The exponential development of biotechnology has lead to a quasi unlimited number of potential products going from biopolymers to vaccines. Cell culture has therefore evolved from the simple cell growth outside its natural environment to its use to produce molecules that they do not naturally produce. This rapid development could not be continued without new control and supervising tools as well as a good process understanding. This requirement involves however a large diversity and a better accessibility of process measurements. In this framework, software sensors show numerous potentialities. The objective of a software sensor is indeed to provide an estimation of the system state variables and particularly those which are not obtained through in situ hardware sensors or laborious and expensive analysis. In this context, This work attempts to join the knowledge of increasing bioprocess complexity and diversity and the time scale of process developments and favours systematic modelling methodology, its flexibility and the speed of development. In the field of state observation, an important modelling constraint is the one induced by the selection of the state to estimate and the available measurements. Another important constraint is the model quality. The central axe of this work is to provide solutions in order to reduce the weight of these constraints to software sensors development. On this purpose, we propose four solutions to four main questions that may arise. The first two ones concern modelling uncertainties. 1."How to develop a software sensor using measurements easily available on pilot scale bioreactor?" The proposed solution is a static software sensor using an artificial neural network. Following this modelling methodology we developed static software sensors for the biomass and ethanol concentrations in a pilot scale S. cerevisae cell culture using the measurement of titrating base quantity, agitation rate and CO₂ concentration in the exhaust gas. 2."How to obtain a reaction scheme and a kinetic model to develop a dynamic observation model?". The proposed solution is to combine three elements: a systematic methodology to generate, identify and select the possible reaction schemes, a general kinetic model and a systematic identification procedure where the last step is particularly dedicated to the identification of observation models. Combining these methodologies allowed us to develop a software sensor for the concentrations of an allergen produced by an animal cell culture using the discrete measurement of glucose, glutamine and ammonium concentrations (which are also estimated in continuous time by the software sensors). The two other questions are dealing with kinetic model uncertainty. 3 "How to correct kinetic model parameters while keeping the system observability?". We consider the possibility to correct some model parameters during the process observation. We propose indeed an adaptive observer based on the theory of the most likely initial conditions observer and exploiting the information from the asymptotic observer. This algorithm allows to jointly estimate the state and some kinetic model parameters. 4 "How to avoid any state observer selection that requires an a priori knowledge on the model quality?". Answering this question lead us to the development of hybrid state observers. The general principle of a hybrid observer is to automatically evaluate the model quality and to select the appropriate state observer. In this work we focus on kinetic model quality and propose hybrid observers that evolves between the state observation from an exponential observer (free convergence rate tuning but model error sensitivity) and the one provided by an asymptotic observer (no kinetic model requirement but a convergence rate depending on the dilution rate). Two strategies are investigated in order to evaluate the model quality and to induce the state observation evolution. Each of them have been validated on two simulated cultures (microbial and animal cells) and one real industrial one (B. subtilis). ∙ In a first strategy, the hybrid observer is based on the determination of a parameter that drives the state estimation from the one obtained with an exponential observer (exponential observation) when the model is of good quality to the one provided by an asymptotic observer (asymptotic observation) when a kinetic model error is detected. The evaluation of this driving parameter is made either with an a priori defined function or is coupled to the identification of the initial conditions in a most likely initial conditions observer. ∙ In another strategy, the hybrid observer is based on a statistical test that compares the state estimations provided by an exponential and an asymptotic observer and corrects the state estimation according to it./ Le rapide développement des biotechnologies permet actuellement d'envisager un nombre quasi illimité de produits potentiels allant du biopolymère au vaccin. La culture cellulaire a dès lors évolué de la simple croissance de cellules en dehors de leur environnement naturel à son exploitation pour la production de molécules qu'elles ne produisent pas naturellement. Un tel développement ne peut se poursuivre sans l'utilisation de nouvelles technologies de contrôle