Дисертації з теми "Swarna"
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Agarwal, Swarna [Verfasser], and Heinrich [Akademischer Betreuer] Planck. "Nanoskalig strukturierte Textilfiltermedien für die Trennung von Öl-Wasser-Emulsionen / Swarna Agarwal. Betreuer: Heinrich Planck." Stuttgart : Universitätsbibliothek der Universität Stuttgart, 2012. http://d-nb.info/1021923419/34.
Повний текст джерелаMcNabb, Andrew W. "Parallel Particle Swarm Optimization and Large Swarms." BYU ScholarsArchive, 2011. https://scholarsarchive.byu.edu/etd/2480.
Повний текст джерелаRiyaz, Firasath Maurer Peter M. Marks Robert J. "Evolving a Disjunctive Predator Prey Swarm using PSO Adapting Swarms with Swarms/." Waco, Tex. : Baylor University, 2005. http://hdl.handle.net/2104/1465.
Повний текст джерелаAshcraft, C. Chace. "Moderating Influence as a Design Principle for Human-Swarm Interaction." BYU ScholarsArchive, 2019. https://scholarsarchive.byu.edu/etd/7406.
Повний текст джерелаNagavalli, Sasanka. "Algorithms for Timing and Sequencing Behaviors in Robotic Swarms." Research Showcase @ CMU, 2018. http://repository.cmu.edu/dissertations/1215.
Повний текст джерелаGrosh, John Rolfes. "Multi-Human Management of a Hub-Based Colony: Efficiency and Robustness in the Cooperative Best M-of-N Task." BYU ScholarsArchive, 2019. https://scholarsarchive.byu.edu/etd/8544.
Повний текст джерелаGrandi, Raffaele <1976>. "Coordination and Control of Autonomous Mobile Robots Swarms by using Particle Swarm Optimization Algorithm and Consensus Theory." Doctoral thesis, Alma Mater Studiorum - Università di Bologna, 2013. http://amsdottorato.unibo.it/5904/1/Grandi_Raffaele_tesi.pdf.
Повний текст джерелаGrandi, Raffaele <1976>. "Coordination and Control of Autonomous Mobile Robots Swarms by using Particle Swarm Optimization Algorithm and Consensus Theory." Doctoral thesis, Alma Mater Studiorum - Università di Bologna, 2013. http://amsdottorato.unibo.it/5904/.
Повний текст джерелаGazi, Veysel. "Stability Analysis of Swarms." The Ohio State University, 2002. http://rave.ohiolink.edu/etdc/view?acc_num=osu1029812963.
Повний текст джерелаAbdenebaoui, Larbi [Verfasser], Hans-Jörg [Akademischer Betreuer] [Gutachter] Kreowski, and Jürgen [Gutachter] Pannek. "Graph-Transfromational Swarms : A Graph-Transformational Approach to Swarm Computation / Larbi Abdenebaoui ; Gutachter: Hans-Jörg Kreowski, Jürgen Pannek ; Betreuer: Hans-Jörg Kreowski." Bremen : Staats- und Universitätsbibliothek Bremen, 2016. http://d-nb.info/112770141X/34.
Повний текст джерелаSwartz, Clinton Keith. "Digital data collection and analysis: what are the effects on students' understanding of chemistry concepts." Montana State University, 2012. http://etd.lib.montana.edu/etd/2012/swartz/SwartzC0812.pdf.
Повний текст джерелаTaranova, D. V. "Swarm robotics." Thesis, Sumy State University, 2016. http://essuir.sumdu.edu.ua/handle/123456789/45874.
Повний текст джерелаKatz, Benji. "Swarm Sounds." Bowling Green State University / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=bgsu1527780021318426.
Повний текст джерелаEngzell, Waldén Frithiof. "Chasing Swans." Thesis, KTH, Arkitektur, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-261662.
