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16th International Conference on applied and Theoretical Electricity, ICATE 2024

作者： Mustata, Laurentiu-Alex Sorea, Gheorghe-Dan Helerea, Elena Transilvania University Electrical Engineering and Computer Science Faculty Electrical Engineering and Applied Physics Department Brasov Romania

ISBN: (纸本)9798350388107

In the case of standalone houses, ensuring a continuous and regulated power supply from renewable sources is crucial. To address their unpredictable nature, an environmentally conscious hybrid renewable energy system combining photovoltaic panels, a battery, and a Diesel generator is considered. A software application simulates the power flow in a standalone house with common AC loads, analysing summer and winter scenarios over 24 hours. The strategy employed provides an optimal energy management by using the Diesel generator only during peak demand when PV is available and to cover loads and maintain battery life when PV is not in operation. The simulation results provide valuable insights for designing such hybrid energy systems and for enhancing system responsiveness to dynamic energy demands. © 2024 IEEE.

关键词： Hybrid power

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Algorithmic optimization of quantum optical storage in solids

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Physical Review Research 2024年第3期6卷 033153-033153页

作者： Yisheng Lei Haechan An Zongfeng Li Mahdi Hosseini Department of Electrical and Computer Engineering and Applied Physics Program Elmore Family School of Electrical and Computer Engineering

Quantum memory devices with high storage efficiency and bandwidth are essential elements for future quantum networks. Solid-state quantum memories can provide broadband storage, but they primarily suffer from low storage efficiency. We use passive optimization and algorithmic optimization techniques to demonstrate nearly a sixfold enhancement in quantum memory efficiency. In this regime, we demonstrate coherent and single-photon-level storage with a high signal-to-noise ratio. The optimization technique presented here can be applied to most solid-state quantum memories to significantly improve the storage efficiency without compromising the memory bandwidth.

关键词： Quantum communication

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Machine Learning for Smart Agriculture: A Comprehensive Survey

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第6期5卷 2568-2588页

作者： Mahmood, M. Rezwanul Matin, Mohammad Abdul Goudos, Sotirios K. Karagiannidis, George North South University Department of Electrical and Computer Engineering Dhaka1229 Bangladesh Aristotle University of Thessaloniki ELEDIA@AUTH Department of Physics Thessaloniki54124 Greece Aristotle University of Thessaloniki Department of Electrical and Computer Engineering Thessaloniki54124 Greece Cyber Security Systems and Applied AI Research Center Beirut1102 2801 Lebanon

As communication technologies and equipment evolve, smart assets become smarter. The agricultural industry is also evolving in line with the implementation of modern communication protocols, intelligent sensors, and equipment. This evolution is enabling large-scale agricultural production processes to operate independently, thus, securing the food supply chain for an ever-growing population. Data processing for such a system with multiple heterogeneous sources requires proper management for effective agricultural operations. Recognizing the advantages of machine learning (ML) in performing large-scale data processing, researchers are investigating the implementation of ML to design an effective intelligent agricultural architecture. The aim of this article is to provide a thorough analysis of the state-of-the-art in smart agriculture, open challenges, and guidelines for the development of further enhanced smart agriculture systems. Specifically, we describe how ML is used to create intelligent agricultural systems supported by state-of-the-art technology. © 2020 IEEE.

关键词： Weed control

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A New Pulse Skipping Control for Series Resonant Converter 3

A New Pulse Skipping Control for Series Resonant Converter

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3rd IEEE Industrial Electronics Society Annual On-Line Conference, ONCON 2024

作者： Bhowmick, Supratik Chakraborty, Soham Maiti, Suman Indian Institute of Technology Kharagpur Department of Electrical Engineering Kharagpur721302 India Calcutta University UG Program of Electrical Engineering Department of Applied Physics Kolkata700009 India

ISBN: (纸本)9798331540319

This paper proposes a pulse skipping based combined frequency and duty ratio control for a series resonant converter which is targeted for voltage regulator applications. The proposed pulse skipping control combines duty ratio based phase shifted control for maintaining output voltage at its reference value, along with frequency based control which assists in development of the pulse skipping algorithm. This algorithm is structured so that pulses are skipped in generated gate pulses for upper switching devices present in the converter whenever the output voltage tries to be higher than its specified maximum value or be lower than its specified minimum value. These maximum and minimum voltages are determined based on its reference voltage and allowable voltage ripple within which the oscillations in output voltage are desired to be in. The converter system with the proposed control method is simulated in MATLAB/Simulink environment to validate its performance under transient conditions e.g., variation in load resistance and output voltage reference. The system performance with proposed method is also compared with conventional method where the proposed control demonstrates its superiority by significantly minimizing the output voltage ripple. © 2024 IEEE.

