Engineering Commons^™

A Digital Twin Based Forecasting Framework For Power Flow Management In Dc Microgrids, Kerry Sado, Jarrett Peskar, Austin Downey, Jamil A. Khan, Kristen Booth

Faculty Publications

The ability to forecast system conditions is integral to the definition and functionality of digital twins. While forecasting methods have been explored for use in digital twin systems, the integration of feedback mechanisms for real-time forecasting and in-situ decision-making in DC microgrids has not been extensively investigated. This research develops a modular forecasting framework tailored for digital twins in DC microgrids to enable real-time monitoring, online forecasting, and decision-making. DC microgrids, characterized by dynamic load variations, benefit from advanced predictive capabilities to maintain stability and operational efficiency. The proposed digital twin-based forecasting framework addresses these challenges by providing real-time predictive …

Zno Nanowires For Biosensing Applications, G.M. Mehedi Hossain, Daniel Garza, Emilio Chavez, Ahmed Hasnain Jalal, Fahmida Alam

Electrical and Computer Engineering Faculty Publications

Zinc oxide Nanowires (ZnO-NWs) are promising biosensor materials and hold the key to overcoming challenges in the field. This chapter provides an introductory overview of biosensing technology, focusing on the fundamental principles and comparing ZnO-NWs with other nanostructures regarding the surface area, reactivity, electrical properties, charge transport behavior, optical, magnetic, and piezoelectric properties, and mechanical flexibility. Providing the synthesis and characterization methods, ZnO-NWs’ biosensing processes are also elaborated on surface modification for selectivity, integration with microfluidic systems, enhancing signal transduction, and connecting with biological elements like enzymes, antibodies, and DNA. The chapter also discusses the applications of ZnO-NWs-based biosensors in …

Limitations In Speech Recognition For Young Adults With Down Syndrome, Franceli L. Cibrian, Yingying 'Yuki' Chen, Kayla Anderson, Cecilia Marie Abrahamsson, Vivian Genaro Motti

Engineering Faculty Articles and Research

Speech recognition has the potential to make technology more accessible to users. However, the accuracy of speech recognition remains limited for users with disabilities, including those with Down Syndrome, and the types and frequencies of recognition errors are poorly understood. This paper characterizes these problems, focusing on errors occurring when recognizing Down Syndrome speech. We analyze the transcripts from six speech recognition algorithms (Google, IBM, Otter.ai, Microsoft, AssemblyAI, OpenAI) using the audio content of 15 individuals with Down Syndrome (331 dialogues; 3428 words). Our analysis shows: (1) significant difference in speech recognition accuracy for people with Down Syndrome compared to …

Leveraging Network Science For Customer Segmentation And Product Recommendation, Ali Nasirzonouzi

Northeast Journal of Complex Systems (NEJCS)

The rapid growth in e-commerce has forced the development and implementation of enhanced customer segmentation and recommendation systems, improving business results and improving customer experience. Traditional approaches, such as RFM analysis and clustering algorithms like K-means, are very helpful in many situations but usually fail to catch complex interdependencies among customers and products. This paper proposes a new approach using network science methodologies, a bipartite graph model, toward the advancement of customer segmentation and product recommendation. It implements a bipartite graph of customers and products using the "Online Retail II" dataset and proceeds with community detection, segmenting customers into unique …

Stochastic Generalization Models Learn To Comprehensively Detect Volatile Organic Compounds Associated With Foodborne Pathogens Via Raman Spectroscopy, Bohong Zhang, Anand K. Nambisan, Abhishek Prakash Hungund, Xavier Jones, Qingbo Yang, Jie Huang

Electrical and Computer Engineering Faculty Research & Creative Works

Ensuring food safety requires continuous innovation, especially in the detection of foodborne pathogens and chemical contaminants. In this study, we present a system that combines Raman spectroscopy with machine learning (ML) algorithms for the precise detection and analysis of VOCs linked to foodborne pathogens in complex liquid mixtures. A remote fiber-optic Raman probe was developed to collect spectral data from 42 distinct VOC mixtures, representing contamination scenarios with dilution levels ranging from undiluted to highly diluted states. A dataset comprising 1445 Raman spectra was analyzed using classification and regression ML models, including multi-layer perceptron (MLP), random forest, and extreme gradient …

