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Electrical and Computer Engineering Faculty Research & Creative Works

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Full-Text Articles in Engineering

A Novel Microgrid Fault Detection And Classification Method Using Maximal Overlap Discrete Wavelet Packet Transform And An Augmented Lagrangian Particle Swarm Optimization-Support Vector Machine, Masoud Ahmadipour, Muhammad Murtadha Othman, Rui Bo, Zainal Salam, Hussein Mohammed Ridha, Kamrul Hasan Nov 2022

A Novel Microgrid Fault Detection And Classification Method Using Maximal Overlap Discrete Wavelet Packet Transform And An Augmented Lagrangian Particle Swarm Optimization-Support Vector Machine, Masoud Ahmadipour, Muhammad Murtadha Othman, Rui Bo, Zainal Salam, Hussein Mohammed Ridha, Kamrul Hasan

Electrical and Computer Engineering Faculty Research & Creative Works

In this paper, an intelligent method for fault detection and classification for a microgrid (MG) was proposed. The idea was based on the combination of three computational tools: signal processing using the maximal overlap discrete wavelet packet transform (MODWPT), parameter optimization by the augmented Lagrangian particle swarm optimization (ALPSO), and machine learning using the support vector machine (SVM). The MODWPT was applied to preprocess half cycle of the post-fault current samples measured at both ends of feeders. The wavelet coefficients derived from the MODWPT were statistically evaluated using the mean, standard deviation, energy, skewness, kurtosis, logarithmic energy entropy, max, min ...


Building Marginal Pattern Library With Unbiased Training Dataset For Enhancing Model-Free Load-Ed Mapping, Qiwei Zhang, Fangxing Li, Wei Feng, Xiaofei Wang, Linquan Bai, Rui Bo Feb 2022

Building Marginal Pattern Library With Unbiased Training Dataset For Enhancing Model-Free Load-Ed Mapping, Qiwei Zhang, Fangxing Li, Wei Feng, Xiaofei Wang, Linquan Bai, Rui Bo

Electrical and Computer Engineering Faculty Research & Creative Works

Input-output mapping for a given power system problem, such as loads versus economic dispatch (ED) results, has been demonstrated to be learnable through artificial intelligence (AI) techniques, including neural networks. However, the process of identifying and constructing a comprehensive dataset for the training of such input-output mapping remains a challenge to be solved. Conventionally, load samples are generated by a pre-defined distribution, and then ED is solved based on those load samples to form training datasets, but this paper demonstrates that such dataset generation is biased regarding load-ED mapping. The marginal unit and line congestion (i.e., marginal pattern) exhibit ...


An Intelligent Distributed Ledger Construction Algorithm For Iot, Charles Rawlins, Jagannathan Sarangapani Jan 2022

An Intelligent Distributed Ledger Construction Algorithm For Iot, Charles Rawlins, Jagannathan Sarangapani

Electrical and Computer Engineering Faculty Research & Creative Works

Blockchain is the next generation of secure data management that creates near-immutable decentralized storage. Secure cryptography created a niche for blockchain to provide alternatives to well-known security compromises. However, design bottlenecks with traditional blockchain data structures scale poorly with increased network usage and are extremely computation-intensive. This made the technology difficult to combine with limited devices, like those in Internet of Things networks. In protocols like IOTA, replacement of blockchain's linked-list queue processing with a lightweight dynamic ledger showed remarkable throughput performance increase. However, current stochastic algorithms for ledger construction suffer distinct trade-offs between efficiency and security. This work ...


