Relevant bibliographies by topics / Rough seas

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Rough seas'

Author: Grafiati
Published: 10 March 2023

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Journal articles on the topic "Rough seas"

1

VAGG, JON. "ROUGH SEAS?" British Journal of Criminology 35, no. 1 (1995): 63–80. http://dx.doi.org/10.1093/oxfordjournals.bjc.a048489.

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DAGANI, RON. "ROUGH SEAS AHEAD." Chemical & Engineering News 85, no. 14 (April 2, 2007): 13. http://dx.doi.org/10.1021/cen-v085n014.p013.

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Künkler, Mirjam, Hanna Lerner, and Shylashri Shankar. "Constitutionalism in Rough Seas." American Behavioral Scientist 60, no. 8 (May 26, 2016): 911–18. http://dx.doi.org/10.1177/0002764216643806.

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Winduo, Steven Edmund. "Gutsini Posa (Rough seas) (review)." Contemporary Pacific 12, no. 2 (2000): 551–54. http://dx.doi.org/10.1353/cp.2000.0070.

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JOHNSON, JEFF. "ROUGH SEAS FOR ENERGY BILLS." Chemical & Engineering News 85, no. 26 (June 25, 2007): 12. http://dx.doi.org/10.1021/cen-v085n026.p012a.

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Hockenbery, David M. "Forecast: Rough Seas for Leukemia." Cancer Discovery 4, no. 3 (March 2014): 278–79. http://dx.doi.org/10.1158/2159-8290.cd-14-0084.

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Kragh, Ed, and Robert Laws. "Rough seas and statistical deconvolution." Geophysical Prospecting 54, no. 4 (July 2006): 475–85. http://dx.doi.org/10.1111/j.1365-2478.2006.00549.x.

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Lacey, Marcia. "Staying Afloat on Rough Seas." Journal - American Water Works Association 101, no. 4 (April 2009): 2. http://dx.doi.org/10.1002/j.1551-8833.2009.tb09869.x.

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Liu, Xing Bo, and Wan Zheng Ai. "Analysis of Torsion Vibration with Safety Navigation Countermeasures under the Condition of Rough Seas and Storms." Applied Mechanics and Materials 327 (June 2013): 263–66. http://dx.doi.org/10.4028/www.scientific.net/amm.327.263.

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Rough seas can bring adversely affects to ships safety navigation. Adversely affects on ship torsion vibration caused by rough seas is analyzed, and measures to decrease these adversely affects is also put forward as to provide references for seaman in this paper.
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Elizabeth Bush. "On Rough Seas (review)." Bulletin of the Center for Children's Books 61, no. 10 (2008): 429. http://dx.doi.org/10.1353/bcc.0.0145.

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Dissertations / Theses on the topic "Rough seas"

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Mockutė, Agota [Verfasser]. "Suitability of Wave Loading Models for Offshore Wind Turbine Monopiles in Rough Seas / Agota Mockute." Düren : Shaker, 2020. http://nbn-resolving.de/urn:nbn:de:101:1-2020090605232739927321.

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Mockute, Agota [Verfasser]. "Suitability of Wave Loading Models for Offshore Wind Turbine Monopiles in Rough Seas / Agota Mockute." Düren : Shaker, 2020. http://d-nb.info/1217164081/34.

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Vural, Hulya. "Comparison Of Rough Multi Layer Perceptron And Rough Radial Basis Function Networks Using Fuzzy Attributes." Master's thesis, METU, 2004. http://etd.lib.metu.edu.tr/upload/12605293/index.pdf.

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The hybridization of soft computing methods of Radial Basis Function (RBF) neural networks, Multi Layer Perceptron (MLP) neural networks with back-propagation learning, fuzzy sets and rough sets are studied in the scope of this thesis. Conventional MLP, conventional RBF, fuzzy MLP, fuzzy RBF, rough fuzzy MLP, and rough fuzzy RBF networks are compared. In the fuzzy neural networks implemented in this thesis, the input data and the desired outputs are given fuzzy membership values as the fuzzy properties &ldquo
low&rdquo
, &ldquo
medium&rdquo
and &ldquo
high&rdquo
. In the rough fuzzy MLP, initial weights and near optimal number of hidden nodes are estimated using rough dependency rules. A rough fuzzy RBF structure similar to the rough fuzzy MLP is proposed. The rough fuzzy RBF was inspected whether dependencies like the ones in rough fuzzy MLP can be concluded.
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Ferone, A. "EXPLOITING HIGHER ORDER UNCERTAINTY IN IMAGE ANALYSIS." Doctoral thesis, Università degli Studi di Milano, 2011. http://hdl.handle.net/2434/155479.

