Τα Τεχνητά Νευρωνικά Δίκτυα είναι μια επιστημονική περιοχή η οποία έχει αναπτυχθεί κατά τις τελευταίες δεκαετίες και επικαλύπτει όλες σχεδόν τις θετικές επιστήμες και την μηχανολογία. Τα Νευρωνικά Δίκτυα αποτελούνται από ένα σύνολο απλών, διασυνδεδεμένων και προσαρμοστικών μονάδων οι οποίες δημιουργούν ένα παράλληλο και πολύπλοκο υπολογιστικό μοντέλο. Στην ουσία είναι προγράμματα που υλοποιούνται στους ηλεκτρονικούς υπολογιστές. Μέχρι σήμερα έχουν χρησιμοποιηθεί σε πολλές εφαρμογές και σε προβλήματα που οι γνωστοί τρόποι αντιμετώπισής τους παρουσιάζουν δυσκολίες, με αποτέλεσμα την αναγκαιότητα των Τεχνητών Νευρωνικών Δικτύων. Η εργασία αυτή αποτελείται από έξι κεφάλαια. Στο πρώτο κεφάλαιο κάνουμε μια εισαγωγή στα Τεχνητά Νευρωνικά Δίκτυα. Αναφέρουμε τις βασικές αρχές τους και την αντιστοιχία τους με τα βιολογικά δίκτυα. Το δεύτερο κεφάλαιο ασχολείται με το δίκτυο Perceptron. Ξεκινάμε με το πιο απλό μοντέλο, τον αισθητήρα και συνεχίζουμε με τα πολυεπίπεδα Νευρωνικά Δίκτυα Perceptron. Αναφέρουμε δύο μεθόδους εκπαίδευσης, τη μέθοδο οπισθοδιάδοσης του λάθους και τον κανόνα Δέλτα. Στο τρίτο κεφάλαιο μελετάμε άλλα είδη δικτύων, όπως τα αναδρομικά δίκτυα, το δίκτυο Hopfield, το δίκτυο SOM και το δίκτυο RBF. Το τέταρτο κεφάλαιο αναφέρεται στον νευρωνικό έλεγχο και στις αρχιτεκτονικές των νευρωνικών ελεγκτών. Στο πέμπτο κεφάλαιο εξετάζουμε κάποιες συγκεκριμένες εφαρμογές των Τεχνητών Νευρωνικών Δικτύων σε διάφορα συστήματα ελέγχου. Στο έκτο κεφάλαιο αναφέρουμε τα συμπεράσματα καθώς και μελλοντικές επεκτάσεις των ΤΝΔ. / Artificial Neural Networks are a research area which has developed over the past decades. Neural Networks consist of a set of simple, interconnected and adaptive plants that create a parallel and complex computational model. They are essentially programs implemented in computers. They have been used in many applications and problems that are very difficult to be solved otherwise. This work consists of six chapters. In the first chapter we make an introduction to Artificial Neural Networks. We mention the basic principles and their correlation with biological networks. The second chapter deals with the network Perceptron. We start with the simplest model, the sensor and continue with the multilayer Neural Network Perceptron. We mention two training methods, the method of error back-propagation and delta rule. In the third chapter we consider other types of networks such as the recurrent networks, Hopfield network, the network SOM and the RBF network. The fourth chapter deals with the neural control and the architectures of neural controllers. In the fifth chapter we examine some specific applications of Artificial Neural Networks in several control systems. The sixth chapter refers to the conclusions of this work and future evolution of ANN.

Nowadays islanding has become a big issue with the increasing use of distributed generators in power system. In order to effectively detect islanding after DG disconnects from main source, author first studied two passive islanding methods in this thesis: THD&VU method and wavelet-transform method. Compared with other passive methods, each of them has small non-detection zone, but both of them are based on the threshold limit, which is very hard to set. What’s more, when these two methods were applied to practical signals distorted with noise, they performed worse than anticipated. Thus, a new composite intelligent based method is presented in this thesis to solve the drawbacks above. The proposed method first uses wavelet-transform to detect the occurrence of events (including islanding and non-islanding) due to its sensitivity of sudden change. Then this approach utilizes artificial neural network (ANN) to classify islanding and non-islanding events. In this process, three features based on THD&VU are extracted as the input of ANN classifier. The performance of proposed method was tested on two typical distribution networks. The obtained results of two cases indicated the developed method can effectively detect islanding with low misclassification.

