Digital Commons Network^™

Perturbation Of 3d Nuclear Architecture, Epigenomic Dysregulation And Aging, And Cannabinoid Synaptopathy Reconfigures Conceptualization Of Cannabinoid Pathophysiology: Part 1–Aging And Epigenomics, Albert Stuart Reece, Gary Kenneth Hulse

Research outputs 2022 to 2026

Much recent attention has been directed toward the spatial organization of the cell nucleus and the manner in which three-dimensional topologically associated domains and transcription factories are epigenetically coordinated to precisely bring enhancers into close proximity with promoters to control gene expression. Twenty lines of evidence robustly implicate cannabinoid exposure with accelerated organismal and cellular aging. Aging has recently been shown to be caused by increased DNA breaks. These breaks rearrange and maldistribute the epigenomic machinery to weaken and reverse cellular differentiation, cause genome-wide DNA demethylation, reduce gene transcription, and lead to the inhibition of developmental pathways, which contribute to …

Readability Of Australian Road Safety Information For The General Public, Catherine Ferguson, Stephen Winn

Research outputs 2022 to 2026

This research was conducted as a result of the authors becoming aware of the Australasian College of Road Safety (ACRS) submission to the National Road Safety Strategy 2021-2030. The ACRS submission outlined six key elements, one of which suggested that the National Road Safety Strategy should include ‘Publication in easily consumable form, for the public, of infrastructure safety star ratings for all road users’ (ACRS, 2021, p.61). This prompted the researchers to consider the road safety information provided to the general public about driving behaviour. Forty excerpts from online data from one Australian State’s road safety website were assessed for …

A Data Taxonomy For Adaptive Multifactor Authentication In The Internet Of Health Care Things, Tance Suleski, Mohiuddin Ahmed

Research outputs 2022 to 2026

The health care industry has faced various challenges over the past decade as we move toward a digital future where services and data are available on demand. The systems of interconnected devices, users, data, and working environments are referred to as the Internet of Health Care Things (IoHT). IoHT devices have emerged in the past decade as cost-effective solutions with large scalability capabilities to address the constraints on limited resources. These devices cater to the need for remote health care services outside of physical interactions. However, IoHT security is often overlooked because the devices are quickly deployed and configured as …

Seagrass Soils Sequester Up To Half The Metal Emissions Of One Of The World's Largest Smelters, Anna Lafratta, Oscar Serrano, Pere Masque´, M. Fernandes, S. Gaylard, Paul Lavery

Research outputs 2022 to 2026

One of the world's largest smelters has been operating in South Australia since 1889, affecting environment and human health. Here we quantified the magnitude of Pb, Zn and Cd emissions from the smelter sequestered in the soil of an adjacent 110 km2 Posidonia australis seagrass meadows. Seagrass core records show that the smelter contaminated the entire area with decreasing sequestration with increasing distance from contamination points. The soil accumulated ~1300 t of Pb, ~3450 t of Zn, and ~ 90 t of Cd since 1889, and sequestered the equivalent of ~20 % of Pb, and ~50 % of Zn and …

Going Beyond: Cyber Security Curriculum In Western Australian Primary And Secondary Schools. Final Report, Nicola F. Johnson, Ahmed Ibrahim, Leslie Sikos, Marnie Mckee

Research outputs 2022 to 2026

There is no doubt cyber security is of national interest given the rife nature of cyber crime and the alarming increase of victims who have endured identify theft, fraud and scams. Curriculum within K-12 schools tends to be fixed and any modifications are subject to extensive consultation within a prolonged review cycle. Therefore, this report has gone beyond curriculum to explore the potential of national awareness campaigns and dynamic digital cyber security licences as alternative possibilities for instigation. The role of leaders in various school sectors and systems is critical for a successful roll out. This final report culminates from …

Generalized Framework For Image And Video Object Segmentation Using Affinity Learning And Message Passing Gnns, Sundaram Muthu, Ruwan Tennakoon, Tharindu Rathnayake, Reza Hoseinnezhad, David Suter, Alireza Bab-Hadiashar

