33 research outputs found
An integrated diagnostic architecture for autonomous robots
Abstract unavailable please refer to PD
NONLINEAR IDENTIFICATION AND CONTROL: A PRACTICAL SOLUTION AND ITS APPLICATION
It is well known that typical welding processes such as laser welding are nonlinear although mostly they are treated as linear system. For the purpose of automatic control, Identification of nonlinear system, especially welding processes is a necessary and fundamental problem. The purpose of this research is to develop a simple and practical identification and control for welding processes. Many investigations have shown the possibility to represent physical processes by nonlinear models, such as Hammerstein structure, consisting of a nonlinearity and linear dynamics in series with each other. Motivated by the fact that typical welding processes do not have non-zeroes, a novel two-step nonlinear Hammerstein identification method is proposed for laser welding processes. The method can be realized both in continuous and discrete case. To study the relation among parameters influencing laser processing, a standard diode laser processing system is built as system prototype. Based on experimental study, a SISO and 2ISO nonlinear Hammerstein model structure are developed to approximate the diode laser welding process. Specific persistent excitation signals such as PRTS (Pseudo-random-ternary-series) to Step signal are used for identification. The model takes welding speed as input and the top surface molten weld pool width as output. A vision based sensor implemented with a Pulse-controlled-CCD camera is proposed and applied to acquire the images and the geometric data of the weld pool. The estimated model is then verified by comparing the simulation and experimental measurement. The verification shows that the model is reasonably correct and can be use to model the nonlinear process for further study. The two-step nonlinear identification method is proved valid and applicable to traditional welding processes and similar manufacturing processes. Based on the identified model, nonlinear control algorithms are also studied. Algorithms include simple linearization and backstepping based robust adaptive control algorithm are proposed and simulated
Pattern Recognition
A wealth of advanced pattern recognition algorithms are emerging from the interdiscipline between technologies of effective visual features and the human-brain cognition process. Effective visual features are made possible through the rapid developments in appropriate sensor equipments, novel filter designs, and viable information processing architectures. While the understanding of human-brain cognition process broadens the way in which the computer can perform pattern recognition tasks. The present book is intended to collect representative researches around the globe focusing on low-level vision, filter design, features and image descriptors, data mining and analysis, and biologically inspired algorithms. The 27 chapters coved in this book disclose recent advances and new ideas in promoting the techniques, technology and applications of pattern recognition
Detection Of Chipping In Ceramic Cutting Inserts From Workpiece Profile Signature During Turning Process Using Machine Vision
Ceramic tools are prone to chipping due to their low impact toughness. Tool
chipping significantly decreases the surface finish quality and dimensional accuracy
of the workpiece. Thus, in-process detection of chipping in ceramic tools is
important especially in unattended machining. Existing in-process tool failure
detection methods using sensor signals have limitations in detecting tool chipping.
The monitoring of tool wear from the workpiece profile using machine vision has
great potential to be applied in-process, however no attempt has been made to detect
tool chipping. In this work, a vision-based approach has been developed to detect
tool chipping in ceramic insert from 2-D workpiece profile signature. The profile of
the workpiece surface was captured using a DSLR camera. The surface profile was
extracted to sub-pixel accuracy using invariant moment method. The effect of
chipping in the ceramic cutting tools on the workpiece profile was investigated using
autocorrelation function (ACF) and fast Fourier transform (FFT). Detection of onset
tool chipping was conducted by using the sub-window FFT and continuous wavelet
transform (CWT). Chipping in the ceramic tool was found to cause the peaks of ACF
of the workpiece profile to decrease rapidly as the lag distance increased and
deviated significantly from one another at different workpiece rotation angles. From
FFT analysis the amplitude of the fundamental feed frequency increases steadily with
cutting duration during gradual wear, however, fluctuates significantly after tool has
chipped. The stochastic behaviour of the cutting process after tool chipping leads to a
sharp increase in the amplitude of spatial frequencies below the fundamental feed
frequency. CWT method was found more effective to detect the onset of tool
chipping at 16.5 s instead of 17.13 s by sub-window FFT. Root mean square of CWT
coefficients for the workpiece profile at higher scale band was found to be more
sensitive to chipping and thus can be used as an indicator to detect the occurrence of
the tool chipping in ceramic inserts
Towards An Intelligent Fuzzy Based Multimodal Two Stage Speech Enhancement System
This thesis presents a novel two stage multimodal speech enhancement system, making use of both visual and audio information to filter speech, and explores the extension of
this system with the use of fuzzy logic to demonstrate proof of concept for an envisaged autonomous, adaptive, and context aware multimodal system. The design of the proposed cognitively inspired framework is scalable, meaning that it is possible for the techniques used in individual parts of the system to be upgraded and there is scope for the initial framework presented here to be expanded.
