Statistical Model to Detect Voids for Curled or Warped Concrete Pavements

2017 ◽  
Vol 2639 (1) ◽  
pp. 28-38 ◽  
Author(s):  
Kevin Alland ◽  
Julie M. Vandenbossche ◽  
John Brigham
Keyword(s):  
Monitoring Program ◽  
Field Trials ◽  
Detection Methods ◽  
Concrete Pavements ◽  
Detection Techniques ◽  
Void Detection ◽  
Jointed Concrete Pavement ◽  
Deflection Analysis ◽  
Selection Operator ◽  
Falling Weight

A statistical classifier was developed to interpret falling weight deflectometer data for the detection of voids under jointed concrete pavement slabs. The classifier was trained with the Seasonal Monitoring Program sections in the Long-Term Pavement Performance (LTPP) database and data from the Minnesota Road Research Facility. A two-level cross-validation process was used to assess the performance of existing void detection methods, according to a threshold of a single variable, and the least absolute shrinkage and selection operator (LASSO) classifier, which is based on several variables. Simple void detection methods based on the normalized 9,000-lb deflection were found to perform better than void detection methods based on variable deflection analysis. The LASSO classifier outperformed any of the existing void detection techniques. The LASSO classifier was validated through two field trials in Pennsylvania and an LTPP general pavement section in which significant faulting had developed.

Determination of Sample Size and Influencing Factors to Characterize Faulting in Jointed Concrete Pavements

2021 ◽  
pp. 036119812199670
Author(s):  
Ruohan Li ◽  
Jorge A. Prozzi
Keyword(s):  
Sample Size ◽  
Plain Concrete ◽  
Concrete Pavements ◽  
Portland Cement Concrete ◽  
Federal Highway ◽  
Significant Difference ◽  
Jointed Concrete Pavement ◽  
Functional Classes ◽  
High Precipitation ◽  
Departments Of Transportation

The objective of this study is to evaluate the field variability of jointed concrete pavement (JCP) faulting and its effects on pavement performance. The standard deviation of faulting along both the longitudinal and transverse directions are calculated. Based on these, the overall variability is determined, and the required sample sizes needed for a given precision at a certain confidence level are calculated and presented. This calculation is very important as state departments of transportation are required to report faulting every 0.1 mi to the Federal Highway Administration as required by the 2015 FAST Act. On average, twice the number of measurements are needed on jointed reinforced concrete pavements (JRCP) to achieve the same confidence and precision as on jointed plain concrete pavements (JPCP). For example, a sample size of 13 is needed to achieve a 95% confidence interval with a precision of 1.0 mm for average faulting of JPCP, while 26 measurements are required for JRCP ones. Average faulting was found to correlate with several climatic, structural, and traffic variables, while no significant difference was found between edge and outer wheelpath measurements. The application of Portland cement concrete overlay and the use of dowel bars (rather than aggregate interlock) are found to significantly reduce faulting. Older sections located on higher functional classes, and in regions of high precipitation or where the daily temperature change is larger, tend to have higher faulting, and might require larger samples sizes as compared with the rest when faulting surveys are to be conducted.

scholarly journals Indirectly Supervised Anomaly Detection of Clinically Meaningful Health Events from Smart Home Data

2021 ◽  
Vol 12 (2) ◽  
pp. 1-18
Author(s):  
Jessamyn Dahmen ◽  
Diane J. Cook
Keyword(s):  
Anomaly Detection ◽  
Time Series Data ◽  
Bayesian Optimization ◽  
Detection Methods ◽  
Series Data ◽  
Detection Techniques ◽  
Health Events ◽  
Warm Start ◽  
Health Related ◽  
Unsupervised Algorithms

