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IEEE International Symposium on Electromagnetic Compatibility (EMC)

作者： Aaron Harmon Wei Zhang Victor Khilkevich Dept. of Electrical and Computer Engineering – Electromagnetic Compatibility Laboratory Missouri University of Science and Technology Rolla MO USA

In this work, the permittivity of a 3D-printed carbon fiber-loaded anisotropic material, XT-CF20, is examined further. The microstructure of XT-CF20 is first examined via optical imaging and is shown to be composed of inclusions that are aligned with the print direction of the sample. The permittivity tensor for the aligned microstructure is then measured using a capacitive measurement technique and simulations are provided to demonstrate the validity of this measurement method. The simulated permittivity values for XT-CF20 samples with varying infill structures are then presented and compared to the measured permittivity values of said samples. An error of less than 12% between the simulated and measured permittivity values was observed validating the measured permittivity tensor and claims about the cause of the anisotropy presented in this work. The pronounced effect of a sample’s infill on the permittivity tensor of the sample is then discussed along with the conclusions of this work and possible future topics of work for the authors.

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Unique MS Lesion Identification from MRI

arXiv

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arXiv 2024年

作者： Rivas, Carlos A. Zhang, Jinwei Wei, Shuwen Remedios, Samuel W. Carass, Aaron Prince, Jerry L. Image Analysis and Communications Laboratory Dept. of Electrical and Computer Engineering Johns Hopkins University BaltimoreMD21218 United States Dept. of Computer Science Johns Hopkins University BaltimoreMD21218 United States

Unique identification of multiple sclerosis (MS) white matter lesions (WMLs) is important to help characterize MS progression. WMLs are routinely identified from magnetic resonance images (MRIs) but the resultant total lesion load does not correlate well with EDSS∗;whereas mean unique lesion volume has been shown to correlate with EDSS. Our approach builds on prior work by incorporating Hessian matrix computation from lesion probability maps before using the random walker algorithm to estimate the volume of each unique lesion. Synthetic images demonstrate our ability to accurately count the number of lesions present. The takeaways, are: 1) that our method correctly identifies all lesions including many that are missed by previous methods;2) we can better separate confluent lesions;and 3) we can accurately capture the total volume of WMLs in a given probability map. This work will allow new more meaningful statistics to be computed from WMLs in brain MRIs. © 2024, CC BY.

关键词： Magnetic resonance imaging

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FASTER AND ACCURATE NEURAL NETWORKS WITH SEMANTIC INFERENCE

arXiv

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arXiv 2023年

作者： Sayyed, Sazzad Ashdown, Jonathan Restuccia, Francesco Dept. of Electrical and Computer Engineering Northeastern University United States Air Force Research Laboratory United States

Deep neural networks (DNNs) usually come with a significant computational and data labeling burden. While approaches such as structured pruning and mobile-specific DNNs have been proposed, they incur in drastic accuracy loss. Conversely from prior work, in this paper we leverage the intrinsic redundancy in latent representations to drastically reduce the computational load with very limited loss in performance. Specifically, we show that semantically similar inputs share a significant number of filter activations, especially in the earlier layers. As such, semantically similar classes can be "clustered" so as to create cluster-specific subgraphs. These may be "turned on" when an input belonging to a semantic cluster is being presented to the DNN, while the rest of the DNN can be "turned off". To this end, we propose a new framework called Semantic Inference (SINF). In short, SINF (i) identifies the semantic cluster the object belongs to using a small additional classifier;and then (ii) executes the subgraph extracted from the base DNN related to that semantic cluster to perform the inference. To extract each cluster-specific subgraph, we propose a new approach named Discriminative Capability Score (DCS) that effectively finds the subgraph with the capability to discriminate among the members of a specific semantic cluster. Importantly, DCS is independent from SINF, as it is a general-purpose quantity that can be applied to any DNN. We benchmark the performance of DCS on the VGG16, VGG19, and ResNet50 DNNs trained on the CIFAR100 dataset against 6 state-of-the-art pruning approaches. Our results show that (i) SINF reduces the inference time of VGG19, VGG16, and ResNet50 respectively by up to 35%, 29% and 15% with only 0.17%, 3.75%, and 6.75% accuracy loss;(ii) DCS achieves respectively up to 3.65%, 4.25%, and 2.36% better accuracy with VGG16, VGG19, and ResNet50 with respect to existing discriminative scores;(iii) when used as a pruning criterion, DCS achieves up to

关键词： Deep neural networks

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Design and Construction of an Arbitrary Pulse Compressive Amplifier

Design and Construction of an Arbitrary Pulse Compressive Am...

