Proposal Complementary Action Detection

2020 ◽  
Vol 16 (2s) ◽  
pp. 1-12
Author(s):  
Suguo Zhu ◽  
Xiaoxian Yang ◽  
Jun Yu ◽  
Zhenying Fang ◽  
Meng Wang ◽  
...  
Keyword(s):  
Action Detection ◽  
Complementary Action

scholarly journals Video action detection by learning graph-based spatio-temporal interactions

2021 ◽  
Vol 206 ◽  
pp. 103187
Author(s):  
Matteo Tomei ◽  
Lorenzo Baraldi ◽  
Simone Calderara ◽  
Simone Bronzin ◽  
Rita Cucchiara
Keyword(s):  
Action Detection ◽  
Spatio Temporal ◽  
Temporal Interactions

scholarly journals Skeleton‐based attention‐aware spatial–temporal model for action detection and recognition

2020 ◽  
Vol 14 (5) ◽  
pp. 177-184
Author(s):  
Ran Cui ◽  
Aichun Zhu ◽  
Jingran Wu ◽  
Gang Hua
Keyword(s):  
Action Detection ◽  
Temporal Model ◽  
Detection And Recognition

scholarly journals Learning Spatial and Temporal Extents of Human Actions for Action Detection

2015 ◽  
Vol 17 (4) ◽  
pp. 512-525 ◽  
Author(s):  
Zhong Zhou ◽  
Feng Shi ◽  
Wei Wu
Keyword(s):  
Human Actions ◽  
Action Detection

scholarly journals Complementary action of the PGC-1 coactivators in mitochondrial biogenesis and brown fat differentiation

2006 ◽  
Vol 3 (5) ◽  
pp. 333-341 ◽  
Author(s):  
Marc Uldry ◽  
Wenli Yang ◽  
Julie St-Pierre ◽  
Jiandie Lin ◽  
Patrick Seale ◽  
...  
Keyword(s):  
Mitochondrial Biogenesis ◽  
Brown Fat ◽  
Complementary Action

scholarly journals Identification of a Novel Saturable Endoplasmic Reticulum Localization Mechanism Mediated by the C-Terminus of aDictyostelium Protein Disulfide Isomerase

2000 ◽  
Vol 11 (10) ◽  
pp. 3469-3484 ◽  
Author(s):  
Jean Monnat ◽  
Eva M. Neuhaus ◽  
Marius S. Pop ◽  
David M. Ferrari ◽  
Barbara Kramer ◽  
...  
Keyword(s):  
Endoplasmic Reticulum ◽  
Protein Disulfide Isomerase ◽  
Fluorescent Protein ◽  
Null Mutation ◽  
Disulfide Isomerase ◽  
Isomerase Activity ◽  
C Terminus ◽  
Complementary Action ◽  
Green Fluorescent ◽  
Protein Disulfide

Localization of soluble endoplasmic reticulum (ER) resident proteins is likely achieved by the complementary action of retrieval and retention mechanisms. Whereas the machinery involving the H/KDEL and related retrieval signals in targeting escapees back to the ER is well characterized, other mechanisms including retention are still poorly understood. We have identified a protein disulfide isomerase (Dd-PDI) lacking the HDEL retrieval signal normally found at the C terminus of ER residents in Dictyostelium discoideum. Here we demonstrate that its 57 residue C-terminal domain is necessary for intracellular retention of Dd-PDI and sufficient to localize a green fluorescent protein (GFP) chimera to the ER, especially to the nuclear envelope. Dd-PDI and GFP-PDI57 are recovered in similar cation-dependent complexes. The overexpression of GFP-PDI57 leads to disruption of endogenous PDI complexes and induces the secretion of PDI, whereas overexpression of a GFP-HDEL chimera induces the secretion of endogenous calreticulin, revealing the presence of two independent and saturable mechanisms. Finally, low-level expression of Dd-PDI but not of PDI truncated of its 57 C-terminal residues complements the otherwise lethal yeast TRG1/PDI1 null mutation, demonstrating functional disulfide isomerase activity and ER localization. Altogether, these results indicate that the PDI57 peptide contains ER localization determinants recognized by a conserved machinery present in D. discoideum and Saccharomyces cerevisiae.

TAD-Net: An approach for real-time action detection based on temporal convolution network and graph convolution network in digital twin shop-floor

Digital Twin ◽  
2021 ◽  
Vol 1 ◽  
pp. 10
Author(s):  
Qing Hong ◽  
Yifeng Sun ◽  
Tingyu Liu ◽  
Liang Fu ◽  
Yunfeng Xie
Keyword(s):  
Real Time ◽  
Sequence Data ◽  
Network Models ◽  
Human Action ◽  
Practical Significance ◽  
Shop Floor ◽  
Action Detection ◽  
Digital Twin ◽  
Actual Application ◽  
Production Processes

Background: Intelligent monitoring of human action in production is an important step to help standardize production processes and construct a digital twin shop-floor rapidly. Human action has a significant impact on the production safety and efficiency of a shop-floor, however, because of the high individual initiative of humans, it is difficult to realize real-time action detection in a digital twin shop-floor. Methods: We proposed a real-time detection approach for shop-floor production action. This approach used the sequence data of continuous human skeleton joints sequences as the input. We then reconstructed the Joint Classification-Regression Recurrent Neural Networks (JCR-RNN) based on Temporal Convolution Network (TCN) and Graph Convolution Network (GCN). We called this approach the Temporal Action Detection Net (TAD-Net), which realized real-time shop-floor production action detection. Results: The results of the verification experiment showed that our approach has achieved a high temporal positioning score, recognition speed, and accuracy when applied to the existing Online Action Detection (OAD) dataset and the Nanjing University of Science and Technology 3 Dimensions (NJUST3D) dataset. TAD-Net can meet the actual needs of the digital twin shop-floor. Conclusions: Our method has higher recognition accuracy, temporal positioning accuracy, and faster running speed than other mainstream network models, it can better meet actual application requirements, and has important research value and practical significance for standardizing shop-floor production processes, reducing production security risks, and contributing to the understanding of real-time production action.

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