The National Oceanic and Atmospheric Administration reports a 95% decline in the oldest Arctic ice over the last 33 years [1], while the National Aeronautics and Space Administration states that summer Arctic Sea Ice ...The National Oceanic and Atmospheric Administration reports a 95% decline in the oldest Arctic ice over the last 33 years [1], while the National Aeronautics and Space Administration states that summer Arctic Sea Ice Extent (SIE) is shrinking by 12.2% per decade since 1979 due to warmer temperatures [2]. Given the rapidly changing Arctic conditions, accurate prediction models are crucial. Deep learning models developed for Arctic forecasts primarily focus on exploring convolutional neural networks (CNN) and convolutional Long Short-Term Memory (LSTM) networks, while the exploration of the power of LSTM networks is limited. In this research, we focus on enhancing the performance of an LSTM network for predicting monthly Arctic SIE. We leverage five climate and atmospheric variables, validated for their correlation with SIE in prior studies [3]. We utilize the Spearman’s rank correlation and ExtraTrees regressor to enhance our understanding of the importance of the five variables in predicting SIE. We further enhance our predictor variables with seasonal information, lagged time steps, and a linear regression simulated SIE that accounts for the influence of past SIE on current SIE. Statistical methods guide our selection of data scalers and best evaluation metrics for our model. By experimenting with hyperparameter optimization and advanced deep learning training techniques, such as batch sizes, number of neurons, early stopping, and model checkpoint, our model achieved a Mean Absolute Error (MAE) of 0.191 and R2 of 0.996, underscoring its ability to account for nearly all the variance in our data and holds great promise for the prediction of SIE.展开更多
为实现英文文本标题的自动化生成,研究一套基于长短期记忆网络的句子级LSTM编码策略,并在标题生成模型中引入注意力机制来获取英文文本的上下文向量,保留文本中的重要信息。在此基础上,通过负对数似然函数来对模型加以训练。最后通过Byt...为实现英文文本标题的自动化生成,研究一套基于长短期记忆网络的句子级LSTM编码策略,并在标题生成模型中引入注意力机制来获取英文文本的上下文向量,保留文本中的重要信息。在此基础上,通过负对数似然函数来对模型加以训练。最后通过Byte Cup 2018数据集对本文提出的英语标题自动生成算法进行实验,并通过过ROUGE-N指标对标题生成质量加以评价。实验研究发现,所提出的句子级LSTM编码方案在英文文本标题生成准确性方面相比于其他常规摘要生成模型来说具有显著优势。展开更多
为及时识别、预测车辆的换道行为,综合考虑目标车辆及周边车辆的时空交互关系,结合时间卷积网络(Temporal Convolutional Network,TCN)的时序处理能力和长短期记忆(Long Short Term Memory,LSTM)神经网络的门控记忆机制,构建了基于TCNL...为及时识别、预测车辆的换道行为,综合考虑目标车辆及周边车辆的时空交互关系,结合时间卷积网络(Temporal Convolutional Network,TCN)的时序处理能力和长短期记忆(Long Short Term Memory,LSTM)神经网络的门控记忆机制,构建了基于TCNLSTM网络的车辆换道意图识别模型。首先,将目标车辆的驾驶意图分为直行、向左换道和向右换道3种类型,从CitySim车辆轨迹数据集中提取出目标车辆及对应同车道、左侧车道、右侧车道的相邻前车和相邻后车的轨迹数据,并利用中值滤波算法获得车辆运行状态指标。其次,针对统计学理论和机器学习方法面临的识别精度不高、训练时间长、参数更新慢等问题,提出利用膨胀卷积技术提取时间序列的时序特征,采用门控记忆单元捕捉时序特征的长期依赖关系,并以目标车辆及周边相邻车辆的速度、加速度、航向角、航向角变化率和相对位置信息等54个车辆状态指标为输入变量,以车辆的换道意图为输出变量,构建了一个基于TCN-LSTM网络的车辆换道意图识别模型。最后,对比分析了不同输入时间步长下TCN、支持向量机(Support Vector Machines,SVM)、LSTM和TCN-LSTM模型的识别精度。结果表明:输入时间序列长度为150帧时,TCN-LSTM模型的识别精度达到最高值96.67%;从整体分类精度来看,相比LSTM、TCN和SVM模型,TCN-LSTM模型的换道意图分类准确率分别提升了1.34、0.84和2.46个百分点,展现出了更高的分类性能。展开更多
