Systems and control : 21th Benelux meeting, 2002, March 19-21, Veldhoven, The Netherlands

Book of abstract

Academic Catalog: 2010-2011

https://digitalcommons.esf.edu/acadcat/1023/thumbnail.jp

Towards the implementation of distributed systems in synthetic biology

The design and construction of engineered biological systems has made great strides over the last few decades and a growing part of this is the application of mathematical and computational techniques to problems in synthetic biology. The use of distributed systems, in which an overall function is divided across multiple populations of cells, has the potential to increase the complexity of the systems we can build and overcome metabolic limitations. However, constructing biological distributed systems comes with its own set of challenges. In this thesis I present new tools for the design and control of distributed systems in synthetic biology. The first part of this thesis focuses on biological computers. I develop novel design algorithms for distributed digital and analogue computers composed of spatial patterns of communicating bacterial colonies. I prove mathematically that we can program arbitrary digital functions and develop an algorithm for the automated design of optimal spatial circuits. Furthermore, I show that bacterial neural networks can be built using our system and develop efficient design tools to do so. I verify these results using computational simulations. This work shows that we can build distributed biological computers using communicating bacterial colonies and different design tools can be used to program digital and analogue functions. The second part of this thesis utilises a technique from artificial intelligence, reinforcement learning, in first the control and then the understanding of biological systems. First, I show the potential utility of reinforcement learning to control and optimise interacting communities of microbes that produce a biomolecule. Second, I apply reinforcement learning to the design of optimal characterisation experiments within synthetic biology. This work shows that methods utilising reinforcement learning show promise for complex distributed bioprocessing in industry and the design of optimal experiments throughout biology

A collaborative, multi-agent based methodology for abnormal events management

Ph.DDOCTOR OF PHILOSOPH

Identification Of Streptococcus Pyogenes Using Raman Spectroscopy

Despite the attention that Raman Spectroscopy has gained recently in the area of pathogen identification, the spectra analyses techniques are not well developed. In most scenarios, they rely on expert intervention to detect and assign the peaks of the spectra to specific molecular vibration. Although some investigators have used machine-learning techniques to classify pathogens, these studies are usually limited to a specific application, and the generalization of these techniques is not clear. Also, a wide range of algorithms have been developed for classification problems, however, there is less insight to applying such methods on Raman spectra. Furthermore, analyzing the Raman spectra requires pre-processing of the raw spectra, in particular, background removing. Various techniques are developed to remove the background of the raw spectra accurately and with or without less expert intervention. Nevertheless, as the background of the spectra varies in the different media, these methods still require expert effort adding complexity and inefficiency to the identification task. This dissertation describes the development of state-of-the-art classification techniques to identify S. pyogenes from other species, including water and other confounding background pathogens. We compared these techniques in terms of their classification accuracy, sensitivity, and specificity in addition to providing a bias-variance insight in selecting the number of principal components in a principal component analysis (PCA). It was observed that Random Forest provided a better result with an accuracy of 94.11%. Next, a novel deep learning technique was developed to remove the background of the Raman spectra and then identify the pathogen. The architecture of the network was discussed and it was found that this method yields an accuracy of 100% in our test samples. This outperforms other traditional machine learning techniques as discussed. In clinical applications of Raman Spectroscopy, the samples have confounding background creates a challenging task for the removal of the spectral background and subsequent identification of the pathogen in real- time. We tested our methodology on datasets composed of confounding background such as throat swabs from patients and discussed the robustness and generalization of the developed method. It was found that the misclassification error of the test dataset was around 3.7%. Also, the realization of the trained model is discussed in detail to provide a better understating and insight into the efficacy of the deep learning architecture. This technique provides a platform for general analysis of other pathogens in confounding environments as well

