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- Genre: Academic theses
- Genre: Masters Thesis
- Creators: Bakkaloglu, Bertan
- Creators: Yang, Yezhou
Description
The goal of reinforcement learning is to enable systems to autonomously solve tasks in the real world, even in the absence of prior data. To succeed in such situations, reinforcement learning algorithms collect new experience through interactions with the environment to further the learning process. The behaviour is optimized by maximizing a reward function, which assigns high numerical values to desired behaviours. Especially in robotics, such interactions with the environment are expensive in terms of the required execution time, human involvement, and mechanical degradation of the system itself. Therefore, this thesis aims to introduce sample-efficient reinforcement learning methods which are applicable to real-world settings and control tasks such as bimanual manipulation and locomotion. Sample efficiency is achieved through directed exploration, either by using dimensionality reduction or trajectory optimization methods. Finally, it is demonstrated how data-efficient reinforcement learning methods can be used to optimize the behaviour and morphology of robots at the same time.
ContributorsLuck, Kevin Sebastian (Author) / Ben Amor, Hani (Thesis advisor) / Aukes, Daniel (Committee member) / Fainekos, Georgios (Committee member) / Scholz, Jonathan (Committee member) / Yang, Yezhou (Committee member) / Arizona State University (Publisher)
Created2019
Description
This work solves the problem of incorrect rotations while using handheld devices.Two new methods which improve upon previous works are explored. The first method
uses an infrared camera to capture and detect the user’s face position and orient the
display accordingly. The second method utilizes gyroscopic and accelerometer data
as input to a machine learning model to classify correct and incorrect rotations.
Experiments show that these new methods achieve an overall success rate of 67%
for the first and 92% for the second which reaches a new high for this performance
category. The paper also discusses logistical and legal reasons for implementing this
feature into an end-user product from a business perspective. Lastly, the monetary
incentive behind a feature like irRotate in a consumer device and explore related
patents is discussed.
uses an infrared camera to capture and detect the user’s face position and orient the
display accordingly. The second method utilizes gyroscopic and accelerometer data
as input to a machine learning model to classify correct and incorrect rotations.
Experiments show that these new methods achieve an overall success rate of 67%
for the first and 92% for the second which reaches a new high for this performance
category. The paper also discusses logistical and legal reasons for implementing this
feature into an end-user product from a business perspective. Lastly, the monetary
incentive behind a feature like irRotate in a consumer device and explore related
patents is discussed.
ContributorsTallman, Riley (Author) / Yang, Yezhou (Thesis advisor) / Liang, Jianming (Committee member) / Chen, Yinong (Committee member) / Arizona State University (Publisher)
Created2020
Description
Respiratory behavior provides effective information to characterize lung functionality, including respiratory rate, respiratory profile, and respiratory volume. Current methods have limited capabilities of continuous characterization of respiratory behavior and are primarily targeting the measurement of respiratory rate, which has relatively less value in clinical application. In this dissertation, a wireless wearable sensor on a paper substrate is developed to continuously characterize respiratory behavior and deliver clinically relevant parameters, contributing to asthma control. Based on the anatomical analysis and experimental results, the optimum site for the wireless wearable sensor is on the midway of the xiphoid process and the costal margin, corresponding to the abdomen-apposed rib cage. At the wearing site, the linear strain change during respiration is measured and converted to lung volume by the wireless wearable sensor utilizing a distance-elapsed ultrasound. An on-board low-power Bluetooth module transmits the temporal lung volume change to a smartphone, where a custom-programmed app computes to show the clinically relevant parameters, such as forced vital capacity (FVC) and forced expiratory volume delivered in the first second (FEV1) and the FEV1/FVC ratio. Enhanced by a simple, yet effective machine-learning algorithm, a system consisting of two wireless wearable sensors accurately extracts respiratory features and classifies the respiratory behavior within four postures among different subjects, demonstrating that the respiratory behaviors are individual- and posture-dependent contributing to monitoring the posture-related respiratory diseases. The continuous and accurate monitoring of respiratory behaviors can track the respiratory disorders and diseases' progression for timely and objective approaches for control and management.