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Soft computing is a group of methodologies that works synergistically to provide flexible information processing capability for handling real-life ambiguous situations. Its aim is to exploit the tolerance for imprecision, uncertainty, approximate reasoning, and partial truth in order to achieve tractability, robustness, and low-cost solutions. Soft computing methodologies (involving fuzzy sets, neural networks, genetic algorithms, and rough sets) have been successfully employed in various image processing tasks including image segmentation, enhancement and classification, both individually or in combination with other soft computing techniques. The reason of such success has its motivation in the fact that soft computing techniques provide a powerful tools to describe uncertainty, naturally embedded in images, which can be exploited in various image processing tasks. The main contribution of this thesis is to present tools for handling uncertainty by means of a rough-fuzzy framework for exploiting feature level uncertainty. The first contribution is the definition of a general framework based on the hybridization of rough and fuzzy sets, along with a new operator called RF-product, as an effective solution to some problems in image analysis. The second and third contributions are devoted to prove the effectiveness of the proposed framework, by presenting a compression method based on vector quantization and its compression capabilities and an HSV color image segmentation technique.
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Fernández, Oliva Alberto. "Estimación probabilística del grado de excepcionalidad de un elemento arbitrario en un conjunto finito de datos: aplicación de la teoría de conjuntos aproximados de precisión variable." Doctoral thesis, Universidad de Alicante, 2010. http://hdl.handle.net/10045/17570.

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Oukbir, Karim. "Indiscernibility and Vagueness in Spatial Information Systems." Doctoral thesis, KTH, Numerical Analysis and Computer Science, NADA, 2003. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-3634.

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We investigate the use of the concept of indiscernibilityand vagueness in spatial information systems. To representindiscernibility and vagueness we use rough sets, respectivelyfuzzy sets. We introduce a theoretical model to supportapproximate queries in information systems and we show howthose queries can be used to perform uncertain classi.cations.We also explore how to assess quality of uncertainclassi.cations and ways to compare those classi.cations to eachother in order to assess accuracies. We implement the querylanguage in an SQL relational language to demonstrate thefeasibility of approximate queries and we perform an experimenton real data using uncertain classi.cations.

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Mazzocco, Alessandro <1991&gt. "L'approccio dei Rough-sets applicato alla valutazione dello stato di salute aziendale." Master's Degree Thesis, Università Ca' Foscari Venezia, 2017. http://hdl.handle.net/10579/10469.

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- Introduzione sul fallimento aziendale e panoramiche dei metodi statistici e variabili utilizzati in letteratura per prevederlo - Rough set Theory - Applicazione dei rough set con indicatori di bilancio di aziende fallite e no per valutarne l’efficacia ai fini della previsione del fallimento aziendale - Conclusione con confronto con gli altri metodi
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Oliveira, Henrique Viana. "Refinamento de Consultas em Lógicas de Descrição Utilizando Teoria dos Rough Sets." reponame:Repositório Institucional da UFC, 2012. http://www.repositorio.ufc.br/handle/riufc/18114.