Characteristic Mode Analysis Prediction And Guidance Of Electromagnetic Coupling Measurements To A Uav Model, Mohamed Z. M. Hamdalla, Benjamin Bissen, James D. Hunter, Yuanzhuo Liu, Victor Khilkevich, Daryl G. Beetner, Anthony N. Caruso, Ahmed M. Hassan Jan 2022

Characteristic Mode Analysis Prediction And Guidance Of Electromagnetic Coupling Measurements To A Uav Model, Mohamed Z. M. Hamdalla, Benjamin Bissen, James D. Hunter, Yuanzhuo Liu, Victor Khilkevich, Daryl G. Beetner, Anthony N. Caruso, Ahmed M. Hassan

Electrical and Computer Engineering Faculty Research & Creative Works

In this work, we study the current coupled to a simplified Unmanned Aerial Vehicle (UAV) model using a dual computational and experimental approach. The simplified surrogate structure reduced the computational burden and facilitated the experimental measurement of the coupled currents. For a practical system, a wide range of simulations and measurements must be performed to analyze the induced current variations with respect to properties of the incident excitation waveform, such as the frequency, angle of incidence, and polarization. To simplify this analysis, Characteristic Mode Analysis (CMA) was used to compute the eigen-currents of the UAV model and predict where and ...


A Deep Learning Approach To Design And Discover Sustainable Cementitious Binders: Strategies To Learn From Small Databases And Develop Closed-Form Analytical Models, Taihao Han, Sai Akshay Ponduru, Rachel Cook, Jie Huang, Gaurav Sant, Aditya Kumar Jan 2022

A Deep Learning Approach To Design And Discover Sustainable Cementitious Binders: Strategies To Learn From Small Databases And Develop Closed-Form Analytical Models, Taihao Han, Sai Akshay Ponduru, Rachel Cook, Jie Huang, Gaurav Sant, Aditya Kumar

Electrical and Computer Engineering Faculty Research & Creative Works

To reduce the energy-intensity and carbon footprint of Portland cement (PC), the prevailing practice embraced by concrete technologists is to partially replace the PC in concrete with supplementary cementitious materials [SCMs: geological materials (e.g., limestone); industrial by-products (e.g., fly ash); and processed materials (e.g., calcined clay)]. Chemistry and content of the SCM profoundly affect PC hydration kinetics; which, in turn, dictates the evolutions of microstructure and properties of the [PC + SCM] binder. Owing to the substantial diversity in SCMs' compositions-plus the massive combinatorial spaces, and the highly nonlinear and mutually-interacting processes that arise from SCM-PC interactions-state-of-the-art computational ...


An Interleaved High Step-Up Dc-Dc Converter Based On Integration Of Coupled Inductor And Built-In-Transformer With Switched-Capacitor Cells For Renewable Energy Applications, Ramin Rahimi, Saeed Habibi, Mehdi Ferdowsi, Pourya Shamsi Jan 2022

An Interleaved High Step-Up Dc-Dc Converter Based On Integration Of Coupled Inductor And Built-In-Transformer With Switched-Capacitor Cells For Renewable Energy Applications, Ramin Rahimi, Saeed Habibi, Mehdi Ferdowsi, Pourya Shamsi

Electrical and Computer Engineering Faculty Research & Creative Works

This paper proposes an interleaved high step-up DC-DC converter with the coupled inductor (CI) and built-in transformer (BIT) for renewable energy applications. Two double-winding (2W) CIs and one triple-winding (3W) BIT are integrated with the switched-capacitor (SC) voltage multiplier cells (VMCs) to achieve high-voltage gains without extreme duty cycles. The CIs and BIT turns-ratios provide two other degrees of freedom -- in addition to the duty cycle -- to adjust the voltage gain that leads to increased design flexibility. The diodes turn off naturally under the zero-current switching (ZCS) conditions because their current falling rates are controlled by the leakage inductances of ...


Forecasting Nodal Price Difference Between Day-Ahead And Real-Time Electricity Markets Using Long-Short Term Memory And Sequence-To-Sequence Networks, Ronit Das, Rui Bo, Haotian Chen, Waqas Ur Rehman, Donald C. Wunsch Jan 2022

Forecasting Nodal Price Difference Between Day-Ahead And Real-Time Electricity Markets Using Long-Short Term Memory And Sequence-To-Sequence Networks, Ronit Das, Rui Bo, Haotian Chen, Waqas Ur Rehman, Donald C. Wunsch

Electrical and Computer Engineering Faculty Research & Creative Works

Price forecasting is at the center of decision making in electricity markets. Much research has been done in forecasting energy prices for a single market while little research has been reported on forecasting price difference between markets, which presents higher volatility and yet plays a critical role in applications such as virtual trading. To this end, this paper takes the first attempt at it and employs novel deep learning architecture with Bidirectional Long-Short Term Memory (LSTM) units and Sequence-to-Sequence (Seq2Seq) architecture to forecast nodal price difference between day-ahead and real-time markets. In addition to value prediction, these deep learning architectures ...


