Traffic forecasting with high precision aids Intelligent Transport Systems(ITS)in formulating and optimizing traffic management strategies.The algorithms used for tuning the hyperparameters of the deep learning models...Traffic forecasting with high precision aids Intelligent Transport Systems(ITS)in formulating and optimizing traffic management strategies.The algorithms used for tuning the hyperparameters of the deep learning models often have accurate results at the expense of high computational complexity.To address this problem,this paper uses the Tree-structured Parzen Estimator(TPE)to tune the hyperparameters of the Long Short-term Memory(LSTM)deep learning framework.The Tree-structured Parzen Estimator(TPE)uses a probabilistic approach with an adaptive searching mechanism by classifying the objective function values into good and bad samples.This ensures fast convergence in tuning the hyperparameter values in the deep learning model for performing prediction while still maintaining a certain degree of accuracy.It also overcomes the problem of converging to local optima and avoids timeconsuming random search and,therefore,avoids high computational complexity in prediction accuracy.The proposed scheme first performs data smoothing and normalization on the input data,which is then fed to the input of the TPE for tuning the hyperparameters.The traffic data is then input to the LSTM model with tuned parameters to perform the traffic prediction.The three optimizers:Adaptive Moment Estimation(Adam),Root Mean Square Propagation(RMSProp),and Stochastic Gradient Descend with Momentum(SGDM)are also evaluated for accuracy prediction and the best optimizer is then chosen for final traffic prediction in TPE-LSTM model.Simulation results verify the effectiveness of the proposed model in terms of accuracy of prediction over the benchmark schemes.展开更多
The paper presents a two-layer,disturbance-resistant,and fault-tolerant affine formation maneuver control scheme that accomplishes the surrounding of a dynamic target with multiple underactuated Quadrotor Unmanned Aer...The paper presents a two-layer,disturbance-resistant,and fault-tolerant affine formation maneuver control scheme that accomplishes the surrounding of a dynamic target with multiple underactuated Quadrotor Unmanned Aerial Vehicles(QUAVs).This scheme mainly consists of predefinedtime estimators and fixed-time tracking controllers,with a hybrid Laplacian matrix describing the communication among these QUAVs.At the first layer,we devise predefined time estimators for leading and following QUAVs,enabling accurate estimation of desired information.In the second layer,we initially devise a fixed-time hybrid observer to estimate unknown disturbances and actuator faults.Fixedtime translational tracking controllers are then proposed,and the intermediary control input from these controllers is used to extract the desired attitude and angular velocities for the fixed-time rotational tracking controllers.We employ an exact tracking differentiator to handle variables that are challenging to differentiate directly.The paper includes a demonstration of the control system stability through mathematical proof,as well as the presentation of simulation results and comparative simulations.展开更多
Wet flue gas desulphurization technology is widely used in the industrial process for its capability of efficient pollution removal.The desulphurization control system,however,is subjected to complex reaction mechanis...Wet flue gas desulphurization technology is widely used in the industrial process for its capability of efficient pollution removal.The desulphurization control system,however,is subjected to complex reaction mechanisms and severe disturbances,which make for it difficult to achieve certain practically relevant control goals including emission and economic performances as well as system robustness.To address these challenges,a new robust control scheme based on uncertainty and disturbance estimator(UDE)and model predictive control(MPC)is proposed in this paper.The UDE is used to estimate and