This paper presents a comprehensive review of various traditional systems of crude oil distillation column design, modeling, simulation, optimization and control methods. Artificial neural network (ANN), fuzzy logic (...This paper presents a comprehensive review of various traditional systems of crude oil distillation column design, modeling, simulation, optimization and control methods. Artificial neural network (ANN), fuzzy logic (FL) and genetic algorithm (GA) framework were chosen as the best methodologies for design, optimization and control of crude oil distillation column. It was discovered that many past researchers used rigorous simulations which led to convergence problems that were time consuming. The use of dynamic mathematical models was also challenging as these models were also time dependent. The proposed methodologies use back-propagation algorithm to replace the convergence problem using error minimal method.展开更多
The variational data assimilation scheme (VAR) is applied to investigating the advective effect and the evolution of the control variables in time splitting semi-Lagrangian framework. Two variational algorithms are us...The variational data assimilation scheme (VAR) is applied to investigating the advective effect and the evolution of the control variables in time splitting semi-Lagrangian framework. Two variational algorithms are used. One is the conjugate code method-direct approach, and another is the numerical backward integration of analytical adjoint equation—indirect approach. Theoretical derivation and sensitivity tests are conducted in order to verify the consistency and inconsistency of the two algorithms under the semi-Lagrangian framework. On the other hand, the sensitivity of the perfect and imperfect initial condition is also tested in both direct and indirect approaches. Our research has shown that the two algorithms are not only identical in theory, but also identical in numerical calculation. Furthermore, the algorithms of the indirect approach are much more feasible and efficient than that of the direct one when both are employed in the semi-Lagrangian framework. Taking advantage of semi-Lagrangian framework, one purpose of this paper is to illustrate when the variational assimilation algorithm is concerned in the computational method of the backward integration, the algorithm is extremely facilitated. Such simplicity in indirect approach should be meaningful for the VAR design in passive model. Indeed, if one can successfully split the diabatic and adiabatic process, the algorithms represented in this paper might be easily used in a more general vision of atmospheric model.展开更多
Prestressed wire winded framework （PWWF） is an advanced structure and the most expensive part in the large-scale equip- ment. The traditional design of PWWF is complicated, highly iterative and cost uncontrolable, b...Prestressed wire winded framework （PWWF） is an advanced structure and the most expensive part in the large-scale equip- ment. The traditional design of PWWF is complicated, highly iterative and cost uncontrolable, because PWWF is a variable stiffness multi-agent structure, with non-linear loading and deformation coordination. In this paper, cost optimization method of large-scale PWWF by multiple-island genetic algorithm （MIGA） is presented. Optimization design flow and optimization model are proposed based on variable-tension wire winding theory. An example of the PWWF cost optimization of isostatic equipment with axial load 6 000 kN is given. The optimization cost is reduced by 21.6% compared with traditional design. It has also been verified by the finite-element analysis and successfully applied to an actual PWWF design of isostatic press. The results show that this method is efficient and reliable. This method can also provide a guide for optimal design for ultra-large dimension muti-frame structure of 546 MN and 907 MN isostatic press equipment.展开更多
In this paper, a new class of over-relaxed proximal point algorithms for solving nonlinear operator equations with (A,η,m)-monotonicity framework in Hilbert spaces is introduced and studied. Further, by using the gen...In this paper, a new class of over-relaxed proximal point algorithms for solving nonlinear operator equations with (A,η,m)-monotonicity framework in Hilbert spaces is introduced and studied. Further, by using the generalized resolvent operator technique associated with the (A,η,m)-monotone operators, the approximation solvability of the operator equation problems and the convergence of iterative sequences generated by the algorithm are discussed. Our results improve and generalize the corresponding results in the literature.展开更多
