<div>Thin walled tubular structures are widely used in the automotive industry because of its weight to energy absorption advantage. A lot of research has been done in different cross sectional shapes and different tapered designs, with design for manufacturability in mind, to achieve high specific energy absorption. </div><div><br></div><div>In this study a novel type of tubular structure is proposed, in which predesigned origami initiators are introduced into conventional square tubes. The crease pattern is designed to achieve extensional collapse mode which results in decreasing the initial buckling forces and at the same time acts as a fold initiator, helping to achieve a extensional collapse mode. The influence of various design parameters of the origami pattern on the mechanical properties (crushing force and deceleration) are extensively investigated using finite element modelling. Thus, showing a predictable and stable collapse behavior. This pattern can be stamped out of a thin sheet of material. </div><div><br></div><div>The results showed that a properly designed origami pattern can consistently trigger a extensional collapse mode which can significantly lower the peak values of crushing forces and deceleration without compromising on the mean values. Also, a comparison has been made with the behavior of proposed origami pattern for extensional mode verses origami pattern with diamond fold.</div>

by Lawrence Chi-Ho Lee. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1998. / Includes bibliographical references (leaves 91-97). / Abstract also in Chinese. / Chapter Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- The Importance of Forecasting --- p.1 / Chapter 1.2 --- Objective --- p.3 / Chapter Chapter 2 --- Holt-Winters Forecasting Procedure --- p.6 / Chapter 2.1 --- Exponential Smoothing and Holt-Winters Method --- p.6 / Chapter 2.2 --- Relationships Between Holt-Winters models and ARIMA Models --- p.13 / Chapter 2.2.1 --- A Steady Model --- p.14 / Chapter 2.2.2 --- A Growth Model --- p.15 / Chapter 2.2.3 --- The Three-Parameter Holt-Winters Model --- p.18 / Chapter 2.3 --- Some Practical Issues --- p.19 / Chapter 2.3.1 --- Normalizing the Seasonal Factors --- p.20 / Chapter 2.3.2 --- Choosing Starting Values --- p.20 / Chapter 2.3.3 --- Choosing the Smoothing Parameters --- p.22 / Chapter Chapter 3 --- Methods of Constructing Simultaneous Prediction Intervals --- p.24 / Chapter 3.1 --- Three Approximation Procedures --- p.25 / Chapter 3.1.1 --- Bonferroni-type Inequality --- p.26 / Chapter 3.1.2 --- Product-type Inequality --- p.28 / Chapter 3.1.3 --- Chi-square-type Inequality --- p.30 / Chapter 3.2 --- The 'Exact' Procedure --- p.31 / Chapter 3.3 --- Summary --- p.32 / Chapter Chapter 4 --- An Illustrative Example --- p.33 / Table 4.1 - 4.7 --- p.47 / Figure 4.1 - 4.5 --- p.55 / Chapter Chapter 5 --- Simulation Study --- p.60 / Chapter 5.1 --- Holt-Winters Forecasting Procedure for Optimal Model --- p.60 / Chapter 5.2 --- Holt-Winters Forecasting Procedure for Some Non-optimal Models --- p.66 / Chapter 5.3 --- A Comparison of Box-Jenkins Method and Holt-Winters Forecasting Procedure --- p.68 / Chapter 5.4 --- Conclusion --- p.74 / Table 5.1-5.10 --- p.75 / Chapter Chapter 6 --- Further Research --- p.82 / APPENDIXES --- p.87 / REFERENCES --- p.91

This report presents the development of a dynamic temperature model for an automatic transmission in a Volvo Cars passenger vehicle. The model should simulate the oil to cooler temperature and flow from the transmission. A mathematical approach to use lumped masses for different parts of the transmission was used. To tune the response of the lumped masses and heat transfer coefficients; temperature measurements were done on a vehicle in a chassis dyno. To verify the model, simple drive cycles were performed with temperature measurement in the same chassis dyno and on the same vehicle. The verification on the model shows that the model can simulate the behavior of a transmission with an error of 2.5 °C during normal behavior and 6.5 °C for a few minutes when a sudden change in the temperature from the cooler have a large transient increase. Because of this, the model is considered to be fairly accurate. However, in order to make the model compatible with Volvo Cars existing simulation software, Vsim, a "cooler model" has to be created.

