In the globalized market environment, increasingly significant economic and environmental factors withincomplex industrial plants impose importance on the optimization of global production indices; such opti-mization ...In the globalized market environment, increasingly significant economic and environmental factors withincomplex industrial plants impose importance on the optimization of global production indices; such opti-mization includes improvements in production efficiency, product quality, and yield, along with reductionsof energy and resource usage. This paper briefly overviews recent progress in data-driven hybrid intelli-gence optimization methods and technologies in improving the performance of global production indicesin mineral processing. First, we provide the problem description. Next, we summarize recent progress indata-based optimization for mineral processing plants. This optimization consists of four layers： optimiza-tion of the target values for monthly global production indices, optimization of the target values for dailyglobal production indices, optimization of the target values for operational indices, and automation systemsfor unit processes. We briefly overview recent progress in each of the different layers. Finally, we point outopportunities for future works in data-based optimization for mineral processing plants.展开更多
In this paper,a data-based scheme is proposed to solve the optimal tracking problem of autonomous nonlinear switching systems.The system state is forced to track the reference signal by minimizing the performance func...In this paper,a data-based scheme is proposed to solve the optimal tracking problem of autonomous nonlinear switching systems.The system state is forced to track the reference signal by minimizing the performance function.First,the problem is transformed to solve the corresponding Bellman optimality equation in terms of the Q-function(also named as action value function).Then,an iterative algorithm based on adaptive dynamic programming(ADP)is developed to find the optimal solution which is totally based on sampled data.The linear-in-parameter(LIP)neural network is taken as the value function approximator.Considering the presence of approximation error at each iteration step,the generated approximated value function sequence is proved to be boundedness around the exact optimal solution under some verifiable assumptions.Moreover,the effect that the learning process will be terminated after a finite number of iterations is investigated in this paper.A sufficient condition for asymptotically stability of the tracking error is derived.Finally,the effectiveness of the algorithm is demonstrated with three simulation examples.展开更多
In this paper, a data-based fault tolerant control(FTC) scheme is investigated for unknown continuous-time(CT)affine nonlinear systems with actuator faults. First, a neural network(NN) identifier based on particle swa...In this paper, a data-based fault tolerant control(FTC) scheme is investigated for unknown continuous-time(CT)affine nonlinear systems with actuator faults. First, a neural network(NN) identifier based on particle swarm optimization(PSO) is constructed to model the unknown system dynamics. By utilizing the estimated system states, the particle swarm optimized critic neural network(PSOCNN) is employed to solve the Hamilton-Jacobi-Bellman equation(HJBE) more efficiently.Then, a data-based FTC scheme, which consists of the NN identifier and the fault compensator, is proposed to achieve actuator fault tolerance. The stability of the closed-loop system under actuator faults is guaranteed by the Lyapunov stability theorem. Finally, simulations are provided to demonstrate the effectiveness of the developed method.展开更多
This paper proposes linear and nonlinear filters for a non-Gaussian dynamic system with an unknown nominal covariance of the output noise.The challenge of designing a suitable filter in the presence of an unknown cova...This paper proposes linear and nonlinear filters for a non-Gaussian dynamic system with an unknown nominal covariance of the output noise.The challenge of designing a suitable filter in the presence of an unknown covariance matrix is addressed by focusing on the output data set of the system.Considering that data generated from a Gaussian distribution exhibit ellipsoidal scattering,we first propose the weighted sum of norms(SON)clustering method that prioritizes nearby points,reduces distant point influence,and lowers computational cost.Then,by introducing the weighted maximum likelihood,we propose a semi-definite program(SDP)to detect outliers and reduce their impacts on each cluster.Detecting these weights paves the way to obtain an appropriate covariance of the output noise.Next,two filtering approaches are presented:a cluster-based robust linear filter using the maximum a posterior(MAP)estimation and a clusterbased robust nonlinear filter assuming that output noise distribution stems from some Gaussian noise resources according to the ellipsoidal clusters.At last,simulation results demonstrate the effectiveness of our proposed filtering approaches.展开更多
