Aligning Manifolds of Double Pendulum Dynamics Under the Influence of Noise

This study presents the results of a series of simulation experiments that evaluate and compare four different manifold alignment methods under the influence of noise. The data was created by simulating the dynamics of two slightly different double pendulums in three-dimensional space. The method of semi-supervised feature-level manifold alignment using global distance resulted in the most convincing visualisations. However, the semi-supervised feature-level local alignment methods resulted in smaller alignment errors. These local alignment methods were also more robust to noise and faster than the other methods.Comment: The final version will appear in ICONIP 2018. A DOI identifier to the final version will be added to the preprint, as soon as it is availabl

Exploiting Low-dimensional Structures to Enhance DNN Based Acoustic Modeling in Speech Recognition

We propose to model the acoustic space of deep neural network (DNN) class-conditional posterior probabilities as a union of low-dimensional subspaces. To that end, the training posteriors are used for dictionary learning and sparse coding. Sparse representation of the test posteriors using this dictionary enables projection to the space of training data. Relying on the fact that the intrinsic dimensions of the posterior subspaces are indeed very small and the matrix of all posteriors belonging to a class has a very low rank, we demonstrate how low-dimensional structures enable further enhancement of the posteriors and rectify the spurious errors due to mismatch conditions. The enhanced acoustic modeling method leads to improvements in continuous speech recognition task using hybrid DNN-HMM (hidden Markov model) framework in both clean and noisy conditions, where upto 15.4% relative reduction in word error rate (WER) is achieved

TimewarpVAE: Simultaneous Time-Warping and Representation Learning of Trajectories

Human demonstrations of trajectories are an important source of training data for many machine learning problems. However, the difficulty of collecting human demonstration data for complex tasks makes learning efficient representations of those trajectories challenging. For many problems, such as for handwriting or for quasistatic dexterous manipulation, the exact timings of the trajectories should be factored from their spatial path characteristics. In this work, we propose TimewarpVAE, a fully differentiable manifold-learning algorithm that incorporates Dynamic Time Warping (DTW) to simultaneously learn both timing variations and latent factors of spatial variation. We show how the TimewarpVAE algorithm learns appropriate time alignments and meaningful representations of spatial variations in small handwriting and fork manipulation datasets. Our results have lower spatial reconstruction test error than baseline approaches and the learned low-dimensional representations can be used to efficiently generate semantically meaningful novel trajectories.Comment: 17 pages, 12 figure

Manifold and base casting of Lunenburg Foundry Atlantic Marine Engine

Thesis: S.B., Massachusetts Institute of Technology, Department of Mechanical Engineering, May, 2020Cataloged from the official PDF of thesis.Includes bibliographical references (page 41).Previous work started by mechanical engineering students in the spring of 2016 established a basis for applying modern machining and modeling techniques to the fabrication of century old sand cast designs, specifically a Lunenburg Foundry Atlantic Engine. This project was continued by mechanical engineering students in the spring of 2019. From there, this groundwork was used to fabricate one of the remaining parts of the engine, the lower base. Due to COVID-19 pandemic and MIT campus shut down, this thesis project was virtualized. From this, casting simulation software (Visual Cast) used to simulate the casting of another engine part, the manifold, with a variety of geometries and process conditions. The simulations were completed to predict the casting quality of the part if physically cast. Additionally, the next steps for completion of the engine are outlined.by Chiaki Kirby.S.B.S.B. Massachusetts Institute of Technology, Department of Mechanical Engineerin