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2024 IEEE-EmBS International Conference on Biomedical and Health Informatics, BHI 2024

作者： King, Ryan Kodali, Shivesh Krueger, Conrad Yang, Tianbao mortazavi, Bobak J. Texas A&m University Computer Science & Engineering College Station United States

ISBN: (纸本)9798350351552

machine learning has revolutionized the modeling of clinical timeseries data. Using machine learning, a Deep Neural Network (DNN) can be automatically trained to learn a complex mapping of its input features for a desired task. This is particularly valuable in Electronic Health Record (EHR) databases, where patients often spend extended periods in intensive care units (ICUs). machine learning serves as an efficient method for extract meaningful information. However, many state-of-the-art (SOTA) methods for training DNNs demand substantial volumes of labeled data, posing significant challenges for clinics in terms of cost and time. Self-supervised learning offers an alternative by allowing practitioners to extract valuable insights from data without the need for costly labels. Yet, current SOTA methods often necessitate large data batches to achieve optimal performance, increasing computational demands. This presents a challenge when working with long clinical timeseries data. To address this, we propose an efficient method of contrastive pretraining tailored for long clinical timeseries data. Our approach utilizes an estimator for negative pair comparison, enabling effective feature extraction. We assess the efficacy of our pretraining using standard self-supervised tasks such as linear evaluation and semi-supervised learning. Additionally, our model demonstrates the ability to impute missing measurements, providing clinicians with deeper insights into patient conditions. We demonstrate that our pretraining is capable of achieving better performance as both the size of the model and the size of the measurement vocabulary scale. Finally, we externally validate our model, trained on the mImIC-III dataset, using the eICU dataset. We demonstrate that our model is capable of learning robust clinical information that is transferable to other clinics. © 2024 IEEE.

关键词： Self-supervised learning

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Leveraging Explainable Extremely Randomized Trees model for Poisoning Attack Detection in Power Grid Stability Assessment 4

Leveraging Explainable Extremely Randomized Trees Model for ...

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4th International Conference on Smart Grid and Renewable Energy, SGRE 2024

作者： massaoudi, mohamed Ghrayeb, Ali Begovic, miroslav Huang, Tingwen Texas A&M University Department of Electrical and Computer Engineering College Station United States Texas A&M University at Qatar Department of Electrical and Computer Engineering Doha Qatar Texas A&M University at Qatar Science Department Doha Qatar

ISBN: (纸本)9798350306262

With the increasing integration of digital technologies and communication networks, the power grid has become more vulnerable to cyber-attacks. Among these threats, false data injection attacks (FDIAs) have emerged as a significant concern. Developing distinctive models using power grid datasets that include poisoning attacks proves to be a complex task, owing to the scarcity of comprehensive data and the tendency of conventional classifiers to be biased towards the majority class. To address this gap, this paper introduces a detection and defense model against poisoning attacks. An extremely randomized tree (ET) model is used to simulate FDIAs targeting the state estimation process, which is crucial for stability assessments. moreover, an oversampling technique is employed to reduce the effect of the imbalanced dataset. The paper explores the susceptibility of electrical grids to such threats by leveraging the ET model to detect irregularities in data patterns. This paper proposes a machine learning-based detection algorithm that demonstrates high accuracy in identifying FDIAs. The robust state estimation method is proposed to significantly reduce the impact of false data, ensuring more accurate grid stability assessments. © 2024 IEEE.

关键词： machine learning

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Accurate Joint Detection of False Data Injection Attacks on Islanded PV Output Power and State of Health Estimation of Lithium-Ion Batteries 4

Accurate Joint Detection of False Data Injection Attacks on ...

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4th International Conference on Smart Grid and Renewable Energy, SGRE 2024

作者： massaoudi, mohamed Davis, Katherine R. Abu-Rub, Haitham Ghrayeb, Ali Huang, Tingwen Texas A&M University Department of Electrical and Computer Engineering College Station United States Texas A&M University at Qatar Department of Electrical and Computer Engineering Doha Qatar Texas A&M University at Qatar Science Department Doha Qatar

ISBN: (纸本)9798350306262

With the widespread adoption of islanded DC micro-grids incorporating photovoltaic (PV) systems and battery energy storage systems (BESSs), safeguarding the cyber-physical security of PV-BESS configurations becomes crucial. While numerous false data injection attack (FDIA) detection techniques have been presented in existing literature, these methods are often tailor-made for specific attack types. This paper introduces an innovative approach for concurrently detecting FDIAs on islanded P V power plants and BESSs. A bespoke two-layer random forest model, meticulously crafted and trained, is deployed for identifying FDIAs instigated by malicious adversaries. Our method uniquely caters to three distinct cyber-attack scenarios: tampered attacks, poisoned attacks, and replay attacks. The efficacy of our FDIA detection approach is rigorously evaluated utilizing real-world datasets and synthetically generated attacks. The results underscore the method's impressive performance in identifying a spectrum of cyber-attacks on both PV system output and the state of health output from the battery data. © 2024 IEEE.

