The principle of minimum dissipation rate is applied to tokamak plasmas with energy and helicity balances imposed as two constraints. The analytical solution on toroidal current distribution are derived from the resul...The principle of minimum dissipation rate is applied to tokamak plasmas with energy and helicity balances imposed as two constraints. The analytical solution on toroidal current distribution are derived from the resulting Euler-Lagrangian equation. Three typical forms of current profiles are found for low-aspect-ratio tokamaks like NSTX. One of them decreases with r on equatorial plane, the second peaks in the inner half part on equatorial plane and the third may have a hole or reverse in the central part.展开更多
随着大量分布式能源的接入,配电系统的运行与控制方式愈加复杂。针对配电网状态估计方法面临分布式电源波动数据辨识困难、估计精度低、鲁棒性与估计时效性差等问题,提出一种基于集成深度神经网络的配电网分布式状态估计方法。首先,利...随着大量分布式能源的接入,配电系统的运行与控制方式愈加复杂。针对配电网状态估计方法面临分布式电源波动数据辨识困难、估计精度低、鲁棒性与估计时效性差等问题,提出一种基于集成深度神经网络的配电网分布式状态估计方法。首先,利用量测数据相关性检验的数据辨识技术识别不良数据和新能源波动数据。在此基础上,利用时域卷积网络(temporal convolutional network,TCN)-双向长短期记忆网络(bidirectional long short term memory,BILSTM)对不良数据进行修正。然后,建立集成深度神经网络(deep neural network,DNN)状态估计模型,采用最大相关-最小冗余(maximum relevance-minimum redundancy,MRMR)的方法优化训练样本,从而提高状态估计的精度和鲁棒性。最后,建立分布式集成深度神经网络模型,弥补了集中式状态估计速度慢的不足,从而提高状态估计效率。基于IEEE123配电网的算例分析表明,所提方法能更准确地辨识分布式电源波动数据和不良数据,同时提高状态估计的精度和效率,且具有较高的鲁棒性。展开更多
MDM (minimum distance method) is a very popular algorithm in staterecognition. But it has a presupposition, that is, the distance within one class must be shorterenough than the distance between classes. When this pre...MDM (minimum distance method) is a very popular algorithm in staterecognition. But it has a presupposition, that is, the distance within one class must be shorterenough than the distance between classes. When this presupposition is not satisfied, the method isno longer valid. In order to overcome the shortcomings of MDM, an improved minimum distance method(IMDM) based on ANN (artificial neural networks) is presented. The simulation results demonstratethat IMDM has two advantages, that is, the rate of recognition is faster and the accuracy ofrecognition is higher compared with MDM.展开更多
In this paper the conception of theoretical determine the relations between material experimental characteristics is presented. On the base of stress-strain relations for nonlinear elastic anisotropic material and geo...In this paper the conception of theoretical determine the relations between material experimental characteristics is presented. On the base of stress-strain relations for nonlinear elastic anisotropic material and geometrical interpretation of deformation state, the general form of strain energy density function was introduced. Using this function and variational methods the relations between material characteristics were achieved. All considerations are illustrated by a short theoretical example.展开更多
Attribute reduction in the rough set theory is an important feature selection method, but finding a minimum attribute reduction has been proven to be a non-deterministic polynomial （NP）-hard problem. Therefore, it i...Attribute reduction in the rough set theory is an important feature selection method, but finding a minimum attribute reduction has been proven to be a non-deterministic polynomial （NP）-hard problem. Therefore, it is necessary to investigate some fast and effective approximate algorithms. A novel and enhanced quantum-inspired shuffled frog leaping based minimum attribute reduction algorithm （QSFLAR） is proposed. Evolutionary frogs are represented by multi-state quantum bits, and both quantum rotation gate and quantum mutation operators are used to exploit the mechanisms of frog population diversity and convergence to the global optimum. The decomposed attribute subsets are co-evolved by the elitist frogs with a quantum-inspired shuffled frog leaping algorithm. The experimental results validate the better feasibility and effectiveness of QSFLAR, comparing with some representa- tive algorithms. Therefore, QSFLAR can be considered as a more competitive algorithm on the efficiency and accuracy for minimum attribute reduction.展开更多
Gas-phase CO_2 catalyzed activation hydrogenation by Ru atoms was studied with density functional theory. Based on the structure optimization of different potential energy surfaces,there are two crossing points betwee...Gas-phase CO_2 catalyzed activation hydrogenation by Ru atoms was studied with density functional theory. Based on the structure optimization of different potential energy surfaces,there are two crossing points between singlet and triplet potential energy surfaces and there is a crossing point between quintet and triplet potential energy surfaces in the whole catalytic cycle. Spin transition probabilities in the vicinity of the intersections have been calculated by the Landau-Zener model theory. There are three minimum energy crossing points（MECPs） with strong spin-orbital coupling effect and higher spin transition probability,and all spin inversion occurred in s orbital and different d orbitals of ruthenium,indicating this is a typical two-state reactivity（TSR） reaction. Finally,the lowest energy reaction path is ensured.展开更多
文摘The principle of minimum dissipation rate is applied to tokamak plasmas with energy and helicity balances imposed as two constraints. The analytical solution on toroidal current distribution are derived from the resulting Euler-Lagrangian equation. Three typical forms of current profiles are found for low-aspect-ratio tokamaks like NSTX. One of them decreases with r on equatorial plane, the second peaks in the inner half part on equatorial plane and the third may have a hole or reverse in the central part.