Augmented Reality(AR)tries to seamlessly integrate virtual content into the real world of the user.Ideally,the virtual content would behave exactly like real objects.This necessitates a correct and precise estimation ...Augmented Reality(AR)tries to seamlessly integrate virtual content into the real world of the user.Ideally,the virtual content would behave exactly like real objects.This necessitates a correct and precise estimation of the user’s viewpoint(or that of a camera)with regard to the virtual content’s coordinate sys-tem.Therefore,the real-time establishment of 3-dimension(3D)maps in real scenes is particularly important for augmented reality technology.So in this paper,we integrate Simultaneous Localization and Mapping(SLAM)technology into augmented reality.Our research is to implement an augmented reality system without markers using the ORB-SLAM2 framework algorithm.In this paper we propose an improved method for Oriented FAST and Rotated BRIEF(ORB)feature extraction and optimized key frame selection,as well as the use of the Progressive Sample Consensus(PROSAC)algorithm for planar estimation of augmented reality implementations,thus solving the problem of increased sys-tem runtime because of the loss of large amounts of texture information in images.In this paper,we get better results by comparing experiments and data analysis.However,there are some improved methods of PROSAC algorithm which are more suitable for the detection of plane feature points.展开更多
With the development of global position system（GPS）,wireless technology and location aware services,it is possible to collect a large quantity of trajectory data.In the field of data mining for moving objects,the pr...With the development of global position system（GPS）,wireless technology and location aware services,it is possible to collect a large quantity of trajectory data.In the field of data mining for moving objects,the problem of anomaly detection is a hot topic.Based on the development of anomalous trajectory detection of moving objects,this paper introduces the classical trajectory outlier detection（TRAOD） algorithm,and then proposes a density-based trajectory outlier detection（DBTOD） algorithm,which compensates the disadvantages of the TRAOD algorithm that it is unable to detect anomalous defects when the trajectory is local and dense.The results of employing the proposed algorithm to Elk1993 and Deer1995 datasets are also presented,which show the effectiveness of the algorithm.展开更多
提出了一种基于CAE_ViT网络模型和顺序层状耦合信息框架(sequential hierarchical coupled information framework,SHCIF)的多粒度多缺陷图像分类识别方法,并结合模糊综合评价(FCE)方法,以桥梁设施为例,对其表面缺陷进行细致的分类及安...提出了一种基于CAE_ViT网络模型和顺序层状耦合信息框架(sequential hierarchical coupled information framework,SHCIF)的多粒度多缺陷图像分类识别方法,并结合模糊综合评价(FCE)方法,以桥梁设施为例,对其表面缺陷进行细致的分类及安全评价。首先,研究提出了SHCIF及对应3个层次粒度的识别模型,并构建和增强了对应不同粒度的数据集。SHCIF框架和跨粒度分类决策旨在通过利用桥梁组件和缺陷类型这两个粒度的信息和准确性,提升对缺陷严重程度的识别。其次,使用迁移学习对CAE_ViT预训练模型进行微调,以满足桥梁缺陷检测的具体需求,并通过跨粒度分类决策进一步提升分类的准确性。最后,基于层次分析法与熵权法(AHP⁃EWM)的权重体系,通过模糊综合评价,综合考虑桥梁部位、桥梁组件、缺陷类型及其严重程度,实现了基于表观健康状态对桥梁安全状态等级的定量评价。实验结果显示,在3个层次粒度的识别模型中的宏平均F1⁃Score分数分别达到94.1%、81.6%和75.3%,而跨粒度分类决策的准确率为82%。最终通过一个桥梁的安全评价案例证明了方法的有效性、系统性和可拓展性。展开更多
文摘This paper presents a comprehensive review of various traditional systems of crude oil distillation column design, modeling, simulation, optimization and control methods. Artificial neural network (ANN), fuzzy logic (FL) and genetic algorithm (GA) framework were chosen as the best methodologies for design, optimization and control of crude oil distillation column. It was discovered that many past researchers used rigorous simulations which led to convergence problems that were time consuming. The use of dynamic mathematical models was also challenging as these models were also time dependent. The proposed methodologies use back-propagation algorithm to replace the convergence problem using error minimal method.
文摘The variational data assimilation scheme (VAR) is applied to investigating the advective effect and the evolution of the control variables in time splitting semi-Lagrangian framework. Two variational algorithms are used. One is the conjugate code method-direct approach, and another is the numerical backward integration of analytical adjoint equation—indirect approach. Theoretical derivation and sensitivity tests are conducted in order to verify the consistency and inconsistency of the two algorithms under the semi-Lagrangian framework. On the other hand, the sensitivity of the perfect and imperfect initial condition is also tested in both direct and indirect approaches. Our research has shown that the two algorithms are not only identical in theory, but also identical in numerical calculation. Furthermore, the algorithms of the indirect approach are much more feasible and efficient than that of the direct one when both are employed in the semi-Lagrangian framework. Taking advantage of semi-Lagrangian framework, one purpose of this paper is to illustrate when the variational assimilation algorithm is concerned in the computational method of the backward integration, the algorithm is extremely facilitated. Such simplicity in indirect approach should be meaningful for the VAR design in passive model. Indeed, if one can successfully split the diabatic and adiabatic process, the algorithms represented in this paper might be easily used in a more general vision of atmospheric model.
