Academic literature on the topic 'Random Encounter Model'
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Journal articles on the topic "Random Encounter Model"
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Yin, Zhang-Cai, Hui Liu, Zhi-Jun Zhang, Zhang-Hao-Nan Jin, San-Juan Li, and Jia-Qiang Xiao. "Probabilistic Model of Random Encounter in Obstacle Space." ISPRS International Journal of Geo-Information 8, no. 1 (2019): 32. http://dx.doi.org/10.3390/ijgi8010032.
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Based on probabilistic time-geography, the encounter between two moving objects is random. The quantitative analysis of the probability of encounter needs to consider the actual geographical environment. The existing encounter probability algorithm is based on homogeneous space, ignoring the wide range of obstacles and their impact on encounter events. Based on this, this paper introduces obstacle factors, proposes encounter events that are constrained by obstacles, and constructs a model of the probability of encounters of moving objects based on the influence of obstacles on visual perception with the line-of-sight view analysis principle. In realistic obstacle space, this method provides a quantitative basis for predicting the encountering possibility of two mobile objects and the largest possible encounter location. Finally, the validity of the model is verified by experimental results. The model uses part of the Wuhan digital elevation model (DEM) data to calculate the encounter probability of two moving objects on it, and analyzes the temporal and spatial distribution characteristics of these probabilities.
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Kiefer, Dale A., and Juli Berwald. "A random encounter model for the microbial planktonic community." Limnology and Oceanography 37, no. 3 (1992): 457–67. http://dx.doi.org/10.4319/lo.1992.37.3.0457.
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Swartzman, Gordon. "Fish school formation and maintenance: a random encounter model." Ecological Modelling 56 (January 1991): 63–80. http://dx.doi.org/10.1016/0304-3800(91)90193-5.
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Yin, Zhang-Cai, Zhang-Hao-Nan Jin, Shen Ying, Hui Liu, San-Juan Li, and Jia-Qiang Xiao. "Distance-Decay Effect in Probabilistic Time Geography for Random Encounter." ISPRS International Journal of Geo-Information 8, no. 4 (2019): 177. http://dx.doi.org/10.3390/ijgi8040177.
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Probabilistic time geography uses a fixed distance threshold for the definition of the encounter events of moving objects. However, because of the distance-decay effect, different distances within the fixed threshold ensure that the encounter events do not always have the same possibility, and, therefore, the quantitative probabilistic time geography analysis needs to consider the actual distance-decay coefficient (DDC). Thus, this paper introduces the DDC and proposes a new encounter probability measure model that takes into account the distance-decay effect. Given two positions of a pair of moving objects, the traditional encounter probability model is that if the distance between the two positions does not exceed a given threshold, the encounter event may occur, and its probability is equal to the product of the probabilities of the two moving objects in their respective positions. Furthermore, the probability of the encounter at two given positions is multiplied by the DDC in the proposed model, in order to express the influence of the distance-decay effect on the encounter probability. Finally, the validity of the proposed model is verified by an experiment, which uses the tracking data of wild zebras to calculate the encounter probability, and compares it with the former method.
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Garland, Laura, Eric Neilson, Tal Avgar, Erin Bayne, and Stan Boutin. "Random Encounter and Staying Time Model Testing with Human Volunteers." Journal of Wildlife Management 84, no. 6 (2020): 1179–84. http://dx.doi.org/10.1002/jwmg.21879.
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SI, XIAMENG, YUN LIU, and ZHENJIANG ZHANG. "OPINION DYNAMICS IN POPULATIONS WITH IMPLICIT COMMUNITY STRUCTURE." International Journal of Modern Physics C 20, no. 12 (2009): 2013–26. http://dx.doi.org/10.1142/s0129183109014898.
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Web encounter facilitate contacts between people from different communities outside space and time. Implicit Community Structure is exhibited because of highly connected links within community and sparse encounters between communities. Considering the imperceptible influence of encounter on opinions, Sznajd updating rules are used to mimic people's behaviors after encountering a stranger in another community. We introduce a model for opinion evolution, in which the interconnectivity between different communities is represented as encounter frequency, and leadership is introduced to control the strength of community's opinion guide. In this scenario, the effects of Implicit Community Structure of contact network on opinion evolution, for asymmetric and random initial distribution but with heterogeneous opinion guide, are investigated respectively. It is shown that large encounter frequency favors consensus of the whole populations and successful opinion spreading, which is qualitatively agree with the results observed in Majority model defined on substrates with predefined community structure.
