scholarly journals Enhanced Vehicle Identification Utilizing Sensor Fusion and Statistical Algorithms

2009 ◽  
Author(s):  
Stephane Roussel ◽  
Hemanth Porumamilla ◽  
Charles Birdsong ◽  
Peter Schuster ◽  
Christopher Clark
Keyword(s):  
Sensor Fusion ◽  
Partial Information ◽  
Probabilistic Approach ◽  
Measurement Data ◽  
Traffic Monitoring ◽  
Magnetic Sensor ◽  
Statistical Techniques ◽  
Vehicle Identification ◽  
Statistical Algorithms ◽  
The Individual

Several studies in the area of vehicle detection and identification involve the use of probabilistic analysis and sensor fusion. While several sensors utilized for identifying vehicle presence and proximity have been researched, their effectiveness in identifying vehicle types has remained inadequate. This study presents the utilization of an ultrasonic sensor coupled with a magnetic sensor and the development of statistical algorithms to overcome this limitation. Mathematical models of both the ultrasonic and magnetic sensors were constructed to first understand the intrinsic characteristics of the individual sensors and also to provide a means of simulating the performance of the combined sensor system and to facilitate algorithm development. Preliminary algorithms that utilized this sensor fusion were developed to make inferences relating to vehicle proximity as well as type. It was noticed that while it helped alleviate the limitations of the individual sensors, the algorithm was affected by high occurrences of false positives. Also, since sensors carry only partial information about the surrounding environment and their measured quantities are partially corrupted with noise, probabilistic techniques were employed to extend the preliminary algorithms to include these sensor characteristics. These statistical techniques were utilized to reconstruct partial state information provided by the sensors and to also filter noisy measurement data. This probabilistic approach helped to effectively utilize the advantages of sensor fusion to further enhance the reliability of inferences made on vehicle identification. In summary, the study investigated the enhancement of vehicle identification through the use of sensor fusion and statistical techniques. The algorithms developed showed encouraging results in alleviating the occurrences of false positive inferences. One of the several applications of this study is in the use of ultrasonic-magnetic sensor combination for advanced traffic monitoring such as smart toll booths.

scholarly journals Multi-Sensor Fusion for Aerial Robots in Industrial GNSS-Denied Environments

2021 ◽  
Vol 11 (9) ◽  
pp. 3921
Author(s):  
Paloma Carrasco ◽  
Francisco Cuesta ◽  
Rafael Caballero ◽  
Francisco J. Perez-Grau ◽  
Antidio Viguria
Keyword(s):  
Sensor Fusion ◽  
Computational Efficiency ◽  
Probabilistic Approach ◽  
Laser Scanner ◽  
Industrial Applications ◽  
Experimental Results ◽  
Added Value ◽  
Aerial Robots ◽  
3D Localization ◽  
High Level

The use of unmanned aerial robots has increased exponentially in recent years, and the relevance of industrial applications in environments with degraded satellite signals is rising. This article presents a solution for the 3D localization of aerial robots in such environments. In order to truly use these versatile platforms for added-value cases in these scenarios, a high level of reliability is required. Hence, the proposed solution is based on a probabilistic approach that makes use of a 3D laser scanner, radio sensors, a previously built map of the environment and input odometry, to obtain pose estimations that are computed onboard the aerial platform. Experimental results show the feasibility of the approach in terms of accuracy, robustness and computational efficiency.

scholarly journals Experimental and simulation-based analysis of asymmetrical spherical roller bearings as main bearings for wind turbines

2021 ◽  
Author(s):  
Amin Loriemi ◽  
Georg Jacobs ◽  
Sebastian Reisch ◽  
Dennis Bosse ◽  
Tim Schröder
Keyword(s):  
Wind Turbine ◽  
Wind Turbines ◽  
Measurement Data ◽  
Contact Angles ◽  
Suspension System ◽  
Roller Bearings ◽  
Axial Forces ◽  
Wind Turbine System ◽  
Bearing Life ◽  
The Individual

AbstractSymmetrical spherical roller bearings (SSRB) used as main bearings for wind turbines are known for their high load carrying capacity. Nevertheless, even designed after state-of-the-art guidelines premature failures of this bearing type occur. One promising solution to overcome this problem are asymmetrical spherical roller bearings (ASRB). Using ASRB the contact angles of the two bearing rows can be adjusted individually to the load situation occurring during operation. In this study the differences between symmetrical and asymmetrical spherical roller bearings are analyzed using the finite element method (FEM). Therefore, FEM models for a three point suspension system of a wind turbine including both bearings types are developed. These FEM models are validated with measurement data gained at a full-size wind turbine system test bench. Taking into account the design loads of the investigated wind turbine it is shown that the use of an ASRB leads to a more uniform load distribution on the individual bearing rows. Considering fatigue-induced damage an increase of the bearing life by 62% can be achieved. Regarding interactions with other components of the rotor suspension system it can be stated that the transfer of axial forces into the gearbox is decreased significantly.