Anomaly detection techniques can extract a wealth of information about unusual events. Unfortunately, these methods yield an abundance of findings that are not of interest, obscuring relevant anomalies. In this work, we improve upon traditional anomaly detection methods by introducing Isudra, an Indirectly Supervised Detector of Relevant Anomalies from time series data. Isudra employs Bayesian optimization to select time scales, features, base detector algorithms, and algorithm hyperparameters that increase true positive and decrease false positive detection. This optimization is driven by a small amount of example anomalies, driving an indirectly supervised approach to anomaly detection. Additionally, we enhance the approach by introducing a warm-start method that reduces optimization time between similar problems. We validate the feasibility of Isudra to detect clinically relevant behavior anomalies from over 2M sensor readings collected in five smart homes, reflecting 26 health events. Results indicate that indirectly supervised anomaly detection outperforms both supervised and unsupervised algorithms at detecting instances of health-related anomalies such as falls, nocturia, depression, and weakness.

scholarly journals Evaluating effects of focal length and viewing angle in a comparison of recent face landmark and alignment methods

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Xiang Li ◽  
Jianzheng Liu ◽  
Jessica Baron ◽  
Khoa Luu ◽  
Eric Patterson
Keyword(s):  
Focal Length ◽  
Ground Truth ◽  
Detection Methods ◽  
Viewing Angle ◽  
Deep Convolutional Neural Networks ◽  
Landmark Detection ◽  
High Performing ◽  
Detection Techniques ◽  
And Performance ◽  
Lens Focal Length

AbstractRecent attention to facial alignment and landmark detection methods, particularly with application of deep convolutional neural networks, have yielded notable improvements. Neither these neural-network nor more traditional methods, though, have been tested directly regarding performance differences due to camera-lens focal length nor camera viewing angle of subjects systematically across the viewing hemisphere. This work uses photo-realistic, synthesized facial images with varying parameters and corresponding ground-truth landmarks to enable comparison of alignment and landmark detection techniques relative to general performance, performance across focal length, and performance across viewing angle. Recently published high-performing methods along with traditional techniques are compared in regards to these aspects.

scholarly journals Standalone Behaviour-Based Attack Detection Techniques for Distributed Software Systems via Blockchain

2021 ◽  
Vol 11 (12) ◽  
pp. 5685
Author(s):  
Hosam Aljihani ◽  
Fathy Eassa ◽  
Khalid Almarhabi ◽  
Abdullah Algarni ◽  
Abdulaziz Attaallah
Keyword(s):  
Attack Detection ◽  
Detection Methods ◽  
Software Systems ◽  
Distributed Software ◽  
Detection Techniques ◽  
Detection Systems ◽  
Novel Approach ◽  
Validation Experiment ◽  
Critical Issues ◽  
Concrete Solution

With the rapid increase of cyberattacks that presently affect distributed software systems, cyberattacks and their consequences have become critical issues and have attracted the interest of research communities and companies to address them. Therefore, developing and improving attack detection techniques are prominent methods to defend against cyberattacks. One of the promising attack detection methods is behaviour-based attack detection methods. Practically, attack detection techniques are widely applied in distributed software systems that utilise network environments. However, there are some other challenges facing attack detection techniques, such as the immutability and reliability of the detection systems. These challenges can be overcome with promising technologies such as blockchain. Blockchain offers a concrete solution for ensuring data integrity against unauthorised modification. Hence, it improves the immutability for detection systems’ data and thus the reliability for the target systems. In this paper, we propose a design for standalone behaviour-based attack detection techniques that utilise blockchain’s functionalities to overcome the above-mentioned challenges. Additionally, we provide a validation experiment to prove our proposal in term of achieving its objectives. We argue that our proposal introduces a novel approach to develop and improve behaviour-based attack detection techniques to become more reliable for distributed software systems.

