Matching Items (127)
Description
A storage system requiring file redundancy and on-line repairability can be represented as a Steiner system, a combinatorial design with the property that every $t$-subset of its points occurs in exactly one of its blocks. Under this representation, files are the points and storage units are the blocks of the Steiner system, or vice-versa. Often, the popularities of the files of such storage systems run the gamut, with some files receiving hardly any attention, and others receiving most of it. For such systems, minimizing the difference in the collective popularity between any two storage units is nontrivial; this is the access balancing problem. With regard to the representative Steiner system, the access balancing problem in its simplest form amounts to constructing either a point or block labelling: an assignment of a set of integer labels (popularity ranks) to the Steiner system's point set or block set, respectively, requiring of the former assignment that the sums of the labelled points of any two blocks differ as little as possible and of the latter that the sums of the labels assigned to the containing blocks of any two distinct points differ as little as possible. The central aim of this dissertation is to supply point and block labellings for Steiner systems of block size greater than three, for which up to this point no attempt has been made. Four major results are given in this connection. First, motivated by the close connection between the size of the independent sets of a Steiner system and the quality of its labellings, a Steiner triple system of any admissible order is constructed with a pair of disjoint independent sets of maximum cardinality. Second, the spectrum of resolvable Bose triple systems is determined in order to label some Steiner 2-designs with block size four. Third, several kinds of independent sets are used to point-label Steiner 2-designs with block size four. Finally, optimal and close to optimal block labellings are given for an infinite class of 1-rotational resolvable Steiner 2-designs with arbitrarily large block size by exploiting their underlying group-theoretic properties.
ContributorsLusi, Dylan (Author) / Colbourn, Charles J (Thesis advisor) / Czygrinow, Andrzej (Committee member) / Fainekos, Georgios (Committee member) / Richa, Andrea (Committee member) / Arizona State University (Publisher)
Created2021
Description
Autonomous Vehicles (AV) are inevitable entities in future mobility systems thatdemand safety and adaptability as two critical factors in replacing/assisting human
drivers. Safety arises in defining, standardizing, quantifying, and monitoring requirements
for all autonomous components. Adaptability, on the other hand, involves
efficient handling of uncertainty and inconsistencies in models and data. First, I address
safety by presenting a search-based test-case generation framework that can be
used in training and testing deep-learning components of AV. Next, to address adaptability,
I propose a framework based on multi-valued linear temporal logic syntax and
semantics that allows autonomous agents to perform model-checking on systems with
uncertainties. The search-based test-case generation framework provides safety assurance
guarantees through formalizing and monitoring Responsibility Sensitive Safety
(RSS) rules. I use the RSS rules in signal temporal logic as qualification specifications
for monitoring and screening the quality of generated test-drive scenarios. Furthermore,
to extend the existing temporal-based formal languages’ expressivity, I propose
a new spatio-temporal perception logic that enables formalizing qualification specifications
for perception systems. All-in-one, my test-generation framework can be
used for reasoning about the quality of perception, prediction, and decision-making
components in AV. Finally, my efforts resulted in publicly available software. One
is an offline monitoring algorithm based on the proposed logic to reason about the
quality of perception systems. The other is an optimal planner (model checker) that
accepts mission specifications and model descriptions in the form of multi-valued logic
and multi-valued sets, respectively. My monitoring framework is distributed with the
publicly available S-TaLiRo and Sim-ATAV tools.
ContributorsHekmatnejad, Mohammad (Author) / Fainekos, Georgios (Thesis advisor) / Deshmukh, Jyotirmoy V (Committee member) / Karam, Lina (Committee member) / Pedrielli, Giulia (Committee member) / Shrivastava, Aviral (Committee member) / Yang, Yezhou (Committee member) / Arizona State University (Publisher)
Created2021
Description
Composite materials have gained interest in the aerospace, mechanical and civil engineering industries due to their desirable properties - high specific strength and modulus, and superior resistance to fatigue. Design engineers greatly benefit from a reliable predictive tool that can calculate the deformations, strains, and stresses of composites under uniaxial and multiaxial states of loading including damage and failure predictions. Obtaining this information from (laboratory) experimental testing is costly, time consuming, and sometimes, impractical. On the other hand, numerical modeling of composite materials provides a tool (virtual testing) that can be used as a supplemental and an alternate procedure to obtain data that either cannot be readily obtained via experiments or is not possible with the currently available experimental setup. In this study, a unidirectional composite (Toray T800-F3900) is modeled at the constituent level using repeated unit cells (RUC) so as to obtain homogenized response all the way from the unloaded state up until failure (defined as complete loss of load carrying capacity). The RUC-based model is first calibrated and validated against the principal material direction laboratory tests involving unidirectional loading states. Subsequently, the models are subjected to multi-directional states of loading to generate a point cloud failure data under in-plane and out-of-plane biaxial loading conditions. Failure surfaces thus generated are plotted and compared against analytical failure theories. Results indicate that the developed process and framework can be used to generate a reliable failure prediction procedure that can possibly be used for a variety of composite systems.
