Air Force Institute of Technology Research Report 2013

This report summarizes the research activities of the Air Force Institute of Technology’s Graduate School of Engineering and Management. It describes research interests and faculty expertise; lists student theses/dissertations; identifies research sponsors and contributions; and outlines the procedures for contacting the school. Included in the report are: faculty publications, conference presentations, consultations, and funded research projects. Research was conducted in the areas of Aeronautical and Astronautical Engineering, Electrical Engineering and Electro-Optics, Computer Engineering and Computer Science, Systems Engineering and Management, Operational Sciences, Mathematics, Statistics and Engineering Physics

ENHANCEMENT OF CHURN PREDICTION ALGORITHMS

Customer churn can be described as the process by which consumers of goods and services discontinue the consumption of a product or service and switch over to a competitor.It is of great concern to many companies. Thus, decision support systems are needed to overcome this pressing issue and ensure good return on investments for organizations. Decision support systems use analytical models to provide the needed intelligence to analyze an integrated customer record database to predict customers that will churn and offer recommendations that will prevent them from churning. Customers churn prediction, unlike most conventional business intelligence techniques, deals with customer demographics, net worth-value, and market opportunities. It is used in determining customers who are likely to churn, those likely to remain loyal to the organization, and for prediction of future churn rates. Customer defection is naturally a slow rate event, and it is not easily detected by most business intelligent solutions available in the market; especially when data is skewed, large, and distinct. Thus, accurate and precise prediction methods are needed to detect the churning trend. In this study, a churn model that applies business intelligence techniques to detect the possibility that a customer will churn using churn trend analysis of customer records is proposed. The model applies clustering algorithms and enhanced SPRINT decision tree algorithms to explore customer record database, and identify the customer profile and behavior patterns. The Model then predicts the possibility that a customer will churn. Additionally, it offers solutions for retaining customers and making them loyal to a business entity by recommending customer-relationship management measures

Word based off-line handwritten Arabic classification and recognition. Design of automatic recognition system for large vocabulary offline handwritten Arabic words using machine learning approaches.

The design of a machine which reads unconstrained words still remains an unsolved problem. For example, automatic interpretation of handwritten documents by a computer is still under research. Most systems attempt to segment words into letters and read words one character at a time. However, segmenting handwritten words is very difficult. So to avoid this words are treated as a whole. This research investigates a number of features computed from whole words for the recognition of handwritten words in particular. Arabic text classification and recognition is a complicated process compared to Latin and Chinese text recognition systems. This is due to the nature cursiveness of Arabic text. The work presented in this thesis is proposed for word based recognition of handwritten Arabic scripts. This work is divided into three main stages to provide a recognition system. The first stage is the pre-processing, which applies efficient pre-processing methods which are essential for automatic recognition of handwritten documents. In this stage, techniques for detecting baseline and segmenting words in handwritten Arabic text are presented. Then connected components are extracted, and distances between different components are analyzed. The statistical distribution of these distances is then obtained to determine an optimal threshold for word segmentation. The second stage is feature extraction. This stage makes use of the normalized images to extract features that are essential in recognizing the images. Various method of feature extraction are implemented and examined. The third and final stage is the classification. Various classifiers are used for classification such as K nearest neighbour classifier (k-NN), neural network classifier (NN), Hidden Markov models (HMMs), and the Dynamic Bayesian Network (DBN). To test this concept, the particular pattern recognition problem studied is the classification of 32492 words using ii the IFN/ENIT database. The results were promising and very encouraging in terms of improved baseline detection and word segmentation for further recognition. Moreover, several feature subsets were examined and a best recognition performance of 81.5% is achieved

Hybrid wheelchair controller for handicapped and quadriplegic patients

In this dissertation, a hybrid wheelchair controller for handicapped and quadriplegic patient is proposed. The system has two sub-controllers which are the voice controller and the head tilt controller. The system aims to help quadriplegic, handicapped, elderly and paralyzed patients to control a robotic wheelchair using voice commands and head movements instead of a traditional joystick controller. The multi-input design makes the system more flexible to adapt to the available body signals. The low-cost design is taken into consideration as it allows more patients to use this system

Speech Recognition

Chapters in the first part of the book cover all the essential speech processing techniques for building robust, automatic speech recognition systems: the representation for speech signals and the methods for speech-features extraction, acoustic and language modeling, efficient algorithms for searching the hypothesis space, and multimodal approaches to speech recognition. The last part of the book is devoted to other speech processing applications that can use the information from automatic speech recognition for speaker identification and tracking, for prosody modeling in emotion-detection systems and in other speech processing applications that are able to operate in real-world environments, like mobile communication services and smart homes

