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Journal articles on the topic 'Ice Detection'

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Author: Grafiati

Published: 14 March 2023

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1

Arvidson, Rhonda, and Stan Jones. "Ice Detection and Avoidance." International Oil Spill Conference Proceedings 2003, no. 1 (April 1, 2003): 453–56. http://dx.doi.org/10.7901/2169-3358-2003-1-453.

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ABSTRACT An extensive risk assessment of oil transportation in Prince William Sound, Alaska was finalized in 1996 that identified drifting icebergs, from Columbia Glacier, as one of the most significant oil spill risks remaining to be addressed. The Prince William Sound Regional Citizens’ Advisory Council (PWS RCAC) was a major participant in this risk analysis. As part of the groundwork for the ice detection project, PWS RCAC has also sponsered extensive studies of Columbia Glacier calving and drift patterns, iceberg size and distribution. A collaborative project, called the ice detection project, was developed by a multi stakeholder working group and provides an opportunity for an immediate and long-term solution using existing technology. One objective of the project is to verify the efficiency, effectiveness and reliability of existing radar technologies to provide mariners and the United States Coast Guard with real time information regarding ice conditions. A secondary objective is to promote the research and development through field testing of new and emerging technologies to determine the possible enhancement of conventional radar. In addition to PWS RCAC, stakeholders responsible for spearheading this project are: Alyeska Pipeline Service Company, Alaska Department of Environmental Conservation, Oil Spill Recovery Institute, United States Coast Guard, Prince William Sound Community College and National Oceanic and Atmospheric Administration. Each of the seven participants brings expertise and backing from the stakeholder they represent. The site chosen for the ice detection radar project is Reef Island (illustration 1), located adjacent to Bligh Reef, Prince William Sound. This location is ideal because of its proximity to Columbia Glacier, the source of the icebergs, as well as providing an unobstructed view of the shipping lanes. A fifty foot tower was installed at the site during the fall of 2001 and a conventional radar system is currently being configured for installation. The expectation is that the system will be up and running by July of 2002, giving real time information on ice in the tanker lanes to mariners in Prince William Sound. A second field test of an UHF radar prototype is planned for the summer of 2002. Field testing and ground truthing of the radar system is scheduled for the next five years.

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2

Brett, Gemma M., Daniel Price, Wolfgang Rack, and Patricia J. Langhorne. "Satellite altimetry detection of ice-shelf-influenced fast ice." Cryosphere 15, no. 8 (August 26, 2021): 4099–115. http://dx.doi.org/10.5194/tc-15-4099-2021.

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Abstract. The outflow of supercooled Ice Shelf Water from the conjoined Ross and McMurdo ice shelf cavity augments fast ice thickness and forms a thick sub-ice platelet layer in McMurdo Sound. Here, we investigate whether the CryoSat-2 satellite radar altimeter can consistently detect the higher freeboard caused by the thicker fast ice combined with the buoyant forcing of a sub-ice platelet layer beneath. Freeboards obtained from CryoSat-2 were compared with 4 years of drill-hole-measured sea ice freeboard, snow depth, and sea ice and sub-ice platelet layer thicknesses in McMurdo Sound in November 2011, 2013, 2017 and 2018. The spatial distribution of higher CryoSat-2 freeboard concurred with the distributions of thicker ice-shelf-influenced fast ice and the sub-ice platelet layer. The mean CryoSat-2 freeboard was 0.07–0.09 m higher over the main path of supercooled Ice Shelf Water outflow, in the centre of the sound, relative to the west and east. In this central region, the mean CryoSat-2-derived ice thickness was 35 % larger than the mean drill-hole-measured fast ice thickness. We attribute this overestimate in satellite-altimeter-obtained ice thickness to the additional buoyant forcing of the sub-ice platelet layer which had a mean thickness of 3.90 m in the centre. We demonstrate the capability of CryoSat-2 to detect higher Ice Shelf Water-influenced fast ice freeboard in McMurdo Sound. Further development of this method could provide a tool to identify regions of ice-shelf-influenced fast ice elsewhere on the Antarctic coastline with adequate information on the snow layer.

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3

Weeks, W. F., Edward O. Lewis, Brian W. Currie, and Simon Kaykin. "Detection and Classification of Ice." Arctic and Alpine Research 20, no. 1 (February 1988): 129. http://dx.doi.org/10.2307/1551711.

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4

Deiler, Christoph, and Nicolas Fezans. "Performance-Based Ice Detection Methodology." Journal of Aircraft 57, no. 2 (March 2020): 209–23. http://dx.doi.org/10.2514/1.c034828.

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5

Gagnon, R. E., J. Groves, and W. Pearson. "Remote ice detection equipment — RIDE." Cold Regions Science and Technology 72 (March 2012): 7–16. http://dx.doi.org/10.1016/j.coldregions.2011.11.004.

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6

Arcone, S. "Detection and classification of ice." Cold Regions Science and Technology 15, no. 1 (February 1988): 95. http://dx.doi.org/10.1016/0165-232x(88)90044-4.

