正则稀疏化的多因子量化选股策略

针对高维度数据集特征之间的复杂性,而传统的L1惩罚项不满足Oracle性质的无偏性,将逻辑回归弹性网（LR-Elastic Net）中的L1惩罚项替换为SCAD（Smoothly Clipped Absolute Deviation）和MCP（Minimax Concave Penalty）惩罚项,分别构建了LR-SCAD和LR-MCP模型,在保留稀疏性的同时满足了无偏性,并利用ADMM（Alternating Direction Method of Multipliers）算法进行求解。通过模拟实验发现,LR-Elastic Net模型能很好地处理特征存在相关性的小样本数据,而LR-SCAD和LR-MCP模型在特征存在相关性的大样本数据中表现较好;建立LR-Elastic Net、LR-SCAD和LR-MCP策略,并应用于沪深300指数成分股数据。回测结果显示,LR-SCAD和LR-MCP策略在股票相关性很强的数据中比LR-Elastic Net策略表现更好。     

稀疏约束的时空正则相关滤波无人机视觉跟踪

目的 基于相关滤波的跟踪算法在无人机(unmanned aerial vehicle,UAV)视觉跟踪领域表现出卓越的性能。现有的相关滤波类跟踪算法从样本区域的所有特征中学习滤波器,然而某些来自遮挡或形变的特征可能会污染滤波器,降低模型判别能力。针对此问题,提出一种稀疏约束的时空正则相关滤波跟踪算法。方法 在相关滤波目标函数上施加空间弹性网络约束以自适应地抑制跟踪过程中的干扰特征,同时集成空间—时间正则相关滤波算法(spatial-temporal regularized correlation filter,STRCF)中的时间正则项以增强滤波器抑制畸变的能力。采用交替方向乘子法(alternating direction method of multipliers,ADMM)将带有约束项的目标函数转化为两个具有闭式解的子问题迭代求局部最优解。此外,提出一种相关滤波框架通用的加速策略,根据当前帧的目标位移量,对检测定位阶段的特征矩阵进行等距离的循环移位,将其作为在线学习阶段的特征矩阵,每帧可节省一次训练样本的特征提取操作,提高跟踪速度。结果 在3个UAV数据集上与14种主流跟踪算法进...     

基于ADMM算法的主动配电网分层优化经济调度

主动配电网(active distribution network,ADN)因其具有调度控制灵活、用户交互性高、能源利用率高等特征正逐渐取代传统配电网,在系统安全运行的前提下实现了经济性最优的目标。文章充分考虑配电网整体运行的经济性,提出一种基于交替方向乘子法(alternating direction method of multipliers, ADMM)的主动配电网分层优化经济调度方法。首先,以最小配电网整体运行成本为目标建立了主动配电网分层优化调度模型,借助ADMM算法分解为上下两层进行求解,上层以最小化配电网整体运行成本为目标进行优化,下层考虑配电网内部节点接入的分布式光伏和储能单元,以最小化本地储能运行成本和购电成本为目标进行优化。其次,上下两层通过有限的边界信息交换,相互迭代,直至满足收敛条件,得出最优解。最后,通过设计算例进行测试,验证了所提调度方法的有效性和可行性。     

Optimal operation of integrated energy system including power thermal and gas subsystems

As a form of hybrid multi-energy systems, the integrated energy system contains different forms of energy such as power, thermal, and gas which meet the load of various energy forms. Focusing mainly on model building and optimal operation of the integrated energy system, in this paper, the dist-flow method is applied to quickly calculate the power flow and the gas system model is built by the analogy of the power system model. In addition, the piecewise linearization method is applied to solve the quadratic Weymouth gas flow equation, and the alternating direction method of multipliers (ADMM) method is applied to narrow the optimal results of each subsystem at the coupling point. The entire system reaches its optimal operation through multiple iterations. The power-thermal-gas integrated energy system used in the case study includes an IEEE-33 bus power system, a Belgian 20 node natural gas system, and a six node thermal system. Simulation-based calculations and comparison of the results under different scenarios prove that the power-thermal-gas integrated energy system enhances the flexibility and stability of the system as well as reducing system operating costs to some extent.     

