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2024 Conference on Lasers and Electro-Optics, CLEO 2024

作者： Shah, Priyansh Mellouk, Elias Ben Levine, Jeremy Mohanty, Aseema Department of Electrical and Computer Engineering Tufts University MedfordMA02155 United States Department of Biomedical Engineering Tufts University MedfordMA02155 United States

We demonstrate mode-division multiplexing at visible wavelengths (473 nm) for the first time using adiabatic mode couplers. We measure less than -15 dB and -20 dB crosstalk for TE2 and TE3 higher-order mode couplers, ... 详细信息

ISBN: (纸本)9781957171395

关键词： Laser beams

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Mode-Division Multiplexing at Visible Wavelengths

Mode-Division Multiplexing at Visible Wavelengths

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CLEO: Science and Innovations in CLEO 2024, CLEO: S and I 2024 - Part of Conference on Lasers and Electro-Optics

关键词： Tellurium compounds

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On-chip hyperspectral detectors for fluorescence lifetime imaging 9

On-chip hyperspectral detectors for fluorescence lifetime im...

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High-Speed biomedical Imaging and Spectroscopy IX 2024

作者： Ahamed, Ahasan Rawat, Amita McPhillips, Lisa N. Marcu, Laura Saif Islam, M. Electrical and Computer Engineering Department University of California DavisCA95616 United States Biomedical Engineering Department University of California DavisCA95616 United States

ISBN: (纸本)9781510669659

Fluorescence Lifetime Imaging (FLIM) is a powerful technique that measures the decay time of fluorophores present in tissue samples alluding to their constituent molecules. FLIM has gained popularity in biomedical imaging for applications such as detecting cancerous tumors, surgical guidance, etc. However, conventional FLIM systems are limited by a reduced number of spectral bands and long acquisition time. Moreover, the large footprint, complexity, and cost of the instrumentation make it difficult for clinical applications. In this paper, we demonstrate a reconstruction-based hyperspectral detector that can resolve decay time and intensities in broad spectral ranges while providing high sensitivity, high gain, and fast response time. The hyperspectral detector is comprised of an array of efficient, ultrafast avalanche photodetectors integrated with nanophotonic structures. We utilize different nanostructures in the detectors to modulate light-matter interactions in spectral channels. This allows us to computationally reconstruct the spectral profile of the incoming fluorescence spectrum without the need for additional filters or dispersive optics. Also, the nanophotonic structures enhance efficiency (by a factor of 2 to 10 over different wavelengths) while providing fast response time. An innovative detector design has been employed to reduce the breakdown of the avalanche photodetectors to-7.8V while maintaining high gain (∼50) across the spectral range. Therefore, enabling low light detection with a high signal-To-noise ratio for FLIM applications. Added spectral channels would provide valuable information about tissue materials, morphology, and disease diagnosis. Such innovative hyperspectral sensors can now be integrated on-chip capable of miniaturizing the FLIM system and making it a commercially viable tool for clinical use. This technology has the potential to revolutionize the current FLIM system with improved detection capabilities opening doors for new ho

关键词： Hyperspectral imaging

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Cyber Attacks Detection using Deep Learning Methods for Resilient Operation in DC Shipboard Microgrids

Cyber Attacks Detection using Deep Learning Methods for Resi...

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2024 IEEE IAS Industrial and Commercial Power System Asia, I and CPS Asia 2024

作者： Ali, Zulfiqar Hussain, Tahir Su, Chun-Lien Jurcut, Anca Delia Baloch, Shazia Sadiq, Muhammad National Kaohsiung University of Science and Technology Department of Electrical Engineering Kaohsiung city Taiwan Graduate School of Informatics and Engineering The University of Electro-Communication Department of Informatics Tokyo Japan School of Computer Science University College Dublin BelfieldDublin 4 Ireland Balochistan University of Engineering and Technology Department of Electrical Engineering Khuzdar Pakistan

ISBN: (纸本)9798350352290

Cyber resilience has become paramount as a transition of maritime systems towards digitization, particularly within DC shipboard microgrids (SMGs). Adopting innovative communication technologies can enhance the resilience of SMGs for stable operation. However, challenges like false data injection and Man-in-The-Middle attacks pose significant threats to SMG operations when integrating these technologies into ship intelligent grids. In this regard, this paper proposes a reliable deep learning (DL) method, especially an Artificial neural network (ANN) with a deep encoder-decoder architecture, for the detection of cyber-intrusions and mitigating their effects, ensuring system control and stability for resilient operation of SMG. Detecting malicious data intrusions is crucial for maintaining optimal grid conditions and preventing disruptions in load dispatch. The proposed method utilizes a fusion of current and voltage data features for comprehensive DL model training, resulting in an adequate level of detection accuracy and providing cybersecurity analysis for SMGs, addressing component-level attacks, and devising defense strategies from aspects of detection, mitigation, and prevention. Furthermore, the deep ANN is fine-tuned with optimal hyperparameters to effectively counter cyberattacks, achieving an enhanced accuracy rate of 97.51% and minimal loss of 0.101%, surpassing conventional machine learning approaches. Rigorous test scenarios are performed to validate the robustness of the proposed method, emphasizing the cyber resilience of DC SMGs for enhanced security and operational integrity. © 2024 IEEE.