ContributorsChen, Ang (Author) / Cao, Yu (Thesis advisor) / Bakkaloglu, Bertan (Committee member) / Allee, David (Committee member) / Goryll, Michael (Committee member) / Arizona State University (Publisher)
Created2020
Description
Imagery data has become important for civil infrastructure operation and
maintenance because imagery data can capture detailed visual information with high
frequencies. Computer vision can be useful for acquiring spatiotemporal details to
support the timely maintenance of critical civil infrastructures that serve society. Some
examples include: irrigation canals need to maintain the leaking sections to avoid water
loss; project engineers need to identify the deviating parts of the workflow to have the
project finished on time and within budget; detecting abnormal behaviors of air traffic
controllers is necessary to reduce operational errors and avoid air traffic accidents.
Identifying the outliers of the civil infrastructure can help engineers focus on targeted
areas. However, large amounts of imagery data bring the difficulty of information
overloading. Anomaly detection combined with contextual knowledge could help address
such information overloading to support the operation and maintenance of civil
infrastructures.
Some challenges make such identification of anomalies difficult. The first challenge is
that diverse large civil infrastructures span among various geospatial environments so
that previous algorithms cannot handle anomaly detection of civil infrastructures in
different environments. The second challenge is that the crowded and rapidly changing
workspaces can cause difficulties for the reliable detection of deviating parts of the
workflow. The third challenge is that limited studies examined how to detect abnormal
behaviors for diverse people in a real-time and non-intrusive manner. Using video andii
relevant data sources (e.g., biometric and communication data) could be promising but
still need a baseline of normal behaviors for outlier detection.
This dissertation presents an anomaly detection framework that uses contextual
knowledge, contextual information, and contextual data for filtering visual information
extracted by computer vision techniques (ADCV) to address the challenges described
above. The framework categorizes the anomaly detection of civil infrastructures into two
categories: with and without a baseline of normal events. The author uses three case
studies to illustrate how the developed approaches can address ADCV challenges in
different categories of anomaly detection. Detailed data collection and experiments
validate the developed ADCV approaches.
maintenance because imagery data can capture detailed visual information with high
frequencies. Computer vision can be useful for acquiring spatiotemporal details to
support the timely maintenance of critical civil infrastructures that serve society. Some
examples include: irrigation canals need to maintain the leaking sections to avoid water
loss; project engineers need to identify the deviating parts of the workflow to have the
project finished on time and within budget; detecting abnormal behaviors of air traffic
controllers is necessary to reduce operational errors and avoid air traffic accidents.
Identifying the outliers of the civil infrastructure can help engineers focus on targeted
areas. However, large amounts of imagery data bring the difficulty of information
overloading. Anomaly detection combined with contextual knowledge could help address
such information overloading to support the operation and maintenance of civil
infrastructures.
Some challenges make such identification of anomalies difficult. The first challenge is
that diverse large civil infrastructures span among various geospatial environments so
that previous algorithms cannot handle anomaly detection of civil infrastructures in
different environments. The second challenge is that the crowded and rapidly changing
workspaces can cause difficulties for the reliable detection of deviating parts of the
workflow. The third challenge is that limited studies examined how to detect abnormal
behaviors for diverse people in a real-time and non-intrusive manner. Using video andii
relevant data sources (e.g., biometric and communication data) could be promising but
still need a baseline of normal behaviors for outlier detection.
This dissertation presents an anomaly detection framework that uses contextual
knowledge, contextual information, and contextual data for filtering visual information
extracted by computer vision techniques (ADCV) to address the challenges described
above. The framework categorizes the anomaly detection of civil infrastructures into two
categories: with and without a baseline of normal events. The author uses three case
studies to illustrate how the developed approaches can address ADCV challenges in
different categories of anomaly detection. Detailed data collection and experiments
validate the developed ADCV approaches.
