Engineering Commons^™

Personalized Driving Using Inverse Reinforcement Learning, Rodrigo J. Gonzalez Salinas

Theses and Dissertations

This thesis introduces an autonomous driving controller designed to replicate individual driving behaviors based on a provided demonstration. The controller employs Inverse Reinforcement Learning (IRL) to formulate the reward function associated with the provided demonstration. IRL is implemented through a dual-feedback loop system. The inner loop utilizes Q-learning, a model-free reinforcement learning technique, to optimize the Hamilton-Jacobi-Bellman (HJB) equation and derive an appropriate control solution. The outer loop leverages this derived control solution to generate parameters for the reward function, which are subsequently integrated into the HJB equation. The ultimate control policy is deduced from the final reward function obtained …

Advances In Electrocatalytic Dehydrogenation Of Ethylamine To Acetonitrile, Yanlin Zhu, Dezhen Wu, Jinyao Tang, Dakota Braaten, Bin Liu, Zhenmeng Peng

Faculty Publications

The electrocatalytic dehydrogenation of ethylamine (EDH), owing to its high hydrogen content, holds broad prospects in electrochemical hydrogen (H2) production, H2 storage, and addressing energy issues, thus deserving wide attention. In this feature article, we first summarized the fundamentals of thermocatalytic and electrocatalytic EDH and reviewed the recent state-of-the-art advances in catalyst research, specifically platinum group metal (PGM) catalysts and non-PGM catalysts. We systematically discussed the potential applications of electrocatalytic EDH in energy storage and conversion. Finally, we provide our perspective on the key challenges and future developments in this field. We believe this feature article will offer helpful guidance …

Opening And Constructing Stable Lithium-Ion Channels Within Polymer Electrolytes, Yangmingyue Zhao, Libo Li, Da Zhou, Yue Ma, Yonghong Zhang, Hang Yang, Shubo Fan, Hao Tong, Suo Li, Wenhua Qu

School of Integrative Biological & Chemical Sciences Faculty Publications

Lithium-ion batteries play an integral role in various aspects of daily life, yet there is a pressing need to enhance their safety and cycling stability. In this study, we have successfully developed a highly secure and flexible solid-state polymer electrolyte (SPE) through the in-situ polymerization of allyl acetoacetate (AAA) monomers. This SPE constructed an efficient Li+ transport channel inside and effectively improved the solid-solid interface contact of solid-state batteries to reduce interfacial impedance. Furthermore, it exhibited excellent thermal stability, an ionic conductivity of 3.82×10-4 S cm-1 at room temperature (RT), and a Li+ transport number (tLi+) of 0.66. The numerous …

Generative Algorithms For Art And Architecture: A Collaborative Teaching Approach, Sam Keene, Benjamin Aranda

Tradition Innovations in Arts, Design, and Media Higher Education

We will present a course that we have been offering for the past few years that engages art, architecture and engineering students and challenges them to collaborate using generative methods to produce creative work. Our work contributes to the long-term understanding of AI in the arts and design in higher education because we have developed a successful course model focused on collaboration between creatives and technologists that can be replicated at other institutions. Feedback between creatives and technologists has been fundamental to opening new frontiers, giving students the tools to collaborate successfully is tremendously important. We will share example of …

Development Of Cyclic Scb Test For Evaluating Fatigue Cracking Resistance Of Asphalt Mixture At Intermediate Temperature, Ye Ma

LSU Doctoral Dissertations

Asphalt pavements are subjected to premature cracking due to traffic loading and environmental effects. The Semi-Circular Bending (SCB) test has been traditionally conducted in a monotonic, displacement-controlled mode at intermediate temperature to assess the fatigue crack resistance of asphalt mixtures. However, fatigue damage is essentially deterioration in material integrity as a result of repeated loading. This study aims to develop a cyclic SCB test to characterize the fatigue cracking properties of asphalt mixture utilizing Paris’ Law model. A secondary goal is to apply the Paris’ Law coefficients measured from the developed cyclic SCB test in predicting the fatigue life of …

Tension–Compression Fatigue Of A Hybrid Polymer-Matrix/Ceramic-Matrix Composite At Elevated Temperature, Marina B. Ruggles-Wrenn, Joshua Schmidt

Faculty Publications

Fully reversed tension–compression fatigue of a hybrid material comprising polymer matrix composite (PMC) co-cured with a ceramic matrix composite (CMC) was investigated. The PMC portion had a polyimide matrix reinforced with 15 plies of carbon fibers woven in an eight-harness satin weave (8HSW). The CMC portion had three plies of a quartz-fiber 8HSW fabric in a zirconia-based ceramic matrix. The hybrid PMC/CMC was developed for use in aerospace thermal protection systems (TPS). Hence, the experimental setup aimed to simulate the TPS service environment—the CMC side was kept at 329 °C, whereas the PMC side was open to laboratory air. Compression …