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IEEE International Symposium on Electromagnetic Compatibility (EMC)

作者： Cody Goins Aaron Harmon Victor Khilkevich Daryl Beetner Dept. of Electrical and Computer Engineering - Electromagnetic Compatibility Laboratory Missouri University of Science and Technology Rolla MO USA

Compressive pulse amplifiers are a class of amplifiers that convert long low amplitude signals into very broadband pulses of high amplitude, yielding a very high instantaneous peak power output pulse. However, in the realm of electronic immunity and susceptibility testing, very broadband short pulses are not always desired. This work presents a design for a compressive amplifier that is aimed at creating arbitrary pulsed signals of varying bandwidths. Limitations of the achievable gain and methods used are discussed.

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Stability of Grid-Forming Inverter on Tomia Island Microgrid

Stability of Grid-Forming Inverter on Tomia Island Microgrid

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IEEE Industry Applications Society Annual Meeting (IAS)

作者： Yunita Muharram Jinho Kim Eduard Muljadi R. M. Nelms Dept. of Electrical and Computer Engineering Auburn University AL USA Pacific Northwest National Laboratory Power System Research Engineer Richland WA

ISBN: (数字)9798350372717

ISBN: (纸本)9798350372724

Microgrids serve as a viable option to supply energy to inhabitants residing in Indonesia's isolated regions, including rural areas and small islands that are distant from the primary power grid. Consequently, the cost of building a transmission line in this region is exceedingly high. Tomia Island, located in the eastern part of Indonesia, relies on a microgrid with four photovoltaic power plants and a diesel generator to supply its electrical energy. This research examines the stability of a grid-forming inverter hybrid microgrid with solar and wind power plants in the Tomia Island microgrid as a means of reducing the amount of fossil fuels used by diesel generators. The microgrid experienced two disruptions: three-phase faults and a loss of PV generation. The stability of the microgrid was confirmed through PSCAD simulation. It was demonstrated that the use of a GFM inverter ensures stability in the microgrid, as evidenced by the voltage and frequency response of the system during various disturbances, including the loss of a major-rated power PV power plant and three-phase faults at the PV power plant located farthest from the system.

关键词： Microgrids Power system stability Inverters Stability analysis Generators Wind turbines PSCAD Power generation Thermal stability Grid forming

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Towards Robust Data-Driven Underwater Acoustic Localization: A Deep CNN Solution with Performance Guarantees for Model Mismatch

Towards Robust Data-Driven Underwater Acoustic Localization:...

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

作者： Amir Weiss Andrew C. Singer Gregory W. Wornell Research Laboratory of Electronics Massachusetts Institute of Technology Dept. of Electrical and Computer Engineering University of Illinois Urbana-Champaign

Key challenges in developing underwater acoustic localization methods are related to the combined effects of high reverberation in intricate environments. To address such challenges, recent studies have shown that with a properly designed architecture, neural networks can lead to unprecedented localization capabilities and enhanced accuracy. However, the robustness of such methods to environmental mismatch is typically hard to characterize, and is usually assessed only empirically. In this work, we consider the recently proposed data-driven method [18] based on a deep convolutional neural network, and demonstrate that it can learn to localize in complex and mismatched environments. To explain this robustness, we provide an upper bound on the localization mean squared error (MSE) in the "true" environment, in terms of the MSE in a "presumed" environment and an additional penalty term related to the environmental discrepancy. Our theoretical results are corroborated via simulation results in a rich, highly reverberant, and mismatch channel.

关键词： Location awareness Upper bound Simulation Neural networks Signal processing Robustness Convolutional neural networks

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How We Lost the Internet

arXiv

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arXiv 2024年

作者： Beck, Micah D. Moore, Terry R. Dept. of Electrical Engineering and Computer Science University of Tennessee Knoxville United States Innovative Computing Laboratory University of Tennessee Knoxville United States

In this paper we reexamine an assumption that underpinned the development of the Internet architecture, namely that a stateless and loosely synchronous point-to-point datagram delivery service would be sufficient to meet the needs of all network applications, including those which deliver content and services to a mass audience at global scale. Such applications are inherently asynchronous and point-to-multipoint in nature. We explain how the inability of distributed systems based on this stateless datagram service to provide adequate and affordable support for them within the public (I.e., universally shared and available) network led to the development of private overlay infrastructures, specifically Content Delivery Networks and distributed Cloud data centers. We argue that the burdens imposed by reliance on these private overlays may have been an obstacle to achieving the Open Data Networking goals of early Internet advocates. The contradiction between those initial goals and the exploitative commercial imperatives of hypergiant overlay operators is offered as a possibly important reason for the negative impact of their most profitable applications (e.g., social media) and monetization strategies (e.g., targeted advertisement). We propose that one important step in resolving this contradiction may be to reconsider the adequacy Internet’s stateless datagram service model. © 2024, CC BY.