Повний текст джерелаSoylu, Umit. "Multi-Target Tracking for Swarm vs. Swarm UAV Systems." Thesis, Monterey, California. Naval Postgraduate School, 2012. http://hdl.handle.net/10945/17462.
Повний текст джерелаUnmanned systems, including unmanned aerial vehicles (UAVs), are developing technologies that are becoming increasingly important. This thesis provides a model for generating a common operational picture (COP) for unmanned systems that is applicable in todayメs technology, and presents results and analysis based on simulation studies. This thesis specifically investigates a swarm versus swarm unmanned systems scenario in which opposing teams of UAVs approach each other. Different methodologies for generating a COP from the perspective of a given team are investigated, and a simulation is designed to explore the performance of the selected strategies for performing multi-target tracking. The results of the simulation show the performance of the presented approach where targets are assumed in the field of view of the tracking agents, false detections may or may not be present, and all entities maneuver according to nondeterministic motion models.
Bratton, Daniel. "Simple and adaptive particle swarms." Thesis, Goldsmiths College (University of London), 2010. http://research.gold.ac.uk/4752/.
Повний текст джерелаMårtensson, Christopher, and Linus Sjövall. "Consensus Algorithms - Flocking and Swarms." Thesis, KTH, Optimeringslära och systemteori, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-105773.
Повний текст джерелаEtt intressant omrade inom matematiken ar att beskriva fenomenet med ockar och svarmar. Med hjalp av grafteori kan man beskriva ett system av agenter som skickar information mellan varandra och med hjalp av algoritmer som beskriver hur varje agents informationen ska uppdateras sa att konsensus nas. Om informationen beskriver en position eller foryttning i rummet kan man observera ett beteende som liknar det hos djurockar eller insektssv armar. Manga andra tillampningsomraden nns ocksa, till exempel i system av robotar nar det saknas central styrning och internt beslutstagande ar onskvart. I denna rapport kommer olika algoritmer for att uppdatera en agents status att undersokas for att bestamma vilka krav som nns for att konsensus skall nas. Forsta delen kommer att behandla ett enklare fall dar varje agent tar emot information fran en oforanderlig uppsattning agenter. Specikt sa kommer en algoritm, dar en agents status bestams av en linjar funktion som beror pa statusen hos de agenter fran vilka information mottages, att studeras. Ett krav for att denna algoritm ska na konsensus ar att varje agent bade skickar och tar emot information fran samtliga ovriga agenter, direkt eller indirekt via andra agenter. Om alla informations overforingar vags lika sa kommer alla agenter na medelvardet av agenternas initialvarden. Konsensus kan ocksa nas under mindre restriktiva villkor, om det nns en agent som skickar information till alla andra noder (direkt eller indirekt). Forandringar av systemets beteende vid olika uppdateringsalgoritmer kommer att studeras och datorsimuleringar av dessa fenomen kommer att ges. Ett intressant fall ar da informationen (ofta position, hastighet eller acceleration) endast kan tas emot fran de agenter som nns inom ett givet avstand. darmed forandras den uppsattning agenter med vilka information overfors med tiden. Detta resulterar i olinjara algoritmer och framforallt kommer simulationer och tolkningar av dessa att ges. En observation ar att om konsensus nas eller inte beror starkt pa densiteten bland agenterna i utgangslaget samt det maximala avstand vid vilket informationsoverforing kan ske.
Henderson, Robert. "Swarms: Spatiotemporal grouping across domains." Springer, 2016. http://hdl.handle.net/10150/622353.
