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
Similarity measure design for discrete data group was proposed. Similarity measure design for continuous membership function was also carried out. Proposed similarity measures were designed based on fuzzy number and d...Similarity measure design for discrete data group was proposed. Similarity measure design for continuous membership function was also carried out. Proposed similarity measures were designed based on fuzzy number and distance measure, and were proved. To calculate the degree of similarity of discrete data, relative degree between data and total distribution was obtained. Discrete data similarity measure was completed with combination of mentioned relative degrees. Power interconnected system with multi characteristics was considered to apply discrete similarity measure. Naturally, similarity measure was extended to multi-dimensional similarity measure case, and applied to bus clustering problem.展开更多
阐述了一种基于表面生成的多波束数据处理方法——CUBE(Combined Uncertainty and Bathymetry Estimator)算法,用该方法可以对观测区域网格节点"真实"水深及相关误差进行估计。与从测量水深中选择出"最佳"数据的手...阐述了一种基于表面生成的多波束数据处理方法——CUBE(Combined Uncertainty and Bathymetry Estimator)算法,用该方法可以对观测区域网格节点"真实"水深及相关误差进行估计。与从测量水深中选择出"最佳"数据的手工交互方式的多波束数据编辑手段不同,CUBE算法具有很强的抗差性和较高的效率,适合于实时多波束数据处理。对南海某测区多波束数据处理结果表明,在没有人工干预的情况下,利用CUBE算法去噪生成的海底DTM图与手工编辑生成的相当吻合。CUBE算法和手工编辑方法综合对比得出,CUBE算法能够很好地保留水深地形细节,在计算效率、误差评估、实时处理等方面比手工编辑方法具有较大的优势。展开更多
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.
基金Project(2010-0020163) supported by Key Research Institute Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education, Science and Technology, Korea
文摘Similarity measure design for discrete data group was proposed. Similarity measure design for continuous membership function was also carried out. Proposed similarity measures were designed based on fuzzy number and distance measure, and were proved. To calculate the degree of similarity of discrete data, relative degree between data and total distribution was obtained. Discrete data similarity measure was completed with combination of mentioned relative degrees. Power interconnected system with multi characteristics was considered to apply discrete similarity measure. Naturally, similarity measure was extended to multi-dimensional similarity measure case, and applied to bus clustering problem.
文摘阐述了一种基于表面生成的多波束数据处理方法——CUBE(Combined Uncertainty and Bathymetry Estimator)算法,用该方法可以对观测区域网格节点"真实"水深及相关误差进行估计。与从测量水深中选择出"最佳"数据的手工交互方式的多波束数据编辑手段不同,CUBE算法具有很强的抗差性和较高的效率,适合于实时多波束数据处理。对南海某测区多波束数据处理结果表明,在没有人工干预的情况下,利用CUBE算法去噪生成的海底DTM图与手工编辑生成的相当吻合。CUBE算法和手工编辑方法综合对比得出,CUBE算法能够很好地保留水深地形细节,在计算效率、误差评估、实时处理等方面比手工编辑方法具有较大的优势。
