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Title: STEEL STRUCTURE RESPONSE UNDER FIRE CONDITIONS MODEL BASED SIMULATION (MBS)
Creator: Coughlin, Kevin James
Date: 2019
Description: This paper addresses the issue of structure design and analysis for conditions of fire loading. It includes an introductory section that...
Show moreThis paper addresses the issue of structure design and analysis for conditions of fire loading. It includes an introductory section that presents the historical and current state of practice using prescriptive methods of design, a qualitative and conceptual development (based on actual field observations) of what is expected to occur in a structure when subjected to fire, and a summary of the current state of research on the subject of structure design for fire loading. Next, a thermo-plastic non-linear finite element shell model was developed for a two member steel beam and column, bolted joint structure used in an actual physical fire test, subjected to beam a bending load and column compressive load, held constant, while the structure was heated up in a furnace. The beam / column bolted joint rotation for the test matched the simulation quite well. Next, further extending this modeling approach, a partial moment frame from the center of a 9 story building designed for dead, live, and seismic loading was modeled with non-linear thermo-plastic shell elements in the fire zone, along with linear elastic beam / line elements for structural components surrounding the fire zone. For this model, the gravity loading (no seismic loading included) was fully applied, and then a thermal load corresponding to the ASTM E119 fire test load was applied to the structure in the fire zone. Simulation of lateral torsional buckling, flange local buckling, web local buckling, and finally overall global buckling of the columns was accomplished in this effort, increasing confidence that complex thermo-plastic structural behavior can be modeled with advanced non-linear finite element technology. Boundary conditions on this model from the floor system had a significant impact on the mode of global buckling (strong axis or weak axis), warranting further investigation and possibly a 3-D frame with a floor system included in future work. Also, extending this modeling approach even further, in future work, using the entire 9 story moment frame, with shell elements in the fire zone and non-linear moment-curvature beam / line elements for surrounding members, is contemplated, the objective being to numerically model a progressive collapse event in a planar frame. Finally, an actual 10 story structure, converted to and industrial open floor structure, based on current design codes and standards, was modeled thermally using the industry standard Hydrocarbon (HC) Temperature vs time curve, and structurally using non-linear thermoplastic shell elements in the “fire room” (to better capture local buckling and overall structure collapse behavior), and thermoplastic beam elements for the rest of the structure. The thermal modeling was performed for steel members both without insulation (bare steel) and with minimal insulation (1/4” coated thickness), and these “decoupled” results then applied to the structural model. The use of even a small layer of insulation demonstrated the dramatic effect of such, insofar as the collapse time of the structure is concerned.
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Title: EVALUATING INTEGRITY FOR MOBILE ROBOT LOCALIZATION SAFETY
Creator: Duenas Arana, Guillermo
Date: 2019
Description: Precise localization is paramount for autonomous navigation. Localization errors are not only dangerous by themselves, but can also mislead...
Show morePrecise localization is paramount for autonomous navigation. Localization errors are not only dangerous by themselves, but can also mislead other dependent systems into moving to a hazardous location. Unfortunately, the problem of quantifying robot localization safety is only sparsely addressed in the robotics literature, and most robotics algorithms still quantify pose estimation performance using a covariance matrix or particle spread, which only accounts for nominal sensor errors. This is insufficient for life- and mission-critical applications, such as autonomous vehicles and other co-robots, where ignoring sensor or sensor or processing faults can lead to catastrophic localization errors. Thus, other methods must be employed to ensure safety.In response, this research leverages prior work in aviation integrity monitoring to tackle the more challenging case of evaluating localization safety in mobile robots. In contrast to aviation applications, that heavily rely on the Global Navigation Satellite System (GNSS) for localization, robots often operate in complex, GNSS-denied environments that require a more sophisticated sensor suite to ensure localization safety. Localization integrity risk is the probability that a robot's pose estimate lies outside pre-defined acceptable limits while no alarm is triggered. In this work, the integrity risk is rigorously upper bounded by accounting for both nominal noise and other non-nominal sensor faults, resulting in a safe upper bound on the localization integrity risk.The main contribution of this dissertation is the design and evaluation of a sequential integrity monitoring methodology applicable to mobile robot localization algorithms that use feature extraction and data association. First, faults introduced during the feature extraction and data association processes are distinguished, and the probability of the latter is rigorously upper bounded using analytical methods. The impact of faults in the estimate error's and fault detector's distributions is then determined to quantify integrity risk, which is evaluated under the worst-possible fault combination. To determine the impact of previous faults without a boundlessly growing number of fault hypotheses, this dissertation presents a novel method that uses a preceding time window to build a limited set of hypotheses and a prior estimate bias to account for faults occurring before the start of the time window. The proposed methodology is applicable to Kalman Filter and fixed-lag smoothing localization. Simulated and experimental results are presented to validate the methodology.
