Engineering Commons^™

Distribution System Reliability Evaluation Considering Protection Coordination Using Petri Nets, Rani Kumari, Bhukya Krishna Naick

Turkish Journal of Electrical Engineering and Computer Sciences

Maintaining reliable and high-quality power delivery becomes increasingly complex with expanding power grids. The lack of protection coordination poses a significant threat, compromising overall system reliability. This research addresses this challenge by proposing a method for coordinating protective devices within the distribution system, specifically during network faults. The proposed approach utilizes a stochastic timed Petri net (STPN) based methodology to model protective device coordination across various fault scenarios. This technique effectively captures the dynamic behavior and interactions of protective equipment, allowing for the anticipation of potential disturbances. This proactive insight facilitates preventative measures to address prewarning situations, thereby preventing cascading …

Proposed Methodology For Correcting Fourier-Transform Infrared Spectroscopy Field-Of-View Scene-Change Artifacts, Kody A. Wilson, Michael L. Dexter, Benjamin F. Akers, Anthony L. Franz

Faculty Publications

Fourier-transform spectrometers are widely used for spectral measurements. Changes in the field of view during measurement introduce oscillations into the measured spectra known as scene-change artifacts. Field-of-view changes also introduce uncertainty about which target the measured spectrum represents. Though scene-change artifacts are often present in dynamic data, their significance is disputed in the current literature. This work presents a theoretical framework and experimental validation for scene-change artifacts. Field-of-view changes introduce variable interferogram offsets, which standard processing techniques assume are constant. The error between the interferogram offset and its estimate is Fourier-transformed, yielding scene-change artifacts, often confused with noise, in the …

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Turkish Journal of Electrical Engineering and Computer Sciences

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Exploitation Prioritization And Residual Risk Assessment Based On Hybrid Mcdm Model, Zibo Wang, Yaofang Zhang, Sicai Lv, Yingzhou Wang, Hongri Liu, Bailing Wang

Turkish Journal of Electrical Engineering and Computer Sciences

Exploitation is one of the most significant ways to launch attacks using vulnerabilities. The increasing number of vulnerabilities and limited allocation of security resources make it impossible to eliminate all exploitations. Because not every vulnerability can be fixed, it is necessary to rank exploitations and subsequently assess the residual risk, which is defined as the remaining threat potential after each elimination. In this paper, a structured and flexible decision support framework based on a hybrid multicriteria decision-making model is proposed for prioritizing exploitations and assessing residual risk. Metrics are treated as criteria in the model. The hybrid model is developed …

A Deep Learning-Based Real-Time No-Reference Image Decolorization Network With Perceptual Preservation, Mengjuan Zhao, Yitao Liang, Weiya Shi, Juan Xia

Turkish Journal of Electrical Engineering and Computer Sciences

Currently, grayscale images are preferred as input data for some specific vision tasks. Decolorization is the transformation of a color image into a grayscale image. Efficient decolorization algorithms can improve the overall task efficiency, while perceptual preservation in decolorization can provide more information for further processing. In recent research, traditional methods focus on preserving contrast or detail information with little attention to perceptual features. Deep-learning methods are beginning to consider perceptual preservation, but they run inefficiently. In addition, the decolorization methods lack the optimal target grayscale images for reference. Therefore, we propose a new deep learning-based real-time no-reference decolorization network …

Noninvasive Condition Monitoring For Eccentricity Fault Detection In Large Hydro Generators, Atena Tazikeh Lemeski, Di̇dem Tekgün, Ozan Keysan, Kemal Leblebi̇ci̇oğlu, Murat Göl

Turkish Journal of Electrical Engineering and Computer Sciences

Eccentricity faults in electric machines remain a critical concern, as they generate uneven magnetic forces that increase vibration and noise, ultimately raising the risk of premature motor failure. This study proposes a method for the early detection of dynamic eccentricity (DE) faults in hydropower plants through an advanced optimization-based parameter identification technique integrated with finite element analysis (FEA). Finite element modeling (FEM) is first used to analyze an existing salient-pole synchronous generator (SPSG) from a hydroelectric power plant in Türkiye. The effects of DE faults on the SPSG’s magnetic equivalent circuit parameters are then examined under various fault severities. A …

