In this paper, the stability of iterative learning control with data dropouts is discussed. By the super vector formulation, an iterative learning control （ILC） system with data dropouts can be modeled as an asynchr...In this paper, the stability of iterative learning control with data dropouts is discussed. By the super vector formulation, an iterative learning control （ILC） system with data dropouts can be modeled as an asynchronous dynamical system with rate constraints on events in the iteration domain. The stability condition is provided in the form of linear matrix inequalities （LMIS） depending on the stability of asynchronous dynamical systems. The analysis is supported by simulations.展开更多
Radiation belt electron dropouts indicate electron flux decay to the background level during geomagnetic storms,which is commonly attributed to the effects of wave-induced pitch angle scattering and magnetopause shado...Radiation belt electron dropouts indicate electron flux decay to the background level during geomagnetic storms,which is commonly attributed to the effects of wave-induced pitch angle scattering and magnetopause shadowing.To investigate the loss mechanisms of radiation belt electron dropouts triggered by a solar wind dynamic pressure pulse event on 12 September 2014,we comprehensively analyzed the particle and wave measurements from Van Allen Probes.The dropout event was divided into three periods:before the storm,the initial phase of the storm,and the main phase of the storm.The electron pitch angle distributions(PADs)and electron flux dropouts during the initial and main phases of this storm were investigated,and the evolution of the radial profile of electron phase space density(PSD)and the(μ,K)dependence of electron PSD dropouts(whereμ,K,and L^*are the three adiabatic invariants)were analyzed.The energy-independent decay of electrons at L>4.5 was accompanied by butterfly PADs,suggesting that the magnetopause shadowing process may be the major loss mechanism during the initial phase of the storm at L>4.5.The features of electron dropouts and 90°-peaked PADs were observed only for>1 MeV electrons at L<4,indicating that the wave-induced scattering effect may dominate the electron loss processes at the lower L-shell during the main phase of the storm.Evaluations of the(μ,K)dependence of electron PSD drops and calculations of the minimum electron resonant energies of H+-band electromagnetic ion cyclotron(EMIC)waves support the scenario that the observed PSD drop peaks around L^*=3.9 may be caused mainly by the scattering of EMIC waves,whereas the drop peaks around L^*=4.6 may result from a combination of EMIC wave scattering and outward radial diffusion.展开更多
This paper is concerned with the estimation problem for discrete-time stochastic linear systems with possible single unit delay and multiple packet dropouts. Based on a proposed uncertain model in data transmission, a...This paper is concerned with the estimation problem for discrete-time stochastic linear systems with possible single unit delay and multiple packet dropouts. Based on a proposed uncertain model in data transmission, an optimal full-order filter for the state of the system is presented, which is shown to be of the form of employing the received outputs at the current and last time instants. The solution to the optimal filter is given in terms of a Riccati difference equation governed by two binary random variables. The optimal filter is reduced to the standard Kalman filter when there are no random delays and packet dropouts. The steady-state filter is also investigated. A sufficient condition for the existence of the steady-state filter is given. The asymptotic stability of the optimal filter is analyzed.展开更多
This paper addresses the problem of fault detection(FD)for networked systems with access constraints and packet dropouts.Two independent Markov chains are used to describe the sequences of channels which are available...This paper addresses the problem of fault detection(FD)for networked systems with access constraints and packet dropouts.Two independent Markov chains are used to describe the sequences of channels which are available for communication at an instant and the packet dropout process,respectively.Performance indexes H∞and H_are introduced to describe the robustness of residual against external disturbances and sensitivity of residual to faults,respectively.By using a mode-dependent fault detection filter(FDF)as residual generator,the addressed FD problem is converted into an auxiliary filter design problem with the above index constraints.A sufficient condition for the existence of the FDF is derived in terms of certain linear matrix inequalities(LMIs).When these LMIs are feasible,the explicit expression of the desired FDF can also be characterized.A numerical example is exploited to show the usefulness of the proposed results.展开更多
