scholarly journals Remote and Non-Destructive Sensing for Precision Crop and Field Managements. I. Remote sensing method as a basis for information-based crop management. Potential and the state of the art.

1997 ◽  
Vol 66 (2) ◽  
pp. 335-344 ◽  
Author(s):  
Yoshio INOUE
2012 ◽  
Vol 229-231 ◽  
pp. 1476-1480 ◽  
Author(s):  
Salah M. Ali Al-Obaidi ◽  
M. Salman Leong ◽  
R.I. Raja Hamzah ◽  
Ahmed M. Abdelrhman

Acoustic emission (AE) measurements are one of many non-destructive testing methods which had found applications in defects detection in machines. This paper reviews the state of the art in AE based condition monitoring with particular emphasis on rotating and reciprocating machinery applications. Advantages and limitations of the AE technique in comparison to other condition monitoring techniques in detecting common machinery faults are also discussed.


2016 ◽  
Vol 85 (2) ◽  
pp. 1223-1248 ◽  
Author(s):  
Md. Shahinoor Rahman ◽  
Liping Di

2013 ◽  
Vol 19 (3) ◽  
pp. 325-334 ◽  
Author(s):  
Krzysztof Schabowicz

The paper presents a methodology for comprehensive use of ultrasonic tomography and impact-echo – the state-of-the-art acoustic techniques – for non-destructive identification of the thickness of unilaterally accessible concrete elements. Since the techniques are not commonly used, they are little known. Therefore, a brief description of the techniques is given to facilitate the understanding of the subsequently presented methodology. The article gives a practical example of the use of the methodology, which demonstrates its suitability for non-destructive identification of the thickness of concrete elements, particularly those only accessible from one side. In the example, the concrete shell of a heat pipe, carrying tunnel located under a river was tested using the ultrasonic tomography and impact-echo techniques. The tests were carried out according to the proposed methodology. It should be noted that the test results yielded by the two methods were similar. In this way, the proposed methodology has been validated.


2021 ◽  
Vol 13 (9) ◽  
pp. 1765
Author(s):  
Juan M. Sánchez ◽  
César Coll ◽  
Raquel Niclòs

The combination of the state-of-the-art in the thermal infrared (TIR) domain [...]


2020 ◽  
Author(s):  
Fabrizio D'Amico ◽  
Chiara Ferrante ◽  
Luca Bianchini Ciampoli ◽  
Alessandro Calvi ◽  
Andrea Benedetto

<p>Recent and dramatic events occurred on the Italian transport networks have pointed out the urgent need for assessing the actual state of health along the national transport assets. Analogous considerations can be addressed towards the high exposition and vulnerability of the transport system to major natural events, such as floods or earthquake.</p><p>Recently, the administrations and managing companies have increasingly made use of non-destructive techniques for achieving a denser knowledge about the health of the asset.</p><p>However, one of the major limitations concerning these methods is that each technology, according to its specific features, is usually suitable for a single specific application and has very limited effectiveness for other tasks. Accordingly, the integration of datasets collected with different NDTs stands as a viable approach to fill technology-specific gaps, thereby ensuring a more comprehensive assessment of the infrastructure [1-3]. Data fusion logic can also potentially allow for further data interpretation from merging different information [4].</p><p>The EXTRATN project aims at overcoming the state-of-the-art research in the field of non-destructive monitoring of linear infrastructures and, through a “data fusion” logic, at achieving a comprehensive rate of knowledge about the actual condition of the asset. The addressed concept is a “fully sensed infrastructure”, being sensed by different technologies and with different scopes. Specifically, interferometric synthetic aperture radar (DInSAR), Laser Imaging Detection and Ranging (LiDAR), Ground-penetrating Radar (GPR) and Falling Weight Deflectometer (FWD) are considered to the purpose.</p><p>A system of transport infrastructure being located in the Province of Salerno (IT), within an area subjected to hydrogeological risk, has been selected as a study case for the integrated approach. This system includes a motorway, a rural highway and a railway.</p><p>As a major advantage with respect to the state-of-the-art, such a methodology allows for analysing the evolution trend of the on-going distresses, meaning a significant upgrade of the monitoring activities that may provide valuable information for a priority-based scheduling of the maintenance.</p><p>Moreover, such an approach enables to simultaneously monitor exogenous and endogenous events that may lead to a decrease of the safety, functionality or strength conditions.</p><p>The research is supported by the Italian Ministry of Education, University and Research under the National Project “Extended resilience analysis of transport networks (EXTRA TN): Towards a simultaneously space, aerial and ground sensed infrastructure for risks prevention”, PRIN 2017, Prot. 20179BP4SM.</p><p> </p><ol><li>Liu W, Chen S, Hauser E (2011) LiDAR-based bridge structure defect detection. Exp Tech 35(6):27–34.</li> <li>Grasmueck M, Viggiano DA (2007) Integration of ground-penetrating radar and laser position sensors for real-time 3-D data fusion. IEEE Trans Geosci Remote Sens 45(1):130–137.</li> <li>Solla M et al (2011) Non-destructive methodologies in the assessment of the masonry arch bridge of Traba, Spain. Eng Fail Anal 18(3):828–835</li> <li>Luo RC, Yih C-C, Su KL (2002) Multisensor fusion and integration: approaches, applications, and future research directions. IEEE Sens J 2(2):107–119.</li> </ol>


