807 research outputs found
Unsupervised learning based coordinated multi-task allocation for unmanned surface vehicles
In recent decades, unmanned surface vehicles (USVs) are attracting increasing attention due to their underlying capability in autonomously undertaking complex maritime tasks in constrained environments. However, the autonomy level of USVs is still limited, especially when being deployed to conduct multiple tasks simultaneously. This paper, therefore, aims to improve USVs autonomy level by investigating and developing an effective and efficient task management algorithm for multi-USV systems. To better deal with challenging requirements such as allocating vast tasks in cluttered environments, the task management has been de-composed into two submissions, i.e., task allocation and task execution. More specifically, unsupervised learning strategies have been used with an improved K-means algorithm proposed to first assign different tasks for a multi-USV system then a self-organising map (SOM) been implemented to deal with the task execution problem based upon the assigned tasks for each USV. Differing to other work, the communication problem that is crucial for USVs in a constrained environment has been specifically resolved by designing a new competition strategy for K-means algorithm. Key factors that will influence the communication capability in practical applications have been taken into account. A holistic task management architecture has been designed by integrating both the task allocation and task execution algorithms, and a number of simulations in both simulated and practical maritime environments have been carried out to validate the effectiveness of the proposed algorithms
Modelling and tracking objects with a topology preserving self-organising neural network
Human gestures form an integral part in our everyday communication. We use
gestures not only to reinforce meaning, but also to describe the shape of objects,
to play games, and to communicate in noisy environments. Vision systems that
exploit gestures are often limited by inaccuracies inherent in handcrafted models.
These models are generated from a collection of training examples which requires
segmentation and alignment. Segmentation in gesture recognition typically involves manual intervention, a time consuming process that is feasible only for a
limited set of gestures. Ideally gesture models should be automatically acquired
via a learning scheme that enables the acquisition of detailed behavioural knowledge only from topological and temporal observation.
The research described in this thesis is motivated by a desire to provide a framework for the unsupervised acquisition and tracking of gesture models. In any
learning framework, the initialisation of the shapes is very crucial. Hence, it would
be beneficial to have a robust model not prone to noise that can automatically correspond the set of shapes. In the first part of this thesis, we develop a framework
for building statistical 2D shape models by extracting, labelling and corresponding
landmark points using only topological relations derived from competitive hebbian learning. The method is based on the assumption that correspondences can
be addressed as an unsupervised classification problem where landmark points
are the cluster centres (nodes) in a high-dimensional vector space. The approach
is novel in that the network can be used in cases where the topological structure of
the input pattern is not known a priori thus no topology of fixed dimensionality is imposed onto the network.
In the second part, we propose an approach to minimise the user intervention
in the adaptation process, which requires to specify a priori the number of nodes
needed to represent an object, by utilising an automatic criterion for maximum
node growth. Furthermore, this model is used to represent motion in image sequences by initialising a suitable segmentation that separates the object of interest
from the background. The segmentation system takes into consideration some illumination tolerance, images as inputs from ordinary cameras and webcams, some
low to medium cluttered background avoiding extremely cluttered backgrounds,
and that the objects are at close range from the camera.
In the final part, we extend the framework for the automatic modelling and
unsupervised tracking of 2D hand gestures in a sequence of k frames. The aim
is to use the tracked frames as training examples in order to build the model and
maintain correspondences. To do that we add an active step to the Growing Neural Gas (GNG) network, which we call Active Growing Neural Gas (A-GNG) that
takes into consideration not only the geometrical position of the nodes, but also the
underlined local feature structure of the image, and the distance vector between
successive images. The quality of our model is measured through the calculation
of the topographic product. The topographic product is our topology preserving
measure which quantifies the neighbourhood preservation.
