High-order asymmetric flatness defects resulting from the abnormal state of roll system are the main issue of precision rolling mill in the manufacturing process of high-strength thin strip.Due to the difficulty of mo...High-order asymmetric flatness defects resulting from the abnormal state of roll system are the main issue of precision rolling mill in the manufacturing process of high-strength thin strip.Due to the difficulty of monitoring and adjusting the abnormal state,the spatial state of roll system cannot be controlled by traditional methods.It is difficult to fundamentally improve these high-order asymmetric flatness defects.Therefore,a digital twin model of flatness control process for S6-high rolling mill was established,which could be used to analyze the influence of the abnormal state on the flatness control characteristic and propose improvement strategies.The internal relationship between the force state of side support roll system and the abnormal state of roll system was proposed.The XGBoost algorithm model was established to analyze the contribution degree of the side support roll system force to the flatness characteristic quantity.The abnormal state of roll system in the S6-high rolling mill can be diagnosed by analyzing the flatness characteristic difference between flatness value of the rolled strip and calculated characteristic value of finite element simulation.The flatness optimization model of the gray wolf optimization–long short-term memory non-dominated sorting whale optimization algorithm(GWO-LSTM-NSWOA)was established,and the decision-making selection was made from the Pareto frontier based on the flatness requirements of cold rolling to regulate the abnormal state of the roll system.The results indicate that the contribution degree of the force of the side support roll system to the flatness characteristics is more than 25%,which is the main influence of high-order asymmetric flatness defect.The performance of the GWO-LSTM flatness feature prediction model has clear advantages over back propagation and LSTM.The practical applications show that optimizing the force of side support roll system can reduce the high point of high-strength strip flatness from 13.2 to 6 IU and decrease the percentage of low-strength strip flatness defects from 1.6%to 1.2%.This optimization greatly reduced the proportion of flatness defects,improved the accuracy level of flatness control of precision rolling mill,and provided a guarantee for the stable production of thin strip.展开更多
This paper addresses the challenge of identifying abnormal states in Lithium-ion Battery(LiB)time series data.As the energy sector increasingly focuses on integrating distributed energy resources,Virtual Power Plants(...This paper addresses the challenge of identifying abnormal states in Lithium-ion Battery(LiB)time series data.As the energy sector increasingly focuses on integrating distributed energy resources,Virtual Power Plants(VPP)have become a vital new framework for energy management.LiBs are key in this context,owing to their high-efficiency energy storage capabilities essential for VPP operations.However,LiBs are prone to various abnormal states like overcharging,over-discharging,and internal short circuits,which impede power transmission efficiency.Traditional methods for detecting such abnormalities in LiB are too broad and lack precision for the dynamic and irregular nature of LiB data.In response,we introduce an innovative method:a Long Short-Term Memory(LSTM)autoencoder based on Dynamic Frequency Memory and Correlation Attention(DFMCA-LSTM-AE).This unsupervised,end-to-end approach is specifically designed for dynamically monitoring abnormal states in LiB data.The method starts with a Dynamic Frequency Fourier Transform module,which dynamically captures the frequency characteristics of time series data across three scales,incorporating a memory mechanism to reduce overgeneralization of abnormal frequencies.This is followed by integrating LSTM into both the encoder and decoder,enabling the model to effectively encode and decode the temporal relationships in the time series.Empirical tests on a real-world LiB dataset demonstrate that DFMCA-LSTM-AE outperforms existing models,achieving an average Area Under the Curve(AUC)of 90.73%and an F1 score of 83.83%.These results mark significant improvements over existing models,ranging from 2.4%–45.3%for AUC and 1.6%–28.9%for F1 score,showcasing the model’s enhanced accuracy and reliability in detecting abnormal states in LiB data.展开更多
