10,672 research outputs found
Computational Intelligence for Condition Monitoring
Condition monitoring techniques are described in this chapter. Two aspects of
condition monitoring process are considered: (1) feature extraction; and (2)
condition classification. Feature extraction methods described and implemented
are fractals, Kurtosis and Mel-frequency Cepstral Coefficients. Classification
methods described and implemented are support vector machines (SVM), hidden
Markov models (HMM), Gaussian mixture models (GMM) and extension neural
networks (ENN). The effectiveness of these features were tested using SVM, HMM,
GMM and ENN on condition monitoring of bearings and are found to give good
results.Comment: 23 page
Word Representations, Tree Models and Syntactic Functions
Word representations induced from models with discrete latent variables
(e.g.\ HMMs) have been shown to be beneficial in many NLP applications. In this
work, we exploit labeled syntactic dependency trees and formalize the induction
problem as unsupervised learning of tree-structured hidden Markov models.
Syntactic functions are used as additional observed variables in the model,
influencing both transition and emission components. Such syntactic information
can potentially lead to capturing more fine-grain and functional distinctions
between words, which, in turn, may be desirable in many NLP applications. We
evaluate the word representations on two tasks -- named entity recognition and
semantic frame identification. We observe improvements from exploiting
syntactic function information in both cases, and the results rivaling those of
state-of-the-art representation learning methods. Additionally, we revisit the
relationship between sequential and unlabeled-tree models and find that the
advantage of the latter is not self-evident.Comment: Add github code repository link. Fix equation 4.
High Performance Human Face Recognition using Gabor based Pseudo Hidden Markov Model
This paper introduces a novel methodology that combines the multi-resolution
feature of the Gabor wavelet transformation (GWT) with the local interactions
of the facial structures expressed through the Pseudo Hidden Markov model
(PHMM). Unlike the traditional zigzag scanning method for feature extraction a
continuous scanning method from top-left corner to right then top-down and
right to left and so on until right-bottom of the image i.e. a spiral scanning
technique has been proposed for better feature selection. Unlike traditional
HMMs, the proposed PHMM does not perform the state conditional independence of
the visible observation sequence assumption. This is achieved via the concept
of local structures introduced by the PHMM used to extract facial bands and
automatically select the most informative features of a face image. Thus, the
long-range dependency problem inherent to traditional HMMs has been drastically
reduced. Again with the use of most informative pixels rather than the whole
image makes the proposed method reasonably faster for face recognition. This
method has been successfully tested on frontal face images from the ORL, FRAV2D
and FERET face databases where the images vary in pose, illumination,
expression, and scale. The FERET data set contains 2200 frontal face images of
200 subjects, while the FRAV2D data set consists of 1100 images of 100 subjects
and the full ORL database is considered. The results reported in this
application are far better than the recent and most referred systems.Comment: 9 pages. arXiv admin note: substantial text overlap with
arXiv:1312.151
Use HMM and KNN for classifying corneal data
These days to gain classification system with high accuracy that can classify
complicated pattern are so useful in medicine and industry. In this article a
process for getting the best classifier for Lasik data is suggested. However at
first it's been tried to find the best line and curve by this classifier in
order to gain classifier fitting, and in the end by using the Markov method a
classifier for topographies is gained
Analysis of Multilingual Sequence-to-Sequence speech recognition systems
This paper investigates the applications of various multilingual approaches
developed in conventional hidden Markov model (HMM) systems to
sequence-to-sequence (seq2seq) automatic speech recognition (ASR). On a set
composed of Babel data, we first show the effectiveness of multi-lingual
training with stacked bottle-neck (SBN) features. Then we explore various
architectures and training strategies of multi-lingual seq2seq models based on
CTC-attention networks including combinations of output layer, CTC and/or
attention component re-training. We also investigate the effectiveness of
language-transfer learning in a very low resource scenario when the target
language is not included in the original multi-lingual training data.
