基于感兴趣区域的小波图像压缩算法

提出了一种基于矢量量化的感兴趣区小波图像压缩算法，该算法充分利用了子带系数带间和带内的相关性并考虑到人眼对背景区和感兴趣区的视觉差异，对背景区和感兴趣区分别采用不同的矢量构成方法，且在对背景区编码时借用零树编码的思想极大的提高了编码比特率．在高压缩比时应用本算法所得恢复图像仍能保持较好的主观质量。     

模糊评价与PSO优化的LSSVM架空输电线路故障率预测

准确计算架空线路故障率对于保障线路安全稳定运行有重要意义。分析确定架空输电线路故障的影响因素,将其划分为4个一级指标和21个二级指标,建立线路故障指标评价体系。分别采用三角分布法和模糊试验法确定定量指标和定性指标的隶属度函数,利用九分度AHP法计算各指标权重,通过模糊运算得到各影响因素的评价值。根据故障造成损失的严重程度,划分故障为缺陷、失效和事故三类;采用粒子群算法对LSSVM进行参数寻优,以运行时间和4个一级指标为支持向量,建立线路I级故障率预测模型,经检验该模型的平均相对误差为2.36%。算例验证了此线路故障率计算方法的有效性,计算结果可为电力部门制定线路运维措施提供参考。     

一种改进的MRVM方法及其在风电机组轴承诊断中的应用

针对风力机电组轴承故障难以诊断的问题,提出一种基于改进多分类相关向量机(MRVM)的风力机电组主轴轴承概率性智能故障诊断方法。首先,为了减少人为设定核参数的主观性以提高其分类性能,提出MRVM最优核参数自适应选取方法;然后,通过仿真实验结果验证所提方法的有效性及优越性;最后,以风电机组主轴滚动轴承故障诊断为实例,提取小波包能量为故障特征输入到改进后的MRVM中进行故障识别。实验结果表明,该方法可提高故障诊断准确率及效率,同时可输出故障诊断结果的概率信息,为实际检修人员提供更多参考信息。此外,通过与其他方法的对比实验进一步表明该方法在智能故障诊断方面的优越性。     

PCA-PSO/GS-SVM组合方法在风电齿轮箱故障预测中的应用研究

在标准支持向量机(SVM)的基础上,引入主成分分析法(PCA)、粒子群算法(PSO)以及网格算法(GS),构建针对风力机故障的PCA-PSO/GS-SVM组合预测模型。相对于标准SVM,该模型采用PSO以及GS算法寻优参数,能够更准确地建立各变量间的相关关系以提高模型的预测准确性。以中国北方某风场2 MW风电齿轮箱在2017年上半年某2个月的SCADA监测数据为例进行分析。结果表明,对于以齿轮箱输出功率为例的骤变信号的预测,采用PSO算法寻优后的绝对误差均值是采用GS算法的3.0647倍,而对于以高速侧轴端温度为例的缓变信号的预测,则采用PSO算法更加合理;同时发现剔除训练样本数据中的奇异点能够有效提高模型的预测精度及其泛化能力。     

错时采样空间矢量调制技术的频域数学分析

错时采样空间矢量调制是一种新型的开关调制策略,能够在较低的器件开关频率下实现较高等效开关频率的效果,具有良好的谐波特性。文中以不连续脉宽调制为例,首先将其调制波显化,并通过双重傅里叶变换得到其频域数学模型。在此基础上,推导了错时采样空间矢量调制技术的频域数学模型,从而在理论上证明该技术能够大大提高装置的等效开关频率,同时没有基波损失。这个结论普遍适用于各种载波脉宽调制技术。仿真和实验验证了理论分析结果。     

基于调峰形势的联络线受电模型及其优化

探讨了基于调峰形势的联络线受电优化模型。该模型根据负荷预测数据,采用分时电价计算方法,综合考虑电网内机组的发电、联络线受电和调峰能力三者的关系,在保证电网安全的条件下,得到了经济效益优化的受电曲线,从而改变了以往受电曲线由人工凭经验制定的状况,为电网的安全经济运行提供了技术依据。     

基于MATLAB的矩阵变换器空间相量调制研究

矩阵式变换器是一种具有优良输入输出特性的新型交-交直接电源变换器.对矩阵变换器进行了建模和运行分析,推导出矩阵式变换器和等效交-直-交结构的开关函数关系,从而由等效交-直-交结构的双空间矢量控制规律得到交-交直接变换的空间相量控制策略.并进行了MATLAB仿真,仿真结果证明矩阵变换器空间相量调制策略的正确性、优越性.同时可将在仿真中优化出的功率器件开关规律直接应用于矩阵变换器的实际控制.     