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OLIVEIRA, Henrique Viana. Refinamento de Consultas em Lógicas de Descrição Utilizando Teoria dos Rough Sets. 2012. 111 f. : Dissertação (mestrado) - Universidade Federal do Ceará, Centro de Ciências, Departamento de Computação, Fortaleza-CE, 2012.
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Query Refinement consists of methods that modify the terms of a consult aiming the change of its result obtained previously. Refinements can be done of several ways and different approaches can be applied to it. This work proposes to apply methods of Query Refinement based on Rough Set theory, using it as an alternative for the refinement problem. The proposed methods will be grounded in the languages of Description Logics, which are commonly used on problems involving knowledge bases or ontologies representation. Two extensions of Description Logics with the Rough Set theory are introduced in this dissertation. We will prove the complexity of satisfiability of these logics, as well as the complexities of the query refinement methods applied to these logics. Finally, we will show quality measures which will aid to choose the results of the refinements obtained.
Refinamento de consulta consiste de técnicas que modificam os termos de uma consulta com o objetivo de alterar os resultados obtidos inicialmente. Para a realização de tal fim, diversas abordagens podem ser aplicadas e diferentes tipos de refinamentos podem ser considerados. Este trabalho propõe aplicar a teoria dos Rough Sets como uma nova alternativa de solução para o problema. Através das noções presentes nessa teoria, iremos desenvolver técnicas que serão aplicadas nas linguagens de Lógicas de Descrição, que são comumente utilizadas em problemas de representação de bases de conhecimento ou ontologias. Além disso, introduziremos duas extensões de Lógicas de Descrição capazes de representar as operações da teoria dos Rough Sets. Provaremos os resultados de complexidade de decisão dessas duas lógicas, assim como os resultados de complexidade das técnicas de refinamentos desenvolvidas. Por fim, mostraremos métricas de qualidade que poderão ser usadas para melhorar o resultado dos refinamentos obtidos.
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Oliveira, Henrique Viana. "Refinamento de Consultas em LÃgicas de DescriÃÃo Utilizando Teoria dos Rough Sets." Universidade Federal do CearÃ, 2012. http://www.teses.ufc.br/tde_busca/arquivo.php?codArquivo=8325.

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CoordenaÃÃo de AperfeiÃoamento de Pessoal de NÃvel Superior
Refinamento de consulta consiste de tÃcnicas que modificam os termos de uma consulta com o objetivo de alterar os resultados obtidos inicialmente. Para a realizaÃÃo de tal fim, diversas abordagens podem ser aplicadas e diferentes tipos de refinamentos podem ser considerados. Este trabalho propÃe aplicar a teoria dos Rough Sets como uma nova alternativa de soluÃÃo para o problema. AtravÃs das noÃÃes presentes nessa teoria, iremos desenvolver tÃcnicas que serÃo aplicadas nas linguagens de LÃgicas de DescriÃÃo, que sÃo comumente utilizadas em problemas de representaÃÃo de bases de conhecimento ou ontologias. AlÃm disso, introduziremos duas extensÃes de LÃgicas de DescriÃÃo capazes de representar as operaÃÃes da teoria dos Rough Sets. Provaremos os resultados de complexidade de decisÃo dessas duas lÃgicas, assim como os resultados de complexidade das tÃcnicas de refinamentos desenvolvidas. Por fim, mostraremos mÃtricas de qualidade que poderÃo ser usadas para melhorar o resultado dos refinamentos obtidos.
Query Refinement consists of methods that modify the terms of a consult aiming the change of its result obtained previously. Refinements can be done of several ways and different approaches can be applied to it. This work proposes to apply methods of Query Refinement based on Rough Set theory, using it as an alternative for the refinement problem. The proposed methods will be grounded in the languages of Description Logics, which are commonly used on problems involving knowledge bases or ontologies representation. Two extensions of Description Logics with the Rough Set theory are introduced in this dissertation. We will prove the complexity of satisfiability of these logics, as well as the complexities of the query refinement methods applied to these logics. Finally, we will show quality measures which will aid to choose the results of the refinements obtained.
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10

Lee, Chang Su. "A framework of adaptive T-S type rough-fuzzy inference systems (ARFIS)." University of Western Australia. School of Electrical, Electronic and Computer Engineering, 2009. http://theses.library.uwa.edu.au/adt-WU2009.0192.

