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MILCOM, Military Communications Conference

作者： Mostafa Ibrahim Sabit Ekin Ali Riza Ekti Department of Engineering Technology Texas A&M University Texas USA Departments of Engineering Technology and Electrical & Computer Engineering Texas A&M University Texas USA Oak Ridge National Laboratory Tennessee USA

Time-frequency warped waveform is a novel type of well-contained waveform composed of raised cosine (RC) pulses with roll-off factors profile customized for spectrally efficient signal containment. The waveform is suited for massive machine-type communication (mMTC), and Internet-of-Things (IoT) applications. The paper is a follow-up to the original work of time-frequency warped waveforms; here, we are proposing a warping function suited for length-independent symbols, which is missing from the previous work. The paper proposes the design steps starting from a piecewise warping function with parts of straight segments and other curved parts. A maximum Out-of-band leakage is used as a criterion to determine the warping function parameters. The roll-off factors profile determined in the previous study is used as a minimum bound for the leakage limits. Gains are presented over Windowed Zero-tail Discrete Fourier Transform-spread-Orthogonal Frequency Division Multiplexing (ZT-DFT-s-OFDM) in the simulations section.

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Digital Control and Phase Governing of Interleaved Multistage Hybrid Boost Capacitor Charger for Improved Efficiency and Power Density

Digital Control and Phase Governing of Interleaved Multistag...

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IEEE Workshop on computers in Power Electronics

作者： Daniel Beniaminson Bar Halivni Michael Evzelman Alon Kuperman Mor Mordechai Peretz Department of Electrical and Computer Engineering The Center for Power Electronics and Mixed-Signal IC Ben-Gurion University of the Negev P.O. Box 653 Beer-Sheva Israel Department of Electrical and Computer Engineering Applied Energy Laboratory Ben-Gurion University of the Negev P.O. Box 653 Beer-Sheva Israel

This paper introduces a digitally controlled multiphase multistage hybrid boost converter (MMHBC) for high voltage capacitor charging applications. Rapid capacitor charging control approach is presented, and topological variations to improve efficiency are examined. The controller provides tight output current regulation, while maintaining real-time current balancing capabilities for superior thermal distribution and converter efficiency. The MMHBC is capable of rapid capacitor bank charging to high voltage by forcing maximum output current at the first stage, which translates into maximal output power during the whole charging period. The controller includes a system governor unit which facilitates additional control aspects for multiphase operation such as phase shedding, soft-start mechanism, and system protection. SMPS operation, rapid capacitor charging, and topological improvements are validated experimentally on a four-phase 12V-to-300V multiphase prototype with a maximum output power of 250W and power density of 2.5kW/Liter.

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A Resilient Controller for Frequency Regulation of Power Grids against Cyber Attacks

A Resilient Controller for Frequency Regulation of Power Gri...

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Renewable Energy and Hydrogen Technologies (GlobConHT), IEEE IAS Global Conference on

作者： Shaohua Yang Keng-Weng Lao Hongxun Hui Yulin Chen Department of Electrical and Computer Engineering State Key Laboratory of Internet of Things for Smart City University of Macau Macao China Hainan Institute of Zhejiang University Sanya China

Maintaining system frequency at a rated value is a fundamental requirement to ensure the stability and security of power grids. However, the advanced frequency regulation process is more cyber-dependent, and raises a cyber-security issue for the power system. Traditional methods cannot avoid the adverse impacts of cyber attacks, which may lead to frequency deviations and, in severe cases, even blackouts. To address this issue, we propose an attack-resilient control algorithm for the smart grid's frequency regulation to defend the system frequency against cyber attacks. The stability of the system with the proposed controller can be guaranteed, which is strictly proved by Lyapunov theorem. Furthermore, the effectiveness of the proposed resilient controller is validated by case studies. The results show that in comparison with the existing methods, the proposed controller can make the system frequency achieve faster recovery with a minor frequency deviation. Specifically, using the proposed method, the maximum frequency deviation can be reduced from 0.17 Hz to approximately 0 Hz even under a cyber attack, which means that the adverse effects of a cyber attack can be almost eliminated. Therefore, the proposed controller can well counter potential cyber attacks, which helps improve the stability of the system frequency.