关键词： Lithium-ion batteries

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OAAFormer:Robust and Efficient Point Cloud Registration Through Overlapping-Aware Attention in Transformer

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Journal of computer science & Technology 2024年第4期39卷 755-770页

作者： Jun-Jie Gao Qiu-Jie Dong Rui-An Wang Shuang-min Chen Shi-Qing Xin Chang-He Tu Wenping Wang School of Computer Science and Technology Shandong UniversityQingdao 266237China School of Information and Technology Qingdao University of Science and TechnologyQingdao 266061China College of Engineering Texas A&M UniversityTexasTX 77843U.S.A.

In the domain of point cloud registration,the coarse-to-fine feature matching paradigm has received significant attention due to its impressive *** paradigm involves a two-step process:first,the extraction of multilevel features,and subsequently,the propagation of correspondences from coarse to fine ***,this approach faces two notable ***,the use of the Dual Softmax operation may promote one-to-one correspondences between superpoints,inadvertently excluding valuable ***,it is crucial to closely examine the overlapping areas between point clouds,as only correspondences within these regions decisively determine the actual *** these issues,we propose OAAFormer to enhance correspondence *** the one hand,we introduce a soft matching mechanism to facilitate the propagation of potentially valuable correspondences from coarse to fine *** the other hand,we integrate an overlapping region detection module to minimize mismatches to the greatest extent ***,we introduce a region-wise attention module with linear complexity during the fine-level matching phase,designed to enhance the discriminative capabilities of the extracted *** on the challenging 3DLomatch benchmark demonstrate that our approach leads to a substantial increase of about 7%in the inlier ratio,as well as an enhancement of 2%-4%in registration ***,to accelerate the prediction process,we replace the Conventional Random Sample Consensus(RANSAC)algorithm with the selection of a limited yet representative set of high-confidence correspondences,resulting in a 100 times speedup while still maintaining comparable registration performance.

关键词： point cloud registration coarse-to-fine overlapping region feature matching Transformer

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Grid Voltage Sensorless model Predictive Control for Single-phase Five-level ANPC-FC Rectifier 50

Grid Voltage Sensorless Model Predictive Control for Single-...

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50th Annual Conference of the IEEE Industrial Electronics Society, IECON 2024

作者： Bayindir, Abdullah Berkay Sharida, Ali Bayhan, Sertac Abu-Rub, Haitham Dept. of Electrical & Computer Engineering Texas A&M University College StationTX77840 United States Dept. of Electrical & Computer Engineering Texas A&M University at Qatar Doha23874 Qatar Qatar Environment and Energy Research Institute Hamad Bin Khalifa University Doha Qatar Dept. of Electrical & Computer Engineering College of Science & Engineering Hamad Bin Khalifa University Doha Qatar

ISBN: (纸本)9781665464543

This paper presents a grid voltage sensorless model predictive control (mPC) technique for a single-phase five-level active neutral point clamped flying capacitor (ANPC-FC) rectifier. The control strategy utilizes mPC to regulate the grid current and ensure equilibrium in the voltages of the flying and DC side capacitors, without measuring the grid voltage. A state feedback observer (SFO) is responsible to provide the grid voltage estimation to the mPC. Achieving this involves integrating all controlled parameters into a finite control set mPC (FCS-mPC). moreover, the reference grid current is generated to regulate the DC link voltage based on a power equilibrium technique supported by a feed-forward loop. The proposed controller is implemented and evaluated using mATLAB/Simulink. Simulation results are carried out to show that the proposed mPC technique effectively achieves control objectives, while ensuring accurate tracking, and resisting voltage distortions. © 2024 IEEE.

关键词： mATLAB

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AR-Classroom: Integrating Conversational Artificial Intelligence with Augmented Reality Technology for Learning Spatial Transformations and Their matrix Representation 54

AR-Classroom: Integrating Conversational Artificial Intellig...

