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arXiv 2024年

作者： Li, Kejia Wang, Mengen Department of Electrical and Computer Engineering and Materials Science and Engineering Program SUNY BinghamtonNY13902 United States

CsPbI3 has been recognized as a promising candidate for optoelectronic device applications. To further improve the efficiency of the devices, it is imperative to better understand the surface properties of CsPbI3, which affect charge carrier transport and defect formation properties. In this study, we perform density functional theory calculations to explore the stability of the (001), (110), and (100) surfaces of orthorhombic CsPbI3, considering different stoichiometries and surface reconstructions. Our results show that, under the chemical potentials confined by the thermodynamically stable region of bulk CsPbI3, the CsI-terminated surfaces of (001) and (110) and the stoichiometric surface of (100) are stable. Among these three surfaces, the CsI-terminated (110) surface has the lowest surface energy and no mid-gap states, which benefits the transport properties of the material. © 2024, CC BY-NC-SA.

关键词： Stoichiometry

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Proposing a Transient Model for H-Section Reinforced Concrete Power Poles

Proposing a Transient Model for H-Section Reinforced Concret...

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International Youth Conference on Energetics (IYCE)

作者： Mohsen M. Kivi Alessandro Bosisio Rouzbeh Shirvani Ali A. Razi-Kazemi Department of Electrical Computer and Biomedical Engineering University of Pavia Pavia Italy Electrical Engineering Department K.N. Toosi University of Technology Tehran IRAN

ISBN: (数字)9798350372380

ISBN: (纸本)9798350372397

Accurate surge impedance measurement is critical in the field of electrical distribution and transmission towers. However, a majority of the studies that have been done so far, have focused on steel tower models, ignoring the common concrete equivalents. This work presents a novel approach to evaluate the electrical properties of H-section reinforced concrete power poles. The key parameters investigated include surge impedance, capacitance, and inductance, all crucial for transient analysis during lightning strikes. The suggested method is then rigorously verified with COMSOL Multiphysics in the following phase. The results demonstrate the substantial impact of material properties, particularly relative permittivity and electrical conductivity of the tower body, on surge impedance values. By bridging the gap in research concerning the transient behaviors of concrete towers, this research takes a major step toward the reliability of power systems infrastructures.

关键词： Impedance measurement Poles and towers Concrete Power system reliability Impedance Steel Reliability

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Textual training for the hassle-free removal of unwanted visual data: case studies on OOD and hateful image detection 24

Textual training for the hassle-free removal of unwanted vis...

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Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Saehyung Lee Jisoo Mok Sangha Park Yongho Shin Dahuin Jung Sungroh Yoon Department of Electrical and Computer Engineering Seoul National University Qualcomm Korea YH Seoul South Korea School of Computer Science and Engineering Soongsil University Department of Electrical and Computer Engineering and Interdisciplinary Program in Artificial Intelligence Seoul National University

ISBN: (纸本)9798331314385

In our study, we explore methods for detecting unwanted content lurking in visual datasets. We provide a theoretical analysis demonstrating that a model capable of successfully partitioning visual data can be obtained using only textual data. Based on the analysis, we propose Hassle-Free Textual Training (HFTT), a streamlined method capable of acquiring detectors for unwanted visual content, using only synthetic textual data in conjunction with pre-trained vision-language models. HFTT features an innovative objective function that significantly reduces the necessity for human involvement in data annotation. Furthermore, HFTT employs a clever textual data synthesis method, effectively emulating the integration of unknown visual data distribution into the training process at no extra cost. The unique characteristics of HFTT extend its utility beyond traditional out-of-distribution detection, making it applicable to tasks that address more abstract concepts. We complement our analyses with experiments in out-of-distribution detection and hateful image detection. Our codes are available at https://***/Saehyung-Lee/HFTT

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Reinforcement Learning-based Receding Horizon Control using Adaptive Control Barrier Functions for Safety-Critical Systems*

Reinforcement Learning-based Receding Horizon Control using ...

