Processes supported by process-aware information systems are subject to continuous and often subtle changes due to evolving operational,organizational,or regulatory factors.These changes,referred to as incremental con...Processes supported by process-aware information systems are subject to continuous and often subtle changes due to evolving operational,organizational,or regulatory factors.These changes,referred to as incremental concept drift,gradually alter the behavior or structure of processes,making their detection and localization a challenging task.Traditional process mining techniques frequently assume process stationarity and are limited in their ability to detect such drift,particularly from a control-flow perspective.The objective of this research is to develop an interpretable and robust framework capable of detecting and localizing incremental concept drift in event logs,with a specific emphasis on the structural evolution of control-flow semantics in processes.We propose DriftXMiner,a control-flow-aware hybrid framework that combines statistical,machine learning,and process model analysis techniques.The approach comprises three key components:(1)Cumulative Drift Scanner that tracks directional statistical deviations to detect early drift signals;(2)a Temporal Clustering and Drift-Aware Forest Ensemble(DAFE)to capture distributional and classification-level changes in process behavior;and(3)Petri net-based process model reconstruction,which enables the precise localization of structural drift using transition deviation metrics and replay fitness scores.Experimental validation on the BPI Challenge 2017 event log demonstrates that DriftXMiner effectively identifies and localizes gradual and incremental process drift over time.The framework achieves a detection accuracy of 92.5%,a localization precision of 90.3%,and an F1-score of 0.91,outperforming competitive baselines such as CUSUM+Histograms and ADWIN+Alpha Miner.Visual analyses further confirm that identified drift points align with transitions in control-flow models and behavioral cluster structures.DriftXMiner offers a novel and interpretable solution for incremental concept drift detection and localization in dynamic,process-aware systems.By integrating statistical signal accumulation,temporal behavior profiling,and structural process mining,the framework enables finegrained drift explanation and supports adaptive process intelligence in evolving environments.Its modular architecture supports extension to streaming data and real-time monitoring contexts.展开更多
In order to suppress the influence of temperature changes on the performance of accelerometers,a digital quartz resonant accelerometer with low temperature drift is developed using a quartz resonator cluster as a tran...In order to suppress the influence of temperature changes on the performance of accelerometers,a digital quartz resonant accelerometer with low temperature drift is developed using a quartz resonator cluster as a transducer element.In addition,a digital intellectual property(IP) is designed in FPGA to achieve signal processing and fusion of integrated resonators.A testing system for digital quartz resonant accelerometers is established to characterize the performance under different conditions.The scale factor of the accelerometer prototype reaches 3561.63 Hz/g in the range of -1 g to +1 g,and 3542.5 Hz/g in the range of-10 g to+10 g.In different measurement ranges,the linear correlation coefficient R~2 of the accelerometer achieves greater than 0.998.The temperature drift of the accelerometer prototype is tested using a constant temperature test chamber,with a temperature change from -20℃ to 80℃.After temperature-drift compensation,the zero bias temperature coefficient falls to 0.08 mg/℃,and the scale factor temperature coefficient is 65.43 ppm/℃.The experimental results show that the digital quartz resonant accelerometer exhibits excellent sensitivity and low temperature drift.展开更多
Many real⁃world machine learning applications face the challenge of dealing with changing data over time,known as concept drift,and the issue of data indeterminacy,where all the true labels available are unrealistic.T...Many real⁃world machine learning applications face the challenge of dealing with changing data over time,known as concept drift,and the issue of data indeterminacy,where all the true labels available are unrealistic.This can lead to a decrease in the accuracy of the prediction models.The aim of this study is to introduce a new approach for detecting drift,which is based on neutrosophic set theory.This approach takes into account uncertainty in the prediction model and is able to handle indeterminate information,considering its impact on the models performance.The proposed method reads data into windows and calculates a set of values based on the concept of neutrosophic membership.These values are then used in the Neutrosophic Support Vector Machine(N⁃SVM).To address the issue of indeterminate true label data,the values issued by N⁃SVM are expressed as entropy and used as input for the ADWIN(Adaptive Windowing)change detector.When a drift is detected,the prediction model is retrained by including only the most recent instances with the original training data set.The proposed method gives promising results in terms of drift detection accuracy compared to the state of existing drift detection methods such as KSWIN,ADWIN,and DWM.展开更多
In this paper,we investigate the weighted Dirichlet eigenvalue problem of polynomial operator of the drifting Laplacian on the cigar soliton■as follows■where is a positive continuous function on,denotes the outward ...In this paper,we investigate the weighted Dirichlet eigenvalue problem of polynomial operator of the drifting Laplacian on the cigar soliton■as follows■where is a positive continuous function on,denotes the outward unit normal to the boundary,and are two nonnegative constants.We establish some universal inequalities for eigenvalues of this problem.展开更多