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Title: ENGINEERING 2D PHOTO-REACTING COF FOR PATTERNING AND DRUG DELIVERY
Creator: Chen, Kuo Hao
Date: 2017, 2017-07
Description: Covalent Organic Frameworks (COFs) are 2-dimensional polymers that exhibit rigid and large surface area as well as porous architectures....
Show moreCovalent Organic Frameworks (COFs) are 2-dimensional polymers that exhibit rigid and large surface area as well as porous architectures. Currently, COFs are tailored for gas storage applications, drug delivery, catalysis and they are used as filtering membranes for water treatment. It is well documented that at the nano/micro scale, COFs can form multi-layered architecture with respect to the basic molecular building blocks. In this picture, it is possible that the 2D intra-layer and 3D inter-layer interactions of the basic molecular units COFs may dictate the overall efficiency of the aforementioned applications. To understand the dimensionality-function relationship of COFs, we are engineering hybrid 1D-2D organic polymers. This hybrid architecture will allow us to study the propagation of energy/exciton transfer within the resulting materials among other applications such as drug delivery and light-induced nano/micro-patterning. To achieve our objectives, I exploited the photo-reacting properties of two molecular systems: The first system is used to prepare the 2D COF of interest and the other system is used to engineer a 1D crystalline solid. Although I have not tested the energy/exciton propagation with the desired material, I have successfully engineered a 1D crystalline solid and synthesized the expected 2D COFs. Using a combination of synthetic strategies, I prepared and characterized photoreacting tetra-phenyl ketone building block that was used to form the desired polymer. I have also engineered 1D needle-like crystals of bisphenyl cyclopropenone compound. Moreover, the two materials were characterized by optical and electron microscopy methods. This thesis will detail the synthesis and characterization of all precursors of the basic molecular units that were used to engineer the 1D crystalline solid and 2D COF materials. Condignly, the optical and scanning electron microscopy images highlight the microscale features of the materials of interest. I am certain that this preliminary investigation will pave the way to study the dimensionality of energy/exciton transfer and reaction propagation in the many organic materials.
M.S. in Chemistry, July 2017
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Title: Capillary Rise of Common Liquids and Nanofluids: Experiments and Modeling
Creator: Wu, Pingkeng
Date: 2018
Description: Capillary dynamics of common liquids and nanofluids is a ubiquitous everyday phenomenon. It has practical applications in diverse fields,...
Show moreCapillary dynamics of common liquids and nanofluids is a ubiquitous everyday phenomenon. It has practical applications in diverse fields, including ink-jet printing, lab-on-a-chip, biotechnology, and coating. Important as it is, this phenomenon has not been fully understood and requires tremendous effort in theoretical analysis and experimental investigations to gain further knowledge and guide the design of practical precesses whenever capillarity is essential.The rise of the main meniscus in rectangular capillaries is important in interpreting the phenomenon of fluid flow in porous media. This thesis presents an experimental study on the rise of the main meniscus in rectangular borosilicate glass and plastic (polystyrene) capillaries using three different liquids (water, ethanol, and hexadecane). A universal model (an extended two-wall model) based on the Laplace equation was developed to predict the equilibrium height of the main meniscus in rectangular capillaries. In capillary dynamics, it is crucial to understand the interaction between fluid molecules and a solid substrate (the wall) in molecular scale. Recent studies reveal that a layered molecularly thin wetting film (LMTWF) will develop ahead of the apparent three-phase contact line for the spreading of a wetting liquid on solid surfaces. Based on this fact, a novel molecular self-layering model is proposed to explain the dynamic wetting considering the role of the molecular shape on self-layering and its effect on the molecularly thin film viscosity in regards to the advancing (dynamic) contact angle. The proposed molecular self-layering model is then incorporated into the Lucas-Washburn-Rideal (LWR) equation to explain the capillary rise dynamics of fluids of spherical, cylindrical, and disk shape molecules in borosilicate glass capillaries. The abilities of the other popular dynamic contact angle models to correct the dynamic contact angle effect in the capillary rise process were also investigated. The LWR equation modified by molecular self-layering model predicts well the capillary rise of carbon tetrachloride, octamethylcyclotetrasiloxane and n-alkanes with the molecular diameter or measured solvation force data. The molecular self-layering model modified LWR equation also has good predictions on the capillary rise of silicone oils covering a wide range of bulk viscosities with the same key parameter W(0), which results from the molecular self-layering. Besides the open capillaries, the proposed molecular self-layering model is applied to explain the spontaneous rise of Newtonian liquids in closed-end capillaries. Contribution of the compressed air inside the closed capillaries is also modeled and experimentally verified. Finally, the research is extended to a liquid phase displacing another immiscible liquid in capillaries with the focus on surfactant solutions containing polymeric nanoparticles (nanofluids), which have been shown to have an improved wetting and spreading on solid surfaces. The polymeric nanoparticles can reduce the frictional coefficient by as much as four times by forming structured layers in the confined wedge film. The role of the interfacial tension on the frictional coefficient is also demonstrated.In summary, this thesis presents the physics of liquid rise in rectangular capillaries, effect of molecular self-layering in capillary dynamics in open and closed-end capillaries, and the contribution of nanofluids in the two-phase displacement dynamics.