Research outputs 2022 to 2026

Despite significant amount of work reported in the computer vision literature, segmenting images or videos based on multiple cues such as objectness, texture and motion, is still a challenge. This is particularly true when the number of objects to be segmented is not known or there are objects that are not classified in the training data (unknown objects). A possible remedy to this problem is to utilize graph-based clustering techniques such as Correlation Clustering. It is known that using long range affinities (Lifted multicut), makes correlation clustering more accurate than using only adjacent affinities (Multicut). However, the former is computationally …

Improved Yolov5-Based Real-Time Road Pavement Damage Detection In Road Infrastructure Management, Abdullah A. Sami, Saadman Sakib, Kaushik Deb, Iqbal H. Sarker

Research outputs 2022 to 2026

Deep learning has enabled a straightforward, convenient method of road pavement infrastructure management that facilitates a secure, cost-effective, and efficient transportation network. Manual road pavement inspection is time-consuming and dangerous, making timely road repair difficult. This research showcases You Only Look Once version 5 (YOLOv5), the most commonly employed object detection model trained on the latest benchmark Road Damage Dataset, Road Damage Detection 2022 (RDD 2022). The RDD 2022 dataset includes four common types of road pavement damage, namely vertical cracks, horizontal cracks, alligator cracks, and potholes. This paper presents an improved deep neural network model based on YOLOv5 for …

Three-Dimensional Food Printing: Its Readiness For A Food And Nutrition Insecure World, Liezhou Zhong, Joshua R. Lewis, Marc Sim, Catherine P. Bondonno, Mark L. Wahlqvist, Amin Mugera, Sharon Purchase, Kadambot H. M. Siddique, Michael J. Considine, Stuart K. Johnson, Amanda Devine, Jonathan M. Hodgson

Research outputs 2022 to 2026

Three-dimensional (3D) food printing is a rapidly emerging technology offering unprecedented potential for customised food design and personalised nutrition. Here, we evaluate the technological advances in extrusion-based 3D food printing and its possibilities to promote healthy and sustainable eating. We consider the challenges in implementing the technology in real-world applications. We propose viable applications for 3D food printing in health care, health promotion and food waste upcycling. Finally, we outline future work on 3D food printing in food safety, acceptability and economics, ethics and regulations. . .

Photocatalytic Reforming Of Lignocellulose: A Review, Xinyuan Xu, Lei Shi, Shu Zhang, Zhimin Ao, Jinqiang Zhang, Shaobin Wang, Hongqi Sun

Research outputs 2022 to 2026

Biomass has been considered as a promising energy resource to combat the exhaustion of fossil fuels, as it is renewable, sustainable, and clean. Photocatalytic reforming is a novel technology to utilize solar energy for upgrading biomass in relatively mild conditions. This process efficiently reforms and recasts biomass into hydrogen and/or valuable chemicals. To date, lignocellulose, including cellulose, hemicellulose and lignin, has attracted extensive studies in facile photocatalytic valorisation. This review summarizes and analyzes the most recent research advances on photoreforming of lignocellulose to provide insights for future research, with a particular emphasis on the reformation of lignin because of its …

A Provable Secure And Efficient Authentication Framework For Smart Manufacturing Industry, Muhammad Hammad, Akhtar Badshah, Ghulam Abbas, Hisham Alasmary, Muhammad Waqas, Wasim A. Khan

Research outputs 2022 to 2026

Smart manufacturing is transforming the manufacturing industry by enhancing productivity and quality, driving growth in the global economy. The Internet of Things (IoT) has played a crucial role in realizing Industry 4.0, where machines can communicate and interact in real-time. Despite these advancements, security remains a major challenge in developing and deploying smart manufacturing. As cyber-attacks become more prevalent, researchers are making security a top priority. Although IoT and Industrial IoT (IIoT) are used to establish smart industries, these systems remain vulnerable to various types of attacks. To address these security issues, numerous authentication methods have been proposed. However, many …

Biofilm Formation On The Surface Of Monazite And Xenotime During Bioleaching, Arya Van Alin, Melissa K. Corbett, Homayoun Fathollahzadeh, M. Christian Tjiam, William D. A. Rickard, Xiao Sun, Andrew Putnis, Jacques Eksteen, Anna H. Kaksonen, Elizabeth Watkin

