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engineering synthetic gauge fields through the coupling phases in cavity magnonics

Physical Review Applied 2024年第6期21卷 064033-064033页

作者： Alan Gardin Guillaume Bourcin Jeremy Bourhill Vincent Vlaminck Christian Person Christophe Fumeaux Giuseppe C. Tettamanzi Vincent Castel School of Physics Lab-STICC UMR CNRS 6285 ARC Centre of Excellence for Engineered Quantum Systems and ARC Centre of Excellence for Dark Matter Particle Physics Department of Physics School of Electrical Engineering and Computer Science School of Chemical Engineering and Advanced Materials

Cavity magnonics, which studies the interaction of light with magnetic systems in a cavity, is a promising platform for quantum transducers and quantum memories. At microwave frequencies, the coupling between a cavity photon and a magnon, the quasiparticle of a spin-wave excitation, is a consequence of the Zeeman interaction between the cavity’s magnetic field and the magnet’s macroscopic spin. For each photon-magnon interaction, a coupling phase factor exists, and this is often neglected in simple systems; however, in “loop-coupled” systems, where there are at least as many couplings as modes, the coupling phases become relevant for the physics and lead to synthetic gauge fields. We present experimental evidence of the existence of such coupling phases by considering two spheres made of yttrium-iron-garnet and two different re-entrant cavities. We predict numerically the values of the coupling phases, and we find good agreement between the theory and the experimental data. These results show that in cavity magnonics, one can engineer synthetic gauge fields, which can be useful for cavity-mediated coupling and engineering dark-mode physics.

关键词： Magnetic coupling Magnons Optical & microwave phenomena Ferrimagnets Ferromagnetic resonance Gauge symmetries Holstein-Primakoff method Microwave techniques

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Liver Tumor Decision Support System on Human Magnetic Resonance Images:A Comparative Study

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computer systems Science & engineering 2023年第8期46卷 1653-1671页

作者： Hiam Alquran Yazan Al-Issa Mohammed Alslatie Isam Abu-Qasmieh Amin Alqudah Wan Azani Mustafa Yasmin Mohd Yacob Department of Biomedical Systems and Informatics Engineering Yarmouk University 556Irbid21163Jordan Department of Biomedical Engineering Jordan University of Science and TechnologyIrbid22110Jordan Department of Computer Engineering Yarmouk University 556Irbid21163Jordan The of Biomedical Technology King Hussein Medical CenterRoyal Jordanian Medical ServiceAmman11855Jordan Faculty of Electrical Engineering&Technology Campus Pauh PutraUniversiti Malaysia PerlisArau02600PerlisMalaysia Faculty of Electronic Engineering&Technology Campus Pauh PutraUniversiti Malaysia PerlisArau02600PerlisMalaysia Advanced Computing Centre of Excellence(CoE)Universiti Malaysia PerlisArau02600PerlisMalaysia

Liver cancer is the second leading cause of cancer death *** tumor detection may help identify suitable treatment and increase the survival *** imaging is a non-invasive tool that can help uncover abnormalities in human *** Resonance Imaging(MRI),in particular,uses magnetic fields and radio waves to differentiate internal human organs ***,the interpretation of medical images requires the subjective expertise of a radiologist and ***,building an automated diagnosis computer-based system can help specialists reduce incorrect *** paper proposes a hybrid automated system to compare the performance of 3D features and 2D features in classifying magnetic resonance liver tumor *** paper proposed two models;the first one employed the 3D features while the second exploited the 2D *** first system uses 3D texture attributes,3D shape features,and 3D graphical deep descriptors beside an ensemble classifier to differentiate between four 3D tumor *** top of that,the proposed method is applied to 2D slices for comparison *** proposed approach attained 100%accuracy in discriminating between all types of tumors,100%Area Under the Curve(AUC),100%sensitivity,and 100%specificity and precision as well in 3D liver *** the other hand,the performance is lower in 2D *** maximum accuracy reached 96.4%for two classes and 92.1%for four *** top-class performance of the proposed system can be attributed to the exploitation of various types of feature selection methods besides utilizing the ReliefF features selection technique to choose the most relevant features associated with different *** novelty of this work appeared in building a highly accurate system under specific circumstances without any processing for the images and human input,besides comparing the performance between 2D and 3D *** the future,the presented work can be extended to be

关键词： Liver tumors ensemble classifier 3D shape features 3D cooccurrence matrix ResNet101

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Enhancing Human-Robot Cohesion through HAT Methods: A Crowd-Avoidance Model for Safety Aware Navigation

Enhancing Human-Robot Cohesion through HAT Methods: A Crowd-...