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7

Grulich, Lucas, Ralf Weigel, Andreas Hildebrandt, Michael Wand, and Peter Spichtinger. "Automatic shape detection of ice crystals." Journal of Computational Science 54 (September 2021): 101429. http://dx.doi.org/10.1016/j.jocs.2021.101429.

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8

Shire, S., J. Quarini, and R. S. Ayala. "Ultrasonic detection of slurry ice flows." Proceedings of the Institution of Mechanical Engineers, Part E: Journal of Process Mechanical Engineering 219, no. 3 (August 1, 2005): 217–25. http://dx.doi.org/10.1243/095440805x33180.

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Experimental work has been carried out to investigate the use of ultrasound in the detection of slurry ice flows within pipes. The work sets out the basis for a novel device that is both portable and adaptable to retrofitting onto existing pipelines. This method of noninvasive pipeline interrogation has applications within many parts of the chemical and process industries. The work described here relates particularly to the use of ultrasound to detect the presence of an ice pig within product pipelines in the food industry. Research has shown that the products tested and the ice slurry have very different ‘sound signatures’. The signals obtained from ultrasonic tests proved to be reproducible, even under dynamic flow conditions. Contamination of products with slush ice was detectable down to the levels of a few per cent of slush ice. The technique was verified for detection of the interface between the product and the ice pig under flow conditions.

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Mendig, C., J. Riemenschneider, H. P. Monner, L. J. Vier, M. Endres, and Hannah Sommerwerk. "Ice detection by ultrasonic guided waves." CEAS Aeronautical Journal 9, no. 3 (March 9, 2018): 405–15. http://dx.doi.org/10.1007/s13272-018-0289-0.

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10

Brown, Michael E., Christopher D. Koresko, and Geoffrey A. Blake. "Detection of Water Ice on Nereid." Astrophysical Journal 508, no. 2 (December 1, 1998): L175—L176. http://dx.doi.org/10.1086/311741.

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11

Friedson, A. James. "Ice giant seismology: prospecting for normal modes." Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 378, no. 2187 (November 9, 2020): 20190475. http://dx.doi.org/10.1098/rsta.2019.0475.

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The properties of ice giant normal mode oscillations, including their periods, spatial structure, stratospheric amplitudes and relative influence on the external gravity field, are surveyed for the purpose of formulating the best strategy for their eventual detection. Measurement requirements for detecting a normal mode's periodic pressure and temperature variations, including a possible stratospheric signal, and its effect on the external gravity field, are discussed in terms of its radial velocity amplitude at the 1 bar pressure level. It is found that for reasonable amplitudes, detection of the pressure and temperature variations of ice giant normal modes presents an extraordinary technical challenge. The prospects for detecting their gravitational influence on an orbiting spacecraft are more promising, with requirements that lie within the range of current technology. This article is part of a discussion meeting issue ‘Future exploration of ice giant systems’.

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12

Terrace, Scott M., Kimberlea D. Bender, Edmundo A. Sierra, Isabelle Marcil, John D'Avirro, Edward Pugacz, and Frank Eyre. "Comparison of Human Ice Detection Capabilities and Ground Ice Detection System Performance under Post Deicing Conditions." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 50, no. 17 (October 2006): 2051–55. http://dx.doi.org/10.1177/154193120605001772.

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13

Baker, Meghan A., Deborah S. Yokoe, John Stelling, Ken Kleinman, Rebecca E. Kaganov, Alyssa R. Letourneau, Neha Varma, et al. "Automated outbreak detection of hospital-associated pathogens: Value to infection prevention programs." Infection Control & Hospital Epidemiology 41, no. 9 (June 10, 2020): 1016–21. http://dx.doi.org/10.1017/ice.2020.233.

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AbstractObjective:To assess the utility of an automated, statistically-based outbreak detection system to identify clusters of hospital-acquired microorganisms.Design:Multicenter retrospective cohort study.Setting:The study included 43 hospitals using a common infection prevention surveillance system.Methods:A space–time permutation scan statistic was applied to hospital microbiology, admission, discharge, and transfer data to identify clustering of microorganisms within hospital locations and services. Infection preventionists were asked to rate the importance of each cluster. A convenience sample of 10 hospitals also provided information about clusters previously identified through their usual surveillance methods.Results:We identified 230 clusters in 43 hospitals involving Gram-positive and -negative bacteria and fungi. Half of the clusters progressed after initial detection, suggesting that early detection could trigger interventions to curtail further spread. Infection preventionists reported that they would have wanted to be alerted about 81% of these clusters. Factors associated with clusters judged to be moderately or highly concerning included high statistical significance, large size, and clusters involving Clostridioides difficile or multidrug-resistant organisms. Based on comparison data provided by the convenience sample of hospitals, only 9 (18%) of 51 clusters detected by usual surveillance met statistical significance, and of the 70 clusters not previously detected, 58 (83%) involved organisms not routinely targeted by the hospitals’ surveillance programs. All infection prevention programs felt that an automated outbreak detection tool would improve their ability to detect outbreaks and streamline their work.Conclusions:Automated, statistically-based outbreak detection can increase the consistency, scope, and comprehensiveness of detecting hospital-associated transmission.

