Engineering Commons^™

Self-Relabeling For Noise-Tolerant Retina Vessel Segmentation Through Label Reliability Estimation, Jiacheng Li, Ruirui Li, Ruize Han, Song Wang

Faculty Publications

Background Retinal vessel segmentation benefits significantly from deep learning. Its performance relies on sufficient training images with accurate ground-truth segmentation, which are usually manually annotated in the form of binary pixel-wise label maps. Manually annotated ground-truth label maps, more or less, contain errors for part of the pixels. Due to the thin structure of retina vessels, such errors are more frequent and serious in manual annotations, which negatively affect deep learning performance.

Methods In this paper, we develop a new method to automatically and iteratively identify and correct such noisy segmentation labels in the process of network training. We consider …

The Lognometer: A New Normalized And Computerized Device For Assessing The Neurodevelopment Of Fine Motor Control In Children, Christian O'Reilly, Rejean Plamondon, Nadir Faci

Publications

Motor skills are fundamental for the development of children. Neurodevelopmental tests currently used by professionals for measuring motor control maturity exhibit several limitations. To address some of these, we have designed the Lognometer, a tablet-based device that can run computerized neuromotor tests. To normalize this tool against a representative population, we collected handwritten triangles from 780 children. We used the Sigma-Lognormal model and a prototype-based parameter estimation algorithm to analyze these movements. To ensure clinical acceptance, we developed an explainable solution relying on statistical regression. We evaluated how well the proposed lognormal decomposition captures the motor control maturation between 6 …

Ksat: Knowledge-Infused Self Attention Transformer - Integrating Multiple Domain-Specific Contexts, Kaushik Roy, Yuxin Zi, Vignesh Narayanan, Manas Gaur, Amit P. Sheth

Publications

Domain-specific language understanding requires integrating multiple pieces of relevant contextual information. For example, we see both suicide and depression related behavior (multiple contexts) in the text “I have a gun and feel pretty bad about my life, and it wouldn’t be the worst thing if I didn’t wake up tomorrow”. Domain specificity in self-attention architectures is handled by fine-tuning on excerpts from relevant domain specific resources (datasets and external knowledge - medical textbook chapters on mental health diagnosis related to suicide and depression). We propose a modified self-attention architecture Knowledge infused Self Attention Transformer (KSAT) that achieves the integration of …

Comparing Symbolic And Connectionist Algorithms For Correlating The Age Of Healthy Children With Sigma-Lognormal Neuromuscular Parameters, Zigeng Zhang, Christian O'Reilly, Rejean Plamondon

Publications

It is important to accurately evaluate the motor control maturity to help physicians diagnose delayed or abnormal motor development in children. Traditionally, it has been challenging to design assessment methods that are practical and accurate at the same time. This study aims to develop an effective algorithm to predict motor control maturity based on the Kinematic Theory of rapid human movements. We used handwritten pen strokes made on an electronic tablet by 513 children (5.5 to 13 years of age). We considered two types of movements: a single stroke and a triangle drawing test. For the analysis, Sigma-Lognormal parameters were …

A Risk-Averse Mechanism For Suicidality Assessment On Social Media, Ramit Sawhney, Atula Tejaswi Neerkaje, Manas Gaur

Publications

Recent studies have shown that social media has increasingly become a platform for users to express suicidal thoughts outside traditional clinical settings. With advances in Natural Language Processing strategies, it is now possible to design automated systems to assess suicide risk. However, such systems may generate uncertain predictions, leading to severe consequences. We hence reformulate suicide risk assessment as a selective prioritized prediction problem over the Columbia Suicide Severity Risk Scale (C-SSRS). We propose SASI, a risk-averse and self-aware transformer-based hierarchical attention classifier, augmented to refrain from making uncertain predictions. We show that SASI is able to refrain from 83% …

Process Knowledge-Infused Learning For Suicidality Assessment On Social Media, Kaushik Roy, Manas Gaur, Qi Zhang, Amit Sheth