This paper investigates the observer-based H-infinity control problem for networked control systems （NCSs） with random packet dropouts. A general packet dropout model with multiple independent stochastic variables i...This paper investigates the observer-based H-infinity control problem for networked control systems （NCSs） with random packet dropouts. A general packet dropout model with multiple independent stochastic variables in the multiple channels case is adopted to describe the data missing in the limited communication channels. With the consideration of the sensor-to-controller and controller-to-actuator packet dropouts at the same time, a new method is pro- posed based on a separation lemma to design an observer-based H-infinity controller, which exponentially stabilizes the closed-loop system in the sense of mean square and also achieves a prescribed H-infinity disturbance attenuation level. A numerical example is given to illustrate the effectiveness of the proposed control method.展开更多
In 2004, Wang Chengyan, a 13-year-old Mongolian girl in the Tumote Left Banner of Inner Mongolia, took up her schoolbag again and marched into the classroom of six grade of a local primary school. With her face shinin...In 2004, Wang Chengyan, a 13-year-old Mongolian girl in the Tumote Left Banner of Inner Mongolia, took up her schoolbag again and marched into the classroom of six grade of a local primary school. With her face shining with brilliance, she told her friends： ＂It is brothers and sisters studying in Japan who have paid my way to school.＂展开更多
A robust sliding mode control algorithm is developed for a class of networked control system with packet dropouts in both sensor-controller channel and controller-actuator channel,and at the same time mismatched param...A robust sliding mode control algorithm is developed for a class of networked control system with packet dropouts in both sensor-controller channel and controller-actuator channel,and at the same time mismatched parametric uncertainty and external disturbance are also taken into consideration.A two-level Bernoulli process has been used to describe the packet dropouts existing in both channels.A novel integral sliding surface is proposed,based on which the H∞performance of system sliding mode motion is analyzed.Then the sufficient condition for system stability and robustness is derived in the form of linear matrix inequality(LMI).A sliding mode controller is designed which can guarantee a relatively ideal system dynamic performance and has certain robustness against unknown parameter perturbations and external disturbances.The results from numerical simulations are presented to corroborate the validity of the proposed controller.展开更多
The globally exponential stability of nonlinear impul- sive networked control systems （NINCS） with time delay and packet dropouts is investigated. By applying Lyapunov function theory, sufficient conditions on the g...The globally exponential stability of nonlinear impul- sive networked control systems （NINCS） with time delay and packet dropouts is investigated. By applying Lyapunov function theory, sufficient conditions on the global exponential stability are derived by introducing a comparison system and estimating the corresponding Cauchy matrix. An impulsive controller is explicitly designed to achieve exponential stability and ensure state con- verge with a given decay rate for the system. The Lorenz oscillator system is presented as a numerical example to illustrate the theo- retical results and effectiveness of the proposed controller design procedure.展开更多
<div style="text-align:justify;"> <span style="font-family:Verdana;">The growing need to use Artificial Intelligence (AI) technologies in addressing challenges in education sectors of d...<div style="text-align:justify;"> <span style="font-family:Verdana;">The growing need to use Artificial Intelligence (AI) technologies in addressing challenges in education sectors of developing countries is undermined by low awareness, limited skill and poor data quality. One particular persisting challenge, which can be addressed by AI, is school dropouts whereby hundreds of thousands of children drop annually in Africa. This article presents a data-driven approach to proactively predict likelihood of dropping from schools and enable effective management of dropouts. The approach is guided by a carefully crafted conceptual framework and new concepts of average absenteeism, current cumulative absenteeism and dropout risk appetite. In this study, a typical scenario of missing quality data is considered and for which synthetic data is generated to enable development of a functioning prediction model using neural network. The results show that, using the proposed approach, the levels of risk of dropping out of schools can be practically determined using data that is largely available in schools. Potentially, the study will inspire further research, encourage deployment of the technologies in real life, and inform processes of formulating or improving policies.</span> </div>展开更多