The particle identification(PID)of hadrons plays a crucial role in particle physics experiments,especially in flavor physics and jet tagging.The cluster counting method,which measures the number of primary ionizations...The particle identification(PID)of hadrons plays a crucial role in particle physics experiments,especially in flavor physics and jet tagging.The cluster counting method,which measures the number of primary ionizations in gaseous detectors,is a promising breakthrough in PID.However,developing an effective reconstruction algorithm for cluster counting remains challenging.To address this challenge,we propose a cluster counting algorithm based on long short-term memory and dynamic graph convolutional neural networks for the CEPC drift chamber.Experiments on Monte Carlo simulated samples demonstrate that our machine learning-based algorithm surpasses traditional methods.It improves the K/πseparation of PID by 10%,meeting the PID requirements of CEPC.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 primary goal of software defect prediction (SDP) is to pinpoint code modules that are likely to contain defects, thereby enabling software quality assurance teams to strategically allocate their resources and manp...The primary goal of software defect prediction (SDP) is to pinpoint code modules that are likely to contain defects, thereby enabling software quality assurance teams to strategically allocate their resources and manpower. Within-project defect prediction (WPDP) is a widely used method in SDP. Despite various improvements, current methods still face challenges such as coarse-grained prediction and ineffective handling of data drift due to differences in project distribution. To address these issues, we propose a fine-grained SDP method called DIDP (drift-immune defect prediction), based on drift-immune graph neural networks (DI-GNN). DIDP converts source code into graph representations and uses DI-GNN to mitigate data drift at the model level. It also analyses key statements leading to file defects for a more detailed SDP approach. We evaluated the performance of DIDP in WPDP by examining its file-level and statement-level accuracy compared to state-of-the-art methods, and by examining its cross-project prediction accuracy. The results of the experiment show that DIDP showed significant improvements in F1-score and Recall@Top20%LOC compared to existing methods, even with large software version changes. DIDP also performed well in cross-project SDP. Our study demonstrates that DIDP achieves impressive prediction results in WPDP, effectively mitigating data drift and accurately predicting defective files. Additionally, DIDP can rank the risk of statements in defective files, aiding developers and testers in identifying potential code issues.展开更多
Purpose:This research addresses the challenge of concept drift in AI-enabled software,particularly within autonomous vehicle systems where concept drift in object recognition(like pedestrian detection)can lead to misc...Purpose:This research addresses the challenge of concept drift in AI-enabled software,particularly within autonomous vehicle systems where concept drift in object recognition(like pedestrian detection)can lead to misclassifications and safety risks.This study introduces a proactive framework to detect early signs of domain-specific concept drift by leveraging domain analysis and natural language processing techniques.This method is designed to help maintain the relevance of domain knowledge and prevent potential failures in AI systems due to evolving concept definitions.Design/methodology/approach:The proposed framework integrates natural language processing and image analysis to continuously update and monitor key domain concepts against evolving external data sources,such as social media and news.By identifying terms and features closely associated with core concepts,the system anticipates and flags significant changes.This was tested in the automotive domain on the pedestrian concept,where the framework was evaluated for its capacity to detect shifts in the recognition of pedestrians,particularly during events like Halloween and specific car accidents.Findings:The framework demonstrated an ability to detect shifts in the domain concept of pedestrians,as evidenced by contextual changes around major events.While it successfully identified pedestrian-related drift,the system’s accuracy varied when overlapping with larger social events.The results indicate the model’s potential to foresee relevant shifts before they impact autonomous systems,although further refinement is needed to handle high-impact concurrent events.Research limitations:This study focused on detecting concept drift in the pedestrian domain within autonomous vehicles,with results varying across domains.To assess generalizability,we tested the framework for airplane-related incidents and demonstrated adaptability.However,unpredictable events and data biases from social media and news may obscure domain-specific drifts.Further evaluation across diverse applications is needed to enhance robustness in evolving AI environments.Practical implications:The proactive detection of concept drift has significant implications for AI-driven domains,especially in safety-critical applications like autonomous driving.By identifying early signs of drift,this framework provides actionable insights for AI system updates,potentially reducing misclassification risks and enhancing public safety.Moreover,it enables timely interventions,reducing costly and labor-intensive retraining requirements by focusing only on the relevant aspects of evolving concepts.This method offers a streamlined approach for maintaining AI system performance in environments where domain knowledge rapidly changes.Originality/value:This study contributes a novel domain-agnostic framework that combines natural language processing with image analysis to predict concept drift early.This unique approach,which is focused on real-time data sources,offers an effective and scalable solution for addressing the evolving nature of domain-specific concepts in AI applications.展开更多