Machine Learning Identifies Liquids Employing A Simple Fiber-Optic Tip Sensor, Wassana Naku, Chen Zhu, Anand K. Nambisan, Rex E. Gerald, Jie Huang Nov 2021

Machine Learning Identifies Liquids Employing A Simple Fiber-Optic Tip Sensor, Wassana Naku, Chen Zhu, Anand K. Nambisan, Rex E. Gerald, Jie Huang

Electrical and Computer Engineering Faculty Research & Creative Works

We proposed an extremely simple fiber-optic tip sensor system to identify liquids by combining their corresponding droplet evaporation events with analyses using machine learning techniques. Pendant liquid droplets were suspended from the cleaved endface of a single-mode fiber during the experiment. The optical fiber-droplet interface and the droplet-air interface served as two partial reflectors of an extrinsic Fabry-Perot interferometer (EFPI) with a liquid droplet cavity. As the liquid pendant droplet evaporated, its length diminished. A light source can be used to observe the effective change in the net reflectivity of the optical fiber sensor system by observing the resulting optical ...


Dc Bus Voltage Selection For A Grid-Connected Low-Voltage Dc Residential Nanogrid Using Real Data With Modified Load Profiles, Saeed Habibi, Ramin Rahimi, Mehdi Ferdowsi, Pourya Shamsi Nov 2021

Dc Bus Voltage Selection For A Grid-Connected Low-Voltage Dc Residential Nanogrid Using Real Data With Modified Load Profiles, Saeed Habibi, Ramin Rahimi, Mehdi Ferdowsi, Pourya Shamsi

Electrical and Computer Engineering Faculty Research & Creative Works

This study examines various low voltage levels applied to a direct current residential nanogrid (DC-RNG) with respect to the efficiency and component cost of the system. Due to the significant increase in DC-compatible loads, on-site Photovoltaic (PV) generation, and local battery storage, DC distribution has gained considerable attention in buildings. To provide an accurate evaluation of the DC-RNG's efficiency and component cost, a one-year load profile of a conventional AC-powered house is considered, and AC appliances' load profiles are scaled to their equivalent available DC appliances. Based on the modified load profiles, proper wiring schemes, converters, and protection devices ...


Open-Ended Hollow Coaxial Cable Resonator Sensor, Jie Huang, Chen Zhu, Rex E. Gerald Ii Oct 2021

Open-Ended Hollow Coaxial Cable Resonator Sensor, Jie Huang, Chen Zhu, Rex E. Gerald Ii

Electrical and Computer Engineering Faculty Research & Creative Works

An open-ended hollow coaxial cable resonator probe configured to receive an aerosol sample for analysis. A metal post shorts the resonator's inner and outer conductors. A metal plate is spaced apart from an open end of the resonator by a dielectric layer that contains the received aerosol sample. Interrogator circuitry coupled to the resonator transmits an electromagnetic wave within the resonator and generates an electric field at the open end of the resonator. The interrogator circuitry is responsive to the generated electric field for determining a resonance frequency and an impedance of the resonator when the aerosol sample is ...


Evolutionary Algorithm-Based Adaptive Robust Optimization For Ac Security Constrained Unit Commitment Considering Renewable Energy Sources And Shunt Facts Devices, Aliasghar Baziar, Rui Bo, Misagh Dehghani Ghotbabadi, Mehdi Veisi, Waqas Ur Rehman Sep 2021

Evolutionary Algorithm-Based Adaptive Robust Optimization For Ac Security Constrained Unit Commitment Considering Renewable Energy Sources And Shunt Facts Devices, Aliasghar Baziar, Rui Bo, Misagh Dehghani Ghotbabadi, Mehdi Veisi, Waqas Ur Rehman

Electrical and Computer Engineering Faculty Research & Creative Works

An AC security constrained unit commitment (AC-SCUC) in the presence of the renewable energy sources (RESs) and shunt flexible AC transmission system (FACTS) devices is conventionally modeled as a deterministic optimization problem to minimize the operation cost of conventional generation units (CGUs) subject to AC optimal power flow (AC-OPF) equations, operation constraints of RESs, shunt FACTS devices, and CGUs. To cope with the uncertainties of load and RES generation, robust and stochastic optimization and linearized formulation have been used to achieve a sub-optimal solution. To arrive at a more optimal solution, an evolutionary algorithm-based adaptive robust optimization (EA-ARO) approach to ...


