风电主齿箱系统低温待机方案的研究

以1.5 MW风力发电机组为例,对目前风电主齿箱系统低温环境待机困难的现状进行研究,通过该类主齿箱系统待机时加热与散热的计算,对循环周期内各时段润滑油的温度进行推算,根据推算结果,解释了主齿箱系统低温环境待机困难的主要原因,并提出了低温待机方案的改进策略.     

Contrastive Meta-learning on Heterogeneous Information Networks for Cold-start Recommendation

In recommender systems, the cold-start issue is challenging due to the lack of interactions between users or items. Such an issue can be alleviated via data-level and model-level strategies. Traditional data-level methods employ auxiliary information like feature information to enhance the learning of user and item embeddings. Recently, Heterogeneous Information Networks (HINs) have been incorporated into the recommender system as they provide more fruitful auxiliary information and meaningful semantics. However, these models are unable to capture the structural and semantic information comprehensively and neglect the unlabeled information of HINs during training. Model-level methods propose to apply the meta-learning framework which naturally fits into the cold-start issue, as it learns the prior knowledge from similar tasks and adapts to new tasks quickly with few labeled samples. Therefore, we propose a contrastive meta-learning framework on HINs named CM-HIN, which addresses the cold-start issue at both data level and model level. In particular, we explore meta-path and network schema views to describe the higher-order and local structural information of HINs. Within meta-path and network schema views, contrastive learning is adopted to mine the unlabeled information of HINs and incorporate these two views. Extensive experiments on three benchmark datasets demonstrate that CM-HIN outperforms all state-of-the-art baselines in three cold-start scenarios.     

基于首次着火循环的低温冷起动特性

从发动机首次着火循环的单一循环特性分析入手,研究了以液化石油气(LPG)为燃料的电控喷射小型点燃式发动机低温冷起动首次着火循环的燃烧情况.当环境温度从-9~4℃变化时,对不同过量空气系数对冷起动的首次着火循环的影响进行分析,结果表明环境温度对冷起动首循环的HC排放有很大影响,而对着火或起动瞬态转速影响不大;过量空气系数直接影响发动机冷起动首循环的着火可靠性和HC排放.通过研究,得出保证此LPG发动机首次着火循环可靠起动时的最佳过量空气系数为0.74左右.     

The Potential for Use of Gold in Automotive Pollution Control Technologies: A Short Review

Recent studies relevant to the future use of gold catalysts in the automotive industry are summarized. Gold catalysts have been examined for their potential in low temperature activity to combat cold-start emission problems and removal of NO _x from lean-burn gasoline and diesel engines. The justification for developing gold catalyst technologies is based both on their promising technical performance and the relatively stable price and greater availability of gold compared with the platinum group metals (PGMs). Use of gold catalysts under mild conditions could also produce lower proportions of CO in the hydrogen streams used for automotive fuel cells and they could be used for the selective oxidation of carbon monoxide in these hydrogen streams in order to increase the efficiency of these fuel cells. Technical barriers are indicated which still remain to be overcome before application of gold catalysts is successful in the automotive sphere.     

钻机用冷启动装置的研制与应用

石油钻机柴油机启动是整台钻机的关键之一，而提供压缩空气又是启动柴油机的必要条件。冷启动机组是为钻机供气的重要部件，相对于辅助电动压缩机而言，构成了柴油机启动双保险；文章叙述了冷启动装置在设计中关键设备的选型以及优点；本装置是由雅马哈汽油发动机和Z系列压缩机组成．具有启动使用可靠、体积小、安全环保等特点；用户使用反映良好，是现代钻机的重要辅助设备。     

Sub-ambient cold-start emissions reduction using SwRI® PO x catalyst technology

This paper describes the application of SwRI’s cold-start PO _x catalyst technology to reduce cold-start hydrocarbon emissions from a US Tier 2 vehicle at −7 °C. A reduction in −7 °C (20 °F) cold-start hydrocarbons will help US Tier 2 vehicles meet the proposed EPA NMOG standards. Improvements in cold temperature hydrocarbon emissions would also be beneficial in many parts of Europe during the winter months. In this work, a total hydrocarbon reduction of 19% was realized at 24 °C, in line with previous results, but only up to 3% at −7 °C. Insufficient oxygen in the engine-out exhaust gas at −7 °C was determined to be the reason why the PO _x catalyst failed to significantly reduce HC emissions. Addition of supplemental oxygen to the exhaust during the cold-start, to simulate an adjustment in the engine calibration to less rich operation, resulted in a total hydrocarbon reduction of 18% with the PO _x catalysts in place, but no benefit when the PO _x catalysts were removed. Hence, the PO _x catalyst approach can be used to good effect, even under sub-ambient cold-start conditions.     