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Eshel, Ilan, and Daphna Weinshall. "Cooperation in a repeated game with random payment function." Journal of Applied Probability 25, no. 3 (1988): 478–91. http://dx.doi.org/10.2307/3213977.
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A model of cooperation versus defection in a sequence of games is analysed under the assumptions that the rules of the game are randomly changed from one encounter to another, that the decisions are to be made each time anew, according to the (random) rules of the specific local game, and that the result of one such game affects the ability of a player to participate and thus, cooperate in the next game. Under plausible assumptions, it is shown that all Nash solutions of the supergame determine cooperation over a non-degenerate range of rules, determining encounters of the prisoner's dilemma type.
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Eshel, Ilan, and Daphna Weinshall. "Cooperation in a repeated game with random payment function." Journal of Applied Probability 25, no. 03 (1988): 478–91. http://dx.doi.org/10.1017/s0021900200041206.
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A model of cooperation versus defection in a sequence of games is analysed under the assumptions that the rules of the game are randomly changed from one encounter to another, that the decisions are to be made each time anew, according to the (random) rules of the specific local game, and that the result of one such game affects the ability of a player to participate and thus, cooperate in the next game. Under plausible assumptions, it is shown that all Nash solutions of the supergame determine cooperation over a non-degenerate range of rules, determining encounters of the prisoner's dilemma type.
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Jousimo, Jussi, and Otso Ovaskainen. "A Spatio-Temporally Explicit Random Encounter Model for Large-Scale Population Surveys." PLOS ONE 11, no. 9 (2016): e0162447. http://dx.doi.org/10.1371/journal.pone.0162447.
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Miura, Kensuke, Tomoki Mori, Haruna Ogawa, et al. "Density estimations of the Asiatic black bear: application of the random encounter model." Journal of Natural History 56, no. 21-24 (2022): 1123–38. https://doi.org/10.1080/00222933.2022.2070441.
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Miura, Kensuke, Mori, Tomoki, Ogawa, Haruna, Umano, Shota, Kato, Haruki, Izumiyama, Shigeyuki, Niizuma, Yasuaki (2022): Density estimations of the Asiatic black bear: application of the random encounter model. Journal of Natural History 56 (21-24): 1123-1138, DOI: 10.1080/00222933.2022.2070441, URL: http://dx.doi.org/10.1080/00222933.2022.2070441
Dissertations / Theses on the topic "Random Encounter Model"
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Poitevin, Caroline Myriam. "Non-random inter-specific encounters between Amazon understory forest birds : what are theyand how do they change." reponame:Biblioteca Digital de Teses e Dissertações da UFRGS, 2016. http://hdl.handle.net/10183/150626.
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Os bandos mistos de aves são agregações sociais complexas estáveis durante o tempo e espaço. Até hoje, a estrutura social dessas espécies foi descrita a partir de estudos subjetivos de campo ou a partir de compilações do número e intensidade das interações a nível de todo o grupo, sem considerar as interações par-a-par individualmente. Nossos objetivos foram buscar evidências de associações não-randômicas entre pares de espécies de aves, delimitar os grupos a partir das espécies com as associações mais fortes e verificar se há diferenças na estrutura social entre os habitat de floresta primária e secundária. Utilizamos dados de ocorrência das espécies coletados a partir de redes de neblina e gravação de vocalizações para identificar pares de espécies que foram co-detectadas mais frequentemente do que o esperado a partir do modelo nulo e compararamos a força dessa interação entre as florestas tropicais primária e secundária Amazônicas. Nós também utilizamos as associações par-a-par para construir as redes de interação social e suas mudanças entre os tipos de habitat. Nós encontramos muitas interações positivas fortes entre as espécies, mas nenhuma evidência de repulsão. As análises das redes de interação revelaram vários grupos de espécies que corroboram com grupos ecológios descritos na literatura. Além disso, tanto a estrutura da rede de interação como a força da interação se alteraram drasticamente com a perturbação do habitat, com formação de algumas associações novas, mas uma tendência geral para quebra de associações entre as espécies. Nossos resultados mostram que as interações sociais entre essas aves podem ser fortemente afetados pela degradação do habitat, sugerindo que a estabilidade das interações desenvolvida entre espécies é ameaçada pelos distúrbios causados pelo homem.<br>Inter-specific associations of birds are complex social phenomena, frequently detected and often stable over time and space. So far, the social structure of these associations has been largely deduced from subjective assessments in the field or by counting the number of inter-specific encounters at the whole-group level, without considering changes to individual pairwise interactions. Here, we look for evidence of non-random association between pairs of bird species, delimit groups of more strongly associated species and examine differences in social structure between old growth and secondary forest habitat. We used records of bird species detection from mist-netting capture and from acoustic recordings to identify pairwise associations that were detected more frequently than expected under a null distribution, and compared the strength of these associations between old-growth and secondary forest Amazonian tropical forest. We also used the pairwise strength associations to visualize the social network structure and its changes between habitat types. We found many strongly positive interactions between species, but no evidence of repulsion. Network analyses revealed several modules of species that broadly agree with the subjective groupings described in the ornithological literature. Furthermore, both network structure and association strength changed drastically with habitat disturbance, with the formation of a few new associations but a general trend towards the breaking of associations between species. Our results show that social grouping in birds is real and may be strongly affected by habitat degradation, suggesting that the stability of the associations is threatened by anthropogenic disturbance.
