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Synthetic helicoidal rotation of a quasielectrostatic ring resonator for ultrabroadband magnet-free nonreciprocity

Physical Review Applied 2024年第6期22卷 064020-064020页

作者： Aravind Nagulu Ahmed Mekawy Mykhailo Tymchenko Dimitrios Sounas Harish Krishnaswamy Andrea Alù Department of Electrical and Systems Engineering Washington University in St. Louis St. Louis Missouri 63130 USA Photonics Initiative Advanced Science Research Center City University of New York New York New York 10031 USA Department of Electrical and Computer Engineering The University of Texas at Austin Austin Texas 78712 USA Department of Electrical Engineering Wayne State University Detroit Michigan 48202 USA Department of Electrical Engineering Columbia University New York New York 10027 USA Physics Program Graduate Center City University of New York New York New York 10016 USA

Suitably tailored forms of spatiotemporal modulation in electronic circuit networks have been recently employed to overcome fundamental challenges in modern electronic systems, including breaking reciprocity, squeezing the footprint of high-Q resonators, and overcoming the delay-bandwidth limit. Rotating patterns of temporal modulations have been used to synthesize angular momentum, which replaces magnetic bias to break reciprocity in integrated circuits. However, this approach is limited by trade-offs between modulation speed, footprint, and bandwidth of operation. Rotating switching patterns in commutated capacitor networks also enables compact filters and quasielectrostatic wave propagation, overcoming the delay-bandwidth limit. In this paper, we combine these mechanisms in an integrated-circuit ring that synthetically rotates in two dimensions, realizing an effective helicoidal motion that provides ultrabroadband quasielectrostatic nonreciprocal responses fitting within a theoretically infinitesimal size. We also analyze the impact of modulation signal noise on time-modulated nonreciprocal components and unveil the role of a dynamic noise mechanism based on which the noise level increases in the presence of a strong signal passing through the component, along with methods to mitigate this effect. We experimentally verify these principles in a three-port integrated circulator based on a 65-nm CMOS process that operates from dc to 1 GHz with a miniaturization factor of 2 × 106.

关键词： Metamaterials Non-reciprocal transmission Photonics Topolectrical circuits

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Enhanced Detection of Demagnetization in Direct-Drive Wind Turbine Generators Using eMIST and Machine Learning

Enhanced Detection of Demagnetization in Direct-Drive Wind T...

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IEEE Workshop on electrical Machines Design, Control and Diagnosis (WEMDCD)

作者： Qiuyi Chen Panagiotis Tsilifis Jaime Renedo Anglada Department of Mechanical Engineering University of Maryland College Park MD USA Robotics AI & Computer Vision Group GE Vernova Advanced Research Niskayuna NY USA Electrical Systems GE Vernova Advanced Research Niskayuna NY USA

ISBN: (数字)9798331520748

ISBN: (纸本)9798331520755

The rapid growth of offshore wind energy needs robust condition monitoring to ensure the reliability and efficiency of direct-drive permanent magnet synchronous generators (PMSGs). Demagnetization, a critical fault in these generators, can severely impact performance and increase operational costs, especially in harsh marine environments. Traditional diagnostic techniques often struggle to detect subtle magnetic property changes under varying load conditions. This paper presents an innovative framework for demagnetization detection in PMSGs, leveraging the electrical Multi-phase Imbalance Separation Technique (eMIST) combined with machine learning models. Using a dataset from offshore wind turbines, including simulated faulty conditions, the proposed methodology enhances anomaly detection by isolating imbalance signatures and correlating them with key operational parameters. Gaussian Process Regression (GPR) is employed to predict anomalies, offering high accuracy and confidence in fault classification. Results demonstrate the approach's ability to identify early-stage demagnetization, enabling predictive maintenance, reducing downtime, and improving the economic viability of offshore wind farms.

关键词： Costs Accuracy Wind energy Machine learning Gaussian processes Wind farms Generators Wind turbines Demagnetization Anomaly detection

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Explainable Symbolic Regression Model for Tool Wear Diagnosis

Explainable Symbolic Regression Model for Tool Wear Diagnosi...

