Computer Engineering Commons^™

How Do Social Network Models Compare To All-To-All Models For Forecasting Tuberculosis Epidemics? A Mathematical Modeling Study, Masabho Peter Milali, Hae-Young Kim, George Corliss, Anna Bershteyn

Electrical and Computer Engineering Faculty Research and Publications

Background. Mathematical models guide tuberculosis (TB) target-setting, yet most assume homogeneous “all-to-all” mixing. We compared projected intervention impacts between an all-to-all compartmental model and a Barabási–Albert (BA) scale‑free social network model under otherwise identical disease assumptions.

Methods. We calibrated transmission parameters so both models produced similar baseline trends, then introduced vaccination (coverage 30–70%; efficacy 80–95%) and treatment (20–50% increases in recovery) after a 400‑day burn‑in. Outcomes were assessed 300 days post‑intervention.

Results. Under 60% coverage, increasing vaccine efficacy from 80% to 95% yielded smaller projected reductions in active TB with the network model than with all‑to‑all mixing. Treatment improvements showed …

Applying Large Language Models For Surgical Case Length Prediction, Adhitya Ramamurthi, Bhabishya Neupane, Priya Deshpande, Ryan Hanson, Srujan Vegesna, Deborah Cray, Bradley H. Crotty, Melek Somai, Kellie R. Brown, Sachin S. Pawar, Bradley Taylor, Anai N. Kothari

Electrical and Computer Engineering Faculty Research and Publications

Importance Accurate prediction of surgical case duration is critical for operating room (OR) management, as inefficient scheduling can lead to reduced patient and surgeon satisfaction while incurring considerable financial costs.

Objective To evaluate the feasibility and accuracy of large language models (LLMs) in predicting surgical case length using unstructured clinical data compared to existing estimation methods.

Design, Setting, and Participants This was a retrospective study analyzing elective surgical cases performed between January 2017 and December 2023 at a single academic medical center and affiliated community hospital ORs. Analysis included 125493 eligible surgical cases, with 1950 used for LLM fine-tuning and …

Heat-Pipe-Based Thermal Management System Design For A 250-Kw Gan-Based Integrated Modular Motor Drive, Seyed Iman Hosseini Sabzevari, Salar Koushan, Armin Ebrahimian, Towhid Islam Chowdhury, Nathan Weise, Ayman El-Refaie

Electrical and Computer Engineering Faculty Research and Publications

Integrated modular motor drive (IMMD) is an effective approach for realizing high-efficiency, high-power-density, and fault-tolerant electric machines. However, designing an efficient thermal management system (TMS) for the motor drive becomes a challenge, particularly due to space constraints. This article presents the design of a TMS based on 3-mm heat pipes for a 250-kW IMMD intended for aviation applications. The power electronics module is simulated using PLECS software where an electrothermal analysis is conducted. A simplified thermal resistance model of the system is developed to estimate the die junction temperature of gallium nitride (GaN) semiconductors. The performance of the proposed TMS …

Performance Enhancement In Rewound Industrial Retrofit Solutions For Five- And Six-Phase Permanent Magnet Assisted Synchronous Reluctance Machines, Kotb B. Tawfiq, Ayman M. El-Refaie, Peter Sergeant, Hatem Zeineldin, Ahmed Al-Durra, Ehab F. El-Sadaany

Electrical and Computer Engineering Faculty Research and Publications

This paper investigates upgrading aging three-phase Permanent Magnet Assisted Synchronous Reluctance Machines (PMaSynRMs) into multiphase configurations—specifically six- and five-phase windings—without modifying the existing stator or rotor laminations. This retrofit supports circular economic principles by extending machine life and reducing material waste and cost. Four configurations are examined: the original three-phase winding, asymmetrical six-phase winding, symmetrical six-phase winding, and five-phase winding. The feasibility of rewinding existing three-phase stators is explored for different slot/pole combinations. Balanced rewound five-phase windings are feasible only when the stator's slot/pole ratio is greater than or equal to 9. Both symmetrical and asymmetrical rewound six-phase windings are …