Books on the topic "Random Encounter Model"
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Viswanathan, Gandhimohan M. The physics of foraging: An introduction to random searches and biological encounters. Cambridge University Press, 2011.
Find full textBook chapters on the topic "Random Encounter Model"
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Burdick, Don, A. Jackson Stenner, and Andrew Kyngdon. "From Model to Measurement with Dichotomous Items." In Explanatory Models, Unit Standards, and Personalized Learning in Educational Measurement. Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-3747-7_12.
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AbstractPsychometric models typically represent encounters between persons and dichotomous items as a random variable with two possible outcomes, one of which can be labeled success. For a given item, the stipulation that each person has a probability of success defines a construct on persons. This model specification defines the construct, but measurement is not yet achieved. The path to measurement must involve replication; unlike coin-tossing, this cannot be attained by repeating the encounter between the same person and the same item. Such replication can only be achieved with more items whose features are included in the model specifications. That is, the model must incorporate multiple items. This chapter examines multi-item model specifications that support the goal of measurement. The objective is to select the model that best facilitates the development of reliable measuring instruments. From this perspective, the Rasch model has important features compared to other models.
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Holton, David, and Robert M. May*. "Chaos and one-dimensional maps." In The Nature of Chaos. Oxford University PressOxford, 1993. http://dx.doi.org/10.1093/oso/9780198539902.003.0005.
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Abstract In our everyday experience we frequently encounter processes which exhibit chaotic or apparently random behaviour: the constantly changing weather pattern, the flow of a turbulent stream, or the fluctuations in the population of a species. These are dramatic examples of systems which display complicated and seemingly indeterminate behaviour. However, these are precisely the kinds of systems which hold most fascination and which we wish most to be able to rationalize, to model, and, using estimates based on the model, to predict future behaviour. The traditional view of the complexity of nature is that there is little hope of unravelling the underlying reasons for randomness. The elements of cause and effect are believed to be so intricate and the interactions so complicated, appearing on so many spatial and temporal scales, that the net result is that the many influences act incoherently and statistically, to yield an unfathomable mess.
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Zwarts, Leo, John T. Cayford, Jan B. Hulscher, Marcel Kersten, Patrick M. Meire, and Patrick Triplet. "Prey size selection and intake rate." In The Oystercatcher. Oxford University PressOxford, 1996. http://dx.doi.org/10.1093/oso/9780198546474.003.0003.
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Abstract Do predators take all the individual prey belonging to one species that they encounter, and if not, to what degree are some prey under-represented in the diet and why? These questions are important in every study of the interaction between predators and their food supply. This chapter first analyses the degree to which the selection of prey sizes within one prey species by Oystercatchers can be understood as a passive process in which a randomly searching bird takes all the prey it encounters; this will be referred to as ‘passive size selection’. As we shall see, however, Oystercatchcrs reject certain size classes of prey which they actually encounter, and hence there is also ‘an active size selection’, The rules that Oystercatchers obey when selecting size classes actively, and whether these can be derived from optimal foraging models, are then discussed.
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Field, Timothy R. "Scattering From General Populations." In Electromagnetic Scattering from Random Media. Oxford University PressOxford, 2008. http://dx.doi.org/10.1093/oso/9780198570776.003.0012.
