Engineering Commons^™

Event-Triggered Optimal Adaptive Control Of Partially Unknown Linear Continuous-Time Systems With State Delay, Rohollah Moghadam, Vignesh Narayanan, Sarangapani Jagannathan

Publications

This paper proposes an event-triggered optimal adaptive output feedback control design approach by utilizing integral reinforcement learning (IRL) for linear time-invariant systems with state delay and uncertain internal dynamics. In the proposed approach, the general optimal control problem is formulated into the game-theoretic framework by treating the event-triggering threshold and the optimal control policy as players. A cost function is defined and a value functional, which includes the delayed system output, is considered. First, by using the value functional and applying stationarity conditions using the Hamiltonian function, the output game delay algebraic Riccati equation (OGDARE) and optimal control policy are …

Tutorial: Knowledge-Infused Learning For Autonomous Driving (Kl4ad), Ruwan Wickramarachchi, Cory Henson, Sebastian Monka, Daria Stepanova, Amit Sheth

Publications

Autonomous Driving (AD) is considered as a testbed for tackling many hard AI problems. Despite the recent advancements in the field, AD is still far from achieving full autonomy due to core technical problems inherent in AD. The emerging field of neuro-symbolic AI and the methods for knowledge-infused learning are showing exciting ways of leveraging external knowledge within machine/deep learning solutions, with the potential benefits for interpretability, explainability, robustness, and transferability. In this tutorial, we will examine the use of knowledge-infused learning for three core state-of-the-art technical achievements within the AD domain. With a collaborative team from both academia and …

Tutorial: Neuro-Symbolic Ai For Mental Healthcare, Kaushik Roy, Usha Lokala, Manas Gaur, Amit Sheth

Publications

Artificial Intelligence (AI) systems for mental healthcare (MHCare) have been ever-growing after realizing the importance of early interventions for patients with chronic mental health (MH) conditions. Social media (SocMedia) emerged as the go-to platform for supporting patients seeking MHCare. The creation of peer-support groups without social stigma has resulted in patients transitioning from clinical settings to SocMedia supported interactions for quick help. Researchers started exploring SocMedia content in search of cues that showcase correlation or causation between different MH conditions to design better interventional strategies. User-level Classification-based AI systems were designed to leverage diverse SocMedia data from various MH conditions, …

Metaversekg: Knowledge Graph For Engineering And Design Application In Industrial Metaverse, Utkarshani Jaimini, Tongtao Zhang, Georgia Olympia Brikis

Publications

While the term Metaverse was first coined by the author Neal Stephenson in 1992 in his science fiction novel “Snow Crash”, today the vision of an integrated virtual world is becoming a reality across different sectors. Applications in gaming and consumer products are gaining traction, industrial metaverse applications are, still in their early stages of development with one of the challenges being interoperability across various metaverse development platforms and existing software tools. In this work we propose the use of a knowledge graph based semantic data exchange layer, the Metaverse Knowledge Graph, to enable seamless transfer of information across platforms. …

Learning To Automate Follow-Up Question Generation Using Process Knowledge For Depression Triage On Reddit Posts, Shrey Gupta, Anmol Agarwal, Manas Gaur, Kaushik Roy, Vignesh Narayanan, Ponnurangam Kumaraguru, Amit Sheth

Publications

Conversational Agents (CAs) powered with deep language models (DLMs) have shown tremendous promise in the domain of mental health. Prominently, the CAs have been used to provide informational or therapeutic services (e.g., cognitive behavioral therapy) to patients. However, the utility of CAs to assist in mental health triaging has not been explored in the existing work as it requires a controlled generation of follow-up questions (FQs), which are often initiated and guided by the mental health professionals (MHPs) in clinical settings. In the context of `depression', our experiments show that DLMs coupled with process knowledge in a mental health questionnaire …

Uav Rapidly-Deployable Stage Sensor With Electro-Permanent Magnet Docking Mechanism For Flood Monitoring In Undersampled Watersheds, Corinne A, Smith, Joud Satme, Jacob Martin, Austin Downey, Nikolaos Vitzilaios, Jasim Imran

Faculty Publications

The availability of historical flood data is vital in recognizing weather-related trends and outlining necessary precautions for at-risk communities. Flood frequency, magnitude, endurance, and volume are traditionally recorded using established streamgages; however, the material and installation costs allow only a few streamgages in a region, which yield a narrow data selection. In particular, stage, the vertical water height in a water body, is an important parameter in determining flood trends. This work investigates a low-cost, compact, rapidly-deployable alternative to traditional stage sensors that will allow for denser sampling within a watershed and a more detailed record of flood events. The …