Cardiovascular diseases(CVDs)continue to present a leading cause ofmortalityworldwide,emphasizing the importance of early and accurate prediction.Electrocardiogram(ECG)signals,central to cardiac monitoring,have increa...Cardiovascular diseases(CVDs)continue to present a leading cause ofmortalityworldwide,emphasizing the importance of early and accurate prediction.Electrocardiogram(ECG)signals,central to cardiac monitoring,have increasingly been integratedwithDeep Learning(DL)for real-time prediction of CVDs.However,DL models are prone to performance degradation due to concept drift and to catastrophic forgetting.To address this issue,we propose a realtime CVDs prediction approach,referred to as ADWIN-GFR that combines Convolutional Neural Network(CNN)layers,for spatial feature extraction,with Gated Recurrent Units(GRU),for temporal modeling,alongside adaptive drift detection and mitigation mechanisms.The proposed approach integratesAdaptiveWindowing(ADWIN)for realtime concept drift detection,a fine-tuning strategy based on Generative Features Replay(GFR)to preserve previously acquired knowledge,and a dynamic replay buffer ensuring variance,diversity,and data distribution coverage.Extensive experiments conducted on the MIT-BIH arrhythmia dataset demonstrate that ADWIN-GFR outperforms standard fine-tuning techniques,achieving an average post-drift accuracy of 95.4%,amacro F1-score of 93.9%,and a remarkably low forgetting score of 0.9%.It also exhibits an average drift detection delay of 12 steps and achieves an adaptation gain of 17.2%.These findings underscore the potential of ADWIN-GFR for deployment in real-world cardiac monitoring systems,including wearable ECG devices and hospital-based patient monitoring platforms.展开更多
A half-size prototype of the multi wire drift chamber for the cooling storage ring external-target experiment(CEE)was assembled and tested in the 350 MeV/u Kr+Fe reactions at the heavy-ion research facility in Lanzhou...A half-size prototype of the multi wire drift chamber for the cooling storage ring external-target experiment(CEE)was assembled and tested in the 350 MeV/u Kr+Fe reactions at the heavy-ion research facility in Lanzhou.The prototype consists of six sense layers,where the sense wires are stretched in three directions X,U,and V;meeting 0?,30?,and-30?,respectively,with respect to the vertical axis.The sensitive area of the prototype is 76 cm×76 cm.The amplified and shaped signals from the anode wires were digitized in a serial capacity array.When operating at a high voltage of 1500 V on the anode wires,the efficiency for each layer is greater than 95%.The tracking residual is approximately 301±2μm.This performance satisfies the requirements of CEE.展开更多
With the gradual penetration of the internet of things(IoT)into all areas of life,the scale of IoT devices shows an explosive growth trend.The era of internet of everything is coming,and the important position of IoT ...With the gradual penetration of the internet of things(IoT)into all areas of life,the scale of IoT devices shows an explosive growth trend.The era of internet of everything is coming,and the important position of IoT security is becoming increasingly prominent.Due to the large number types of IoT devices,there may be different security vulnerabilities,and unknown attack forms and virus samples are appear.In other words,large number of IoT devices,large data volumes,and various attack forms pose a big challenge of malicious traffic identification.To solve these problems,this paper proposes a concept drift detection and adaptation(CDDA)method for IoT security framework.The AI model performance is evaluated by verifying the effectiveness of IoT traffic for data drift detection,so as to select the best AI model.The experimental test are given to confirm that the feasibility of the framework and the adaptive method in practice,and the effect on the performance of IoT traffic identification is also verified.展开更多
Understanding the drifting motion of a small semi-submersible drifter is of vital importance regarding monitoring surface currents and the floating pollutants in coastal regions. This work addresses this issue by esta...Understanding the drifting motion of a small semi-submersible drifter is of vital importance regarding monitoring surface currents and the floating pollutants in coastal regions. This work addresses this issue by establishing a mechanistic drifting forecast model based on kinetic analysis. Taking tide–wind–wave into consideration, the forecast model is validated against in situ drifting experiment in the Radial Sand Ridges. Model results show good performance with respect to the measured drifting features, characterized by migrating back and forth twice a day with daily downwind displacements. Trajectory models are used to evaluate the influence of the individual hydrodynamic forcing. The tidal current is the fundamental dynamic condition in the Radial Sand Ridges and has the greatest impact on the drifting distance. However, it loses its leading position in the field of the daily displacement of the used drifter. The simulations reveal that different hydrodynamic forces dominate the daily displacement of the used drifter at different wind scales. The wave-induced mass transport has the greatest influence on the daily displacement at Beaufort wind scale 5–6; while wind drag contributes mostly at wind scale 2–4.展开更多
