Synthesis of Multistable Equilibrium Compliant Mechanisms.

Whenever an engineering system operates far from its equilibrium position, the control and actuation scheme can become excessively complicated and power consuming. However, in a multistable compliant mechanism (MSCM), a passive subsystem can be integrated to afford discrete adaptability in function by providing additional equilibria thereby simplifying the actuation and control scheme. This dissertation explores the design and application of devices that exhibit multistability by exploiting compliance in design. MSCMs have the essential advantages of compliant mechanisms including reduced part-count, assembly time, wear, and increased precision, durability and reliability. However, there is no systematic method to design MSCMs and the majority of prior research is limited to design of specific types of bistable mechanisms. This is due to the fact that design of MSCMs is not intuitive and it requires enormous computational time to overcome the complexity of nonlinear behaviors. This study is motivated by the need to design MSCMs systematically without excessive computational time and complexity. The design methodology developed in this dissertation has two major components: (i) generalized methods for synthesizing bistable mechanisms and (ii) synthesis of multistable mechanisms by combining multiple bistable mechanisms. A mathematical formalism to ensure bistable behavior is first introduced. Two methods for synthesizing bistable mechanism are developed (i) by choosing “buckled” shape as initial configuration and (ii) by utilizing a reverse-lateral deformation of a clamped-pinned beam to provide bistability. Each bistable compliant mechanism works as a building block, providing either one or two additional stable states. A simplified mathematical scheme is introduced to capture essential parameters of bistable behaviors to aid in synthesis of more sophisticated multistable mechanisms. The methodology enables designers to capture design requirements mathematically, decompose the problem into feasible sub-problems, synthesize the desired MSCMs from pre-compiled combination libraries, and efficiently evaluate the designs without computationally intensive nonlinear FEA. The method also yields robust designs that are insensitive to manufacturing and other imperfections. The synthesis methodology can benefit a variety of applications including MEMS, space mechanisms, ergonomic devices, and general product design. Several novel designs and working prototypes of MSCMs are developed to demonstrate the effectiveness of the synthesis methods.Ph.D.Mechanical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/61608/1/ohys_1.pd

Thermal and Electrical Transport in Ultralow Density Single‐Walled Carbon Nanotube Networks

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/98216/1/adma_201300059_sm_suppl.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/98216/2/2926_ftp.pd

A novel approach to managing the dynamic nature of semantic relatedness

This research proposes a novel method of measuring the dynamics of semantic relatedness. Research on semantic relatedness has a long history in the fields of computational linguistics, psychology, computer science, as well as information systems. Computing semantic relatedness has played a critical role in various situations, such as data integration and keyword recommendation. Many researchers have tried to propose more sophisticated techniques to measure semantic relatedness. However, little research has considered the change of semantic relatedness with the flow of time and occurrence of events. The authors' proposed method is validated by actual corpus data collected from a particular context over a specific period of time. They test the feasibility of our proposed method by constructing semantic networks by using the corpus collected during a different period of time. The experiment results show that our method can detect and manage the changes in semantic relatedness between concepts. Based on the results, the authors discuss the need for a dynamic semantic relatedness paradigm

A novel approach to managing the dynamic nature of semantic relatedness

This research proposes a novel method of measuring the dynamics of semantic relatedness. Research on semantic relatedness has a long history in the fields of computational linguistics, psychology, computer science, as well as information systems. Computing semantic relatedness has played a critical role in various situations, such as data integration and keyword recommendation. Many researchers have tried to propose more sophisticated techniques to measure semantic relatedness. However, little research has considered the change of semantic relatedness with the flow of time and occurrence of events. The authors' proposed method is validated by actual corpus data collected from a particular context over a specific period of time. They test the feasibility of our proposed method by constructing semantic networks by using the corpus collected during a different period of time. The experiment results show that our method can detect and manage the changes in semantic relatedness between concepts. Based on the results, the authors discuss the need for a dynamic semantic relatedness paradigm

Preformed Nanoporous Carbon Nanotube Scaffold-Based Multifunctional Polymer Composites

Multifunctional polymer nanocomposites that simultaneously possess high modulus and strength, high thermal stability, novel optical responses, and high electrical and thermal conductivity have been actively researched. Carbon nanotubes are considered an ideal additive for composites because of their superlative physical, electronic and optical properties. While nanotubes have successfully added electrical conductivity, thermal stability, and novel optical responses to polymers, mechanical reinforcements, although substantial, have been well below any theoretical estimations. Here, we integrated preformed hydrogels and aerogels of individually dispersed nanotubes with polymer to increase elastic modulus of composites according to Halpin–Tsai model up to at least 25 vol % of nanotubes. Our solution-based fabrication method allowed us to create bulk composites with tunable form-factors, and with polymers that were incompatible with nanotubes. Further, in this approach, nanotubes were not covalently linked among themselves and to the polymer, so intrinsic optical, electrical, and thermal properties of nanotubes could be exploited. The optically active nanotubes, for example, added a strain-dependent, spatially resolved fluorescence to these composites. Finally, the nanoporous nanotube networks suppressed the polymer glass transition and extended the mechanical integrity of polymer well above its polymer melting point, and both the nanotubes and polymer remained thermally stable above their decomposition temperatures

RGB-D Dense Visual Odometry through Pixel Level Segmentation in Dynamic Environments

Estimating a camera pose in dynamic environments is one of the challenging problems in Visual Odometry. We propose an RGB-D Dense Visual Odometry (Dense-VO) system which uses preprocessed images that passed the Convolutional Neural Network (CNN). The algorithm adopts the CNN that tracks the designated dynamic object. The tracked dynamic object is excluded when the Dense-VO estimates the camera motion by minimizing photometric error between consecutive images. The system was tested in two datasets which includes a dynamic object. The proposed approach containing the preprocessing procedure estimates the camera trajectory with less drift in a dynamic environment.N

Edge Adaptive Color Demosaicking Based on the Spatial Correlation of the Bayer Color Difference

An edge adaptive color demosaicking algorithm that classifies the region types and estimates the edge direction on the Bayer color filter array (CFA) samples is proposed. In the proposed method, the optimal edge direction is estimated based on the spatial correlation on the Bayer color difference plane, which adopts the local directional correlation of an edge region of the Bayer CFA samples. To improve the image quality with the consistent edge direction, we classify the region of an image into three different types, such as edge, edge pattern, and flat regions. Based on the region types, the proposed method estimates the edge direction adaptive to the regions. As a result, the proposed method reconstructs clear edges with reduced visual distortions in the edge and the edge pattern regions. Experimental results show that the proposed method outperforms conventional edge-directed methods on objective and subjective criteria.</p

The Photometric Brightness Variation of Geostationary Orbit Satellite

Photometric observation is one of the most effective techniques for determining the physical characteristics of unknown space objects and space debris. In this research, we examine the change in brightness of the Communication, Ocean, Meteorological Satellite-1 (COMS-1) Geostationary Orbit Satellite (GEO), and compare it to our estimate model. First, we calculate the maximum brightness time using our calculation method and then derive the light curve shape using our rendering model. The maximum brightness is then calculated using the induced equation from Pogson's formula. For a comparison with our estimation, we carried out photometric observation using an optical telescope. The variation in brightness and the shape of the light curve are similar to the calculations achieved using our model, but the maximum brightness shows a slightly different value from our calculation result depending on the input parameters. This paper examines the photometric phenomenon of the variation in brightness of a GEO satellite, and the implementation of our approach to understanding the characteristics of space objects