State of Research on Negative Pressure Techniques Applied to Leak Detection in Liquid Pipelines

2004 ◽  
Author(s):  
Wei Liang ◽  
Lai-bin Zhang ◽  
Zhao-hui Wang
Keyword(s):  
Pressure Wave ◽  
Analytical Techniques ◽  
Leak Detection ◽  
Point Of View ◽  
Detection Methods ◽  
Propagation Mechanism ◽  
Detection Techniques ◽  
Wave Detection ◽  
State Of Research ◽  
Signal Processing Methods

In China, the rarefaction-pressure wave techniques are widely used to diagnose the leakage fault for liquid pipelines. Many leaking propagating assumptions, such as stable single-phased flow hypothesis and none rarefaction wave front hypothesis, are often uncertain in the process of leak detection, which can easily result in some errors. Thus the rarefaction-pressure wave techniques should be integrated with other analytical techniques to compute a more accurate leak location. Additionally, the development trends of rarefaction-pressure wave techniques lie in three aspects. First, rarefaction-pressure wave detection techniques will be integrated with other compatible detection techniques and modern signal processing methods to solve the complex problems encountered in leak detection. Second, studies of rarefaction-pressure wave techniques have advanced to a new stage. The deductions on propagation mechanism of rarefaction-pressure wave have been successfully applied to determine leaks qualitatively. Third, analysis on rarefaction-pressure wave detection techniques will be made from a quantitative point of view. The quantitative data have been used to deduce leak amounts and location. The purpose of this paper is to present the recent achievements in the study of improved rarefaction-pressure wave detection techniques. The rarefaction-pressure wave detection methods, effects of incomplete information conditions, the improvements of rarefaction-pressure wave detection techniques with modified factors and propagation mechanisms are comprehensively investigated. The disfigurements of rarefaction-pressure wave are analyzed. The corresponding methods for resolving such problems as ill diagnostic information and weak amplitude values are put forward. Several methods for stronger small leakage detection ability, higher leakage positioning precision, lower false alarm rates are proposed. The application of rarefaction-pressure wave detection techniques to safety protection of liquid pipelines is also introduced. Finally, the prospect of rarefaction-pressure wave detection techniques is predicted.

Acquire Ocean Bottom Seismic Data and Time-Lapse Geochemistry Data Simultaneously to Identify Compartmentalization and Map Hydrocarbon Movement

2021 ◽  
Author(s):  
Rick Schrynemeeckers
Keyword(s):  
High Resolution ◽  
Seismic Data ◽  
Time Lapse ◽  
Field Trials ◽  
Detection Methods ◽  
Ocean Bottom ◽  
Field Development ◽  
Dense Grid ◽  
Hydrocarbon Charge ◽  
Sweet Spots

Abstract Current offshore hydrocarbon detection methods employ vessels to collect cores along transects over structures defined by seismic imaging which are then analyzed by standard geochemical methods. Due to the cost of core collection, the sample density over these structures is often insufficient to map hydrocarbon accumulation boundaries. Traditional offshore geochemical methods cannot define reservoir sweet spots (i.e. areas of enhanced porosity, pressure, or net pay thickness) or measure light oil or gas condensate in the C7 – C15 carbon range. Thus, conventional geochemical methods are limited in their ability to help optimize offshore field development production. The capability to attach ultrasensitive geochemical modules to Ocean Bottom Seismic (OBS) nodes provides a new capability to the industry which allows these modules to be deployed in very dense grid patterns that provide extensive coverage both on structure and off structure. Thus, both high resolution seismic data and high-resolution hydrocarbon data can be captured simultaneously. Field trials were performed in offshore Ghana. The trial was not intended to duplicate normal field operations, but rather provide a pilot study to assess the viability of passive hydrocarbon modules to function properly in real world conditions in deep waters at elevated pressures. Water depth for the pilot survey ranged from 1500 – 1700 meters. Positive thermogenic signatures were detected in the Gabon samples. A baseline (i.e. non-thermogenic) signature was also detected. The results indicated the positive signatures were thermogenic and could easily be differentiated from baseline or non-thermogenic signatures. The ability to deploy geochemical modules with OBS nodes for reoccurring surveys in repetitive locations provides the ability to map the movement of hydrocarbons over time as well as discern depletion affects (i.e. time lapse geochemistry). The combined technologies will also be able to: Identify compartmentalization, maximize production and profitability by mapping reservoir sweet spots (i.e. areas of higher porosity, pressure, & hydrocarbon richness), rank prospects, reduce risk by identifying poor prospectivity areas, accurately map hydrocarbon charge in pre-salt sequences, augment seismic data in highly thrusted and faulted areas.

