Search results for "Markov"
showing 10 items of 628 documents
Co-jumps and Markov Counting Systems in Random Environments
2020
Motivated by the analysis of multi-strain infectious disease data, we provide closed-form transition rates for continuous-time Markov chains that arise from subjecting Markov counting systems to correlated environmental noises. Noise correlation induces co-jumps or counts that occur simultaneously in several counting processes. Such co-jumps are necessary and sufficient for infinitesimal correlation between counting processes of the system. We analyzed such infinitesimal correlation for a specific infectious disease model by randomizing time of Kolmogorov’s Backward system of differential equations based on appropriate stochastic integrals.
On Capturing Rent from a Non-Renewable Resource International Monopoly: A Dynamic Game Approach
2005
In this paper we model the case of an international non-renewable resource monopolist as a dynamic game between a monopolist and n importing countries governments, and we investigate whether a tariff on resource imports can be advantageous for the consumers of the importing countries when the monopolist sets the price and the importing countries governments act in a non-cooperative way. We find that a tariff is advantageous for the consumers even when there is not commitment to the trade policy although the part of the rent that can be reaped by the importing countries decreases substantially with the number of importing countries. The optimality of the tariff in our dynamic game is explain…
Efficient and accurate methodology for solving multiserver retrial systems
2005
Proposed is a novel methodology for solving retrial systems which is based on the aggregation of levels of the Markov model beyond a given one. Its evaluation concludes that it is more accurate than previous approximations while requiring a low computational cost.
Creation and cognition for humanoid live dancing
2016
Abstract Computational creativity in dancing is a recent and challenging research field in Artificial Intelligence and Robotics. We present a cognitive architecture embodied in a humanoid robot capable to create and perform dances driven by the perception of music. The humanoid robot is able to suitably move, to react to human mate dancers and to generate novel and appropriate sequences of movements. The approach is based on a cognitive architecture that integrates Hidden Markov Models and Genetic Algorithms. The system has been implemented on a NAO robot and tested in public setting-up live performances, obtaining positive feedbacks from the audience.
An automatic system for humanoid dance creation
2016
Abstract The paper describes a novel approach to allow a robot to dance following musical rhythm. The proposed system generates a dance for a humanoid robot through the combination of basic movements synchronized with the music. The system made up of three parts: the extraction of features from audio file, estimation of movements through the Hidden Markov Models and, finally, the generation of dance. Starting from a set of given movements, the robot choices sequence of movements a suitable Hidden Markov Model, and synchronize them processing musical input. The proposed approach has the advantage that movement execution probabilities could be changed according evaluation of the dance executi…
Modeling and Performance Analysis of Channel Assembling in Multichannel Cognitive Radio Networks With Spectrum Adaptation
2012
[EN] To accommodate spectrum access in multichannel cognitive radio networks (CRNs), the channel-assembling technique, which combines several channels together as one channel, has been proposed in many medium access control (MAC) protocols. However, analytical models for CRNs enabled with this technique have not been thoroughly investigated. In this paper, two representative channel-assembling strategies that consider spectrum adaptation and heterogeneous traffic are proposed, and the performance of these strategies is evaluated based on the proposed continuous-time Markov chain (CTMC) models. Moreover, approximations of these models in the quasistationary regime are analyzed, and closed-fo…
Part of Speech Tagging Using Hidden Markov Models
2020
Abstract In this paper, we present a wide range of models based on less adaptive and adaptive approaches for a PoS tagging system. These parameters for the adaptive approach are based on the n-gram of the Hidden Markov Model, evaluated for bigram and trigram, and based on three different types of decoding method, in this case forward, backward, and bidirectional. We used the Brown Corpus for the training and the testing phase. The bidirectional trigram model almost reaches state of the art accuracy but is disadvantaged by the decoding speed time while the backward trigram reaches almost the same results with a way better decoding speed time. By these results, we can conclude that the decodi…
Non-equilibrium Markov state modeling of periodically driven biomolecules
2019
Molecular dynamics simulations allow to study the structure and dynamics of single biomolecules in microscopic detail. However, many processes occur on time scales beyond the reach of fully atomistic simulations and require coarse-grained multiscale models. While systematic approaches to construct such models have become available, these typically rely on microscopic dynamics that obey detailed balance. In vivo, however, biomolecules are constantly driven away from equilibrium in order to perform specific functions and thus break detailed balance. Here we introduce a method to construct Markov state models for systems that are driven through periodically changing one (or several) external p…
Molecular dynamics simulations in hybrid particle-continuum schemes: Pitfalls and caveats
2017
Heterogeneous multiscale methods (HMM) combine molecular accuracy of particle-based simulations with the computational efficiency of continuum descriptions to model flow in soft matter liquids. In these schemes, molecular simulations typically pose a computational bottleneck, which we investigate in detail in this study. We find that it is preferable to simulate many small systems as opposed to a few large systems, and that a choice of a simple isokinetic thermostat is typically sufficient while thermostats such as Lowe-Andersen allow for simulations at elevated viscosity. We discuss suitable choices for time steps and finite-size effects which arise in the limit of very small simulation bo…
Bag-of-word based brand recognition using Markov Clustering Algorithm for codebook generation
2015
International audience; In order to address the issue of counterfeiting online, it is necessary to use automatic tools that analyze the large amount of information available over the Internet. Analysis methods that extract information about the content of the images are very promising for this purpose. In this paper, a method that automatically extract the brand of objects in images is proposed. The method does not explicitly search for text or logos. This information is implicitly included in the Bag-of-Words representation. In the Bag-of-Words paradigm, visual features are clustered to create the visual words. Despite its shortcomings, k-means is the most widely used algorithm. With k-mea…