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IEEE Transportation Electrification Conference and Expo (ITEC)

作者： Chris Bracken Richard Beddingfield Mark A. Juds Bharadwaj Reddy Andapally Brandon Grainger Paul R. Ohodnicki Department of Mechanical Engineering and Materials Science University of Pittsburgh Pittsburgh United States Department of Electrical and Computer Engineering North Carolina State University Raleigh United States Companhia Brasileira de Metalurgia e Mineração (CBMM) Flevoland Netherlands Department of Electrical and Computer Engineering University of Pittsburgh Pittsburgh United States

Magnetic core and component characterization presents technical challenges, particularly with a focus on achieving higher frequency and voltage operation with advances in power electronics converters due to wide bandgap semiconductors. Accurate characterization plays a critical role in benchmarking the performance of existing core materials and construction for various component designs. In addition, characterization of fabricated components with application relevant excitation is critical for accurate assessment of technical figures of merit. Standardization of core and component characterization plays a foundational role in benchmarking and quantifying performance improvement of new magnetic materials, core constructions, and component designs. In this article, we discuss existing standards relevant for magnetic core characterization and several new standard development activities currently underway relevant for medium frequency and power applications. We also discuss the application of relevant measurements to selected magnetic core materials and components currently active within the Advanced Magnetics for Power and Energy Development (AMPED) Consortium. Example current transformer components are designed and tested as an example of standardized testing that is currently underway, to be expanded in the future to also include inductors and transformers amongst others.

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3D imaging of moving cells within droplets with a staggered bifocal microlens array

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Optics Express 2025年第12期33卷 26152-26163页

作者： Xinglong Huang Yingzhe Piao Jiayan Zhuang Chunhui Wu Dongrui Zhang Xing Cheng Boris Stoeber Department of Mechanical Engineering the University of British Columbia Vancouver V6T1Z4 Canada Department of Materials Science and Engineering Southern University of Science and Technology Shenzhen 518055 China Department of Materials Science and Engineering University of Toronto Toronto M5S1A1 Canada SUSTech-Jiaxing Research Center Jiaxing 314050 China chengx@*** Department of Electrical and Computer Engineering the University of British Columbia Vancouver V6T1Z4 Canada boris.stoeber@ubc.ca (boris.stoeber@ubc.ca)

Three-dimensional (3D) observation of moving cells offers critical insights into fundamental biological processes. However, the limited temporal resolution of traditional scanning-based 3D imaging techniques constrains their use in live-cell imaging. Light-field microscopy (LFM) has emerged as a scanning-free alternative, enabling rapid 3D volumetric data acquisition. Despite this advantage, LFM's spatial resolution and reconstruction artifacts remain challenges that hinder its broader application in biological imaging. This study proposes a custom-designed staggered bifocal microlens array (MLA), fabricated through a cost-effective, high-throughput method, to overcome these limitations. This approach enhances spatial resolution while minimizing reconstruction artifacts compared to conventional uniform MLAs. We demonstrate the technique through 3D imaging of microbeads at varying depths and live cells suspended in droplets. This bifocal MLA presents a promising advancement for extending LFM's applications in biological research.

关键词： Biomedical imaging Imaging systems Imaging techniques Microlens arrays Spatial resolution Three dimensional imaging

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Synthetic helicoidal rotation of a quasielectrostatic ring resonator for ultrabroadband magnet-free nonreciprocity

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Physical Review Applied 2024年第6期22卷 064020-064020页

作者： Aravind Nagulu Ahmed Mekawy Mykhailo Tymchenko Dimitrios Sounas Harish Krishnaswamy Andrea Alù Department of Electrical and Systems Engineering Washington University in St. Louis St. Louis Missouri 63130 USA Photonics Initiative Advanced Science Research Center City University of New York New York New York 10031 USA Department of Electrical and Computer Engineering The University of Texas at Austin Austin Texas 78712 USA Department of Electrical Engineering Wayne State University Detroit Michigan 48202 USA Department of Electrical Engineering Columbia University New York New York 10027 USA Physics Program Graduate Center City University of New York New York New York 10016 USA