Automatic foreground extraction from imperfect backgrounds using multi-agent consensus equilibrium

Extracting accurate foreground objects from a scene is an essential step for many video applications. Traditional background subtraction algorithms can generate coarse estimates, but generating high quality masks requires professional softwares with significant human interventions, e.g., providing trimaps or labeling key frames. We propose an automatic foreground extraction method in applications where a static but imperfect background is available. Examples include filming and surveillance where the background can be captured before the objects enter the scene or after they leave the scene. Our proposed method is very robust and produces significantly better estimates than state-of-the-art background subtraction, video segmentation and alpha matting methods. The key innovation of our method is a novel information fusion technique. The fusion framework allows us to integrate the individual strengths of alpha matting, background subtraction and image denoising to produce an overall better estimate. Such integration is particularly important when handling complex scenes with imperfect background. We show how the framework is developed, and how the individual components are built. Extensive experiments and ablation studies are conducted to evaluate the proposed method.     

Magnetic resonance imaging reconstruction via non‐convex total variation regularization

Magnetic resonance imaging (MRI) reconstruction model based on total variation (TV) regularization can deal with problems such as incomplete reconstruction, blurred boundary, and residual noise. In this article, a non‐convex isotropic TV regularization reconstruction model is proposed to overcome the drawback. Moreau envelope and minmax‐concave penalty are firstly used to construct the non‐convex regularization of L₂ norm, then it is applied into the TV regularization to construct the sparse reconstruction model. The proposed model can extract the edge contour of the target effectively since it can avoid the underestimation of larger nonzero elements in convex regularization. In addition, the global convexity of the cost function can be guaranteed under certain conditions. Then, an efficient algorithm such as alternating direction method of multipliers is proposed to solve the new cost function. Experimental results show that, compared with several typical image reconstruction methods, the proposed model performs better. Both the relative error and the peak signal‐to‐noise ratio are significantly improved, and the reconstructed images also show better visual effects. The competitive experimental results indicate that the proposed approach is not limited to MRI reconstruction, but it is general enough to be used in other fields with natural images.     

基于加权场景先验的海上红外弱小目标检测

为了提高海上红外弱小目标检测的检测精度和实时性,提出了一种基于加权场景先验的红外弱小目标检测方法.该方法首先利用目标的稀疏特性以及海面场景的非局部自相关特性,将目标和背景的分离问题转化为恢复低秩和稀疏矩阵的鲁棒主成分分析(Robust Principal Component Analysis,RPCA)问题.之后,将海面背景的先验特征信息通过加权核范数的方式加入模型,加快算法中目标和背景图像块矩阵的分解速度.最后,通过引入交替方向乘子法(ADMM)算法进一步加速求解的迭代速度.实验结果表明:该算法能有效地提高目标检测准确率,算法实时性较原算法提高了120%.     

快速双非凸回归算法及其电力数据预测应用

为适应产能输出、运营效益等电力数据预测应用,文中提出一种快速双非凸回归(double nonconvex regression,DNR)预测算法。首先,将经典稀疏编码分类技术解释为预测回归模型,并划分为训练阶段和测试阶段,使之适合标量预测应用;其次,针对经典Lasso模型存在的稀疏性不足以及噪声拟合单一问题,该算法通过l_p范数约束逼近原始稀疏编码问题的误差重构项和系数正则项,具有更为灵活的模型形式和应用范围。最后,通过交替方向乘子框架实现了重构系数的优化升级策略。为确保ADMM优化子问题具有快速解,提出一种改进的迭代阈值规则用于更新非凸l_p约束项,解决了原始算法陷入的局部最优问题。在电力企业实际运行产出和运营指标数据上的实验结果表明,DNR在预测效果和预测效率上均优于经典的支持向量机、BP神经网络以及非凸约束预测方法。     

基于自适应步长ADMM的电-气混联系统多时间尺度优化调度

随着微型燃气轮机和以其为基础的热电联供单元的大量配置,电力、天然气系统之间的联合优化调度越来越受到关注。针对电-气混联系统的优化调度问题,提出双层多时间尺度优化调度框架,考虑到各子系统间信息的不透明特性,上层基于日前预测数据和自适应步长交替方向乘子法(ADMM)建立以系统成本为目标函数的分布式日前优化调度模型;针对可再生能源和负荷的波动,下层基于短时预测数据建立以遵循日前调度方案为目标的实时调度模型。通过算例分析验证了所提电-气混联系统优化调度模型和框架的有效性。