Digital Commons Network^™

Joint Assessment Of Characteristics Of Object Parameters And Statistical Characteristics Of Disturbing Influences, Yulduz Mukhtarkhodzhaevna Abdurakhmanova

Technical science and innovation

The paper considers the problem of joint estimation of object parameters and statistical characteristics of random disturbances based on data from independent experiments. It is indicated that the use of the method of moments for the problem under consideration gives estimates that are not effective in the Rao-Kramer sense and the most common asymptotically effective estimation method, in this sense, is the maximum likelihood method(ML). However, in the case of objects with several disturbances that have distribution densities of a general form, the implementation of the ML method is difficult, since to find the values of the distribution density it …

Mathematical Model Of Control Of A Pumping Unit Used In The Technological Process Of Wastewater Treatment, Nodira Batirdjanovna Alimova Dsc, Professor, Aziza Ruzimamamovna Khaitova

Technical science and innovation

Strict environmental and health regulations, as well as the cost-effectiveness of wastewater treatment processes, have made control technology an important priority in the wastewater treatment industry. Control strategies operate on input variables (setpoint values) by choosing the values they should take to influence the evolution of state variables so that output variables (controllable variables) achieve desired values. The object is a pumping unit used in wastewater treatment technology, and its mathematical model is a set of structures reflecting the characteristics of the simulated processes. The main parameters in mathematical models are temperature and vibration level during pump operation, coordinates of …

Qualitative Analysis Of The Circuits Of Autonomous Inverters With Shut-Off Valves, Shukhrat Badreddinovich Umarov

Technical science and innovation

The article presents the results of a qualitative analysis of circuits of autonomous current and voltage inverters with cut-off valves; the influence of the charge value of the switching capacitor in parallel and series equivalent circuits on the restoration of the switching properties of the thyristors of the inverter power circuit is studied. It is shown that due to the energy periodically accumulated in the inductive elements of the load, the voltage on the switching capacitor in the cut-off state is greater than in a conventional parallel autonomous current inverter. This circumstance ensures an increase in the switching stability of …

Method For Calculating Characteristic Frequency Ab (Rechargeable Batteries), Alisher Ganisher Ugli Bozorov

Technical science and innovation

A method for calculating the characteristic frequency is proposed, based on automatic interpolation of the frequency dependence of the reactance and subsequent calculation of the characteristic frequency as one of the roots of the interpolation function. The developed algorithm allows you to calculate the characteristic frequency from only 4 points of the frequency dependence of the reactance. In this case, the error in calculating the resonant frequency parameter does not exceed ~2 Hz.

Obviously, the values of the coefficients of the equation depend on the number of resistance values X and the corresponding frequencies of the test signal. The indicated …

Design, Control, And Evaluation Of A Photovoltaic Snow Removal Strategy Based On A Bidirectional Dc-Dc Converter For Photovoltaic–Electric Vehicle Application, Salma Elakkad, Mohamed Hesham, Hany Ayad Bastawrous, Peter Makeen

Electrical Engineering

A novel self-heating technique is proposed to clear snow from photovoltaic panels as a solution to the issue of winter snow accumulation in photovoltaic (PV) power plants. This approach aims to address the shortcomings of existing methods. It reduces PV cell wear, resource loss, and safety risks, without the need for additional devices. A self-heating current is applied to the solar panel to melt the snow covering its surface, which is then allowed to slide off the panel due to gravity. The proposed system consists of a bidirectional DC-DC converter, which removes the snow cover by heating the solar PV …

Safety And Optimality Monitors For Learning-Enabled Systems Using Conformal Prediction, Jackson Cox

McKelvey School of Engineering Graduate Student Theses & Dissertations

The use of machine learning to create data-driven plant models and controllers has led to an increased need for safety and optimality monitors for model-based systems. System plant models are subject to uncertainty due to learning constraints such as unseen data and overfitting or physical constraints such as unknown dynamics and noise. This uncertainty is detrimental to safety-critical systems and must be properly regulated. To curb this uncertainty, we create prediction sets using the guarantees provided by Conformal Prediction. With a user-specified high probability, these prediction sets contain the true plant system states for an entire prediction horizon, which we …

Modelling V-Band Atmospheric Loss With Deep Learning, Ralph Lyndon Gesner

Electrical and Computer Engineering ETDs

Modelling atmospheric loss is fundamental to implementation of future mm-wave terrestrial and earth space communication links. Atmospheric losses have traditionally been estimated by calculating the contributions of individual weather phenomena through in-situ meteorological measurements and statistically derived models for estimating localized atmospheric water content. Collecting data for all points along the propagation path and calculating the physical effects of gases, clouds, and precipitation individually is both time consuming and impractical for large-scale use cases.

