scholarly journals Ultrasonic Flaw Imaging via Multipath Exploitation

2012 ◽  
Vol 2012 ◽  
pp. 1-12 ◽  
Author(s):  
Yimin D. Zhang ◽  
Xizhong Shen ◽  
Ramazan Demirli ◽  
Moeness G. Amin
Keyword(s):  
Ultrasonic Imaging ◽  
Real Data ◽  
Sensor Data ◽  
Point Spread Functions ◽  
Virtual Sensors ◽  
Ultrasonic Flaw ◽  
Virtual Array ◽  
Bottom Side ◽  
Material Conditions ◽  
Detection And Localization

We consider ultrasonic imaging for the visualization of flaws in a material. Ultrasonic imaging is a powerful nondestructive testing (NDT) tool which assesses material conditions via the detection, localization, and classification of flaws inside a structure. We utilize reflections of ultrasonic signals which occur when encountering different media and interior boundaries. These reflections can be cast as direct paths to the target corresponding to the virtual sensors appearing on the top and bottom side of the target. Some of these virtual sensors constitute a virtual aperture, whereas in others, the aperture changes with the transmitter position. Exploitations of multipath extended virtual array apertures provide enhanced imaging capability beyond the limitation of traditional multisensor approaches. The waveforms observed at the physical as well as the virtual sensors yield additional measurements corresponding to different aspect angles, thus allowing proper multiview imaging of flaws. We derive the wideband point spread functions for dominant multipaths and show that fusion of physical and virtual sensor data improves the flaw perimeter detection and localization performance. The effectiveness of the proposed multipath exploitation approach is demonstrated using real data.

scholarly journals Damage Detection and Localization under Variable Environmental Conditions Using Compressed and Reconstructed Bayesian Virtual Sensor Data

Sensors ◽  
2021 ◽  
Vol 22 (1) ◽  
pp. 306
Author(s):  
Jyrki Kullaa
Keyword(s):  
Damage Detection ◽  
Data Compression ◽  
Sensor Network ◽  
Measurement Data ◽  
Sensor Data ◽  
Extreme Value Statistics ◽  
Virtual Sensors ◽  
Actual Measurement ◽  
Redundant Data ◽  
Detection And Localization

Structural health monitoring (SHM) with a dense sensor network and repeated vibration measurements produces lots of data that have to be stored. If the sensor network is redundant, data compression is possible by storing the signals of selected Bayesian virtual sensors only, from which the omitted signals can be reconstructed with higher accuracy than the actual measurement. The selection of the virtual sensors for storage is done individually for each measurement based on the reconstruction accuracy. Data compression and reconstruction for SHM is the main novelty of this paper. The stored and reconstructed signals are used for damage detection and localization in the time domain using spatial or spatiotemporal correlation. Whitening transformation is applied to the training data to take the environmental or operational influences into account. The first principal component of the residuals is used to localize damage and also to design the extreme value statistics control chart for damage detection. The proposed method was studied with a numerical model of a frame structure with a dense accelerometer or strain sensor network. Only five acceleration or three strain signals out of the total 59 signals were stored. The stored and reconstructed data outperformed the raw measurement data in damage detection and localization.

scholarly journals Spatial Data Lake for Smart Cities: From Design to Implementation

2020 ◽  
Vol 1 ◽  
pp. 1-15
Author(s):  
Rodrique Kafando ◽  
Rémy Decoupes ◽  
Lucile Sautot ◽  
Maguelonne Teisseire
Keyword(s):  
Remote Sensing ◽  
Spatial Data ◽  
Smart Cities ◽  
Real Data ◽  
Heterogeneous Data ◽  
Geographic Information ◽  
Sensor Data ◽  
Remote Sensing Images ◽  
Thematic Information ◽  
General Data

Abstract. In this paper, we propose a methodology for designing data lake dedicated to Spatial Data and an implementation of this specific framework. Inspired from previous proposals on general data lake Design and based on the Geographic information – Metadata normalization (ISO 19115), the contribution presented in this paper integrates, with the same philosophy, the spatial and thematic dimensions of heterogeneous data (remote sensing images, textual documents and sensor data, etc). To support our proposal, the process has been implemented in a real data project in collaboration with Montpellier Métropole Méditerranée (3M), a metropolis in the South of France. This framework offers a uniform management of the spatial and thematic information embedded in the elements of the data lake.

