Automatic recognition and localization of underground pipelines in GPR B-scans using a deep learning model

Liu, Hai; Yue, Yunpeng; Liu, Chao; Spencer, Billie F.; Cui, Jie

doi:10.1016/j.tust.2022.104861

Cited by 41 publications

(8 citation statements)

References 40 publications

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“…Vankadara et al [49] performed a similar study, showing Dion minima at different Local Solar Time (LST) locations, leading to equatorial plasma bubble developments. Our results have shown differences in longitude because of magnetospheric convection processes and electric field penetration [59]. In this scheme, all three American regions have shown clear variations in the initial phases of both the June 2015 and August 2018 storms, but none in the main phases of either storm (Figures 5,7,11,12,14 and 15).…”

Section: Discussionmentioning

confidence: 54%

Ionospheric–Thermospheric Responses to Geomagnetic Storms from Multi-Instrument Space Weather Data

et al. 2023

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We analyze vertical total electron content (vTEC) variations from the Global Navigation Satellite System (GNSS) at different latitudes in different continents of the world during the geomagnetic storms of June 2015, August 2018, and November 2021. The resulting ionospheric perturbations at the low and mid-latitudes are investigated in terms of the prompt penetration electric field (PPEF), the equatorial electrojet (EEJ), and the magnetic H component from INTERMAGNET stations near the equator. East and Southeast Asia, Russia, and Oceania exhibited positive vTEC disturbances, while South American stations showed negative vTEC disturbances during all the storms. We also analyzed the vTEC from the Swarm satellites and found similar results to the retrieved vTEC data during the June 2015 and August 2018 storms. Moreover, we observed that ionospheric plasma tended to increase rapidly during the local afternoon in the main phase of the storms and has the opposite behavior at nighttime. The equatorial ionization anomaly (EIA) crest expansion to higher latitudes is driven by PPEF during daytime at the main and recovery phases of the storms. The magnetic H component exhibits longitudinal behavior along with the EEJ enhancement near the magnetic equator.

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Section: Discussionmentioning

confidence: 54%

Ionospheric–Thermospheric Responses to Geomagnetic Storms from Multi-Instrument Space Weather Data

et al. 2023

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“…By using a deep learning model for automatic recognition and localization, the methodology offers several advantages. It reduces the manual effort required for pipeline identification, improves efficiency, and provides consistent results ( Liu et al, 2023 ).…”

Section: Related Workmentioning

confidence: 98%

Integrating multi-criteria decision-making with hybrid deep learning for sentiment analysis in recommender systems

Angamuthu,

Trojovský

2023

PeerJ Computer Science

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Expert assessments with pre-defined numerical or language terms can limit the scope of decision-making models. We propose that decision-making models can incorporate expert judgments expressed in natural language through sentiment analysis. To help make more informed choices, we present the Sentiment Analysis in Recommender Systems with Multi-person, Multi-criteria Decision Making (SAR-MCMD) method. This method compiles the opinions of several experts by analyzing their written reviews and, if applicable, their star ratings. The growth of online applications and the sheer amount of available information have made it difficult for users to decide which information or products to select from the Internet. Intelligent decision-support technologies, known as recommender systems, leverage users’ preferences to suggest what they might find interesting. Recommender systems are one of the many approaches to dealing with information overload issues. These systems have traditionally relied on single-grading algorithms to predict and communicate users’ opinions for observed items. To boost their predictive and recommendation abilities, multi-criteria recommender systems assign numerous ratings to various qualities of products. We created, manually annotated, and released the technique in a case study of restaurant selection using ‘TripAdvisor reviews’, ‘TMDB 5000 movies’, and an ‘Amazon dataset’. In various areas, cutting-edge deep learning approaches have led to breakthrough progress. Recently, researchers have begun to focus on applying these methods to recommendation systems, and different deep learning-based recommendation models have been suggested. Due to its proficiency with sparse data in large data systems and its ability to construct complex models that characterize user performance for the recommended procedure, deep learning is a formidable tool. In this article, we introduce a model for a multi-criteria recommender system that combines the best of both deep learning and multi-criteria decision-making. According to our findings, the suggested system may give customers very accurate suggestions with a sentiment analysis accuracy of 98%. Additionally, the metrics, accuracy, precision, recall, and F1 score are where the system truly shines, much above what has been achieved in the past.

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“…The You Only Look Once (YOLO) algorithm is a cutting-edge real-time object detection system that uses CNN principles [ 63 , 64 ]. YOLO divides the input image into a S × S grid [ 64 , 65 ]. Each grid cell predicts only one object and a fixed number of boundary boxes.…”

Section: Literature Reviewmentioning

confidence: 99%

Unmanned Aerial Systems and Deep Learning for Safety and Health Activity Monitoring on Construction Sites

Akinsemoyin

Awolusi

Chakraborty

et al. 2023

Sensors

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Construction is a highly hazardous industry typified by several complex features in dynamic work environments that have the possibility of causing harm or ill health to construction workers. The constant monitoring of workers’ unsafe behaviors and work conditions is considered not only a proactive but also an active method of removing safety and health hazards and preventing potential accidents on construction sites. The integration of sensor technologies and artificial intelligence for computer vision can be used to create a robust management strategy and enhance the analysis of safety and health data needed to generate insights and take action to protect workers on construction sites. This study presents the development and validation of a framework that implements the use of unmanned aerial systems (UASs) and deep learning (DL) for the collection and analysis of safety activity metrics for improving construction safety performance. The developed framework was validated using a pilot case study. Digital images of construction safety activities were collected on active construction sites using a UAS, and the performance of two different object detection deep-learning algorithms/models (Faster R-CNN and YOLOv3) for safety hardhat detection were compared. The dataset included 7041 preprocessed and augmented images with a 75/25 training and testing split. From the case study results, Faster R-CNN showed a higher precision of 93.1% than YOLOv3 (89.8%). The findings of this study show the impact and potential benefits of using UASs and DL in computer vision applications for managing safety and health on construction sites.

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Automatic recognition and localization of underground pipelines in GPR B-scans using a deep learning model

Cited by 41 publications

References 40 publications

Ionospheric–Thermospheric Responses to Geomagnetic Storms from Multi-Instrument Space Weather Data

Ionospheric–Thermospheric Responses to Geomagnetic Storms from Multi-Instrument Space Weather Data

Integrating multi-criteria decision-making with hybrid deep learning for sentiment analysis in recommender systems

Unmanned Aerial Systems and Deep Learning for Safety and Health Activity Monitoring on Construction Sites

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