Engineering Commons^™

Low Cost Additive Manufacturing Of Segmented Stator Composite Polymer Permanent Magnet Dc Motors, Ben Goldberg, Jordan Bailey, Connor Hawkins, Colin Haskins, Razvan Voicu

Symposium of Student Scholars

This study presents a novel approach to the design, manufacture, and optimization of segmented stators for composite construction axial flux permanent magnet DC motors. Traditional axial flux stator manufacturing is both challenging and expensive, creating a bottleneck in rapid prototyping and innovation. To overcome these limitations, the stator is divided into individually fabricated segments using advanced composite polymer materials and low-cost additive manufacturing techniques. This segmentation not only drastically reduces production complexity and cost but also allows for customized coil geometries that maximize the surface area for improved heat dissipation.

A key innovation of our design is the integration of …

2d Probabilistic Scour Model For Predicting The Time-Rate Of Scour For Spillways And Overtopping Dams Based On The Erodibility Index Method, M. F. George, G. W. Annandale

5th International Seminar on Dam Protections Against Overtopping

Time-dependent prediction of rock scour for dams and spillways has largely remained elusive given limited available data on rock erosion rate parameters in literature. Recently, a theoretical framework for determining the rate of rock scour was developed by Annandale (2025), enhancing the Erodibility Index Method (EIM). The EIM is the most commonly used and accepted approach for evaluation of scour in rock for dam applications. This theoretical framework has been incorporated into the 2D probabilistic jet impingement scour model (George & Annandale, 2023) so that scour progression due to head-cutting in a spillway channel (lined or unlined) or overtopping onto …

Deep Learning Algorithms For Traffic Flow Predictions, Adegoke Ojeniyi, Prince Pal Singh, Ankita Vashisht, Swati Kumari, Karan Karan

AUIQ Technical Engineering Science

Given the growing complexity of urban transportation systems, precise traffic flow forecasting is essential for reducing not only issues of congestion but also, for boosting road safety and enhancing mobility management. This study integrates Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM), Long Short-Term Memory (LSTM), and Recurrent Neural Networks (RNN) to present a hybrid deep learning framework for traffic prediction. Of these, the CNN-LSTM model is a reliable option for real-time traffic forecasting since it successfully captures both spatial and temporal dependencies, resulting in superior predictive performance. The dataset used to assess the framework includes 48,120 records from a traffic monitoring …

Investigating The Impact Of Waterhead, Time And Temperature On Dam Displacement: Application Of Computer Aided Models, Maaz Abdullah

AUIQ Technical Engineering Science

Dam displacement is a crucial indicator for assessing the safety of a concrete dam through structural health monitoring. Since the displacement data exhibits a non-linear and complex relationship with influencing factors like waterhead, time and temperature, machine learning models are deployed to accurately predict dam displacement. Furthermore, the limited availability of monitored data in the majority of the dams renders the studies conducted with a large number of observations valueless. In order to address the aforementioned issues, this study proposes a feature selection approach to predict dam displacement by examining the ability of four ensemble machine learning models on different …

Comparative Assessment Of Land Surface Temperature In Urbanized Areas Of The Saudi Arabia, Rauf Khan, Syed Ilyas, Ziaul Haq Doost

AUIQ Technical Engineering Science

Land Surface Temperature (LST) has become a critical urban climate concern due to rapid urbanization and land cover transformation in Saudi Arabian cities. This study aimed to analyze the spatial and temporal variability of LST in three climatically distinct cities encompassing Riyadh (hot desert), Dhahran (hot humid), and Abha (cold desert) over the summer months (May to September) from 2017 to 2021. LST was retrieved through the RSLab Landsat LST web application. A total of 80 samples per year were compiled for each city using a structured sampling approach, and results were aggregated using the RS-LST Aggregation Framework (RSLAF). The …

Optimizing Hydrological Pan Evaporation Prediction Using Advanced Machine Learning Techniques With Spectral Clustering, Labib Sharrar

AUIQ Technical Engineering Science

Accurate prediction of pan evaporation remains a significant challenge due to inconsistencies across different climatic regions. This study aims to enhance pan evaporation estimation by developing a robust hybrid machine learning (ML) model that integrates spectral clustering with advanced regression techniques, specifically the Histogram-based Gradient Boosting Regressor (HGBR) and Extreme Gradient Boosting Regressor (XGBR), to improve prediction accuracy and adaptability across diverse environments. The research developed a novel methodology by employing spectral clustering for models' performance enhancement, followed by rigorous hyperparameter tuning, sensitivity analysis to assess the impact of individual features on each model. Finally, models underwent lack of fit …