Suitably tailored forms of spatiotemporal modulation in electronic circuit networks have been recently employed to overcome fundamental challenges in modern electronic systems, including breaking reciprocity, squeezing the footprint of high-Q resonators, and overcoming the delay-bandwidth limit. Rotating patterns of temporal modulations have been used to synthesize angular momentum, which replaces magnetic bias to break reciprocity in integrated circuits. However, this approach is limited by trade-offs between modulation speed, footprint, and bandwidth of operation. Rotating switching patterns in commutated capacitor networks also enables compact filters and quasielectrostatic wave propagation, overcoming the delay-bandwidth limit. In this paper, we combine these mechanisms in an integrated-circuit ring that synthetically rotates in two dimensions, realizing an effective helicoidal motion that provides ultrabroadband quasielectrostatic nonreciprocal responses fitting within a theoretically infinitesimal size. We also analyze the impact of modulation signal noise on time-modulated nonreciprocal components and unveil the role of a dynamic noise mechanism based on which the noise level increases in the presence of a strong signal passing through the component, along with methods to mitigate this effect. We experimentally verify these principles in a three-port integrated circulator based on a 65-nm CMOS process that operates from dc to 1 GHz with a miniaturization factor of 2 × 106.

关键词： Metamaterials Non-reciprocal transmission Photonics Topolectrical circuits

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Evolution-Assisted Deep Reinforcement Learning for Fast Charging Station Coordinated Operation 13

Evolution-Assisted Deep Reinforcement Learning for Fast Char...

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13th IEEE Congress on Evolutionary Computation, CEC 2024

作者： Yang, Xiaoying Gu, Yujing Jia, Fuhua Li, Yiran Wang, Hongru Du, Nanjiang Cui, Tianxiang Ye, Yujian Bai, Ruibin State Grid Nantong Power Supply Company Nantong China University of Nottingham Ningbo China Department of Mechanical Materials and Manufacturing Engineering Ningbo China School of Electrical Engineering Southeast University Nanjing China School of Computer Science University of Nottingham Ningbo China Ningbo China University of Nottingham Ningbo China The Digital Port Technologies Lab Ningbo China Nottingham Ningbo China Beacons of Excellence Research and Innovation Institute Ningbo China

ISBN: (纸本)9798350308365

The shift towards transportation electrification, marked by the rising use of electric vehicles (EVs) and the development of fast charging stations (FCS), plays a crucial role in transport decarbonization initiatives. To optimize the rollout of FCS and set appropriate charging service fees (CSF)-a process referred to as the coupled FCS multi-stage bi-level operation problem (FCS-MBOP)-is essential for improving both investment and operational efficiency within the integrated power distribution and transportation network (CPTN). For operators, it's not only necessary to adapt to short-term fluctuations within the environment but also to swiftly respond to changes in the FCS layout resulting from various long-term investment decisions. To address this complexity, we introduce a dual-timescale evolutionary assist deep reinforcement learning framework, which includes two specialized agents with distinct functions: an investment agent (planner) and an operational agent (operator). The planner focuses on annual investments, evolving long-term strategies that weigh social benefits against investment costs through the use of a genetic algorithm (GA). In contrast, the operator acts on an hourly basis, fine-tuning CSF to alleviate traffic congestion and minimize the social costs, while taking into account the planner's feasible investment decisions. Leveraging the integrated capabilities of a graph neural network (GNN), long-short-term memory (LSTM), and attention mechanisms, our framework's agents are adept at extracting both temporal and spatial features and facilitating the transfer of experiences across different investment stages. Empirical evidence underscores the effectiveness of our approach, showcasing its ability to surpass conventional methodologies in delivering high-quality solutions. © 2024 IEEE.

关键词： Deep reinforcement learning

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Investigating the Behavior of Diffusion Models for Accelerating Electronic Structure Calculations

arXiv

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arXiv 2023年

作者： Rothchild, Daniel Rosen, Andrew S. Taw, Eric Robinson, Connie Gonzalez, Joseph E. Krishnapriyan, Aditi S. Department of Electrical Engineering and Computer Science University of California Berkeley United States Department of Materials Science and Engineering University of California Berkeley United States Materials Science Division Lawrence Berkeley National Laboratory United States Department of Chemical and Biomolecular Engineering University of California Berkeley United States Department of Chemistry University of California Berkeley United States

We present an investigation into diffusion models for molecular generation, with the aim of better understanding how their predictions compare to the results of physics-based calculations. The investigation into these models is driven by their potential to significantly accelerate electronic structure calculations using machine learning, without requiring expensive first-principles datasets for training interatomic potentials. We find that the inference process of a popular diffusion model for de novo molecular generation is divided into an exploration phase, where the model chooses the atomic species, and a relaxation phase, where it adjusts the atomic coordinates to find a low-energy geometry. As training proceeds, we show that the model initially learns about the first-order structure of the potential energy surface, and then later learns about higher-order structure. We also find that the relaxation phase of the diffusion model can be re-purposed to sample the Boltzmann distribution over conformations and to carry out structure relaxations. For structure relaxations, the model finds geometries with ∼ 10× lower energy than those produced by a classical force field for small organic molecules. Initializing a density functional theory (DFT) relaxation at the diffusion-produced structures yields a > 2× speedup to the DFT relaxation when compared to initializing at structures relaxed with a classical force field. Copyright © 2023, The Authors. All rights reserved.