Research outputs 2022 to 2026

Microbial attachment and biofilm formation is a ubiquitous behaviour of microorganisms and is the most crucial prerequisite of contact bioleaching. Monazite and xenotime are two commercially exploitable minerals containing rare earth elements (REEs). Bioleaching using phosphate solubilizing microorganisms is a green biotechnological approach for the extraction of REEs. In this study, microbial attachment and biofilm formation of Klebsiella aerogenes ATCC 13048 on the surface of these minerals were investigated using confocal laser scanning microscopy (CLSM) and scanning electron microscopy (SEM). In a batch culture system, K. aerogenes was able to attach and form biofilms on the surface of three phosphate …

Application Of A Variable Path Length Repetitive Process Control For Direct Energy Deposition Of Thin-Walled Structures, Elias B. Snider, Douglas A. Bristow

Mechanical and Aerospace Engineering Faculty Research & Creative Works

Direct Energy Deposition (DED) Additive Manufacturing is Well Suited to Fabricating Large Thin-Walled Metal Structures Such as Rocket Nozzles but Suffers from Layer-To-Layer Defect Propagation. Propagating Defects May Exhibit as Slumping or a Ripple in Bead Geometry. Recent Works Have Used Repetitive Process Control (RPC) Methods for Additive Manufacturing to Stabilize the Layer-Wise Defect Propagation, But These Methods Require Repetition of the Same Path. However, Typical Thin-Wall DED Applications, Sometimes Referred to as Vase Structures, Have Changing Paths with Each Layer Such as Expanding or Contracting Diameters and Changing Profiles. This Paper Presents an Extension to Optimal RPC that Uses …

Thermal Diffusivity Materials Characterization Via Active Microwave Thermography, Logan M. Wilcox, Kristen M. Donnell

Electrical and Computer Engineering Faculty Research & Creative Works

Active microwave thermography (AMT) is a relatively new nondestructive evaluation method which is proposed in this work for thermal materials characterization. Specifically, AMT is investigated as a single-sided measurement option for out-of-plane thermal diffusivity (a parameter traditionally measured using a two-sided technique). Simulation and measurement results support the use of AMT for such a characterization for materials backed by an electromagnetically absorptive material. Both lossless and lossy materials may be measured, with better accuracy for lossless materials. The effect of heating time was also considered. The results indicate that for the 50 W system used here, 100 seconds of electromagnetic …

Managing Reserve Deliverability Risk Of Integrated Electricity-Heat Systems In Day-Ahead Market: A Distributionally Robust Joint Chance Constrained Approach, Yang Chen, Jianxue Wang, Siyuan Wang, Rui Bo, Chenjia Gu, Qingtao Li

Electrical and Computer Engineering Faculty Research & Creative Works

The integrated electricity and heat system (IEHS) is an emerging demand-side flexible resource for power systems. IEHS operators participating in electricity markets considering their capabilities in reserve provision will face the reserve deliverability risk due to the energy-limited storage nature of heat systems. To address this challenge and increase profitability, a distributionally robust joint chance-constrained mechanism with enhanced quantifications is adopted for the heating system and reserve deployment uncertainties. Detailed pipeline storage representation for thermal networks and integrated demand response are incorporated into this strategic participation model. A two-stage distributionally robust joint chance constrained program is then incorporated to effectively …

Continual Reinforcement Learning Formulation For Zero-Sum Game-Based Constrained Optimal Tracking, Behzad Farzanegan, Sarangapani Jagannathan

Electrical and Computer Engineering Faculty Research & Creative Works

This study provides a novel reinforcement learning-based optimal tracking control of partially uncertain nonlinear discrete-time (DT) systems with state constraints using zero-sum game (ZSG) formulation. To address optimal tracking, a novel augmented system consisting of tracking error and its integral value, along with an uncertain desired trajectory, is constructed. A barrier function (BF) with a tradeoff factor is incorporated into the cost function to keep the state trajectories to remain within a compact set and to balance safety with optimality. Next, by using the modified value functional, the ZSG formulation is introduced wherein an actor–critic neural network (NN) framework is …

Three-Terminal Noise Source Extraction From A Qi-Based Wireless Power Transfer System For Predicting Conducted Emissions, Srinath Penugonda, Siqi Bai, Varittha Sanphuang, Nevin Altunyurt, Hongseok Kim, Daryl G. Beetner, Jun Fan