Classification Of Daily Weather Conditions Using Decision Tree-Based Machine Learning Models: A Case Study Of Kabul, Afghanistan, Ahmad Bilal Ahmadullah, Ahmad Shah Irshad, Basir Ahmad Khaled

AUIQ Technical Engineering Science

As global climate variability intensifies, the need for accurate and reliable weather forecasting becomes increasingly important. This study aimed to classify daily weather conditions in Kabul, Afghanistan, by comparing two decision-tree-based machine learning (ML) models that includes Decision Tree Classifier (DTC) and Extra Trees Classifier (ETC). A complete year dataset consisting of 366 daily meteorological observations collected from a central weather station in the region for 2024 was used. Results revealed that the DTC model consistently outperformed the ETC model, obtained an overall accuracy of 99% in both the training and testing phases, compared to the ETC model's accuracy of …

Distal Weight-Bearing Implants Design Featuring An Integrated Groove Or Thread, Muntadher Saleh Mahdi, Dunya Abdulsahib Hamdi

AUIQ Technical Engineering Science

The distal weight-bearing implant was selected from a pool of approximately 17 implant systems that utilize the osseointegration mechanism currently available globally. It stands out due to its modernity, rarity, and creative concept, offering amputees with ``above-knee amputation'' a range of options that ensure their satisfaction and fulfill their requirements. However, this implant requires additional refinement and adaptation to achieve the highest level of perfection. This research implemented various modifications to the mechanical design of the implant, which were subsequently evaluated using the finite element analysis software ``ANSYS.'' Modifications included substituting the threads along the femoral stem with a groove …

Surface Urban Heat Island Effects Analysis In The Most Populated City In Thailand: Towards Sustainable Urban Development, Abdul Maulud Khairul Nizam, Muhammad Noor, Zafar Iqbal

AUIQ Technical Engineering Science

The fast and unprecedented urban growth process may violate cities' responsibilities by causing an urban heat island (UHI) and increasing the public's risk of heat-related illnesses due to vegetative area reduction and urban area inclination. The Bangkok Metropolitan Area (BMA)'s variations in Land Use Land Cover (LULC), Land Surface Temperature (LST), UHI, and several geospatial indicators, as well as the daytime and nighttime LST, are the main subjects of the study. Between 2015 and 2021, the highest Daytime LST (DLST) rose consistently from 36.77°C to 38.14°C, then slightly decreased to 37.22°C in 2023. There is a general increase in the …

A Comprehensive Review Of Advancements In Materials And Manufacturing For 3d Knee Implants, Huda Ali Hashim, Ghaidaa A. Khalid

AUIQ Technical Engineering Science

Over the past three decades, knee implant design has significantly advanced to address the challenges of replacing damaged knee joint bone with durable and efficient prosthetics. The aim of this review explore key developments in materials and manufacturing processes, focusing on biocompatible options such as zirconium, titanium alloys, UHMWP, and smart materials, as well as coatings designed for metal-sensitive patients. The study examines the mechanical forces acting on implants during daily activities, highlighting wear and infection risks, and evaluates the role of innovative manufacturing techniques in improving implant precision, cost-efficiency, and durability. Simulation methods, including Finite Element Analysis (FEA), are …

Investigating The Differential Effects Of Smote Variants On Class Imbalance And Exploring Their Applicability To A Thalassemia Prediction Model, Hussam Mezher Merdas, Ayad Hameed Mousa

AUIQ Technical Engineering Science

Researchers work around the clock on many datasets provided by various institutions. These researchers strive to come up with highly efficient Artificial Intelligence models. Often, researchers face the problem of imbalance in the distribution of classes in a particular feature in the selected dataset, which creates an Artificial Intelligence model biased towards one class at the expense of another class that is no less important than the first. On the other hand, thalassemia is a disease that affects people of different ages. The degree of disease varies according to the thalassemia class. This study proposes an improved Machine Learning model …

Wide Band Single-Mode Optical Fiber Design For Decreasing Bending Loss, Zahraa M. Kassem Alasady, Ahmed Al-Amiery

AUIQ Technical Engineering Science

Radiation loss due to fiber curving or bending is a major challenge in advanced technical applications like fiber-optic sensing or biomedical applications. This study focuses on the basic features that characterize a single-mode fiber (SMF) and its critical parameters in view of the recent improvements made. The planned design of SMF is, however, intended to resolve these problems by proposing minimizing bend loss by adopting a five-layer fiber structure designed to keep the optical field within the fiber core. The proposed SMF design exhibit ultra-low bending sensitivity, with estimated bending loss of 2×10-3 dB/turn for bending radius of 5 mm. …