The recovery and recycling of the spent chemicals from the kraft pulping process are economically and environmentally essential in an integrated kraft pulp and paper mill. The recovery process can be optimised by firing high-solids black liquor in the recovery boiler. Unfortunately, due to a corresponding increase in the liquor viscosity, in many mills, black liquor is fired at reduced solids concentration to avoid possible rheological problems. Online measurement, monitoring and control of the liquor viscosity are deemed essential for the recovery boiler optimization. However, in most mills, including those in New Zealand, black liquor viscosity is not routinely measured. Four batches of black liquors having solids concentrations ranging between 47 % and 70 % and different residual alkali (RA) contents were obtained from Carter Holt Harvey Pulp and Paper (CHHP&P), Kinleith mill, New Zealand. Weak black liquor samples were obtained by diluting the concentrated samples with deionised water. The viscosities of the samples at solids concentrations ranging from 0 to 70 % were measured using open-cup rotational viscometers at temperatures ranging from 0 to 115 oC and shear rates between 10 and 2000 s-1. The effect of post-pulping process, liquor heat treatment (LHT) on the liquors’ viscosities was investigated in an autoclave at a temperature >=180 oC for at least 15 mins. The samples exhibit both Newtonian and non-Newtonian behaviours depending on temperature and solids concentration; the onsets of these behaviours are liquor-dependent. In conformity with the literature data, at high solids concentrations (> 50 %) and low temperatures, they exhibit shear-thinning behaviour with or without thixotropy but the shear-thinning/thixotropic characteristics disappear at high temperatures (>= 80 oC). Generally, when the apparent viscosities of the liquors are <= ~1000 cP, the liquors show a Newtonian or a near-Newtonian behaviour. These findings demonstrate that New Zealand black liquors can be safely treated as Newtonian fluids under industrial conditions. Further observations show that at low solids concentrations (< 50 %), viscosity is fairly independent of the RA content; however at solids concentrations > 50 %, viscosity decreases with increasing RA content of the liquor. This shows that the RA content of black liquor can be manipulated to control the viscosity of high-solids black liquors. The LHT process had negligible effect on the low-solids liquor viscosity but led to a significant and permanent reduction of the high-solids liquor viscosity by a factor of at least 6. Therefore, the incorporation of a LHT process into an existing kraft recovery process can help to obtain the benefits of high-solids liquor firing without a concern for the attending rheological problems. A variety of the existing and proposed viscosity models using the traditional regression modelling tools and an artificial neural network (ANN) paradigm were obtained under different constraints. Hitherto, the existing models rely on the traditional regression tools and they were mostly applicable to limited ranges of process conditions. On the one hand, composition-dependent models were obtained as a direct function of solids concentration and temperature, or solids concentration, temperature and shear rate; the relationships between these variables and the liquor viscosity are straight forward. The ANN-based models developed in this work were found to be superior to the traditional models in terms of accuracy, generalization capability and their applicability to a wide range of process conditions. If the parameters of the resulting ANN models can be successfully correlated with the liquor composition, the models would be suitable for online application. Unfortunately, black liquor viscosity depends on its composition in a complex manner; the direct correlation of its model parameters with the liquor composition is not yet a straight forward issue. On the other hand, for the first time in the Australasia, the limitations of the composition-dependent models were addressed using centrifugal pump performance parameters, which are easy to measure online. A variety of centrifugal pump-based models were developed based on the estimated data obtained via the Hydraulic Institute viscosity correction method. This is opposed to the traditional approaches, which depend largely on actual experimental data that could be difficult and expensive to obtain. The resulting age-independent centrifugal pump-based model was implemented online as a black liquor viscosity soft sensor at the number 5 recovery boiler at the CHHP&P, Kinleith mill, New Zealand where its performance was evaluated. The results confirm its ability to effectively account for variations in the liquor composition. Furthermore, it was able to give robust viscosity estimates in the presence of the changing pump’s operating point. Therefore, it is concluded that this study opens a new and an effective way for kraft black liquor viscosity sensor development.

After a digital photo has been taken by a camera, it can be manipulated to be more appealing. Two ways of doing that are to reduce noise and to increase the saturation. With time and skills in an image manipulating program, this is usually done by hand. In this thesis, automatic image improvement based on artificial neural networks is explored and evaluated qualitatively and quantitatively. A new approach, which builds on an existing method for colorizing gray scale images is presented and its performance compared both to simpler methods and the state of the art in image denoising. Saturation is lowered and noise added to original images, which the methods receive as inputs to improve upon. The new method is shown to improve in some cases but not all, depending on the image and how it was modified before given to the method.

Time series forecasting and modeling are challenging problems during the past decades, because of its plenty of properties and underlying correlated relationships. As a result, researchers proposed a lot of models to deal with the time series. However, the proposed models such as Autoregressive integrated moving average (ARIMA) and artificial neural networks (ANNs) only describe part of the properties of time series. In this thesis, we introduce a new hybrid model integrated filter structure to improve the prediction accuracy. Case studies with real data from University of Kentucky HealthCare are carried out to examine the superiority of our model. Also, we applied our model to operating room (OR) to reduce the inefficiency cost. The experiment results indicate that our model always outperforms compared with other models in different conditions.