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Title: 3D reconstruction of lake surface using camera and lidar sensor fusion
Creator: Khan, Shahrukh
Date: 2020
Description: Global Navigation Satellite System Reflectometry (GNSS-R) relies upon detecting the GNSS signals reflected off a surface and then analyzing...
Show moreGlobal Navigation Satellite System Reflectometry (GNSS-R) relies upon detecting the GNSS signals reflected off a surface and then analyzing the reflected signal to obtain surface characteristics. GNSS-R has become one of the many additional applications of the readily available GNSS signals, alongside more traditional remote sensing of ionospheric monitoring, beyond the intended GNSS purposes of providing position, navigation, and timing estimation. In previous work, GPS signals reflected off Lake Michigan in Chicago have been collected using a specially designed portable sensor suite. The data collected is then analyzed to differentiate between surface ice and water conditions, as well as obtain other characteristic information such as surface reflectivity. The goal is to provide a way for remote sensing of seasonal ice formation beyond just satellite imagery which can be affected by cloud cover. To confirm the validity of the GNSS-R results there needs to be a separate reference against which to compare. This work demonstrates the sensor fusion between camera and lidar to reconstruct the lake surface, to provide that truth reference for comparison against the results of the GPS reflectometry signal processing. For this setup, the camera provides visual information about the lake surface, while the lidar provides distance information with respect to the sensor suite. Combining the data from the two sensors allows backward projection of the camera image to reconstruct the lake surface and its features. The backward projection relies upon knowledge of the camera's intrinsic properties alongside distance information of the features captured by the camera. Each pixel of the camera image is then transformed to its 3D position relative to the sensor system. This produces a 3D map of the lake surface, as captured by the sensors. The estimated point at which the GPS signal reflects off the surface, the specular point, is calculated by the satellite position at the time of interest and the receiver location. This point is then mapped onto the reconstructed surface to identify the exact location where the signal reflected and compare the surface visually to the results from the signal analysis.Time-varying camera-lidar-specular-point maps of the data campaigns conducted for this project are created for comparison with the GPS signal analysis. Multiple data campaigns were performed during which the Lake Michigan surface had surface ice, water or a mixture of the two. The lake surface is reconstructed for different timestamps, using the appropriate image frame and lidar frame. Combining chronologically, the changes in the lake surface can then be observed along with the movement of the specular point, due to the movement of the GPS satellites. Any satellites passing over a boundary between water and ice on the lake surface are identified and time stamped, to then be compared to the GPS signal analysis results.
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Title: Effects of the Silicon Content on the Dimensional Changes of Electrodes for Lithium-ion Cells: An Electrochemical Dilatometry Study
Creator: Rodrigues Prado, Andressa Yasmim
Date: 2021
Description: The continuous growth of the electric vehicle market has significantly increased the demand for Li-ion batteries (LIBs). However, state-of-the...
