Τα συστήματα διεπαφών ανθρώπινου εγκεφάλου-υπολογιστή (BCIs: Brain-Computer Interfaces) απαιτούν την πραγματικού χρόνου, αποτελεσματική επεξεργασία των μετρήσεων των ηλεκτροεγκεφαλογραφικών (ΗΕΓ) σημάτων του χρήστη τους, προκειμένου να μεταφράσουν τις νοητικές διεργασίες/προθέσεις του σε σήματα ελέγχου εξωτερικών διατάξεων ή συστημάτων. Στο πλαίσιο της εργασίας αυτής μελετήθηκε το θεωρητικό υπόβαθρο του προβλήματος και αναλύθηκαν συνοπτικά οι κυριότερες τεχνικές που χρησιμοποιούνται σήμερα. Επιπρόσθετα, παρουσιάστηκε μία μέθοδος ταξινόμησης των νοητικών προθέσεων της αριστερής και δεξιάς κίνησης των χεριών ενός χρήστη η οποία εφαρμόστηκε σε πραγματικά ιατρικά δεδομένα. Η εξαγωγή των χαρακτηριστικών που διαφοροποιούνται μεταξύ των δύο καταστάσεων βασίστηκε σε πληροφορίες του πεδίου χρόνου-συχνότητας, οι οποίες αντλούνται με το φιλτράρισμα των ακατέργαστων ΗΕΓ δεδομένων και με τη βοήθεια των αιτιατών κυματιδίων Morlet, ενώ για την επακόλουθη ταξινόμηση των χαρακτηριστικών αναπτύχθηκαν και συγκρίθηκαν δύο αξιόπιστες μέθοδοι. Η πρώτη αφορά στη δημιουργία πιθανοθεωρητικών προτύπων κανονικής κατανομής για κάθε κατηγορία πρόθεσης κίνησης, με την τελική απόφαση ταξινόμησης να λαμβάνεται με εφαρμογή του απλού ταξινομητή του Bayes, ενώ η δεύτερη δημιουργεί ένα πρότυπο ταξινόμησης με βάση το θεωρητικό πλαίσιο των Μηχανών Διανυσμάτων Υποστήριξης (SVM). Στόχος του προβλήματος της δυαδικής ταξινόμησης είναι να αποφασίζεται σε ποια από τις δύο κατηγορίες ανήκει μία δεδομένη νοητική πρόθεση όσο το δυνατόν ταχύτερα και αξιόπιστα, έτσι ώστε ο σχεδιαζόμενος αλγόριθμος να εξυπηρετήσει ένα πλαίσιο ανατροφοδότησης της τελικής απόφασης στο χρήστη σε συνθήκες πραγματικού χρόνου. / Brain-Computer Interfaces (BCIs) demand the efficient processing of EEG data in order to translate one's thought or wish into a control signal that can be applied as input to external devices. Here we present a method to classify left from right hand movements, by extracting features from the data with Morlet wavelets and classifying with two different models, SVMs and Naive Bayes Classifier.

In recent years knowledge has become an important resource to enhance the business and many activities are required to manage these knowledge resources well and help companies to remain competitive within industrial environments. The data available in most industrial setups is complex in nature and multiple different data formats may be generated to track the progress of different projects either related to developing new products or providing better services to the customers. Knowledge Discovery from different databases requires considerable efforts and energies and data mining techniques serve the purpose through handling structured data formats. If however the data is semi-structured or unstructured the combined efforts of data and text mining technologies may be needed to bring fruitful results. This thesis focuses on issues related to discovery of knowledge from semi-structured or unstructured data formats through the applications of textual data mining techniques to automate the classification of textual information into two different categories or classes which can then be used to help manage the knowledge available in multiple data formats. Applications of different data mining techniques to discover valuable information and knowledge from manufacturing or construction industries have been explored as part of a literature review. The application of text mining techniques to handle semi-structured or unstructured data has been discussed in detail. A novel integration of different data and text mining tools has been proposed in the form of a framework in which knowledge discovery and its refinement processes are performed through the application of Clustering and Apriori Association Rule of Mining algorithms. Finally the hypothesis of acquiring better classification accuracies has been detailed through the application of the methodology on case study data available in the form of Post Project Reviews (PPRs) reports. The process of discovering useful knowledge, its interpretation and utilisation has been automated to classify the textual data into two classes.

Continued increase in system load leading to a reduction in operating margins, as well as the tendency to move towards a deregulated grid with renewable energy sources has increased the vulnerability of the grid to blackouts. Advanced intelligent techniques are therefore required to design new monitoring schemes that enable smart grid operation in a secure and robust manner. As the grid is highly interconnected, monitoring of transmission and distribution systems is increasingly relying on digital communication. Conventional security assessment techniques are slow, hampering real-time decision making. Hence, there is a need to develop fast and accurate security monitoring techniques. Intelligent techniques that are capable of processing large amounts of captured data are finding increasing scope as essential enablers for the smart grid. The research work presented in this thesis has evolved from the need for enhanced monitoring in transmission and distribution grids. The potential of intelligent techniques for enhanced system monitoring has been demonstrated for disturbed scenarios in an integrated power system. In transmission grids, one of the challenging problems is network partitioning, also known as network area-decomposition. In this thesis, an approach based on relative electrical distance (RED) has been devised to construct zonal dynamic equivalents such that the dynamic characteristics of the original system are retained in the equivalent system within the desired accuracy. Identification of coherent generators is another key aspect in power system dynamics. In this thesis, a support vector clustering-based coherency identification technique is proposed for large interconnected multi-machine power systems. The clustering technique is based on coherency measure which is formulated using the generator rotor measurements. These rotor measurements can be obtained with the help of Phasor Measurement Units (PMUs). In distribution grids, accurate and fast fault identification of faults is a key challenge. Hence, an automated fault diagnosis technique based on multi class support vector machines (SVMs) has been developed in this thesis. The proposed fault location scheme is capable of accurately identify the fault type, location of faulted line section and the fault impedance in the distributed generation (DG) systems. The proposed approach is based on the three phase voltage and current measurements available at all the sources i.e. substation and at the connection points of DGs. An approach for voltage instability monitoring in 3-phase distribution systems has also been proposed in this thesis. The conventional single phase L-index measure has been extended to a 3-phase system to incorporate information pertaining to unbalance in the distribution system. All the approaches proposed in this thesis have been validated using standard IEEE test systems and also on practical Indian systems.