对环境的适应是软件保证其可信的重要手段.当应用场景超出开发阶段的预设时,软件的环境适应能力需要能够在线调整,以保证其行为和结果仍可符合用户预期.这一调整的前提是软件工程层面的高效支持机制.基于关注点分离原则和动态软件体系...对环境的适应是软件保证其可信的重要手段.当应用场景超出开发阶段的预设时,软件的环境适应能力需要能够在线调整,以保证其行为和结果仍可符合用户预期.这一调整的前提是软件工程层面的高效支持机制.基于关注点分离原则和动态软件体系结构技术,提出了一种支持软件环境适应能力细粒度在线调整的构件模型ACOE(adaptive component model for open environment).ACOE将软件环境适应能力中的感知、决策、执行等关注点封装为独立的构件和连接子,通过动态软件体系结构技术来支持它们的在线重配置,从而使第三方可在必要时通过有选择性的更新来调整适应能力.实现了支持ACOE构件模型的容器原型,并通过实验验证了其有效性.展开更多
The crustal movements of the Chinese mainland include an average regional movement trend of the mainland and complex local deformations. Thus, both trends in the crustal movement of the mainland and local distortions ...The crustal movements of the Chinese mainland include an average regional movement trend of the mainland and complex local deformations. Thus, both trends in the crustal movement of the mainland and local distortions should be simultaneously taken into consideration in crustal movement estimations. A combined collocation model based on Euler vector (taken as trend parameters) and local distortions (taken as signals) is proposed in this paper. We assume that prior covariance matrices between signals and observations should be consistent with their uncertainties. Otherwise, the station movement estimates provided by the collocation will be distorted. Thus, an adaptive collocation estimator based on simplified Helmert variance components is applied. This means that the contributions of signals and observations to estimates of crustal movements are balanced and reasonable, and consistent covariance matrices of the signals and observations are achieved through the adjustment of the adaptive factor. The calculation of actual horizontal movements of the Chinese crust shows that the estimates of horizontal crustal movement velocities are made more accurate by the adaptive collocation model.展开更多
Multi-way principal component analysis (MPCA) had been successfully applied to monitoring the batch and semi-batch process in most chemical industry. An improved MPCA approach, step-by-step adaptive MPCA (SAMPCA), usi...Multi-way principal component analysis (MPCA) had been successfully applied to monitoring the batch and semi-batch process in most chemical industry. An improved MPCA approach, step-by-step adaptive MPCA (SAMPCA), using the process variable trajectories to monitoring the batch process is presented in this paper. It does not need to estimate or fill in the unknown part of the process variable trajectory deviation from the current time until the end. The approach is based on a MPCA method that processes the data in a sequential and adaptive manner. The adaptive rate is easily controlled through a forgetting factor that controls the weight of past data in a summation. This algorithm is used to evaluate the industrial streptomycin fermentation process data and is compared with the traditional MPCA. The results show that the method is more advantageous than MPCA, especially when monitoring multi-stage batch process where the latent vector structure can change at several points during the batch.展开更多
In chemical process, a large number of measured and manipulated variables are highly correlated. Principal component analysis(PCA) is widely applied as a dimension reduction technique for capturing strong correlation ...In chemical process, a large number of measured and manipulated variables are highly correlated. Principal component analysis(PCA) is widely applied as a dimension reduction technique for capturing strong correlation underlying in the process measurements. However, it is difficult for PCA based fault detection results to be interpreted physically and to provide support for isolation. Some approaches incorporating process knowledge are developed, but the information is always shortage and deficient in practice. Therefore, this work proposes an adaptive partitioning PCA algorithm entirely based on operation data. The process feature space is partitioned into several sub-feature spaces. Constructed sub-block models can not only reflect the local behavior of process change, namely to grasp the intrinsic local information underlying the process changes, but also improve the fault detection and isolation through the combination of local fault detection results and reduction of smearing effect.The method is demonstrated in TE process, and the results show that the new method is much better in fault detection and isolation compared to conventional PCA method.展开更多
