This note settles the complexity of the single genotype resolution problemshowing it is NP-complete. This solves an open problem raised by P. Bonizzoni, G.D. Vedova, R.Dondi, and J. Li. The same proof also gives an al...This note settles the complexity of the single genotype resolution problemshowing it is NP-complete. This solves an open problem raised by P. Bonizzoni, G.D. Vedova, R.Dondi, and J. Li. The same proof also gives an alternative and simpler reduction of the NP-hardnessof Maximum Resolution problem.展开更多
This paper presentes a novel resolution method, T-resolution, based on the first order temporal logic. The primary claim of this method is its soundness and completeness. For this purpose, we construct the correspondi...This paper presentes a novel resolution method, T-resolution, based on the first order temporal logic. The primary claim of this method is its soundness and completeness. For this purpose, we construct the corresponding semantic trees and extend Herbrand's Theorem.展开更多
The smart grid is an evolving critical infrastructure,which combines renewable energy and the most advanced information and communication technologies to provide more economic and secure power supply services.To cope ...The smart grid is an evolving critical infrastructure,which combines renewable energy and the most advanced information and communication technologies to provide more economic and secure power supply services.To cope with the intermittency of ever-increasing renewable energy and ensure the security of the smart grid,state estimation,which serves as a basic tool for understanding the true states of a smart grid,should be performed with high frequency.More complete system state data are needed to support high-frequency state estimation.The data completeness problem for smart grid state estimation is therefore studied in this paper.The problem of improving data completeness by recovering highfrequency data from low-frequency data is formulated as a super resolution perception(SRP)problem in this paper.A novel machine-learning-based SRP approach is thereafter proposed.The proposed method,namely the Super Resolution Perception Net for State Estimation(SRPNSE),consists of three steps:feature extraction,information completion,and data reconstruction.Case studies have demonstrated the effectiveness and value of the proposed SRPNSE approach in recovering high-frequency data from low-frequency data for the state estimation.展开更多
文摘This note settles the complexity of the single genotype resolution problemshowing it is NP-complete. This solves an open problem raised by P. Bonizzoni, G.D. Vedova, R.Dondi, and J. Li. The same proof also gives an alternative and simpler reduction of the NP-hardnessof Maximum Resolution problem.
文摘This paper presentes a novel resolution method, T-resolution, based on the first order temporal logic. The primary claim of this method is its soundness and completeness. For this purpose, we construct the corresponding semantic trees and extend Herbrand's Theorem.
基金the Training Program of the Major Research Plan of the National Natural Science Foundation of China(91746118)the Shenzhen Municipal Science and Technology Innovation Committee Basic Research project(JCYJ20170410172224515)。
文摘The smart grid is an evolving critical infrastructure,which combines renewable energy and the most advanced information and communication technologies to provide more economic and secure power supply services.To cope with the intermittency of ever-increasing renewable energy and ensure the security of the smart grid,state estimation,which serves as a basic tool for understanding the true states of a smart grid,should be performed with high frequency.More complete system state data are needed to support high-frequency state estimation.The data completeness problem for smart grid state estimation is therefore studied in this paper.The problem of improving data completeness by recovering highfrequency data from low-frequency data is formulated as a super resolution perception(SRP)problem in this paper.A novel machine-learning-based SRP approach is thereafter proposed.The proposed method,namely the Super Resolution Perception Net for State Estimation(SRPNSE),consists of three steps:feature extraction,information completion,and data reconstruction.Case studies have demonstrated the effectiveness and value of the proposed SRPNSE approach in recovering high-frequency data from low-frequency data for the state estimation.