A Deep Learning Model For Accurate Tomato Leaf Disease Identification, Maheen Shahzad, Muhammad Abdullah Javed, Erum Ashraf, Hafiz Ishfaq Ahmad, Sabeen Masood

Turkish Journal of Electrical Engineering and Computer Sciences

Recent advances in machine learning and deep learning have greatly improved how we detect plant diseases, making diagnoses more accurate, faster, and easier to scale. However, many existing solutions depend on large, pretrained models that need powerful hardware, which limits their use in the field, especially in areas with limited resources. To tackle this, we designed a custom lightweight convolutional neural network (CNN) built from scratch using 20,000 carefully selected images from the PlantVillage tomato dataset. Our model uses Squeeze-and-Excitation (SE) blocks and Swish activation functions to boost performance, reaching an accuracy of 97.7% while using far fewer computing resources …

Influence Of Sinr And Noise Variance On Outage Probability For Mimo-Noma System In 5g And Beyond, Sadiq Ur Rehman, Jawwad Ahmed, Muhammad Zubair, Syed Sajjad Hussain Rizvi

Turkish Journal of Electrical Engineering and Computer Sciences

Nonorthogonal multiple access (NOMA) communication presents a promising solution to the limitations of traditional orthogonal multiple access techniques, offering potential improvements in achievable rates. Multiple-input multiple-output (MIMO), when combined with NOMA (MIMO-NOMA), further enhances these benefits by leveraging the diversity advantages of multiple antennas. Looking ahead, the future of wireless communication hinges on deploying heterogeneous networks (HetNets), facilitating the coexistence of various wireless access networks in a hierarchical fashion. However, the advent of 5G and 6G communications brings shorter channel coherence times, rendering channel reciprocity unreliable. Consequently, conventional channel estimation methods relying on uplink (UL) pilots for downlink (DL) transmission …

A Novel Approach To Maximum Weighted Traffic Flow Method For Effective Signal Control, Zülal Hi̇lal Yildiz Budak, Seyi̇t Alperen Çeltek, Aki̇f Durdu

Turkish Journal of Electrical Engineering and Computer Sciences

Traffic signal management is a critical challenge due to its environmental, economic, and public health impacts. The maximum weighted flow method (MaxWeightedFlow) was developed to optimize traffic flow at isolated and coordinated urban intersections. This study proposes a new method, the novel MaxWeightedFlow, which includes two key strategies to enhance the classical approach. The first strategy reduces computational burden by estimating vehicle approach times based on instantaneous speeds, improving real-time performance. The second employs regression analysis to optimize the alpha parameter, representing the vehicle waiting coefficient. The proposed approach, the novel MaxWeightedFlow, was evaluated using real-world traffic data from Kilis, …

Improving Rail System Signaling Efficiency Through Ai-Based Driving Profile Generation: A Comparative Performance Analysis, Mehmet Taci̇ddi̇n Akçay, Abdurrahi̇m Akgündoğdu

Turkish Journal of Electrical Engineering and Computer Sciences

In this study, a dataset comprising 3600 discrete operational snapshots (rather than continuous time-series data) derived from real-field operations is used to obtain a high-accuracy driving profile equation using a second-degree Polynomial Regression method. This equation demonstrates the model’s interpretability. The performance metrics obtained with the second-degree polynomial regression model’s equation are as follows: a coefficient of determination (R2) of 0.84, a Pearson Correlation Coefficient of 0.91, and an RMSE of 11.13. These results indicate the effectiveness of artificial intelligence-based approaches in improving the efficiency of the railway signaling system. The same dataset is also utilized with other machine learning …

