基于动态行为学习的空中目标识别方法

针对空中移动目标识别问题,提出了一种基于动态行为学习的识别方法。首先,从数据源中提取目标要素,经过标注形成目标数据特征集;其次,基于应用场景进行特征组合,形成特征模型要素;然后,基于深度学习算法进行模型训练,得到评估最优的模型参数;最后,利用训练后的模型进行目标识别处理。通过数值仿真验证,该方法能够有效地识别无业务规则的目标身份。     

Effect of α phase morphology on fatigue crack growth behavior of Ti−5Al−5Mo−5V−1Cr−1Fe alloy

Taking a Ti−5Al−5Mo−5V−1Cr−1Fe alloy as exemplary case, the fatigue crack growth sensitivity and fracture features with various tailored α phase morphologies were thoroughly investigated using fatigue crack growth rate (FCGR) test, optical microscopy (OM) and scanning electron microscopy (SEM). The tailored microstructures by heat treatments include the fine and coarse secondary α phase, as well as the widmanstatten and basket weave features. The sample with coarse secondary α phase exhibits better comprehensive properties of good crack propagation resistance (with long Paris regime ranging from 15 to 60 MPa·m^1/2), high yield strength (1113 MPa) and ultimate strength (1150 MPa), and good elongation (11.6%). The good crack propagation resistance can be attributed to crack deflection, long secondary crack, and tortuous crack path induced by coarse secondary α phase.     

A process fault diagnosis method using multi-time scale dynamic feature extraction based on convolutional neural network

Unlike many other techniques used in process control, which are widely applied in practice and play significant roles, abnormal situation management (ASM) still relies heavily on human experience, not least because the problem of fault detection and diagnosis (FDD) has not been well addressed. In this paper, a process fault diagnosis method using multi-time scale dynamic feature extraction based on convolutional neural network (CNN) consisting of similarity measurement, variable ranking, and multi-time scale dynamic feature extraction is proposed. The CNN-based model containing the fixed multiple sampling (FMS) layer can extract dynamic characteristics of process data at different time scales. The benchmark Tennessee Eastman (TE) process is used to verify the performance of the proposed method.     

Feature-based ontological framework for semantic interoperability in product development

An essential requirement in integrating tasks in product development is to have a seamless exchange of product information through the entire product lifecycle. A key challenge in the integration is the exchange of shape semantics in terms of understandable labels and representations. A unified taxonomy is proposed to represent, classify, and extract shape features. This taxonomy is built using the Domain-Independent Form Feature (DIFF) model as the representation of features. All the shape features in a product model are classified under three main classes, namely, volumetric features, deformation features and free-form surface features. Shape feature ontology is developed using the unified taxonomy, which brings the shape features under a single reasoning framework. One-to-many reasoning framework is presented for mapping semantically equivalent information (label and representation) of the feature to be exchanged to target applications, and the reconstruction of the shape model automatically in that target application. An algorithm has been developed to extract the semantics of shape features and construct the model in the target application. The algorithm developed has been tested for shape models taken from literature and test cases are selected based on variations of topology and geometry. Results of exchanging product information are presented and discussed. Finally, the limitations of the proposed method for exchanging product information are explained.     

长江经济带工业集聚对水环境污染的影响——基于空间计量与门槛效应的检验

经济发展与生态环境动态良性循环已经成为社会经济发展的重要议题。为实现长江经济带高质量发展，以2007—2018年长江经济带11个省市为样本，采用空间计量和面板门槛模型考察工业集聚与水环境污染之间的关系。研究表明：长江经济带各省市水环境污染具有显著的空间自相关性；长江经济带工业集聚水平和水环境污染表现出先降低后增强的“U”形变化特征，说明工业集聚水平对水环境污染的影响存在一个阈值；长江经济带工业集聚产生的规模效应和结构效应对水环境污染存在显著的双门槛效应，在不同的工业集聚水平下，规模效应和结构效应表现出不同的外部性特征；工业集聚产生的技术溢出效应对水环境污染的影响同样具有显著的双门槛作用；随着各地区工业集聚水平的提高，技术溢出效应对水环境污染的减排作用表现出先强后弱的趋势。     

数据中心内服务器能耗数据采集及特征分析

数据中心的高能耗低能效问题正受到广泛关注与研究，但目前没有公开的数据中心内服务器能耗数据集供研究人员使用，且现有过滤式特征选择并不能满足运维人员的需求。为此，提出了一套模拟数据中心内服务器运行状态的仿真环境架构，基于该架构采集了服务器运行各类任务时的多项性能指标和能耗数据。然后将基于因果关系的特征选择应用于能耗数据集的特征分析中，构建出可解释的特征子集和能耗预测结果。实验结果表明，因果特征子集大小约为过滤式特征子集大小的1/3到1/6，并且使用因果特征子集训练的模型在75%的情况下都取得了最优预测精度。     

Effects of liquid phases on densification of TiO2-doped Al2O3–ZrO2 composite ceramics

This study investigated the densification behaviors and microstructural evolution of Al₂O₃–ZrO₂ (3Y) composite ceramics doped with four different amounts of TiO₂ (0, 1, 4, and 8 wt%; denoted as 0T, 1T, 4T, and 8T, respectively) to clarify the effect of TiO₂ dopants on densification. The shrinkage rate during densification increased with the increase in the amount of TiO₂. The development of grain boundary feature was also examined. The undoped ceramic showed clean grain boundaries. Thin liquid grain boundary phases were observed in 1T, whereas large liquid phases were found on the grain boundary and at the junction pockets in 4T and 8T. The results were discussed in terms of the relationship between densification and grain boundary feature.     

7.63m焦炉循环氨水系统运行特点及检修技术

介绍了7.63m焦炉循环氨水系统的设计参数、应急系统设计、氨水工艺流程、主要设备和生产运行中出现的问题,论述了循环氨水系统检修方案的制定实施、焦炉生产作业调整、冷却水量平衡保供及生产、环保风险的防控等实践活动。