ContributorsKatusele, Daniel Mutahwa (Author) / Rajan, Subramaniam (Thesis advisor) / Mobasher, Barzin (Committee member) / Neithalath, Narayanan (Committee member) / Arizona State University (Publisher)
Created2021
Description
Imitation learning is a promising methodology for teaching robots how to physically interact and collaborate with human partners. However, successful interaction requires complex coordination in time and space, i.e., knowing what to do as well as when to do it. This dissertation introduces Bayesian Interaction Primitives, a probabilistic imitation learning framework which establishes a conceptual and theoretical relationship between human-robot interaction (HRI) and simultaneous localization and mapping. In particular, it is established that HRI can be viewed through the lens of recursive filtering in time and space. In turn, this relationship allows one to leverage techniques from an existing, mature field and develop a powerful new formulation which enables multimodal spatiotemporal inference in collaborative settings involving two or more agents. Through the development of exact and approximate variations of this method, it is shown in this work that it is possible to learn complex real-world interactions in a wide variety of settings, including tasks such as handshaking, cooperative manipulation, catching, hugging, and more.
ContributorsCampbell, Joseph (Author) / Ben Amor, Heni (Thesis advisor) / Fainekos, Georgios (Thesis advisor) / Yamane, Katsu (Committee member) / Kambhampati, Subbarao (Committee member) / Arizona State University (Publisher)
Created2021
Description
Many real-world engineering problems require simulations to evaluate the design objectives and constraints. Often, due to the complexity of the system model, simulations can be prohibitive in terms of computation time. One approach to overcome this issue is to construct a surrogate model, which approximates the original model. The focus of this work is on the data-driven surrogate models, in which empirical approximations of the output are performed given the input parameters. Recently neural networks (NN) have re-emerged as a popular method for constructing data-driven surrogate models. Although, NNs have achieved excellent accuracy and are widely used, they pose their own challenges. This work addresses two common challenges, the need for: (1) hardware acceleration and (2) uncertainty quantification (UQ) in the presence of input variability. The high demand in the inference phase of deep NNs in cloud servers/edge devices calls for the design of low power custom hardware accelerators. The first part of this work describes the design of an energy-efficient long short-term memory (LSTM) accelerator. The overarching goal is to aggressively reduce the power consumption and area of the LSTM components using approximate computing, and then use architectural level techniques to boost the performance. The proposed design is synthesized and placed and routed as an application-specific integrated circuit (ASIC). The results demonstrate that this accelerator is 1.2X and 3.6X more energy-efficient and area-efficient than the baseline LSTM. In the second part of this work, a robust framework is developed based on an alternate data-driven surrogate model referred to as polynomial chaos expansion (PCE) for addressing UQ. In contrast to many existing approaches, no assumptions are made on the elements of the function space and UQ is a function of the expansion coefficients. Moreover, the sensitivity of the output with respect to any subset of the input variables can be computed analytically by post-processing the PCE coefficients. This provides a systematic and incremental method to pruning or changing the order of the model. This framework is evaluated on several real-world applications from different domains and is extended for classification tasks as well.
ContributorsAzari, Elham (Author) / Vrudhula, Sarma (Thesis advisor) / Fainekos, Georgios (Committee member) / Ren, Fengbo (Committee member) / Yang, Yezhou (Committee member) / Arizona State University (Publisher)
Created2021
Description
Autonomous Vehicles (AVs) have the potential to significantly evolve transportation. AVs are expected to make transportation safer by avoiding accidents that happen due to human errors. When AVs become connected, they can exchange information with the infrastructure or other Connected Autonomous Vehicles (CAVs) to efficiently plan their future motion and therefore, increase the road throughput and reduce energy consumption. Cooperative algorithms for CAVs will not be deployed in real life unless they are proved to be safe, robust, and resilient to different failure models. Since intersections are crucial areas where most accidents happen, this dissertation first focuses on making existing intersection management algorithms safe and resilient against network and computation time, bounded model mismatches and external disturbances, and the existence of a rogue vehicle. Then, a generic algorithm for conflict resolution and cooperation of CAVs is proposed that ensures the safety of vehicles even when other vehicles suddenly change their plan. The proposed approach can also detect deadlock situations among CAVs and resolve them through a negotiation process. A testbed consisting of 1/10th scale model CAVs is built to evaluate the proposed algorithms. In addition, a simulator is developed to perform tests at a large scale. Results from the conducted experiments indicate the robustness and resilience of proposed approaches.
