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Phase- and Angle-Sensitive Terahertz Hot-Electron Bolometric Plasmonic Detectors Based on Fets with Graphene Channel and Composite H-BN/Black-P/H-BN Gate Layer

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International Journal of High Speed Electronics and Systems 2024年第4期33卷

作者： Ryzhii, Victor Tang, Chao Otsuji, Taiichi Shur, Michael S. Ryzhii, Maxim Mitin, Vladimir Research Institute For Electrical Communication Tohoku University Miyagi Sendai980-8577 Japan Department Of Electrical Computer And Systems Engineering Rensselaer Polytechnic Institute TroyNY United States Department Of Computer Science And Engineering University Of Aizu Aizu-Wakamatsu Fukushima965-8580 Japan Department Of Electrical Engineering University At Buffalo Suny BuffaloNY United States

In this paper, we propose and analyze the terahertz (THz) bolometric vector detectors based on the graphene-channel field-effect transistors (GC-FET) with the black-P gate barrier layer or with the composite b-BN/black-P/b-BN gate layer. The phase difference between the signal received by the FET source and drain substantially affects the plasmonic resonances. This results in a resonant variation of the detector response on the incoming THz signal phase shift and the THz radiation angle of incidence. © 2024 World Scientific Publishing Company.

关键词： Graphene

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Wavefront engineering With Reconfigurable Intelligent Surfaces for Improved Sensing Capabilities in The THz Band

Wavefront Engineering With Reconfigurable Intelligent Surfac...

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IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP)

作者： Arjun Singh Priyansghu Sen Josep Miquel Jornet Department of Engineering SUNY Polytechnic Institute Utica NY Department of Electrical and Computer Engineering Northeastern University Boston MA

The terahertz (THz) band offers the advantages of mas-sive available bandwidth and small signal wavelengths with revolutionary potential in both communication and sensing applications. However, the increased noise power from the larger bandwidth, as well as the increased path losses due to smaller wavelengths severely restrict the signal-to-noise ratio (SNR). In this light, reconfigurable intelligent surfaces (RISs) have been proposed as breakthrough devices that can be readily scaled up to create high-gain aperture systems, which are likely to have near-field applications. Here, wavefronts specifically engineered within the near field provide opportunities for improved sensing capabilities. Specifically, in this paper, we highlight how the greater depth of focus through Bessel beams can be utilized to improve the resolution capability of THz-band sensing. We numerically derive and show that Bessel beams can help improve target classification due to increased SNR in detection and increase resolution through increased bandwidth exploitation as well.

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Net Load Forecasting with Disaggregated Behind-the-Meter PV Generation

Net Load Forecasting with Disaggregated Behind-the-Meter PV ...

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2022 IEEE Industry Applications Society Annual Meeting, IAS 2022

作者： Stratman, Anne Hong, Tianqi Yi, Ming Zhao, Dongbo University of Nebraska-Lincoln Department of Electrical and Computer Engineering LincolnNE United States Argonne National Lab Energy Systems Division LemontIL United States Rensselaer Polytechnic Institute Department of Electrical Computer and Systems Engineering TroyNY United States

ISBN: (数字)9781665478151

ISBN: (纸本)9781665478151

With increasing adoption of residential PV systems, net load forecasting is gradually shifting from forecasting pure load to forecasting pure load with PV generation. This paper explicitly compares two methods of net load forecasting for systems with high behind-the-meter (BTM) PV penetration. The first method is an additive method, in which PV generation and pure load are forecasted separately and combined to produce a net load forecast. First, a disaggregation algorithm is applied to aggregate net load measurements of residential homes to separate the pure load and PV generation. Then, a long short-term memory (LSTM) model is used to forecast pure load and PV separately using the historical disaggregated pure load and PV, respectively, and weather factors. The results are combined to generate a net load forecast. The additive model is compared to a direct net load forecast from an LSTM model. Results show that over the five-month test horizon, the additive method decreases the root mean square error (RMSE), maximum absolute error, and mean absolute error (MAE) of the net load forecast by 6.13%, 3.63%, and 6.06% respectively, compared to the direct method. © 2022 IEEE.

