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4th International Conference on Expert Clouds and Applications, ICOECA 2024

作者： Gorde, Swapnil Gulabrao Singh, Archana Tamilselvi, M. Ravivarman, G. Rajendiran, M. Maranan, Ramya MIT College of Management MIT Art Design & Technology University Pune India Private University Maharashtra India Roever Engineering College Department of Computer Science and Engineering Tamil Nadu Perambalur India Karpagam Academy of Higher Education Department of Electrical and Electronics Engineering Tamil Nadu Coimbatore India Panimalar Engineering College Department of Computer Science and Engineering Poonamallee Tamil Nadu Chennai India Saveetha School of Engineering SIMATS Department of Research and Innovation Tamil Nadu Chennai India

ISBN: (纸本)9798350385793

This study suggests a powerful Three- Tier Architecture that combines machine learning models, Internet of Things (IoT) devices, and sophisticated sensors to provide continuous cardiac health monitoring. A range of physiological data is provided by the sensors in the Data Collection Tier, which measure blood pressure, temperature, acceleration, and electrocardiogram (ECG). In the Data Processing Tier, a local server applies complex algorithms to efficiently assess the gathered data and provide insightful outcomes. By providing easy access, authentication, and alarm production, the Application Tier acts as the interface and guarantees efficient contact with healthcare providers. The unique aspect of the system is the integration of machine learning models, which were trained on recorded data for predictive analysis. k-Nearest Neighbors (KNN), Artificial Neural Networks (ANN), Decision Trees (DT), and Recurrent Neural Networks (RNN) are some examples of these models. Impressive accuracy levels are seen in the results, with ANN leading at 96.44% The measures of accuracy, precision, recall, and F1 score all demonstrate the algorithms' capacity to anticipate possible health risks, which supports the models' effectiveness. This proactive paradigm in healthcare is made possible by the comprehensive approach of continuous heart health monitoring, which enables prompt interventions and customized treatment strategies. This study increases our knowledge of the dynamics of cardiovascular health and the rapidly growing field of health care technology by demonstrating the synergy of sensor-based monitoring, IoT connectivity, and machine learning analytics in a unified architecture. In the end, patient care is improved. © 2024 IEEE.

关键词： Blood pressure

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Competition and Diversity in Generative AI

arXiv

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arXiv 2024年

作者： Raghavan, Manish MIT Sloan School of Management Department of Electrical Engineering and Computer Science United States

Recent evidence suggests that the use of generative artificial intelligence reduces the diversity of content produced. In this work, we develop a game-theoretic model to explore the downstream consequences of content homogeneity when producers use generative AI to compete with one another. At equilibrium, players indeed produce content that is less diverse than optimal. However, stronger competition mitigates homogeneity and induces more diverse production. Perhaps more surprisingly, we show that a generative AI model that performs well in isolation (i.e., according to a benchmark) may fail to do so when faced with competition, and vice versa. We validate our results empirically by using language models to play Scattergories, a word game in which players are rewarded for producing answers that are both correct and unique. We discuss how the interplay between competition and homogeneity has implications for the development, evaluation, and use of generative AI. Copyright © 2024, The Authors. All rights reserved.

关键词： Generative adversarial networks

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A New Switched Reluctance Motor with Embedded Permanent Magnets for Transportation Electrification

A New Switched Reluctance Motor with Embedded Permanent Magn...

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Annual Conference of Industrial Electronics Society

作者： Gholamreza Davarpanah Sajjad Mohammadi Department of Electrical Engineering Amirkabir University of Technology Tehran Iran Department of Electrical Engineering and Computer Science MIT Cambridge USA

ISBN: (数字)9781665464543

ISBN: (纸本)9781665464550

A new three-phase hybrid-excited multi-tooth switched reluctance motor with embedded permanent magnets is proposed, capable of achieving higher torque density for transportation electrification applications. Operating principles and design considerations are discussed. A magnetic equivalent circuit is developed. Finite element method is employed in the field analysis. The advantages of the proposed topology over existing designs for switched reluctance motors and flux switching motors are presented. Finally, the optimized design is prototyped to experimentally confirm the design and simulation results.