In response to these present limitations, this work proposes the use of novel deep learning models for estimating atmospheric losses from tabular meteorological sensor data and …

Theory And Algorithms To Learn, Propagate, And Exploit Uncertainty For Stochastic Optimal Control Of Dynamical Systems, Vignesh Sivaramakrishnan

Electrical and Computer Engineering ETDs

Non-Gaussian uncertainty frequently arises in learning and control problems involving stochastic dynamical systems, particularly in autonomous vehicles, UAVs, satellites, and robotics. In this dissertation, we propose a new framework that leverages characteristic functions that provides a frequency-domain representation of random variables. The dissertation is structured into three key areas. First, we address model-based stochastic optimal control for linear systems with non-Gaussian noise, demonstrating that characteristic functions can be used to enforce chance constraints and control systems toward desired distributions. Second, we explore data-driven stochastic control, utilizing empirical characteristic functions to handle systems with unknown disturbances. In addition, we derive several …

Jovial Family Farm Facilitates Intergenerational Learning Opportunities Through Urban Farming, Maera Williams

Purdue Journal of Service-Learning and International Engagement

Maera Williams is a sophomore in Environmental and Ecological Engineering at Purdue University. She joined the Engineering Projects in Community Service (EPICS) Urban Farming team as a freshman and now serves as project manager of ten undergraduate students collaborating with Jovial Family Farm to design and develop three indoor hydroponics systems for a laboratory space on their property in Broad Ripple, IN. Jovial Family Farm is a nonprofit organization founded by Drs. Bobbie and Brian Jellison with the mission to provide senior citizens, their families, and their communities with meaningful engagement opportunities on an intergenerational scale through urban agricultural activities. …

Ge/Sige Quantum Wells: Material For The Post-Moore Era, Troy Alexander Hutchins-Delgado

Optical Science and Engineering ETDs

This dissertation demonstrates high-quality germanium quantum wells on a 200 mm silicon wafer platform, enabling novel device possibilities. Partnering with a commercial silicon-germanium epitaxy supplier, we obtained shallow, undoped germanium quantum wells with high-crystalline quality, confirmed through x-ray diffraction, secondary ion mass spectroscopy, high-resolution scanning transmission electron microscopy, and energy dispersive x-ray spectroscopy. Hall bar devices fabricated on single quantum wells revealed that surface preparation can tune transport properties while maintaining peak mobilities around 10⁵ cm²V⁻¹s⁻¹. Manganese-germanide spintronic contacts were integrated via solid-state reaction, with contact quality assessed through Schottky diodes, transfer length …

Identifying Subject Bias In Wifi-Based Human Activity Recognition Evaluation Methods, Amany Elkelany, Robert J. Ross, Susan Mckeever

Conference papers

WiFi-based Human Activity Recognition (HAR) has emerged as a promising approach for monitoring and analysing human activities in a non-intrusive manner, leveraging WiFi signals for activity classification. Despite advancements, existing WiFi-based HAR research lacks consideration of subject (human) bias. This results in learning models performing well on individuals used in the training samples but failing to generalise to new/unseen subjects, in contrast to known good practices in machine learning. In this paper, we address this oversight directly by systematically examining the evaluation methodology for the WiFi-based HAR context. Specifically, we investigate the impact of Leave-One-Subject-Out Cross-Validation (LOSOCV) in a hybrid …

Applying Positive Unlabeled Learning Techniques And Using The Kullback-Leibler Divergence To Improve Geothermal Surveying Assessments, Martín Thomas Rodriguez

Dissertations and Theses

As we face the current climate crisis, the discovery of geothermal energy resources has the potential to greatly reduce our dependence on fossil fuels worldwide. However, the development of any new energy infrastructure is expensive and depends on the willingness of energy agencies and developers to make initial investments based on calculated risk measures. One such measure, called geothermal favorability, is the likelihood that a site has conditions favorable for geothermal systems containing recoverable energy potential. Its prediction from existing geophysical datasets proves to be a nontrivial task. The prediction of geothermal favorability can be framed as a binary classification …

Analysis Of Learning Mechanisms In Spiking Neural Networks With R(T) Elements And Memristive Synapses, Farhana Afrin