Drift phenomenon has been known as the drawback of sensors and causes inaccuracy on the long-term measurement. In general,there are two methods to reduce the drift problem.One is to tune the parameters of the fabricat...Drift phenomenon has been known as the drawback of sensors and causes inaccuracy on the long-term measurement. In general,there are two methods to reduce the drift problem.One is to tune the parameters of the fabrication process to improve the properties of the front-ended device.Another is to compensate the drift phenomenon by adding extra drift compensation circuit or software in the back-ended readout circuit.In this study,a drift calibration method for the potentiometric sensor was presented and the drift calibration method was performed by using the circuit.According to experimental results,the drift phenomenon of the SnO_2 pH electrode was reduced by the drift calibration device.展开更多
A data gathering system is designed for the interferometric fiber optic gyroscope (IFOG) of land strapdown inertial system. IFOG is tested and the testing curve is given. The test data of IFOG are analyzed with Allan ...A data gathering system is designed for the interferometric fiber optic gyroscope (IFOG) of land strapdown inertial system. IFOG is tested and the testing curve is given. The test data of IFOG are analyzed with Allan variance method and each error coefficient is identified. Furthermore, a random drift error model for IFOG is built by the method of time series analysis. The conclusion provides supports for improving IFOG design and compensating for errors of IFOG in practice.展开更多
Based on micro-displacement measurement principles of the spherical differential capacitance sensor, the relationship between the capacitance variation and the micro-displacement of each pair of detecting electrodes f...Based on micro-displacement measurement principles of the spherical differential capacitance sensor, the relationship between the capacitance variation and the micro-displacement of each pair of detecting electrodes for the superconducting gyroscope （SCG） with eight detecting electrodes is analyzed. The model of the SCG rotor drift is established through dimensionless processing, linearization within micro-displacement and the least-square approach. Both the measurement scheme of the SCG rotor drift based on the model and its parameter relationship are presented. To guarantee the potential of the suspension rotor to be zero, the distributing scheme of four pairs of detecting electrodes is presented. The scheme can measure the magnitude and the direction of the rotor drift. The negative factors for affecting the measurement precision of .the SCG rotor drift and simulation results of the total effects are given. Simulation results show that the distributing capacitance of these differential capacitance sensors, the zero potential of the rotor and the model error are the major negative factors. The methods for eliminating those negative factors and the application range of the model are given. The model ensures the relationship between the output voltage and the rotor drift be linear.展开更多
A state space aproach for modeling nonstationary time series is employed in analysing gyro transient process. Based on the concept of smoothness priors constraint, the overall model is using the Kalman filter and Akai...A state space aproach for modeling nonstationary time series is employed in analysing gyro transient process. Based on the concept of smoothness priors constraint, the overall model is using the Kalman filter and Akaike's AIC criterion.Some numerical results of gyro drift models are obtained for analysis of gyro system. As the trend and irregular components of the observed time series can be modeled simultaneously, it is statistically more accurate and efficient than that modeled separately.展开更多
In summer of 2001, 2002 and 2003, ten, six and seventeen satellite-tracked surface drifters with drogues centered at 15 and 4 m were deployed, respectively, in the southern Yellow Sea (YS). 23 drifters of them transmi...In summer of 2001, 2002 and 2003, ten, six and seventeen satellite-tracked surface drifters with drogues centered at 15 and 4 m were deployed, respectively, in the southern Yellow Sea (YS). 23 drifters of them transmitted useful data of at least 30 days. The wind-driven component of the drift was removed from the original drift velocity of drifters. The wind data used are from NCEP (National Center for Environmental Prediction), USA.Trajectories and drift velocities of the 23 drifters depicted the upper circulation structure in the southern YS.There exists an anti-cyclonic eddy with a mean speed and radius of 0.063 m/s and 50km in the central southern YS, whose center lingered within 35.3-36.0°N / 123.5-124.0°E. Showed by 6 drifters, a basin-scale elliptic cyclonic gyre with a mean speed of 0.114 m/s, long and short radius of 250 and 200 km surrounds the anti-cyclonic eddy. In the southwestern part of the southern YS has obvious frontal eddy activities within about100 km with a mean speed about 0.076 m/s. All the drifters passing Korean coast were staggering for more than10 days west of a protruding cape of central Korea. A small-scale cyclonic eddy centered at around 120.5°E/35.1°N with a mean speed of 0.048 m/s was observed in western part of the southern YS.展开更多
Abstract Monthly mean sea ice motion vectors and monthly mean sea level pressure (SLP) for the period of 1979-2006 are investigated to understand the spatial and temporal changes of Arctic sea-ice drift. According to ...Abstract Monthly mean sea ice motion vectors and monthly mean sea level pressure (SLP) for the period of 1979-2006 are investigated to understand the spatial and temporal changes of Arctic sea-ice drift. According to the distinct differences in monthly mean ice velocity field as well as in the distribution of SLP, there are four primary types in the Arctic Ocean: Beaufort Gyre+Transpolar Drift, Anticyclonic Drift, Cyclonic Drift and Double Gyre Drift. These four types account for 81% of the total, and reveal distinct seasonal variations. The Cyclonic Drift with a large-scale anticlockwise ice motion pattern trends to prevail in summer while the Anticyclonic Drift with an opposite pattern trends to prevail in winter and spring. The prevailing seasons for the Beaufort Gyre+Transpolar Drift are spring and autumn, while the Double Gyre Drift trends to prevail in winter, especially in Feb- ruary. The annual occurring times of the Anticyclonic Drift and the Cyclonic Drift are closely correlated with the yearly mean Arc- tic Oscillation (AO) index, with a correlation coefficient of -0.54 and 0.54 (both significant with the confident level of 99%), re- spectively. When the AO index stays in a high positive (negative) condition, the sea-ice motion in the Arctic Ocean demonstrates a more anticlockwise (clockwise) drifting pattern as a whole. When the AO index stays in a neutral condition, the sea-ice motion becomes much more complicated and more transitional types trend to take place.展开更多
