Computational Engineering Commons^™

Exploring The Identification Of Autoregression Model By General Least Deviation Method, Mostafa Abotaleb, Tatiana Makarovskikh, Ramadhan, Ali J

Al-Bahir

We are considering a novel method for analyzing time series data that relies on quasi-linear recurrence relations. Unlike neural networks, this approach allows for directly formulating high-quality quasi-linear difference equations that accurately represent the studied process. Techniques for determining the parameters of a single equation have been devised and validated. This work discusses and tests a technique for identifying the parameters of a quasi-linear recurrence equation. This approach is employed to tackle the issue of regression analysis including observable variables that are mutually dependent. It enables the utilization of the Generalized Least Deviations Method (GLDM). This model was utilized in …

Development Of Message Passing-Based Graph Convolutional Networks For Classifying Cancer Pathology Reports, Hong Jun Yoon, Hilda B. Klasky, Andrew E Blanchard, J. Blair Christian, Eric B Durbin, Xiao Cheng Wu, Antoinette Stroup, Jennifer Doherty, Linda Coyle, Lynne Penberthy, Georgia D Tourassi

School of Public Health Faculty Publications

Background: Applying graph convolutional networks (GCN) to the classification of free-form natural language texts leveraged by graph-of-words features (TextGCN) was studied and confirmed to be an effective means of describing complex natural language texts. However, the text classification models based on the TextGCN possess weaknesses in terms of memory consumption and model dissemination and distribution. In this paper, we present a fast message passing network (FastMPN), implementing a GCN with message passing architecture that provides versatility and flexibility by allowing trainable node embedding and edge weights, helping the GCN model find the better solution. We applied the FastMPN model to …

A Random Forest Classifier Model For Predicting The Impact Of Viral Infections On Adults With Chronic Conditions, Fungai Jacqueline Kiwa, Martin Muduva

African Conference on Information Systems and Technology

This study investigates the impact of viral infections on adults with chronic illnesses, focusing on the development of a Random Forest classifier model. The research aims to predict outcomes among individuals with conditions like diabetes, cancer, and tuberculosis, analyzing severity, age groups, and travel patterns. The study aims to assist healthcare professionals in resource allocation and patient prioritization based on disease severity. It reviews literature on viral infection risks for chronic illness patients and explores machine learning applications in infectious disease management. Methodologically, the study adopts a structured approach similar to the Cross-Industry Standard Process for Data Mining (CRISP -DM) …

Ai Bioelectricity Management System, Fungai Jacqueline Kiwa, Tawanda Bundukutu, Thoko Matnell Mawoyo, Batsiranai Linda Chiduku, Martin Muduva, Belinda Ndlovu

African Conference on Information Systems and Technology

This document emphasizes on the generation of electricity from trees and its usability in all the sectors of Zimbabwe. The research focused on positively changing the lives of citizens through the provision of uninterrupted and reliable bioelectricity. The literature review was completely and accurately performed through finding out the current news associated with the use of trees in producing electricity and the use of AI to manage the flow. The Scrum’s development model was adopted and followed during the research project to address issues like transparency, early mitigation of risks and constant feedback. The Scrum-model is one of the best …

2024 Summer Proceedings Teuscher Lab, Teuscher Group, Christof Teuscher, Chelsea Ogbede, Lauren Sanday, Sofia Vargas, Artem Arefev

altREU Projects

How will computation evolve in the coming years? What problems can be tackled using artificial intelligence, in a world increasingly driven by data? And how can that data be used to better inform our decisions as a society? In this unique collection of research projects, each chapter represents a distinct work undertaken by a single individual or a group of students as part of the altREU program led by Christof Teuscher. The projects, rooted in applications of artificial intelligence and innovative computation techniques, examine impactful solutions to numerous pressing challenges affecting communities around the world.

Aligning Human And Computational Coherence Evaluations, Jia Peng Lim, Hady Wirawan Lauw

Research Collection School Of Computing and Information Systems

Automated coherence metrics constitute an efficient and popular way to evaluate topic models. Previous work presents a mixed picture of their presumed correlation with human judgment. This work proposes a novel sampling approach to mining topic representations at a large scale while seeking to mitigate bias from sampling, enabling the investigation of widely used automated coherence metrics via large corpora. Additionally, this article proposes a novel user study design, an amalgamation of different proxy tasks, to derive a finer insight into the human decision-making processes. This design subsumes the purpose of simple rating and outlier-detection user studies. Similar to the …

Collision Dynamics Of Compound Droplets In Microchannels: A Combined Numerical And Data-Driven Study, S M Abdullah Al Mamun

Dissertations

Understanding and predicting the hydrodynamic interactions of micron-scale droplets is crucial in a wide range of industrial and real-life applications, including microfluidics, pharmaceutics, drug delivery, food science, and enhanced oil recovery. These multi-phase and multi-scale phenomena are further complicated by the presence of core droplets of an immiscible fluid within shell droplets, known as compound droplets. The collisions and interactions of droplets in emulsions are influenced by various physical and geometric parameters, leading to distinct rheological and dynamic responses. This research employs numerical methods for a systematic parametric study of both simple and compound droplet pair collisions under confined shear …