关键词： Density functional theory

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Two-Dimensional Magnetic Exciton Polariton with Strongly Coupled Atomic and Photonic Anisotropies

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Physical Review Letters 2024年第26期133卷 266901-266901页

作者： Qiuyang Li Xin Xie Adam Alfrey Christiano W. Beach Nicholas McLellan Yang Lu Jiaqi Hu Wenhao Liu Nikhil Dhale Department of Physics University of Michigan Ann Arbor Michigan 48109 USA Applied Physics Program University of Michigan Ann Arbor Michigan 48109 USA Department of Electrical Engineering and Computer Science University of Michigan Ann Arbor Michigan 48109 USA Department of Physics The University of Texas at Dallas Richardson Texas 75080 USA

Anisotropy is a fundamental property of both material and photonic systems. The interplay between material and photonic anisotropies, however, has hardly been explored due to the vastly different length scales. Here we demonstrate exciton polaritons in a 2D antiferromagnet, CrSBr, coupled with an anisotropic photonic crystal cavity, where the spin, atomic, and photonic anisotropies are strongly correlated. Atomic anisotropy led to exceptionally strong coupling between anisotropic exciton and optical modes, which are stable against excitation densities a few orders of magnitude higher than polaritons in isotropic materials. The resulting polaritons feature anisotropic polarizations determined by the interplay of not only the anisotropies but also the dissipations and coupling of both exciton and photon modes, tunable by tens of degrees via many parameters. The work provides insights of excitons in CrSBr and demonstrates a prototype where atomic- and photonic-scale orders strongly couple, giving rise to unconventional properties in quantum materials and photonic devices.

关键词： Atoms

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Max-Plus Synchronization in Decentralized Trading Systems

arXiv

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arXiv 2023年

作者： Riess, Hans Munger, Michael Zavlanos, Michael M. Department of Electrical and Computer Engineering Duke University United States Department of Political Science Duke University United States Department of Economics Duke University United States Dept. of Mechanical Engineering & Materials Science Duke University United States

We introduce a decentralized mechanism for pricing and exchanging alternatives constrained by transaction costs. We characterize the time-invariant solutions of a heat equation involving a (weighted) Tarski Laplacian operator, defined for max-plus matrix-weighted graphs, as approximate equilibria of the trading system. We study algebraic properties of the solution sets as well as convergence behavior of the dynamical system. We apply these tools to the "economic problem" of allocating scarce resources among competing uses. Our theory suggests differences in competitive equilibrium, bargaining, or cost-benefit analysis, depending on the context, are largely due to differences in the way that transaction costs are incorporated into the decision-making process. We present numerical simulations of the synchronization algorithm (RRAggU), demonstrating our theoretical *** Codes 15A80, 90C24 Copyright © 2023, The Authors. All rights reserved.

关键词： Cost benefit analysis

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Compressing Image-to-Image Translation GANs Using Local Density Structures on Their Learned Manifold

arXiv

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arXiv 2023年

作者： Ganjdanesh, Alireza Gao, Shangqian Alipanah, Hirad Huang, Heng Department of Computer Science University of Maryland College Park College ParkMD20742 United States Department of Electrical and Computer Engineering University of Pittsburgh PittsburghPA15261 United States Department of Mechanical Engineering and Materials Science University of Pittsburgh PittsburghPA15261 United States

Generative Adversarial Networks (GANs) have shown remarkable success in modeling complex data distributions for image-to-image translation. Still, their high computational demands prohibit their deployment in practical scenarios like edge devices. Existing GAN compression methods mainly rely on knowledge distillation or convolutional classifiers’ pruning techniques. Thus, they neglect the critical characteristic of GANs: their local density structure over their learned manifold. Accordingly, we approach GAN compression from a new perspective by explicitly encouraging the pruned model to preserve the density structure of the original parameter-heavy model on its learned manifold. We facilitate this objective for the pruned model by partitioning the learned manifold of the original generator into local neighborhoods around its generated samples. Then, we propose a novel pruning objective to regularize the pruned model to preserve the local density structure over each neighborhood, resembling the kernel density estimation method. Also, we develop a collaborative pruning scheme in which the discriminator and generator are pruned by two pruning agents. We design the agents to capture interactions between the generator and discriminator by exchanging their peer’s feedback when determining corresponding models’ architectures. Thanks to such a design, our pruning method can efficiently find performant sub-networks and can maintain the balance between the generator and discriminator more effectively compared to baselines during pruning, thereby showing more stable pruning dynamics. Our experiments on image translation GAN models, Pix2Pix and CycleGAN, with various benchmark datasets and architectures demonstrate our method’s effectiveness. © 2023, CC BY-NC-SA.