Повний текст джерелаThis paper presents cross-domain evidence that natural language makes use of (at least) two ways of individuating collective entities that differ in terms of how they cohere. The first kind, which I call swarm reference, picks out higher-order collective entities defined in terms of the spatial and temporal configuration of their constituent individuals. The second, which corresponds to canonical cases of group reference (e.g. committee, team, etc.), makes use of non-spatiotemporal notions. To motivate this distinction, I present systematic differences in how these two types of collective reference behave linguistically, both in the individual and event domains. These differences support two primary results. First, they are used as tests to isolate a new class of collective nouns that denote swarm individuals, both in English, as well as other languages like Romanian. I then consider a crosslinguistically common type of pluractionality, called event-internal in the previous literature (Cusic 1981, Wood 2007), and show that its properties are best explained if the relevant verbs denote swarm events. By reducing event-internal pluractionality to a type of collective reference also available for nouns, this work generates a new strong argument that pluractionality involves the same varieties of plural reference in the event domain that are seen in the individual domain.
Pitonakova, Lenka. "Design patterns for robot swarms." Thesis, University of Southampton, 2017. https://eprints.soton.ac.uk/410360/.
Повний текст джерелаJung, Shin-Young. "Shaping Swarms Through Coordinated Mediation." BYU ScholarsArchive, 2013. https://scholarsarchive.byu.edu/etd/5516.
Повний текст джерелаDiukman, Anner Gaby. "Swarm Observations Implementing Integration Theory to Understand an Opponent Swarm." Thesis, Monterey, California. Naval Postgraduate School, 2012. http://hdl.handle.net/10945/17358.
Повний текст джерелаSwarm counter measure systems currently use enhanced weapons and sensor capabilities to address the threat of opponent swarms. However, there is a gap in current defense capabilities to counter swarm attacks, because brute force, or the enhancement of current defense systems by adding to defense capabilities are inadequate because of the inherent robustness, flexibility and adaptation of swarm attacks. Because of this, an overarching model is sought to understand the underlying command and control mechanism of an observed swarm threat, so that mechanisms that determine swarm behaviors can be understood. This will enable the development of countermeasures to counter swarms using specialized systems or tactics for certain behavior types. Integration theory provides an abstract model adequate throughout disparate swarm intelligence-domains (such as biology, computer algorithms, physics, and sociology). Integration theory, used with agent based modeling and analytical methods such as fractal dimensions, entropy, correlation and spatiotemporal structures, shows that it is possible to differentiate among the underlying C2 mechanisms by observing the swarm movement patterns. Adopting a swarm analytical observation approach is advised to promote the implementation of effective future countermeasures.
Ekberg, Pontus. "Swarm-Intelligent Localization." Thesis, Uppsala University, Department of Information Technology, 2009. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-108042.
Повний текст джерелаIn wireless sensor networks we often want to know where individual sensor nodesare physically positioned in order to make sense of the data that they report. Theprocess of obtaining such position information for the nodes is known as localization.Simple solutions to the problem of localization are to either place the nodes manuallyat specified places, or to use some special localization hardware such as GPSreceivers. However, these solutions can be impractical or too costly, especially forlarge networks. Instead we can use some algorithm to try to compute the nodes'positions based on available data. We present a new distributed algorithm, which wecall Swarm-Intelligent Localization (SIL), for computing these positions. Our algorithmassumes that a fraction of the nodes, the so-called anchors, have an a prioriknowledge of their positions, and that noisy range measurements can be madebetween neighbouring nodes in the network. The average computational complexityper node running SIL is constant in the network size, and linear in the connectivity ofthe network. We evaluate the algorithm through simulations of different networktopologies with varying parameters, such as network size, range measurement errors,fraction of anchors and connectivity. The results of the simulations indicate that inmost cases SIL can successfully locate the majority of sensor nodes with reasonableaccuracy, even in the face of difficulties such as large distance measurement errors.
Devarakonda, SaiPrasanth. "Particle Swarm Optimization." University of Dayton / OhioLINK, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1335827032.
Повний текст джерелаSwain, Jo Elyn Christiansen. "The influence of relational trust between the superintendent and union president." Diss., Montana State University, 2007. http://etd.lib.montana.edu/etd/2007/swain/SwainJ1207.pdf.
Повний текст джерелаShang, Beining. "Hardware variation in robotic swarm and behavioural sorting with swarm chromatography." Thesis, University of Southampton, 2017. https://eprints.soton.ac.uk/417270/.
