33,427 research outputs found
Learning to automatically detect features for mobile robots using second-order Hidden Markov Models
In this paper, we propose a new method based on Hidden Markov Models to
interpret temporal sequences of sensor data from mobile robots to automatically
detect features. Hidden Markov Models have been used for a long time in pattern
recognition, especially in speech recognition. Their main advantages over other
methods (such as neural networks) are their ability to model noisy temporal
signals of variable length. We show in this paper that this approach is well
suited for interpretation of temporal sequences of mobile-robot sensor data. We
present two distinct experiments and results: the first one in an indoor
environment where a mobile robot learns to detect features like open doors or
T-intersections, the second one in an outdoor environment where a different
mobile robot has to identify situations like climbing a hill or crossing a
rock.Comment: 200
Activity Recognition using Hierarchical Hidden Markov Models on Streaming Sensor Data
Activity recognition from sensor data deals with various challenges, such as
overlapping activities, activity labeling, and activity detection. Although
each challenge in the field of recognition has great importance, the most
important one refers to online activity recognition. The present study tries to
use online hierarchical hidden Markov model to detect an activity on the stream
of sensor data which can predict the activity in the environment with any
sensor event. The activity recognition samples were labeled by the statistical
features such as the duration of activity. The results of our proposed method
test on two different datasets of smart homes in the real world showed that one
dataset has improved 4% and reached (59%) while the results reached 64.6% for
the other data by using the best methods
Temporal and Spatial Data Mining with Second-Order Hidden Models
In the frame of designing a knowledge discovery system, we have developed
stochastic models based on high-order hidden Markov models. These models are
capable to map sequences of data into a Markov chain in which the transitions
between the states depend on the \texttt{n} previous states according to the
order of the model. We study the process of achieving information extraction
fromspatial and temporal data by means of an unsupervised classification. We
use therefore a French national database related to the land use of a region,
named Teruti, which describes the land use both in the spatial and temporal
domain. Land-use categories (wheat, corn, forest, ...) are logged every year on
each site regularly spaced in the region. They constitute a temporal sequence
of images in which we look for spatial and temporal dependencies. The temporal
segmentation of the data is done by means of a second-order Hidden Markov Model
(\hmmd) that appears to have very good capabilities to locate stationary
segments, as shown in our previous work in speech recognition. Thespatial
classification is performed by defining a fractal scanning ofthe images with
the help of a Hilbert-Peano curve that introduces atotal order on the sites,
preserving the relation ofneighborhood between the sites. We show that the
\hmmd performs aclassification that is meaningful for the agronomists.Spatial
and temporal classification may be achieved simultaneously by means of a 2
levels \hmmd that measures the \aposteriori probability to map a temporal
sequence of images onto a set of hidden classes
Nonparametric Bayesian Double Articulation Analyzer for Direct Language Acquisition from Continuous Speech Signals
Human infants can discover words directly from unsegmented speech signals
without any explicitly labeled data. In this paper, we develop a novel machine
learning method called nonparametric Bayesian double articulation analyzer
(NPB-DAA) that can directly acquire language and acoustic models from observed
continuous speech signals. For this purpose, we propose an integrative
generative model that combines a language model and an acoustic model into a
single generative model called the "hierarchical Dirichlet process hidden
language model" (HDP-HLM). The HDP-HLM is obtained by extending the
hierarchical Dirichlet process hidden semi-Markov model (HDP-HSMM) proposed by
Johnson et al. An inference procedure for the HDP-HLM is derived using the
blocked Gibbs sampler originally proposed for the HDP-HSMM. This procedure
enables the simultaneous and direct inference of language and acoustic models
from continuous speech signals. Based on the HDP-HLM and its inference
procedure, we developed a novel double articulation analyzer. By assuming
HDP-HLM as a generative model of observed time series data, and by inferring
latent variables of the model, the method can analyze latent double
articulation structure, i.e., hierarchically organized latent words and
phonemes, of the data in an unsupervised manner. The novel unsupervised double
articulation analyzer is called NPB-DAA.
The NPB-DAA can automatically estimate double articulation structure embedded
in speech signals. We also carried out two evaluation experiments using
synthetic data and actual human continuous speech signals representing Japanese
vowel sequences. In the word acquisition and phoneme categorization tasks, the
NPB-DAA outperformed a conventional double articulation analyzer (DAA) and
baseline automatic speech recognition system whose acoustic model was trained
in a supervised manner.Comment: 15 pages, 7 figures, Draft submitted to IEEE Transactions on
Autonomous Mental Development (TAMD
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