负荷预测的准确率会影响电力生产和经济发展,根据目前广东电力现货市场的出清机制,超短期负荷预测的准确度对未来电力现货市场出清电价有着重大影响。文章采用数据横向纵向修正法对历史负荷数据进行修正,通过长短期记忆网络(Long Short ...负荷预测的准确率会影响电力生产和经济发展,根据目前广东电力现货市场的出清机制,超短期负荷预测的准确度对未来电力现货市场出清电价有着重大影响。文章采用数据横向纵向修正法对历史负荷数据进行修正,通过长短期记忆网络(Long Short Term Memory Network,LSTM)的预测方法,同时考虑现货市场实际运行时间间隔,对未来15min的负荷进行预测。根据应用情况表明,该方法简单实用,能满足现货市场实际运行出清时的负荷预测要求。展开更多
文摘The National Oceanic and Atmospheric Administration reports a 95% decline in the oldest Arctic ice over the last 33 years [1], while the National Aeronautics and Space Administration states that summer Arctic Sea Ice Extent (SIE) is shrinking by 12.2% per decade since 1979 due to warmer temperatures [2]. Given the rapidly changing Arctic conditions, accurate prediction models are crucial. Deep learning models developed for Arctic forecasts primarily focus on exploring convolutional neural networks (CNN) and convolutional Long Short-Term Memory (LSTM) networks, while the exploration of the power of LSTM networks is limited. In this research, we focus on enhancing the performance of an LSTM network for predicting monthly Arctic SIE. We leverage five climate and atmospheric variables, validated for their correlation with SIE in prior studies [3]. We utilize the Spearman’s rank correlation and ExtraTrees regressor to enhance our understanding of the importance of the five variables in predicting SIE. We further enhance our predictor variables with seasonal information, lagged time steps, and a linear regression simulated SIE that accounts for the influence of past SIE on current SIE. Statistical methods guide our selection of data scalers and best evaluation metrics for our model. By experimenting with hyperparameter optimization and advanced deep learning training techniques, such as batch sizes, number of neurons, early stopping, and model checkpoint, our model achieved a Mean Absolute Error (MAE) of 0.191 and R2 of 0.996, underscoring its ability to account for nearly all the variance in our data and holds great promise for the prediction of SIE.
文摘为实现英文文本标题的自动化生成,研究一套基于长短期记忆网络的句子级LSTM编码策略,并在标题生成模型中引入注意力机制来获取英文文本的上下文向量,保留文本中的重要信息。在此基础上,通过负对数似然函数来对模型加以训练。最后通过Byte Cup 2018数据集对本文提出的英语标题自动生成算法进行实验,并通过过ROUGE-N指标对标题生成质量加以评价。实验研究发现,所提出的句子级LSTM编码方案在英文文本标题生成准确性方面相比于其他常规摘要生成模型来说具有显著优势。
文摘为及时识别、预测车辆的换道行为,综合考虑目标车辆及周边车辆的时空交互关系,结合时间卷积网络(Temporal Convolutional Network,TCN)的时序处理能力和长短期记忆(Long Short Term Memory,LSTM)神经网络的门控记忆机制,构建了基于TCNLSTM网络的车辆换道意图识别模型。首先,将目标车辆的驾驶意图分为直行、向左换道和向右换道3种类型,从CitySim车辆轨迹数据集中提取出目标车辆及对应同车道、左侧车道、右侧车道的相邻前车和相邻后车的轨迹数据,并利用中值滤波算法获得车辆运行状态指标。其次,针对统计学理论和机器学习方法面临的识别精度不高、训练时间长、参数更新慢等问题,提出利用膨胀卷积技术提取时间序列的时序特征,采用门控记忆单元捕捉时序特征的长期依赖关系,并以目标车辆及周边相邻车辆的速度、加速度、航向角、航向角变化率和相对位置信息等54个车辆状态指标为输入变量,以车辆的换道意图为输出变量,构建了一个基于TCN-LSTM网络的车辆换道意图识别模型。最后,对比分析了不同输入时间步长下TCN、支持向量机(Support Vector Machines,SVM)、LSTM和TCN-LSTM模型的识别精度。结果表明:输入时间序列长度为150帧时,TCN-LSTM模型的识别精度达到最高值96.67%;从整体分类精度来看,相比LSTM、TCN和SVM模型,TCN-LSTM模型的换道意图分类准确率分别提升了1.34、0.84和2.46个百分点,展现出了更高的分类性能。
文摘负荷预测的准确率会影响电力生产和经济发展,根据目前广东电力现货市场的出清机制,超短期负荷预测的准确度对未来电力现货市场出清电价有着重大影响。文章采用数据横向纵向修正法对历史负荷数据进行修正,通过长短期记忆网络(Long Short Term Memory Network,LSTM)的预测方法,同时考虑现货市场实际运行时间间隔,对未来15min的负荷进行预测。根据应用情况表明,该方法简单实用,能满足现货市场实际运行出清时的负荷预测要求。