This paper focuses on developing a system that allows presentation authors to effectively retrieve presentation slides for reuse from a large volume of existing presentation materials. We assume episodic memories of t...This paper focuses on developing a system that allows presentation authors to effectively retrieve presentation slides for reuse from a large volume of existing presentation materials. We assume episodic memories of the authors can be used as contextual keywords in query expressions to efficiently dig out the expected slides for reuse rather than using only the part-of-slide-descriptions-based keyword queries. As a system, a new slide repository is proposed, composed of slide material collections, slide content data and pieces of information from authors＇ episodic memories related to each slide and presentation together with a slide retrieval application enabling authors to use the episodic memories as part of queries. The result of our experiment shows that the episodic memory-used queries can give more discoverability than the keyword-based queries. Additionally, an improvement model is discussed on the slide retrieval for further slide-finding efficiency by expanding the episodic memories model in the repository taking in the links with the author-and-slide-related data and events having been post on the private and social media sites.展开更多
To cope with the challenges of CoViD-19,europe has adopted relevant measures of a data-based approach to governance,on which scholars have huge differences,and the related researches are conducive to further discussio...To cope with the challenges of CoViD-19,europe has adopted relevant measures of a data-based approach to governance,on which scholars have huge differences,and the related researches are conducive to further discussion on the differences.By sorting out the challenges posed by the pandemic to public security and data protection in europe,we can summarize the“european Solution”of the data-based approach to governance,including legislation,instruments,supervision,international cooperation,and continuity.The“Solution”has curbed the spread of the pandemic to a certain extent.However,due to the influence of the traditional values of the EU,the“Solution”is too idealistic in the balance between public security and data protection,which intensifies the dilemma and causes many problems,such as ambiguous legislation,inadequate effectiveness and security of instruments,an arduous endeavor in inter national cooperation,and imperfect regulations on digital green certificates.Therefore,in a major public health crisis,there is still a long way to go in exploring a balance between public security and data protection.展开更多
With the advancement of the rural revitalization strategy,preventing poverty recurrence among previously impoverished populations has become a crucial social concern.The application of big data technology in poverty r...With the advancement of the rural revitalization strategy,preventing poverty recurrence among previously impoverished populations has become a crucial social concern.The application of big data technology in poverty recurrence monitoring and agricultural product sales systems can effectively enhance precise identification and early warning capabilities,promoting the sustainable development of rural economies.This paper explores the application of big data technology in poverty recurrence monitoring,analyzes its innovative integration with agricultural product sales systems,and proposes an intelligent monitoring and sales platform model based on big data,aiming to provide a reference for relevant policy formulation.展开更多
需求的波动性与对预测精度的高要求使得销售预测成为学界与业界研究的重难点问题,销售预测的准确性极大影响着企业的生产及最终收益。本文针对饮品零售行业的销售预测问题进行了详细的研究,构建了引入天气因素的改进HyperGBM机器学习预...需求的波动性与对预测精度的高要求使得销售预测成为学界与业界研究的重难点问题,销售预测的准确性极大影响着企业的生产及最终收益。本文针对饮品零售行业的销售预测问题进行了详细的研究,构建了引入天气因素的改进HyperGBM机器学习预测模型,全面比较了传统预测方法(如ARIMA、SARIMA和Prophet)与HyperGBM在预测准确性上的差异,并分析了加入天气因素对HyperGBM预测效果的影响。由于南北方气候差异较大,本文的研究选择西安代表北方的天气特征,选择昆明代表南方季节性特征显著的天气,并根据历史销售数据对SKU进行分类,分析适用于不同类别SKU的销售预测方法。基于企业提供的72个饮品的SKU历史数据的研究表明,HyperGBM在63个(共71个)SKU上相较于传统预测方法的预测效果更好,RMSE指标平均提升22.9%。对于天气因素的进一步研究表明,将天气数据融合到HyperGBM后,预测的准确度较无天气因素的模型最高提升了31.6%。本文根据季节趋势分解法(seasonal-trend decomposition using loess,STL)和增广迪基-富勒测试(augmented dickey-fuller,ADF)两项检测结果,将SKU按周期性和稳定性的强弱均匀划分为四个类别:周期性强且稳定、周期性弱且稳定、周期性强但不稳定、周期性弱但不稳定。研究发现,不同类别的SKU适用于不同的预测方法,周期性强的SKU类别适合采用SARIMA预测方法,稳定性强的SKU类别适合采用HyperGBM机器学习算法。本文的结论可以为饮品零售行业的销售预测提供帮助,指导企业按照商品类别选择适用的销售预测方法。展开更多
文摘In the globalized market environment, increasingly significant economic and environmental factors withincomplex industrial plants impose importance on the optimization of global production indices; such opti-mization includes improvements in production efficiency, product quality, and yield, along with reductionsof energy and resource usage. This paper briefly overviews recent progress in data-driven hybrid intelli-gence optimization methods and technologies in improving the performance of global production indicesin mineral processing. First, we provide the problem description. Next, we summarize recent progress indata-based optimization for mineral processing plants. This optimization consists of four layers： optimiza-tion of the target values for monthly global production indices, optimization of the target values for dailyglobal production indices, optimization of the target values for operational indices, and automation systemsfor unit processes. We briefly overview recent progress in each of the different layers. Finally, we point outopportunities for future works in data-based optimization for mineral processing plants.