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IEEE International Conference on Human-Machine systems

作者： Timothy Sellers Tingjun Lei Chaomin Luo Lantao Liu Daniel W. Carruth Department of Electrical and Computer Engineering Mississippi State University Mississippi State MS USA Department of Intelligent Systems Engineering Indiana University Bloomington IN USA Center for Advanced Vehicular Systems Mississippi State University MS USA

ISBN: (数字)9798350315790

ISBN: (纸本)9798350315806

In the world of autonomous robots, achieving complete accuracy remains a challenge, underscoring the importance of human intervention, especially regarding safety. In Human-Autonomy Teaming (HAT), ensuring safe and effective human-robot cooperation in dynamic indoor settings is crucial. This paper introduces a framework designed to address this accuracy shortfall, enhancing safety and robotic interactions in these environments. Central to our approach is a hybrid graph system that melds the Generalized Voronoi Diagram (GVD) with spatio-temporal graphs, harmonizing human feedback, environmental elements, and key waypoints. An essential component is the improved Node Selection Algorithm (iNSA), which utilizes the revised Grey Wolf Optimization (rGWO) for enhanced adaptability and performance. Human insights are instrumental, from supplying initial environmental data and determining key waypoints to stepping in during unexpected challenges or dynamic changes in the environment. Extensive simulation and comparison tests confirm the reliability and effectiveness of our model, highlighting its unique advantages in the realm of HAT.

关键词： Adaptation models Accuracy Navigation Robot kinematics Heuristic algorithms Instruments Path planning

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Skeleton-based Action Recognition Using Two-stream Graph Convolutional Network with Pose Refinement 41

Skeleton-based Action Recognition Using Two-stream Graph Con...

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第41届中国控制会议

作者： Biao Zheng Luefeng Chen Min Wu Witold Pedrycz Kaoru Hirota School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Department of Electrical and Computer Engineering University of Alberta Tokyo Institute of Technology

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

With the development of science and technology,graph convolutional network has made great progress in improving the accuracy of action ***,there still exists some deficiencies in current ***,the human skeleton point coordinates entering into the network are barely refined,which may cause large ***,the second-order information(the length and direction of bones),which can reflect action characteristics discriminatively,is rarely *** solve the above issues,a two stream graph convolutional network with pose refinement for skeleton based action recognition is ***,we use an adaptive block to to help improve the *** test our method on Kinetics dataset and the experiment show it can get better results than some recent methods,which plays a positive role in future research.

关键词： Graph convolutional network Pose refinement Skeleton based action recognition Adaptive block

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Distributed Model-Predictive Energy Management Strategy for Shipboard Power systems Considering Battery Degradation

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IFAC-PapersOnLine 2024年第28期58卷 360-365页

作者： Satish Vedula Seyyed Shaho Alaviani Olugbenga Moses Anubi Department of Electrical and Computer Engineering FAMU-FSU College of Engineering Tallahassee FL 32310 USA Center for Advanced Power Systems (CAPS) Florida State University and National High Magnetic Field Laboratory (MagLab) Tallahassee FL 32310 USA

With the integration of loads such as pulse power loads, a new control challenge is presented in meeting their high ramp rate requirements. Existing onboard generators are ramp rate limited. The inability to meet the load power due to ramp rate limitation may lead to instability. The addition of energy storage elements in addition to the existing generators proves a viable solution in addressing the control challenges presented by high ramp rate loads. A distributed energy management strategy maximizing generator efficiency and minimizing energy storage degradation is developed that facilitates an optimal adaptive power split between generators and energy storage elements. The complex structure of the energy storage degradation model makes it tough for its direct integration into the optimization problem and is not practical for real-time implementation. A degradation heuristic to minimize absolute power extracted from the energy storage elements is proposed as a degradation heuristic measure. The designed strategy is tested through a numerical case study of a consolidated shipboard power system model consisting of a single generator, energy storage element, and load model. The results show the impact of the designed energy management strategy in effectively managing energy storage health.