This paper proposed a feedback feedforward Iterative Learning Control(ILC)law for nonlinear system with iteratively variable trial lengths under a networked systems structure,where the both sensor and actuator occurs ...This paper proposed a feedback feedforward Iterative Learning Control(ILC)law for nonlinear system with iteratively variable trial lengths under a networked systems structure,where the both sensor and actuator occurs random data lost separately.The feedforward ILC part includes the calculated input signal,actual input signal,and the modified tracking error of last iteration.Some tracking signal would be lost at last iteration because of the iterative varying trial lengths.In order to offset the missing signal of last trial,the tracking error of present trial is adopted by feedback control part.It is established that the convergence relied on the feedforward control gain merely,while the rate of convergence is also expedited by the feedback control component.When the initial state expectation equals to the reference one,it is established that the tracking error expectation can be controlled to zero.With an illustrative simulation,the effectiveness of the developed algorithm can be demonstrated.展开更多
This paper utilizes a switched systems approach to deal with the problem of fault detectio for uncertain delta operator networked control system with packet dropouts and timevarying delays.Uncertainties exist in the m...This paper utilizes a switched systems approach to deal with the problem of fault detectio for uncertain delta operator networked control system with packet dropouts and timevarying delays.Uncertainties exist in the matrices of the systems and are norm-bounded time-varying.Two parts of packet dropouts are considered in this paper:From sensors to controllers,and from controllers to actuators.Two independent Bernoulli distributed white sequences are introduced to account for packet dropouts.Then an FD filter is designed under an arbitrary switching law.Furthermore,the sufficient conditions for the NCSs under consideration that are exponentially stable in the mean-square sense and satisfy H∞performance are obtained in terms of linear matrix inequalitie,multiple Lyapunov function and average dwell-tim approach.The explicit expression of the desired filter parameters is given.Finally,a numerical example verifies the effectiveness of the proposed method.展开更多
Customer churn is the rate at which customers discontinue doing business with a company over a given time period.It is an essential measure for businesses to monitor high churn rates,as they often indicate underlying ...Customer churn is the rate at which customers discontinue doing business with a company over a given time period.It is an essential measure for businesses to monitor high churn rates,as they often indicate underlying issues with services,products,or customer experience,resulting in considerable income loss.Prediction of customer churn is a crucial task aimed at retaining customers and maintaining revenue growth.Traditional machine learning(ML)models often struggle to capture complex temporal dependencies in client behavior data.To address this,an optimized deep learning(DL)approach using a Regularized Bidirectional Long Short-Term Memory(RBiLSTM)model is proposed to mitigate overfitting and improve generalization error.The model integrates dropout,L2-regularization,and early stopping to enhance predictive accuracy while preventing over-reliance on specific patterns.Moreover,this study investigates the effect of optimization techniques on boosting the training efficiency of the developed model.Experimental results on a recent public customer churn dataset demonstrate that the trained model outperforms the traditional ML models and some other DL models,such as Long Short-Term Memory(LSTM)and Deep Neural Network(DNN),in churn prediction performance and stability.The proposed approach achieves 96.1%accuracy,compared with LSTM and DNN,which attain 94.5%and 94.1%accuracy,respectively.These results confirm that the proposed approach can be used as a valuable tool for businesses to identify at-risk consumers proactively and implement targeted retention strategies.展开更多
