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Target Localization for Distributed Hybrid Active-Passive Radars

IEEE Transactions on Aerospace and Electronic Systems 2025年

作者： Zhou, Qiyu Yuan, Ye Li, Hongbin Lai, Yangming Greco, Maria Sabrina Gini, Fulvio Yi, Wei University of Electronic Science and Technology of China School of Information and Communication Engineering Chengdu611731 China Stevens Institute of Technology Department of Electrical and Computer Engineering HobokenNJ07030 United States Fuzhou University School of Advanced Manufacturing Fuzhou350108 China University of Pisa Department of Information Engineering Pisa56122 Italy

Hybrid active-passive radar (HAPR) can effectively reduce the radiation power of active nodes, thus improving the radar system's electromagnetic environmental friendliness and anti-interception capability. Meanwhile, active nodes in HAPR can provide cooperative transmitters with known signals for passive nodes, overcoming performance losses caused by non-cooperative illuminators of opportunity (IOs). Although the advantages of HAPR have been gradually recognized in recent years, there is currently almost no systematic solution to the joint localization problem involving active and passive nodes. To address this problem, we propose two target localization algorithms for distributed HAPRs based on two-step and, respectively, direct localization. In the two-step localization, we establish two geometric equation sets with consistent structures to handle different geometric relationships among the target, transmitters/IOs, and receivers associated with the active and passive measurements. In direct localization, we reduce the high-dimensional joint likelihood function constructed from active and passive received signals into a low-dimensional one through two dimensionality reductions, significantly reducing the complexity. These algorithms combine active and passive received signals, enhancing localization performance. We also derive the Cramér-Rao lower bound (CRLB), which provides a useful performance indicator. The results indicate that the two-step localization algorithm yields suboptimal performance, with localization errors approaching the CRLB only under specific conditions. The direct localization algorithm approaches the CRLB and outperforms systems using only active or passive nodes, thereby confirming its effectiveness and robustness. © 1965-2011 IEEE.

关键词： Transmitters

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Core Loss Separation Method for High Q Inductors

Core Loss Separation Method for High Q Inductors

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Wireless Power Week (WPW)

作者： Aaron Wadsworth Wenting Zhang Matthew G.S. Pearce Duleepa J. Thrimawithana Lei Zhao Zongzhen Li Department of Electrical Computer and Software Engineering The University of Auckland Auckland New Zealand College of Automation Chongqing University Chongqing China Jiangsu JITRI Advanced Energy Materials Research Institute Changzhou China

ISBN: (数字)9798350349139

ISBN: (纸本)9798350349146

Magnetic components used in power electronics are typically the largest and least efficient part of the design. For designers of power magnetics it is necessary to have reliable measurements of loss so the appropriate core can be selected. However, this is a challenging task since small errors in phase measurements can cause large errors in core loss. This is particularly challenging for cores with a phase angle close to 90°, i.e. a high Q. In this work a loss separation method and new phase correction method are proposed. Experimental results show the proposed method has lower error than existing state of the art techniques.

关键词： Wireless communication Power measurement Phase measurement Magnetic cores Magnetic separation Core loss Measurement techniques

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Circuit Optimization Techniques for Efficient Ex-Situ Training of Robust Memristor Based Liquid State Machine 22

Circuit Optimization Techniques for Efficient Ex-Situ Traini...

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Proceedings of the 17th ACM International Symposium on Nanoscale Architectures

作者： Alex Henderson Chris Yakopcic Steven Harbour Tarek Taha Cory Merkel Hananel Hazan Dayton Engineering Advanced Projects Lab Southwest Research Institute Beavercreek OH USA Department of Electrical and Computer Engineering University of Dayton Dayton OH USA Department of Computer Engineering Rochester Institute of Technology Rochester NY USA Allen Discovery Center Tufts University Medford MA USA

ISBN: (纸本)9781450399388

Spiking neural network hardware offers a high performance, power-efficient and robust platform for the processing of complex data. Many of these systems require supervised learning, which poses a challenge when using gradient-based algorithms due to the discontinuous properties of SNNs. Memristor based hardware can offer gains in portability, power reduction, and throughput efficiency when compared to pure CMOS. This paper proposes a memristor-based spiking liquid state machine (LSM). The inherent dynamics of the LSM permit the use of supervised learning without backpropagation for weight updates. To carry out the design space evaluation of the LSM for optimal hardware performance, several temporal signal classification tasks are performed. It is found that the binary neuron activations in the output layer improve testing accuracy by 3.7% and 5% for classification, while reducing training time. A power and energy analysis of the proposed hardware is presented, resulting in an approximately 50% reduction in power consumption and cycle energy.

