Fossil fuel depletion and environmental pollution problems promote development of renewable energy(RE)glob-ally.With increasing penetration of RE,operation security and economy of power systems(PS)are greatly impacted...Fossil fuel depletion and environmental pollution problems promote development of renewable energy(RE)glob-ally.With increasing penetration of RE,operation security and economy of power systems(PS)are greatly impacted by fluctuation and intermittence of renewable power.In this paper,information gap decision theory(IGDT)is adapted to handle uncertainty of wind power generation.Based on conventional IGDT method,linear regulation strategy(LRS)and robust linear optimization(RLO)method are integrated to reformulate the model for rigorously considering security constraints.Then a robustness assessment method based on hybrid RLO-IGDT approach is proposed for analyzing robustness and economic performance of PS.Moreover,a risk-averse linearization method is adapted to convert the proposed assessment model into a mixed integer linear programming(MILP)problem for convenient optimization without robustness loss.Finally,results of case studies validate superiority of proposed method in guaranteeing operation security rigorously and effectiveness in assessment of RSR for PS without overestimation.Index Terms-Hybrid RLO-IGDT approach,information gap decision theory(IGDT),operation security,robustness assessment,robustness security region(RSR).展开更多
无线自组网按需平面距离向量(ad hoc on-demand distance vector,AODV)路由协议以其较低的控制开销、能量消耗和带宽占用而广泛应用于移动自组织网络(mobile ad hoc networks,MANET).为了改善AODV在进行路由修复时存在的路由延迟的问题...无线自组网按需平面距离向量(ad hoc on-demand distance vector,AODV)路由协议以其较低的控制开销、能量消耗和带宽占用而广泛应用于移动自组织网络(mobile ad hoc networks,MANET).为了改善AODV在进行路由修复时存在的路由延迟的问题,提出了基于判定区域的AODV路由协议的自适应修复算法(adaptive repair algorithm for AODV routing based on decision region,AR-AODV).首先根据抢险救灾网络中节点是被统一调配的特点,提出搜寻公式,求出该公式的最优解;然后确定自修复过程发起的条件阈值;最后给出判定寻优区域的算法,减少控制开销.仿真结果表明,该修复算法改善了路由的效率.以接受统一调配的车载等移动设备为网络节点,在实际抢险救灾环境场景中对提出的自适应修复算法进行测试.结果显示,与仿真结果基本一致,整体性能改善明显.展开更多
Classification can be regarded as dividing the data space into decision regions separated by decision boundaries.In this paper we analyze decision tree algorithms and the NBTree algorithm from this perspective.Thus,a ...Classification can be regarded as dividing the data space into decision regions separated by decision boundaries.In this paper we analyze decision tree algorithms and the NBTree algorithm from this perspective.Thus,a decision tree can be regarded as a classifier tree,in which each classifier on a non-root node is trained in decision regions of the classifier on the parent node.Meanwhile,the NBTree algorithm,which generates a classifier tree with the C4.5 algorithm and the naive Bayes classifier as the root and leaf classifiers respectively,can also be regarded as training naive Bayes classifiers in decision regions of the C4.5 algorithm.We propose a second division (SD) algorithm and three soft second division (SD-soft) algorithms to train classifiers in decision regions of the naive Bayes classifier.These four novel algorithms all generate two-level classifier trees with the naive Bayes classifier as root classifiers.The SD and three SD-soft algorithms can make good use of both the information contained in instances near decision boundaries,and those that may be ignored by the naive Bayes classifier.Finally,we conduct experiments on 30 data sets from the UC Irvine (UCI) repository.Experiment results show that the SD algorithm can obtain better generali-zation abilities than the NBTree and the averaged one-dependence estimators (AODE) algorithms when using the C4.5 algorithm and support vector machine (SVM) as leaf classifiers.Further experiments indicate that our three SD-soft algorithms can achieve better generalization abilities than the SD algorithm when argument values are selected appropriately.展开更多
Although the construction of underground dams is one of the best methods to conserve water resources in arid and semi-arid regions,applying efficient methods for the selection of suitable sites for subsurface dam cons...Although the construction of underground dams is one of the best methods to conserve water resources in arid and semi-arid regions,applying efficient methods for the selection of suitable sites for subsurface dam construction remains a challenge.Due to the costly and time-consuming methods of site selection for underground dam construction,this study aimed to present a new method using geographic information systems techniques and decision-making processes.The exclusionary criteria including fault,slope,hypsometry,land use,soil,stream,geology,and chemical properties of groundwater were selected for site selection of dam construction and inappropriate regions were omitted by integration and scoring layers in ArcGIS based on the Boolean logic.Finally,appropriate sites were prioritized using the Multi-Attribute Utility Theory.According to the results of the utility coefficient,seven sites were selected as the region for underground dam construction based on all criteria and experts’opinions.The site of Nazarabad dam was the best location for underground dam construction with a utility coefficient of 0.7137 followed by sites of Akhavan with a utility coefficient of 0.4633 and Mirshamsi with a utility coefficient of 0.4083.This study proposed a new approach for the construction of the subsurface dam at the proper site and help managers and decision-makers achieve sustainable water resources with limited facilities and capital and avoid wasting national capital.展开更多
基金supported by the National Key R&D Program of China(No.2022YFB2404000).
