Modeling the ionosphere, where the effects of solar dynamo becomes more effective to space based and ground borne activities, has an undeniable importance for telecommunication and navigation purposes. Mid-latitude electron density trough is an interesting phenomenon in characterizing the behavior of the ionosphere, especially during disturbed conditions. Modeling the mid-latitude electron density trough is a very popular research subject which has been studied by several researchers until now. In this work, an operational technique has been developed for a probabilistic space weather forecast using fuzzy modeling and computer based detection of trough in two steps. First step is to detect the appropriate geomagnetical conditions for trough formation, depending on the values of 3-h planetary K index (Kp), magnetic season, latitude and local time, by using fuzzy modeling technique. Once the suitable geomagnetic conditions are detected, second step is to find the lower latitude position (LLP) and minimum position (MP) of the observed trough being two main identifiers of the mid-latitude electron density trough. A number of case studies were performed on ARIEL 4 satellite data, composed of different geomagnetic, annual and diurnal characteristics. The results obtained from fuzzy modeling show that the model is able to detect the appropriate conditions for trough occurrence and the trough shape was effectively identified for each selected case by using the predefined descriptions of mid-latitude electron density trough. The overall results are observed to be promising.

Nowadays, low-cost and tiny sensors are started to be commonly used due to developing technology. Wireless sensor networks become the solution for a variety of applications such as military applications. For military applications, classification of a target in a battlefield plays an important role. Target classification can be done effectively by using wireless sensor networks. A wireless sensor node has the ability to sense the raw signal data in battlefield, extract the feature vectors from sensed signal and produce a local classification result using a classifier. Although only one sensor is enough to produce a classification result, decision fusion of the local classification results for the sensor nodes improves classification accuracy and loads lower computational burden on the sensor nodes. Decision fusion performance can also be improved by picking optimum sensor nodes for target classification. In this thesis, we propose fuzzy decision fusion methods for single target classification in wireless sensor networks. Our proposed fusion algorithms use fuzzy logic for selecting the appropriate sensor nodes to be used for classification. Our solutions provide better classification accuracy over some popular decision fusion algorithms. In addition to fusion algorithms, we present some techniques for feature vector size reduction on sensor nodes, and training set formation for classifiers.

It is a known fact that construction projects do not often complete on time due to several reasons related with the unexpected changes in the project conditions, external factors or performance of project participants. Since construction projects are unique and limited information is available at the beginning of the projects, prediction of delays is a difficult task. However, if the delays can be assessed at the early stages, their impacts might be minimized, some of the delays can even be eliminated. This study introduces a delay assessment methodology which can be used to predict delays both at the activity level and project level. Fuzzy logic and fuzzy network analysis form the basis of this methodology. A software has been developed using the proposed delay assessment methodology and a delay taxonomy developed by Bilgin (2011). Project management teams can use the developed tool to predict delays and also evaluate impacts of delays on a project`s schedule.

Factors of very rapidly rising oil prices, the running out time limits on continued use of fossil fuels, as well as elements of the Kyoto Protocol, have greatly arouses the increasing emphasis on natural and renewable energy sources. 40 minutes of total solar radiation on earth could provide enough power to meet the energy needs of all human beings for approximately one year. The potential of solar energy is virtually limitedless. Moreover, by means of solar powered batteries, solar energy can be directly converted to electric power. Since it neither pollutes the environment or ecology, solar is an extremely clean source of energy. The life-span of solar cell is very long, possibly 20 years or more. The capability of solar batteries to provide energy is approximately proportional to the intensity of the sunlight. This thesis proposes the use of Artificial intelligence for "Sun Track Solar Energy System". This system employs Fuzzy Logic Control Theory, combined with Grey Relational Analysis, for tracking the angle of the sun, and further control the motor to adjust the angle for tracking, so direct sunlight could be acquired to increase power output. As a result of the experiment, comparing the electricity generated from the fix angle solar battery with the AI-based Sun Track Solar Energy System, the latter one has an efficiency increase up to 23% for the same amount of sunlight.