Optimization-Based Distributed Controller For Multi-Agents System In Microgrid Secondary Control, Fahad S. Alshammari, Ayman El-Refaie, Saleh Alyahya, Sheroz Khan

Electrical and Computer Engineering Faculty Research and Publications

Micro-grids function to connect to power system power produced by the renewable energy resources. In islanded micro-grids, grid-forming units collaborate to maintain the micro-grids voltage and frequency by utilizing droop control technique that includes primary, secondary and tertiary levels. Secondary control intervenes to improve power sharing and restore voltage and frequency to their nominal levels. However, the conventional droop control applied to a grid with mismatched line parameters experiences a trade-off between reactive power sharing and voltage regulations. This paper applies real-time trajectory tracking convex optimization to ensure by communicating power sharing between units in a consensus topology. The optimization …

Advanced Day-Ahead Scheduling Of Hvac Demand Response Control Using Novel Strategy Of Q-Learning, Model Predictive Control, And Input Convex Neural Networks, Rahman Heidarykiany, Cristinel Ababei

Electrical and Computer Engineering Faculty Research and Publications

In this paper, we present a Q-Learning optimization algorithm for smart home HVAC systems. The proposed algorithm combines new convex deep neural network models with model predictive control (MPC) techniques. More specifically, new input convex long short-term memory (ICLSTM) models are employed to predict dynamic states in an MPC optimal control technique integrated within a Q-Learning reinforcement learning (RL) algorithm to further improve the learned temporal behaviors of nonlinear HVAC systems. As a novel RL approach, the proposed algorithm generates day-ahead HVAC demand response (DR) signals in smart homes that optimally reduce and/or shift peak energy usage, reduce electricity costs, …

Understanding The Breadth And Impact Of The Ias [President’S Message], Ayman El-Refaie

Electrical and Computer Engineering Faculty Research and Publications

No abstract provided.

Understanding The Breadth And Impact Of The Ias [Presidents Message], Ayman El-Refaie

Electrical and Computer Engineering Faculty Research and Publications

No abstract provided.

Heat-Pipe-Based Thermal Management System Design For A 250 Kw Gan-Based Integrated Modular Motor Drive, Seyed Iman Hosseini Sabzevari, Salar Koushan, Armin Ebrahimian, Towhid Islam Chowdhury, Nathan Weise, Ayman El-Refaie

Electrical and Computer Engineering Faculty Research and Publications

Integrated modular motor drive (IMMD) is an effective approach for realizing high-efficiency, high-power-density, and fault-tolerant electric machines. However, designing an efficient thermal management system (TMS) for the motor drive becomes a challenge, particularly due to space constraints. This article presents the design of a TMS based on 3-mm heat pipes for a 250-kW IMMD intended for aviation applications. The power electronics module is simulated using PLECS software where an electrothermal analysis is conducted. A simplified thermal resistance model of the system is developed to estimate the die junction temperature of gallium nitride (GaN) semiconductors. The performance of the proposed TMS …

Industry Applications Society And Conferences [President’S Message], Ayman El-Refaie

Electrical and Computer Engineering Faculty Research and Publications

No abstract provided.

Development And Validation Of An Artificial Intelligence System For Surgical Case Length Prediction, Adhitya Ramamurthi, Bhabishya Neupane, Priya Deshpande, Ryan Hanson, Kellie R. Brown, Kathleen K. Christians, Douglas B. Evans, Anai N. Kothari

Electrical and Computer Engineering Faculty Research and Publications

Background

Accurate case length estimation is a vital part of optimizing operating room use; however, significant inaccuracies exist with current solutions. The purpose of this study was to develop and validate an artificial intelligence system for improved surgical case length prediction by applying natural language processing and machine-learning methods.