ContributorsChen, Jiawei (Author) / Tang, Pingbo (Thesis advisor) / Ayer, Steven (Committee member) / Yang, Yezhou (Committee member) / Arizona State University (Publisher)
Created2020
Description
The need for incorporating game engines into robotics tools becomes increasingly crucial as their graphics continue to become more photorealistic. This thesis presents a simulation framework, referred to as OpenUAV, that addresses cloud simulation and photorealism challenges in academic and research goals. In this work, OpenUAV is used to create a simulation of an autonomous underwater vehicle (AUV) closely following a moving autonomous surface vehicle (ASV) in an underwater coral reef environment. It incorporates the Unity3D game engine and the robotics software Gazebo to take advantage of Unity3D's perception and Gazebo's physics simulation. The software is developed as a containerized solution that is deployable on cloud and on-premise systems.
This method of utilizing Gazebo's physics and Unity3D perception is evaluated for a team of marine vehicles (an AUV and an ASV) in a coral reef environment. A coordinated navigation and localization module is presented that allows the AUV to follow the path of the ASV. A fiducial marker underneath the ASV facilitates pose estimation of the AUV, and the pose estimates are filtered using the known dynamical system model of both vehicles for better localization. This thesis also investigates different fiducial markers and their detection rates in this Unity3D underwater environment. The limitations and capabilities of this Unity3D perception and Gazebo physics approach are examined.
This method of utilizing Gazebo's physics and Unity3D perception is evaluated for a team of marine vehicles (an AUV and an ASV) in a coral reef environment. A coordinated navigation and localization module is presented that allows the AUV to follow the path of the ASV. A fiducial marker underneath the ASV facilitates pose estimation of the AUV, and the pose estimates are filtered using the known dynamical system model of both vehicles for better localization. This thesis also investigates different fiducial markers and their detection rates in this Unity3D underwater environment. The limitations and capabilities of this Unity3D perception and Gazebo physics approach are examined.
ContributorsAnand, Harish (Author) / Das, Jnaneshwar (Thesis advisor) / Yang, Yezhou (Committee member) / Berman, Spring M (Committee member) / Arizona State University (Publisher)
Created2020
Description
This work analyzes and develops a point-of-load (PoL) synchronous buck converter using enhancement-mode Gallium Nitride (e-GaN), with emphasis on optimizing reverse conduction loss by using a well-known technique of placing an anti-parallel Schottky diode across the synchronous power device. This work develops an improved analytical switching model for the GaN-based converter with the Schottky diode using piecewise linear approximations.
To avoid a shoot-through between the power switches of the buck converter, a small dead-time is inserted between gate drive switching transitions. Despite optimum dead-time management for a power converter, optimum dead-times vary for different load conditions. These variations become considerably large for PoL applications, which demand high output current with low output voltages. At high switching frequencies, these variations translate into losses that contribute significantly to the total loss of the converter. To understand and quantify power loss in a hard-switching buck converter that uses a GaN power device in parallel with a Schottky diode, piecewise transitions are used to develop an analytical switching model that quantifies the contribution of reverse conduction loss of GaN during dead-time.
The effects of parasitic elements on the dynamics of the switching converter are investigated during one switching cycle of the converter. A designed prototype of a buck converter is correlated to the predicted model to determine the accuracy of the model. This comparison is presented using simulations and measurements at 400 kHz and 2 MHz converter switching speeds for load (1A) condition and fixed dead-time values. Furthermore, performance of the buck converter with and without the Schottky diode is also measured and compared to demonstrate and quantify the enhanced performance when using an anti-parallel diode. The developed power converter achieves peak efficiencies of 91.7% and 93.86% for 2 MHz and 400 KHz switching frequencies, respectively, and drives load currents up to 6A for a voltage conversion from 12V input to 3.3V output.
In addition, various industry Schottky diodes have been categorized based on their packaging and electrical characteristics and the developed analytical model provides analytical expressions relating the diode characteristics to power stage performance parameters. The performance of these diodes has been characterized for different buck converter voltage step-down ratios that are typically used in industry applications and different switching frequencies ranging from 400 KHz to 2 MHz.
To avoid a shoot-through between the power switches of the buck converter, a small dead-time is inserted between gate drive switching transitions. Despite optimum dead-time management for a power converter, optimum dead-times vary for different load conditions. These variations become considerably large for PoL applications, which demand high output current with low output voltages. At high switching frequencies, these variations translate into losses that contribute significantly to the total loss of the converter. To understand and quantify power loss in a hard-switching buck converter that uses a GaN power device in parallel with a Schottky diode, piecewise transitions are used to develop an analytical switching model that quantifies the contribution of reverse conduction loss of GaN during dead-time.