Simulations are a good method for the verification of the correct operation of solar-powered sensor nodes over the desired lifetime. They do, however, require accurate models to capture the influences of the loads and solar energy harvesting system. Artificial neural networks promise a simplification and acceleration of the modeling process in comparison to state-of-the-art modeling methods. This work focuses on the influence of the modeling process's different configurations on the accuracy of the model. It was found that certain parameters, such as the network's number of neurons and layers, heavily influence the outcome, and that these factors need to be determined individually for each modeled harvesting system. But having found a good configuration for the neural network, the model can predict the supercapacitor's charge depending on the solar current fairly accurately. This is also true in comparison to the reference models in this work. Nonetheless, the results also show a crucial need for improvements regarding the acquisition and composition of the neural network's training set.

Vehicle dynamics development is strongly based on subjective assessments (SA) of vehicle prototypes, which is expensive and time consuming. Consequently, in the age of computer- aided engineering (CAE), there is a drive towards reducing this dependency on physical test- ing. However, computers are known for their remarkable processing capacity, not for their feelings. Therefore, before SA can be computed, it is required to properly understand the cor- relation between SA and objective metrics (OM), which can be calculated by simulations, and to understand how this knowledge can enable a more efficient and effective development process. The approach to this research was firstly to identify key OM and SA in vehicle dynamics, based on the multicollinearity of OM and of SA, and on interviews with expert drivers. Sec- ondly, linear regressions and artificial neural network (ANN) were used to identify the ranges of preferred OM that lead to good SA-ratings. This result is the base for objective require- ments, a must in effective vehicle dynamics development and verification. The main result of this doctoral thesis is the development of a method capable of predicting SA from combinations of key OM. Firstly, this method generates a classification map of ve- hicles solely based on their OM, which allows for a qualitative prediction of the steering feel of a new vehicle based on its position, and that of its neighbours, in the map. This prediction is enhanced with descriptive word-clouds, which summarizes in a few words the comments of expert test drivers to each vehicle in the map. Then, a second superimposed ANN displays the evolution of SA-ratings in the map, and therefore, allows one to forecast the SA-rating for the new vehicle. Moreover, this method has been used to analyse the effect of the tolerances of OM requirements, as well as to verify the previously identified preferred range of OM. This thesis focused on OM-SA correlations in summer conditions, but it also aimed to in- crease the effectiveness of vehicle dynamics development in general. For winter conditions, where objective testing is not yet mature, this research initiates the definition and identifica- tion of robust objective manoeuvres and OM. Experimental data were used together with CAE optimisations and ANOVA-analysis to optimise the manoeuvres, which were verified in a second experiment. To improve the quality and efficiency of SA, Volvo’s Moving Base Driving Simulator (MBDS) was validated for vehicle dynamics SA-ratings. Furthermore, a tablet-app to aid vehicle dynamics SA was developed and validated. Combined this research encompasses a comprehensive method for a more effective and ob- jective development process for vehicle dynamics. This has been done by increasing the un- derstanding of OM, SA and their relations, which enables more effective SA (key SA, MBDS, SA-app), facilitates objective requirements and therefore CAE development, identi- fies key OM and their preferred ranges, and which allow to predict SA solely based on OM. / <p>QC 20170223</p> / iCOMSA

