Electrical and Computer Engineering Commons^™

Solving High-Dimensional Differential Equations Using Recurrent And Residual Neural Network Architectures, Hind Khaled Kolaib

Knowledge Engineering and Data Science

High-dimensional Partial Differential Equations (PDEs) form the foundation of complex process modeling in various scientific and engineering applications, including finance, physics, and optimal control. However, classical numerical methods are adversely affected by the curse of dimensionality, making them inapplicable for large-scale problems. Recently, however, deep learning-based approaches have provided a new toolbox for these high-dimensional PDEs, including methods such as the Deep Backward Stochastic Differential Equation (Deep BSDE) method. Our approach draws on a more sophisticated deep learning backbone, using neural networks (in our case, a Residual Neural Network and a Long Short-Term Memory network (LSTM) integrated into the Deep …

A Full Polymer Piezoelectric Flextensional Energy Harvester, Nadia Ahbab, Sidra Naz, Bingqi Zhao, Tian-Bing Xu

Mechanical & Aerospace Engineering Faculty Publications

This study presents a full polymer piezoelectric flextensional energy harvester (FPPFEH) comprising a single-layer poly(vinylidene fluoride) (PVDF) film bonded to a 3D-printed polylactic acid (PLA) flextensional frame. For an arm inclination angle of θ=10°, the free-body model gives a theoretical geometric force-amplification factor of M_F=cot θ ≈ 5.67; this value represents an ideal upper bound and was not independently validated by local force or strain measurements. During assembly, the film was tensioned only to remove visible slack and maintain a flat configuration. No intentional pretension was applied, and any residual tension was not measured. Off-resonance force-controlled tests showed …

Waveguide-Assisted Single-Mode Fiber Bragg Gratings In A Highly Multimode Coreless Fiber Via Femtosecond Laser Inscription For Extreme Temperature Quasi-Distributed Thermal Sensing, Farhan Mumtaz, Koustav Dey, Bohong Zhang, Jeffrey D. Smith, Ronald J. O'Malley, Jie Huang

Electrical and Computer Engineering Faculty Research & Creative Works

This research reports a potential quasi-distributed thermal mapping optical sensing system for extreme temperatures, leveraging femtosecond (fs) laser inscribed single-mode fiber Bragg gratings (FBGs) and a waveguide within coreless, highly multimode optical fiber, resulting in a single-mode structure. Unlike doped single-mode fibers, coreless fibers composed of silica rods prevent issues associated with dopant migration and ensure data accuracy. The strategic placement of point-by-point FBGs in a cascaded formation on the fs-laser inscribed waveguide facilitates localized multipoint sensing. The long-term stability of the proposed waveguide-assisted FBG system was assessed over 24 hours at elevated temperatures (1000°C), showing no hysteresis during heating …

Multi-Objective Optimization Of Energy Costs And Ev Battery Health In V2g Enabled Homes, Dzifa M. Hodey

Theses and Dissertations--Computer Science

Electric vehicles (EVs) and rooftop solar photovoltaic (PV) systems are increasingly being integrated into residential settings, creating new opportunities for vehicle-to-grid (V2G) and vehicle-to-home (V2H) operations. In these systems, the EV battery functions as a controllable energy storage unit that can charge from the grid or PV and discharge energy to supply household load or export to the grid for a profit. By intelligently scheduling this bidirectional power exchange, households can reduce electricity costs and enhance PV utilization. Realizing these benefits requires optimization strategies that balance cost reduction with EV battery health preservation. However, existing V2G/V2H studies largely emphasize cost …

Advancing Task-Oriented Dialog Systems: Scalability, Generalization, And Evaluation, Adib Mosharrof