Publications

Improving the performance and natural language explanations of deep learning algorithms is a priority for adoption by humans in the real world. In several domains, such as healthcare, such technology has significant potential to reduce the burden on humans by providing quality assistance at scale. However, current methods rely on the traditional pipeline of predicting labels from data, thus completely ignoring the process and guidelines used to obtain the labels. Furthermore, post hoc explanations on the data to label prediction using explainable AI (XAI) models, while satisfactory to computer scientists, leave much to be desired to the end users due …

Wise Causal Models: Wisdom Infused Semantics Enhanced Causal Models - A Study In Suicidality Diagnosis, Kaushik Roy, Yuxin Zi, Vignesh Narayanan, Manas Gaur, Sanjay Chandrasekar, Amit Sheth

Publications

The COVID-19 Pandemic has highlighted the gap between the number of mental health care seekers and care providers. Netizens have taken to internet-based platforms such as Reddit to express their experiences. Mental illness diagnosis processes have clinically accepted causal interpretations and semantics. Curiously, mental illness diagnosis accuracy is low relative to similar well-studied illnesses. Motivated by this discrepancy, we propose Wisdom Infused Semantics Enhanced (WISE) causal models, inspired by the wisdom of the crowd idea that learns from a collective agreement among causal models and their semantics for mental illness diagnoses. We use suicidality diagnosis task descriptions, datasets, and baseline …

Knowledge-Infused Reinforcement Learning, Kaushik Roy, Manas Gaur, Qi Zhang, Amit Sheth

Publications

Virtual health agents (VHAs) have received considerable attention, but the early focus has been on collecting data, helping patients follow generic health guidelines, and providing reminders for clinical appointments. While presenting the collected data and frequency of visits to the clinician is useful, further context and personalization are needed for a VHA to interpret and understand what the data means in clinical terms. This has made their use in managing health limited. Such understanding enables patient empowerment and self-appraisal – i.e., aiding the patient in interpreting the data to understand the changes in the patient’s health conditions, and self-management – …

Rapid Ac Electrokinetic Micromixer With Electrically Conductive Sidewalls, Fang Yang, Wei Zhao, Cuifang Kuang, Guiren Wang

Faculty Publications

We report a quasi T-channel electrokinetics-based micromixer with electrically conductive sidewalls, where the electric field is in the transverse direction of the flow and parallel to the conductivity gradient at the interface between two fluids to be mixed. Mixing results are first compared with another widely studied micromixer configuration, where electrodes are located at the inlet and outlet of the channel with electric field parallel to bulk flow direction but orthogonal to the conductivity gradient at the interface between the two fluids to be mixed. Faster mixing is achieved in the micromixer with conductive sidewalls. Effects of Re numbers, applied …

Machine-Learning Algorithms For Forecast-Informed Reservoir Operation (Firo) To Reduce Flood Damages, Manizhe Zarei, Omid Bozorg-Haddad, Sahar Baghban, Mohammad Delpasand, Erfan Goharian, Hugo A. Loaiciga

Faculty Publications

Water is stored in reservoirs for various purposes, including regular distribution, flood control, hydropower generation, and meeting the environmental demands of downstream habitats and ecosystems. However, these objectives are often in conflict with each other and make the operation of reservoirs a complex task, particularly during flood periods. An accurate forecast of reservoir inflows is required to evaluate water releases from a reservoir seeking to provide safe space for capturing high flows without having to resort to hazardous and damaging releases. This study aims to improve the informed decisions for reservoirs management and water prerelease before a flood occurs by …

Defining And Detecting Toxicity On Social Media: Context And Knowledge Are Key, Amit Sheth, Valerie Shalin, Ugur Kursuncu