Methods

All inpatient elective surgical cases longer than 30 minutes completed between 2017 and 2023 at a single, quaternary care hospital were considered. Data were split into training, test, and hold-out validation for model training and testing. Linear regression, CategoricalBoost, and feed-forward neural network each were trained and used embeddings created …

An Iterative Shifting Disaggregation Algorithm For Multi-Source, Irregularly Sampled, And Overlapped Time Series, Colin O. Quinn, Ronald H. Brown, George F. Corliss, Richard J. Povinelli

Electrical and Computer Engineering Faculty Research and Publications

Accurate time series forecasting often requires higher temporal resolution than that provided by available data, such as when daily forecasts are needed from monthly data. Existing temporal disaggregation techniques, which typically handle only single, uniformly sampled time series, have limited applicability in real-world, multi-source scenarios. This paper introduces the Iterative Shifting Disaggregation (ISD) algorithm, designed to process and disaggregate time series derived from sensor-sourced low-frequency measurements, transforming multiple, nonuniformly sampled sensor data streams into a single, coherent high-frequency signal. ISD operates in an iterative, two-phase process: a prediction phase that uses multiple linear regression to generate high-frequency series from low-frequency …

Computer Vision-Based Framework For Data Extraction From Heterogeneous Financial Tables: A Comprehensive Approach To Unlocking Financial Insights, Iftakhar Ali Khandokar, Priya Deshpande

Electrical and Computer Engineering Faculty Research and Publications

Information extraction from financial document images is crucial in computer vision and NLP, as financial data often exists in image or PDF format, enabling organizations to analyze and make informed business decisions using OCR advancements. The table contents of financial document images are one of the prominent structures to confine important portions of data of the document and many Deep learning-based methods have been proposed to detect Table regions inside document images. The shortcomings of the current approach are that it is bounded within the detection of the table region and struggles in cases such as handling different layouts and …

Uninterruptable Non-Isolated Integrated Power Electronics Converter (Unipec) For Commercial Truck Auxiliary Power Unit, Pouya Zolfi, Ahmad Alzahrani, Ayman M. El-Refaie

Electrical and Computer Engineering Faculty Research and Publications

A low component count high efficiency and high-power density three-port DC-DC converter named UNIPEC is introduced for commercial trucks auxiliary power unit (APU) application. The proposed UNIPEC is a single magnetic core topology which is achieved by integration and modification of switched capacitor technology and bidirectional multi-level DC-DC converter. With peak simulation efficiency of 97.3% and power density of 12.6 kW/L, proposed unit is an attractive candidate for conventional and refrigerated freight transportation applications. Comparison study and low power experimental validation results are presented. Reliability analysis of UNIPEC is also conducted and mean time to failure (MTTF) reported. A 1 …

Retrofitting Of A Chevy-Bolt Ipm Motor With A Pm-Assisted Synchronous Reluctance Rotor Enabled With A Blend Of Heavy-Rare-Earth-Free Neodymium And Ferrite Magnets, Praveen Kumar, Ali Hussain Al-Qarni, Robin Wilson, Ayman M. El-Refaie

Electrical and Computer Engineering Faculty Research and Publications

In this paper, the rotor of an existing Chevy-Bolt Interior Permanent Magnet (IPM) motor, consisting of rare-earth permanent magnet based rotor, is replaced and retrofitted with a PM-Assisted Synchronous Reluctance Machine (PMASynRM) rotor enabled with a combination of Heavy-Rare-Earth (Dy-free) Neodymium and Ferrite magnets. For this, a multi-objective design optimization of the PMASynRM rotor, aimed at minimizing rare-earth magnet volume and permanent demagnetization risk of magnets, while achieving performance comparable to the Chevy-Bolt motor across its operating speed range, is performed. This is followed by mechanical stress optimization of rotor lamination at maximum speed, to ensure its structural integrity. The …

Analysis Of Multivariable Sensor Responses To Multi-Analyte Gas Samples In The Presence Of Interferents And Humidity, Sakin Sarwar Satter, Florian Bender, Nicholas Post, Antonio J. Ricco, Fabien Josse