The effects of parasitic elements on the dynamics of the switching converter are investigated during one switching cycle of the converter. A designed prototype of a buck converter is correlated to the predicted model to determine the accuracy of the model. This comparison is presented using simulations and measurements at 400 kHz and 2 MHz converter switching speeds for load (1A) condition and fixed dead-time values. Furthermore, performance of the buck converter with and without the Schottky diode is also measured and compared to demonstrate and quantify the enhanced performance when using an anti-parallel diode. The developed power converter achieves peak efficiencies of 91.7% and 93.86% for 2 MHz and 400 KHz switching frequencies, respectively, and drives load currents up to 6A for a voltage conversion from 12V input to 3.3V output.
In addition, various industry Schottky diodes have been categorized based on their packaging and electrical characteristics and the developed analytical model provides analytical expressions relating the diode characteristics to power stage performance parameters. The performance of these diodes has been characterized for different buck converter voltage step-down ratios that are typically used in industry applications and different switching frequencies ranging from 400 KHz to 2 MHz.
ContributorsKoli, Gauri (Author) / Kitchen, Jennifer (Thesis advisor) / Bakkaloglu, Bertan (Committee member) / Ozev, Sule (Committee member) / Arizona State University (Publisher)
Created2020
Description
Many real-world planning problems can be modeled as Markov Decision Processes (MDPs) which provide a framework for handling uncertainty in outcomes of action executions. A solution to such a planning problem is a policy that handles possible contingencies that could arise during execution. MDP solvers typically construct policies for a problem instance without re-using information from previously solved instances. Research in generalized planning has demonstrated the utility of constructing algorithm-like plans that reuse such information. However, using such techniques in an MDP setting has not been adequately explored.
This thesis presents a novel approach for learning generalized partial policies that can be used to solve problems with different object names and/or object quantities using very few example policies for learning. This approach uses abstraction for state representation, which allows the identification of patterns in solutions such as loops that are agnostic to problem-specific properties. This thesis also presents some theoretical results related to the uniqueness and succinctness of the policies computed using such a representation. The presented algorithm can be used as fast, yet greedy and incomplete method for policy computation while falling back to a complete policy search algorithm when needed. Extensive empirical evaluation on discrete MDP benchmarks shows that this approach generalizes effectively and is often able to solve problems much faster than existing state-of-art discrete MDP solvers. Finally, the practical applicability of this approach is demonstrated by incorporating it in an anytime stochastic task and motion planning framework to successfully construct free-standing tower structures using Keva planks.
This thesis presents a novel approach for learning generalized partial policies that can be used to solve problems with different object names and/or object quantities using very few example policies for learning. This approach uses abstraction for state representation, which allows the identification of patterns in solutions such as loops that are agnostic to problem-specific properties. This thesis also presents some theoretical results related to the uniqueness and succinctness of the policies computed using such a representation. The presented algorithm can be used as fast, yet greedy and incomplete method for policy computation while falling back to a complete policy search algorithm when needed. Extensive empirical evaluation on discrete MDP benchmarks shows that this approach generalizes effectively and is often able to solve problems much faster than existing state-of-art discrete MDP solvers. Finally, the practical applicability of this approach is demonstrated by incorporating it in an anytime stochastic task and motion planning framework to successfully construct free-standing tower structures using Keva planks.
ContributorsKala Vasudevan, Deepak (Author) / Srivastava, Siddharth (Thesis advisor) / Zhang, Yu (Committee member) / Yang, Yezhou (Committee member) / Arizona State University (Publisher)
Created2020
Description
Image super-resolution (SR) is a low-level image processing task, which has manyapplications such as medical imaging, satellite image processing, and video enhancement,
etc. Given a low resolution image, it aims to reconstruct a high resolution
image. The problem is ill-posed since there can be more than one high resolution
image corresponding to the same low-resolution image. To address this problem, a
number of machine learning-based approaches have been proposed.