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Heyn, Hans-Martin, and Roger Skjetne. "Fast onboard detection of ice drift changes under stationkeeping in ice." Cold Regions Science and Technology 196 (April 2022): 103483. http://dx.doi.org/10.1016/j.coldregions.2022.103483.

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15

Shenoy, Erica S., Eric S. Rosenthal, Yu-Ping Shao, Siddharth Biswal, Manohar Ghanta, Erin E. Ryan, Dolores Suslak, et al. "Real-Time, Automated Detection of Ventilator-Associated Events: Avoiding Missed Detections, Misclassifications, and False Detections Due to Human Error." Infection Control & Hospital Epidemiology 39, no. 07 (May 17, 2018): 826–33. http://dx.doi.org/10.1017/ice.2018.97.

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OBJECTIVETo validate a system to detect ventilator associated events (VAEs) autonomously and in real time.DESIGNRetrospective review of ventilated patients using a secure informatics platform to identify VAEs (ie, automated surveillance) compared to surveillance by infection control (IC) staff (ie, manual surveillance), including development and validation cohorts.SETTINGThe Massachusetts General Hospital, a tertiary-care academic health center, during January–March 2015 (development cohort) and January–March 2016 (validation cohort).PATIENTSVentilated patients in 4 intensive care units.METHODSThe automated process included (1) analysis of physiologic data to detect increases in positive end-expiratory pressure (PEEP) and fraction of inspired oxygen (FiO2); (2) querying the electronic health record (EHR) for leukopenia or leukocytosis and antibiotic initiation data; and (3) retrieval and interpretation of microbiology reports. The cohorts were evaluated as follows: (1) manual surveillance by IC staff with independent chart review; (2) automated surveillance detection of ventilator-associated condition (VAC), infection-related ventilator-associated complication (IVAC), and possible VAP (PVAP); (3) senior IC staff adjudicated manual surveillance–automated surveillance discordance. Outcomes included sensitivity, specificity, positive predictive value (PPV), and manual surveillance detection errors. Errors detected during the development cohort resulted in algorithm updates applied to the validation cohort.RESULTSIn the development cohort, there were 1,325 admissions, 479 ventilated patients, 2,539 ventilator days, and 47 VAEs. In the validation cohort, there were 1,234 admissions, 431 ventilated patients, 2,604 ventilator days, and 56 VAEs. With manual surveillance, in the development cohort, sensitivity was 40%, specificity was 98%, and PPV was 70%. In the validation cohort, sensitivity was 71%, specificity was 98%, and PPV was 87%. With automated surveillance, in the development cohort, sensitivity was 100%, specificity was 100%, and PPV was 100%. In the validation cohort, sensitivity was 85%, specificity was 99%, and PPV was 100%. Manual surveillance detection errors included missed detections, misclassifications, and false detections.CONCLUSIONSManual surveillance is vulnerable to human error. Automated surveillance is more accurate and more efficient for VAE surveillance.Infect Control Hosp Epidemiol 2018;826–833

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Pegau, W. Scott, Jessica Garron, Leonard Zabilansky, Christopher Bassett, Job Bello, John Bradford, Regina Carns, et al. "Detection of oil in and under ice." International Oil Spill Conference Proceedings 2017, no. 1 (May 1, 2017): 1857–76. http://dx.doi.org/10.7901/2169-3358-2017.1.1857.

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ABSTRACT (2017-147) In 2014, researchers from ten organizations came to the U.S. Army Corps of Engineers, Cold Regions Research and Engineering Laboratory (CRREL) in New Hampshire to conduct a first of its kind large-scale experiment aimed at determining current sensor capabilities for detecting oil in and under sea ice. This project was the second phase of the Oil Spill Detection in Low Visibility and Ice research project of the International Association of Oil and Gas Producers (IOGP), Arctic Oil Spill Response Technology - Joint Industry Programme. The objectives of the project were to:Acquire acoustic, thermal, optical and radar signatures of oil on, within, and underneath a level sheet of laboratory sea ice.Determine the capabilities of various sensors to detect oil in specific ice environments created in a test tank, including freeze-up, growth and melt.Model the potential performance of the sensors under realistic field conditions using the test data for validation.Recommend the most effective sensor suite of existing sensors for detecting oil in the ice environment. The sensor testing spanned a two-month ice growth phase and a one-month decay/melt period. The growth phase produced an 80 centimeter thick level sheet of salt water ice representative of natural sea ice grown under quiescent conditions. Above-ice sensors included frequency modulated continuous wave radar, ground penetrating radar, laser fluorescence polarization sensor, spectral radiometer, visible and infrared cameras. Below-ice sensors included acoustics (broadband, narrowband, and multibeam sonars), spectral radiometers, cameras, and fluorescence polarization. Measurements of physical and electrical properties of the ice and oil within the ice were provided to optical, acoustic, and radar modelers as inputs into their models. The models were then used to extrapolate the sensors’ laboratory performance to potential performance over a range of field conditions. All selected sensors detected oil under some conditions. The radar systems were the only above-ice sensors capable of detecting oil below or trapped within the ice. Cameras below the ice detected oil at all stages of ice growth, and the acoustic and fluorescence systems detected encapsulated oil through limited amounts of new ice growth beneath the oil. No single sensor detected oil in and below ice under all conditions tested. However, we used the test results to identify suites of sensors that could be deployed today both above and below the ice to detect and map an oil spill within ice covered waters.