文摘Fossil fuel depletion and environmental pollution problems promote development of renewable energy(RE)glob-ally.With increasing penetration of RE,operation security and economy of power systems(PS)are greatly impacted by fluctuation and intermittence of renewable power.In this paper,information gap decision theory(IGDT)is adapted to handle uncertainty of wind power generation.Based on conventional IGDT method,linear regulation strategy(LRS)and robust linear optimization(RLO)method are integrated to reformulate the model for rigorously considering security constraints.Then a robustness assessment method based on hybrid RLO-IGDT approach is proposed for analyzing robustness and economic performance of PS.Moreover,a risk-averse linearization method is adapted to convert the proposed assessment model into a mixed integer linear programming(MILP)problem for convenient optimization without robustness loss.Finally,results of case studies validate superiority of proposed method in guaranteeing operation security rigorously and effectiveness in assessment of RSR for PS without overestimation.Index Terms-Hybrid RLO-IGDT approach,information gap decision theory(IGDT),operation security,robustness assessment,robustness security region(RSR).
文摘无线自组网按需平面距离向量(ad hoc on-demand distance vector,AODV)路由协议以其较低的控制开销、能量消耗和带宽占用而广泛应用于移动自组织网络(mobile ad hoc networks,MANET).为了改善AODV在进行路由修复时存在的路由延迟的问题,提出了基于判定区域的AODV路由协议的自适应修复算法(adaptive repair algorithm for AODV routing based on decision region,AR-AODV).首先根据抢险救灾网络中节点是被统一调配的特点,提出搜寻公式,求出该公式的最优解;然后确定自修复过程发起的条件阈值;最后给出判定寻优区域的算法,减少控制开销.仿真结果表明,该修复算法改善了路由的效率.以接受统一调配的车载等移动设备为网络节点,在实际抢险救灾环境场景中对提出的自适应修复算法进行测试.结果显示,与仿真结果基本一致,整体性能改善明显.
基金supported by the National Natural Science Foundation of China (No.60970081)the National Basic Research Program (973) of China (No.2010CB327903)
文摘Classification can be regarded as dividing the data space into decision regions separated by decision boundaries.In this paper we analyze decision tree algorithms and the NBTree algorithm from this perspective.Thus,a decision tree can be regarded as a classifier tree,in which each classifier on a non-root node is trained in decision regions of the classifier on the parent node.Meanwhile,the NBTree algorithm,which generates a classifier tree with the C4.5 algorithm and the naive Bayes classifier as the root and leaf classifiers respectively,can also be regarded as training naive Bayes classifiers in decision regions of the C4.5 algorithm.We propose a second division (SD) algorithm and three soft second division (SD-soft) algorithms to train classifiers in decision regions of the naive Bayes classifier.These four novel algorithms all generate two-level classifier trees with the naive Bayes classifier as root classifiers.The SD and three SD-soft algorithms can make good use of both the information contained in instances near decision boundaries,and those that may be ignored by the naive Bayes classifier.Finally,we conduct experiments on 30 data sets from the UC Irvine (UCI) repository.Experiment results show that the SD algorithm can obtain better generali-zation abilities than the NBTree and the averaged one-dependence estimators (AODE) algorithms when using the C4.5 algorithm and support vector machine (SVM) as leaf classifiers.Further experiments indicate that our three SD-soft algorithms can achieve better generalization abilities than the SD algorithm when argument values are selected appropriately.
文摘Although the construction of underground dams is one of the best methods to conserve water resources in arid and semi-arid regions,applying efficient methods for the selection of suitable sites for subsurface dam construction remains a challenge.Due to the costly and time-consuming methods of site selection for underground dam construction,this study aimed to present a new method using geographic information systems techniques and decision-making processes.The exclusionary criteria including fault,slope,hypsometry,land use,soil,stream,geology,and chemical properties of groundwater were selected for site selection of dam construction and inappropriate regions were omitted by integration and scoring layers in ArcGIS based on the Boolean logic.Finally,appropriate sites were prioritized using the Multi-Attribute Utility Theory.According to the results of the utility coefficient,seven sites were selected as the region for underground dam construction based on all criteria and experts’opinions.The site of Nazarabad dam was the best location for underground dam construction with a utility coefficient of 0.7137 followed by sites of Akhavan with a utility coefficient of 0.4633 and Mirshamsi with a utility coefficient of 0.4083.This study proposed a new approach for the construction of the subsurface dam at the proper site and help managers and decision-makers achieve sustainable water resources with limited facilities and capital and avoid wasting national capital.