关键词： Errors

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EMT-Based Co-Simulation of Power System and Communication Networks

EMT-Based Co-Simulation of Power System and Communication Ne...

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IEEE International Conference on Smart Grid Communications (SmartGridComm)

作者： Milad Beikbabaei Ashwin Venkataramanan Ali Mehrizi-Sani The Bradley Department of Electrical and Computer Engineering Virginia Polytechnic Institute and State University Blacksburg VA

Communication-based devices are extensively used in the power grid, and studying the power and communication systems as a cyber-physical system before their deployment is essential. Co-simulation can be used for studying power system stability, cyberattack detection and mitigation strategies, and controller performance. In co-simulation, both the power and communication layers are simulated simultaneously. Previous work has developed co-simulation platforms; however, it does not provide a comprehensive comparison of different co-simulation platforms. Furthermore, the slow simulation speed of co-simulation in some of the proposed platforms reduces the scalability capability dramatically. In this paper, five cyber-physical co-simulation platforms are implemented using PSCAD v5, and their merits and demerits are comprehensively evaluated.

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Improved Transient Response in Inverter-Based Resources using Deep Reinforcement Learning

Improved Transient Response in Inverter-Based Resources usin...

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IEEE International Symposium on Industrial Electronics (ISIE)

作者： Ashwin Venkataramanan Ali Mehrizi-Sani The Bradley Department of Electrical and Computer Engineering Virginia Polytechnic Institute and State University Blacksburg VA

In this work, a deep reinforcement learning (DRL)-based controller is presented to improve the performance of an existing control process in inverter-based resources (IBR). A DRL algorithm is utilized to learn a model-free value function which further improves the transient response of an IBR over fast response time frames. In addition, the developed controller provides black-box control, that is, it performs the desired control action without accessing or modifying the internal parameters of the existing controller in an IBR. The developed controller is tested in case studies utilizing EMT simulations in PSCAD/EMTDC and its performance, resilience, and adaptiveness are verified.

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A Nested Ensemble Method to Bilevel Machine Learning

A Nested Ensemble Method to Bilevel Machine Learning

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Lisha Chen Momin Abbas Tianyi Chen Electrical Computer and Systems Engineering Department Rensselaer Polytechnic Institute Troy NY United States

Modern machine learning problems, such as hyperparameter optimization, meta learning, and adversarial training, adopt a bilevel learning formulation. Such problems involve a nested relation between inner- and outer-level problems, which often have suboptimal solutions with poor generalization ability. To address this issue, this paper proposes an ensemble method tailored to bilevel learning. Our method finds a nested ensemble of inner and outer parameters that improve generalization. We instantiate our general results with meta learning. We show theoretically and empirically that the diversity and the smoother loss landscape of the proposed ensemble methods lead to improved generalization over the state-of-the-art method.

关键词： Training Signal processing Benchmark testing Hyperparameter optimization Acoustics Ensemble learning Speech processing

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Open Problems and Modern Solutions for Deep Reinforcement Learning

arXiv

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arXiv 2023年

作者： Chen, Weiqin Department of Electrical Computer and Systems Engineering Rensselaer Polytechnic Institute TroyNY United States

Deep Reinforcement Learning (DRL) has achieved great success in solving complicated decision-making problems. Despite the successes, DRL is frequently criticized for many reasons, e.g., data inefficient, inflexible and intractable reward design. In this paper, we review two publications that investigate the mentioned issues of DRL and propose effective solutions. One designs the reward for human-robot collaboration by combining the manually designed extrinsic reward with a parameterized intrinsic reward function via the deterministic policy gradient, which improves the task performance and guarantees a stronger obstacle avoidance. The other one applies selective attention and particle filters to rapidly and flexibly attend to and select crucial pre-learned features for DRL using approximate inference instead of backpropagation, thereby improving the efficiency and flexibility of DRL. Potential avenues for future work in both domains are discussed in this paper. . Copyright © 2023, The Authors. All rights reserved.

关键词： Reinforcement learning

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Resilient Power Sharing in a 100% Inverter-Based Power System Under GPS Spoofing Attacks

Resilient Power Sharing in a 100% Inverter-Based Power Syste...

