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2024 IEEE Energy Conversion Congress and Exposition, ECCE 2024

作者： Hwang, Hyeonji Park, Sumi Kim, Myung-Yong Lee, Jaehong Lee, Seung-Hwan University of Seoul School of Electrical and Computer Engineering Seoul Korea Republic of Korea Railroad Research Institute Smart Electrical & Signaling Division Uiwang16105 Korea Republic of

ISBN: (纸本)9798350376067

A transformerless multilevel inductive power transfer (IPT) system converts medium-voltage (MV, 6.6 ~ 25 kV) by employing a multilevel converter and excitation coils. To deliver outputs of N number of inverters with MV-level isolation, N:1 coupled excitation coils and a transmitter coil are investigated. The transmitter coil augments magnetic fields generated by the multiple excitation coils. Then, receiver coils can collect power from the transmitter coil via inductive couplings. Applications of the proposed IPT system are MV online IPT systems for trains and large-scale wireless charging stations for electric vehicles. This paper addresses key technical challenges in realizing the MV transformerless multilevel IPT system, such as the multilevel converter, its controllers, and the resonant tank design. In addition, the MV isolation strategy of the excitation coil and the impact of the reinforced concrete on the IPT system are discussed. For evaluation, a 6-level IPT system was fabricated in the PLECS simulation tool. The multi-load charging capacity of the proposed IPT system was evaluated as well. © 2024 IEEE.

关键词： Inductive power transmission

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Sense and Jam: ISAC-Aided Robust Physical Layer Security 58

Sense and Jam: ISAC-Aided Robust Physical Layer Security

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58th Asilomar Conference on Signals, Systems and computers, ACSSC 2024

作者： Naim Shaikh, Mohd Hamza Celik, Abdulkadir Eltawil, Ahmed M. Nauryzbayev, Galymzhan School of Engineering and Digital Sciences Nazarbayev University Department of Electrical and Computer Engineering Astana010000 Kazakhstan King Abdullah University of Science and Technology Cemse Division Thuwal Saudi Arabia

ISBN: (纸本)9798350354058

This work proposes a physical layer security (PLS) framework leveraging integrated sensing and communication (ISAC) to facilitate secure communication. The framework employs a multi-antenna full-duplex (FD), dual-functional access point (AP) to establish a secure communication zone around the intended user. The AP performs target sensing to detect the desired user and simultaneously employs artificial noise (AN) to jam any undesired users within the zone. In the first time slot, the AP sweeps the beam from a discrete Fourier transform-based codebook to sense the environment via its transmit antenna array, while the receive antenna array captures echoes from targets. If undesired users are detected within the secure communication zone, the downlink (DL) signal is augmented with friendly AN to obstruct their overhearing attempts. For the uplink (UL), the same set of transmit antennas beams AN towards the undesired users, while the receive antennas capture the UL signals from the desired user. By combining optimal beam selection, target sensing, and AN transmission, an FD secure communication link is established between the AP and desired user. Simulation results show that the proposed scheme significantly improves DL and UL secrecy rates compared to conventional methods. © 2024 IEEE.

关键词： Discrete Fourier transforms

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Supervised Local Training With Backward Links for Deep Neural Networks

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第2期5卷 854-867页

作者： Guo, Wenzhe Fouda, Mohammed E. Eltawil, Ahmed M. Salama, Khaled Nabil King Abdullah University of Science and Technology Division of Computer Electrical and Mathematical Sciences and Engineering Thuwal23955 Saudi Arabia University of California Center for Embedded and Cyber-Physical Systems IrvineCA92612 United States

The restricted training pattern in the standard backpropagation (BP) requires end-to-end error propagation, causing large memory costs and prohibiting model parallelization. Existing local training methods aim to resolve the training obstacles by completely cutting off the backward path between modules and isolating their gradients. These methods prevent information exchange between modules and result in inferior performance. This work proposes a novel local training algorithm, BackLink, which introduces intermodule backward dependence and facilitates information to flow backward along with the network. To preserve the computational advantage of local training, BackLink restricts the error propagation length within the module. Extensive experiments performed in various deep convolutional neural networks demonstrate that our method consistently improves the classification performance of local training algorithms over other methods. For example, our method can surpass the conventional greedy local training method by 6.45% in accuracy in ResNet32 classifying CIFAR100 and recent work by 2.58% in ResNet110 classifying STL-10 with much lower complexity. Analysis of computational costs reveals that small overheads are incurred in GPU memory costs and runtime on multiple GPUs. Our method can lead up to a 79% reduction in memory cost and 52% in simulation runtime in ResNet110 compared to the standard BP. Therefore, our method could create new opportunities for improving training algorithms toward better efficiency for real-time learning applications. © 2023 IEEE.