Faids: Artificial Intelligence Developmental Systems Framework For Predicting And Preventing Cyberattacks In Supply Chain Networks, Lordt Becklines

Research & Publications

Cyber threats and attacks disrupt and damages supply chain networks (SCNs), which are complex and interlinked. Current methods to predict and prevent cyberattacks are inadequate and ineffective. This research proposes an AI developmental systems framework (FAIDS) to protect SCNs from cyberattacks. The framework has four components: (1) an AI threat intelligence system; (2) an AI risk assessment system; (3) an AI decision support system; and (4) an AI learning and adaptation system. The framework is tested on a simulated retail SCN. The results show that the framework can predict and prevent cyberattacks and improve the network's resilience and security. The …

Implementation Of Machine Learning Using Deep Neural Networks To Estimate The Failure Risk Caused By Leakage In Pressure Relief Devices, Adi Yudho Wijayanto, Yossi Andreano, M. Ali Yafi Rizky, Dedi Priadi

Journal of Materials Exploration and Findings

The primary objective of deploying Pressure Relief Device (PRD) equipment is to ensure the safety of pressure vessels within a pressurized system. Over time, PRD equipment may degrade and fail to perform its intended function, which must be identified as a failure mode. To mitigate potential risks associated with this, it is recommended that an approach such as risk-based inspection (RBI) be implemented. Despite the widespread adoption of RBI, the method relies on qualitative techniques, leading to significant variations in equipment risk assessments. This study proposes a novel risk analysis method that uses deep learning-based machine learning to develop a …

Exploiting Physical Side-Channel Information For Offensive And Defensive Ends, Sisheng Liang

All Dissertations

Side-channel information consists of side effects of computation that range from microarchitectural to physical phenomena. Empirical studies have demonstrated the practical exploitability of these side effects in real-world systems for malicious attacks and effective defenses. In this dissertation, we discover, analyze, and exploit certain physical side-channel information for end-to-end attacks and defense across three studies.

In the first study, we demonstrate a new DNN model extraction attack named Clairvoyance that exploits certain far-field electromagnetic signals emitted from a GPU to steal DNN models several meters away from the victim machine, even with some physical obstacles in between. Using Clairvoyance, an …

Classification Model For Discovering The Type Of Crop To Plant Using Ensemble Techniques, Uma Mahesh Addanki

Electronic Theses, Projects, and Dissertations

Farming plays a role in ensuring survival, especially with the growing need for increased agricultural output. It is vital for farmers to efficiently choose the crops to cultivate. By using crop recommendation systems farmers can make decisions on what crops to plant leading to yields and improved resource management. The success of crop production depends on maintaining the balance of soil nutrients and favorable weather conditions. In this research project, we created a crop recommendation system utilizing learning methods to predict the appropriate crops based on essential soil nutrients and weather patterns. We worked with a dataset sourced from Kaggle, …

Large-Scale Hpc-Empowered Power Electronics Modeling And Simulation In Photovoltaic Applications, Liwei Wang

All Dissertations

The rising popularity of renewable energy sources requires advanced, efficient power electronic systems for energy conversion, grid integration, and system management, thereby raising expectations for power electronics in the energy industry. The complexity of modern power electronic systems requires comprehensive simulations and in-depth analysis to predict performance accurately, but this process is impeded by prolonged simulation times. The primary objective of this dissertation is to develop a high-fidelity, high-speed event-driven simulator to tackle challenges related to mass data processing, uncertainty evaluation, as well as modeling and simulation issues in assessing the reliability of power electronics in large-scale Photovoltaic (PV) systems. …

Medical Imaging Dataset Management Leveraging Deep Learning Frameworks In Breast Cancer Screening, Inchan Hwang

Dissertations

In the domain of Computer-Aided Diagnosis (CADx) for breast cancer diagnosis through mammography, prevailing models have traditionally been trained and validated using old film-based mammography. However, contemporary U.S. hospital practices involve the utilization of Full Field Digital Mammography (FFDM), offering more detailed images captured at various angles than old film-scanned mammography. Despite this shift, the existing body of research predominantly focuses on old-film based datasets, the implications of FFDM for CADx systems have not been understood. This dissertation addresses the issues emerged from FFDM such as data augmentation between old film-based set and new FFDM whether they are more effective …

Pyrolysis Kinetics Of Spirulina Platensis And Non-Condensable Gas Product Distribution In A Fixed-Bed Reactor, Ahmad Yusril Aminullah, Sukarni Sukarni, Retno Wulandari, Muhammad Shahbaz

Journal of Mechanical Engineering Science and Technology (JMEST)

Energy is a fundamental factor for civilization development and sustainability. However, energy sources are dominated by non-renewable fractions, such as fossil fuels. Renewable biomass is projected to be a future fuel source. Spirulina platensis (SP) has numerous advantages compared to other biomass, and it is considered 3rd generation biomass that does not interfere with food and land usage and has a relatively low main decomposition temperature at 325.7℃. Thermogravimetric analysis (TGA) was conducted to observe SP kinetics parameters, especially activation energy. Kissinger-Akahira-Sunose (KAS), Ozawa-Flynn-Wall (OFW), and Starink iso-conversional methods reveal that SP has an activation energy of 152.33, 154.56, and …