关键词： Network intrusion

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In Search for Model-Driven eXplainable Artificial Intelligence 1

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10th IFIP International Workshop on Artificial Intelligence for Knowledge Management, AI4KMES 2023

作者： Ligęza, Antoni Sepiolo, Dominik Faculty of Electrical Engineering Automatics Computer Science and Biomedical Engineering Department of Applied Computer Science AGH University of Krakow al. Mickiewicza 30 Krakow30-059 Poland

ISBN: (数字)9783031610691

ISBN: (纸本)9783031610684

This paper reports on ongoing and innovative research in the area of eXplainable Artificial Intelligence (XAI). A classical XAI task is considered as finding an explanation of the model generated via Machine Learning by identifying the most influential variables for local decision-making. Such an approach suffers from severe limitations. The proposed approach moves the explanatory process to a new, knowledge-level dimension. It is oriented towards Model Discovery, i.e. the internal structure and functions of the components. The concept of Model-Driven XAI is put forward and explained with examples. An experiment on Function Discovery via Grammatical Evolution is reported in brief. © IFIP International Federation for Information Processing 2024.

关键词： Decision making

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Assessing Predictor Influence in LSTM Models for Enhanced Solar Energy Forecasting 26

Assessing Predictor Influence in LSTM Models for Enhanced So...

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26th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing, SYNASC 2024

作者： Ciobanu, Alexandru-Adrian Bacanin, Nebojsa Sanders, Tilman Stoean, Catalin University of Craiova Department of Computer Science Craiova Romania Singidunum University Faculty of Informatics and Computing Belgrade Serbia FH Muenster Department of Electrical Engineering and Computer Science Steinfurt Germany

ISBN: (纸本)9798331532833

Accurate forecasting of solar energy production is highly important for an adequate integration of renewable energy into the power grid. This study explores the importance of various predictors for enhancing the accuracy of solar energy forecasting using two distinct Long Short-Term Memory models: one that relies solely on historical power production data and past exogenous weather data, and another that additionally incorporates future exogenous weather data. Various settings are tried to improve the results and relatively similar parameters are used by both, yielding more accurate results for the model that also uses information from the future. The results also demonstrate that predictor importance varies significantly between the two models. These findings suggest that different models benefit from distinct predictor combinations and incorporating exogenous data can significantly enhance forecasting performance. © 2024 IEEE.

关键词： Prediction models

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Optimizing Gabor texture features for materials recognition by convolutional neural networks

Optimizing Gabor texture features for materials recognition ...

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London Imaging Meeting, LIM 2023

作者： Bianconi, Francesco Cusano, Claudio Napoletano, Paolo Schettini, Raimondo Department of Engineering Universitá degli Studi di Perugia Italy Department of Electrical Computer and Biomedical Engineering University of Pavia Italy Department of Informatics Systems and Communication University of Milano Bicocca Italy

ISBN: (纸本)9780892083633

In this paper, we present a novel technique that allows for customized Gabor texture features by leveraging deep learning neural networks. Our method involves using a Convolutional Neural Network to refactor traditional, hand-designed filters on specific datasets. The refactored filters can be used in an off-the shelf manner with the same computational cost but significantly improved accuracy for material recognition. We demonstrate the effectiveness of our approach by reporting a gain in discrimina tion accuracy on different material datasets. Our technique is particularly appealing in situations where the use of the entire CNN would be inadequate, such as analyzing non-square im ages or performing segmentation tasks. Overall, our approach provides a powerful tool for improving the accuracy of material recognition tasks while retaining the advantages of handcrafted filters. © 2023 Society for Imaging Science and Technology.