Boise State University Theses and Dissertations

As Moore's law ends, the conventional von Neumann computer architecture with binary-coded data representation has reached its bottleneck because of having separate computing and memory modules. In this architecture, continuous power is required due to sequential processing, making it challenging to improve efficiency further. The human brain can be regarded as the most energy-efficient computer architecture. In the brain, data is represented as small voltage pulses as spikes. That is why the neural units are called spiking neural networks (SNN). In SNNs, energy is required only when there is a spike, making it more energy efficient than the von-Neumann computer …

Spectrum Optimization For Advanced Air Mobility Communications Using Deep Reinforcement Learning., Rafael D. Apaza

Electronic Theses and Dissertations

As aviation operations expand and new participants enter the National Airspace System (NAS), the demand for aeronautical communications will experience a significant rise. This surge is propelled by increased air travel and the emergence of Urban Air Mobility (UAM) operations, a subset of Advanced Air Mobility (AAM). UAM aims to facilitate intra-city transportation of people and cargo utilizing remotely piloted aircraft capable of electric vertical takeoff and landing operations. The growing dependence on efficient wireless communication systems underscores the critical importance of intelligent spectrum allocation and effective airspace management to ensure safe, seamless, and technologically advanced air operations. However, the …

Development Of A Square Wave Voltage Field-Oriented Controller For A Linear Vapor Compressor., Thomas Everson

Electronic Theses and Dissertations

In this thesis, a novel control strategy for an inverter-driven linear compressor system is proposed in which the overall electrical losses of a coupled inverter-linear motor system are reduced through the use of a modified square wave inverter modulation scheme to minimize inverter switching losses at the expense of a modest increase in conduction losses. The controller is based on a single-phase field-oriented control strategy, and to achieve the FOC control objectives, the fundamental and dc components of the applied square wave voltage are matched to the corresponding values requested by the current controller. To validate the efficiency improvements offered …

Learning To Represent Temporal Dynamics And Generative Factors For Intelligent Visual Navigation, Sahand Khoshdel

All Theses

Visual navigation systems are crucial in various applications, including autonomous driving, unmanned aerial systems (UAS), and industrial automation. For these systems to operate efficiently in dynamic environments, they must not only interpret complex surroundings but also anticipate changes over time. Temporal prediction—forecasting environmental changes like moving obstacles or shifting lighting conditions—enables navigation systems to act proactively, enhancing both safety and performance. This dissertation investigates representation learning methods both as a backbone feature extractor for RL agents as well as a proxy for systems oriented for Explainable AI (XAI). Two main projects are presented as case studies to achieve the aforementioned …

Reinforcement Learning-Based Optimal Control Of Uncertain Nonlinear Systems, Miguel Garcia, Wenjie Dong

Electrical and Computer Engineering Faculty Publications

This paper considers the optimal control of a second-order nonlinear system with unknown dynamics. A new reinforcement learning based approach is proposed with the aid of direct adaptive control. By the new approach actor-critic reinforcement learning algorithms are proposed with three neural network approximation. Simulation results are presented to show the effectiveness of the proposed algorithms.

Surgical Suturing Skill Assessment Using Estimated Hand Roll Angle From A Deep-Learning Computer Vision Algorithm, Jianxin Gao, Amir Mehdi Shayan, Simar P. Singh, Joe Bible, Ravikiran Singapogu, Richard E. Groff

Publications

This paper proposes a deep-learning computer vision algorithm to estimate hand roll angles for metric-based assessment of surgical suturing skills. The number of rolls metric, previously calculated directly from IMU data, counts the number of hand roll reversals during a single suture. To calculate this metric using computer vision, we apply a deep-learning algorithm that can reliably estimate hand roll angles after training on suturing videos collected on the SutureCoach simulator. Results show that the estimation accuracy of the deep-learning algorithm is robust to different video backgrounds. The number of rolls metrics were used to analyze suturing performance in the …

Ellipticity-Controlled Exceptional Points From Nanoscale Metasurfaces, Benjamin Goldberg

McKelvey School of Engineering Graduate Student Theses & Dissertations

Precise control over light polarization is critical for advancing technologies in telecommunica- tions, quantum computing, and image sensing. However, existing methods for manipulating polarization around exceptional points in non-Hermitian systems, have exclusively focused on circular polarization and work with reflected light. To address this limitation, we de- velop a novel metasurface platform with high-Q resonators that enables tunable control of polarization exceptional points across arbitrary ellipticity for transmitted light. Our design uses orthogonally polarized guided mode resonators in a two-layer silicon metasurface, where careful tuning of the dipolar guided mode resonances (DGMRs) and layer spacing allows us to control the …

