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.     

潜江东干渠治理工程生态湿地规划设计

正东干渠是湖北省四湖流域六大骨干排水沟渠之一,是四湖上区田北片和中区潜江市境内的排水干渠,起于荆门市沙洋县境内的李市,由李市流经漳湖垸农场、江汉油田总部(广华寺)、高场、熊口、徐李闸等地,于潜江市西大垸农场附近的冉家集汇入四湖总干渠,全长60.26 km,流域面积335.4 km2。东干渠在高场被田关河分为上下两段,上段李市~高场(以下简称东干渠上段)长约25 km,流域面积213.6     

基于知识蒸馏算法的电缆专业知识库应用

电缆全寿命周期内的标准、规程、法规是电缆从业人员开展工作的依据,而齐全、权威且方便查阅的标准和规程是指导电缆各项工作开展和城市电网安全稳定运行的重要保障。介绍了所开发的电缆专业知识库系统,基于小规模样本知识蒸馏算法,实现电缆专业知识库的关键词匹配搜索、笔记摘抄学习以及论坛的学习探讨等功能,提升了电缆从业人员查阅资料、员工学习的效率。     

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.     

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.