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IEEE Conference on Decision and Control

作者： Ehsan Sabouni H.M. Sabbir Ahmad Vittorio Giammarino Christos G. Cassandras Ioannis Ch. Paschalidis Wenchao Li Division of Systems Engineering Boston University Boston MA USA Department of Electrical and Computer Engineering and the Division of Systems Engineering Boston University Boston MA USA Department of Electrical and Computer Engineering Division of Systems Engineering and Department of Biomedical Engineering Boston University Boston MA USA

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

Optimal control methods provide solutions to safety-critical problems but easily become intractable. Control Barrier Functions (CBFs) have emerged as a popular technique that facilitates their solution by provably guaranteeing safety, through their forward invariance property, at the expense of some performance loss. This approach involves defining a performance objective alongside CBF-based safety constraints that must always be enforced with both performance and solution feasibility significantly impacted by two key factors: (i) the selection of the cost function and associated parameters, and (ii) the calibration of parameters within the CBF-based constraints, which capture the trade-off between performance and conservativeness. To address these challenges, we propose a Reinforcement Learning (RL)-based Receding Horizon Control (RHC) approach leveraging Model Predictive Control (MPC) with CBFs (MPC-CBF). In particular, we parameterize our controller and use bilevel optimization, where RL is used to learn the optimal parameters while MPC computes the optimal control input. We validate our method by applying it to the challenging automated merging control problem for Connected and Automated Vehicles (CAVs) at conflicting roadways. Results demonstrate improved performance and a significant reduction in the number of infeasible cases compared to traditional heuristic approaches used for tuning CBF-based controllers, showcasing the effectiveness of the proposed method. In order to guarantee reproducibility, our code is provided here: link 1 . 1 ***/EhsanSabouni/CDC2024_RL_adpative_MPC_CBF/tree/main

关键词： Roads System performance Merging Optimal control Reinforcement learning Cost function Reproducibility of results Safety Tuning Predictive control

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Predicting Multiple Sclerosis Relapses Using Patient Exposure Trajectories 25

Predicting Multiple Sclerosis Relapses Using Patient Exposur...

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25th Working Notes of the Conference and Labs of the Evaluation Forum, CLEF 2024

作者： Bosoni, Pietro Vazifehdan, Mahin Pala, Daniele Tavazzi, Eleonora Bergamaschi, Roberto Bellazzi, Riccardo Dagliati, Arianna Department of Electrical Computer and Biomedical Engineering University of Pavia Via Ferrata 5 Pavia27100 Italy Multiple Sclerosis Centre Department of Clinical Neurology Neurological Institute C. Mondino Via Mondino 2 Pavia27100 Italy

Multiple Sclerosis (MS) is a prevalent autoimmune neurodegenerative disease characterized by progressive nerve inflammation, leading to increasingly severe symptoms. Approximately 85% of MS cases exhibit a relapse-remitting pattern, where sudden symptom exacerbations (relapses) are followed by periods of improvement (remissions). Previous research has shown that MS progression is influenced by external factors such as weather and air pollution. In this paper, we present a Machine Learning-based approach to predict the timing of MS relapses based on environmental exposure. This work was conducted as part of the Intelligent Disease Progression Prediction (iDPP) CLEF 2024 Challenge, which focused on the impact of environmental exposure on MS progression using retrospective data. Specifically, we utilized two anonymized datasets from clinical institutions in Pavia and Turin, Italy, containing real patient data. We employed Topological Data Analysis to compute personal exposure trajectories and used two predictive approaches, one based on the application of Linear Regression, Random Forest, and Extreme Gradient Boosting models to the last follow-up data, and one based on the application of Mixed-Effects modeling on longitudinal data from the first to the last follow-up. Results suggest that integrating environmental variables yields valuable insights for predicting MS relapses, emphasizing the need for improved methods of calculating personal pollution exposure patterns to enhance the accuracy of MS progression predictions. © 2024 Copyright for this paper by its authors.

关键词： Prediction models

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Development of Novel- Epoxy-Based Transformer Bushing Insulator for High Breakdown Voltage (BDV)

Development of Novel- Epoxy-Based Transformer Bushing Insula...

