梯级水电站群短期优化调度方法研究

梯级水电站优化调度对于增加系统发电量,降低耗水率,充分利用流域水能资源,提高整个梯级水电站群的经济效益和运行水平具有重要意义。建立"一库多级"梯级水电站群短期优化调度模型,研究采用逐步优化算法(POA算法)进行模型求解的方法和步骤,在此基础上开发调度软件,并以金溪流域梯级电站群为例对算法实际应用效果进行分析。研究结果表明,采用POA算法能够有效提高水电站发电量3%以上,且POA算法具有易于计算机程序实现的特点,在水电站自动优化调度方面具有较大优势。     

粒子群优化算法

系统地介绍了粒子群优化算法，归纳了其发展过程中的各种改进如惯性权重、收敛因子、跟踪并优化动态目标等模型。阐述了算法在目标函数优化、神经网络训练、模糊控制系统等基本领域的应用并给出其在工程领域的应用进展，最后，对粒子群优化算法的研究和应用进行了总结和展望，指出其在计算机辅助工艺规划领域的应用前景。     

一种分布式数据库多属性划分查询优化算法

简单阐述了分布式数据库中查询优化的查询目的,并简单介绍了直接连接优化算法中的Hash划分和Partition算法.通过分析,指出Partition算法的不足,并加以改进.在改进算法中提出了查询图划分方法,缩短查询操作的响应时间.     

时空众包中基于质量感知的在线激励机制

在实时、复杂的网络环境中，如何激励工人参与任务并得到高质量的感知数据是时空众包研究的重点。基于此，提出一种基于质量感知的时空众包在线激励机制。首先，为了适应时空众包实时性的特点，提出一种阶段性在线选择工人算法（POA），该算法在预算约束下将整个众包活动周期分为多个阶段，每个阶段在线选择工人；其次，为了提高质量预估的精度与效率，提出一种改进的最大期望（IEM）算法，该算法在算法迭代的过程中优先考虑可信度高的工人提交的任务结果；最后，通过真实数据集上的对比实验，验证了所提激励机制在提高平台效用方面的有效性。实验结果表明，POA相较于改进的两阶段拍卖（ITA）算法、多属性与两阶段相结合的拍卖（M-ITA）算法，以及L-VCG(Lyapunov-based Vickrey-Clarke-Groves)等拍卖算法，效率平均提高了11.11%，工人的额外奖励金额平均提升了12.12%，可以激励工人向冷门偏远地区移动；在质量预估方面，IEM算法相比其他质量预估算法，在精度和效率上分别平均提高了5.06%和14.2%。     

遗传算法的改进及其应用研究

基于工程优化命题的特点对传统遗传算法进行了改进，改进算法在尿素工业过程神经网络建模和专家系统知识获取中的应用结果表明，该算法适用范围广，可有效地解决若干工程优化问题。     

一种混合优化算法及其收敛性证明

针对改进的混沌优化方法和Alopex算法的特性，将改进的Alopex算法嵌入到改进的混沌优化算法中，提出一种混合优化算法，此算法充分发挥了改进的Alopex算法的快速搜索能力和改进的混沌优化方法细致寻优的特性，提高了算法的收敛速度，避免了优化算法陷入局部最优；同时对改进的混沌优化算法和混合优化算法的收敛性进行了证明，仿真结果表明了算法的有效性。     

可移植对象适配器策略机制的分析与设计

可移植对象适配器POA模型是CORBA规范中一个核心模型，它是CORBA为了规范服务器应用程序的开发以及解决服务器程序在不同ORB产品之间无需修改地进行移植问题而提出的 ，POA策略是其关键特性。本文对POA模型中的七个标准策略的特点、应用场合以及各种策略组合进行了深入的分析，在此基础上设计实现了POA策略机制，提供了灵活的策略创建、策略管理手段，使得应用开发者可以根据需要灵活地对POA进行动态配置，从而满足了不同应用的需求。     

蚁群优化算法的研究现状及研究展望

本文首先简要地介绍蚁群优化算法的来源、对应的生物原理和算法实现的框架．然后详细地讨论了算法的研究现状以及在各种优化问题中的应用情况，同时也指出了蚁群优化算法在当前应用中的一些不足．针对这些不足提出了解决方法，描述了几种蚁群优化算法的修正策略.最后对蚁群优化算法下一步的研究方向进行了展望．     

改进的群搜索优化算法在MATLAB中的实现

文章介绍了一种改进的带趋势预测的群搜索优化（Group Search Optimizer,GSO）算法,描述了此算法在MATLAB中的程序实现。同时根据函数优化的概念,举例说明了改进算法在函数优化中的应用方法。     

一种基于植物群落算法的无线传感器网络定位方法

针对DV-Hop算法定位精度低的问题，提出了一种基于植物群落行为在DV-Hop算法中的定位方法。首先，该算法利用植物群落算法与DV-Hop算法的第2阶段相结合实现最小误差的平均跳距；其次，植物群落算法在后期求解未知节点坐标时对其值进一步优化。最后，仿真结果显示，改进算法在不增加额外开销的情况下，定位误差进一步降低，相比其他传统群智能优化算法对DV-Hop方法的改进，算法的定位精度有很大的优势。     

Binormalized data-reusing adaptive filtering algorithm for active control of impulsive sources

The main objective of active noise control (ANC) is to provide attenuation for the environmental acoustic noise. The adaptive algorithms for ANC systems work well to attenuate the Gaussian noise; however, their performance may degrade for non-Gaussian impulsive noise sources. Recently, we have proposed variants of the most famous ANC algorithm, the filtered-x least mean square (FxLMS) algorithm, where an improved performance has been realized by thresholding the input data or by efficiently normalizing the step-size. In this paper, we propose a modified binormalized data-reusing (BNDR)-based adaptive algorithm for impulsive ANC. The proposed algorithm is derived by minimizing a modified cost function, and is based on reusing the past and present samples of data. The main contribution of the paper is to develop a practical DR-type adaptive algorithm, which incorporates an efficiently normalized step-size, and is well suited for ANC of impulsive noise sources. The computer simulations are carried out to demonstrate the effectiveness of the proposed algorithm. It is shown that an improved performance has been realized with a reasonable increase in the computational complexity.     