scholarly journals Probabilistic assessment of pile capacity based on CPTu probing including random pile foundation depth

2018 ◽  
Vol 196 ◽  
pp. 01058 ◽  
Author(s):  
Marek Wyjadłowski ◽  
Irena Bagińska ◽  
Jakub Reiner
Keyword(s):  
Bearing Capacity ◽  
Objective Assessment ◽  
Probabilistic Approach ◽  
Measurement Data ◽  
Direct Methods ◽  
Side Surface ◽  
Design Stage ◽  
Direct Data ◽  
Design Calculations ◽  
The Impact

The modern recognition of subsoil with the use of CPTu static probes allows to obtain detailed information necessary for the designing. Registered basic two quantities, i.e. cone resistance qc and friction on the sleeve fs, often become direct data, which allow to estimate bearing capacity of the base and the side surface of the pile. Direct methods use similarity of the pile work and piezo-cone work during the examination. An important design stage is the appropriate development of measurement data prior to the commencement of the procedure of determining the pile bearing capacity. Algorithms generated on the basis of empirical experiments are often applied with the simultaneous use of test loads. The probabilistic approach is also significant, because it enables objective assessment of the reliability level of performed design calculations. This work contains an analysis of the impact on the estimated bearing capacity and reliability of a pile of variable random depth of the pile base. It also includes the determination of probabilities of obtaining the assumed safety index for the designed solution at random foundation depth.

scholarly journals Revised Estimation Method for Emissions from Automated Plunger Lift Liquid Unloadings

Environments ◽  
2020 ◽  
Vol 7 (4) ◽  
pp. 25
Author(s):  
Adam Pacsi ◽  
David W. Sullivan ◽  
David T. Allen
Keyword(s):  
Direct Measurement ◽  
Oil And Gas ◽  
Measurement Data ◽  
Estimation Method ◽  
The United States ◽  
Gas Production ◽  
Small Subset ◽  
Emission Estimate ◽  
National Measurement ◽  
The Individual

A variety of liquid unloading techniques are used to clear accumulated liquids from the wellbore to increase production rates for oil and gas wells. Data from national measurement studies indicate that a small subset of wells with plunger lift assist, that vent with high frequency and short event duration, contribute a significant fraction of methane emissions from liquid unloading activities in the United States. Compared to direct measurement of emissions at 24 wells in a field campaign, the most commonly used engineering emission estimate for this source category, which is based on the volume of gas in the wellbore, does not accurately predict emissions at the individual well (R2 = 0.06). An alternative emission estimate is proposed that relies on the duration of the venting activity and the gas production rate of the well, which has promising statistical performance characteristics when compared to direct measurement data. This work recommends well parameters that should be collected from future field measurement campaigns that are focused on this emission source.

scholarly journals COVIDSAVIOR: A Novel Sensor-Fusion and Deep Learning Based Framework for Virus Outbreaks

2021 ◽  
Vol 9 ◽  
Author(s):  
Sharnil Pandya ◽  
Anirban Sur ◽  
Nitin Solke
Keyword(s):  
Deep Learning ◽  
Body Temperature ◽  
Real Time ◽  
Sensor Fusion ◽  
Face Mask ◽  
Temperature Threshold ◽  
Mask Condition ◽  
Essential Information ◽  
The Individual ◽  
Scanning Mechanism

The presented deep learning and sensor-fusion based assistive technology (Smart Facemask and Thermal scanning kiosk) will protect the individual using auto face-mask detection and auto thermal scanning to detect the current body temperature. Furthermore, the presented system also facilitates a variety of notifications, such as an alarm, if an individual is not wearing a mask and detects thermal temperature beyond the standard body temperature threshold, such as 98.6°F (37°C). Design/methodology/approach—The presented deep Learning and sensor-fusion-based approach can also detect an individual in with or without mask situations and provide appropriate notification to the security personnel by raising the alarm. Moreover, the smart tunnel is also equipped with a thermal sensing unit embedded with a camera, which can detect the real-time body temperature of an individual concerning the prescribed body temperature limits as prescribed by WHO reports. Findings—The investigation results validate the performance evaluation of the presented smart face-mask and thermal scanning mechanism. The presented system can also detect an outsider entering the building with or without mask condition and be aware of the security control room by raising appropriate alarms. Furthermore, the presented smart epidemic tunnel is embedded with an intelligent algorithm that can perform real-time thermal scanning of an individual and store essential information in a cloud platform, such as Google firebase. Thus, the proposed system favors society by saving time and helps in lowering the spread of coronavirus.

