No description available.

Die Adipositas stellt eine der größten medizinischen und soziökonomischen Herausforderungen für unsere modernen Gesundheitssysteme dar. Als wichtige der Adipositas zugrundeliegende Faktoren wurden in früheren Studien typische Verhaltensunterschiede, abweichende hirnmorphologische und -funktionelle Befunde sowie unterschiedliche Aktivitäten in den Anteilen des autonomen Nervensystems im Vergleich adipöser und schlanker Männer und Frauen festgestellt. Diese Unterschiede könnten nach weiterer differenzierter Untersuchung wichtige Ansatzpunkte neuer Therapieformen liefern. In der vorliegenden Studie untersuchten wir Lernperformanz und kardiale Reaktionsmuster während verstärkenden Lernens unter dem Einfluss von Feedback-Valenz, Geschlecht und Adipositas auf Lernleistung und autonome Reaktionen anhand einer probabilistischen Lernaufgabe. Um exakt zwischen dem Lernverhalten bei positivem gegenüber negativem Feedback differenzieren zu können verwendeten wir ein spezielles Aufgaben-Design eines probabilistischen Lernexperiments zur operanten Konditionierung mittels monetären Feedbacks. Neben der Lernleistung untersuchten wir die Unterschiede in der kardialen Reaktivität bei der Verarbeitung der beiden Feedback-Valenzen sowie die Einflüsse von Geschlecht und Adipositas auf diese Prozesse. In der Analyse der Stärke der phasischen kardialen Reaktionen auf die Präsentation von Feedback zeigte sich ein direkter Zusammenhang zur Stärke des Vorhersagefehlers. Dieser kodiert als neuronales Signal für die Neubewertung von kortikalen Werte-Repräsentationen, falls das tatsächliche Ergebnis einer Entscheidung von dem erwarteten Ergebnis abweicht. Folglich bestehen direkte Wechselwirkungen zwischen phasischen Herzraten-Dezelerationen und höheren Prozessen des Feedback-Monitorings, was in der vorliegenden Studie nach unserem besten Wissen erstmalig als direkter Zusammenhang aufgezeigt werden konnte. Die beobachteten geschlechtsabhängigen Defizite bei verstärkendem Lernen waren nicht durch Differenzen in der Aneignung von Wissen, sondern in einer unzureichenden Anwendung des Erlernten begründet. Dabei zeigten besonders weibliche Probanden in der Belohnungsbedingung ein stärker inkonsistentes Verhalten im Vergleich zu männlichen Probanden, was in dieser Aufgabe zu einer geringeren Anzahl an vorteilhaften Entscheidungen führte und damit einer geringeren Lernperformanz. Darüber hinaus liefern unsere Ergebnisse weitere wichtige Hinweise für adipositasspezifische Unterschiede im Lernverhalten. In der initialen Lernphase war der Lernprozess im Vermeiden von Bestrafung bei adipösen Probanden verlangsamt, was im Einklang mit Ergebnissen aus der Literatur zu Einschränkungen in der Vermeidung negativer Langzeit-Folgen steht. Dieser Fund sollte in folgenden Studien differenzierter untersucht werden, um so die Entwicklung geeigneter Therapieformen weiter voran zu treiben.:1. Einführung in die Thematik 1.1 Adipositas 1.2 Lernen 1.3 Adipositasspezifische Lerndefizite 1.4 Geschlechtsunterschiede im Lernverhalten 1.5 Lernen und das autonome Nervensystem 1.6 Adipositasspezifische Veränderungen des autonomen Nervensystems 1.7 Phasische Herzreaktionen – Internet Intervals 1.8 Rationale der Studie 2. Paper 3. Zusammenfassung der Arbeit 3.1 Behaviorale Ergebnisse 3.2 Einfluss der Adipositas auf den Lernvorgang 3.3. Einfluss des Geschlechts auf den Lernvorgang 3.4 Zusammenhänge zwischen physischen Herzreaktionen und dem Lernvorgang 3.5 Schlussfolgerungen 4. Literaturverzeichnis 5. Appendix 5.1 Zusatzmaterial 5.1.1 Herzratenvariabilität (HRV) 5.1.2 Interbeat Intervals (IBIs) 5.3 Selbstständigkeitserklärung 5.4 Lebenslauf 5.5 Danksagung