Theses and Dissertations--Computer Science

Task-oriented dialog (TOD) systems enable conversational interfaces for complex tasks like flight booking and restaurant reservations. However, deploying TOD systems at scale faces three critical barriers: scalability, generalization, and evaluation. Scalability is primarily restricted by the human-annotation bottleneck, as current systems depend on vast quantities of manually labeled data for every new domain, making deployment prohibitively expensive. Generalization remains a persistent challenge, as systems optimized for known domains often suffer significant performance degradation when encountering new, unseen ones. Existing evaluation metrics measure response quality and fluency, but fail to measure functional task success. As TOD systems are deployed across diverse …

Vision‑Based Online Quality Tracking In Wire Arc Additive Manufacturing Via Hybrid Unsupervised Deep Learning–Statistical Process Monitoring, Giulio Mattera, Yue Cao, Yuming Zhang, Luigi Nele

Electrical and Computer Engineering Faculty Publications

Vision-based monitoring of Wire Arc Additive Manufacturing (WAAM) using supervised deep learning represents the state of the art in anomaly detection, but such approaches require large labeled datasets that are costly to obtain and typically limited to laboratory conditions. To address these limitations, this work proposes a hybrid deep learning–statistical process monitoring (SPM) framework tailored to the stochastic nature of conventional arc welding processes such as GMAW-based additive manufacturing, where existing methods often overfit. The framework integrates a residual convolutional autoencoder (Res-CAE) with skip connections, which jointly analyzes video frames to generate refined latent-space features that are subsequently monitored using …

Co-Sputtered Cuni Heteroatomic Electrocatalyst For Enhanced 5-Hydroxymethylfurfural Selective Electrochemical Conversion, Moumita Dikshit, Baleeswaraiah Muchharla, Luz Vazquez Rivera, Kapil Kumar, Sunita Sanwaria, Kishor Kumar Sadasivuni, Abdennaceur Karoui, Sandeep Kumar, Adetayo Adedeji, Bijandra Kumar

Civil & Environmental Engineering Faculty Publications

The electrochemical conversion of biomass-derived 5-hydroxymethylfurfural (HMF) represents a promising, economically viable, and environmentally sustainable approach for producing value-added chemicals using renewable energy and in situ hydrogen generated through water electrolysis. However, the electrochemical hydrogenation (ECH) of HMF remains challenging due to the inherently low catalytic activity and selectivity of the electrodes, compounded by competition with the kinetically favored hydrogen evolution reaction (HER) in aqueous electrolytes. In this work, we demonstrate that Cu_xNi_100-x heteroatomic thin films, fabricated via direct current (DC) magnetron co-sputtering, achieve a more than one order of magnitude increase in the HMF to 2,5-Bis-hydroxymethylfuran …

High-Resolution, Fast-Response Optical Fiber Temperature Sensor With A Large Measurement Range Based On Fiber-Tip Alumina Fabry-Pérot Interferometer, Ruimin Jie, Chen Zhu, Robert Abbott, Michael Davis, Xiong Zhang, Jie Huang

Electrical and Computer Engineering Faculty Research & Creative Works

We present an alumina-tip optical fiber Fabry-Pérot interferometric temperature sensor exhibiting high-temperature performance, rapid response, and high resolution. Fabricated by fusion splicing an alumina micro disk directly to a single-mode fiber, the sensor achieves robust, stable operation without complex fabrication processes or adhesives. Experimental evaluation confirms a measurement range extending to 1000°C, with sensitivity of 28.66 pm/°C, a resolution of 0.042°C, and a rapid response time of approximately 13 ms. Compared to state-of-the-art optical fiber FPI sensors, our alumina-tip sensor offers superior overall performance, effectively addressing critical demands for high-resolution, fast-response temperature measurement in extreme environments including aerospace, structural monitoring, …

Embeddable Optical Fiber Sensor For Simultaneous Strain And Temperature Monitoring, Amardeep Kaur, Sudharshan Anandan, Steve Eugene Watkins, Yinan Zhang, Kumbla Chandrashekhara, Hai Xiao