In this note,we study the state estimation problem for a multi-sensor system subject to multiple packet dropouts.A novel optimal distributed fusion estimator is derived by applying a resending mechanism and a parallel...In this note,we study the state estimation problem for a multi-sensor system subject to multiple packet dropouts.A novel optimal distributed fusion estimator is derived by applying a resending mechanism and a parallel information filtering structure.It is shown that the proposed distributed fusion estimator has smaller estimation error covariance and less computation complexity when compared with the centralised Kalman like estimator with multiple intermittent measurements.展开更多
Recently, Massive Open Online Courses(MOOCs) have become a major online learning methodology for millions of people worldwide. However, the dropout rates from several current MOOCs are high. Usually, dropout predictio...Recently, Massive Open Online Courses(MOOCs) have become a major online learning methodology for millions of people worldwide. However, the dropout rates from several current MOOCs are high. Usually, dropout prediction aims to predict whether a learner will exhibit learning behaviors during several consecutive days in the future. Therefore, the information related to the learning behaviors of a learner in several consecutive days should be considered. After in-depth analysis of the learning behavior patterns of the MOOC learners, this study reports that learners often exhibit similar learning behaviors on several consecutive days, i.e., the learning status of a learner for the subsequent day is likely to be similar to that for the previous day. Based on this characteristic of MOOC learning,this study proposes a new simple feature matrix for keeping information related to the local correlation of learning behaviors and a new Convolutional Neural Network(CNN) model for predicting the dropout. Extensive experimental validations illustrate that the local correlation of learning behaviors should not be neglected. The proposed CNN model considers this characteristic and improves the dropout prediction accuracy. Furthermore, the proposed model can be used to predict dropout temporally and early when sufficient data are collected.展开更多
Single-cell RNA sequencing(scRNA-seq)provides a powerful tool to determine expression patterns of thousands of individual cells.However,the analysis of scRNA-seq data remains a computational challenge due to the high ...Single-cell RNA sequencing(scRNA-seq)provides a powerful tool to determine expression patterns of thousands of individual cells.However,the analysis of scRNA-seq data remains a computational challenge due to the high technical noise such as the presence of dropout events that lead to a large proportion of zeros for expressed genes.Taking into account the cell heterogeneity and the relationship between dropout rate and expected expression level,we present a cell sub-population based bounded low-rank(PBLR)method to impute the dropouts of scRNA-seq data.Through application to both simulated and real scRNA-seq datasets,PBLR is shown to be effective in recovering dropout events,and it can dramaimprove the low・dimensional representation and the recovery of gene-gene relationships masked by dropout events compared to several state-of-the-art methods・Moreover,PBLR also detects accurate and robust cell sub-populations automatically,shedding light on its flexibility and generality for scRNA-seq data analysis.展开更多
基金supported by General Program (No. 60774022)State Key Program (No. 60834001) of National Natural Science Foundation of China
文摘In this paper, the stability of iterative learning control with data dropouts is discussed. By the super vector formulation, an iterative learning control （ILC） system with data dropouts can be modeled as an asynchronous dynamical system with rate constraints on events in the iteration domain. The stability condition is provided in the form of linear matrix inequalities （LMIS） depending on the stability of asynchronous dynamical systems. The analysis is supported by simulations.
基金This work was supported by the B-type Strategic Priority Program of the Chinese Academy of Sciences(grant no.XDB41000000)the National Natural Science Foundation of China(grant nos.42025404,41704162,41974186,41674163,41904144,41904143)+1 种基金the pre-research projects on Civil Aerospace Technologies(grant nos.D020303,D020308,D020104)the China National Space Administration,and the China Postdoctoral Science Foundation Project(grant no.2019M662700).We also acknowledge the Van Allen Probes mission,particularly the ECT and EMFISIS team,for providing particle and wave data.The electron flux data were obtained from http://www.rbsp-ect.lanl.gov/data_pub/.The wave data from the EMFISIS instrument were obtained from http://emfisis.physics.uiowa.edu/data/index.The solar wind parameters and geomagnetic indices were obtained from the online OMNIWeb(http://omniweb.gsfc.nasa.gov/).