Exploring Runtime Sparsification Of Yolo Model Weights During Inference, Tanzeel-Ur-Rehman Khan, Sanghamitra Roy, Koushik Chakraborty

Electrical and Computer Engineering Student Research

In the pursuit of real-time object detection with constrained computational resources, the optimization of neural network architectures is paramount. We introduce novel sparsity induction methods within the YOLOv4-Tiny framework to significantly improve computational efficiency while maintaining high accuracy in pedestrian detection. We present three sparsification approaches: Homogeneous, Progressive, and Layer-Adaptive, each methodically reducing the model’s complexity without compromising its detection capability. Additionally, we refine the model’s output with a memory-efficient sliding window approach and a Bounding Box Sorting Algorithm, ensuring precise Intersection over Union (IoU) calculations. Our results demonstrate a substantial reduction in computational load by zeroing out over 50% …

Robust And High-Efficiency Demodulation Of Ultra-Weak Fbg Arrays In Ofdr-Based Distributed Sensing, Zhaopeng Zhang, Yuxuan Cao, Xu Liu, Dingcheng Wang, Bo Liu, Chen Zhu

Electrical and Computer Engineering Faculty Research & Creative Works

A robust and high-efficiency demodulation scheme for optical frequency domain reflectometry (OFDR) based ultra-weak fiber Bragg grating (UWFBG) array detection system, originating from the Buneman frequency estimation (BFE) algorithm, is proposed and experimentally demonstrated. Due to the current limitations and imperfections of FBG inscription technology, the quasi-continuous inscription approach, along with its less-than-ideal outcomes, gives rise to problems of grating spectrum splitting and spectral distortion during the grating demodulation process. This renders the traditional approach of directly applying the BFE algorithm for grating demodulation ineffective, despite its significant enhancement of demodulation efficiency. To address this issue, we propose utilizing the …

Temperature Dependent High Frequency Performance Of A 62% Algan Channel Hemt, Jiahao Chen, Abdullah Al Mamun Mazumder, Parthasarathy Seshadri, Dheekshinn Nandakumar, Ruixin Bai, Rafael Andrew Choudhury, M. Asif Khan, Chirag Gupta

Faculty Publications

This article reports on the temperature dependent performance of a HEMT with an Al_0.62Ga_0.38N channel layer and an Al_0.84Ga_0.16N barrier layer grown by metal–organic chemical vapor deposition. The device in this report was measured at room temperature and elevated temperatures of 100–150 °C. The sheet resistance increased from 3.5 kΩ/sq (25 °C) to 5.4 kΩ/sq (150 °C), while the contact resistance remained nominally similar. For a device with 160 nm gate length and 2 µm source-to-drain length, excellent electrical characteristics have been achieved when the device was operated at 150 °C with …

Optimized Resnet-18 Architecture For Multi-Class Oral Diseases Classification, Ahmed Ahmed

Karbala International Journal of Modern Science

In recent years, the classification of oral diseases has gained significant attention due to its influence on public health and the necessity for early and accurate diagnosis. Traditional diagnosis depends on manual clinical assessment, which can be slow and subjective. An optimized and subsequently quantized model is required to provide a faster and more consistent diagnostic support tool. This paper proposes an optimized ResNet-18 architecture for the classification of six oral diseases. The optimization process is based on removing the Rectified Linear Unit (ReLU), Batch Normalization (BN), and convolutional layers from the base ResNet-18 blocks that contain 128 filters. This …

Model-Based Investigation Of The Influence Of Environmental Conditions On The Energy Supply Of Multirotor Uavs, Morten Roßberg, Hanna Dibbern, Claudia Werner

Journal of Aviation Technology and Engineering

Flight time of unmanned aerial vehicles (UAVs) is limited by available energy, which is affected by the mission profile and external factors, such as environmental conditions. Two main environmental conditions that need to be considered are the impact of wind and ambient temperature on the UAV and its energy supply. The purpose of this study is to examine the effects of wind and ambient temperature to conduct a preliminary evaluation of flight performance and flight limitations. For this reason, three locations in the United States—New York City, Miami, and Fairbanks—are selected and the impact of the considered environmental conditions of …