Electrical and Computer Engineering Faculty Research & Creative Works

We present an embeddable hybrid optical fiber sensor based on a cascaded extrinsic Fabry–Pérot interferometer (EFPI) and intrinsic Fabry–Pérot interferometer (IFPI) for simultaneous strain and temperature monitoring in high-performance composite materials. The sensor is fabricated using femtosecond laser micromachining and is embedded within bismaleimide composite laminates manufactured via an out-of-autoclave process. Experimental results demonstrate linear and decoupled responses to strain and temperature, with the EFPI showing minimal temperature sensitivity (1.7 pm/°C) and the IFPI exhibiting high temperature sensitivity (16.1 pm/°C). Strain sensitivities for both components were consistent at 0.6pm/με in embedded conditions. The sensor maintained structural integrity and stable spectral …

Distributed Temperature Sensing In The Spray-Cooled Shell Of A 150-Ton Dc Electric Arc Furnace Using Brillouin Optical Fiber Technology, Farhan Mumtaz, Yeshwanth Reddy Mekala, Koustav Dey, Rony Kumer Saha, Ogbole Collins Inalegwu, Manoj Kumar Pullagura, Bohong Zhang, Muhammad Roman, Nicholas Dionise, Zane Voss, Jeffrey D. Smith, Ronald J. O'Malley, Rex E. Gerald, Jie Huang

Electrical and Computer Engineering Faculty Research & Creative Works

This paper presents the deployment and validation of a Brillouin - distributed temperature sensing (DTS) system for real-time thermal monitoring of the spray-cooled upper shell of a 150-ton direct current Electric Arc Furnace (DC EAF) at Big River Steel Plant, Osceola, AR, USA. A four-channel Brillouin DTS system from OZ Optics was employed, with one active channel instrumented using an in-house-fabricated Brillouin scattering-depressed single-mode optical fiber (SMF28e+). The 60 m optical fiber sensor was fabricated, with 20 m allocated for thermal measurement and 40 m used as lead-in fiber to isolate the interrogator from the furnace environment. The fiber was …

Self-Calibrating Uav Navigation: Reinforcement Learning Approaches For Horizontal Trajectory Estimation, Shirin Nasr-Esfahani, S. Jagannathan

Electrical and Computer Engineering Faculty Research & Creative Works

Accurate unmanned aerial vehicle (UAV) trajectory estimation is essential for autonomous navigation, particularly in GPS-denied environments. Visualodometry and simultaneous localization and mapping (SLAM) approaches require precise camera intrinsic parameters, which are typically obtained through predefined or offline calibration. Instead, in this work, we propose a reinforcement learning (RL)-based self-calibration framework that estimates camera intrinsic parameters directly from monocular video sequences, without requiring prior knowledge of the camera, environment, or calibration targets. This intrinsic parameter estimation is then leveraged to achieve robust UAV trajectory estimation using only video data. We formulate the problem as a sequential decision-making task, where an RL …

Integrating Environmental Awareness In Underwater Acoustic Networks: A Comprehensive Review, Sadaf Vahabli, Iftekhar Ahmad, Quoc Viet Phung, Daryoush Habibi, Walid K. Hasan, Ruba Zaheer

Research outputs 2022 to 2026

Underwater Acoustic Networks (UANs) are critical for enabling long-range underwater communication, supporting a wide range of applications, such as environmental monitoring, resource exploration, disaster prevention and marine security. Unfortunately, UANs operate within a limited acoustic spectrum, where these spectrums often overlap with those used by marine animals for communication, navigation and foraging. Additionally, anthropogenic noise from industrial activities contributes to acoustic pollution, intensifying this problem. This frequency overlap poses significant risks to marine life. Therefore, this review highlights the urgent need to develop environmentally aware UANs that minimize harmful acoustic interference with marine mammals, fish and invertebrates. Further, it focuses …

C2p-M: Critical Connection Protection In Multiplex Graphs, Conggai Li, Wei Ni, Ming Ding, Youyang Qu, Jianjun Chen, Wenjie Zhang, Thierry Rakotoarivelo