Nuclear Energy: The Key To Sustainable Power For Ai And Emerging Technologies, Joshua Luke Ponsell, James Zurawski, Eli Musgrave, Johnny Demont, Eduardo B. Farfan

Symposium of Student Scholars

Skeptics question whether Artificial Intelligence (AI) can be powered sustainably. Nuclear may just be the best option. AI is transforming various spaces, from cybersecurity to accessibility, by enhancing the detection and prevention of fraud and phishing attacks to improving real-time services like subtitle generation, and supporting language translation. AI's impact also extends to applications like handwriting and speech recognition, streamlining processes and increasing accessibility for individuals with disabilities. As AI becomes more integrated into everyday life, the increasing demand for its computational power raises concerns about energy consumption. By researching datacenter power demands empirically and comparing nuclear options as opposed …

Preparing Students For The Quantum Era: Qml Training And Applications, Triveni Kandimalla, Valentina Nino

Symposium of Student Scholars

Quantum Machine Learning (QML) emerges as a transformative approach to addressing the growing complexities of modern data processing and computational challenges. Classical machine learning (CML) techniques, while powerful, face limitations in handling vast amounts of high-dimensional data and solving complex optimization problems efficiently. Despite its potential, QML remains underrepresented in academia, highlighting the need for accessible, hands-on learning experiences and knowledgeable faculty. This project seeks to advance QML education by incorporating it into diverse curricula, creating practical learning materials, and fostering workforce readiness. Using Google Colab, an open source labware has been designed to provide interactive learning modules (M0 to …

Human-Ai Teaming For Academic Performance Analysis, Kendarius Ward, Elise Hernandez, Tom Antony, Md Abdullah Al Hafiz Khan, Kazi Aminul Islam, Abm Adnan Azmee

Symposium of Student Scholars

Educators today often work with students who are struggling academically, but limited time and resources make it difficult to uncover the root causes and provide timely assistance. Artificial intelligence (AI) is a growing, viable tool for analyzing large datasets and solving problems in different domains; however, human expertise is required to enhance the AI model’s performance. This study will utilize human-AI teaming to assess student performance based on factors such as their academic involvement, hours spent studying, and grade-point-average, among others. These findings will help instructors better grasp each student's academic needs. By incorporating humans into the AI pipeline, we …

Building Resilience: Strategies For Sustainable Wildfire Management In California, Jaiden Rennie, Pegah Zamani

Symposium of Student Scholars

Wildfires in California are becoming more intense and destructive, threatening communities, ecosystems, and livelihoods. This project explores how resilience and sustainability can work together to reduce wildfire risks and long-term damage. By reviewing research, case studies, and expert insights, it examines what’s driving these fires, climate change, poor land management, and human activity, and looks at solutions that can make a real difference. From community education programs to AI-driven fire detection and smarter policies, the findings highlight practical ways to better prepare for and manage wildfires. The goal is to contribute to a future where communities and the environment are …

Using Rf Hardware Technologies To Counteract Interference Within Dsrc Caused By Adjacent Unlicensed Bands, Grayson Hatcher, Billy Kihei, Nathan Kirkwood, Marco Tello

Symposium of Student Scholars

In recent years, competition within Radio Frequency (RF) communication bands has led to an overlap between the U-NII-4 band used by low-cost consumer devices, and the Dedicated Short Communication Band (DSRC) used by the Georgia Department of Transportation. Past research con- firmed that this interference leads to drastic reduction in Packet Reception Rate (PRR) for Roadside Units (RSUs) and On- Board Units (OBUs) throughout the country, resulting in a reduction of stability between different components of the Intelligent Transportation System (ITS). Our research team set a goal to prove that a low-cost and simple modification can be made to RSUs …

Bridging The Gap: Care Team’S Perspectives On Technology And Ai Integration In Healthcare, Sarah Fernandes, Pranathi Boyina, Awatef Ergai Dr., Wellstar Health System, Mohammad Yousef Mousa Naser, Sylvia Bhattacharya Dr.

Symposium of Student Scholars

As healthcare systems increasingly integrate digital solutions, understanding the perspectives of frontline healthcare workers on technology adoption is critical. This study explores how Registered Nurses (RNs), Licensed Practical Nurses (LPNs), and Certified Nursing Assistants (CNAs), collectively referred to as the Care Team, interact with existing and emerging healthcare technologies, including artificial intelligence (AI). Given the growing reliance on digital tools for clinical and administrative tasks, this research examines the challenges and benefits perceived by healthcare professionals when incorporating AI-driven solutions into their workflows.