Whenever we make a prediction we will make an error of a varying degree. What is worse,positive errors or negative ones? This question is important to answer before estimating amodel. When estimating a model a loss function is chosen, a function that gives an instruction of how to transform a particular error. Previous research hints at applications whereasymmetric loss functions provide more optimal models than using symmetric loss functions.Through a simulation study, this thesis highlights the consequences of using symmetric andasymmetric loss functions when assuming the actual problem is asymmetric. This thesis isconducted to cover a gap in literature as well as to correct a common statistical misunderstanding. Our core findings are that the models that take the asymmetry into account havethe lowest prediction errors, while also demonstrating that the larger the degree of asymmetry leads to a greater difference in performance between asymmetric and symmetric modelsin favour of the models estimated with asymmetric loss functions. This confirms what isdemonstrated in existing literature and what can be found in statistical theory.

No description available.

No description available.

It has been suggested that reward processing and related neural substrates are disrupted in some common mental disorders such as depression, addiction, and anxiety. An increasing number of psychiatric studies have been applying reinforcement learning (RL) models to examine these disruptions in self-regarding learning (learning about rewards delivered to the learners themselves). A review of RL alterations associated with mental disorders in extant studies will be beneficial for uncovering the mechanisms of these health problems. Although impaired social reward processing is common in some mental disorders [e.g., post-traumatic stress disorder (PTSD), social anxiety and autism], RL has not been widely used to detect the potentially disrupted social reward learning, especially for other-regarding learning (learning about rewards delivered to others). Meanwhile, it has not been clear whether some drugs, e.g., oxytocin (OT), can alter other-regarding learning, so they may serve as a therapeutic intervention when related deficits occur. In the present set of studies, we summarized common and distinct features in terms of self-regarding RL disturbances among depression, addiction and anxiety disorders based on previous findings (Paper I), tested whether behavioral and neural self- and other-regarding RL were impaired in PTSD with and without comorbid depression (Paper II), and investigated OT's behavioral and neural effects on self- and other-regarding RL in healthy males (Paper III). The results of our literature review showed that the commonalities in all three mental disorders were inflexibility and inconsistent choices, and the differences included decreased learning rates in depression, a higher weight to rewards versus punishments in addiction, and hypersensitivity to punishments in anxiety. The results of the PTSD study demonstrated impaired behavioral other-regarding learning in PTSD patients with and without depression, supposedly due to their hypervigilance to unexpected outcomes for others, as evidenced by the heightened responses in their inferior parietal lobule. The OT study detected OT's effects of attenuating behavioral other-regarding learning, as well as the neural coding of unexpected outcomes for others in the anterior cingulate cortex. These findings provide new evidence of self- and other-regarding RL alterations in mental disorders, reveal potential targets for their treatments, and bring caution for using OT as a therapeutic intervention. / Doctor of Philosophy / People learn to make choices to gain rewards and to avoid punishments delivered to themselves. As social animals, people also take account of outcomes delivered to others when learning. With the help of computational modeling, previous studies have found abnormal reward learning for oneself in people with mental health problems. To better understand mental illnesses, we summarized the similarities and differences of the learning abnormalities reported in previous studies about depression, addiction, and anxiety. We have found that people with these mental illnesses all tend to be inflexible and make more random choices when learning. As for the differences, people with depression tend to learn slower; people with addiction tend to see gaining rewards as more important than avoiding punishments; and people with anxiety tend to be oversensitive to punishments. Using computational modeling and imaging of brain function, we also tested whether learning for other was abnormal in post-traumatic stress disorder (PTSD), and found that, compared to healthy people, PTSD patients had slower learning for others' rewards, and the inferior parietal lobule, a brain region for processing social information, showed higher responses to unexpected outcomes for others. In another study, we examined whether oxytocin (OT), a neuropeptide that has been reported to change people's social functions, could influence reward learning for others in healthy males. The results showed that OT slowed down people's learning for others, and also decreased the neural learning signals in the anterior cingulate cortex, a region involved in processing other's outcomes. Our findings provide new information about how reward learning for oneself and others are changed in mental illnesses, reveal potential targets for their treatments, and bring caution for using OT as a therapeutic intervention.

