With the growing penetration of wind power in power systems, more accurate prediction of wind speed and wind power is required for real-time scheduling and operation. In this paper, a novel forecast model for shortter...With the growing penetration of wind power in power systems, more accurate prediction of wind speed and wind power is required for real-time scheduling and operation. In this paper, a novel forecast model for shortterm prediction of wind speed and wind power is proposed,which is based on singular spectrum analysis(SSA) and locality-sensitive hashing(LSH). To deal with the impact of high volatility of the original time series, SSA is applied to decompose it into two components: the mean trend,which represents the mean tendency of the original time series, and the fluctuation component, which reveals the stochastic characteristics. Both components are reconstructed in a phase space to obtain mean trend segments and fluctuation component segments. After that, LSH is utilized to select similar segments of the mean trend segments, which are then employed in local forecasting, so that the accuracy and efficiency of prediction can be enhanced. Finally, support vector regression is adopted forprediction, where the training input is the synthesis of the similar mean trend segments and the corresponding fluctuation component segments. Simulation studies are conducted on wind speed and wind power time series from four databases, and the final results demonstrate that the proposed model is more accurate and stable in comparison with other models.展开更多
为了提高图像检索的准确度和检索效率,提出一种基于卷积神经网络和局部敏感哈希(Locality-Sensitive Hashing,LSH)算法的图像检索算法。使用图像库ImageNet对视觉几何小组16(Visual Geometry Group 16,VGG16)网络进行训练,获取初始化参...为了提高图像检索的准确度和检索效率,提出一种基于卷积神经网络和局部敏感哈希(Locality-Sensitive Hashing,LSH)算法的图像检索算法。使用图像库ImageNet对视觉几何小组16(Visual Geometry Group 16,VGG16)网络进行训练,获取初始化参数。以卷积神经网络为基础,增加哈希层代替VGG16全连接层,获取图像的高维特征向量。利用哈希函数满足p-稳定分布的LSH算法将高维特征向量映射为哈希码,并将相似图像映射到同一个哈希桶中作为粗检候选集,计算并排序候选集中特征向量欧氏距离完成图像检索,从而得到最终的检索结果。实验结果表明,与其他基于不同哈希算法的图像检索算法相比,所提算法具有较高的准确性和较快的检索速度。展开更多
随着在线用户和物品数量的不断增长,有必要通过追踪和筛选历史数据,为用户提供机制可参考的决策建议。构建统计预测算法是实现启发式预测用户兴趣的有效机制。因此,在充分利用用户自身历史偏好和潜在偏好的前提下,提出兴趣相似度传递思...随着在线用户和物品数量的不断增长,有必要通过追踪和筛选历史数据,为用户提供机制可参考的决策建议。构建统计预测算法是实现启发式预测用户兴趣的有效机制。因此,在充分利用用户自身历史偏好和潜在偏好的前提下,提出兴趣相似度传递思想,分析用户的社交关联强度,计算用户的邻近社交兴趣和选择趋向特征,设计并实现了可扩展的局部敏感哈希(Improved Local Sensitivity Hashing,ILSH)统计预测算法。实验表明,该算法在有利于相似度计算量剧增的背景下,在提高兴趣预测的准确性和可靠性方面优于其他近似算法。展开更多
基金supported by the Guangdong Innovative Research Team Program(No.201001N0104744201)the State Key Program of the National Natural Science Foundation of China(No.51437006)
文摘With the growing penetration of wind power in power systems, more accurate prediction of wind speed and wind power is required for real-time scheduling and operation. In this paper, a novel forecast model for shortterm prediction of wind speed and wind power is proposed,which is based on singular spectrum analysis(SSA) and locality-sensitive hashing(LSH). To deal with the impact of high volatility of the original time series, SSA is applied to decompose it into two components: the mean trend,which represents the mean tendency of the original time series, and the fluctuation component, which reveals the stochastic characteristics. Both components are reconstructed in a phase space to obtain mean trend segments and fluctuation component segments. After that, LSH is utilized to select similar segments of the mean trend segments, which are then employed in local forecasting, so that the accuracy and efficiency of prediction can be enhanced. Finally, support vector regression is adopted forprediction, where the training input is the synthesis of the similar mean trend segments and the corresponding fluctuation component segments. Simulation studies are conducted on wind speed and wind power time series from four databases, and the final results demonstrate that the proposed model is more accurate and stable in comparison with other models.
文摘为了提高图像检索的准确度和检索效率,提出一种基于卷积神经网络和局部敏感哈希(Locality-Sensitive Hashing,LSH)算法的图像检索算法。使用图像库ImageNet对视觉几何小组16(Visual Geometry Group 16,VGG16)网络进行训练,获取初始化参数。以卷积神经网络为基础,增加哈希层代替VGG16全连接层,获取图像的高维特征向量。利用哈希函数满足p-稳定分布的LSH算法将高维特征向量映射为哈希码,并将相似图像映射到同一个哈希桶中作为粗检候选集,计算并排序候选集中特征向量欧氏距离完成图像检索,从而得到最终的检索结果。实验结果表明,与其他基于不同哈希算法的图像检索算法相比,所提算法具有较高的准确性和较快的检索速度。
文摘随着在线用户和物品数量的不断增长,有必要通过追踪和筛选历史数据,为用户提供机制可参考的决策建议。构建统计预测算法是实现启发式预测用户兴趣的有效机制。因此,在充分利用用户自身历史偏好和潜在偏好的前提下,提出兴趣相似度传递思想,分析用户的社交关联强度,计算用户的邻近社交兴趣和选择趋向特征,设计并实现了可扩展的局部敏感哈希(Improved Local Sensitivity Hashing,ILSH)统计预测算法。实验表明,该算法在有利于相似度计算量剧增的背景下,在提高兴趣预测的准确性和可靠性方面优于其他近似算法。
