Non-destructive Electromagnetic Wave Sensor for Hazardous Biological Materials

Teng, Kah Hou; Idowu, Ibijoke; Kot, Patryk; Shaw, Andy; Muradov, Magomed

doi:10.1109/dese.2019.00122

Cited by 2 publications

(2 citation statements)

References 13 publications

(8 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The benefit of this part of the EM spectrum is it is none ionizing and providing high depth penetration through dielectric materials offering great potential to be commercialized and applied across several industrial sectors. This technique has been broadly used for water and wastewater quality control [10,11], detection of substances deposited on various materials [12,13], cultural heritage [14] and healthcare applications [15].…”

Section: Sweep Frequency Techniquementioning

confidence: 99%

Recent Advancements in Non-Destructive Testing Techniques for Structural Health Monitoring

et al. 2021

Self Cite

View full text Add to dashboard Cite

Structural health monitoring (SHM) is an important aspect of the assessment of various structures and infrastructure, which involves inspection, monitoring, and maintenance to support economics, quality of life and sustainability in civil engineering. Currently, research has been conducted in order to develop non-destructive techniques for SHM to extend the lifespan of monitored structures. This paper will review and summarize the recent advancements in non-destructive testing techniques, namely, sweep frequency approach, ground penetrating radar, infrared technique, fiber optics sensors, camera-based methods, laser scanner techniques, acoustic emission and ultrasonic techniques. Although some of the techniques are widely and successfully utilized in civil engineering, there are still challenges that researchers are addressing. One of the common challenges within the techniques is interpretation, analysis and automation of obtained data, which requires highly skilled and specialized experts. Therefore, researchers are investigating and applying artificial intelligence, namely machine learning algorithms to address the challenges. In addition, researchers have combined multiple techniques in order to improve accuracy and acquire additional parameters to enhance the measurement processes. This study mainly focuses on the scope and recent advancements of the Non-destructive Testing (NDT) application for SHM of concrete, masonry, timber and steel structures.

show abstract

Section: Sweep Frequency Techniquementioning

confidence: 99%

Recent Advancements in Non-Destructive Testing Techniques for Structural Health Monitoring

et al. 2021

Self Cite

View full text Add to dashboard Cite

show abstract

“…Technological tools are nowadays a means for the automatic detection of biohazards [1], [2] as well as for the analysis of waste such as plastic paper or metal that may involve the same type of risk [3]. Identifying, locating, and detecting biohazards is of utmost importance to safeguard human lives [4].…”

Section: Introductionmentioning

confidence: 99%

Paper biological risk detection through deep learning and fuzzy system

Sanabria

Moreno²,

Baquero³

2023

IJECE

View full text Add to dashboard Cite

<span lang="EN-US">Given the recent events worldwide due to viral diseases that affect human health, automatic monitoring systems are one of the strong points of research that has gained strength, where the detection of biohazardous waste of a sanitary nature is highlighted related to viral diseases stands out. It is essential in this field to generate developments aimed at saving lives, where robotic systems can operate as assistants in various fields. In this work an artificial intelligence algorithm based on two stages is presented, one is the recognition of paper debris using a ResNet-50, chosen for its object localization capacity, and the other is a fuzzy inference system for the generation of alarm states due to biological risk by such debris, where fuzzy logic helps to establish a model for a non-predictive system as the one exposed. A biohazard detection algorithm for paper waste is described, oriented to operate on an assistive robot in a residential environment. The training parameters of the network, which achieve 100% accuracy with confidence levels between 82% for very small waste and 100% in direct view, are presented. Timing cycles are established for validation of the exposure time of the waste, where through the fuzzy system, risk alarms are generated, which allows establishing a system with an average reliability of 98%.</span>

show abstract

Non-destructive Electromagnetic Wave Sensor for Hazardous Biological Materials

Cited by 2 publications

References 13 publications

Recent Advancements in Non-Destructive Testing Techniques for Structural Health Monitoring

Recent Advancements in Non-Destructive Testing Techniques for Structural Health Monitoring

Paper biological risk detection through deep learning and fuzzy system

Contact Info

Product

Resources

About