Show moreThe continuous growth of the electric vehicle market has significantly increased the demand for Li-ion batteries (LIBs). However, state-of-the-art LIBs are not yet able to meet the EV industry demand for high energy density and long cycle life rechargeable batteries, prompting efforts to improve the performance of Li-ion cells. In this context, silicon became the most promising next-generation active material for LIBs negative electrodes, especially because Si can significantly increase the lithium storage capacity of the commonly available anodes. Nonetheless, commercialization of Si-based electrodes has been hindered by the poor electrochemical performance of these electrodes, which is mainly attributed to the severe volumetric changes in the silicon particles related to the electrochemical reactions with Li. Since the electrodes are composites with a complex combination of various materials interspaced by pores, the electrode-level swelling may differ significantly from the particle-scale expansion. Furthermore, an increase in electrode thickness due to silicon expansion can have a direct effect on how Li-ion cells are designed, as the accommodation of electrode dilation requires additional cell space to prevent significant dynamic stresses. Thus, the actual volumetric energy density of a LIB cell depends on the electrode swelling, since the higher the magnitude of the electrode expansion, the lower the gains in energy density. Monitoring the electrode dilation is just as important as the electrochemical evaluation when designing cells with Si-based anodes.In this work, we use high-resolution operando electrochemical dilatometry to quantify the (de)lithiation-induced expansion/contraction of silicon, blended silicon-graphite and graphite electrodes, upon electrochemical cycling. We evaluate the relationship between electrode capacity and dilation and observe that while the lithiation capacity improved with increasing the silicon content, the electrode swelling is highly aggravated. For silicon-rich anodes, the electrode dilation can be higher than 300%, and the expansion profile consists of a combination of slow swelling at low levels of lithiation followed by an accelerated increase at higher lithium contents. This non-linear dilation allows for narrowing the swelling by limiting the electrode capacity. In addition, we investigate how electrode properties, such as porosity, affect the dilation profile, and quantify the irreversible expansion of the electrodes. Finally, we discuss some of the challenges associated with the dilatometry technique and suggest experimental approaches for obtaining consistent and reliable data.
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Title: ENHANCED OPTICAL TOMOGRAPHY IN DIFFUSE MEDIA USING OPTICAL GATING OF EARLY PHOTONS
Creator: Ghosh, Aishwarya
Date: 2020
Description: Tissue biopsies, where a volume of tissue is removed from a patient, typically through needle extraction, provides critical information about...
Show moreTissue biopsies, where a volume of tissue is removed from a patient, typically through needle extraction, provides critical information about the cellular and molecular aspects of an individual patient’s health and/or disease. However, current pathological assessments of tissue biopsies evaluate less than 1% of the volume of the tissue (e.g., one to a few 5-micron slices are sectioned out of the biopsy and stained/processed for microscopic analysis). Since the bulk of tissue biopsy is carried out through optical imaging (absorption or fluorescence), a more 3D, “whole-biopsy” view is conceivably possible with optical projection tomography (OPT). The challenge with OPT has been that for clinically relevant sized biopsies, most photons undergo multiple scattering events that lead to loss of spatial resolution that makes accurate pathological analysis intractable. In my MS thesis, I worked on the development of an enhanced OPT system that employs optical gating based on non-linear up-conversion of infrared ultrashort laser pulses to isolate “early-arriving” photons that experience significantly less scatter than the bulk of photons transiting a scattering biological sample. Considering the complexity of such a system, the entirety of my MS thesis work was spent constructing and testing the femtosecond optical gated OPT system and though I was unable to validate its operation in biological samples, simulations suggest that the properties we were able to achieve could allow high resolution optical imaging in 0.1-1 cm-diameter specimens.
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Title: Gas Turbine Vane Heat Transfer and Cooling Under Freestream Turbulence
Creator: Kanani, Yousef
Date: 2020
Description: The effects of the inflow turbulence on the fluid flow and heat transfer of a gas turbine passage flow have been investigated using wall...
Show moreThe effects of the inflow turbulence on the fluid flow and heat transfer of a gas turbine passage flow have been investigated using wall-resolved large eddy simulations. Numerical simulations are conducted in a linear vane cascade at different levels of inflow turbulence up to 12.4% at nominal exit chord Reynolds number of 500,000. At this Reynolds number and without any inflow turbulence, the boundary layer remains laminar on both sides of the vane. The presence of the velocity disturbances at the inlet augments the heat transfer on the leading edge and pressure side, triggers transition to turbulence over the suction side and alters the structure of the secondary flow in the turbine passage.The detailed analysis of the flow field indicates formation of large scale leading edge structures that wrap around the large leading edge and extend into both suction and pressure sides of the vane. These structures disturb the boundary layer and form streaky structures which augment the heat transfer on the pressure side. The perturbed boundary layer on the suction side eventually breaks up to turbulence due to the inner mode secondary instability which was reported earlier in a handful of studies.The vane and endwall heat transfer in regions affected by the secondary flows in the turbine passage are also studied in detail. A new representation on the origin and evolution of the passage vortex is presented. The passage vortex in the current geometry is originated from the pressure side passage circulation and not the pressure leg of the horseshoe vortex at the leading edge. Furthermore, it is observed that the distribution of the heat transfer coefficient on the endwall is significantly altered by the change in the level of the freestream turbulence and the approach boundary layer thickness. Finally, the effect of the freestream turbulence on the effectiveness of a slot cooling system in a symmetrical airfoil is studied. The large eddy simulations are conducted for a Reynolds number of 250,000 (based on the approach velocity and the leading edge diameter) and freestream turbulence levels of up to 13.7%. Current predictions capture the decay of the film cooling effectiveness at higher turbulence levels due to the higher mixing of the incoming hot gases and the coolant. It is been shown that the presence of arrays of pin fins in the preconditioning section of the slot cooling system plays a major role in the near field film cooling effectiveness and surface temperature distribution.