Enhancing remanufacturing automation using deep learning approach

In recent years, remanufacturing has significant interest from researchers and practitioners to improve efficiency through maximum value recovery of products at end-of-life (EoL). It is a process of returning used products, known as EoL products, to as-new condition with matching or higher warranty than the new products. However, these remanufacturing processes are complex and time-consuming to implement manually, causing reduced productivity and posing dangers to personnel. These challenges require automating the various remanufacturing process stages to achieve higher throughput, reduced lead time, cost and environmental impact while maximising economic gains. Besides, as highlighted by various research groups, there is currently a shortage of adequate remanufacturing-specific technologies to achieve full automation. -- This research explores automating remanufacturing processes to improve competitiveness by analysing and developing deep learning-based models for automating different stages of the remanufacturing processes. Analysing deep learning algorithms represents a viable option to investigate and develop technologies with capabilities to overcome the outlined challenges. Deep learning involves using artificial neural networks to learn high-level abstractions in data. Deep learning (DL) models are inspired by human brains and have produced state-of-the-art results in pattern recognition, object detection and other applications. The research further investigates the empirical data of torque converter components recorded from a remanufacturing facility in Glasgow, UK, using the in-case and cross-case analysis to evaluate the remanufacturing inspection, sorting, and process control applications. -- Nevertheless, the developed algorithm helped capture, pre-process, train, deploy and evaluate the performance of the respective processes. The experimental evaluation of the in-case and cross-case analysis using model prediction accuracy, misclassification rate, and model loss highlights that the developed models achieved a high prediction accuracy of above 99.9% across the sorting, inspection and process control applications. Furthermore, a low model loss between 3x10-3 and 1.3x10-5 was obtained alongside a misclassification rate that lies between 0.01% to 0.08% across the three applications investigated, thereby highlighting the capability of the developed deep learning algorithms to perform the sorting, process control and inspection in remanufacturing. The results demonstrate the viability of adopting deep learning-based algorithms in automating remanufacturing processes, achieving safer and more efficient remanufacturing. -- Finally, this research is unique because it is the first to investigate using deep learning and qualitative torque-converter image data for modelling remanufacturing sorting, inspection and process control applications. It also delivers a custom computational model that has the potential to enhance remanufacturing automation when utilised. The findings and publications also benefit both academics and industrial practitioners. Furthermore, the model is easily adaptable to other remanufacturing applications with minor modifications to enhance process efficiency in today's workplaces.In recent years, remanufacturing has significant interest from researchers and practitioners to improve efficiency through maximum value recovery of products at end-of-life (EoL). It is a process of returning used products, known as EoL products, to as-new condition with matching or higher warranty than the new products. However, these remanufacturing processes are complex and time-consuming to implement manually, causing reduced productivity and posing dangers to personnel. These challenges require automating the various remanufacturing process stages to achieve higher throughput, reduced lead time, cost and environmental impact while maximising economic gains. Besides, as highlighted by various research groups, there is currently a shortage of adequate remanufacturing-specific technologies to achieve full automation. -- This research explores automating remanufacturing processes to improve competitiveness by analysing and developing deep learning-based models for automating different stages of the remanufacturing processes. Analysing deep learning algorithms represents a viable option to investigate and develop technologies with capabilities to overcome the outlined challenges. Deep learning involves using artificial neural networks to learn high-level abstractions in data. Deep learning (DL) models are inspired by human brains and have produced state-of-the-art results in pattern recognition, object detection and other applications. The research further investigates the empirical data of torque converter components recorded from a remanufacturing facility in Glasgow, UK, using the in-case and cross-case analysis to evaluate the remanufacturing inspection, sorting, and process control applications. -- Nevertheless, the developed algorithm helped capture, pre-process, train, deploy and evaluate the performance of the respective processes. The experimental evaluation of the in-case and cross-case analysis using model prediction accuracy, misclassification rate, and model loss highlights that the developed models achieved a high prediction accuracy of above 99.9% across the sorting, inspection and process control applications. Furthermore, a low model loss between 3x10-3 and 1.3x10-5 was obtained alongside a misclassification rate that lies between 0.01% to 0.08% across the three applications investigated, thereby highlighting the capability of the developed deep learning algorithms to perform the sorting, process control and inspection in remanufacturing. The results demonstrate the viability of adopting deep learning-based algorithms in automating remanufacturing processes, achieving safer and more efficient remanufacturing. -- Finally, this research is unique because it is the first to investigate using deep learning and qualitative torque-converter image data for modelling remanufacturing sorting, inspection and process control applications. It also delivers a custom computational model that has the potential to enhance remanufacturing automation when utilised. The findings and publications also benefit both academics and industrial practitioners. Furthermore, the model is easily adaptable to other remanufacturing applications with minor modifications to enhance process efficiency in today's workplaces

Academic Catalog: 2009-2010

https://digitalcommons.esf.edu/acadcat/1022/thumbnail.jp

Academic Catalog: 2008-2009

https://digitalcommons.esf.edu/acadcat/1021/thumbnail.jp

Academic Catalog: 2007-2008

https://digitalcommons.esf.edu/acadcat/1020/thumbnail.jp

Expanding the Horizons of Manufacturing: Towards Wide Integration, Smart Systems and Tools

This research topic aims at enterprise-wide modeling and optimization (EWMO) through the development and application of integrated modeling, simulation and optimization methodologies, and computer-aided tools for reliable and sustainable improvement opportunities within the entire manufacturing network (raw materials, production plants, distribution, retailers, and customers) and its components. This integrated approach incorporates information from the local primary control and supervisory modules into the scheduling/planning formulation. That makes it possible to dynamically react to incidents that occur in the network components at the appropriate decision-making level, requiring fewer resources, emitting less waste, and allowing for better responsiveness in changing market requirements and operational variations, reducing cost, waste, energy consumption and environmental impact, and increasing the benefits. More recently, the exploitation of new technology integration, such as through semantic models in formal knowledge models, allows for the capture and utilization of domain knowledge, human knowledge, and expert knowledge toward comprehensive intelligent management. Otherwise, the development of advanced technologies and tools, such as cyber-physical systems, the Internet of Things, the Industrial Internet of Things, Artificial Intelligence, Big Data, Cloud Computing, Blockchain, etc., have captured the attention of manufacturing enterprises toward intelligent manufacturing systems