et de supervision ainsi q'une bonne compréhension et maîtrise du biprocédé. Cette exigence nécessite cependant une meilleure accessibilité et une plus grande variabilité des mesures des différentes variables de ce procédé. Dans ce contexte, les capteurs logiciels présentent de nombreuses potentialités. L'objectif d'un capteur logiciel est en effet de fournir une estimation des états d'un système et particulièrement de ceux qui ne sont pas mesurés par des capteurs physiquement installés sur le système ou par de longues et coûteuses analyses. Cet objectif peut être obtenu en combinant un modèle du système avec certaines mesures physiques au sein d'un algorithme d'observation d'état. Dans ce domaine de l'observation des bioprocédés, ce travail tente de considérer, à la fois, l'augmentation de la complexité et de la diversité des bioprocédés et l'exigence d'un développement rapide en favorisant le caractère systématique, flexible et rapide des méthodes proposées. Dans le cadre de l'observation des bioprocédés, une importante contrainte de modélisation est induite par la sélection des états à estimer et des mesures disponibles pour cette estimation. Une seconde contrainte est la qualité du modèle. L'axe central de ce travail est de fournir certaines solutions afin de réduire le poids de ces contraintes dans le développement de capteurs logiciels. Pour ce faire, nous proposons quatre réponses à quatre questions qui peuvent survenir lors de ce développement. Les deux premières questions concernent l'incertitude de modélisation. Quant aux deux questions suivantes, elles concernent l'incertitude du modèle lui-même. 1."Comment développer un capteur logiciel exploitant des mesures facilement disponibles sur un bioréacteur pilote?". La réponse que nous apportons à cette question est le développement d'un capteur logiciel statique basé sur un réseau de neurones artificiels. Cette structure nous a permis de développer des capteurs logiciels de concentrations en biomasse et éthanol au sein d'une culture de S. cerevisae utilisant les mesures en ligne de quantité de base titrante, de vitesse d'agitation et de concentration en CO₂ dans le gaz sortant du réacteur. 2."Comment obtenir un schéma réactionnel et un modèle cinétique pour l'identification d'un modèle dynamique d'observation". Afin de répondre à cette question, nous proposons de combiner trois éléments: une méthode de génération systématique de schémas réactionnels, une structure générale de modèle cinétique et une méthode d'identification dont la dernière étape est particulièrement dédiée à l'identification de modèles d'observation. La combinaison de ces éléments nous a permis de développer un capteur logiciel permettant l'estimation continue de la concentration en un allergène produit par une culture de cellules animales en utilisant des mesures échantillonnées de glucose, glutamine et ammonium (qui sont elles aussi estimées en continu par le capteur logiciel). 3."Comment corriger certains paramètres cinétiques tout en maintenant l'observabilité du système?". Nous considérons ici la possibilité de corriger certains paramètres du modèle cinétique durant le procédé de culture. Nous proposons, en effet, un observateur d'état adaptatif exploitant la théorie de l'observateur par identification des conditions initiales les plus vraisemblables et l'information fournie par un observateur asymptotique. L'algorithme proposé permet ainsi de fournir une estimation conjointe de l'état et de certains paramètres cinétiques. 4."Comment éviter la sélection d'un observateur d'état nécessitant une connaissance, a priori, de la qualité du modèle?". La dernière contribution de ce travail concerne le développement d'observateurs d'état hybrides. Le principe général d'un observateur hybride est d'évaluer automatiquement la qualité du modèle et de sélectionner l'observateur d'état approprié. Au sein de ce travail nous considérons la qualité du modèle cinétique et proposons des observateurs d'état hybrides évoluant entre un observateur dit exponentiel (libre ajustement de la vitesse de convergence mais forte sensibilité aux erreurs de mesures) et un observateur asymptotique (ne nécessite aucun modèle cinétique mais présente une vitesse de convergence dépendante du taux de dilution). Afin de réaliser cette évaluation et d'induire l'évolution de l'observation d'état entre ces deux extrémités, deux stratégies sont proposées. Chacune d'elle est illustrée sur deux cultures simulées (une croissance bactérienne et une culture de cellules animales) et une culture réelle de B. subtilis. ∙ Une première stratégie est basée sur la détermination d'un paramètre de pondération entre l'observation fournie par un observateur exponentiel et un observateur asymptotique. L'évaluation de ce paramètre peut être obtenue soit au moyen d'une fonction définie a priori soit par une identification conjointe aux conditions initiales d'un observateur par identification des conditions initiales les plus vraisemblables. ∙ Une seconde stratégie est basée sur une comparaison statistique entre les observations fournies par les deux types d'observateurs. Le résultat de cette comparaison, lorsqu'il indique une incohérence entre les deux observateurs d'état, est alors utilisé pour corriger l'estimation fournie par l'observateur exponentiel.