Integrating Neural Networks For Predictive Torque Control And Obstacle Avoidance In Autonomous Robot, Viswanath Kodali, Harsha Vardhan Borra, Kiran P

Northeast Journal of Complex Systems (NEJCS)

In the field of robotics, precise motion control and accurate computation of joint forces are critical for ensuring optimal performance. Traditional methods, such as using the Jacobian matrix for joint angle determination and Euler-Lagrange equations for torque computation, are reliable but computationally intensive, making them less suitable for real-time applications. This paper presents an advanced approach to improving the productivity and efficiency of a 3-Degree of Freedom (DOF) robotic arm by utilizing Artificial Neural Network (ANN). The proposed system dynamically predicts joint angles and torque, enabling faster and more efficient motion control.

To address the challenge of obstacle avoidance in …

Multimodal Search On A Line, Jared Coleman, Dmitry Ivanov, Evangelos Kranakis, Danny Krizanc, Oscar Morales Ponce

Computer Science Faculty Works

Inspired by the diverse set of technologies used in underground object detection and imaging, we introduce a novel multimodal linear search problem whereby a single searcher starts at the origin and must find a target that can only be detected when the searcher moves through its location using the correct of p possible search modes. The target’s location, its distance d from the origin, and the correct search mode are all initially unknown to the searcher. We prove tight upper and lower bounds on the competitive ratio for this problem. Specifically, we show that when p is odd, the optimal …

Si-Doped Ain Using Pulsed Metalorganic Chemical Vapor Deposition And Doping, Tariq Jamil, Abdullah Al Mamun Mazumder, Mohammod Ali, Jingyu Lin, Hongxing Jiang, Grigory Simin, M. Asif Khan

Faculty Publications

In this paper we describe a pulsed metalorganic chemical vapor deposition (MOCVD) Si-doping approach for AlN epilayers over bulk AlN. The Al-rich growth/doping conditions in the pulsed MOCVD process resulted in n-AlN layers with transmission line model currents that were an order higher than for structures on layers that were grown/doped at identical temperatures using the conventional MOCVD process. Our work demonstrated that like the other reported approaches such as UV exposure during growth, the pulsed MOCVD process is also very effective in reducing point defects by the defect quasi-Fermi level-chemical potential control.

Fault Diagnosis And Fault Tolerant Structure For Multilevel Inverters Using Machine Learning Techniques, Sudha V

Theses and Dissertations

A paradigm shift towards electric drives in domestic and industrial sectors has significantly increased the use of multilevel inverters (MLI). MLIs are constructed using more semiconductor devices, which hinders safety and reliability. Literature states 31.2% of failures in MLIs are due to semiconductor devices. Hence, there is a need for fault detection and tolerant mechanisms to ensure the safety and reliability of MLIs.

MLIs like Cascaded H-bridge(CHB) and Packed U cell(PUC) are mostly preferred due to low harmonic distortion, which is considered in this work. The complexity associated with fault diagnosis with more components in MLIs is addressed by machine …

The Hidden Cost Of Using Time Series Aggregation For Modeling Low-Carbon Industrial Energy Systems: An Investors’ Perspective, Markus Fleschutz

Publications

Time series aggregation (TSA) is commonly used in energy system optimization to reduce model complexity and computational expenses by selecting periods to represent the entire time series. TSA’s accuracy has traditionally been assessed by comparing the objective values between the original and TSA models (assumed error). However, evaluating TSA from an investor’s standpoint involves analyzing the performance of TSA-based energy system designs using the original time series. Therefore, we introduce the hidden error and total error, novel error metrics, to evaluate the financial implications of TSA through backtesting the TSA-based system designs using the original time series. Our analysis extends …