基于上下文相似度和社会网络的移动服务推荐方法

针对传统的基于协同过滤的移动服务推荐方法存在的数据稀疏性和用户冷启动问题,提出一种基于上下文相似度和社会网络的移动服务推荐方法(Context-similarity and Social-network based Mobile Service Recommendation,CSMSR).该方法将基于用户的上下文相似度引入个性化服务推荐过程,并挖掘由移动用户虚拟交互构成的社会关系网络,按照信任度选取信任用户;然后结合基于用户评分相似度计算发现的近邻,分别从相似用户和信任用户中选择相应的邻居用户,对目标用户进行偏好预测和推荐.实验表明,与已有的服务推荐方法TNCF、SRMTC及CF-DNC相比,CSMSR方法有效地缓解数据稀疏性并提高推荐准确率,有利于发现用户感兴趣的服务,提升用户个性化服务体验.     

Hydrocarbon storage modeling for diesel oxidation catalysts

A simple hydrocarbon (HC) storage model was proposed which represents the adsorption-desorption processes in zeolites incorporated in a diesel oxidation catalyst (DOC). Using four experiments in which the HC was stepped up and the reactor run until steady state, a Langmuir isotherm was generated that was sufficient to represent the equilibrium data, and the remaining rate constant capturing the adsorption time scale was obtained by fitting. The model thus developed was validated using reactor data which was obtained by stepping the inlet HC concentration to zero after outlet reached equilibrium.A typical DOC which contained both a storage component (zeolite) and noble metal component (for oxidation) was studied using a full scale 1D reactor model which includes the storage kinetics developed here with the oxidation kinetics developed in our previous work [Sampara et al., 2008. Global kinetics for a commercial diesel oxidation catalyst with two exhaust hydrocarbons. Industrial & Engineering Chemistry Research 47, 311-322]. Simulations of a simplified warm-up process indicated that the zeolite storage component reduces the overall cold start HC emissions by at least a factor of two if the warm-up rate achieves , a range commonly observed during start-up. Modeling results also showed that the HC oxidation for these reactors commonly starts at the rear end of the reactor due to reduced CO inhibition. The rates of the individual processes during cold start were analyzed in detail and compared with the rate of inlet temperature increase provided from the exhaust.     

Cold-Start Link Prediction via Weighted Symmetric Nonnegative Matrix Factorization with Graph Regularization

Link prediction has attracted wide attention among interdisciplinary researchers as an important issue in complex network. It aims to predict the missing links in current networks and new links that will appear in future networks. Despite the presence of missing links in the target network of link prediction studies, the network it processes remains macroscopically as a large connected graph. However, the complexity of the real world makes the complex networks abstracted from real systems often contain many isolated nodes. This phenomenon leads to existing link prediction methods not to efficiently implement the prediction of missing edges on isolated nodes. Therefore, the cold-start link prediction is favored as one of the most valuable subproblems of traditional link prediction. However, due to the loss of many links in the observation network, the topological information available for completing the link prediction task is extremely scarce. This presents a severe challenge for the study of cold-start link prediction. Therefore, how to mine and fuse more available non-topological information from observed network becomes the key point to solve the problem of cold-start link prediction. In this paper, we propose a framework for solving the cold-start link prediction problem, a joint-weighted symmetric nonnegative matrix factorization model fusing graph regularization information, based on low-rank approximation algorithms in the field of machine learning. First, the nonlinear features in high-dimensional space of node attributes are captured by the designed graph regularization term. Second, using a weighted matrix, we associate the attribute similarity and first order structure information of nodes and constrain each other. Finally, a unified framework for implementing cold-start link prediction is constructed by using a symmetric nonnegative matrix factorization model to integrate the multiple information extracted together. Extensive experimental validation on five real networks with attributes shows that the proposed model has very good predictive performance when predicting missing edges of isolated nodes.     

基于改进降噪自动编码器的点击率预测

点击率(CTR)预测是个性化广告和推荐系统中的一项基本任务. 针对提升点击率预测效果和处理冷启动问题, 本文中提出了一种基于改进降噪自动编码器的点击率预测模型ADVAE (ADditional Variational AutoEncoder),该模型在输入数据加入高斯随机噪声, 利用改进的降噪自动编码器生成新的嵌入特征, 然后分别进行低阶和高阶的特征交互来预测用户点击行为. 该方法可以在数据稀疏以及系统冷启动情况下, 更深层地学习特征嵌入与交叉之间的关系. 该模型关注特征域之间的交互, 动态修复低频数据的特征嵌入, 具有更强的鲁棒性. 此外, 该方法可以动态应用到其他深度学习模型, 具有更高的灵活性. 实验结果表明, 该方法在点击率预测和系统冷启动问题上的性能表现均优于现有方法.