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Kwon, Jennie H., Kimberly Reske, Caroline A. O’Neil, Candice Cass, Sondra Seiler, Meghan A. Wallace, Tiffany Hink, et al. "Assessment of antibiotic-resistant organism transmission among rooms of hospitalized patients, healthcare personnel, and the hospital environment utilizing surrogate markers and selective bacterial cultures." Infection Control & Hospital Epidemiology 41, no. 5 (January 23, 2020): 539–46. http://dx.doi.org/10.1017/ice.2019.376.

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AbstractObjective:To assess potential transmission of antibiotic-resistant organisms (AROs) using surrogate markers and bacterial cultures.Design:Pilot study.Setting:A 1,260-bed tertiary-care academic medical center.Participants:The study included 25 patients (17 of whom were on contact precautions for AROs) and 77 healthcare personnel (HCP).Methods:Fluorescent powder (FP) and MS2 bacteriophage were applied in patient rooms. HCP visits to each room were observed for 2–4 hours; hand hygiene (HH) compliance was recorded. Surfaces inside and outside the room and HCP skin and clothing were assessed for fluorescence, and swabs were collected for MS2 detection by polymerase chain reaction (PCR) and selective bacterial cultures.Results:Transfer of FP was observed for 20 rooms (80%) and 26 HCP (34%). Transfer of MS2 was detected for 10 rooms (40%) and 15 HCP (19%). Bacterial cultures were positive for 1 room and 8 HCP (10%). Interactions with patients on contact precautions resulted in fewer FP detections than interactions with patients not on precautions (P < .001); MS2 detections did not differ by patient isolation status. Fluorescent powder detections did not differ by HCP type, but MS2 was recovered more frequently from physicians than from nurses (P = .03). Overall, HH compliance was better among HCP caring for patients on contact precautions than among HCP caring for patients not on precautions (P = .003), among nurses than among other nonphysician HCP at room entry (P = .002), and among nurses than among physicians at room exit (P = .03). Moreover, HCP who performed HH prior to assessment had fewer fluorescence detections (P = .008).Conclusions:Contact precautions were associated with greater HCP HH compliance and reduced detection of FP and MS2.

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Li, Zhen, Anton Verhoef, and Ad Stoffelen. "Bayesian Sea Ice Detection Algorithm for CFOSAT." Remote Sensing 14, no. 15 (July 25, 2022): 3569. http://dx.doi.org/10.3390/rs14153569.

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This paper describes the adaptation of the Bayesian sea ice detection algorithm for the rotating fan-beam scatterometer CSCAT onboard the China–France Oceanography Satellite (CFOSAT). The algorithm was originally developed and applied for fixed fan-beam and rotating pencil-beam scatterometers. It is based on the probability of the wind and ice backscatter distances from the measurements to their corresponding geophysical model functions (GMFs). The new rotating Ku-band fan-beam design introduces very diverse geometry distributions across the swath, which leads to three main adaptations of the algorithm: (1) a new probability distribution function fit for the backscatter distances over open sea; (2) a linear ice GMF as a function of incidence angle; (3) the separation of outer swath wind vector cells ((WVCs) number 1, 2, 41, 42) from the other WVCs to form two sets of probability distribution function fits for these two WVC groups. The results are validated against sea ice extents from the active microwave ASCAT and the passive microwave SSMI. The validation shows good agreement with both instruments, despite the discrepancies with SSMI during the melting season, and this discrepancy is caused by the lower sensitivity of the passive microwave to detect the ice at a low concentration with a mixed water/ice state, while the scatterometer is more tolerant regarding this situation. We observed that the sea-ice GMF regression between HH and VV sea-ice backscatter at low and high incidence angles decorrelates at around −12 dB (28) and −20 dB (50) and an experiment with truncated backscatter values at these incidence angles is executed, which significantly improves the year-long average sea ice extents. In conclusion, the adapted algorithm for CSCAT works effectively and yields consistent sea ice extents compared with active and passive microwave instruments. As such, it can, in principle, contribute to the long-term global scatterometer sea ice record, and as the algorithm was adapted for a rotating fan-beam scatterometer, it also can serve as a guideline for the recently launched, dual-frequency, rotating fan-beam scatterometer WindRAD.

