基于非对称Copula函数的风电场黑启动储能优化配置

在高比例风电接入的区域电网中,为加快停电后的恢复速度,可以采用风电场配置储能系统作为黑启动电源。针对启动发电机组过程中风电出力平滑问题,提出一种基于非对称Copula函数的储能优化配置方法,以投资成本最小为目标,使风电场具备黑启动电源的能力。将储能的功率配置和容量配置看作2个随机变量,根据样本数据分析两者之间的相关特性,基于非对称Copula函数建立二元联合概率分布进行准确描述。利用核密度估计得到2个边缘概率分布,通过极大似然估计和拟合度测试确定最佳Copula函数。仿真结果表明,所提非对称Copula函数拟合准确度更高,得到的储能配置成本更低。     

A high-throughput maize kernel traits scorer based on line-scan imaging

Maize kernel traits such as kernel length, kernel width and kernel number determine kernel weight and, consequently, maize yield. Therefore, the measurement of kernel traits is important for maize breeding and the evaluation of maize yield. The conventional method for measuring kernel traits is still manual, which is time consuming, costly and subjective. In this study, a novel maize kernel traits scorer (MKTS) was developed for the automatic measurement of 12 maize kernel traits based on line-scan imaging, image processing, and automatic control techniques. Here, total of 615 samples were measured to evaluate the system performance. The results showed that the MKTS was capable of evaluating maize kernel traits with the mean absolute percentage error of the manual and automatic measurements less than 5% and the measurement efficiency of approximately 72 s for the measurement of 6 ears. In conclusion, this high-throughput scorer will provide maize scientists with a novel tool to assist in maize functional genetics and maize breeding.     

Simultaneous determination of neonicotinoid insecticides in sunflower-treated seeds (hull and kernel) by LC-MS/MS

A validated analytical method to determine seven neonicotinoids (dinotefuran, nitenpyram, thiamethoxam, clothianidin, imidacloprid, acetamiprid and thiacloprid) in sunflower seeds (hull and kernel) using HPLC coupled to electrospray ionisation mass spectrometry (ESI-MS) is presented. Sample clean-up based on a solid–liquid extraction, and the removal of lipid fraction, in the case of kernels, is proposed and optimised. Low limits of detection and quantification were obtained, ranging from 0.3 × 10^–3 to 1.2 × 10^–3 µg g^–1 and from 1.0 × 10^–3 to 4.0 × 10^–3 µg g^–1, with good precision, and recovery values ranged from 90% to 104% for hulls and kernels. The method was applied for the analysis of five thiamethoxam-dressed sunflower seeds and four non-treated seeds, where, besides thiamethoxam, residues of the other neonicotinoid, clothianidin, were also detected and confirmed via tandem mass spectrometry (LC-ESI-MS/MS). Finally, the presence of residues of thiamethoxam and clothianidin in collected sunflower seeds (hulls) coming from coated seeds confirmed the translocation of these neonicotinoids through the plant up to these seeds.     

核Fisher判别分析在数字信号分类中的应用

为了提高通信信号的识别精度,提出了一种基于核Fisher判别分析(KFDA)的数字调制信号分类器设计方法.将接收信号的高阶累积量作为分类特征向量,利用核函数的思想把非线性向量映射到一个高维空间,并在高维空间中利用线性Fisher判别分析实现数字信号的分类.将多类分类器分解成一系列二类问题,并给出了KFDA用于信号分类的详细流程.仿真实验结果表明,当选择合适的核参数时,基于KFDA的分类精度与支持向量机相当,但由于避免了求解非线性优化问题,故计算复杂度低,训练时间短.     

简化核函数FFT运算复杂度和动态性能的优化

针对简化核函数FFT能提高FFT运算速度却引入一定频谱杂散的问题,讨论了其运算复 杂度和动态性能的优化方法。首先推导简化核函数Cooley-Tukey FFT算法的运算量公式, 给出不同简化点数下的运算量比较;然后通过仿真分析了不同核函数简化点数的频谱杂散分 布 规律;最后综合评价不同简化点数的运算时间和瞬时动态性能。所得结论对宽带数字接收机 的性能优化有一定指导意义。     

Quality Performance,Proximate Composition and Sensory Evaluation of Developed Flavoured Instant Popcorn