文摘随着大量分布式能源的接入,配电系统的运行与控制方式愈加复杂。针对配电网状态估计方法面临分布式电源波动数据辨识困难、估计精度低、鲁棒性与估计时效性差等问题,提出一种基于集成深度神经网络的配电网分布式状态估计方法。首先,利用量测数据相关性检验的数据辨识技术识别不良数据和新能源波动数据。在此基础上,利用时域卷积网络(temporal convolutional network,TCN)-双向长短期记忆网络(bidirectional long short term memory,BILSTM)对不良数据进行修正。然后,建立集成深度神经网络(deep neural network,DNN)状态估计模型,采用最大相关-最小冗余(maximum relevance-minimum redundancy,MRMR)的方法优化训练样本,从而提高状态估计的精度和鲁棒性。最后,建立分布式集成深度神经网络模型,弥补了集中式状态估计速度慢的不足,从而提高状态估计效率。基于IEEE123配电网的算例分析表明,所提方法能更准确地辨识分布式电源波动数据和不良数据,同时提高状态估计的精度和效率,且具有较高的鲁棒性。
基金This work was financially supported by the National Natural Science Foundation of China under the contract No.69372031.]
文摘MDM (minimum distance method) is a very popular algorithm in staterecognition. But it has a presupposition, that is, the distance within one class must be shorterenough than the distance between classes. When this presupposition is not satisfied, the method isno longer valid. In order to overcome the shortcomings of MDM, an improved minimum distance method(IMDM) based on ANN (artificial neural networks) is presented. The simulation results demonstratethat IMDM has two advantages, that is, the rate of recognition is faster and the accuracy ofrecognition is higher compared with MDM.
文摘In this paper the conception of theoretical determine the relations between material experimental characteristics is presented. On the base of stress-strain relations for nonlinear elastic anisotropic material and geometrical interpretation of deformation state, the general form of strain energy density function was introduced. Using this function and variational methods the relations between material characteristics were achieved. All considerations are illustrated by a short theoretical example.
基金supported by the National Natural Science Foundation of China(6113900261171132)+4 种基金the Funding of Jiangsu Innovation Program for Graduate Education(CXZZ11 0219)the Natural Science Foundation of Jiangsu Education Department(12KJB520013)the Applying Study Foundation of Nantong(BK2011062)the Open Project Program of State Key Laboratory for Novel Software Technology,Nanjing University(KFKT2012B28)the Natural Science Pre-Research Foundation of Nantong University(12ZY016)
文摘Attribute reduction in the rough set theory is an important feature selection method, but finding a minimum attribute reduction has been proven to be a non-deterministic polynomial （NP）-hard problem. Therefore, it is necessary to investigate some fast and effective approximate algorithms. A novel and enhanced quantum-inspired shuffled frog leaping based minimum attribute reduction algorithm （QSFLAR） is proposed. Evolutionary frogs are represented by multi-state quantum bits, and both quantum rotation gate and quantum mutation operators are used to exploit the mechanisms of frog population diversity and convergence to the global optimum. The decomposed attribute subsets are co-evolved by the elitist frogs with a quantum-inspired shuffled frog leaping algorithm. The experimental results validate the better feasibility and effectiveness of QSFLAR, comparing with some representa- tive algorithms. Therefore, QSFLAR can be considered as a more competitive algorithm on the efficiency and accuracy for minimum attribute reduction.
基金supported by the National Natural Science Foundation of China(21263023)
文摘Gas-phase CO_2 catalyzed activation hydrogenation by Ru atoms was studied with density functional theory. Based on the structure optimization of different potential energy surfaces,there are two crossing points between singlet and triplet potential energy surfaces and there is a crossing point between quintet and triplet potential energy surfaces in the whole catalytic cycle. Spin transition probabilities in the vicinity of the intersections have been calculated by the Landau-Zener model theory. There are three minimum energy crossing points（MECPs） with strong spin-orbital coupling effect and higher spin transition probability,and all spin inversion occurred in s orbital and different d orbitals of ruthenium,indicating this is a typical two-state reactivity（TSR） reaction. Finally,the lowest energy reaction path is ensured.
