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Relevant bibliographies by topics / Large scale swarm intelligence / Dissertations / Theses

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Dissertations / Theses on the topic 'Large scale swarm intelligence'

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Published: 4 June 2021

Last updated: 2 February 2022

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1

Wittner, Otto. "Emergent behavior based implements for distributed network management." Doctoral thesis, Norwegian University of Science and Technology, Department of Telematics, 2003. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-1787.

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<p>Network and system management has always been of concern for telecommunication and computer system operators. The need for standardization was recognised already 20 years ago, hence several standards for network management exist today. However, the ever-increasing number of units connected to networks and the ever-increasing number of services being provided results in significant increased complexity of average network environments. This challenges current management systems. In addition to the general increase in complexity the trend among network owners and operators of merging several s

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2

Geuther, Brian Q. "Towards Bacteria Inspired Stochastic Control Strategies for Microrobotic Swarm Intelligence." Thesis, Virginia Tech, 2013. http://hdl.handle.net/10919/23751.

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Collective robotic behavior poses significant advantages over classical control methods such as system response and robustness. Biological cooperative communities have provided great insights for development of many control algorithms. Localized chemical signaling within bacterial communities is used for directed movement and dynamic density measurements. Both individual and population scale models have been created to adequately model community dynamics. These dynamics, including directed motion due to chemotaxis and density controlled functionality from quorum sensing, are modeled through an

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3

Nairouz, Bassem R. "Conceptual design methodology of distributed intelligence large scale systems." Diss., Georgia Institute of Technology, 2013. http://hdl.handle.net/1853/49077.

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Distributed intelligence systems are starting to gain dominance in the field of large-scale complex systems. These systems are characterized by nonlinear behavior patterns that are only predicted through simulation-based engineering. In addition, the autonomy, intelligence, and reconfiguration capabilities required by certain systems introduce obstacles adding another layer of complexity. However, there exists no standard process for the design of such systems. This research presents a design methodology focusing on distributed control architectures while concurrently considering the systems d

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Ealey, Douglas. "Natural language acquisition in large scale neural semantic networks." Thesis, University of Southampton, 1999. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.310843.

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5

Zhao, Haixiang. "Artificial Intelligence Models for Large Scale Buildings Energy Consumption Analysis." Phd thesis, Ecole Centrale Paris, 2011. http://tel.archives-ouvertes.fr/tel-00658767.

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The energy performance in buildings is influenced by many factors, such as ambient weather conditions, building structure and characteristics, occupancy and their behaviors, the operation of sub-level components like Heating, Ventilation and Air-Conditioning (HVAC) system. This complex property makes the prediction, analysis, or fault detection/diagnosis of building energy consumption very difficult to accurately and quickly perform. This thesis mainly focuses on up-to-date artificial intelligence models with the applications to solve these problems. First, we review recently developed models

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6

Takane, Marina. "Inference of gene regulatory networks from large scale gene expression data." Thesis, McGill University, 2003. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=80883.

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With the advent of the age of genomics, an increasing number of genes have been identified and their functions documented. However, not as much is known of specific regulatory relations among genes (e.g. gene A up-regulates gene B). At the same time, there is an increasing number of large-scale gene expression datasets, in which the mRNA transcript levels of tens of thousands of genes are measured at a number of time points, or under a number of different conditions. A number of studies have proposed to find gene regulatory networks from such datasets. Our method is a modification of th

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7

Oldewage, Elre Talea. "The perils of particle swarm optimization in high dimensional problem spaces." Diss., University of Pretoria, 2005. http://hdl.handle.net/2263/66233.

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Particle swarm optimisation (PSO) is a stochastic, population-based optimisation algorithm. PSO has been applied successfully to a variety of domains. This thesis examines the behaviour of PSO when applied to high dimensional optimisation problems. Empirical experiments are used to illustrate the problems exhibited by the swarm, namely that the particles are prone to leaving the search space and never returning. This thesis does not intend to develop a new version of PSO speci cally for high dimensional problems. Instead, the thesis investigates why PSO fails in high dimensional search spaces.

