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Journal articles on the topic 'UXO'

Author: Grafiati

Published: 4 June 2021

Last updated: 1 June 2024

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1

Kappler, K. N., and E. Gasperikova. "A Hybrid Method for UXO vs. Non-UXO Discrimination." Journal of Environmental & Engineering Geophysics 16, no. 4 (2011): 177–89. http://dx.doi.org/10.2113/jeeg16.4.177.

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2

Butler, Dwain K. "Landmines and UXO." Leading Edge 16, no. 10 (1997): 1460. http://dx.doi.org/10.1190/1.1437514.

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3

Stanbridge, Andrew. "UXO in Laos." Anthropology Now 3, no. 2 (2011): 48–61. http://dx.doi.org/10.1080/19492901.2011.11728321.

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4

Kay, Simon. "UXO? MM? QED." British Journal of Plastic Surgery 58, no. 2 (2005): 145–46. http://dx.doi.org/10.1016/j.bjps.2005.01.001.

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5

Hameed, Qabas A., Harith A. Hussein, Mohamed A. Ahmed, Mahmood M. Salih, Reem D. Ismael, and Mohammed Basim Omar. "UXO-AID: A New UXO Classification Application Based on Augmented Reality to Assist Deminers." Computers 11, no. 8 (2022): 124. http://dx.doi.org/10.3390/computers11080124.

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Unexploded ordnance (UXO) is a worldwide problem and a long-term hazard because of its ability to harm humanity by remaining active and destructive decades after a conflict has concluded. In addition, the current UXO clearance methods mainly involve manual clearance and depend on the deminer’s experience. However, this approach has a high misclassification rate, which increases the likelihood of an explosion ending the deminer’s life. This study proposes a new approach to identifying the UXO based on augmented reality technology. The methodology is presented based on two phases. Firstly, a new

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6

Bowers, R. Josh, and Beatrice A. Bidwell. "Geophysics and UXO detection." Leading Edge 18, no. 12 (1999): 1389–91. http://dx.doi.org/10.1190/1.1438224.

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7

Huang, Haoping, Bill SanFilipo, and I. J. Won. "Optimizing decision threshold and weighting parameter for UXO discrimination." GEOPHYSICS 71, no. 6 (2006): G313—G320. http://dx.doi.org/10.1190/1.2353802.

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Establishing a decision threshold to separate unexploded ordnance (UXO) from clutter is a critical task in UXO discrimination procedures. It is, however, a difficult task because the decision threshold depends on many parameters such as local geology, distribution of UXO and clutter, and cultural and environmental noise at a given UXO cleanup site. Some discrimination algorithms also use site-dependent weighting factors. In practice, one determines these parameters empirically from calibration experiments. To facilitate this task, we introduce a simple function called the degree of discriminat

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8

Potić, Ivan, Nenad Komazec, Ljiljana Mihajlović, Aleksandar Milić, and Saša Bakrač. "Risk management of unexploded ordnance in the Republic of Serbia for environmental protection: Borovac case study." Vojnotehnicki glasnik 71, no. 4 (2023): 1087–114. http://dx.doi.org/10.5937/vojtehg71-44656.

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Introduction/purpose: Decades of dealing with unexploded ordnance (UXO) in some areas of the Republic of Serbia have confirmed that it presents a substantial hazard to the security of people, property, and the environment. Even though the terrain has been cleaned, various threats from difficult-tofind UXO components remain serious. Inadequate systemic solutions for UXO management can have significant harmful consequences. Methods: Based on the spatial distribution analysis and different UXO types and quantities, this article studies the effects of dangerous components of unexploded ordnance on

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9

Brighouse, Jack, Martin Wood, Eoin McGregor, et al. "Inverse modelling and classification of magnetic responses to improve marine unexploded ordnance rationalization." Geophysical Journal International 237, no. 1 (2024): 123–44. http://dx.doi.org/10.1093/gji/ggad490.