文摘Processes supported by process-aware information systems are subject to continuous and often subtle changes due to evolving operational,organizational,or regulatory factors.These changes,referred to as incremental concept drift,gradually alter the behavior or structure of processes,making their detection and localization a challenging task.Traditional process mining techniques frequently assume process stationarity and are limited in their ability to detect such drift,particularly from a control-flow perspective.The objective of this research is to develop an interpretable and robust framework capable of detecting and localizing incremental concept drift in event logs,with a specific emphasis on the structural evolution of control-flow semantics in processes.We propose DriftXMiner,a control-flow-aware hybrid framework that combines statistical,machine learning,and process model analysis techniques.The approach comprises three key components:(1)Cumulative Drift Scanner that tracks directional statistical deviations to detect early drift signals;(2)a Temporal Clustering and Drift-Aware Forest Ensemble(DAFE)to capture distributional and classification-level changes in process behavior;and(3)Petri net-based process model reconstruction,which enables the precise localization of structural drift using transition deviation metrics and replay fitness scores.Experimental validation on the BPI Challenge 2017 event log demonstrates that DriftXMiner effectively identifies and localizes gradual and incremental process drift over time.The framework achieves a detection accuracy of 92.5%,a localization precision of 90.3%,and an F1-score of 0.91,outperforming competitive baselines such as CUSUM+Histograms and ADWIN+Alpha Miner.Visual analyses further confirm that identified drift points align with transitions in control-flow models and behavioral cluster structures.DriftXMiner offers a novel and interpretable solution for incremental concept drift detection and localization in dynamic,process-aware systems.By integrating statistical signal accumulation,temporal behavior profiling,and structural process mining,the framework enables finegrained drift explanation and supports adaptive process intelligence in evolving environments.Its modular architecture supports extension to streaming data and real-time monitoring contexts.
文摘In order to suppress the influence of temperature changes on the performance of accelerometers,a digital quartz resonant accelerometer with low temperature drift is developed using a quartz resonator cluster as a transducer element.In addition,a digital intellectual property(IP) is designed in FPGA to achieve signal processing and fusion of integrated resonators.A testing system for digital quartz resonant accelerometers is established to characterize the performance under different conditions.The scale factor of the accelerometer prototype reaches 3561.63 Hz/g in the range of -1 g to +1 g,and 3542.5 Hz/g in the range of-10 g to+10 g.In different measurement ranges,the linear correlation coefficient R~2 of the accelerometer achieves greater than 0.998.The temperature drift of the accelerometer prototype is tested using a constant temperature test chamber,with a temperature change from -20℃ to 80℃.After temperature-drift compensation,the zero bias temperature coefficient falls to 0.08 mg/℃,and the scale factor temperature coefficient is 65.43 ppm/℃.The experimental results show that the digital quartz resonant accelerometer exhibits excellent sensitivity and low temperature drift.
文摘Many real⁃world machine learning applications face the challenge of dealing with changing data over time,known as concept drift,and the issue of data indeterminacy,where all the true labels available are unrealistic.This can lead to a decrease in the accuracy of the prediction models.The aim of this study is to introduce a new approach for detecting drift,which is based on neutrosophic set theory.This approach takes into account uncertainty in the prediction model and is able to handle indeterminate information,considering its impact on the models performance.The proposed method reads data into windows and calculates a set of values based on the concept of neutrosophic membership.These values are then used in the Neutrosophic Support Vector Machine(N⁃SVM).To address the issue of indeterminate true label data,the values issued by N⁃SVM are expressed as entropy and used as input for the ADWIN(Adaptive Windowing)change detector.When a drift is detected,the prediction model is retrained by including only the most recent instances with the original training data set.The proposed method gives promising results in terms of drift detection accuracy compared to the state of existing drift detection methods such as KSWIN,ADWIN,and DWM.
基金Supported by National Natural Science Foundation of China(11001130,12272062)Fundamental Research Funds for the Central Universities(30917011335).
文摘In this paper,we investigate the weighted Dirichlet eigenvalue problem of polynomial operator of the drifting Laplacian on the cigar soliton■as follows■where is a positive continuous function on,denotes the outward unit normal to the boundary,and are two nonnegative constants.We establish some universal inequalities for eigenvalues of this problem.
