In this paper, we study the distributed economic dispatch problem where the supply demand balance, capacity constraints and ramp-rate constraints are considered. In order to accommodate varying power load and power st...In this paper, we study the distributed economic dispatch problem where the supply demand balance, capacity constraints and ramp-rate constraints are considered. In order to accommodate varying power load and power storage in reality, we introduce two variables where each variable is time-varing and has its own dynamics. The renewable power is also considered. Barrier functions are introduced to deal with the local constraints by means of imposing penalty terms into the objective function to ensure that the optimal solution satisfies the corresponding constraints. Based on the Langrange dual theory, the primal optimization problem is transformed into the dual problem, which is solved by the primary-dual algorithm proposed in this paper. Under the assumption that the communication graph is an undirected and connected graph, we analyze the convergence of the proposed algorithm. The simulations on IEEE six-bus test systems are carried out to verify the performance of the algorithm, which shows that the proposed algorithm converges to the optimal solution, while all the constraints are met.展开更多
To address the problem that traditional keypoint detection methods are susceptible to complex backgrounds and local similarity of images resulting in inaccurate descriptor matching and bias in visual localization, key...To address the problem that traditional keypoint detection methods are susceptible to complex backgrounds and local similarity of images resulting in inaccurate descriptor matching and bias in visual localization, keypoints and descriptors based on cross-modality fusion are proposed and applied to the study of camera motion estimation. A convolutional neural network is used to detect the positions of keypoints and generate the corresponding descriptors, and the pyramid convolution is used to extract multi-scale features in the network. The problem of local similarity of images is solved by capturing local and global feature information and fusing the geometric position information of keypoints to generate descriptors. According to our experiments, the repeatability of our method is improved by 3.7%, and the homography estimation is improved by 1.6%. To demonstrate the practicability of the method, the visual odometry part of simultaneous localization and mapping is constructed and our method is 35% higher positioning accuracy than the traditional method.展开更多
基金supported by the National Natural Science Foundation of China(Grant No.61673107)the Stable Supporting Fund of Science and Technology on Underwater Vehicle Technology(Grant No.SXJQR2018WDKT05)the Jiangsu Provincial Key Laboratory of Networked Collective Intelligence(Grant No.BM2017002)
文摘In this paper, we study the distributed economic dispatch problem where the supply demand balance, capacity constraints and ramp-rate constraints are considered. In order to accommodate varying power load and power storage in reality, we introduce two variables where each variable is time-varing and has its own dynamics. The renewable power is also considered. Barrier functions are introduced to deal with the local constraints by means of imposing penalty terms into the objective function to ensure that the optimal solution satisfies the corresponding constraints. Based on the Langrange dual theory, the primal optimization problem is transformed into the dual problem, which is solved by the primary-dual algorithm proposed in this paper. Under the assumption that the communication graph is an undirected and connected graph, we analyze the convergence of the proposed algorithm. The simulations on IEEE six-bus test systems are carried out to verify the performance of the algorithm, which shows that the proposed algorithm converges to the optimal solution, while all the constraints are met.
基金Supported by the National Natural Science Foundation of China (61802253)。
文摘To address the problem that traditional keypoint detection methods are susceptible to complex backgrounds and local similarity of images resulting in inaccurate descriptor matching and bias in visual localization, keypoints and descriptors based on cross-modality fusion are proposed and applied to the study of camera motion estimation. A convolutional neural network is used to detect the positions of keypoints and generate the corresponding descriptors, and the pyramid convolution is used to extract multi-scale features in the network. The problem of local similarity of images is solved by capturing local and global feature information and fusing the geometric position information of keypoints to generate descriptors. According to our experiments, the repeatability of our method is improved by 3.7%, and the homography estimation is improved by 1.6%. To demonstrate the practicability of the method, the visual odometry part of simultaneous localization and mapping is constructed and our method is 35% higher positioning accuracy than the traditional method.
