Communication-Wear: User Feedback as Part of a Co-Design Process

Communication-Wear is a clothing concept that augments the mobile phone by enabling expressive messages to be exchanged remotely, by conveying a sense of touch, and presence. It proposes to synthesise conventions and cultures of fashion with those of mobile communications, where there are shared attributes in terms of communication and expression. Using garment prototypes as research probes as part of an on-going iterative co-design process, we endeavoured to mobilise participants’ tacit knowledge in order to gauge user perceptions on touch communication in a lab-based trial. The aim of this study was to determine whether established sensory associations people have with the tactile qualities of textiles could be used as signs and metaphors for experiences, moods, social interactions and gestures, related to interpersonal touch. The findings are used to inspire new design ideas for textile actuators for use in touch communication in successive iterations

Aerospike Rocket Motor Structural Webbing

A labscale hybrid rocket motor test stand has been developed for research at Cal Poly. The primary focus of research using this rig has been the development of regenerative cooling techniques using nitrous oxide as coolant and oxidizer, as well as validation of technologies relating to the annular aerospike nozzle. In order to prevent undesirable deflection of the cantilevered spike, a structural stiffening web, referred to as “The Spider,” is proposed. The Spider resembles a three-spoked wheel, with the aerospike held by the inner hub and the chamber walls abutting the outer radius. The Spider, placed upstream of the nozzle, is subject to thermal loads due to radiation and convection from the gases, and conduction from the outer annulus, as well as mechanical loads from thermal expansion and gas flow. Simulation tools are developed in three phases to produce an accurate model of the spatio-temporal distribution of these loads. A prototype of the Spider instrumented with thermocouple probes is designed, manufactured, and subjected to a series of hotfire tests. Results from three experimental runs are gathered and compared to simulated results. Good agreement is shown for the most part between the two datasets, with a single noticeable discrepancy for one measured temperature location. The high fidelity in the mean rate of temperature change for all stations indicates that the convective heat load is accurately modeled. The simulation results, confirmed by experiment, indicate that in order for the Spider to survive in the steady-state during an actual burn, an active cooling strategy is necessary. Two actively cooled concept designs are presented and discussed, and future avenues of research are suggested

Machine analysis of engineering drawings

While engineering information is increasingly developed and communicated digitally, traditional media like technical prints are still in wide use for both manufacturing and inspection. Digitizing this information, either to preserve archival drawings or to interface with modern computer-controlled devices like CNC tools or CMMs, is time-consuming and may require an expert operator. This work presents the first holistic approach to semi-automatically extracting 3-dimensional geometry directly from a raster scan of engineering drawings. This approach is capable of running autonomously or semi-autonomously, with a human operator correcting errors in each processing stage. This work addresses four key subproblems in this task: identification, segmentation, association, and reconstruction. First, a custom convolutional neural network is developed to identify and localize all of the text and annotation elements in a drawing. Through the study of neural network training techniques, this work introduces novel, general techniques to improve and accelerate training in both generative and discriminative models. Furthermore, a high-quality, domain-specific character recognition dataset comprising more than 500,000 annotations which we use to train these models is presented. Second, to address the problem of part geometry segmentation and vectorization, a novel technique for extracting geometric primitives from line drawings based on neural networks and graph methods is proposed. Combining this model with a novel knowledge capture and data simulation pipeline makes it possible to scale to any number of primitive classes with only minimal need for class-specific postprocessing. Third, this work leverages the latent rules that underlie drawing production to develop heuristics for tackling the problem of association: grouping identified drawing elements into callouts, and determining what part geometry the callouts refer to. Finally, this work employs projection geometry methods that leverage the results of solving the first three problems to produce three-dimensional CAD models. This work advances the state of the art in neural network training and provides a scalable framework for developing complete drawing analysis systems, providing a stepping stone towards the next generation of intelligent design software

FreezeOut: Accelerate Training by Progressively Freezing Layers

The early layers of a deep neural net have the fewest parameters, but take up the most computation. In this extended abstract, we propose to only train the hidden layers for a set portion of the training run, freezing them out one-by-one and excluding them from the backward pass. Through experiments on CIFAR, we empirically demonstrate that FreezeOut yields savings of up to 20% wall-clock time during training with 3% loss in accuracy for DenseNets, a 20% speedup without loss of accuracy for ResNets, and no improvement for VGG networks. Our code is publicly available at https://github.com/ajbrock/FreezeOutComment: Extended Abstrac

SMASH: One-Shot Model Architecture Search through HyperNetworks

Designing architectures for deep neural networks requires expert knowledge and substantial computation time. We propose a technique to accelerate architecture selection by learning an auxiliary HyperNet that generates the weights of a main model conditioned on that model's architecture. By comparing the relative validation performance of networks with HyperNet-generated weights, we can effectively search over a wide range of architectures at the cost of a single training run. To facilitate this search, we develop a flexible mechanism based on memory read-writes that allows us to define a wide range of network connectivity patterns, with ResNet, DenseNet, and FractalNet blocks as special cases. We validate our method (SMASH) on CIFAR-10 and CIFAR-100, STL-10, ModelNet10, and Imagenet32x32, achieving competitive performance with similarly-sized hand-designed networks. Our code is available at https://github.com/ajbrock/SMAS