Developing Robust Bidding Strategy For Virtual Bidders In Day-Ahead Electricity Markets, Hossein Mehdipourpicha, Siyuan Wang, Rui Bo Sep 2021

Developing Robust Bidding Strategy For Virtual Bidders In Day-Ahead Electricity Markets, Hossein Mehdipourpicha, Siyuan Wang, Rui Bo

Electrical and Computer Engineering Faculty Research & Creative Works

Purely financial players without any physical assets can participate in day-Ahead electricity markets as virtual bidders. They can arbitrage the price difference between day-Ahead (DA) and real-Time (RT) markets to maximize profits. Virtual bidders encounter various monetary risks and uncertainties in their decision-making due to the high volatility of the price difference. Therefore, this paper proposes a max-min two-level optimization model to derive the optimal bidding strategy of virtual bidders. In this model, the risks of uncertainties associated with the rivals' strategies and RT market prices are managed by robust optimization. The proposed max-min two-level model is turned into a ...


Physical-Based Training Data Collection Approach For Data-Driven Lithium-Ion Battery State-Of-Charge Prediction, Jie Li, Will Ziehm, Jonathan W. Kimball, Robert Landers, Jonghyun Park Sep 2021

Physical-Based Training Data Collection Approach For Data-Driven Lithium-Ion Battery State-Of-Charge Prediction, Jie Li, Will Ziehm, Jonathan W. Kimball, Robert Landers, Jonghyun Park

Electrical and Computer Engineering Faculty Research & Creative Works

Data-Driven approaches for State of Charge (SOC) prediction have been developed considerably in recent years. However, determining the appropriate training dataset is still a challenge for model development and validation due to the considerably varieties of lithium-ion batteries in terms of material, types of battery cells, and operation conditions. This work focuses on optimization of the training data set by using simple measurable data sets, which is important for the accuracy of predictions, reduction of training time, and application to online estimation. It is found that a randomly generated data set can be effectively used for the training data set ...


New Infeed Correction Methods For Distance Protection In Distribution Systems, Fahd Hariri, Mariesa Crow Aug 2021

New Infeed Correction Methods For Distance Protection In Distribution Systems, Fahd Hariri, Mariesa Crow

Electrical and Computer Engineering Faculty Research & Creative Works

The reliability and security of power systems may be jeopardized by the increase in the amounts of renewable generation and the uncertainties produced by these devices. In particular, the protection schemes of traditional power systems have been challenged by the integration of distributed generation (DG) resources. Distance relays (DRs), which have been mainly employed to protect transmission systems, are increasingly proposed as one of the solutions to protect distribution systems with a heavy penetration of DGs. However, conventional distance protection faces several drawbacks that might lead to maloperation. One of those challenges is the "infeed effect", which causes the impedance ...


Off-Axis Microsphere Photolithography Patterned Nanohole Array And Other Structures On An Optical Fiber Tip For Glucose Sensing, Jiayu Liu, Ibrahem Jasim, Tao Liu, Jie Huang, Edward Kinzel, Mahmoud Almasri Jul 2021

Off-Axis Microsphere Photolithography Patterned Nanohole Array And Other Structures On An Optical Fiber Tip For Glucose Sensing, Jiayu Liu, Ibrahem Jasim, Tao Liu, Jie Huang, Edward Kinzel, Mahmoud Almasri

Electrical and Computer Engineering Faculty Research & Creative Works

Microsphere photolithography (MPL) using off-axis UV exposure is a technique that uses a layer of self-assembled microspheres as an optical mask to project different periodic nanopatterns. This paper introduces MPL as an alternative fabrication technique to pattern complex metasurfaces on an optical single mode fiber tip as a sensor for measuring refractive index. Based on the hexagonal close packing microsphere array, complicated metasurfaces were successfully created by changing the UV illumination angle. Using the same self-assembled microspheres monolayer, multiple UV illumination jets were projected to create multiple hole group patterns. Fiber sensors with three-hole group and four-hole group patterns were ...