A Novel Self-Assembled Cobalt-Free Perovskite Composite Cathode With Triple-Conduction For Intermediate Proton-Conducting Solid Oxide Fuel Cells, Hua Tong, Min Fu, Yang Yang, Fanglin Chen, Zetian Tao

Faculty Publications

A traditional composite cathode for proton-conducting solid oxide fuel cells (H-SOFCs) is typically obtained by mixing cathode materials and proton conducting electrolyte of BaCe_0.7Y_0.2Z_r0.1O_3–δ (BZCY), providing chemical and thermal compatibility with the electrolyte. Here, a series of triple-conducing and cobalt-free iron-based perovskites as cathodes for H-SOFCs is reported. Specifically, BaCe_xFe_1–xO_3–δ (x = 0.36, 0.43, and 0.50) shows various contents of two single phase perovskites with an in situ heterojunction structure as well as triple conductivity by tailoring the Ce/Fe ratios. The cell performance with the optimized BaCe_{0.36 …}

Towards Efficient Scoring Of Student-Generated Long-Form Analogies In Stem, Thilini Wijesiriwardene, Ruwan Wickramarachchi, Valerie L. Shalin, Amit P. Sheth

Publications

Switching from an analogy pedagogy based on comprehension to analogy pedagogy based on production raises an impractical manual analogy scoring problem. Conventional symbol-matching approaches to computational analogy evaluation focus on positive cases, and challenge computational feasibility. This work presents the Discriminative Analogy Features (DAF) pipeline to identify the discriminative features of strong and weak long-form text analogies. We introduce four feature categories (semantic, syntactic, sentiment, and statistical) used with supervised vector-based learning methods to discriminate between strong and weak analogies. Using a modestly sized vector of engineered features with SVM attains a 0.67 macro F1 score. While a semantic feature …

Securing Information On A Web Application System To Facilitate Online Blood Donation Booking, Hrishitva Patel

Faculty Publications

Blood donation has saved many lives in the past. According to statistics presented by the American Red Cross, a patient is in need of a blood transfusion every two seconds. There are many benefits that arise from blood donation to both the donor and the blood recipients. With blood donation, cancer patients, people involved in accidents, or those battling diseases that require blood donation have access to enough blood to sustain their survival. There is a need to digitize the blood donation booking to facilitate blood donation across the United States, and ensure patients in need of blood, receive their …

Integrated Socio-Environmental Vulnerability Assessment Of Coastal Hazards Using Data-Driven And Multi-Criteria Analysis Approaches, Ahad Hasan Tanim, Erfan Goharian

Faculty Publications

Coastal hazard vulnerability assessment has been centered around the multi-variate analysis of geo-physical and hydroclimate data. The representation of coupled socio-environmental factors has often been ignored in vulnerability assessment. This study develops an integrated socio-environmental Coastal Vulnerability Index (CVI), which simultaneously combines information from five vulnerability groups: biophysical, hydroclimate, socio-economic, ecological, and shoreline. Using the Multi-Criteria Decision Making (MCDM) approach, two CVI (CVI-50 and CVI-90) have been developed based on average and extreme conditions of the factors. Each CVI is then compared to a data-driven CVI, which is formed based on Probabilistic Principal Component Analysis (PPCA). Both MCDM and PPCA …

Structural Health Monitoring Of Fatigue Cracks For Steel Bridges With Wireless Large-Area Strain Sensors, Sdiq Anwar Taher, Jian Li, Jong-Hyun Jeong, Simon Laflamme, Hongki Jo, Caroline Bennett, William N. Collins, Austin Downey

Faculty Publications

This paper presents a field implementation of the structural health monitoring (SHM) of fatigue cracks for steel bridge structures. Steel bridges experience fatigue cracks under repetitive traffic loading, which pose great threats to their structural integrity and can lead to catastrophic failures. Currently, accurate and reliable fatigue crack monitoring for the safety assessment of bridges is still a difficult task. On the other hand, wireless smart sensors have achieved great success in global SHM by enabling long-term modal identifications of civil structures. However, long-term field monitoring of localized damage such as fatigue cracks has been limited due to the lack …

An Ontology For Cardiothoracic Surgical Education And Clinical Data Analytics, Maryam Panahiazar, Yorick Chern, Ramon Riojas, Omar S.Latif, Usha Lokala, Dexter Hadley, Amit Sheth, Ramin E.Beygui

Faculty Publications

The development of an ontology facilitates the organization of the variety of concepts used to describe different terms in different resources. The proposed ontology will facilitate the study of cardiothoracic surgical education and data analytics in electronic medical records (EMR) with the standard vocabulary.

