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Articles 1 - 8 of 8
Full-Text Articles in Physical Sciences and Mathematics
Dynamic Neuromechanical Sets For Locomotion, Aravind Sundararajan
Dynamic Neuromechanical Sets For Locomotion, Aravind Sundararajan
Doctoral Dissertations
Most biological systems employ multiple redundant actuators, which is a complicated problem of controls and analysis. Unless assumptions about how the brain and body work together, and assumptions about how the body prioritizes tasks are applied, it is not possible to find the actuator controls. The purpose of this research is to develop computational tools for the analysis of arbitrary musculoskeletal models that employ redundant actuators. Instead of relying primarily on optimization frameworks and numerical methods or task prioritization schemes used typically in biomechanics to find a singular solution for actuator controls, tools for feasible sets analysis are instead developed …
Application Of Crowdsourced Data In Transportation Operations And Safety, Nima Hoseinzadeh
Application Of Crowdsourced Data In Transportation Operations And Safety, Nima Hoseinzadeh
Doctoral Dissertations
Crowdsourcing refers to the acquisition of data from users who contribute their information via smartphone, social media, or the internet. In transportation systems, crowdsourcing turns users into real-time sensors, providing data on traffic speed, travel time, mile traveled, incidents, roadway conditions, weather severity, irregularities in traffic patterns, and hazards. These data can be collected actively or passively in quantitative or qualitative forms. With the emergence of smartphones and navigation apps, crowdsourced data are gaining increased attention in transportation. Crowdsourced data have advantages over traditional fixed-location sensors and camera monitoring: low implementation costs, extended geographic coverage, high resolution, real-time application, increased …
Nonparametric Bayesian Deep Learning For Scientific Data Analysis, Devanshu Agrawal
Nonparametric Bayesian Deep Learning For Scientific Data Analysis, Devanshu Agrawal
Doctoral Dissertations
Deep learning (DL) has emerged as the leading paradigm for predictive modeling in a variety of domains, especially those involving large volumes of high-dimensional spatio-temporal data such as images and text. With the rise of big data in scientific and engineering problems, there is now considerable interest in the research and development of DL for scientific applications. The scientific domain, however, poses unique challenges for DL, including special emphasis on interpretability and robustness. In particular, a priority of the Department of Energy (DOE) is the research and development of probabilistic ML methods that are robust to overfitting and offer reliable …
Root Stage Distributions And Their Importance In Plant-Soil Feedback Models, Tyler Poppenwimer
Root Stage Distributions And Their Importance In Plant-Soil Feedback Models, Tyler Poppenwimer
Doctoral Dissertations
Roots are fundamental to PSFs, being a key mediator of these feedbacks by interacting with and affecting the soil environment and soil microbial communities. However, most PSF models aggregate roots into a homogeneous component or only implicitly simulate roots via functions. Roots are not homogeneous and root traits (nutrient and water uptake, turnover rate, respiration rate, mycorrhizal colonization, etc.) vary with age, branch order, and diameter. Trait differences among a plant’s roots lead to variation in root function and roots can be disaggregated according to their function. The impact on plant growth and resource cycling of changes in the distribution …
Random Search Plus: A More Effective Random Search For Machine Learning Hyperparameters Optimization, Bohan Li
Random Search Plus: A More Effective Random Search For Machine Learning Hyperparameters Optimization, Bohan Li
Masters Theses
Machine learning hyperparameter optimization has always been the key to improve model performance. There are many methods of hyperparameter optimization. The popular methods include grid search, random search, manual search, Bayesian optimization, population-based optimization, etc. Random search occupies less computations than the grid search, but at the same time there is a penalty for accuracy. However, this paper proposes a more effective random search method based on the traditional random search and hyperparameter space separation. This method is named random search plus. This thesis empirically proves that random search plus is more effective than random search. There are some case …
Resource-Saving Technologies For The Production Of Elastic Leather Materials: Collective Monograph, Olena Korotych, Anatolii Danylkovych, Serhii Bilinskyi, Serhii Bondarenko, Slava Branovitska, Vasyl Chervinskyi, Nataliia Khliebnikova, Alona Kudzieva, Viktor Lishchuk, Nataliia Lysenko, Olena Mokrousova, Nataliia Omelchenko, Vera Palamar, Yuliia Potakh, Oksana Romanyuk, Olga Sanginova, Oleksandr Zhyhotsky
Resource-Saving Technologies For The Production Of Elastic Leather Materials: Collective Monograph, Olena Korotych, Anatolii Danylkovych, Serhii Bilinskyi, Serhii Bondarenko, Slava Branovitska, Vasyl Chervinskyi, Nataliia Khliebnikova, Alona Kudzieva, Viktor Lishchuk, Nataliia Lysenko, Olena Mokrousova, Nataliia Omelchenko, Vera Palamar, Yuliia Potakh, Oksana Romanyuk, Olga Sanginova, Oleksandr Zhyhotsky
Chemistry Publications and Other Works
This monograph contains a collection of recent research papers focusing on advancing existing technologies and developing new technologies to improve the environmentally friendliness and save resources during the production of elastic leather materials. The papers are organized based on the type of technological process used to preserve raw hides. A lot of attention is devoted to mathematical planning, simulations, and multicriteria optimization of the technological processes using newly developed chemical reagents. The monograph contains a complex study of physicochemical properties and characteristics of the resulting leather materials. The developed technologies were tested by the private joint-stock company Chinbar (Kyiv, Ukraine) …
Empirical Modeling Of Used Nuclear Fuel Radiation Emissions For Safeguards Purposes, Amanda M. Bachmann
Empirical Modeling Of Used Nuclear Fuel Radiation Emissions For Safeguards Purposes, Amanda M. Bachmann
Masters Theses
For nuclear nonproliferation safeguards, the ability to characterize used nuclear fuel (UNF) is a vital process. Fuel characterization allows for independent verification by inspectors of operator declarations of the special nuclear material flow and nuclear related activities within a facility, and an estimation of fissile material remaining in a fuel assembly. Current methods to verify this information rely heavily on non-destructive assay techniques, such as gamma spectroscopy and neutron detection measurements. While these measurements are effective tools for estimating a specific characteristic of the fuel, such as burnup or cooling time, they often require an accurate estimation of a select …
Bayesian Topological Machine Learning, Christopher A. Oballe
Bayesian Topological Machine Learning, Christopher A. Oballe
Doctoral Dissertations
Topological data analysis encompasses a broad set of ideas and techniques that address 1) how to rigorously define and summarize the shape of data, and 2) use these constructs for inference. This dissertation addresses the second problem by developing new inferential tools for topological data analysis and applying them to solve real-world data problems. First, a Bayesian framework to approximate probability distributions of persistence diagrams is established. The key insight underpinning this framework is that persistence diagrams may be viewed as Poisson point processes with prior intensities. With this assumption in hand, one may compute posterior intensities by adopting techniques …