In this dissertation, I present my works on single image super-resolution (SISR)
and accelerated magnetic resonance imaging (MRI) (a.k.a. super-resolution on MR
images), followed by the investigation on transfer learning for accelerated MRI reconstruction.
For the SISR, a dictionary-based approach and two reconstruction based
approaches are presented. To be precise, a convex dictionary learning (CDL)
algorithm is proposed by constraining the dictionary atoms to be formed by nonnegative
linear combination of the training data, which is a natural, desired property.
Also, two reconstruction-based single methods are presented, which make use
of (i)the joint regularization, where a group-residual-based regularization (GRR) and
a ridge-regression-based regularization (3R) are combined; (ii)the collaborative representation
and non-local self-similarity. After that, two deep learning approaches
are proposed, aiming at reconstructing high-quality images from accelerated MRI
acquisition. Residual Dense Block (RDB) and feedback connection are introduced
in the proposed models. In the last chapter, the feasibility of transfer learning for
accelerated MRI reconstruction is discussed.
etc. Given a low resolution image, it aims to reconstruct a high resolution
image. The problem is ill-posed since there can be more than one high resolution
image corresponding to the same low-resolution image. To address this problem, a
number of machine learning-based approaches have been proposed.
In this dissertation, I present my works on single image super-resolution (SISR)
and accelerated magnetic resonance imaging (MRI) (a.k.a. super-resolution on MR
images), followed by the investigation on transfer learning for accelerated MRI reconstruction.
For the SISR, a dictionary-based approach and two reconstruction based
approaches are presented. To be precise, a convex dictionary learning (CDL)
algorithm is proposed by constraining the dictionary atoms to be formed by nonnegative
linear combination of the training data, which is a natural, desired property.
Also, two reconstruction-based single methods are presented, which make use
of (i)the joint regularization, where a group-residual-based regularization (GRR) and
a ridge-regression-based regularization (3R) are combined; (ii)the collaborative representation
and non-local self-similarity. After that, two deep learning approaches
are proposed, aiming at reconstructing high-quality images from accelerated MRI
acquisition. Residual Dense Block (RDB) and feedback connection are introduced
in the proposed models. In the last chapter, the feasibility of transfer learning for
accelerated MRI reconstruction is discussed.
ContributorsDing, Pak Lun Kevin (Author) / Li, Baoxin (Thesis advisor) / Wu, Teresa (Committee member) / Wang, Yalin (Committee member) / Yang, Yezhou (Committee member) / Arizona State University (Publisher)
Created2020
Description
A complex social system, whether artificial or natural, can possess its macroscopic properties as a collective, which may change in real time as a result of local behavioral interactions among a number of agents in it. If a reliable indicator is available to abstract the macrolevel states, decision makers could use it to take a proactive action, whenever needed, in order for the entire system to avoid unacceptable states or con-verge to desired ones. In realistic scenarios, however, there can be many challenges in learning a model of dynamic global states from interactions of agents, such as 1) high complexity of the system itself, 2) absence of holistic perception, 3) variability of group size, 4) biased observations on state space, and 5) identification of salient behavioral cues. In this dissertation, I introduce useful applications of macrostate estimation in complex multi-agent systems and explore effective deep learning frameworks to ad-dress the inherited challenges. First of all, Remote Teammate Localization (ReTLo)is developed in multi-robot teams, in which an individual robot can use its local interactions with a nearby robot as an information channel to estimate the holistic view of the group. Within the problem, I will show (a) learning a model of a modular team can generalize to all others to gain the global awareness of the team of variable sizes, and (b) active interactions are necessary to diversify training data and speed up the overall learning process. The complexity of the next focal system escalates to a colony of over 50 individual ants undergoing 18-day social stabilization since a chaotic event. I will utilize this natural platform to demonstrate, in contrast to (b), (c)monotonic samples only from “before chaos” can be sufficient to model the panicked society, and (d) the model can also be used to discover salient behaviors to precisely predict macrostates.