Experimental Study On The Effect Of Micro Concentrations Of Hybrid Nanofluids Through Microchannel Heat Sinks, Mohamed Salaheldin Elsherbiny, Moustafa Ali, Moatsem Shahin, Mahmoud El-Kady

Renewable Mechanical Energy

The current study examines the convective heat transfer coefficient experimentally. It assesses the performance of Al2O3, CuO, ZnO, and Ag/ distilled water nanofluids up to a tetra level of hybridization utilized in microprocessor cooling systems equipped with MCHS. An equal ratio of 50%, 33.3%, and 25% for di-, tri-, and tetra-nanofluids, respectively, with a focus on micro-volume fraction 0.025% and comparing it with 0.05%. Operating conditions were as follows: heat loading from 136.4 up to 196.4 watts; and flow rate from 0.35 up to 0.5 LPM. The findings showed that all types of mono nanofluids showed an increase in Nu …

Improving Biomimetic Passive Dynamics Of A Quadruped Robot Hind Leg Hip Joint With A 3d Printed Torsion Spring, Haonan Zheng

Dissertations and Theses

The Agile and Adaptive Robotics Lab is interested in researching the neuromuscular control network of legged animal locomotion. To validate the lab's understanding of biological neural control, synthetic neural networks are developed and applied to biologically inspired legged robots. For a synthetic neural network to operate in the same manner as a biological neural network and produce the same locomotor behavior, the robot that the synthetic neural network is controlling must also mimic its biological counterpart. Previous research produced a quadruped robot hind leg designed with biomimetic passive dynamics by installing spring and damper pairs at each joint, however, an …

Series Reports From Professor Wei’S Group Of Chongqing University: Advancements In Electrochemical Energy Conversions (1/4): Report 1: High-Performance Oxygen Reduction Catalysts For Fuel Cells, Fa-Dong Chen, Zhuo-Yang Xie, Meng-Ting Li, Si-Guo Chen, Wei Ding, Li Li, Jing Li, Zi-Dong Wei

Journal of Electrochemistry

Two major challenges, high cost and short lifespan, have been hindering the commercialization process of low-temperature fuel cells. Professor Wei’s group has been focusing on decreasing cathode Pt loadings without losses of activity and durability, and their research advances in this area over the past three decades are briefly reviewed herein. Regarding the Pt-based catalysts and the low Pt usage, they have firstly tried to clarify the degradation mechanism of Pt/C catalysts, and then demonstrated that the activity and stability could be improved by three strategies: regulating the nanostructures of the active sites, enhancing the effects of support materials, and …

Kai Wu, The Editorial Board Member Of Journal Of Electrochemistry And Chief Scientist Of Catl, Receives The National Science And Technology Progress Award, Editorial Office Of J.Electrochem.

Journal of Electrochemistry

No abstract provided.

Sorbitol-Electrolyte-Additive Based Reversible Zinc Electrochemistry, Qiong Sun, Hai-Hui Du, Tian-Jiang Sun, Dian-Tao Li, Min Cheng, Jing Liang, Hai-Xia Li, Zhan-Liang Tao

Journal of Electrochemistry

The unstable zinc (Zn)/electrolyte interfaces formed by undesired dendrites and parasitic side reactions greatly hinder the development of aqueous zinc ion batteries. Herein, the hydroxy-rich sorbitol was used as an additive to reshape the solvation structure and modulate the interface chemistry. The strong interactions among sorbitol and both water molecules and Zn electrode can reduce the free water activity, optimize the solvation shell of water and Zn²⁺ ions, and regulate the formation of local water (H₂O)-poor environment on the surface of Zn electrode, which effectively inhibit the decomposition of water molecules, and thus, achieve the thermodynamically stable …

The Effect Of Tio2 On The Electrochemical Performance Of Sb2o3 Anodes For Li-Ion Batteries, Kithzia Gomez, Elizabeth M. Fletes, Jason Parsons, Mataz Alcoutlabi

Mechanical Engineering Faculty Publications

Antimony (Sb) and its composites have been recognized as potentially good anode materials for lithium-ion batteries (LIBs) due to their relatively high theoretical capacity of 660 mAh g−1 and to their low cost. However, Sb-based anodes suffer from a high-volume change during the lithiation/delithiation process that results in capacity fading and anode degradation after prolonged charge/discharge cycles. To address this issue, Sb2O3/TiO2 nanocomposite electrodes can be synthesized and used as anodes for LIBs with high capacity and good electrochemical stability. In the present work, TiO2@Sb2O3 composites with different (TiO2:Sb2O3) ratios of 0:1, 1:1, 1:4 and 3:1 were synthesized and directly …