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International Conference on Control, Decision and Information Technologies (CoDIT)

作者： Debasish Mishra Seulki Han Krishna R. Pattipati George M. Bollas Pratt & Whitney Institute of Advanced Systems Engineering University of Connecticut Storrs CT USA Department of Chemical & Biomolecular Engineering University of Connecticut Storrs CT USA Department of Electrical & Computer Engineering University of Connecticut Storrs CT USA

In precision machining, predicting the tool health can improve productivity, job quality, and reduce machine downtime and energy consumption. While deep learning (DL) algorithms have garnered recent interest, they lack the physics understanding associated with machining processes. To address this limitation, we present a symbolic regression framework for tool wear diagnostics. The method explores analytical symbolic expressions using health indicators and cutting settings. Tool health indicators are computed from wavelet subspaces of vibration signals by applying a distance metric to the wavelet coefficients. These indicators are strongly correlated with tool wear measurements, making them suitable for tool wear diagnostics. We applied the developed framework to the IEEE PHM 2010 data, which comprises three sets of run-to-failure machining tests conducted with three tools. The framework predicted tool wear with an $R^{2}$ of 0.947 and a mean absolute error (MAE) of 0.006 mm across test sets. The results demonstrate the effectiveness of the symbolic regression approach for tool wear diagnostics, showcasing the richness of information in the indicators and the quality of the developed model $\mathbf{for}$ tool wear estimation.

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Comparative Study of Alternative-Pole Flux Switching and Inter-Modular Biased-Flux Permanent Magnet Motors

Comparative Study of Alternative-Pole Flux Switching and Int...

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electrical Machines and Drives (ICEMD), International Conference on

作者： Mohammad Afrank Alireza Eikani Ehsan Farmahini Farahani Mojtaba Mirsalim Department of Electrical Engineering Amirkabir University of Technology Tehran Iran Advanced Motion Systems Research Lab School of Electrical and Computer Engineering College of Engineering University of Tehran Tehran Iran Electrical Power Research Group School of Engineering Newcastle University Newcastle upon Tyne U.K

This paper executes a comparative analysis between alternative-pole flux-switching and inter-modular biased-flux permanent magnet (PM) motors. Despite their close structural similarities, the former puts magnets not only between stator modules, but also close to the armature windings. However, the inter-modular PM (IMPM) motor only occupies spaces between stator modules. This type of occupation allows this structure to provide a safe operation point for PMs. Not considering the PM’s recoil-phenomenon, the torque density of the IMPM motor is not as significant as the alternative-pole flux-switching PM (AP-FSPM) motor. This is because of the four-operating mode of the AP-FSPM structure which enables it to increase the torque density by utilizing the PMs in every moment of the operation. But, when the demagnetization of the AP-FSPM’s PMs is taken into account, the torque density and torque per PM values of the AP-FSPM structure plummets. A complete full-load demagnetization analysis is done in order to get a better understanding of how the performance of the IMPM structure supersedes the AP-FSPM structure. 2D finite element analysis is used to extract the most important indexes of both topologies.

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Design and Application of a One-Stop Mobile Application Sharing platform for Steel Enterprises 42

Design and Application of a One-Stop Mobile Application Shar...

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42nd Chinese Control Conference, CCC 2023

作者： Chen, Xinzhe Chen, Luefeng Hu, Jie Yang, Qingfeng Wu, Min Pedrycz, Witold School of Automation China University of Geosciences Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan430074 China University of Alberta Department of Electrical and Computer Engineering EdmontonABT6R 2G7 Canada

ISBN: (纸本)9789887581543

As China's steel production accounts for an increasing share of the world's output, the intelligent transformation of the steel industry is becoming increasingly urgent. To address issues such as low levels of mobile informationization in steel enterprises and the lack of an industry-specific mobile application platform, it is of great significance to establish a shared mobile application platform for the steel industry. In this paper, the requirements of the platform were analyzed, and the platform's functions were designed. The software design of the platform was then carried out, and the entire mobile application sharing platform was developed, effectively improving the production management efficiency of steel enterprises. The results indicate that the platform can effectively meet the needs of steel enterprises and has significant engineering significance. © 2023 Technical Committee on Control Theory, Chinese Association of Automation.