文摘Prestressed wire winded framework （PWWF） is an advanced structure and the most expensive part in the large-scale equip- ment. The traditional design of PWWF is complicated, highly iterative and cost uncontrolable, because PWWF is a variable stiffness multi-agent structure, with non-linear loading and deformation coordination. In this paper, cost optimization method of large-scale PWWF by multiple-island genetic algorithm （MIGA） is presented. Optimization design flow and optimization model are proposed based on variable-tension wire winding theory. An example of the PWWF cost optimization of isostatic equipment with axial load 6 000 kN is given. The optimization cost is reduced by 21.6% compared with traditional design. It has also been verified by the finite-element analysis and successfully applied to an actual PWWF design of isostatic press. The results show that this method is efficient and reliable. This method can also provide a guide for optimal design for ultra-large dimension muti-frame structure of 546 MN and 907 MN isostatic press equipment.
文摘In this paper, a new class of over-relaxed proximal point algorithms for solving nonlinear operator equations with (A,η,m)-monotonicity framework in Hilbert spaces is introduced and studied. Further, by using the generalized resolvent operator technique associated with the (A,η,m)-monotone operators, the approximation solvability of the operator equation problems and the convergence of iterative sequences generated by the algorithm are discussed. Our results improve and generalize the corresponding results in the literature.
基金supported by the Hainan Provincial Natural Science Foundation of China(project number:621QN269)the Sanya Science and Information Bureau Foundation(project number:2021GXYL251).
文摘Augmented Reality(AR)tries to seamlessly integrate virtual content into the real world of the user.Ideally,the virtual content would behave exactly like real objects.This necessitates a correct and precise estimation of the user’s viewpoint(or that of a camera)with regard to the virtual content’s coordinate sys-tem.Therefore,the real-time establishment of 3-dimension(3D)maps in real scenes is particularly important for augmented reality technology.So in this paper,we integrate Simultaneous Localization and Mapping(SLAM)technology into augmented reality.Our research is to implement an augmented reality system without markers using the ORB-SLAM2 framework algorithm.In this paper we propose an improved method for Oriented FAST and Rotated BRIEF(ORB)feature extraction and optimized key frame selection,as well as the use of the Progressive Sample Consensus(PROSAC)algorithm for planar estimation of augmented reality implementations,thus solving the problem of increased sys-tem runtime because of the loss of large amounts of texture information in images.In this paper,we get better results by comparing experiments and data analysis.However,there are some improved methods of PROSAC algorithm which are more suitable for the detection of plane feature points.
基金supported by the Aeronautical Science Foundation of China(20111052010)the Jiangsu Graduates Innovation Project (CXZZ120163)+1 种基金the "333" Project of Jiangsu Provincethe Qing Lan Project of Jiangsu Province
文摘With the development of global position system（GPS）,wireless technology and location aware services,it is possible to collect a large quantity of trajectory data.In the field of data mining for moving objects,the problem of anomaly detection is a hot topic.Based on the development of anomalous trajectory detection of moving objects,this paper introduces the classical trajectory outlier detection（TRAOD） algorithm,and then proposes a density-based trajectory outlier detection（DBTOD） algorithm,which compensates the disadvantages of the TRAOD algorithm that it is unable to detect anomalous defects when the trajectory is local and dense.The results of employing the proposed algorithm to Elk1993 and Deer1995 datasets are also presented,which show the effectiveness of the algorithm.
文摘提出了一种基于CAE_ViT网络模型和顺序层状耦合信息框架(sequential hierarchical coupled information framework,SHCIF)的多粒度多缺陷图像分类识别方法,并结合模糊综合评价(FCE)方法,以桥梁设施为例,对其表面缺陷进行细致的分类及安全评价。首先,研究提出了SHCIF及对应3个层次粒度的识别模型,并构建和增强了对应不同粒度的数据集。SHCIF框架和跨粒度分类决策旨在通过利用桥梁组件和缺陷类型这两个粒度的信息和准确性,提升对缺陷严重程度的识别。其次,使用迁移学习对CAE_ViT预训练模型进行微调,以满足桥梁缺陷检测的具体需求,并通过跨粒度分类决策进一步提升分类的准确性。最后,基于层次分析法与熵权法(AHP⁃EWM)的权重体系,通过模糊综合评价,综合考虑桥梁部位、桥梁组件、缺陷类型及其严重程度,实现了基于表观健康状态对桥梁安全状态等级的定量评价。实验结果显示,在3个层次粒度的识别模型中的宏平均F1⁃Score分数分别达到94.1%、81.6%和75.3%,而跨粒度分类决策的准确率为82%。最终通过一个桥梁的安全评价案例证明了方法的有效性、系统性和可拓展性。