The integration of cloud computing into traditional industrial control systems is accelerating the evolution of Industrial Cyber-Physical System(ICPS),enhancing intelligence and autonomy.However,this transition also e...The integration of cloud computing into traditional industrial control systems is accelerating the evolution of Industrial Cyber-Physical System(ICPS),enhancing intelligence and autonomy.However,this transition also expands the attack surface,introducing critical security vulnerabilities.To address these challenges,this article proposes a hybrid intrusion detection scheme for securing ICPSs that combines system state anomaly and network traffic anomaly detection.Specifically,an improved variation-Bayesian-based noise covariance-adaptive nonlinear Kalman filtering(IVB-NCA-NLKF)method is developed to model nonlinear system dynamics,enabling optimal state estimation in multi-sensor ICPS environments.Intrusions within the physical sensing system are identified by analyzing residual discrepancies between predicted and observed system states.Simultaneously,an adaptive network traffic anomaly detection mechanism is introduced,leveraging learned traffic patterns to detect node-and network-level anomalies through pattern matching.Extensive experiments on a simulated network control system demonstrate that the proposed framework achieves higher detection accuracy(92.14%)with a reduced false alarm rate(0.81%).Moreover,it not only detects known attacks and vulnerabilities but also uncovers stealthy attacks that induce system state deviations,providing a robust and comprehensive security solution for the safety protection of ICPS.展开更多
The liver performs several vital functions such as metabolism,toxin removal,and glucose storage through the coordination of various cell types.With the recent breakthrough of the single-cell/single-nucleus RNAseq(sc/s...The liver performs several vital functions such as metabolism,toxin removal,and glucose storage through the coordination of various cell types.With the recent breakthrough of the single-cell/single-nucleus RNAseq(sc/snRNA-seq)techniques,there is a great opportunity to establish a reference cell map of the liver at single-cell resolution with transcriptome-wise features.In this study,we build a unified liver cell atlas uniLIVER(http://lifeome.net/database/uniliver)by integrative analysis of a large-scale sc/snRNA-seq data collection of normal human liver with 331,125 cells and 79 samples from 6 datasets.Moreover,we introduce LiverCT,a machine learning based method for mapping any query dataset to the liver reference map by introducing the definition of“variant”cellular states analogous to the sequence variants in genomic analysis.Applying LiverCT on liver cancer datasets,we find that the“deviated”states of T cells are highly correlated with the stress pathway activities in hepatocellular carcinoma,and the enrichments of tumor cells with the hepatocyte-cholangiocyte“intermediate”states significantly indicate poor prognosis.Besides,we find that the tumor cells of different patients have different zonation tendencies and this zonation tendency is also significantly associated with the prognosis.This reference atlas mapping framework can also be extended to any other tissues.展开更多
Abnormal movement states for a mobile robot were identified by four multi-layer perceptron. In the presence ot abnormality, avoidance strategies were designed to guarantee the safety of the robot. Firstly, the kinemat...Abnormal movement states for a mobile robot were identified by four multi-layer perceptron. In the presence ot abnormality, avoidance strategies were designed to guarantee the safety of the robot. Firstly, the kinematics of the normal and abnormal movement states were exploited, 8 kinds of features were extracted. Secondly, 4 multi-layer pereeptrons were employed to classify the features for four 4-driving wheels into 4 kinds of states, i.e. normal, blocked, deadly blocked, and slipping. Finally, avoidance strategies were designed based on this. Experiment results show that the methods can identify most abnormal movement states and avoid the abnormality correctly and timely.展开更多
With the advent of Industry 4.0,water treatment systems(WTSs)are recognized as typical industrial cyber-physical systems(iCPSs)that are connected to the open Internet.Advanced information technology(IT)benefits the WT...With the advent of Industry 4.0,water treatment systems(WTSs)are recognized as typical industrial cyber-physical systems(iCPSs)that are connected to the open Internet.Advanced information technology(IT)benefits the WTS in the aspects of reliability,efficiency,and economy.However,the vulnerabilities exposed in the communication and control infrastructure on the cyber side make WTSs prone to cyber attacks.The traditional IT system oriented defense mechanisms cannot be directly applied in safety-critical WTSs because the availability and real-time requirements are of great importance.In this paper,we propose an entropy-based intrusion detection(EBID)method to thwart cyber attacks against widely used controllers(e.g.,programmable logic controllers)in WTSs to address this issue.Because of the varied WTS operating conditions,there is a high false-positive rate with a static threshold for detection.Therefore,we propose a dynamic threshold adjustment mechanism to improve the performance of EBID.To validate the performance of the proposed approaches,we built a high-fidelity WTS testbed with more than 50 measurement points.We conducted experiments under two attack scenarios with a total of 36attacks,showing that the proposed methods achieved a detection rate of 97.22%and a false alarm rate of 1.67%.展开更多
基金financially supported by the National Key Research and Development Program of China(Grant No.2023YFB3812602).
