In recent years, the interest in Augmented Reality (AR) applications for entertainment and productivity has grown. One company exploring this technology is LAZER WOLF STUDIOS, the developers behind Thrymd: an AR-driven board games platform powered by the Unity engine.  This paper details the development of a visual scripting framework, meant to provide end users with a means of developing their own games for the platform, without significant programming or background knowledge required. A graph-based visual language was implemented in a custom Unity editor window, in order to maintain a familiar and consistent feel for users. The graph consists of a series of branching, interconnected nodes which pass data in-between each other, and execute in succession. The graph is serialized as a Unity asset, and can easily be interacted with through regular C# scripts.  A small number of nodes were implemented, but for the system to be viable, more are needed. For that reason, extensibility was a core ideal; creating new node types must be fast and painless. As with any script layer, performance is generally worse than compiled code. Further work is needed to improve user experience. / Intresset för användandet av Augmenterad Verklighet (AR) för underhållning och produktivitetssyften har ökat på senare tid. LAZER WOLF STUDIOS är utvecklarna bakom Thrymd, en AR-driven brädspelsplattform byggd i spelmotorn Unity.  Denna rapport dokumenterar utvecklingsprocessen av ett visuellt skriptramverk byggt med avsikt att låta slutanvändare utveckla sina egna spel till plattformen utan större förkunskapskrav. Ett graf-baserat visuellt skriptspråk implementerades i en skräddarsydd editormiljö inuti Unity, för att bibehålla en bekant och konsekvent användarupplevelse. Grafen består av en serie förgrenande, sammankopplade noder som skickar data mellan varann och exekveras i sekvens. Grafen sparas som en resurs på hårddisken och är lätt att interagera med genom traditionella C#-skript i Unity.  Ett mindre antal noder implementerades, men fler krävs för att systemet ska vara brukbart. Av detta skäl designades språket med vidareutveckling i åtanke, då det måste vara enkelt att skapa nya noder. Som med de flesta skriptspråk är prestandan överlag sämre än kompilerad kod. Ytterligare arbete krävs för att förbättra användarupplevelsen.

This dissertation proposes two novel manifold graph-based ranking systems for Content-Based Image Retrieval (CBIR). The two proposed systems exploit the synergism between relevance feedback-based transductive short-term learning and semantic feature-based long-term learning to improve retrieval performance. Proposed systems first apply the active learning mechanism to construct users' relevance feedback log and extract high-level semantic features for each image. These systems then create manifold graphs by incorporating both the low-level visual similarity and the high-level semantic similarity to achieve more meaningful structures for the image space. Finally, asymmetric relevance vectors are created to propagate relevance scores of labeled images to unlabeled images via manifold graphs. The extensive experimental results demonstrate two proposed systems outperform the other state-of-the-art CBIR systems in the context of both correct and erroneous users' feedback.

京都大学 / 0048 / 新制・課程博士 / 博士(情報学) / 甲第21397号 / 情博第683号 / 新制||情||118(附属図書館) / 京都大学大学院情報学研究科社会情報学専攻 / (主査)教授 吉川 正俊, 教授 黒田 知宏, 教授 守屋 和幸 / 学位規則第4条第1項該当 / Doctor of Informatics / Kyoto University / DFAM

<p> Design and optimization of aircraft thermal management systems (TMS) is typically conducted by considering a single system architecture at steady-state conditions, using per?formance metrics such as bleed air flow rate, fuel burn flow rate, or total system mass. However, when trying to increase the overall performance of a legacy system or analyzing new system architectures, it can be difficult to identify how individual component or sub?system changes will propagate throughout the overall TMS. In this thesis, new knowledge and tools are presented that will advance the use of exergy-based design techniques for next generation aircraft thermal management systems (TMS). This is motivated by the fact that exergy destruction is a quantity that can be calculated for any subsystem or component, regardless of energy domain or function. The relationship between exergy destruction min?imization (EDM) and conventional design metrics is investigated and quantified. This is performed through the use of a steady-state analysis and by leveraging a high fidelity model of a complex TMS. It is shown that exergy destruction is not only sensitive to individual component parameters in a manner consistent with conventional performance metrics, but that due to its generalizability, it also captures how changes in one subsystem propagate throughout the overall TMS. Specifically, through a design case study, it is shown that minimizing system-wide exergy destruction rate (without an engine model) yields a similar engine fuel burn rate as when fuel burn is minimized directly, but also results in a signif?icantly lower system mass. Building on these results, a transient design and analysis tool for TMS is developed using a graph theoretic approach. The tool is used on a case study of an air cycle machine (ACM) and on an architecture enumeration case study for a notional TMS. The transient exergy-based analysis is shown to provide insight into how efficiently energy is used at a component level, and captures the differences in thermal performance between architectures. </p>

