Bibliografie tematiche / Multidimensionnal network

Letteratura scientifica selezionata sul tema "Multidimensionnal network"

Autore: Grafiati

Pubblicato: 1 giugno 2024

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Articoli di riviste sul tema "Multidimensionnal network":

Xuan, Qi, Xiaodi Ma, Chenbo Fu, Hui Dong, Guijun Zhang e Li Yu. "Heterogeneous multidimensional scaling for complex networks". International Journal of Modern Physics C 26, n. 02 (febbraio 2015): 1550023. http://dx.doi.org/10.1142/s0129183115500230.

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Many real-world networks are essentially heterogeneous, where the nodes have different abilities to gain connections. Such networks are difficult to be embedded into low-dimensional Euclidean space if we ignore the heterogeneity and treat all the nodes equally. In this paper, based on a newly defined heterogeneous distance and a generalized network distance under the constraints of network and triangle inequalities, respectively, we propose a new heterogeneous multidimensional scaling method (HMDS) to embed different networks into proper Euclidean spaces. We find that HMDS behaves much better than the traditional multidimensional scaling method (MDS) in embedding different artificial and real-world networks into Euclidean spaces. Besides, we also propose a method to estimate the appropriate dimensions of Euclidean spaces for different networks, and find that the estimated dimensions are quite close to the real dimensions for those geometrical networks under study. These methods thus can help to better understand the evolution of real-world networks, and have practical importance in network visualization, community detection, link prediction and localization of wireless sensors.

Abrahão, Felipe S., Klaus Wehmuth, Hector Zenil e Artur Ziviani. "Algorithmic Information Distortions in Node-Aligned and Node-Unaligned Multidimensional Networks". Entropy 23, n. 7 (29 giugno 2021): 835. http://dx.doi.org/10.3390/e23070835.

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In this article, we investigate limitations of importing methods based on algorithmic information theory from monoplex networks into multidimensional networks (such as multilayer networks) that have a large number of extra dimensions (i.e., aspects). In the worst-case scenario, it has been previously shown that node-aligned multidimensional networks with non-uniform multidimensional spaces can display exponentially larger algorithmic information (or lossless compressibility) distortions with respect to their isomorphic monoplex networks, so that these distortions grow at least linearly with the number of extra dimensions. In the present article, we demonstrate that node-unaligned multidimensional networks, either with uniform or non-uniform multidimensional spaces, can also display exponentially larger algorithmic information distortions with respect to their isomorphic monoplex networks. However, unlike the node-aligned non-uniform case studied in previous work, these distortions in the node-unaligned case grow at least exponentially with the number of extra dimensions. On the other hand, for node-aligned multidimensional networks with uniform multidimensional spaces, we demonstrate that any distortion can only grow up to a logarithmic order of the number of extra dimensions. Thus, these results establish that isomorphisms between finite multidimensional networks and finite monoplex networks do not preserve algorithmic information in general and highlight that the algorithmic information of the multidimensional space itself needs to be taken into account in multidimensional network complexity analysis.

Garcez, Thalles V., e Przemyslaw Szufel. "Multidimensional Risk Management for Underground Electricity Networks". Studies in Logic, Grammar and Rhetoric 37, n. 1 (8 agosto 2014): 51–69. http://dx.doi.org/10.2478/slgr-2014-0017.

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Abstract In the paper we consider an electricity provider company that makes decision on allocating resources on electric network maintenance. The investments decrease malfunction rate of network nodes. An accidental event (explosion, fire, etc.) or a malfunctioning on underground system can have various consequences and in different perspectives, such as deaths and injuries of pedestrians, fires in nearby locations, disturbances in the flow of vehicular traffic, loss to the company image, operating and financial losses, etc. For this reason it is necessary to apply an approach of the risk management that considers the multidimensional view of the consequences. Furthermore an analysis of decision making should consider network dependencies between the nodes of the electricity distribution system. In the paper we propose the use of the simulation to assess the network effects (such as the increase of the probability of other accidental event and the occurrence of blackouts of the dependent nodes) in the multidimensional risk assessment in electricity grid. The analyzed effects include node overloading due to malfunction of adjacent nodes and blackouts that take place where there is temporarily no path in the grid between the power plant and a node. The simulation results show that network effects have crucial role for decisions in the network maintenance – outcomes of decisions to repair a particular node in the network can have significant influence on performance of other nodes. However, those dependencies are non-linear. The effects of network connectivity (number of connections between nodes) on its multidimensional performance assessment depend heavily on the overloading effect level. The simulation results do not depend on network type structure (random or small world) – however simulation outcomes for random networks have shown higher variance compared to small-world networks.

Obrubov, M., e S. Kirillova. "USING LSTM NETWORK FOR SOLVING THE MULTIDIMENTIONAL TIME SERIES FORECASTING PROBLEM". National Association of Scientists 2, n. 68 (1 luglio 2021): 43–48. http://dx.doi.org/10.31618/nas.2413-5291.2021.2.68.450.

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The article discusses using of the recurrent neural networks technology to the multidimensional time series prediction problem. There is an experimental determination of the neural network architecture and its main hyperparameters carried out to achieve the minimum error. The revealed network structure going to be used further to detect anomalies in multidimensional time series.

Veiga, André, E. Glen Weyl e Alexander White. "Multidimensional Platform Design". American Economic Review 107, n. 5 (1 maggio 2017): 191–95. http://dx.doi.org/10.1257/aer.p20171044.

