为了检测风电机组发电机异常、减少由故障引起的停机事件发生,基于真实风电场的数据采集与监视控制(Supervisory Control and Data Acquisition,SCADA)系统记录的多维传感器参数,提出一种K-CNN(Convolutional Neural Network,卷积神经网...为了检测风电机组发电机异常、减少由故障引起的停机事件发生,基于真实风电场的数据采集与监视控制(Supervisory Control and Data Acquisition,SCADA)系统记录的多维传感器参数,提出一种K-CNN(Convolutional Neural Network,卷积神经网络)和N-GRU(Gated Recurrent Unit,门控循环单元)相结合的深度学习框架,建立风电机组发电机状态预测模型。首先,用Pearson相关系数分析状态参数相关性;之后,通过权重系数加权得到一维融合参数;其次,针对传统特征提取过程中忽略浅层特征的问题,采用CNN分层提取一维融合参数的特征,并利用核主成分分析(Kernel Principal Component Analysis,KPCA)将不同层的特征提取结果降为一维;然后,针对传统GRU算法参数欠优化问题,利用神经网络架构搜索改进GRU算法,得到N-GRU模型,将降维后的特征提取结果输入N-GRU做预测并得到重构误差,通过设定告警阈值实现状态评估;最后,以新疆某风场中2 MW风电机组为例,验证了该模型的有效性与准确性。展开更多
Technique for horror video recognition is important for its application in web content filtering and surveillance, especially for preventing children from being threaten. In this paper, a novel horror video recognitio...Technique for horror video recognition is important for its application in web content filtering and surveillance, especially for preventing children from being threaten. In this paper, a novel horror video recognition algorithm based on fuzzy comprehensive evolution model is proposed. Three low-level video features are extracted as typical features, and they are video key-light, video colour energy and video rhythm. Analytic Hierarchy Process （AHP） is adopted to estimate the weights of extracted features in fuzzy evolution model. Horror evaluation （membership function） is on shot scale and it is constructed based on the knowledge that videos which share the same affective have similar low-level features. K-Means algorithm is implemented to help finding the most representative feature vectors. The experimental results demonstrate that the proposed approach has good performance in recognition precision, recall rate and F1 measure.展开更多
文摘为了检测风电机组发电机异常、减少由故障引起的停机事件发生,基于真实风电场的数据采集与监视控制(Supervisory Control and Data Acquisition,SCADA)系统记录的多维传感器参数,提出一种K-CNN(Convolutional Neural Network,卷积神经网络)和N-GRU(Gated Recurrent Unit,门控循环单元)相结合的深度学习框架,建立风电机组发电机状态预测模型。首先,用Pearson相关系数分析状态参数相关性;之后,通过权重系数加权得到一维融合参数;其次,针对传统特征提取过程中忽略浅层特征的问题,采用CNN分层提取一维融合参数的特征,并利用核主成分分析(Kernel Principal Component Analysis,KPCA)将不同层的特征提取结果降为一维;然后,针对传统GRU算法参数欠优化问题,利用神经网络架构搜索改进GRU算法,得到N-GRU模型,将降维后的特征提取结果输入N-GRU做预测并得到重构误差,通过设定告警阈值实现状态评估;最后,以新疆某风场中2 MW风电机组为例,验证了该模型的有效性与准确性。
文摘Technique for horror video recognition is important for its application in web content filtering and surveillance, especially for preventing children from being threaten. In this paper, a novel horror video recognition algorithm based on fuzzy comprehensive evolution model is proposed. Three low-level video features are extracted as typical features, and they are video key-light, video colour energy and video rhythm. Analytic Hierarchy Process （AHP） is adopted to estimate the weights of extracted features in fuzzy evolution model. Horror evaluation （membership function） is on shot scale and it is constructed based on the knowledge that videos which share the same affective have similar low-level features. K-Means algorithm is implemented to help finding the most representative feature vectors. The experimental results demonstrate that the proposed approach has good performance in recognition precision, recall rate and F1 measure.