Research outputs 2022 to 2026

Multiplex graphs represent diverse real-world interactions among entities, where multiple relationship types coexist within the same set of entities. These graphs introduce privacy risks, as data collectors can exploit cross-layer dependencies to infer hidden and sensitive connections. In this work, we propose a C2P-M framework that identifies and protects critical connections while preserving the structural information in multiplex graphs. Unlike conventional methods for single-layer graphs that perturb all edges uniformly, C2P-M selectively protects critical connections, maintaining the analytical usability of the graph. To achieve this, we introduce the multiplex p-cohesion model, which incorporates new score functions that account for both …

Analytical And Semi-Analytical Modeling Of Solar Cells Using The Lambert W Function: A Comprehensive Review Of Equivalent Circuits, Adel El-Shahat, Martin Ćalasan, Snežana Vujoševic, Shady H. E. Abdel Aleem

Engineering Technology Faculty Publications

The modeling of photovoltaic (PV) cells through equivalent circuits forms a central element in the analysis, simulation, and optimization of solar energy systems. Traditional approaches often depend on iterative numerical methods to solve the implicit current–voltage (I–V) equations. In contrast, the Lambert W function has emerged as an effective mathematical tool that enables closed-form or semi-analytical expressions for a wide range of PV models. This paper presents a Lambert W-centered review of analytical and semi-analytical formulations for PV equivalent-circuit models, covering classical single-diode and multi-diode structures and modern variants incorporating additional elements, voltage-dependent parameters, and topology rearrangements. The models are …

A Comprehensive Survey Of Prompt Engineering Techniques In Large Language Models, Tonmoy Debnath, Md Nurul Absar Siddiky, Muhammad Enayetur Rahman, Prosenjit Das, Antu Kumar Guha, Muhammad Rezaur Rahman, H. M. Dipu Kabir

Electrical & Computer Engineering Faculty Publications

Prompt engineering has arisen as a pivotal discipline in optimizing the performance of Large Language Models (LLMs) by structuring inputs to enhance coherence, accuracy, and task alignment. This paper comprehensively surveys various prompting techniques, systematically categorizing them according to their application domains and methodological foundations. Fundamental approaches like zero-shot and few-shot prompting are examined along with advanced strategies, including chain-of-thought reasoning, retrieval-augmented generation, and self-consistency mechanisms. A rigorous qualitative analysis is conducted to evaluate each technique's strengths, limitations, and optimal use cases, offering a structured framework for selecting the most effective prompting strategies. Theoretical insights and empirical findings are consolidated …

Physics-Informed Temperature Prediction Of Lithium-Ion Batteries Using Decomposition-Enhanced Lstm And Bilstm Models, Seyed Saeed Madani, Yasmin Shabeer, Michael Fowler, Satyam Panchal, Carlos Ziebert, Hicham Chaoui, François Allard

Electrical & Computer Engineering Faculty Publications

Accurately forecasting the operating temperature of lithium-ion batteries (LIBs) is essential for preventing thermal runaway, extending service life, and ensuring the safe operation of electric vehicles and stationary energy-storage systems. This work introduces a unified, physics-informed, and data-driven temperature-prediction framework that integrates mathematically governed preprocessing, electrothermal decomposition, and sequential deep learning architectures. The methodology systematically applies the governing relations to convert raw temperature measurements into trend, seasonal, and residual components, thereby isolating long-term thermal accumulation, reversible entropy-driven oscillations, and irreversible resistive heating. These physically interpretable signatures serve as structured inputs to machine learning and deep learning models trained on temporally …

Comparative Assessment Of Energy And Emission Costs For Geothermal Heat Pumps And Fossil-Fuel Heating Systems Across U.S. Climatic Zones, Md Shahin Alam, Shima Afshar, Seyed Ali Arefifar, Mohammad Haq