The Use of Auxiliary Airborne Sensor Data in SPACE-M Photogrammetric Block Adjustments

1984 ◽  
Vol 38 (1) ◽  
pp. 3-14
Author(s):  
J. A. R. Blais ◽  
M. A. Chapman
Keyword(s):  
Global Positioning System ◽  
Mathematical Formulation ◽  
Real Data ◽  
Sensor Data ◽  
Navigation Systems ◽  
Test Results ◽  
Inertial Navigation Systems ◽  
Adjustment Program ◽  
Global Positioning ◽  
Topographical Mapping

The mathematical formulation used in the photogrammetric block adjustment program SPACE-M has recently been extended to accommodate auxiliary airborne sensor data corresponding to the position and/or attitude of the aerial camera at the time of film exposure. Examples of such systems are statoscopes, laser profilometers, Inertial Navigation Systems (INS) and the Global Positioning System (GPS). The description of the use of these auxiliary data in SPACE-M is outlined and references are given to other related formulations. Test results with simulated and limited real data are presented with some analysis of the implications for topographical mapping and other applications.

Risk Evaluation by Human Trajectory Simulation Based on Real Data

2011 ◽  
Vol 15 (2) ◽  
pp. 220-225
Author(s):  
Kiyoshi Izumi ◽  
◽  
Yoshifumi Nishida ◽  
Yoichi Motomura ◽  
Keyword(s):  
Risk Evaluation ◽  
Real Data ◽  
Public Spaces ◽  
Layout Design ◽  
Sensor Data ◽  
Agent Based Simulation ◽  
New Approach ◽  
Agent Based ◽  
Trajectory Simulation ◽  
Simulation Based

This paper proposes a new approach integrating the modeling of moving persons from sensor data and agent-based simulation for indoor layout design viewed from preventing children’s accidents. Our model focuses on interaction between indoor objects and children to estimate the risk of indoor accidents. We discuss the agent-based simulation of multiple persons moving in public spaces and its application to evaluating information presentation for guidance.

scholarly journals A Methodology for Design and Analysis of Sensor Fusion with Real Data in UAV Platforms

2018 ◽  
Author(s):  
Jesús García ◽  
Jose Manuel Molina ◽  
Jorge Trincado
Keyword(s):  
Sensor Fusion ◽  
Performance Indicators ◽  
Performance Metrics ◽  
Ground Truth ◽  
Real Data ◽  
Experimental Methodology ◽  
Sensor Data ◽  
Data Processing Software ◽  
Real Performance ◽  
Best Parameters

This paper presents a methodology to design sensor fusion parameters using real performance indicators of navigation in UAVs based on PixHawk flight controller and peripherals. This methodology and the selected performance indicators allows to find the best parameters for the fusion system of a determined configuration of sensors and a predefined real mission. The selected real platform is described with stress on available sensors and data processing software, and the experimental methodology is proposed to characterize sensor data fusion output and determine the best choice of parameters using quality measurements of tracking output with performance metrics not requiring ground truth.

Recover Missing Sensor Data with GRNN-based Cascade Scheme

2020 ◽  
Vol 10 ◽  
Author(s):  
Roman Tkachenko ◽  
Ivan Izonin ◽  
Ivanna Dronyuk ◽  
Mykola Logoyda ◽  
Pavlo Tkachenko
Keyword(s):  
Computational Intelligence ◽  
Prediction Method ◽  
Real Data ◽  
High Accuracy ◽  
Sensor Data ◽  
Optimal Parameters ◽  
Data Set ◽  
Advantages And Disadvantages ◽  
General Regression Neural Networks ◽  
General Regression

Background: Today, using of systems on the base of Internet of Things (ІоТ) devices is very widespread in various applications. Intellectual analysis of the data collected by similar devices is an important task for efficient and successful functioning of such systems. In particular, the reliability of such kind of analysis has greatly influence on the ability to partially or fully automate certain processes or subsystems. However, imperfect devices of data collection, transportation errors, etc. cause data missing to appear. A number of limitations cause this problem, and in the work, they makes it impossible an effective intellectual analysis for specific use. That is why the scientific and applied problem of effectively filling the missing in the data collected by the sensors of specific characteristics should be considered. Methods: The authors propose a new prediction method for solving this problem based on the use of General Regression Neural Networks (GRNN). Results: The possibility of approximation and partial elimination of the error of computational intelligence of this type has been analytically proved. A cascade of two sequentially connected GRNN was developed. The optimal parameters of the developed cascade were selected. The simulation of its work was performed to solve the problem of recover missing sensor data in the dataset for monitoring the state of air environment. A high number of missing for one reason or another characterizes this real data set, collected by IoT device. Conclusion: High accuracy of cascade operation in comparison with existing methods of this class is inserted. All advantages and disadvantages are described. Perspectives of further research are outlined.