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Successful platforms attract not just many users, but also those of the right kind. 'The right kind of user' is one who can either be directly monetized or who differentially attracts other valuable users. Bonacich centrality on the network of user sorting with direct value of monetization captures this feedback loop and thus characterizes the value of user characteristics. We use this value to determine optimal steady-state platform design and reliable means for platforms to reach such a steady state. We apply these results respectively to explain the dynamic growth strategy of social networks and urban development policies of cities.

MANGAL, MANISH, e MANU PRATAP SINGH. "ANALYSIS OF MULTIDIMENSIONAL XOR CLASSIFICATION PROBLEM WITH EVOLUTIONARY FEEDFORWARD NEURAL NETWORKS". International Journal on Artificial Intelligence Tools 16, n. 01 (febbraio 2007): 111–20. http://dx.doi.org/10.1142/s0218213007003229.

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This paper describes the application of two evolutionary algorithms to the feedforward neural networks used in classification problems. Besides of a simple backpropagation feedforward algorithm, the paper considers the genetic algorithm and random search algorithm. The objective is to analyze the performance of GAs over the simple backpropagation feedforward in terms of accuracy or speed in this problem. The experiments considered a feedforward neural network trained with genetic algorithm/random search algorithm and 39 types of network structures and artificial data sets. In most cases, the evolutionary feedforward neural networks seemed to have better of equal accuracy than the original backpropagation feedforward neural network. We found few differences in the accuracy of the networks solved by applying the EAs, but found ample differences in the execution time. The results suggest that the evolutionary feedforward neural network with random search algorithm might be the best algorithm on the data sets we tested.

BLOCK, PER, e THOMAS GRUND. "Multidimensional homophily in friendship networks". Network Science 2, n. 2 (agosto 2014): 189–212. http://dx.doi.org/10.1017/nws.2014.17.

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AbstractHomophily—the tendency for individuals to associate with similar others—is one of the most persistent findings in social network analysis. Its importance is established along the lines of a multitude of sociologically relevant dimensions, e.g. sex, ethnicity and social class. Existing research, however, mostly focuses on one dimension at a time. But people are inherently multidimensional, have many attributes and are members of multiple groups. In this article, we explore such multidimensionality further in the context of network dynamics. Are friendship ties increasingly likely to emerge and persist when individuals have an increasing number of attributes in common? We analyze eleven friendship networks of adolescents, draw on stochastic actor-oriented network models and focus on the interaction of established homophily effects. Our results indicate that main effects for homophily on various dimensions are positive. At the same time, the interaction of these homophily effects is negative. There seems to be a diminishing effect for having more than one attribute in common. We conclude that studies of homophily and friendship formation need to address such multidimensionality further.

Li, Siming, Zhangxi Lin, Jiaxian Qiu, Roozmehr Safi e Zhongyi Xiao. "How friendship networks work in online P2P lending markets". Nankai Business Review International 6, n. 1 (2 marzo 2015): 42–67. http://dx.doi.org/10.1108/nbri-01-2014-0010.

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Purpose – The purpose of this paper is to study the effects of multidimensional friendship networks on economic outcomes in the domain of online people-to-people (P2P) lending markets. Design/methodology/approach – The empirical analysis is based on the data set of transactions and friendship networks from PPDai.com market, the most prominent P2P lending market in China. A friendship hierarchy is proposed in this paper to conceptualize friendship network types. Furthermore, methodologies of t-test, logistic regression and ordinary least squares regression are implemented to measure the impact of multidimensional friendship network variables on the probability of successful funding, as well as the interest rates on funded loans. Findings – The study demonstrates significant effects of structural, relational and cognitive friendship networks using PPDai.com data. The results indicate that structural friendship network measured in terms of the number of friendship ties is a significant factor of funding performance. Additionally, borrowers, who are involved in higher-quality friendship networks, are more likely to be funded and pay lower interest rates on funded loans. Also, the deeper the level of the relationship is in the friendship hierarchy, the more significant will be the effect of friendship on the final economic results. Furthermore, quality is more important than quantity in determining funding performance. Originality/value – This paper is the first to study the effects of multidimensional friendship networks on economic outcome variables in the domain of online P2P lending, thus broadening the theory of multidimensional social capital, which can deepen our understanding about how social networks work and have significant implications practically and theoretically.

Pavan, Elena. "Embedding Digital Communications Within Collective Action Networks: A Multidimensional Network Approach". Mobilization: An International Quarterly 19, n. 4 (1 dicembre 2014): 441–55. http://dx.doi.org/10.17813/maiq.19.4.w24rl524u074126k.

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In this article, we conceive of digital media as embedded within social networks, and use this perspective to examine the role of online communications in collective action. We claim that the adoption of this perspective requires two shifts: first, rethinking the ontological separation between media and social networks of action that has, so far, characterized research in this domain; second, the adoption of flexible tools that enable us to account, simultaneously, for the multiplicity of relations underpinning collective efforts and the hybrid interplay between direct and technology-mediated interactions. After discussing the necessity and the implications of considering communication technologies as endogenous to social networks of collective action, we introduce multidimensional networks (MDNs) as a suitable perspective to advance the application of a relational approach to the study of collective action, thus meeting the challenges posed by the diffusion of interactive and networking digital media.

Dařena, František, Alexander Troussov e Jan Žižka. "Simulating activation propagation in social networks using the graph theory". Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis 58, n. 3 (2010): 21–28. http://dx.doi.org/10.11118/actaun201058030021.

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The social-network formation and analysis is nowadays one of objects that are in a focus of intensive research. The objective of the paper is to suggest the perspective of representing social networks as graphs, with the application of the graph theory to problems connected with studying the network-like structures and to study spreading activation algorithm for reasons of analyzing these structures. The paper presents the process of modeling multidimensional networks by means of directed graphs with several characteristics. The paper also demonstrates using Spreading Activation algorithm as a good method for analyzing multidimensional network with the main focus on recommender systems. The experiments showed that the choice of parameters of the algorithm is crucial, that some kind of constraint should be included and that the algorithm is able to provide a stable environment for simulations with networks.