<p dir="ltr">Managing transient heat loads has become more challenging with the increasing electrification of ground, air, and marine vehicles. Doing so requires novel designs of thermal management systems, or in some cases, novel retrofits of legacy TMSs to accommodate the addition of more electrified subsystems. However, design tools that are well suited for examining and optimizing the dynamic response of TMS over candidate operation or mission profiles are limited. In this thesis, a principled methodology and associated tools for the enumeration and dynamic optimization of all feasible architectures of an air cycle machine are presented. Graph-based modeling is pivotal for exploring and optimizing ACM architectures, providing a structured representation of system components and interactions. By modeling the ACM as a graph, with vertices and edges representing components and interactions, respectively, various component configurations and performance metrics can be systematically analyzed. This approach enables efficient exploration of design alternatives and consideration of dynamic boundary conditions (representing, for example, a complex mission profile) during optimization. Another unique contribution of this thesis is a novel application of a multi-state graph-based modeling approach for developing dynamic models of turbomachinery components. By representing multiple states within each control volume or component and connecting them through power flows, this approach accurately captures both first and second law dynamics, enabling the computation of dynamic entropy generation rates. A detailed case study demonstrates the optimization of ACM architectures based on entropy generation minimization and dynamic bleed air flow rate minimization. This study highlights the trade-offs between different optimization criteria and the potential for generalizing the tool to more complex thermofluid systems in thermal management applications. The results underscore the importance of entropy-based analysis in comparing the thermodynamic losses across various system architectures.</p>

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Récemment, nous avons pu observer un intérêt grandissant pour l'application de l'analogie formelle à l'analyse morphologique. L'intérêt premier de ce concept repose sur ses parallèles avec le processus mental impliqué dans la création de nouveaux termes basée sur les relations morphologiques préexistantes de la langue. Toutefois, l'utilisation de ce concept reste tout de même marginale due notamment à son coût de calcul élevé.Dans ce document, nous présenterons le système à base de graphe Moranapho fondé sur l'analogie formelle. Nous démontrerons par notre participation au Morpho Challenge 2009 (Kurimo:10) et nos expériences subséquentes, que la qualité des analyses obtenues par ce système rivalise avec l'état de l'art. Nous analyserons aussi l'influence de certaines de ses composantes sur la qualité des analyses morphologiques produites. Nous appuierons les conclusions tirées de nos analyses sur des théories bien établies dans le domaine de la linguistique. Ceci nous permet donc de fournir certaines prédictions sur les succès et les échecs de notre système, lorsqu'appliqué à d'autres langues que celles testées au cours de nos expériences. / Recently, we have witnessed a growing interest in applying the concept of formal analogy to unsupervised morphology acquisition. The attractiveness of this concept lies in its parallels with the mental process involved in the creation of new words based on morphological relations existing in the language. However, the use of formal analogy remain marginal partly due to their high computational cost. In this document, we present Moranapho, a graph-based system founded on the concept of formal analogy. Our participation in the 2009 Morpho Challenge (Kurimo:10) and our subsequent experiments demonstrate that the performance of Moranapho are favorably comparable to the state-of-the-art. We studied the influence of some of its components on the quality of the morphological analysis produced as well. Finally, we will discuss our findings based on well-established theories in the field of linguistics. This allows us to provide some predictions on the successes and failures of our system when applied to languages other than those tested in our experiments.