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54th IEEE Frontiers in Education Conference, FIE 2024

作者： monjoree, Uttamasha Aguilar, Samantha D. Qian, Chengyuan Van Huyck, Carl Yeh, Shu-Hao Tranbarger, Preston Duane-Tessier, Luke Solitare-Renaldo, Leo Burte, Heather Yasskin, Philip Liew, Jeffrey Song, Dezhen Quek, Francis Yan, Wei Texas A&m University Dept. Architecture College Station United States Texas A&m University Dept. Educational Psychology College Station United States Texas A&m University Dept. Computer Sci. & Eng. College Station United States Texas A&m University Dept. Visualization College Station United States Texas A&m University Dept. Mathematics College Station United States Haverford College Haverford United States Carnegie Mellon University Dept. Computer Science Pittsburgh United States Texas A&m University Dept. Teaching Learning & Culture College Station United States

ISBN: (纸本)9798350351507

This research full paper describes the AR-Classroom application that utilizes augmented reality (AR) and physical and virtual manipulatives to enable undergraduate students to build intuition about the relation between spatial transformations and their mathematical representations. To further build on the app's usability and functionality, additional features are being prototyped to continue improving the user-app interaction with the AR-Classroom. Some of the challenges the students faced when using AR-Classroom were recalling basic matrix operations without geometric context, basic trigonometric functions and their applications in the two-dimensional space, loss of AR registration for not understanding the AR environment, and User Interface (UI) issues. To address these issues, a conversational Artificial Intelligence (AI)-based multi-sensory and interactive assistance has been added to the AR-Classroom. Integrating sophisticated language processing and response generation of AI with immersive three-dimensional capabilities of AR can create a more engaging learning experience than the previous versions of the app. This integration focuses on creating a symbiosis between AR and AI. It creates an elevated user experience by offering real-time, personalized assistance to students dealing with issues related to understanding mathematical concepts and functionalities of the app. A qualitative exploratory usability study was done to assess the user's interaction with the AI implemented in the AR-Classroom, aiming to explore the AI's ability to guide students in using AR technology and aid in introductory matrix algebra learning, to effectively serve the students' learning. Based on the thematic analysis of the user experiment we found four main themes related to users' perceptions of AR-Classroom AI features usability: (1) AI chatbot ease-of-use, (2) Need for answer elaboration from AI, (3) Desire for visual information, and (4) Increased understanding of the content ar

关键词： Students

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Accurate State of Health Estimation of Lithium-ion Batteries: An Efficient ResNet-LSTm Approach 4

Accurate State of Health Estimation of Lithium-ion Batteries...

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4th International Conference on Smart Grid and Renewable Energy, SGRE 2024

作者： Eddin, Ez Eddin Ez massaoudi, mohamed Ghrayeb, Ali Qatar University Computer Science and Engineering Qatar Texas A&M University Department of Electrical and Computer Engineering College Station United States Texas A&M University at Qatar Department of Electrical and Computer Engineering Doha Qatar

ISBN: (纸本)9798350306262

Accurate estimation of the state of health (SoH) of lithium-ion battery (LIB) application systems is a critical concern in the domain of electric vehicles (EVs). Precise SoH holds significance due to its direct impact on the performance, longevity, and safety of LIBs. This paper proposes an efficient deep learning (DL) method for SoH estimation. The proposed DL model incorporates a residual neural network (ResNet) with long short-term memory (LSTm), extracting spatial-temporal features from LIB data. The ResNet-LSTm approach ensures high accuracy in SoH prediction. Extensive experimental results derived from real-world LIBs are presented, demonstrating the model's superiority and competitiveness in score measures when compared to existing SoH estimation models. The key contributions of this paper include (1) the simulation of a highly effective model explicitly tailored for SoH estimation in LIBs for EVs;(2) empirical verification of the effectiveness and superiority of the proposed model on the widely used NASA battery dataset. Simulation results are carried out to demonstrate the generalization and feasibility of the ResNet-LSTm model in SOH estimation with an overall root-mean-squared error of 0.69%. © 2024 IEEE.

关键词： Long short-term memory

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Design and Analysis of Digital Twin models for Dual Active Bridge 50

Design and Analysis of Digital Twin Models for Dual Active B...