关键词： Renewable energy sources Hydrogen Power system stability Smart grids Security Frequency control Cyberattack

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Quadrotor Autonomous Landing on Moving Platform

Quadrotor Autonomous Landing on Moving Platform

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2022 International Symposium on Biomimetic Intelligence and robotics, ISBIR 2022

作者： Wang, Pengyu Wang, Chaoqun Wang, Jiankun Meng, Max Q.-H. Shenzhen Key Laboratory of Robotics Perception and Intelligence Shenzhen China Department of Electronic and Electrical Engineering Southern University of Science and Technology Shenzhen China School of Control Science and Engineering Shandong University Jinan China Jiaxing Research Institute Southern University of Science and Technology Jiaxing China Department of Electronic Engineering The Chinese University of Hong Kong Hong Kong Shenzhen Research Institute The Chinese University of Hong Kong Hong Kong

This paper introduces a quadrotor's autonomous take-off and landing system on a moving platform. The designed system addresses three challenging problems: fast pose estimation, restricted external localization, and effective obstacle avoidance. Specifically, first, we design a landing recognition and positioning system based on the AruCo marker to help the quadrotor quickly calculate the relative pose;second, we leverage a gradient-based local motion planner to generate collision-free reference trajectories rapidly for the quadrotor;third, we build an autonomous state machine that enables the quadrotor to complete its take-off, tracking and landing tasks in full autonomy;finally, we conduct experiments in simulated, real-world indoor and outdoor environments to verify the system's effectiveness and demonstrate its potential. © 2022 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0) Peer-review under responsibility of the scientific committee of the Proceedings of International Symposium on Biomimetic Intelligence and robotics.

关键词： Motion planning

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Improved Multistep Model Predictive Control for Six-Step Operation of Traction PMSM Drives

Improved Multistep Model Predictive Control for Six-Step Ope...

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Workshop on Predictive Control of electrical Drives and Power Electronics (PRECEDE)

作者： Zhenyao Sun Shuai Xu Guangtong Ma Juri Jatskevich State Key Laboratory of Rail Transit Vehicle System Southwest Jiaotong University Chengdu China Department of Electrical and Computer Engineering The University of British Columbia Vancouver Canada

Model predictive control (MPC) has recently been considered for many permanent magnet synchronous machine (PMSM) drive applications. However, the conventional MPC lacks accurate current tracking and fast dynamic responses in the overmodulation region and the six-step operation, which limits its application areas. This paper proposes a six-step operation scheme based on MPC for PMSM drives in traction applications. The current trajectory long-horizon prediction and the average current-based objective function are introduced to determine the optimal switching instants. To enhance the method’s precision, a trapezoidal integration rule has been used in the prediction step. The voltage angle control principle is introduced to clamp the voltage vectors. The effectiveness of the proposed method has been validated by detailed studies. The proposed method is demonstrated to have excellent current tracking accuracy and fast dynamic response in six-step operation, which is an advantage over existing methods.

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A Novel Microwave Sensor for Traditional Chinese Medicine Decoctions Detection

A Novel Microwave Sensor for Traditional Chinese Medicine De...

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IEEE International Workshop on Radio-Frequency Integration Technology (RFIT)

作者： Ming-Feng Xu Jing Wu Hua-Feng Li Jin-Peng He Zhong-Qi Zhang Min Chen Rui-Sen Chen Guan-Long Huang Zi-Yu Pang Kam-Weng Tam School of Electronics and Information Engineering Foshan University Foshan Guangdong China Guangdong Provincial Key Laboratory of Industrial Intelligent Inspection Technology Foshan University Foshan China Department of Electrical and Computer Engineering University of Macau Macau SAR China

ISBN: (数字)9798331541095

ISBN: (纸本)9798331541101

This paper demonstrates a high-sensitivity substrate-integrated waveguide (SIW) microwave sensor for traditional Chinese medicine decoctions detection. The designed microwave sensor is based on elliptical electric-LC(EELC) and interdigital capacitors (IDC). The regent under test (RUT) with different permittivities from 2 to 5 and dimensions of 9 mm $\times 11$ mm $\times 0.8$ mm is placed on the sensitive area of the sensor. According to the simulated result, the resonant frequency of the sensor shifts between 4.06 and 5.63 GHz with the variation of the RUT permittivity. The designed sensor can achieve sensitivity up to 8.28%, which shows a good feasibility in detecting liquid dielectric properties.

关键词： Radio frequency Sensitivity Dielectric constant Capacitors Resonant frequency Metals Microwave sensors Sensors Permittivity Substrates

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Autoregressive pairwise Graphical Models efficiently find ground state representations of stoquastic Hamiltonians

arXiv

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arXiv 2025年

作者： Pang, Yuchen Jayakumar, Abhijith McKinney, Evan Coffrin, Carleton Vuffray, Marc Lokhov, Andrey Y. University of Illinois at Urbana-Champaign Department of Computer Science Champaign IL United States Theoretical Division Los Alamos National Laboratory Los AlamosNM United States Department of Electrical and Computer Engineering University of Pittsburgh PittsburghPA United States Advanced Network Science Initiative Los Alamos National Laboratory Los AlamosNM United States

We introduce Autoregressive Graphical Models (AGMs) as an Ansatz for modeling the ground states of stoquastic Hamiltonians. Exact learning of these models for smaller systems show the dominance of the pairwise terms in the autoregressive decomposition, which informs our modeling choices when the Ansatz is used to find representations for ground states of larger systems. We find that simple AGMs with pairwise energy functions trained using first-order stochastic gradient methods often outperform more complex non-linear models trained using the more expensive stochastic reconfiguration method. We also test our models on Hamiltonians with frustration and observe that the simpler linear model used here shows faster convergence to the variational minimum in a resource-limited setting. © 2025, CC BY.