文摘Radiation belt electron dropouts indicate electron flux decay to the background level during geomagnetic storms,which is commonly attributed to the effects of wave-induced pitch angle scattering and magnetopause shadowing.To investigate the loss mechanisms of radiation belt electron dropouts triggered by a solar wind dynamic pressure pulse event on 12 September 2014,we comprehensively analyzed the particle and wave measurements from Van Allen Probes.The dropout event was divided into three periods:before the storm,the initial phase of the storm,and the main phase of the storm.The electron pitch angle distributions(PADs)and electron flux dropouts during the initial and main phases of this storm were investigated,and the evolution of the radial profile of electron phase space density(PSD)and the(μ,K)dependence of electron PSD dropouts(whereμ,K,and L^*are the three adiabatic invariants)were analyzed.The energy-independent decay of electrons at L>4.5 was accompanied by butterfly PADs,suggesting that the magnetopause shadowing process may be the major loss mechanism during the initial phase of the storm at L>4.5.The features of electron dropouts and 90°-peaked PADs were observed only for>1 MeV electrons at L<4,indicating that the wave-induced scattering effect may dominate the electron loss processes at the lower L-shell during the main phase of the storm.Evaluations of the(μ,K)dependence of electron PSD drops and calculations of the minimum electron resonant energies of H+-band electromagnetic ion cyclotron(EMIC)waves support the scenario that the observed PSD drop peaks around L^*=3.9 may be caused mainly by the scattering of EMIC waves,whereas the drop peaks around L^*=4.6 may result from a combination of EMIC wave scattering and outward radial diffusion.
基金supported by Agency for Science,Technology and Research Grant(SERC)(No.0521010037)Natural Science Foundation of China(No.60874062,60828006)NSFC-Guangdong Joint Foundation(No.U0735003)
文摘This paper is concerned with the estimation problem for discrete-time stochastic linear systems with possible single unit delay and multiple packet dropouts. Based on a proposed uncertain model in data transmission, an optimal full-order filter for the state of the system is presented, which is shown to be of the form of employing the received outputs at the current and last time instants. The solution to the optimal filter is given in terms of a Riccati difference equation governed by two binary random variables. The optimal filter is reduced to the standard Kalman filter when there are no random delays and packet dropouts. The steady-state filter is also investigated. A sufficient condition for the existence of the steady-state filter is given. The asymptotic stability of the optimal filter is analyzed.
基金supported by the National Natural Science Foundation of China(6057408860874053)
文摘This paper addresses the problem of fault detection(FD)for networked systems with access constraints and packet dropouts.Two independent Markov chains are used to describe the sequences of channels which are available for communication at an instant and the packet dropout process,respectively.Performance indexes H∞and H_are introduced to describe the robustness of residual against external disturbances and sensitivity of residual to faults,respectively.By using a mode-dependent fault detection filter(FDF)as residual generator,the addressed FD problem is converted into an auxiliary filter design problem with the above index constraints.A sufficient condition for the existence of the FDF is derived in terms of certain linear matrix inequalities(LMIs).When these LMIs are feasible,the explicit expression of the desired FDF can also be characterized.A numerical example is exploited to show the usefulness of the proposed results.
基金supported by the Defence Science Organisation-National Laboratories (DSO-NL),Singapore (No.DSOCL06184)
文摘This paper investigates the observer-based H-infinity control problem for networked control systems （NCSs） with random packet dropouts. A general packet dropout model with multiple independent stochastic variables in the multiple channels case is adopted to describe the data missing in the limited communication channels. With the consideration of the sensor-to-controller and controller-to-actuator packet dropouts at the same time, a new method is pro- posed based on a separation lemma to design an observer-based H-infinity controller, which exponentially stabilizes the closed-loop system in the sense of mean square and also achieves a prescribed H-infinity disturbance attenuation level. A numerical example is given to illustrate the effectiveness of the proposed control method.
文摘In 2004, Wang Chengyan, a 13-year-old Mongolian girl in the Tumote Left Banner of Inner Mongolia, took up her schoolbag again and marched into the classroom of six grade of a local primary school. With her face shining with brilliance, she told her friends： ＂It is brothers and sisters studying in Japan who have paid my way to school.＂
基金Projects(51476187,51506221,51606219) supported by the National Natural Science Foundation of China
文摘A robust sliding mode control algorithm is developed for a class of networked control system with packet dropouts in both sensor-controller channel and controller-actuator channel,and at the same time mismatched parametric uncertainty and external disturbance are also taken into consideration.A two-level Bernoulli process has been used to describe the packet dropouts existing in both channels.A novel integral sliding surface is proposed,based on which the H∞performance of system sliding mode motion is analyzed.Then the sufficient condition for system stability and robustness is derived in the form of linear matrix inequality(LMI).A sliding mode controller is designed which can guarantee a relatively ideal system dynamic performance and has certain robustness against unknown parameter perturbations and external disturbances.The results from numerical simulations are presented to corroborate the validity of the proposed controller.