文摘High-order asymmetric flatness defects resulting from the abnormal state of roll system are the main issue of precision rolling mill in the manufacturing process of high-strength thin strip.Due to the difficulty of monitoring and adjusting the abnormal state,the spatial state of roll system cannot be controlled by traditional methods.It is difficult to fundamentally improve these high-order asymmetric flatness defects.Therefore,a digital twin model of flatness control process for S6-high rolling mill was established,which could be used to analyze the influence of the abnormal state on the flatness control characteristic and propose improvement strategies.The internal relationship between the force state of side support roll system and the abnormal state of roll system was proposed.The XGBoost algorithm model was established to analyze the contribution degree of the side support roll system force to the flatness characteristic quantity.The abnormal state of roll system in the S6-high rolling mill can be diagnosed by analyzing the flatness characteristic difference between flatness value of the rolled strip and calculated characteristic value of finite element simulation.The flatness optimization model of the gray wolf optimization–long short-term memory non-dominated sorting whale optimization algorithm(GWO-LSTM-NSWOA)was established,and the decision-making selection was made from the Pareto frontier based on the flatness requirements of cold rolling to regulate the abnormal state of the roll system.The results indicate that the contribution degree of the force of the side support roll system to the flatness characteristics is more than 25%,which is the main influence of high-order asymmetric flatness defect.The performance of the GWO-LSTM flatness feature prediction model has clear advantages over back propagation and LSTM.The practical applications show that optimizing the force of side support roll system can reduce the high point of high-strength strip flatness from 13.2 to 6 IU and decrease the percentage of low-strength strip flatness defects from 1.6%to 1.2%.This optimization greatly reduced the proportion of flatness defects,improved the accuracy level of flatness control of precision rolling mill,and provided a guarantee for the stable production of thin strip.
基金supported by“Regional Innovation Strategy(RIS)”through the National Research Foundation of Korea(NRF)funded by the Ministry of Education(MOE)(2021RIS-002)the Technology Development Program(RS-2023-00278623)funded by the Ministry of SMEs and Startups(MSS,Korea).
文摘This paper addresses the challenge of identifying abnormal states in Lithium-ion Battery(LiB)time series data.As the energy sector increasingly focuses on integrating distributed energy resources,Virtual Power Plants(VPP)have become a vital new framework for energy management.LiBs are key in this context,owing to their high-efficiency energy storage capabilities essential for VPP operations.However,LiBs are prone to various abnormal states like overcharging,over-discharging,and internal short circuits,which impede power transmission efficiency.Traditional methods for detecting such abnormalities in LiB are too broad and lack precision for the dynamic and irregular nature of LiB data.In response,we introduce an innovative method:a Long Short-Term Memory(LSTM)autoencoder based on Dynamic Frequency Memory and Correlation Attention(DFMCA-LSTM-AE).This unsupervised,end-to-end approach is specifically designed for dynamically monitoring abnormal states in LiB data.The method starts with a Dynamic Frequency Fourier Transform module,which dynamically captures the frequency characteristics of time series data across three scales,incorporating a memory mechanism to reduce overgeneralization of abnormal frequencies.This is followed by integrating LSTM into both the encoder and decoder,enabling the model to effectively encode and decode the temporal relationships in the time series.Empirical tests on a real-world LiB dataset demonstrate that DFMCA-LSTM-AE outperforms existing models,achieving an average Area Under the Curve(AUC)of 90.73%and an F1 score of 83.83%.These results mark significant improvements over existing models,ranging from 2.4%–45.3%for AUC and 1.6%–28.9%for F1 score,showcasing the model’s enhanced accuracy and reliability in detecting abnormal states in LiB data.
基金supported by the National Natural Science Foundation of China(NSFC)under grant No.62371187the Hunan Provincial Natural Science Foundation of China under Grant Nos.2024JJ8309 and 2023JJ50495.