Advances in Public Transport Platform for the Development of Sustainability Cities

Modern societies demand high and varied mobility, which in turn requires a complex transport system adapted to social needs that guarantees the movement of people and goods in an economically efficient and safe way, but all are subject to a new environmental rationality and the new logic of the paradigm of sustainability. From this perspective, an efficient and flexible transport system that provides intelligent and sustainable mobility patterns is essential to our economy and our quality of life. The current transport system poses growing and significant challenges for the environment, human health, and sustainability, while current mobility schemes have focused much more on the private vehicle that has conditioned both the lifestyles of citizens and cities, as well as urban and territorial sustainability. Transport has a very considerable weight in the framework of sustainable development due to environmental pressures, associated social and economic effects, and interrelations with other sectors. The continuous growth that this sector has experienced over the last few years and its foreseeable increase, even considering the change in trends due to the current situation of generalized crisis, make the challenge of sustainable transport a strategic priority at local, national, European, and global levels. This Special Issue will pay attention to all those research approaches focused on the relationship between evolution in the area of transport with a high incidence in the environment from the perspective of efficiency

Learning to visually predict terrain properties for planetary rovers

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2009.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Includes bibliographical references (p. 174-180).For future planetary exploration missions, improvements in autonomous rover mobility have the potential to increase scientific data return by providing safe access to geologically interesting sites that lie in rugged terrain, far from landing areas. This thesis presents an algorithmic framework designed to improve rover-based terrain sensing, a critical component of any autonomous mobility system operating in rough terrain. Specifically, this thesis addresses the problem of predicting the mechanical properties of distant terrain. A self-supervised learning framework is proposed that enables a robotic system to learn predictions of mechanical properties of distant terrain, based on measurements of mechanical properties of similar terrain that has been previously traversed. The proposed framework relies on three distinct algorithms. A mechanical terrain characterization algorithm is proposed that computes upper and lower bounds on the net traction force available at a patch of terrain, via a constrained optimization framework. Both model-based and sensor-based constraints are employed. A terrain classification method is proposed that exploits features from proprioceptive sensor data, and employs either a supervised support vector machine (SVM) or unsupervised k-means classifier to assign class labels to terrain patches that the rover has traversed. A second terrain classification method is proposed that exploits features from exteroceptive sensor data (e.g. color and texture), and is automatically trained in a self-supervised manner, based on the outputs of the proprioceptive terrain classifier.(cont.) The algorithm includes a method for distinguishing novel terrain from previously observed terrain. The outputs of these three algorithms are merged to yield a map of the surrounding terrain that is annotated with the expected achievable net traction force. Such a map would be useful for path planning purposes. The algorithms proposed in this thesis have been experimentally validated in an outdoor, Mars-analog environment. The proprioceptive terrain classifier demonstrated 92% accuracy in labeling three distinct terrain classes. The exteroceptive terrain classifier that relies on self-supervised training was shown to be approximately as accurate as a similar, human-supervised classifier, with both achieving 94% correct classification rates on identical data sets. The algorithm for detection of novel terrain demonstrated 89% accuracy in detecting novel terrain in this same environment. In laboratory tests, the mechanical terrain characterization algorithm predicted the lower bound of the net available traction force with an average margin of 21% of the wheel load.by Christopher A. Brooks.Ph.D

Konzept zur datengetriebenen Analyse und Modellierung des preisbeeinflussten Verbrauchsverhaltens