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[Truncated abstract] Fuzzy inference systems (FIS) are information processing systems using fuzzy logic mechanism to represent the human reasoning process and to make decisions based on uncertain, imprecise environments in our daily lives. Since the introduction of fuzzy set theory, fuzzy inference systems have been widely used mainly for system modeling, industrial plant control for a variety of practical applications, and also other decisionmaking purposes; advanced data analysis in medical research, risk management in business, stock market prediction in finance, data analysis in bioinformatics, and so on. Many approaches have been proposed to address the issue of automatic generation of membership functions and rules with the corresponding subsequent adjustment of them towards more satisfactory system performance. Because one of the most important factors for building high quality of FIS is the generation of the knowledge base of it, which consists of membership functions, fuzzy rules, fuzzy logic operators and other components for fuzzy calculations. The design of FIS comes from either the experience of human experts in the corresponding field of research or input and output data observations collected from operations of systems. Therefore, it is crucial to generate high quality FIS from a highly reliable design scheme to model the desired system process best. Furthermore, due to a lack of a learning property of fuzzy systems themselves most of the suggested schemes incorporate hybridization techniques towards better performance within a fuzzy system framework. ... This systematic enhancement is required to update the FIS in order to produce flexible and robust fuzzy systems for unexpected unknown inputs from real-world environments. This thesis proposes a general framework of Adaptive T-S (Takagi-Sugeno) type Rough-Fuzzy Inference Systems (ARFIS) for a variety of practical applications in order to resolve the problems mentioned above in the context of a Rough-Fuzzy hybridization scheme. Rough set theory is employed to effectively reduce the number of attributes that pertain to input variables and obtain a minimal set of decision rules based on input and output data sets. The generated rules are examined by checking their validity to use them as T-S type fuzzy rules. Using its excellent advantages in modeling non-linear systems, the T-S type fuzzy model is chosen to perform the fuzzy inference process. A T-S type fuzzy inference system is constructed by an automatic generation of membership functions and rules by the Fuzzy C-Means (FCM) clustering algorithm and the rough set approach, respectively. The generated T-S type rough-fuzzy inference system is then adjusted by the least-squares method and a conjugate gradient descent algorithm towards better performance within a fuzzy system framework. To show the viability of the proposed framework of ARFIS, the performance of ARFIS is compared with other existing approaches in a variety of practical applications; pattern classification, face recognition, and mobile robot navigation. The results are very satisfactory and competitive, and suggest the ARFIS is a suitable new framework for fuzzy inference systems by showing a better system performance with less number of attributes and rules in each application.
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Books on the topic "Rough seas"

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Hull, Nancy L. On rough seas. New York: Clarion Books, 2008.

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Stella, Regis N. Gutsini posa =: Rough seas. Suva, Fiji: Institute of Pacific Studies, 1999.

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Hodges, David. Delayed by rough seas. Caldey Island: Caldey Abbey, 2003.

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Zidle, Marcia. Career savvy: 150 life preservers for today's rough seas and tomorrow's raging waters. Dubuque, Iowa: Kendall/Hunt Pub. Co., 1996.

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M, Steinman Alan, and United States. Coast Guard. Office of Research and Development., eds. A Comparison of the protection against immersion hypothermia provided by Coast Guard anti-exposure clothing in calm versus rough seas. Washington, D.C: U.S. Dept. of Transportation, U.S. Coast Guard, Office of Research and Development, 1985.

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Piech, Henryk. Scheduling with applying rough sets theory. Częstochowa: Publishing Office of Czestochowa University of Technology, 2008.

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F, Peters James, ed. Transactions on rough sets. Berlin: Springer, 2004.

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Düntsch, Ivo. Rough set data analysis: A road to non-invasive knowledge discovery. Bangor: Metho[delta]os Publishers, 2000.

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Ewa, Orlowska, ed. Incomplete information: Rough set analysis. New York: Physica-Verlag, 1997.

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Xu xin xi xi tong yu cu cao ji. Beijing: Ke xue chu ban she, 2013.

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Book chapters on the topic "Rough seas"

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Rolfes, Raimund, Moritz Häckell, and Tanja Grießmann. "A Firm Hold in Rough Seas." In Sea – Wind – Power, 31–43. Berlin, Heidelberg: Springer Berlin Heidelberg, 2016. http://dx.doi.org/10.1007/978-3-662-53179-2_4.

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Chaison, Gary. "The Unions Adrift in Rough Seas." In SpringerBriefs in Economics, 1–14. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-31982-7_1.

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Thorp, Rosemary. "Colombia in the Rough Seas of the 1980s." In Economic Management and Economic Development in Peru and Colombia, 179–94. London: Palgrave Macmillan UK, 1991. http://dx.doi.org/10.1007/978-1-349-21468-6_9.

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Mockute, A., C. Borri, E. Marino, and C. Lugni. "Wind–Wave Loading and Response of OWT Monopiles in Rough Seas." In Lecture Notes in Civil Engineering, 507–18. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-12815-9_39.

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Qu, Fengde, Fengwu Wang, Zongmo Yang, and Jian Sun. "Navigation Safety Assessment of Ship in Rough Seas Based on Bayesian Network." In Algorithms and Architectures for Parallel Processing, 470–78. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-11194-0_39.

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Katalinić, M., P. Matić, T. Petranović, and J. Parunov. "Full-scale measurements of ship motion in rough seas in the Adriatic Sea." In Trends in Maritime Technology and Engineering Volume 1, 365–71. London: CRC Press, 2022. http://dx.doi.org/10.1201/9781003320272-40.

