Guangxi Key Lab of Multi-source Information Mining&Security(MIMS22-04)National Natural Science Youth Foundation of China(No.12305214).
文摘To correct spectral peak drift and obtain more reliable net counts,this study proposes a long short-term memory(LSTM)model fused with a convolutional neural network(CNN)to accurately estimate the relevant parameters of a nuclear pulse signal by learning of samples.A predefined mathematical model was used to train the CNN-LSTM model and generate a dataset composed of distorted pulse sequences.The trained model was validated using simulated pulses.The relative errors in the amplitude estimation of pulse sequences with different degrees of distortion were obtained using triangular shaping,CNN-LSTM,and LSTM models.As a result,for severely distorted pulses,the relative error of the CNN-LSTM model in estimating the pulse parameters was reduced by 14.35%compared with that of the triangular shaping algorithm.For slightly distorted pulses,the relative error of the CNN-LSTM model was reduced by 0.33%compared with that of the triangular shaping algorithm.The model was then evaluated considering two performance indicators,the correction ratio and the efficiency ratio,which represent the proportion of the increase in peak area of the two characteristic peak regions of interest(ROIs)to the peak area of the corrected characteristic peak ROI and the proportion of the increase in peak area of the two characteristic peak ROIs to the peak areas of the two shadow peak ROI,respectively.Ten measurement results of the iron ore samples indicate that approximately 86.27%of the decreased peak area of the shadow peak ROI was corrected to the characteristic peak ROI,and the proportion of the corrected peak area to the peak area of the characteristic peak ROI was approximately 1.72%.The proposed CNN-LSTM model can be applied to X-ray energy spectrum correction,which is of great significance for X-ray spectroscopy and elemental content analyses.
文摘Handling sentiment drifts in real time twitter data streams are a challen-ging task while performing sentiment classifications,because of the changes that occur in the sentiments of twitter users,with respect to time.The growing volume of tweets with sentiment drifts has led to the need for devising an adaptive approach to detect and handle this drift in real time.This work proposes an adap-tive learning algorithm-based framework,Twitter Sentiment Drift Analysis-Bidir-ectional Encoder Representations from Transformers(TSDA-BERT),which introduces a sentiment drift measure to detect drifts and a domain impact score to adaptively retrain the classification model with domain relevant data in real time.The framework also works on static data by converting them to data streams using the Kafka tool.The experiments conducted on real time and simulated tweets of sports,health care andfinancial topics show that the proposed system is able to detect sentiment drifts and maintain the performance of the classification model,with accuracies of 91%,87%and 90%,respectively.Though the results have been provided only for a few topics,as a proof of concept,this framework can be applied to detect sentiment drifts and perform sentiment classification on real time data streams of any topic.
基金supported by the Institute of Information and Communications Technology Planning and Evaluation(IITP)grant funded by the Korean government(MSIT)(No.2022-0-00089,Development of clustering and analysis technology to identify cyber attack groups based on life cycle)the Institute of Civil Military Technology Cooperation funded by the Defense Acquisition Program Administration and Ministry of Trade,Industry and Energy of Korean government under Grant No.21-CM-EC-07.
文摘Cybersecurity increasingly relies on machine learning(ML)models to respond to and detect attacks.However,the rapidly changing data environment makes model life-cycle management after deployment essential.Real-time detection of drift signals from various threats is fundamental for effectively managing deployed models.However,detecting drift in unsupervised environments can be challenging.This study introduces a novel approach leveraging Shapley additive explanations(SHAP),a widely recognized explainability technique in ML,to address drift detection in unsupervised settings.The proposed method incorporates a range of plots and statistical techniques to enhance drift detection reliability and introduces a drift suspicion metric that considers the explanatory aspects absent in the current approaches.To validate the effectiveness of the proposed approach in a real-world scenario,we applied it to an environment designed to detect domain generation algorithms(DGAs).The dataset was obtained from various types of DGAs provided by NetLab.Based on this dataset composition,we sought to validate the proposed SHAP-based approach through drift scenarios that occur when a previously deployed model detects new data types in an environment that detects real-world DGAs.The results revealed that more than 90%of the drift data exceeded the threshold,demonstrating the high reliability of the approach to detect drift in an unsupervised environment.The proposed method distinguishes itself fromexisting approaches by employing explainable artificial intelligence(XAI)-based detection,which is not limited by model or system environment constraints.In conclusion,this paper proposes a novel approach to detect drift in unsupervised ML settings for cybersecurity.The proposed method employs SHAP-based XAI and a drift suspicion metric to improve drift detection reliability.It is versatile and suitable for various realtime data analysis contexts beyond DGA detection environments.This study significantly contributes to theMLcommunity by addressing the critical issue of managing ML models in real-world cybersecurity settings.Our approach is distinguishable from existing techniques by employing XAI-based detection,which is not limited by model or system environment constraints.As a result,our method can be applied in critical domains that require adaptation to continuous changes,such as cybersecurity.Through extensive validation across diverse settings beyond DGA detection environments,the proposed method will emerge as a versatile drift detection technique suitable for a wide range of real-time data analysis contexts.It is also anticipated to emerge as a new approach to protect essential systems and infrastructures from attacks.