In the proposed system, the concept of single modality two stage filtering is extended to include the visual modality. Noisy speech information received by a microphone array is first pre-processed by visually derived Wiener filtering employing the novel use of the Gaussian Mixture Regression (GMR) technique, making use of associated visual speech information, extracted using a state of the art Semi Adaptive Appearance Models (SAAM) based lip tracking approach. This pre-processed speech is then enhanced further by audio only beamforming using a state of the art Transfer Function Generalised Sidelobe Canceller (TFGSC) approach. This results in a system which is designed to function in challenging noisy speech environments (using speech sentences with different speakers from the GRID corpus and a range of noise recordings), and both objective and subjective test results (employing the widely used Perceptual Evaluation of Speech Quality (PESQ) measure, a composite objective measure, and subjective listening tests), showing that this initial system is capable of delivering very encouraging results with regard to filtering speech mixtures in difficult reverberant speech environments.
Some limitations of this initial framework are identified, and the extension of this multimodal system is explored, with the development of a fuzzy logic based framework and a proof of concept demonstration implemented. Results show that this proposed autonomous,adaptive, and context aware multimodal framework is capable of delivering very positive results in difficult noisy speech environments, with cognitively inspired use of audio and visual information, depending on environmental conditions. Finally some concluding remarks
are made along with proposals for future work
Machine learning methods for the characterization and classification of complex data
This thesis work presents novel methods for the analysis and classification of medical images and, more generally, complex data. First, an unsupervised machine learning method is proposed to order anterior chamber OCT (Optical Coherence Tomography) images according to a patient's risk of developing angle-closure glaucoma. In a second study, two outlier finding techniques are proposed to improve the results of above mentioned machine learning algorithm, we also show that they are applicable to a wide variety of data, including fraud detection in credit card transactions. In a third study, the topology of the vascular network of the retina, considering it a complex tree-like network is analyzed and we show that structural differences reveal the presence of glaucoma and diabetic retinopathy. In a fourth study we use a model of a laser with optical injection that presents extreme events in its intensity time-series to evaluate machine learning methods to forecast such extreme events.El presente trabajo de tesis desarrolla nuevos métodos para el análisis y clasificación de imágenes médicas y datos complejos en general. Primero, proponemos un método de aprendizaje automático sin supervisión que ordena imágenes OCT (tomografía de coherencia óptica) de la cámara anterior del ojo en función del grado de riesgo del paciente de padecer glaucoma de ángulo cerrado. Luego, desarrollamos dos métodos de detección automática de anomalías que utilizamos para mejorar los resultados del algoritmo anterior, pero que su aplicabilidad va mucho más allá, siendo útil, incluso, para la detección automática de fraudes en transacciones de tarjetas de crédito. Mostramos también, cómo al analizar la topología de la red vascular de la retina considerándola una red compleja, podemos detectar la presencia de glaucoma y de retinopatía diabética a través de diferencias estructurales. Estudiamos también un modelo de un láser con inyección óptica que presenta eventos extremos en la serie temporal de intensidad para evaluar diferentes métodos de aprendizaje automático para predecir dichos eventos extremos.Aquesta tesi desenvolupa nous mètodes per a l’anàlisi i la classificació d’imatges mèdiques i dades complexes. Hem proposat, primer, un mètode d’aprenentatge automàtic sense supervisió que ordena
imatges OCT (tomografia de coherència òptica) de la cambra anterior de l’ull en funció del grau de risc del pacient de patir glaucoma d’angle tancat. Després, hem desenvolupat dos mètodes de detecció automàtica d’anomalies que hem utilitzat per millorar els resultats de l’algoritme anterior, però que la seva aplicabilitat va molt més enllà, sent útil, fins i tot, per a la detecció automàtica de fraus en transaccions de targetes de crèdit. Mostrem també, com en analitzar la topologia de la xarxa vascular de la retina considerant-la una xarxa complexa, podem detectar la presència de glaucoma i de retinopatia diabètica a través de diferències estructurals. Finalment, hem estudiat un làser amb injecció òptica, el qual presenta esdeveniments extrems en la sèrie temporal d’intensitat.