关键词： Convolutional neural networks

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Improving Thyroid Disorder Diagnosis via Ensemble Stacking and Bidirectional Feature Selection

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computers, Materials & Continua 2024年第3期78卷 4225-4241页

作者： Muhammad Armghan Latif Zohaib Mushtaq Saad Arif Sara Rehman Muhammad Farrukh Qureshi Nagwan Abdel Samee Maali Alabdulhafith Yeong Hyeon Gu Mohammed A.Al-masni Department of Computer and Information System Cleveland State UniversityOhio44115USA Department of Electrical Electronics and Computer SystemsCollege of Engineering and TechnologyUniversity of SargodhaSargodha40100Pakistan Department of Mechanical Engineering College of EngineeringKing Faisal UniversityAl-Ahsa31982Saudi Arabia Department of Biomedical Engineering Riphah International UniversityIslamabad44000Pakistan Department of Electrical Engineering Riphah International UniversityIslamabad44000Pakistan Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Artificial Intelligence College of Software and Convergence TechnologySejong UniversitySeoul05006Korea

Thyroid disorders represent a significant global health challenge with hypothyroidism and hyperthyroidism as two common conditions arising from dysfunction in the thyroid *** and timely diagnosis of these disorders is crucial for effective treatment and patient *** research introduces a comprehensive approach to improve the accuracy of thyroid disorder diagnosis through the integration of ensemble stacking and advanced feature selection *** forward feature selection,sequential backward feature elimination,and bidirectional feature elimination are investigated in this *** ensemble learning,random forest,adaptive boosting,and bagging classifiers are *** effectiveness of these techniques is evaluated using two different datasets obtained from the University of California Irvine-Machine Learning Repository,both of which undergo preprocessing steps,including outlier removal,addressing missing data,data cleansing,and feature *** experimentation demonstrates the remarkable success of proposed ensemble stacking and bidirectional feature elimination achieving 100%and 99.86%accuracy in identifying hyperthyroidism and hypothyroidism,*** enhancing detection accuracy,the ensemble stacking model also demonstrated a streamlined computational complexity which is pivotal for practical medical *** significantly outperformed existing studies with similar objectives underscoring the viability and effectiveness of the proposed *** research offers an innovative perspective and sets the platform for improved thyroid disorder diagnosis with broader implications for healthcare and patient well-being.

关键词： Ensemble learning random forests boosting dimensionality reduction machine learning smart healthcare computer aided diagnosis

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Analysis of selected dielectric parameters of insulating liquids 23

Analysis of selected dielectric parameters of insulating liq...

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23rd International Scientific Conference on Electric Power engineering, EPE 2023

作者： Pluzek, Aleksandra Nagi, Lukasz Koziol, Michal Zygarlicka, Malgorzata Opole University of Technology Faculty of Electrical Engineering Automatic Control and Informatics Department of Electrical Power and Renewable Energy Opole Poland Opole University of Technology Faculty of Electrical Engineering Automatic Control and Informatics Department of Computer Science Opole Poland

ISBN: (纸本)9798350335934

During the last few years, there has been a growing interest in the topic of using natural or synthetic esters as an alternative to mineral oils in oil transformers due to the easier way to obtain them and their ability to exhibit better electrical properties. In addition, more and more researchers are describing in their work that doping esters with nanoparticles is capable of further improving these properties. Therefore, in the current work, it was decided to see what effect the addition of a hydrophobic carbon shell iron nanoparticle to ORLEN mineral oil and Midel 7131 synthetic ester would have on selected electrical parameters of the insulating fluid, i.e. breakdown voltage and electrical strength. © 2023 IEEE.

关键词： Nanoparticles

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A Time-Frequency Analysis for Broken Rotor Bar Detection in Closed Loop Inverter Fed Induction Motor at Imposed Speed 14

A Time-Frequency Analysis for Broken Rotor Bar Detection in ...

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14th IEEE International Symposium on Diagnostics for electrical Machines, Power Electronics and Drives, SDEMPED 2023

作者： Mantione, Lorenzo Fernandez-Cavero, Vanesa Morinigo-Sotelo, Daniel Frosini, Lucia University of Pavia Department of Electrical Computer and Biomedical Engineering Pavia Italy Universidad de Valladolid School of Industrial Engineering Department of Electrical Engineering Valladolid Spain Valladolid Spain

ISBN: (纸本)9798350320770

In this paper, an analysis to detect broken rotor bars while the motor is operating in closed loop control is presented. Due to changes in the supply frequency, which occur to keep the speed constant, a time-frequency technique is required even when the transient is completed. Signals from the phase current of the induction motor are analysed using traditional methods such as the Fast Fourier Transform and the Short Time Fourier Transform. Finally, the same signals are analysed through the Dragon Transform, that ensures high resolution frequency and time and allows to detect trajectories of the harmonics with different load conditions. © 2023 IEEE.

关键词： Induction motors

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