Research Progress On Generating Perfect Vortex Beams Based On Metasurfaces, Xiujuan Liu, Manna Gu, Ying Tian, Mingfeng Zheng, Bo Fang, Zhi Hong, Chee Leong Tan, Xufeng Jing

Electrical and Computer Engineering Faculty Publications and Presentations

This article reviews the latest advances in the generation and control of perfect vector beams using metasurfaces. In recent years, metasurfaces have garnered increasing interest due to their simple fabrication and easy integration. Perfect vortex beams (PVBs), as a type of vector beams, exhibit complex polarization states that require the superposition of multiple phases for their generation. The use of metasurfaces provides a compact platform for the generation of perfect vortex beams and enables more complex vortex beam control tasks, which are quite challenging for traditional optics. This paper begins by introducing the principle of perfect vortex beam generation using …

Universal Sound Separation: Distance-Aware Mixture Simulation, Co-Occurrence Conditioning, And Chain-Of-Inference, Wonjun Park

Computer Science and Engineering Theses - Archive

Universal Sound Separation (USS) -- the task of disentangling arbitrary sound sources from a single-channel acoustic mixture -- remains an open challenge due to the ill-posed nature of the problem and the distributional gap between synthetic training data and real-world recordings. This thesis addresses three distinct bottlenecks in the USS pipeline: training data realism, inference strategy, and conditioning richness. We first present two knowledge-guided approaches to sound source separation. The first is a distance-aware mixing strategy that leverages Large Language Models (LLMs) to assign plausible loudness relationships between audio sources during training data synthesis. By querying an LLM about the …

Study Of Clustering Technique And Communication Topologies For Cooperative Control-Based Volt-Var Optimization, Gaurav Yadav, Yuan Liao, Dan M. Ionel

Electrical and Computer Engineering Faculty Publications

Introducing renewable distributed generation (DG) in the power distribution system causes rapid voltage fluctuations due to its intermittency. This intermittency renders conventional voltage regulation devices such as on-load tap changers (OLTCs) and capacitor banks (CBs) inefficient to regulate rapid voltage changes and leads to reduced equipment lifetime and high operation and maintenance costs. Hence, this calls for non-conventional methods to mitigate such voltage fluctuations. This paper presents a cooperative control-based method aimed to optimally control the reactive power of DG inverters to mitigate the voltage deviations by establishing communication among the DG nodes, and between DG and non-DG nodes. This …

A. Vs. I In Ai: Is There A Threshold To "Engineered" Intelligence?, Joshit Mohanty

Engineering Management & Systems Engineering Faculty Publications

Despite artificial intelligence reshaping the world, its development generates uncertainties regarding future capabilities. AI simultaneously exists as an artifact of engineering design and as autonomous intelligence, creating an observer-participant feedback loop. This paper proposes that embodied AI faces a bandwidth-limited intelligence threshold T_h that it arises from B = min(C_sens,C_Act). However, Shannon capacity measures bits while intelligence operates on concepts, necessitating a dual-channel model separating physical bandwidth B_io from representational capacity B_rep. Intelligence emerges as multi-dimensional rather than scalar, with components exhibiting different bandwidth dependencies. Surpassing T_h requires either new sensing methods expanding B, enhanced representational frameworks, or reconceptualization within …

Parking Information And Supervision System, Joshua A. Thum, Alex J. Kinch, Jacob A. Dye

Williams Honors College, Honors Research Projects

In densely populated areas, finding parking can be an arduous and time-consuming struggle, especially in large and tall parking decks. Drivers would benefit from a convenient way to find an open parking spot without having to scour the entire lot first. The goal of this project is to sense available parking spots in a parking garage or parking lot using physical object detection and visual detection with computer vision verification, and display the open spots to drivers entering the lot. This information will be displayed locally at the lot and in an app, with the latter allowing someone to see …

