Trigger-action (TA) programming is a programming paradigm that allows end-users to automate and connect IoT devices and online services using if-trigger-then-action rules. Early studies have demonstrated this paradigms usability, but more recent work has also highlighted complexities that arise in realistic scenarios. To facilitate end-users in TA programming, we propose AutoTAR, a context-aware conversational recommendation technique for recommending TA rules. AutoTAR leverages a TA knowledge graph to encode semantic features and abstract functionalities of rules, and then takes a two-phase method to recommend TA rules to end-users: during the context-aware recommendation phase, it elicits user preferences from programming context and recommends the top-N rules using a mixed content and collaborative technique; during the conversational recommendation phase, it justifies recommendations by iteratively raising questions and collecting feedback from end-users. We evaluate AutoTAR on Mturk and real data collected from the IFTTT community. The results show that our method outperforms state-of-the-arts significantly — its context-aware recommendation outperforms RecRules by 26% on R@5 and 21% on NDCG@5; its conversational recommendation outperforms LARecommender (a conversational recommender with the LA model) by 67.64% on accuracy. In addition, AutoTAR is effective in solving three problems frequently occurring in TA rule recommendations, i.e., the cold-start problem, the repeat-consumption problem, and the incomplete-intent problem.

为了预防运载火箭总装过程中因动态事件引发的工期延误问题,保证火箭总装任务的按期交付,提出一种工期延误预警方法.该方法包括3个关键步骤:警情监测、警兆识别与警度预报.通过分析火箭总装工期的各种扰动因素及其作用机理,设计定量模型衡量各扰动因素的预警指标,实现火箭总装任务的进度监测.通过充分考虑各警度等级样本数量的不平衡性,应用不平衡分类算法实现警兆识别.通过综合考虑预警样本拖期程度与预警时间节点调整的难易度,设计相应的警度等级以实现警度预报.将该预警方法应用于上海某航天总装厂的实际总装过程数据,以验证该方法的有效性与优越性.

炉管区是半导体生产线的主要瓶颈之一,对整个生产线的性能影响较大.当前针对炉管区调度研究以规则调度为主,且考虑约束较为简单,忽略了存在前后道工序影响的多机台调度以及晶圆加工的重入特性.针对具有等待时间约束、不兼容工艺菜单和重入特性的炉管区β1→β2调度问题,构建了目标为最小化晶圆平均流动时间的β1→β2调度模型.将该调度问题分成组批、设备选择及批排序3个阶段,提出了一种基于混合蚁群优化算法的炉管区调度算法.针对组批阶段,设计了一种可变阈值控制策略.针对批排序阶段,设计了混合蚁群优化算法.进行了历史生产数据的不同规模54组算例实验,结果表明:混合蚁群算法的性能均优于几种常用启发式规则和遗传算法的性能.将所提出的混合蚁群算法应用于实际晶圆生产线,能够有效减少生产过程中晶圆的流动时间.

In a modernized manufacturing workshop, myriads of data are incessantly produced and a large part of those are stored in the industrial big data platform of the modern manufacturing enterprise in the form of structuralized unlabeled raw data. Those manufacturing data are of great latent exploitative value, because of their characteristics of high-noise and high-redundancy, however, direct analysis and utilization of them are impossible. Aiming at reducing the redundancy of manufacturing procedural data and excavating their local structure, a two-stage unsupervised feature selection method is proposed. In the first stage of the method, subset of the original feature set generated by genetic algorithm(GA) is utilized as the input features of radius basis function neural network(RBFNN), to reconstruct the unabridged original feature set. The ratio of dimensionality reduction and reconstructional accuracy are calculated jointly as the fitness function of GA, which is optimized by i teration to learn a low-dimensional representation of high-dimensional features, removing redundant and noisy features of the origin feature set. In the second stage, Laplacian score(LS) is employed to evaluate the locality preserving power of the remainin g features, unearthing features which are prone to improving the performance of classification. By comparing with other unsupervised feature selecti on method, the method proposed here is proven more effective in reducing the redundancy of manufacturing data and simultaneously enhancing the performance of classification.

针对运载火箭总装过程中由于各种不确定因素和动态事件可能引发的火箭不能按时交付的问题,提出一种基于栈式自动编码器的火箭总装完工时间预测方法.通过逐层训练浅层自动编码器代替传统方法中的特征提取过程,利用无监督学习过程学习完工时间相关因素的非线性压缩特征;通过堆叠浅层自动编码器构成精调网络,利用监督学习过程及参数优化过程精确预测火箭完工时间.通过仿真数据以及上海某航天设备制造厂火箭总装实际数据中的测试数据集,验证了该方法比传统预测方法具有更优秀的泛化性能,能够提升预测精度.

