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2024 Conference on Lasers and Electro-Optics, CLEO 2024

作者： Liu, Shuai Zhang, Yuheng Zhang, Zheshen Department of Electrical Engineering and Computer Science University of Michigan Ann ArborMI48109 United States

ISBN: (纸本)9781957171395

We present a robust process for fabricating high-Q, dispersion-engineered Si3N4 photonic chips using amorphous silicon hardmask etching with PECVD SiO2 cladding, achieving an intrinsic quality factor up to ∼ 17.5×106 and an average intrinsic quality factor of ∼ 13×106. We anticipate a further reduction in propagation loss through LPCVD SiO2 cladding. © Optica Publishing Group 2024, © 2023 The Author(s)

关键词： Silicon nitride

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Fair-Over-Time Distributed Energy Resource Coordination 60

Fair-Over-Time Distributed Energy Resource Coordination

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60th Annual Allerton Conference on Communication, Control, and Computing, Allerton 2024

作者： Moring, Hannah Farrell, Xavier Mathieu, Johanna L. University of Michigan Department of Electrical Engineering and Computer Science Ann ArborMI48109 United States

ISBN: (纸本)9798331541033

There are numerous strategies to coordinate distributed energy resources (DERs) to provide a variety of services to the power grid. DER coordination can affect resources and participants unequally, for example, by excessively degrading or curtailing particular DERs more than others. However, few DER coordination strategies explicitly take into account fairness, equity, or justice. In this paper, we explore fairness metrics and their applicability to the DER coordination problem. In particular, we investigate metrics from machine learning to identify metrics that could be incorporated into DER coordination problems and we summarize fairness metrics that have been used in the power systems literature. A key challenge is that most existing fairness metrics are static - ensuring fairness at a point in time. DER coordination problems are inherently dynamic and we often care about fairness over time, not at each time. The machine learning literature offers some ways to think about fairness over time and, more generally, how to incorporate fairness into dynamic power systems problems. We use a specific DER coordination problem - the problem of computing dynamic operating envelopes - to demonstrate how incorporation of a fair-over-time metric changes DER coordination solutions, and highlight the trade-offs that arise. © 2024 IEEE.

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Graphene Photodetector for Time-of-Flight Optical Ranging Using Optoelectronic Mixing

Graphene Photodetector for Time-of-Flight Optical Ranging Us...

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2024 Conference on Lasers and Electro-Optics, CLEO 2024

作者： Kienitz, Paul Bablich, Andreas Bornemann, Rainer Müller, Maurice Bolívar, Peter Haring University of Siegen Department of Electrical Engineering and Computer Science Hölderlinstrasse 3 Siegen57076 Germany

A graphene optoelectronic mixer device utilizing indirect Time-of-Flight (ToF) for enhanced 3D imaging applications is presented. The prototype enables ranging up to 1 m with a mean accuracy of 25.6 mm. © Optica ... 详细信息

ISBN: (纸本)9781957171395

关键词： Three dimensional displays

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Improving Speed of MUX-FSM-based Stochastic Computing for On-device Neural Networks

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Journal of Computing science and engineering 2022年第2期16卷 79-87页

作者： Kang, Jongsung Kim, Taewhan Department of Electrical Engineering and Computer Science Seoul National University Seoul Korea Republic of

We propose an acceleration technique for processing multiplication operations using stochastic computing (SC) in ondevice neural networks. Recently, multiplexor driven finite state machine (MUX-FSM)-based SCs, which employ a MUX controlled by an FSM to generate a (repeated but short) bit sequence of a binary number to count up for a multiplication operation, considerably reduce the processing time of MAC operations over the traditional stochastic number generator (SNG) based SC. Nevertheless, the existing MUX-FSM-based SCs still do not meet the multiplication processing time required for the wide adoption of on-device neural networks in practice even though it offers a very economical hardware implementation. In this respect, this work proposes a solution that speeds up the conventional MUX-FSMbased SCs. Precisely, we analyze the bit counting pattern produced by MUX-FSM and replace the counting redundancy by shift operation, resulting in a shortening of the length of the required bit sequence significantly, together with analytically formulating the number of computation cycles. Through experiments, we have shown that the enhanced SC technique can reduce the processing time by 44.1% on average over the conventional MUX-FSM-based SCs. © 2022. The Korean Institute of Information Scientists and Engineers

关键词： Embedded systems

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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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A Novel Topology of a Three-Phase Five-Level Reduced Switch Reduced Component Multilevel Inverter 3

A Novel Topology of a Three-Phase Five-Level Reduced Switch ...

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3rd IEEE International Conference on Smart Technologies for Power, Energy and Control, STPEC 2023

作者： Mohanty, Sarthak Parida, Jyotiranjan Parida, Shubhranshu Mohan Rout, Pravat Kumar Samanta, Indu Sekhar Department of Electrical Engineering Bhubaneswar India Department of Electrical and Electronics Engineering Bhubaneswar India Department of Computer Science and Engineering Bhubaneswar India

ISBN: (纸本)9798350304732

This paper introduces a novel three-phase Reduced Switch Reduced Component (RSRC) Multilevel Inverter (MLI) topology to reduce switching losses, cost-effectiveness, and overall performance within power electronic systems. The suggested topology addresses the challenges of complexity and high cost associated with traditional MLIs by significantly minimizing the number of necessary components. Our design process, in-depth analysis, and comprehensive evaluation center around two key performance indicators: voltage waveform quality and total harmonic distortion (THD). To illustrate the superiority of the new design, we draw comparative analyses between our proposed topology and prevailing MLI topologies, underlining the novel advantages our design offers. Particularly designed for compact applications, our topology is relevant to e-mobility loads. Simulation results show that applying an assortment of Pulse Width Modulation (PWM) techniques makes a notable reduction in THD achievable, thus solidifying the proposed topology as a feasible and economically-sound solution for future prototyping. Future work will explore applying optimization methods to regulate duty cycle signals and introducing advanced modulation techniques for achieving further THD reduction. © 2023 IEEE.