Les Graphes sont des structures mathématiques puissantes constituant un outil de modélisation universel utilisé dans différents domaines de l'informatique, notamment dans le domaine de la reconnaissance de formes. L'appariement de graphes est l'opération principale dans le processus de la reconnaissance de formes à base de graphes. Dans ce contexte, trouver des solutions d'appariement de graphes, garantissant l'optimalité en termes de précision et de temps de calcul est un problème de recherche difficile et d'actualité. Dans cette thèse, nous nous intéressons à la résolution de ce problème dans deux domaines : la reconnaissance de formes 2D et 3D. Premièrement, nous considérons le problème d'appariement de graphes géométriques et ses applications sur la reconnaissance de formes 2D. Dance cette première partie, la reconnaissance des Kites (structures archéologiques) est l'application principale considérée. Nous proposons un "framework" complet basé sur les graphes pour la reconnaissance des Kites dans des images satellites. Dans ce contexte, nous proposons deux contributions. La première est la proposition d'un processus automatique d'extraction et de transformation de Kites a partir d'images réelles en graphes et un processus de génération aléatoire de graphes de Kites synthétiques. En utilisant ces deux processus, nous avons généré un benchmark de graphes de Kites (réels et synthétiques) structuré en 3 niveaux de bruit. La deuxième contribution de cette première partie, est la proposition d'un nouvel algorithme d'appariement pour les graphes géométriques et par conséquent pour les Kites. L'approche proposée combine les invariants de graphes au calcul de l'édition de distance géométrique. Deuxièmement, nous considérons le problème de reconnaissance des formes 3D ou nous nous intéressons à la reconnaissance d'objets déformables représentés par des graphes c.à.d. des tessellations de triangles. Nous proposons une décomposition des tessellations de triangles en un ensemble de sous structures que nous appelons triangle-étoiles. En se basant sur cette décomposition, nous proposons un nouvel algorithme d'appariement de graphes pour mesurer la distance entre les tessellations de triangles. L'algorithme proposé assure un nombre minimum de structures disjointes, offre une meilleure mesure de similarité en couvrant un voisinage plus large et utilise un ensemble de descripteurs qui sont invariants ou au moins tolérants aux déformations les plus courantes. Finalement, nous proposons une approche plus générale de l'appariement de graphes. Cette approche est fondée sur une nouvelle formalisation basée sur le problème de mariage stable. L'approche proposée est optimale en terme de temps d'exécution, c.à.d. la complexité est quadratique O(n2), et flexible en terme d'applicabilité (2D et 3D). Cette approche se base sur une décomposition en sous structures suivie par un appariement de ces structures en utilisant l'algorithme de mariage stable. L'analyse de la complexité des algorithmes proposés et l'ensemble des expérimentations menées sur les bases de graphes des Kites (réelle et synthétique) et d'autres bases de données standards (2D et 3D) attestent l'efficacité, la haute performance et la précision des approches proposées et montrent qu'elles sont extensibles et générales / Graphs are powerful mathematical modeling tools used in various fields of computer science, in particular, in Pattern Recognition. Graph matching is the main operation in Pattern Recognition using graph-based approach. Finding solutions to the problem of graph matching that ensure optimality in terms of accuracy and time complexity is a difficult research challenge and a topical issue. In this thesis, we investigate the resolution of this problem in two fields: 2D and 3D Pattern Recognition. Firstly, we address the problem of geometric graphs matching and its applications on 2D Pattern Recognition. Kite (archaeological structures) recognition in satellite images is the main application considered in this first part. We present a complete graph based framework for Kite recognition on satellite images. We propose mainly two contributions. The first one is an automatic process transforming Kites from real images into graphs and a process of generating randomly synthetic Kite graphs. This allowing to construct a benchmark of Kite graphs (real and synthetic) structured in different level of deformations. The second contribution in this part, is the proposition of a new graph similarity measure adapted to geometric graphs and consequently for Kite graphs. The proposed approach combines graph invariants with a geometric graph edit distance computation. Secondly, we address the problem of deformable 3D objects recognition, represented by graphs, i.e., triangular tessellations. We propose a new decomposition of triangular tessellations into a set of substructures that we call triangle-stars. Based on this new decomposition, we propose a new algorithm of graph matching to measure the distance between triangular tessellations. The proposed algorithm offers a better measure by assuring a minimum number of triangle-stars covering a larger neighbourhood, and uses a set of descriptors which are invariant or at least oblivious under most common deformations. Finally, we propose a more general graph matching approach founded on a new formalization based on the stable marriage problem. The proposed approach is optimal in term of execution time, i.e. the time complexity is quadratic O(n2) and flexible in term of applicability (2D and 3D). The analyze of the time complexity of the proposed algorithms and the extensive experiments conducted on Kite graph data sets (real and synthetic) and standard data sets (2D and 3D) attest the effectiveness, the high performance and accuracy of the proposed approaches and show that the proposed approaches are extensible and quite general