Electrical and Computer Engineering Faculty Research and Publications

This work presents an adaptive sensor signal-processing approach to enable quantification, using a single gas sensor or a small sensor array, of multianalyte mixtures of aromatic hydrocarbons in the presence of various interferents and humidity for environmental-monitoring applications. Dynamic sensor responses are analyzed by extracting multivariable sensing parameters to provide necessary sensitivity and selectivity. This is achieved by integrating the Levenberg–Marquardt-modified, exponentially weighted, recursive-least-squares-estimation (LM-modified EW-RLSE) algorithm and principal-component analysis (PCA). Achieving measured detection limits as low as 3 μg/L (≤1 ppm by volume) for 6 target analytes, the system exhibits excellent PCA cluster separation for all analytes in the …

Improved Test Fixture For Collecting Microcontact Performance And Reliability Data, Turja Nandy, Ronald A. Coutu Jr., Rafee Mahbub

Electrical and Computer Engineering Faculty Research and Publications

Microelectromechanical systems (MEMS) ohmic contact switches are considered to be a promising candidate for wireless communication applications. The longevity of MEMS switches is directly related to the reliability and performance of microcontacts. In this work, an improved microcontact test fixture with high actuation rates (KHz) and highly precise position control (nm) and force (nN) control was developed. Here, we collected microcontact performance data from initial contact tests (ICT) and microcontact reliability data from cold switched tests (CST). To perform these tests with our test fixture, we fabricated MEMS microcontact test structures with relatively high Young’s modulus electroplated Nickel (Ni)-based, fixed–fixed …

Hvac Energy Cost Minimization In Smart Grids: A Cloud-Based Demand Side Management Approach With Game Theory Optimization And Deep Learning, Rahman Heidarykiany, Cristinel Ababei

Electrical and Computer Engineering Faculty Research and Publications

In this paper, we present a novel cloud-based demand side management (DSM) optimization approach for the cost reduction of energy usage in heating, ventilation and air conditioning (HVAC) systems in residential homes at the district level. The proposed approach achieves optimization through scheduling of HVAC energy usage within permissible bounds set by house users. House smart home energy management (SHEM) devices are connected to the utility/aggregator via a dedicated communication network that is used to enable DSM. Each house SHEM can predict its own HVAC energy usage for the next 24 h using minimalistic deep learning (DL) prediction models. These …

Critical Risk Assessment, Diagnosis, And Survival Analysis Of Breast Cancer, Shamiha Binta Manir, Priya Deshpande

Electrical and Computer Engineering Faculty Research and Publications

Breast cancer is the most prevalent type of cancer in women. Risk factor assessment can aid in directing counseling regarding risk reduction and breast cancer surveillance. This research aims to (1) investigate the relationship between various risk factors and breast cancer incidence using the BCSC (Breast Cancer Surveillance Consortium) Risk Factor Dataset and create a prediction model for assessing the risk of developing breast cancer; (2) diagnose breast cancer using the Breast Cancer Wisconsin diagnostic dataset; and (3) analyze breast cancer survivability using the SEER (Surveillance, Epidemiology, and End Results) Breast Cancer Dataset. Applying resampling techniques on the training dataset …

Texture-Based Classification To Overcome Uncertainty Between Covid-19 And Viral Pneumonia Using Machine Learning And Deep Learning Techniques, Omar Farghaly, Priya Deshpande

Electrical and Computer Engineering Faculty Research and Publications

The SARS-CoV-2 virus, responsible for COVID-19, often manifests symptoms akin to viral pneumonia, complicating early detection and potentially leading to severe COVID pneumonia and long-term effects. Particularly affecting young individuals, the elderly, and those with weakened immune systems, the accurate classification of COVID-19 poses challenges, especially with highly dimensional image data. Past studies have faced limitations due to simplistic algorithms and small, biased datasets, yielding inaccurate results. In response, our study introduces a novel classification model that integrates advanced texture feature extraction methods, including GLCM, GLDM, and wavelet transform, within a deep learning framework. This innovative approach enables the effective …

Ai-Assisted Reconfiguration Of Battery Packs For Cell Balancing To Extend Driving Runtime, Yuqin Weng, Cristinel Ababei