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Abstract In this chapter we derive the stochastic dynamics for scattering of a wavelike field from a large population of scatterers whose dynamics is arbitrary. This leads to a result concerning the observability of the scattering cross-section in terms of the resultant phase fluctuations that is independent of the population dynamics. An emergent concept is a certain notion of an ideal filter. The dffusion-based model of K-scattering arises, encountered in Chapter 8, as a special case. The experimental implications of the results in a variety of contexts are discussed later in Part III.
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Nguyen, Hung T., Vladik Kreinovich, and Gang Xiang. "Random Fuzzy Sets." In Intelligent Data Analysis. IGI Global, 2009. http://dx.doi.org/10.4018/978-1-59904-982-3.ch002.
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It is well known that in decision making under uncertainty, while we are guided by a general (and abstract) theory of probability and of statistical inference, each specific type of observed data requires its own analysis. Thus, while textbook techniques treat precisely observed data in multivariate analysis, there are many open research problems when data are censored (e.g., in medical or bio-statistics), missing, or partially observed (e.g., in bioinformatics). Data can be imprecise due to various reasons, for example, due to fuzziness of linguistic data. Imprecise observed data are usually called coarse data. In this chapter, we consider coarse data which are both random and fuzzy. Fuzziness is a form of imprecision often encountered in perception-based information. In order to develop statistical reference procedures based on such data, we need to model random fuzzy data as bona fide random elements, that is, we need to place random fuzzy data completely within the rigorous theory of probability. This chapter presents the most general framework for random fuzzy data, namely the framework of random fuzzy sets. We also describe several applications of this framework.
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Hassell, Michael P. "Heterogeneity in host-parasitoid interactions." In The Spatial and Temporal Dynamics of Host–Parasitoid Interactions. Oxford University PressOxford, 2000. http://dx.doi.org/10.1093/oso/9780198540892.003.0004.
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Abstract With the exception of the negative binomial model of parasitism (Griffiths 1969b; May 1978), the host-parasitoid models considered in the last two chapters viewed populations as homogeneous entities in which roaming natural enemies encountered hosts at random, like molecules colliding in an ideal gas (Thompson 1924; Nicholson and Bailey 1935; Watt 1959; Hassell and Varley 1969). This tradition of assuming that all host individuals are equally susceptible to parasitism by identical parasitoids is an obvious starting point for developing any general theory, but it is highly simplistic. Within any natural population, individuals of varying phenotype and phenology will almost inevitably be unevenly distributed within habitats that are highly structured and variable. It is therefore inconceivable that individuals in real populations will all experience the same probability of survival and reproduction throughout their lives.
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Abiraami T V, Maithili K, and Nivetha J E. "Parallel Computing Enabled Cloudd-Based IOT Applications." In Advances in Parallel Computing Technologies and Applications. IOS Press, 2021. http://dx.doi.org/10.3233/apc210149.
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In delay-sensitive IoT applications, the acquisition and processing of data from the sensor devices to the cloud-based computing environment result in higher computational cost and inefficient system. The cloud servers dedicated to performing larger tasks are found quite difficult as IoT applications collect data frequently. Hence there is a constant retrieval and updating leads to the synchronization of data in the cloud. The potential of cloud servers and virtual machines tend to lose the ability of computing larger tasks. Hence the scope of parallel computing in cloud-based IoT applications are proposed with certain parallel shared models of computation. The Parallel Random Access Machine is introduced in cloud-based IoT applications. The parallel algorithms are designed to eliminate the conflicts encountered in the proposed model. Hence Conflicts of Concurrent Read Concurrent Write PRAM and Conflicts of Concurrent Read Exclusive Write PRAM algorithms are introduced which promotes the efficiency of cloud-based IoT applications.
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Hassell, Michael P. "Epilogue." In The Spatial and Temporal Dynamics of Host–Parasitoid Interactions. Oxford University PressOxford, 2000. http://dx.doi.org/10.1093/oso/9780198540892.003.0008.
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Abstract The question of what regulates populations was first posed early in this century by entomologists, particularly Howard and Fiske (1911) and Thompson (1924) who were engaged in the design and execution of biological-control programmes, and by Nicholson (1933) who was interested in more general questions of population dynamics. Thompson and Nicholson’s models were both framed in discrete generations and had random parasitoid encounters with hosts, but they made quite different assumptions about the efficiency of parasitoids in finding hosts. Thompson’s parasitoids were always egg-limited and his models were inherently unstable. Nicholson’s parasitoids had a potentially unlimited egg supply and were only limited by their searching ability, encapsulated in the searching efficiency parameter, the area of discovery. Ironically for someone who championed the cause of population regulation by density-dependent factors, his models were also unstable showing divergent oscillations in host and parasitoid population densities.