In the past research, smart sensor devices have become mature enough for large, distributed networks of such sensors to start to be deployed. Such networks can include tens or hundreds of independent nodes that can perform their functions without human interactions such as recharging of batteries, the configuration of network routes and others. Each of the sensors in the wireless sensor network is considered as microsystem, which consists of memory, processor, transducers and low bandwidth as well as a low range radio transceiver. This study investigates an adaptive sampling strategy for WSS aimed at reducing the number of data samples by sensing data only when a significant change in these processes is detected. This detection strategy is based on an extension to Holt's Method and statistical model. To investigate this strategy, the water consumption in a household is used as a case study. A query distribution approach is proposed, which is presented in detail in chapter 5. Our developed wireless sensor query engine is programmed on Sensinode testbed cc2430. The implemented model used on the wireless sensor platform and the architecture of the model is presented in chapters six, seven, and eight. This thesis presents a contribution by designing the experimental simulation setup and by developing the required database interface GUI sensing system, which enables the end user to send the inquiries to the sensor s network whenever needed, the On-Demand Query Sensing system ODQS is enhanced with a probabilistic model for the purpose of sensing only when the system is insufficient to answer the user queries. Moreover, a dynamic aggregation methodology is integrated so as to make the system more adaptive to query message costs. Dynamic on-demand approach for aggregated queries is implemented, based in a wireless sensor network by integrating the dynamic programming technique for the most optimal query decision, the optimality factor in our experiment is the query cost. In-network query processing of wireless sensor networks is discussed in detail in order to develop a more energy efficient approach to query processing. Initially, a survey of the research on existing WSN query processing approaches is presented. Building on this background, novel primary achievements includes an adaptive sampling mechanism and a dynamic query optimiser. These new approaches are extremely helpful when existing statistics are not sufficient to generate an optimal plan. There are two distinct aspects in query processing optimisation; query dynamic adaptive plans, which focus on improving the initial execution of a query, and dynamic adaptive statistics, which provide the best query execution plan to improve subsequent executions of the aggregation of on-demand queries requested by multiple end-users. In-network query processing is attractive to researchers developing user-friendly sensing systems. Since the sensors are a limited resource and battery powered devices, more robust features are recommended to limit the communication access to the sensor nodes in order to maximise the sensor lifetime. For this reason, a new architecture that combines a probability modelling technique with dynamic programming (DP) query processing to optimise the communication cost of queries is proposed. In this thesis, a dynamic technique to enhance the query engine for the interactive sensing system interface is developed. The probability technique is responsible for reducing communication costs for each query executed outside the wireless sensor networks. As remote sensors have limited resources and rely on battery power, control strategies should limit communication access to sensor nodes to maximise battery life. We propose an energy-efficient data acquisition system to extend the battery life of nodes in wireless sensor networks. The system considers a graph-based network structure, evaluates multiple query execution plans, and selects the best plan with the lowest cost obtained from an energy consumption model. Also, a genetic algorithm is used to analyse the performance of the approach. Experimental testing are provided to demonstrate the proposed on-demand sensing system capabilities to successfully predict the query answer injected by the on-demand sensing system end-user based-on a sensor network architecture and input query statement attributes and the query engine ability to determine the best and close to the optimal execution plan, given specific constraints of these query attributes . As a result of the above, the thesis contributes to the state-of-art in a network distributed wireless sensor network query design, implementation, analysis, evaluation, performance and optimisation.

Métodos baseados em grafos consistem em uma poderosa forma de representação e abstração de dados que proporcionam, dentre outras vantagens, representar relações topológicas, visualizar estruturas, representar grupos de dados com formatos distintos, bem como, fornecer medidas alternativas para caracterizar os dados. Esse tipo de abordagem tem sido cada vez mais considerada para solucionar problemas de aprendizado de máquina, principalmente no aprendizado não supervisionado, como agrupamento de dados, e mais recentemente, no aprendizado semissupervisionado. No aprendizado supervisionado, por outro lado, o uso de algoritmos baseados em grafos ainda tem sido pouco explorado na literatura. Este trabalho apresenta um algoritmo não paramétrico baseado em grafos para problemas de classificação com distribuição estacionária, bem como sua extensão para problemas que apresentam distribuição não estacionária. O algoritmo desenvolvido baseia-se em dois conceitos, a saber, 1) em uma estrutura chamada grafo K-associado ótimo, que representa o conjunto de treinamento como um grafo esparso e dividido em componentes; e 2) na medida de pureza de cada componente, que utiliza a estrutura do grafo para determinar o nível de mistura local dos dados em relação às suas classes. O trabalho também considera problemas de classificação que apresentam alteração na distribuição de novos dados. Este problema caracteriza a mudança de conceito e degrada o desempenho do classificador. De modo que, para manter bom desempenho, é necessário que o classificador continue aprendendo durante a fase de aplicação, por exemplo, por meio de aprendizado incremental. Resultados experimentais sugerem que ambas as abordagens apresentam vantagens na classificação de dados em relação aos algoritmos testados / Graph-based methods consist in a powerful form for data representation and abstraction which provides, among others advantages, representing topological relations, visualizing structures, representing groups of data with distinct formats, as well as, supplying alternative measures to characterize data. Such approach has been each time more considered to solve machine learning related problems, mainly concerning unsupervised learning, like clustering, and recently, semi-supervised learning. However, graph-based solutions for supervised learning tasks still remain underexplored in literature. This work presents a non-parametric graph-based algorithm suitable for classification problems with stationary distribution, as well as its extension to cope with problems of non-stationary distributed data. The developed algorithm relies on the following concepts, 1) a graph structure called optimal K-associated graph, which represents the training set as a sparse graph separated into components; and 2) the purity measure for each component, which uses the graph structure to determine local data mixture level in relation to their classes. This work also considers classification problems that exhibit modification on distribution of data flow. This problem qualifies concept drift and worsens any static classifier performance. Hence, in order to maintain accuracy performance, it is necessary for the classifier to keep learning during application phase, for example, by implementing incremental learning. Experimental results, concerning both algorithms, suggest that they had presented advantages over the tested algorithms on data classification tasks