期刊文献+
共找到2篇文章
< 1 >
每页显示 20 50 100
Optimal Allocation of Public Transport Hub Based on Load Loss Value and the Economy of Distribution Network
1
作者 Yuying Zhang Chen Liang +2 位作者 Bo Sun QiangChen mingyang lei 《Energy Engineering》 EI 2022年第6期2211-2229,共19页
The rapid development of electric buses has brought a surge in the number of bus hubs and their charging and discharging capacities.Therefore,the location and construction scale of bus hubs will greatly affect the ope... The rapid development of electric buses has brought a surge in the number of bus hubs and their charging and discharging capacities.Therefore,the location and construction scale of bus hubs will greatly affect the operation costs and benefits of an urban distribution network in the future.Through the scientific and reasonable planning of public transport hubs on the premise of meeting the needs of basic public transport services,it can reduce the negative impact of electric bus charging loads upon the power grids.Furthermore,it can use its flexible operation characteristics to provide flexible support for the distribution network.In this paper,taking the impact of public transport hub on the reliability of distribution network as the starting point,a three-level programming optimization model based on the value and economy of distribution network load loss is proposed.Through the upper model,several planning schemes can be generated,which provides boundary conditions for the expansion of middle-level optimization.The normal operation dispatching scheme of public transport hub obtained from the middle-level optimization results provides boundary conditions for the development of lower level optimization.Through the lower level optimization,the expected load loss of the whole distribution system including bus hub under the planning scheme given by the upper level can be obtained.The effectiveness of the model is verified by an IEEE-33 bus example. 展开更多
关键词 Distribution network public transport hub optimal allocation value of lost load ECONOMY
在线阅读 下载PDF
Detecting Designated Building Areas From Remote Sensing Images Using Hierarchical Structural Constraints
2
作者 Fukun BI mingyang lei +2 位作者 Zhihua YANG Jinyuan HOU Yanyan QIN 《Photonic Sensors》 SCIE EI CSCD 2020年第1期45-56,共12页
Automatic detection of a designated building area(DBA)is a research hotspot in the field of target detection using remote sensing images.Target detection is urgently needed for tasks such as illegal building monitorin... Automatic detection of a designated building area(DBA)is a research hotspot in the field of target detection using remote sensing images.Target detection is urgently needed for tasks such as illegal building monitoring,dynamic land use monitoring,antiterrorism efforts,and military reconnaissance.The existing detection methods generally have low efficiency and poor detection accuracy due to the large size and complexity of remote sensing scenes.To address the problems of the current detection methods,this paper presents a DBA detection method that uses hierarchical structural constraints in remote sensing images.Our method was conducted in two main stages.(1)During keypoint generation,we proposed a screening method based on structural pattern descriptors.The local pattern feature of the initial keypoints was described by a multilevel local pattern histogram(MLPH)feature;then,we used one-class support vector machine(OC-SVM)merely to screen those building attribute keypoints.(2)To match the screened keypoints,we proposed a reliable DBA detection method based on matching the local structural similarities of the screened keypoints.We achieved precise keypoint matching by calculating the similarities of the local skeletal structures in the neighboring areas around the roughly matched keypoints to achieve DBA detection.We tested the proposed method on building area sets of different types and at different time phases.The experimental results show that the proposed method is both highly accurate and computationally efficient. 展开更多
关键词 DBA detection local structural constraint multilevel local pattern histogram(MLPH) similarity of the local structure scale invariant feature transform(SIFT)
原文传递
上一页 1 下一页 到第
使用帮助 返回顶部