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IEEE General Meeting Power& Energy Society

作者： Brady Alexander Ardavan Mohammadhassani Ali Mehrizi-Sani The Bradley Department of Electrical and Computer Engineering Virginia Polytechnic Institute and State University Blacksburg VA

Utilizing power sharing controllers in a fully inverter-based power system (FIBPS) improves the system response to disturbances, despite the lack of mechanical inertia. However, a power sharing controller synchronizes its internal clock with the global positioning system (GPS) to improve its timing accuracy. This may expose the power sharing controller to a GPS spoofing attack that disrupts its operation. This paper proposes a power sharing strategy for an FIBPS that is resilient to GPS spoofing attacks. The impact of a GPS spoofing attack on the employed grid-supporting power sharing controller is studied. Then, a state observer is designed to estimate the voltage angle of the grid-supporting inverter. Finally, a control loop is designed to mitigate GPS spoofing attack by correcting the voltage angle according to its estimated value. The performance of the proposed method is evaluated using time-domain simulation case studies on the IEEE 9-bus benchmark system in PSCAD/EMTDC software.

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Human-as-advisor in the loop for autonomous lane-keeping

Human-as-advisor in the loop for autonomous lane-keeping

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American Control Conference (ACC)

作者： Rene Mai Sandipan Mishra Agung Julius Department of Mechanical Aerospace and Nuclear Engineering Department of Electrical Computer and Systems Engineering Rensselaer Polytechnic Institute Troy NY

This paper presents a human-as-advisor architecture for shared human-machine autonomy in dynamic systems. In the human-as-advisor architecture, the human provides suggested control actions to the autonomous system; the system uses a model of the human controller to ascertain the system’s state as perceived by the human. The system combines this information with additional sensor measurements, yielding an improved state estimate. We apply this architecture to the problem of lane-centering an autonomous vehicle in the presence of conflicting lane markings that render the true lane center uncertain. We model conflicting lane markings with a multi-component Gaussian mixture model. The human-suggested course of action is interpreted as an additional sensor measurement, which a Kalman filter is designed to combine with a speedometer and camera for improving the state estimate. With human input from our human-as-advisor architecture, the vehicle centers itself in the lane; without human input, the vehicle does not center itself. We also demonstrate the human-as-advisor architecture is robust to additive output matrix uncertainty and non-linear perturbations in the human model used to interpret the human-suggested control actions.

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Single-Stage 48V-to-1V Regulator with a Half-Turn Transformer and Current-Doubler Rectifier

Single-Stage 48V-to-1V Regulator with a Half-Turn Transforme...

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Annual IEEE Conference on Applied Power Electronics Conference and Exposition (APEC)

作者： Xinmiao Xu Qiang Li Department of Electrical and Computer Engineering Center for Power Electronics Systems Virginia Polytechnic Institute and State University Blacksburg VA United States

ISBN: (数字)9798331516116

ISBN: (纸本)9798331516123

Single-stage 48V-to-1V regulated solutions for high-performance processors become increasingly popular. This paper presents a full-bridge transformer-based buck converter with the current-doubler rectifier. The transformer adopts a half-turn winding structure for reduced resistance and leakage inductance. Operating principles of the converter with the half-turn transformer and the converter’s optimized printed circuit board layout are illustrated. Synchronous rectifiers are integrated in high-frequency loops and are treated carefully to reduce parasitic-inductor-induced loss. With the help of recent advances in gallium nitride (GaN) and silicon transistors, a 48V-to-1.8V GaN-based prototype switching at 700 kHz is constructed, which achieves 95.34% peak efficiency, 92.79% full-load efficiency (including gating loss) and 576 W/in 3 power density with 50-A current per phase. The same hardware achieves 94.00% peak efficiency and 89.75% full-load efficiency with 55-A current per phase in 48V-to-1.0V conversion. Experimental results with 54-V input are also included in this paper, demonstrating the great potential of the single-stage solution for future data centers.

关键词： Inductance Regulators Switching frequency Windings Layout Printed circuits Rectifiers Prototypes Transformers Gallium nitride

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