With the rapid growth of spatial data,POI(Point of Interest)is becoming ever more intensive,and the text description of each spatial point is also gradually increasing.The traditional query method can only address the...With the rapid growth of spatial data,POI(Point of Interest)is becoming ever more intensive,and the text description of each spatial point is also gradually increasing.The traditional query method can only address the problem that the text description is less and single keyword query.In view of this situation,the paper proposes an approximate matching algorithm to support spatial multi-keyword.The fuzzy matching algorithm is integrated into this algorithm,which not only supports multiple POI queries,but also supports fault tolerance of the query keywords.The simulation results demonstrate that the proposed algorithm can improve the accuracy and efficiency of query.展开更多
Reverse k nearest neighbor （RNNk） is a generalization of the reverse nearest neighbor problem and receives increasing attention recently in the spatial data index and query. RNNk query is to retrieve all the data po...Reverse k nearest neighbor （RNNk） is a generalization of the reverse nearest neighbor problem and receives increasing attention recently in the spatial data index and query. RNNk query is to retrieve all the data points which use a query point as one of their k nearest neighbors. To answer the RNNk of queries efficiently, the properties of the Voronoi cell and the space-dividing regions are applied. The RNNk of the given point can be found without computing its nearest neighbors every time by using the rank Voronoi cell. With the elementary RNNk query result, the candidate data points of reverse nearest neighbors can he further limited by the approximation with sweepline and the partial extension of query region Q. The approximate minimum average distance （AMAD） can be calculated by the approximate RNNk without the restriction of k. Experimental results indicate the efficiency and the effectiveness of the algorithm and the approximate method in three varied data distribution spaces. The approximate query and the calculation method with the high precision and the accurate recall are obtained by filtrating data and pruning the search space.展开更多
K-th number query是计算机算法中的一个基础问题,被广泛作为很多算法实现的重要步骤。对该问题进行了深入研究,并找到了单询问渐近时间复杂度最优的算法。目前一般对于多询问的K-th number query问题使用平衡二叉树解决,询问的时间复...K-th number query是计算机算法中的一个基础问题,被广泛作为很多算法实现的重要步骤。对该问题进行了深入研究,并找到了单询问渐近时间复杂度最优的算法。目前一般对于多询问的K-th number query问题使用平衡二叉树解决,询问的时间复杂度为O(lbn)。但该算法实现比较复杂,并且常系数较大,提出了基于Bit Indexed Tree数据结构的算法解决,在同等时间复杂度的前提下,实现简单,隐含的常系数很小。最后进行了实验测试,分析显示该新算法不论在时间上还是空间上都优于现有的算法。展开更多
针对电池储能系统(battery energy storage system,BESS)进行光伏波动平抑时寿命损耗高及荷电状态(state of charge,SOC)一致性差的问题,提出了光伏波动平抑下改进K-means的BESS动态分组控制策略。首先,采用最小最大调度方法获取光伏并...针对电池储能系统(battery energy storage system,BESS)进行光伏波动平抑时寿命损耗高及荷电状态(state of charge,SOC)一致性差的问题,提出了光伏波动平抑下改进K-means的BESS动态分组控制策略。首先,采用最小最大调度方法获取光伏并网指令。其次,设计了改进侏儒猫鼬优化算法(improved dwarf mongoose optimizer,IDMO),并利用它对传统K-means聚类算法进行改进,加快了聚类速度。接着,制定了电池单元动态分组原则,并根据电池单元SOC利用改进K-means将其分为3个电池组。然后,设计了基于充放电函数的电池单元SOC一致性功率分配方法,并据此提出BESS双层功率分配策略,上层确定电池组充放电顺序及指令,下层计算电池单元充放电指令。对所提策略进行仿真验证,结果表明,所设计的IDMO具有更高的寻优精度及更快的寻优速度。所提BESS平抑光伏波动策略在有效平抑波动的同时,降低了BESS运行寿命损耗并提高了电池单元SOC的均衡性。展开更多
文摘With the rapid growth of spatial data,POI(Point of Interest)is becoming ever more intensive,and the text description of each spatial point is also gradually increasing.The traditional query method can only address the problem that the text description is less and single keyword query.In view of this situation,the paper proposes an approximate matching algorithm to support spatial multi-keyword.The fuzzy matching algorithm is integrated into this algorithm,which not only supports multiple POI queries,but also supports fault tolerance of the query keywords.The simulation results demonstrate that the proposed algorithm can improve the accuracy and efficiency of query.
基金Supported by the National Natural Science Foundation of China (60673136)the Natural Science Foundation of Heilongjiang Province of China (F200601)~~
文摘Reverse k nearest neighbor （RNNk） is a generalization of the reverse nearest neighbor problem and receives increasing attention recently in the spatial data index and query. RNNk query is to retrieve all the data points which use a query point as one of their k nearest neighbors. To answer the RNNk of queries efficiently, the properties of the Voronoi cell and the space-dividing regions are applied. The RNNk of the given point can be found without computing its nearest neighbors every time by using the rank Voronoi cell. With the elementary RNNk query result, the candidate data points of reverse nearest neighbors can he further limited by the approximation with sweepline and the partial extension of query region Q. The approximate minimum average distance （AMAD） can be calculated by the approximate RNNk without the restriction of k. Experimental results indicate the efficiency and the effectiveness of the algorithm and the approximate method in three varied data distribution spaces. The approximate query and the calculation method with the high precision and the accurate recall are obtained by filtrating data and pruning the search space.
文摘K-th number query是计算机算法中的一个基础问题,被广泛作为很多算法实现的重要步骤。对该问题进行了深入研究,并找到了单询问渐近时间复杂度最优的算法。目前一般对于多询问的K-th number query问题使用平衡二叉树解决,询问的时间复杂度为O(lbn)。但该算法实现比较复杂,并且常系数较大,提出了基于Bit Indexed Tree数据结构的算法解决,在同等时间复杂度的前提下,实现简单,隐含的常系数很小。最后进行了实验测试,分析显示该新算法不论在时间上还是空间上都优于现有的算法。
文摘针对电池储能系统(battery energy storage system,BESS)进行光伏波动平抑时寿命损耗高及荷电状态(state of charge,SOC)一致性差的问题,提出了光伏波动平抑下改进K-means的BESS动态分组控制策略。首先,采用最小最大调度方法获取光伏并网指令。其次,设计了改进侏儒猫鼬优化算法(improved dwarf mongoose optimizer,IDMO),并利用它对传统K-means聚类算法进行改进,加快了聚类速度。接着,制定了电池单元动态分组原则,并根据电池单元SOC利用改进K-means将其分为3个电池组。然后,设计了基于充放电函数的电池单元SOC一致性功率分配方法,并据此提出BESS双层功率分配策略,上层确定电池组充放电顺序及指令,下层计算电池单元充放电指令。对所提策略进行仿真验证,结果表明,所设计的IDMO具有更高的寻优精度及更快的寻优速度。所提BESS平抑光伏波动策略在有效平抑波动的同时,降低了BESS运行寿命损耗并提高了电池单元SOC的均衡性。