Learning To Automate Follow-Up Question Generation Using Process Knowledge For Depression Triage On Reddit Posts, Shrey Gupta, Anmol Agarwal, Manas Gaur, Kaushik Roy, Vignesh Narayanan, Ponnurangam Kumaraguru, Amit Sheth

Publications

Conversational Agents (CAs) powered with deep language models (DLMs) have shown tremendous promise in the domain of mental health. Prominently, the CAs have been used to provide informational or therapeutic services (e.g., cognitive behavioral therapy) to patients. However, the utility of CAs to assist in mental health triaging has not been explored in the existing work as it requires a controlled generation of follow-up questions (FQs), which are often initiated and guided by the mental health professionals (MHPs) in clinical settings. In the context of ‘depression’, our experiments show that DLMs coupled with process knowledge in a mental health questionnaire …

Illustrative Application Of The Nth-Order Comprehensive Adjoint Sensitivity Analysis Methodology For Nonlinear Systems To The Nordheim–Fuchs Reactor Dynamics/Safety Model, Dan Gabriel Cacuci

Faculty Publications

The application of the recently developed “nth-order comprehensive sensitivity analysis methodology for nonlinear systems” (abbreviated as “nth-CASAM-N”) has been previously illustrated on paradigm nonlinear space-dependent problems. To complement these illustrative applications, this work illustrates the application of the nth-CASAM-N to a paradigm nonlinear time-dependent model chosen from the field of reactor dynamics/safety, namely the well-known Nordheim–Fuchs model. This phenomenological model describes a short-time self-limiting power transient in a nuclear reactor system having a negative temperature coefficient in which a large amount of reactivity is suddenly inserted, either intentionally or by accident. This model is sufficiently complex to demonstrate all the …

The NTh-Order Comprehensive Adjoint Sensitivity Analysis Methodology For Nonlinear Systems (Nth-Casam-N): Mathematical Framework, Dan Gabriel Cacuci

Faculty Publications

This work presents the n^th-Order Comprehensive Adjoint Sensitivity Analysis Methodology for Nonlinear Systems (n^th-CASAM-N), which enables the most efficient computation of exactly determined expressions of arbitrarily high-order sensitivities of generic nonlinear system responses with respect to model parameters, uncertain boundaries, and internal interfaces in the model’s phase space. The mathematical framework underlying the n^th-CASAM-N is proven to be correct by using mathematical induction. The n^th-CASAM-N is formulated in linearly increasing higher-dimensional Hilbert spaces—as opposed to exponentially increasing parameter-dimensional spaces—thus overcoming the curse of dimensionality in sensitivity analysis of nonlinear systems.

Illustrative Application Of The Nth-Order Comprehensive Adjoint Sensitivity Analysis Methodology For Nonlinear Systems To The Nordheim–Fuchs Reactor Dynamics/Safety Model, Dan Gabriel Cacuci

Faculty Publications

The application of the recently developed “nth-order comprehensive sensitivity analysis methodology for nonlinear systems” (abbreviated as “nth-CASAM-N”) has been previously illustrated on paradigm nonlinear space-dependent problems. To complement these illustrative applications, this work illustrates the application of the nth-CASAM-N to a paradigm nonlinear time-dependent model chosen from the field of reactor dynamics/safety, namely the well-known Nordheim–Fuchs model. This phenomenological model describes a short-time self-limiting power transient in a nuclear reactor system having a negative temperature coefficient in which a large amount of reactivity is suddenly inserted, either intentionally or by accident. This model is sufficiently complex to demonstrate all the …

Can Language Models Capture Graph Semantics? From Graphs To Language Model And Vice-Versa, Tarun Garg, Kaushik Roy, Amit Sheth

Publications

Knowledge Graphs are a great resource to capture semantic knowledge in terms of entities and relationships between the entities. However, current deep learning models takes as input distributed representations or vectors. Thus, the graph is compressed in a vectorized representation. We conduct a study to examine if the deep learning model can compress a graph and then output the same graph with most of the semantics intact. Our experiments show that Transformer models are not able to express the full semantics of the input knowledge graph. We find that this is due to the disparity between the directed, relationship and …

Knowledge-Driven Drug-Use Namedentity Recognition With Distant Supervision, Goonmeet Bajaj, Ugur Kursuncu, Manas Gaur, Usha Lokala, Ayaz Hyder, Srinivasan Parthasarathy, Amit Sheth

