10 research outputs found
Intelligent X-ray imaging inspection system for the food industry.
The inspection process of a product is an important stage of a modern
production factory. This research presents a generic X-ray imaging inspection system
with application for the detection of foreign bodies in a meat product for the food
industry. The most important modules in the system are the image processing module
and the high-level detection system.
This research discusses the use of neural networks for image processing and
fuzzy-logic for the detection of potential foreign bodies found in x-ray images of
chicken breast meat after the de-boning process. The meat product is passed under a
solid-state x-ray sensor that acquires a dual-band two-dimensional image of the meat (a
low- and a high energy image). A series of image processing operations are applied to
the acquired image (pre-processing, noise removal, contrast enhancement). The most
important step of the image processing is the segmentation of the image into meaningful
objects. The segmentation task is a difficult one due to the lack of clarity of the acquired
X-ray images and the resulting segmented image represents not only correctly identified
foreign bodies but also areas caused by overlapping muscle regions in the meat which
appear very similar to foreign bodies in the resulting x-ray image. A Hopfield neural
network architecture was proposed for the segmentation of a X-ray dual-band image. A
number of image processing measurements were made on each object (geometrical and
grey-level based statistical features) and these features were used as the input into a
fuzzy logic based high-level detection system whose function was to differentiate
between bones and non-bone segmented regions. The results show that system's
performance is considerably improved over non-fuzzy or crisp methods. Possible noise
affecting the system is also investigated.
The proposed system proved to be robust and flexible while achieving a high
level of performance. Furthermore, it is possible to use the same approach when
analysing images from other applications areas from the automotive industry to
medicine
Integrative (Synchronisations-)Mechanismen der (Neuro-)Kognition vor dem Hintergrund des (Neo-)Konnektionismus, der Theorie der nichtlinearen dynamischen Systeme, der Informationstheorie und des Selbstorganisationsparadigmas
Der Gegenstand der vorliegenden Arbeit besteht darin, aufbauend auf dem (Haupt-)Thema, der Darlegung und Untersuchung der Lösung des Bindungsproblems anhand von temporalen integrativen (Synchronisations-)Mechanismen im Rahmen der kognitiven (Neuro-)Architekturen im (Neo-)Konnektionismus mit Bezug auf die Wahrnehmungs- und Sprachkognition, vor allem mit Bezug auf die dabei auftretende Kompositionalitäts- und Systematizitätsproblematik, die Konstruktion einer noch zu entwickelnden integrativen Theorie der (Neuro-)Kognition zu skizzie-ren, auf der Basis des Repräsentationsformats einer sog. „vektoriellen Form“, u.z. vor dem Hintergrund des (Neo-)Konnektionismus, der Theorie der nichtlinearen dynamischen Systeme, der Informationstheorie und des Selbstorganisations-Paradigmas
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The application of artificial neural networks to interpret acoustic emissions from submerged arc welding
This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.Automated fusion welding processes play a fundamental role in modern manufacturing industries. The proliferation of joint geometries together with the large permutation of associated process variable configurations has given rise to research into complex system modelling and control strategies. Many of these techniques have involved monitoring of not only the electrical characteristics of the process but visual and acoustic information. Acoustic information derived from certain welding processes is well documented as it is an established fact that skilled manual welders utilise such information as an aid to creating an optimum weld. The experimental investigation presented in this thesis is dedicated to the feasibility of monitoring airborne acoustic emissions of Submerged Arc Welding (SAW) for diagnostic and real time control purposes. The experimental method adopted for this research takes a cybernetic approach to data processing and interpretation in an attempt to replicate the robustness of human biological functions. A custom designed audio hardware system was used to analyse signals obtained from bead on mild steel plate fusion welds. Time and frequency domains were used in an attempt to establish salient characteristics or identify the signatures associated with changes of the process variables. The featured parameters were voltage / current and weld travel speed, due to their ease of validation. However, consideration has also been given to weld defect prediction due to process instabilities. As the data proved to be highly correlated and erratic when subjected to off line statistical analysis, extensive investigation was given to the application of artificial neural networks to signal processing and real time control scenarios. As a consequence, a dedicated neural based software system was developed, utilising supervised and unsupervised neural techniques to monitor the process. The research was aimed at proving the feasibility of monitoring the electrical process parameters and stability of the welding process in real time. It was shown to be possible, by the exploitation of artificial neural networks, to generate a number of monitoring parameters indicative of the welding process state. The limitations of the present neural method and proposed developments are discussed, together with an overview of applied neural network technology and its impact on artificial intelligence and robotic control. Further developments are considered together with recommendations for future areas of research
Classifying the suras by their lexical semantics :an exploratory multivariate analysis approach to understanding the Qur'an
PhD ThesisThe Qur'an is at the heart of Islamic culture. Careful, well-informed interpretation of
it is fundamental both to the faith of millions of Muslims throughout the world, and
also to the non-Islamic world's understanding of their religion. There is a long and
venerable tradition of Qur'anic interpretation, and it has necessarily been based on
literary-historical methods for exegesis of hand-written and printed text.
Developments in electronic text representation and analysis since the second half of
the twentieth century now offer the opportunity to supplement traditional techniques
by applying the newly-emergent computational technology of exploratory
multivariate analysis to interpretation of the Qur'an. The general aim of the present
discussion is to take up that opportunity.
Specifically, the discussion develops and applies a methodology for discovering the
thematic structure of the Qur'an based on a fundamental idea in a range of
computationally oriented disciplines: that, with respect to some collection of texts, the
lexical frequency profiles of the individual texts are a good indicator of their semantic
content, and thus provide a reliable criterion for their conceptual categorization
relative to one another. This idea is applied to the discovery of thematic
interrelationships among the suras that constitute the Qur'an by abstracting lexical
frequency data from them and then analyzing that data using exploratory multivariate
methods in the hope that this will generate hypotheses about the thematic structure of
the Qur'an.
The discussion is in eight main parts. The first part introduces the discussion. The
second gives an overview of the structure and thematic content of the Qur'an and of
the tradition of Qur'anic scholarship devoted to its interpretation. The third part
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defines the research question to be addressed together with a methodology for doing
so. The fourth reviews the existing literature on the research question. The fifth
outlines general principles of data creation and applies them to creation of the data on
which the analysis of the Qur'an in this study is based. The sixth outlines general
principles of exploratory multivariate analysis, describes in detail the analytical
methods selected for use, and applies them to the data created in part five. The
seventh part interprets the results of the analyses conducted in part six with reference
to the existing results in Qur'anic interpretation described in part two. And, finally, the
eighth part draws conclusions relative to the research question and identifies
directions along which the work presented in this study can be developed
Flow-3D CFD model of bifurcated open channel flow: setup and validation
Bifurcation is a morphological feature present in most of fluvial systems; where a river splits into two channels, each bearing a portion of the flow and sediments. Extensive theoretical studies of river bifurcations were performed to understand the nature of flow patterns at such diversions. Nevertheless, the complexity of the flow structure in the bifurcated channel has resulted in various constraints on physical experimentation, so computational modelling is required to investigate the phenomenon. The advantages of computational modelling compared with experimental research (e.g. simple variable control, reduced cost, optimize design condition etc.) are widely known. The great advancement of computer technologies and the exponential increase in power, memory storage and affordability of high-speed machines in the early 20th century led to evolution and wide application of numerical fluid flow simulations, generally referred to as Computational Fluid Dynamics {CFD). In this study, the open-channel flume with a lateral channel established by Momplot et al (2017) is modelled in Flow-3D. The original investigation on divided flow of equal widths as simulated in ANSYS Fluent and validated with velocity measurements