Più fonti

Tesi sul tema "Multidimensionnal network":

Ben, Chaabene Nour El Houda. "Détection d'utilisateurs violents et de menaces dans les réseaux sociaux". Electronic Thesis or Diss., Institut polytechnique de Paris, 2022. http://www.theses.fr/2022IPPAS001.

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Les réseaux sociaux en ligne font partie intégrante de l'activité sociale quotidienne des gens. Ils fournissent des plateformes permettant de mettre en relation des personnes du monde entier et de partager leurs intérêts. Des statistiques récentes indiquent que 56% de la population mondiale utilisent ces médias sociaux. Cependant, ces services de réseau ont également eu de nombreux impacts négatifs et l'existence de phénomènes d'agressivité et d'intimidation dans ces espaces est inévitable et doit donc être abordée. L'exploration de la structure complexe des réseaux sociaux pour détecter les comportements violents et les menaces est un défi pour l'exploration de données, l'apprentissage automatique et l'intelligence artificielle. Dans ce travail de thèse, nous visons à proposer de nouvelles approches de détection des comportements violents dans les réseaux sociaux. Nos approches tentent de résoudre cette problématique pour plusieurs raisons pratiques. Premièrement, des personnes différentes ont des façons différentes d'exprimer le même comportement violent. Il est souhaitable de concevoir une approche qui fonctionne pour tout le monde en raison de la variété des comportements et des diverses manières dont ils sont exprimés. Deuxièmement, les approches doivent avoir un moyen de détecter les comportements anormaux potentiels non vus et de les ajouter automatiquement à l'ensemble d'apprentissage. Troisièmement, la multimodalité et la multidimensionnalité des données disponibles sur les sites de réseaux sociaux doivent être prises en compte pour le développement de solutions d'exploration de données qui seront capables d'extraire des informations pertinentes utiles à la détection de comportements violents. Enfin, les approches doivent considérer la nature variable dans le temps des réseaux pour traiter les nouveaux utilisateurs et liens et mettre automatiquement à jour les modèles construits. A la lumière de cela et pour atteindre les objectifs susmentionnés, les principales contributions de cette thèse sont les suivantes: - La première contribution propose un modèle de détection des comportements violents sur Twitter. Ce modèle prend en charge la nature dynamique du réseau et est capable d'extraire et d'analyser de données hétérogènes. - La deuxième contribution introduit une approche de détection des comportements atypiques sur un réseau multidimensionnel. Cette approche se base sur l'exploration et l'analyse des relations entre les individus présents sur cette structure sociale multidimensionnelle. - La troisième contribution présente un framework d'identification des personnes anormales. Ce cadre intelligent s'appuie sur l'exploitation d'un modèle multidimensionnel qui prend en entrée des données multimodales provenant de plusieurs sources, capable d'enrichir automatiquement l'ensemble d'apprentissage par les comportements violents détectés et considère la dynamicité des données afin de détecter les nouveaux comportements violents qui apparaissent sur le réseau. Cette thèse décrit des réalisations combinant les techniques d'exploration de données avec les nouvelles techniques d’apprentissage automatique. Pour prouver la performance de nos résultats d'expérimentation, nous nous sommes basés sur des données réelles extraites de trois réseaux sociaux populaires
Online social networks are an integral part of people's daily social activity. They provide platforms to connect people from all over the world and share their interests. Recent statistics indicate that 56% of the world's population use these social media. However, these network services have also had many negative impacts and the existence of phenomena of aggression and intimidation in these spaces is inevitable and must therefore be addressed. Exploring the complex structure of social networks to detect violent behavior and threats is a challenge for data mining, machine learning, and artificial intelligence. In this thesis work, we aim to propose new approaches for the detection of violent behavior in social networks. Our approaches attempt to resolve this problem for several practical reasons. First, different people have different ways of expressing the same violent behavior. It is desirable to design an approach that works for everyone because of the variety of behaviors and the various ways in which they are expressed. Second, the approaches must have a way to detect potential unseen abnormal behaviors and automatically add them to the training set. Third, the multimodality and multidimensionality of the data available on social networking sites must be taken into account for the development of data mining solutions that will be able to extract relevant information useful for the detection of violent behavior. Finally, approaches must consider the time-varying nature of networks to process new users and links and automatically update built models. In the light of this and to achieve the aforementioned objectives, the main contributions of this thesis are as follows: - The first contribution proposes a model for detecting violent behavior on Twitter. This model supports the dynamic nature of the network and is capable of extracting and analyzing heterogeneous data. - The second contribution introduces an approach for detecting atypical behaviors on a multidimensional network. This approach is based on the exploration and analysis of the relationships between the individuals present on this multidimensional social structure. - The third contribution presents a framework for identifying abnormal people. This intelligent framework is based on the exploitation of a multidimensional model which takes as input multimodal data coming from several sources, capable of automatically enriching the learning set by the violent behaviors detected and considers the dynamicity of the data in order to detect new violent behaviors that appear on the network. This thesis describes achievements combining data mining techniques with new machine learning techniques. To prove the performance of our experimental results, we sums based on real data taken from three popular social networks

Possamai, Lino <1978&gt. "Multidimensional analysis of complex networks". Doctoral thesis, Alma Mater Studiorum - Università di Bologna, 2013. http://amsdottorato.unibo.it/5389/1/possamai_lino_tesi.pdf.

