As a new mode and means of smart manufacturing,smart cloud manufacturing(SCM)faces great challenges in massive supply and demand,dynamic resource collaboration and intelligent adaptation.To address the problem,this pa...As a new mode and means of smart manufacturing,smart cloud manufacturing(SCM)faces great challenges in massive supply and demand,dynamic resource collaboration and intelligent adaptation.To address the problem,this paper proposes an SCM-oriented dynamic supply-demand(SD)intelligent adaptation model for massive manufacturing services.In this model,a collaborative network model is established based on the properties of both the supply-demand and their relationships;in addition,an algorithm based on deep graph clustering(DGC)and aligned sampling(AS)is used to divide and conquer the large adaptation domain to solve the problem of the slow computational speed caused by the high complexity of spatiotemporal search in the collaborative network model.At the same time,an intelligent supply-demand adaptation method driven by the quality of service(QoS)is established,in which the experiences of adaptation are shared among adaptation subdomains through deep reinforcement learning(DRL)powered by a transfer mechanism to improve the poor adaptation results caused by dynamic uncertainty.The results show that the model and the solution proposed in this paper can performcollaborative and intelligent supply-demand adaptation for themassive and dynamic resources in SCM through autonomous learning and can effectively performglobal supply-demand matching and optimal resource allocation.展开更多
EGP(English for General Purposes)教学转向ESP(English for Specific Purposes)教学是我国硕士研究生英语教学的大趋势,学生对专业英语学习的需求及动机决定了研究生英语学习的结果,同时也决定了ESP教学目标的真正实现。所以,对研究生...EGP(English for General Purposes)教学转向ESP(English for Specific Purposes)教学是我国硕士研究生英语教学的大趋势,学生对专业英语学习的需求及动机决定了研究生英语学习的结果,同时也决定了ESP教学目标的真正实现。所以,对研究生ESP学习的需求和动机分析可以为ESP教学实践提供积极的理论借鉴。展开更多
城市不同区域网约车供需缺口预测可为车辆调度策略提供支持,从而提高车辆运行效率和乘客服务水平.为实现网约车供需缺口短时预测,提出一种基于时空数据挖掘的深度学习预测模型(Spatio-Temporal Deep Learning Model, S-TDL).该模型由时...城市不同区域网约车供需缺口预测可为车辆调度策略提供支持,从而提高车辆运行效率和乘客服务水平.为实现网约车供需缺口短时预测,提出一种基于时空数据挖掘的深度学习预测模型(Spatio-Temporal Deep Learning Model, S-TDL).该模型由时空变量模型、空间属性变量模型和环境变量模型3个子模型融合而成,可捕捉时空关联性、区域差异性和环境变化对供需缺口的影响.同时,提出特征聚类—最大信息系数两阶段特征选择方法,筛选与供需缺口相关性强的特征变量,提高训练效率,减少过拟合.滴滴出行实例分析证明,特征选择后的STDL模型预测精度显著优于BP神经网络、长短期记忆网络和卷积神经网络.展开更多
基金This paper was supported in part by the National Natural Science Foundation of China under Grant 62172235in part by Natural Science Foundation of Jiangsu Province of China under Grant BK20191381in part by Primary Research&Development Plan of Jiangsu Province Grant BE2019742.
文摘As a new mode and means of smart manufacturing,smart cloud manufacturing(SCM)faces great challenges in massive supply and demand,dynamic resource collaboration and intelligent adaptation.To address the problem,this paper proposes an SCM-oriented dynamic supply-demand(SD)intelligent adaptation model for massive manufacturing services.In this model,a collaborative network model is established based on the properties of both the supply-demand and their relationships;in addition,an algorithm based on deep graph clustering(DGC)and aligned sampling(AS)is used to divide and conquer the large adaptation domain to solve the problem of the slow computational speed caused by the high complexity of spatiotemporal search in the collaborative network model.At the same time,an intelligent supply-demand adaptation method driven by the quality of service(QoS)is established,in which the experiences of adaptation are shared among adaptation subdomains through deep reinforcement learning(DRL)powered by a transfer mechanism to improve the poor adaptation results caused by dynamic uncertainty.The results show that the model and the solution proposed in this paper can performcollaborative and intelligent supply-demand adaptation for themassive and dynamic resources in SCM through autonomous learning and can effectively performglobal supply-demand matching and optimal resource allocation.
文摘EGP(English for General Purposes)教学转向ESP(English for Specific Purposes)教学是我国硕士研究生英语教学的大趋势,学生对专业英语学习的需求及动机决定了研究生英语学习的结果,同时也决定了ESP教学目标的真正实现。所以,对研究生ESP学习的需求和动机分析可以为ESP教学实践提供积极的理论借鉴。
文摘城市不同区域网约车供需缺口预测可为车辆调度策略提供支持,从而提高车辆运行效率和乘客服务水平.为实现网约车供需缺口短时预测,提出一种基于时空数据挖掘的深度学习预测模型(Spatio-Temporal Deep Learning Model, S-TDL).该模型由时空变量模型、空间属性变量模型和环境变量模型3个子模型融合而成,可捕捉时空关联性、区域差异性和环境变化对供需缺口的影响.同时,提出特征聚类—最大信息系数两阶段特征选择方法,筛选与供需缺口相关性强的特征变量,提高训练效率,减少过拟合.滴滴出行实例分析证明,特征选择后的STDL模型预测精度显著优于BP神经网络、长短期记忆网络和卷积神经网络.
