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International Conference on Renewable Energy Research and Applications (ICRERA)

作者： Souma Jinno Hiroshi Toki Masayuki Abe Dept. of Electrical and Electronic Systems Engineering Osaka Institute of Technology Osaka Japan Research Center for Nuclear Physics (RCNP) Osaka University Osaka Japan Grahuate School of Engineering Science Osaka University Osaka Japan

ISBN: (数字)9798350375589

ISBN: (纸本)9798350375596

This paper presents a near-field antenna analysis using electromagnetic potentials. We introduce the concept of source-potential energy representation and visualize the spatiotemporal distribution of energy densities around a dipole antenna. Our results demonstrate that the source-potential expression enables clear distinction between the conductive energy within the antenna and the radiated energy from the antenna. This visualization provides a novel perspective on energy dynamics in antenna systems, offering insights into the interplay between internal conduction and external radiation processes.

关键词： Antenna measurements Visualization Electric potential Dipole antennas Statistical analysis Conductors Timing Electromagnetics Antenna accessories Time-varying systems

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An Efficient GPU-based Implementation for Noise Robust Sound Source Localization

arXiv

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arXiv 2025年

作者： Lin, Zirui Takigahira, Masayuki Terakado, Naoya Gulzar, Haris Busto, Monikka Roslianna Eda, Takeharu Itoyama, Katsutoshi Nakadai, Kazuhiro Amano, Hideharu Dept. of Systems and Control Engineering School of Engineering Tokyo Institute of Technology Tokyo Japan Honda Research Institute Japan Co. Ltd. Saitama Japan NTT Software Innovation Center Tokyo Japan Dept. of Information and Computer Science Keio University Kanagawa Japan

Robot audition, encompassing Sound Source Localization (SSL), Sound Source Separation (SSS), and Automatic Speech Recognition (ASR), enables robots and smart devices to acquire auditory capabilities similar to human hearing. Despite their wide applicability, processing multi-channel audio signals from microphone arrays in SSL involves computationally intensive matrix operations, which can hinder efficient deployment on Central Processing Units (CPUs), particularly in embedded systems with limited CPU resources. This paper introduces a GPU-based implementation of SSL for robot audition, utilizing the Generalized Singular Value Decomposition-based Multiple Signal Classification (GSVD-MUSIC), a noise-robust algorithm, within the HARK platform, an open-source software suite. For a 60-channel microphone array, the proposed implementation achieves significant performance improvements. On the Jetson AGX Orin, an embedded device powered by an NVIDIA GPU and ARM®Cortex™-A78AE v8.2 64-bit CPUs, we observe speedups of 4645.1× for GSVD calculations and 8.8× for the SSL module, while speedups of 2223.4× for GSVD calculation and 8.95× for the entire SSL module on a server configured with an NVIDIA A100 GPU and AMD EPYC™ 7352 CPUs, making real-time processing feasible for large-scale microphone arrays and providing ample capacity for real-time processing of potential subsequent machine learning or deep learning tasks. © 2025, CC BY-NC-ND.

关键词： Acoustic generators

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Learning on Transformers is Provable Low-Rank and Sparse: A One-layer Analysis

Learning on Transformers is Provable Low-Rank and Sparse: A ...

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Sensor Array and Multichannel Signal Processing Workshop

作者： Hongkang Li Meng Wang Shuai Zhang Sijia Liu Pin-Yu Chen Dept. Electrical Computer and System Engineering Rensselaer Polytechnic Institute Troy NY USA Dept. Data Science New Jersey Institute of Technology Newark NJ USA Dept. Computer Science and Engineering Michigan State University East Lansing MI USA IBM Thomas J. Watson Research Center Yorktown Heights NY USA

ISBN: (数字)9798350344813

ISBN: (纸本)9798350344820

Efficient training and inference algorithms, such as low-rank adaption and model pruning, have shown impressive performance for learning Transformer-based large foundation models. However, due to the technical challenges of the non-convex optimization caused by the complicated architecture of Transformers, the theoretical study of why these methods can be applied to learn Transformers is mostly elusive. To the best of our knowledge, this paper shows the first theoretical analysis of the property of low-rank and sparsity of one-layer Transformers by characterizing the trained model after convergence using stochastic gradient descent. By focusing on a data model based on label-relevant and label-irrelevant patterns, we quantify that the gradient updates of trainable parameters are low-rank, which de-pends on the number of label-relevant patterns. We also analyze how model pruning affects the generalization while improving computation efficiency and conclude that proper magnitude-based pruning has a slight effect on the testing performance. We implement numerical experiments to support our findings.

关键词： Training Analytical models Adaptation models Computational modeling Neurons Stochastic processes Signal processing algorithms

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Practical Numerical Modeling Approach for a Design of a Two-Dimensional Linear Synchronous Actuator

Practical Numerical Modeling Approach for a Design of a Two-...