Electrical & Computer Engineering Faculty Publications

In response to growing concerns over global warming and energy sustainability, transitioning from fossil-fuel-based heating systems to renewable alternatives is essential. This study evaluates the economic and environmental performance of geothermal heat pumps for building heating and compares it with conventional coal-fired boilers, natural-gas boilers, and diesel furnaces. Using the heating degree-day (HDD) method, heating energy demand was analyzed for four U.S. cities—Anchorage (AK), San Francisco (CA), Salt Lake City (UT), and Las Vegas (NV)—representing diverse climatic zones. The analysis integrates thermodynamic and economic parameters, including the coefficient of performance (COP = 2–5) and annual fuel-utilization efficiency (AFUE = 80–97%), …

Machine Learning-Based Lifetime Prediction Of Lithium Batteries: A Comparative Assessment For Electric Vehicle Applications, Abdelilah Hammou, Raffaele Petrone, Demba Diallo, Boubekeur Tala-Ighil, Philippe Makany Boussiengue, Hicham Chaoui, Hamid Gualous

Electrical & Computer Engineering Faculty Publications

This paper evaluates and compares four data-driven methods (Gaussian Process Regression (GPR), echo state network (ESN), gated recurrent unit (GRU), and long short-term memory (LSTM)) for lithium-ion capacity prognostics adapted to electric vehicle conditions. This comparison aims to find the most efficient prognosis method considering two constraints: the limitation of computational power and the unavailability of on-board capacity measurement that requires full charge and discharge conditions. The machine learning models are trained using capacity values estimated under vehicle conditions. The ageing data is collected from cycling tests of two battery chemistries, Lithium Fer Phosphate (LFP) and Nickel Manganese Cobalt (NMC), …

Pin-Plane Electrical Discharge Driven By A Mosfet Dc Current Source, Myles Perry, Sidmar Holoman, Daniel Wozniak, Shirshak Kumar Dhali

Electrical & Computer Engineering Faculty Publications

The generation of atmospheric pressure nonequilibrium plasma using electrical discharges is an active area of research due to its significance in a wide spectrum of applications including medicine, combustion, and manufacturing. In our attempt to create a helium plasma jet in a pin-plane discharge with a constant current source, we observed self-pulsating behavior. We present the results of the electrical, optical, and spectroscopic measurements carried out to characterize the discharge. The duration of the discharge is a few tens of nanoseconds, and the repetition rate is in the few tens of kHz. The effect of the gap distance and gas …

Healthcare Digital Twins: A Methodological Literature Review On Integrating Iot And Ai For Personalized Medicine And Predictive Care, Sara Shahnazinia, Mahsa Tavasoli, Abdolhossein Sarrafzadeh, Ali Karimoddini

Electrical & Computer Engineering Faculty Publications

Digital Twin (DT) technology has the potential to revolutionize healthcare delivery and enhance patient outcomes through personalized and precision medicine, simulation models for operations and interventions, and drug discovery. However, successful implementation of DTs in Internet of Things (IoT) and artificial intelligence (AI) healthcare is contingent upon addressing key challenges such as privacy, ethics, and robust data security. This paper presents a methodological literature review of DT applications in healthcare, systematically analyzing the current state of research, key enabling technologies, and implementation challenges. The review summarizes DT categorization approaches (application-based, technology-based, and real-time function-based); delineates core DT components such as …

An Explainable Cs-Mitigation Triangular (Ecsmt) Framework To Secure Graph Neural Networks, Sabah Ettahri, Sergio Pallas Enguita, Chung-Hao Chen, Wen-Chao Yang

Electrical & Computer Engineering Faculty Publications

This research addresses cyber risk by defending against backdoor attacks on Graph Neural Networks (GNNs). We propose the Explainable Complex System-Mitigation Triangular (ECSMT) Framework, which integrates Robust Training, Graph Regularization, and Data Sanitization into a lightweight, hardware-efficient defense layer. To evaluate structural generalizability, we conducted empirical evaluations across three distinct benchmark domains (AIDS, MUTAG, and PROTEINS) using a Graph Isomorphism Network (GIN) backbone. Under a baseline 5% backdoor subgraph trigger injection ratio, ECSMT achieves excellent utility retention, securing a Clean Accuracy (CA) of 97.33% (±0.62%) while reducing the Attack Success Rate (ASR) from 97.00% down to 69.45% on the primary …