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SUMMARY Unexploded ordnance (UXO), often deployed in WWI and WWII, are still present on the seabed posing a risk to seabed construction. UXO surveys identify potential UXO targets which are then investigated and removed where necessary; however, approximately 96 per cent of targets investigated on projects included in this study were non-UXO (false positives). Here we investigate methods of improving statistical confidence in targets picked through the improved classification of magnetic response, utilizing simulation methods and historic data. A simulation of the induced magnetism of UXO was

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10

Wigh, Mark David, Thomas Mejer Hansen, and Arne Døssing. "Inference of unexploded ordnance (UXO) by probabilistic inversion of magnetic data." Geophysical Journal International 220, no. 1 (2019): 37–58. http://dx.doi.org/10.1093/gji/ggz421.

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SUMMARY Magnetic modelling of unexploded ordnance (UXO) is a well-documented method used to interpret magnetic anomalies occurring in UXO excavation surveys. By treating UXO as a ferrous spheroidal object, the induced dipole moment can be estimated by approximation of UXO characteristics such as shape, size and orientation. Inversion of magnetic data with respect to UXO requires one to solve the equation for the induced dipole moment, while also determining the location and orientation of the object. This is a highly nonlinear, non-unique problem, where many solutions often are present, which

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11

Gamey, T. J. "Magnetic Response of Clustered UXO Targets." Journal of Environmental & Engineering Geophysics 13, no. 3 (2008): 211–21. http://dx.doi.org/10.2113/jeeg13.3.211.

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12

Daily, William, Abelardo Ramirez, Robin Newmark, and Victor George. "Imaging UXO Using Electrical Impedance Tomography." Journal of Environmental and Engineering Geophysics 5, no. 4 (2000): 11–23. http://dx.doi.org/10.4133/jeeg5.4.11.

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13

Constantinoiu, Laurențiu-Florin, Eugen Rusu, and Maria-Emanuela Mihailov. "UXO Assessment on the Romanian Black Sea Coast." Journal of Marine Science 4, no. 2 (2022): 7. http://dx.doi.org/10.30564/jms.v4i2.4497.

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This paper aims to provide the reader with the results of the Unexploded Ordnance (UXO) survey of the defensive historical naval minefields launched by the Romanian and German Navies on the Romanian Black Sea coast, during the Second World War. This UXO survey was carried out between 2015-2018 by the Romanian Navy’s hydrographic ship “Commander Alexandru Cătuneanu” and Romanian Mine Warfare Data Center, using towed side-scan sonar technology and oceanographic observations. After explaining the materials and methodology, the results are presented and discussed: mosaics of the minefields, side-s

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14

Gasperikova, Erika, J. Torquil Smith, H. Frank Morrison, Alex Becker, and Karl Kappler. "UXO detection and identification based on intrinsic target polarizabilities — A case history." GEOPHYSICS 74, no. 1 (2009): B1—B8. http://dx.doi.org/10.1190/1.2997419.

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Electromagnetic induction data parameterized in time-dependent object intrinsic polarizabilities can discriminate unexploded ordnance (UXO) from false targets (scrap metal). Data from a cart-mounted system designed to discriminate UXO of [Formula: see text] in diameter are used. Discriminating UXO from irregular scrap metal is based on the principal dipole polarizabilities of a target. Nearly intact UXO displays a single major polarizability coincident with the long axis of the object and two equal, smaller transverse polarizabilities, whereas metal scraps have distinct polarizability signatur

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15

Rancich, Tom. "Search and Recovery of Munitions by Divers." Marine Technology Society Journal 45, no. 6 (2011): 75–79. http://dx.doi.org/10.4031/mtsj.45.6.9.

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AbstractThe trend in the United States is to treat the underwater unexploded ordnance (UXO) problem the same as the land problem. This is fundamentally flawed. Not only must underwater sites be treated differently than land sites, but each underwater site must be treated differently, bringing to bear all possible solutions to develop the best course(s) of action. Although many sites will have similar assets applied to the solution, there will be no cookie-cutter solution. Because of the dynamic nature of the underwater environment, an underwater UXO operation is distinctly unique from a land o

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16

Han, Songtong, Xiaoli Rong, Leixiang Bian, Mingyou Zhong, and Lining Zhang. "The Application of Magnetometers and Electromagnetic Induction Sensors in UXO Detection." E3S Web of Conferences 131 (2019): 01045. http://dx.doi.org/10.1051/e3sconf/201913101045.

