The machining unit of hobbing machine tool accounts for a large portion of the energy consumption during the operating phase.The optimization design is a practical means of energy saving and can reduce energy consumpt...The machining unit of hobbing machine tool accounts for a large portion of the energy consumption during the operating phase.The optimization design is a practical means of energy saving and can reduce energy consumption essentially.However,this issue has rarely been discussed in depth in previous research.A comprehensive function of energy consumption of the machining unit is built to address this problem.Surrogate models are established by using effective fitting methods.An integrated optimization model for reducing tool displacement and energy consumption is developed on the basis of the energy consumption function and surrogate models,and the parameters of the motor and structure are considered simultaneously.Results show that the energy consumption and tool displacement of the machining unit are reduced,indicating that energy saving is achieved and the machining accuracy is guaranteed.The influence of optimization variables on the objectives is analyzed to inform the design.展开更多
目的系统评价基于极端梯度提升(eXtreme Gradient Boosting,XGBoost)算法构建的重症加强护理病房(Intensive Care Unit,ICU)死亡风险预测模型的研究现况。方法检索知网、万方、维普、PubMed、Embase、Web of Science、Scopus数据库,搜...目的系统评价基于极端梯度提升(eXtreme Gradient Boosting,XGBoost)算法构建的重症加强护理病房(Intensive Care Unit,ICU)死亡风险预测模型的研究现况。方法检索知网、万方、维普、PubMed、Embase、Web of Science、Scopus数据库,搜集有关基于XGBoost算法构建的ICU死亡风险预测模型的研究,检索时限均为建库至2023年2月18日。由2名研究者独立筛选文献,提取资料并评价纳入研究的偏倚风险后,进行定性系统评价。结果共纳入12篇文献,纳入模型的受试者工作特征曲线下面积为0.750~0.941。10篇文献适用性较好,其余2篇文献适用性不清楚。12篇文献均存在高偏倚风险,偏倚主要来自于不合适的研究数据来源、研究对象的纳排标准不清晰、预测因子定义与评估不一致、基于单因素分析法筛选预测因子、缺乏完善的模型性能评估等。结论现有基于XGBoost算法构建的ICU死亡风险预测模型具有较好的区分度,但其临床预测的准确性还尚不明确。未来还需进一步完善相关研究设计,避免研究中的各类偏倚风险,加强模型的外部验证,确保模型在临床实践中的可行性及有效性。展开更多
针对数控机床能量源多、能量消耗动态变化复杂的特点,提出了一种基于业务流程模型和符号(Business process model and notation,BPMN)的数控机床多源动态能耗建模方法。依据数控机床不同子系统的功率特性,将其划分为时变能耗单元和非时...针对数控机床能量源多、能量消耗动态变化复杂的特点,提出了一种基于业务流程模型和符号(Business process model and notation,BPMN)的数控机床多源动态能耗建模方法。依据数控机床不同子系统的功率特性,将其划分为时变能耗单元和非时变能耗单元,分析了其工作状态及耦合关系对数控机床能耗的影响;基于BPMN2.0规范,构建了能耗单元工作状态BPMN流程模型和BPMN耦合模型,提出了能耗单元工作状态能耗数据及耦合关系时序数据与BPMN模型的数据集成方法,构建了数控机床能耗多源动态特性模型。以某数控铣床加工过程为例验证了所述模型及方法的有效性。展开更多
基金This work was supported in part by the National Natural Science Foundation of China(Grant Nos.51975075 and 52105506)the Chongqing Technology Innovation and Application Program,China(Grant No.cstc2020jscx-msxmX0221).
文摘The machining unit of hobbing machine tool accounts for a large portion of the energy consumption during the operating phase.The optimization design is a practical means of energy saving and can reduce energy consumption essentially.However,this issue has rarely been discussed in depth in previous research.A comprehensive function of energy consumption of the machining unit is built to address this problem.Surrogate models are established by using effective fitting methods.An integrated optimization model for reducing tool displacement and energy consumption is developed on the basis of the energy consumption function and surrogate models,and the parameters of the motor and structure are considered simultaneously.Results show that the energy consumption and tool displacement of the machining unit are reduced,indicating that energy saving is achieved and the machining accuracy is guaranteed.The influence of optimization variables on the objectives is analyzed to inform the design.
文摘目的系统评价基于极端梯度提升(eXtreme Gradient Boosting,XGBoost)算法构建的重症加强护理病房(Intensive Care Unit,ICU)死亡风险预测模型的研究现况。方法检索知网、万方、维普、PubMed、Embase、Web of Science、Scopus数据库,搜集有关基于XGBoost算法构建的ICU死亡风险预测模型的研究,检索时限均为建库至2023年2月18日。由2名研究者独立筛选文献,提取资料并评价纳入研究的偏倚风险后,进行定性系统评价。结果共纳入12篇文献,纳入模型的受试者工作特征曲线下面积为0.750~0.941。10篇文献适用性较好,其余2篇文献适用性不清楚。12篇文献均存在高偏倚风险,偏倚主要来自于不合适的研究数据来源、研究对象的纳排标准不清晰、预测因子定义与评估不一致、基于单因素分析法筛选预测因子、缺乏完善的模型性能评估等。结论现有基于XGBoost算法构建的ICU死亡风险预测模型具有较好的区分度,但其临床预测的准确性还尚不明确。未来还需进一步完善相关研究设计,避免研究中的各类偏倚风险,加强模型的外部验证,确保模型在临床实践中的可行性及有效性。
文摘针对数控机床能量源多、能量消耗动态变化复杂的特点,提出了一种基于业务流程模型和符号(Business process model and notation,BPMN)的数控机床多源动态能耗建模方法。依据数控机床不同子系统的功率特性,将其划分为时变能耗单元和非时变能耗单元,分析了其工作状态及耦合关系对数控机床能耗的影响;基于BPMN2.0规范,构建了能耗单元工作状态BPMN流程模型和BPMN耦合模型,提出了能耗单元工作状态能耗数据及耦合关系时序数据与BPMN模型的数据集成方法,构建了数控机床能耗多源动态特性模型。以某数控铣床加工过程为例验证了所述模型及方法的有效性。