Debtor Eligibility Prediction Using Deep Learning With Chatbot-Based Testing, Reski Noviania, Enny Itje Sela, Luther Alexander Latumakulita, Steven R. Sentinuwo

Knowledge Engineering and Data Science

Predicting debtor eligibility is essential for effective risk management and minimizing lousy credit risks. However, financial institutions face challenges such as imbalanced data, inefficient feature selection, and limited user accessibility. This study combines Recursive Feature Elimination (RFE) and Deep Learning (DL) to improve prediction accuracy. It integrates a chatbot interface for user-friendly testing. RFE effectively identifies critical features, while the DL model achieves a validation accuracy of 97.62%, surpassing previous studies with less comprehensive methodologies. The chatbot's novel design not only ensures accessibility but also enhances user engagement through flexible input options, such as approximate values, enabling non experts to …

Optimal Strategy For Handling Unbalanced Medical Datasets: Performance Evaluation Of K-Nn Algorithm Using Sampling Techniques, Yulita Salim, Aulia Putri Utami, Abdul Rachman Manga, Huzain Azis, Fadhila Tangguh Admojo

Knowledge Engineering and Data Science

This study addresses the critical role of medical image classification in enhancing healthcare effectiveness and tackling the challenges of imbalanced medical datasets. It focuses on optimizing classification performance by integrating Canny edge detection for segmentation and Hu-moment feature extraction and applying oversampling and undersampling techniques. Five diverse medical datasets were utilized, covering Alzheimer’s and Parkinson’s diseases, COVID-19, brain tumours, and lung cancer. The K-Nearest Neighbors (K-NN) algorithm was implemented to enhance classification accuracy, aiming to develop a more robust framework for medical image analysis. The evaluation, conducted using cross-validation, demonstrated notable improvements in key metrics. Specifically, oversampling significantly enhanced lung …

A Hierarchical Density-Based Spatial Clustering Of Applications With Noise (Hdbscan) Approach For Identifying Potential Villages In Buleleng Regency, Dina Nur Amalina, Achmad Fauzan

Knowledge Engineering and Data Science

Buleleng Regency, located in Bali Province, possesses diverse village potential, including agricultural production and tourist attractions. However, this potential has not been fully optimized. Therefore, it is important to enhance village potential by clustering villages based on their specific characteristics to identify and prioritize those requiring special attention. This approach aims to promote equitable village development and reduce poverty levels. This study clusters villages in Buleleng Regency based on their potential using the Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN) method. The data utilized in this study comprises village potential data obtained from the Buleleng Regency Statistics Office …

Manifold Learning And Undersampling Approaches For Imbalanced Class Sentiment Classification, L.M. Risman Dwi Jumansyah, Agus Mohamad Soleh, Utami Dyah Syafitri

Knowledge Engineering and Data Science

Movie reviews are crucial in determining a film's success by influencing audience decisions. Automating sentiment classification is essential for efficient public opinion analysis. However, it faces challenges such as high-dimensional data and imbalanced class distributions. This study addresses these issues by applying manifold learning techniques, Principal Component Analysis (PCA) and Laplacian Eigenmaps (LE) to reduce data complexity and undersampling strategies (Random Undersampling (RUS) and EasyEnsemble) to balance data and improve predictions for both sentiment classes. On reviews of The Raid 2: Berandal, EasyEnsemble achieved the highest average G-Mean of 0.694 using Term Frequency-Inverse Document Frequency (TF IDF) features with a …

Constructing Qur’An Recitation Classification Using Alexnet Algorithm, Harits Ar Rosyid, Dzulkifli Abdullah, Mohammed S. Alqahtani

Knowledge Engineering and Data Science

The growing demands for accurate and efficient methods in the Qur'an recitation classification highlight the limitations of existing models, particularly in assisting the memorization process. This study aims to address these challenges by implementing the AlexNet Convolutional Neural Network architecture, widely recognized for its effectiveness in image classification, to classify the Qur'an recitations using the Mel Frequency Cepstral Coefficient (MFCC) as the feature extraction method. The research involves several stages, including data collection, preprocessing (audio segmentation by verse), data augmentation, feature extraction, and classification using the AlexNet architecture, followed by performance evaluation. Key results demonstrate that the combination of MFCC …

Deep Learning Approach For Dental Anomalies X-Ray Imaging Using Yolov8, Amelia Ritahani Ismail, Md Salim Sadman Taseen