2015 ◽  
Vol 727-728 ◽  
pp. 785-789 ◽  
Author(s):  
Zhi Bin Lin ◽  
Fardad Azarmi ◽  
Qusay Al-Kaseasbeh ◽  
Mohsen Azimi ◽  
Fei Yan

Non-destructive evaluation (NDE) methods are widely accepted for quality control of welding in steel bridges. Recent development of advanced ultrasonic testing technologies enriched the categories of NDE methods used for steel bridges and more importantly these enhanced techniques provided more effective flaw detection and characterization. No guidelines, however, is available in existing bridge welding code for their more widespread applications to bridges. In this study, we overview the state-of-the-art advanced ultrasonic testing technologies in welding inspection. Benefits of the enhanced ultrasonic testing technologies are summarized, aiming to pave the way for deciding methods need for various steel bridge welding inspections.


Sensors ◽  
2021 ◽  
Vol 21 (21) ◽  
pp. 7316
Author(s):  
Bo Zhong ◽  
Jiang Du ◽  
Minghao Liu ◽  
Aixia Yang ◽  
Junjun Wu

Semantic segmentation for high-resolution remote-sensing imagery (HRRSI) has become increasingly popular in machine vision in recent years. Most of the state-of-the-art methods for semantic segmentation of HRRSI usually emphasize the strong learning ability of deep convolutional neural network to model the contextual relationship in the image, which takes too much consideration on every pixel in images and subsequently causes the problem of overlearning. Annotation errors and easily confused features can also lead to the confusion problem while using the pixel-based methods. Therefore, we propose a new semantic segmentation network—the region-enhancing network (RE-Net)—to emphasize the regional information instead of pixels to solve the above problems. RE-Net introduces the regional information into the base network, to enhance the regional integrity of images and thus reduce misclassification. Specifically, the regional context learning procedure (RCLP) can learn the context relationship from the perspective of regions. The region correcting procedure (RCP) uses the pixel aggregation feature to recalibrate the pixel features in each region. In addition, another simple intra-network multi-scale attention module is introduced to select features at different scales by the size of the region. A large number of comparative experiments on four different public datasets demonstrate that the proposed RE-Net performs better than most of the state-of-the-art ones.


2021 ◽  
Vol 9 (1) ◽  
pp. 2
Author(s):  
Eleni Vrochidou ◽  
Christos Bazinas ◽  
George A. Papakostas ◽  
Theodore Pachidis ◽  
Vassilis G. Kaburlasos

This work highlights the most recent machine vision methodologies and algorithms proposed for estimating the ripening stage of grapes. Destructive and non-destructive methods are overviewed for in-field and in-lab applications. Integration principles of innovative technologies and algorithms to agricultural agrobots, namely, Agrobots, are investigated. Critical aspects and limitations, in terms of hardware and software, are also discussed. This work is meant to be a complete guide of the state-of-the-art machine vision algorithms for grape ripening estimation, pointing out the advantages and barriers for the adaptation of machine vision towards robotic automation of the grape and wine industry.


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