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Complex Networks analysis turn out to be a very promising field of research, testified by many research projects and works that span different fields. Those analysis have been usually focused on characterize a single aspect of the system and a study that considers many informative axes along with a network evolve is lacking. We propose a new multidimensional analysis that is able to inspect networks in the two most important dimensions, space and time. To achieve this goal, we studied them singularly and investigated how the variation of the constituting parameters drives changes to the network as a whole. By focusing on space dimension, we characterized spatial alteration in terms of abstraction levels. We proposed a novel algorithm that, by applying a fuzziness function, can reconstruct networks under different level of details. We verified that statistical indicators depend strongly on the granularity with which a system is described and on the class of networks. We keep fixed the space axes and we isolated the dynamics behind networks evolution process. We detected new instincts that trigger social networks utilization and spread the adoption of novel communities. We formalized this enhanced social network evolution by adopting special nodes (called sirens) that, thanks to their ability to attract new links, were able to construct efficient connection patterns. We simulated the dynamics of the system by considering three well-known growth models. Applying this framework to real and synthetic networks, we showed that the sirens, even when used for a limited time span, effectively shrink the time needed to get a network in mature state. In order to provide a concrete context of our findings, we formalized the cost of setting up such enhancement and provided the best combinations of system's parameters, such as number of sirens, time span of utilization and attractiveness.

Possamai, Lino <1978&gt. "Multidimensional analysis of complex networks". Doctoral thesis, Alma Mater Studiorum - Università di Bologna, 2013. http://amsdottorato.unibo.it/5389/.

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Ringienė, Laura. "Hybrid neural network for multidimensional data visualization". Doctoral thesis, Lithuanian Academic Libraries Network (LABT), 2014. http://vddb.library.lt/obj/LT-eLABa-0001:E.02~2014~D_20140912_140117-42267.

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The area of research is data mining based on multidimensional data visual analysis. This allows researcher to participate in the process of data analysis directly, to understand the complex data better and to make the best decisions. The objective of the dissertation is to create a method for making a multidimensional data projection on the plane such that the researcher could see and assess the intergroup similarities and differences of multidimensional points. In order to achieve the target, a new hybrid neural network is proposed and investigated. This neural network integrates the ideas both of the radial basis function neural network and that of a multilayer perceptron, which has the properties of a ''bottleneck'' neural network. The new network is used for the visual analysis of multidimensional data in such a way that the output values of the neurons of the last hidden layer are the two-dimensional or three-dimensional projections of the multidimensional data, when the multidimensional data is given to the network. A peculiarity of the network is that the visualization results on the plane reflect the general structure of the data (clusters, proximity between clusters, intergroup similarities of points) rather than the location of multidimensional points.
Šio darbo tyrimų sritis yra duomenų tyryba remiantis daugiamačių duomenų vizualia analize. Tai leidžia tyrėjui betarpiškai dalyvauti duomenų analizės procese, geriau pažinti sudėtingus duomenis ir priimti geriausius sprendimus. Disertacijos tikslas yra sukurti metodą tokios duomenų projekcijos radimui plokštumoje, kad tyrėjas galėtų pamatyti ir įvertinti daugiamačių taškų tarpgrupinius panašumus/skirtingumus. Šiam tikslui pasiekti yra pasiūlytas radialinių bazinių funkcijų ir daugiasluoksnio perceptrono, turinčio ,,butelio kaklelio“ neuroninio tinklo savybes, junginys. Naujas tinklas naudojamas vizualiai daugiamačių duomenų analizei, kai atidėjimui plokštumoje arba trimatėje erdvėje taškai gaunami paskutinio paslėpto neuronų sluoksnio išėjimuose, kai į tinklo įėjimą paduodami daugiamačiai duomenys. Šio tinklo ypatybė yra ta, kad gautas vaizdas plokštumoje labiau atspindi bendrą duomenų struktūrą (klasteriai, klasterių tarpusavio artumas, taškų tarpklasterinis panašumas) nei daugiamačių taškų tarpusavio išsidėstymą.

Zignani, M. "FROM SMALL-WORLDS TO BIG DATA:TEMPORAL AND MULTIDIMENSIONAL ASPECTS OF HUMAN NETWORKS". Doctoral thesis, Università degli Studi di Milano, 2014. http://hdl.handle.net/2434/233322.