A cross-sectional research design was employed, involving 30 semi-structured interviews with Care Team members from an Intensive …

Sustainable Smart Farming Device With Leafit Adaptive Growth Technology, Saville Atkins, Anthony Iwejuo, Julian Pitts, Luis Mercado, Rachnicha Rojjhanarittikorn, Sandip Das, Hai Ho

Symposium of Student Scholars

For farmers, gardeners, and horticulture enthusiasts worldwide, one immutable reality is that maintaining a consistent physical presence to care for plants is not always feasible. In addition, different plants have unique needs for watering, nutrients, and environmental conditions to thrive. Failing to meet these specific needs can result in poor plant health, reduced yields, and inefficient resource usage. In this research project, we have designed and developed ‘LeaFit’ – a cutting-edge Internet of Things (IoT) device that offers a sophisticated and sustainable smart farming and gardening solution. Equipped with intelligent soil moisture, ambient temperature, humidity, and light sensors, LeaFit autonomously …

Algorithms For Feature Extraction And Optimisation Of Object Recognition Operator, Hudayberdiev Xakkulmirzayevich Mirzaakbar, Bobomurod Mamitjonovich Tojiboev, Feruza Komiljonovna Samadova

Chemical Technology, Control and Management

This paper deals with the development and analysis of feature extraction and optimisation algorithms for object recognition operators. Different algorithms are used to improve the efficiency of recognition operators in the automatic analysis of remote sensing images. The information model of objects and methods for selecting, extracting and optimising their features have been studied. The issue of extracting important features of objects using spectral, textural and statistical features and constructing optimal operators based on them has also been studied. Effective approaches based on the theory of convex hulls and multidimensional analysis methods have been proposed, taking into account the mutual …

Determination Of Optimal Parameters For Quality Drying Of Garlic Using The Vibro-Convective Method, Jasur Safarov, Shakhnoza Sultanova, Abdurakhmon Mirkomilov

Chemical Technology, Control and Management

This article examines the process of drying garlic using a low-temperature vibratory-convective drying system. At the "Service Technology" department of Tashkent State Technical University, a vibratory dryer was developed to ensure high-quality drying of garlic while maximizing the preservation of its biologically active compounds. Through experimental studies, optimal drying parameters were determined, including the degree of moisture removal, process duration, airflow velocity, and vibration acceleration. The results obtained contribute to enhancing the efficiency and quality of garlic drying, which holds practical significance for agricultural producers and consumers interested in healthy nutrition.

Towards Human Modeling For Human-Robot Collaboration And Digital Twins In Industrial Environments: Research Status, Prospects, And Challenges, Guoyi Xia, Zied Gharairi, Thorsten Wuest, Karl Hribernik, Aaron Heuermann, Furui Liu, Hui Liu, Klaus-Dieter Thoben

Faculty Publications

Human-Robot Collaboration (HRC) and Digital Twins (DT) have significantly advanced industrial development and digital transformation. Human representations and models are essential in Industry 5.0, where human-centric is one of the key features. Despite the growing interest in human models for HRC and DT, a comprehensive overview of these models and enabling technologies currently needs to be provided. This paper aims to present the research status, prospects, applications, and challenges of human modeling for HRC and DT in industrial environments. This paper adopts a Systematic Literature Review (SLR) approach. Moreover, a framework is proposed to systematize human modeling aspects, the technologies …

Improving Image Quality In Electrical Capacitance Tomography Using Otsu Thresholding, Josiah Nombo

Tanzania Journal of Engineering and Technology (TJET)

Electrical Capacitance Tomography (ECT) is an imaging technique used in industrial process monitoring, particularly for monitoring and measuring the composition of multiphase flows. Despite its widespread application, the commonly used Linear Back Projection (LBP) algorithm often produces low-quality images due to its limited ability to handle high permittivity contrasts and nonlinearities. This study investigates the use of Otsu thresholding as a post-processing technique to enhance ECT image quality. By maximizing inter-class variance in the image histogram, Otsu thresholding improves contrast, clarity, and structural definition, enabling more effective segmentation of oil and gas components in multiphase flows. The proposed Otsu-based reconstruction …

Artificial Intelligence Applications For Grid-Connected Solar Inverters, Utkirjon Ubaydullaev, Sarvinoz Mirzaeva, Hasan Mustafoev

Chemical Technology, Control and Management

The increasing global demand for renewable energy has highlighted the importance of grid-connected solar inverters in ensuring efficient and stable power conversion. However, challenges such as fluctuations in solar energy generation, grid disturbances, and power quality issues necessitate advanced control strategies. The integration of artificial intelligence (AI) into solar inverters presents a transformative solution, enhancing performance, adaptability, and reliability in real-world applications.