Modeling, Analysis, And Control Design Of A Single-Stage Boost Inverter, Md Rasheduzzaman, Poria Fajri, Jonathan W. Kimball, Brad Deken Jul 2021

Modeling, Analysis, And Control Design Of A Single-Stage Boost Inverter, Md Rasheduzzaman, Poria Fajri, Jonathan W. Kimball, Brad Deken

Electrical and Computer Engineering Faculty Research & Creative Works

A single-phase, single-stage, differential boost inverter comprises two independently-controlled boost DC-DC converters, with the load connected between their outputs. The net voltage on the load is sinusoidal and has a controllable frequency and magnitude that is larger than that of the DC source. The present work first derives steady-state and small-signal models of the inverter with parasitic elements. The results obtained from the line-to-output transfer function, control-to-output transfer function, open-loop input impedance, and open-loop output impedance models are compared with that of the ones obtained from the experimental testbed. Using the new models, a voltage mode controller is designed in ...


Single Step 2-Port Device De-Embedding Algorithm For Fixture-Dut-Fixture Network Assembly, Simone Scafati, Enza Pellegrino, Francesco De Paulis, Carlo Olivieri, James L. Drewniak, Antonio Orlandi Jun 2021

Single Step 2-Port Device De-Embedding Algorithm For Fixture-Dut-Fixture Network Assembly, Simone Scafati, Enza Pellegrino, Francesco De Paulis, Carlo Olivieri, James L. Drewniak, Antonio Orlandi

Electrical and Computer Engineering Faculty Research & Creative Works

The de-embedding of measurement fixtures is relevant for an accurate experimental characterization of radio frequency and digital electronic devices. The standard technique consists in removing the effects of the measurement fixtures by the calculation of the transfer scattering parameters (T-parameters) from the available measured (or simulated) global scattering parameters (S-parameters). The standard de-embedding is achieved by a multiple steps process, involving the S-to-T and subsequent T-to-S parameter conversion. In a typical measurement setup, two fixtures are usually placed before and after the device under test (DUT) allowing the connection of the device to the calibrated vector network analyzer coaxial ports ...


Sensitivity-Enhanced Microwave-Photonic Optical Fiber Interferometry Based On The Vernier Effect, Chen Zhu, Jie Huang May 2021

Sensitivity-Enhanced Microwave-Photonic Optical Fiber Interferometry Based On The Vernier Effect, Chen Zhu, Jie Huang

Electrical and Computer Engineering Faculty Research & Creative Works

This paper proposes optical carrier microwave interferometry (OCMI)-based optical fiber interferometers for sensing applications with improved measurement sensitivity with the assistance of the Vernier effect. Fabry-Perot interferometers (FPIs) are employed in the proof of concept. A single-FPI-OCMI system is first demonstrated for measurements of variations of temperatures by tracking the spectral shift of the interferogram in microwave domain. By cascading two FPIs with slightly different optical lengths, the Vernier effect is generated in the magnitude spectrum of the system with a typical amplitude-modulated signal. By tracking the shift of the envelope signal, temperature measurements are experimentally demonstrated with greatly ...