关键词： Deep neural networks

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Towards Non-Invasive Swallowing Assessment: An AI-Powered Interface for Swallowing Kinematic Analysis using High-Resolution Cervical Auscultation 46

Towards Non-Invasive Swallowing Assessment: An AI-Powered In...

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46th Annual International Conference of the IEEE engineering in Medicine and Biology Society, EMBC 2024

作者： Luo, Yuewen Anwar, Ayman Ren, Siyi Coyle, James L. Sejdic, Ervin University of Toronto Division of Engineering Science Faculty of Applied Science & Engineering TorontoON Canada University of Toronto Faculty of Applied Science & Engineering Department of Electrical and Computer Engineering TorontoON Canada University of Pittsburgh School of Health and Rehabilitation Sciences Department of Communication Science and Disorders PittsburghPA United States University of Toronto North York General Hospital Faculty of Applied Science & Engineering Department of Electrical and Computer Engineering TorontoON Canada

ISBN: (纸本)9798350371499

Swallowing is a pivotal physiological function for human sustenance and hydration. Dysfunctions, termed dysphagia, necessitate prompt and precise diagnosis. Videofluoroscopic swallowing studies (VFSS) remain the gold standard for swallowing assessment but pose accessibility and radiation exposure concerns. High-resolution cervical auscultation (HRCA) presents a non-invasive alternative with comparable accuracy. Yet, existing studies have only assessed HRCA in tandem with VFSS, without applying it to clinical settings. We propose an AI-enhanced PC software deploying HRCA for real-time bedside swallowing screening. This software proficiently detects abnormalities and precisely measures key kinematic events, including upper esophageal sphincter opening and laryngeal vestibule closure duration. The system was evaluated through two studies, encompassing a VFSS comparative analysis and a usability study. Our findings underscored the software's superior operational efficiency and diagnostic accuracy, positioning our HRCA-based system as a refined and pragmatic alternative for dysphagia assessment. © 2024 IEEE.

关键词： User centered design

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Probabilistic Tube-based Control Synthesis of Stochastic Multi-Agent Systems under Signal Temporal Logic 63

Probabilistic Tube-based Control Synthesis of Stochastic Mul...

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63rd IEEE Conference on Decision and Control, CDC 2024

作者： Vlahakis, Eleftherios E. Lindemann, Lars Sopasakis, Pantelis Dimarogonas, Dimos V. Kth Royal Institute of Technology Division of Decision and Control Systems School of Electrical Engineering and Computer Science Stockholm10044 Sweden University of Southern California Viterbi School of Engineering Department of Computer Science Los AngelesCA90089 United States Queen's University School of Electronics Electrical Engineering and Computer Science Northern Ireland Belfast United Kingdom

ISBN: (纸本)9798350316339

We consider the control design of stochastic discrete-time linear multi-agent systems (MASs) under a global signal temporal logic (STL) specification to be satisfied at a predefined probability. By decomposing the dynamics into deterministic and error components, we construct a probabilistic reachable tube (PRT) as the Cartesian product of reachable sets of the individual error systems driven by disturbances lying in confidence regions (CRs) with a fixed probability. By bounding the PRT probability with the specification probability, we tighten all state constraints induced by the STL specification by solving tractable optimization problems over segments of the PRT, and relax the underlying stochastic problem with a deterministic one. This approach reduces conservatism compared to tightening guided by the STL structure. Additionally, we propose a recursively feasible algorithm to attack the resulting problem by decomposing it into agent-level subproblems, which are solved iteratively according to a scheduling policy. We demonstrate our method on a ten-agent system, where existing approaches are impractical. © 2024 IEEE.

关键词： Stochastic systems

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Enhancing pH Sensing Capabilities Through Hydroxylated Surface Groups on RuOxFlexible EGFET Sensor

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IEEE Sensors Journal 2024年第9期24卷 13863-13869页

作者： Singh, Kanishk Chen, Chao-Hung Tai, Li-Chia Pang, See-Tong Pan, Tung-Ming Chang Gung University Department of Electronics Engineering Taoyuan City33302 Taiwan National Yang Ming Chiao Tung University Department of Electrical and Computer Engineering Hsinchu30010 Taiwan Division of Urology Chang Gung Memorial Hospital Linkou Medical Center Taoyuan City33305 Taiwan