关键词： Informatization

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Accelerating Deep Neural Networks with a Low-Cost Lossless Compression

Accelerating Deep Neural Networks with a Low-Cost Lossless C...

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International Conference on Electronics, Information and Communications (ICEIC)

作者： Seongwook Kim Gwangeun Byeon Seungeui Lee Youngseok Bae Seokin Hong Department of Electrical and Computer Engineering Sungkyunkwan University Suwon Korea Advanced R&D Center Hanwha Systems Co. Ltd. Emerging Science and Technology Directorate Agency for Defense Development

ISBN: (数字)9798331510756

ISBN: (纸本)9798331510763

Since deep learning inference involves a significant amount of computations, there have been a lot of efforts to accelerate the inference process by eliminating ineffectual compu-tations. As a solution to this problem, several sparsity-aware deep learning accelerators have been proposed. These accelerators perform the inference on compressed data (filters and feature maps) and dynamically remove the ineffectual multiplications with zero operands. For efficiently handling the compressed data on the accelerators, a simple and effective compression scheme, which encodes the whole data with its nonzero values and their positions, is necessary. However, prior approaches have several shortcomings, such as low compression efficiency and high complexity in compression and decompression hardware. This paper proposes a low-cost compression scheme that can be applied to sparsity-aware deep-learning accelerators. The compression scheme synergistically combines two low-cost compression schemes: Zero-Value Compression (ZVC) and Base-Delta-Immediate Compression (BDI). Our experimental evaluation shows that the proposed compression scheme achieves an average compression ratio of 2.26, resulting in 2.33x performance improvement,

关键词： Deep learning Filters Artificial neural networks Performance gain Complexity theory Hardware acceleration

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An Investigation Method Incorporates with Contextual Information and Transformer Residual BiLSTM to Improve the Accuracy of Detecting Sensitive Content in Various Contexts

An Investigation Method Incorporates with Contextual Informa...

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International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)

作者： Niharika Karne Chandra Shekar Ramagundam Ranga Rao Patnala Shashishekhar Ramagundam Sai Nitisha Tadiboina Department of Electrical Engineering Masters in Electrical Engineering Sr. Principal Systems Engineer in C&RS Platform Services DT Amtrak University of South Alabama USA Senior Software Engineer in Cloud Platform Services City Of Philadelphia Pennsylvania USA Vice President US AL. Department of Computer Science Masters In Computer Science Software Development & Engineering in Content Discovery & AI Comcast Troy University Montgomery Campus USA Department of Information Technology GEICO - Chevy Chase MD United States

ISBN: (数字)9798350376425

ISBN: (纸本)9798350376432

In the modern era, the internet is an emerging technology with a rapidly growing user. Sensitive content uploaders consider this platform as a way to transmit disturbing visual content, such as animated cartoon videos to circulate unsuitable content among youngsters. Previous researches show that the interaction of online sensitive information has substantial offline effects on historically impoverished communities. As a result, it is strongly recommended that social media platforms have an automatic real-time sensitive content filtering method for detecting contextual information. In this proposed model, a novel deep-learning framework is developed for detecting sensitive content from contextual information. For this, the suggested architecture performs under three stages (i) text collection, (ii) preprocessing, and (iii) detection. Initially, the required texts from the contextual information are gathered from the standard available data resource. The collected text is subjected to text preprocessing, where the text is cleaned and prepared for further analysis. Finally, in the detection phase, the developed Transformer Residual Bidirectional Long Short-Term Memory (TR-BiLSTM) is employed to learn the effective representation from the preprocessed text and perform the sensitive content detection. Furthermore, performance comparisons are carried out between the proposed frameworks with other standard approaches. The developed architecture achieved better results in detecting the sensitive content from the text.