dynamically compensate acting disturbances,whereas MPC is deployed for optimal feedback regulation of the resultant dynamics.By viewing the system nonlinearities and unknown dynamics as disturbances,the proposed control framework allows to locally treat the considered nonlinear plant as a linear one.The obtained simulation results confirm that the utilization of UDE makes the tracking error negligibly small,even in the presence of unmodeled dynamics.In the conducted comparison study,the introduced control scheme outperforms both the standard MPC and PID(proportional-integral-derivative)control strategies in terms of transient performance and robustness.Furthermore,the results reveal that a lowpass-filter time constant has a significant effect on the robustness and the convergence range of the tracking error.展开更多
We study the Nadaraya-Watson estimators for the drift function of two-sided reflected stochastic differential equations.The estimates,based on either the continuously observed process or the discretely observed proces...We study the Nadaraya-Watson estimators for the drift function of two-sided reflected stochastic differential equations.The estimates,based on either the continuously observed process or the discretely observed process,are considered.Under certain conditions,we prove the strong consistency and the asymptotic normality of the two estimators.Our method is also suitable for one-sided reflected stochastic differential equations.Simulation results demonstrate that the performance of our estimator is superior to that of the estimator proposed by Cholaquidis et al.(Stat Sin,2021,31:29-51).Several real data sets of the currency exchange rate are used to illustrate our proposed methodology.展开更多
In this paper,we consider the limit distribution of the error density function estima-tor in the rst-order autoregressive models with negatively associated and positively associated random errors.Under mild regularity...In this paper,we consider the limit distribution of the error density function estima-tor in the rst-order autoregressive models with negatively associated and positively associated random errors.Under mild regularity assumptions,some asymptotic normality results of the residual density estimator are obtained when the autoregressive models are stationary process and explosive process.In order to illustrate these results,some simulations such as con dence intervals and mean integrated square errors are provided in this paper.It shows that the residual density estimator can replace the density\estimator"which contains errors.展开更多
Regularized system identification has become the research frontier of system identification in the past decade.One related core subject is to study the convergence properties of various hyper-parameter estimators as t...Regularized system identification has become the research frontier of system identification in the past decade.One related core subject is to study the convergence properties of various hyper-parameter estimators as the sample size goes to infinity.In this paper,we consider one commonly used hyper-parameter estimator,the empirical Bayes(EB).Its convergence in distribution has been studied,and the explicit expression of the covariance matrix of its limiting distribution has been given.However,what we are truly interested in are factors contained in the covariance matrix of the EB hyper-parameter estimator,and then,the convergence of its covariance matrix to that of its limiting distribution is required.In general,the convergence in distribution of a sequence of random variables does not necessarily guarantee the convergence of its covariance matrix.Thus,the derivation of such convergence is a necessary complement to our theoretical analysis about factors that influence the convergence properties of the EB hyper-parameter estimator.In this paper,we consider the regularized finite impulse response(FIR)model estimation with deterministic inputs,and show that the covariance matrix of the EB hyper-parameter estimator converges to that of its limiting distribution.Moreover,we run numerical simulations to demonstrate the efficacy of ourtheoretical results.展开更多