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Chen, Chen, Kenji Sasa, Teruo Ohsawa, and Daisuke Terada. "Effects of GPV datasets on WRF modelling of ocean surface wind in rough seas." In Developments in Maritime Technology and Engineering, 11–18. London: CRC Press, 2021. http://dx.doi.org/10.1201/9781003216599-2.

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Degtyarev, Alexander B., Arthur M. Reed, and Vladimir Mareev. "Modeling of Incident Waves Near the Ship’s Hull (Application of Autoregressive Approach in Problems of Simulation of Rough Seas)." In Contemporary Ideas on Ship Stability, 25–35. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-00516-0_2.

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Banerjee, Mohua, and M. K. Chakraborty. "Rough Consequence and Rough Algebra." In Rough Sets, Fuzzy Sets and Knowledge Discovery, 196–207. London: Springer London, 1994. http://dx.doi.org/10.1007/978-1-4471-3238-7_24.

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Hu, Mengjun, Xiaofei Deng, and Yiyu Yao. "An Application of Bayesian Confirmation Theory for Three-Way Decision." In Rough Sets, 3–15. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-22815-6_1.

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Conference papers on the topic "Rough seas"

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Kragh, E., and R. Laws. "Rough Seas and Statistical Deconvolution." In 63rd EAGE Conference & Exhibition. European Association of Geoscientists & Engineers, 2001. http://dx.doi.org/10.3997/2214-4609-pdb.15.a-22.

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Kragh, E., and R. Laws. "Rough Seas and Time-Lapse Seismic." In 62nd EAGE Conference & Exhibition. European Association of Geoscientists & Engineers, 2000. http://dx.doi.org/10.3997/2214-4609-pdb.28.l15.

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Ryan, Frank. "Ducted millimeter wave propagation over rough seas using PWE." In 2014 United States National Committee of URSI National Radio Science Meeting (USNC-URSI NRSM). IEEE, 2014. http://dx.doi.org/10.1109/usnc-ursi-nrsm.2014.6928026.

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Bashir, Musa B., Mathew Evans, Simon D. Benson, and Alan J. Murphy. "Simulations of Dynamic Interaction Between a Bluff Body and Installation Vessel During Launch and Recovery in Rough Seas." In ASME 2017 36th International Conference on Ocean, Offshore and Arctic Engineering. American Society of Mechanical Engineers, 2017. http://dx.doi.org/10.1115/omae2017-61319.

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Simulation of marine operations for launch and recovery of bluff bodies such as autonomous underwater vehicles (AUV), remotely operated vehicles (ROV) or subsea templates is traditionally performed in calm to moderate sea conditions. The reason for doing so is partly due to the interaction between the complex dynamic response of an installation vessel, a moving bluff body and the wave kinematics of the rough sea condition. This is in addition to the need for accurate hydrodynamic coefficients that would enable proper simulation and modelling of the launch and recovery process. The key objective of the current methodology is to minimize risks of damage to the vessel and total loss of assets during the deployment and recovery process for marine operations in rough sea conditions. The aim of this paper is to present the results of experimental and numerical investigation on the prediction of dynamic response of a bluff body during launch and recovery from a surface vessel in rough sea condition. Experimental measurements of hydrodynamic coefficients and responses of a large scale bluff body using a scaled model were completed. Further studies using a time-domain numerical tool have been undertaken to measure the response characteristic of bluff bodies in rough seas. The study also predicted the contributions of vessel motion in rough seas to the dynamic response of the bluff bodies. The results obtained have shown that simulation of launch and recovery operations in rough seas can be carried out efficiently if their hydrodynamic coefficients through the wave active regions of the rough seas are predicted and then adequately implemented in the simulations.
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Vrugt, Rik, Piet Hein Noordenbos, and Ed Dudson. "Rough Seas and Small Passenger Ferries The Damen 3717 SWATH Solution." In High Speed Craft: Technology and Operations. RINA, 2002. http://dx.doi.org/10.3940/rina.hs.2002.15.

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Yang, Xilin, Matt Garratt, and Hemanshu Pota. "An Autonomous Recovery System for a Rotorcraft UAV Operating in rough Seas." In AIAA Guidance, Navigation, and Control Conference. Reston, Virigina: American Institute of Aeronautics and Astronautics, 2010. http://dx.doi.org/10.2514/6.2010-7733.