Electrical and Computer Engineering Faculty Research and Publications

State of charge (SoC) cell balancing is one of the most important roles of battery management systems (BMS). The performance and lifespan of a battery pack can be significantly degraded and reduced by the presence of imbalance in cells SoC. Recently, we have shown that using a machine learning driven battery pack reconfiguration technique based on a network of controllable switches, one can periodically change the battery pack topology to effectively achieve better cell SoC equalization. As a result, the driving runtime achieved with a better balanced battery pack is increased. In this paper, we build on these promising results …

Cross-Temporal Hierarchical Forecast Reconciliation Of Natural Gas Demand, Colin O. Quinn, George F. Corliss, Richard J. Povinelli

Electrical and Computer Engineering Faculty Research and Publications

Local natural gas distribution companies (LDCs) require accurate demand forecasts across various time periods, geographic regions, and customer class hierarchies. Achieving coherent forecasts across these hierarchies is challenging but crucial for optimal decision making, resource allocation, and operational efficiency. This work introduces a method that structures the gas distribution system into cross-temporal hierarchies to produce accurate and coherent forecasts. We apply our method to a case study involving three operational regions, forecasting at different geographical levels and analyzing both hourly and daily frequencies. Trained on five years of data and tested on one year, our model achieves a 10% reduction …

Data-Integrity Aware Stochastic Model For Cascading Failures In Power Grids, Rezoan Ahmed Shuvro, Pankaz Das, Jamir Shariar Jyoti, Joana M. Abreu, Majeed M. Hayat

Electrical and Computer Engineering Faculty Research and Publications

The reliable operation of power grids during cascading failures is heavily dependent on the interdependencies between the power grid components and the supporting communications and control networks. Moreover, the system operators' expertise in dealing with cascading failures can play a pivotal role during contingencies. In this paper, a dynamical probabilistic model is developed based on Markov-chains, which captures the dynamics of cascading failures in the power grid. Specifically, a previously developed Markov-chain based model is extended to capture the trade-off between the benefits of having a robust communication infrastructure and its vulnerability from data integrity (e.g., cyber-attacks). State-space reduction of …

A Comprehensive Characterization Of Hollow Conductor Additively Manufactured Coils And Thermal Management System For A 250 Kw Spm Machine, Sina Vahid, Salar Koushan, Towhid Islam Chowdhury, Ayman El-Refaie

Electrical and Computer Engineering Faculty Research and Publications

This paper provides an extensive and comprehensive analysis on characterization of additively manufactured coils and heat pipe based liquid cooling thermal management system, for a 250 kW and 5,000 rpm machine. AC and DC electrical tests at 140 Arms and 70 ADC are conducted on AlSi10Mg additively manufactured coils to extract their thermal and electrical characteristics. Heat pipes and liquid cooling test setups are described and discussed. The thermal discussion is concluded by experimental results showing the effectiveness of the thermal management system proposed in this paper. FEA are performed on the coils using ANSYS Maxwell to accurately predict the …

Mitigating Cascading Failures In Power Grids Via Markov Decision-Based Load-Shedding With Dc Power Flow Model, Pankaz Das, Rezoan Ahmed Shuvro, Kassie Povinelli, Francesco Sorrentino, Majeed M. Hayat

Electrical and Computer Engineering Faculty Research and Publications

Despite the reliability of modern power systems, large blackouts due to cascading failures (CFs) do occur in power grids with enormous economic and societal costs. In this article, CFs in power grids are theoretically modeled proposing a Markov decision process (MDP) framework with the aim of developing optimal load-shedding (LS) policies to mitigate CFs. The embedded Markov chain of the MDP, established earlier to capture the dynamics of CFs, features a reduced state-space and state-dependent transition probabilities. We introduce appropriate actions affecting the dynamics of CFs and associated costs. Optimal LS policies are computed that minimize the expected cumulative cost …