ContributorsKhayatian, Mohammad (Author) / Shrivastava, Aviral (Thesis advisor) / Fainekos, Georgios (Committee member) / Ben Amor, Heni (Committee member) / Yang, Yezhou (Committee member) / Lou, Yingyan (Committee member) / Iannucci, Bob (Committee member) / Arizona State University (Publisher)
Created2021
Description
Alkali activated mine tailing-slag blends and mine tailing-cement blends containing mine tailings as the major binder constituent are evaluated for their setting time behavior,
reactivity properties, flow characteristics, and compressive strengths. Liquid sodium
silicate and sodium hydroxide are used as the activator solution. The effects of varying
alkali oxide-to-powder ratio (n value) and silicon oxide-to-alkali oxide ratio (Ms value) is
explored. The reactivity of all blends prepared in this study is studied using an isothermal
calorimeter. Mine tailing-cement blends show a higher initial heat release peak than mine
tailing-slag blends, whereas their cumulative heat release is comparable for higher n values
of 0.050 to 0.100. Compressive strength tests and rheological studies were done for the
refined blends selected based on setting time criterion. Setting times and compressive
strengths are found to depend significantly on the activator parameters and binder
compositions, allowing fine-tuning of the mix proportion parameters based on the intended
end applications. The compressive strength of the selected mine tailing-slag blends and
mine tailing-cement blends are in the range of 7-40 MPa and 4-11 MPa, respectively.
Higher compressive strength is generally achieved at lower Ms and higher n values for mine
tailing-slag blends, while a higher Ms yields better compressive strength in the case of mine
tailing-cement blends. Rheological studies indicate a decrease in yield stress and viscosity
with increase in the replacement ratio, while a higher activator concentration increase both.
Oscillatory shear studies were used to evaluate the storage modulus and loss modulus of
the mine tailing binders. The paste is seen to exhibit a more elastic behavior at n values of
0.05 and 0.075, however the viscous behavior is seen to dominate at higher n value of 0.1
at similar replacement ratios and Ms value. A higher Ms value is also seen to increase the
onset point of the drop in both the storage and loss modulus of the pastes. The studied also
investigated the potential use of mine tailing blends for coating applications. The pastes
with higher alkalinity showed a lesser crack percentage, with a 10% slag replacement ratio
having a better performance compared to 20% and 30% slag replacement ratios. Overall,
the study showed that the activation parameters and mine tailings replacement level have
a significant influence on the properties of both mine tailing-slag binders and mine tailing-cement binders, thereby allowing selection of suitable mix design for the desired end
application, allowing a sustainable approach to dispose the mine tailings waste
ContributorsRamasamy Jeyaprakash, Rijul Kanth (Author) / Neithalath, Narayanan (Thesis advisor) / Rajan, Subramaniam (Committee member) / Mobasher, Barzin (Committee member) / Arizona State University (Publisher)
Created2023
Description
Concrete develops strength rapidly after mixing and is highly influenced by temperature and curing process. The material characteristics and the rate of property development, along with the exposure conditions influences volume change mechanisms in concrete, and the cracking propensity of the mixtures. Furthermore, the structure geometry (due to restraint as well as the surface area-to-volume ratio) also influences shrinkage and cracking. Thus, goal of this research is to better understand and predict shrinkage cracking in concrete slab systems under different curing conditions. In this research, different concrete mixtures are evaluated on their propensity to shrink based on free shrinkage and restrained shrinkage tests.Furthermore, from the data obtained from restrained ring test, a casted slab is measured for shrinkage. Effects of different orientation of restraints are studied and compared to better understand the shrinking behavior of the concrete mixtures. The results show that the maximum shrinkage is near the edges of the slab and decreases towards the center. Shrinkage near the edges with no restraint is found out to be more than the shrinkage towards the edges with restraining effects.
ContributorsNimbalkar, Atharwa Samir (Author) / Neithalath, Narayanan (Thesis advisor) / Mobasher, Barzin (Thesis advisor) / Rajan, Subramaniam (Committee member) / Arizona State University (Publisher)
Created2023
Description
Recent advances in Artificial Intelligence (AI) have brought AI closer to laypeople than ever before. This leads to a pervasive problem: how would a user ascertain whether an AI system will be safe, reliable, or useful in a given situation? This problem becomes particularly challenging when it is considered that most autonomous systems are not designed by their users; the internal software of these systems may be unavailable or difficult to understand; and the functionality of these systems may even change from initial specifications as a result of learning. To overcome these challenges, this dissertation proposes a paradigm for third-party autonomous assessment of black-box taskable AI systems. The four main desiderata of such assessment systems are: (i) interpretability: generating a description of the AI system's functionality in a language that the target user can understand; (ii) correctness: ensuring that the description of AI system's working is accurate; (iii) generalizability creating a solution approach that works well for different types of AI systems; and (iv) minimal requirements: creating an assessment system that does not place complex requirements on AI systems to support the third-party assessment, otherwise the manufacturers of AI system's might not support such an assessment. To satisfy these properties, this dissertation presents algorithms and requirements that would enable user-aligned autonomous assessment that helps the user understand the limits of a black-box AI system's safe operability. This dissertation proposes a personalized AI assessment module that discovers the high-level ``capabilities'' of an AI system with arbitrary internal planning algorithms/policies and learns an accurate symbolic description of these capabilities in terms of concepts that a user understands. Furthermore, the dissertation includes the associated theoretical results and the empirical evaluations. The results show that (i) a primitive query-response interface can enable the development of autonomous assessment modules that can derive a causally accurate user-interpretable model of the system's capabilities efficiently, and (ii) such descriptions are easier to understand and reason with for the users than the agent's primitive actions.