Soil compaction is a widely spread problem in agricultural soils that has negative agronomic and environmental impacts. The former may lead to poor crop growth and yield, whereas the latter may lead to poor hydraulic properties of soils, and high risk to flooding, soil erosion and degradation. Therefore, the elimination of soil compaction must be done on regular bases. One of the main parameters to quantify soil compaction is soil bulk density (BD). Mapping of within field variation in soil BD will be a main requirement for within field management of soil compaction. The aim of this research was to develop a new approach for the measurement of soil BD as an indicator of soil compaction. The research relies on the fusion of data from visible and near infrared spectroscopy (vis-NIRS), to measure soil gravimetric moisture content (ω), with frequency domain reflectometry (FDR) data to measure soil volumetric moisture content (θv). The values of the estimated ω and θv, for the same undisturbed soil samples were collected from selected locations, textures, soil moisture contents and land use systems to derive soil BD. A total of 1013 samples were collected from 32 sites in the England and Wales. Two calibration techniques for vis-NIRS were evaluated, namely, partial least squares regression (PLSR) and artificial neural networks (ANN). ThetaProbe calibration was performed using the general formula (GF), soil specific calibration (SSC), the output voltage (OV) and artificial neural networks (ANN). ANN analyses for both ω and θv properties were based either on a single input variable or multiple input variables (data fusion). Effects of texture, moisture content, and land use on the prediction accuracy on ω, θv and BD were evaluated to arrive at the best experimental conditions for the measurement of BD with the proposed new system. A prototype was developed and tested under laboratory conditions and implemented in-situ for mapping of ω, θv and BD. When using the entire dataset (general data set), results proved that high measurement accuracy can be obtained for ω and θv with PLSR and the best performing traditional calibration method of the ThetaProbe with R2 values of 0.91 and 0.97, and root mean square error of prediction (RMSEp) of 0.027 g g-1 and 0.019 cm3 cm-3, respectively. However, the ANN – data fusion method resulted in improved accuracy (R2 = 0.98 and RMSEp = 0.014 g g-1 and 0.015 cm3 cm-3, respectively). This data fusion approach gave the best accuracy for BD assessment when only vis-NIRS spectra and ThetaProbe V were used as an input data (R2 = 0.81 and RMSEp = 0.095 g cm-3). The moisture level (L) impact on BD prediction revealed that the accuracy improved with soil moisture increasing, with RMSEp values of 0.081, 0.068 and 0.061 g cm-3, for average ω of 0.11, 0.20 and 0.28 g g-1, respectively. The influence of soil texture was discussed in relation with the clay content in %. It was found that clay positively affected vis-NIRS accuracy for ω measurement and no obvious impact on the dielectric sensor readings was observed, hence, no clear influence of the soil textures on the accuracy of BD prediction. But, RMSEp values of BD assessment ranged from 0.046 to 0.115 g cm-3. The land use effect of BD prediction showed measurement of grassland soils are more accurate compared to arable land soils, with RMSEp values of 0.083 and 0.097 g cm-3, respectively. The prototype measuring system showed moderate accuracy during the laboratory test and encouraging precision of measuring soil BD in the field test, with RMSEp of 0.077 and 0.104 g cm-3 of measurement for arable land and grassland soils, respectively. Further development of the prototype measuring system expected to improve prediction accuracy of soil BD. It can be concluded that BD can be measured accurately by combining the vis-NIRS and FDR techniques based on an ANN-data fusion approach.

A design optimization based on coupled solid–fluid analysis is investigated in this work to achieve specific flow rate through a peristaltic micropump. A micropump consisting of four pneumatically actuated nozzle/diffuser shaped moving actuators on the sidewalls is considered for numerical study. These actuators are used to create pressure difference in the four pump chambers, which in turn drives the fluid through the pump in one direction. Genetic algorithms along with artificial neural networks are used for optimizing the pump geometry and the actuation frequency. A simple example with moving walls is considered for validation by developing an exact analytical solution of Navier–Stokes equation and comparing it with numerical simulations. Possible applications of these pumps are in microelectronics cooling and drug delivery. Based on the results obtained from the fluid–structure interaction analysis, three optimized geometries result in flow rates which match the predicted flow rates with 95% accuracy. These geometries need further investigation for fabrication and manufacturing issues.

Computational joint models provide insight into the biomechanical function of human joints. Through both deformable and rigid body modeling, the structure-function relationship governing joint behavior is better understood, and subsequently, knowledge regarding normal, diseased, and/or injured function is garnered. Given the utility of these computational models, it is imperative to supply them with appropriate inputs such that model function is representative of true joint function. In these models, Magnetic Resonance Imaging (MRI) or Computerized Tomography (CT) scans and literature inform the bony anatomy and mechanical properties of muscle and ligamentous tissues, respectively. In the case of the latter, literature reports a wide range of values or average values with large standard deviations due to the inability to measure the mechanical properties of soft tissues in vivo. This makes it difficult to determine which values within the published literature to assign to computational models, especially patient-specific models. Therefore, while the use of published literature serves as a reasonable first approach to set up a computational model, a means of improving the supplied input data was sought. This work details the application of artificial neural networks (ANNs), specifically feedforward and radial basis function networks, to the optimization of ligament stiffnesses for the improved performance of pre- and post-operative, patient-specific foot/ankle computational models. ANNs are mathematical models that utilize learning rules to determine relationships between known sets of inputs and outputs. Using knowledge gained from these training data, the ANN may then predict outputs for similar, never‑before-seen inputs. Here, an optimal network of each ANN type was found, per mean square error and correlation data, and then both networks were used to predict optimal ligament stiffnesses corresponding to a single patient’s radiographic measurements. Both sets of predictions were ultimately supplied to the patient-specific computational models, and the resulting kinematics illustrated an improvement over the existing models that utilized literature-assigned stiffnesses. This research demonstrated that neural networks are a viable means to hone in on ligament stiffnesses for the overall objective of improving the predictive ability of a patient-specific computational model.