The movement of a robotic arm in the working environment requires efficient and adequate motion planning.The procedure of collision detection based on the object geometry is crucial to plan the motion trajectories,and...The movement of a robotic arm in the working environment requires efficient and adequate motion planning.The procedure of collision detection based on the object geometry is crucial to plan the motion trajectories,and usually requires intensive resource and considerable time.Many learning-based collision detection schemes have been developed to improve the efficiency of collision detection.However,current learning-based collision detection methods are either not accurate enough or prone to low efficiency.We propose a simple,yet highly accurate collision distance estimator,a spatial information assisted distance estimator,i.e.,SPADE,in which spatial information of both robotic arms and obstacles are encoded by multiple encoders.With evaluation in both static and dynamic environments,our model shows higher prediction accuracy than multiple baselines,and higher accuracy can be corroborated by experiment with our model under the premise of equal inference efficiency.In addition,our model shows better robustness than baseline in real-world path planning.展开更多
目的 基于国际癌症研究机构发布的GLOBOCAN 2018、2020及2022版本,系统分析2018–2022年间全球及中国胰腺癌的发病和死亡情况,并总结其主要影响因素,为我国胰腺癌的防控策略制定与临床诊疗实践提供参考。方法 收集并整理GLOBOCAN数据库...目的 基于国际癌症研究机构发布的GLOBOCAN 2018、2020及2022版本,系统分析2018–2022年间全球及中国胰腺癌的发病和死亡情况,并总结其主要影响因素,为我国胰腺癌的防控策略制定与临床诊疗实践提供参考。方法 收集并整理GLOBOCAN数据库中胰腺癌的发病例数、死亡例数,以及粗发病率、粗死亡率、世界年龄标准化发病率(age-standardized incidence rate by world standard population,ASIRW)、世界年龄标准化死亡率(age-standardized mortality rate by world standard population,ASMRW)等指标;同时结合人类发展指数(human development index,HDI)、国家收入水平等社会经济学参数,对全球与中国胰腺癌在不同地区、不同年龄与不同性别中的分布特点进行比较分析。结果 从2018年至2022年,全球胰腺癌新发病例数由45.8万增至51.1万,粗发病率从5.4/10万上升至6.5/10万;死亡例数从43.2万上升至46.7万,粗死亡率从5.7/10万增至5.9/10万,ASMRW从4.4/10万下降至4.3/10万。中国胰腺癌新发病例数从11.6万增至11.9万,占全球的23.3%,粗发病率维持在(8~9)/10万;死亡例数从11.0万下降至10.6万,粗死亡率从7.8/10万降至7.5/10万,ASMRW从4.9/10万降至3.9/10万。2022年非常高HDI国家的胰腺癌ASIRW为7.9/10万,ASMRW为6.9/10万,远高于低HDI国家的1.4/10万、1.3/10万。胰腺癌发病呈明显年龄相关性,≥75岁组全球新发191 157例(粗发病率为63.3/10万),中国新发37 722例(粗发病率为51.2/10万);全球和中国的男性发病率和死亡率均高于女性。结论 胰腺癌正成为全球和中国重要的公共卫生挑战,其发病与死亡在未来仍可能继续攀升。应进一步加强控烟、肥胖管理、糖尿病监测等综合防控措施;针对高危人群开展早期筛查及规范化诊治对于提升胰腺癌生存率至关重要。完善全国肿瘤登记体系,并结合多学科协作模式,可为精准防治胰腺癌奠定坚实基础。展开更多
文摘Traffic forecasting with high precision aids Intelligent Transport Systems(ITS)in formulating and optimizing traffic management strategies.The algorithms used for tuning the hyperparameters of the deep learning models often have accurate results at the expense of high computational complexity.To address this problem,this paper uses the Tree-structured Parzen Estimator(TPE)to tune the hyperparameters of the Long Short-term Memory(LSTM)deep learning framework.The Tree-structured Parzen Estimator(TPE)uses a probabilistic approach with an adaptive searching mechanism by classifying the objective function values into good and bad samples.This ensures fast convergence in tuning the hyperparameter values in the deep learning model for performing prediction while still maintaining a certain degree of accuracy.It also overcomes the problem of converging to local optima and avoids timeconsuming random search and,therefore,avoids high computational complexity in prediction accuracy.The proposed scheme first performs data smoothing and normalization on the input data,which is then fed to the input of the TPE for tuning the hyperparameters.The traffic data is then input to the LSTM model with tuned parameters to perform the traffic prediction.The three optimizers:Adaptive Moment Estimation(Adam),Root Mean Square Propagation(RMSProp),and Stochastic Gradient Descend with Momentum(SGDM)are also evaluated for accuracy prediction and the best optimizer is then chosen for final traffic prediction in TPE-LSTM model.Simulation results verify the effectiveness of the proposed model in terms of accuracy of prediction over the benchmark schemes.