<p>The mental workload for fighter pilots in modern air combat is extremely high. The pilot has to make fast dynamic decisions under high uncertainty and high time pressure. This is hard to perform in close encounters, but gets even harder when operating beyond visual range when the sensors of an aircraft become the pilot's eyes and ears. Although sensors provide good estimates for position and speed of an opponent, there is a big loss in the assessment of a situation. Important tactical events or situations can occur without the pilot noticing, which can change the outcome of a mission completely. This makes the development of an automated situation assessment system very important for future fighter aircraft. </p><p>This Master Thesis investigates the possibilities to design and implement an automated situation assessment system in a fighter aircraft. A Fuzzy-Bayesian hybrid technique is used in order to cope with the stochastic environment and making the development of the tactical situations library as clear and simple as possible.</p>

Many of the first robots ever built, decades even before the first industrial robots, were humanoids. It seems like researchers have always sought to imitate the human form with their robots, and with good reason. Humans are incredibly flexible; they can perform a huge variety of tasks, from locomotion over rough terrain, to delicate assembly, to heavy lifting. A human’s second arm allows him to lift twice as much weight. His workspace is approximately doubled, and he can perform a broader variety of tasks as items are passed back and forth between hands. We sought to impart some of that same functionality to a strong, rigid, dual-arm robot. Specifically, we developed a control law that allows two robot arms to lift and manipulate an object in cooperation. As opposed to the prior art, our control law is tailored for industrial robots. These robots do not usually allow torque control and their control frequency is generally 60 Hz. Through the use of fuzzy logic, the control law is quite robust at 60 Hz control rates. Its simple structure reduces the computational cost of the algorithm by approximately 75% over Jacobian-based methods. Stability is proven and the controller parameters can be adjusted to handle perturbances of arbitrary magnitude. Since the robots behave as an admittance, torque control is not required. Several experiments were conducted to benchmark and validate the performance of this control law. The controller is able to maintain a clamp force within ± 4N despite a wide variation in trajectory and control frequency. This fine level of force control makes the controller suitable for delicate tasks. The conclusion suggests several extensions that would make this control law more useful. For example, adaptive control would improve the performance. A position feedback controller should be cascaded so that the robot arms’ tracking accuracy is improved. Many tasks (such as co-robotics) require external compliance, and we show how external compliance could easily be incorporated. / text

This thesis presents a novel two stage multimodal speech enhancement system, making use of both visual and audio information to filter speech, and explores the extension of this system with the use of fuzzy logic to demonstrate proof of concept for an envisaged autonomous, adaptive, and context aware multimodal system. The design of the proposed cognitively inspired framework is scalable, meaning that it is possible for the techniques used in individual parts of the system to be upgraded and there is scope for the initial framework presented here to be expanded. In the proposed system, the concept of single modality two stage filtering is extended to include the visual modality. Noisy speech information received by a microphone array is first pre-processed by visually derived Wiener filtering employing the novel use of the Gaussian Mixture Regression (GMR) technique, making use of associated visual speech information, extracted using a state of the art Semi Adaptive Appearance Models (SAAM) based lip tracking approach. This pre-processed speech is then enhanced further by audio only beamforming using a state of the art Transfer Function Generalised Sidelobe Canceller (TFGSC) approach. This results in a system which is designed to function in challenging noisy speech environments (using speech sentences with different speakers from the GRID corpus and a range of noise recordings), and both objective and subjective test results (employing the widely used Perceptual Evaluation of Speech Quality (PESQ) measure, a composite objective measure, and subjective listening tests), showing that this initial system is capable of delivering very encouraging results with regard to filtering speech mixtures in difficult reverberant speech environments. Some limitations of this initial framework are identified, and the extension of this multimodal system is explored, with the development of a fuzzy logic based framework and a proof of concept demonstration implemented. Results show that this proposed autonomous,adaptive, and context aware multimodal framework is capable of delivering very positive results in difficult noisy speech environments, with cognitively inspired use of audio and visual information, depending on environmental conditions. Finally some concluding remarks are made along with proposals for future work.

Lexical vagueness is a ubiquitous phenomenon in natural language. Most of previous works in natural language processing (NLP) consider lexical ambiguity as the main problem in natural language understanding rather than lexical vagueness. Lexical vagueness is usually considered as a solution rather than a problem in natural language understanding since precise information is usually failed to be provided in conversations. However, lexical vagueness is obviously an obstacle in human robot interaction (HRI) since the robots are expected to precisely understand their users' utterances in order to provide reliable services to their users. This research aims to develop novel lexical vagueness handling techniques to enable service robots to precisely understand their users' utterance so that they can provide the reliable services to their users. A novel integrated system to handle lexical vagueness is proposed in this research based on an in-depth understanding of lexical ambiguity and lexical vagueness including why they exist, how they are presented, what differences are in between them, and the mainstream techniques to handle lexical ambiguity and lexical vagueness. The integrated system consists of two blocks: the block of lexical ambiguity handling and the block of lexical vagueness handling. The block of lexical ambiguity handling first removes syntactic ambiguity and lexical ambiguity. The block of lexical vagueness handling is then used to model and remove lexical vagueness. Experimental results show that the robots endowed with the developed integrated system are able to understand their users' utterances. The reliable services to their users, therefore, can be provided by the robots.