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Kaiten, Juan Carlos, Kara Stonehouse, and Sonja Niederhumer. "Large Scale Collaboration towards Strategic Sustainable Development." Thesis, Blekinge Tekniska Högskola, Sektionen för ingenjörsvetenskap, 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-2851.

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As humanity transitions towards a global society, opportunities and challenges have grown in equal magnitude. Severe, global depletion of ecosystems and social turmoil have initiated a widespread public response. If the many small actions were to connect and collaborate strategically, then a greater impact could be made on the system to move towards sustainability. Along with continuous technological and scientific development, society is discovering new ways of organizing itself for a more just, sustainable and harmonious world. This is giving birth to a large social body that is becoming awa

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9

Moon, Yoon Keon 1959. "High performance simulation-based optimization environment for large scale systems." Diss., The University of Arizona, 1996. http://hdl.handle.net/10150/282263.

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Modelling large scale systems with natural and artificial components requires storage of voluminous amounts of knowledge/information as well as computing speed for simulations to provide reliable answers in reasonable time. Computing technology is becoming powerful enough to support such high performance modelling and simulation. This dissertation proposes a high performance simulation based optimization environment to support the design and modeling of large scale systems with high levels of resolution. The proposed environment consists of three layers--modeling, simulation and searcher layer

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10

Schlick, Sandra. "Dynamic approach to competitive intelligence : case studies of large-scale Swiss telecom firms." Thesis, De Montfort University, 2016. http://hdl.handle.net/2086/12226.

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The research aim is to understand how the competitive intelligence (CI) process in large-scale Swiss telecom companies contributes to management decision-making. Studying CI activities of the Swiss large-scale telecom firms (Swisscom, Sunrise, Orange/Salt, Cablecom) in a dynamic European context offers useful insight into the critical challenges that service firms now face when developing intelligence in disruptive market contexts where aggressive competitive behaviour is evident. In considering CI theory, this study has reviewed perspectives drawn from research on the CI process, studies on k

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11

Devaney, Mark David. "Plan recognition in a large-scale multi-agent tactical domain." Diss., Georgia Institute of Technology, 2003. http://hdl.handle.net/1853/9195.

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12

Al-Shayji, Khawla Abdul Mohsen. "Modeling, Simulation, and Optimization of large-Scale Commercial Desalination Plants." Diss., Virginia Tech, 1998. http://hdl.handle.net/10919/30462.

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This dissertation introduces desalination processes in general and multistage flash (MSF) and reverse osmosis (RO) in particular. It presents the fundamental and practical aspects of neural networks and provides an overview of their structures, topology, strengths, and limitations. This study includes the neural network applications to prediction problems of large-scale commercial MSF and RO desalination plants in conjunction with statistical techniques to identify the major independent variables to optimize the process performance. In contrast to several recent studies, this work utilizes

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13

Zeng, Chunqiu. "Large Scale Data Mining for IT Service Management." FIU Digital Commons, 2016. http://digitalcommons.fiu.edu/etd/3051.

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More than ever, businesses heavily rely on IT service delivery to meet their current and frequently changing business requirements. Optimizing the quality of service delivery improves customer satisfaction and continues to be a critical driver for business growth. The routine maintenance procedure plays a key function in IT service management, which typically involves problem detection, determination and resolution for the service infrastructure. Many IT Service Providers adopt partial automation for incident diagnosis and resolution where the operation of the system administrators and automat

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14

Sharma, Astha. "Emotion Recognition Using Deep Convolutional Neural Network with Large Scale Physiological Data." Scholar Commons, 2018. https://scholarcommons.usf.edu/etd/7570.

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Classification of emotions plays a very important role in affective computing and has real-world applications in fields as diverse as entertainment, medical, defense, retail, and education. These applications include video games, virtual reality, pain recognition, lie detection, classification of Autistic Spectrum Disorder (ASD), analysis of stress levels, and determining attention levels. This vast range of applications motivated us to study automatic emotion recognition which can be done by using facial expression, speech, and physiological data. A person’s physiological signals such are hea

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15

Rihawi, Omar. "Modelling and simulation of distributed large scale situated multi-agent systems." Thesis, Lille 1, 2014. http://www.theses.fr/2014LIL10148/document.