关键词： Magnetic flux Torque Transportation Switches Switched reluctance motors Electrification Motors Permanent magnets Finite element analysis Saturation magnetization

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Identifying Cyber Extremism Sentiments using ROBERTA

Identifying Cyber Extremism Sentiments using ROBERTA

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2023 IEEE International Conference on Intelligent and Innovative Technologies in Computing, electrical and Electronics, ICIITCEE 2023

作者： Patel, Sudhanshu Raja, K. Duela, J. Shiny Chen, Thomas M Sathiyanarayanan, mithileysh SRM Institute of Science and Technology Department of Computer Science and Engineering Chennai India School of Science and Technology City Department of Electrical and Electronic Engineering University of London United Kingdom Research & Innovation MIT Square University of London London United Kingdom

ISBN: (纸本)9781665462631

It is critical to identify and mitigate cyber extremist sentiments in order to prevent terrorist acts and maintain national security. The use of online platforms to promote extremist ideologies and incite violence is referred to as cyber extremism. To detect and classify extremist content on the internet, machine learning models have been used, making the process more efficient and scalable. To identify cyber extremism sentiments using machine learning models, the model is typically trained on a large dataset of extremist and non-extremist materials. The model then learns to recognize patterns and features associated with extremism, such as the use of specific words and phrases, as well as image and video-based features like facial expressions and gestures. We use ROBERTA for this purpose in this study and compare its results to those of BERT and ALBERT. The use of machine learning to detect cyber extremism sentiments can improve law enforcement and other organizations' ability to detect and respond to potential threats, ultimately helping to keep communities safe. © 2023 IEEE.

关键词： National security

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Topology Optimization in Three-Phase C-Core SRMs

Topology Optimization in Three-Phase C-Core SRMs

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Annual Conference of Industrial Electronics Society

作者： Gholamreza Davarpanah Sajjad Mohammadi James L. Kirtley Department of Electrical Engineering Amirkabir University of Technology Tehran Iran Department of Electrical Engineering and Computer Science MIT Cambridge USA

ISBN: (数字)9781665464543

ISBN: (纸本)9781665464550

This paper investigates the optimized combination of rotor and stator teeth in a three-phase switched reluctance motor featuring a connected C-core topology to attain a larger winding area and, thus, a higher electrical loading capability, leading to a higher torque density. The general formulation is discussed. Additionally, the shorter flux path within the design leads to a reduced core loss. The finite element method is employed in the design and field analysis. A comparison with a conventional motor shows the superiority of the proposed configuration. The optimized design is prototyped and tested. Both static and dynamic torques are extracted. A good correlation between simulation and experimental results is observed.

关键词： Stator cores Torque Loading Windings Rotors Stator windings Switched reluctance motors Motors Topology Finite element analysis

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A Novel Radial-Flux IPM Eddy-Current Coupler for Wind Generator Applications

arXiv

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arXiv 2024年

作者： Mohammadi, Sajjad Davarpanah, Gholamreza Kirtley, James L. Electrical Engineering and Computer Science MIT Cambridge United States Department of Electrical Engineering Amirkabir University of Technology Tehran Iran

A novel radial-flux eddy-current coupler with interior permanent magnets (IPM) is proposed, providing higher demagnetization tolerance, making it well-suited for applications with limited accessibility, such as offshore wind generation. Finite element analysis is employed in the design and derivation of coupler quantities. Finally, the coupler is prototyped to experimentally validate the design and the simulation results. Copyright © 2024, The Authors. All rights reserved.

关键词： Demagnetization

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Mean-field underdamped langevin dynamics and its spacetime discretization 24

Mean-field underdamped langevin dynamics and its spacetime d...

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Proceedings of the 41st International Conference on Machine Learning

作者： Qiang Fu Ashia Wilson Department of Computer Science Yale University New Haven CT Department of Electrical Engineering and Computer Science MIT Cambridge MA

We propose a new method called the N-particle underdamped Langevin algorithm for optimizing a special class of non-linear functionals defined over the space of probability measures. Examples of problems with this formulation include training mean-field neural networks, maximum mean discrepancy minimization and kernel Stein discrepancy minimization. Our algorithm is based on a novel spacetime discretization of the meanfield underdamped Langevin dynamics, for which we provide a new, fast mixing guarantee. In addition, we demonstrate that our algorithm converges globally in total variation distance, bridging the theoretical gap between the dynamics and its practical implementation.

