伊洛河流域水资源系统优化调度模型研究

针对河流并联供水库群水资源优化分配问题,建立了以相对缺水率最小为目标的流域水量调度应用模型。该模型运用大系统分解协调的基本原理,将并联供水库群分解为多个独立的子系统,子系统间存在控制反馈的联系,同时考虑河道水流传播对系统的影响。利用建立的模型,以伊洛河流域供水库群为研究对象,通过建立虚拟水库,将伊洛河供水库群分为三个子系统,选用整体改进的遗传算法(GA)进行计算求解,并采用常规调度方法进行校核。以水平年2020年为例,得到不同来水条件下伊洛河流域水量调度方案,并对特枯来水情况制定供水三级预警系统。     

The Cascade Reservoirs Multi-Objective Ecological Operation Optimization Considering Different Ecological Flow Demand

Water Resources Management - In order to coordinate the power generation and downstream ecological protection benefits,the optimal ecological scheduling of cascade reservoirs is pivotal. In current...     

The Short-Term Economical Operation Problem for Hydropower Station Using Chaotic Normal Cloud Model Based Discrete Shuffled Frog Leaping Algorithm

Water Resources Management - The short-term economical operation (STEO) in hydropower station is nonlinear mixed integer problem, satisfying complex hydrologic constraints simultaneously. In this...     

Short-Term Hydropower Generation Scheduling Using an Improved Cloud Adaptive Quantum-Inspired Binary Social Spider Optimization Algorithm

Water Resources Management - Short-term hydropower generation scheduling (STHGS), a highly complicated nonlinear optimization problem with various equality and inequality constraints, plays an...     

A Novel Adaptive Multi-Objective Particle Swarm Optimization Based on Decomposition and Dominance for Long-term Generation Scheduling of Cascade Hydropower System

Water Resources Management - Multi-objective long-term generation scheduling (MLGS) considering ecological flow demands is important for comprehensive utilization of water resources in cascade...     

重加权稀疏主成分分析算法及其在人脸识别中的应用

针对主成分分析（PCA）算法获取的主成分向量不够稀疏,拥有较多的非零元这一问题,使用重加权方法对PCA算法进行优化,提出了一个新的提取高维数据特征的方法,即重加权稀疏主成分分析（RSPCA）算法。首先,将重加权l₁最优化框架和LASSO回归模型引入到PCA算法数学模型中,建立新的数据降维模型;然后,使用交替最小化算法、奇异值分解算法、最小角回归算法等方式对模型进行求解;最后,使用人脸识别实验对算法效果进行了验证。在实验中使用K折交叉验证的方法针对ORL人脸数据集分别使用PCA算法和RSPCA算法进行识别实验。实验结果表明,RSPCA算法在获取更稀疏解的情况下仍拥有着不弱于PCA算法的表现,平均识别准确率达到95.1%,所提算法与表现最好的sPCA-rSVD算法相比,识别准确率提高了6.2个百分点;针对手写数字识别这一具体现实应用进行求解,获取到平均识别准确率96.4%的良好实验效果。证明了所提方法在人脸识别及书写数字识别方面的优异性。