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Rodier, S., Y. Hu, and M. Vaughan. "Sea ice detection with space-based LIDAR." Cryosphere Discussions 7, no. 5 (September 13, 2013): 4681–701. http://dx.doi.org/10.5194/tcd-7-4681-2013.

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Abstract. Monitoring long-term climate change in the Polar Regions relies on accurate, detailed and repeatable measurements of geophysical processes and states. These regions are among the Earth's most vulnerable ecosystems, and measurements there have shown rapid changes in the seasonality and the extent of snow and sea ice coverage. The authors have recently developed a promising new technique that uses lidar surface measurements from the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) mission to infer ocean surface ice-water phase. CALIPSO's 532 nm depolarization ratio measurements of the ocean surface are uniquely capable of providing information about the ever-changing sea surface state within the Polar Regions. With the finer resolution of the CALIPSO footprint (90 m diameter, spaced 335 m apart) and its ability to acquire measurements during both daytime and nighttime orbit segments and in the presence of clouds, the CALIPSO sea ice product provides fine-scale information on mixed phase scenes and can be used to assess/validate the estimates of sea-ice concentration currently provided by passive sensors. This paper describes the fundamentals of the CALIPSO sea-ice detection and classification technique. We present retrieval results from a six-year study, which are compared to existing data sets obtained by satellite-based passive remote sensors.

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Fingas, Merv, and Carl Brown. "Detection of Oil in Ice and Snow." Journal of Marine Science and Engineering 1, no. 1 (November 22, 2013): 10–20. http://dx.doi.org/10.3390/jmse1010010.

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21

Jang, Seohyun, Joo-Hyung Kim, and Jihyun Kim. "Detection of Microplastics in Water and Ice." Remote Sensing 13, no. 17 (September 6, 2021): 3532. http://dx.doi.org/10.3390/rs13173532.

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It is possible to detect various microplastics (MPs) floating on water or contained in ice due to the unique optical characteristics of plastics of various chemical compositions and structures. When the MPs are measured in the spectral region between 800 and 1000 nm, which has relatively little influence on the temperature change in water, they are frequently perceived as noise or obscured by the surrounding reflection spectra because of the small number and low intensity of the representative peak wavelengths. In this study, we have applied several mathematical methods, including the convex hull, Gaussian deconvolution, and curve fitting to amplify and normalize the reflectance and thereby find the spectral properties of each polymer, namely polypropylene (PP), polyethylene terephthalate (PET), methyl methacrylate (PMMA), and polyethylene (PE). Blunt-shaped spectra with a relatively large maximum of normalized reflectance (NRmax) can be decomposed into several Gaussian peak wavelengths: 889, 910, and 932 nm for the PP and 898 and 931 nm for the PE. Moreover, unique peak wavelengths with the meaningful measure at 868 and 907 nm for the PET and 887 nm for the PMMA were also obtained. Based on the results of the study, one can say that each plastic can be identified with up to 81% precision by compensating based on the spectral properties even when they are hidden in water or ice.

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Rizk, Patrick, Rafic Younes, Adrian Ilinca, and Jihan Khoder. "Wind turbine ice detection using hyperspectral imaging." Remote Sensing Applications: Society and Environment 26 (April 2022): 100711. http://dx.doi.org/10.1016/j.rsase.2022.100711.

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Bianchi, M., G. d’Ambrosio, R. Massa, and M. D. Migliore. "Microwave Devices for Ice Detection on Aircraft." Journal of Microwave Power and Electromagnetic Energy 31, no. 2 (January 1996): 83–86. http://dx.doi.org/10.1080/08327823.1996.11688298.

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Bao, Xiao‐Qi, Vasundara V. Varadan, and Vijay K. Varadan. "Sensors for ice detection on aerospace structures." Journal of the Acoustical Society of America 101, no. 5 (May 1997): 3035. http://dx.doi.org/10.1121/1.418650.

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Zhao, Xiang, and Joseph L. Rose. "Ultrasonic guided wave tomography for ice detection." Ultrasonics 67 (April 2016): 212–19. http://dx.doi.org/10.1016/j.ultras.2015.12.005.

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Kofman, Wlodek, Roberto Orosei, and Elena Pettinelli. "Radar Signal Propagation and Detection Through Ice." Space Science Reviews 153, no. 1-4 (March 31, 2010): 249–71. http://dx.doi.org/10.1007/s11214-010-9642-2.

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Foster, M. "Detection of Water Ice on 2060 Chiron." Icarus 141, no. 2 (October 1999): 408–10. http://dx.doi.org/10.1006/icar.1999.6180.

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Ding, Fuhong, Hui Shen, William Perrie, and Yijun He. "Is Radar Phase Information Useful for Sea Ice Detection in the Marginal Ice Zone?" Remote Sensing 12, no. 11 (June 8, 2020): 1847. http://dx.doi.org/10.3390/rs12111847.