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In this thesis we address the close interplay among mobility, offline relationships and online interactions and the related human networks at different dimensional scales and temporal granularities. By generally adopting a data-driven approach, we move from small datasets about physical interactions mediated by human-carried devices, describing small social realities, to large-scale graphs that evolve over time, as well as from human mobility trajectories to face-to-face contacts occurring in different geographical contexts. We explore in depth the relation between human mobility and the social structure induced by the overlapping of different people's trajectories on GPS traces collected in urban and metropolitan areas. We define the notions of geo-location and geo-community which are operational in describing in a unique framework both spatial and social aspects of human behavior. Through the concept of geo-community we model the human mobility adopting a bipartite graph. Thanks to this graph representation we can generate a social structure that is plausible w.r.t. the real interactions. In general the modeling approach have the merit for reporting the mobility in a graph-theoretic framework making the study of the interplay mobility/sociality more affordable and intuitive. Our modeling approach also results in a mobility model, Geo-CoMM, which lies on and exploits the idea of geo-community. The model represents a particular instance of a general framework we provide. A framework where the social structure behind the preferred-location based mobility models emerges. We validate Geo-CoMM on spatial, temporal, pairwise connectivity and social features showing that it reproduces the main statistical properties observed in real traces. As concerns the offline/online interplay we provide a complete overview of the close connection between online and offline sociality. To reach our goal we gather data about offline contacts and social interactions on Facebook of a group of students and we propose a multidimensional network analysis which allows us to deeply understand how the characteristics of users in the distinct networks impact each other. Results show how offline and Facebook friends are different. This way we confirm and worsen the general intuition that online social networks have shifted away from their original goal to mirror the offline sociality of individuals. As for the role and the social importance, it becomes apparent that social features such as user popularity or community structure do not transfer along social dimensions, as confirmed by our correlation analysis of the network layers and by the comparison among the communities. In the last chapters we analyze the evolution of the online social network from a physical time perspective, i.e. considering the graph evolution as a graph time-series and not as a function of the network basic properties (number of nodes or links). As for the physical time in a user-centric viewpoint, we investigate the bursty nature of the link creation process in online social network. We prove not only that it is a highly inhomogeneous process, but also identify patterns of burstiness common to all nodes. Then we focus on the dynamic formation of two fundamental network building components: dyads and triads. We propose two new metrics to aid the temporal analysis on physical time: link creation delay and triangle closure delay. These two metrics enable us to study the dynamic creation of dyads and triads, and to highlight network behavior that would otherwise remain hidden. In our analysis, we find that link delays are generally very low in absolute time and are largely independent of the dates people join the network. To highlight the social nature of this metric, we introduce the term \textit{peerness} to quantify how well linked users overlap in lifetimes. As for triadic closure delay we first introduce an algorithm to extract of temporal triangle which enables us to monitor the triangle formation process, and to detect sudden changes in the triangle formation behavior, possibly related to external events. In particular, we show that the introduction of new service functionalities had a disruptive impact on the triangle creation process in the network.

Li, Yan. "Multidimensional protein separations in a plastic microfluidic network". College Park, Md. : University of Maryland, 2003. http://hdl.handle.net/1903/194.

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Thesis (Ph. D.)--University of Maryland, College Park, 2003.
Thesis research directed by: Chemistry. Title from t.p. of PDF. Includes bibliographical references. Published by UMI Dissertation Services, Ann Arbor, Mich. Also available in paper.

Suh, Young-Joo. "Realizations of efficient collective communication in multidimensional processor arrays". Diss., Georgia Institute of Technology, 1996. http://hdl.handle.net/1853/13343.

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Tulabandula, Sridhar. "Localization of wireless sensor networks using multidimensional scaling". Diss., Columbia, Mo. : University of Missouri-Columbia, 2007. http://hdl.handle.net/10355/4986.

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Thesis (M.S.)--University of Missouri-Columbia, 2007.
The entire dissertation/thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file (which also appears in the research.pdf); a non-technical general description, or public abstract, appears in the public.pdf file. Title from title screen of research.pdf file (viewed on April 17, 2008) Includes bibliographical references.

Sumanasena, M. G. Buddika. "A multidimensional systems approach to grid sensor networks". Thesis, University of Notre Dame, 2014. http://pqdtopen.proquest.com/#viewpdf?dispub=3578994.

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A method for distributed information processing in rectangular grid based wireless sensor networks is presented, employing the Givone-Roesser and the Fornasini-Marchesini state space models for m-D systems. It can be used for distributed implementation of any general linear system on a grid sensor network. The method is highly scalable and requires only communication between immediate neighbors.

Usage of finite precision schemes for the representation of numbers and computations introduce nonlinearities to the otherwise linear m-D system models. Nonlinearities caused by fixed point and floating point number representation schemes used for in node computations and inter-node communication are modeled. Stability of the system is analyzed with special consideration given to the influence of inter-node communication on system dynamics. Necessary and sufficient conditions for the global asymptotic stability under both fixed point and floating point arithmetic is derived. It has been shown that the global asymptotic stability of the sensor networks is equivalent to that of a 1-D system for both the cases of fixed point and floating number representation.

Issues posed by communication time delay, in real-time implementation of the proposed method, are discussed. It is shown that, in order to implement a real-time sensor network, system matrices of the state space models have to satisfy certain conditions. A necessary and sufficient condition for a transfer function to be realizable in the constrained state space models is established. Realization algorithms to derive state space models of the desired form given an admissible transfer function are also presented. Node and link failure introduce complications not encountered in centralized implementation of m-D systems. Givone-Roesser and the Fornasini-Marchesini state space models are extended to include node and link failure. Necessary and sufficient conditions for mean square stability are then derived with the help of these two state space models. Input output stability of the distributed systems under node and link failure is also discussed.

The utility of the proposed method is demonstrated by examples. In particular a distributed Kalman filter is proposed for grid sensor networks. Implementation of the proposed Kalman filter on grid sensor networks is discussed in some detail. A method for contaminant detection and its implementation using the proposed method is also presented.

Ahmed, Ahmed Abdol-Monem. "Experimental study of localization in sensor networks and design of adaptive localization". Diss., Columbia, Mo. : University of Missouri-Columbia, 2005. http://hdl.handle.net/10355/5829.

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Thesis (Ph.D.)--University of Missouri-Columbia, 2005.
The entire dissertation/thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file (which also appears in the research.pdf); a non-technical general description, or public abstract, appears in the public.pdf file. Title from title screen of research.pdf file viewed on (Novembr 15, 2006) Vita. Includes bibliographical references.

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Libri sul tema "Multidimensionnal network":

Murthy, G. Rama. Multidimensional neural networks: Unified theory. New Delhi: New Age International (P) Ltd., Publishers, 2008.

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Arena, Paolo, Luigi Fortuna, Giovanni Muscato e Maria Gabriella Xibilia, a cura di. Neural Networks in Multidimensional Domains. London: Springer-Verlag, 1998. http://dx.doi.org/10.1007/bfb0047683.