Robotic Air Hockey Table, William Forcey, Andrew Piunno, Kaden Carpenter, Xander Zavatchen

Williams Honors College, Honors Research Projects

Air hockey, a popular arcade game, is traditionally designed for two players. This limits the game’s accessibility for individuals who wish to practice or enjoy it as a single player. To solve this problem, a robotic system was implemented to play air hockey against a human player. The speed and acceleration of the puck and mallet were measured from a game played between humans to inform the required movement capabilities of the robot. The robotic opponent implemented observes the location of the puck on the table using a camera and predicts where it will be in the future. A Cartesian …

Heat Input Control And Deep Learning-Based Indirect Measure Of Process And Deposition Stability In Wire Arc Additive Manufacturing, Alessandra Caggiano, Giulio Mattera, Yuming Zhang, Roberto Teti

Electrical and Computer Engineering Faculty Publications

A process qualification-oriented data-driven framework for Wire Arc Additive Manufacturing (WAAM) integrating qualification data, process monitoring and feedback control, is presented. A proportional control strategy regulating heat input by varying the Contact Tip–to–Workpiece Distance (CTWD) is developed to enhance process stability, ensure consistent layer geometry and maintain the qualified heat-input conditions for process qualification. To assess the control strategy stability, deep learning-based CTWD soft sensing from high-frequency welding signals is combined with an uncertainty-aware process quality index. The framework is validated on Invar 36 alloy, but it supports extension to other alloys and arc welding-based additive processes.

Safety Aware Continual Reinforcement Learning-Based Output Tracking Control Of Nonlinear Continuous-Time Systems, Irfan Ganie, Sarangapani Jagannathan

Electrical and Computer Engineering Faculty Research & Creative Works

An output feedback (OF)-based control scheme utilizing both a scalable multilayer neural network (MNN) observer and actor–critic MNN via integral reinforcement learning (IRL)/adaptive dynamics programming (ADP) approach for a class of nonlinear systems with output constraints is introduced. The proposed observer, critic, and actor MNN weight updates are derived using a singular value decomposition (SVD) of MNN activation function gradient along with output error, Bellman and control input errors, respectively. Next, the approach incorporates continual learning (CL), utilizing a penalty function in the weight update laws for both actor–critic MNNs to consolidate knowledge from previous tasks and enhance learning in …

Data For: Optimizing Finite Structures To Suppress The Photonic Density Of States, Prakash Mishra, Sukhad Dnyanesh Joshi, Quintin A. Hatzis, Aditya Bahulikar, M. Cenk Gursoy, Rodrick Kuate Defo

Electrical Engineering and Computer Science - All Scholarship

We propose a topology-optimization framework for optimizing finite structures of arbitrary shape by combining density-based methods with level-set approaches. We first optimize regular polygonal structures to suppress the photonic density of states and find that the best performing polygon is consistent with a tiling of space with hexagonal unit cells. We next show that introducing cavities into hexagonal structures further suppresses the photonic density of states, particularly when the cavity is also hexagonal. Such a result would find application in the design of fiber-optic cables. We then describe an approach for optimizing arbitrary x-simple or y-simple designs that can recover …

Localized Air Pollution Impacts From Data Centers In Northern Virginia, Damian Pitt, Ivan Suen, Ellie Plisko

Institute for Sustainable Energy and Environment Publications and Presentations

This report examins the extent of air pollution emissions resulting from the use of backup generators at data centers in Northern Virginia, including both actual current emissions and potential future emissions, and how those emissions totals compare to other sources of air pollution in the Northern Virginia region. It also examines how exposure to data center emissions correlates with demographic characteristics such as race, income, and education.

