In this study, an improved bit-array representation method for structural topology optimization using the Genetic Algorithm (GA) is proposed. The issue of representation degeneracy is fully addressed and the importance of structural connectivity in a design is further emphasized. To evaluate the constrained objective function, Deb's constraint handling approach is further developed to ensure that feasible individuals are always better than infeasible ones in the population to improve the efficiency of the GA. A hierarchical violation penalty method is proposed to drive the GA search towards the topologies with higher structural performance, less unusable material and fewer separate objects in the design domain in a hierarchical manner. Numerical results of structural topology optimization problems of minimum weight and minimum compliance designs show the success of this novel bit-array representation method and suggest that the GA performance can be significantly improved by handling the design connectivity properly. / Singapore-MIT Alliance (SMA)

Computing surface and/or object intersections is a cornerstone of many algorithms in Geometric Modeling and Computer Graphics, for example Set Operations between solids, or surface Ray Casting display. We present an object centered, information preserving, hierarchical representation for polyhedra called Prism Tree. We use the representation to decompose the intersection algorithms into two steps: the localization of intersections, and their processing. When dealing with polyhedra with many faces (typically more than one thousand), the first step is by far the most expensive. The Prism Tree structure is used to compute efficiently this localization step. A preliminary implementation of the Set Operations and Ray casting algorithms has been constructed.

Computational modules early in the human vision system typically generate sparse information about the shapes of visible surfaces in the scene. Moreover, visual processes such as stereopsis can provide such information at a number of levels spanning a range of resolutions. In this paper, we extend this multi-level structure to encompass the subsequent task of reconstructing full surface descriptions from the sparse information. The mathematical development proceeds in three steps. First, the surface most consistent with the sparse constraints is characterized as the solution to an equilibrium state of a thin flexible plate. Second, local, finite element representations of surfaces are introduced and, by applying the finite element method, the continuous variational principle is transformed into a discrete problem in the form of a large system of linear algebraic equations whose solution is computable by local-support, cooperative mechanisms. Third, to exploit the information available at each level of resolution, a hierarchy of discrete problems is formulated and a highly efficient multi-level algorithm, involving both intra-level relaxation processes and bi-directional inter-level algorithm, involving both intra-level relaxation processes and bidirectional inter-level local interpolation processes is applied to their simultaneous solution.. Examples of the generation of hierarchies of surface representations from stereo constraints are given. Finally, the basic surface approximation problem is revisited in a broader mathematical context whose implications are of relevance to vision.

This report describes the organization and content of lexical information required for the task of machine translation. In particular, the lexical-conceptual basis for UNITRAN, an implemented machine translation system, will be described. UNITRAN uses an underlying form called lexical conceptual structure to perform lexical selection and syntactic realization. Lexical word entries have two levels of description: the first is an underlying lexical-semantic representation that is derived from hierarchically organized primitives, and the second is a mapping from this representation to a corresponding syntactic structure. The interaction of these two levels will be discussed and the lexical selection and syntactic realization processes will be described.

In the field of Music Data Mining, Mood and Topic information has been considered as a high level metadata. The extraction of mood and topic information is difficult but is regarded as very valuable. The immense growth of Web 2.0 resulted in Social Tags being a direct interaction with users (humans) and their feedback through tags can help in classification and retrieval of music. One of the major shortcomings of the approaches that have been employed so far is the improper filtering of social tags. This thesis delves into the topic of information extraction from songs’ tags and lyrics. The main focus is on removing all erroneous and unwanted tags with help of other features. The hierarchical clustering method is applied to create clusters of tags. The clusters are based on semantic information any given pair of tags share. The lyrics features are utilized by employing CLOPE clustering method to form lyrics clusters, and Naïve Bayes method to compute probability values that aid in classification process. The outputs from classification are finally used to estimate the accuracy of a tag belonging to the song. The results obtained from the experiments all point towards the success of the method proposed and can be utilized by other research projects in the similar field.