Multi-marginal optimal transport using partial information with applications in robust localization and sensor fusion

2020 ◽  
Vol 171 ◽  
pp. 107474 ◽  
Author(s):  
Filip Elvander ◽  
Isabel Haasler ◽  
Andreas Jakobsson ◽  
Johan Karlsson
Keyword(s):  
Sensor Fusion ◽  
Optimal Transport ◽  
Partial Information ◽  
Robust Localization

Intelligent Transportation System Data Archiving: Statistical Techniques for Determining Optimal Aggregation Widths for Inductive Loop Detector Speed Data

2000 ◽  
Vol 1719 (1) ◽  
pp. 85-93 ◽  
Author(s):  
Byron J. Gajewski ◽  
Shawn M. Turner ◽  
William L. Eisele ◽  
Clifford H. Spiegelman
Keyword(s):  
Traffic Management ◽  
Intelligent Transportation System ◽  
Intelligent Transportation ◽  
Traffic Monitoring ◽  
Transportation System ◽  
Monitoring Data ◽  
Statistical Techniques ◽  
Parameter Distribution ◽  
Data Archiving ◽  
Wide Range

Although most traffic management centers collect intelligent transportation system (ITS) traffic monitoring data from local controllers in 20-s to 30-s intervals, the time intervals for archiving data vary considerably from 1 to 5, 15, or even 60 min. Presented are two statistical techniques that can be used to determine optimal aggregation levels for archiving ITS traffic monitoring data: the cross-validated mean square error and the F-statistic algorithm. Both techniques seek to determine the minimal sufficient statistics necessary to capture the full information contained within a traffic parameter distribution. The statistical techniques were applied to 20-s speed data archived by the TransGuide center in San Antonio, Texas. The optimal aggregation levels obtained by using the two algorithms produced reasonable and intuitive results—both techniques calculated optimal aggregation levels of 60 min or more during periods of low traffic variability. Similarly, both techniques calculated optimal aggregation levels of 1 min or less during periods of high traffic variability (e.g., congestion). A distinction is made between conclusions about the statistical techniques and how the techniques can or should be applied to ITS data archiving. Although the statistical techniques described may not be disputed, there is a wide range of possible aggregation solutions based on these statistical techniques. Ultimately, the aggregation solutions may be driven by nonstatistical parameters such as cost (e.g., “How much do we/the market value the data?”), ease of implementation, system requirements, and other constraints.

Aerodynamic Performance of Turbine Center Frames With Purge Flows—Part I: The Influence of Turbine Purge Flow Rates

2018 ◽  
Vol 140 (6) ◽  
Author(s):  
Stefan Zerobin ◽  
Andreas Peters ◽  
Sabine Bauinger ◽  
Ashwini Bhadravati Ramesh ◽  
Michael Steiner ◽  
...  
Keyword(s):  
Measurement Data ◽  
Leading Edge ◽  
Aerodynamic Performance ◽  
Flow Structures ◽  
Flow Rates ◽  
Purge Flow ◽  
Mass Flows ◽  
The Individual ◽  
The Impact ◽  
First Time

This two-part paper deals with the influence of high-pressure turbine (HPT) purge flows on the aerodynamic performance of turbine center frames (TCF). Measurements were carried out in a product-representative one and a half-stage turbine test setup. Four individual purge mass flows differing in flow rate, pressure, and temperature were injected through the hub and tip, forward and aft cavities of the unshrouded HPT rotor. Two TCF designs, equipped with nonturning struts, were tested and compared. In this first part of the paper, the influence of different purge flow rates (PFR) is discussed, while in the second part of the paper, the impact of the individual hub and tip purge flows on the TCF aerodynamics is investigated. The acquired measurement data illustrate that the interaction of the ejected purge flow with the main flow enhances the secondary flow structures through the TCF duct. Depending on the PFR, the radial migration of purge air onto the strut surfaces directly impacts the loss behavior of the duct. The losses associated with the flow close to the struts and in the strut wakes are highly dependent on the relative position between the HPT vane and the strut leading edge (LE), as well as the interaction between vane wake and ejected purge flow. This first-time experimental assessment demonstrates that a reduction in the purge air requirement benefits the engine system performance by lowering the TCF total pressure loss.

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