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Les systèmes multi-agents sont constitués d'entités autonomes qui interagissent avec leur environnement pour résoudre un objectif collectif. Si l'on souhaite modéliser des systèmes contenant des millions d'agents, une puissance de calcul et de stockage importante devient nécessaire. Pour atteindre de telles simulations large échelle, distribuer le simulateur sur un réseau de machines est nécessaire, mais il faut prendre en compte quelques aspects. Le premier aspect se concentre sur deux types de répartition de la charge de calcul : la première basée sur l'environnement, la deuxième basée sur l

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Sagahyroon, Assim Abdelrahman. "An intelligence driven test system for detection of stuck-open faults in CMOS sequential circuits." Diss., The University of Arizona, 1989. http://hdl.handle.net/10150/184763.

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This paper discusses an intelligence driven test system for generation of test sequences for stuck-open faults in CMOS VLSI sequential circuits. The networks in system evaluation are compiled from an RTL representation of the digital system. To excite a stuck-open fault it is only necessary that the output of the gate containing the fault take on opposite values during two successive clock periods. Excitation of the fault must therefore constrain two successive input/present-state vectors, referred to in the paper as the pregoal and goal nodes respectively. An initialization procedure is used

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Jain, Himalaya. "Learning compact representations for large scale image search." Thesis, Rennes 1, 2018. http://www.theses.fr/2018REN1S027/document.

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Cette thèse aborde le problème de la recherche d'images à grande échelle. Pour aborder la recherche d'images à grande échelle, il est nécessaire de coder des images avec des représentations compactes qui peuvent être efficacement utilisées pour comparer des images de manière significative. L'obtention d'une telle représentation compacte peut se faire soit en comprimant des représentations efficaces de grande dimension, soit en apprenant des représentations compactes de bout en bout. Le travail de cette thèse explore et avance dans ces deux directions. Dans notre première contribution, nous éte

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Graehling, Quinn R. "Feature Extraction Based Iterative Closest Point Registration for Large Scale Aerial LiDAR Point Clouds." University of Dayton / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1607380713807017.

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Hazarika, Subhashis. "Statistical and Machine Learning Approaches For Visualizing and Analyzing Large-Scale Simulation Data." The Ohio State University, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=osu1574692702479196.

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20

Boufoy-Bastick, Zacharyas Amaury. "Internet democracy : the political science and computer science of direct democracy at the large scale." Thesis, Paris 4, 2014. http://www.theses.fr/2014PA040182.

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La démocratie représentative souffre de nombreuses lacunes qui remettent en question la légitimité même des gouvernements démocratiques modernes. Tandis que la représentation directe pourrait théoriquement éliminer ces incongruités, elle a jusqu'à présent été considérée comme irréalisable en raison de limitations spatio-temporelles. Cette thèse adresse ces problèmes en introduisant le concept de Démocratie Internet - distinct de l’e-démocratie et de l’e-gouvernement existant. La Démocratie Internet consiste à cerner la représentation démocratique de telle manière qu’elle puisse être opérationn

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21

Schwartz, Yannick. "Large-scale functional MRI analysis to accumulate knowledge on brain functions." Thesis, Paris 11, 2015. http://www.theses.fr/2015PA112056/document.

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Comment peut-on accumuler de la connaissance sur les fonctions cérébrales ? Comment peut-on bénéficier d'années de recherche en IRM fonctionnelle (IRMf) pour analyser des processus cognitifs plus fins et construire un modèle exhaustif du cerveau ? Les chercheurs se basent habituellement sur des études individuelles pour identifier des régions cérébrales recrutées par les processus cognitifs. La comparaison avec l'historique du domaine se fait généralement manuellement pas le biais de la littérature, qui permet de définir des régions d'intérêt dans le cerveau. Les méta-analyses permettent de dé

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22

Yang, Zhiguo. "MODELING LARGE-SCALE CROSS EFFECT IN CO-PURCHASE INCIDENCE: COMPARING ARTIFICIAL NEURAL NETWORK TECHNIQUES AND MULTIVARIATE PROBIT MODELING." UKnowledge, 2015. http://uknowledge.uky.edu/busadmin_etds/6.