关键词： Resonant converters

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Performance Optimization of Series Resonant Converter by Combined Frequency and Duty Ratio Control 3

Performance Optimization of Series Resonant Converter by Com...

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3rd IEEE Industrial Electronics Society Annual On-Line Conference, ONCON 2024

作者： Chakraborty, Soham Bhowmick, Supratik Maiti, Suman Calcutta University UG Program of Electrical Engineering Department of Applied Physics Kolkata700009 India Indian Institute of Technology Kharagpur Department of Electrical Engineering Kharagpur721302 India

ISBN: (纸本)9798331540319

Resonant converters are widely used for voltage regulator applications. The basic converters i.e., Series resonant Converter (SRC) and Parallel Resonant Converter (PRC) are having some merits and limitations. These are addressed in multi element topologies at the cost of adding extra elements. This work takes up the SRC and reports a combined frequency and duty ratio control for performance optimization of the SRC. The key issue of SRC is that the voltage cannot be controlled at a low Q (i.e. Quality factor) operation. For example, at no load Q becomes zero making the converter inert to frequency variation. This however can be eliminated by pulse width modulation technique. Duty ratio can be properly controlled to achieve the output voltage at an extended load range. The work further takes up challenge to make efficiency optimized. For this a combined control of duty ratio and frequency are proposed. Efficiency Optimization Control (EOC) requires two voltage sensors (one at the input side and another at the output) and a current sensor. Following this, a new minimum rms current algorithm is developed that only requires a current and a voltage sensor. All the proposed techniques are simulated and verified using MATLAB/Simulink. © 2024 IEEE.

关键词： Resonant converters

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Dzyaloshinskii-Moriya-type spin-spin interaction from mixed-parity superconductivity

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Physical Review B 2025年第6期111卷 L060504-L060504页

作者： Jabir Ali Ouassou Takehito Yokoyama Jacob Linder Department of Computer Science Electrical Engineering and Mathematical Sciences Western Norway University of Applied Sciences NO-5528 Haugesund Norway Department of Physics Tokyo Institute of Technology Meguro Tokyo 152-8551 Japan Center for Quantum Spintronics Department of Physics Norwegian University of Science and Technology NO-7491 Trondheim Norway

Interacting impurity spins adsorbed on surfaces have been suggested as basic components for applications in quantum computation and spintronics. Such spins usually prefer a parallel or antiparallel configuration, but weakly noncollinear alignments are possible due to the Dzyaloshinskii-Moriya interaction (DMI) that arises in the presence of relativistic spin-orbit coupling. Here, we show that an effective Dzyaloshinskii-Moriya-type interaction (DMTI) can emerge purely from superconducting correlations without any spin-orbit interaction. We give an analytical proof and provide a numerical study which shows that DMTI arises in mixed-parity superconductors solely from the superconducting pairing. Moreover, we show that the same effect can be realized in Josephson junctions between s-wave and p-wave superconductors, where a phase bias toggles the DMTI entirely on and off. These results enable a way to engineer spin textures using superconducting order.

关键词： Shear waves

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physics-Constrained Adversarial Training for Neural Networks in Stochastic Power Grids

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第3期5卷 1121-1131页

作者： Li, Wenting Deka, Deepjyoti Wang, Ren Paternina, Mario R. Arrieta LANL Applied Mathematics and Plasmas Physics Division Los AlamosNM87544 United States Illinois Institute of Technology Department of Electrical and Computer Engineering ChicagoIL60616 United States National Autonomous University of Mexico Department of Electrical Engineering Mexico City04510 Mexico

The growth in distributed energy resources (DERs) is an important step toward solving the global climate crisis. However, many DERs, such as wind and solar power, are random and intermittent, causing the data in power grids to be perturbed with high uncertainty. Such perturbations degrade the performance of data-driven algorithms introduced in power grids for sensing and control. Existing approaches can strengthen machine learning models against well-designed malicious attacks. However, such physics-agnostic efforts fail to ensure the robustness of natural perturbations in power grids, that occur due to varying loads and changes in control inputs. This article proposes a novel physics-constrained adversarial training method for robustifying neural networks for the crucial problem of locating edge-faults in power grids. Our approach relies on first deriving analytical physical laws that are satisfied by state perturbations in realistic grids and then using the descriptive sets during adversarial training. Compared to state-of-the-art methods, our perturbation-robust neural networks have higher robust accuracy as well as training efficiency. Also, we demonstrate that the proposed approach achieves a tighter upper bound of robust risks than traditional efforts. The numerical experimental results in the IEEE 68-bus benchmark system validate our adversarial training in two scenarios when loads and control inputs vary randomly. The proposed method shows superior performance in accuracy and efficiency for different perturbed datasets. In addition, we testify the influence of different inputs on the robustness. © 2023 IEEE.