基金supported by the National Natural Science Foundation of China (6090402060574006)the Research Fund for the Doctoral Program of Higher Eolucation of China (20070286039)
文摘The globally exponential stability of nonlinear impul- sive networked control systems （NINCS） with time delay and packet dropouts is investigated. By applying Lyapunov function theory, sufficient conditions on the global exponential stability are derived by introducing a comparison system and estimating the corresponding Cauchy matrix. An impulsive controller is explicitly designed to achieve exponential stability and ensure state con- verge with a given decay rate for the system. The Lorenz oscillator system is presented as a numerical example to illustrate the theo- retical results and effectiveness of the proposed controller design procedure.
文摘<div style="text-align:justify;"> <span style="font-family:Verdana;">The growing need to use Artificial Intelligence (AI) technologies in addressing challenges in education sectors of developing countries is undermined by low awareness, limited skill and poor data quality. One particular persisting challenge, which can be addressed by AI, is school dropouts whereby hundreds of thousands of children drop annually in Africa. This article presents a data-driven approach to proactively predict likelihood of dropping from schools and enable effective management of dropouts. The approach is guided by a carefully crafted conceptual framework and new concepts of average absenteeism, current cumulative absenteeism and dropout risk appetite. In this study, a typical scenario of missing quality data is considered and for which synthetic data is generated to enable development of a functioning prediction model using neural network. The results show that, using the proposed approach, the levels of risk of dropping out of schools can be practically determined using data that is largely available in schools. Potentially, the study will inspire further research, encourage deployment of the technologies in real life, and inform processes of formulating or improving policies.</span> </div>
基金supported by the National Natural Science Foundation of China(Nos.61903096 and 62173101)the Key Laboratory of On-Chip Communication and Sensor Chip of Guangdong Higher Education Institutes(No.2023KSYS002).
文摘This paper proposed a feedback feedforward Iterative Learning Control(ILC)law for nonlinear system with iteratively variable trial lengths under a networked systems structure,where the both sensor and actuator occurs random data lost separately.The feedforward ILC part includes the calculated input signal,actual input signal,and the modified tracking error of last iteration.Some tracking signal would be lost at last iteration because of the iterative varying trial lengths.In order to offset the missing signal of last trial,the tracking error of present trial is adopted by feedback control part.It is established that the convergence relied on the feedforward control gain merely,while the rate of convergence is also expedited by the feedback control component.When the initial state expectation equals to the reference one,it is established that the tracking error expectation can be controlled to zero.With an illustrative simulation,the effectiveness of the developed algorithm can be demonstrated.
基金supported by the National Natural Science Foundation of China under Grant No.61471323。
文摘This paper utilizes a switched systems approach to deal with the problem of fault detectio for uncertain delta operator networked control system with packet dropouts and timevarying delays.Uncertainties exist in the matrices of the systems and are norm-bounded time-varying.Two parts of packet dropouts are considered in this paper:From sensors to controllers,and from controllers to actuators.Two independent Bernoulli distributed white sequences are introduced to account for packet dropouts.Then an FD filter is designed under an arbitrary switching law.Furthermore,the sufficient conditions for the NCSs under consideration that are exponentially stable in the mean-square sense and satisfy H∞performance are obtained in terms of linear matrix inequalitie,multiple Lyapunov function and average dwell-tim approach.The explicit expression of the desired filter parameters is given.Finally,a numerical example verifies the effectiveness of the proposed method.