关键词： Battery degradation model predictive control energy management

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Adaptive Deep Reinforcement Learning-based Resource Management for Complex Decision Making in Industry Internet of Things Applications

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Procedia computer Science 2025年 252卷 231-240页

作者： Niharika Karne Chandra Shekar Ramagundam Ranga Rao Patnala Shashishekhar Ramagundam Sai Nitisha Tadiboina Sr. Principal Systems Engineer in C&RS Platform services DT Amtrak USA University of south alabama Department of electrical engineering Masters in electrical engineering Senior Software Engineer in Cloud platform services City Of Philadelphia Pennsylvania USA Vice president Neuberger Berman US Software Development & Engineering in Content Discovery & AI Comcast USA Troy University Montgomery Campus AL. Department of computer Science Masters In Computer Science Department of Information Technology GEICO - Chevy Chase Chevy Chase MD 20815 United States

An independent industrial system may transform into a connected network through the assistance of Industrial Internet of Things (IIoT). The deployed sensors in the IIoT maintain surveillance of the industrial machinery and equipment. As a result, safety and reliability emerge as the primary concerns in IIoT. This presents a variety of well-known and increasing issues related to the industrial system. The IIoT devices are exposed to a wide range of malware, threats, and assaults. To prevent the IIoT devices from malware effects, effective protection plans must be implemented. But adequate security mechanisms are not be incorporated in IIoT devices with limited resources. It is essential to ensure the accuracy and dependability of information gathered by IIoT devices. Decisions taken with incomplete or inaccurate data might be devastating. To overcome these difficulties deep learning with reinforcement learning for complex decision-making in industry applications is developed in this research work. In this developed model, an Adaptive Deep Reinforcement learning (ADRL)-based resource management is performed to reduce the operation cost associated with IIoT deployments. Energy efficiency is essential in IIoT ecosystem, particularly for the devices that run on batteries. Through dynamic resource allocation based on workload needs and energy limits, ADRL-based resource management optimizes the usage of energy. The reliability of the designed model is enhanced by fine-tuning the parameters from DRL using the Ship Rescue Optimization (SRO) algorithm. Thus, ADRL-based resource management systems make real-time decisions based on current environmental conditions and system requirements. This helps the IIoT systems to react quickly to change demands and optimize resource allocation. Finally, the experimental analysis is performed to find the success rate of the developed resource management system via various metrics. Throughout the validation, the statistical analysis of the

关键词： Resource Management Industrial Internet of Things Applications Complex Decision Making Adaptive Deep Reinforcement Learning Ship Rescue Optimization

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Human-Autonomy Teaming-Based Robot Informative Path Planning and Mapping Algorithms with Tree Search Mechanism 3

Human-Autonomy Teaming-Based Robot Informative Path Planning...

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3rd IEEE International Conference on Human-Machine systems, ICHMS 2022

作者： Lei, Tingjun Chintam, Pradeep Carruth, Daniel W. Jan, Gene Eu Luo, Chaomin Mississippi State University Mississippi State Department of Electrical and Computer Engineering MS39762 United States Mississippi State University Center for Advanced Vehicular Systems MS39759 United States National Taipei University Tainan National University of the Arts Department of Electrical Engineering Taiwan

ISBN: (数字)9781665452380

ISBN: (纸本)9781665452380

Human-autonomy teaming (HAT)-based informative path planning (IPP) approaches are proposed to enable an autonomous robot to explore hazardous environments more efficiently in this paper. The proposed informative path planning approaches based on multi-objective optimization enable the robot to explore the environments, while also visiting several high-interest sites more frequently. Furthermore, the robot can be tailored to human needs;for instance, it reaches the human-set destination point or the direction indicated. One proposed HAT-based IPP method utilizes Rapid-exploration Random Tree (RRT*) to select the most informative tree nodes while traveling to human-set target points within global path planning. The other utilizes the direction set by humans, converts it into direction information. Thus, HAT is integrated in the information map for multi-target Pareto Monte Carlo Tree Search (MCTS) in local path planning. Simulation and comparison studies validate the effectiveness and robustness of the proposed approaches. The results demonstrate that our proposed approaches significantly improve the object search time and trajectory length. © 2022 IEEE.

关键词： Motion planning

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Condition Monitoring with Incomplete Data: An Integrated Variational Autoencoder and Distance Metric Framework

Condition Monitoring with Incomplete Data: An Integrated Var...