文摘The integration of cloud computing into traditional industrial control systems is accelerating the evolution of Industrial Cyber-Physical System(ICPS),enhancing intelligence and autonomy.However,this transition also expands the attack surface,introducing critical security vulnerabilities.To address these challenges,this article proposes a hybrid intrusion detection scheme for securing ICPSs that combines system state anomaly and network traffic anomaly detection.Specifically,an improved variation-Bayesian-based noise covariance-adaptive nonlinear Kalman filtering(IVB-NCA-NLKF)method is developed to model nonlinear system dynamics,enabling optimal state estimation in multi-sensor ICPS environments.Intrusions within the physical sensing system are identified by analyzing residual discrepancies between predicted and observed system states.Simultaneously,an adaptive network traffic anomaly detection mechanism is introduced,leveraging learned traffic patterns to detect node-and network-level anomalies through pattern matching.Extensive experiments on a simulated network control system demonstrate that the proposed framework achieves higher detection accuracy(92.14%)with a reduced false alarm rate(0.81%).Moreover,it not only detects known attacks and vulnerabilities but also uncovers stealthy attacks that induce system state deviations,providing a robust and comprehensive security solution for the safety protection of ICPS.
基金funded by the National Key Research and Development Program of China(No.2021YFF1200901)the National Natural Science Foundation of China(Nos.61721003,62133006,and 92268104)。
文摘The liver performs several vital functions such as metabolism,toxin removal,and glucose storage through the coordination of various cell types.With the recent breakthrough of the single-cell/single-nucleus RNAseq(sc/snRNA-seq)techniques,there is a great opportunity to establish a reference cell map of the liver at single-cell resolution with transcriptome-wise features.In this study,we build a unified liver cell atlas uniLIVER(http://lifeome.net/database/uniliver)by integrative analysis of a large-scale sc/snRNA-seq data collection of normal human liver with 331,125 cells and 79 samples from 6 datasets.Moreover,we introduce LiverCT,a machine learning based method for mapping any query dataset to the liver reference map by introducing the definition of“variant”cellular states analogous to the sequence variants in genomic analysis.Applying LiverCT on liver cancer datasets,we find that the“deviated”states of T cells are highly correlated with the stress pathway activities in hepatocellular carcinoma,and the enrichments of tumor cells with the hepatocyte-cholangiocyte“intermediate”states significantly indicate poor prognosis.Besides,we find that the tumor cells of different patients have different zonation tendencies and this zonation tendency is also significantly associated with the prognosis.This reference atlas mapping framework can also be extended to any other tissues.
基金Project (60234030) supported by the National Natural Science Foundation of China
文摘Abnormal movement states for a mobile robot were identified by four multi-layer perceptron. In the presence ot abnormality, avoidance strategies were designed to guarantee the safety of the robot. Firstly, the kinematics of the normal and abnormal movement states were exploited, 8 kinds of features were extracted. Secondly, 4 multi-layer pereeptrons were employed to classify the features for four 4-driving wheels into 4 kinds of states, i.e. normal, blocked, deadly blocked, and slipping. Finally, avoidance strategies were designed based on this. Experiment results show that the methods can identify most abnormal movement states and avoid the abnormality correctly and timely.
基金Project supported by the National Natural Science Foundation of China(No.61833015)。
文摘With the advent of Industry 4.0,water treatment systems(WTSs)are recognized as typical industrial cyber-physical systems(iCPSs)that are connected to the open Internet.Advanced information technology(IT)benefits the WTS in the aspects of reliability,efficiency,and economy.However,the vulnerabilities exposed in the communication and control infrastructure on the cyber side make WTSs prone to cyber attacks.The traditional IT system oriented defense mechanisms cannot be directly applied in safety-critical WTSs because the availability and real-time requirements are of great importance.In this paper,we propose an entropy-based intrusion detection(EBID)method to thwart cyber attacks against widely used controllers(e.g.,programmable logic controllers)in WTSs to address this issue.Because of the varied WTS operating conditions,there is a high false-positive rate with a static threshold for detection.Therefore,we propose a dynamic threshold adjustment mechanism to improve the performance of EBID.To validate the performance of the proposed approaches,we built a high-fidelity WTS testbed with more than 50 measurement points.We conducted experiments under two attack scenarios with a total of 36attacks,showing that the proposed methods achieved a detection rate of 97.22%and a false alarm rate of 1.67%.