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Title: Frictional behavior of bronze-graphite composite as sliding element in the base isolation system
Creator: You, Da
Date: 2021
Description: There are many calamities around the world, one of the most dangerous disasters is earthquake which threatens the safety of people and the...
Show moreThere are many calamities around the world, one of the most dangerous disasters is earthquake which threatens the safety of people and the structures. Almost every year, there are a lot of property losses and casualties caused by earthquakes. To mitigate the bad effect of the earthquake, the base isolation system was proposed by previous researchers. With the contribution of many researchers, several seismic isolations have been developed. Until now, many structures have installed seismic isolations to resist seismic energy and vibration. The seismic isolation system works well during the earthquake period, and it does help reduce the casualty and property loss induced by earthquakes. There are two main types of bearings used in the seismic isolation system. One is the elastomeric bearings and the other is the sliding bearings. The mechanics of the seismic isolation system preventing the influence of the earthquake and reducing the horizontal acceleration of the structure is to elongate the natural frequency of structure. As for the sliding bearings, the simplest way to increase the period is to reduce the friction coefficient of the two sliding elements. In conventional, two stainless steel plates are commonly used in the pure flat sliding bearing. This study tries to use bronze-graphite composite in the sliding bearing to decrease the friction coefficient.Consequently, the testing results suggest that the bronze-graphite composite has a lower friction coefficient, especially the graphite acting as a lubricant. The friction coefficient of the bronze-graphite plate is in the range of 0.12 to 0.23 under the load of 160 kg - 800kg. With a higher ratio of graphite to bronze at the sliding surface, the effect of reducing the friction coefficient more obviously. And the friction coefficient changes during the increasing loads period. It decreases at the beginning, and starts to increase at a certain load applied on it. Finally, it is reasonable to bronze-graphite composite in a low rise structure which has a relatively low weight. Because the load applied in the test is not high enough, the consequence may not work for high or heavy structure. Taken together, the use of new material with similar properties in the seismic isolation system can help improve the performance of resisting the earthquake. It should be accounted for further research in this field.
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Title: Developing Adaptive and Predictive Modules for the Second Generation of Multivariable Insulin Delivery System for People with Type-1 Diabetes
Creator: Askari, Mohammad Reza
Date: 2023
Description: In this research, we are developing the second generation of multivariable automated insulin delivery system (mvAID) for people with Type 1...
Show moreIn this research, we are developing the second generation of multivariable automated insulin delivery system (mvAID) for people with Type 1 diabetes (T1D). AID system is improved by integrating missing data from sensors into the system, reconciling outliers in the data, and eliminating the effects of artifacts in signals from wearable devices. Behavioral patterns of individuals with T1D are captured by data-driven models. The model predictive control algorithm of the mvAID uses these patterns for making decisions and predicting glucose concentrations in the future more accurately. A pipeline algorithm is developed for removing noise and motion artifacts from wristband signals. Then, energy expenditure, physical activity, and acute psychological stress (APS) are estimated from wearable device signals to detect and quantify disturbances affecting the concentration of blood glucose concentration. Additionally, different modules were designed for predicting risky glycemic episodes and are used to build the second generation of the mvAID system. The techniques developed are tested with historical data sets from various clinical experiments and free-living data, and with simulations made by using our multivariable glucose, insulin and physiological variables simulator (mGIPsim).
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Title: Melt Growth of Indium-Iodide on Earth and in Microgravity
Creator: Riabov, Vladimir
Date: 2023
Description: Indium Iodide is a heavy metal halide and a wide band-gap semiconductor which has a potential for application in room temperature γ- and X-ray...