A Novel Hybrid Gwo-Ls Estimator For Harmonic Estimation Problem In Time Varying Noisy Environment, Muhammad Abdullah, Tahir N. Malik, Ali Ahmed, Muhammad F. Nadeem, Irfan A. Khan, Rui Bo May 2021

A Novel Hybrid Gwo-Ls Estimator For Harmonic Estimation Problem In Time Varying Noisy Environment, Muhammad Abdullah, Tahir N. Malik, Ali Ahmed, Muhammad F. Nadeem, Irfan A. Khan, Rui Bo

Electrical and Computer Engineering Faculty Research & Creative Works

The power quality of the Electrical Power System (EPS) is greatly affected by electrical harmonics. Hence, accurate and proper estimation of electrical harmonics is essential to design appropriate filters for mitigation of harmonics and their associated effects on the power quality of EPS. This paper presents a novel statistical (Least Square) and meta-heuristic (Grey wolf optimizer) based hybrid technique for accurate detection and estimation of electrical harmonics with minimum computational time. The non-linear part (phase and frequency) of harmonics is estimated using GWO, while the linear part (amplitude) is estimated using the LS method. Furthermore, harmonics having transients are also ...


Fuzzyart: An R Package For Art-Based Clustering, Louis Steinmeister, Donald C. Wunsch May 2021

Fuzzyart: An R Package For Art-Based Clustering, Louis Steinmeister, Donald C. Wunsch

Electrical and Computer Engineering Faculty Research & Creative Works

Adaptive Resonance Theory (ART) was introduced by Steven Grossberg as a theory of human cognitive information processing (Grossberg 1976, 1980). Extending the capabilities of the ART 1 model, which can learn to categorize patterns in binary data, fuzzy ART as described in (Carpenter, Grossberg, and Rosen 1991) has become one of the most commenly used Adaptive Resonance Theory models (Brito da Silva, Elnabarawy, and Wunsch 2019). By incorporating fuzzy set theroy operators, fuzzy ART is capable of learning from binaray and bounded real valued data. Its advantage over other unsupervised learning algorithms lies in the flexibility of the learning rule ...


Fiber Optic Sensor Embedded Smart Helmet For Real-Time Impact Sensing And Analysis Through Machine Learning, Yiyang Zhuang, Qingbo Yang, Taihao Han, Ryan O'Malley, Aditya Kumar, Rex E. Gerald Ii, Jie Huang Mar 2021

Fiber Optic Sensor Embedded Smart Helmet For Real-Time Impact Sensing And Analysis Through Machine Learning, Yiyang Zhuang, Qingbo Yang, Taihao Han, Ryan O'Malley, Aditya Kumar, Rex E. Gerald Ii, Jie Huang

Electrical and Computer Engineering Faculty Research & Creative Works

Background: Mild traumatic brain injury (mTBI) strongly associates with chronic neurodegenerative impairments such as post-traumatic stress disorder (PTSD) and mild cognitive impairment. Early detection of concussive events would significantly enhance the understanding of head injuries and provide better guidance for urgent diagnoses and the best clinical practices for achieving full recovery. New method: A smart helmet was developed with a single embedded fiber Bragg grating (FBG) sensor for real-time sensing of blunt-force impact events to helmets. The transient signals provide both magnitude and directional information about the impact event, and the data can be used for training machine learning (ML ...


High-Temperature Stable Fbgs Fabricated By A Point-By-Point Femtosecond Laser Inscription For Multi-Parameter Sensing, Chen Zhu, Dinesh Alla, Jie Huang Feb 2021

High-Temperature Stable Fbgs Fabricated By A Point-By-Point Femtosecond Laser Inscription For Multi-Parameter Sensing, Chen Zhu, Dinesh Alla, Jie Huang

Electrical and Computer Engineering Faculty Research & Creative Works

A high-temperature resistant fiber Bragg grating (FBG) with pronounced cladding modes is fabricated using a simple, fast, and flexible point-by-point femtosecond laser inscription. Cladding modes of different orders exhibit various sensitivities to changes in refractive index of the surrounding medium and temperature, while the Bragg wavelength is only dependent on the ambient temperature. By combining the resonance of cladding modes and the Bragg resonance, measurement of variations of temperature and surrounding refractive index can be achieved. Survivability of the cladding modes and the core mode in the fabricated FBG at elevated temperatures up to 1000°C is demonstrated.