Integration Of Artificial Intelligence With A Customized Four-Probe Station For I-V Characteristic Classification And Prediction, Sameh O. Abdellatif, Ahmed Ghanem, Ahmmat Abdel Whahid, Amr Hatem, Belal Ahmed

Electrical Engineering

The incorporation of Artificial Intelligence (AI) is pivotal in automating intricate technical tasks, significantly enhancing accuracy and efficiency while alleviating the burdens of repetitive monitoring traditionally borne by technicians. This study focuses on developing a customized four-probe station integrated with sophisticated AI models aimed at classifying current–voltage () characteristics and extracting essential parameters. Our methodology encompasses the fabrication of precision-engineered gold-plated probes, meticulously assembled with a three-dimensional (3D) moving head to ensure optimal contact and measurement fidelity across a variety of electronic and optoelectronic devices. Data acquisition is executed via a source meter unit, followed by rigorous post-processing utilizing advanced …

Nonlinear Self-Synchronizing Current Control For Single-Phase Ac Inverters, Shruti Pandey, Michael Mclntyre

Electrical and Computer Engineering Faculty Research & Creative Works

Grid-connected single-phase inverters require accurate phase detection for synchronization and power control. Traditionally, phase-locked loops (PLLs) are used to estimate grid parameters. This paper proposes a novel approach that determines the grid phase angle using only current feedback, eliminating the need for grid voltage measurements or cascaded control schemes. The proposed method integrates a phase angle observer with a current controller to regulate real and reactive power. Lyapunov stability analysis and hardware experiments validate the effectiveness of the approach.

Phase Of The Seabed Frequency-Domain Reflection Coefficient: Measurements And Modeling, Charles W. Holland

Electrical and Computer Engineering Faculty Publications and Presentations

The phase of the seabed frequency-domain reflection coefficient potentially contains valuable information on the geoacoustic properties in a layered/refracting seabed. However, heretofore, the phase has not been exploited. Measurements of phase are presented in an area of thick mud at the New England Mud Patch. In addition, a model is presented along with the modeling results. While this is only a first step towards understanding the potential value of exploiting the phase, it seems clear that in some instances, the phase not only contains valuable geoacoustic information, but carries a higher information content than the magnitude.

Design And Implementation Of Uvm-Based Verification Framework For Deep Learning Accelerators, Randa Ahmed Hussein Aboudeif

Theses and Dissertations

Recent advancements in deep learning (DL) have made hardware accelerators, known as deep learning accelerators (DLAs), a preferred solution for numerous high-performance computing (HPC) applications, including speech recognition, computer vision, and image classification. DLAs are composed of hundreds of parallel processing engines to speed up computations and can gain access to pre-trained networks from the cloud or through on-chip memory to implement the DNN inference process. DLA verification is becoming an important and challenging phase. The verification process is required to handle the complex DLA design. Moreover, the reliability of DLAs is critical for assessment as they are involved in …

New Insights Into Controlling The Functional Properties Of Tin Oxide-Based Materials, Alexandra Kuriganova, Nina Smirnova

Journal of Electrochemistry

Development of methodologies for fabrications of nanostructured materials that provide control over their microstructural features and compositions represents a fundamental step in the advancement of technologies for productions of materials with well-defined functional properties. Pulse electrolysis, a top-down electrochemical approach, has been demonstrated to be a viable method for producing nanostructured materials with a particular efficacy in the synthesis of tin oxides. This method allows for significant control over the composition and shape of the resulting tin oxides particles by modifying the anionic composition of the aqueous electrolyte, obviating the need for additional capping agents in the synthesis process and …

A Precoding, Companding, And Nonlinearity Reduction Approach To Optimize High-Speed Ado-Ofdm For Visible Light Communication, Swaminathan S