Повний текст джерелаScheepers, Christiaan. "Multi-guided particle swarm optimization : a multi-objective particle swarm optimizer." Thesis, University of Pretoria, 2017. http://hdl.handle.net/2263/64041.
Повний текст джерелаThesis (PhD)--University of Pretoria, 2017.
Computer Science
PhD
Unrestricted
Semeghini, Michele. "Swarm constructability: designing through embedded tectonic behaviors in swarm robotic systems." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2015. http://amslaurea.unibo.it/8505/.
Повний текст джерелаPendleton, Brian O. "Human-Swarm Interaction: Effects on Operator Workload, Scale, and Swarm Topology." BYU ScholarsArchive, 2013. https://scholarsarchive.byu.edu/etd/3999.
Повний текст джерелаIlaya, Omar, and o. ilaya@student rmit edu au. "Cooperative Control for Multi-Vehicle Swarms." RMIT University. Aerospace, Mechanical & Manufacturing Engineering, 2009. http://adt.lib.rmit.edu.au/adt/public/adt-VIT20091027.112852.
Повний текст джерелаMoritz, Ruby Louisa Viktoria. "Cooperation in self-organized heterogeneous swarms." Doctoral thesis, Universitätsbibliothek Leipzig, 2015. http://nbn-resolving.de/urn:nbn:de:bsz:15-qucosa-161633.
Повний текст джерелаOthman, Wan Amir Fuad Wajdi. "Formation and organisation in robot swarms." Thesis, Sheffield Hallam University, 2009. http://shura.shu.ac.uk/20156/.
Повний текст джерелаLiu, Yang. "Stability analysis of asynchronous foraging swarms /." The Ohio State University, 2002. http://rave.ohiolink.edu/etdc/view?acc_num=osu1486402544588482.
Повний текст джерелаPimenta, Luciano Cunha de Araujo. "Techniques for Controlling Swarms of Robots." Universidade Federal de Minas Gerais, 2009. http://hdl.handle.net/1843/GASP-7Y5F4W.
Повний текст джерелаEsta tese aborda o problema de controle de grandes grupos de robôs, referidos como enxames. São propostas soluções escaláveis as quais não necessitam da identificação única dos robôs. Todos os robôs executam o mesmo código e o sucesso na execução de uma tarefa não depende de membros específicos do grupo. Robustez à adição e remoção dinâmica de agentes também é uma vantagem das abordagens propostas. Na primeira metodologia, o enxame é modelado como um fluido imerso numa região onde um campo de forças externas livre de mínimos locais é definido. Neste caso, utiliza-se o método de Hidrodinâmica de Partículas Suavizadas (HPS) para modelar o fluido robótico'', mais especificamente, para modelar as interações entre robôs do grupo. O Método de Elementos Finitos (MEF) também é utilizado neste trabalho para calcular os campos vetoriais que determinam as forças externas. Esta abordagem é instanciada num problema de geração de padrões e também num problema de cobertura de ambientes. Na segunda metodologia, um problema de cobertura ótima de ambientes utilizando robôs equipados com sensores é tratado por meio de ferramentas provenientes da teoria de Otimização Locacional. São apresentadas três extensões importantes de resultados já conhecidos na literatura: (i) sensores com diferentes campos de visão, (ii) robôs com formato circular e (iii) ambientes poligonais não-convexos. Ambas metodologias são verificadas em simulações. A primeira metodologia é também implementada e testada em robôs reais.
Adams, Joshua S. "Transmitter Localization Using Autonomous Robotic Swarms." DigitalCommons@USU, 2010. https://digitalcommons.usu.edu/etd/632.
Повний текст джерелаSwan, Laura Elizabeth. "Role of the mGRIP1 homologue DGrip in the Drosophila neuromuscular system." [S.l.] : [s.n.], 2005. http://webdoc.sub.gwdg.de/diss/2005/swan.