基于一种NW-FLNN神经网络的短期电价预测

针对传统神经网络收敛速度慢、容易陷入局部极值的问题,文中提出一种改进型小波神经网络以实现网络全局最优化。首先,将小波神经网络与随机矢量函数连接型网络相融合构建一种新型小波链神经网络(NW-FLNN);其次,以小波基函数作为NW-FLNN的隐含层的传递函数,并利用梯度修正法训练该模型各参数;最后,选用澳大利亚新南威尔士州电价数据作为实验数据集,分别对NW-FLNN神经网络、逆传播BP神经网络与小波神经网络进行预测性能比较。实验结果表明:该新型网络预测模型较BP神经网络与小波神经网络性能更优,可明显减少网络迭代次数与隐层神经元数目,且平均百分比误差最大降低至0. 0317,满足实时性要求。     

A segment‐based sparse matrix–vector multiplication on CUDA

The challenge for Sparse Matrix–Vector multiplication (SpMV) performance is memory bandwidth, which mostly depends on input matrices and underlying computing platforms. To solve this challenge, many researchers have explored a variety of optimization techniques. One of the most promising aspects focuses on designing storage formats to represent sparse matrices. However, lots of prior storage formats cannot fully take advantage of the underlying computing platforms, resulting in unsatisfactory performance and large memory footprint. Therefore, a novel storage format, called Segmented Hybrid ELL + Compressed Sparse Row (CSR) (SHEC for short), is proposed to further improve the throughput and lessen memory footprint on Graphics Processing Unit (GPU). SHEC format employs an interleaved combination pattern, which combines certain amount of compressed rows to form a new SHEC row. Segmentation is brought in to balance load and reduce memory footprint. According to the empirical data, an automatic SHEC‐based SpMV is developed to fit for all the matrices. Experimental results show that SHEC approach outperforms the best results of NVIDIA SpMV library and exhibits a comparable performance with state‐of‐the‐art storage formats on the standard dataset. Copyright © 2012 John Wiley & Sons, Ltd.     

Tridiagonalization of a dense symmetric matrix on multiple GPUs and its application to symmetric eigenvalue problems

For software to fully exploit the computing power of emerging heterogeneous computers, not only must the required computational kernels be optimized for the specific hardware architectures but also an effective scheduling scheme is needed to utilize the available heterogeneous computational units and to hide the communication between them. As a case study, we develop a static scheduling scheme for the tridiagonalization of a symmetric dense matrix on multicore CPUs with multiple graphics processing units (GPUs) on a single compute node. We then parallelize and optimize the Basic Linear Algebra Subroutines (BLAS)‐2 symmetric matrix‐vector multiplication, and the BLAS‐3 low rank symmetric matrix updates on the GPUs. We demonstrate the good scalability of these multi‐GPU BLAS kernels and the effectiveness of our scheduling scheme on twelve Intel Xeon processors and three NVIDIA GPUs. We then integrate our hybrid CPU‐GPU kernel into computational kernels at higher‐levels of software stacks, that is, a shared‐memory dense eigensolver and a distributed‐memory sparse eigensolver. Our experimental results show that our kernels greatly improve the performance of these higher‐level kernels, not only reducing the solution time but also enabling the solution of larger‐scale problems. Because such symmetric eigenvalue problems arise in many scientific and engineering simulations, our kernels could potentially lead to new scientific discoveries. Furthermore, these dense linear algebra algorithms present algorithmic characteristics that can be found in other algorithms. Hence, they are not only important computational kernels on their own but also useful testbeds to study the performance of the emerging computers and the effects of the various optimization techniques. Copyright © 2013 John Wiley & Sons, Ltd.