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IEEE International Conference on Automation Science and engineering (CASE)

作者： Maryam Ahang Mostafa Abbasi Todd Charter Homayoun Najjaran Department of Electrical and Computer Engineering University of Victoria Victoria Canada Advanced Control and Intelligent Systems Laboratory (ACIS) University of Victoria Victoria Canada Department of Mechanical Engineering University of Victoria Victoria Canada

ISBN: (数字)9798350358513

ISBN: (纸本)9798350358520

Condition monitoring of industrial systems is crucial for ensuring safety and maintenance planning, yet notable challenges arise in real-world settings due to the limited or non-existent availability of fault samples. This paper introduces an innovative solution to this problem by proposing a new method for fault detection and condition monitoring for unseen data. Adopting an approach inspired by zero-shot learning, our method can identify faults and assign a relative health index to various operational conditions. Typically, we have plenty of data on normal operations, some data on compromised conditions, and very few (if any) samples of severe faults. We use a variational autoencoder to capture the probabilistic distribution of previously seen and new unseen conditions. The health status is determined by comparing each sample’s deviation from a normal operation reference distribution in the latent space. Faults are detected by establishing a threshold for the health indexes, allowing the model to identify severe, unseen faults with high accuracy, even amidst noise. We validate our approach using the run-to-failure IMS-bearing dataset and compare it with other methods. The health indexes generated by our model closely match the established descriptive model of bearing wear, attesting to the robustness and reliability of our method. These findings highlight the potential of our methodology in augmenting fault detection capabilities within industrial domains, thereby contributing to heightened safety protocols and optimized maintenance practices.

关键词： Condition monitoring Fault diagnosis Protocols Fault detection Zero shot learning Probabilistic logic Robustness Safety Maintenance Indexes

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Estimating Remaining Useful Life of Cutting Tools in Machining Using an Extended Kalman Filter

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IFAC-PapersOnLine 2024年第19期58卷 1264-1269页

作者： Qian Yang Debasish Mishra Krishna R. Pattipati George M. Bollas Department of Electrical and Computer Engineering University of Connecticut Storrs CT 06269 USA Data Science & Information Systems Krea University 5655 Central Expressway Sri City 517646 India Pratt & Whitney Institute of Advanced Systems Engineering University of Connecticut Storrs CT 06269 USA

Monitoring the condition of the cutting tool and forecasting the evolution of its wear during machining are vital to ensure workpiece quality and the safety of machine elements. The accurate prediction of tool wear is crucial to advance predictive maintenance in machining. This article presents a methodology to estimate the remaining useful life (RUL) of cutting tools. The methodology involves extracting features from vibration signals using discrete wavelet transform (DWT) and computing the concomitant indicators of tool health by applying distance metrics to the features. These indicators have a high correlation with tool wear measurements. The RUL is estimated by fusing these indicators in a support vector regression (SVR) algorithm. The SVR outputs then serve as inputs to an extended Kalman filter (EKF) that employs a rectified linear activation unit (ReLU) function for state evolution, enabling real-time estimation of the RUL. The proposed methodology is demonstrated on the IEEE PHM 2010 dataset, showcasing its reliability and effectiveness in accurately estimating the RUL for cutting tools.

关键词： Remaining useful life (RUL) Health indicator Distance metrics Support vector regression Extended Kalman filter (EKF) Cutting tool Machining

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Hierarchical Codebook Design Using Scale-Changeable Reconfigurable Holographic Surfaces in Near-Far Field Communications

Hierarchical Codebook Design Using Scale-Changeable Reconfig...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Shupei Zhang Boya Di Hongliang Zhang H. Vincent Poor State Key Laboratory of Advanced Optical Communication Systems and Networks Peking University Beijing China Department of Electrical and Computer Engineering Princeton University USA

ISBN: (数字)9798350351255

ISBN: (纸本)9798350351262

Reconfigurable holographic surfaces (RHSs) have been proposed as a cost-effective and power-efficient solution for extremely large-scale arrays, where the amplitude of electromagnetic waves radiated at each element is controlled to achieve high directive gain. However, the complexity of acquiring real-time channel state information (CSI) required for beamforming is prohibitively high, especially when the near-field expansion brought by the large-scale RHS is considered. In this paper, we propose a codebook-based beam training scheme for a large-scale RHS-enabled communication system to bypass CSI estimation. Unlike traditional phase-controlled arrays, the amplitude-controlled property of the RHS implies that each RHS element can be selectively activated. This motivates an array reconfiguration method where a scale-changeable RHS array is constructed to generate gain-flat beams with different coverage in the angle-range domain. A hierarchical RHS codebook is then proposed where the coverage of the codewords in each layer is progressively refined. To address the substantial beam search overhead in the near-far field, a two-stage beam training scheme is performed in the proposed codebook, thereby reducing the overhead to a logarithmic level of the element number. The simulation results show that the proposed scheme performs better than phased arrays given the same input power in terms of sum rate, and it also approaches the upper bound achieved by the exhaustive search at a significantly reduced overhead.

关键词： Training Phased arrays Upper bound Surface waves Simulation Electromagnetic scattering Estimation Real-time systems Complexity theory Global communication

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