氧化亚铁(FeO)含量是衡量烧结矿强度和还原性的重要指标,烧结过程FeO含量的实时准确预测对于提升烧结质量、优化烧结工艺具有重要意义.然而烧结过程热状态参数缺失、过程参数波动频繁给FeO含量的高精度预测带来巨大的挑战,为此,提出一...氧化亚铁(FeO)含量是衡量烧结矿强度和还原性的重要指标,烧结过程FeO含量的实时准确预测对于提升烧结质量、优化烧结工艺具有重要意义.然而烧结过程热状态参数缺失、过程参数波动频繁给FeO含量的高精度预测带来巨大的挑战,为此,提出一种基于知识与变权重回声状态网络融合(Fusion of data-knowledge and adaptive weight echo state network, DK-AWESN)的烧结过程FeO含量预测方法.首先,针对烧结过程热状态参数缺失的问题,建立烧结料层最高温度分布模型,实现基于料层温度分布特征的FeO含量等级划分;其次,针对烧结过程参数波动频繁的问题,提出基于核函数高维映射的多尺度数据配准方法,有效抑制离群点的影响,提升建模数据的质量;最后,针对烧结过程数据驱动模型缺乏机理认知致使模型预测精度不高的问题,将过程数据中提取得到的FeO含量等级知识与AW-ESN (Adaptive weight echo state network)结合,建立DK-AWESN模型,有效提升复杂工况下FeO含量的预测精度.现场工业数据试验表明,所提方法能实时准确地预测烧结过程FeO含量,为烧结过程的智能化调控提供实时有效的FeO含量反馈信息.展开更多
The use of Amazon Web Services is growing rapidly as more users are adopting the technology.It has various functionalities that can be used by large corporates and individuals as well.Sentiment analysis is used to bui...The use of Amazon Web Services is growing rapidly as more users are adopting the technology.It has various functionalities that can be used by large corporates and individuals as well.Sentiment analysis is used to build an intelligent system that can study the opinions of the people and help to classify those related emotions.In this research work,sentiment analysis is performed on the AWS Elastic Compute Cloud(EC2)through Twitter data.The data is managed to the EC2 by using elastic load balancing.The collected data is subjected to preprocessing approaches to clean the data,and then machine learning-based logistic regression is employed to categorize the sentiments into positive and negative sentiments.High accuracy of 94.17%is obtained through the proposed machine learning model which is higher than the other models that are developed using the existing algorithms.展开更多
亚马逊云科技(Amazon Web Services,AWS)作为全球领先的云计算平台之一,宣布从2024年2月1日起,对其提供的公共IPv4地址进行收费,并鼓励客户向IPv6迁移。AWS表示,在过去5年时间里,购买单个IPv4地址的成本增加了300%以上,因此选择通过收...亚马逊云科技(Amazon Web Services,AWS)作为全球领先的云计算平台之一,宣布从2024年2月1日起,对其提供的公共IPv4地址进行收费,并鼓励客户向IPv6迁移。AWS表示,在过去5年时间里,购买单个IPv4地址的成本增加了300%以上,因此选择通过收费来降低运营成本。届时AWS用户将在每个公共IPv4地址上收取0.005美元/小时的费用。展开更多
研究了带有Chaplygin压力的耦合Aw-Rascle(CAR)交通模型的黎曼问题.通过令耦合模型两侧压力同时消失,得到上述黎曼解的极限,并证明了该极限具有相同初值的无压气体动力(Pressureless Gas Dynamics,PGD)模型的黎曼解.更进一步,证得极限后...研究了带有Chaplygin压力的耦合Aw-Rascle(CAR)交通模型的黎曼问题.通过令耦合模型两侧压力同时消失,得到上述黎曼解的极限,并证明了该极限具有相同初值的无压气体动力(Pressureless Gas Dynamics,PGD)模型的黎曼解.更进一步,证得极限后的delta激波解的权重和速度与PGD模型的delta激波解的权重和速度完全一致.此外,由解的渐近行为,可以观察到稀疏接触间断到接触间断的转化.展开更多
文摘氧化亚铁(FeO)含量是衡量烧结矿强度和还原性的重要指标,烧结过程FeO含量的实时准确预测对于提升烧结质量、优化烧结工艺具有重要意义.然而烧结过程热状态参数缺失、过程参数波动频繁给FeO含量的高精度预测带来巨大的挑战,为此,提出一种基于知识与变权重回声状态网络融合(Fusion of data-knowledge and adaptive weight echo state network, DK-AWESN)的烧结过程FeO含量预测方法.首先,针对烧结过程热状态参数缺失的问题,建立烧结料层最高温度分布模型,实现基于料层温度分布特征的FeO含量等级划分;其次,针对烧结过程参数波动频繁的问题,提出基于核函数高维映射的多尺度数据配准方法,有效抑制离群点的影响,提升建模数据的质量;最后,针对烧结过程数据驱动模型缺乏机理认知致使模型预测精度不高的问题,将过程数据中提取得到的FeO含量等级知识与AW-ESN (Adaptive weight echo state network)结合,建立DK-AWESN模型,有效提升复杂工况下FeO含量的预测精度.现场工业数据试验表明,所提方法能实时准确地预测烧结过程FeO含量,为烧结过程的智能化调控提供实时有效的FeO含量反馈信息.
基金This research project was supported by the Deanship of Scientific Research,Prince Sattam Bin Abdulaziz University,KSA,Project Grant No.2021/01/17783,Sha M,www.psau.edu.sa.
文摘The use of Amazon Web Services is growing rapidly as more users are adopting the technology.It has various functionalities that can be used by large corporates and individuals as well.Sentiment analysis is used to build an intelligent system that can study the opinions of the people and help to classify those related emotions.In this research work,sentiment analysis is performed on the AWS Elastic Compute Cloud(EC2)through Twitter data.The data is managed to the EC2 by using elastic load balancing.The collected data is subjected to preprocessing approaches to clean the data,and then machine learning-based logistic regression is employed to categorize the sentiments into positive and negative sentiments.High accuracy of 94.17%is obtained through the proposed machine learning model which is higher than the other models that are developed using the existing algorithms.
文摘亚马逊云科技(Amazon Web Services,AWS)作为全球领先的云计算平台之一,宣布从2024年2月1日起,对其提供的公共IPv4地址进行收费,并鼓励客户向IPv6迁移。AWS表示,在过去5年时间里,购买单个IPv4地址的成本增加了300%以上,因此选择通过收费来降低运营成本。届时AWS用户将在每个公共IPv4地址上收取0.005美元/小时的费用。
文摘研究了带有Chaplygin压力的耦合Aw-Rascle(CAR)交通模型的黎曼问题.通过令耦合模型两侧压力同时消失,得到上述黎曼解的极限,并证明了该极限具有相同初值的无压气体动力(Pressureless Gas Dynamics,PGD)模型的黎曼解.更进一步,证得极限后的delta激波解的权重和速度与PGD模型的delta激波解的权重和速度完全一致.此外,由解的渐近行为,可以观察到稀疏接触间断到接触间断的转化.
