In this paper, we present a cluster-based algorithm for time series outlier mining.We use discrete Fourier transformation (DFT) to transform time series from time domain to frequency domain. Time series thus can be ma...In this paper, we present a cluster-based algorithm for time series outlier mining.We use discrete Fourier transformation (DFT) to transform time series from time domain to frequency domain. Time series thus can be mapped as the points in k -dimensional space.For these points, a cluster-based algorithm is developed to mine the outliers from these points.The algorithm first partitions the input points into disjoint clusters and then prunes the clusters,through judgment that can not contain outliers.Our algorithm has been run in the electrical load time series of one steel enterprise and proved to be effective.展开更多
针对部分场景下标签较少、样本不均衡的时序数据,为了更好的捕捉序列之间的逐步依赖关系,本文一方面使用具有因果关系属性的时域卷积网络构建生成对抗网络,另一方面使用长短期记忆网络构建嵌入网络和复现网络,以实现模型同时处理短期依...针对部分场景下标签较少、样本不均衡的时序数据,为了更好的捕捉序列之间的逐步依赖关系,本文一方面使用具有因果关系属性的时域卷积网络构建生成对抗网络,另一方面使用长短期记忆网络构建嵌入网络和复现网络,以实现模型同时处理短期依存项和长期依存项,从而提出一种基于时域卷积网络和长短期记忆网络的时间序列生成对抗网络(A Time-series Generative Adversarial Network based on Temporal convolutional network and Long-short term memory network, TL-TimeGAN)。采用覆盖性、有用性和相似度检验的综合分析方法作为合成数据质量的评价指标,进一步全面地评价合成数据的覆盖性、预测程度和相似性。最终,基于以太坊欺诈检测数据集,使用Tabnet网络对扩增数据进行异常检测并获得局部特征重要性以及全局特征重要性,以增强扩增数据应用于实际工作的实践指导价值。展开更多
Mill vibration is a common problem in rolling production,which directly affects the thickness accuracy of the strip and may even lead to strip fracture accidents in serious cases.The existing vibration prediction mode...Mill vibration is a common problem in rolling production,which directly affects the thickness accuracy of the strip and may even lead to strip fracture accidents in serious cases.The existing vibration prediction models do not consider the features contained in the data,resulting in limited improvement of model accuracy.To address these challenges,this paper proposes a multi-dimensional multi-modal cold rolling vibration time series prediction model(MDMMVPM)based on the deep fusion of multi-level networks.In the model,the long-term and short-term modal features of multi-dimensional data are considered,and the appropriate prediction algorithms are selected for different data features.Based on the established prediction model,the effects of tension and rolling force on mill vibration are analyzed.Taking the 5th stand of a cold mill in a steel mill as the research object,the innovative model is applied to predict the mill vibration for the first time.The experimental results show that the correlation coefficient(R^(2))of the model proposed in this paper is 92.5%,and the root-mean-square error(RMSE)is 0.0011,which significantly improves the modeling accuracy compared with the existing models.The proposed model is also suitable for the hot rolling process,which provides a new method for the prediction of strip rolling vibration.展开更多
短期预测在智能电网建设中扮演着重要角色,深刻影响电网发输变配用各个环节的智能化改造。短期预测一般基于系统实测数据,而传感器故障,数据传输错误等原因会导致数据质量下降,严重影响短期预测的精确性。为建立数据质量受损情况下的精...短期预测在智能电网建设中扮演着重要角色,深刻影响电网发输变配用各个环节的智能化改造。短期预测一般基于系统实测数据,而传感器故障,数据传输错误等原因会导致数据质量下降,严重影响短期预测的精确性。为建立数据质量受损情况下的精确短期预测模型,提出了结合数据预处理和双向长短期记忆(bi-directional long short-term memory,Bi-LSTM)的短期预测框架Bi-LSTM-DP(bi-directional long short-term memory data preprocessing)。在Bi-LSTM-DP中,采集的数据首先通过均值填补缺失值,进而基于Savitzky-Golay滤波器对数据降噪,最后采用Bi-LSTM提取时间序列的信息,实现短期预测。为了评估所提方法的性能,文中使用实测的公开数据集分别预测风电发电量和负荷需求,与其他参考方法对比表明了所述方法的有效性和鲁棒性。展开更多
文摘In this paper, we present a cluster-based algorithm for time series outlier mining.We use discrete Fourier transformation (DFT) to transform time series from time domain to frequency domain. Time series thus can be mapped as the points in k -dimensional space.For these points, a cluster-based algorithm is developed to mine the outliers from these points.The algorithm first partitions the input points into disjoint clusters and then prunes the clusters,through judgment that can not contain outliers.Our algorithm has been run in the electrical load time series of one steel enterprise and proved to be effective.