Optimal Tracking Current Control Of Switched Reluctance Motor Drives Using Reinforcement Q-Learning Scheduling, Hamad Alharkan, Sepehr Saadatmand, Mehdi Ferdowsi, Pourya Shamsi Jan 2021

Optimal Tracking Current Control Of Switched Reluctance Motor Drives Using Reinforcement Q-Learning Scheduling, Hamad Alharkan, Sepehr Saadatmand, Mehdi Ferdowsi, Pourya Shamsi

Electrical and Computer Engineering Faculty Research & Creative Works

In this article, a novel Q-learning scheduling method for the current controller of a switched reluctance motor (SRM) drive is investigated. The Q-learning algorithm is a class of reinforcement learning approaches that can find the best forward-in-time solution of a linear control problem. An augmented system is constructed based on the reference current signal and the SRM model to allow for solving the algebraic Riccati equation of the current-tracking problem. This article introduces a new scheduled-Q-learning algorithm that utilizes a table of Q-cores that lies on the nonlinear surface of an SRM model without involving any information about the model ...


Optimal Bidding Strategy For Physical Market Participants With Virtual Bidding Capability In Day-Ahead Electricity Markets, Hossein Mehdipourpicha, Rui Bo Jan 2021

Optimal Bidding Strategy For Physical Market Participants With Virtual Bidding Capability In Day-Ahead Electricity Markets, Hossein Mehdipourpicha, Rui Bo

Electrical and Computer Engineering Faculty Research & Creative Works

Virtual bidding provides a mechanism for financial players to participate in wholesale day-ahead (DA) electricity markets. The price difference between DA and real-time (RT) markets creates financial arbitrage opportunities for financial players. Physical market participants (MP), referred to as participants with physical assets, can also take advantage of virtual bidding but in a different way, which is to further amplify the value of their physical assets. Therefore, this work proposes a model for such physical MPs to maximize the profits. This model employs a bi-level optimization approach, where the upper-level subproblem maximizes the total profit from both physical generations and ...


Quality-Of-Service Architecture For Cloud Computing Networking, Ken R. Owens Jr., Steve Eugene Watkins Jan 2021

Quality-Of-Service Architecture For Cloud Computing Networking, Ken R. Owens Jr., Steve Eugene Watkins

Electrical and Computer Engineering Faculty Research & Creative Works

Quality-of-service (QoS) performance is an important consideration for real-time and high-priority traffic on internet protocol (IP) networks. Service differentiation can provide a more efficient and customer-oriented internet. The “best-effort” internet models in use today cannot provide guarantees or service differentiation for end-to-end individual and aggregate data flows. Hardware-based models and software-based models do not completely address the total service-enabled solution. We propose a hybrid architecture that combines software and hardware features to handle network traffic with diverse QoS requirements. Since cloud providers leverage IP networks today, the model is based on a systems engineering approach that uses cloud computing technologies ...


Microsphere Photolithography Patterned Nanohole Array On An Optical Fiber, Ibrahem Jasim, Jiayu Liu, Chen Zhu, Muhammad Roman, Jie Huang, Edward Kinzel, Mahmoud Almasri Jan 2021

Microsphere Photolithography Patterned Nanohole Array On An Optical Fiber, Ibrahem Jasim, Jiayu Liu, Chen Zhu, Muhammad Roman, Jie Huang, Edward Kinzel, Mahmoud Almasri

Electrical and Computer Engineering Faculty Research & Creative Works

Microsphere Photolithography (MPL) is a nanopatterning technique that utilizes a self-assembled monolayer of microspheres as an optical element to focus incident radiation inside a layer of photoresist. The microspheres produces a sub-diffraction limited photonic-jet on the opposite side of each microsphere from the illumination. When combined with pattern transfer techniques such as etching/lift-off, MPL provides a versatile, low-cost fabrication method for producing hexagonal close-packed metasurfaces. This article investigates the MPL process for creating refractive index (RI) sensors on the cleaved tips of optical fiber. The resonant wavelength of metal elements on the surface is dependent on the local dielectric ...