Interestingly, we found multilingual features superior to multilingual models,
and this finding suggests that we can efficiently combine the benefits of the
HMM system with the seq2seq system through these multilingual feature
techniques.Comment: arXiv admin note: text overlap with arXiv:1810.0345
Building Prior Knowledge: A Markov Based Pedestrian Prediction Model Using Urban Environmental Data
Autonomous Vehicles navigating in urban areas have a need to understand and
predict future pedestrian behavior for safer navigation. This high level of
situational awareness requires observing pedestrian behavior and extrapolating
their positions to know future positions. While some work has been done in this
field using Hidden Markov Models (HMMs), one of the few observed drawbacks of
the method is the need for informed priors for learning behavior. In this work,
an extension to the Growing Hidden Markov Model (GHMM) method is proposed to
solve some of these drawbacks. This is achieved by building on existing work
using potential cost maps and the principle of Natural Vision. As a
consequence, the proposed model is able to predict pedestrian positions more
precisely over a longer horizon compared to the state of the art. The method is
tested over "legal" and "illegal" behavior of pedestrians, having trained the
model with sparse observations and partial trajectories. The method, with no
training data, is compared against a trained state of the art model. It is
observed that the proposed method is robust even in new, previously unseen
areas.Comment: 15 th International Conference on Control, Automation, Robotics and
Vision (ICARCV 2018), Nov 2018, Singapore, Singapor
Architectures for Detecting Interleaved Multi-stage Network Attacks Using Hidden Markov Models
With the growing amount of cyber threats, the need for development of
high-assurance cyber systems is becoming increasingly important. The objective
of this paper is to address the challenges of modeling and detecting
sophisticated network attacks, such as multiple interleaved attacks. We present
the interleaving concept and investigate how interleaving multiple attacks can
deceive intrusion detection systems. Using one of the important statistical
machine learning (ML) techniques, Hidden Markov Models (HMM), we develop two
architectures that take into account the stealth nature of the interleaving
attacks, and that can detect and track the progress of these attacks. These
architectures deploy a database of HMM templates of known attacks and exhibit
varying performance and complexity. For performance evaluation, in the presence
of multiple multi-stage attack scenarios, various metrics are proposed which
include (1) attack risk probability, (2) detection error rate, and (3) the
number of correctly detected stages. Extensive simulation experiments are used
to demonstrate the efficacy of the proposed architectures
Pay Attention to What You Read: Non-recurrent Handwritten Text-Line Recognition
The advent of recurrent neural networks for handwriting recognition marked an
important milestone reaching impressive recognition accuracies despite the
great variability that we observe across different writing styles. Sequential
architectures are a perfect fit to model text lines, not only because of the
inherent temporal aspect of text, but also to learn probability distributions
over sequences of characters and words. However, using such recurrent paradigms
comes at a cost at training stage, since their sequential pipelines prevent
parallelization. In this work, we introduce a non-recurrent approach to
recognize handwritten text by the use of transformer models. We propose a novel
method that bypasses any recurrence. By using multi-head self-attention layers
both at the visual and textual stages, we are able to tackle character
recognition as well as to learn language-related dependencies of the character
sequences to be decoded. Our model is unconstrained to any predefined
vocabulary, being able to recognize out-of-vocabulary words, i.e. words that do
not appear in the training vocabulary. We significantly advance over prior art
and demonstrate that satisfactory recognition accuracies are yielded even in
few-shot learning scenarios
Know Your Master: Driver Profiling-based Anti-theft Method
Although many anti-theft technologies are implemented, auto-theft is still
increasing. Also, security vulnerabilities of cars can be used for auto-theft
by neutralizing anti-theft system. This keyless auto-theft attack will be
increased as cars adopt computerized electronic devices more. To detect
auto-theft efficiently, we propose the driver verification method that analyzes
driving patterns using measurements from the sensor in the vehicle. In our
model, we add mechanical features of automotive parts that are excluded in
previous works, but can be differentiated by drivers' driving behaviors. We
design the model that uses significant features through feature selection to
reduce the time cost of feature processing and improve the detection
performance. Further, we enrich the feature set by deriving statistical
features such as mean, median, and standard deviation. This minimizes the
effect of fluctuation of feature values per driver and finally generates the
reliable model. We also analyze the effect of the size of sliding window on
performance to detect the time point when the detection becomes reliable and to
inform owners the theft event as soon as possible. We apply our model with real
driving and show the contribution of our work to the literature of driver
identification.Comment: 8 pages, 11 figures, Accepted for PST 2016 : 14th International
Conference on Privacy, Security and Trus
Online Signature Verification using Deep Representation: A new Descriptor
This paper presents an accurate method for verifying online signatures. The
main difficulty of signature verification come from: (1) Lacking enough
training samples (2) The methods must be spatial change invariant. To deal with
these difficulties and modeling the signatures efficiently, we propose a method
that a one-class classifier per each user is built on discriminative features.
First, we pre-train a sparse auto-encoder using a large number of unlabeled
signatures, then we applied the discriminative features, which are learned by
auto-encoder to represent the training and testing signatures as a self-thought
learning method (i.e. we have introduced a signature descriptor). Finally,
user's signatures are modeled and classified using a one-class classifier. The
proposed method is independent on signature datasets thanks to self-taught
learning. The experimental results indicate significant error reduction and
accuracy enhancement in comparison with state-of-the-art methods on SVC2004 and
SUSIG datasets.Comment: arXiv admin note: substantial text overlap with arXiv:1505.0815
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