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International Symposium on Linear Drives for Industry Applications (LDIA)

作者： Noriaki Ito Takafumi Koseki Wataru Ohnishi Yuichiro Nakamura Kenji Takahashi Hiroyuki Sekiguchi dept. of Electrical Engineering and Information systems The University of Tokyo Tokyo Japan Advanced Technology R&D Center Mitsubishi Electric Corporation Hyogo Japan

This paper proposes the design of a magnetically levitated planar motor, the configuration of a basic numerical method to embody the control concept, and the design procedure for an experimental test rig. This motor is stable in the levitating direction but unstable in the driving direction because it utilizes the repulsive force between the stator permanent magnets and the moving coils. One problem with magnetically levitated planar motors is the time-consuming nature of the three-dimensional finite element analysis. This analysis requires significant computational resources and can result in significant delays in the design process. To address this problem, a less time-consuming method using Gaussian process regression is presented. In addition, the proposed analysis is verified using a test rig equipped with several sensors. These sensors will provide measurements of static force characteristics and flux linkage to evaluate the performance and effectiveness of the motor under various operating conditions.

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Flight Trajectory Prediction Based on Hybrid-Recurrent Networks

Flight Trajectory Prediction Based on Hybrid-Recurrent Netwo...

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2021 IEEE Cognitive Communications for Aerospace Applications Workshop, CCAAW 2021

作者： Schimpf, Nathan Knoblock, Eric J. Wang, Zhe Apaza, Rafael D. Li, Hongxiang University of Louisville Dept. of Electrical and Computer Engineering Louisville United States Nasa Glenn Research Center Communications and Intelligent Systems Division Cleveland United States

ISBN: (纸本)9781665412582

The development of future technologies for the National Airspace System (NAS) will be reliant on a new communications infrastructure capable of managing the limited available spectrum for communications among aircraft and ground systems. Emerging approaches to autonomous allocation of aviation spectrum mostly rely on machine learning techniques, where 4D (longitude, latitude, altitude, time) trajectory prediction is an important data input to enable real-time resource allocation. This study explores and evaluates effective data sources and deep recurrent neural network techniques when determining flight trajectories. Specifically, data are collected and evaluated in a 14-day period. Sources of data include NASA Sherlock Data Warehouse, MIT Lincoln Labs Corridor Integrated Weather Service (CIWS), and assorted NOAA weather datasets. Deep learning models for 4D predictions all utilize a hybrid-recurrent technique. A baseline model is considered via the convolutional-recurrent design from the existing literature. The modified design considers Gated Recurrent Units (GRU), Independently Recurrent Neural Networks (IndRNN), and stand-alone self-attention layers. Results indicate the effectiveness of Long-Short Term Memory (LSTM) and GRU cells for state-of-the-art data processing (interpolation). Attention mechanisms provide notable performance improvements to convolutional layers and may extend dimensional capabilities of a learning model. Finally, NOAA measurements provide only a supplemental value by improving predictions along a flight altitude;however, tailored products such as CIWS Echo Top remain the most holistically accurate. © 2021 IEEE.

关键词： Convolution

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A Compensation Mechanism for EV Flexibility Services using Discrete Utility Functions

arXiv

引用

arXiv 2022年

作者： Giraldo, Juan S. Arias, Nataly Bañol Duque, Edgar Mauricio Salazar Hoogsteen, Gerwin Hurink, Johann L. Dept. of Electrical Engineering Mathematics and Computer Science University of Twente Netherlands Electrical Energy Systems Group Eindhoven University of Technology Netherlands

Compensation mechanisms are used to counterbalance the discomfort suffered by users due to quality service issues. Such mechanisms are currently used for different purposes in the electrical power and energy sector, e.g., power quality and reliability. This paper proposes a compensation mechanism using EV flexibility management of a set of charging sessions managed by a charging point operator (CPO). Users' preferences and bilateral agreements with the CPO are modelled via discrete utility functions for the energy not served. A mathematical proof of the proposed compensation mechanism is given and applied to a test scenario using historical data from an office building with a parking lot in the Netherlands. Synthetic data for 400 charging sessions was generated using multivariate elliptical copulas to capture the complex dependency structures in EV charging data. Numerical results validate the usefulness of the proposed compensation mechanism as an attractive measure both for the CPO and the users in case of energy not served. © 2022, CC BY.

关键词： Electric vehicles

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Designing Classical Header Bits for Quantum Wrapper Networking with O-band Quantum Signal

Designing Classical Header Bits for Quantum Wrapper Networki...