ContributorsVerma, Pulkit (Author) / Srivastava, Siddharth (Thesis advisor) / Cooke, Nancy (Committee member) / Fainekos, Georgios (Committee member) / Zhang, Yu (Committee member) / Arizona State University (Publisher)
Created2024
Description
Being a remarkably versatile and inexpensive building material, concrete has found tremendous use in development of modern infrastructure and is the most widely used material in the world. Extensive research in the field of concrete has led to the development of a wide array of concretes with applications ranging from building of skyscrapers to paving of highways. These varied applications require special cementitious composites which can satisfy the demand for enhanced functionalities such as high strength, high durability and improved thermal characteristics among others.
The current study focuses on the fundamental understanding of such functional composites, from their microstructural design to macro-scale application. More specifically, this study investigates three different categories of functional cementitious composites. First, it discusses the differences between cementitious systems containing interground and blended limestone with and without alumina. The interground systems are found to outperform the blended systems due to differential grinding of limestone. A novel approach to deduce the particle size distribution of limestone and cement in the interground systems is proposed. Secondly, the study delves into the realm of ultra-high performance concrete, a novel material which possesses extremely high compressive-, tensile- and flexural-strength and service life as compared to regular concrete. The study presents a novel first principles-based paradigm to design economical ultra-high performance concretes using locally available materials. In the final part, the study addresses the thermal benefits of a novel type of concrete containing phase change materials. A software package was designed to perform numerical simulations to analyze temperature profiles and thermal stresses in concrete structures containing PCMs.
The design of these materials is accompanied by material characterization of cementitious binders. This has been accomplished using techniques that involve measurement of heat evolution (isothermal calorimetry), determination and quantification of reaction products (thermo-gravimetric analysis, x-ray diffraction, micro-indentation, scanning electron microscopy, energy-dispersive x-ray spectroscopy) and evaluation of pore-size distribution (mercury intrusion porosimetry). In addition, macro-scale testing has been carried out to determine compression, flexure and durability response. Numerical simulations have been carried out to understand hydration of cementitious composites, determine optimum particle packing and determine the thermal performance of these composites.
The current study focuses on the fundamental understanding of such functional composites, from their microstructural design to macro-scale application. More specifically, this study investigates three different categories of functional cementitious composites. First, it discusses the differences between cementitious systems containing interground and blended limestone with and without alumina. The interground systems are found to outperform the blended systems due to differential grinding of limestone. A novel approach to deduce the particle size distribution of limestone and cement in the interground systems is proposed. Secondly, the study delves into the realm of ultra-high performance concrete, a novel material which possesses extremely high compressive-, tensile- and flexural-strength and service life as compared to regular concrete. The study presents a novel first principles-based paradigm to design economical ultra-high performance concretes using locally available materials. In the final part, the study addresses the thermal benefits of a novel type of concrete containing phase change materials. A software package was designed to perform numerical simulations to analyze temperature profiles and thermal stresses in concrete structures containing PCMs.
The design of these materials is accompanied by material characterization of cementitious binders. This has been accomplished using techniques that involve measurement of heat evolution (isothermal calorimetry), determination and quantification of reaction products (thermo-gravimetric analysis, x-ray diffraction, micro-indentation, scanning electron microscopy, energy-dispersive x-ray spectroscopy) and evaluation of pore-size distribution (mercury intrusion porosimetry). In addition, macro-scale testing has been carried out to determine compression, flexure and durability response. Numerical simulations have been carried out to understand hydration of cementitious composites, determine optimum particle packing and determine the thermal performance of these composites.
ContributorsArora, Aashay (Author) / Neithalath, Narayanan (Thesis advisor) / Rajan, Subramaniam D. (Committee member) / Mobasher, Barzin (Committee member) / Chawla, Nikhilesh (Committee member) / Hoover, Christian G (Committee member) / Arizona State University (Publisher)
Created2018