Efficient point-by-point engine calibration using machine learning and sequential design of experiment strategies

Modern engines are controlled by electronic control units, which operate all the engine actuators based on the signals from various sensors in the engine. Traditionally, the control parameters of the actuators are obtained through huge amount of trial-and-error experiments. However, using traditional approach to calibrate these parameters becomes more challenging with the increasing incorporation of new technologies into advanced engines. In order to reduce the number of experiments required in the calibration process of modern engines, a novel point-by-point engine calibration approach based on machine learning methods is proposed in this study. It is an iterative procedure that, for a given operating point, sequential design of experiment (DoE) strategy is utilized to measure the responses of different engine sensors corresponding to different actuator signals, and a machine learning algorithm called initial-training-free online extreme learning machine is utilized to incrementally learn the relationship between the sensors and actuators based on the measurement acquired. In each iterative cycle, meta-heuristic optimization is performed on the machine-learning-based model to search for the best parameters, which are then used as the initial parameters for generating DoE plan of the next cycle. The iteration is repeated until the optimal parameters of that operating point are found. To verify the effectiveness of the proposed approach, experiments on both simulation engine in commercial software and real engine in test bench have been conducted. The results show that the engine calibration can be carried out with significant fewer experiments and time by using the proposed approach.     

社会化网络、情境因素与知识协同关系实证研究——基于安徽大学学生的样本调查

[目的/意义]知识经济时代,社会化网络越来越得到重视,而探究其对于知识协同的作用一直处于摸索阶段。[方法/过程]本文基于对社会化网络、知识协同基本定义的界定上,利用维度衡量、模型构建为架构,利用数据对模型进行验证分析。并引入情境因素作为外生环境因素探究其对于社会化网络及知识协同的影响。[结果/结论]通过实证分析,表明社会化网络对于知识协同以及情境因素中部分维度有着显著影响;用户因素对于知识协同存在显著影响,情境因素在社会化网络与知识协同之间存在部分中介效应。     

Graph-based discovery and analysis of atomic-scale one-dimensional materials

Recent decades have witnessed an exponential growth in the discovery of low-dimensional materials (LDMs), benefiting from our unprecedented capabilities in characterizing their structure and chemistry with the aid of advanced computational techniques. Recently, the success of two-dimensional compounds has encouraged extensive research into one-dimensional (1D) atomic chains. Here, we present a methodology for topological classification of structural blocks in bulk crystals based on graph theory, leading to the identification of exfoliable 1D atomic chains and their categorization into a variety of chemical families. A subtle interplay is revealed between the prototypical 1D structural motifs and their chemical space. Leveraging the structure graphs, we elucidate the self-passivation mechanism of 1D compounds imparted by lone electron pairs, and reveal the dependence of the electronic band gap on the cationic percolation network formed by connections between structure units. This graph-theory-based formalism could serve as a source of stimuli for the future design of LDMs.     

The interplay between teacher empathy,students’ sense of school belonging,and learning achievement

European Journal of Psychology of Education - Teacher empathy has become an essential topic in educational research for enhancing students’ learning achievement. However, most existing...     

高职院校校歌的文化建设与传承

高职校歌注重技能、敬业精神和爱国主义精神的培养,展示高职院校蓬勃向上的精神风貌,引导学生积极向上的责任担当,凝聚广大师生团结一致向前看。目前,我国高职院校已进入校歌创作快速发展的阶段,但高职校歌的文化建设工作还不完善,需要进一步加强此项工作。高职院校应重视校歌的创作工作,重视校歌的时代感、数字化和大众化,积极发挥校歌独特的教育功能,为系统地培养全面发展的高技能人才服务。     

对当前科学技术期刊工作的几点认识

近半个多世纪，特别是近20年以来，科学技术的生产、传播和应用活动有序地发生了巨大的变革，说明这三者是一个有机的、互动共长的整体。科技期刊工作不论是宏观还是微观，都应当从这个整体出发考虑问题，这三者结合得越紧密越协调，科技期刊工作就越有成效。科技编辑人员在科学技术活动中扮演着选家、守门人、传播者的角色，一些促进科技活动的功能性社会机制也要编辑人员在编辑活动的过程中启动实施，因此，在新形势下应当高度重视编辑人才资源的开发，强化编辑队伍建设。现在我国科技期刊的运营规模狭小、分散，资金和技术力量薄弱，实现产业化、集团化已是大势所趋，不论哪种运营模式都应坚持社会效益第一的最高准则。