Publications

As Named Entity Recognition (NER) has been essential in identifying critical elements of unstructured content, generic NER tools remain limited in recognizing entities specific to a domain, such as drug use and public health. For such high-impact areas, accurately capturing relevant entities at a more granular level is critical, as this information influences real-world processes. On the other hand, training NER models for a specific domain without handcrafted features requires an extensive amount of labeled data, which is expensive in human effort and time. In this study, we employ distant supervision utilizing a domain-specific ontology to reduce the need for …

Tropospheric Attenuation Prediction For Future Millimeter Wave Terrestrial Systems: Estimating Statistics And Extremes, Jinwen Liu, David W. Matolak, Ismail Güvenç, Hani Mehrpouyan

Faculty Publications

Tropospheric attenuations can be significant in the millimeter wave (mmWave) frequency bands; hence, accurate prediction modeling of tropospheric attenuation is important for reliable mmWave communication. Several models have been established by the International Telecommunication Union (ITU), yet estimation accuracy is limited due to the large spatial scales used for model input parameters. In this paper, we address this and apply local precipitation data to analyze tropospheric attenuation statistics and compare to results when using ITU regional input rain data. Specifically, tropospheric attenuation is predicted via simulations using the ITU method at 30, 60, and 90 GHz in four distinct geographic …

Scalable Deeper Graph Neural Networks For High-Performance Materials Property Prediction, Sadman Sadeed Omee, Steph-Yves Louis, Nihang Fu, Lai Wei, Sourin Dey, Rongzhi Dong, Qinyang Li, Jianjun Hu

Faculty Publications

Machine-learning-based materials property prediction models have emerged as a promising approach for new materials discovery, among which the graph neural networks (GNNs) have shown the best performance due to their capability to learn high-level features from crystal structures. However, existing GNN models suffer from their lack of scalability, high hyperparameter tuning complexity, and constrained performance due to over-smoothing. We propose a scalable global graph attention neural network model DeeperGATGNN with differentiable group normalization (DGN) and skip connections for high-performance materials property prediction. Our systematic benchmark studies show that our model achieves the state-of-the-art prediction results on five out of six …

Chiral Liquid Crystal Microdroplets For Sensing Phospholipid Amphiphiles, Sepideh Norouzi, Jose A. Martinez Gonzalez, Monirosadat Sadati

Faculty Publications

Designing simple, sensitive, fast, and inexpensive readout devices to detect biological molecules and biomarkers is crucial for early diagnosis and treatments. Here, we have studied the interaction of the chiral liquid crystal (CLC) and biomolecules at the liquid crystal (LC)-droplet interface. CLC droplets with high and low chirality were prepared using a microfluidic device. We explored the reconfiguration of the CLC molecules confined in droplets in the presence of 1,2-diauroyl-sn-glycero3-phosphatidylcholine (DLPC) phospholipid. Cross-polarized optical microscopy and spectrometry techniques were employed to monitor the effect of droplet size and DLPC concentration on the structural reorganization of the CLC molecules. Our results …

Chiral Liquid Crystal Microdroplets For Sensing Phospholipid Amphiphiles, Sepideh Norouzi, Jose A. Martinez Gonzalez, Monirosadat (Sanaz) Sadati

Faculty Publications

Designing simple, sensitive, fast, and inexpensive readout devices to detect biological molecules and biomarkers is crucial for early diagnosis and treatments. Here, we have studied the interaction of the chiral liquid crystal (CLC) and biomolecules at the liquid crystal (LC)-droplet interface. CLC droplets with high and low chirality were prepared using a microfluidic device. We explored the reconfiguration of the CLC molecules confined in droplets in the presence of 1,2-diauroyl-sn-glycero3-phosphatidylcholine (DLPC) phospholipid. Cross-polarized optical microscopy and spectrometry techniques were employed to monitor the effect of droplet size and DLPC concentration on the structural reorganization of the CLC molecules. Our results …

A Dual-Band Filtering Structure For Highly Selective Reconfigurable Bandpass Filter And Filtering Balun, Jinqun Ge, Guoan Wang