Electrical and Computer Engineering Faculty Research & Creative Works

An equivalent method to extract a three-terminal noise source from a Qi-based wireless power transfer system is proposed in this article. This method is capable of extracting a source for both the positive (P) and negative (N) lines with respect to the third terminal (the ground chassis or reference plane). The extracted sources are independent of the setup configuration and can be used to predict currents or voltages on the P and N lines on a setup that is different from the extraction setup. In the following article, a three-terminal model independent of the setup configuration is first extracted. The …

Continual Optimal Adaptive Tracking Of Uncertain Nonlinear Continuous-Time Systems Using Multilayer Neural Networks, Irfan Ganie, S. (Sarangapani) Jagannathan

Electrical and Computer Engineering Faculty Research & Creative Works

This study provides a lifelong integral reinforcement learning (LIRL)-based optimal tracking scheme for uncertain nonlinear continuous-time (CT) systems using multilayer neural network (MNN). In this LIRL framework, the optimal control policies are generated by using both the critic neural network (NN) weights and single-layer NN identifier. The critic MNN weight tuning is accomplished using an improved singular value decomposition (SVD) of its activation function gradient. The NN identifier, on the other hand, provides the control coefficient matrix for computing the control policies. An online weight velocity attenuation (WVA)-based consolidation scheme is proposed wherein the significance of weights is derived by …

Optimal Tracking Of Nonlinear Discrete-Time Systems Using Zero-Sum Game Formulation And Hybrid Learning, Behzad Farzanegan, S. (Sarangapani) Jagannathan

Electrical and Computer Engineering Faculty Research & Creative Works

This paper presents a novel hybrid learning-based optimal tracking method to address zero-sum game problems for partially uncertain nonlinear discrete-time systems. An augmented system and its associated discounted cost function are defined to address optimal tracking. Three multi-layer neural networks (NNs) are utilized to approximate the optimal control and the worst-case disturbance inputs, and the value function. The critic weights are tuned using the hybrid technique, whose weights are updated once at the sampling instants and in an iterative manner over finite times within the sampling instants. The proposed hybrid technique helps accelerate the convergence of the approximated value functional …

Securing The Transportation Of Tomorrow: Enabling Self-Healing Intelligent Transportation, Elanor Jackson, Sahra Sedigh Sarvestani

Electrical and Computer Engineering Faculty Research & Creative Works

The safety of autonomous vehicles relies on dependable and secure infrastructure for intelligent transportation. The doctoral research described in this paper aims to enable self-healing and survivability of the intelligent transportation systems required for autonomous vehicles (AV-ITS). The proposed approach is comprised of four major elements: qualitative and quantitative modeling of the AV-ITS, stochastic analysis to capture and quantify interdependencies, mitigation of disruptions, and validation of efficacy of the self-healing process. This paper describes the overall methodology and presents preliminary results, including an agent-based model for detection of and recovery from disruptions to the AV-ITS.

Personalizing Student Graduation Paths Using Expressed Student Interests, Nicolas Dobbins, Ali R. Hurson, Sahra Sedigh

Electrical and Computer Engineering Faculty Research & Creative Works

This paper proposes an intelligent recommendation approach to facilitate personalized education and help students in planning their path to graduation. The goal is to identify a path that aligns with a student's interests and career goals and approaches optimality with respect to one or more criteria, such as time-to-graduation or credit hours taken. The approach is illustrated and verified through application to undergraduate curricula at the Missouri University of Science and Technology.

A Resilience-Oriented Multi-Stage Adaptive Distribution System Planning Considering Multiple Extreme Weather Events, Siyuan Wang, Rui Bo

Electrical and Computer Engineering Faculty Research & Creative Works

Climate Change May Increase the Risk of an Area Being Hit by Multiple Extreme Weather Events, Which Brings Significant Challenges for Distribution System Planners in an Increasing Renewable Penetration Era. There is an Urgent Need for Planning Approaches to Be More Flexible and Allow for Adaptive Adjustments in the Future to Hedge Against High Uncertainties in Extreme Weather Event Scenarios. in This Work, We Propose a Resilience-Oriented Distribution System Planning Approach that Considers Multiple Extreme Weather Events. a Multi-Stage Hybrid-Stochastic-And-Robust Formulation is Developed to Model Decisions Not Only for Initial Investments, But Also for Adaptive Investments and Emergent Operations in …