基金supported by National Science Foundation of China(U2139203)National Key R&D Program of China (2018YFC1503400)+3 种基金China Earthquake Science Experiment Project,CEA (2019CSES0107)HKSAR Research Grant Council GRF Grant (14303721,14306122)State Key Lab of Earthquake Dynamics (LED2021B03)the Faculty of Science,CUHK。
文摘Temporary seismic network deployments often suffer from incorrect timing records and thus pose a challenge to fully utilize the valuable data.To inspect and fix such time problems,the ambient noise cross-correlation function(NCCF)has been widely adopted by using daily waveforms.However,it is still challenging to detect the shortterm clock drift and overcome the influence of local noise on NCCF.To address these challenges,we conduct a study on two temporary datasets,including an ocean-bottom-seismometer(OBS)dataset from the southern Mariana subduction zone and a dataset from a temporary dense network from the Weiyuan shale gas field,Sichuan,China.We first inspect the teleseismic and local event waveforms to evaluate the overall clock drift and data quality for both datasets.For the OBS dataset,NCCF using different time segments(3,6,and 12-h)beside daily waveforms data is computed to select the data length with optimal detection capability.Eventually,the 6-h segment is the preferred choice with high detection efficiency and low noise level.For the land dataset,higher drift detection is achieved by NCCF using the daily long waveforms.Meanwhile,we find that NCCF symmetry on the dense array is highly influenced by localized intense noise for large interstation distances(>1 km)but is well preserved for short interstation distances.The results have shown that the use of different segments of daily waveform data in the OBS dataset,and the careful selection of interstation distances in the land dataset substantially improved the NCCF results.All the clock drifts in both datasets are successfully corrected and verified with waveforms and NCCF.The newly developed strategies using short-segment NCCF help to overcome the existing issues to correct the clock drift of seismic data.
基金Project supported by the National Natural Science Foundation of China(Grant Nos.62205231 and 22002102)the Postgraduate Research&Practice Innovation Program of Jiangsu Province,China(Grant No.KYCX223271)Jiangsu Key Laboratory for Environment Functional Materials。
文摘Multilevel phase-change memory is an attractive technology to increase storage capacity and density owing to its high-speed,scalable and non-volatile characteristics.However,the contradiction between thermal stability and operation speed is one of key factors to restrain the development of phase-change memory.Here,N-doped Ge_(2)Sb_(2)Te_(5)-based optoelectronic hybrid memory is proposed to simultaneously implement high thermal stability and ultrafast operation speed.The picosecond laser is adopted to write/erase information based on reversible phase transition characteristics whereas the resistance is detected to perform information readout.Results show that when N content is 27.4 at.%,N-doped Ge_(2)Sb_(2)Te_(5)film possesses high ten-year data retention temperature of 175℃and low resistance drift coefficient of 0.00024 at 85℃,0.00170 at 120℃,and 0.00249 at 150℃,respectively,owing to the formation of Ge–N,Sb–N,and Te–N bonds.The SET/RESET operation speeds of the film reach 520 ps/13 ps.In parallel,the reversible switching cycle of the corresponding device is realized with the resistance ratio of three orders of magnitude.Four-level reversible resistance states induced by various crystallization degrees are also obtained together with low resistance drift coefficients.Therefore,the N-doped Ge_(2)Sb_(2)Te_(5)thin film is a promising phase-change material for ultrafast multilevel optoelectronic hybrid storage.
文摘According to the drive of planetary-scale upper magma fluid motions associated with the core-magma angular momentum exchange in the early Earth’s interior, this paper reviewed the results of continental drift studied over the last three decades. The theoretical speculation is in good fit to the traces of geological events left on the Earth’s surface. A northeastward drift directionality of the Australian, African, and South American continents relative to the Antarctica Continent in the Southern Hemisphere is reanalyzed according to the slowing down of the early Earth’s rotation. Six traces of significant back-and-forth drifts of the Australian and Asian continents left respectively on the Southwest and Northwest Pacific seafloors are reidentified according to the gradually decreasing amplitude of core-magma angular momentum exchange during early geological evolution. Finally, the thickening and shortening of different continents during the early drift processes are re-simulated by using a simple magma fluid dynamical model.