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Kaza, Vijaya Sindhoori, Anisha P. R., and Rithika Badam. "From Fields to Satellites." In Practice, Progress, and Proficiency in Sustainability. IGI Global, 2023. http://dx.doi.org/10.4018/978-1-6684-9819-4.ch004.
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This chapter explores the impact of millet cultivation and sustainable development on agriculture, focusing on advancements in production and environmental practices. Key factors influencing production quantity, quality, location, etc., are analysed. It presents an overview of food security assessments, modelling, mapping, and monitoring techniques, utilizing data from various satellites, from spectroradiometers, and ground-truthing, the grain yield of different millet varieties is estimated, accounting for regional variations and the influence of soil bunds on crop growth. Through the application of the random forest technique, the chapter demonstrates how crop yields can be identified and predicted using regression models based on rainfall patterns and millet suitability in specific Indian states. The findings showcase how monitoring techniques can empower farmers in various regions to achieve higher yields, sustainable food production, and improved crop quality along with addressing the limitations or challenges encountered during the research process.
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Pulleyblank, Douglas, and William J. Turkel. "Learning Phonology: Genetic Algorithms and Yoruba Tongue—Root Harmony." In Optimality Theory. Oxford University PressOxford, 2000. http://dx.doi.org/10.1093/oso/9780198238430.003.0016.
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Abstract This chapter presents an implementation of a genetic algorithm which acquires the constraint rankings of an Optimality-theoretic grammar. The chapter begins by sketching the structure of a grammar within Optimality Theory, goes on to outline the nature of an acquisition model employing genetic algorithms, and finally, applies genetic algorithms to Optimality Theory, using as a case study the acquisition of tongue-root harmony systems. The basic structure of a phonological grammar in Optimality Theory is established by the appropriate ranking of a universally determined set of violable constraints. This structure is well suited for an acquisition model based on genetic algorithms, a form of evolution that occurs on a computer. In a genetic algorithm, a problem of some sort is encoded in the form of strings of symbols. In the case of an Optimality-theoretic grammar, each such string of symbols directly corresponds to a ranked set of constraints. A population of these strings (ranked constraint sets) is randomly generated, each corresponding to a possible grammar. The goodness (or ‘fitness’) of each of the strings is then assessed for how well it describes the sort of data encountered. The fitness of most of the randomly generated strings will be very poor—especially if the search space is large—but some strings are better than others.
Conference papers on the topic "Random Encounter Model"
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Oshman, Y., J. Shinar, and S. Weizman. "Using a multiple model adaptive estimator in a random evasion missile/aircraft encounter." In Guidance, Navigation, and Control Conference and Exhibit. American Institute of Aeronautics and Astronautics, 1999. http://dx.doi.org/10.2514/6.1999-4141.
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Dostal, Leo, and Edwin Kreuzer. "Assessment of Extreme Rolling of Ships in Random Seas." In ASME 2014 33rd International Conference on Ocean, Offshore and Arctic Engineering. American Society of Mechanical Engineers, 2014. http://dx.doi.org/10.1115/omae2014-23458.
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The goal of our analysis is to improve intact stability criteria for ships. For this, we develop analytical formulas for mean first passage times of critical roll amplitudes of ships travelling in long crested random seas with an arbitrary wave encounter angle. Starting from a model for coupled heave-pitch-roll motion, we average the fast oscillatory dynamics of roll over the roll period, which yields equations for drift and diffusion of roll energy. The results are used to calculate the desired mean first passage times. Finally, we apply the proposed theory to a sample ship design.
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de Kat, Jan O., Dirk-Jan Pinkster, and Kevin A. McTaggart. "Random Waves and Capsize Probability Based on Large Amplitude Motion Analysis." In ASME 2002 21st International Conference on Offshore Mechanics and Arctic Engineering. ASMEDC, 2002. http://dx.doi.org/10.1115/omae2002-28477.
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The objective of this paper is to apply a methodology aimed at the probabilistic capsize assessment of two naval ships: a frigate and a corvette. Use is made of combined knowledge of the wave and wind climate a ship will be exposed to during its lifetime and of the physical behavior of that ship in the various sea states it is likely to encounter. This includes the behavior in extreme wave conditions that have a small probability of occurrence, but which may be critical to the safe operation of a ship. Time domain simulations provide the basis for deriving short-term and long-term statistics for extreme roll angles. The numerical model is capable of predicting the 6 DOF behavior of a steered vessel in wind and waves, including conditions that may lead to broaching and capsizing.