Cooperative Deep $Q$ -Learning Framework For Environments Providing Image Feedback, Krishnan Raghavan, Vignesh Narayanan, Sarangapani Jagannathan

Electrical and Computer Engineering Faculty Research & Creative Works

In This Article, We Address Two Key Challenges in Deep Reinforcement Learning (DRL) Setting, Sample Inefficiency and Slow Learning, with a Dual-Neural Network (NN)-Driven Learning Approach. in the Proposed Approach, We Use Two Deep NNs with Independent Initialization to Robustly Approximate the Action-Value Function in the Presence of Image Inputs. in Particular, We Develop a Temporal Difference (TD) Error-Driven Learning (EDL) Approach, Where We Introduce a Set of Linear Transformations of the TD Error to Directly Update the Parameters of Each Layer in the Deep NN. We Demonstrate Theoretically that the Cost Minimized by the EDL Regime is an Approximation …

Advances In Fiber-Optic Extrinsic Fabry-Perot Interferometric Physical And Mechanical Sensors: A Review, Chen Zhu, Hongkun Zheng, Lingmei Ma, Zheyi Yao, Bo Liu, Jie Huang, Yunjiang Rao

Electrical and Computer Engineering Faculty Research & Creative Works

Fabry-Perot Interferometers Have Found a Multitude of Scientific and Industrial Applications Ranging from Gravitational Wave Detection, High-Resolution Spectroscopy, and Optical Filters to Quantum Optomechanics. Integrated with Optical Fiber Waveguide Technology, the Fiber-Optic Fabry-Perot Interferometers Have Emerged as a Unique Candidate for High-Sensitivity Sensing and Have Undergone Tremendous Growth and Advancement in the Past Two Decades with their Successful Applications in an Expansive Range of Fields. the Extrinsic Cavity-Based Devices, I.e., the Fiber-Optic Extrinsic Fabry-Perot Interferometers (EFPIs), Enable Great Flexibility in the Design of the Sensitive Fabry-Perot Cavity Combined with State-Of-The-Art Micromachining and Conventional Mechanical Fabrication, Leading to the Development of …

Decoupling Optimization For Complex Pdn Structures Using Deep Reinforcement Learning, Ling Zhang, Li Jiang, Jack Juang, Zhiping Yang, Er Ping Li, Chulsoon Hwang

Electrical and Computer Engineering Faculty Research & Creative Works

This Article Presents a New Optimization Method for Complex Power Distribution Networks (PDNs) with Irregular Shapes and Multilayer Structures using Deep Reinforcement Learning (DRL), Which Has Not Been Considered Before. a Fast Boundary Integration Method is Applied to Compute the Impedance Matrix of a PDN Structure. Subsequently, a New DRL Algorithm based on Proximal Policy Optimization (PPO) is Proposed to Optimize the Decoupling Capacitor (Decap) Placement by Minimizing the Number of Decaps While Satisfying the Desired Target Impedance. in the Proposed Approach, the PDN Structure Information is Encoded into Matrices and Serves as the Input of the DRL Algorithm, Which …

Decentralized And Coordinated V-F Control For Islanded Microgrids Considering Der Inadequacy And Demand Control, Buxin She, Fangxing Li, Hantao Cui, Jinning Wang, Liang Min, Oroghene Oboreh-Snapps, Rui Bo

Electrical and Computer Engineering Faculty Research & Creative Works

This Paper Proposes a Decentralized and Coordinated Voltage and Frequency (V-F) Control Framework for Islanded Microgrids, with Full Consideration of the Limited Capacity of Distributed Energy Resources (DERs) and V-F Dependent Load. First, the Concept of DER Inadequacy is Illustrated with the Challenges It Poses. Then, a Decentralized and Coordinated Control Framework is Proposed to Regulate the Output of Inverter-Based Generations and Reallocate Limited DER Capacity for V-F Control. the Control Framework is Composed of a Power Regulator and a V-F Regulator, Which Generates the Supplementary Signals for the Primary Controller. the Power Regulator Regulates the Output of Grid-Forming Inverters …

Skin Lesion Segmentation In Dermoscopic Images With Noisy Data, Norsang Lama, Jason Hagerty, Anand Nambisan, Ronald Joe Stanley, William Van Stoecker

