基于高斯混合模型及近似线性规划的风电系统校正控制方法

提出一种基于高斯混合模型及近似线性规划的风电系统校正控制方法。考虑到风电及负荷功率预测误差的非高斯特性及相关性,建立了描述风电及负荷功率联合概率分布的高斯混合模型。考虑切负荷、发电机功率调整以及统一潮流控制器等控制措施,以控制代价最小为目标函数,构建了计及电压、频率及线路潮流等静态安全约束的风电系统校正控制优化问题的数学模型。为求解含不确定变量的非线性优化问题,依据高斯混合模型的性质,在稳态运行点的邻域内将非线性约束近似转化为线性约束,用线性规划方法求解,并不断迭代直至收敛。最后,将该方法应用于修改后的IEEE 10机39节点算例系统验证其有效性,与场景法的对比结果表明该方法求解效率较高,且具有较好的经济性。     

Cooperative particle swarm optimizer with depth first search strategy for global optimization of multimodal functions

This paper presents a Cooperative Particle Swarm Optimizer with Depth First Search Strategy (DFS-CPSO), which has better seacrch capality than classical Particle Swarm Optimizer (PSO) in solving multimodal optimization problems. In order to improve the quality of information exchange, the Depth First Search (DFS) strategy is hybridized to Cooperative Particle Swarm Optimization(CPSO), which makes information transfer more effectively and generates better quality solution. Specifically, DFS strategy enables different components of solution vector to exchange information separately with PSO and increases the diversity of the population, so that the information of solution components could be preserved by multiple iterations in CPSO. Confirmatory experiments are performed to prove the effectiveness of employing the DFS strategy to CPSO. The comparative results demonstrate superior performance of DFS-CPSO in solving high dimensional multimodal functions than CPSO and other advanced methods.

     

基于减量化理念的绿色包装设计研究

减量化包装是绿色包装设计的重要组成部分,传统的减量化包装多以减少包装材料的使用量来实现,具有较大的局限性。为使减量化包装理念能更广泛地应用于绿色包装设计中,以分析包装工业发展的特点为基础,阐述了减量化包装在新形式下的设计特点,扩展了减量化包装的定义,介绍了实施减量化包装的意义;并从包装材料、包装结构、延长包装生命周期3个方面分析了减量化包装的设计方法和阻碍减量化包装理念推广的影响因素。通过分析可知,在新的包装环境中,只有正确理解减量化包装的设计理念,才能更好的发展包装工业,促进社会经济可持续发展。     

新型咔唑衍生物密度泛函理论的研究

运用量子化学中的密度泛函和含时密度泛函理论法,计算1种新型咔唑衍生物,以探讨其几何构型、电子结构、前线分子轨道和电子光谱性质。计算结果表明当2-(4-吡啶基)乙烯基的反式构型与咔唑环作用时比顺式稳定,引入取代基后使化合物的HOMO和LUMO之间的能隙降低,吸收光谱红移。电子被激发时,电子从咔唑环向吡啶环转移,说明新型咔唑类化合物具有很好的光学性能,可以作为空穴传输材料。     

自主角色导航技术综述

自主角色导航技术是影响角色行为真实性的关键性因素.通过全面对比分析机器人路径规划、飞行器航路规划、虚拟人足迹规划、自动制导武器制导技术等研究成果,研究可用于角色导航的全局路径规划、局部路径规划所使用的主要算法,简要介绍了各算法的基本原理及优缺点.提出了两种模拟视觉的实现思路,研究了基于模拟视觉的角色仿生导航方法,最后对导航技术的发展作了总结和展望.     

HD-GDD: high dimensional graph dominance drawing approach for reachability query

Efficiently answering reachability queries, which checks whether one vertex can reach another in a directed graph, has been studied extensively during recent years. However, the size of the graph that people are facing and generating nowadays is growing so rapidly that simple algorithms, such as BFS and DFS, are no longer feasible. Although Refined Online Search algorithms can scale to large graphs, they all suffer from the false positive problem. In this paper, we analyze the cause of false positive and propose an efficient High Dimensional coordinate generating method based on Graph Dominance Drawing (HD-GDD) to answer reachability queries in linear or even constant time. We conduct experiments on different graph structures and different graph sizes to fully evaluate the performance and behavior of our proposal. Empirical results demonstrate that our method outperforms state-of-the-art algorithms and can handle extensive graphs.     

Batch Nearest Neighbor Search for Video Retrieval

To retrieve similar videos to a query clip from a large database, each video is often represented by a sequence of high- dimensional feature vectors. Typically, given a query video containing m feature vectors, an independent nearest neighbor (NN) search for each feature vector is often first performed. After completing all the NN searches, an overall similarity is then computed, i.e., a single content-based video retrieval usually involves m individual NN searches. Since normally nearby feature vectors in a video are similar, a large number of expensive random disk accesses are expected to repeatedly occur, which crucially affects the overall query performance. Batch nearest neighbor (BNN) search is stated as a batch operation that performs a number of individual NN searches. This paper presents a novel approach towards efficient high-dimensional BNN search called dynamic query ordering (DQO) for advanced optimizations of both I/O and CPU costs. Observing the overlapped candidates (or search space) of a pervious query may help to further reduce the candidate sets of subsequent queries, DQO aims at progressively finding a query order such that the common candidates among queries are fully utilized to maximally reduce the total number of candidates. Modelling the candidate set relationship of queries by a candidate overlapping graph (COG), DQO iteratively selects the next query to be executed based on its estimated pruning power to the rest of queries with the dynamically updated COG. Extensive experiments are conducted on real video datasets and show the significance of our BNN query processing strategy.