Show moreIndium Iodide is a heavy metal halide and a wide band-gap semiconductor which has a potential for application in room temperature γ- and X-ray detectors. Its physical properties are similar to those of other materials used as room temperature radiation detectors. Over the years the technology of purification and crystal growth of InI was developed. Significant advances were made to improve purity, crystal structure and resulting electronic properties of the material. Nevertheless, the desired detector performance has not been achieved yet. Stress-induced crystal lattice defects resulting from solidification in contact with crucible are suspected to be responsible for the limited performance. Microgravity environment was previously used to study its effects on the process of crystal growth from the melt applied to semiconductors. It was observed that unlike on Earth materials can solidify without contact with the wall, when the sample is confined by the crucible. It was also shown that such detached solidification can drastically reduce stress-induced defects of the crystal lattice and improve electronic properties of the material. In this study crystal growth of InI was studied in microgravity, attempting to achieve detached solidification, and observe it in a transparent zone of a furnace. Partially detached solidification (a large free surface) has occurred in one of the samples. The resulting crystals were characterized by measuring their electronic properties and estimating the radiation detector performance of the devices manufactured using the crystals.
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Title: INTELLIGENT STREET LIGHTING AND REMOTE POWER UNITS AS CASE STUDIES FOR CITIES TO DECARBONIZE
Creator: Burgess, Patrick G.
Date: 2022
Description: There is a scientific consensus that atmospheric warming caused by the release of emissions will reach critical levels in our lifetime if...
Show moreThere is a scientific consensus that atmospheric warming caused by the release of emissions will reach critical levels in our lifetime if significant efforts are not made to decarbonize our buildings and power grid. The City of Los Angeles is a prime example of the challenges of decarbonizing, balancing global, federal, and state policies and issues and addressing environmental justice. The first research case studies of the details and challenges of decarbonization efforts include the implementation of the first networked light-emitting diode (LED) streetlights in the city of Chicago on IIT’s campus to improve the reliability and economics of its main campus, 2.5 mi south of downtown Chicago. Research shows that these networked LED streetlights greatly reduce a city's rising energy costs, but the CSMART project team has set out to prove the benefits of integrating an intelligent communications and control system with an existing smart grid infrastructure, such as an existing network and supervisory control and data acquisition (SCADA) systems. In addition to assessing the economic and environmental drivers for the intelligent streetlight solution, the project team is dedicated to assessing the potential cybersecurity vulnerabilities of such a system and working to mitigate or eliminate them. The second research case study covers off-grid remote power units providing continuous illumination for safer streets and safer driving that is unaffected by power outages. Thanks to individual lighting control potentially allowing for dimming, blinking, and even color changing, streetlights powered by RPUs can be used as emergency signaling devices, directing traffic during a city evacuation or other emergency. The RPU control and monitoring can be accessed through the cloud, thereby avoiding reliance on local servers.
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Title: Data-Driven Methods for Soft Robot Control and Turbulent Flow Models
Creator: Lopez, Esteban Fernando
Date: 2022
Description: The world today has seen an exponential increase in its usage of computers for communication and measurement. Thanks to recent technologies,...
Show moreThe world today has seen an exponential increase in its usage of computers for communication and measurement. Thanks to recent technologies, we are now able to collect more data than ever before. This has dawned a new age of data-driven methods which can describe systems and behaviors with increasing accuracy. Whereas before we relied on the expertise of a few professionals with domain-specific knowledge developed over years of rigorous study, we are now able to rely on collected data to reveal patterns, develop novel ideas, and offer solutions to the world’s engineering problems. No domain is safe. Within the engineering realm, data-driven methods have seen vast usage in the areas of control and system identification. In this thesis we explore two areas of data-driven methods, namely reinforcement learning and data-driven causality. Reinforcement learning is a method by which an agent learns to increase its selection of ideal actions and behaviors which result in an increasing reward. This method was applied to a soft-robotic concept called the JAMoEBA to solve various tasks of interest in the robotics community, specifically tunnel navigation, obstacle field navigation, and object manipulation. A validation study was conducted to show the complications that arise when applying reinforcement learning to such a complex system. Nevertheless, it was shown that reinforcement learning is capable of solving three key tasks (static tunnel navigation, obstacle field navigation, and object manipulation) using specific simulation and learning hyperparameters. Data-driven causality encompasses a range of metrics and methods which attempt to uncover causal relationships between variables in a system. Several information theoretic causal metrics were developed and applied to nine mode turbulent flow data set which represents the Moehlis model. It was shown that careful consideration into the method used was required to identify significant causal relationships. Causal relationships were shown to converge over several hundred realizations of the turbulent model. Furthermore, these results match the expected causal relationships given known information of self-sustaining processes in turbulence, validating the method’s ability to identify causal relationships in turbulence.