Generalized Inverter Fault Detection Using Normalized Current Features And A Lightweight Bilstm Network, Mohammad Zamani Khaneghah, Mohamad Alzayed, Hicham Chaoui

Electrical & Computer Engineering Faculty Publications

Fault detection and diagnosis of three-phase inverter-fed motor drives is essential for ensuring system reliability, safety, and continuous operation in applications such as electric vehicles and industrial automation. This paper proposes a data-driven fault detection framework based on normalized current features and a lightweight bidirectional long short-term memory (BiLSTM) network which can be generalized to different motor power rating in the same controller system. A compact set of six time-domain features, consisting of the mean and root-mean-square (RMS) values of the phase currents, is extracted and normalized with respect to the average RMS value. This normalization effectively removes dependency on …

Dermatology Skin Lesion Image Analysis, Victoria Wegley, Joshua Hoog, Tristan Crawford, Keith Miller, William Fons, K. Pugh, J. Taylor, S. Swinfard, A. Fernandes, G. Patel, J. Hagerty, W. V. Stoecker, Ronald Joe Stanley

Research Data

Dermatology skin lesion image analysis research has been ongoing at Missouri S&T (previously UMR) since the 1980s. Our group has been successful in finding over 20 key dermoscopic structures in melanoma, melanoma mimics, and nonmelanoma skin cancers using iterative structure-based analysis. Structures are chosen to reduce system errors which are concentrated in a few classes: amelanotic/featureless, regressed, and small in situ melanomas, and the most difficult benign lesions to identify: Clark nevi, lentigines, and seborrheic keratoses [1]. Preliminary research detecting and using annotated lesion structures [2-7] and lesion artifacts [8,9] guided by clinical experience [10] with image processing and deep …

State-Dependent Queueing For Adaptive Signal Control: A Simulation-Based Performance Evaluation, Shaimaa Alseddiek, Usama Elrawy Shahdah, Hala B. Nafea, Hossam El-Din Moustafa, El-Said Ahmed Marzouk, Mohamed M. Ashour

Mansoura Engineering Journal

Urban traffic congestion persists as a critical challenge to transportation system efficiency, sustainability, and safety. Traditional queuing models utilizing fixed service rates inadequately represent the dynamic feedback between congestion and capacity in real vehicular flow. State-Dependent Queuing Models (SDQMs) address this limitation by modelling service rate as a function of queue length or density. This research advances SDQM application for adaptive traffic signal control through development of a calibrated state-dependent departure rate implemented within a microscopic simulation environment using SUMO and TraCI. Six control strategies including fixed-time, actuated, and two SDQM variants were evaluated across traffic demands ranging from undersaturated …

A Review On Underwater Beamforming: Techniques, Challenges, And Future Directions, Ruba Zaheer, Quoc Viet Phung, Iftekhar Ahmad, Asma Aziz, Daryoush Habibi, Yue Rong, Walid K. Hasan

Research outputs 2022 to 2026

This paper comprehensively reviews recent advancements in Underwater Beamforming (UWB) systems, highlighting its pivotal role in underwater communication, sensing, and environmental monitoring. It explores the various beamforming applications, ranging from maritime surveillance to marine life monitoring, and indicates its significance in enhancing signal clarity, spatial resolution, and noise suppression in underwater acoustic environments. The unique challenges posed by the underwater environment that introduce complexities into the beamforming process such as non-stationary noise interference, severe signal attenuation, multipath propagation, and dynamic environmental variability are thoroughly discussed. The review systematically discusses and examines conventional, adaptive, and learning-based beamforming techniques, analyzing their strengths, …