This review explores the role of AI techniques, including machine learning (ML), deep learning (DL), fuzzy logic, and reinforcement learning (RL), in optimizing key inverter functionalities such as maximum power point tracking (MPPT), fault detection, power quality enhancement, …

Detection Of Http Flood Attacks Based On Machine Learning Algorithms, Norbek Karimov, Furkat Rakhmatov, Oybek Xolmuminov

Chemical Technology, Control and Management

This paper analyzes the effectiveness of Random Forest and SVM models for detecting HTTP Flood attacks. Experimental results demonstrate that both models achieve high accuracy. Evaluation was conducted using Precision, Recall, and F1 Score metrics. Additionally, key features of network traffic were extracted through correlation analysis to enable real-time application of the models in attack detection. The findings provide important insights into detecting DDoS attacks using machine learning and improving model performance.

Security In Biometric Access Control Systems Based On Fingerprints, Elmurad Jamuradovich Qilichev, Baxodir Saydullaevich Achilov, Ismoil Ergash OʻGʻLi Isroilov, Mirshod Mirkomil O'G'Li Ahmadov

Chemical Technology, Control and Management

This article analyzes biometric access control systems using fingerprints. The article considers the advantages of using biometric technologies, including solutions aimed at ensuring security, protecting users' personal data, and increasing the efficiency of systems. A detailed explanation of the principles of operation of fingerprint biometric systems, their underlying algorithms, and technological advances is provided. Problems that arise when assessing the level of security, ease of use, and technological and social aspects of biometric access systems and methods for combating them are also covered. At the end of the article, opinions are expressed about the future of fingerprint-based biometric systems and …

Studying The Technology For Obtaining Zinc Chloride From Spent Supporter Waste In Ammonia Production, A.T. Dadaxo‘Jaev, Sodikjon Kadirov

Chemical Technology, Control and Management

This article presents the results of research on the recycling of spent zinc adsorbents formed during the ammonia production process, aimed at obtaining zinc chloride (ZnCl₂), which can be utilized in various industrial sectors, including electroplating. The research covers the optimization of technological parameters such as hydrochloric acid concentration, temperature, and reaction time to efficiently extract zinc chloride. The experiments revealed that the best results are achieved by using a 20% hydrochloric acid solution, at a temperature of 60°C, with a reaction time of 60 minutes. The resulting 40% zinc chloride solution was tested in electroplating production, where it met …

Development Of Fuzzy Ontology For Explainable Artificial Intelligence For Decision-Making In Fuzzy Environment, Pavel Kosov

Chemical Technology, Control and Management

In modern artificial intelligence systems, there is an acute need to understand the decision-making logic of "black box" algorithms. Our research proposes an innovative method for increasing the transparency of such systems through the formalization of fuzzy explanatory mechanisms. We have developed an extension of existing ontological approaches by introducing the concept of fuzziness into the structure of explanatory properties, which allows overcoming the fundamental limitations of traditional XAI methods. The proposed formalization is based on the theory of collective mental models and principles of fuzzy logic, providing a more accurate reflection of uncertainty and subjectivity in expert knowledge. Our …

Research Of The Dynamic Characteristics Of A Time-Pulse Ultrasonic Sensor, Aliev Ravshan, A.U. Djalilov

Chemical Technology, Control and Management

This article investigates the dynamic characteristics of a time-pulsed ultrasonic sensor used to measure water flow in open channels. The operating principle of the sensor, its response time in various hydrodynamic conditions, speed, and factors affecting measurement accuracy are analyzed. In the process of research, the improved ultrasonic sensor was tested and its effectiveness in measuring water flow in real time was evaluated. The results obtained showed that the sensor adapts to the velocity, temperature and turbulence level of the water flow. Based on the results of the research, the possibilities of working of time-impulse ultrasound sensors in open channels …

Static Characteristics Of An Electromagnetic Three-Phase Reactive Power Sensor For Converting Asymmetric Currents, Timur Urunbayevich Kurbaniyazov

Chemical Technology, Control and Management

This article discusses the processes in the elements and structures of three-phase electromagnetic current sensors used in measuring and controlling three-phase asymmetric reactive power in power supply systems, research models of quantities and parameters, physical mechanisms, and mathematical formulas based on graphical models.