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Title: RADIAL MAP ASSESSMENT APPROACH FOR DEEP LEARNING DENOISED CARDIAC MAGNETIC RESONANCE RECONSTRUCTION SHARPNESS
Creator: Mo, Fei
Date: 2021
Description: Deep Learning (DL) and Artificial Intelligence (AI) play important roles in the computer-aided medical diagnostics and precision medicine...
Show moreDeep Learning (DL) and Artificial Intelligence (AI) play important roles in the computer-aided medical diagnostics and precision medicine fields, capable of complementing human operators in disease diagnosis and treatment but optimizing and streamlining medical image display. While incredibly powerful, images produced via Deep Learning or Artificial Intelligence should be analyzed critically in order to be cognizant of how the algorithms are producing the new image and what the new imagine is. One such opportunity arose in the form of a unique collaborative project: the technical development of an image assessment tool that would analyze outputs between DL-based and non DL-based Magnetic Resonance Imaging reconstruction methods.More specifically, we examine the operator input dependence of the existing reference method in terms of accuracy and precision performance, and subsequently propose a new metric approach that preserves the heuristics of the intended quantification, overcomes operator dependence, and provides a relative comparative scoring approach that may normalize for angular dependence of examined images. In chapter 2 of this thesis, we provide a background description pertaining to the two imaging science principles that yielded our proposed method description and study design. First, if treated naively, the examined linear measurement approach exhibits potential bias with respect to the coordinate lattice space of the examined image. Second, the examined DL-based image reconstruction methods used in this thesis warrants an elaborate and explicit description of the measured noise and signal present in the reconstructed images. This specific reconstruction approach employs an iterative scheme with an embedded DL-based substep or filter to which we are blinded. In chapters 3 and 4 of this thesis, the imaging and DL-based image reconstruction experiments are described. These experiments employ cardiac MRI datasets from multiple clinical centers. We first outline the clinical and technical background for this approach, and then examine the quality of DL-based reconstructed image sharpness by two alternative methods: 1) by employing the gold-standard method that addresses the lattice point irregularity using a ‘re-gridding’ method, and 2) by applying our novel proposed method inspired by radial MRI k-space sampling, which exploits the mathematical properties of uniform radial sampling to yield the target voxel counts in the ‘gridded’ polar coordinate system. This new measure of voxel counts is shown to overcome the limitation due to the operator-dependence for the conventional approach. Furthermore, we propose this metric as a relative and comparative index between two alternative reconstruction methods from the same MRI k-space.
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Title: DEEP LEARNING AND COMPUTER VISION FOR INDUSTRIAL APPLICATIONS: CELLULAR MICROSCOPIC IMAGE ANALYSIS AND ULTRASOUND NONDESTRUCTIVE TESTING
Creator: Yuan, Yu
Date: 2022
Description: For decades, researchers have sought to develop artificial intelligence (AI) systems that can help human beings on decision making, data...