关键词： spiking neural network liquid state machine memristor

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A study on the Efficient Design of Adders Using Adiabatic Quantum-Flux-Parametron Circuits 11

A Study on the Efficient Design of Adders Using Adiabatic Qu...

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11th IEEE Global Conference on Consumer Electronics, GCCE 2022

作者： Yamauchi, Tomoharu San, Hao Yoshikawa, Nobuyuki Chen, Olivia Tokyo City University Graduate School of Integrative Science and Engineering Tokyo Japan Tokyo City University Department of Electrical Electronics and Communication Engineering Tokyo Japan Institute of Advanced Sciences Yokohama National University Kanagawa Japan Tokyo City University Department of Computer Science Tokyo Japan

ISBN: (纸本)9781665492324

Adiabatic quantum-flux-parametron (AQFP) logic is a promising technology for future energy-efficient, high-performance information processing systems. It has significantly low power consumption thanks to the adiabatic switching of Josephson junctions. In this paper, we proposed an AQFP-based Kogge-Stone adder (KSA) to achieve low energy consumption. Moreover, we proposed an offset buffer based XOR macro to improve the hardware overhead. The proposed work achieves a 21% and 6% reduction in total Josephson junction count and circuit area, respectively. © 2022 IEEE.

关键词： Timing circuits

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Position: Social Choice Should Guide AI Alignment in Dealing with Diverse Human Feedback 41

Position: Social Choice Should Guide AI Alignment in Dealing...

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41st International Conference on Machine Learning, ICML 2024

作者： Conitzer, Vincent Freedman, Rachel Heitzig, Jobst Holliday, Wesley H. Jacobs, Bob M. Lambert, Nathan Mossé, Milan Pacuit, Eric Russell, Stuart Schoelkopf, Hailey Tewolde, Emanuel Zwicker, William S. Foundations of Cooperative AI Lab Computer Science Department Carnegie Mellon University Pittsburgh United States Institute for Ethics in AI University of Oxford Oxford United Kingdom Center for Human-Compatible AI Department of Electrical Engineering and Computer Sciences University of California Berkeley United States Potsdam Institute for Climate Impact Research Brandenburg Potsdam Germany Department of Philosophy University of California Berkeley United States Department of Philosophy and Moral Sciences Ghent University Ghent Belgium Allen Institute for AI BerkeleyCA United States Department of Philosophy University of Maryland College Park United States EleutherAI United States Department of Mathematics Union College Schenectady United States Murat Sertel Center for Advanced Economic Studies Istanbul Bilgi University Istanbul Turkey

Foundation models such as GPT-4 are fine-tuned to avoid unsafe or otherwise problematic behavior, such as helping to commit crimes or producing racist text. One approach to fine-tuning, called reinforcement learning from human feedback, learns from humans' expressed preferences over multiple outputs. Another approach is constitutional AI, in which the input from humans is a list of high-level principles. But how do we deal with potentially diverging input from humans? How can we aggregate the input into consistent data about "collective" preferences or otherwise use it to make collective choices about model behavior? In this paper, we argue that the field of social choice is well positioned to address these questions, and we discuss ways forward for this agenda, drawing on discussions in a recent workshop on Social Choice for AI Ethics and Safety held in Berkeley, CA, USA in December 2023. Copyright 2024 by the author(s)

关键词： Reinforcement learning

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Unlocking the Global Synergies in Low-Rank Adapters

arXiv

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arXiv 2024年

作者： Zhang, Zixi Zhang, Cheng Gao, Xitong Mullins, Robert D. Constantinides, George A. Zhao, Yiren Department of Computer Science University of Cambridge Cambridge United Kingdom Department of Electrical and Eletronic Engineering Imperial College London London United Kingdom Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Guangdong China