基金supported by the National Natural Science Foundation of China(61921004,U1713209,61803085,and 62041301)。
文摘In this paper,a data-based scheme is proposed to solve the optimal tracking problem of autonomous nonlinear switching systems.The system state is forced to track the reference signal by minimizing the performance function.First,the problem is transformed to solve the corresponding Bellman optimality equation in terms of the Q-function(also named as action value function).Then,an iterative algorithm based on adaptive dynamic programming(ADP)is developed to find the optimal solution which is totally based on sampled data.The linear-in-parameter(LIP)neural network is taken as the value function approximator.Considering the presence of approximation error at each iteration step,the generated approximated value function sequence is proved to be boundedness around the exact optimal solution under some verifiable assumptions.Moreover,the effect that the learning process will be terminated after a finite number of iterations is investigated in this paper.A sufficient condition for asymptotically stability of the tracking error is derived.Finally,the effectiveness of the algorithm is demonstrated with three simulation examples.
基金supported in part by the National Natural ScienceFoundation of China(61533017,61973330,61773075,61603387)the Early Career Development Award of SKLMCCS(20180201)the State Key Laboratory of Synthetical Automation for Process Industries(2019-KF-23-03)。
文摘In this paper, a data-based fault tolerant control(FTC) scheme is investigated for unknown continuous-time(CT)affine nonlinear systems with actuator faults. First, a neural network(NN) identifier based on particle swarm optimization(PSO) is constructed to model the unknown system dynamics. By utilizing the estimated system states, the particle swarm optimized critic neural network(PSOCNN) is employed to solve the Hamilton-Jacobi-Bellman equation(HJBE) more efficiently.Then, a data-based FTC scheme, which consists of the NN identifier and the fault compensator, is proposed to achieve actuator fault tolerance. The stability of the closed-loop system under actuator faults is guaranteed by the Lyapunov stability theorem. Finally, simulations are provided to demonstrate the effectiveness of the developed method.
文摘This paper proposes linear and nonlinear filters for a non-Gaussian dynamic system with an unknown nominal covariance of the output noise.The challenge of designing a suitable filter in the presence of an unknown covariance matrix is addressed by focusing on the output data set of the system.Considering that data generated from a Gaussian distribution exhibit ellipsoidal scattering,we first propose the weighted sum of norms(SON)clustering method that prioritizes nearby points,reduces distant point influence,and lowers computational cost.Then,by introducing the weighted maximum likelihood,we propose a semi-definite program(SDP)to detect outliers and reduce their impacts on each cluster.Detecting these weights paves the way to obtain an appropriate covariance of the output noise.Next,two filtering approaches are presented:a cluster-based robust linear filter using the maximum a posterior(MAP)estimation and a clusterbased robust nonlinear filter assuming that output noise distribution stems from some Gaussian noise resources according to the ellipsoidal clusters.At last,simulation results demonstrate the effectiveness of our proposed filtering approaches.