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This paper introduces the physical model of unexploded ordnance (UXO) in the magnetization process and the formula derivation of the magnetic dipole model. The application of magnetometers and electromagnetic induction sensors in the detection of UXO is introduced. The magnetometers introduce the total field measurements technology of the CS optical-pump marine magnetometer and the MagSTAR (Magnetic Scalar Triangulation and Ranging) gradient detection technology. The electromagnetic induction sensors introduce the working theories and the popular product models of Geophex company and Geonics L

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17

Davis, Kristofer, Yaoguo Li, and Misac Nabighian. "Automatic detection of UXO magnetic anomalies using extended Euler deconvolution." GEOPHYSICS 75, no. 3 (2010): G13—G20. http://dx.doi.org/10.1190/1.3375235.

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We have developed an algorithm for the automatic detection of prospective unexploded ordnance (UXO) anomalies in total-field or gradient magnetic data based on the concept of the structural index (SI) of a magnetic anomaly. Identifying magnetic anomalies having specific structural indices enables the direct detection of potential UXO targets. The total magnetic field produced by a dipolelike source, such as a UXO, decays with inverse distance cubed and therefore has an SI of three, whereas the gradient data have an SI of four. The developed extended Euler deconvolution method based on the Hilb

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18

Mendonça, Luís Felipe, Jorge Arigony-Neto, Claudio Mendes Junior, Virginia Santos, and Ricardo Jaña. "ESTIMATIVA DAS VARIAÇÕES SAZONAIS NO FLUXO DA GELEIRA GREY, PATAGÔNIA, POR IMAGENS SAR." Revista Brasileira de Cartografia 67, no. 7 (2019): 1541–55. http://dx.doi.org/10.14393/rbcv67n7-49197.

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Este trabalho apresenta estimativas da velocidade de fl uxo da geleira Grey, localizada no Campo de Gelo PatagônicoSul, para o período de outubro de 2011 e janeiro a abril de 2012. Obtidos através de um algoritmo baseado no cálculode correlação cruzada entre pares de imagens SAR COSMO-SkyMed, os valores de velocidade foram posteriormenteinterpolados, de forma a gerar superfícies contínuas que ilustrem a direção e intensidade de fl uxo da geleira. Os resultados foram comparados com dados meteorológicos, disponibilizados pelo Serviço Meteorológico Argentino, parainterpretar o padrão sazonal na v

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19

Grezmak, John, and Kathryn A. Daltorio. "Probing with Each Step: How a Walking Crab-like Robot Classifies Buried Cylinders in Sand with Hall-Effect Sensors." Sensors 24, no. 5 (2024): 1579. http://dx.doi.org/10.3390/s24051579.

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Shallow underwater environments around the world are contaminated with unexploded ordnances (UXOs). Current state-of-the-art methods for UXO detection and localization use remote sensing systems. Furthermore, human divers are often tasked with confirming UXO existence and retrieval which poses health and safety hazards. In this paper, we describe the application of a crab robot with leg-embedded Hall effect-based sensors to detect and distinguish between UXOs and non-magnetic objects partially buried in sand. The sensors consist of Hall-effect magnetometers and permanent magnets embedded in lo

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20

Son, Derac. "Magnertic Methods to Detect Mines and UXO." Journal of the Korean Magnetics Society 30, no. 3 (2020): 106–13. http://dx.doi.org/10.4283/jkms.2020.30.3.106.

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21

Arcone, Steven, David Finnegan, and Ginger Boitnott. "GPR characterization of a lacustrine UXO site." GEOPHYSICS 75, no. 4 (2010): WA221—WA239. http://dx.doi.org/10.1190/1.3467782.