Theses and Dissertations

Using light-emitting diodes (LEDs) for data transfer, visible light communication (VLC) presents a strong substitute for radio frequency communication. Yet, because of the non-linearity of LEDs, conventional Orthogonal Frequency-Division Multiplexing (OFDM) approaches in VLC are limited regarding spectrum efficiency, peak-to-average power ratio (PAPR), and system linearity. Multi-carrier asymmetrically Clipped Optical OFDM (MADO-OFDM) is introduced in this study. For VLC operations based on Optical Orthogonal Frequency Division Multiplexing (O-OFDM), a model-driven Deep Learning (DL) approach has been presented. Utilizing an Auto Encoder (AE) network technology reduced the non-linearity of the LEDs. A proposed improved ADO-OFDM protocol, called MADO-OFDM, adaptively modifies the …

A Cnt Intercalated Co Porphyrin-Based Metal Organic Framework Catalyst For Oxygen Reduction Reaction, Pei-Pei He, Jin-Hua Shi, Xiao-Yu Li, Ming-Jie Liu, Zhou Fang, Jing He, Zhong-Jian Li, Xin-Sheng Peng, Qing-Gang He

Journal of Electrochemistry

The poor electronic conductivity of metal-organic framework (MOF) materials hinders their direct application in the field of electrocatalysis in fuel cells. Herein, we proposed a strategy of embedding carbon nanotubes (CNTs) during the growth process of MOF crystals, synthesizing a metalloporphyrin-based MOF catalyst TCPPCo-MOF-CNT with a unique CNT-intercalated MOF structure. Physical characterization revealed that the CNTs enhance the overall conductivity while retaining the original characteristics of the MOF and metalloporphyrin. Simultaneously, the insertion of CNTs generated adequate mesopores and created a hierarchical porous structure that enhances mass transfer efficiency. X-ray photoelectron spectroscopic analysis confirmed that the C atom in CNT …

Nanostructured Graphitic Carbon Nitride For Photocatalytic And Electrochemical Applications, Muhammad Abdul Qadeer, Iqra Fareed, Asif Hussaine, Muhammad Asim Farid, Sadia Nazir, Faheem K. Butt, Ji-Jun Zou, Muhammad Tahir, Shang-Feng Du

Journal of Electrochemistry

Graphitic carbon nitride (g-C₃N₄) exhibits great mechanical as well as thermal characteristics, making it a valuable material for use in photoelectric conversion devices, an accelerator for synthesis of organic compounds, an electrolyte for fuel cell applications or power sources, and a hydrogen storage substance and a fluorescence detector. It is fabricated using different methods, and there is a variety of morphologies and nanostructures such as zero to three dimensions that have been designed for different purposes. There are many reports about g-C₃N₄ in recent years, but a comprehensive review which covers nanostructure dimensions …

Sensing Rotational Direction Using Paramagnetic Nanoparticles For Gyroscopic Applications, Jacob C. Pung

Masters Theses

Gyroscopes have long been used for measuring an object’s angular orientation. Methods range from mechanical spinning disks to oscillating spring-mass systems. However, previous gyroscopes can suffer from high maintenance or fragility. A recently invented novel gyroscope aims to curb these restrictions through the use of ferrofluid.

This thesis proposes a new method of modeling and measuring the signals from the novel gyroscopic device. Rather than simply measuring the device’s average current consumption via slow RMS measurements, an attempt was made to model the voltage amplitude at any time. This allows a faster examination of both the changing magnitude and phase …

Role Of Sulphur In Resistive Switching Behavior Of Natural Rubber-Based Memory, Muhammad Awais, Nadras Othman, Mohamad Danial Shafiq, Feng Zhao, Kuan Yew Cheong

Electrical and Computer Engineering Faculty Research & Creative Works

The rising environmental awareness has spurred the extensive use of green materials in electronic applications, with bio-organic materials emerging as attractive alternatives to inorganic and organic materials due to their natural biocompatibility, biodegradability, and eco-friendliness. This study showcases the natural rubber (NR) based resistive switching (RS) memory devices and how varying Sulphur concentrations (0-0.8 wt.%) in NR thin films impact the RS characteristics. The NR was formulated and processed into a thin film deposited on an indium tin oxide substrate as the bottom electrode and with an Ag film as the top electrode. The addition of Sulphur modifies the degree …