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Melikov, Agassi, e Leonid Ponomarenko. Multidimensional Queueing Models in Telecommunication Networks. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-08669-9.

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Vlachou, Akrivi. Peer-to-peer query processing over multidimensional data. New York: Springer, 2012.

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Henderson, Geraldine R. Brand constructs: The complementarity of consumer associative networks and multidimensional scaling. Cambridge, Mass: Marketing Science Institute, 1998.

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Camara, Antonio S. Environmental systems: A multidimensional approach. Oxford: Oxford University Press, 2002.

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Henderson, Geraldine R. Brand constructs: The complementarity of consumer associative networks and multidimensional scaling. Cambridge, Mass: Marketing Science Institute, 1998.

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Zuzevičiūtė, Vaiva. E-learning as a socio-cultural system: A multidimensional analysis. Hershey, PA: Information Science Reference, an imprint of IGI Global, 2014.

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Park, June S. Delay analysis for multidimensional queueing process in CSMA/CD local area networks. Monterey, Calif: Naval Postgraduate School, 1991.

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Varlamov, Oleg. Mivar databases and rules. ru: INFRA-M Academic Publishing LLC., 2021. http://dx.doi.org/10.12737/1508665.

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The multidimensional open epistemological active network MOGAN is the basis for the transition to a qualitatively new level of creating logical artificial intelligence. Mivar databases and rules became the foundation for the creation of MOGAN. The results of the analysis and generalization of data representation structures of various data models are presented: from relational to "Entity — Relationship" (ER-model). On the basis of this generalization, a new model of data and rules is created: the mivar information space "Thing-Property-Relation". The logic-computational processing of data in this new model of data and rules is shown, which has linear computational complexity relative to the number of rules. MOGAN is a development of Rule - Based Systems and allows you to quickly and easily design algorithms and work with logical reasoning in the "If..., Then..." format. An example of creating a mivar expert system for solving problems in the model area "Geometry"is given. Mivar databases and rules can be used to model cause-and-effect relationships in different subject areas and to create knowledge bases of new-generation applied artificial intelligence systems and real-time mivar expert systems with the transition to"Big Knowledge". The textbook in the field of training "Computer Science and Computer Engineering" is intended for students, bachelors, undergraduates, postgraduates studying artificial intelligence methods used in information processing and management systems, as well as for users and specialists who create mivar knowledge models, expert systems, automated control systems and decision support systems. Keywords: cybernetics, artificial intelligence, mivar, mivar networks, databases, data models, expert system, intelligent systems, multidimensional open epistemological active network, MOGAN, MIPRA, KESMI, Wi!Mi, Razumator, knowledge bases, knowledge graphs, knowledge networks, Big knowledge, products, logical inference, decision support systems, decision-making systems, autonomous robots, recommendation systems, universal knowledge tools, expert system designers, logical artificial intelligence.

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Capitoli di libri sul tema "Multidimensionnal network":

Graves, Alex. "Multidimensional Networks". In Studies in Computational Intelligence, 95–108. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-24797-2_8.

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Lu, Rongxing. "Privacy-Preserving Multidimensional Data Aggregation". In Wireless Networks, 41–60. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-32899-7_3.

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Dzemyda, Gintautas, Olga Kurasova e Julius Žilinskas. "Combining Multidimensional Scaling with Artificial Neural Networks". In Multidimensional Data Visualization, 113–77. New York, NY: Springer New York, 2012. http://dx.doi.org/10.1007/978-1-4419-0236-8_4.

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Chouchane, Amani, Oualid Boutemine e Mohamed Bouguessa. "On Detecting Multidimensional Communities". In Lecture Notes in Social Networks, 45–78. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-11286-8_3.

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Johnson, Jeffrey. "The Multidimensional Networks of Complex Systems". In Networks in Action, 49–79. Berlin, Heidelberg: Springer Berlin Heidelberg, 1995. http://dx.doi.org/10.1007/978-3-642-57843-4_3.

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Araújo, Tanya, e Sven Banisch. "Multidimensional Analysis of Linguistic Networks". In Understanding Complex Systems, 107–31. Berlin, Heidelberg: Springer Berlin Heidelberg, 2016. http://dx.doi.org/10.1007/978-3-662-47238-5_5.

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Kazienko, Przemysław, Katarzyna Musial, Elżbieta Kukla, Tomasz Kajdanowicz e Piotr Bródka. "Multidimensional Social Network: Model and Analysis". In Computational Collective Intelligence. Technologies and Applications, 378–87. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-23935-9_37.

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Gao, Zhenfeng, e Guodong Shan. "Billing System and 5G Network Slicing Service". In New Approaches for Multidimensional Signal Processing, 289–97. Singapore: Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-8558-3_24.

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Shi, Yansong, Shiyou Xing, Peijian Wang e Dai Jiawei. "Neural Network Algorithm Applied in Electrical Engineering Automation". In New Approaches for Multidimensional Signal Processing, 143–50. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-7842-5_13.

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Melikov, Agassi, e Leonid Ponomarenko. "Priority Schemes in Packet Switching Networks". In Multidimensional Queueing Models in Telecommunication Networks, 141–86. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-08669-9_5.

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Atti di convegni sul tema "Multidimensionnal network":

Sharony, Jacob, e Yao Li. "Multidimensional broadcast networks". In OSA Annual Meeting. Washington, D.C.: Optica Publishing Group, 1993. http://dx.doi.org/10.1364/oam.1993.wx.5.