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50th Annual Conference of the IEEE Industrial Electronics Society, IECON 2024

作者： Bayindir, Abdullah Berkay Al-Khateeb, Ahmad Sharida, Ali Alnuweiri, Hussein Bayhan, Sertac Abu-Rub, Haitham Dept. of Electrical & Computer Engineering Texas A&M University College StationTX77840 United States Dept. of Electrical & Computer Engineering Texas A&M University at Qatar Doha23874 Qatar Qatar Environment and Energy Research Institute Hamad Bin Khalifa University Doha Qatar Dept. of Electrical & Computer Engineering College of Science & Engineering Hamad Bin Khalifa University Doha Qatar

ISBN: (纸本)9781665464543

Digital twins (DTs) are emerging as effective tools for power electronic converters, addressing important objectives such as real-time monitoring, fault detection and predictive maintenance. Among these power converters, the dual active bridge (DAB) stands out for DC-DC conversion applications. However, the non-linear dynamics of DABs pose considerable challenges to their accurate modeling. To address these challenges, this paper investigates the use of machine learning (mL) and deep learning (DL) models for DT implementation in DABs. Specifically, XGBoost and dense neural network (DNN) models are employed to construct DTs compatible with low-cost microcontrollers. These DTs are operated in parallel with the physical DAB converter to predict its output voltage in real-time. Experimental results demonstrate that both of the models perform well in terms of accuracy and applicability to low-cost microcontroller. However, the DNN is more reliable and the XGBoost is simpler. © 2024 IEEE.

关键词： microcontrollers

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Thermal Homography in Photovoltaic Panels: Evaluating Deep Learning and Feature-Based methods

Thermal Homography in Photovoltaic Panels: Evaluating Deep L...

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2025 IEEE texas Power and Energy Conference, TPEC 2025

作者： Dahmani, Hajer Yaqoob, mohammed Ansari, mohammed Yusuf Flushing, Eduardo Feo Carnegie Mellon University in Qatar Computer Science Doha Qatar Texas A&M University Electrical and Computer Engineering College StationTX United States Texas A&M University at Qatar Electrical and Computer Engineering Doha Qatar

ISBN: (纸本)9798331541125

Thermal imaging has become a vital tool for analyzing temperature variations in various fields, including medical diagnostics, industrial inspection, and environmental monitoring. However, the application of homography techniques in the thermal imaging domain, particularly for photovoltaic (PV) panels, remains underexplored. This paper presents a comprehensive evaluation of state-of-the-art deep homography models, specifically HomographyNet, in comparison with traditional feature-based methods such as ORB+RANSAC and SIFT+RANSAC. These methods are applied to close-range thermal images of PV panels, and their performance is assessed using the mean average corner error (mACE) metric. Extensive experiments analyze the accuracy and robustness of these techniques, as well as the influence of colormap representations on model performance. Key contributions include the evaluation of homography methods for thermal imaging, an in-dept. analysis of colormap effects, and the introduction of a novel high-resolution thermal image dataset for PV panels. Results demonstrate that HomographyNet outperforms traditional methods, achieving a mACE of 13.65 with the Noon dataset compared to 24.78 with the morning dataset. HomographyNets superior performance with magma colormap makes it particularly useful in PV applications such as identifying microcracks or hotspots in solar cells, where accurate thermal image alignment can enhance the visualization of subtle yet critical temperature anomalies. © 2025 IEEE.

关键词： Thermography (imaging)

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Power Flow modeling of the Impacts of Weather and Other Resiliency Hazards With a Focus on Transmission Planning 58

Power Flow Modeling of the Impacts of Weather and Other Resi...

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58th Hawaii International Conference on System sciences, HICSS 2025

作者： Safdarian, Farnaz Cook, Jordan Zhgun, Kseniia Overbye, Thomas J. Snodgrass, Jonathan Dept. of Electrical and Computer Engineering Texas A&M University College StationTX United States

ISBN: (纸本)9780998133188

This paper presents an approach for modeling weather and other environmental inputs (ENIs) in the power flow and related tools with a focus on electric grid transmission planning. Such work is needed because of the rapidly growing dependence of electric grids on the weather and the need to consider the impact of more severe resiliency events. The paper presents a modeling approach, and then demonstrates it using several large-scale electric grids. Validation is also considered. A key contribution is to show that environmental inputs can be directly integrated into existing power flow and related tools such as contingency analysis and optimal power flow. © 2025 IEEE computer Society. All rights reserved.

关键词： Electric power transmission networks

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