Feasibility And Acceptability Of The Mazi Umntanakho Digital Tool In South African Settings: A Qualitative Evaluation, Catherine E. Draper, Caylee J. Cook, Elizabeth A. Ankrah, Jesus A. Beltran, Franceli L. Cibrian, Kimberley D. Lakes, Hanna Mofid, Lucretia Williams, Gillian R. Hayes

Engineering Faculty Articles and Research

To address the need for interventions targeting social emotional development and mental health of young children in South Africa, the Mazi Umntanakho (‘know your child’) digital tool was co-designed, and piloted with caregivers and 3–5-year-old children involved in home visiting programmes promoting early childhood development. The aim of this study was to qualitatively evaluate the feasibility and acceptability of this tool in four urban and four rural low-income communities, from the perspective of home visitors and caregivers. Focus groups were conducted with home visitors (n = 117) and caregivers (n = 72). Issues relating to the feasibility of …

Novel Approach For The Micro Cracks Detection Of Solar Wafers And Cells, Mohd Israil, Arvind Kumar Sharma, Ekta Gupta

Al-Bahir

This paper deals with the review of various existing technique for the microcracks detection in silicon solar cell and wafer. In addition to this, we proposed a novel approach for the machine learning technique for the inspection of the cracks those are existed in the solar cell and wafer and not able to detect by the naked eyes. There are many techniques have been developed by the various researchers around the world to inspect solar cells for defect. All the techniques discussed in this article having some features and some weakness too. This paper present here gives the two-fold solution …

Dunbar’S Number In Motion: Agent-Based Simulations Of Friendship Formation, Christopher R. Cooke, Cameron D. Lutz

Northeast Journal of Complex Systems (NEJCS)

By contrasting Lévy flight and random walk strategies in simulated agents, we discern the effect of movement behavior on the total duration of social interactions. Our agent-based simulation results approximate empirically observed Dunbar social circle formation using simple behavioral rules of interaction and compatibility to mimic exogenous attribute-based friendship formation. We simulate the complexities of social interactions among agents with unique attributes and a time budget for social engagement over a one-year period. Two distinct simulations were conducted to evaluate the behavioral contributions of Lévy flight and random walk movement patterns on cumulative interaction duration and the formation of Dunbar …

A Novel Mu-Metal Based Weak Magnetic Energy Harvester For Self-Powered Monitoring Of Power Grid Assets, Arsalan Habib Khawaja, Hassan Pervaiz, Dongsheng Cai, Jian Li, Qi Huang

Turkish Journal of Electrical Engineering and Computer Sciences

This paper presents a novel magnetic field driven contactless energy harvester with improved flux concentration capabilities for potential utilization in Power system monitoring where stray magnetic field is abundant and readily available. The designed harvester employs multilayered Mu-Metal based cone shaped core to maximize magnetic flux density. To achieve the final design, this work investigates magnetic flux concentration ability of various geometries and material properties in magnetic flux conditions typical to overhead 11 kV power distribution circuits. Impact of layers in core-coil region of harvesting coil on magnetic flux concentration is evaluated by means of Finite Element analysis. Resultantly, the …

Advanced Prediction Of Events And Temporal Expressions In Medical Text Using The Jena Api: Integrating Ontologies And Deep Learning, Hafida Tiaiba, Lyazid Sabri, Okba Kazar

Turkish Journal of Electrical Engineering and Computer Sciences

The automatic recognition of medical concepts and temporal expressions in narrative clinical text enhances the utility of electronic health records (EHRs) and supports clinical decision-making and research. However, challenges arise due to the complexity of medical language, ambiguity of terms, and variability in expression. To address these issues, the use of medical ontologies significantly improves data management in healthcare. A novel approach integrates various medical ontologies covering drugs, symptoms, diseases, anatomy, disease drivers, and food, and with convolutional neural networks (CNNs) -including Standard, Transposed, and Separable convolution models (CONSEPTR)- to extract both medical events (e.g., clinical departments, treatments, problems) and …

Machine Learning Models Approach For The Quantitative Classification Of Ferricyanide Compound Using Electrochemical Detection With Cpe-Fe3o4nps, Süleyman Aşir, Nemah Abu Shama, Najya Maroof Saleem, Devri̇m Kayali, Kami̇l Di̇mi̇li̇ler