Повний текст джерелаAl-kazemi, Buthainah Sabeeh No'man. "Multiphase particle swarm optimization." Related electronic resource: Current Research at SU : database of SU dissertations, recent titles available full text, 2002. http://wwwlib.umi.com/cr/syr/main.
Повний текст джерелаGarattoni, Lorenzo. "Cognitive Abilities in Swarm Robotics: Developing a swarm that can collectively sequence tasks." Doctoral thesis, Universite Libre de Bruxelles, 2021. https://dipot.ulb.ac.be/dspace/bitstream/2013/317235/5/contratLG.pdf.
Повний текст джерелаDoctorat en Sciences de l'ingénieur et technologie
info:eu-repo/semantics/nonPublished
Jayabalan, Adhavan. "Decentralized Persistent Connectivity Deployment in Robot Swarms." Digital WPI, 2018. https://digitalcommons.wpi.edu/etd-theses/1231.
Повний текст джерелаDickie, Alistair James. "Modeling robot swarms using agent-based simulation." Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 2002. http://library.nps.navy.mil/uhtbin/hyperion-image/02Jun%5FDickie.pdf.
Повний текст джерелаChen, Jianing. "Cooperation in swarms of robots without communication." Thesis, University of Sheffield, 2015. http://etheses.whiterose.ac.uk/8319/.
Повний текст джерелаLang, Andreas. "Face Detection using Swarm Intelligence." Universitätsbibliothek Chemnitz, 2011. http://nbn-resolving.de/urn:nbn:de:bsz:ch1-qucosa-64415.
Повний текст джерелаAlbin, Aaron Thomas. "Musical swarm robot simulation strategies." Thesis, Georgia Institute of Technology, 2011. http://hdl.handle.net/1853/42862.
Повний текст джерелаKeshtkar, Abolfazl. "Swarm intelligence-based image segmentation." Thesis, University of Ottawa (Canada), 2007. http://hdl.handle.net/10393/27525.
Повний текст джерелаCorbett, Ian Rudy Paul. ""SWANG!" for symphonic wind orchestra." Diss., UMK access, 2005.
Знайти повний текст джерела"A dissertation in music composition." Advisor: Paul Rudy. Typescript. Vita. Title from "catalog record" of the print edition Description based on contents viewed Nov. 12, 2007 Online version of the print edition.
Stoops, David. "Rule discovery from swarm systems." Thesis, University of Ulster, 2011. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.550967.
Повний текст джерелаSun, Yanxia. "Improved particle swarm optimisation algorithms." Thesis, Paris Est, 2011. http://encore.tut.ac.za/iii/cpro/DigitalItemViewPage.external?sp=1000395.
Повний текст джерелаParticle Swarm Optimisation (PSO) is based on a metaphor of social interaction such as birds flocking or fish schooling to search a space by adjusting the trajectories of individual vectors, called "particles" conceptualized as moving points in a multidimensional space. This thesis presents several algorithms/techniques to improve the PSO's global search ability. Simulation and analytical results confirm the efficiency of the proposed algorithms/techniques when compared to the other state of the art algorithms.
Lang, Andreas. "Face Detection using Swarm Intelligence." Technische Universität Chemnitz, 2010. https://monarch.qucosa.de/id/qucosa%3A19439.
Повний текст джерелаDjaneye-Boundjou, Ouboti Seydou Eyanaa. "Particle Swarm Optimization Stability Analysis." University of Dayton / OhioLINK, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1386413941.
Повний текст джерелаHettiarachchi, Suranga D. "Distributed evolution for swarm robotics." Laramie, Wyo. : University of Wyoming, 2007. http://proquest.umi.com/pqdweb?did=1445057141&sid=1&Fmt=2&clientId=18949&RQT=309&VName=PQD.
Повний текст джерелаTiboni, Ivan. "I principi della swarm intelligence." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2012. http://amslaurea.unibo.it/4051/.
Повний текст джерела