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Javanmardi, Mohammadreza, Javad A. Mehr, and Zi Hsiang Heng. "Experimental Investigation on the Natural Frequency and Damping Coefficient of the Roll From Vessel Response in Random Seas." In ASME 2023 42nd International Conference on Ocean, Offshore and Arctic Engineering. American Society of Mechanical Engineers, 2023. http://dx.doi.org/10.1115/omae2023-105399.
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Abstract Autonomous shipping is inevitably the future of sea transportation which requires accurate decision-making systems. Analysis of big data as a key element to develop these decision-making systems is much easier than before. The precise prediction of vessel roll response, as a critical component of ship motions, by directly calculating hydrodynamic parameters from vessel response during the voyage, would tremendously aid in the real time adjustments to avoid dangerous situations the vessel would encounter. This investigation aims to extract the natural period and damping coefficient of the roll from vessel response in random seas. Experimental tests were conducted to record the roll motions of vessel models, P&O Nedlloyd Hoorn container ship and Midi Mak Marad F bulk carrier, in random waves at Australian Maritime College Model Test Basin. The natural period obtained from the vessels’ roll response in random seas were in close agreement with the one obtained from the vessels’ roll decay test with negligible percentage error of under 1% for both models. Random Decrement Method (RDM) was used to produce roll decay from random vessel response and estimate the damping coefficient of the models. The estimated damping ratio from RDM show the good agreement with roll decay tests. This great achievement can help us to precisely predict the vessel roll response in random seas in order to avoid dangerous situation.
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Shulman, Ami, and Jorge Soto-Andrade. "A random walk in stochastic dance." In LINK 2021. Tuwhera Open Access, 2021. http://dx.doi.org/10.24135/link2021.v2i1.71.
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Stochastic music, developed last century by Xenakis, has older avatars, like Mozart, who showed how to compose minuets by tossing dice, in a similar way that contemporary choreographer Cunningham took apart the structural elements of what was considered to be a cohesive choreographic work (including movement, sound, light, set and costume) and reconstructed them in random ways. We intend to explore an enactive and experiential analogue of stochastic music, in the realm of dance, where the poetry of a choreographic spatial/floor pattern is elicited by a mathematical stochastic process, to wit a random walk – a stochastic dance of sorts. Among many possible random walks, we consider two simple examples, embodied in the following scenarios, proposed to the students/dancers: - a frog, jumping randomly on a row of stones, choosing right and left as if tossing a coin, - a person walking randomly on a square grid, starting a given node, and choosing each time randomly, equally likely N, S, E or W, and walking non-stop along the corresponding edge, up to the next node, and so on.When the dancers encounter these situations, quite natural questions arise for the choreographer, like: Where will the walker/dancer be after a while? Several ideas for a choreography emerge, which are more complex than just having one or more dancers perform the random walk, and which surprisingly turn our random process into a deterministic one!For instance, for the first random walk, 16 dancers start at the same node of a discrete line on the stage, and execute, each one, a different path of the 16 possible 4 – jump paths the frog can follow. They would need to agree first on how to carry this out. Interestingly, they may proceed without a Magister Ludi handing out scripts to every dancer. After arriving to their end node/position, they could try to retrace their steps, to come back all to the starting node.Analogously for the grid random walk, where we may have now 16 dancers enacting the 16 possible 2-edge paths of the walker. The dancers could also enter the stage (the grid or some other geometric pattern to walk around), one by one, sequentially, describing different random paths, or deterministic intertwined paths, in the spirit of Beckett’s Quadrat. Also, the dancers could choose their direction ad libitum, after some spinning, each time, on a grid-free stage, but keeping the same step length, as in statistician Pearson’s model for a mosquito random flight.We are interested in various possible spin-offs of these choreographies, which intertwine dance and mathematical cognition: For instance, when the dancers choose each one a different path, they will notice that their final distribution on the nodes is uneven (interesting shapes emerge). In this way, just by moving, choreographer and dancers can find a quantitative answer to the impossible question: where will the walker/dancer be after a while? Indeed, the percentage of dancers ending up at each node gives the probability of the random walker landing there.