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This dissertation examines cross-category effects in consumer purchases from the big data and analytics perspectives. It uses data from Nielsen Consumer Panel and Scanner databases for its investigations. With big data analytics it becomes possible to examine the cross effects of many product categories on each other. The number of categories whose cross effects are studied is called category scale or just scale in this dissertation. The larger the category scale the higher the number of categories whose cross effects are studied. This dissertation extends research on models of cross effects b

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23

Isholt, Carl, and Mohammad Mosleh. "AI implementation in inbound logistics operations at large-scale manufacturing companies in Sweden : - Implementation levels and challenges." Thesis, Linnéuniversitetet, Institutionen för ekonomistyrning och logistik (ELO), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-104607.

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Purpose of the thesis This study aims to propose an AI maturity model in order to be able to evaluate to whatextent large-scale manufacturing companies utilize AI in their inbound logistical operationsand also to reveal the challenges related to the implementation of AI. Methodology This study employs a qualitative research method with the use of a survey conducted on 10companies within 5 industries. Additionally, semi-structured interviews were employed attwo out of these 10 companies. Theoretical framework The theoretical framework provides a deeper theoretical understanding of inbound logi

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Benea, Marius-Tudor. "Collective Intelligence in AmI Environments." Electronic Thesis or Diss., Sorbonne université, 2018. http://www.theses.fr/2018SORUS142.

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La thèse intersecte le domaine de l'Intelligence Ambiante (AmI) avec le domaine de l'Intelligence Collective (IC) dans un point qui facilite le développement des environnements AmI à grande échelle, caractérisés par un haut degré de l'intelligence. Le modèle CAmIE est introduit, pour le développement ascendant des environnements AmI complexes, à grande échelle, avec un niveau élevé de l'IC émergente. Il offre des outils pour le développement facile des applications AmI et il facilite les interactions entre leurs back-ends. De plus, CAmIE encourage la diversité, un des aspects clés pour l'émerg

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Tchappi, haman Igor. "Dynamic Multilevel and Holonic Model for the Simulation of a Large-Scale Complex System with Spatial Environment : Application to Road Traffic Simulation." Thesis, Bourgogne Franche-Comté, 2020. http://www.theses.fr/2020UBFCA004.

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De nos jours, avec l’émergence d’objets et de voitures connectés, les systèmes de trafic routier deviennent de plus en plus complexes et présentent des comportements hiérarchiques à plusieurs niveaux de détail. L'approche de modélisation multiniveaux est une approche appropriée pour représenter le trafic sous plusieurs perspectives. Les modèles multiniveaux constituent également une approche appropriée pour modéliser des systèmes complexes à grande échelle comme le trafic routier. Cependant, la plupart des modèles multiniveaux de trafic proposés dans la littérature sont statiques car ils utili

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Paulin, Mattis. "De l'apprentissage de représentations visuelles robustes aux invariances pour la classification et la recherche d'images." Thesis, Université Grenoble Alpes (ComUE), 2017. http://www.theses.fr/2017GREAM007/document.

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Ce mémoire de thèse porte sur l’élaboration de systèmes de reconnaissance d’image qui sont robustes à la variabilité géométrique. La compréhension d’une image est un problème difficile, de par le fait qu’elles sont des projections en deux dimensions d’objets 3D. Par ailleurs, des représentations qui doivent appartenir à la même catégorie, par exemple des objets de la même classe en classification, peuvent être visuellement très différentes. Notre but est de rendre ces systèmes robustes à la juste quantité de déformations, celle-ci étant automatiquement déterminée à partir des données. Nos deux

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Iqbal, Sumaiya. "Machine Learning based Protein Sequence to (un)Structure Mapping and Interaction Prediction." ScholarWorks@UNO, 2017. http://scholarworks.uno.edu/td/2379.

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Proteins are the fundamental macromolecules within a cell that carry out most of the biological functions. The computational study of protein structure and its functions, using machine learning and data analytics, is elemental in advancing the life-science research due to the fast-growing biological data and the extensive complexities involved in their analyses towards discovering meaningful insights. Mapping of protein’s primary sequence is not only limited to its structure, we extend that to its disordered component known as Intrinsically Disordered Proteins or Regions in proteins (IDPs/IDRs

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Knychala, Tucci Thadeu. "Distributed algorithms for programmable matter : target shape description and self-assembly planning." Thesis, Bourgogne Franche-Comté, 2018. http://www.theses.fr/2018UBFCD028.