Simulation Of The Performance Of Kevlar Impregnated Shear Thickening Fluid Ballistic Test Results, Ridwan Prasetya, Andoko Andoko, Suprayitno Suprayitno

Journal of Mechanical Engineering Science and Technology (JMEST)

This study explores the enhancement of Kevlar fabric’s ballistic performance through impregnation with Shear Thickening Fluid (STF) for potential application in soft body armor. The experimental approach often fails to elucidate mechanical phenomena critical for the development of lightweight and high-strength body armor designs. To address this limitation, the finite element method, specifically using ANSYS/LS-DYNA R.13, was employed for a comprehensive analysis. The simulation aimed to evaluate the impact of STF on Kevlar fabric by assessing projectile velocity, force exerted by the projectile onto the fabric, displacement, stress distribution, and fabric failure mechanisms. Kevlar yarn was modeled as a shell …

Metal Casting Furnace Design Development Using Computer Simulation, Ramadhan Wahyu Irianto, Yanuar R.A. Pradana, Suprayitno Suprayitno

Journal of Mechanical Engineering Science and Technology (JMEST)

Metal foundries still rely heavily on crucible furnaces. The current furnace design, which is currently being used by the partner industry, has been found to be not properly designed and will result in a reduction in efficiency. CFD simulation will be used to find the optimal melting furnace design. This research simulation consists of 3 stages: pre-processing, solving, and post-processing. There are two furnace geometries, cylindrical and hexagonal, while the burner location will be divided into 3 positions, namely P1, P2, and P3. The most optimal furnace design will be used as a basis for the verification testing process. The …

Evolution Of Residual Stress, Crystal Orientation, And Texture On Preheating Weld Treatment Of Low Carbon Steel Astm A572 Grade 42, Suratno Adi Saputro, Andoko Andoko, Poppy Puspitasari

Journal of Mechanical Engineering Science and Technology (JMEST)

Preheating is one type of heat treatment on the material prior to starting the welding process by increasing the temperature of the material. It is used to reduce the cooling rate during the welding processes to minimize the risk of residual stress and cracking during welding. Besides the benefits of welding, it also has a negative impact, especially on the integrity of the material, because the heating process at high temperatures will cause residual stress, which will affect the mechanical properties, chemical composition, and microstructure of the material, especially on Heat Affected Zone (HAZ). This study aims to analyze the …

Static And Modal Analysis Of A Box Structured Satellite Deployment Mechanism With Self-Actuated Torsion Joint, Dongping Sheng, Renzhe Ma, Chun Su

Journal of Mechanical Engineering Science and Technology (JMEST)

A brand-new satellite deployment mechanism with boxed structure and self-actuated torsion joints is proposed and an effective method to verify the feasibility of this mechanism is established in this paper. This mechanism has the characteristics of high base frequency, high ratio of deployed and folded space occupation ratio. In order to meet the design requirements, the related analysis and optimization need to be conducted, and several conclusions are obtained. Firstly, the modal analysis of deployment mechanism at folded and fully deployed state is analyzed, and the result showed that the proper wall thickness is the key parameter to satisfy the …

A Review Of Fault Detecting Devices For Belt Conveyor Idlers, Fahad Alharbi, Suhuai Luo

Journal of Mechanical Engineering Science and Technology (JMEST)

The shift towards automated inspection methods represents considerable progress in conveyor system maintenance, enhancing safety and efficiency while posing challenges in data analysis and implementation costs. This study critically analyses sensor technologies and inspection methods for detecting faults in conveyor belt idlers, highlighting their essential role in preserving the operational integrity of industrial conveyor systems. By synthesizing various research findings, the study assesses the effectiveness of different sensor devices in identifying defects, including built-in sensors, fixed sensor options like acoustic, ultrasonic sensors, cameras, accelerometers, and Distributed Optical Fibre Sensors (DOFS), as well as mobile sensor systems. Our findings emphasize the …

Analysis Of Avgas Fuel Spraying Schemes Using The Ansys Application Approach, Dimas Endrawan Putra, Nasrul Ilminnafik, M Fahrur Rozy Hentihu, Muh. Nurkoyim Kustanto, Danang Yudistiro, Skriptyan N.H. Syuhri

Journal of Mechanical Engineering Science and Technology (JMEST)

Avgas fuel consumption on Cessna trainer aircraft is very high. However, there has been little research regarding spray impacts in Cessna aircraft engines. The phenomenon of avgas spray colliding with the cylinder wall may occur during fuel injection, resulting in a changed spray radius and height, which will affect the mixing of fuel and air. In several aspects, this affects engine performance and exhaust emissions on Cessna aircraft. This research aims to determine and study the phenomenon of spray impact on avgasfueled aircraft engines. The fuel spray in the study occurred in the combustion chamber using pressure from a fuel …