关键词： Distributed cloud

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On-Board AC Charging Topology Integrated with Electric Vehicle Motor Drive System

On-Board AC Charging Topology Integrated with Electric Vehic...

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

作者： Md Khalid Mahmud Bin Azam Aquib Ahmed Mohammad Muntasir Islam Afsana Hossain Dristy Ashraf Siddiquee Yilmaz Sozer Mithat John Kisacikoglu Dept. of Electrical and Computer Engineering The University of Akron Akron OH Center for Integrated Mobility Sciences National Renewable Energy Laboratory Golden CO

On-board AC charging is a convenient and widely adopted method for recharging electric vehicles (EVs) directly from standard alternating current (AC) power sources. This paper presents a novel topology for AC charging of EVs that utilizes EV 3-phase electric machine windings as the input inductors, thus eliminating the requirement for bulky grid interfacing inductors and resulting in a compact and cost-effective integrated motor drive and charger system. The proposed approach leverages the motor windings and parallel operating half-bridge inverter during the charging process by interconnecting the inverter phases with the motor windings in a mechanically interleaved and electrically paralleled manner. The implementation of this unique and innovative idea, achieved through precise control and arrangement of the motor winding as a series inductor, successfully eliminates the possibility of unintended motion of the electric machine during the charging process.

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Design Automation for Cyber-Physical Production Systems: Lessons Learned from the DeFacto Project

Design Automation for Cyber-Physical Production Systems: Les...

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Design, Automation and Test in Europe Conference and Exhibition

作者： Michele Lora Sebastiano Gaiardelli Chanwook Oh Stefano Spellini Pierluigi Nuzzo Franco Fummi Dept. of Engineering for Innovation Medicine Industrial Computer Engineering Laboratory University of Verona Italy Dept. of Electrical and Computer Engineering University of Southern California Los Angeles CA U.S.A Factoryal S.r.l. San Giovanni Lupatoto Italy

ISBN: (数字)9783981926385

ISBN: (纸本)9798350348606

The DeFacto project, supported by the European Commission via a Marie Skłodowska-Curie Global Individual Fellowship, tackles the complexity arising from the transformation of industrial manufacturing systems into intricate cyber-physical systems. This evolution offers unprecedented opportunities but also poses intellectual and engineering challenges. DeFacto aims to advance the design automation of cyber-physical production systems by developing innovative modeling paradigms, scalable algorithms, software architectures, and tools. In the DeFacto approach, production systems are managed through service-oriented manufacturing software architectures. System-level models capture the features and the requirements of production systems, representing both production and computational processes as services provided by the infrastructure. Methodologies for system analysis and optimization rely on compositional abstractions of system behaviors grounded in assume-guarantee contracts. This paper outlines key research endeavors, findings, and lessons learned from the DeFacto project.

关键词： Design automation Software architecture Computational modeling Software algorithms Europe Cyber-physical systems System analysis and design

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Distributed Event-Triggered Adaptive Cooperative Circumnavigation Control of Networked Unmanned Aerial Vehicles

Distributed Event-Triggered Adaptive Cooperative Circumnavig...

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IEEE SSD International Multi-Conference on Systems, Signals and Devices

作者： Jawhar Ghommam Raouf Fareh Ibrahim Al Naimi Department of Electrical and Computer Engineering College of Engineering Sultan Qaboos University Laboratory of Control Energy Management ENIS Tunisa Electrical Engineering Dept. University of Sharjah UAE

In this paper, the objective for a group of unmanned aerial vehicle agents (UAVs) to achieve three dimensional circumnavigation around a moving target which information is made available to all agents in the group. The cooperative circumnavigation is to drive the UAVs to orbit around the target according to a given elliptical desired spatial formation. Due to the thrust limitation needed to fly the drone, existing cyclic pursuit algorithms cannot be extended directly to achieve this objective. Thus the proposed algorithm is worked out to take into account this constraint in order to achieve such objective. The drones are subject to unknown external disturbance, also the masses of those agent drones are assumed to be unknown. Furthermore, the communication cost can be decreased and the Zeno behavior is shown to be excluded. The proposed controller guarantees the bounded control effort irrespective of the external disturbance and model uncertainties of the drone. Numerical simulations are conducted to illustrate the efficacy of the approach.

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