针对常规基于二阶广义积分发生器的锁频环(second-order generalized integrator based frequency locked-loop,SOGI-FLL)在单相并网逆变器电压控制中对直流及谐波分量抑制能力不足,从而引起输出电压频率、相位振荡的问题,提出一种基于...针对常规基于二阶广义积分发生器的锁频环(second-order generalized integrator based frequency locked-loop,SOGI-FLL)在单相并网逆变器电压控制中对直流及谐波分量抑制能力不足,从而引起输出电压频率、相位振荡的问题,提出一种基于改进型SOGI-FLL的单相并网逆变器电压控制方法。该方法在常规SOGI-FLL控制的基础上,在电压信号输入端加入级联型谐振滤波环节来消除谐波分量;同时引入直流控制环节,借助输入电压误差估计值来消除直流分量,达到电网电压频率和相位快速跟踪效果,从而实现电压的自适应控制。使用MATLAB及RT-LAB硬件在环半实物平台,在频率突变、含直流分量及谐波分量的非理想电网环境中,对二阶广义积分器锁相环、双二阶广义积分器锁频环与改进型SOGI-FLL 3种控制方法进行仿真及实验。结果表明,所提改进型SOGI-FLL控制方法在消除直流及谐波干扰的同时,能在0.025 s内实现频率锁定,且频率偏差小于2%,可增强系统对非理想电网信号的适应能力,实现并网电压的快速跟踪,具有良好动态性能。展开更多
Regarding the spatial profile extraction method of a multi-field co-simulation dataset,different extraction directions,locations,and numbers of profileswill greatly affect the representativeness and integrity of data....Regarding the spatial profile extraction method of a multi-field co-simulation dataset,different extraction directions,locations,and numbers of profileswill greatly affect the representativeness and integrity of data.In this study,a multi-field co-simulation data extractionmethod based on adaptive infinitesimal elements is proposed.Themultifield co-simulation dataset based on related infinitesimal elements is constructed,and the candidate directions of data profile extraction undergo dimension reduction by principal component analysis to determine the direction of data extraction.Based on the fireworks algorithm,the data profile with optimal representativeness is searched adaptively in different data extraction intervals to realize the adaptive calculation of data extraction micro-step length.The multi-field co-simulation data extraction process based on adaptive microelement is established and applied to the data extraction process of the multi-field co-simulation dataset of the sintering furnace.Compared with traditional data extraction methods for multi-field co-simulation,the approximate model constructed by the data extracted from the proposed method has higher construction efficiency.Meanwhile,the relative maximum absolute error,root mean square error,and coefficient of determination of the approximationmodel are better than those of the approximation model constructed by the data extracted from traditional methods,indicating higher accuracy,it is verified that the proposed method demonstrates sound adaptability and extraction efficiency.展开更多
文摘对环境的适应是软件保证其可信的重要手段.当应用场景超出开发阶段的预设时,软件的环境适应能力需要能够在线调整,以保证其行为和结果仍可符合用户预期.这一调整的前提是软件工程层面的高效支持机制.基于关注点分离原则和动态软件体系结构技术,提出了一种支持软件环境适应能力细粒度在线调整的构件模型ACOE(adaptive component model for open environment).ACOE将软件环境适应能力中的感知、决策、执行等关注点封装为独立的构件和连接子,通过动态软件体系结构技术来支持它们的在线重配置,从而使第三方可在必要时通过有选择性的更新来调整适应能力.实现了支持ACOE构件模型的容器原型,并通过实验验证了其有效性.
基金supported by National Natural Science Foundation of China (Grant Nos. 41020144004 and 41004013)
文摘The crustal movements of the Chinese mainland include an average regional movement trend of the mainland and complex local deformations. Thus, both trends in the crustal movement of the mainland and local distortions should be simultaneously taken into consideration in crustal movement estimations. A combined collocation model based on Euler vector (taken as trend parameters) and local distortions (taken as signals) is proposed in this paper. We assume that prior covariance matrices between signals and observations should be consistent with their uncertainties. Otherwise, the station movement estimates provided by the collocation will be distorted. Thus, an adaptive collocation estimator based on simplified Helmert variance components is applied. This means that the contributions of signals and observations to estimates of crustal movements are balanced and reasonable, and consistent covariance matrices of the signals and observations are achieved through the adjustment of the adaptive factor. The calculation of actual horizontal movements of the Chinese crust shows that the estimates of horizontal crustal movement velocities are made more accurate by the adaptive collocation model.
基金Supported by the National High-tech Program of China (No. 2001 AA413110).