文摘针对部分场景下标签较少、样本不均衡的时序数据,为了更好的捕捉序列之间的逐步依赖关系,本文一方面使用具有因果关系属性的时域卷积网络构建生成对抗网络,另一方面使用长短期记忆网络构建嵌入网络和复现网络,以实现模型同时处理短期依存项和长期依存项,从而提出一种基于时域卷积网络和长短期记忆网络的时间序列生成对抗网络(A Time-series Generative Adversarial Network based on Temporal convolutional network and Long-short term memory network, TL-TimeGAN)。采用覆盖性、有用性和相似度检验的综合分析方法作为合成数据质量的评价指标,进一步全面地评价合成数据的覆盖性、预测程度和相似性。最终,基于以太坊欺诈检测数据集,使用Tabnet网络对扩增数据进行异常检测并获得局部特征重要性以及全局特征重要性,以增强扩增数据应用于实际工作的实践指导价值。
基金Project(2023JH26-10100002)supported by the Liaoning Science and Technology Major Project,ChinaProjects(U21A20117,52074085)supported by the National Natural Science Foundation of China+1 种基金Project(2022JH2/101300008)supported by the Liaoning Applied Basic Research Program Project,ChinaProject(22567612H)supported by the Hebei Provincial Key Laboratory Performance Subsidy Project,China。
文摘Mill vibration is a common problem in rolling production,which directly affects the thickness accuracy of the strip and may even lead to strip fracture accidents in serious cases.The existing vibration prediction models do not consider the features contained in the data,resulting in limited improvement of model accuracy.To address these challenges,this paper proposes a multi-dimensional multi-modal cold rolling vibration time series prediction model(MDMMVPM)based on the deep fusion of multi-level networks.In the model,the long-term and short-term modal features of multi-dimensional data are considered,and the appropriate prediction algorithms are selected for different data features.Based on the established prediction model,the effects of tension and rolling force on mill vibration are analyzed.Taking the 5th stand of a cold mill in a steel mill as the research object,the innovative model is applied to predict the mill vibration for the first time.The experimental results show that the correlation coefficient(R^(2))of the model proposed in this paper is 92.5%,and the root-mean-square error(RMSE)is 0.0011,which significantly improves the modeling accuracy compared with the existing models.The proposed model is also suitable for the hot rolling process,which provides a new method for the prediction of strip rolling vibration.
文摘短期预测在智能电网建设中扮演着重要角色,深刻影响电网发输变配用各个环节的智能化改造。短期预测一般基于系统实测数据,而传感器故障,数据传输错误等原因会导致数据质量下降,严重影响短期预测的精确性。为建立数据质量受损情况下的精确短期预测模型,提出了结合数据预处理和双向长短期记忆(bi-directional long short-term memory,Bi-LSTM)的短期预测框架Bi-LSTM-DP(bi-directional long short-term memory data preprocessing)。在Bi-LSTM-DP中,采集的数据首先通过均值填补缺失值,进而基于Savitzky-Golay滤波器对数据降噪,最后采用Bi-LSTM提取时间序列的信息,实现短期预测。为了评估所提方法的性能,文中使用实测的公开数据集分别预测风电发电量和负荷需求,与其他参考方法对比表明了所述方法的有效性和鲁棒性。
