DM-CTSA: a discriminative multi-focused and complementary temporal/spatial attention framework for action recognition

Neural Computing and Applications - Video-based human action recognition remains a challenging task. There are three main limitations: (1) Most works are only restricted to single temporal scale...     

Adsorption characteristics of hexadecyl ammonium with different numbers of carbon chains in montmorillonite and the structure of the prepared composites

Journal of Porous Materials - The dynamics and thermodynamics of adsorption of hexadecyl ammonium with different numbers of carbon chains in montmorillonite (Mt) with different layer charge density...     

Loop Closure Detection based on Image Covariance Matrix Matching for Visual SLAM

International Journal of Control, Automation and Systems - Loop closure detection is an indispensable part of visual simultaneous location and mapping (SLAM). Correct detection of loop closure can...     

A spatial variable selection method for monitoring product surface

Two-dimensional (2-D) data maps are generated in certain advanced manufacturing processes. Such maps contain rich information about process variation and product quality status. As a proven effective quality control technique, statistical process control (SPC) has been widely used in different processes for shift detection and assignable cause identification. However, charting algorithms for 2-D data maps are still vacant. This paper proposes a variable selection-based SPC method for monitoring 2-D wafer surface. The fused LASSO algorithm is firstly employed to identify potentially shifted sites on the surface; a charting statistic is then developed to detect statistically significant shifts. As the variable selection algorithm can nicely preserve shift patterns in spatial clusters, the newly proposed chart is proved to be both effective in detecting shifts and capable of providing diagnostic information for process improvement. Extensive Monte Carlo simulations and a real example have been used to demonstrate the effectiveness and usage of the proposed method.     

工控运维中基于语义分析的智能电网控制攻击防御

为了降低控制相关攻击对智能电网的威胁,在数据采集与监视控制（supervisory control and data acquisition,SCADA）系统中,提出一种语义分析框架,利用智能电网的信息和物理工控设备,检测控制相关攻击。首先,对控制网络进行监控,识别控制命令,并从网络提取出与控制命令相关的语义和更新的测量值,并评估命令的物理后果;然后,采用自适应通用潮流分析算法,从控制命令提取出语义信息,并调整参数。在IEEE24节点、28节点和38节点系统以及一个2 730节点系统上进行实验,结果表明：与交流潮流相比,所提算法将24节点和38节点系统的检测延迟均降低了约50%,将2 730节点系统检测延迟降低了66%,平均漏警率为0.01%,最差情况下误警率为0.78%。     

Imbalanced fault diagnosis of rotating machinery using autoencoder-based SuperGraph feature learning

Existing fault diagnosis methods usually assume that there are balanced training data for every machine health state. However, the collection of fault signals is very difficult and expensive, resulting in the problem of imbalanced training dataset. It will degrade the performance of fault diagnosis methods significantly. To address this problem, an imbalanced fault diagnosis of rotating machinery using autoencoder-based SuperGraph feature learning is proposed in this paper. Unsupervised autoencoder is firstly used to compress every monitoring signal into a low-dimensional vector as the node attribute in the SuperGraph. And the edge connections in the graph depend on the relationship between signals. On the basis, graph convolution is performed on the constructed SuperGraph to achieve imbalanced training dataset fault diagnosis for rotating machinery. Comprehensive experiments are conducted on a benchmarking publicized dataset and a practical experimental platform, and the results show that the proposed method can effectively achieve rotating machinery fault diagnosis towards imbalanced training dataset through graph feature learning.     

Process adjustment with an asymmetric quality loss function

Controlling an engineering process usually focuses on maintaining the process output on-target with minimum variation. Conventionally, the quality loss incurred by the deviation from the nominal values is assumed symmetric. However, in some engineering processes, the penalties incurred by positive and negative deviations of the quality variables are different. In such cases, we need to redesign the controller so that the overall quality loss is minimized. In this work, motivated by a real ingot growing process, a new controller is proposed for an asymmetric quality loss function. Its performance and stability are studied via numerical simulation. The effectiveness of the method is also demonstrated in the real engineering process.     

热扩散对镀锡银钎料界面组织及熔化特性的影响

对带锡镀覆层的银钎料进行热扩散处理,采用差示扫描量热仪(DSC)、扫描电镜(SEM)、X射线衍射仪(XRD)研究热扩散工艺对其熔化温度和扩散界面组织、物相的影响. 结果表明,在扩散时间一定条件下,随着扩散温度升高,扩散界面层厚度增加;随着扩散温度升高或扩散时间延长,钎料的固、液相线温度均降低,熔化温度区间缩小;扩散界面层物相主要由棒状Ag₃Sn相和块状Cu₃Sn相组成;最佳热扩散工艺为220 ℃,24 h. 经最佳工艺处理后,扩散界面层厚度为9.1 μm,钎料中Sn含量为7.2%,此时钎料熔化温度区间为642.34 ℃~676.37 ℃. 与传统熔炼合金化方法相比,钎料中Sn含量提高近31%.