Publications

As the role of online platforms has become increasingly prominent for communication, toxic behaviors, such as cyberbullying and harassment, have been rampant in the last decade. On the other hand, online toxicity is multi-dimensional and sensitive in nature, which makes its detection challenging. As the impact of exposure to online toxicity can lead to serious implications for individuals and communities, reliable models and algorithms are required for detecting and understanding such communications. In this paper We define toxicity to provide a foundation drawing social theories. Then, we provide an approach that identifies multiple dimensions of toxicity and incorporates explicit knowledge …

The NTh-Order Comprehensive Adjoint Sensitivity Analysis Methodology For Response-Coupled Forward/Adjoint Linear Systems (NTh-Casam-L): Ii. Illustrative Application, Dan Gabriel Cacuci

Faculty Publications

This work illustrates the application of the n^th-order comprehensive adjoint sensitivity analysis methodology for response-coupled forward/adjoint linear systems (abbreviated as “n^th-CASAM-L”) to a paradigm model that describes the transmission of particles (neutrons and/or photons) through homogenized materials, as encountered in radiation protection and shielding. The first-, second-, and third-order sensitivities of responses that depend on both the forward and adjoint particle fluxes are obtained exactly, in closed-form, underscoring the principles and methodology underlying the n^th-CASAM-L. The results presented in this work underscore the fundamentally important role of the n^th-CASAM-L in the quest …

The NTh-Order Comprehensive Adjoint Sensitivity Analysis Methodology For Response-Coupled Forward/Adjoint Linear Systems (NTh-Casam-L): I. Mathematical Framework, Dan Gabriel Cacuci

Faculty Publications

This work presents the mathematical framework of the n^th-Order Comprehensive Adjoint Sensitivity Analysis Methodology for Response-Coupled Forward/Adjoint Linear Systems (abbreviated as “n^th-CASAM-L”), which is conceived for obtaining the exact expressions of arbitrarily-high-order (n^th-order) sensitivities of a generic system response with respect to all of the parameters (including boundary and initial conditions) underlying the respective forward/adjoint systems. Since many of the most important responses for linear systems involve the solutions of both the forward and the adjoint linear models that correspond to the respective physical system, the sensitivity analysis of such responses makes it necessary …

The NTh-Order Comprehensive Adjoint Sensitivity Analysis Methodology For Response-Coupled Forward/Adjoint Linear Systems (NTh-Casam-L): I. Mathematical Framework, Dan Gabriel Cacuci

Faculty Publications

This work presents the mathematical framework of the n^th-Order Comprehensive Adjoint Sensitivity Analysis Methodology for Response-Coupled Forward/Adjoint Linear Systems (abbreviated as “n^th-CASAM-L”), which is conceived for obtaining the exact expressions of arbitrarily-high-order (n^th-order) sensitivities of a generic system response with respect to all of the parameters (including boundary and initial conditions) underlying the respective forward/adjoint systems. Since many of the most important responses for linear systems involve the solutions of both the forward and the adjoint linear models that correspond to the respective physical system, the sensitivity analysis of such responses makes it necessary …

Simultaneous Dirac-Like Cones At Two Energy States In Tunable Phononic Crystals: An Analytical And Numerical Study, Mustahseen M. Indaleeb, Sourav Banerjee

Faculty Publications

Simultaneous occurrence of Dirac-like cones at the center of the Brillouin zone (Γ) at two different energy states is termed Dual-Dirac-like cones (DDC) in this article. The occurrence of DDC is a rare phenomenon. Thus, the generation of multiple Dirac-like cones at the center of the Brillouin zone is usually non-manipulative and poses a challenge to achieve through traditional accidental degeneracy. However, if predictively created, DDC will have multiple engineering applications with acoustics and vibration. Thus, the possibilities of creating DDC have been identified herein using a simple square periodic array of tunable square phononic crystals (PnCs) in air media. …

Atlantis: A Benchmark For Semantic Segmentation Of Waterbody Images, Mohammad H. Erfani, Zhenyao Wu, Xinyi Wu, Song Wang, Erfan Goharian