Knowledge Engineering and Data Science

Dental X-ray imaging is a critical diagnostic tool for identifying various dental anomalies. However, manual interpretation is time-consuming, prone to human error, and requires specialized expertise. Deep learning models, particularly object detection frameworks like YOLO, have demonstrated promising results in automating medical image analysis. This study aims to develop and evaluate a YOLOv8-based deep learning model for automated detection and classification of 14 dental anomaly categories, including Caries, Crowns, Fillings, Implants, and Periapical lesions. The proposed approach addresses limitations in previous YOLO versions by leveraging anchor-free detection and enhanced feature extraction for improved accuracy. The model was trained on a …

Classification Of Anxiety Levels Entering The World Of Work In Final Year Students Using The Neighbor Weighted K-Nearest Neighbor Method, Awang Hendrianto Pratomo, Muhammad Fahmi Adam, Dessyanto Boedi Prasetyo

Knowledge Engineering and Data Science

This study evaluates the accuracy of the Neighbor Weighted K-Nearest Neighbor (NWKNN) method in classifying the anxiety levels of final-year students as they prepare to enter the workforce, particularly in cases of unbalanced data distribution. The system was developed using the prototype method, and NWKNN was applied to classify anxiety levels into low, medium, and high categories. Testing using the Confusion Matrix demonstrated strong performance, achieving an accuracy of 94% based on a dataset of 1009 students, with a 90:10 ratio of training to test data. The results indicate that NWKNN effectively provides classification input values, making it a reliable …

Comparative Analysis Of Bpnn And Lvq For Sundanese Character Recognition, Haviluddin Haviluddin, Herman Santoso Pakpahan, Dinda Izmya Nurpadillah, Hario Jati Setyadi, Medi Taruk, Rayner Alfred

Knowledge Engineering and Data Science

The Sundanese script (Aksara Sunda), an essential part of Sundanese cultural heritage, has been used since the 14th century AD. However, recognizing handwritten Sundanese characters remains challenging due to variations in individual writing styles. This study compares the performance of Backpropagation Neural Network (BPNN) and Learning Vector Quantization (LVQ) for recognizing handwritten Sundanese vowel (Swara) characters. A dataset was collected from 15 individuals, each writing seven Sundanese vowel characters, which were then used for training and testing the recognition models. Experimental results show that BPNN outperforms LVQ, achieving a higher classification accuracy (95.23%), lower Mean Squared Error (MSE), and faster …

Mechanically Cost-Effective Approach For Bipedal Walking In Robots Using Instantaneous Collision Angle, Smit R. Patel

UNLV Theses, Dissertations, Professional Papers, and Capstones

Humans, as bipedal locomotors, are effective at reducing the mechanical cost of transport (CoTmech) by adopting movement strategies and gaits that minimize energy expenditure for a given distance. By using different gaits at different speeds, leveraging their long spring-like tendons and muscle elasticity which store and release energy during movement, humans reduce the mechanical effort required for locomotion. Current locomotion solutions offered in bipedal robots, based on legacy walking and running gait models, are not great at energy efficiency unless walking at very low speeds. Additionally, the control system of robots, designed to ensure stability and adaptability, requires substantial resources, …

Clusteredlog: Optimizing Log Structures For Efficient Data Recovery And Integrity Management In Database Systems, Mariha Siddika Ahmad, Brajendra Panda

Electrical Engineering and Computer Science Faculty Publications and Presentations

In modern database systems, efficient log management is crucial for ensuring data integrity and facilitating swift recovery from potential data corruption or system failures. Traditional log structures, which store operations sequentially as they occur, often lead to significant delays in accessing and recovering specific data objects due to their scattered nature across the log. ClusteredLog addresses the limitations of traditional logging methods by implementing a novel logical organization of log entries. Instead of simply storing operations sequentially, it groups related operations for each data item into clusters. As a result, ClusteredLog enables faster identification and recovery of damaged data items …

Investigation Of Conductive Die-Top Thermal Capacitors During Short Circuit Operation Of Silicon Carbide Devices, Youssef Abotaleb

Department of Electrical and Computer Engineering: Dissertations, Theses, and Student Research

Silicon carbide (SiC) power devices have garnered significant attention in recent years due to their superior thermal and electrical properties compared to traditional silicon devices. However, SiC power devices suffer from severe reliability issues arising from their mechanical properties. This is because the Young's modulus of SiC is about three times larger than that of silicon, which correspondingly raises the mechanical stresses acting on the bonding structure, often leading to device failure under power shock events.

Traditional packaging materials and designs tend to constrain SiC performance, as hot-spot temperature resulting from a power shock is one of the key factors …