Data-Driven Viewpoint For Developing Next-Generation Mg-Ion Solid-State Electrolytes, Fang-Ling Yang, Ryuhei Sato, Eric Jian-Feng Cheng, Kazuaki Kisu, Qian Wang, Xue Jia, Shin-Ichi Orimo, Hao Li

Journal of Electrochemistry

Magnesium (Mg) is a promising alternative to lithium (Li) in solid-state batteries due to its abundance and high theoretical volumetric capacity. However, the sluggish Mg-ion conduction in the lattice of solid-state electrolytes (SSEs) is one of the key challenges that hamper the development of Mg-ion solid-state batteries. Though various Mg-ion SSEs have been reported in recent years, key insights are hard to be derived from a single literature report. Besides, the structure-performance relationships of Mg-ion SSEs need to be further unraveled to provide a more precise design guideline for SSEs. In this Viewpoints article, we analyze the structural characteristics of …

First Announcement Of 76th Annual Meeting Of The International Society Of Electrochemistry, International Society Of Electrochemistry (Ise)

Journal of Electrochemistry

No abstract provided.

Professor Yong Yang, The Editorial Board Member Of Journal Of Electrochemistry, Is Elected As A Fellow Of The Electrochemical Society, Editorial Office Of J.Electrochem.

Journal of Electrochemistry

No abstract provided.

Integrated Multi-Omics Analysis Of Cerebrospinal Fluid In Postoperative Delirium, Bridget A. Tripp, Simon T. Dillon, Min Yuan, John M. Asara, Sarinnapha M. Vasunilashorn, Tamara G. Fong, Sharon K. Inouye, Long H. Ngo, Edward R. Marcantonio, Zhongcong Xie, Towia A. Libermann, Hasan H. Otu

Department of Electrical and Computer Engineering: Faculty Publications

Preoperative risk biomarkers for delirium may aid in identifying high-risk patients and developing intervention therapies, which would minimize the health and economic burden of postoperative delirium. Previous studies have typically used single omics approaches to identify such biomarkers. Preoperative cerebrospinal fluid (CSF) from the Healthier Postoperative Recovery study of adults ≥ 63 years old undergoing elective major orthopedic surgery was used in a matched pair delirium case–no delirium control design. We performed metabolomics and lipidomics, which were combined with our previously reported proteomics results on the same samples. Differential expression, clustering, classification, and systems biology analyses were applied to individual …

Exploring Small-Scale Propellers Performance At Low Advance Ratios, Ali Akber Mollick, Sara Catto, Forrest E. Ames, Clement Tang

Mechanical Engineering Student Publications

The performance of small-scale propellers was experimentally measured at low advance ratios and Reynolds numbers. The propeller diameters range from 14 to 19 inches, with all propellers having the same pitch value of 12. Using a thrust stand situated in a low-speed open-circuit wind tunnel, the propeller thrust, torque, and angular speed were measured at various freestream velocities. The results from this study offer insights on the performance of small propellers at low advance ratios and low Reynolds numbers. Propeller performance results at these conditions tend to be limited in the literatures. At the same pitch value of 12, the …

A New Approach: Ordinal Predictive Maintenance With Ensemble Binary Decomposition (Opmeb), Ozlem Ece Yurek, Derya Birant

Turkish Journal of Electrical Engineering and Computer Sciences

Predictive maintenance (PdM), a fundamental element of modern industrial systems, employs machine learning to monitor equipment conditions, estimate failure probabilities, and optimize maintenance schedules. Its core objective is to enhance equipment reliability, extend lifespan, and minimize costs through data-driven insights by enabling efficient maintenance scheduling, reducing downtime, and optimizing resource allocation. In this paper, we propose a novel ordinal predictive maintenance with ensemble binary decomposition (OPMEB) method for the PdM domain, considering the hierarchical nature of class labels reflecting the machine's health status, including categories like healthy, low risk, moderate risk, and high risk. The proposed OPMEB method was validated …

Enrichment Of Turkish Question Answering Systems Using Knowledge Graphs, Okan Çi̇ftçi̇, Fati̇h Soygazi̇, Selma Teki̇r