Faculty Publications

Vision-based semantic segmentation of waterbodies and nearby related objects provides important information for managing water resources and handling flooding emergency. However, the lack of large-scale labeled training and testing datasets for water-related categories prevents researchers from studying water-related issues in the computer vision field. To tackle this problem, we present ATLANTIS, a new benchmark for semantic segmentation of waterbodies and related objects. ATLANTIS consists of 5,195 images of waterbodies, as well as high quality pixel-level manual annotations of 56 classes of objects, including 17 classes of man-made objects, 18 classes of natural objects and 21 general classes. We analyze ATLANTIS …

Modeling Electrokinetics Of Oxygen Electrodes In Solid Oxide Electrolyzer Cells, Korey Cook, Jacob Wrubel, Zhiwen Ma, Kevin Huang, Xinfang Jin

Faculty Publications

A microscale model is presented in this study to simulate electrode kinetics of the oxygen electrode in a solid oxide electrolyzer cell (SOEC). Two mixed ionic/electronic conducting structures are examined for the oxygen producing electrode in this work: single layer porous lanthanum strontium cobalt ferrite (LSCF), and bilayer LSCF/SCT (strontium cobalt tantalum oxide) structures. A yttrium-stabilized zirconia (YSZ) electrolyte separates the hydrogen and oxygen electrodes, as well as a gadolinium doped-ceria (GDC) buffer layer on the oxygen electrode side. Electrochemical reactions occurring at the two-phase boundaries (2PBs) and three-phase boundaries (3PBs) of single-layer LSCF and bilayer LSCF/SCT oxygen electrodes are …

An Optimized Machine Learning And Big Data Approach To Crime Detection, Ashokkumar Palanivinayagam, Siva Shankar Gopal, Sweta Bhattacharya, Noble Anumbe, Ebuka Ibeke, Cresantus Biamba

Faculty Publications

Crime detection is one of the most important research applications in machine learning. Identifying and reducing crime rates is crucial to developing a healthy society. Big Data techniques are applied to collect and analyse data: determine the required features and prime attributes that cause the emergence of crime hotspots. The traditional crime detection and machine learning-based algorithms lack the ability to generate key prime attributes from the crime dataset, hence most often fail to predict crime patterns successfully. This paper is aimed at extracting the prime attributes such as time zones, crime probability, and crime hotspots and performing vulnerability analysis …

Estimating Bounds Of Aerodynamic, Mass And Auxiliary Load Impacts On Autonomous Vehicles: A Powertrain Simulation Approach, Yuche Chen, Ruixiao Sun, Xuanke Wu

Faculty Publications

Vehicle automation requires new onboard sensors, communication equipment, and/or data processing units, and may encourage modifications to existing onboard components (such as the steering wheel). These changes impact the vehicle’s mass, auxiliary load, coefficient of drag, and frontal area, which then change vehicle performance. This paper uses the powertrain simulation model FASTSim to quantify the impact of autonomy-related design changes on a vehicle’s fuel consumption. Levels 0, 2, and 5 autonomous vehicles are modeled for two battery-electric vehicles (2017 Chevrolet Bolt and 2017 Nissan Leaf) and a gasoline powered vehicle (2017 Toyota Corolla). Additionally, a level 5 vehicle is divided …

Estimating Bounds Of Aerodynamic, Mass, And Auxiliary Load Impacts On Autonomous Vehicles: A Powertrain Simulation Approach, Yuche Chen, Ruixiao Sun, Xuanke Wu

Faculty Publications

Vehicle automation requires new onboard sensors, communication equipment, and/or data processing units, and may encourage modifications to existing onboard components (such as the steering wheel). These changes impact the vehicle’s mass, auxiliary load, coefficient of drag, and frontal area, which then change vehicle performance. This paper uses the powertrain simulation model FASTSim to quantify the impact of autonomy-related design changes on a vehicle’s fuel consumption. Levels 0, 2, and 5 autonomous vehicles are modeled for two battery-electric vehicles (2017 Chevrolet Bolt and 2017 Nissan Leaf) and a gasoline powered vehicle (2017 Toyota Corolla). Additionally, a level 5 vehicle is divided …