We while the air pollution emissions from individual data centers is minor, the collective impact from the 100+ such facilities in the Northern Virginia region is significant. Notably, the cumulative emissions exposure in …

Ransomware As Organization: A Comparative Analysis Of Corporate And Criminal Structures In Conti, George Urling

Theses, Dissertations and Capstones

Cybercriminal groups continue to pose major threats to global cybersecurity. One of the most common types of cybercriminal groups are, “Ransomware-as-a-Service (RaaS)" groups, who create and sell ransomware. While research is conducted into the development of ransomware, there is limited reporting on the organizational structure and habits of RaaS groups. In 2022, prominent RaaS group Conti had their chat logs leaked, with the logs ranging from 2020 to 2022. This study seeks to provide a deeper understanding of RaaS group structures by utilizing the Conti leaked logs as a case study. The study, entitled “Ransomware as Organization: A Comparative Analysis …

Goal-Driven Shared Control In Eeg-Based Brain Machine Interface For Freewill Reaching And Grasping With Movement Intention Detection And Goal Position Decoding, Bhoj Raj Thapa

Theses and Dissertations--Electrical and Computer Engineering

Upper limb motor impairments can severely limit a person’s ability to perform everyday reaching and grasping tasks. Electroencephalogram (EEG)-based brain machine interfaces (BMIs) offer a non-invasive approach for translating neural activity into control signals for assistive devices such as robotic arms. However, traditional EEG-based BMI studies have generally focused on externally cued paradigms, where both movement timing and target selection are specified by the experimenter rather than freely chosen by the user. In addition, shared control offers a practical framework for assistive BMI operation by dividing responsibility between the user and the intelligent robotic system. However, in many EEG-based shared …

Robust Broadband Characterization Of Flexible Absorbers, Sheet Material, And Liquids Using A Coaxial Structure, Joseph Christopher Stecher

Masters Theses

Modern high-frequency measurement systems require reliable calibration and sample positioning to ensure measurement fidelity. This thesis presents three studies addressing practical limitations in broadband material parameter extraction and instrumentation.

The first study introduces a modified Nicolson–Ross–Weir (NRW) technique for flexible, compression-sensitive materials from 100 MHz to 18 GHz. Rigid 3D-printed spacers ensure precise sample positioning, and a T-matrix–based de-embedding procedure removes spacer effects. Validation using microstrip measurements and full-wave simulation confirms accurate permittivity extraction across compression levels.

The second study extends NRW to sheet materials enabling accurate material characterization. Independent validation using toroidal inductors with leakage correction and parallel-plate capacitors …

Validating A Low-Cost Radio Frequency Characterization Framework For Development-Grade Software-Defined Radios In Short-Duration Cubesat Missions, Thomas Wayne Francois

Masters Theses

Software-defined radios (SDRs) and CubeSat platforms have reduced the cost and complexity of space-based communication systems, enabling broader participation in satellite missions. While low-cost radio hardware is increasingly accessible, the ability to characterize and validate its performance remains constrained by the high cost and limited access to traditional RF test equipment. This disparity creates a challenge for small satellite development teams, which must characterize communication-system technical performance with limited access to laboratory-grade instrumentation.

This thesis presents a low-cost RF characterization framework for assessing key radio-frequency performance metrics using readily available hardware and measurement techniques. The approach integrates frequency translation, SDR-based …

Formalizing Asymmetric Control-Telemetry Separation In Distributed Industrial Control Systems, Andrew Manison

College of Graduate Studies: Theses & Dissertations

Distributed industrial control systems often place control and telemetry traffic on the same communication substrate even though the two workloads impose different requirements. Control paths need bounded request-response latency and predictable acknowledgement semantics, whereas telemetry paths benefit from scalable publish-subscribe fanout and tolerance for consumer-side delay. This thesis argues that, for the tested class of mixed workloads on shared commodity infrastructure, these communication roles should be separated architecturally rather than forced through a single protocol. To evaluate that claim, the thesis formalizes an asymmetric control- telemetry pattern and instantiates it in the Asymtra framework using gRPC for synchronous control and …