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Frontiers in Optics + Laser Science 2023, FiO, LS 203: Part of Frontiers in Optics + Laser Science 2023

作者： Gül, G. Berkay On, M. Singh, S.K. Proietti, R. Kanter, G.S. Yoo, S.J.B. Kumar, P. Graduate Program in Applied Physics Northwestern University 2145 Sheridan Road EvanstonIL60208 United States Department of Electrical and Computer Engineering University of California Davis DavisCA95616 United States Department of Electronics and Communication Eng. Indian Institute of Technology Roorkee247667 India Department of Electronics and Telecommunications Engineering Politecnico di Torino Italy Center for Photonic Communication and Computing Dept. of Electrical and Computer Eng. Northwestern University EvanstonIL60208 United States

We investigate the impact of wavelength choice for the classical headers in quantum wrapper networking with O-band quantum payload. We show that a C-band header performs substantially better than an O-band header. ... 详细信息

ISBN: (纸本)9781957171296

关键词： Bandpass filters Beam splitters Bit error rate Entanglement distribution Quantum teleportation Raman scattering

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Designing Classical Header Bits for Quantum Wrapper Networking with O-band Quantum Signal

Designing Classical Header Bits for Quantum Wrapper Networki...

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2023 Laser Science, LS 2023

作者： Gül, G. On, M. Berkay Singh, S.K. Proietti, R. Kanter, G.S. Yoo, S.J.B. Kumar, P. Graduate Program in Applied Physics Northwestern University 2145 Sheridan Road EvanstonIL60208 United States Department of Electrical and Computer Engineering University of California Davis DavisCA95616 United States Department of Electronics and Communication Eng. Indian Institute of Technology Roorkee247667 India Department of Electronics and Telecommunications Engineering Politecnico di Torino Italy Center for Photonic Communication and Computing Dept. of Electrical and Computer Eng. Northwestern University EvanstonIL60208 United States

关键词： Quantum electronics

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On the Ergodic Rate of Uplink Rate-Splitting Multiple Access

On the Ergodic Rate of Uplink Rate-Splitting Multiple Access

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IEEE Conference on Wireless Communications and Networking

作者： Athanasios P. Chrysologou Sotiris A. Tegos Panagiotis D. Diamantoulakis Nestor D. Chatzidiamantis Paschalis C. Sofotasios George K. Karagiannidis Dept. of Electrical and Computer Engineering Aristotle University of Thessaloniki Thessaloniki Greece Department of Computer and Communication Engineering C2PS and 6G Center Khalifa University Abu Dhabi UAE Cyber Security Systems and Applied AI Research Center Lebanese American University (LAU) Lebanon

ISBN: (数字)9798350303582

ISBN: (纸本)9798350303599

One of the principal challenges anticipated for the forthcoming sixth-generation (6G) wireless networks is the imper-ative need to design advanced multiple access techniques capable of enabling massive connectivity. In this direction, rate splitting multiple access (RSMA) has been reported as a promising approach. In this work, we investigate the ergodic rate (ER) performance of an uplink RSMA network which consists of two sources. Specifically, analytical closed-form expressions for the sources' ERs and the system ergodic sum rate (ESR) are derived under the case of perfect successive interference cancellation $(\text{pSIC})$ and perfect channel state information (pCSI) as well as under the more realistic scenario of imperfect SIC (ipSIC) and imperfect CSI (ipCSI). Furthermore, an asymptotic analysis for the high signal-to-noise ratio regime is presented, which provides useful insights into the sources' behavior in all considered cases. The accuracy of the provided analytical expressions is validated by simulation results, which not only explore how different system parameters affect the extracted expressions, but also reveal the detrimental impact of ipSIC and ipCSI on system performance.

关键词： 6G mobile communication Interference cancellation Closed-form solutions Accuracy Wireless networks System performance Simulation

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Mitigating Insecure Outputs in Large Language Models(LLMs): A Practical Educational Module

Mitigating Insecure Outputs in Large Language Models(LLMs): ...

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IEEE Annual International computer Software and Applications Conference (COMPSAC)

作者： MD Abdul Barek Md Mostafizur Rahman Shapna Akter A.B.M Kamrul Islam Riad Md Abdur Rahman Hossain Shahriar Akond Rahman Fan Wu Department of Intelligent Systems and Robotics University of West Florida USA Dept. of Cybersecurity and Information Technology University of West Florida USA Center for Cybersecurity University of West Florida USA Computer Science and Software Engineering Auburn University USA Dept. of Computer Science Tuskegee University USA

ISBN: (数字)9798350376968

ISBN: (纸本)9798350376975

Large Language Models (LLMs) have extensive ability to produce promising output. Nowadays, people are increasingly relying on them due to easy accessibility, rapid and outstanding outcomes. However, the use of these results without appropriate scrutiny poses serious security risks, particularly when they are integrated with other software, APIs, or plugins. This is because the LLM outputs are highly dependent on the prompts they receive. Therefore, it is essential to carefully clean these outputs before using them in additional software environments. This paper is designed to teach students about the potential dangers of contaminated LLM output within the context of web development through prelab, hands-on, and postlab experiences. Hands-on lab provides practical guidance on how to handle LLM vulnerabilities to make applications safe with some real-world examples in Python. This approach aims to provide students with a deeper understanding of the precautions necessary to ensure software against the vulnerabilities introduced by LLM output.

关键词： Training Navigation Large language models Computational modeling Software Security Software development management

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