Biorreatores, que são explorados desde a antiguidade, são sistemas capazes de realizar a fermentação de compostos orgânicos, continuam sendo amplamente utilizados atualmente devido à diversidade de aplicações. Esses sistemas podem operar em diferentes modos de fermentação, entretanto, os mais utilizados são: fermentação contínua, semicontínua e descontínua. Esse último, juntamente com o processo de digestão anaeróbia (ausência de oxigênio), permitem que uma determinada matéria orgânica seja degradada e transformada em biogás, um dos fatores chave para geração de energia limpa. Percebe-se, portanto, que o estudo de biorreatores em modo de operação descontínuo e em processo de digestão anaeróbia é fundamental para o desenvolvimento de pesquisas relacionadas à geração de energia renovável. Para facilitar o entendimento desse processo, alguns autores propuseram estudos baseados na identificação de parâmetros em modelos não-lineares descritivos, do tipo caixa-branca, que hoje são vastamente utilizados na modelagem de biorreatores. A grande limitação dessa abordagem é que o processo de identificação de sistemas utilizando esses modelos pode ser complexo e demorado, ou, ainda, os parâmetros dos sistemas representados podem não ser identificáveis, inviabilizando o procedimento. Tentando amenizar essas dificuldades, propomos neste trabalho a utilização de modelos polinomiais NAR e NARMA do tipo caixa-preta para a modelagem de biorreatores em modo de fermentação descontínua. Modelos caixa-preta representam sistemas reais por meio de sua saída, sem informação sobre os mecanismos internos desse sistema, simplificando a identificação. Frente a esse contexto, o objetivo deste estudo é investigar a predição e, por consequência, realizar o monitoramento da produção de metano utilizando os modelos caixa-preta propostos em sistemas de biorreatores em modo descontínuo e em processo de digestão anaeróbia. Realizamos estudos que abarcam a investigação de dados simulados e de dados reais. Num primeiro momento são propostos modelos polinomiais dos tipos NAR e NARMA. A partir desses modelos são estimados os parâmetros dos sistemas simulados, com e sem ruído na saída, baseados em condições iniciais propostas na literatura, que denominamos Grupo de Controle. Posteriormente realizamos as validações desses modelos. Em seguida, passamos à etapa de investigação do domínio de validade dos modelos caixa-preta propostos, realizando um estudo em que modificamos as condições iniciais do sistema que representa biorreatores em modo de fermentação descontínua. Por fim, utilizamos dados de um experimento real para realizar o processo de estimação de parâmetros e de validação dos modelos. Os resultados mostraram que os modelos polinomiais NAR e NARMA são bastante adequados para predição de metano em biorreatores em modo de fermentação descontínua em processo de digestão anaeróbia, tanto para os dados simulados quanto para os dados reais. / Bioreactors, which are explored since antiquity, are systems that are capable of performing the fermentation of organic compounds. Nowadays, they are widely applied due to its diversity of applications. These systems can operate in different fermentation modes: continuous, fed-batch and batch. This last fermentation method along with the process of anaerobic digestion allow organic matter to be degraded and converted into biogas, which is a key factor for clean energy generation. It is thus realized that the study of bioreactors in batch mode and anaerobic digestion process is crucial to the development of research related to renewable energy generation. For a better understanding of the process, some authors have proposed studies based on parameters identification in descriptive nonlinear models, white-box models, which are widely used in bioreactors modeling. The main limitation of this approach is that the system identification procedure using these models can be complex and time-consuming, or even the parameters of the systems may not be identifiable. In order to overcome these difficulties, we propose in this work the use of black-box polynomial models for bioreactor modeling in batch mode, with NAR and NARMA model structures. Black-box models represent real systems using its output, without explicitly considering the inner mechanisms of the system, simplifying the identification procedure. Thus, the aim of this work is to investigate the prediction and monitoring methane production using the black-box models proposed using bioreactor systems in batch and anaerobic digestion process. The investigation uses numerical simulation and experimental data. At first, polynomial models of the types NAR and NARMA are proposed. The parameters from these models using simulation data with and without noise at the output, based on initial conditions proposed in the literature, are estimated. Subsequently we perform validations of these models. The next step is the study of the validity domain of the proposed black-box models, which is performed by testing many different initial conditions of the system that represents bioreactors in batch fermentation mode. Finally, we used real experimental data to perform the estimation of the parameters from the process and validation of models. The results, both simulated and experimental, indicate that the polynomial models NAR and NARMA are appropriate for prediction of methane fermentation in batch bioreactors.