基金supported by Natural Science Basic Research Plan in Shaanxi Province of China(No.2023-JC-QN-0733)Guangdong Basic and Applied Basic Research Foundation,China(No.2022A1515110753)+2 种基金China Postdoctoral Science Foundation(No.2022M722583)China Industry-UniversityResearch Innovation Foundation(No.2022IT188)National Key Laboratory of Air-based Information Perception and Fusion and the Aeronautic Science Foundation of China(No.20220001068001)。
文摘The paper presents a two-layer,disturbance-resistant,and fault-tolerant affine formation maneuver control scheme that accomplishes the surrounding of a dynamic target with multiple underactuated Quadrotor Unmanned Aerial Vehicles(QUAVs).This scheme mainly consists of predefinedtime estimators and fixed-time tracking controllers,with a hybrid Laplacian matrix describing the communication among these QUAVs.At the first layer,we devise predefined time estimators for leading and following QUAVs,enabling accurate estimation of desired information.In the second layer,we initially devise a fixed-time hybrid observer to estimate unknown disturbances and actuator faults.Fixedtime translational tracking controllers are then proposed,and the intermediary control input from these controllers is used to extract the desired attitude and angular velocities for the fixed-time rotational tracking controllers.We employ an exact tracking differentiator to handle variables that are challenging to differentiate directly.The paper includes a demonstration of the control system stability through mathematical proof,as well as the presentation of simulation results and comparative simulations.
基金supported by the key project of the National Nature Science Foundation of China(51736002).
文摘Wet flue gas desulphurization technology is widely used in the industrial process for its capability of efficient pollution removal.The desulphurization control system,however,is subjected to complex reaction mechanisms and severe disturbances,which make for it difficult to achieve certain practically relevant control goals including emission and economic performances as well as system robustness.To address these challenges,a new robust control scheme based on uncertainty and disturbance estimator(UDE)and model predictive control(MPC)is proposed in this paper.The UDE is used to estimate and dynamically compensate acting disturbances,whereas MPC is deployed for optimal feedback regulation of the resultant dynamics.By viewing the system nonlinearities and unknown dynamics as disturbances,the proposed control framework allows to locally treat the considered nonlinear plant as a linear one.The obtained simulation results confirm that the utilization of UDE makes the tracking error negligibly small,even in the presence of unmodeled dynamics.In the conducted comparison study,the introduced control scheme outperforms both the standard MPC and PID(proportional-integral-derivative)control strategies in terms of transient performance and robustness.Furthermore,the results reveal that a lowpass-filter time constant has a significant effect on the robustness and the convergence range of the tracking error.
基金partially supported by the National Natural Science Foundation of China(11871244)the Fundamental Research Funds for the Central Universities,JLU。
文摘We study the Nadaraya-Watson estimators for the drift function of two-sided reflected stochastic differential equations.The estimates,based on either the continuously observed process or the discretely observed process,are considered.Under certain conditions,we prove the strong consistency and the asymptotic normality of the two estimators.Our method is also suitable for one-sided reflected stochastic differential equations.Simulation results demonstrate that the performance of our estimator is superior to that of the estimator proposed by Cholaquidis et al.(Stat Sin,2021,31:29-51).Several real data sets of the currency exchange rate are used to illustrate our proposed methodology.
基金supported by the National Natural Science Foundation of China(12131015,12071422)。
文摘In this paper,we consider the limit distribution of the error density function estima-tor in the rst-order autoregressive models with negatively associated and positively associated random errors.Under mild regularity assumptions,some asymptotic normality results of the residual density estimator are obtained when the autoregressive models are stationary process and explosive process.In order to illustrate these results,some simulations such as con dence intervals and mean integrated square errors are provided in this paper.It shows that the residual density estimator can replace the density\estimator"which contains errors.
基金supported in part by the National Natural Science Foundation of China(No.62273287)by the Shenzhen Science and Technology Innovation Council(Nos.JCYJ20220530143418040,JCY20170411102101881)the Thousand Youth Talents Plan funded by the central government of China.