An Efficient Track-Scale Model For Laser Powder Bed Fusion Additive Manufacturing: Part 1- Thermal Model, Reza Tangestani, Trevor Sabiston, Apratim Chsktsborty, Waqas Muhammad, Lang Yuan, Étienne Martin

Faculty Publications

This is the first of two manuscripts that presents a computationally efficient full field deterministic model for laser powder bed fusion (LPBF). A new Hybrid Line (HL) heat input model integrates an exponentially decaying (ED) heat input over a portion of a laser path to significantly reduce the computational time. Experimentally measured properties of the high gamma prime nickel-based superalloy RENÉ 65 are implemented in the model to predict the in-process temperature distribution, stresses, and distortions. The model accounts for specific properties of the material as different phases. The first manuscript presents the HL heat transfer model, which is compared …

An Efficient Track-Scale Model For Laser Powder Bed Fusion Additive Manufacturing: Part 2-Mechanical Model, Reza Tangestani, Trevor Sabiston, Apratim Chakraborty, Lang Yuan, Nicholas Krutz, Étienne Martin

Faculty Publications

This is the second of two manuscripts that presents a computationally efficient full-field deterministic model for laser powder bed fusion (LPBF). The Hybrid Line (HL) thermal model developed in part I is extended to predict the in-process residual stresses due to laser processing of a nickel-based superalloy, RENÉ 65. The computational efficiency and accuracy of the HL thermo-mechanical model is first compared to the exponential decaying heat input model on a single-track simulation. LPBF thin-wall builds with three different laser powers and four printing patterns are evaluated in this study and compared with part-scale simulations. The simulations show good agreements …

Meshfree Simulation And Experimental Validation Of Extreme Thermomechanical Conditions In Friction Stir Extrusion, Lei Li, Varun Gupta, Xiao Li, Anthony P. Reynolds, Glenn Grant, Ayoub Soulami

Faculty Publications

Friction stir extrusion (FSE) is a novel solid-phase processing technique that consolidates and extrudes metal powders, flakes, chips, or billets into high-performance parts by plastic deformation, which has the potential to save substantial processing time and energy. Currently, most studies on FSE are experimental and only a few numerical models have been developed to explain and predict the complex physics of the process. In this work, a meshfree simulation framework based on smoothed particle hydrodynamics (SPH) was developed for FSE. Unlike traditional grid-based methods, SPH is a Lagrangian particle-based method that can handle severe material deformations, capture moving interfaces and …

Evaluation Of Orthogonal Strain Components In Friction Extrusion, X Li, Md. Reza-E Rabby, M Ryan, G. Grant, Anthony P. Reynolds

Faculty Publications

Friction extrusion is a thermomechanical process that combines conventional extrusion with the action of a rotating die. The plastic deformation of the material being sheared and extruded is the primary source of process heat and it produces strain distributions unlike those resulting from conventional extrusion. This paper proposes an improved strain analysis that evaluates three main strain components in a series of rate-controlled friction extrusions in which the steady state was achieved. Cylindrical AA1100 extrusion billets with two embedded markers were extruded to wire with a 10:1 diametral reduction. The shape change of the embedded markers was determined via serial, …

Soft Elastomeric Capacitor For Angular Rotation Sensing In Steel Components, Han Liu, Simon Laflamme, Jian Li, Caroline Bennett, William N. Collins, Austin Downey, Paul Ziehl, Hongki Jo

Faculty Publications

The authors have previously proposed corrugated soft elastomeric capacitors (cSEC) to create ultra compliant scalable strain gauges. The cSEC technology has been successfully demonstrated in engineering and biomechanical applications for in-plane strain measurements. This study extends work on the cSEC to evaluate its performance at measuring angular rotation when installed folded at the junction of two plates. The objective is to characterize the sensor’s electromechanical behavior anticipating applications to the monitoring of welded connections in steel components. To do so, an electromechanical model that maps the cSEC signal to bending strain induced by angular rotation is derived and adjusted using …