Turkish Journal of Electrical Engineering and Computer Sciences

Most common electrochemical analysis techniques used to evaluate enzymes, proteins, and heavy metals over a wide potential range include electrochemical impedance EIS, differential pulse voltammetry DPV, and square wave voltammetry SQWV. Machine leaning algorithms MLA are employed to classify the Potassium ferricyaniyde K3Fe(CN)6 concentrations using a modified carbon paste electrode CPE embedded with iron (II, III) oxide (Fe3O4) NPs. The CV, DPV, and SQWV voltametric data collected from all K3Fe(CN)6 concentrations were used as input data to the machine learning algorithms. Signaling current of K3Fe(CN)6 concentrations improved at Fe3O4 modified with nanoparticles NPs CPE in a comparison with the unmodified …

Balancing Anarchy And Efficiency: Partial Team Formations And Learning In Potential Games, Muhammed Sayin

Turkish Journal of Electrical Engineering and Computer Sciences

Non-cooperative multi-agent learning, focusing on individual rationality (anarchy), often falls short in achieving system-wide efficiency in potential games, a class of games with applications in decentralized control and optimization. On the other hand, cooperative approaches prioritize system efficiency but often via global coordination, which could be impractical, e.g., for large-scale and less controlled environments. To address this dilemma, we propose a novel framework that introduces partial team formations, allowing team members with shared objectives to coordinate their actions while maintaining team-wise rationality for improved system-wide efficiency without the burden of global coordination. We model such interactions as a multi-team game …

Long-Range Inertia Prediction Considering Contemporary Evolution Of Power Grid Networks, Peter M. Makolo

Tanzania Journal of Engineering and Technology (TJET)

Reduced network inertia due to high penetration levels of non-synchronous generators in modern power systems is becoming a pressing issue. As a result, very quick inertial responses are observed after contingency events in networks. Due to quick inertial responses, there is a practically very limited time interval for control actions in real-time. Thus, system operators need to understand the prior inertia values to plan, control, and operate the network securely. Long-range forecasting of the network's inertia values, in contrast to short-range forecasting techniques, can pinpoint when the network is most likely to be vulnerable in a reasonable time ahead. Thus, …

A Content-Based Recommender System For The Uav Caching In The Field Of Entertainment In Fog Computing, Elham Darbanian, Mohsen Nickray

Turkish Journal of Electrical Engineering and Computer Sciences

The Unmanned Aerial Vehicle (UAV) can be used as good flying base stations to cache popular content and follow a user mobility pattern, to help them in a suitable services. Conventional edge caching algorithms often prioritize cache contents with higher popularity. Nevertheless, the cache capacity of mobile devices is restricted, and diverse clients may have expansive varieties in content inclination designs. In this manner, the performance and effectiveness of the cache will be so constrained without great strategies. The composition of recommender system and edge caching is considered as a new research topic, which is used to reduce cost and …

2 Kv Al0.64Ga0.36N-Channel High Electron Mobility Transistors With Passivation And Field Plates, Md Tahmidul Alam, Jiahao Chen, Kenneth Stephenson, Md Abdullah-Al Mamun, Abdullah Al Mamun Mazumder, Shubhra S. Pasayat, Asif Khan, Chirag Gupta

Faculty Publications

High voltage (∼2 kV) Al_0.64Ga_0.36N-channel high electron mobility transistors were fabricated with an on-resistance of ∼75 Ω. mm (∼21 mΩ. cm²). Two field plates of variable dimensions were utilized to optimize the breakdown voltage. The breakdown voltage reached >3 kV (tool limit) before passivation however it reduced to ∼2 kV after Si₃N₄ surface passivation and field plate deposition. The breakdown voltage and on-resistance demonstrated a strong linear correlation in a scattered plot of ∼50 measured transistors. The fabricated transistors were electrically characterized and benchmarked against the state-of-the-art high-voltage (> 1 kV) …