OPTIMAL ESTIMATION OF TOTAL PLANTAR FORCE FOR MONITORING GAIT IN DAILY LIFE ACTIVITIES WITH LOW-PRICE INSOLE SYSTEM

2014 ◽  
Vol 14 (03) ◽  
pp. 1450037 ◽  
Author(s):  
FANG PU ◽  
YANG YANG ◽  
XIAOYA FAN ◽  
SHUYU LI ◽  
YAN LI ◽  
...  
Keyword(s):  
Daily Life ◽  
Vertical Jump ◽  
Optimal Estimation ◽  
Real Data ◽  
Optimal Model ◽  
Virtual Sensors ◽  
Daily Life Activities ◽  
Level Walking ◽  
Explained Variance ◽  
Plantar Force

This study presented optimal estimation of total plantar force with a suitable sensor layout and a reliable equation for monitoring gait in daily life activities. The plantar pressure of 10 subjects during level walking was measured by Pedar® insoles at 100 Hz for establishing models and selecting the optimal one. Four types of virtual sensors with different sizes were designed. Stepwise linear regressions were performed to reconstruct total plantar force based on each particular type of virtual sensor. 14 models were established, which met the requirements of the explained variance of the regression model and the multicollinearity of the predictors. Estimated total plantar force from each model was compared with the real data from the Pedar® insoles. According to the correlation coefficient (R) and the root mean square error divided by the peak force (RMSE/PF), the optimal model had three sensors located under the heel and metatarsal. Another four subjects were used for validating the optimal model by performing level walking, running, vertical jump-landing, stair ascending and descending. For these five common activities, the correlation was high (R > 0.970) and the error was low (RMSE/PF < 10%). Therefore, this model can accurately estimate total plantar force in daily life activities.

scholarly journals Analyses of body measurement with depth image data using motioncapture sensor

2020 ◽  
Vol 71 (06) ◽  
pp. 530-537
Author(s):  
HAKAN YÜKSEL ◽  
MELIHA OKTAV BULUT
Keyword(s):  
Low Cost ◽  
Image Data ◽  
Real Data ◽  
Image Sensor ◽  
Industrial Applications ◽  
Sensor Data ◽  
Depth Image ◽  
Depth Data ◽  
Military Security ◽  
The Difference

Sensors can capture and scan many objects in real time for military, security, health and industrial applications. Sensorscan be made smaller, cheaper and more energy efficient due to rapid changes in technology. Low-cost sensors areattractive alternatives to high cost laser scanners in recent years. The Kinect sensor can measure depth data with lowcost and high resolution by scanning the environment. In this study, this sensor collected data on users in front of ascanner, and the depth data results were tested. The process was repeated with four different body positions, and theresults were analysed. The sensor data was reliable versus real measurements. When compared the depth data takenby the sensor with the real measures, the reliability rate is found significance. The difference between the depth imagedata of different users, different positions and different body measures and real data is 0.35 to 1.15 cm. This shows thatthe sensor’s results are close to real data. When the accuracy of the sensor against real measurements is examined,it is seen that these values are between 98.46 % and 99.6 %. Thus, this depth image sensor is reliable and can be usedas an alternative and cheaper way for body measurements.

scholarly journals Analyses of body measurement with depth image data using motioncapture sensor

2020 ◽  
Vol 71 (06) ◽  
pp. 530-537
Author(s):  
HAKAN YÜKSEL ◽  
MELIHA OKTAV BULUT
Keyword(s):  
Low Cost ◽  
Image Data ◽  
Real Data ◽  
Image Sensor ◽  
Industrial Applications ◽  
Sensor Data ◽  
Depth Image ◽  
Depth Data ◽  
Military Security ◽  
The Difference

Sensors can capture and scan many objects in real time for military, security, health and industrial applications. Sensorscan be made smaller, cheaper and more energy efficient due to rapid changes in technology. Low-cost sensors areattractive alternatives to high cost laser scanners in recent years. The Kinect sensor can measure depth data with lowcost and high resolution by scanning the environment. In this study, this sensor collected data on users in front of ascanner, and the depth data results were tested. The process was repeated with four different body positions, and theresults were analysed. The sensor data was reliable versus real measurements. When compared the depth data takenby the sensor with the real measures, the reliability rate is found significance. The difference between the depth imagedata of different users, different positions and different body measures and real data is 0.35 to 1.15 cm. This shows thatthe sensor’s results are close to real data. When the accuracy of the sensor against real measurements is examined,it is seen that these values are between 98.46 % and 99.6 %. Thus, this depth image sensor is reliable and can be usedas an alternative and cheaper way for body measurements.

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