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Zhou, Qianyu, and Jiong Tang. "Efficient Fault Detection in Bearings: Synergizing Transformer Adaptations With Convolutional Kernel." In 2024 International Symposium on Flexible Automation. American Society of Mechanical Engineers, 2024. http://dx.doi.org/10.1115/isfa2024-141122.
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Abstract Efficient and accurate bearing fault diagnosis and prognostics are crucial for predictive maintenance in industrial settings. Deep learning approaches have significantly advanced the field of time series classification, bringing powerful models to the forefront of prognostic and health management. Despite these advancements, current methods, including conventional Transformer models, encounter specific limitations with computational efficiency and the effective processing of long sequences. Addressing these challenges, our research presents an optimized Transformer-based model tailored for surmounting these hurdles, enhancing efficiency and ensuring high performance. By eschewing the traditional decoder in favor of an encoder-only architecture, our model capitalizes on the encoder’s ability to distill contextual information from sensor data. Enhanced with random convolutional kernel transform (ROCKET) for its exceptional feature extraction in time sequences and refined through principal component analysis (PCA) for dimensionality reduction, the proposed approach significantly boosts model efficiency, markedly trimming training duration, while achieving an exemplary accuracy of 99.10% on the benchmark bearing dataset. The proven efficiency and outstanding performance of our model hold significant promise for real-time predictive maintenance, offering a powerful tool for prompt and accurate fault detection.
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Neelamani, S., and K. Al-Banaa. "Inline and Vertical Wave Force Variation due to Burial of Submarine Pipeline in Random Wave Fields." In ASME 2011 30th International Conference on Ocean, Offshore and Arctic Engineering. ASMEDC, 2011. http://dx.doi.org/10.1115/omae2011-49431.
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Marine pipelines encounter significant dynamic forces due to the action of waves. In order to reduce such forces, they are buried below the seabed. The wave force on the pipeline at any depth of burial for the given hydrodynamic condition depends on the properties of the sea bed soil. Physical model is used for assessing the hydrodynamic force on the pipeline for a wide range of random wave conditions, for different burial depths and in four types of soils. It is found that for all the four soil types, the horizontal force reduces with increase in depth of burial, whereas the vertical force generally increases up to certain depth of burial, mainly due to the significant change in the magnitude as well as the phase lag between the pore water pressures in the vertical direction. Among the soils, well graded soil is good for half burial of pipeline, since the least vertical force occurs for this soil. On the other hand, uniformly graded and low hydraulic conductivity soil attracts the maximum vertical force for half burial. On the other hand, such soil is good for full burial or further increase of burial, since it attracts less vertical force when compared to the other soils. The results of this study will help the submarine pipeline design engineers to select the minimum safe burial depth in a range of cohesion-less soil.
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Mu, Jianfeng, Yuheng Fei, Fang Wang, and Xinyue Zeng. "Machine Learning Based Flight State Prediction for Improving UAV Resistance to Uncertainty." In SAE 2023 Intelligent Urban Air Mobility Symposium. SAE International, 2023. http://dx.doi.org/10.4271/2023-01-7114.
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<div class="section abstract"><div class="htmlview paragraph">Unmanned Aerial Vehicles (UAVs) encounter various uncertainties, including unfamiliar environments, signal delays, limited control precision, and other disturbances during task execution. Such factors can significantly compromise flight safety in complex scenarios. In this paper, to enhance the safety of UAVs amidst these uncertainties, a control accuracy prediction model based on ensemble learning abnormal state detection is designed. By analyzing the historical state data, the trained model can be used to judge the current state and obtain the command tracking control accuracy of the UAV at that instant. Ensemble learning offers superior classification capabilities compared to weak learners, particularly for anomaly detection in flight data. The learning efficacy of support vector machine, random forest classifier is compared and achieving a peak accuracy of 95% for the prediction results using random forest combined with adaboost model . Subsequently, a trajectory planning method leveraging the DWA(Dynamic Window approach) algorithm was designed to mitigate the safety risks associated with uncertain control command tracking. By employing the obtained model of nominal command execution results of UAVs subjected to uncertainty, and by adjusting the original assessment criteria to a probability-weighted comprehensive optimal metric, optimal control commands that factor in uncertainty are derived. The simulation results affirm the effectiveness of the designed method.</div></div>
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Yu, Bingbin, Dale G. Karr, Huimin Song, and Senu Sirnivas. "A Surface Ice Module for Wind Turbine Dynamic Response Simulation Using FAST." In ASME 2013 32nd International Conference on Ocean, Offshore and Arctic Engineering. American Society of Mechanical Engineers, 2013. http://dx.doi.org/10.1115/omae2013-11179.