published_or_final_version / Mechanical Engineering / Master / Master of Philosophy

Στην παρούσα διδακτορική διατριβή μελετήθηκε η διασύνδεση ενός υπεράκτιου Αιολικού Πάρκου (ΑΠ) επαγωγικών μηχανών ονομαστικής ισχύος 90MW με ένα απομακρυσμένο ασθενές δίκτυο ΕΡ. Η περίπτωση αυτή παρουσιάζει σημαντικές δυσκολίες λόγω του χαμηλού λόγου ισχύων βραχυκύκλωσης ανάμεσα στις δύο πλευρές και των έντονων διακυμάνσεων της ισχύος που απομαστεύεται από τον άνεμο. Με σκοπό να περιορισθούν οι δυσκολίες αυτές και δεδομένου ότι τα διασυνδεδεμένα άκρα είναι απομακρυσμένα (άρα επιβάλλεται η μεταφορά με καλώδιο ΣΡ), επιλέχθηκε η εφαρμογή της διασύνδεσης του ΑΠ στο δίκτυο ΕΡ μέσω της τεχνολογίας Εναλλασσομένου Ρεύματος/ Συνεχούς Ρεύματος/ Εναλλασσομένου Ρεύματος (ΕΡ/ΣΡ/ΕΡ) με Μετατροπείς Πηγής Τάσης (ΜΠΤ). Η τεχνολογία αυτή επιτρέπει τον ανεξάρτητο έλεγχο της πραγματικής από την άεργο ισχύ και έτσι καθιστά δυνατή τη διασύνδεση δικτύων με χαμηλό λόγο ισχύων βραχυκύκλωσης. Ένα επιπλέον πρόβλημα το οποίο επιλύεται μέσω της χρήσης της τεχνολογίας ΕΡ/ΣΡ/ΕΡ με ΜΠΤ είναι αυτό της δυναμικής αντιστάθμισης αέργου ισχύος των επαγωγικών γεννητριών. Ωστόσο, παρόλα τα οφέλη που αποκομίζονται εφαρμόζοντας την τεχνολογία ΕΡ/ΣΡ/ΕΡ με ΜΠΤ, ο έλεγχος της διασύνδεσης παραμένει μια απαιτητική διαδικασία. Ο σχεδιασμός του συστήματος ελέγχου απαιτεί λεπτομερή γνώση της συμπεριφοράς του συστήματος και ακριβή ρύθμιση ώστε να αποκτηθεί η επιθυμητή έξοδος. Η παρουσία έντονα μεταβαλλόμενων και με θόρυβο εισόδων μπορούν να καταστήσουν πολύ δύσκολη την επιλογή των παραμέτρων ελέγχου οι οποίες παρέχουν σωστή συμπεριφορά σε οποιεσδήποτε συνθήκες λειτουργίας. Σύμφωνα με τα παραπάνω κριτήρια ελέγχου, το σύστημα ελέγχου που επιλέχθηκε για την παραπάνω διασύνδεση σχεδιάστηκε σύμφωνα με την υπολογιστική νοημοσύνη, η οποία βασίζεται στην ποιοτική περιγραφή των ελεγχόμενων διαδικασιών και στη μίμηση του συλλογισμού ενός έμπειρου ανθρώπου-χειριστή της διαδικασίας. Οι κλάδοι της υπολογιστικής νοημοσύνης που εφαρμόσθηκαν είναι τα ασαφή συστήματα και τα υβριδικά νευρο-ασαφή συστήματα. Πιο συγκεκριμένα, για τον έλεγχο της διασύνδεσης του ΑΠ με το ασθενές δίκτυο ΕΡ, σχεδιάστηκαν και μελετήθηκαν τρεις διαφορετικές εκδοχές, οι οποίες περιελάμβαναν: 1. ένα ασαφές σύστημα ελέγχου, 2. ένα νευρο-ασαφές σύστημα ελέγχου, 3. ένα προσαρμοζόμενο ασαφές σύστημα ελέγχου, το οποίο ρυθμίζεται σε πραγματικό χρόνο. Προκειμένου να ελεγχθεί η αποτελεσματικότητα του συστήματος ελέγχου, αλλά και να συγκριθούν οι τρεις εκδοχές του ως προς την αποτελεσματικότητά τους σε διαφορετικές διαταραχές, η αξιολόγηση του συστήματος περιέλαβε τρία μέρη. Στο πρώτο μέρος παρουσιάσθηκε η απόκριση του συστήματος σε μια βηματική αύξηση της μέσης τιμής της ταχύτητας του ανέμου. Η μεταβολή που επιλέχθηκε είναι ιδιαίτερα απότομη με σκοπό να ελεγχθεί η απόκριση του συστήματος υπό ακραίες συνθήκες. Στο δεύτερο μέρος παρουσιάσθηκε η απόκριση του συστήματος σε μια βηματική μείωση της μέσης τιμής της ταχύτητας του ανέμου, εξίσου απότομη με την προηγούμενη, ώστε να σχηματισθεί μια ολοκληρωμένη εικόνα για τη δυναμική συμπεριφορά του συστήματος κατά τη μετάβασή του στις διάφορες περιοχές λειτουργίας. Στο τρίτο μέρος έγινε μια αξιολόγηση της αρμονικής παραμόρφωσης, η οποία προκαλείται από τη διακοπτική λειτουργία του αντιστροφέα, στις κυματομορφές του ρεύματος και της τάσης στο ΣΚΣΔ, σύμφωνα με τα προτεινόμενα όρια του κανονισμού ΙΕΕΕ 519. Μέσω εκτενών αποτελεσμάτων εξομοίωσης αποδείχθηκε ότι το προτεινόμενο σύστημα ελέγχου και στις τρεις περιπτώσεις αντιδρά γρήγορα στις μεταβολές ισχύος του ανέμου και η διασύνδεση ΕΡ/ΣΡ/ΕΡ με ΜΠΤ τροφοδοτεί ομαλά το σύστημα ΕΡ με πραγματική ισχύ υπό σχεδόν σταθερή εναλλασσόμενη τάση. Επιπρόσθετα, λόγω του ελέγχου μεταβλητών στροφών, το ΑΠ οδηγείται σε μέγιστη αεροδυναμική