Real-time monitoring of electromechanical oscillations is of great significance for power system operators; to this aim, software solutions (algorithms) that use synchrophasor measurements have been developed for this purpose. This thesis investigates different approaches for improving mode estimation process by offering new methods and deepening the understanding of different stages in the mode estimation process. One of the problems tackled in this thesis is the selection of synchrophasor signals used as the input for mode estimation. The proposed selection is performed using a quantitative criterion that is based on the variance of the critical mode estimate. The proposed criterion and associated selection method, offer a systematic and quantitative approach for PMU signal selection. The thesis also analyzes methods for model order selection used in mode estimation. Further, negative effects of forced oscillations and non-white noise load random changes on mode estimation results have been addressed by exploiting the intrinsic power system property that the characteristics of electromechanical modes are predominately determined by the power generation and transmission network. An improved accuracy of the mode estimation process can be obtained by intentionally injecting a probing disturbance. The thesis presents an optimization method that finds the optimal spectrum of the probing signals. In addition, the probing signal with the optimal spectrum is generated considering arbitrary time domain signal constraints that can be imposed by various probing signal generating devices. Finally, the thesis provides a comprehensive description of a practical implementation of a real-time mode estimation tool. This includes description of the hardware, software architecture, graphical user interface, as well as details of the most important components such as the Statnett’s SDK that allows easy access to synchrophasor data streams. / <p>The Doctoral Degrees issued upon completion of the programme are issued by Comillas Pontifical University, Delft University of Technology and KTH Royal Institute of Technology. The invested degrees are official in Spain, the Netherlands and Sweden, respectively.</p><p>QC 20160218</p> / FP7 iTesla

Predictive control relies on predictions of the future behaviour of the system to be controlled. These predictions are calculated from a model of this system, thus making the model the cornerstone of the predictive controller. Furthermore predictive control is the only advanced control methodology that has managed to become widely used in the industry. The necessity of good models in the predictive control context can thus be motivated both from the very nature of predictive control and from its widespread use in industry. This thesis is concerned with examining the use of multiple models in the predictive controller. In order to do this the standard predictive control formulation has been extended to incorporate the use of multiple models. The most general case of this new formulation allows the use of an individual model for each prediction horizon. The models are estimated using measurements of the input and output sequences from the true system. When using this data to find a good model of the system it is important to remember the intended purpose of the model. In this case the model is going to be used in a predictive controller and the most important feature of the models is to deliver good k-step ahead predictions. The identification algorithms used to estimate the models thus strives for estimating models good at calculating these predictions. Finally this thesis presents some complete simulations of these ideas showing the potential of using multiple models in the predictive control framework.

<p>Predictive control relies on predictions of the future behaviour of the system to be controlled. These predictions are calculated from a model of this system, thus making the model the cornerstone of the predictive controller. Furthermore predictive control is the only advanced control methodology that has managed to become widely used in the industry. The necessity of good models in the predictive control context can thus be motivated both from the very nature of predictive control and from its widespread use in industry. </p><p>This thesis is concerned with examining the use of multiple models in the predictive controller. In order to do this the standard predictive control formulation has been extended to incorporate the use of multiple models. The most general case of this new formulation allows the use of an individual model for each prediction horizon. </p><p>The models are estimated using measurements of the input and output sequences from the true system. When using this data to find a good model of the system it is important to remember the intended purpose of the model. In this case the model is going to be used in a predictive controller and the most important feature of the models is to deliver good k-step ahead predictions. The identification algorithms used to estimate the models thus strives for estimating models good at calculating these predictions. </p><p>Finally this thesis presents some complete simulations of these ideas showing the potential of using multiple models in the predictive control framework.</p>

