Web-Based Data Inquiry and Real-Time Control of Structural Health Monitoring Sensor Networks

2009 ◽  
Author(s):  
Bo Chen ◽  
Wenjia Liu ◽  
Jinjiang Wang ◽  
Justin Slepak
Keyword(s):  
Real Time ◽  
Operating Mode ◽  
Sensor Nodes ◽  
Sensor Data ◽  
Data Repository ◽  
Real Time Control ◽  
Web Based ◽  
Structural Health ◽  
Time Control ◽  
Data Inquiry

This paper presents a Web-based data inquiry and real-time control of sensor’s operating mode for structural health monitoring sensor networks. The main objective of the presented system is to provide a Web interface for real-time sensor data visualization, sensor-level damage diagnosis, and control of sensor’s operating mode. Web services are available both on distributed sensor nodes and a data repository machine. Users can request Web pages hosted on the sensor nodes or the data repository machine by specifying corresponding sensor IDs. The ability of directly accessing data on sensor nodes via internet allows users to monitor a structure’s performance in a timely manner. The damage diagnosis algorithms implemented on the sensor nodes help users to assess the structural health conditions without the need of transmitting sensor data to a central data station. The presented system also provides the capability of dynamically changing sensor’s operating mode through the Web interface. This feature greatly enhances the flexibility of the system to accommodate different sensing needs and achieve a long lifespan. The system has been tested in the Laboratory to validate its capabilities.

A solution of web-based real-time control laboratories

2009 ◽  
Author(s):  
Yuliang Qiao ◽  
Guo-ping Liu ◽  
Geng Zheng ◽  
Wenshan Hu
Keyword(s):  
Real Time ◽  
Real Time Control ◽  
Web Based ◽  
Time Control

Design of Web-based Real-time Control Laboratory for Diversely Located Test Rigs

2008 ◽  
Vol 41 (2) ◽  
pp. 12643-12648 ◽  
Author(s):  
W.S. Hu ◽  
G.P. Liu ◽  
D. Rees ◽  
Y.L. Qiao
Keyword(s):  
Real Time ◽  
Control Laboratory ◽  
Real Time Control ◽  
Web Based ◽  
Time Control

scholarly journals Real-Time Reconstruction of Contaminant Dispersion from Sparse Sensor Observations with Gappy POD Method

Energies ◽  
2020 ◽  
Vol 13 (8) ◽  
pp. 1956 ◽  
Author(s):  
Zheming Tong ◽  
Yue Li
Keyword(s):  
Real Time ◽  
Field Data ◽  
Complex Geometry ◽  
Sensor Placement ◽  
Three Dimensional ◽  
Time Estimation ◽  
Sensor Data ◽  
Real Time Control ◽  
Time Control ◽  
Gappy Pod

Real-time estimation of three-dimensional field data for enclosed spaces is critical to HVAC control. This task is challenging, especially for large enclosed spaces with complex geometry, due to the nonuniform distribution and nonlinear variations of many environmental variables. Moreover, constructing and maintaining a network of sensors to fully cover the entire space is very costly, and insufficient sensor data might deteriorate system performance. Facing such a dilemma, gappy proper orthogonal decomposition (POD) offers a solution to provide three-dimensional field data with a limited number of sensor measurements. In this study, a gappy POD method for real-time reconstruction of contaminant distribution in an enclosed space is proposed by combining the POD method with a limited number of sensor measurements. To evaluate the gappy POD method, a computational fluid dynamics (CFD) model is utilized to perform a numerical simulation to validate the effectiveness of the gappy POD method in reconstructing contaminant distributions. In addition, the optimal sensor placement is given based on a quantitative metric to maximize the reconstruction accuracy, and the sensor placement constraints are also considered during the sensor design process. The gappy POD method is found to yield accurate reconstruction results. Further works will include the implementation of real-time control based on the POD method.

Modeling and Performance Identification for General Production System Using Sensor Data

2016 ◽  
Author(s):  
Jing Zou ◽  
Qing Chang ◽  
Yong Lei ◽  
Jorge Arinez ◽  
Guoxian Xiao
Keyword(s):  
Real Time ◽  
Production System ◽  
Complex Dynamics ◽  
Production Systems ◽  
General Structure ◽  
Sensor Data ◽  
Production Loss ◽  
Real Time Control ◽  
Time Control ◽  
And Performance

The productivity and efficiency of production systems are greatly influenced by their configuration and complex dynamics subject to constant changes caused by technology insertion, engineering modification, as well as disruption events. In this paper, we develop a mathematical model of production systems with general structure (tandem line, parallel, and etc.) to estimate the status of the system (production counts and processing speeds of the stations, buffer levels and production loss) by using sensor data of disruption events. Real-time production system performance such as effective disruption events, opportunity window, and permanent production loss are identified, which is very useful in real-time control to increase overall system efficiency.

Mapping Algorithms for Real-Time Control of an Avatar Using Eight Sensors

1998 ◽  
Vol 7 (1) ◽  
pp. 1-21 ◽  
Author(s):  
Sudhanshu K. Semwal ◽  
Ron Hightower ◽  
Sharon Stansfield
Keyword(s):  
Real Time ◽  
Inverse Kinematics ◽  
Iterative Algorithms ◽  
Sensor Data ◽  
Real Time Control ◽  
Mapping Algorithms ◽  
Physical Motion ◽  
Time Control ◽  
Inverse Kinematics Problem ◽  
Sensor Configuration

In a virtual environment for small groups of interacting participants, it is important that the physical motion of each participant be replicated by synthetic human forms in real time. Sensors on a user's body are used to drive an inverse kinematics algorithm. Such iterative algorithms for solving the general inverse kinematics problem are too slow for a real-time interactive environment. In this paper we present analytic, constant time methods to solve the inverse kinematics problem and drive an avatar figure. Our sensor configuration has only eight sensors per participant, so the sensor data is augmented with information about natural body postures. The algorithm is fast, and the resulting avatar motion approximates the actions of the participant quite well. This new analytic solution resolves a problem with an earlier iterative algorithm that had a tendency to position knees and elbows of the avatar in awkward and unnatural positions.

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