απόδοση, χωρίς να απαιτείται μέτρηση της ταχύτητας του ανέμου ή του ρότορα των μηχανών. / In this thesis is studied the connection of an offshore Wind Farm (WF) with induction generators to a weak ac grid. This case presents several difficulties, as a result of the low short circuit ratio between the two interconnected ends and the fluctuating and unstable nature of the wind power. In order to constrain these difficulties and taking into account the long distance between the two interconnected ends (which necessitates the use of a dc cable), it was chosen to use the technology of the High Voltage Direct Current (HVDC) link based on Voltage Sourced Converters(VSCs) for the connection of the WF to the weak ac grid. This technology presents the advantages of dc transmission and due to the high switching capability of the VSCs’ valves, it can instantly regulate the reactive power and consequently the ac voltage, independently of the real power flow. So, the short circuit ratio between the two ends of the link does not have to be high. Furthermore, through this link, the dynamic reactive power compensation of the induction generations can be achieved. However, despite the benefits which are obtained through the technology of the HVDC link based on VSCs, the control of the link is still a demanding procedure. The design of the control system demands a comprehensive knowledge of the system behavior and accurate tuning in order to achieve the desirable output. The presence of highly fluctuating and noisy input signals can make the selection of the control parameters which ensure proper behavior in any operating conditions very difficult. Taking into account the above requirements, the control theory that was implemented to the control system of the above mentioned link is that of computational intelligence. Computational intelligence-based controllers do not require precise mathematical modeling of the system nor complex computations. They rely on the human ability to understand the system behavior and are based on qualitative control rules. In addition, they have inherent abilities to deal with imprecise or noisy data. The categories of computational intelligence that were used in the control system design are fuzzy systems and hybrid neuro-fuzzy systems. More precisely, three alternative versions of the control system of the link between the WF and the weak ac grid were designed: 1. a simple, manually tuned fuzzy control system , 2. a hybrid neuro-fuzzy control System 3. an adaptive, on-line tuned fuzzy control system. In order to test the performance of the control system and compare its three versions, the test of the system includes three parts. In the fist part was studied the performance of the system under a step increase of the wind speed. The step increase was chosen to be very steep, in order to test the system under extreme conditions. In the second part was studied the response of the system under a respective step decrease of the wind speed, in order to test the system performance under any operating condition. In the third part was examined the harmonic content of waveforms of the current and voltage at the point of common coupling, according to the limits of the international standard IEEE 519. Through extended simulation results it was shown that the proposed control system quickly reacts to the step changes of the wind power and the HVDC link based on VSCs manages to feed the weak ac grid with the power from the wind under almost stable ac voltage. In addition, due to the ability of the control system to adjust the stator frequency of the induction generators in relation to the wind velocity, maximum power absorption of the WF is achieved, without monitoring the wind speed or the rotor speed.