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We used [Formula: see text] ground-penetrating-radar (GPR) reflection profiles to determine if high-explosive (HE) depth bombs were possibly detonated within a small New England lake formerly used as a bombing range, as well as the depths and distribution of inert and possibly unexploded ordnance (UXO) within the subbottom sediments. Maximum water depth was [Formula: see text]. The primary munitions were unarmed practice bombs, but terrestrial fragments from HE depth bombs led us to seek stratigraphic obliteration and distortion as evidence of their use. The low water conductivity and quartz-r

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22

Paoletti, V., A. Buggi, and R. Pašteka. "UXO Detection by Multiscale Potential Field Methods." Pure and Applied Geophysics 176, no. 10 (2019): 4363–81. http://dx.doi.org/10.1007/s00024-019-02202-7.

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23

Kolster, Mick Emil, and Arne Døssing. "Scalar magnetic difference inversion applied to UAV-based UXO detection." Geophysical Journal International 224, no. 1 (2020): 468–86. http://dx.doi.org/10.1093/gji/ggaa483.

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SUMMARY During scalar magnetic surveys, where the amplitude of the magnetic field is measured, small changes in towed sensor positions can produce complex noise-resembling signals in the data. For well-constructed measurement systems, these signals often contain valuable information, rather than noise, but it can difficult to realize their potential. We present a simple, general approach, which can be used to directly invert data from scalar magnetic surveys, regardless of dynamic or unexpected sensor position variations. The approach generalizes classic along-track gradients to an iterative,

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24

Brown, Daniel C., Scott Brumbaugh, Mark Fanton, Richard Meyer, Shawn Johnson, and Cale Brownstead. "Projector design for imaging of buried unexploded ordnance." Journal of the Acoustical Society of America 151, no. 4 (2022): A57. http://dx.doi.org/10.1121/10.0010646.

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Recent modeling and field experimentation has shown very-shallow-water acoustic imaging of buried unexploded ordnance (UXO) can place stringent requirements on the sonar transmit system. Achieving high-resolution imagery requires large transmit bandwidth. Imaging deeply buried UXO requires low operating frequencies and high source levels to overcome sediment attenuation. Imaging UXO near the sediment–water interface requires short transmit waveforms that require projectors with a low mechanical quality factor for accurate reproduction. This work presents the results of a recent design study, d

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25

Lee, Jong Ki, and Christopher Jekeli. "Rao-Blackwellized Unscented Particle Filter for a Handheld Unexploded Ordnance Geolocation System using IMU/GPS." Journal of Navigation 64, no. 2 (2011): 327–40. http://dx.doi.org/10.1017/s0373463310000548.

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The existence of Unexploded Ordnance (UXO) is a serious environmental hazard, especially in areas being converted from military to civilian use. The detection and discrimination performance of UXO detectors depends on the sensor technology as well as on the processing methodology that inverts the data to infer UXO. The detection systems, typically electro-magnetic induction (EMI) devices, require very accurate positioning (or geolocation) in order to discriminate candidate UXO from non-hazardous items. For this paper, a hand-held geolocation system based on a tactical-grade IMU, such as the HG

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26

Beran, Laurens, Barry Zelt, Leonard Pasion, et al. "Practical strategies for classification of unexploded ordnance." GEOPHYSICS 78, no. 1 (2013): E41—E46. http://dx.doi.org/10.1190/geo2012-0236.1.

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We have developed practical strategies for discriminating between buried unexploded ordnance (UXO) and metallic clutter. These methods are applicable to time-domain electromagnetic data acquired with multistatic, multicomponent sensors designed for UXO classification. Each detected target is characterized by dipole polarizabilities estimated via inversion of the observed sensor data. The polarizabilities are intrinsic target features and so are used to distinguish between UXO and clutter. We tested this processing with four data sets from recent field demonstrations, with each data set charact

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27

Cristaudo, Demetra, Benedict M. Gross, and Jack A. Puleo. "Momentum Balance Analysis of Spherical Objects and Long-Term Field Observations of Unexploded Ordnance (UXO) in the Swash Zone." Journal of Marine Science and Engineering 11, no. 1 (2023): 79. http://dx.doi.org/10.3390/jmse11010079.