文摘This paper focuses on developing a system that allows presentation authors to effectively retrieve presentation slides for reuse from a large volume of existing presentation materials. We assume episodic memories of the authors can be used as contextual keywords in query expressions to efficiently dig out the expected slides for reuse rather than using only the part-of-slide-descriptions-based keyword queries. As a system, a new slide repository is proposed, composed of slide material collections, slide content data and pieces of information from authors＇ episodic memories related to each slide and presentation together with a slide retrieval application enabling authors to use the episodic memories as part of queries. The result of our experiment shows that the episodic memory-used queries can give more discoverability than the keyword-based queries. Additionally, an improvement model is discussed on the slide retrieval for further slide-finding efficiency by expanding the episodic memories model in the repository taking in the links with the author-and-slide-related data and events having been post on the private and social media sites.
基金the phased achievement of the major research project of the National Social Science Fund of China(Project Approval No.21VGQ010)supported by the 2021 Central University Basic Scientific Research Project of Lanzhou University(Project Approval No.21lzujbkyjd002).
文摘To cope with the challenges of CoViD-19,europe has adopted relevant measures of a data-based approach to governance,on which scholars have huge differences,and the related researches are conducive to further discussion on the differences.By sorting out the challenges posed by the pandemic to public security and data protection in europe,we can summarize the“european Solution”of the data-based approach to governance,including legislation,instruments,supervision,international cooperation,and continuity.The“Solution”has curbed the spread of the pandemic to a certain extent.However,due to the influence of the traditional values of the EU,the“Solution”is too idealistic in the balance between public security and data protection,which intensifies the dilemma and causes many problems,such as ambiguous legislation,inadequate effectiveness and security of instruments,an arduous endeavor in inter national cooperation,and imperfect regulations on digital green certificates.Therefore,in a major public health crisis,there is still a long way to go in exploring a balance between public security and data protection.
基金2025 College Students’Innovation Training Program“Return to Poverty Monitoring and Agricultural Products Sales System”2024 College Students’Innovation Training Program“Promoting Straw Recycling to Accelerate the Sustainable Development of Agriculture”(202413207010)。
文摘With the advancement of the rural revitalization strategy,preventing poverty recurrence among previously impoverished populations has become a crucial social concern.The application of big data technology in poverty recurrence monitoring and agricultural product sales systems can effectively enhance precise identification and early warning capabilities,promoting the sustainable development of rural economies.This paper explores the application of big data technology in poverty recurrence monitoring,analyzes its innovative integration with agricultural product sales systems,and proposes an intelligent monitoring and sales platform model based on big data,aiming to provide a reference for relevant policy formulation.
文摘需求的波动性与对预测精度的高要求使得销售预测成为学界与业界研究的重难点问题,销售预测的准确性极大影响着企业的生产及最终收益。本文针对饮品零售行业的销售预测问题进行了详细的研究,构建了引入天气因素的改进HyperGBM机器学习预测模型,全面比较了传统预测方法(如ARIMA、SARIMA和Prophet)与HyperGBM在预测准确性上的差异,并分析了加入天气因素对HyperGBM预测效果的影响。由于南北方气候差异较大,本文的研究选择西安代表北方的天气特征,选择昆明代表南方季节性特征显著的天气,并根据历史销售数据对SKU进行分类,分析适用于不同类别SKU的销售预测方法。基于企业提供的72个饮品的SKU历史数据的研究表明,HyperGBM在63个(共71个)SKU上相较于传统预测方法的预测效果更好,RMSE指标平均提升22.9%。对于天气因素的进一步研究表明,将天气数据融合到HyperGBM后,预测的准确度较无天气因素的模型最高提升了31.6%。本文根据季节趋势分解法(seasonal-trend decomposition using loess,STL)和增广迪基-富勒测试(augmented dickey-fuller,ADF)两项检测结果,将SKU按周期性和稳定性的强弱均匀划分为四个类别:周期性强且稳定、周期性弱且稳定、周期性强但不稳定、周期性弱但不稳定。研究发现,不同类别的SKU适用于不同的预测方法,周期性强的SKU类别适合采用SARIMA预测方法,稳定性强的SKU类别适合采用HyperGBM机器学习算法。本文的结论可以为饮品零售行业的销售预测提供帮助,指导企业按照商品类别选择适用的销售预测方法。
