"Army of One" to "Army Strong" : visual media and U.S. Army recruitment during Bush"s "War on Terror"

From Bush’s September 20, 2001 “War on Terror” speech to Congress to President-Elect Barack Obama’s acceptance speech on November 4, 2008, the U.S. Army produced visual recruitment material that addressed the concerns of falling enlistment numbers—due to the prolonged and difficult war in Iraq—with quickly-evolving and compelling rhetorical appeals: from the introduction of an “Army of One” (2001) to “Army Strong” (2006); from messages focused on education and individual identity to high-energy adventure and simulated combat scenarios, distributed through everything from printed posters and music videos to first-person tactical-shooter video games. These highly polished, professional visual appeals introduced to the American public during a time of an unpopular war fought by volunteers provide rich subject matter for research and analysis. This dissertation takes a multidisciplinary approach to the visual media utilized as part of the Army’s recruitment efforts during the War on Terror, focusing on American myths—as defined by Barthes—and how these myths are both revealed and reinforced through design across media platforms. Placing each selection in its historical context, this dissertation analyzes how printed materials changed as the War on Terror continued. It examines the television ad that introduced “Army Strong” to the American public, considering how the combination of moving image, text, and music structure the message and the way we receive it. This dissertation also analyzes the video game America’s Army, focusing on how the interaction of the human player and the computer-generated player combine to enhance the persuasive qualities of the recruitment message. Each chapter discusses how the design of the particular medium facilitates engagement/interactivity of the viewer. The conclusion considers what recruitment material produced during this time period suggests about the persuasive strategies of different media and how they create distinct relationships with their spectators. It also addresses how theoretical frameworks and critical concepts used by a variety of disciplines can be combined to analyze recruitment media utilizing a Selber inspired three literacy framework (functional, critical, rhetorical) and how this framework can contribute to the multimodal classroom by allowing instructors and students to do a comparative analysis of multiple forms of visual media with similar content.

MODULARITY BASED FUZZY COMMUNITY DETECTION IN SOCIAL NETWORKS

Fuzzy community detection is to identify fuzzy communities in a network, which are groups of vertices in the network such that the membership of a vertex in one community is in [0,1] and that the sum of memberships of vertices in all communities equals to 1. Fuzzy communities are pervasive in social networks, but only a few works have been done for fuzzy community detection. Recently, a one-step forward extension of Newman’s Modularity, the most popular quality function for disjoint community detection, results into the Generalized Modularity (GM) that demonstrates good performance in finding well-known fuzzy communities. Thus, GMis chosen as the quality function in our research. We first propose a generalized fuzzy t-norm modularity to investigate the effect of different fuzzy intersection operators on fuzzy community detection, since the introduction of a fuzzy intersection operation is made feasible by GM. The experimental results show that the Yager operator with a proper parameter value performs better than the product operator in revealing community structure. Then, we focus on how to find optimal fuzzy communities in a network by directly maximizing GM, which we call it Fuzzy Modularity Maximization (FMM) problem. The effort on FMM problem results into the major contribution of this thesis, an efficient and effective GM-based fuzzy community detection method that could automatically discover a fuzzy partition of a network when it is appropriate, which is much better than fuzzy partitions found by existing fuzzy community detection methods, and a crisp partition of a network when appropriate, which is competitive with partitions resulted from the best disjoint community detections up to now. We address FMM problem by iteratively solving a sub-problem called One-Step Modularity Maximization (OSMM). We present two approaches for solving this iterative procedure: a tree-based global optimizer called Find Best Leaf Node (FBLN) and a heuristic-based local optimizer. The OSMM problem is based on a simplified quadratic knapsack problem that can be solved in linear time; thus, a solution of OSMM can be found in linear time. Since the OSMM algorithm is called within FBLN recursively and the structure of the search tree is non-deterministic, we can see that the FMM/FBLN algorithm runs in a time complexity of at least O (n2). So, we also propose several highly efficient and very effective heuristic algorithms namely FMM/H algorithms. We compared our proposed FMM/H algorithms with two state-of-the-art community detection methods, modified MULTICUT Spectral Fuzzy c-Means (MSFCM) and Genetic Algorithm with a Local Search strategy (GALS), on 10 real-world data sets. The experimental results suggest that the H2 variant of FMM/H is the best performing version. The H2 algorithm is very competitive with GALS in producing maximum modularity partitions and performs much better than MSFCM. On all the 10 data sets, H2 is also 2-3 orders of magnitude faster than GALS. Furthermore, by adopting a simply modified version of the H2 algorithm as a mutation operator, we designed a genetic algorithm for fuzzy community detection, namely GAFCD, where elite selection and early termination are applied. The crossover operator is designed to make GAFCD converge fast and to enhance GAFCD’s ability of jumping out of local minimums. Experimental results on all the data sets show that GAFCD uncovers better community structure than GALS.

DC MICROGRID STABILIZATION THROUGH FUZZY CONTROL OF INTERLEAVED, HETEROGENEOUS STORAGE ELEMENTS

As microgrid power systems gain prevalence and renewable energy comprises greater and greater portions of distributed generation, energy storage becomes important to offset the higher variance of renewable energy sources and maximize their usefulness. One of the emerging techniques is to utilize a combination of lead-acid batteries and ultracapacitors to provide both short and long-term stabilization to microgrid systems. The different energy and power characteristics of batteries and ultracapacitors imply that they ought to be utilized in different ways. Traditional linear controls can use these energy storage systems to stabilize a power grid, but cannot effect more complex interactions. This research explores a fuzzy logic approach to microgrid stabilization. The ability of a fuzzy logic controller to regulate a dc bus in the presence of source and load fluctuations, in a manner comparable to traditional linear control systems, is explored and demonstrated. Furthermore, the expanded capabilities (such as storage balancing, self-protection, and battery optimization) of a fuzzy logic system over a traditional linear control system are shown. System simulation results are presented and validated through hardware-based experiments. These experiments confirm the capabilities of the fuzzy logic control system to regulate bus voltage, balance storage elements, optimize battery usage, and effect self-protection.