Comparative Study And Design Optimization Of A Dual-Mechanical-Port Electric Machine For Hybrid Electric Vehicle Applications, Hao Chen, Ayman M. El-Refaie, Yuefei Zuo, Shun Cai, Libing Cao, Christopher H. T. Lee

Electrical and Computer Engineering Faculty Research and Publications

A new dual-mechanical-port (DMP) electric machine for hybrid electric vehicle applications, particularly in the power-split continuously variable transmission systems, is proposed in this paper. In order to comprehensively and quantitatively evaluate the pros and cons of the proposed machine, a comparative study of four DMP electric machines with different topologies is conducted. These four investigated DMP electric machines include a conventional DMP machine, a DMP machine with spoke-type permanent magnets, a DMP machine with reluctance rotor, and a DMP machine with open slots which is the proposed machine in this paper. Even though these four machines have similar topologies, they …

Supermodal Decomposition Of The Linear Swing Equation For Multilayer Networks, Kshitij Bhatta, Amirhossein Nazerian, Francesco Sorrentino, Majeed M. Hayat

Electrical and Computer Engineering Faculty Research and Publications

We study the swing equation in the case of a multilayer network in which generators and motors are modeled differently; namely, the model for each generator is given by second order dynamics and the model for each motor is given by first order dynamics. We also remove the commonly used assumption of equal damping coefficients in the second order dynamics. Under these general conditions, we are able to obtain a decomposition of the linear swing equation into independent modes describing the propagation of small perturbations. In the process, we identify symmetries affecting the structure and dynamics of the multilayer network …

Identification And Quantitation Of Aqueous Single- And Multianalyte Solutions Of The Isomers Ethylbenzene, M-, P-, And O-Xylene Using A Single Specifically Tailored Sensor Coating And Estimation Theory-Based Signal Processing, Nicholas Post, Florian Bender, Fabien Josse, Edwin E. Yaz, Antonio J. Ricco

Electrical and Computer Engineering Faculty Research and Publications

The isomer-specific detection and quantitation of m-, p-, and o-xylene and ethylbenzene, dissolved singly and as mixtures in aqueous solutions at concentrations from 100 to 1200 ppb by volume, is reported for a specifically designed polymer-plasticizer coating on a shear-horizontal surface acoustic wave (SH-SAW) device. The polystyrene-ditridecyl phthalate-blend coating was designed utilizing Hansen solubility parameters and considering the dipole moment and polarizability of the analytical targets and coating components to optimize the affinity of the sensor coating for the four chemical isomers. The two key coating sorption properties, sensitivity and response time constant, are determined by the …

Electrothermal Design Of A Gan-Based Axially Stator Iron-Mounted Fully Integrated Modular Motor Drive, Armin Ebrahimian, Waqar A. Khan, S. Iman Hosseini, Nathan Weise

Electrical and Computer Engineering Faculty Research and Publications

The concept of More Electric Aircraft (MEA) has gained a lot of attention from researchers recently. For such an application, two of the pivotal requirements are having a power dense and energy efficient propulsion system. To that end, in the design procedure of the electric motor and its drive system, high power density and efficiency over the entire operating range is the ultimate goal. Thus, the integration of the electric motor and drive system into a single unit has been introduced as an effective method to meet the design objectives. Therefore, this paper presents the design procedure of a module …

Low-Latency And Fresh Content Provision In Information-Centric Vehicular Networks, Shan Zhang, Junjie Li, Hongbin Luo, Jie Gao, Lian Zhao, Xuemin Sherman Shen

Electrical and Computer Engineering Faculty Research and Publications

In this paper, the content service provision of information-centric vehicular networks (ICVNs) is investigated from the aspect of mobile edge caching, considering the dynamic driving-related context information. To provide up-to-date information with low latency, two schemes are designed for cache update and content delivery at the roadside units (RSUs). The roadside unit centric (RSUC) scheme decouples cache update and content delivery through bandwidth splitting, where the cached content items are updated regularly in a round-robin manner. The request adaptive (ReA) scheme updates the cached content items upon user requests with certain probabilities. The performance of both proposed schemes are analyzed, …