关键词： Multilevel inverters (MLIs) Pulse Width Modulation (PWM) Reduced Switch Reduced Component (RSRC) Three-phase RSRC MLI topology Total Harmonic Distortion (THD)

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Excitonic Single- and Two-Photon Photoluminescence Excitations in Bilayer GaN/AlN Nanowires

Excitonic Single- and Two-Photon Photoluminescence Excitatio...

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

作者： Cui, Liangqing Wu, You Wu, Yuanpeng Mi, Zetian Kira, Mackillo Norris, Theodore B. Department of Electrical Engineering and Computer Science University of Michigan Ann ArborMI48109 United States

The energy difference between the 1s and 2p exciton states of bilayer GaN/AlN is measured and computed by comparing excitonic resonances in photoluminescence excitation spectra following single- versus two-photon abso... 详细信息

关键词： Photoluminescence

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Successful Applications and Future Challenges of Machine Learning for Power Systems

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IEEE ELECTRIFICATION MAGAZINE 2022年第4期10卷 90-+页

作者： Li, Fangxing Department of Electrical Engineering and Computer Science University of Tennessee Knoxville TN USA

Machine learning (ML), or artificial intelligence (AI), is one of the emerging technologies for implementing the next-generation smart grid. In recent years, significant efforts have been devoted to exploring the potentials of ML and AI for solving complex power system problems. The IEEE WG on Machine Learning for Power Systems has recently organized several events in this heatedly studied area. These events include the following:

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Drivers Real time Drowsiness, Attention Detection and Alarm System using Eye Aspect Ratio(EAR) Analysis 3

Drivers Real time Drowsiness, Attention Detection and Alarm ...

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3rd International Conference on Advancement in electrical and Electronic engineering, ICAEEE 2024

作者： Ahmed, Nawshad Romiz, Sk Niyaz Amin, Rohul Shan, Mahe Shahriar Mahmud, Masroor Bangladesh Army University of Science and Technology Department of Electrical and Electronic Engineering Dinajpur Bangladesh Bangladesh Army University of Science and Technology Department of Electrical and Electronic Engineering Satkhira Bangladesh Bangladesh Army University of Science and Technology Department of Computer Science and Engineering Tangail Bangladesh Bangladesh Army University of Science and Technology Department of Electrical and Electronic Engineering Dhaka Bangladesh Bangladesh Army University of Science and Technology Department of Computer Science and Engineering Satkhira Bangladesh

ISBN: (纸本)9798350388282

There is an immediate threat to the highway transportation system from road accidents, which can cause death, serious injury, and property damage. Accidents involving motor vehicles cause significant injury or death to a large number of individuals annually. Fatigue, lack of focus, and lethargy are key contributors to vehicle collisions. In order to identify driver inattention or exhaustion in real-time, this research lays forth a system that combines an emergency alarm system with Eye Aspect Ratio (EAR), which is based on facial landmarks. The method's strength is that it can accurately differentiate between closed eyes and inattention, even in low-light conditions. The experimental results demonstrate the efficacy of the proposed method in resolving these critical issues, offering a practical means to enhance road safety. © 2024 IEEE.

关键词： Aspect ratio

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Optimizing auditory brainstem response detection through NSGA-II guided feature selection

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Neural Computing and Applications 2025年第15期37卷 8925-8943页

作者： Majidpour, Jafar Hassanzadeh, Hiwa Khezri, Edris Arabi, Hossein Department of Software Engineering University of Raparin Kurdistan Region Rania Iraq Department of Computer Science University of Raparin Kurdistan Region Rania Iraq Department of Computer Engineering Islamic Azad University Boukan Branch Boukan Iran Division of Nuclear Medicine and Molecular Imaging Department of Medical Imaging Geneva University Hospital Geneva1211 Switzerland

Hearing-impaired people undergo auditory brainstem response (ABR) testing to assess their peripheral auditory nerve system. Audiologists apply diagnostic labels to ABR data using reference-based indicators such as peak latency, waveform shape, amplitude, and others. ABR test scoring errors may invalidate auditory nerve system integrity results. Machine learning, especially deep learning, may reduce human error in ABR analysis. This work offers a complete methodology to handle ABR analysis problems. First, extract ABR test images from PDF reports to create a dataset. The next step uses Elastic Distortion approach to create high-quality ABR images while conserving data. ResNet-50 deep learning extracts ABR image characteristics in the third phase. The fourth step finds the most relevant features using a trainable multi-objective non-dominated sorting genetic algorithm II optimizer. Finally, ABR images are classified using machine learning algorithms. The proposed model achieved significant performance metrics: an accuracy of 99.46%, precision of 99.33%, specificity of 99.57%, sensitivity of 99.57%, F1-score of 99.43%, and a mean squared error of 0.0054 when using a support vector machine classifier with both augmented and original images. These findings demonstrate the effectiveness of trainable feature selection and high-quality ABR images in improving ABR analysis accuracy. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Mean square error

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