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Ott, Randolph H., Michael E. Pollock, Vincent P. Pusateri, and Thomas E. Tice. "Clutter response of rough seas at near-grazing incidence for UWB radar." In OE/LASE '92, edited by Ivan J. LaHaie. SPIE, 1992. http://dx.doi.org/10.1117/12.59029.

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Clauss, Gu¨nther F., Janou Hennig, Heike Cramer, and Stefan Kru¨ger. "Development of Safer Ships by Deterministic Analysis of Extreme Roll Motions in Harsh Seas." In ASME 2003 22nd International Conference on Offshore Mechanics and Arctic Engineering. ASMEDC, 2003. http://dx.doi.org/10.1115/omae2003-37174.

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For modern ship design — as confirmed by many examples of ship design and operation — the current intact stability code (IMO (2002)) does not provide a reliable basis for the assessment of ship safety in rough seas. In ship design the application of the IS-Code is not supporting design decisions towards increased safety in rough seas (Cramer and Kru¨ger (2001)), and ship operators find their cargo and/or vessels endangered by large roll angles and accelerations (France et al. (2001)). Thus, there is a great need for procedures to analyze ship safety in rough seas. This paper presents innovative deterministic seakeeping test procedures which are used to identify the physical mechanism endangering intact ships by evaluating cause-reaction relations of wave/structure interaction. Rogue wave sequences are embedded in severe seas for computer controlled capsizing tests of different vessels at the Hamburg Ship Model Basin. The model test results are used as a basis for the development of non-linear numerical methods to simulate ship motions in extreme seas with the target to design safer ships with reduced capsizing risk. Polar plots following from the non-linear simulations allow the evaluation of ship safety in severe seas with reference to course, speed and trim.
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Zhao, Yu, Beibei Yang, Jinping Sun, and Yuan Zhang. "SAR Imaging for Ships in Rough Seas Based on Deconvolutive STFT Spectrogram Method." In The International Conference on Remote Sensing,Environment and Transportation Engineering. Paris, France: Atlantis Press, 2013. http://dx.doi.org/10.2991/rsete.2013.163.

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Grion, Sergio, Ahmad Azmi, Jonathan Pollatos, Nick Riddalls, and R. Gareth Williams. "Broadband processing with calm and rough seas: Observations from a North Sea survey." In SEG Technical Program Expanded Abstracts 2013. Society of Exploration Geophysicists, 2013. http://dx.doi.org/10.1190/segam2013-1048.1.

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Reports on the topic "Rough seas"

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Devlin, Colleen, Sarah M. Glaser, Ciera Villegas, and Natasia Poinsatte. Rough Seas: The Causes and Consequences of Fisheries Conflict in Somali Waters. One Earth Future, January 2020. http://dx.doi.org/10.18289/oef.2020.042.

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Moore, Michael D. The U.S. Navy as Joint Forces Air Component Commander (JFACC) - Smooth Sailing or Rough Seas. Fort Belvoir, VA: Defense Technical Information Center, June 1992. http://dx.doi.org/10.21236/ada250270.

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Dorger, Charles, and Andrew Nickerson. Rough Sea Transfer Ship. Fort Belvoir, VA: Defense Technical Information Center, July 2008. http://dx.doi.org/10.21236/ada495490.

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Jamil, Khalid, and Robert J. Burkholder. Simulations of Radar Scattering Over a Rough Sea Surface. Fort Belvoir, VA: Defense Technical Information Center, March 2004. http://dx.doi.org/10.21236/ada423387.

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5

Ivakin, Anatoliy N. Geoacoustic Modeling: High-Frequency Scattering From Heterogeneous Rough Sea Beds at Shallow Grazing Angles. Fort Belvoir, VA: Defense Technical Information Center, September 2009. http://dx.doi.org/10.21236/ada531659.

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Burkholder, R. J., P. Janpugdee, and D. Colak. Development of Computational Tools for Predicting the Radar Scattering from Targets on a Rough Sea Surface. Fort Belvoir, VA: Defense Technical Information Center, January 2001. http://dx.doi.org/10.21236/ada389464.

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Burkholder, R. J., M. R. Pino, and D. H. Kwon. Development of Ray-Optical Methods for Studying the RCS of 2D Targets on a Rough Sea Surface. Fort Belvoir, VA: Defense Technical Information Center, January 1999. http://dx.doi.org/10.21236/ada362540.

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