Show moreFor decades, researchers have sought to develop artificial intelligence (AI) systems that can help human beings on decision making, data analysis and pattern recognition applications where analytical methods are ineffective. In recent years, Deep Learning (DL) has been proven to be an effective AI technique that can outperform other methods in applications such as computer vision, natural language processing, autonomous driving. Realizing the potential of deep learning techniques, researchers have also started to apply deep learning on other industrial applications. Today, deep learning based models are used to innovate and accelerate automation, guidance, and decision making in various industries including automotive industry, pharmaceutical industry, finance, agriculture and more. In this research, several important industrial applications (on Biomedicine and Non-Destructive Testing) utilizing deep learning algorithms will be introduced and analyzed. The first biopharmaceutical application focuses on developing a deep learning based model to automate the visual inspection process in Median Tissue Culture Infectious Dose(TCID50). TCID50 is one of the most popular methods for viral quantification. An important step of TCID50 is to visually inspect the sample and decide if it exhibits cytopathic effect(CPE) or not. Two novel models have been developed to detect CPE in microscopic images of cell culture in 96 well-plates. The first model consists of a convolutional neural network (CNN) and support vector machine(SVM). The second model is a fully convolutional network (FCN) followed by morphological post-processing steps. The models are tested on 4 cell lines and achieve very high accuracy. Another biopharmaceutical application developed for cellular microscopic images is the clonal selection. Clonal selection is one of the mandatory steps in cell line development process. It focuses on verifying the clonality of the cell culture. The researchers used to visually inspect the microscopic images to verify the clonality. In this work, a novel deep learning based model and a workflow is developed to accelerate the process. This algorithm consists of multiple steps, including image analysis after incubation to detect the cell colonies, and verify its clonality in day0 image. The results and common mis-classification cases are shown in this thesis. Image analysis method is not the only technology that has been advancing for cellular image analysis in biopharmaceutical industry. A new class of instruments are currently used in biopharmaceutical industry which enable more opportunities for image analysis. To make the most of these new instruments, a convolutional neural network based architecture is used to perform accurate cell counting and cell morphology based segmentation. This analysis can provide more insight of the cells at very early stage in characterization process of cell line development. The architecture and the testing results are presented in this work. The proposed algorithm has achieved very high accuracy on both applications, and the cell morphology based segmentation enables a brand new feature for scientists to predict the potential productivity of the cells. Next part of this dissertation is focused on hardware implementation of Ultrasonic Non-Destructive Testing (NDT) methods based on deep learning, which can be highly useful in flaw detection and classification applications. With the help of a smart and mobile Non-Destructive Testing device, engineers can accurately detect and locate the flaws inside the materials without reliance on high performance computation resources. The first NDT application presents a hardware implementation of a deep learning algorithm on Field-programmable gate array(FPGA) for Ultrasound flaw detection. The Ultrasound flaw detection algorithm consists of a wavelet transform followed by a LeNet inspired convolutional neural network called Ultra-LeNet. This work is focused on implementing the computationally difficult part of this algorithm: Ultra-LeNet, so that it can be used in the field where high performance computation resources (e.g., AWS) are not accessible. The implementation uses resource partitioning to design two dedicated pipelined accelerators for convolutional layers and fully connected layers respectively. Both accelerators utilize loop unrolling, loop pipelining and batch processing techniques to maximize the throughput. The comparison to other work has shown that the implementation has achieved higher hardware utilization efficiency. The second NDT application is also focused on implementing a deep learning based algorithm for Ultrasound flaw detection on a FPGA. Instead of implementing the Ultra-LeNet, the deep learning model used in this application is Meta-learning based Siamese Network, which is capable for multi-class classification and it can also classify a new class even if it does not appear in the training dataset with the help of automated learning features. The hardware implementation is significantly different than the previous algorithm. In order to improve the inference operation efficiency, the model is compressed with both pruning and quantization, and the FPGA implementation is specifically designed to accelerate the compressed CNN with high efficiency. The CNN model compression method and hardware design are novel methods introduced in this work. Comparison against other compressed CNN accelerators is also presented.
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Title: High-latitude plasma drift structuring from a first principles ionospheric model
Creator: Kim, Heejin
Date: 2020
Description: In the high-latitude ionosphere dense plasma formations called polar cap patches are sometimes observed. These patches are often associated...
Show moreIn the high-latitude ionosphere dense plasma formations called polar cap patches are sometimes observed. These patches are often associated with ionospheric scintillation, a rapid fluctuation in the amplitude and phase of a radio signal that degrades communications and navigation systems. Predicting polar cap patch movement across the polar cap is an important subject for enabling forecasting of the scintillation.Lagrangian coherent structures (LCSs) are ridges indicating regions of maximum fluid separation in a time-varying flow. In previous studies, the Ionosphere-Thermosphere Algorithm for Lagrangian Coherent Structures (ITALCS) predicted the location of LCSs. These LCSs were shown to constrain polar cap patch source and transport regions for flow assumed to due to $\vec{E} \times \vec{B}$ plasma drift. The LCSs were predicted based on an empirical model of the high-latitude electric field for $\vec{E}$. In this thesis, the LCSs are generated using the first principles ionospheric model SAMI3 (SAMI3 is Another Model of the Ionosphere) as the model for electric field. The work relies on an understanding of various magnetic coordinate systems in space science, and includes three different approaches for attempting to generate the $\vec{E} \times \vec{B}$ drift as the flow fields that are to input to ITALCS. Finally, a representative LCS result is obtained with SAMI3 and shown to be at the high latitudes on the dayside, similar to prior work, but spanning a shorter longitudinal range.
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