文摘Multi-way principal component analysis (MPCA) had been successfully applied to monitoring the batch and semi-batch process in most chemical industry. An improved MPCA approach, step-by-step adaptive MPCA (SAMPCA), using the process variable trajectories to monitoring the batch process is presented in this paper. It does not need to estimate or fill in the unknown part of the process variable trajectory deviation from the current time until the end. The approach is based on a MPCA method that processes the data in a sequential and adaptive manner. The adaptive rate is easily controlled through a forgetting factor that controls the weight of past data in a summation. This algorithm is used to evaluate the industrial streptomycin fermentation process data and is compared with the traditional MPCA. The results show that the method is more advantageous than MPCA, especially when monitoring multi-stage batch process where the latent vector structure can change at several points during the batch.
基金Support by the National Natural Science Foundation of China(61174114)the Research Fund for the Doctoral Program of Higher Education in China(20120101130016)Zhejiang Provincial Science and Technology Planning Projects of China(2014C31019)
文摘In chemical process, a large number of measured and manipulated variables are highly correlated. Principal component analysis(PCA) is widely applied as a dimension reduction technique for capturing strong correlation underlying in the process measurements. However, it is difficult for PCA based fault detection results to be interpreted physically and to provide support for isolation. Some approaches incorporating process knowledge are developed, but the information is always shortage and deficient in practice. Therefore, this work proposes an adaptive partitioning PCA algorithm entirely based on operation data. The process feature space is partitioned into several sub-feature spaces. Constructed sub-block models can not only reflect the local behavior of process change, namely to grasp the intrinsic local information underlying the process changes, but also improve the fault detection and isolation through the combination of local fault detection results and reduction of smearing effect.The method is demonstrated in TE process, and the results show that the new method is much better in fault detection and isolation compared to conventional PCA method.
文摘针对常规基于二阶广义积分发生器的锁频环(second-order generalized integrator based frequency locked-loop,SOGI-FLL)在单相并网逆变器电压控制中对直流及谐波分量抑制能力不足,从而引起输出电压频率、相位振荡的问题,提出一种基于改进型SOGI-FLL的单相并网逆变器电压控制方法。该方法在常规SOGI-FLL控制的基础上,在电压信号输入端加入级联型谐振滤波环节来消除谐波分量;同时引入直流控制环节,借助输入电压误差估计值来消除直流分量,达到电网电压频率和相位快速跟踪效果,从而实现电压的自适应控制。使用MATLAB及RT-LAB硬件在环半实物平台,在频率突变、含直流分量及谐波分量的非理想电网环境中,对二阶广义积分器锁相环、双二阶广义积分器锁频环与改进型SOGI-FLL 3种控制方法进行仿真及实验。结果表明,所提改进型SOGI-FLL控制方法在消除直流及谐波干扰的同时,能在0.025 s内实现频率锁定,且频率偏差小于2%,可增强系统对非理想电网信号的适应能力,实现并网电压的快速跟踪,具有良好动态性能。
基金This work is supported by the NationalNatural Science Foundation of China(No.52075350)the Major Science and Technology Projects of Sichuan Province(No.2022ZDZX0001)the Special City-University Strategic Cooperation Project of Sichuan University and Zigong Municipality(No.2021CDZG-3).
文摘Regarding the spatial profile extraction method of a multi-field co-simulation dataset,different extraction directions,locations,and numbers of profileswill greatly affect the representativeness and integrity of data.In this study,a multi-field co-simulation data extractionmethod based on adaptive infinitesimal elements is proposed.Themultifield co-simulation dataset based on related infinitesimal elements is constructed,and the candidate directions of data profile extraction undergo dimension reduction by principal component analysis to determine the direction of data extraction.Based on the fireworks algorithm,the data profile with optimal representativeness is searched adaptively in different data extraction intervals to realize the adaptive calculation of data extraction micro-step length.The multi-field co-simulation data extraction process based on adaptive microelement is established and applied to the data extraction process of the multi-field co-simulation dataset of the sintering furnace.Compared with traditional data extraction methods for multi-field co-simulation,the approximate model constructed by the data extracted from the proposed method has higher construction efficiency.Meanwhile,the relative maximum absolute error,root mean square error,and coefficient of determination of the approximationmodel are better than those of the approximation model constructed by the data extracted from traditional methods,indicating higher accuracy,it is verified that the proposed method demonstrates sound adaptability and extraction efficiency.