The Nernst equation defines the maximum sensitivity of metal oxide pH sensors, which has a theoretical value of 59.2 mV/pH. However, the phenomenon of super-Nernstian pH response of a metal oxide sensing membrane is related to the hydration of the sensing film. In this context, the purpose of this research is to compare the pH sensing capabilities of distilled water (DI)-pretreated RuOx-based extended gate field-effect transistor (EGFET) pH sensors with those of as-deposited RuOx sensing films. The surface composition of the film was explored by X-ray photoelectron spectroscopy. For the as-deposited film, the RuOx sensor exhibited a sub-Nernstian pH sensitivity of 58.91 mV/pH. The DI-pretreated RuOx sample, however, demonstrated a super-Nernstian pH response of 64.13 mV/pH, with a linearity of 0.997. It is possible to attribute the enhanced sensitivity to the hydration process, which increases the amount of effective surface sites available for pH sensing. The result is more proton (H+) adsorption and desorption sites, exceeding the Nernstian limit of 59.2 mV/pH. Additionally, the DI-pretreated RuOx EGFET sensor demonstrated several advantages, including a low hysteresis voltage (∼2 mV), a minimal drift rate (0.25 mV/h), good flexibility, long-term stability (120 days), and excellent repeatability (with 97.95% retention). © 2001-2012 IEEE.

关键词： Surface treatment

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Machine learning electron-phonon interactions in two-dimensional semiconducting materials: The case of zero-point renormalization

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Physical Review Materials 2024年第10期8卷 L101001页

作者： Anubhab Haldar Quentin Clark Marios Zacharias Feliciano Giustino Sahar Sharifzadeh Department of Electrical and Computer Engineering Department of Philosophy Oden Institute for Computational Engineering and Sciences Department of Physics Division of Materials Science and Engineering

We utilize first-principles theory to investigate the role of electron-phonon interactions within a dataset of monolayer materials. Using density functional theory to describe excited-state transitions and the special displacement method to describe the role of phonons, we analyze the relationship between simple physical observables and electron-phonon coupling strength. For over 100 materials, we compute the band gap renormalization due to zero-point vibrational (ZPR) motion as a measure of electron-phonon interactions and train a machine learning model based on physical parameters. We demonstrate that the strength of electron-phonon interactions is highly dependent on the band gap, dielectric constant, and degree of ionicity, all of which can be physically justified. We then apply this model to 1302 2D materials, predicting the ZPR, which for five randomly selected materials tested agree well with the first-principles predictions. This work provides an approach for quantitatively predicting the ZPR as a measure of electron-phonon interactions in 2D materials.

关键词： Electron-phonon coupling Electronic structure 2-dimensional systems Machine learning

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Quantum computer-Aided Design Automation: Optimization Algorithms and Visualization Interfaces

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IEEE Nanotechnology Magazine 2023年第2期17卷 15-25页

作者： Kuo, Shu-Yu Jiang, Yu-Chi Chou, Yao-Hsin Kuo, Sy-Yen Kung, Sun-Yuan National Taiwan University Department of Physics and Center for Theoretical Physics Taipei10617 Taiwan National Taiwan University Department of Electrical Engineering Taipei10617 Taiwan National Chi Nan University Department of Computer Science and Information Engineering Nantou54561 Taiwan National Center for Theoretical Sciences Physics Division Taipei Taiwan Center for Quantum Science and Engineering Taipei Taiwan Princeton University Department of Electrical and Computer Engineering PrincetonNJ08544 United States

Quantum computing is an essential issue for taking advantage of quantum properties in real-world applications. Realizing quantum computing with a corresponding quantum circuit is a critical step. This study introduces the AngelQ system, which provides quantum computing-as-a-service (QCaaS) for the cloud. The AngelQ system integrates quantum design automation (QDA) to achieve quantum and reversible circuit synthesis by a quantum-inspired optimization (QiO) technique and has visualization software interfaces to verify the QiO process and the correctness of the synthesized quantum and reversible circuits. First, the proposed novel QiO method has four significant features: Adaptive strategy, quantum-Not gate, update mechanism by Global solutions, and Entanglement Local search, named Angel QiO. which is a flexible technique to realize various circuits under different gate libraries with a few alterations. Angel QiO can construct diversified function-equivalent circuits to assess the circuit composition and boost the development of circuit synthesis. Second, researchers can observe the QiO algorithm process and inspect the intermediate result formula through AngelQ system's visualization interface to further adjust the parameters and improve the performance. Moreover, it benefits beginners' learning and adds convenience to education. In summary, the AngelQ system can extend and facilitate the future design and automation of quantum computing. © 2007-2011 IEEE.