关键词： Visualization Accuracy Recurrent neural networks Noise Transformers Real-time systems Long short term memory Standards Context modeling Videos

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Graph Based Power Flow Algorithm for Single Phase Radial and Weakly Meshed Distribution System in the presence of Distributed Generations

Graph Based Power Flow Algorithm for Single Phase Radial and...

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IEEE Electric Ship Technologies Symposium, ESTS

作者： Krishna Murari Tuyen Van Vu MD Isfakul Anam James Langston Michael Steurer Department of Electrical and Computer Engineering Clarkson University Potsdam New York USA Center for Advanced Power Systems Florida State University Tallahassee United States

This work focuses on proposing a graph theory-based power flow (PF) solution for distributed energy resources (DERs) integrated single-phase radial and weakly meshed distribution networks. Several Matrices have been formulated which have been further classified into two categories. The first category of the matrix depicts the configuration and topology of the network, and the second one is deployed for the computation of PF parameters utilizing the property of the first one. The PF problem of distribution networks requires unique treatments to deal with weakly meshed configurations, and multiple DGs modeled as PV buses. For such a scenario, a compensating matrix has been formulated to calculate the pertinent additional current to be injected to compensate for the voltage magnitude difference between the two ends of each mesh break-point, and/or between computed and specified values at the PV bus. This computed current needs to be injected into the respective mesh breakpoints, and/or into the respective PV bus present in the network. The calculated pertinent injections are contemplated in the net branch current matrix. This helps in solving the PF problem of the network with weakly meshed structures, and DERs in a similar fashion to the radial network problem. Pertinent tests have been completed to examine the convergence nature and computational ability of the proposed PF technique.

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An Iterative Method for a Frequency Domain PID Controller Synthesis

An Iterative Method for a Frequency Domain PID Controller Sy...

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SICE Annual Conference

作者： Kenjiro Minamikawa Yasushi Kami Eitaku Nobuyama Atsushi Hosokawa Department of Mechanical and Electronic System Engineering Advanced Course of National Institute of Technology Akashi College Hyogo Japan Department of Electrical Systems Engineering Hiroshima Institute of Technology Hiroshima Japan Department of Intelligent and Control Systems Kyushu Institute of Technology Fukuoka Japan Department of Electrical and Computer Engineering National Institute of Technology Akashi College Hyogo Japan

This study is concerned with a control system design method with frequency response data named a finite number of frequency responses(an FNFR) model. In this paper a numerical descent direction is introduced and an iterative method for solving control problems formulated with the FNFR model is proposed with the descent direction. One feature of our approach is to update controller variables so that they approach to a feasible region of considering control problems from the outside. The efficiency of our method is shown by numerical examples.

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Deduplication of Textual Data by NLP Approaches

Deduplication of Textual Data by NLP Approaches

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IEEE Conference on Vehicular Technology (VTC)

作者： Kiana Ghassabi Peyman Pahlevani Daniel E. Lucani Department of Computer Science Institute for Advanced Studies in Basic Sciences (IASBS) Zanjan Iran Department of Electronic Systems Aalborg University Copenhagen Denmark Department of Electrical and Computer Engineering Aarhus University Aarhus Denmark

With the increasing amount of digital data, data deduplication has become an increasingly popular method for reducing data in large-scale storage systems. Generalized deduplication is an alternative technique for reducing the cost of data storage by identifying similar data chunks. This paper proposes TL-GD, a method for improving cloud storage efficiency using generalized deduplication focusing on textual datasets. The core concept of this study is to develop an efficient deduplication system that combines an alternative technique for splitting data into smaller pieces and a new approach for transforming data pieces into bases and deviations. The performance of the system has been validated using two real-world datasets. We also compare the results to state-of-the-art deduplication methods. Our evaluation results show that TL-GD achieves nearly 67% lossless compression for textual navigation instructions datasets, which is a 25% improvement on average compared to existing deduplication techniques.

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