The exercise recommendation system is emerging as a promising application in online learning scenarios,providing personalized recommendations to assist students with explicit learning directions.Existing solutions gen...The exercise recommendation system is emerging as a promising application in online learning scenarios,providing personalized recommendations to assist students with explicit learning directions.Existing solutions generally follow a collaborative filtering paradigm,while the implicit connections between students(exercises)have been largely ignored.In this study,we aim to propose an exercise recommendation paradigm that can reveal the latent connections between student-student(exercise-exercise).Specifically,a new framework was proposed,namely personalized exercise recommendation with student and exercise portraits(PERP).It consists of three sequential and interdependent modules:Collaborative student exercise graph(CSEG)construction,joint random walk,and recommendation list optimization.Technically,CSEG is created as a unified heterogeneous graph with students’response behaviors and student(exercise)relationships.Then,a joint random walk to take full advantage of the spectral properties of nearly uncoupled Markov chains is performed on CSEG,which allows for full exploration of both similar exercises that students have finished and connections between students(exercises)with similar portraits.Finally,we propose to optimize the recommendation list to obtain different exercise suggestions.After analyses of two public datasets,the results demonstrated that PERP can satisfy novelty,accuracy,and diversity.展开更多
针对纯电动商用车在连续制动时,气源压力偏低会导致驱动轴耦合制动力响应速度变慢,影响制动能量回收效率的问题,提出一种基于比例继动阀的解耦式制动能量回收系统(uncoupled braking energy recovery system,URBS)方案。首先,基于比例...针对纯电动商用车在连续制动时,气源压力偏低会导致驱动轴耦合制动力响应速度变慢,影响制动能量回收效率的问题,提出一种基于比例继动阀的解耦式制动能量回收系统(uncoupled braking energy recovery system,URBS)方案。首先,基于比例继动阀的迟滞特性,采用前馈-单神经元PID控制方法,实现制动气压的准确输出;其次,以电池SOC、车速等为约束条件,根据气源压力信号确定供压模式,并制定解耦式制动能量回收控制策略;最后,基于AMESim,MATLAB/Simulink及TruckSim搭建联合仿真平台,选取单次制动工况与循环工况验证了制动力耦合效果及系统的制动能量回收效果。结果表明,基于比例继动阀的URBS可实现耦合制动力的快速响应,达到稳态压力值75%的时间小于0.1 s,且在中国重型商用车行驶工况和中国重型商用车瞬态工况下有效制动能量回收率分别为10.13%,17.17%。所提URBS方案能有效提高驱动轴耦合制动力的响应速度及耦合精度,可为纯电动商用车气压式URBS方案设计提供参考。展开更多
基金supported by the Industrial Support Project of Gansu Colleges under Grant No.2022CYZC-11Gansu Natural Science Foundation Project under Grant No.21JR7RA114+1 种基金National Natural Science Foundation of China under Grants No.622760736,No.1762078,and No.61363058Northwest Normal University Teachers Research Capacity Promotion Plan under Grant No.NWNU-LKQN2019-2.
文摘The exercise recommendation system is emerging as a promising application in online learning scenarios,providing personalized recommendations to assist students with explicit learning directions.Existing solutions generally follow a collaborative filtering paradigm,while the implicit connections between students(exercises)have been largely ignored.In this study,we aim to propose an exercise recommendation paradigm that can reveal the latent connections between student-student(exercise-exercise).Specifically,a new framework was proposed,namely personalized exercise recommendation with student and exercise portraits(PERP).It consists of three sequential and interdependent modules:Collaborative student exercise graph(CSEG)construction,joint random walk,and recommendation list optimization.Technically,CSEG is created as a unified heterogeneous graph with students’response behaviors and student(exercise)relationships.Then,a joint random walk to take full advantage of the spectral properties of nearly uncoupled Markov chains is performed on CSEG,which allows for full exploration of both similar exercises that students have finished and connections between students(exercises)with similar portraits.Finally,we propose to optimize the recommendation list to obtain different exercise suggestions.After analyses of two public datasets,the results demonstrated that PERP can satisfy novelty,accuracy,and diversity.
