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1.
Due to its outstanding ability in processing large quantity and high-dimensional
data, machine learning models have been used in many cases, such as pattern recognition,
classification, spam filtering, data mining and forecasting. As an outstanding machine
learning algorithm, K-Nearest Neighbor (KNN) has been widely used in different situations,
yet in selecting qualified applicants for winning a funding is almost new. The major problem
lies in how to accurately determine the importance of attributes. In this paper, we propose a
Feature-weighted Gradient Decent K-Nearest Neighbor (FGDKNN) method to classify
funding applicants in to two types: approved ones or not approved ones. The FGDKNN is
based on a gradient decent learning algorithm to update weight. It updatesthe weight of labels
by minimizing error ratio iteratively, so that the importance of attributes can be described
better. We investigate the performance of FGDKNN with Beijing Innofund. The results show
that FGDKNN performs about 23%, 20%, 18%, 15% better than KNN, SVM, DT and ANN,
respectively. Moreover, the FGDKNN has fast convergence time under different training
scales, and has good performance under different settings. 相似文献
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系统发生不对称故障时,短路电流、电压中出现负序分量,导致光伏逆变器直流母线电压中产生二倍频振荡,该振荡分量经过控制回路流通后将在光伏逆变器输出短路电流中生成3次谐波。推导了逆变器输出短路电流3次谐波的解析表达式,计及光伏逆变器的限幅控制特性、负序控制策略、低电压穿越策略,研究了3次谐波的变化特性,然后分析了3次谐波对变压器差动保护电流互感器饱和判据的影响,指出某些工况下光伏逆变器输出短路电流中的3次谐波含量较大,可能造成变压器差动保护误闭锁,导致变压器区内故障无法切除,并提出了相应的应对策略。大量的仿真试验结果验证了理论分析的正确性。 相似文献
4.
针对传统字典学习方法的训练样本信号单一、重构效果差等缺点,提出一种带标签信息子字典级联的学习方法,对电能质量扰动信号进行扰动识别。该方法首先对不同类别电能质量扰动测试和训练样本采用主成分分析方法进行降维特征提取,对训练样本添加标签信息,其次对不同类别的电能质量样本训练成冗余子字典并级联成结构化字典,最后将级联的字典优化学习并由冗余误差最小值来判断目标的归属类别。仿真实验结果表明,该方法下的识别效果优于支持向量机(SVM)和稀疏表达分类(SRC),抗噪声鲁棒性更强,在信噪比20 dB以上的环境中电能质量复合扰动识别率达到91.40%以上。 相似文献
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Bo Zhong Jinze Zhang Hanqun Wang Long Xia Chunyu Wang Xiaodong Zhang Xiaoxiao Huang Guangwu Wen 《International Journal of Applied Ceramic Technology》2020,17(6):2598-2611
Novel SiC-based nanomaterials, namely the nitrogen and aluminum co-doped SiC@SiO2 core-shell nanowires and nitrogen-doped SiO2/Al2O3 nanoparticles, have been fabricated through a facile thermal treatment process based on the chemical vapor deposition and vapor-liquid reaction. These nanomaterials show remarkable hydrophobicity with a water contact angle (CA) over 140°, which are aroused by the surface zigzag morphology of the nanostructures and the hydrocarbyl groups generated during the preparation process. Moreover the nanocomposites also exhibit relatively prominent microwave absorption (MA) properties in the frequency range of 2.0-18.0 GHz. The minimum reflection loss (RL) value as low as −23.68 dB can be observed at 14.16 GHz when the absorber thickness is 2.6 mm with a loading rate of 16.7 wt%. And the nanocomposites-based absorbent can achieve an effective absorption bandwidth (RL < −10 dB) of 4.48 GHz with the absorbent thickness of 2.5 mm. This enhanced microwave attenuation performance can be attributed to multiple polarizations and perfect impedance matching conditions, as well as multiple internal reflections. These marvelous properties make these N and Al co-doped SiC@SiO2 core-shell nanowires and N-doped SiO2/Al2O3 nanoparticles display extensive application potential as MA materials in harsh environment. 相似文献
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以某黄金矿山为实例,介绍了分段空场嗣后充填采矿法安全高效开采工艺应用,包括采切工程布置、回采工艺流程及参数、采矿设备等,并叙述了采空区CMS探测及残矿回采设计过程。该矿山多年的开采实践表明,该采矿方法能大幅度提高生产能力,降低综合成本,提高作业安全,实现矿体开采的安全、高效、经济的技术目标。 相似文献