Photolithography machines are the common bottleneck in the semiconductor manufacturing system. The operation constraints in photolithography machines are very complicated, including wafers arriving over time, dedicated machine constraints for critical layers, auxiliary resources’ constraints, and dynamic manufacturing environment. In previous studies, the dynamic manufacturing environment has never been considered, which would make remaining cycle time seriously deviate from the expected value, and then result in the deterioration of scheduling performance. In this paper, an imperialist competitive algorithm incorporating remaining cycle prediction is proposed for photolithography machines’ scheduling problem with the objective of total completion time minimization. A deep autoencoder neural network is presented at first to predict remaining cycle time, responding to the environmental changes. Secondly, an imperialist competitive algorithm in the framework of a rolling horizon strategy is proposed to address the scheduling problem, incorporated with the accurately predicted remaining cycle time. Several procedures are designed to improve the performance of the algorithm. To verify the proposed algorithm, a simulation model of a semiconductor manufacturing system is constructed and numerical tests are conducted in the model. Results show that the algorithm proposed can significantly decrease wafers’ average cycle time.

针对同一个教学班学生课程学习效果存在差异这一现象,本文分析了课程初期学生差异对学生采用深层学习法还是采用表层学习法的影响,以及最终对学生获得不同学习效果的影响.研究表明,影响学生学习效果的重要因素是学生对课程学习的兴趣和激情.

为适应知识、能力与素质三位一体的人才培养模式,在“设计制造一体化”思想指导下,进行了“设计制造基础”课程建设与改革.以课程设计为主线,培养学生的现代设计能力、工程实践能力、创新能力以及团队合作与交流能力.

The paper proposed a new method,Templated LCA(TDLCA),to apply LCA theory to Small and Medium-sized Enterprises(SMEs).The definition of TDLCA and its theoretical basis and ways of building the assessment template were talked about separately.Then,a LCA software package SME-LCA,specially designed for SMEs was developed based on the design idea.SME-LCA has the same basic functions as current commercial LCA software.However,the operation difficulty was largely reduced,and improved LCA efficiency at the same time.TDLCA combined the advantages of LCA researchers and SME product developers,and improved to be an effective way for SMEs to perform LCA.

In the process of one order-reduction approximation of Bezier curve,the error between the reducing curve and the reduced curve must meet the demand of the error bound.Recently using the middle point of the curve as the division point has been applied practically.If division once can not be satisfied,more division must be used.In the presented method,selecting the middle point and the inflection point as the division point is the better way.It not only is efficient,but also can reduce the number of division.Moreover it can also improve the smooth of the Bezier curve.Through the analysis and sample,it is concluded that this method can get a better reduction effect.

结合数据库技术与参数化建模原理提出一种新的建模方法,该方法在UG中采用二次开发的方式,通过建立通用的数据结构与尺寸驱动技术实现了对结构相似轿运车的快速建模,极大地提高了建模效率。在模型的基础上,针对轿运车装车过程提出一种基于商品车行进轨迹的装车仿真算法,该算法在对商品车行进轨迹进行离散的基础上,通过商品车在行进过程中的约束条件计算其仿真时的几何调整量,逼真再现装车过程。通过建立轿运车三维模型并对其装车过程进行仿真,可以有效分析轿运车的使用状况,及早发现问题,从而在产品设计阶段就对其进行修改,极大提高产品的质量与设计效率。

在紧凑型轿车上,发动机的进气歧管受到来自于多种热源的加热,周围的温度较高,影响了汽车的燃油经济性和动力性.用可视化方法研究了发动机舱内温度在不同工况下的变化规律,使温度变化规律更加直观,有助于找出问题的关键所在和改进方案的制定.由此而提出的在发动机罩盖上开孔的改进方案,使发动机舱内温度得到明显降低.实验表明发动机充气效率和扭矩均得到显著提高,燃烧效率得到了改善,从而提高了汽车动力性和经济性.

By using UG software, 3D solid modeling of thread connectors was implemented and used in 3DMAX to realize 3D assembly animation for thread connection. Through the elaboration with video and audio, we had got good teaching effect by motivating students' interest based on good understanding.