文摘Customer churn is the rate at which customers discontinue doing business with a company over a given time period.It is an essential measure for businesses to monitor high churn rates,as they often indicate underlying issues with services,products,or customer experience,resulting in considerable income loss.Prediction of customer churn is a crucial task aimed at retaining customers and maintaining revenue growth.Traditional machine learning(ML)models often struggle to capture complex temporal dependencies in client behavior data.To address this,an optimized deep learning(DL)approach using a Regularized Bidirectional Long Short-Term Memory(RBiLSTM)model is proposed to mitigate overfitting and improve generalization error.The model integrates dropout,L2-regularization,and early stopping to enhance predictive accuracy while preventing over-reliance on specific patterns.Moreover,this study investigates the effect of optimization techniques on boosting the training efficiency of the developed model.Experimental results on a recent public customer churn dataset demonstrate that the trained model outperforms the traditional ML models and some other DL models,such as Long Short-Term Memory(LSTM)and Deep Neural Network(DNN),in churn prediction performance and stability.The proposed approach achieves 96.1%accuracy,compared with LSTM and DNN,which attain 94.5%and 94.1%accuracy,respectively.These results confirm that the proposed approach can be used as a valuable tool for businesses to identify at-risk consumers proactively and implement targeted retention strategies.
基金supported by the National Natural Science Foundation of China(61473306).
文摘In this note,we study the state estimation problem for a multi-sensor system subject to multiple packet dropouts.A novel optimal distributed fusion estimator is derived by applying a resending mechanism and a parallel information filtering structure.It is shown that the proposed distributed fusion estimator has smaller estimation error covariance and less computation complexity when compared with the centralised Kalman like estimator with multiple intermittent measurements.
基金partially supported by the National Natural Science Foundation of China (Nos. 61866007, 61363029, 61662014, 61763007, and U1811264)the Natural Science Foundation of Guangxi District (No. 2018GXNSFDA138006)+2 种基金Guangxi Key Laboratory of Trusted Software (No. KX201721)Humanities and Social Sciences Research Projects of the Ministry of Education (No. 17JDGC022)Chongqing Higher Education Reform Project (No. 183137)
文摘Recently, Massive Open Online Courses(MOOCs) have become a major online learning methodology for millions of people worldwide. However, the dropout rates from several current MOOCs are high. Usually, dropout prediction aims to predict whether a learner will exhibit learning behaviors during several consecutive days in the future. Therefore, the information related to the learning behaviors of a learner in several consecutive days should be considered. After in-depth analysis of the learning behavior patterns of the MOOC learners, this study reports that learners often exhibit similar learning behaviors on several consecutive days, i.e., the learning status of a learner for the subsequent day is likely to be similar to that for the previous day. Based on this characteristic of MOOC learning,this study proposes a new simple feature matrix for keeping information related to the local correlation of learning behaviors and a new Convolutional Neural Network(CNN) model for predicting the dropout. Extensive experimental validations illustrate that the local correlation of learning behaviors should not be neglected. The proposed CNN model considers this characteristic and improves the dropout prediction accuracy. Furthermore, the proposed model can be used to predict dropout temporally and early when sufficient data are collected.
基金This work was supported by the National Key R&D Program of China(2019YFA0709501)the National Natural Science Foundation of China(11661141019 and 61621003)+1 种基金the Nati onal Ten Thousa nd Tale nt Program for Young Top-notch Talents,the CAS Frontier Science Research Key Project for Top Young Scientist(QYZDB-SSW-SYS008)and Shanghai Municipal Science and Technology Major Project(2017SHZDZX01).
文摘Single-cell RNA sequencing(scRNA-seq)provides a powerful tool to determine expression patterns of thousands of individual cells.However,the analysis of scRNA-seq data remains a computational challenge due to the high technical noise such as the presence of dropout events that lead to a large proportion of zeros for expressed genes.Taking into account the cell heterogeneity and the relationship between dropout rate and expected expression level,we present a cell sub-population based bounded low-rank(PBLR)method to impute the dropouts of scRNA-seq data.Through application to both simulated and real scRNA-seq datasets,PBLR is shown to be effective in recovering dropout events,and it can dramaimprove the low・dimensional representation and the recovery of gene-gene relationships masked by dropout events compared to several state-of-the-art methods・Moreover,PBLR also detects accurate and robust cell sub-populations automatically,shedding light on its flexibility and generality for scRNA-seq data analysis.