Der verstärkte Ausbau von Erneuerbare-Energien-Anlagen in Deutschland führt dazu, dass der Anteil der erneuerbaren Energien am gesamten Bruttostromverbrauch im Jahr 2016 bei ca. 32% lag. Die Erzeugungscharakteristik von Photovoltaik- und Windkraftanlagen macht jedoch nachfrage- und erzeugungsseitige Anpassungen im deutschen Elektrizitätsmarkt notwendig. Im Energiesystem der Zukunft (Smart Grid) wird die Bereitstellung von lastseitigen Flexibilitäten eine bedeutende Rolle einnehmen. Die Veränderung der Verbraucherlast durch Preisanreize steht dabei ausdrücklich nicht in Konkurrenz zum verstärkten Ausbau von Energiespeichern, Energieübertragungs- und -verteilnetzen. Die Verbraucherbeeinflussung mithilfe von Steuer- (Demand Side Management, DSM) und Preissignalen (Demand Response, DR) setzt allerdings voraus, dass die Auswirkungen von Preissignalen für Energieverbraucher auf deren Verbrauchsverhalten untersucht, mathematisch modelliert und validiert werden. Die vorliegende Doktorarbeit beschäftigt sich aus diesem Grund mit der datengetriebenen Analyse und Modellierung des Verbrauchsverhaltens als Reaktion auf variable Stromtarife. Dazu wurde ein Gesamtkonzept entwickelt, das aus verschiedenen Vorverarbeitungs-, Analyse- und Modellierungsmethoden besteht. Das Konzept behandelt die gesamte Prozesskette von der Erfassung der Smart-Meter-Rohdaten bis hin zur Analyse und Modellierung des preisbeeinflussten Verbrauchsverhaltens. Das Zeitreihen-Clustering als wichtiger Bestandteil des neuen Konzeptes erlaubt, Aussagen über saisonale, wochentagsbedingte, tarifbedingte Unterschiede für eine Demand-Response-Maßnahme treffen zu können. Des Weiteren erlaubt das neue Konzept, dass haushaltsindividuelle Unterschiede in der Verbrauchsreaktion (Responder-, Semi-Responder, Non-Responder-Haushalte) identifiziert werden können. Zudem wurde eine neue Demand-Response-Modellklasse (Virtuelle-Speicher-Modelle) entwickelt, deren Modelle aus einem System von Differenzengleichungen bestehen und das Verbrauchsverhalten von Haushaltsstromkunden als Reaktion auf verschiedene Preissignale beschreiben

Méthodes de séparation aveugle de sources pour le démélange d'images de télédétection

Nous proposons dans le cadre de cette thèse, de nouvelles méthodes de séparation aveugle de mélanges linéaires instantanés pour des applications de télédétection. La première contribution est fondée sur la combinaison de deux grandes classes de méthodes de Séparation Aveugle de Sources (SAS) : l'Analyse en Composantes Indépendantes (ACI), et la Factorisation en Matrices Non-négatives (NMF). Nous montrons comment les contraintes physiques de notre problème peuvent être utilisées pour éliminer une partie des indéterminations liées à l'ACI et fournir une première approximation des spectres de endmembers et des fractions d'abondance associées. Ces approximations sont ensuite utilisées pour initialiser un algorithme de NMF, avec pour objectif de les améliorer. Les résultats obtenus avec notre méthode sont satisfaisants en comparaison avec les méthodes de la littérature utilisées dans les tests réalisés. La deuxième méthode proposée est fondée sur la parcimonie ainsi que sur des propriétés géométriques. Nous commençons par mettre en avant quelques propriétés facilitant la présentation des hypothèses considérées dans cette méthode, puis nous mettons en lumière les grandes lignes de cette dernière qui est basée sur la détermination des zones bi-sources contenues dans une image de télédétection, ceci à l'aide d'un critère de corrélation. A partir des intersections des droites générées par ces zones bi-sources, nous détaillons le moyen d'obtention des colonnes de la matrice de mélange et enfin des sources recherchées. Les résultats obtenus, en comparaison avec plusieurs méthodes de la littérature sont très encourageants puisque nous avons obtenu les meilleures performances.Within this thesis, we propose new blind source separation (BSS) methods intended for instantaneous linear mixtures, aimed at remote sensing applications. The first contribution is based on the combination of two broad classes of BSS methods : Independent Component Analysis (ICA), and Non-negative Matrix Factorization (NMF). We show how the physical constraints of our problem can be used to eliminate some of the indeterminacies related to ICA and provide a first approximation of endmembers spectra and associated sources. These approximations are then used to initialize an NMF algorithm with the goal of improving them. The results we reached are satisfactory as compared with the classical methods used in our undertaken tests. The second proposed method is based on sparsity as well as on geometrical properties. We begin by highlighting some properties facilitating the presentation of the hypotheses considered 153 in the method. We then provide the broad lines of this approach which is based on the determination of the two-source zones that are contained in a remote sensing image, with the help of a correlation criterion. From the intersections of the lines generated by these two-source zones, we detail how to obtain the columns of the mixing matrix and the sought sources. The obtained results are quite attractive as compared with those reached by several methods from literature