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With continuing sea ice reductions in the Arctic, dynamic physical and ecological processes have more active roles compared to the ice-locked, isolated Arctic Ocean of previous decades. To better understand these changes, observations of high-resolution sea ice conditions are needed. Remote sensing is a useful tool for observations in the harsh Arctic environment. For unsupervised ice detection, we demonstrate the promising value of radar phase difference from polarimetric radar measurements in this study, based on full polarimetric complex RADARSAT-2 SAR images in the marginal ice zone. It is demonstrated that the phase difference from co-polarized and cross-polarized synthetic aperture radar (SAR) images show promising capability for high resolution sea ice discrimination from open water. In particular, the phase difference shows superior potential for the detection of frazil ice compared to the traditional methodology based on the radar intensity ratio. The relationship between phase difference and radar incidence angle is also analyzed, as well as the potential influence of high sea state. The new methodology provides an additional tool for ice detection. In order to make the best use of this tool, directions for further studies are discussed for operational ice detection and possible ice classification.

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Baker, Meghan A., Susan S. Huang, Alyssa R. Letourneau, Rebecca E. Kaganov, Jennifer R. Peeples, Marci Drees, Richard Platt, and Deborah S. Yokoe. "Lack of Comprehensive Outbreak Detection in Hospitals." Infection Control & Hospital Epidemiology 37, no. 4 (January 12, 2016): 466–68. http://dx.doi.org/10.1017/ice.2015.325.

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Timely identification of outbreaks of hospital-associated infections is needed to implement control measures and minimize impact. Survey results from 33 hospitals indicated that most hospitals lacked a formal cluster definition and all targeted a very limited group of prespecified pathogens. Standardized, statistically based outbreak detection could greatly improve current practice.Infect. Control Hosp. Epidemiol. 2016;37(4):466–468

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Ikiades, Aris, Glen Howard, David J. Armstrong, Mary Konstantaki, and Sam Crossley. "Measurement of optical diffusion properties of ice for direct detection ice accretion sensors." Sensors and Actuators A: Physical 140, no. 1 (October 2007): 24–31. http://dx.doi.org/10.1016/j.sna.2007.05.036.

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Wei, Kexiang, Yue Yang, Hongyan Zuo, and Dingqing Zhong. "A review on ice detection technology and ice elimination technology for wind turbine." Wind Energy 23, no. 3 (December 23, 2019): 433–57. http://dx.doi.org/10.1002/we.2427.

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Ashoka K, Dr. "Iceberg Detection using Satellite Images." International Journal for Research in Applied Science and Engineering Technology 9, no. VII (July 20, 2021): 1896–901. http://dx.doi.org/10.22214/ijraset.2021.36782.

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Chunks of ice present genuine risks for transport route and seaward establishments. Subsequently, there is a huge interest to limit them ideal and over tremendous regions. As a result of their autonomy of overcast cover and sunlight, satellite Synthetic Aperture Radar (SAR) pictures are among the favoured information hotspots for functional ice conditions and ice sheet events. The picture spatial goal for the most part utilized for chunk of ice observing changes between a couple and 100 m. Prepared SAR information are portrayed by dot clamour, which causes a grainy appearance of the pictures making the distinguishing proof of ice shelves amazingly troublesome. The techniques for satellite checking of hazardous ice developments, similar to ice shelves in the Arctic oceans address a danger to the security of route and monetary action on the Arctic rack. Along these lines, here we have thought of a thought of an application which distinguishes the Iceberg pictures utilizing satellite pictures and it is proposed by utilizing Convolutional Neural Networks (CNN) grouping.

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Zhuge, Jing Chang, Zhi Jing Yu, and Jian Shu Gao. "Ice Detection Based on Near Infrared Image Analysis." Applied Mechanics and Materials 121-126 (October 2011): 3960–64. http://dx.doi.org/10.4028/www.scientific.net/amm.121-126.3960.

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In order to detect the ice on aircraft wings, a method based on near infrared image processing is proposed. According to the variety of near-infrared reflectivity, four images of one object are obtained under different detection wavelengths. Water and ice can be distinguished by the different variation trends of near infrared images. In this paper, 1.10μm, 1.16μm, 1.26μm and 1.28μm are selected to be the detection wavelengths. The images of Carbon Fiber Composite material aircraft wings partially covered by water or ice are obtained and analyzed. Parameter D can reflect the variation trend of relative near-infrared reflectivity, so that Parameter D also can be the distinguish basis. The results of the experiment show that the method proposed in this paper is available for ice detection.

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Jones, Barbara E., Kevin Antoine Brown, Makoto M. Jones, Benedikt D. Huttner, Tom Greene, Brian C. Sauer, Karl Madaras-Kelly, Michael A. Rubin, Matthew Bidwell Goetz, and Matthew H. Samore. "Variation in Empiric Coverage Versus Detection of Methicillin-Resistant Staphylococcus aureus and Pseudomonas aeruginosa in Hospitalizations for Community-Onset Pneumonia Across 128 US Veterans Affairs Medical Centers." Infection Control & Hospital Epidemiology 38, no. 8 (June 21, 2017): 937–44. http://dx.doi.org/10.1017/ice.2017.98.