In our country farmers have to face many difficulties because of the poor irrigation system. During flood situation, excessive waters will be staged in paddy field producing great loss and pain to farmers. So, proper Irrigation mechanism is an essential component of paddy production. Poor irrigation methods and crop management are rapidly depleting the country’s water table. Most small hold farmers cannot afford new wells or lawns and they are looking for alternative methods to reduce their water consumption. So proper irrigation mechanism not only leads to high crop production but also pave a way for water saving techniques. Automation of irrigation system has the potential to provide maximum water usage efficiency by monitoring soil moistures. The control unit based on Artificial Neural Network is the pivotal block of entire irrigation system. Using this control unit certain factors like temperature, kind of the soil and crops, air humidity, radiation in the ground were estimated and this will help to control the flow of water to acquire optimized results. / Water is an essential resource in the earth. It is also essential for irrigation, so irrigation technique is essential for agriculture. To irrigate large area of lands is a tedious process. In our country farmers are not following proper irrigation techniques. Currently, most of the irrigation scheduling systems and their corresponding automated tools are in fixed rate. Variable rate irrigation is very essential not only for the improvement of irrigation system but also to save water resource for future purpose. Most of the irrigation controllers are ON/OFF Model. These controllers cannot give optimal results for varying time delays and system parameters. Artificial Neural Network (ANN) based intelligent control system is used for effective irrigation scheduling in paddy fields. The input parameters like air, temperature, soil moisture, radiations and humidity are modeled. Using appropriate method, ecological conditions, Evapotranspiration, various growing stages of crops are considered and based on that the amount of water required for irrigation is estimated. Using this existing ANN based intelligent control system, the water saving procedure in paddy field can be achieved. This model will lead to avoid flood in paddy field during the rainy seasons and save that water for future purposes.

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Water distribution utilities are responsible for supplying both clean and safe drinking water, while under constraints of operating at an efficient and acceptable performance level. The City of Kingston, Ontario is currently experiencing elevated costs to repair its aging buried water main assets. Utilities Kingston is opting for a more efficient and practical means of forecasting pipe breaks and the application of a predictive water main break models allows Utilities Kingston to forecast future pipe failures and plan accordingly. The objective of this thesis is to develop an artificial neural network (ANN) model to forecast pipe breaks in the Kingston water distribution network. Data supplied by Utilities Kingston was used to develop the predictive ANN water main break model incorporating multiple variables including pipe age, diameter, length, and surrounding soil type. The constructed ANN model from historical break data was utilized to forecast pipe breaks for 1-year, 2-year, and 5-year planning periods. Simulated results were evaluated by statistical performance metrics, proving the overall model to be adequate for testing and forecasting. Predicted breaks were as follows, 33 breaks for 2011-2012, 22 breaks for 2012-2013 and 35 breaks for 2013-2016. Additionally, GIS plots were developed to highlight areas in need of potential rehabilitation for the distribution system. The goal of the model is to provide a practical means to assist in the management and development of Kingston’s pipe rehabilitation program, and to enable Utilities Kingston to reduce water main repair costs and to improve water quality at the customer's tap. / Thesis (Master, Civil Engineering) -- Queen's University, 2013-10-21 15:30:10.288

This thesis presents a fuzzy logic controller aimed at maintaining constant tension between two adjacent stands in tandem rolling mills. The fuzzy tension controller monitors tension variation by resorting to electric current comparison of different operation modes and sets the reference for speed controller of the upstream stand. Based on modeling the rolling stand as a single input single output linear discrete system, which works in the normal mode and is subject to internal and external noise, the element settings and parameter selections in the design of the fuzzy controller are discussed. To improve the performance of the fuzzy controller, a dynamic fuzzy controller is proposed. By switching the fuzzy controller elements in relation to the step response, both transient and stationary performances are enhanced. To endow the fuzzy controller with intelligence of generalization, flexibility and adaptivity, self-learning techniques are introduced to obtain fuzzy controller parameters. With the inclusion of supervision and concern for conventional control criteria, the parameters of the fuzzy inference system are tuned by a backward propagation algorithm or their optimal values are located by means of a genetic algorithm. In simulations, the neuro-fuzzy tension controller exhibits the real-time applicability, while the genetic fuzzy tension controller reveals an outstanding global optimization ability.

