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
The accurate estimation of road traffic states can provide decision making for travelers and traffic managers. In this work,an algorithm based on kernel-k nearest neighbor(KNN) matching of road traffic spatial charact...The accurate estimation of road traffic states can provide decision making for travelers and traffic managers. In this work,an algorithm based on kernel-k nearest neighbor(KNN) matching of road traffic spatial characteristics is presented to estimate road traffic states. Firstly, the representative road traffic state data were extracted to establish the reference sequences of road traffic running characteristics(RSRTRC). Secondly, the spatial road traffic state data sequence was selected and the kernel function was constructed, with which the spatial road traffic data sequence could be mapped into a high dimensional feature space. Thirdly, the referenced and current spatial road traffic data sequences were extracted and the Euclidean distances in the feature space between them were obtained. Finally, the road traffic states were estimated from weighted averages of the selected k road traffic states, which corresponded to the nearest Euclidean distances. Several typical links in Beijing were adopted for case studies. The final results of the experiments show that the accuracy of this algorithm for estimating speed and volume is 95.27% and 91.32% respectively, which prove that this road traffic states estimation approach based on kernel-KNN matching of road traffic spatial characteristics is feasible and can achieve a high accuracy.展开更多
Land surface water(LSW) is one of the most important resources for human survival and development, and it is also a main component of global water recycling. A full understanding of the spatial distribution of land su...Land surface water(LSW) is one of the most important resources for human survival and development, and it is also a main component of global water recycling. A full understanding of the spatial distribution of land surface water and a continuous measuring of its dynamics can support to diagnose the global ecosystem and environment. Based on the Global Land 30-water 2000 and Global Land 30-water 2010 products, this research analyzed the spatial distribution pattern and temporal fluctuation of land surface water under scale-levels of global, latitude and longitude, continents, and climate zones. The Global Land 30-water products were corrected the temporal inconsistency of original remotely sensed data using MODIS time-series data, and then calculated the indices such as water area, water ration and coefficient of spatial variation for further analysis. Results show that total water area of land surface is about 3.68 million km2(2010), and occupies 2.73% of land area. The spatial distribution of land surface water is extremely uneven and is gathered mainly in mid- to high-latitude area of the Northern Hemisphere and tropic area. The comparison of water ratio between 2000 and 2010 indicates the overall fluctuation is small but spatially differentiated. The Global Land 30-water products and the statistics provided the fundamental information for analyzing the spatial distribution pattern and temporal fluctuation of land surface water and diagnosing the global ecosystem and environment.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Projects(LQ16E080012,LY14F030012)supported by the Zhejiang Provincial Natural Science Foundation,ChinaProject(61573317)supported by the National Natural Science Foundation of ChinaProject(2015001)supported by the Open Fund for a Key-Key Discipline of Zhejiang University of Technology,China
文摘The accurate estimation of road traffic states can provide decision making for travelers and traffic managers. In this work,an algorithm based on kernel-k nearest neighbor(KNN) matching of road traffic spatial characteristics is presented to estimate road traffic states. Firstly, the representative road traffic state data were extracted to establish the reference sequences of road traffic running characteristics(RSRTRC). Secondly, the spatial road traffic state data sequence was selected and the kernel function was constructed, with which the spatial road traffic data sequence could be mapped into a high dimensional feature space. Thirdly, the referenced and current spatial road traffic data sequences were extracted and the Euclidean distances in the feature space between them were obtained. Finally, the road traffic states were estimated from weighted averages of the selected k road traffic states, which corresponded to the nearest Euclidean distances. Several typical links in Beijing were adopted for case studies. The final results of the experiments show that the accuracy of this algorithm for estimating speed and volume is 95.27% and 91.32% respectively, which prove that this road traffic states estimation approach based on kernel-KNN matching of road traffic spatial characteristics is feasible and can achieve a high accuracy.
基金supported by the National High-Tech Research Program of China(Grant Nos.2009AA122001 and 2009AA122004)
文摘Land surface water(LSW) is one of the most important resources for human survival and development, and it is also a main component of global water recycling. A full understanding of the spatial distribution of land surface water and a continuous measuring of its dynamics can support to diagnose the global ecosystem and environment. Based on the Global Land 30-water 2000 and Global Land 30-water 2010 products, this research analyzed the spatial distribution pattern and temporal fluctuation of land surface water under scale-levels of global, latitude and longitude, continents, and climate zones. The Global Land 30-water products were corrected the temporal inconsistency of original remotely sensed data using MODIS time-series data, and then calculated the indices such as water area, water ration and coefficient of spatial variation for further analysis. Results show that total water area of land surface is about 3.68 million km2(2010), and occupies 2.73% of land area. The spatial distribution of land surface water is extremely uneven and is gathered mainly in mid- to high-latitude area of the Northern Hemisphere and tropic area. The comparison of water ratio between 2000 and 2010 indicates the overall fluctuation is small but spatially differentiated. The Global Land 30-water products and the statistics provided the fundamental information for analyzing the spatial distribution pattern and temporal fluctuation of land surface water and diagnosing the global ecosystem and environment.