关键词： Hamiltonians

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Data Association for Maneuvering Targets Through a Combined Siamese Network and Xgboost Model

SSRN

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SSRN 2023年

作者： Gao, Chang Yan, Junkun Chen, Bo Varshney, Pramod K. Jia, Tianyi Liu, Hongwei National Laboratory of Radar Signal Processing Xidian University Xian710071 China Department of Electrical Engineering and Computer Science Syracuse University NY13244 United States

Data association plays an important role in forming target tracks when false alarms exist. Its accuracy is key to reducing the computational burden of the combinatorial explosion problem inherent to target tracking in environments where false alarms are densely distributed. Traditional methods are usually developed according to some assumed motion models and suffer performance degradation when there is mismatch between assumed models and the actual motion, which may often be the case for maneuvering targets. To cope with this problem, we propose a data-driven method that learns the association criteria directly from data. By use of the domain knowledge and a convolutional Siamese network, features of different attributes present in the observations are extracted. Then the features are fused through the use of XGBoost. Simulation results show that the proposed method performs better than the traditional model-based methods in correlating observations of maneuvering targets and avoiding correlations of false alarms. By visualizing the decision process of the feature fusion model, it is found that the proposed method learns from the data to make comprehensive use of multiple features and does not simply set hard thresholds for the features. The computational complexity is also analyzed both theoretically and experimentally. © 2023, The Authors. All rights reserved.

关键词： Target tracking

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Edge-InversionNet: Enabling Efficient Inference of InversionNet on Edge Devices

arXiv

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arXiv 2023年

作者： Wang, Zhepeng Putla, Isaacshubhanand Jiang, Weiwen Lin, Youzuo George Mason University Department of Electrical and Computer Engineering VA United States Los Alamos National Laboratory Energy and Natural Resources Security NM United States

Seismic full waveform inversion (FWI) is a widely used technique in geophysics for inferring subsurface structures from seismic data. And InversionNet is one of the most successful data-driven machine learning models that is applied to seismic FWI. However, the high computing costs to run InversionNet have made it challenging to be efficiently deployed on edge devices that are usually resource-constrained. Therefore, we propose to employ the structured pruning algorithm to get a lightweight version of InversionNet, which can make an efficient inference on edge devices. And we also made a prototype with Raspberry Pi to run the lightweight InversionNet. Experimental results show that the pruned InversionNet can achieve up to 98.2 % reduction in the computing resources with moderate model performance degradation. Copyright © 2023, The Authors. All rights reserved.

关键词： Inference engines

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Byzantine-Robust Distributed Online Learning: Taming Adversarial Participants in An Adversarial Environment

arXiv

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arXiv 2023年

作者： Dong, Xingrong Wu, Zhaoxian Ling, Qing Tian, Zhi The School of Computer Science and Engineering The Guangdong Provincial Key Laboratory of Computational Science Sun Yat-Sen University Guangdong Guangzhou510006 China The Department of Electrical and Computer Engineering George Mason University FairfaxVA22030 United States

This paper studies distributed online learning under Byzantine attacks. The performance of an online learning algorithm is often characterized by (adversarial) regret, which evaluates the quality of one-step-ahead decision-making when an environment incurs adversarial losses, and a sublinear regret bound is preferred. But we prove that, even with a class of state-of-the-art robust aggregation rules, in an adversarial environment and in the presence of Byzantine participants, distributed online gradient descent can only achieve a linear adversarial regret bound, which is tight. This is the inevitable consequence of Byzantine attacks, even though we can control the constant of the linear adversarial regret to a reasonable level. Interestingly, when the environment is not fully adversarial so that the losses of the honest participants are i.i.d. (independent and identically distributed), we show that sublinear stochastic regret, in contrast to the aforementioned adversarial regret, is possible. We develop Byzantine-robust distributed online momentum algorithms to attain such sublinear stochastic regret bounds for a class of robust aggregation rules. Numerical experiments corroborate our theoretical analysis. Copyright © 2023, The Authors. All rights reserved.

关键词： Decision making

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