关键词： Generative adversarial networks

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Fully-Printed Sensor Based Extended Gate Field Effect Transistors for Wireless Monitoring of Potassium and Ammonium Ions

Fully-Printed Sensor Based Extended Gate Field Effect Transi...

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IEEE SENSORS

作者： Munia Ferdoushi Mohammad Shafiqul Islam Wenxin Cai Sandra Lara Galindo Md Farhad Hassan Yasser Khan Ming Hsieh Department of Electrical and Computer Engineering University of Southern California Los Angeles California Department of Chemical Engineering and Materials Science University of Southern California Los Angeles California Alfred E. Mann Department of Biomedical Engineering University of Southern California Los Angeles California

ISBN: (数字)9798350363517

ISBN: (纸本)9798350363524

This paper presents an extended gate field effect transistor based ion sensing platform utilizing flexible printed ion selective electrodes. A custom-printed circuit board with a Bluetooth-enabled microcontroller module enables wireless monitoring of the transistor's transfer characteristics modulated by the ion concentration in the target solution. The utilization of printing techniques such as inkjet printing and direct 3D writing provide the scope for facile and cost-effective fabrication. The system has been utilized for remote monitoring of Potassium and Ammonium ions with ideal Nernstian response, high sensitivity, and selectivity. This work provides a promising path for real-time and continuous ion monitoring which has crucial implications for health, industry, and environmental applications.

关键词： Wireless communication Wireless sensor networks Sensitivity Field effect transistors Logic gates Writing Ions Three-dimensional printing Sensors Potassium

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Study on the Flow, Foaming Characteristics and Structural Strength of Polypropylene Structural Foam Injection Molding by Innovative Nitrogen and Molten Plastic Mixing Mechanism

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Polymers 2023年第9期15卷 2116页

作者： Huang, Po-Wei Peng, Hsin-Shu Hwang, Sheng-Jye Huang, Chao-Tsai Program of Mechanical and Aeronautical Engineering Feng Chia University of Engineering and Science Taichung407102 Taiwan Department of Mechanical and Computer Aided Engineering Feng Chia University of Engineering and Science Taichung407102 Taiwan Department of Mechanical Engineering National Cheng Kung University of Engineering Tainan70101 Taiwan Department of Chemical and Materials Engineering Tamkang University of Engineering New Taipei City251301 Taiwan

Plastic foam molding methods include thermoforming, extrusion and injection molding. Injection foam molding is a one-time molding method with high production efficiency and good product quality. It is suitable for foamed plastic products with complex shapes and strict size requirements. It is also the main method for producing structural bubbles. In this investigation, we developed a structural foam injection molding technology using the gas supply equipment connected to the unique plasticizing mechanism of the injection machine and studied its influence on the specimens’ melt rheology quality and foam structures. In the experiment, the forming material was polypropylene (PP), and the gas for mixing/forming foaming characteristics was nitrogen (N2). Additionally, in order to observe the rheological properties of N2/melt mixing, a melt flow specimen mold cavity was designed and the change in the melt viscosity index was observed using a melt pressure sensing element installed at the nozzle position. With the nitrogen supply equipment connected to a unique plasticizing mechanism, the mixing of gas and molten plastic can be achieved at the screw plasticizing stage, where the foaming effect is realized during the melt-filling process due to the thermodynamic instability of the gas. It was also found that an increase in N2 fill content increased melt fluidity, and the trend of melt pressure and melt viscosity index showed that the higher the gas content, the lower the trend. The foaming characteristic depends on the gas thermodynamic instability and the pressure release, so it can be seen from the melt fill path that, the greater the pressure near the gate, the lower the foaming amount and the internal structure (SEM) after molding;the farther from the gate, the greater the relative increase in the foaming growth/amount. This phenomenon will be more obvious when the N2 fill content is increased. © 2023 by the authors.

关键词： Gases

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