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Military activity has resulted in unexploded ordnance (UXO) existing in the nearshore. Understanding and predicting UXO behavior is important for object identification, and management. Here, two studies (laboratory and fieldwork) have been conducted to observe UXO surrogates in the swash zone and relate burial and migration to the underlying forcing conditions. A small-scale laboratory dam-break study was conducted to quantify migration of varying density spherical objects at different locations on a sloping, mobile, sandy bed. A moment balance was applied to derive two data-driven relationshi

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28

Llopis, J. L. "Introduction to This UXO Special Issue of JEEG." Journal of Environmental & Engineering Geophysics 13, no. 3 (2008): v—vi. http://dx.doi.org/10.2113/jeeg13.3.fmv.

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29

Weichman, Peter B. "Validation of Advanced EM Models for UXO Discrimination." IEEE Transactions on Geoscience and Remote Sensing 51, no. 7 (2013): 3954–67. http://dx.doi.org/10.1109/tgrs.2012.2227490.

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30

Zhang, Qing, Waleed Al-Nuaimy, and Yi Huang. "Detection of Deeply Buried UXO Using CPT Magnetometers." IEEE Transactions on Geoscience and Remote Sensing 45, no. 2 (2007): 410–17. http://dx.doi.org/10.1109/tgrs.2006.886178.

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31

Bray, Matthew P., and Curtis A. Link. "Learning Machine Identification of Ferromagnetic UXO Using Magnetometry." IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 8, no. 2 (2015): 835–44. http://dx.doi.org/10.1109/jstars.2014.2362920.

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32

McKenna, Sean A., and Brent Pulsipher. "Quantifying and reducing uncertainty in UXO site characterization." Stochastic Environmental Research and Risk Assessment 23, no. 2 (2008): 153–54. http://dx.doi.org/10.1007/s00477-007-0203-5.

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33

McKenna, Sean A. "UXO target area identification with hidden Markov models." Stochastic Environmental Research and Risk Assessment 23, no. 2 (2008): 193–202. http://dx.doi.org/10.1007/s00477-007-0209-z.

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34

Nelson, H. H., and J. R. McDonald. "Multisensor towed array detection system for UXO detection." IEEE Transactions on Geoscience and Remote Sensing 39, no. 6 (2001): 1139–45. http://dx.doi.org/10.1109/36.927427.

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35

Ibraheem, Ismael M., Hasan Aladad, Mohamad Faek Alnaser, and Randell Stephenson. "IAS: A New Novel Phase-Based Filter for Detection of Unexploded Ordnances." Remote Sensing 13, no. 21 (2021): 4345. http://dx.doi.org/10.3390/rs13214345.

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A new phase-based filter (called improved analytical signal (IAS)) is introduced to detect buried unexploded ordnance (UXO) precisely from magnetic fields using the arcsine function of the ratio of the first-order vertical derivative of the analytical signal to the first-order derivatives of the x-, y-, and z-components of the analytical signal. The calculations are computed in the frequency domain and then transformed back into the space domain using the inverse Fourier transform. The filter has been tested on magnetic data collected at a test site with UXO bodies of variable orientation. It

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36

Hussein, Harith A., Qabas A. Hameed, Reem D. Ismael, Mustafa Zuhaer Nayef Al-Dabagh, and Moudher Khalid Abdalhammed. "Applying Web Augmented Reality to Unexplosive Ordnance Risk Education." Computers 12, no. 2 (2023): 31. http://dx.doi.org/10.3390/computers12020031.

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Unexploded Ordnances (UXOs) are considered a global concern and a persistent hazard due to their capability to endanger civilians and the place where they are located, and the probability of remaining active explosives even after decades of ending a conflict. Hence, risk education is crucial for providing individuals with life-saving knowledge on recognizing, avoiding, and reporting UXO threats. The main objective of this study is to develop a web augmented reality (AR) application to investigate the effect of WAR on non-explosive ordnance risk education. Firstly, UXO 3D models are edited and

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37

Theera-Umpon, Nipon. "Unexploded Ordnance Detection Using Region of Interest in Range Domain of Ground Penetrating Radar." Journal of Advanced Computational Intelligence and Intelligent Informatics 11, no. 10 (2007): 1184–88. http://dx.doi.org/10.20965/jaciii.2007.p1184.

