Traditional track dynamic geometric state(TDGS)simulation incurs substantial computational burdens,posing challenges for developing reliability assessment approach that accounts for TDGS.To overcome these,firstly,a si...Traditional track dynamic geometric state(TDGS)simulation incurs substantial computational burdens,posing challenges for developing reliability assessment approach that accounts for TDGS.To overcome these,firstly,a simulation-based TDGS model is established,and a surrogate-based model,grid search algorithm-particle swarm optimization-genetic algorithm-multi-output least squares support vector regression,is established.Among them,hyperparameter optimization algorithm’s effectiveness is confirmed through test functions.Subsequently,an adaptive surrogate-based probability density evolution method(PDEM)considering random track geometry irregularity(TGI)is developed.Finally,taking curved train-steel spring floating slab track-U beam as case study,the surrogate-based model trained on simulation datasets not only shows accuracy in both time and frequency domains,but also surpasses existing models.Additionally,the adaptive surrogate-based PDEM shows high accuracy and efficiency,outperforming Monte Carlo simulation and simulation-based PDEM.The reliability assessment shows that the TDGS part peak management indexes,left/right vertical dynamic irregularity,right alignment dynamic irregularity,and track twist,have reliability values of 0.9648,0.9918,0.9978,and 0.9901,respectively.The TDGS mean management index,i.e.,track quality index,has reliability value of 0.9950.These findings show that the proposed framework can accurately and efficiently assess the reliability of curved low-stiffness track-viaducts,providing a theoretical basis for the TGI maintenance.展开更多
现有的侧信息集成序列推荐模型中存在对用户表示学习及优化不足的问题,针对此问题提出基于多序列交互与对比学习的侧信息集成序列推荐模型(side-information integrated sequential recommendation model based on multi-sequence inter...现有的侧信息集成序列推荐模型中存在对用户表示学习及优化不足的问题,针对此问题提出基于多序列交互与对比学习的侧信息集成序列推荐模型(side-information integrated sequential recommendation model based on multi-sequence interaction and contrastive learning,MICL)。首先,引入多序列交互注意力机制,对项目序列和侧信息序列构建序列内和序列间的深度关联,从项目和侧信息两个角度捕获用户偏好,生成两个视角的用户表示。其次,采用用户表示优化模块,结合动态难负样本采样策略构建正负样本对,利用自监督信号优化用户表示。最后,通过多任务动态权重调整策略在推荐任务与属性预测任务之间实现动态平衡优化目标,提升模型的鲁棒性和泛化能力。在Beauty、Sports、Toys和Yelp四个公共数据集上进行实验,与效果较好的基线模型相比,MICL的召回率(recall)和归一化折损率(NDCG)平均提升了1.63%和2.35%,验证了MICL对学习和优化用户表示方面的有效性。展开更多
基金Project(52072412)supported by the National Natural Science Foundation of China。
文摘Traditional track dynamic geometric state(TDGS)simulation incurs substantial computational burdens,posing challenges for developing reliability assessment approach that accounts for TDGS.To overcome these,firstly,a simulation-based TDGS model is established,and a surrogate-based model,grid search algorithm-particle swarm optimization-genetic algorithm-multi-output least squares support vector regression,is established.Among them,hyperparameter optimization algorithm’s effectiveness is confirmed through test functions.Subsequently,an adaptive surrogate-based probability density evolution method(PDEM)considering random track geometry irregularity(TGI)is developed.Finally,taking curved train-steel spring floating slab track-U beam as case study,the surrogate-based model trained on simulation datasets not only shows accuracy in both time and frequency domains,but also surpasses existing models.Additionally,the adaptive surrogate-based PDEM shows high accuracy and efficiency,outperforming Monte Carlo simulation and simulation-based PDEM.The reliability assessment shows that the TDGS part peak management indexes,left/right vertical dynamic irregularity,right alignment dynamic irregularity,and track twist,have reliability values of 0.9648,0.9918,0.9978,and 0.9901,respectively.The TDGS mean management index,i.e.,track quality index,has reliability value of 0.9950.These findings show that the proposed framework can accurately and efficiently assess the reliability of curved low-stiffness track-viaducts,providing a theoretical basis for the TGI maintenance.
文摘现有的侧信息集成序列推荐模型中存在对用户表示学习及优化不足的问题,针对此问题提出基于多序列交互与对比学习的侧信息集成序列推荐模型(side-information integrated sequential recommendation model based on multi-sequence interaction and contrastive learning,MICL)。首先,引入多序列交互注意力机制,对项目序列和侧信息序列构建序列内和序列间的深度关联,从项目和侧信息两个角度捕获用户偏好,生成两个视角的用户表示。其次,采用用户表示优化模块,结合动态难负样本采样策略构建正负样本对,利用自监督信号优化用户表示。最后,通过多任务动态权重调整策略在推荐任务与属性预测任务之间实现动态平衡优化目标,提升模型的鲁棒性和泛化能力。在Beauty、Sports、Toys和Yelp四个公共数据集上进行实验,与效果较好的基线模型相比,MICL的召回率(recall)和归一化折损率(NDCG)平均提升了1.63%和2.35%,验证了MICL对学习和优化用户表示方面的有效性。