Super-Resolution Learning Inspired Spectral-Spatial Correlation Network For Hyperspectral Target Detection, Jiaping Zhong, Yunsong Li, Jianxin Li, Yanzi Shi, Weiying Xie, Paolo Gamba

Research outputs 2022 to 2026

Hyperspectral target detection (HTD) aims at extracting targets from complex backgrounds while overcoming noise interference. Existing deep learning models for HTD usually suffer from low spatial resolution and unitary representation, especially in space-borne platforms. Super-resolution, as a critical technology to enhance the spatial details, could effectively address the aforementioned issue. To make super-resolution absolutely pose positive effects on target detection, this paper proposes an end-to-end novel super-resolution learning inspired spectral-spatial correlation network for hyperspectral target detection (SR-HTD) from the perspective of spatial and spectral regularization to achieve high-precision detection. Specifically, we designed a Spatial Correlation Aggregation (SCA) module inspired by …

Enhancing Deep Reinforcement Learning With Expert Demonstrations For Mobile Robot Navigation In Unstructured Off-Road Environments, Dulitha Dabare

Theses: Doctorates and Masters

Advancements in the field of Deep Learning has ushered in a boom in autonomous navigation research. Most of the work being conducted in this space, however, has focused on on-road urban navigation scenarios, with unstructured outdoor terrain navigation receiving much more limited attention. Given the wide range of applications that exist for legged and wheeled ground robots in off-road environments in areas such as agriculture, mining and disaster recovery, there is a growing need for research work to improve the navigational capabilities of mobile robots deployed in these challenging environments.

A promising candidate for application to these navigation challenges in …

Analysis Of Policies And Incentives For The Successful Implementation Of Hydrogen-Fueled Medium-Duty And Heavy-Duty Vehicles In Humboldt County, California, Alka Verma

Cal Poly Humboldt theses and projects

The 21st century has seen a significant rise in global greenhouse gas (GHG) emissions, with the transportation sector contributing 23% of these emissions. Medium-duty and heavy-duty vehicles (MD/HD) are particularly impactful, accounting for over a quarter of transport-related emissions. In Humboldt County, California, transportation represents 53% of total emissions, with MD/HD vehicles being a major contributor. As light-duty vehicles shift to zero-emission alternatives, the MD/HD sector faces unique challenges. Hydrogen fuel cell vehicles offer a promising solution, providing longer range, higher energy density, and quicker refueling compared to battery electric vehicles (BEVs). These features make hydrogen an attractive option for …

A Novel Hybrid Robust Transfer Learning-Based Adaptive Fractional-Order Super-Twisting Sliding Mode Controller Enhanced For Speed Regulation Of Brushless Dc Motor, Seyyed Morteza Ghamari, Asma Aziz, Daryoush Habibi

Research outputs 2022 to 2026

Brushless DC (BLDC) motors are widely used in applications that are highly-efficient, reliable, and compact, such as electric vehicles, robotics, and medical devices. However, the inherent nonlinearities and load sensitivity of BLDC motors require a robust and adaptive control strategy to ensure satisfactory performance under various operating conditions. Sliding mode control (SMC) has been widely used for the BLDC drives. However, because of its simplicity and robustness, the control effectiveness of the control is limited by the sensitivity to the disturbances and the chattering phenomenon. To remedy this, super-twisting (ST) technique has been proposed to achieve smoother response and better …

Renewable And Affordable Energy For Apartments. Inquiry Into Renewable And Affordable Energy For Apartments, Nishadi Ruwandima Weerasinghe Mudiyanselage, Asma Aziz

Research outputs 2022 to 2026

Recent literature identifies a persistent disparity between detached housing and apartments in accessing renewable and affordable electricity. While rooftop solar PV deployment in Australia  has expanded rapidly since 2017, access to these technologies in multi-unit dwellings (MUDs) remains limited, resulting in higher electricity costs for apartment residents compared with standalone houses. This inequity is increasingly significant given rising electricity prices and the growing share of Australians living in apartments.