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2023 IEEE Energy Conversion Congress and Exposition, ECCE 2023

作者： Bossi, Giuseppe Buccella, Concettina Cecati, Carlo Damiano, Alfonso Floris, Andrea Simonetti, Francesco University of Cagliari Department of Electrical and Electronic Engineering Cagliari09123 Italy University of Aquila Computer Science and Mathematics Department of Information Engineering L'Aquila67100 Italy

ISBN: (纸本)9798350316445

This paper deals with a battery-operated auxiliary DC/AC power conditioning system for all-electric aircraft, consisting of a Triple Active Bridge coupled with a five-level cascaded H-bridge converter with implemented Pulse Amplitude Width Modulation. The proposed system has been developed for an ultra-light electric aircraft and is characterized by 5kW output power, supplied at 115 V and 400 Hz. The synthesis of the adopted control algorithm and the experimental validation on a scaled-down prototype confirm the worth of the proposed solution and design procedure. © 2023 IEEE.

关键词： all electric aircraft DC/DC and DC/AC power conversion energy storage system multilevel inverter triple active bridge

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Design and Development of Three-Phase High-Frequency AC Power Distribution Architecture for Space Application

Design and Development of Three-Phase High-Frequency AC Powe...

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2023 IEEE Energy Conversion Congress and Exposition, ECCE 2023

作者： Keesara, Sairakshith Mazumder, Surjakanta Roy, Shamibrota Kishore Kundu, Utsab Peter, Pradeep K Prasad, Enjeti Basu, Kaushik Indian Institute of Science Department of Electrical Engineering Bengalore India U R Rao Satellite Centre Bangalore India Texas A&m College of Engineering Department of Electrical and Computer Engineering Texas United States

ISBN: (纸本)9798350316445

This paper proposes a new three-phase (3φ) high-frequency ac (HFAC) power distribution architecture for space applications. Advantages of this approach include high-quality HFAC generation with improved THD, load-independent voltage gain, etc. A design procedure for a 10kHz ac power distribution is detailed. Inverter switching frequency is selected around 200 kHz. A systematic method is adopted for selecting the inverter switches based on specific performance parameters and power loss. Considering sine-triangle PWM, the AC-side filter inductor is designed to keep the inductor current ripple within a specified limit. The filter capacitor is also selected based on a ripple criterion. Since the inverter is switched at 200 kHz, the critical considerations for high-frequency PCB layout are also discussed. Finally, the proposed inverter design approach is validated experimentally at 1kW, 30 Vph(peak), 10 kHz 3φ HFAC system. © 2023 IEEE.

关键词： High-frequency PWM inveter LC filter trap filters

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Gap-surface Plasmon and Plasmon Polariton Induced Multiband Absorption on a Nanostructured Metasurface

Gap-surface Plasmon and Plasmon Polariton Induced Multiband ...

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IEEE Photonics Conference (IPC)

作者： Partha Mondal Omar Alkhazragi Tien Khee Ng Boon S. Ooi Hakan Bagci Electrical and Computer Engineering (ECE) Program Computer Electrical and Mathematical Science and Engineering (CEMSE) Division King Abdullah University of Science and Technology (KAUST) Thuwal Saudi Arabia

A plasmonic nanostructured metal-dielectric-metal metasurface that supports multiband absorption is fabricated and characterized. Multispectral absorption is observed across the near-IR to telecom frequencies due to t... 详细信息

ISBN: (数字)9798350361957

ISBN: (纸本)9798350361964

关键词： Filters Absorption Plasmons Metasurfaces Frequency conversion Nanoscale devices Telecommunications Optoelectronic and photonic sensors Photonics

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Revolutionizing Marine Stability: A Review of Cutting-Edge Gyro Stabilizer Designs and Techniques for Vessels

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IEEE Access 2025年 13卷 89542-89555页