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One-dimensional broadcast networks are common, e.g., in the space dimension we have the crossbar networks, and in the wavelength dimension we have the star-based networks (e.g., “broadcast and select”). These networks have limited connectivity: the space-division networks are limited by the switch count and complexity of control, and the wavelength-division networks have a relatively small number of wavelength-channels due to spectrum limitation. In the proposed k-dimensional networks,1 a communication channel between input and output is consists of k coordinates, one per dimension. Each input in the network broadcasts its fixed k coordinates to all the outputs. A connection is established when an output selects the appropriate coordinates (one per dimension) of a specific input. These networks are strictly non-blocking and have very high connectivity (>>1000) using only several dimensions (say 3). The high connectivity is achieved by relaxing the constraints on the size of each dimension since several dimensions share the load of the network, e.g., a 3-dimensional network with connectivity 1000 would require only 10 channels in each dimension. The network is optimal in essence that its complexity achieves the information lower bound through hierarchical multiplexing. An example of a 3-idimensional optical-wavelength, microwave-frequency and space is described and compared with some traditional networks.

Wang, Mingxian, Wei Chen, Yun Huang, Noshir S. Contractor e Yan Fu. "A Multidimensional Network Approach for Modeling Customer-Product Relations in Engineering Design". In ASME 2015 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2015. http://dx.doi.org/10.1115/detc2015-46764.

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Analytical modeling of customer preferences in product design is inherently difficult as it faces challenges in modeling heterogeneous human behavior and product offerings. In this paper, the customer-product interactions are viewed as a complex socio-technical system and analyzed using social network theory and techniques. We propose a Multidimensional Customer-Product Network (MCPN) framework, where separate networks of “customers” and “products” are simultaneously modeled, and multiple types of relations, such as consideration and purchase, product associations, and customer social networks are considered. We start with the simplest unimodal network configuration where customer cross-shopping behaviors and product similarities are analyzed to inform designers about the implied product competition, market segmentation, and product positions in the market. We then progressively extend the network to a multidimensional structure that integrates customer preference decisions with product feature similarities to enable the modeling of preference heterogeneity, product association and decision dependency. Finally, social influences on new product adoption are analyzed in the same framework by introducing customer-customer relations together with other product-product and customer-product relations. Beyond the traditional network descriptive analysis, we employ the Exponential Random Graph Model (ERGM) as a unified statistical inference framework for analyzing multiple relations in MCPN to support engineering design decisions. Our approach broadens the traditional utility-based logit approaches by considering the dependency among product choices and the “irrationality” of customer behavior induced by social influence. While this paper is focused on presenting the conceptual framework of the proposed methodology, examples on customer vehicle preferences are presented to illustrate the progressive development of the MCPN framework from a simple unimodal configuration to a complex multidimensional structure.

Liu, Xuejun, Shuigeng Zhou, Guangwei Bai e Diwen Zhu. "Multidimensional Similarity In-network Query for Large-Scale Sensor Networks". In 2009 Tenth International Conference on Mobile Data Management: Systems, Services and Middleware. IEEE, 2009. http://dx.doi.org/10.1109/mdm.2009.45.

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Dabideen, S., e J. J. Garcia-Luna-Aceves. "Multidimensional Routing". In 17th International Conference on Computer Communications and Networks 2008. IEEE, 2008. http://dx.doi.org/10.1109/icccn.2008.ecp.113.

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Khaniev, R. E., e S. A. K. Diane. "3D Approach to Visualization of Error Function in the Neural Network Tuning Problem". In 33rd International Conference on Computer Graphics and Vision. Keldysh Institute of Applied Mathematics, 2023. http://dx.doi.org/10.20948/graphicon-2023-371-376.

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The report presents an approach to visualization and analysis of the effectiveness of a neural network using one-dimensional and three-dimensional graphs. A generalized block diagram of algorithmic support for solving the problems of configuring and visualizing neural network models is proposed. As part of the study of the influence of several macroparameters on the quality of network learning, particular one-dimensional dependencies of RMSE errors and a generalized three-dimensional error function are given, which takes into account the number of neurons in the hidden layer, the number of training examples and the number of training epochs. Software implementation of algorithms for calculation, training and visualization of neural networks is carried out in Python. The analysis of the obtained three-dimensional graph made it possible to conclude about the nonlinear nature of the obtained multidimensional dependence and to determine the best combination of macroparameters using the expert evaluation function.

Corbisiero, Fabio, e Antonella Avolio. "Migrazioni e networks urbani". In International Conference Virtual City and Territory. Roma: Centre de Política de Sòl i Valoracions, 2014. http://dx.doi.org/10.5821/ctv.7987.

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Ripercorrendo l’ampio dibattito sul tema emerge quanto numerose siano le definizioni di integrazione elaborate dagli studiosi che si sono occupati di migrazioni. Soprattutto in anni più recenti, in forza dei rilevanti cambiamenti dei fenomeni migratori in atto, in molti concordano che questi processi sono aperti a molteplici esiti, in gran parte collegati a fattori di contesto politico, sociale, economico e culturale. Questi diversi fattori rappresentano altrettante dimensioni con cui si può guardare all’integrazione, che pertanto si configura come concetto multidimensionale, oltre che dinamico, e che può essere declinato a diversi livelli di analisi. Il livello relazionale (livello meso) rappresenta il punto di convergenza di fattori di integrazione macro e micro: i percorsi di inserimento urbano spesso dipendono dall’efficacia delle reti nelle quali si è inseriti. Questo contributo presenta i risultati di una ricerca condotta nel quartiere Mercato a Napoli, che ha avuto come oggetto di analisi l’integrazione della comunità cabardina, attraverso la metodologia e gli strumenti della Social Network Analysis. There are many definitions of integration developed by scholars of migration. They agree – especially in recent years, due to the significant changes in migration – that these processes are open to multiple outcomes, largely related political, social, economic and cultural factors. These different factors represent the different dimension which you can look to the integration; a term that appears as a multidimensional concept, as well as dynamic, and can be declined at different levels of analysis. The relational level (meso-level) represents the point of convergence between macro and micro factors of integration. In fact, the urban integration processes often depend on the effectiveness of their own social networks. This paper presents the results of a survey in the Mercato neighborhood (Naples). The aim is to analyze the integration of Kabardians community, through Social Network Analysis methods.