In our system we have applied specific restrictions in the velocity and the appearance of the gestures to simplify the difficulty of the motion analysis in the gesture representation. The proposed framework has been validated on applications
related to sign language. The work has great potential in Virtual Reality (VR) applications where the learning and the representation of gestures becomes natural
without the need of expensive wear cable sensors
Fast 2D/3D object representation with growing neural gas
This work presents the design of a real-time system to model visual objects with the use of self-organising networks. The architecture of the system addresses multiple computer vision tasks such as image segmentation, optimal parameter estimation and object representation. We first develop a framework for building non-rigid shapes using the growth mechanism of the self-organising maps, and then we define an optimal number of nodes without overfitting or underfitting the network based on the knowledge obtained from information-theoretic considerations. We present experimental results for hands and faces, and we quantitatively evaluate the matching capabilities of the proposed method with the topographic product. The proposed method is easily extensible to 3D objects, as it offers similar features for efficient mesh reconstruction
Forecasting Long-Term Government Bond Yields: An Application of Statistical and AI Models
This paper evaluates several artificial intelligence and classical algorithms on their ability of forecasting the monthly yield of the US 10-year Treasury bonds from a set of four economic indicators. Due to the complexity of the prediction problem, the task represents a challenging test for the algorithms under evaluation. At the same time, the study is of particular significance for the important and paradigmatic role played by the US market in the world economy. Four data-driven artificial intelligence approaches are considered, namely, a manually built fuzzy logic model, a machine learned fuzzy logic model, a self-organising map model and a multi-layer perceptron model. Their performance is compared with the performance of two classical approaches, namely, a statistical ARIMA model and an econometric error correction model. The algorithms are evaluated on a complete series of end-month US 10-year Treasury bonds yields and economic indicators from 1986:1 to 2004:12. In terms of prediction accuracy and reliability of the modelling procedure, the best results are obtained by the three parametric regression algorithms, namely the econometric, the statistical and the multi-layer perceptron model. Due to the sparseness of the learning data samples, the manual and the automatic fuzzy logic approaches fail to follow with adequate precision the range of variations of the US 10-year Treasury bonds. For similar reasons, the self-organising map model gives an unsatisfactory performance. Analysis of the results indicates that the econometric model has a slight edge over the statistical and the multi-layer perceptron models. This suggests that pure data-driven induction may not fully capture the complicated mechanisms ruling the changes in interest rates. Overall, the prediction accuracy of the best models is only marginally better than the prediction accuracy of a basic one-step lag predictor. This result highlights the difficulty of the modelling task and, in general, the difficulty of building reliable predictors for financial markets.interest rates; forecasting; neural networks; fuzzy logic.
Spiking neurons in 3D growing self-organising maps
In Kohonen’s Self-Organising Maps (SOM) learning, preserving the map topology to simulate the actual input features appears to be a significant process. Misinterpretation of the training samples can lead to failure in identifying the important features that may affect the outcomes generated by the SOM model. Nonetheless, it is a challenging task as most of the real problems are composed of complex and insufficient data. Spiking Neural Network (SNN) is the third generation of Artificial Neural Network (ANN), in which information can be transferred from one neuron to another using spike, processed, and trigger response as output. This study, hence, embedded spiking neurons for SOM learning in order to enhance the learning process. The proposed method was divided into five main phases. Phase 1 investigated issues related to SOM learning algorithm, while in Phase 2; datasets were collected for analyses carried out in Phase 3, wherein neural coding scheme for data representation process was implemented in the classification task. Next, in Phase 4, the spiking SOM model was designed, developed, and evaluated using classification accuracy rate and quantisation error. The outcomes showed that the proposed model had successfully attained exceptional classification accuracy rate with low quantisation error to preserve the quality of the generated map based on original input data. Lastly, in the final phase, a Spiking 3D Growing SOM is proposed to address the surface reconstruction issue by enhancing the spiking SOM using 3D map structure in SOM algorithm with a growing grid mechanism. The application of spiking neurons to enhance the performance of SOM is relevant in this study due to its ability to spike and to send a reaction when special features are identified based on its learning of the presented datasets. The study outcomes contribute to the enhancement of SOM in learning the patterns of the datasets, as well as in proposing a better tool for data analysis
ASPECT: A spectra clustering tool for exploration of large spectral surveys
We present the novel, semi-automated clustering tool ASPECT for analysing
voluminous archives of spectra. The heart of the program is a neural network in
form of Kohonen's self-organizing map. The resulting map is designed as an icon
map suitable for the inspection by eye. The visual analysis is supported by the
option to blend in individual object properties such as redshift, apparent
magnitude, or signal-to-noise ratio. In addition, the package provides several
tools for the selection of special spectral types, e.g. local difference maps
which reflect the deviations of all spectra from one given input spectrum (real
or artificial). ASPECT is able to produce a two-dimensional topological map of
a huge number of spectra. The software package enables the user to browse and
navigate through a huge data pool and helps him to gain an insight into
underlying relationships between the spectra and other physical properties and
to get the big picture of the entire data set. We demonstrate the capability of
ASPECT by clustering the entire data pool of 0.6 million spectra from the Data
Release 4 of the Sloan Digital Sky Survey (SDSS). To illustrate the results
regarding quality and completeness we track objects from existing catalogues of
quasars and carbon stars, respectively, and connect the SDSS spectra with
morphological information from the GalaxyZoo project.Comment: 15 pages, 14 figures; accepted for publication in Astronomy and
Astrophysic
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