关键词： Neural networks

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Multiregion Light Control in Diffusive Media via Wavefront Shaping

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Physical Review Letters 2024年第14期133卷 146901页

作者： Liam Shaughnessy Rohin E. McIntosh Arthur Goetschy Chia Wei Hsu Nicholas Bender Hasan Yılmaz Alexey Yamilov Hui Cao Department of Applied Physics Ming Hsieh Department of Electrical and Computer Engineering School of Applied and Engineering Physics Institute of Materials Science and Nanotechnology Physics Department

Wavefront shaping allows focusing light through or inside strongly scattering media, but the background intensity also increases which reduces the target’s contrast. By combining transmission or deposition matrices for different regions, we construct joint operators to achieve spatially resolved control of light in diffusive systems. The eigenmode of a contrast operator can maximize the power contrast between a target and its surrounding. A difference operator enhances the power delivery to a target while avoiding the background increase. This work opens the door to coherent control of nonlocal effects in wave transport for practical applications.

关键词： Optics & lasers Random & disordered media Coherent control

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All-photonic artificial-neural-network processor via nonlinear optics

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Physical Review applied 2024年第1期22卷 014009-014009页

作者： Jasvith Raj Basani Mikkel Heuck Dirk R. Englund Stefan Krastanov Department of Electrical and Electronics Engineering Department of Electrical and Computer Engineering Institute for Research in Electronics and Applied Physics and Department of Electrical Engineering and Computer Science Department of Electrical and Photonics Engineering College of Information and Computer Sciences

Optics and photonics have recently captured interest as a platform to accelerate linear matrix processing, otherwise a bottleneck in traditional digital electronics. In this paper we propose an all-photonic computational accelerator wherein information is encoded in the amplitudes of frequency modes stored in a single ring resonator. Interaction among these modes is enabled by nonlinear optical processes. Both the matrix multiplication and elementwise activation functions on these modes (the artificial neurons) are performed through coherent processes, enabling the direct representation of negative and complex numbers without having to pass through digital electronics, a common limitation in today’s photonic architectures. This design also has a drastically lower hardware footprint compared with today’s electronic and optical accelerators, as the entirety of the matrix multiplication happens in a single multimode resonator on chip. Our architecture is unique in providing a completely unitary, reversible mode of computation, enabling on-chip analog Hamiltonian-echo backpropagation for gradient descent and other self-learning tasks. Moreover, the computational speed increases with the power of the pumps to arbitrarily high rates, as long as the circuitry can sustain the higher optical power. Lastly, the design presented here is a less demanding version of a future room-temperature quantum computational device. Therefore, while this architecture is already viable today, direct reinvestments in it would be enabling its evolution into quantum computational hardware.

关键词： Machine learning Neuromorphic computing Nonlinear optics Photonics Artificial neural networks Deep learning Neural network simulations Optical computing

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Long-time nanoscopic bioimaging by phase-intensity nanoscopy 10

Long-time nanoscopic bioimaging by phase-intensity nanoscopy

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Quantitative Phase Imaging X 2024

作者： Cui, Guangjie Liu, Yunbo Zu, Di Kim, Do Young Lee, Somin Eunice Department of Electrical & Computer Engineering Biomedical Engineering Applied Physics Biointerfaces Institute Macromolecular Science & Engineering University of Michigan Ann ArborMI United States

ISBN: (纸本)9781510669635

Light-matter interactions play essential roles in realizing a new generation of nanoscale imaging to overcome traditional trade-offs between spatial resolution and time capabilities. By harnessing engineered nanophotonic materials, scattered light can be shaped to enhance light matter interactions for nanoscale bioimaging. We achieved nanoscale bioimaging with record long time capabilities. Long time nanoscopy has the potential to unlock a wide range of new dynamical studies and insights in assembly, organization and pattern formation in biological and material systems, where nanoscopic rearrangements drive group-level movements at the macroscale over time. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Bioimaging

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