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OBJECTIVETo examine variation in antibiotic coverage and detection of resistant pathogens in community-onset pneumonia.DESIGNCross-sectional study.SETTINGA total of 128 hospitals in the Veterans Affairs health system.PARTICIPANTSHospitalizations with a principal diagnosis of pneumonia from 2009 through 2010.METHODSWe examined proportions of hospitalizations with empiric antibiotic coverage for methicillin-resistant Staphylococcus aureus (MRSA) and Pseudomonas aeruginosa (PAER) and with initial detection in blood or respiratory cultures. We compared lowest- versus highest-decile hospitals, and we estimated adjusted probabilities (AP) for patient- and hospital-level factors predicting coverage and detection using hierarchical regression modeling.RESULTSAmong 38,473 hospitalizations, empiric coverage varied widely across hospitals (MRSA lowest vs highest, 8.2% vs 42.0%; PAER lowest vs highest, 13.9% vs 44.4%). Detection rates also varied (MRSA lowest vs highest, 0.5% vs 3.6%; PAER lowest vs highest, 0.6% vs 3.7%). Whereas coverage was greatest among patients with recent hospitalizations (AP for anti-MRSA, 54%; AP for anti-PAER, 59%) and long-term care (AP for anti-MRSA, 60%; AP for anti-PAER, 66%), detection was greatest in patients with a previous history of a positive culture (AP for MRSA, 7.9%; AP for PAER, 11.9%) and in hospitals with a high prevalence of the organism in pneumonia (AP for MRSA, 3.9%; AP for PAER, 3.2%). Low hospital complexity and rural setting were strong negative predictors of coverage but not of detection.CONCLUSIONSHospitals demonstrated widespread variation in both coverage and detection of MRSA and PAER, but probability of coverage correlated poorly with probability of detection. Factors associated with empiric coverage (eg, healthcare exposure) were different from those associated with detection (eg, microbiology history). Providing microbiology data during empiric antibiotic decision making could better align coverage to risk for resistant pathogens and could promote more judicious use of broad-spectrum antibiotics.Infect Control Hosp Epidemiol 2017;38:937–944

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Hammond, W. R., and K. F. Sprenke. "Radar detection of subglacial sulfides." GEOPHYSICS 56, no. 6 (June 1991): 870–73. http://dx.doi.org/10.1190/1.1443105.

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Using an ice radar system, we detected anomalous reflection strengths over subglacial disseminated sulfide zones beneath the Mt. Henry Clay Glacier in southeast Alaska. The subglacial sulfide zones, which were verified by drill holes, were not detected by previous magnetic, helicopter EM, or ground‐based time‐domain EM surveys. The sulfide zones were mapped by measuring lateral variations in the strength of radar echoes from the ice‐bedrock interface at the base of the glacier. The reflected power from these disseminated occurrences ranged from 20 percent to 60 percent of the theoretically predicted reflected power from a perfect conductor at the base of the ice. The empirical results of this experiment suggest that ice radar may be a useful tool for direct mineral exploration in ice‐covered terrain.

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Iguchi, Toshio, Nozomi Kawamoto, and Riko Oki. "Detection of Intense Ice Precipitation with GPM/DPR." Journal of Atmospheric and Oceanic Technology 35, no. 3 (March 2018): 491–502. http://dx.doi.org/10.1175/jtech-d-17-0120.1.

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AbstractDetection of ice precipitation is one of the objectives in the Global Precipitation Measurement (GPM) mission. The dual-frequency precipitation radar (DPR) can provide precipitation echoes at two different frequencies, which may enable differentiating solid precipitation echoes from liquid precipitation echoes. A simple algorithm that flags the pixels that contain intense ice precipitation above the height of C is implemented in version 5 of the DPR products. In the inner swath of DPR measurements in which both Ku- and Ka-band radar echoes are available, the measured dual-frequency ratio () together with the measured radar reflectivity factor is used to judge the existence of intense ice precipitation. Comparisons of the flagged pixels with surface measurements show that the algorithm correctly identifies relatively intense ice precipitation regions. The global distribution of the flagged pixels indicates an interesting difference between land and ocean, in particular in the distribution of ice precipitation that reaches the surface. The flag is also expected to be useful for improving precipitation retrieval algorithms by microwave radiometers.

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Gao, Jian Shu, Ren Yi Han, Zhi Jing Yu, and Wen Qiao. "An Improved Algorithm to Near Infrared Multispectral Ice Detection." Applied Mechanics and Materials 121-126 (October 2011): 4367–71. http://dx.doi.org/10.4028/www.scientific.net/amm.121-126.4367.

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In order to improve the accuracy of near infrared multispectral ice detection on aircraft wings, we present an improved unit detection algorithm to reduce the noise influenced by the ice detection system itself. The improved algorithm, first select matrices being 3*3 as a detection unit, then calculate the contrast value of the five pixel points in the cross region of the unit, and finally count the number of pixel points, whose contrast value are larger than the threshold value of ice on the surface. The experimental results show that the unit detection method has high accuracy and efficiency compared with conventional point-by-point method.