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Emerging Trends in electrical, Control, and Telecommunication engineering (ETECTE), International Conference on

作者： Abubakar Siddique Laraib Akram Waseem Aslam Areeba Mishal Muhammad Usama Shahid Haseeb Ahmed Department of Electrical and Biomedical Engineering Khwaja Fareed University of Engineering and Information Technology Rahim Yar Khan Pakistan Department of Electrical Electronics & Computer Engineering University of Sargodha Sargodha Pakistan

ISBN: (数字)9798331504861

ISBN: (纸本)9798331504878

In today's trends, nanocomposites have garnered significant interest due to their potential to enhance dielectric properties and be used as insulators. Incorporating nanoparticles into the base epoxy makes achieving highly efficient dielectric materials feasible. However, the low electric field stress management capability and insufficient relative permittivity of typical epoxy provide a hindrance. Efforts have been made to overcome these limitations by adding nanosized fillers with high relative permittivity to the epoxy to enhance the dielectric properties. This research study focuses on the development of Novel-Epoxy-Based Transformer Bushing Insulator using nanomaterial $\text{Al}_2 \mathrm{O}_3$ and $\text{SiO}_{2}$ • The epoxy resin served as the base material, with nano-fillers added to improve its dielectric properties. The hardener was added to initiate the curing process and create a highly crosslinked structure. Surfactants were introduced to improve the interaction between the epoxy resin and nanoparticles. The nano-silica and nano-alumina particles were combined in different ratio configurations to create the nanocomposites by using the preparation method; Sol-Gel and Green Synthesis, which were subsequently tested for their dielectric properties. The proposed sample is the nanocomposite made by the green synthesis method and with the combination of 0.3 g + 0.3 g of the nanoparticle. Results show improved dielectric strength of the proposed nanocomposites compared to conventional porcelain materials. This paper presents an environment friendly approach for developing epoxy-based transformer bushing insulators, offering enhanced performance.

关键词： Nanoparticles Porcelain Nanocomposites Green products Insulators Market research Dielectrics Permittivity Stress Power transformer insulation

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A CT-based Prediction System for Determining Respiratory Support Level in COVID-19 Patients

A CT-based Prediction System for Determining Respiratory Sup...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Sharafeldeen, Ahmed Balaha, Hossam Magdy Farahat, Ibrahim Shawky Ghazal, Mohammed Connelly, James van Bogaert, Eric El-Baz, Ayman Bioengineering Department J.B. Speed School of Engineering University of Louisville LouisvilleKY United States Department of Computer Science Mansoura University Mansoura Egypt Electrical Computer and Biomedical Engineering Department Abu Dhabi University Abu Dhabi United Arab Emirates Department of Radiology Faculty of Medicine University of Louisville LouisvilleKY United States

ISBN: (纸本)9798350368741

A novel diagnostic system is introduced for assessing the required level of respiratory support for COVID-19 patients. It bases its assessments on the correlation between detected COVID-19 lesions and the respiratory support levels administered to the patients. The correlation with computed tomography (CT) scans will focus on three respiratory support levels, including minimal support (Level 0), non-invasive support (Level 1, such as soft oxygen), and invasive support (Level 3, e.g., mechanical ventilation). The system initially outlines the pulmonary regions from CT scans, then identifies COVID-19 lesions within these segmented lung regions. Subsequently, three second-order texture features are extracted from the identified COVID-19 lesion regions to detect differences and abnormalities across varying severity levels in COVID-19 cases. To obtain the suitable level of respiratory support for each patient, a fusion mechanism based on backpropagation neural network is utilized to integrate the diagnosis of these three features, individually generated by the support vector machine (SVM) classifier. The system's performance is evaluated on 307 COVID-19 patients using a hold-out validation approach. This evaluation included various metrics, such as sensitivity, specificity, F1-score, Cohen's kappa, and accuracy, demonstrating impressive results. Specifically, it achieved a score of 97.25%, 98.56%, 97.26%, 95.79%, and 97.25%, respectively. The results demonstrate the effectiveness of the integrated system, which uses various second-order features, in predicting respiratory support needs for COVID-19 patients, outperforming both its individual components and other machine learning-based classification systems. © 2025 IEEE.