Electrical and Computer Engineering Faculty Research & Creative Works

We Propose a Deep Learning Approach to Segment the Skin Lesion in Dermoscopic Images. the Proposed Network Architecture Uses a Pretrained Efficient Net Model in the Encoder and Squeeze-And-Excitation Residual Structures in the Decoder. We Applied This Approach on the Publicly Available International Skin Imaging Collaboration (ISIC) 2017 Challenge Skin Lesion Segmentation Dataset. This Benchmark Dataset Has Been Widely Used in Previous Studies. We Observed Many Inaccurate or Noisy Ground Truth Labels. to Reduce Noisy Data, We Manually Sorted All Ground Truth Labels into Three Categories — Good, Mildly Noisy, and Noisy Labels. Furthermore, We Investigated the Effect of Such …

Inverter Pq Control With Trajectory Tracking Capability For Microgrids Based On Physics-Informed Reinforcement Learning, Buxin She, Fangxing Li, Hantao Cui, Hang Shuai, Oroghene Oboreh-Snapps, Rui Bo, Nattapat Praisuwanna, Jingxin Wang, Leon M. Tolbert

Electrical and Computer Engineering Faculty Research & Creative Works

The increasing penetration of inverter-based resources (IBRs) calls for an advanced active and reactive power (PQ) control strategy in microgrids. To enhance the controllability and flexibility of the IBRs, this paper proposed an adaptive PQ control method with trajectory tracking capability, combining model-based analysis, physics-informed reinforcement learning (RL), and power hardware-in-the-loop (HIL) experiments. First, model-based analysis proves that there exists an adaptive proportional-integral controller with time-varying gains that can ensure any exponential PQ output trajectory of IBRs. These gains consist of a constant factor and an exponentially decaying factor, which are then obtained using a model-free deep reinforcement learning approach …

Datasets From: Application Of Aerial Insar To Measure Glacier Elevations, Bryce Glenn, Andrew G. Fountain, Delywn Moller

Geology Faculty Datasets

In September 2016 NASA flew an interferometric synthetic aperture radar (GLISTIN) over most of the glacier-covered regions of the coterminous USA for the purpose of mapping the surface elevation of the glaciers. Where multiple passes were flown over the same ground track, the data were mosaiced together to improve data coverage and elevation accuracy. These data are to be used with elevations collected by past and future efforts to calculate volume change of glaciers.

The data were the basis for:

Glenn, Bryce Allen, "Assessing Airborne Radar to Map Glacier Elevations in Alpine Terrain Including Estimated Glacier Volume Change …

Bidirectional Asymmetric Clllc Resonant Dc-Dc Converter For Onboard Electric Vehicle Chargers, Angshuman Sharma, Alvaro Cardoza, Kartikeya J.P. Veeramraju, Oroghene Oboreh-Snapps, Jonathan W. Kimball

Electrical and Computer Engineering Faculty Research & Creative Works

Bidirectional CLLLC resonant dc-dc converters with an asymmetric tank can narrow the switching frequency bandwidth required to meet the asymmetric voltage gains in the two directions of power flow. Consequently, higher power density and efficiency may be feasible. in this paper, a CLLLC resonant converter with asymmetric primary and secondary capacitances is investigated for charging and discharging the next-generation 900 V traction battery of an electric vehicle. First, a new voltage gain equation is formulated using the First Harmonic Approximation. Then, the proposed gain equation is used to design the asymmetric resonant tank. the effects of asymmetric capacitances on the …

Retracted: A Robust Integrated Approach For Optimal Management Of Power Networks Encompassing Wind Power Plants, Aliasghar Baziar, Mohammad Reza Akbarizadeh, Amin Hajizadeh, Mousa Marzband, Rui Bo

Electrical and Computer Engineering Faculty Research & Creative Works

This paper basically concentrates on providing some significant steps for congestion management of the power systems based on an interval-Based robust chance constrained transmission switching (IBRCC-TS) approach for decreasing the congestion of the system while increasing the robustness of the system against uncertainties of the wind turbines. However, the utilization of TS approach in the power system is a severe challenge, since there is no limitation over the switching rate during a certain timespan in the network as well as the power switches' failure uncertainty. Besides, the frequent switching by the TS method decreases the switch maintenance and puts the …