Turkish Journal of Electrical Engineering and Computer Sciences

Recent capabilities of large language models (LLMs) have transformed many tasks in Natural Language Processing (NLP), including question answering. The state-of-the-art systems do an excellent job of responding in a relevant, persuasive way but cannot guarantee factuality. Knowledge graphs, representing facts as triplets, can be valuable for avoiding errors and inconsistencies with real-world facts. This work introduces a knowledge graph-based approach to Turkish question answering. The proposed approach aims to develop a methodology capable of drawing inferences from a knowledge graph to answer complex multihop questions. We construct the Beyazperde Movie Knowledge Graph (BPMovieKG) and the Turkish Movie Question Answering …

Ensemble Learning For Accurate Prediction Of Heart Sounds Using Gammatonegram Images, Sinam Ashinikumar Singh, Sinam Ajitkumar Singh, Aheibam Dinamani Singh

Turkish Journal of Electrical Engineering and Computer Sciences

The analysis of heart sound signals constitutes a pivotal domain in healthcare, with the prediction of imbalanced heart sounds offering critical diagnostic insights. However, the inherent diversity in cardiac sound patterns presents a substantial challenge in predicting imbalanced signals. Many scientific disciplines have focused a great deal of emphasis on the problem of class inequality. We introduce an ensemble learning approach employing a convolutional neural network model-based deep learning algorithm to effectively tackle the challenges associated with predicting imbalanced heart sound signals. We use a Gammatone filter bank to extract relevant features from the heard sound signal. Our approach leverages …

Detection And Classification Of Unauthorized Use Of Irrigation Motors In Agricultural Irrigation, Önder Ci̇velek, Sedat Görmüş, Hali̇l İbrahi̇m Okumuş, Orhan Gazi̇ Kederoglu

Turkish Journal of Electrical Engineering and Computer Sciences

The decarbonisation of electricity generation requires the real-time monitoring and control of grid components in order to efficiently and timely dispatch demand. This highly automated system, known as the Smart Grid, relies on smart or sensor-equipped distribution network components to optimise energy flow and minimise losses. However, energy theft, a major obstacle to efficient resource utilisation, poses a significant challenge to achieving this goal. This study proposes and evaluates a real-time telemetry and control system designed to mitigate energy theft in agricultural irrigation applications. The system increases energy efficiency by tracking the energy use in agricultural irrigation. The key challenge …

A New Dynamic Classifier Selection Method For Text Classification, İsmai̇l Terzi̇, Alper Kürşat Uysal

Turkish Journal of Electrical Engineering and Computer Sciences

The primary objective of employing multiple classifier systems (MCS) in pattern recognition is to enhance classification accuracy. Dynamic classifier selection (DCS) and dynamic ensemble selection (DES) are two purposeful forms of multiple classifier systems. While DES involves the selection of a classifier set followed by decision combination, DCS opts for the choice of a single competent classifier, eliminating the necessity for classifier combination. As a consequence, DCS methods exhibit superior efficiency in terms of processing time and memory usage compared to DES methods. Moreover, a substantial performance gap exists between the performance of Oracle and both DES and DCS methods. …

Reduction In Porosity In Gmaw-P Welds Of Cp780 Galvanized Steel With Er70s-3 Electrode Using The Taguchi Methodology, Maleni García-Gómez, Francisco Fernando Curiel-López, Jaime Taha-Tijerina, Victor Hugo Lopez-Morelos, Julio César Verduzco-Juárez, Carlos Adrián García-Ochoa

Informatics and Engineering Systems Faculty Publications

In this study, the theoretical welding parameters influencing porosity formation were examined with the aim of reducing or minimizing porosity levels. An experimental design was implemented using the Taguchi methodology for data analysis, resulting in an L9 orthogonal array matrix of experiments. The welding variables considered in the orthogonal array were peak current, peak time, and frequency. Nine lap welds were performed on CP780 steel using the gas metal arc welding process with pulsed arc (GMAW-P), employing an ER70S-3 electrode as filler metal. The percentage of porosity was determined as a response variable, and the actual heat input was treated …

A Real-Time Embedded System Designed For Nilm Studies With A Novel Competitive Decision Process Algorithm, Sai̇d Mahmut Çinar, Rasi̇m Doğan, Emre Akarslan

Turkish Journal of Electrical Engineering and Computer Sciences

This paper explores the determination of any load or load combination in a power system at any moment. This process requires measurements at the main electric utility service entry of a house, known as nonintrusive measurement. To accurately identify loads, total harmonic distortion, RMS, third harmonic currents, and power consumption are considered their fingerprints. Based on these fingerprints, an algorithm called the competitive decision process is developed and integrated into an embedded system. This algorithm has a two-level decision mechanism. In the first stage, the winner loads with the highest similarity scores from each feature are determined, and the loads …