关键词： computer-Aided Diagnosis (CAD) GLCM GLRLM Machine Learning (ML) Neural Network (NN) SFM

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Wavelet Analysis of Noninvasive EEG Signals Discriminates Complex and Natural Grasp Types

arXiv

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arXiv 2024年

作者： Rabiee, Ali Ghafoori, Sima Cetera, Anna Abiri, Reza Department of Electrical Computer and Biomedical Engineering University of Rhode Island KingstonRI United States

This research aims to decode hand grasps from Electroencephalograms (EEGs) for dexterous neuroprosthetic development and Brain-computer Interface (BCI) applications, especially for patients with motor disorders. Particularly, it focuses on distinguishing two complex natural power and precision grasps in addition to a neutral condition as a no-movement condition using a new EEG-based BCI platform and wavelet signal processing. Wavelet analysis involved generating time-frequency and topographic maps from wavelet power coefficients. Then, by using machine learning techniques with novel wavelet features, we achieved high average accuracies: 85.16% for multiclass, 95.37% for No-Movement vs Power, 95.40% for No-Movement vs Precision, and 88.07% for Power vs Precision, demonstrating the effectiveness of these features in EEG-based grasp differentiation. In contrast to previous studies, a critical part of our study was permutation feature importance analysis, which highlighted key features for grasp classification. It revealed that the most crucial brain activities during grasping occur in the motor cortex, within the alpha and beta frequency bands. These insights demonstrate the potential of wavelet features in real-time neuroprosthetic technology and BCI applications. © 2024, CC BY.

关键词： Electroencephalography

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Controlling action potentials with magnetoelectric nanoparticles

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Brain Stimulation 2024年第5期17卷 1005-1017页

作者： Zhang, Elric Shotbolt, Max Chang, Chen-Yu Scott-Vandeusen, Aidan Chen, Shawnus Liang, Ping Radu, Daniela Khizroev, Sakhrat Department of Electrical and Computer Engineering University of Miami Coral Gables FL United States Department of Biomedical Engineering University of Miami Coral Gables FL United States Department of Mechanical and Materials Engineering Florida International University Miami FL United States Department of Chemical Environmental and Materials Engineering University of Miami Coral Gables FL United States Cellular Nanomed Irvine CA United States The Miami Project to Cure Paralysis Department of Biochemistry and Molecular Biology Miller School of Medicine University of Miami Miami FL United States

Non-invasive or minutely invasive and wireless brain stimulation that can target any region of the brain is an open problem in engineering and neuroscience with serious implications for the treatment of numerous neurological diseases. Despite significant recent progress in advancing new methods of neuromodulation, none has successfully replicated the efficacy of traditional wired stimulation and improved on its downsides without introducing new complications. Due to the capability to convert magnetic fields into local electric fields, MagnetoElectric NanoParticle (MENP) neuromodulation is a recently proposed framework based on new materials that can locally sensitize neurons to specific, low-strength alternating current (AC) magnetic fields (50Hz 1.7 kOe field). However, the current research into this neuromodulation concept is at a very early stage, and the theoretically feasible game-changing advantages remain to be proven experimentally. To break this stalemate phase, this study leveraged understanding of the non-linear properties of MENPs and the nanoparticles' field interaction with the cellular microenvironment. Particularly, the applied magnetic field's strength and frequency were tailored to the M − H hysteresis loop of the nanoparticles. Furthermore, rectangular prisms instead of the more traditional “spherical” nanoparticle shapes were used to: (i) maximize the magnetoelectric effect and (ii) improve the nanoparticle-cell-membrane surface interface. Neuromodulation performance was evaluated in a series of exploratory in vitro experiments on 2446 rat hippocampus neurons. Linear mixed effect models were used to ensure the independence of samples by accounting for fixed adjacency effects in synchronized firing. Neural activity was measured over repeated 4-min segments, containing 90 s of baseline measurements, 90 s of stimulation measurements, and 60 s of post stimulation measurements. 87.5 % of stimulation attempts produced statistically significant (P < 0.0

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A journal that promotes excellence through uncompromising review process: Re°ection of freedom of speech and scientific publication

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International Journal of Neural Systems 2025年

作者： Kam, Zvi Nicora, Giovanna Department of Molecular Cell Biology Weizmann Institute of Science Rehovot7610001 Israel Department of Electrical Computer and Biomedical Engineering University of Pavia Italy

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