作者： Rahiman, Wan Ghazali, Mohamad Hazwan Mohd Mahmood, Iskandar Al-Thani Universiti Sains Malaysia Engineering Campus School of Electrical and Electronic Engineering Penang Nibong Tebal14300 Malaysia Daffodil International University Daffodil Robotics Lab Department of Computer Science and Engineering Dhaka1216 Bangladesh Universiti Kebangsaan Malaysia Faculty of Engineering and Built Environment Department of Mechanical and Manufacturing Engineering Selangor Bangi43600 Malaysia PETRONAS Research Sdn Bhd Selangor Kajang43000 Malaysia

Advancement in marine vessel stabilization technologies has greatly improved the safety and comfort for crews, cargo, and passengers, while also lowering the risk of capsizing. Marine vessels operating in open waters experience disturbances from unpredictable waves and strong winds, leading to instability and potential accidents. To mitigate these risks, stabilizers are integrated into ships to maintain stability, safety, and seaworthiness. As modern vessels evolve, stabilizers play an increasingly vital role, with gyro stabilizers emerging as a highly effective solution for mitigating roll motion without external appendages. This paper provides a comprehensive review of gyro stabilizer designs and techniques, covering their historical development, mathematical modeling, advantages, limitations, and case studies. A review of current research highlights a strong emphasis on simulating gyro stabilizer control systems, demonstrating their potential in enhancing vessel stability. However, key challenges persist, including accurate wave moment prediction during the design phase and gyroscopic drift, which can impact long-term performance and efficiency. Despite these challenges, recent advancements in control algorithms and optimization techniques have improved gyro stabilizer adaptability under varying sea conditions. By exploring the latest developments, this review serves as a valuable reference for researchers, engineers, and maritime professionals seeking to advance marine stabilization technologies. The findings underscore the potential of next-generation gyro stabilizers, including fiber optic gyros, in achieving higher precision, stability, and operational efficiency. © 2013 IEEE.

关键词： Warships

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EEG-Based Motion Intention Detection for Robotic Rehabilitation: Evaluating Classification and Regression Algorithms

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SN computer science 2024年第8期5卷 1057页

作者： Khan, Abdul Manan Khan, Fatima Dwivedi, Anshu Kumar Semwal, Vijay Bhaskar Martha, Sheshikala Bijalwan, Vishwanath Department of Aerospace Engineering University of Bristol Bristol United Kingdom Graduate School of Cancer Science and Policy National Cancer Center Ilsan South Korea School of Computer Science Engineering and Technology Bennett University Greater Noida India Department of CSE Maulana Azad National Institute of Technology Bhopal India School of Computer Science & amp Artificial Intelligence SR University Warangal Warangal India Department of Electrical and Electronics Engineering University of West London London United Kingdom

Rehabilitation robots have shown significant efficacy in restoring lost motor functions in stroke survivors by providing compliant assistance during rehabilitation tasks. However, accurately understanding the desired motion intention (DMI) of the subjects is a significant challenge in providing effective robotic assistance. This work aims to determine the desired motion intention (DMI) for compliant robotic assistance through the analysis of upper limb motions using electroencephalography (EEG) signals. A group of fifteen individuals who were in good health carried out six distinct exercises. These movements were categorized using decision trees, random forest, and deep learning algorithms. Using deep learning, we achieved the highest accuracy rates, reaching 82% for movement-to-movement classification and 90% for movement-to-rest classification. The random forest method achieved superior performance compared to the other algorithms in regression tests, with an accuracy rate of 83%. The results demonstrate the effectiveness of our proposed EEG-based classification and regression techniques in detecting DMI for robotic help in *** study was conducted with a small sample size, exclusively involving healthy subjects, which is a critical factor to consider. This may limit the generalizability of the results, particularly when populations with motor impairments are taken into account. However, the study offers valuable insights into the potential of the EEG-based models for real-time rehabilitation applications despite this limitation. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： C-RNN Deep learning Desired motion intention EEG signals Machine learning Rehabilitation

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NEAR-OPTIMAL OFFLINE REINFORCEMENT LEARNING WITH LINEAR REPRESENTATION: LEVERAGING VARIANCE INFORMATION WITH PESSIMISM 10

NEAR-OPTIMAL OFFLINE REINFORCEMENT LEARNING WITH LINEAR REPR...