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Buried unexploded ordnance (UXO) and land mines are grave threats to civilians who go back to contaminated areas in the postwar period. In this research, we propose a new technique to detect UXOs from ground penetrating radar (GPR) signals. The technique is based on an energy-based feature of the region of interest (ROI). A ROI selection technique in range domain is also proposed. The proposed method was tested with the UXO data collected by Battelle company and the Ohio State University. This data set was made available to public through the Unexploded Ordnance Center of Excellence (UXOCOE),

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38

Billings, Stephen, and Laurens Beran. "Optimizing electromagnetic sensors for unexploded ordnance detection." GEOPHYSICS 82, no. 3 (2017): EN25—EN31. http://dx.doi.org/10.1190/geo2016-0232.1.

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Time-domain electromagnetic (TEM) instruments are the predominant geophysical sensor for detection of buried unexploded ordnance (UXO). Detection surveys commonly use towed TEM sensor arrays to acquire a digital map for target detection. We use a dipolar model to predict a detection threshold for a UXO at a specified clearance depth, given an arbitrary sensor geometry. In general, the minimum target response is obtained for a horizontally oriented target. We find that for multistatic sensors, the minimum response can also depend on the azimuth of the target. By considering the statistics of th

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39

Blachnik, Marcin, Roman Przyłucki, Sławomir Golak, Piotr Ściegienka, and Tadeusz Wieczorek. "On the Development of a Digital Twin for Underwater UXO Detection Using Magnetometer-Based Data in Application for the Training Set Generation for Machine Learning Models." Sensors 23, no. 15 (2023): 6806. http://dx.doi.org/10.3390/s23156806.

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Scanning underwater areas using magnetometers in search of unexploded ordnance is a difficult challenge, where machine learning methods can find a significant application. However, this requires the creation of a dataset enabling the training of prediction models. Such a task is difficult and costly due to the limited availability of relevant data. To address this challenge in the article, we propose the use of numerical modeling to solve this task. The conducted experiments allow us to conclude that it is possible to obtain high compliance with the numerical model based on the finite element

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40

Ki Lee, Jong, and Christopher Jekeli. "Neural Network Aided Adaptive Filtering and Smoothing for an Integrated INS/GPS Unexploded Ordnance Geolocation System." Journal of Navigation 63, no. 2 (2010): 251–67. http://dx.doi.org/10.1017/s0373463309990397.

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The precise geolocation of buried unexploded ordnance (UXO) is a significant component of the detection, characterization, and remediation process. Traditional geolocation methods associated with these procedures are inefficient in helping to distinguish buried UXO from relatively harmless geologic magnetic sources or anthropic clutter items such as exploded ordnance fragments and agricultural or industrial artefacts. The integrated INS/GPS geolocation system can satisfy both high spatial resolution and robust, uninterrupted positioning requirements for successful UXO detection and characteriz

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41

Da Silva, Suzy Cristina Pedroza, and Gustavo Macedo de Mello Baptista. "MODELAGENS DE SEQUESTRO FLORESTAL DE CARBONO E DOS CONTEÚDOS DE CO2 ATMOSFÉRICO COM DADOS HYPERION EM DIFERENTES AMBIENTES NA AMAZÔNIA BRASILEIRA." Revista Brasileira de Cartografia 67, no. 7 (2019): 1509–21. http://dx.doi.org/10.14393/rbcv67n7-49195.