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The simulation software FAST is an open source CAE package maintained by the National Renewable Energy Laboratory. A new module of FAST for assessing the dynamic response of offshore wind turbines subjected to ice forcing is presented. This paper describes the recent addition of capabilities for analyzing the response of wind turbines subjected forces resulting from ice impact on the turbine support structure. Several models are presented which involve both prescribed forcing and coupled response. For conditions in which the ice forcing is essentially decoupled from the structural response, ice forces are established from existing models for brittle and ductile ice failure modes. For conditions in which the ice failure and the structural response are coupled, such as lock-in conditions, a rate-dependent ice model is described, which is developed in conjunction with a new modularization framework for FAST. The ice loading module includes ice mechanics models, that incorporate ice floe forcing, deformation, and failure. For lower speeds, forces slowly build until the ice strength is reached and ice fails resulting in a quasi-static condition where the frequency of the forcing is about an order of magnitude lower than the response of the structure. For intermediate speeds, the ice failure response can be coupled with the structural response resulting in response where the ice feature failure period and the response period of the structure approximately coincide. A third response regime occurs at high speeds of encounter in which brittle fracturing of the ice feature in contact with the structure occur in a more or less random pattern, which results in random vibration excitation of the structure. These three conditions are specifically addressed in the ISO standard 19906:2010 for consideration in the design of arctic offshore structures. Special consideration of lock-in vibrations is required due to the detrimental effects of such response with regard to fatigue and foundation/soil response. The use of FAST for transient, time domain simulation with the new ice module is well suited for such analyses.
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Narayan, Rajnikant, and Mr Vinay Kumar. "SPAM EMAIL DETECTION USING DEEP LEARNING MODEL." In Computing for Sustainable Innovation: Shaping Tomorrow’s World. Innovative Research Publication, 2024. http://dx.doi.org/10.55524/csistw.2024.12.1.47.
Full textAbstract:
The persistent menace of spam emails poses a formidable cybersecurity threat, demanding the implementation of robust classification models for prompt detection and prevention. This research addresses the intricate challenge of classifying spam emails by evaluating diverse machine learning and deep learning models. The focal point of the identified problem lies in the inherent complexity of distinguishing spam emails from legitimate ones, leading to potential security vulnerabilities. Existing models' limitations, particularly in managing false positives and false negatives, underscore the imperative for enhanced techniques. The chosen methodology incorporates conventional machine learning models, such as Random Forest, Multinomial Naive Bayes (MNB), and Support Vector Machine (SVM), in conjunction with the Netcraft dataset. Additionally, deep learning models, including Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), Convolutional Neural Network (CNN), and Bidirectional LSTM (BiLSTM), are deployed with meticulous hyperparameter tuning. An attention mechanism is introduced to dynamically allocate distinct weights to email headers and bodies based on their significance. The outcomes unveil the consistently superior performance of the Random Forest model, attaining a flawless F1-score of 1.0 for spam email classification. The SVM model also exhibits excellence with impeccable precision and recall across all folds. Nevertheless, the MNB model encounters difficulties in classifying legitimate emails, influencing overall metrics. The deep learning models, especially LSTM, CNN, BiL STM, and RNN, demonstrate exceptional efficacy with remarkably minimal test losses and elevated accuracies, underscoring their prowess in spam email classification tasks.
Reports on the topic "Random Encounter Model"
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Li, Yuan, Benjamin Metcalf, Sopio Chochua та ін. Validation of β-lactam minimum inhibitory concentration predictions for pneumococcal isolates with newly encountered penicillin binding protein (PBP) sequences [Supporting data]. Centers for Disease Control and Prevention (U.S.), 2017. http://dx.doi.org/10.15620/cdc/147467.
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The datafiles, R scripts, MIC tables, and other files were used to evaluate the prediction performance of a penicillin-binding protein (PBP) typing system and two methods (Random Forest (RF) and Mode MIC (MM) previously developed by this research team. This data and these files support the finding of the paper "Validation of β-lactam minimum inhibitory concentration predictions for pneumococcal isolates with newly encountered penicillin binding protein (PBP) sequences" at https://doi.org/10.1186%2Fs12864-017-4017-7 or at https://stacks.cdc.gov/view/cdc/47684.