基金supported by National Natural Science Foundation of China(NSFC)(Nos.12475200 and 12275296)Joint Fund of Research utilizing Large-Scale Scientific Facility of the NSFC and CAS(No.U2032114)Institute of High Energy Physics(Chinese Academy of Sciences)Innovative Project on Sciences and Technologies(Nos.E3545BU210 and E25456U210).
文摘The particle identification(PID)of hadrons plays a crucial role in particle physics experiments,especially in flavor physics and jet tagging.The cluster counting method,which measures the number of primary ionizations in gaseous detectors,is a promising breakthrough in PID.However,developing an effective reconstruction algorithm for cluster counting remains challenging.To address this challenge,we propose a cluster counting algorithm based on long short-term memory and dynamic graph convolutional neural networks for the CEPC drift chamber.Experiments on Monte Carlo simulated samples demonstrate that our machine learning-based algorithm surpasses traditional methods.It improves the K/πseparation of PID by 10%,meeting the PID requirements of CEPC.
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 authors would like to express appreciation to the National Natural Science Foundation of China(Grant No.61762067)for their financial support.
文摘The primary goal of software defect prediction (SDP) is to pinpoint code modules that are likely to contain defects, thereby enabling software quality assurance teams to strategically allocate their resources and manpower. Within-project defect prediction (WPDP) is a widely used method in SDP. Despite various improvements, current methods still face challenges such as coarse-grained prediction and ineffective handling of data drift due to differences in project distribution. To address these issues, we propose a fine-grained SDP method called DIDP (drift-immune defect prediction), based on drift-immune graph neural networks (DI-GNN). DIDP converts source code into graph representations and uses DI-GNN to mitigate data drift at the model level. It also analyses key statements leading to file defects for a more detailed SDP approach. We evaluated the performance of DIDP in WPDP by examining its file-level and statement-level accuracy compared to state-of-the-art methods, and by examining its cross-project prediction accuracy. The results of the experiment show that DIDP showed significant improvements in F1-score and Recall@Top20%LOC compared to existing methods, even with large software version changes. DIDP also performed well in cross-project SDP. Our study demonstrates that DIDP achieves impressive prediction results in WPDP, effectively mitigating data drift and accurately predicting defective files. Additionally, DIDP can rank the risk of statements in defective files, aiding developers and testers in identifying potential code issues.
基金supported by U.S.Office of Naval Research(ONR)Grant number G2A62826.
文摘Purpose:This research addresses the challenge of concept drift in AI-enabled software,particularly within autonomous vehicle systems where concept drift in object recognition(like pedestrian detection)can lead to misclassifications and safety risks.This study introduces a proactive framework to detect early signs of domain-specific concept drift by leveraging domain analysis and natural language processing techniques.This method is designed to help maintain the relevance of domain knowledge and prevent potential failures in AI systems due to evolving concept definitions.Design/methodology/approach:The proposed framework integrates natural language processing and image analysis to continuously update and monitor key domain concepts against evolving external data sources,such as social media and news.By identifying terms and features closely associated with core concepts,the system anticipates and flags significant changes.This was tested in the automotive domain on the pedestrian concept,where the framework was evaluated for its capacity to detect shifts in the recognition of pedestrians,particularly during events like Halloween and specific car accidents.Findings:The framework demonstrated an ability to detect shifts in the domain concept of pedestrians,as evidenced by contextual changes around major events.While it successfully identified pedestrian-related drift,the system’s accuracy varied when overlapping with larger social events.The results indicate the model’s potential to foresee relevant shifts before they impact autonomous systems,although further refinement is needed to handle high-impact concurrent events.Research limitations:This study focused on detecting concept drift in the pedestrian domain within autonomous vehicles,with results varying across domains.To assess generalizability,we tested the framework for airplane-related incidents and demonstrated adaptability.However,unpredictable events and data biases from social media and news may obscure domain-specific drifts.Further evaluation across diverse applications is needed to enhance robustness in evolving AI environments.Practical implications:The proactive detection of concept drift has significant implications for AI-driven domains,especially in safety-critical applications like autonomous driving.By identifying early signs of drift,this framework provides actionable insights for AI system updates,potentially reducing misclassification risks and enhancing public safety.Moreover,it enables timely interventions,reducing costly and labor-intensive retraining requirements by focusing only on the relevant aspects of evolving concepts.This method offers a streamlined approach for maintaining AI system performance in environments where domain knowledge rapidly changes.Originality/value:This study contributes a novel domain-agnostic framework that combines natural language processing with image analysis to predict concept drift early.This unique approach,which is focused on real-time data sources,offers an effective and scalable solution for addressing the evolving nature of domain-specific concepts in AI applications.