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Stamm, Michael, Helge Pfeiffer, Johan Reynaert, and Martine Wevers. "Using Acoustic Emission Measurements for Ice-Melting Detection." Applied Sciences 9, no. 24 (December 9, 2019): 5387. http://dx.doi.org/10.3390/app9245387.

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Aircraft operators being faced with water accumulation in fuel tanks on a daily basis and are looking for reliable detection systems to determine the remaining amount of accumulated ice during maintenance after flight. Using such a technology, an increase in the safety and efficiency of the aircraft operation would be possible in this highly competitive market. This article presents the use of the Acoustic Emission Technique (AE) for the reliable and non-invasive monitoring of the melting of ice in fuel tanks. This technology is in principle based on the fact that a phase transition comes frequently along with stress relaxation that can be used for monitoring the process. Therefore, the melting of water can, in essence, be monitored with AE without accessing the ice directly. The analysis of the AE signals has been carried out in the time domain since it was the melting of ice needed to be monitored as a function of temperature rise time. The insights presented in this paper can possibly lead to new technologies for ice detection, especially in remote areas that are not easily accessible with other techniques.

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Sheppard, A. J., C. S. J. Shen, and T. S. Rudolf. "Detection of Vegetable Oil Adulteration in Ice Cream." Journal of Dairy Science 68, no. 5 (May 1985): 1103–8. http://dx.doi.org/10.3168/jds.s0022-0302(85)80935-8.

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Render, P. M., and L. R. Jenkinson. "Investigation into ice detection parameters for turboprop aircraft." Journal of Aircraft 33, no. 1 (January 1996): 125–30. http://dx.doi.org/10.2514/3.46912.

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Rakhmatullin, R. R., and J. N. Zatsarinnaya. "DETECTION OF ICE ON OVERHEAD ELECTRIC POWER LINES." Transactions of Academenergo 55, no. 2 (June 2019): 98–103. http://dx.doi.org/10.34129/2070-4755-2019-55-2-98-103.

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Zakharova, Elena A., Sara Fleury, Kévin Guerreiro, Sascha Willmes, Frédérique Rémy, Alexei V. Kouraev, and Günther Heinemann. "Sea Ice Leads Detection Using SARAL/AltiKa Altimeter." Marine Geodesy 38, sup1 (September 10, 2015): 522–33. http://dx.doi.org/10.1080/01490419.2015.1019655.

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Cooper, Paul D., Marla H. Moore, and Reggie L. Hudson. "Infrared Detection of HO2and HO3Radicals in Water Ice." Journal of Physical Chemistry A 110, no. 26 (July 2006): 7985–88. http://dx.doi.org/10.1021/jp062765k.

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Penrose, John D., M. Conde, and T. J. Pauly. "Acoustic detection of ice crystals in Antarctic waters." Journal of Geophysical Research 99, no. C6 (1994): 12573. http://dx.doi.org/10.1029/93jc03507.

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Sierra, Edmundo A., Kimberlea Bender, Isabelle Marcil, John D'Avirro, Edward Pugacz, and Frank Eyre. "Ice Detection Capabilities under Aircraft Post Deicing Conditions." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 50, no. 1 (October 2006): 76–80. http://dx.doi.org/10.1177/154193120605000117.

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Schnell, R. C., R. G. Barry, M. W. Miles, E. L. Andreas, L. F. Radke, C. A. Brock, M. P. McCormick, and J. L. Moore. "Lidar detection of leads in Arctic sea ice." Nature 339, no. 6225 (June 1989): 530–32. http://dx.doi.org/10.1038/339530a0.

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Zou, Jianhong, Lin Ye, Junfeng Ge, and Chengrui Zhao. "Novel fiber optic sensor for ice type detection." Measurement 46, no. 2 (February 2013): 881–86. http://dx.doi.org/10.1016/j.measurement.2012.09.020.

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Bindschadler, Robert A., Ted A. Scambos, Hyeungu Choi, and Terry M. Haran. "Ice sheet change detection by satellite image differencing." Remote Sensing of Environment 114, no. 7 (July 2010): 1353–62. http://dx.doi.org/10.1016/j.rse.2010.01.014.

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Li, Jun, Xiaojun Liu, and Guangyou Fang. "Master Control Design of Polar Detection Ice Radar." IOP Conference Series: Earth and Environmental Science 17 (March 18, 2014): 012137. http://dx.doi.org/10.1088/1755-1315/17/1/012137.

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Owen, T. C., D. P. Cruikshank, C. M. Dalle Ore, T. R. Geballe, T. L. Roush, and C. de Bergh. "Detection of Water Ice on Saturn's Satellite Phoebe." Icarus 139, no. 2 (June 1999): 379–82. http://dx.doi.org/10.1006/icar.1999.6116.

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