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Position: A Roadmap to Pluralistic Alignment 41

Position: A Roadmap to Pluralistic Alignment

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41st International Conference on Machine Learning, ICML 2024

作者： Sorensen, Taylor Moore, Jared Fisher, Jillian Gordon, mitchell Mireshghallah, Niloofar Rytting, Christopher Michael Ye, Andre Jiang, Liwei Lu, Ximing Dziri, Nouha Althoff, Tim Choi, Yejin Department of Computer Science University of Washington SeattleWA United States Department of Computer Science Stanford University StanfordCA United States Department of Statistics University of Washington SeattleWA United States Department of Electrical Engineering and Computer Science MIT CambridgeMA United States Allen Institute for Artificial Intelligence SeattleWA United States

With increased power and prevalence of AI systems, it is ever more critical that AI systems are designed to serve all, i.e., people with diverse values and perspectives. However, aligning models to serve pluralistic human values remains an open research question. In this piece, we propose a roadmap to pluralistic alignment, specifically using large language models as a test bed. We identify and formalize three possible ways to define and operationalize pluralism in AI systems: 1) Overton pluralistic models that present a spectrum of reasonable responses;2) Steerably pluralistic models that can steer to reflect certain perspectives;and 3) Distributionally pluralistic models that are well-calibrated to a given population in distribution. We also formalize and discuss three possible classes of pluralistic benchmarks: 1) Multi-objective benchmarks, 2) Tradeoff steerable benchmarks that incentivize models to steer to arbitrary trade-offs, and 3) Jury-pluralistic benchmarks that explicitly model diverse human ratings. We use this framework to argue that current alignment techniques may be fundamentally limited for pluralistic AI;indeed, we highlight empirical evidence, both from our own experiments and from other work, that standard alignment procedures might reduce distributional pluralism in models, motivating the need for further research on pluralistic alignment. Copyright 2024 by the author(s)

关键词： Artificial intelligence

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A Universal Maximum Likelihood GRAND Decoder in 40nm CMOS 14

A Universal Maximum Likelihood GRAND Decoder in 40nm CMOS

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14th International Conference on COMmunication Systems and NETworkS, COMSNETS 2022

作者： Riaz, Arslan Medard, Muriel Duffy, Ken R. Yazicigil, Rabia Tugce Boston University Department of Electrical and Computer Engineering BostonMA United States MIT Department of Electrical Engineering and Computer Science CambridgeMA United States Maynooth University Hamilton Institute Ireland

ISBN: (纸本)9781665421041

As optimally accurate Maximum-likelihood (ML) decoding is NP-complete in code-length, all existing decoders are code-specific and so restricted in code-rate and code-length. Guessing Random Additive Noise Decoding (GRAND) is a recently introduced theoretically universal decoder suitable for all moderate redundancy codes, as required for ultra-reliable low-latency communication by the 5G New Radio Standard. Here we demonstrate the first-integrated universal GRAND chip in 40nm CMOS technology. It efficiently decodes all binary linear codes of any rate from 0.66 to 1 with code length up to 128 bits. For a bit-flip probability of 10-5, GRAND chip measurements show that it achieves 1 μs latency at 68 MHz. Dynamic clock gating leveraging noise statistics reduces the average power dissipation to 3.75 mW at 1.1 V consuming 30.6 pJ/decoded bit of energy with a throughput of 122.6 Mb/s. The chip allows seamless swapping between codebooks with no downtime, enabling use by multiple applications simultaneously. © 2022 IEEE.

关键词： Additive noise

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Intelligent Energy Management Strategies for Renewable Energy Sources

Intelligent Energy Management Strategies for Renewable Energ...

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AIML-Applications for engineering & Technology (ICAET), International Conference on

作者： Sangita Patil Disha Deotale Ramesh Mali Department of Electrical & Electronics Engineering MIT School of Computing MIT ADT University Pune India Department of Computer Science & Engineering MIT School of Computing MIT ADT University Pune India

ISBN: (数字)9798350355611

ISBN: (纸本)9798350355628

There have been several developments in renewable resources, standby sources of energy, and storage technologies. Because renewable sources are inconsistent, the best method to ensure supply continuity is to combine them with traditional energy to serve mini-grids or the isolated loads that are not linked to the main grid. In hybrid power system, many different energy sources are blended. Every time, effective and efficient Energy Management Strategies (EMS) are necessary when more than a source of energy is employed to supply a given load. This strategy directs the energy flow via the supply chain. This is necessary not just for standalone hybrid systems, but for hybrid power system that are linked to the main network. The intelligent control of hybrid MG has been the subject of a large number of serious research projects. A comprehensive assessment of the approaches used by a number of writers in their research work on applying energy management approaches is conducted. Standalone hybrid energy systems and the hybrid systems those are linked to the electrical grids are examples of these solutions. The aim of this paper is to give summary of various energy management schemes utilized in sustainable hybrid systems. Brief study is conducted for a number of freestanding and grid-linked hybrid system designs in order to make sure and find that which energy management technique that works well and will be applied for one renewable source is not always the best for another.

关键词： Renewable energy sources Solid modeling Wind energy Supply chains Solids Hybrid power systems Batteries Energy management System analysis and design Intelligent control

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