Unsupervised, dynamic identification of physiological and activity context in wearable computing

2004 ◽  
Author(s):  
A. Krause ◽  
D.P. Siewiorek ◽  
A. Smailagic ◽  
J. Farringdon
Keyword(s):  
Wearable Computing ◽  
Dynamic Identification

Methodology and Application of Dynamic Identification System for Ecosystem Services

2010 ◽  
Vol 11 (4) ◽  
pp. 498-504
Author(s):  
Feng WU ◽  
Jinyan ZHAN ◽  
Xiangzheng DENG ◽  
Nana SHI
Keyword(s):  
Ecosystem Services ◽  
Identification System ◽  
Dynamic Identification

Wearable Computing With Distributed Deep Learning Hierarchy: A Study of Fall Detection

2020 ◽  
Vol 20 (16) ◽  
pp. 9408-9416
Author(s):  
Xiaoye Qian ◽  
Huan Chen ◽  
Haotian Jiang ◽  
Justin Green ◽  
Haoyou Cheng ◽  
...  
Keyword(s):  
Deep Learning ◽  
Wearable Computing ◽  
Fall Detection

scholarly journals Autonomous Identification and Positioning of Trucks during Collaborative Forage Harvesting

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1166
Author(s):  
Wei Zhang ◽  
Liang Gong ◽  
Suyue Chen ◽  
Wenjie Wang ◽  
Zhonghua Miao ◽  
...  
Keyword(s):  
Field Experiments ◽  
Absolute Error ◽  
Identification Accuracy ◽  
Depth Image ◽  
Dynamic Identification ◽  
Random Sample Consensus ◽  
Harvesting Efficiency ◽  
Value Decomposition ◽  
Forage Harvester ◽  
Ransac Algorithm

In the process of collaborative operation, the unloading automation of the forage harvester is of great significance to improve harvesting efficiency and reduce labor intensity. However, non-standard transport trucks and unstructured field environments make it extremely difficult to identify and properly position loading containers. In this paper, a global model with three coordinate systems is established to describe a collaborative harvesting system. Then, a method based on depth perception is proposed to dynamically identify and position the truck container, including data preprocessing, point cloud pose transformation based on the singular value decomposition (SVD) algorithm, segmentation and projection of the upper edge, edge lines extraction and corner points positioning based on the Random Sample Consensus (RANSAC) algorithm, and fusion and visualization of results on the depth image. Finally, the effectiveness of the proposed method has been verified by field experiments with different trucks. The results demonstrated that the identification accuracy of the container region is about 90%, and the absolute error of center point positioning is less than 100 mm. The proposed method is robust to containers with different appearances and provided a methodological reference for dynamic identification and positioning of containers in forage harvesting.

Issues in wearable computing

1997 ◽  
Vol 29 (4) ◽  
pp. 34-39 ◽  
Author(s):  
Len Bass ◽  
Steve Mann ◽  
Dan Siewiorek ◽  
Chris Thompson
Keyword(s):  
Wearable Computing

scholarly journals Aspects of Vibration-Based Methods for the Prestressing Estimate in Concrete Beams with Internal Bonded or Unbonded Tendons

2021 ◽  
Vol 6 (6) ◽  
pp. 83
Author(s):  
Angelo Aloisio
Keyword(s):  
Elastic Modulus ◽  
Structural Reliability ◽  
Natural Frequencies ◽  
Concrete Beams ◽  
Experimental Testing ◽  
Hybrid Approach ◽  
Reliable Estimate ◽  
Axial Deformation ◽  
Dynamic Identification ◽  
Concrete Girders

The estimate of internal prestressing in concrete beams is essential for the assessment of their structural reliability. Many scholars have tackled multiple and diverse methods to estimate the measurable effects of prestressing. Among them, many experimented with dynamics-based techniques; however, these clash with the theoretical independence of the natural frequencies of the forces of internally prestressed beams. This paper examines the feasibility of a hybrid approach based on dynamic identification and the knowledge of the elastic modulus. Specifically, the author considered the effect of the axial deformation on the beam length and the weight per unit of volume. It is questioned whether the uncertainties related to the estimate of the elastic modulus and the first natural frequency yield reasonable estimates of the internal prestressing. The experimental testing of a set of full-scale concrete girders with known design prestressing supports a discussion about its practicability. The author found that the uncertainty in estimating the natural frequencies and elastic modulus significantly undermines a reliable estimate of the prestressing state.

scholarly journals Intelligent Dynamic Identification Technique of Industrial Products in a Robotic Workplace

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1797
Author(s):  
Ján Vachálek ◽  
Dana Šišmišová ◽  
Pavol Vašek ◽  
Jan Rybář ◽  
Juraj Slovák ◽  
...  
Keyword(s):  
Machine Learning ◽  
Control Charts ◽  
Nearest Neighbor ◽  
Learning Algorithm ◽  
Conveyor Belt ◽  
Standard Uncertainty ◽  
K Nearest Neighbor ◽  
Industrial Products ◽  
Dynamic Identification ◽  
Identification Technique

The article deals with aspects of identifying industrial products in motion based on their color. An automated robotic workplace with a conveyor belt, robot and an industrial color sensor is created for this purpose. Measured data are processed in a database and then statistically evaluated in form of type A standard uncertainty and type B standard uncertainty, in order to obtain combined standard uncertainties results. Based on the acquired data, control charts of RGB color components for identified products are created. Influence of product speed on the measuring process identification and process stability is monitored. In case of identification uncertainty i.e., measured values are outside the limits of control charts, the K-nearest neighbor machine learning algorithm is used. This algorithm, based on the Euclidean distances to the classified value, estimates its most accurate iteration. This results into the comprehensive system for identification of product moving on conveyor belt, where based on the data collection and statistical analysis using machine learning, industry usage reliability is demonstrated.

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