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Esse estudo analisou a concentração e o fl uxo de dióxido de carbono das torres micrometeorológicas pelo modelo desequestro fl orestal de carbono denominado de CO2fl ux, validando essa modelagem pelo índice ICO2, em diferentespaisagens na Amazônia. No estudo foram utilizadas 21 imagens hiperespectrais, obtidas pelo sensor Hyperion/EO-1que opera com 242 bandas espectrais nos comprimentos de onda de 0,4 a 2,5 µm, com uma resolução espacial de 30metros. O CO2fl ux mede a efi ciência do processo de sequestro de carbono pela vegetação. Para sua determinação, aintegração de dois índices de vegetação

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42

Holladay, J. S., W. E. Doll, L. P. Beard, J. L. C. Lee, and D. T. Bell. "UXO Time-Constant Estimation from Helicopter-Borne TEM Data." Journal of Environmental & Engineering Geophysics 11, no. 1 (2006): 43–52. http://dx.doi.org/10.2113/jeeg11.1.43.

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43

Pasion, Leonard R., and Douglas W. Oldenburg. "A Discrimination Algorithm for UXO Using Time Domain Electromagnetics." Journal of Environmental and Engineering Geophysics 6, no. 2 (2001): 91–102. http://dx.doi.org/10.4133/jeeg6.2.91.

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44

Pippett, Timothy, and Stephen Lee. "UXO Location using Total Field Magnetics in SE Asia." ASEG Extended Abstracts 2004, no. 1 (2004): 1–2. http://dx.doi.org/10.1071/aseg2004ab114.

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45

Young, J. A., S. T. Keenan, D. A. Clark, et al. "A Superconducting Magnetic Tensor Gradiometer for Underwater UXO Detection." ASEG Extended Abstracts 2010, no. 1 (2010): 1–4. http://dx.doi.org/10.1081/22020586.2010.12041889.

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46

Bucaro, J. A., B. H. Houston, M. Saniga, et al. "Broadband acoustic scattering measurements of underwater unexploded ordnance (UXO)." Journal of the Acoustical Society of America 123, no. 2 (2008): 738–46. http://dx.doi.org/10.1121/1.2821794.

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47

Chirgwin, Carl. "Managing land mine and UXO contamination in exploration projects." Leading Edge 24, no. 4 (2005): 376–77. http://dx.doi.org/10.1190/1.1901387.

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Lin-Ping Song, Douglas W. Oldenburg, L. R. Pasion, S. D. Billings, and L. Beran. "Temporal Orthogonal Projection Inversion for EMI Sensing of UXO." IEEE Transactions on Geoscience and Remote Sensing 53, no. 2 (2015): 1061–72. http://dx.doi.org/10.1109/tgrs.2014.2332992.

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Forbes, Jonathan A., Ian Laughlin, Shane Newberry, Michael Ryhn, Jason Pasley, and Travis Newberry. "Stratification of risk to the surgical team in removal of small arms ammunition implanted in the craniofacial region: case report." Journal of Neurosurgery 125, no. 3 (2016): 661–66. http://dx.doi.org/10.3171/2015.6.jns15779.

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Abstract:

In cases of penetrating injury with implantation of small arms ammunition, it can often be difficult to tell the difference between simple ballistics and ballistics associated with unexploded ordnances (UXOs). In the operative environment, where highly flammable substances are often close to the surgical site, detonation of UXOs could have catastrophic consequences for both the patient and surgical team. There is a paucity of information in the literature regarding how to evaluate whether an implanted munition contains explosive material. This report describes a patient who presented during Op

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Tebbal, Ibtissam, and Abdelhak Ferhat Hamida. "Effects of Crossover Operators on Genetic Algorithms for the Extraction of Solar Cell Parameters from Noisy Data." Engineering, Technology & Applied Science Research 13, no. 3 (2023): 10630–37. http://dx.doi.org/10.48084/etasr.5417.

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This study analyzed the accuracy of solar cell modeling parameters extracted from noisy data using Genetic Algorithms (GAs). Three crossover operators (XOs) were examined, namely the Uniform (UXO), Arithmetic (AXO), and Blend (BXO) operators. The data used were an experimental benchmark cell and a simulated curve where noise levels (p) from 0 to 10% were added. For each XO, the analysis was carried out by running GAs 100 times and varying p and population size (Npop). Simulation results showed that UXO and AXO suffered from premature convergence and failed to provide parameters with good preci

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