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Equations of motion with required boundary conditions for doubly curved deep and thick composite shells are shown using two formulations. The first is based upon the formulation that was presented initially by Rath and Das (1973, J. Sound and Vib.) and followed by Reddy (1984, J. Engng. Mech. ASCE). In this formulation, plate stiffness parameters are used for thick shells, which reduced the equations to those applicable for shallow shells. This formulation is widely used but its accuracy has not been completely tested. The second formulation is based upon that of Qatu (1995, Compos. Press. Vessl. Indust.; 1999, Int. J. Solids Struct.). In this formulation, the stiffness parameters are calculated by using exact integration of the stress resultant equations. In addition, Qatu considered the radius of twist in his formulation. In both formulations, first order polynomials for in-plane displacements in the z-direction are utilized allowing for the inclusion of shear deformation and rotary inertia effects (first order shear deformation theory or FSDT). Also, FSDTQ has been modified in this dissertation using the radii of each laminate instead of using the radii of mid-plane in the moment of inertias and stress resultants equations. Exact static and free vibration solutions for isotropic and symmetric and anti-symmetric cross-ply cylindrical shells for different length-to-thickness and length-to-radius ratios are obtained using the above theories. Finally, the equations of motion are put together with the equations of stress resultants to arrive at a system of seventeen first-order differential equations. These equations are solved numerically with the aid of General Differential Quadrature (GDQ) method for isotropic, cross-ply, angle-ply and general lay-up cylindrical shells with different boundary conditions using the above mentioned theories. Results obtained using all three theories (FSDT, FSDTQ and modified FSDTQ) are compared with the results available in literature and those obtained using a three-dimensional (3D) analysis. The latter (3D) is used here mainly to test the accuracy of the shell theories presented here.

Lesbians have historically lived in obscurity and isolation because living outwardly as a lesbian carried with it the almost certain loss of social standing, family, and friends (Blando, 2001). For lesbians who grew up in the Deep South, isolation and the pressure to conform was greater than anywhere in the United States (Barton, 2010). Most Deep Southerners were homophobic, especially in rural areas where people were deeply religious and had little exposure to sexual minorities. The researcher used a qualitative phenomenological approach to explore the meaning and significance of growing up lesbian in the rural Deep South. The sample included 12 Caucasian lesbians, ages 45 to 62. Four clusters of themes emerged from the interviews. Those clusters were: (1) emerging sexuality, (2) the mark of fatal difference, (3) denial of lesbian identity, and (4) conforming to Deep Southern social mores. Themes within those clusters described how delays in both lesbian identity development (Cass, 1984) and psychosocial development (Erikson, 1975) occurred in each of the participants because of the intensely religious and homophobic environments in which they were raised. Denunciation of participants' personal identities began with the first expressions of their sexual identities in elementary school. Ridiculed at a young age because of attractions to girls, participants cycled back through developmental crises involving shame, doubt, and inferiority. They entered adolescence disturbed about their developing sexualities, to discover that parents and faith-based communities were homophobic. Therefore, at the time when participants faced the most critical developmental crises of their lives (Erikson, 1975), they feared rejection by their parents, communities, and God. Participants sought to suppress or deny their lesbianism. Suppression of lesbian identity came with emotional and developmental costs, including substance abuse, unwanted marriages, and role confusion. Unable to find needed resources and role models, participants conformed to the social mores of the rural community for periods ranging from five to twenty years. Eventually, each participant in this study left her rural origins to begin claiming her lesbian identity. Retrospectively, each woman recognized that in the era in which they grew up, communities in the rural Deep South demanded conformity and resisted allowing members to individuate. Thus, participants in this study entered adulthood, and sometimes middle age, with a number of unresolved developmental crises, particularly as those crises related to sexual orientation.

A rigorous first order shear deformation theory (FSDT) is employed along with modified ABD parameters to analyze static and free vibration behavior of generally laminated beams and shafts. Different approaches for calculating composite beam stiffness parameters have been considered and the most accurate one that accounts for material couplings have been used to analyze static and free vibration behaviors of straight beams with different laminates and boundary conditions. In order to analyze curved beams, the term (1+z/R) is exactly integrated into ABD parameters formulation and an equivalent modulus of elasticity is used instead of traditional stiffness terms to account for both the deepness and material coupling of the beam structures. The model has been solved analytically for simply supported boundary conditions and the general differential quadrature (GDQ) technique has been used for other boundary conditions. The results for deflection, moment resultants, and natural frequencies of straight and curved beams with different deepness ratio (often called depth ratio), slenderness ratio, lamination, and boundary conditions are compared with those obtained from accurate three dimensional finite element simulations using ANSYS. The results were in close proximity to three dimensional finite element results. The model is then applied to transverse vibration analysis of multi-span generally laminated composite shafts with a lumped mass using GDQ. The results for natural frequencies are compared to experimental and other analytical models as well as finite element simulation. The results in the present analyses were found accurate. Conclusively, it has been shown that when considering more accurate stiffness parameters, a First Order Shear Deformation Theory can accurately predict static and free vibration behaviors of composite beams and multispan shafts of any deepness, lamination and boundary conditions.

No description available.

No description available.

Trimap-free natural image matting problem is an important computer vision task in which we extract foreground objects from given images without extra trimap input. Compared with trimap-based matting algorithms, trimap-free algorithms are easier to make false detection when the foreground object is not well defined. To solve the problem, we design a novel structure (SegMatting) to handle foreground segmentation and alpha matte prediction simultaneously, which is able to produce high-quality mattes based on RGB inputs alone. This entangled structure enables information exchange between the binary segmentation task and the alpha matte prediction task interactively, and we further design a hybrid loss to adaptively balance two tasks during the multitask learning process. Additionally, we adopt a salient object detection dataset to pretrain our network so that we could obtain a more accurate foreground segment before our training process. Experiments indicate that the proposed SegMatting qualitatively and quantitatively outperforms most previous trimap-free models with a significant margin, while remains competitive among trimap-based methods. / Thesis / Master of Science in Electrical and Computer Engineering (MSECE)

Anomaly detection is the process of finding unusual events in a given dataset. Anomaly detection is often performed on datasets with a fixed set of predefined features. As a result of this, if the normal features bear a close resemblance to the anomalous features, most anomaly detection algorithms exhibit poor performance. This work seeks to answer the question, can we deform these features so as to make the anomalies standout and hence improve the anomaly detection outcome? We employ a Deep Learning and an Active Learning framework to learn features for anomaly detection. In Active Learning, an Oracle (usually a domain expert) labels a small amount of data over a series of training rounds. The deep neural network is trained after each round to incorporate the feedback from the Oracle into the model. Results on the MNIST, CIFAR-10 and Galaxy Zoo datasets show that our algorithm, Ahunt, significantly outperforms other anomaly detection algorithms used on a fixed, static, set of features. Ahunt can therefore overcome a poor choice of features that happen to be suboptimal for detecting anomalies in the data, learning more appropriate features. We also explore the role of the loss function and Active Learning query strategy, showing these are important, especially when there is a significant variation in the anomalies.