Comparison of Water and Gas Tracers Field Breakthrough

2021 ◽  
Author(s):  
Hsieh Chen ◽  
Sehoon Chang ◽  
Gawain Thomas ◽  
Wei Wang ◽  
Afnan Mashat ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Gas Phase ◽  
Detection Method ◽  
Tracer Tests ◽  
Field Tests ◽  
Field Trials ◽  
Breakthrough Curves ◽  
Detection Methods ◽  
Reservoir Conditions ◽  
Gas Tracer

Abstract We are developing new classes of barcoded advanced tracers, which, compared to present commercial offerings, can be optically detected in an automated fashion. The eventual goal for the advanced tracers is to deploy cost-effective, ubiquitous, long-term, and full-field tracer tests in supporting large-scale waterflooding optimization for improved oil recovery. In this paper, we compare model predictions to breakthrough data from two field tests of advanced tracers in a pilot during water alternating gas (WAG) cycles, where gas tracer tests have recently been performed as well. Two advanced tracer injections were performed at the test site. For the first injection, only a dipicolinic acid based advanced tracer (DPA) was injected. For the second injection, DPA and a phenanthroline- based advanced tracer, 4,7-bis(sulfonatophenyl)-1,10-phenanthroline-2,9-dicarboxylic acid (BSPPDA), was injected in conjunction with a commercially available fluorobenzoic acid-based tracer (FBA) to benchmark their performance. Produced water samples were collected weekly for tracer analysis. Both newly developed 2D-high performance liquid chromatography/time-resolved fluorescence optical detection method (2D-HPLC/TRF) and liquid chromatography-mass spectrometry (LC-MS) were used to construct the breakthrough curves for the advanced tracers. In parallel, gas chromatography-mass spectrometry (GC-MS) was used to detect FBA tracer. Gas tracer tests have been performed on the same field. Since DPA, BSPPDA and FBA tracers were water tracers as designed, they were expected to appear in between gas tracer breakthroughs, and we observed exactly that for BSPPDA and FBA. Unexpectedly, the DPA predominantly appeared along with gas tracer breakthroughs, suggesting its favorable compatibility with the gas phase. We suspect the presence of some gas components rendered the medium more acidic, which likely protonates DPA molecules, thereby alters its hydrophilicity. A wealth of information could be gathered from the field tests. First, all tracers survived not only the harsh reservoir conditions but also the irregular WAG injections. Their successful detection from the producers suggested robustness of these materials for reservoir applications. Second, the breakthrough curves of the BSPPDA tracers using optical detection method were very similar to those of FBA tracers detected by GC-MS, substantiating the competency of our in-house materials and detection methods to the present commercial offerings. Finally, even though DPA has passed prior lab tests as a good water tracer, its high solubility to gas phase warrants further investigation. This paper summarizes key results from two field trials of the novel barcoded advanced tracers, of which both the tracer materials and detection methods are new to the industry. Importantly, the two co- injected advanced tracers showed opposite correlations to the gas tracers, highlighting the complex physicochemical interactions in reservoir conditions. Nevertheless, the information collected from the field trials is invaluable in enabling further design and utilization of the advanced tracers in fulfilling their wonderful promises.

Deepfake videos: synthesis and detection techniques –a survey

2021 ◽  
pp. 1-21
Author(s):  
Shahela Saif ◽  
Samabia Tehseen
Keyword(s):  
Social Systems ◽  
Generative Models ◽  
Detection Methods ◽  
Future Research ◽  
Fake News ◽  
Research Directions ◽  
Detection Techniques ◽  
Future Research Directions ◽  
Video Creation ◽  
The One