This thesis is concerned with one important question in artificial neural networks, that is, how biologically inspired connectivity of a network affects its associative memory performance. In recent years, research on the mammalian cerebral cortex, which has the main responsibility for the associative memory function in the brains, suggests that the connectivity of this cortical network is far from fully connected, which is commonly assumed in traditional associative memory models. It is found to be a sparse network with interesting connectivity characteristics such as the “small world network” characteristics, represented by short Mean Path Length, high Clustering Coefficient, and high Global and Local Efficiency. Most of the networks in this thesis are therefore sparsely connected. There is, however, no conclusive evidence of how these different connectivity characteristics affect the associative memory performance of a network. This thesis addresses this question using networks with different types of connectivity, which are inspired from biological evidences. The findings of this programme are unexpected and important. Results show that the performance of a non-spiking associative memory model is found to be predicted by its linear correlation with the Clustering Coefficient of the network, regardless of the detailed connectivity patterns. This is particularly important because the Clustering Coefficient is a static measure of one aspect of connectivity, whilst the associative memory performance reflects the result of a complex dynamic process. On the other hand, this research reveals that improvements in the performance of a network do not necessarily directly rely on an increase in the network’s wiring cost. Therefore it is possible to construct networks with high associative memory performance but relatively low wiring cost. Particularly, Gaussian distributed connectivity in a network is found to achieve the best performance with the lowest wiring cost, in all examined connectivity models. Our results from this programme also suggest that a modular network with an appropriate configuration of Gaussian distributed connectivity, both internal to each module and across modules, can perform nearly as well as the Gaussian distributed non-modular network. Finally, a comparison between non-spiking and spiking associative memory models suggests that in terms of associative memory performance, the implication of connectivity seems to transcend the details of the actual neural models, that is, whether they are spiking or non-spiking neurons.

Towards having a one size fits all solution to cocaine detection at borders; this thesis proposes a systematic cocaine detection methodology that can use raw data output from a fibre optic sensor to produce a set of unique features whose decisions can be combined to lead to reliable output. This multidisciplinary research makes use of real data sourced from cocaine analyte detecting fibre optic sensor developed by one of the collaborators - City University, London. This research advocates a two-step approach: For the first step, the raw sensor data are collected and stored. Level one fusion i.e. analyses, pre-processing and feature extraction is performed at this stage. In step two, using experimentally pre-determined thresholds, each feature decides on detection of cocaine or otherwise with a corresponding posterior probability. High level sensor fusion is then performed on this output locally to combine these decisions and their probabilities at time intervals. Output from every time interval is stored in the database and used as prior data for the next time interval. The final output is a decision on detection of cocaine. The key contributions of this thesis includes investigating the use of data fusion techniques as a solution for overcoming challenges in the real time detection of cocaine using fibre optic sensor technology together with an innovative user interface design. A generalizable sensor fusion architecture is suggested and implemented using the Bayesian and Dempster-Shafer techniques. The results from implemented experiments show great promise with this architecture especially in overcoming sensor limitations. A 5-fold cross validation system using a 12 13 - 1 Neural Network was used in validating the feature selection process. This validation step yielded 89.5% and 10.5% true positive and false alarm rates with 0.8 correlation coefficient. Using the Bayesian Technique, it is possible to achieve 100% detection whilst the Dempster Shafer technique achieves a 95% detection using the same features as inputs to the DF system.