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La matière programmable peut être vue comme un énorme robot modulaire dans lequel chaque module peut communiquer avec ses voisins connectés et travailler tous ensemble pour atteindre un objectif commun, modifiant la forme du robot et l'adaptant à nouvelles fonctionnalités.Afin de parvenir à une coordination entre un groupe de plusieurs milliers de robots, des règles locales et des algorithmes distribués profiteraient à ce type d'environnement. De la même manière, utiliser de petits modules signifient qu'il existe également des ressources restreintes et que les algorithmes doivent être conçus p

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Kao, Shih-Yu, and 高世宇. "Implementing an Improved Particle Swarm Algorithm for Large-scale Optimization Problem." Thesis, 2013. http://ndltd.ncl.edu.tw/handle/14333964011965946334.

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碩士<br>國立暨南國際大學<br>資訊管理學系<br>101<br>Particle swarm algorithm (PSO) is a popular method used for continuous optimization problems. It simulates the social behavior of bird flocking for food. This algorithm was first published on 1995, and it is commonly used to solve the real world problems with all the different evolutions. Since this algorithm mimics the behavior of bird hunting food, it is very likely the whole flock traps in local minima if the leading bird fails to reach the global optima. How to increase the diversity to avoid the premature condition so it can obtain the global optimizatio

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Jang, Myeong-Wuk. "Efficient communication and coordination for large-scale multi-agent systems /." 2006. http://gateway.proquest.com/openurl?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&res_dat=xri:pqdiss&rft_dat=xri:pqdiss:3223620.

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Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2006.<br>Source: Dissertation Abstracts International, Volume: 67-07, Section: B, page: 3901. Adviser: Gul Agha. Includes bibliographical references (leaves 126-137) Available on microfilm from Pro Quest Information and Learning.

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Crites, Robert Harry. "Large-scale dynamic optimization using teams of reinforcement learning agents." 1996. https://scholarworks.umass.edu/dissertations/AAI9709586.

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Recent algorithmic and theoretical advances in reinforcement learning (RL) are attracting widespread interest. RL algorithms have appeared that approximate dynamic programming (DP) on an incremental basis. Unlike traditional DP algorithms, these algorithms do not require knowledge of the state transition probabilities or reward structure of a system. This allows them to be trained using real or simulated experiences, focusing their computations on the areas of state space that are actually visited during control, making them computationally tractable on very large problems. RL algorithms can b

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32

Tos̆ić, Predrag. "Modeling and analysis of the collective dynamics of large-scale multi-agent systems /." 2006. http://gateway.proquest.com/openurl?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&res_dat=xri:pqdiss&rft_dat=xri:pqdiss:3250334.

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Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2006.<br>Source: Dissertation Abstracts International, Volume: 68-02, Section: B, page: 1090. Adviser: Gul Agha. Includes bibliographical references (leaves 190-205) Available on microfilm from Pro Quest Information and Learning.

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Gray, Pamela N., University of Western Sydney, College of Business, and School of Law. "Legal knowledge engineering methodology for large-scale expert systems." 2007. http://handle.uws.edu.au:8081/1959.7/20012.

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Legal knowledge engineering methodology for epistemologically sound, large scale legal expert systems is developed in this dissertation. A specific meta-epistemological method is posed for the transformation of legal domain epistemology to large scale legal expert systems; the method has five stages: 1. domain epistemology; 2. computational domain epistemology; 3. shell epistemology; 4. programming epistemology; and 5. application epistemology and ontology. The nature of legal epistemology is defined in terms of a deep model that divides the information of the ontology of legal possibilities i

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Chen, Bo-Yu, and 陳柏育. "Application of Binary Particle Swarm Optimization for Inter-Feeder Load Transfer in Large-Scale Contingencies of Distribution Systems." Thesis, 2008. http://ndltd.ncl.edu.tw/handle/25153523032328755616.