Burnout is a tripartite syndrome consisting of the constructs of emotional exhaustion (EE), depersonalization (DP), and reduced feelings of personal accomplishment (PA) (Azeem & Nazir, 2008; Law, 2010; Yong & Yue, 2007). Teachers in the midst of burnout are in a “state of physical, emotional, and mental exhaustion caused by long-term involvement in situations that are emotionally demanding” (Harrison, 1996, p. 25). The purpose of this study was to explore the relationship between middle school teacher burnout and perceived principal support. The combination of the constructs of burnout and principal behaviors (i.e., supportive, directive, and restrictive) provided the theoretical framework for the study. Participants included 282 middle school teachers from 9 schools in East Tennessee. Instruments utilized were the Maslach Burnout Inventory for Educators Survey (MBI-ES), the Organizational Climate Description Questionnaire—Revised for Middle Schools (OCDQ-RM); and a researcher‑created demographics questionnaire. Schools were also coded as either rural or non‑rural, and this classification was used as demographic information. Analyses included the calculation of Pearson r correlations and a three-stage hierarchical multiple regression. Of the participants, 43.6% rated themselves as experiencing high levels of EE and 45.4% reported low levels of personal accomplishment. In straight correlations, the EE factors of burnout were significant between gender of teachers, those who taught courses that were tested (i.e., math, reading/language arts, science, and social studies), and those who held higher degrees. For teacher behaviors, those reporting collegial and disengaged behavior had a direct relationship between all factors of burnout, as were principal behaviors categorized as supportive and restrictive. This study, however, focused on the hierarchical multiple regression to determine whether principal behaviors had a greater effect on burnout than did other variables. From the findings, the researcher realized directive principal behavior had no significant effect on any of the factors of burnout; however, those teachers with principals who they perceived as supportive were less likely to exhibit EE or DP. Restrictive principal behavior was the only factor in the regression that was significant for all factors of burnout, indicating that employees of these principals exhibited higher levels of EE and DP and lower levels of PA.

The complexity of distributed computing systems has raised two unprecedented challenges for system management. First, various customers need to be assured by meeting their required service-level agreements such as response time and throughput. Second, system power consumption must be controlled in order to avoid system failures caused by power capacity overload or system overheating due to increasingly high server density. However, most existing work, unfortunately, either relies on open-loop estimations based on off-line profiled system models, or evolves in a more ad hoc fashion, which requires exhaustive iterations of tuning and testing, or oversimplifies the problem by ignoring the coupling between different system characteristics (ie, response time and throughput, power consumption of different servers). As a result, the majority of previous work lacks rigorous guarantees on the performance and power consumption for computing systems, and may result in degraded overall system performance. In this thesis, we extensively study adaptive performance/power management and power-efficient performance management for distributed computing systems such as information dissemination systems, power grid management systems, and data centers, by proposing Multiple-Input-Multiple-Output (MIMO) control and hierarchical designs based on feedback control theory. For adaptive performance management, we design an integrated solution that controls both the average response time and CPU utilization in information dissemination systems to achieve bounded response time for high-priority information and maximized system throughput in an example information dissemination system. In addition, we design a hierarchical control solution to guarantee the deadlines of real-time tasks in power grid computing by grouping them based on their characteristics, respectively. For adaptive power management, we design MIMO optimal control solutions for power control at the cluster and server level and a hierarchical solution for large-scale data centers. Our MIMO control design can capture the coupling among different system characteristics, while our hierarchical design can coordinate controllers at different levels. For power-efficient performance management, we discuss a two-layer coordinated management solution for virtualized data centers. Experimental results in both physical testbeds and simulations demonstrate that all the solutions outperform state-of-the-art management schemes by significantly improving overall system performance.

A flexible workcell controller has been developed using a three level control hierarchy (workcell, workstation, equipment). The cell controller is automatically generated from a model input by the user. The model consists of three sets of graphs. One set of graphs describes the process plans of the parts produced by the manufacturing system, one set describes movements into, out of and within workstations, and the third set describes movements of parts/transporters between workstations. The controller uses an event driven Petri net to maintain state information and to communicate with lower level controllers. The control logic is contained in an artificial neural network. The Petri net state information is used as the input to the neural net and messages that are Petri net events are output from the neural net. A genetic algorithm was used to search over alternative operation choices to find a "good" solution. The system was fully implemented and several test cases are described.

This thesis develops a GIS-based Bayesian approach for area-wide traffic crash analysis. Five years of crash data from Houston, Texas, are analyzed using a geographic information system (GIS), and spatial-temporal patterns of relative crash risk are identified based on a hierarchical Bayesian approach. This Bayesian approach is used to filter the uncertainty in the data and identify and rank roadway segments with potentially high relative risks for crashes. The results provide a sound basis to take preventive actions to reduce the risks in these segments. To capture the real safety indications better, this thesis differentiates the risks in different directions of the roadways, disaggregates different road types, and utilizes GIS to analyze and visualize the spatial relative crash risks in 3-D views according to different temporal scales. Results demonstrate that the approach is effective in spatially smoothing the relative crash risks, eliminating the instability of estimates while maintaining real safety trends. The posterior risk maps show high-risk roadway segments in 3-D views, which is more reader friendly than the conventional 2-D views. The results are also useful for travelers to choose relatively safer routes.

This thesis concerns three-dimensional finite element modelling of Biot's equations for elasto-acoustic modelling of wave propagation in layered media including porous elastic materials. The concept of hierarchical (p-version) finite elements are combined with various weak forms of Biot's equations. Computationally efficient methods providing accurate solutions of sound propagation in layered porous media are discussed. The research falls within the areas linear acoustics and numerical acoustics. Important applications of the developed methods may be found within vehicle interior acoustics, e.g. engineering design of damping treatment based on multiple layers of porous materials. / QC 20100618