关键词： Optimization

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Fast prediction of indoor airflow distribution inspired by synthetic image generation artificial intelligence

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Building Simulation 2023年第7期16卷 1219-1238页

作者： Cary A.Faulkner Dominik S.Jankowski John E.Castellini Jr Wangda Zuo Philipp Epple Michael D.Sohn Ali Taleb Zadeh Kasgari Walid Saad Department of Mechanical Engineering University of Colorado BoulderBoulderCOUSA HySON Institut SonnebergGermany Department of Architectural Engineering Pennsylvania State UniversityUniversity ParkPAUSA Department of Mechanical Engineering Coburg University of Applied SciencesCoburgGermany Energy Analysis and Environmental Impacts Division Lawrence Berkeley National LaboratoryBerkeleyCAUSA Department of Electrical and Computer Engineering Virginia TechArlingtonVAUSA

Prediction of indoor airflow distribution often relies on high-fidelity,computationally intensive computational fluid dynamics(CFD)*** intelligence(Al)models trained by CFD data can be used for fast and accurate prediction of indoor airflow,but current methods have limitations,such as only predicting limited outputs rather than the entire flow ***,conventional Al models are not always designed to predict different outputs based on a continuous input range,and instead make predictions for one or a few discrete *** work addresses these gaps using a conditional generative adversarial network(CGAN)model approach,which is inspired by current state-of-the-art Al for synthetic image *** create a new Boundary Condition CGAN(BC-CGAN)model by extending the original CGAN model to generate 2D airflow distribution images based on a continuous input parameter,such as a boundary ***,we design a novel feature-driven algorithm to strategically generate training data,with the goal of minimizing the amount of computationally expensive data while ensuring training quality of the Al *** BC-CGAN model is evaluated for two benchmark airflow cases:an isothermal lid-driven cavity flow and a non-isothermal mixed convection flow with a heated *** also investigate the performance of the BC-CGAN models when training is stopped based on different levels of validation error *** results show that the trained BC-CGAN model can predict the 2D distribution of velocity and temperature with less than 5%relative error and up to about 75,ooo times faster when compared to reference CFD *** proposed feature-driven algorithm shows potential for reducing the amount of data and epochs required to train the Al models while maintaining prediction accuracy,particularly when the flow changes non-linearlywith respectto an input.

关键词： artificial intelligence indoor airflow conditional generative adversarial network computational fluid dynamics

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A Blockchain-Assisted Distributed Edge Intelligence for Privacy-Preserving Vehicular Networks

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computers, Materials & Continua 2023年第9期76卷 2959-2978页

作者： Muhammad Firdaus Harashta Tatimma Larasati Kyung-Hyune Rhee Department of Artificial Intelligence Convergence Pukyong National UniversityBusan48513Korea School of Electrical Engineering and Informatics Institut Teknologi BandungBandung40132Indonesia College of Information Technology and Convergence Division of Computer Engineering and AIPukyong National UniversityBusan48513Korea

The enormous volume of heterogeneous data fromvarious smart device-based applications has growingly increased a deeply interlaced cyber-physical *** order to deliver smart cloud services that require low latency with strong computational processing capabilities,the Edge Intelligence System(EIS)idea is now being employed,which takes advantage of Artificial Intelligence(AI)and Edge Computing Technology(ECT).Thus,EIS presents a potential approach to enforcing future Intelligent Transportation Systems(ITS),particularly within a context of a Vehicular Network(VNets).However,the current EIS framework meets some issues and is conceivably vulnerable tomultiple adversarial attacks because the central aggregator server handles the entire ***,this paper introduces the concept of distributed edge intelligence,combining the advantages of Federated Learning(FL),Differential Privacy(DP),and blockchain to address the issues raised *** performing decentralized data management and storing transactions in immutable distributed ledger networks,the blockchain-assisted FL method improves user privacy and boosts traffic prediction ***,DP is utilized in defending the user’s private data from various threats and is given the authority to bolster the confidentiality of data-sharing *** model has been deployed in two strategies:First,DP-based FL to strengthen user privacy by masking the intermediate data during model ***,blockchain-based FL to effectively construct secure and decentralized traffic management in vehicular *** simulation results demonstrated that our framework yields several benefits for VNets privacy protection by forming a distributed EIS with privacy budget(ε)of 4.03,1.18,and 0.522,achieving model accuracy of 95.8%,93.78%,and 89.31%,respectively.

关键词： Edge intelligence federated learning differential privacy blockchain vehicular networks

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