Hem avaluat diferents mètodes per tal de predir-los.Postprint (published version
Salford postgraduate annual research conference (SPARC) 2012 proceedings
These proceedings bring together a selection of papers from the 2012 Salford Postgraduate Annual Research Conference (SPARC). They reflect the breadth and diversity of research interests showcased at the conference, at which over 130 researchers from Salford, the North West and other UK universities presented their work. 21 papers are collated here from the humanities, arts, social sciences, health, engineering, environment and life sciences, built environment and business
Automatic assessment of honey bee cells using deep learning
Temporal assessment of honey bee colony strength is required for different applications
in many research projects, which often involves counting the number of comb cells with
brood and food reserves multiple times a year. There are thousands of cells in each comb,
which makes manual counting a time-consuming, tedious and thereby an error-prone
task. Therefore, the automation of this task using modern imaging processing techniques
represents a major advance. Herein, we developed a software capable of (i) detecting
each cell from comb images, (ii) classifying its content and (iii) display the results to
the researcher in a simple way. The cells’ contents typically display a high variation of
patterns which make their classification by software a challenging endeavour. To address
this challenge, we used Deep Neural Networks (DNNs). DNNs are known for achieving the
state of art in many fields of study including image classification, because they can learn
features that best describe the content being classified by themselves. Our DNN model
was trained with over 70,000 manually labelled cell images whose cells were separated
into seven classes. Our contribution is an end-to-end software capable of doing automatic
background removal, cell detection, and classification of cell content based on an input
comb image. With this software, colony assessment achieves an average accuracy of 94%
across the seven classes in our dataset, representing a substantial progress regarding the
approximation methods (e.g. Lieberfeld) currently used by honey bee researchers and
previous techniques based on machine learning that used handmade features like colour
and texture.A análise temporal sobre a qualidade e força de colônias de abelha melífera (Apis mellifera
L.) é necessária em muitos projetos de pesquisa. Ela pode ser realizada contando alvéolos
com alimento (pólen e néctar) e criação. É comum que ela seja feita diversas vezes ao
ano. A grande quantidade de alvéolos em cada favo torna a tarefa demorada e tediosa
ao pesquisador. Assim, frequentemente essa contagem é feita forma aproximada usando
métodos como o de Lieberfeld. Automatizar este processo usando técnicas modernas de
processamento de imagem representa um grande avanço, pois resultados mais precisos e
padronizados poderão ser obtidos em menos tempo. O objetivo deste trabalho é construir
de um software capaz de detectar, classificar e contar alvéolos a partir de uma imagem.
Após, ele deverá apresentar os dados de forma simplificada ao usuário. Para tratar da alta
variação de padrões como textura, cor e iluminação presente nas alvéolos, usaremos Deep
Neural Network (DNN), que são modelos computacionais conhecidos por terem alcançado
o estado da arte em várias tarefas relacionadas a processamento de sinais e imagens. Para o
treinamento desses modelos utilizamos mais de 70.000 alvéolos anotadas por um apicultor
experiente, separadas em sete classes. Entre nossas contribuições estão métodos de préprocessamento
que garantem uma alta taxa de detecção de alvéolos, aliados a modelos
de segmentação baseados em DNNs que asseguram uma baixa taxa de falsos positivos.
Com nossos classificadores conseguimos uma acurácia média de 94% em nosso dataset e
obtivemos resultados superiores a outros métodos baseados em contagens aproximadas e
técnicas de análise por imagem que não utilizam DNNs.This research was conducted in the framework of the project BEEHOPE, funded
through the 2013-2014 BiodivERsA/FACCE-JPI Joint call for research proposals, with
the national founders FCT(Portugal), CNRS(France), and MEC(Spain)