Generative and Discriminative Voxel Modeling with Convolutional Neural Networks

When working with three-dimensional data, choice of representation is key. We explore voxel-based models, and present evidence for the viability of voxellated representations in applications including shape modeling and object classification. Our key contributions are methods for training voxel-based variational autoencoders, a user interface for exploring the latent space learned by the autoencoder, and a deep convolutional neural network architecture for object classification. We address challenges unique to voxel-based representations, and empirically evaluate our models on the ModelNet benchmark, where we demonstrate a 51.5% relative improvement in the state of the art for object classification.Comment: 9 pages, 5 figures, 2 table

Testing the usefulness of pine stomata as a proxy in lake sediment cores from low-latitude environments

Paleoecological research, using lake cores to reconstruct past climatic and anthropogenic changes, is a burgeoning field in the circum-Caribbean. The Dominican Republic’s Las Lagunas region is being studied for this purpose using many proxies. One possible proxy for study there is pine stomata. Concentrations of pine stomata in lake sediments have been used in high-latitude and alpine locations to reconstruct tree-line movement and stand invasion, but have never been used in low-latitude environments. In this thesis I present results of analyses of Pinus occidentalis Swartz (Hispaniolan pine or West Indian pine) stomata concentrations in lake-sediment cores from two lakes in the Las Lagunas region, Laguna Castilla and Laguna de Salvador. Stomata concentrations, along with prior pollen counts, provide a detailed, site-specific view of historic pine distribution near the lakes. Previous higher-latitude studies provide background and context for this project, which aims to establish pine stomata as a useful proxy in low-latitude environments. Stomata concentrations in Castilla and Salvador, though never high, improved the interpretability of previous pine pollen counts. Pollen and stomata tended to co-vary down the Salvador core, and more weakly in the Castilla core where stomata concentrations were lower. Overall, pine stomata proved a useful proxy at Las Lagunas that can be used in future paleoecological studies of other low-latitude environments. In addition to the Las Lagunas temporal study, this thesis examines spatial patterns of stomata deposition in mid-latitude Crystal Lake, Knoxville, Tennessee, from the edge of the lake to its middle. Typically, sediment cores are taken centrally in lakes, so this study examines whether stomata are distributed evenly enough across lakes to be well represented in central cores. The Crystal Lake study provided useful insights into the deposition and redeposition paths followed by stomata after they enter water bodies. Concentrations of stomata decreased on a dry weight basis, traversing away from shore, with a slight increase where a typical core site location would be, at the lake’s center. Based on these results, central coring sites might often fall short of yielding representative concentrations of the stomata entering lakes

Representing moisture fluxes and phase changes in glacier debris cover using a reservoir approach

Due to the complexity of treating moisture in supraglacial debris, surface energy balance models to date have neglected moisture infiltration and phase changes in the debris layer. The latent heat flux (QL) is also often excluded due to the uncertainty in determining the surface vapour pressure. To quantify the importance of moisture on the surface energy and climatic mass balance (CMB) of debris-covered glaciers, we developed a simple reservoir parameterization for the debris ice and water content, as well as an estimation of the latent heat flux. The parameterization was incorporated into a CMB model adapted for debris-covered glaciers. We present the results of two point simulations, using both our new “moist” and the conventional “dry” approaches, on the Miage Glacier, Italy, during summer 2008 and fall 2011. The former year coincides with available in situ glaciological and meteorological measurements, including the first eddy-covariance measurements of the turbulent fluxes over supraglacial debris, while the latter contains two refreeze events that permit evaluation of the influence of phase changes. The simulations demonstrate a clear influence of moisture on the glacier energy and mass-balance dynamics. When water and ice are considered, heat transmission to the underlying glacier ice is lower, as the effective thermal diffusivity of the saturated debris layers is reduced by increases in both the density and the specific heat capacity of the layers. In combination with surface heat extraction by QL, subdebris ice melt is reduced by 3.1% in 2008 and by 7.0% in 2011 when moisture effects are included. However, the influence of the parameterization on the total accumulated mass balance varies seasonally. In summer 2008, mass loss due to surface vapour fluxes more than compensates for the reduction in ice melt, such that the total ablation increases by 4.0 %. Conversely, in fall 2011, the modulation of basal debris temperature by debris ice results in a decrease in total ablation of 2.1 %. Although the parameterization is a simplified representation of the moist physics of glacier debris, it is a novel attempt at including moisture in a numerical model of debris-covered glaciers and one that opens up additional avenues for future research