Multi-Label Voice Disorder Classification Using Raw Waveforms, Gökay Di̇şken

Turkish Journal of Electrical Engineering and Computer Sciences

Automated voice disorder systems that distinguish pathological voices from healthy ones have been developed with the aid of machine learning methods. Both clinicians and patients can benefit from these systems as they provide many advantages, compared to the invasive techniques. These systems can produce binary (healthy/pathological) or multi-class (healthy/selected pathologies) decisions. However, multiple disorders might exist in an individual’s voice. Multi-label classification should be considered in such cases. By this time, only a single report is available on this topic, where hand-crafted features were used, and a data augmentation technique was utilized to overcome class imbalances. In this study, a …

Network Intrusion Detection Based On Machine Learning Strategies: Performance Comparisons On Imbalanced Wired, Wireless, And Software-Defined Networking (Sdn) Network Traffics, Hi̇lal Hacilar, Zafer Aydin, Vehbi̇ Çağri Güngör

Turkish Journal of Electrical Engineering and Computer Sciences

The rapid growth of computer networks emphasizes the urgency of addressing security issues. Organizations rely on network intrusion detection systems (NIDSs) to protect sensitive data from unauthorized access and theft. These systems analyze network traffic to detect suspicious activities, such as attempted breaches or cyberattacks. However, existing studies lack a thorough assessment of class imbalances and classification performance for different types of network intrusions: wired, wireless, and software-defined networking (SDN). This research aims to fill this gap by examining these networks’ imbalances, feature selection, and binary classification to enhance intrusion detection system efficiency. Various techniques such as SMOTE, ROS, ADASYN, …

Adaptable Quantum Education Platform Using Learning Objects, Krishna Puja Anumula

Master's Theses

In recent years, the need to make classroom learning more interactive and engaging has become increasingly important. The lack of workforce in interdisciplinary fields such as quantum networking and quantum internet requires a new approach that addresses every learner’s individual needs. To address this challenge, this thesis introduces an adaptive learning platform rooted in the theory of learning objects and Kolb’s experiential learning model. The platform aids educators and learners in designing and utilizing various learning objects for quantum networking and quantum internet.

The platform enables educators and learners to build their own lessons and lesson plans using learning objects …

Efficient Deep Neural Network Compression For Environmental Sound Classification On Microcontroller Units, Shan Chen, Na Meng, Haoyuan Li, Weiwei Fang

Turkish Journal of Electrical Engineering and Computer Sciences

Environmental sound classification (ESC) is one of the important research topics within the non-speech audio classification field. While deep neural networks (DNNs) have achieved significant advances in ESC recently, their high computational and memory demands render them highly unsuitable for direct deployment on resource-constrained Internet of Things (IoT) devices based on microcontroller units (MCUs). To address this challenge, we propose a novel DNN compression framework specifically designed for such devices. On the one hand, we leverage pruning techniques to significantly compress the large number of model parameters in DNNs. To reduce the accuracy loss that follows pruning, we propose a …

Enhancement Of Mechanical Properties Of Pcl/Pla/Dmso2 Composites For Bone Tissue Engineering, Kyung-Eun Min, Jae-Won Jang, Cheolhee Kim, Sung Yi

Mechanical and Materials Engineering Faculty Publications and Presentations

Bone tissue engineering shows potential for regenerating or replacing damaged bone tissues by utilizing biomaterials renowned for their biocompatibility and structural support capabilities. Among these biomaterials, polycaprolactone (PCL) and polylactic acid (PLA) have gained attention due to their biodegradability and versatile applications. However, challenges such as low degradation rates and poor mechanical properties limit their effectiveness. Dimethyl sulfone (DMSO₂) has emerged as a potential additive to address these limitations, offering benefits such as reduced viscosity, increased degradation time, and enhanced surface tension. In this study, we investigate tailored composites comprising PLA, PCL, and DMSO₂ to enhance mechanical …

Rational Design Of Lignin-First Biorefineries Through Lignin Analysis And Deconstruction Modeling Tools, Aditya Ponukumati

McKelvey School of Engineering Graduate Student Theses & Dissertations

Lignocellulosic biomass is produced photosynthetically from atmospheric CO2 and is one of the few sources of renewable, reduced carbon that is generated on a scale comparable to demand for chemical commodities. For a sense of scale, the U.S. Energy Security and Independence Act targets the production of approximately 16 billion gallons per year of second-generation lignocellulosic biofuels by 2025. That production would generate approximately 62 million tons per year of dry lignin byproducts. The focus of this dissertation is on the valorization of these lignin components of biomass. Currently, lignin is either discarded or burned for local heat or electricity …