基金supported by Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2025R196)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘Cardiovascular diseases(CVDs)continue to present a leading cause ofmortalityworldwide,emphasizing the importance of early and accurate prediction.Electrocardiogram(ECG)signals,central to cardiac monitoring,have increasingly been integratedwithDeep Learning(DL)for real-time prediction of CVDs.However,DL models are prone to performance degradation due to concept drift and to catastrophic forgetting.To address this issue,we propose a realtime CVDs prediction approach,referred to as ADWIN-GFR that combines Convolutional Neural Network(CNN)layers,for spatial feature extraction,with Gated Recurrent Units(GRU),for temporal modeling,alongside adaptive drift detection and mitigation mechanisms.The proposed approach integratesAdaptiveWindowing(ADWIN)for realtime concept drift detection,a fine-tuning strategy based on Generative Features Replay(GFR)to preserve previously acquired knowledge,and a dynamic replay buffer ensuring variance,diversity,and data distribution coverage.Extensive experiments conducted on the MIT-BIH arrhythmia dataset demonstrate that ADWIN-GFR outperforms standard fine-tuning techniques,achieving an average post-drift accuracy of 95.4%,amacro F1-score of 93.9%,and a remarkably low forgetting score of 0.9%.It also exhibits an average drift detection delay of 12 steps and achieves an adaptation gain of 17.2%.These findings underscore the potential of ADWIN-GFR for deployment in real-world cardiac monitoring systems,including wearable ECG devices and hospital-based patient monitoring platforms.
基金supported by the National Natural Science Foundation of China(Nos.11927901,11875301,11875302,U1867214,U1832105,U1832167)the Strategic Priority Research Program of Chinese Academy of Sciences(No.XDB34000000)+2 种基金the National Key R&D Program of China(No.2018YFE0205200)the CAS"Light of West China"Programthe Tsinghua University Initiative Scientific Research Program。
文摘A half-size prototype of the multi wire drift chamber for the cooling storage ring external-target experiment(CEE)was assembled and tested in the 350 MeV/u Kr+Fe reactions at the heavy-ion research facility in Lanzhou.The prototype consists of six sense layers,where the sense wires are stretched in three directions X,U,and V;meeting 0?,30?,and-30?,respectively,with respect to the vertical axis.The sensitive area of the prototype is 76 cm×76 cm.The amplified and shaped signals from the anode wires were digitized in a serial capacity array.When operating at a high voltage of 1500 V on the anode wires,the efficiency for each layer is greater than 95%.The tracking residual is approximately 301±2μm.This performance satisfies the requirements of CEE.
基金supported by 2023 Teaching Research Project of the Education Department of Anhui Province:Exploration of Optimizing Teaching Strategies for Embedded Courses in the Context of“New Engineering”(Project No.2023jyxm0460)2024 High-quality Course on Ideological and Political Education Integrated into Curriculum at Anhui University of Engineering:“Data Structures and Algorithms”(Project No.2024szyzk40)Industry-University-Research Cooperation Project of Anhui University of Engineering:“Online detection of surface quality defects in high-speed wire rod”(Project No.HX-2024-11-003).
文摘With the gradual penetration of the internet of things(IoT)into all areas of life,the scale of IoT devices shows an explosive growth trend.The era of internet of everything is coming,and the important position of IoT security is becoming increasingly prominent.Due to the large number types of IoT devices,there may be different security vulnerabilities,and unknown attack forms and virus samples are appear.In other words,large number of IoT devices,large data volumes,and various attack forms pose a big challenge of malicious traffic identification.To solve these problems,this paper proposes a concept drift detection and adaptation(CDDA)method for IoT security framework.The AI model performance is evaluated by verifying the effectiveness of IoT traffic for data drift detection,so as to select the best AI model.The experimental test are given to confirm that the feasibility of the framework and the adaptive method in practice,and the effect on the performance of IoT traffic identification is also verified.
基金supported by the National Key Research and Development Program of China(Grant No.2017YFC0405401)the National Science&Technology Pillar Program(Grant No.2012BAB03B01)+1 种基金the Fundamental Research Funds for the Central Universities,Hohai University(Grant No.2014B30914)the Natural Science Foundation of Jiangsu Province(Grant No.BK2012411)
文摘Understanding the drifting motion of a small semi-submersible drifter is of vital importance regarding monitoring surface currents and the floating pollutants in coastal regions. This work addresses this issue by establishing a mechanistic drifting forecast model based on kinetic analysis. Taking tide–wind–wave into consideration, the forecast model is validated against in situ drifting experiment in the Radial Sand Ridges. Model results show good performance with respect to the measured drifting features, characterized by migrating back and forth twice a day with daily downwind displacements. Trajectory models are used to evaluate the influence of the individual hydrodynamic forcing. The tidal current is the fundamental dynamic condition in the Radial Sand Ridges and has the greatest impact on the drifting distance. However, it loses its leading position in the field of the daily displacement of the used drifter. The simulations reveal that different hydrodynamic forces dominate the daily displacement of the used drifter at different wind scales. The wave-induced mass transport has the greatest influence on the daily displacement at Beaufort wind scale 5–6; while wind drag contributes mostly at wind scale 2–4.