Low-rank Adaption (LoRA) has been the de-facto parameter-efficient fine-tuning technique for large language models. We present HeteroLoRA, a light-weight search algorithm that leverages zero-cost proxies to allocate the limited LoRA trainable parameters across the model for better fine-tuned performance. In addition to the allocation for the standard LoRA-adapted models, we also demonstrate the efficacy of HeteroLoRA by performing the allocation in a more challenging search space that includes LoRA modules and LoRA-adapted shortcut connections. Experiments show that HeteroLoRA enables improvements in model performance given the same parameter budge. For example, on MRPC, we see an improvement of 1.6% in accuracy with similar training parameter budget. We will open-source our algorithm once the paper is accepted. © 2024, CC BY.

关键词： Budget control

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An Artificial Intelligence Solution for Energy Management in Smart Grid

An Artificial Intelligence Solution for Energy Management in...

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2023 IEEE International Conference on Artificial Intelligence and Green Energy, ICAIGE 2023

作者： Naceur, Ferdaws Ben Toumi, Sana Salah, Chokri Ben Mahjoub, Mohamed Ali National School of Engineers of Sousse University of Sousse LATIS- Laboratory of Advanced Technology and Intelligent Systems Sousse4023 Tunisia Higher Institute of Computer Science and Communication Technologies of Hammam Sousse University of Sousse Sousse4011 Tunisia Department of Electrical Engineering Monastir Tunisia Brest29238 France Issat Sousse Sousse Tunisia

ISBN: (纸本)9798350325539

This paper deals with an intelligent decision-making strategy of energy management for a smart grid within an element storage in order to reduce the system cost and improve its reliability. Indeed, the integration of distributed energy storage devices allows the customers to store energy and reuse at a later time or delayed some appliances operation according to a time-varying price signal. The adopted strategy is based on an artificial intelligence control algorithm 'fuzzy logic'. The main objective is to determine the appropriate source (grid or battery) used for meeting the consumer. The decision solution shows the system reliability where the switches are synchronized. Switches draw electricity from the grid for both load and battery when price is low and in contrary when electricity price is high, we find a variation's price correspondance with the switch drawing electricity from the battery. The obtained results are verified carrying out Matlab simulation using real data such as the electricity price (Price (t)) and the battery state of charge (SOC (t)). © 2023 IEEE.

关键词： Decision making

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Cervical Cancer Classification Using Combined Machine Learning and Deep Learning Approach

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computers, Materials & Continua 2022年第9期72卷 5117-5134页

作者： Hiam Alquran Wan Azani Mustafa Isam Abu Qasmieh Yasmeen Mohd Yacob Mohammed Alsalatie Yazan Al-Issa Ali Mohammad Alqudah Department of Biomedical Engineering Jordan University of Science and TechnologyIrbid22110Jordan Department of Biomedical Systems and Informatics Engineering Yarmouk University 556Irbid21163Jordan Faculty of Electrical Engineering Technology Campus Pauh PutraUniversiti Malaysia Perlis02000ArauPerlisMalaysia Advanced Computing Centre of Excellence(CoE)Universiti Malaysia Perlis(UniMAP)02000ArauPerlisMalaysia The Institute of Biomedical Technology King Hussein Medical CenterRoyal Jordanian Medical ServiceAmman11855Jordan Department of Computer Engineering Yarmouk UniversityIrbid21163Jordan

Cervical cancer is screened by pap smear methodology for detection and classification *** smear images of the cervical region are employed to detect and classify the abnormality of cervical *** this paper,we proposed the first system that it ables to classify the pap smear images into a seven classes *** smear images are exploited to design a computer-aided diagnoses system to classify the abnormality in cervical images *** features that have been extracted using ResNet101 are employed to discriminate seven classes of images in Support Vector Machine(SVM)*** success of this proposed system in distinguishing between the levels of normal cases with 100%accuracy and 100%*** top of that,it can distinguish between normal and abnormal cases with an accuracy of 100%.The high level of abnormality is then studied and classified with a high *** the other hand,the low level of abnormality is studied separately and classified into two classes,mild and moderate dysplasia,with∼92%*** proposed system is a built-in cascading manner with five models of polynomial(SVM)*** overall accuracy in training for all cases is 100%,while the overall test for all seven classes is around 92%in the test phase and overall accuracy reaches 97.3%.The proposed system facilitates the process of detection and classification of cervical cells in pap smear images and leads to early diagnosis of cervical cancer,which may lead to an increase in the survival rate in women.