无线传感网数据信息的一种压缩算法

提出了一种改进型自适应Huffman编码算法,目的在于压缩传输数据的容量,该算法适用于内存和计算资源受限的无线传感网络节点。它与修剪树自适应Huffman编码算法lll相比较,能够更有效地利用内存空间,提供更好的压缩比。     

SRTM DEM enhancement using a single set of PolSAR data based on the polarimetry-clinometry model

ABSTRACT

The freely available global and near-global digital elevation models (DEMs) have shown great potential for various remote sensing applications. The Shuttle Radar Topography Mission (SRTM) data sets provide the near-global DEM of the Earth’s surface obtained using the interferometry synthetic aperture radar (InSAR). Although free accessibility and generality are the advantages of these data sets, many applications require more detailed and accurate DEMs. In this paper, we proposed a modified and advanced polarimetry-clinometry algorithm for improving SRTM topography model which requires only one set of polarimetric synthetic aperture radar (PolSAR) data. The azimuth and range slope components estimation based on polarization orientation angle (POA) shifts and the intensity-based Lambertian model formed the bases of the proposed method. This method initially compensated for the polarimetry topography effect corresponding to SRTM using the DEM-derived POA. In the second step, using a modified algorithm, POA was obtained from the compensated PolSAR data. The POA shifts by the azimuth and range slopes’ variations based on the polarimetric model. In addition to the polarimetric model, a clinometry model based on the Lambertian scattering model related to the terrain slope was employed. Next, two unknown parameters, i.e. azimuth and range slope values, were estimated in a system of equations by two models from the compensated PolSAR data. Azimuth and range slopes of SRTM were enhanced by PolSAR-derived slopes. Finally, a weighted least-square grid adjustment (WLSG) method was proposed to integrate the enhanced slopes’ map and estimate enhanced heights. The National Aeronautics and Space Administration Jet Propulsion Laboratory (NASA JPL) AIRSAR was utilized to illustrate the potential of the proposed method in SRTM enhancement. Also, the InSAR DEM was employed for evaluation experiments. Results showed that the accuracy of SRTM DEM is improved up to 2.91 m in comparison with InSAR DEM.     

随机系统自适应控制的完整性设计

本文在文献[1]提出的自适应算法基础上,引入了加权阵Λ,使修改后的自适应算法在进行完整性设计时,不但设计方法更简便,而且具有更强的收敛性。     

基于逐次优化和禁忌搜索算法的混沌优化方法

结合逐次优化、禁忌搜索和变尺度混沌优化方法的优点,提出了基于逐次优化和禁忌搜索算法的混沌优化方法.该方法具有逐次优化算法的隐性并行性和收敛性,禁忌搜索的智能性和变尺度混沌优化方法的快速性.仿真计算表明,该方法具有实现简单,优化效率高,鲁棒性强等特点,是求解大规模非线性规划问题的一种有效手段.     

Computations of probabilistic output admissible set for uncertain constrained systems

This paper considers uncertain constrained systems, and develops two algorithms for computing a probabilistic output admissible (POA) set which is a set of initial states probabilistically assured to satisfy the constraint. The first algorithm is inspired by an existing randomized sequential technique. The second algorithm alleviates the computational effort based on heuristics. The present algorithms terminate in a finite number of iterations and provide a POA set. Additionally, we can obtain information on the size of the resulting set a posteriori. A numerical simulation demonstrates the applicability of the POA set to a control system design scheme.     

Vector quantization of images using modified adaptive resonancealgorithm for hierarchical clustering

A modified adaptive resonance theory (ART2) learning algorithm, which we employ in this paper, belongs to the family of NN algorithms whose main goal is the discovery of input data clusters, without considering their actual size. This feature makes the modified ART2 algorithm very convenient for image compression tasks, particularly when dealing with images with large background areas containing few details. Moreover, due to the ability to produce hierarchical quantization (clustering), the modified ART2 algorithm is proved to significantly reduce the computation time required for coding, and therefore enhance the overall compression process. Examples of the results obtained are presented, suggesting the benefits of using this algorithm for the purpose of VQ, i.e., image compression, over the other NN learning algorithms.     

混合混沌优化方法及其在非线性规划问题中的应用

结合逐次优化、禁忌搜索和变尺度混沌优化方法的优点,提出了一种混合混沌优化方法。该方法具有逐次优化算法的隐性并行性和收敛性,禁忌搜索的智能性和变尺度混沌优化方法的快速性。仿真计算表明,该方法具有实现简单,优化效率高,鲁棒性强等特点。     

An adaptive amoeba algorithm for constrained shortest paths

The constrained shortest path problem (CSP) is one of the basic network optimization problems, which plays an important part in real applications. In this paper, an adaptive amoeba algorithm is combined with the Lagrangian relaxation algorithm to solve the CSP problem. The proposed method is divided into two steps: (1) the adaptive amoeba algorithm is modified to solve the shortest path problem (SPP) in a directed network; (2) the modified adaptive amoeba algorithm is combined with the Lagrangian relaxation method to solve the CSP problem. In addition, the evolving processes of the adaptive amoeba model have been detailed in the paper. Two examples are used to illustrate the efficiency of the proposed method. The results show that the proposed method can deal with the CSP problem effectively.