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10th International Conference on Learning Representations, ICLR 2022

作者： Yin, Ming Duan, Yaqi Wang, Mengdi Wang, Yu-Xiang Department of Computer Science University of California Santa Barbara United States Department of Electrical and Computer Engineering Princeton University United States

Offline reinforcement learning, which seeks to utilize offline/historical data to optimize sequential decision-making strategies, has gained surging prominence in recent studies. Due to the advantage that appropriate function approximators can help mitigate the sample complexity burden in modern reinforcement learning problems, existing endeavors usually enforce powerful function representation models (e.g. neural networks) to learn the optimal policies. However, a precise understanding of the statistical limits with function representations, remains elusive, even when such a representation is linear. Towards this goal, we study the statistical limits of offline reinforcement learning with linear model representations. To derive the tight offline learning bound, we design the variance-aware pessimistic value iteration (VAPVI), which adopts the conditional variance information of the value function for time-inhomogeneous episodic linear Markov decision processes (MDPs). VAPVI leverages estimated variances of the value functions to reweight the Bellman residuals in the least-square pessimistic value iteration and provides improved offline learning bounds over the best-known existing results (whereas the Bellman residuals are equally weighted by design). More importantly, our learning bounds are expressed in terms of system quantities, which provide natural instance-dependent characterizations that previous results are short of. We hope our results draw a clearer picture of what offline learning should look like when linear representations are provided. © 2022 ICLR 2022 - 10th International Conference on Learning Representationss. All rights reserved.

关键词： Reinforcement learning

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Single extracellular vesicle trapping, and manipulation assisted by plasmonic mirror-enhanced optical anapole mode

Single extracellular vesicle trapping, and manipulation assi...

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2023 Conference on Lasers and Electro-Optics, CLEO 2023

作者： Hong, Chuchuan Hong, Ikjun Ndukaife, Justus C. Vanderbilt Institute ofNanoscale Science and Engineering Vanderbilt University NashvilleTN United States Department of Electrical Engineering and Computer Science Vanderbilt University NashvilleTN United States

We experimentally demonstrate trapping and manipulation of single extracellular vesicle using optical anapole state for the first time. It provides a promising platform for single extracellular vesicle analysis with h...

ISBN: (纸本)9781957171258

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RelaxVR: Cybersickness Reduction in Immersive Virtual Reality Through Explainable AI and Large Language Models

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IEEE Access 2025年 13卷 84689-84712页

作者： Kumar Kundu, Ripan Anuarul Hoque, Khaza University of Missouri-Columbia Department of Electrical Engineering and Computer Science ColumbiaMO65211 United States

Virtual reality (VR) systems are susceptible to cybersickness, significantly hindering user immersion. Very recently, researchers introduced explainable artificial intelligence (XAI) enabled methods for detecting and explaining cybersickness features. Since XAI methods can identify the dominant features causing cybersickness, we argue that this knowledge can also guide the dynamic adoption of effective cybersickness reduction strategies, unlike state-of-the-art static approaches that rely on fixed methods. This paper introduces a new dataset MazeSick and RelaxVR, an interactive XAI-guided VR cybersickness reduction framework to predict, explain, and reduce cybersickness. Specifically, we propose an innovative XAI-guided cybersickness reduction engine, which selects the most appropriate reduction technique based on XAI-provided feature importance scores from a cybersickness reduction library of different reduction techniques. We also design an interactive dialogue system powered by large language models, enabling users to engage in the VR simulation via voice commands to understand the reasoning of cybersickness and select effective reduction strategies through natural language interaction. We deployed RelaxVR on a consumer-grade VR headset (e.g., HTC VIVE Pro) and validated it through a user study on cybersickness evaluation, where participants were immersed in a custom-built VR Maze simulation. Our results demonstrate that RelaxVR effectively reduces cybersickness with minimal impact on immersion, and 94% of the users agreed that RelaxVR is highly effective for reducing cybersickness and easier to use through the dialogue engine for selecting effective reduction strategies. © 2013 IEEE.