Neural networks have frequently been found to give accurate solutions to hard classification problems. However neural networks do not make explained classifications because the class boundaries are implicitly defined by the network weights, and these weights do not lend themselves to simple analysis. Explanation is desirable because it gives problem insight both to the designer and to the user of the classifier. Many methods have been suggested for explaining the classification given by a neural network, but they all suffer from one or more of the following disadvantages: a lack of equivalence between the network and the explanation; the absence of a probability framework required to express the uncertainty present in the data; a restriction to problems with binary or coarsely discretised features; reliance on axis-aligned rules, which are intrinsically poor at describing the boundaries generated by neural networks. The structure of the solution presented in this thesis rests on the following steps: Train a standard neural network to estimate the class conditional probabilities. Bayes’ rule then defines the optimal class boundaries. Obtain an explicit representation of these class boundaries using a piece-wise linearisation technique. Note that the class boundaries are otherwise only implicitly defined by the network weights. Obtain a safe but possibly partial description of this explicit representation using rules based upon the city-block distance to a prototype pattern. The methods required to achieve the last two represent novel work which seeks to explain the answers given by a proven neural network solution to the classification problem.

This thesis describes the use of a Real-Time Evolutionary Algorithm (RTEA) to optimise an Artificial Neural Network (ANN) on-line (in this context “on-line” means while it is in use). Traditionally, Evolutionary Algorithms (Genetic Algorithms, Evolutionary Strategies and Evolutionary Programming) have been used to train networks before use - that is “off-line,” as have other learning systems like Back-Propagation and Simulated Annealing. However, this means that the network cannot react to new situations (which were not in its original training set). The system outlined here uses a Simulated Legged Robot as a test-bed and allows it to adapt to a changing Fitness function. An example of this in reality would be a robot walking from a solid surface onto an unknown surface (which might be, for example, rock or sand) while optimising its controlling network in real-time, to adjust its locomotive gait, accordingly. The project initially developed a Central Pattern Generator (CPG) for a Bipedal Robot and used this to explore the basic characteristics of RTEA. The system was then developed to operate on a Quadruped Robot and a test regime set up which provided thousands of real-environment like situations to test the RTEA’s ability to control the robot. The programming for the system was done using Borland C++ Builder and no commercial simulation software was used. Through this means, the Evolutionary Operators of the RTEA were examined and their real-time performance evaluated. The results demonstrate that a RTEA can be used successfully to optimise an ANN in real-time. They also show the importance of Neural Functionality and Network Topology in such systems and new models of both neurons and networks were developed as part of the project. Finally, recommendations for a working system are given and other applications reviewed.

This thesis proposes an innovative wormhole detection scheme to detect wormhole attacks using computational intelligence and an artificial neural network (ANN). The aim of the proposed research is to develop a detection scheme that can detect wormhole attacks (In-band, out of band, hidden wormhole attack, active wormhole attack) in both uniformly and non-uniformly distributed sensor networks. Furthermore, the proposed research does not require any special hardware and causes no significant network overhead throughout the network. Most importantly, the probable location of the wormhole nodes can be tracked down by the proposed ANN-based detection scheme. We evaluate the efficacy of the proposed detection scheme in terms of detection accuracy, false positive rate, and false negative rate. The performance of the proposed model is also compared with other machine learning techniques (i.e. SVM and regularized nonlinear logistic regression (LR) based detection models) based detection schemes. The simulation results show that proposed ANN-based detection model outperforms the SVM and LR based detection schemes in terms of detection accuracy, false positive rate, and false negative rates. / February 2017