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碩士<br>國立高雄應用科技大學<br>電機工程系博碩士班<br>96<br>In the cases of large-scale contingencies of distribution systems operations of inter-feeder load transfer plays an important role to reduce the influence of the outages to the lowest. This thesis exploits the algorithm of binary particle swarm (BPS) optimization to find the optimal inter-feeder switching operations for distribution systems in case of large-scale contingencies. The solutions are adapted to the crew scheduling for maintenance with fuzzy reasoning and verified by load flow program. To improve the solving efficiency of the proposed algor

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Upadhyay, Nitesh. "Large Scale Graph Processing in a Distributed Environment." Thesis, 2017. http://etd.iisc.ernet.in/2005/3625.

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Graph algorithms are ubiquitously used across domains. They exhibit parallelism, which can be exploited on parallel architectures, such as multi-core processors and accelerators. However, real world graphs are massive in size and cannot fit into the memory of a single machine. Such large graphs are partitioned and processed in a distributed cluster environment which consists of multiple GPUs and CPUs. Existing frameworks that facilitate large scale graph processing in the distributed cluster have their own style of programming and require extensive involvement by the user in communication and

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(10723926), Adefolarin Alaba Bolaji. "Community Detection of Anomaly in Large-Scale Network Dissertation - Adefolarin Bolaji .pdf." Thesis, 2021.

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<p>The detection of anomalies in real-world networks is applicable in different domains; the application includes, but is not limited to, credit card fraud detection, malware identification and classification, cancer detection from diagnostic reports, abnormal traffic detection, identification of fake media posts, and the like. Many ongoing and current researches are providing tools for analyzing labeled and unlabeled data; however, the challenges of finding anomalies and patterns in large-scale datasets still exist because of rapid changes in the threat landscape. </p><p>In this study, I impl

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"Computational Methods for Knowledge Integration in the Analysis of Large-scale Biological Networks." Doctoral diss., 2012. http://hdl.handle.net/2286/R.I.15204.

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abstract: As we migrate into an era of personalized medicine, understanding how bio-molecules interact with one another to form cellular systems is one of the key focus areas of systems biology. Several challenges such as the dynamic nature of cellular systems, uncertainty due to environmental influences, and the heterogeneity between individual patients render this a difficult task. In the last decade, several algorithms have been proposed to elucidate cellular systems from data, resulting in numerous data-driven hypotheses. However, due to the large number of variables involved in the proces

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Koopman, Sharene Grace. "The experiences of senior leaders driving large scale change in a construction company." Diss., 2013. http://hdl.handle.net/10500/8590.

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A phenomenological study was conducted to explore and understand the personal experiences and meaning ascribed by senior leaders within a large multidisciplinary construction company. An effort was made to understand how they personally experienced leading others, what they learnt about themselves, what challenged them most, and what support, if any they had during their leadership of large-scale organisational change. The research found that there is a significant personal cost to the individual. This cost comes in terms of career, work-life balance and even reputation. It provides an o

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"Large-Scale Matrix Completion Using Orthogonal Rank-One Matrix Pursuit, Divide-Factor-Combine, and Apache Spark." Master's thesis, 2014. http://hdl.handle.net/2286/R.I.24857.

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abstract: As the size and scope of valuable datasets has exploded across many industries and fields of research in recent years, an increasingly diverse audience has sought out effective tools for their large-scale data analytics needs. Over this period, machine learning researchers have also been very prolific in designing improved algorithms which are capable of finding the hidden structure within these datasets. As consumers of popular Big Data frameworks have sought to apply and benefit from these improved learning algorithms, the problems encountered with the frameworks have motivated a n

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Vijayanarasimhan, Sudheendra. "Active visual category learning." Thesis, 2011. http://hdl.handle.net/2152/ETD-UT-2011-05-3014.

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Visual recognition research develops algorithms and representations to autonomously recognize visual entities such as objects, actions, and attributes. The traditional protocol involves manually collecting training image examples, annotating them in specific ways, and then learning models to explain the annotated examples. However, this is a rather limited way to transfer human knowledge to visual recognition systems, particularly considering the immense number of visual concepts that are to be learned. I propose new forms of active learning that facilitate large-scale transfer of human knowl

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