Popcorn is a world famous snack food with significant commercial demand. Its market has been continuously growing in Sri Lanka. At the same time, different variety of instant popcorn products should be tested for sensory attributes, proximate composition and quality performance. The flavoured instant products were developed by adding 15%, 25% and 35% butter and butter oil as separately and 0.5 g, 1.0 g and 1.5 g salt respectively for 20 g of raw popcorn grains. 35% butter incorporated popcorn had significantly higher median score for appearance, taste and overall acceptability. There was no any effect of level of salt added. Proximate composition was determined for raw seed, raw popped flakes and flavoured popped flakes. Butter flavoured popped corn flakes were showed higher level for crude fat content and mineral content while lowest content for carbohydrate 16.71%, 2.4% and 64.2% respectively. Kernels were popped using a microwave oven and visually sorted into three different polymorphisms depending on whether the appendages were expanded unilaterally, bilaterally, or multilaterally. The expansion volume before sorting was comparatively lower and it was 10-11cm3/g. When popped, 37.37%, 14.02%, and 33.57% of kernels were expanded unilaterally, bilaterally, and multilaterally, respectively, while 14.2% of kernels remained unpopped. Expansion volumes in respect to flake weight were shown significant differences for unilaterally, bilaterally, and multilaterally expanded polymorphisms of 9.34, 8.86 and 12.29cm3/g, respectively.     

多策略改进MPA算法与HKELM的变压器故障辨识

为解决目前变压器故障诊断精度低的问题,提出一种多策略改进海洋捕食者算法( MPA) 与混合核极限学习机(HKELM)的变压器故障辨识方法。 首先通过核主成分分析法(KPCA)对高维线性不可分的变压器故障数据进行降维,获取特征支持数据;然后通过伯努利混沌映射、改进阶段转换判据、最佳候选者等策略综合改进 MPA,加强全局开发能力;最后使用改进的 IMPA 算法对 HKELM 的参数寻优,构建变压器故障诊断模型。 为验证模型有效性,分析比较常用算法优化的 HKELM 的 4种变压器故障诊断模型。 其中 IMPA-HKELM 的诊断精度为 94. 7%,相比于另外 3 种基础算法优化的模型,诊断精度分别提升了 5. 4%、8%、10. 7%。 结果表明,提出模型有效提升了故障诊断的分类性能,并实现了较高的故障诊断精度。     

基于FT-NIR光谱技术结合KPCA-MD-SVM对白酒基酒的快速判别

为保证在摘酒过程中基酒分段的准确,研究通过利用傅里叶变换近红外设备对整个摘酒过程中的基酒样品进行光谱采集,使用支持向量机（SVM）对最优预处理的基酒光谱建立基酒分段模型,其模型训练集的正确率为93.02%,测试集判别率为90.08%。为减少建模时间和提高模型的可靠性,使用核主成分分析（KPCA）对基酒光谱数据降维,并对此建立基酒分段模型。其训练集正确率为94.81%,测试集判别率为90.75%,相比无KPCA分析时的分段模型训练集高1.79%,测试集高0.67%。为进一步提高模型的判别能力,使用马氏距离（MD）剔除了降维后的异常数据样品,创建的基酒分段模型训练集对基酒段数的正确率为98.72%,测试集正确率为98.75%。剔除异常样品后的分段模型的训练集正确率提高了3.91%,测试集判别率提高了8%。以上研究表明了KPCA+MD+ SVM基酒分段模型能对基酒进行快速判别,为近红外光谱在自动化摘酒方面提供了一种理论可能。     

用于行人轨迹预测的场景限制时空图卷积网络

目的 针对行人轨迹预测问题,已有的几种结合场景信息的方法基于合并操作通过神经网络隐式学习场景与行人运动的关联,无法直观地解释场景对单个行人运动的调节作用。除此之外,基于图注意力机制的时空图神经网络旨在学习全局模式下行人之间的社会交互,在人群拥挤场景下精度不佳。鉴于此,本文提出一种场景限制时空图卷积神经网络（scene-constrained spatial-temporal graph convolutional neural network,Scene-STGCNN）。方法 Scene-STGCNN由运动模块、基于场景的微调模块、时空卷积和时空外推卷积组成。运动模块以时空图卷积提取局部行人时空特征,避免了时空图神经网络在全局模式下学习交互的局限性。基于场景的微调模块将场景信息嵌入为掩模矩阵,用来调节运动模块生成的中间运动特征,具备实际场景下的物理解释性。通过最小化核密度估计下真实轨迹的负对数似然,增强Scene-STGCNN输出的多模态性,减少预测误差。结果 实验在公开数据集ETH （包含ETH和HOTEL）和UCY （包含UNIV、ZARA1和ZARA2）上与其他7种主流方法进行比较,就平均值而言,相对于性能第2的模型,平均位移误差（average displacement error,ADE）值减少了12%,最终位移误差（final displacement error,FDE）值减少了9%。在同样的数据集上进行了消融实验以验证基于场景的微调模块的有效性,结果表明基于场景的微调模块能有效建模场景对行人轨迹的调节作用,从而减小算法的预测误差。结论 本文提出的场景限制时空图卷积网络能有效融合场景和行人运动,在学习局部模式下行人交互的同时基于场景特征对轨迹特征做实时性调节,相比于其他主流方法,具有更优的性能。