This thesis is focused on increasing productive actions in construction by a procedure known as Activity Analysis. Activity Analysis is a continuous productivity improvement tool for identifying barriers to site productivity with the goal of decreasing them and thereby increasing the direct work rate. A preceding study validated this approach, however it had two limitations. No reevaluation was conducted on projects in Canada by the authors, and not enough resources or data were available to understand behaviour of the activity rates in absolute value terms across many projects. Based on three case studies and data collected over 17 days by the author and a colleague, Activity Analysis was validated as being applicable in Canadian conditions. A desired value, known as a target rate, was then studied in order to be able to set expectations with respect to the productivity to be achieved in each cycle. The premise behind setting a “target rate” is that 100% direct work is neither possible nor desirable, since some time must always be spent on communications and planning. However, a higher direct work rate is generally better than a lower rate. Thus, a target rate is needed. A mathematical model called ANFIS was developed as a means of setting the desired level of activities. Through consideration of a variety of factors that affect labour productivity, the developed model was trained based on 65 data points. The model was found to be easy to use and flexible enough to be appropriate for all of the factors considered. Based on the data points available from 5 different past projects and 3 recent projects and the experience associated with these projects, three additional methods of defining the target rate were developed. The impact of these results is that companies now have appropriate methods and an initial data set for industrial construction in order to establish target rates for direct work and supporting activities. This should help reduce project costs and improve productivity.

Είναι ενδιαφέρουσα αλλά συνάμα δύσκολη η ανάλυση ιατρικών εικόνων, επειδή υπάρχουν πολύ μικρές διακυμάνσεις και μεγάλος όγκος δεδομένων για επεξεργασία. Είναι αρκετά δύσκολο να αναπτυχθεί ένα αυτοματοποιημένο σύστημα αναγνώρισης, το οποίο θα μπορούσε να επεξεργάζεται μεγάλο όγκο πληροφοριών των ασθενών και να παρέχει μια σωστή εκτίμηση. Στην ιατρική, η συμβατική διαγνωστική μέθοδος για εικόνες MR γονάτου για αναγνώριση ανωμαλιών, είναι από την επίβλεψη έμπειρων ιατρών. Η τεχνική της ασαφούς λογικής είναι πιο ακριβής, αλλά αυτό εξαρτάται πλήρως από τη γνώση των εμπειρογνωμόνων, η οποία μπορεί να μην είναι πάντα διαθέσιμη. Στη παρούσα εργασία, τμηματοποιούμε την MR εικόνα του γονάτου με την τεχνική Mean Shift, αναγνωρίζουμε τα κύρια μέρη με τη βοήθεια των ΗΜRF και τέλος εκπαιδεύουμε ταξινομητή ANFIS. Η απόδοση του ταξινομητή ANFIS αξιολογήθηκε όσον αφορά την απόδοση της εκαπαίδευσης και της ακρίβειας ταξινόμησης. Επιβεβαιώθηκε ότι ο ταξινομητής είχε μεγάλη ακρίβεια στην ανίχνευση ανωμαλιών στις ακτινογραφίες Στην εργασία αυτή περιγράφεται η προτεινόμενη στρατηγική για την διάγνωση ανωμαλιών στις εικόνες μαγνητικής τομογραφίας γόνατος. / It is a challenging task to analyze medical images because there are very minute variations & larger data set for analysis. It is a quite difficult to develop an automated recognition system which could process on a large information of patient and provide a correct estimation. The conventional method in medicine for knee MR images classification and diseases detection is by human inspection. Fuzzy logic technique is more accurate but it fully depends on expert knowledge, which may not always available. Here we extract the feature using Mean Shift segmentation and region recognition with HMRF and after that training using the ANFIS tool. The performance of the ANFIS classifier was evaluated in terms of training performance and classification accuracy. Here the result confirmed that the proposed ANFIS classifier with high accuracy in detecting the knee diseases. This work describes the proposed strategy to medical image classification of patient’s MRI scan images of the knee.

This thesis is focused on increasing productive actions in construction by a procedure known as Activity Analysis. Activity Analysis is a continuous productivity improvement tool for identifying barriers to site productivity with the goal of decreasing them and thereby increasing the direct work rate. A preceding study validated this approach, however it had two limitations. No reevaluation was conducted on projects in Canada by the authors, and not enough resources or data were available to understand behaviour of the activity rates in absolute value terms across many projects. Based on three case studies and data collected over 17 days by the author and a colleague, Activity Analysis was validated as being applicable in Canadian conditions. A desired value, known as a target rate, was then studied in order to be able to set expectations with respect to the productivity to be achieved in each cycle. The premise behind setting a “target rate” is that 100% direct work is neither possible nor desirable, since some time must always be spent on communications and planning. However, a higher direct work rate is generally better than a lower rate. Thus, a target rate is needed. A mathematical model called ANFIS was developed as a means of setting the desired level of activities. Through consideration of a variety of factors that affect labour productivity, the developed model was trained based on 65 data points. The model was found to be easy to use and flexible enough to be appropriate for all of the factors considered. Based on the data points available from 5 different past projects and 3 recent projects and the experience associated with these projects, three additional methods of defining the target rate were developed. The impact of these results is that companies now have appropriate methods and an initial data set for industrial construction in order to establish target rates for direct work and supporting activities. This should help reduce project costs and improve productivity.

DC to DC converters stabilize the voltage obtained from voltage sources such as solar power system, wind energy sources, wave energy sources, rectified voltage from alternators, and so forth. Hence, the need for improving its control algorithm is inevitable. Many algorithms are applied to DC to DC converters. This thesis designs fuzzy adaptive dynamic programming (Fuzzy ADP) algorithm. Also, this thesis implements both adaptive dynamic programming (ADP) and Fuzzy ADP on DC to DC converters to observe the performance of the output voltage trajectories.

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The thesis deals with artificial neural networks theory. Subsequently, fuzzy sets are being described and fuzzy logic is explained. The hybrid neuro-fuzzy system stemming from ANFIS system is designed on the basis of artificial neural networks, fuzzy sets and fuzzy logic. The upper-mentioned systems' functionality has been demonstrated on an inverted pendulum controlling problem. The three controllers have been designed for the controlling needs - the first one is on the basis of artificial neural networks, the second is a fuzzy one, and the third is based on ANFIS system.  The thesis is aimed at comparing the described systems, which the controllers have been designed on the basis of, and evaluating the hybrid neuro-fuzzy system ANFIS contribution in comparison with particular theory solutions. Finally, some experiments with the systems are demonstrated and findings are assessed.