关键词： Virtual reality

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Adaptive Confidence Threshold for ByteTrack in Multi-Object Tracking 12

Adaptive Confidence Threshold for ByteTrack in Multi-Object ...

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12th IEEE International Conference on Control, Automation and Information sciences, ICCAIS 2023

作者： Ma, Linh Van Hussain, Muhammad Ishfaq Park, JongHyun Kim, Jeongbae Jeon, Moongu Gwangju Institute of Science and Technology School of Electrical Engineering and Computer Science Korea Republic of Pusan National University Department of Mechanical-IT Convergence System Engineering Busan Korea Republic of

ISBN: (纸本)9798350328783

We investigate the application of ByteTrack in the realm of multiple object tracking. ByteTrack, a simple tracking algorithm, enables the simultaneous tracking of multiple objects by strategically incorporating detections with a low confidence threshold. Conventionally, objects are initially associated with high confidence threshold detections. When the association between objects and detections becomes ambiguous, ByteTrack extends the association to lower confidence threshold detections. One notable drawback of the existing ByteTrack approach is its reliance on a fixed threshold to differentiate between high and low-confidence detections. In response to this limitation, we introduce a novel and adaptive approach. Our proposed method entails a dynamic adjustment of the confidence threshold, leveraging insights derived from overall detections. Through experimentation, we demonstrate the effectiveness of our adaptive confidence threshold technique while maintaining running time compared to ByteTrack. © 2023 IEEE.

关键词： Object detection

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Energy-Efficient Resource Allocation for Smart Grids using Deep Reinforcement Learning in IoT Cloud Systems 8

Energy-Efficient Resource Allocation for Smart Grids using D...

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8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2024

作者： Patil, Harshal Kansal, Vipashi Balasubramani, R. Manohar, Talari Vekariya, Daxa Kannan, Sathish Iilm University School of Computer Science & Engineering Uttar Pradesh Greater Noida201 306 India Graphic Era Deemed to be University Department of Computer Science & Engineering Uttarakhand Dehradun248 002 India Department of Information Science and Engineering Karnataka Nitte574110 India Anantha Lakshmi Institute of Technology and Sciences Department of Electrical and Electronics Engineering Andhra Pradesh Ananthapuramu515721 India Parul Institute of Engineering and Technology Parul University Department of Computer Science & Engineering Post Limda Gujarat 391760 India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Vlsi Microelectronics Tamil Nadu Chennai602105 India

ISBN: (纸本)9798350376425

This study describes the use of deep reinforcement learning (DRL) models to fine-tune resource allocation for smart grids in order to reduce energy waste and operational costs. The research experiment evaluates four different DRL algorithms, which include SAC, DDPG, PPO, and A3C. It demonstrates performance through various evaluation metrics, which include impeccable energy efficiency, perfect resource utilization, load balancing ratio, average response time, and computational overhead. SAC is proven to achieve 92.5% energy efficiency, 88.7% resource utilization, 90% load balancing ratio, and response time of 120ms. The computational overhead assessed supplements to 15 GFLOPS. DDPG showcased 90.1% energy efficiency and 85.4% resource utilization, 88.9% load balancing, a response time of 110 ms, and a computational overhead of 14.8 GFLOPS. On the other hand, PPO and A3C also proved to work well and have a promising future to be implemented in a dynamic grid. This analysis demonstrates that practical applications from DRL, including real-time decision-making and adaptive control approaches, can prove vital while assigning and working on energy tasks and renewable conduct. These models prove important and beneficial in enhancing the operational efficiency of the smart grid, understanding and learning continuously from real-time data. It will be a vital concept and idea that would be helpful in ensuring that smart grids work perfectly to help in a reduction in carbon footprints and effective energy resource management to ensure end-use service provider satisfactions. © 2024 IEEE.

关键词： Deep reinforcement learning

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