A Reduced-Order Lumped Model For Li-Ion Battery Packs During Operation, Paul T. Coman, Eric C. Darcy, Brad Strangways, Ralph E. White

Faculty Publications

Modeling heat distribution in Li-ion battery packs can be challenging, especially if the battery pack is large and the cells are operated at high C-rates, which usually requires high-order physics-based mathematical models. Reduced and simplifying models can, however, be used at lower rates. This paper presents a fast novel reduced lumped model (RLM) that can be used to calculate the temperature increase during the high-current discharge of cylindrical Li-ion cells in a subscale of a battery pack. By reducing the PDE utilized to calculate the state of charge (SoC) to ODE's and solving them analytically, the reduced model can be …

Parameter Estimation And Application Of Anisotropic Yield Criteria For Cylindrical Aluminum Extrusions: Theoretical Developments And Stereodic Measurements, Farzana Yasmeen, Michael A. Sutton, Xiaomin Deng, Megan Ryan, Anthony P. Reynolds

Faculty Publications

Theoretical and experimental studies are presented to characterize the anisotropic plastic response under torsion loading of two nominally identical aluminum Al6061-T6 extruded round bars. Theoretical models are developed using isotropic (Von Mises 1913) and anisotropic (Barlat 1991) yield criteria, along with isotropic strain hardening formulae, to model post-yield behavior under simple torsion loading. For the case of simple shear loading, incremental plasticity theory is used to determine the theoretical elastic, plastic, and total shear strains. A set of experiments are performed to calibrate Barlat’s 1991 yield function. Several specimens are extracted at different orientations to the longitudinal direction of each …

Occurrence And Removal Of Engineered Nanoparticles In Drinking Water Treatment And Wastewater Treatment A Review, Cheng Yu, Sewoon Kim, Min Jang, Chang Min Park, Yeomin Yoon

Faculty Publications

Engineered nanoparticles (ENPs) are widely used in various industrial products and consumer goods, resulting in their widespread existence, particularly in natural water systems and water and wastewater treatment plants. Their presence in surface water for human consumption may severely harm human health. Therefore, this review examines new findings and developments in the removal technology of ENPs in drinking water and wastewater treatment processes since the publication of the literature by Park et al. [1]. By evaluating recent articles, this review investigates the occurrence of ENPs, discusses the transport of nanoparticles (NPs) in various drinking water and wastewater treatment …

High-Order Deterministic Sensitivity Analysis And Uncertainty Quantification: Review And New Developments, Dan Gabriel Cacuci

Faculty Publications

This work reviews the state-of-the-art methodologies for the deterministic sensitivity analysis of nonlinear systems and deterministic quantification of uncertainties induced in model responses by uncertainties in the model parameters. The need for computing high-order sensitivities is underscored by presenting an analytically solvable model of neutron scattering in a hydrogenous medium, for which all of the response’s relative sensitivities have the same absolute value of unity. It is shown that the wider the distribution of model parameters, the higher the order of sensitivities needed to achieve a desired level of accuracy in representing the response and in computing the response’s expectation, …

On The Need To Determine Accurately The Impact Of Higher-Order Sensitivities On Model Sensitivity Analysis, Uncertainty Quantification And Best-Estimate Predictions, Dan Gabriel Cacuci

Faculty Publications

This work aims at underscoring the need for the accurate quantification of the sensitivities (i.e., functional derivatives) of the results (a.k.a. “responses”) produced by large-scale computational models with respect to the models’ parameters, which are seldom known perfectly in practice. The large impact that can arise from sensitivities of order higher than first has been highlighted by the results of a third-order sensitivity and uncertainty analysis of an OECD/NEA reactor physics benchmark, which will be briefly reviewed in this work to underscore that neglecting the higher-order sensitivities causes substantial errors in predicting the expectation and variance of model responses. The …