ContributorsChoi, Taeyeong (Author) / Pavlic, Theodore (Thesis advisor) / Richa, Andrea (Committee member) / Ben Amor, Heni (Committee member) / Yang, Yezhou (Committee member) / Liebig, Juergen (Committee member) / Arizona State University (Publisher)
Created2020
Description
Power management circuits have been more and more widely used in various applications, while providing fully integrated voltage regulation remains a challenging topic. Switched-capacitor (SC) voltage converters have received attentions in integrated power conversion for fixed-ratio voltage conversions with good efficiency and feasibility of integration. During my PhD study, an on-chip current sensing technique is proposed to dynamically modulate both switching frequency and switch widths of SC voltage converters, enhancing fast transient response and higher efficiency across a wide range of load currents. In conjunction with SC converters, a low-dropout regulator (LDO) is implemented which is driven by a push-pull operational transconductance amplifier (OTA), whose current is mirrored and sensed with minimal power and efficiency overhead. The sensed load current directly controls the frequency and width of SC converters through a voltage-controlled oscillator (VCO) and a time-to-digital converter, respectively.
Theoretical analysis and optimization for SC DC-DC converters have been presented in prior works, however optimization of different capacitors, namely flying and input/output decoupling capacitors, in SC voltage regulators (SCVRs) under an area constraint has not been addressed. A methodology to optimize flying and decoupling capacitance for area-constrained on-chip SCVRs to achieve the highest system-level power efficiency. Considering both conversion efficiency and droop voltage against fast load transients, the proposed model determines the optimal ratio between flying and decoupling.
Based on the previous design, a fully integrated switched-capacitor voltage regulator with voltage comparison and on-chip lossless current sensing control is proposed. Based on the voltage comparison result and sensed current as the load current changes, the frequency of the SC converters are modulated for optimal efficiency. The voltage regulator targets 2.1V input voltage and 0.9V output voltage, which offers higher-voltage power transfer across chip package. A 17-phase interleaved structure is used to reduce output voltage ripple.
In 65nm CMOS, the regulator is implemented with MIM-capacitor, targeting 2.1V input voltage and 0.9V output voltage. According to the measurement results, the proposed SC voltage regulator achieves 69.6% peak efficiency at 60mA load current, which corresponds to a 4.2mW/mm2 power-area density and 12.5mW
F power-capacitance density. The efficiency across 20mA to 92mA regulator load current range is above 62%. The steady-state output voltage ripple across 22x load current range of 3.5mA-76mA is between 50mV to 60mV.
Theoretical analysis and optimization for SC DC-DC converters have been presented in prior works, however optimization of different capacitors, namely flying and input/output decoupling capacitors, in SC voltage regulators (SCVRs) under an area constraint has not been addressed. A methodology to optimize flying and decoupling capacitance for area-constrained on-chip SCVRs to achieve the highest system-level power efficiency. Considering both conversion efficiency and droop voltage against fast load transients, the proposed model determines the optimal ratio between flying and decoupling.
Based on the previous design, a fully integrated switched-capacitor voltage regulator with voltage comparison and on-chip lossless current sensing control is proposed. Based on the voltage comparison result and sensed current as the load current changes, the frequency of the SC converters are modulated for optimal efficiency. The voltage regulator targets 2.1V input voltage and 0.9V output voltage, which offers higher-voltage power transfer across chip package. A 17-phase interleaved structure is used to reduce output voltage ripple.
In 65nm CMOS, the regulator is implemented with MIM-capacitor, targeting 2.1V input voltage and 0.9V output voltage. According to the measurement results, the proposed SC voltage regulator achieves 69.6% peak efficiency at 60mA load current, which corresponds to a 4.2mW/mm2 power-area density and 12.5mW
F power-capacitance density. The efficiency across 20mA to 92mA regulator load current range is above 62%. The steady-state output voltage ripple across 22x load current range of 3.5mA-76mA is between 50mV to 60mV.
ContributorsMi, Xiaoyang (Author) / Seo, Jae-Sun (Thesis advisor) / Bakkaloglu, Bertan (Committee member) / Ogras, Umit Y. (Committee member) / Kitchen, Jennifer (Committee member) / Arizona State University (Publisher)
Created2020