Η παρούσα διπλωματική εργασία έχει ως αντικείμενο την μελέτη και ανάπτυξη μίας τεχνικής ανίχνευσης φάσματος (spectrum sensing technique), η οποία να μπορεί να χρησιμοποιηθεί σε περιβάλλον Δυναμικής Εκχώρησης Φάσματος από Γνωστικά Συστήματα Ραδιοεπικοινωνιών (Cognitive Radio Systems). Οι παραδοσιακές στατικές στρατηγικές καταμερισμού του φάσματος έχουν δημιουργήσει προβλήματα έλλειψης διαθέσιμου φάσματος. Ταυτόχρονα, πρόσφατες μετρήσεις δείχνουν ότι μεγάλα τμήματα του φάσματος που έχουν εκχωρηθεί με άδεια σε συγκεκριμένα συστήματα υποχρησιμοποιούνται. Είναι επομένως αναγκαίο να υιοθετηθούν νέες πολιτικές διαχείρισης του φάσματος οι οποίες θα επιτρέπουν σε μη αδειοδοτημένα δίκτυα να κάνουν χρήση τμημάτων του αδειοδοτημένου φάσματος. Τα Γνωστικά Συστήματα Ραδιοεπικοινωνιών είναι ευφυή συστήματα τα οποία έχουν γνώση του περιβάλλοντός τους και μπορούν να προσαρμόζουν κατάλληλα τις παραμέτρους λειτουργίας τους σε αυτό. Τα συστήματα αυτά μπορούν να ανιχνεύουν περιοδικά το φάσμα, να εντοπίζουν τις ζώνες συχνοτήτων οι οποίες δε χρησιμοποιούνται από τους αδειοδοτημένους χρήστες τους και να τις αξιοποιούν. Όπως γίνεται εύκολα αντιληπτό από τα παραπάνω η ανίχνευση φάσματος αποτελεί ένα ιδιαιτέρως κρίσιμο θέμα για τα Γνωστικά Συστήματα Ραδιοεπικοινωνιών. Στο στάδιο αυτό, το σύστημα ανιχνεύει και παρακολουθεί στο περιβάλλον μέσα στο οποίο ενεργεί, το κατά πόσο το φάσμα είναι ελεύθερο ανά πάσα χρονική στιγμή και αξιοποιεί αυτά τα φασματικά κενά. Ουσιαστικά η ανίχνευση φάσματος εφαρμόζεται για να δώσει στον cognitive χρήστη μία όσο το δυνατόν πιστότερη εικόνα του περιβάλλοντος μέσα στο οποίο βρίσκεται. Η δική μας μελέτη επικεντρώθηκε στις τεχνικές ανίχνευσης φάσματος (spectrum sensing) και συγκεκριμένα αναπτύσσουμε μία μέθοδο ανίχνευσης φασματικών κενών βασιζόμενη στη χρήση ενός προβλεπτή (predictor) και στη χρησιμοποίηση του σφάλματος πρόβλεψης του σήματος που προκύπτει από αυτόν ως μετρική για τη λήψη απόφασης σχετικά με την ύπαρξη ή την απουσία σήματος ακόμα και σε θορυβώδη περιβάλλοντα (πολύ χαμηλό SNR). H τεχνική ανίχνευσης φάσματος που προτείνουμε μοντελοποιήθηκε στο περιβάλλον μοντελοποίησης MATLAB. Στη συνέχεια, διενεργήθηκαν εκτενείς προσομοιώσεις για ποικίλες τιμές των διαφόρων παραμέτρων του συστήματος αλλά και για διαφορετικά συστήματα, ούτως ώστε να αξιολογηθεί η επίδοση της τεχνικής σε διάφορες συνθήκες. / In the present thesis, we will study spectrum sensing techniques of Cognitive Radio SIMO systems. The conventional approach to spectrum management is not flexible, as most of the useful part of the spectrum is bounded. Hence it is extremely difficult to find free frequencies in order to deploy new services or to enhance the already existing ones. At the same time, various measurements show that the licensed spectrum is heavily underutilized in terms of both the time domain as well as the space domain. Thus Cognitive Radio technology comes to offer solutions, mainly with regard to the issues mentioned above, providing a dynamic utilization of the spectrum. Cognitive Radio has been proposed for lower priority secondary systems intending to improve spectral efficiency through spectrum sensing thus allowing these systems to transmit at frequency bands that are detected to be unused. As we can easily understand from the above, spectrum sensing is a critical issue for cognitive systems. In order to achieve adaptive transmission in unused portions of the spectrum without interferences to the licensed users of these portions (Primary Users-PUs), spectrum sensing is the first and one of the most important steps as high reliability is demanded on PUs' signal detection. That is, Secondary Users (SUs) should know if the spectrum is being used in order to exploit the available spectrum in the most efficient way. Essentially, spectrum sensing is used in order to provide the cognitive user with a representation of its operating environment which is as faithful as possible. The scope of this thesis is the study and the creation of algorithms that will give the SU of a SIMO system the opportunity to detect the existence of spectrum holes. The implementation we used is based on a predictor. More specifically, the received signal passes through a backward linear predictor from which we compute the difference between the actual signal and the predicted signal, which is the prediction error. By properly exploiting the prediction error, more precisely the power of the prediction error, we can trustworthily detect the existence or the absence of a signal, even in noisy environments, that is, for low values of the signal-to-noise ratio. In order to test the performance of our algorithms, the system above was simulated by MATLAB for different conditions and channels.