Sensor Placement for Real Time Infiltration Parameter Evaluation

1988 ◽  
Vol 31 (4) ◽  
pp. 1159-1166 ◽  
Author(s):  
Behzad Izadi ◽  
Dale F. Heermann ◽  
Harold R. Duke
Keyword(s):  
Real Time ◽  
Sensor Placement ◽  
Parameter Evaluation ◽  
Infiltration Parameter

scholarly journals Comprehensive detection device and physical testing for mechanical properties of seabed sediments and shallow gas

2021 ◽  
Vol 2083 (2) ◽  
pp. 022105
Author(s):  
Zhe Yun Li ◽  
Qing Li
Keyword(s):  
Mechanical Properties ◽  
Real Time ◽  
Gas Separation ◽  
Sensor Placement ◽  
Gas Detection ◽  
Shallow Gas ◽  
Seabed Sediment ◽  
Transmission Part ◽  
Seabed Sediments ◽  
Water Gas

Abstract In this paper, a comprehensive detection device for the mechanical properties of seabed sediments and shallow gas is designed, which is mainly composed of the seabed sediment mechanical properties detection part, the shallow gas detection part and the ultrasonic wireless transmission part. The mud water gas separation structure of the shallow gas detection part separates the shallow gas from the mud water, and then the methane concentration in the shallow gas is measured by the non-dispersive infrared methane sensor, which realizes the collection of the submarine shallow gas and the automatic real-time monitoring of the concentration. The measurement of the mechanical properties of seabed sediments realizes the real-time measurement of the three parameters of cone resistance, sidewall friction and pore water pressure, which characterize the mechanical properties of seabed sediments, through strain-sensitive elements. The ultrasonic wireless data transmission part is mainly for the data detected by the mechanical properties of the seabed sediments to be wirelessly transmitted to the sensor placement room through the ultrasonic transducer across the mud-water-gas separation structure. Finally, the data measured by the two parts are transmitted to the mother ship through the cable located in the sensor placement room. The experimental results show that it has the ability to comprehensively detect the mechanical properties of seabed sediments and shallow gas, and has strong operability.

scholarly journals An adaptive, real-time cadence algorithm for unconstrained sensor placement

2018 ◽  
Vol 52 ◽  
pp. 49-58 ◽  
Author(s):  
B.T. van Oeveren ◽  
C.J. de Ruiter ◽  
P.J. Beek ◽  
S.M. Rispens ◽  
J.H. van Dieën
Keyword(s):  
Real Time ◽  
Sensor Placement

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.

Application of Bayesian Sensor Placement Optimization for Real–Time Health Monitoring

2012 ◽  
Author(s):  
Masoud Pourali ◽  
Ali Mosleh
Keyword(s):  
Real Time ◽  
Health Monitoring ◽  
Sensor Placement ◽  
The State ◽  
Physical Parameters ◽  
Real Time System ◽  
System Health Monitoring ◽  
Sensor Placement Optimization ◽  
Safety And Reliability ◽  
Real Time Information

Sensors are being increasingly used for real–time health monitoring of complex systems. The measured quantities are expected to provide real–time information about the state of the system, its subsystems, components, and internal and external physical parameters. A complex system normally requires many sensors to extract required information from the sensed environment. The increasing costs of aging systems and infrastructures have become a major concern and real–time health monitoring systems could ensure increased safety and reliability of these systems. Real–time system health monitoring, assesses the state of systems’ health and, through appropriate data processing and interpretation, can predict the remaining life of the system. This paper introduces a method based on Bayesian networks and attempts to find optimum locations of sensors for the best estimate a system health. Information metrics are used for optimized sensor placement based on the value of information that each possible sensor placement scenario provides.

Next Generation ADC Massive Parallel Testing with Real Time Parameter Evaluation

2006 ◽  
Vol 22 (4-6) ◽  
pp. 337-350
Author(s):  
Heinz Mattes ◽  
Stéphane Kirmser ◽  
Sebastian Sattler
Keyword(s):  
Real Time ◽  
Time Parameter ◽  
Next Generation ◽  
Parallel Testing ◽  
Parameter Evaluation

Real-time optimal spatiotemporal sensor placement for monitoring air pollutants

2020 ◽  
Vol 22 (10) ◽  
pp. 2091-2105
Author(s):  
Rajib Mukherjee ◽  
Urmila M. Diwekar ◽  
Naresh Kumar
Keyword(s):  
Real Time ◽  
Air Pollutants ◽  
Sensor Placement ◽  
Time Optimal
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