Deep learning has been used in computer vision to accomplish many tasks that were previously considered too complex or resource-intensive to be feasible. One remarkable application is the creation of deepfakes. Deepfake images change or manipulate a person’s face to give a different expression or identity by using generative models. Deepfakes applied to videos can change the facial expressions in a manner to associate a different speech with a person than the one originally given. Deepfake videos pose a serious threat to legal, political, and social systems as they can destroy the integrity of a person. Research solutions are being designed for the detection of such deepfake content to preserve privacy and combat fake news. This study details the existing deepfake video creation techniques and provides an overview of the deepfake datasets that are publicly available. More importantly, we provide an overview of the deepfake detection methods, along with a discussion on the issues, challenges, and future research directions. The study aims to present an all-inclusive overview of deepfakes by providing insights into the deepfake creation techniques and the latest detection methods, facilitating the development of a robust and effective deepfake detection solution.

Can Soy Methyl Esters Improve Concrete Pavement Joint Durability?

2012 ◽  
Vol 2290 (1) ◽  
pp. 60-68 ◽  
Author(s):  
Michael Golias ◽  
Javier Castro ◽  
Alva Peled ◽  
Tommy Nantung ◽  
Bernard Tao ◽  
...  
Keyword(s):  
Methyl Esters ◽  
Field Trials ◽  
Chemical Degradation ◽  
Maintenance Cost ◽  
Concrete Durability ◽  
Concrete Pavements ◽  
Freeze Thaw ◽  
Fluid Saturation ◽  
Soy Methyl Ester ◽  
Term Performance

Although many concrete pavements provide excellent long-term performance, some pavements (primarily in the Midwest) have shown premature deterioration at the joints. This premature deterioration is a concern because such deterioration can shorten the life of pavements that are otherwise functioning well. Previous work has hypothesized that these joints may be susceptible to preferential fluid saturation, which can lead to freeze–thaw damage or chemical degradation. This work examines the use of soy methyl ester–polystyrene (SME-PS) blends as a method to reduce the rate of fluid ingress into the pore system of the concrete and thereby make the concrete more resistant to deterioration. SME-PS is derived from soybeans and has demonstrated an ability to reduce fluid absorption in concrete when used as a topical treatment. A series of experiments was developed to evaluate the effectiveness of various dosage rates of SME-PS for increasing concrete durability at pavement joints. The experiments show that SME-PS reduces fluid ingress, salt ingress, and the potential for freeze–thaw damage. As a result of the positive experimental results, the Indiana Department of Transportation is conducting field trials that use SME-PS on concrete pavements that are beginning to show signs of premature deterioration with the expectation that SME-PS will extend the life of the joints and thereby reduce maintenance cost and extend the life of concrete pavements.

Partial Discharge Detection Using Acoustic and Optical Methods in High Voltage Power Equipments: A Review

2013 ◽  
Vol 845 ◽  
pp. 283-286 ◽  
Author(s):  
Malik Abdul Razzaq Al Saedi ◽  
Mohd Muhridza Yaacob
Keyword(s):  
Acoustic Emission ◽  
High Voltage ◽  
Power Transformer ◽  
Partial Discharge ◽  
Optical Detection ◽  
Detection Threshold ◽  
Optical Methods ◽  
Detection Methods ◽  
Detection Techniques ◽  
Advantages And Disadvantages

There is a high risk of insulation system dielectric instability when partial discharge (PD) occurs. Therefore, measurement and monitoring of PD is an important preventive tool to safeguard high-voltage equipment from wanton damage. PD can be detected using optical method to increase the detection threshold and to improve the performance of on-line measurement of PD in noise environment. The PD emitted energy as acoustic emission. We can use this emitted energy to detect PD signal. The best method to detect PD in power transformer is by using acoustic emission. Optical sensor has some advantages such as; high sensitivity, more accuracy small size. Furthermore, in on-site measurements and laboratory experiments, it isoptical methodthat gives very moderate signal attenuations. This paper reviews the available PD detection methods (involving high voltage equipment) such as; acoustic detection and optical detection. The advantages and disadvantages of each method have been explored and compared. The review suggests that optical detection techniques provide many advantages from the consideration of accuracy and suitability for the applications when compared to other techniques.

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