文摘Drift phenomenon has been known as the drawback of sensors and causes inaccuracy on the long-term measurement. In general,there are two methods to reduce the drift problem.One is to tune the parameters of the fabrication process to improve the properties of the front-ended device.Another is to compensate the drift phenomenon by adding extra drift compensation circuit or software in the back-ended readout circuit.In this study,a drift calibration method for the potentiometric sensor was presented and the drift calibration method was performed by using the circuit.According to experimental results,the drift phenomenon of the SnO_2 pH electrode was reduced by the drift calibration device.
文摘A data gathering system is designed for the interferometric fiber optic gyroscope (IFOG) of land strapdown inertial system. IFOG is tested and the testing curve is given. The test data of IFOG are analyzed with Allan variance method and each error coefficient is identified. Furthermore, a random drift error model for IFOG is built by the method of time series analysis. The conclusion provides supports for improving IFOG design and compensating for errors of IFOG in practice.
文摘Based on micro-displacement measurement principles of the spherical differential capacitance sensor, the relationship between the capacitance variation and the micro-displacement of each pair of detecting electrodes for the superconducting gyroscope （SCG） with eight detecting electrodes is analyzed. The model of the SCG rotor drift is established through dimensionless processing, linearization within micro-displacement and the least-square approach. Both the measurement scheme of the SCG rotor drift based on the model and its parameter relationship are presented. To guarantee the potential of the suspension rotor to be zero, the distributing scheme of four pairs of detecting electrodes is presented. The scheme can measure the magnitude and the direction of the rotor drift. The negative factors for affecting the measurement precision of .the SCG rotor drift and simulation results of the total effects are given. Simulation results show that the distributing capacitance of these differential capacitance sensors, the zero potential of the rotor and the model error are the major negative factors. The methods for eliminating those negative factors and the application range of the model are given. The model ensures the relationship between the output voltage and the rotor drift be linear.
文摘A state space aproach for modeling nonstationary time series is employed in analysing gyro transient process. Based on the concept of smoothness priors constraint, the overall model is using the Kalman filter and Akaike's AIC criterion.Some numerical results of gyro drift models are obtained for analysis of gyro system. As the trend and irregular components of the observed time series can be modeled simultaneously, it is statistically more accurate and efficient than that modeled separately.
文摘In summer of 2001, 2002 and 2003, ten, six and seventeen satellite-tracked surface drifters with drogues centered at 15 and 4 m were deployed, respectively, in the southern Yellow Sea (YS). 23 drifters of them transmitted useful data of at least 30 days. The wind-driven component of the drift was removed from the original drift velocity of drifters. The wind data used are from NCEP (National Center for Environmental Prediction), USA.Trajectories and drift velocities of the 23 drifters depicted the upper circulation structure in the southern YS.There exists an anti-cyclonic eddy with a mean speed and radius of 0.063 m/s and 50km in the central southern YS, whose center lingered within 35.3-36.0°N / 123.5-124.0°E. Showed by 6 drifters, a basin-scale elliptic cyclonic gyre with a mean speed of 0.114 m/s, long and short radius of 250 and 200 km surrounds the anti-cyclonic eddy. In the southwestern part of the southern YS has obvious frontal eddy activities within about100 km with a mean speed about 0.076 m/s. All the drifters passing Korean coast were staggering for more than10 days west of a protruding cape of central Korea. A small-scale cyclonic eddy centered at around 120.5°E/35.1°N with a mean speed of 0.048 m/s was observed in western part of the southern YS.
基金the National Natural Science Foundation of China (Grant no. 40631006)the National Major Science Project of China for Global Change Research (Grant no. 2010CB951403)
文摘Abstract Monthly mean sea ice motion vectors and monthly mean sea level pressure (SLP) for the period of 1979-2006 are investigated to understand the spatial and temporal changes of Arctic sea-ice drift. According to the distinct differences in monthly mean ice velocity field as well as in the distribution of SLP, there are four primary types in the Arctic Ocean: Beaufort Gyre+Transpolar Drift, Anticyclonic Drift, Cyclonic Drift and Double Gyre Drift. These four types account for 81% of the total, and reveal distinct seasonal variations. The Cyclonic Drift with a large-scale anticlockwise ice motion pattern trends to prevail in summer while the Anticyclonic Drift with an opposite pattern trends to prevail in winter and spring. The prevailing seasons for the Beaufort Gyre+Transpolar Drift are spring and autumn, while the Double Gyre Drift trends to prevail in winter, especially in Feb- ruary. The annual occurring times of the Anticyclonic Drift and the Cyclonic Drift are closely correlated with the yearly mean Arc- tic Oscillation (AO) index, with a correlation coefficient of -0.54 and 0.54 (both significant with the confident level of 99%), re- spectively. When the AO index stays in a high positive (negative) condition, the sea-ice motion in the Arctic Ocean demonstrates a more anticlockwise (clockwise) drifting pattern as a whole. When the AO index stays in a neutral condition, the sea-ice motion becomes much more complicated and more transitional types trend to take place.