Berlingerio, Michele, Michele Coscia, Fosca Giannotti, Anna Monreale e Dino Pedreschi. "Foundations of Multidimensional Network Analysis". In 2011 International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2011). IEEE, 2011. http://dx.doi.org/10.1109/asonam.2011.103.

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Guha, Rupkatha, e S. K. Setua. "A Multidimensional secured network model". In 2013 3rd International Conference on Computer Science and Network Technology (ICCSNT). IEEE, 2013. http://dx.doi.org/10.1109/iccsnt.2013.6967259.

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Healey, Peter, Steven A. Cassidy e David W. Smith. "Multidimensional optical interconnection networks". In OE/LASE '90, 14-19 Jan., Los Angeles, CA, a cura di Raymond Arrathoon. SPIE, 1990. http://dx.doi.org/10.1117/12.18063.

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Quintuna, Veronica, e Maxime Laye. "Modeling and optimization of Content Delivery Networks with heuristics solutions for the Multidimensional Knapsack Problem". In 2016 17th International Telecommunications Network Strategy and Planning Symposium (Networks). IEEE, 2016. http://dx.doi.org/10.1109/netwks.2016.7751146.

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Rapporti di organizzazioni sul tema "Multidimensionnal network":

Park, June S., e Keebom Kang. Delay Analysis for Multidimensional Queueing Process in CSMA/CD Local Area Networks. Fort Belvoir, VA: Defense Technical Information Center, settembre 1991. http://dx.doi.org/10.21236/ada242364.

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Soloviev, Volodymyr Mykolayovych, e Viktoriya Volodymyrivna Solovyova. Universal tools of modeling different nature complex systems. ФОП Однорог Т.В., 2018. http://dx.doi.org/10.31812/123456789/2865.

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It is shown that there is а powerful set of tools for the study of self-organization in complex systems, both natural and artificial origin. They characterize the multidimensional nature of complexity - multifractality, irreversibility, non-linearity, recurrence, nonstability, emeregence, etc., and quantitative evaluation of individual dynamical measures of complexity allows for monitoring, predicting and preventing unwanted critical or crisis. Particular attention is paid to measures of network complexity, which are fully applicable to build synergistic network of pedagogical systems.

Li, Jing. Various New Statistical Models for Modeling and Change Detection in Multidimensional Dynamic Networks. Fort Belvoir, VA: Defense Technical Information Center, gennaio 2014. http://dx.doi.org/10.21236/ada606729.

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Johnson, Joseph E., e Vladimir Gudkov. Cyberspace Assurance Metrics: Utilizing Models of Networks, Complex Systems Theory, Multidimensional Wavelet Analysis, and Generalized Entrophy Measures. Fort Belvoir, VA: Defense Technical Information Center, aprile 2005. http://dx.doi.org/10.21236/ada434351.

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Ramm-Granberg, Tynan, F. Rocchio, Catharine Copass, Rachel Brunner e Eric Nelsen. Revised vegetation classification for Mount Rainier, North Cascades, and Olympic national parks: Project summary report. National Park Service, febbraio 2021. http://dx.doi.org/10.36967/nrr-2284511.

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Field crews recently collected more than 10 years of classification and mapping data in support of the North Coast and Cascades Inventory and Monitoring Network (NCCN) vegetation maps of Mount Rainier (MORA), Olympic (OLYM), and North Cascades (NOCA) National Parks. Synthesis and analysis of these 6000+ plots by Washington Natural Heritage Program (WNHP) and Institute for Natural Resources (INR) staff built on the foundation provided by the earlier classification work of Crawford et al. (2009). These analyses provided support for most of the provisional plant associations in Crawford et al. (2009), while also revealing previously undescribed vegetation types that were not represented in the United States National Vegetation Classification (USNVC). Both provisional and undescribed types have since been submitted to the USNVC by WNHP staff through a peer-reviewed process. NCCN plots were combined with statewide forest and wetland plot data from the US Forest Service (USFS) and other sources to create a comprehensive data set for Washington. Analyses incorporated Cluster Analysis, Nonmetric Multidimensional Scaling (NMS), Multi-Response Permutation Procedure (MRPP), and Indicator Species Analysis (ISA) to identify, vet, and describe USNVC group, alliance, and association distinctions. The resulting revised classification contains 321 plant associations in 99 alliances. A total of 54 upland associations were moved through the peer review process and are now part of the USNVC. Of those, 45 were provisional or preliminary types from Crawford et al. (2009), with 9 additional new associations that were originally identified by INR. WNHP also revised the concepts of 34 associations, wrote descriptions for 2 existing associations, eliminated/archived 2 associations, and created 4 new upland alliances. Finally, WNHP created 27 new wetland alliances and revised or clarified an additional 21 as part of this project (not all of those occur in the parks). This report and accompanying vegetation descriptions, keys and synoptic and environmental tables (all products available from the NPS Data Store project reference: https://irma.nps.gov/DataStore/Reference/Profile/2279907) present the fruit of these combined efforts: a comprehensive, up-to-date vegetation classification for the three major national parks of Washington State.