关键词： Classification deep learning machine learning pap smear images resnet101 support vector machines

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Nanocrystalline Core Loss in High Power IPT Systems for EV Charging Applications

Nanocrystalline Core Loss in High Power IPT Systems for EV C...

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Wireless Power Week (WPW)

作者： Wenting Zhang Seho Kim Duleepa J. Thrimawithana Grant A. Covic Lei Zhao Zongzhen Li Department of Electrical Computer and Software Engineering University of Auckland Auckland New Zealand School of Automation Chongqing University Chongqing China Jiangsu JITRI Advanced Energy Materials Research Institute Changzhou China

ISBN: (数字)9798350349139

ISBN: (纸本)9798350349146

Mn-Zn ferrite is commonly used in inductive power transfer (IPT) systems to enhance magnetic coupling and reduce flux leakage. However, ferrite is not ideal for high power transfer due to the large volume of material needed for its low saturation flux density, brittleness, and high core loss leading to thermal issues. Nanocrystalline alloys are potential alternatives to replace ferrite in IPT pads to improve magnetic performance. This paper analyses the core loss and equivalent series resistance (ESR) of ferrite and nanocrystalline cores in high-power IPT pads based on the measurement of magnetic properties using the partial cancellation method. The experimental findings show that the ESR of IPT pads using nanocrystalline cores decreases with higher excitation current in the pads, contrary to the typical behaviour of IPT pads using ferrite.

关键词： Ferrites Wireless communication Magnetic flux leakage Magnetic cores Current measurement Core loss Metals

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Agent-based Modeling and Simulation for the Electricity Market with Residential Demand Response

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CSEE Journal of Power and Energy Systems 2021年第2期7卷 368-380页

作者： Shuyang Xu Xingying Chen Jun Xie Saifur Rahman Jixiang Wang Hongxun Hui Tao Chen College of Energy and Electrical Engineering Hohai UniversityNanjing 211100China Department of Electrical and Computer Engineering Viginia TechAlington VA 22203 USAand also with the Advanced Research InstituteViginia TechArlingtonVA 22203 USA State Key Laboratory of Internet of Things for Smart City University of MacaoMacao 519031China Collegue of Electrical Engineering Southeast UniversityNanjing 210009China.DOI:10.17775/CSEEJPES.2019.01750

Currently,critical peak load caused by residential customers has attracted utility companies and policymakers to pay more attention to residential demand response(RDR)*** typical RDR programs,residential customers react to the price or incentive-based signals,but the actions can fall behind flexible market *** those residential customers equipped with smart meters,they may contribute more DR loads if they can participate in DR events in a proactive *** this paper,we propose a comprehensive market framework in which residential customers can provide proactive RDR actions in a day-ahead market(DAM).We model and evaluate the interactions between generation companies(GenCos),retailers,residential customers,and the independent system operator(ISO)via an agent-based modeling and simulation(ABMS)*** simulation framework contains two main procedures—the bottom-up modeling procedure and the reinforcement learning(RL)*** bottom-up modeling procedure models the residential load profiles separately by household types to capture the RDR potential differences in advance so that residential customers may rationally provide automatic DR *** and GenCos optimize their bidding strategies via the RL *** modified optimization approach in this procedure can prevent the training results from falling into local optimum *** ISO clears the DAM to maximize social welfare via Karush-Kuhn-Tucker(KKT)*** on realistic residential data in China,the proposed models and methods are verified and compared in a large multi-scenario test case with 30,000 residential *** show that proactive RDR programs and interactions between market entities may yield significant benefits for both the supply and demand *** models and methods in this paper may be used by utility companies,electricity retailers,market operators,and policy makers to evaluate the consequences of a proactive RDR and the interactions

关键词： Agent-based modeling and simulation(ABMS) electricity market residential demand response(RDR) reinforcement learning(RL)

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