文摘Regularized system identification has become the research frontier of system identification in the past decade.One related core subject is to study the convergence properties of various hyper-parameter estimators as the sample size goes to infinity.In this paper,we consider one commonly used hyper-parameter estimator,the empirical Bayes(EB).Its convergence in distribution has been studied,and the explicit expression of the covariance matrix of its limiting distribution has been given.However,what we are truly interested in are factors contained in the covariance matrix of the EB hyper-parameter estimator,and then,the convergence of its covariance matrix to that of its limiting distribution is required.In general,the convergence in distribution of a sequence of random variables does not necessarily guarantee the convergence of its covariance matrix.Thus,the derivation of such convergence is a necessary complement to our theoretical analysis about factors that influence the convergence properties of the EB hyper-parameter estimator.In this paper,we consider the regularized finite impulse response(FIR)model estimation with deterministic inputs,and show that the covariance matrix of the EB hyper-parameter estimator converges to that of its limiting distribution.Moreover,we run numerical simulations to demonstrate the efficacy of ourtheoretical results.
文摘The movement of a robotic arm in the working environment requires efficient and adequate motion planning.The procedure of collision detection based on the object geometry is crucial to plan the motion trajectories,and usually requires intensive resource and considerable time.Many learning-based collision detection schemes have been developed to improve the efficiency of collision detection.However,current learning-based collision detection methods are either not accurate enough or prone to low efficiency.We propose a simple,yet highly accurate collision distance estimator,a spatial information assisted distance estimator,i.e.,SPADE,in which spatial information of both robotic arms and obstacles are encoded by multiple encoders.With evaluation in both static and dynamic environments,our model shows higher prediction accuracy than multiple baselines,and higher accuracy can be corroborated by experiment with our model under the premise of equal inference efficiency.In addition,our model shows better robustness than baseline in real-world path planning.
文摘目的 基于国际癌症研究机构发布的GLOBOCAN 2018、2020及2022版本,系统分析2018–2022年间全球及中国胰腺癌的发病和死亡情况,并总结其主要影响因素,为我国胰腺癌的防控策略制定与临床诊疗实践提供参考。方法 收集并整理GLOBOCAN数据库中胰腺癌的发病例数、死亡例数,以及粗发病率、粗死亡率、世界年龄标准化发病率(age-standardized incidence rate by world standard population,ASIRW)、世界年龄标准化死亡率(age-standardized mortality rate by world standard population,ASMRW)等指标;同时结合人类发展指数(human development index,HDI)、国家收入水平等社会经济学参数,对全球与中国胰腺癌在不同地区、不同年龄与不同性别中的分布特点进行比较分析。结果 从2018年至2022年,全球胰腺癌新发病例数由45.8万增至51.1万,粗发病率从5.4/10万上升至6.5/10万;死亡例数从43.2万上升至46.7万,粗死亡率从5.7/10万增至5.9/10万,ASMRW从4.4/10万下降至4.3/10万。中国胰腺癌新发病例数从11.6万增至11.9万,占全球的23.3%,粗发病率维持在(8~9)/10万;死亡例数从11.0万下降至10.6万,粗死亡率从7.8/10万降至7.5/10万,ASMRW从4.9/10万降至3.9/10万。2022年非常高HDI国家的胰腺癌ASIRW为7.9/10万,ASMRW为6.9/10万,远高于低HDI国家的1.4/10万、1.3/10万。胰腺癌发病呈明显年龄相关性,≥75岁组全球新发191 157例(粗发病率为63.3/10万),中国新发37 722例(粗发病率为51.2/10万);全球和中国的男性发病率和死亡率均高于女性。结论 胰腺癌正成为全球和中国重要的公共卫生挑战,其发病与死亡在未来仍可能继续攀升。应进一步加强控烟、肥胖管理、糖尿病监测等综合防控措施;针对高危人群开展早期筛查及规范化诊治对于提升胰腺癌生存率至关重要。完善全国肿瘤登记体系,并结合多学科协作模式,可为精准防治胰腺癌奠定坚实基础。