An Explainable And Statistically Validated Ensemble Clustering Model Applied To The Identification Of Traumatic Brain Injury Subgroups, Dacosta Yeboah, Louis Steinmeister, Daniel B. Hier, Bassam Hadi, Donald C. Wunsch, Gayla R. Olbricht, Tayo Obafemi-Ajayi Sep 2020

An Explainable And Statistically Validated Ensemble Clustering Model Applied To The Identification Of Traumatic Brain Injury Subgroups, Dacosta Yeboah, Louis Steinmeister, Daniel B. Hier, Bassam Hadi, Donald C. Wunsch, Gayla R. Olbricht, Tayo Obafemi-Ajayi

Electrical and Computer Engineering Faculty Research & Creative Works

We present a framework for an explainable and statistically validated ensemble clustering model applied to Traumatic Brain Injury (TBI). The objective of our analysis is to identify patient injury severity subgroups and key phenotypes that delineate these subgroups using varied clinical and computed tomography data. Explainable and statistically-validated models are essential because a data-driven identification of subgroups is an inherently multidisciplinary undertaking. In our case, this procedure yielded six distinct patient subgroups with respect to mechanism of injury, severity of presentation, anatomy, psychometric, and functional outcome. This framework for ensemble cluster analysis fully integrates statistical methods at several stages of ...


Evaluation Of Standard And Semantically-Augmented Distance Metrics For Neurology Patients, Daniel B. Hier, Jonathan Kopel, Steven U. Brint, Donald C. Wunsch, Gayla R. Olbricht, Sima Azizi, Blaine Allen Aug 2020

Evaluation Of Standard And Semantically-Augmented Distance Metrics For Neurology Patients, Daniel B. Hier, Jonathan Kopel, Steven U. Brint, Donald C. Wunsch, Gayla R. Olbricht, Sima Azizi, Blaine Allen

Electrical and Computer Engineering Faculty Research & Creative Works

Background: Patient distances can be calculated based on signs and symptoms derived from an ontological hierarchy. There is controversy as to whether patient distance metrics that consider the semantic similarity between concepts can outperform standard patient distance metrics that are agnostic to concept similarity. The choice of distance metric can dominate the performance of classification or clustering algorithms. Our objective was to determine if semantically augmented distance metrics would outperform standard metrics on machine learning tasks.

Methods: We converted the neurological findings from 382 published neurology cases into sets of concepts with corresponding machine-readable codes. We calculated patient distances by ...


Transceivers As A Resource: Scheduling Time And Bandwidth In Software-Defined Radio, Nathan D. Price, Maciej Jan Zawodniok, Ivan G. Guardiola Jul 2020

Transceivers As A Resource: Scheduling Time And Bandwidth In Software-Defined Radio, Nathan D. Price, Maciej Jan Zawodniok, Ivan G. Guardiola

Electrical and Computer Engineering Faculty Research & Creative Works

In the future, software-defined radio may enable a mobile device to support multiple wireless protocols implemented as software applications. These applications, often referred to as waveform applications, could be added, updated, or removed from a software-radio device to meet changing demands. Current software-defined radio solutions grant an active waveform exclusive ownership of a specific transceiver or analog front-end. Since a wireless device has a limited number of front-ends, this approach puts a hard constraint on the number of concurrent waveform applications a device can support. A growing trend in software-defined radio research is to virtualize front-ends to allow sharing and ...


An Algorithm For The Evolutionary-Fuzzy Generation Of On-Line Signature Hybrid Descriptors, Marcin Zalasinski, Krzysztof Cpalka, Lukasz Laskowski, Donald C. Wunsch, Krzystof Przybyszewski Jul 2020

An Algorithm For The Evolutionary-Fuzzy Generation Of On-Line Signature Hybrid Descriptors, Marcin Zalasinski, Krzysztof Cpalka, Lukasz Laskowski, Donald C. Wunsch, Krzystof Przybyszewski

Electrical and Computer Engineering Faculty Research & Creative Works

In biometrics, methods which are able to precisely adapt to the biometric features of users are much sought after. They use various methods of artificial intelligence, in particular methods from the group of soft computing. In this paper, we focus on on-line signature verification. Such signatures are complex objects described not only by the shape but also by the dynamics of the signing process. In standard devices used for signature acquisition (with an LCD touch screen) this dynamics may include pen velocity, but sometimes other types of signals are also available, e.g. pen pressure on the screen surface (e ...