American Kestrel Nest Box Programs have been established since the mid 1960’s. The population of American Kestrels (Falco sparverius) along nest box trails has decreased by 47 percent since their original implementation. There are existing technologies that can help in the location of prime kestrel habitat (open fields with conspicuous perching locations) along highways, which reduces the amount of labor in searching for new box locations. These technologies can also help increase the efficiency of monitoring and maintaining kestrel nest trail programs. This study provides an example of how Google Maps can be implemented into a kestrel trail and explains the multiple benefits of the integration.

abstract: Reverse engineering is critical to reasoning about how a system behaves. While complete access to a system inherently allows for perfect analysis, partial access is inherently uncertain. This is the case foran individual agent in a distributed system. Inductive Reverse Engineering (IRE) enables analysis under such circumstances. IRE does this by producing program spaces consistent with individual input-output examples for a given domain-specific language. Then, IRE intersects those program spaces to produce a generalized program consistent with all examples. IRE, an easy to use framework, allows this domain-specific language to be specified in the form of Theorist s, which produce Theory s, a succinct way of representing the program space. Programs are often much more complex than simple string transformations. One of the ways in which they are more complex is in the way that they follow a conversation-like behavior, potentially following some underlying protocol. As a result, IRE represents program interactions as Conversations in order to more correctly model a distributed system. This, for instance, enables IRE to model dynamically captured inputs received from other agents in the distributed system. While domain-specific knowledge provided by a user is extremely valuable, such information is not always possible. IRE mitigates this by automatically inferring program grammars, allowing it to still perform efficient searches of the program space. It does this by intersecting conversations prior to synthesis in order to understand what portions of conversations are constant. IRE exists to be a tool that can aid in automatic reverse engineering across numerous domains. Further, IRE aspires to be a centralized location and interface for implementing program synthesis and automatic black box analysis techniques. / Dissertation/Thesis / Masters Thesis Computer Science 2019

acase@tulane.edu

The relative profitability of sugar beets and competitive crops were studied in Box Elder and Cache counties , Utah . Profitability ratios based on enterprise budget data and resource use requirements were used as a basis for comparison . Sugar beets was found to be more profitable than competing crops in returns per acre , in returns to water used and in returns to fixed investment and management. However, sugar beets was found to be less profitable than certain other crops in returns to operating capital and returns to labor . Also , on land rated low in productivity sugar beets was found to be less profitable than most competing crops . Linear programming techniques were used in studying the production response of sugar beets at various price levels . An aggregated supply curve was developed showing the acreage response in sugar beet production at varying sugar beet prices for the two county area . The price range over which sugar beet acreage was responsive ranged from $11 .70 per ton to a high of $16.70 per ton where the maximum acreage permitted in the model was attained .

This thesis presents the results of a geologic investigation of the Rendezvous Peak area, Cache and Box Elder Counties, Utah (Figure 1). The area lies between the Bear River Range on the east and the Northern Wasatch Mountains on the west (Figure 2). It is south of Cache Valley in which Logan, Utah, is located and north of Ogden Valley, east of the Wasatch Range near Ogden, Utah.

A wide range of modern large-scale systems relies on recovering a signal from noisy linear measurements. In many applications, the useful signal has inherent properties, such as sparsity, low-rankness, or boundedness, and making use of these properties and structures allow a more efficient recovery. Hence, a significant amount of work has been dedicated to developing and analyzing algorithms that can take advantage of the signal structure. Especially, since the advent of Compressed Sensing (CS) there has been significant progress towards this direction. Generally speaking, the signal structure can be harnessed by solving an appropriate regularized or constrained M-estimator. In modern Multi-input Multi-output (MIMO) communication systems, all transmitted signals are drawn from finite constellations and are thus bounded. Besides, most recent modulation schemes such as Generalized Space Shift Keying (GSSK) or Generalized Spatial Modulation (GSM) yield signals that are inherently sparse. In the recovery procedure, boundedness and sparsity can be promoted by using the ℓ1 norm regularization and by imposing an ℓ∞ norm constraint respectively. In this thesis, we propose novel optimization algorithms to recover certain classes of structured signals with emphasis on MIMO communication systems. The exact analysis permits a clear characterization of how well these systems perform. Also, it allows an automatic tuning of the parameters. In each context, we define the appropriate performance metrics and we analyze them exactly in the High Dimentional Regime (HDR). The framework we use for the analysis is based on Gaussian process inequalities; in particular, on a new strong and tight version of a classical comparison inequality (due to Gordon, 1988) in the presence of additional convexity assumptions. The new framework that emerged from this inequality is coined as Convex Gaussian Min-max Theorem (CGMT).