Faculty Publications

This article proposes a filtering structure consisting of two half-wavelength resonators and two open-stub loaded resonators, which generates two third-order passbands. Multiple transmission zeros are introduced by the newly developed coupling scheme, resulting in extremely sharp roll-off desirable for highly selective filters. The proposed structure is applied to design a PIN-diodes switch-controlled reconfigurable dual-band bandpass filter (BPF) with four-state filtering responses: both passbands ON, both passbands OFF, high-frequency passband ON, and low-frequency passband ON. Stepped-impedance open stubs and one-end-grounded coupled lines are studied and employed in the design to suppress unwanted responses. In addition, two filtering structures are placed symmetrically …

Audio-Based Wildfire Detection On Embedded Systems, Hung-Tien Huang, Austin Downey, Jason D. Bakos

Faculty Publications

The occurrence of wildfires often results in significant fatalities. As wildfires are notorious for their high speed of spread, the ability to identify wildfire at its early stage is essential in quickly obtaining control of the fire and in reducing property loss and preventing loss of life. This work presents a machine learning wildfire detecting data pipeline that can be deployed on embedded systems in remote locations. The proposed data pipeline consists of three main steps: audio preprocessing, feature engineering, and classification. Experiments show that the proposed data pipeline is capable of detecting wildfire effectively with high precision and is …

Audio-Based Wildfire Detection On Embedded Systems, Hung-Tien Huang, Austin Downey, Jason D. Bakos

Faculty Publications

The occurrence of wildfires often results in significant fatalities. As wildfires are notorious for their high speed of spread, the ability to identify wildfire at its early stage is essential in quickly obtaining control of the fire and in reducing property loss and preventing loss of life. This work presents a machine learning wildfire detecting data pipeline that can be deployed on embedded systems in remote locations. The proposed data pipeline consists of three main steps: audio preprocessing, feature engineering, and classification. Experiments show that the proposed data pipeline is capable of detecting wildfire effectively with high precision and is …

Editorial For Gels 6th Anniversary Special Issue, Esmaiel Jabbari, Gulden Camci-Unal

Faculty Publications

No abstract provided.

Editorial For Gels 6th Anniversary Special Issue, Esmaiel Jabbari, Gulden Camci-Unal

Faculty Publications

Note: In lieu of an abstract, this is an excerpt from the first page.

This Special Issue celebrates many outstanding quality papers published in Gels over the past six years since its first issue was published in 2015 [...]

Antiviral Polymers: A Review, Ali Akbari, Ashkan Bigham, Vahid Rahimkhoei, Sina Sharifi, Esmaiel Jabbari

Faculty Publications

Polymers, due to their high molecular weight, tunable architecture, functionality, and buffering effect for endosomal escape, possess unique properties as a carrier or prophylactic agent in preventing pandemic outbreak of new viruses. Polymers are used as a carrier to reduce the minimum required dose, bioavailability, and therapeutic effectiveness of antiviral agents. Polymers are also used as multifunctional nanomaterials to, directly or indirectly, inhibit viral infections. Multifunctional polymers can interact directly with envelope glycoproteins on the viral surface to block fusion and entry of the virus in the host cell. Polymers can indirectly mobilize the immune system by activating macrophages and …

Beyond Profitable Shifts To Green Energies, Towards Energy Sustainability, Farboud Khatami, Erfan Goharian

Faculty Publications

The traditional carbon-based approach towards sustainability has long caused the concepts of green and sustainable energies to be used interchangeably. Recent studies have tried to advance this archaic view by considering more aspects of sustainability. However, almost all major studies have been concerned with only the economic and environmental aspects of electricity generation, whereas the concept of sustainability is beyond these two criteria. In this paper, we seek to provide a methodology for a more comprehensive definition of electricity generation sustainability based on the lessons learned from previous studies and additional metrics suggested by them. The main characteristics of select …

Atmospheric Plasma Spraying To Fabricate Metal-Supported Solid Oxide Fuel Cells With Open-Channel Porous Metal Support, Jie Lin, Haixia Li, Wanhua Wang, Peng Qiu, Greg Tao, Kevin Huang, Fanglin Chen

Faculty Publications

Metal-supported solid oxide fuel cells (MS-SOFCs) have been fabricated by applying phase-inversion tape-casting and atmospheric plasma spraying (APS). The effect of the binder amount of the phase-inversion slurries on the microstructure development of the 430L stainless steel metal support was investigated. The pore structures, the viscosity of the slurry, porosity and permeability of the as-prepared metal supports are significantly influenced by the amount of the binder. NiO–scandia-stabilized zirconia (ScSZ) anode, ScSZ electrolyte and La_0.6Sr_0.4Co_0.2Fe0.8O_3−δ (LSCF) cathode layers were consecutively deposited on the metal support with an ideal microstructure by APS process. The effect …