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Design and Control of a High-Bandwidth Dual Active Bridge DC-DC Converter 14

Design and Control of a High-Bandwidth Dual Active Bridge DC...

引用

14th Annual IEEE Applied Power Electronics Conference and Exposition, APEC 2025

作者： Uzum, Alper Hasan, Syed Imam Sozer, Yilmaz Loparo, Kenneth A. The University of Akron Dept. of Electrical and Computer Engineering AkronOH United States Case Western Reserve University Dept. of Electrical Computer and Systems Engineering ClevelandOH United States

ISBN: (纸本)9798331516116

For the stable operation of sensitive systems, achieving high-bandwidth control in power converters is critical, as the current and voltage ripple directly reflects the quality of the synthesized waveform. The selection of an appropriate DC-link capacitor, which significantly influences the system's bandwidth, plays a pivotal role in ensuring performance. This paper explores the intricate relationship between input and output DC-link capacitance and its effect on the system bandwidth in Dual Active Bridge (DAB) converters, a topic that remains relatively unexplored despite numerous research on high-bandwidth control for other power converters. To address the challenges posed by the bulky DC-link capacitor typically required to manage ripple current, the proposed approach integrates a harmonic trap filter to effectively mitigate these limitations. Using state-space modeling and state-variable feedback control, the design and control strategy is validated on a 5 kW DAB converter, demonstrating its ability to enhance system bandwidth without compromising ripple performance. © 2025 IEEE.

关键词： Feedback control

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Network-based Intrusion Detection Datasets: A Survey 23

Network-based Intrusion Detection Datasets: A Survey

引用

23rd International Arab Conference on Information Technology, ACIT 2022

作者： Ahmed, Lubna Ali Hassan Hamad, Yahia Abdalla Mohamed Abdalla, Ahmed Abdallah Mohamed Ali Sudan University of Science and Technology Dept. of Electronics Engineering Khartoum Sudan Sudan University of Science and Technology Dept. of Computer Networks and Systems Khartoum Sudan

ISBN: (纸本)9798350320244

The increasing reliance on computers and the internet has increased the need for protection against attacks and security threats. A network-based intrusion detection system (NIDS) is one of the promising research fields that could highly contribute to cyber-Attacks detection and mitigation. Datasets are necessary for NIDS training and evaluation. This paper provides a comprehensive survey of publicly available NIDS datasets. The study covers the most used NIDS datasets, each dataset is briefly described along with its main properties and shortcomings. Moreover, the paper highlights the most important dataset characteristics and provides a clear datasets comparison accordingly. Finally, the paper discusses observations and provides some recommendations to help researchers identify future work. © 2022 IEEE.

关键词： Network security

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Dynamic SpikFormer: Low-Latency & Energy-Efficient Spiking Neural Networks with Dynamic Time Steps for Vision Transformers

Dynamic SpikFormer: Low-Latency & Energy-Efficient Spiking N...

引用

International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Gourav Datta Zeyu Liu Anni Li Peter A. Beerel Dept. of Electrical Computer & Systems Engineering Case Western Reserve University Cleveland USA Ming Hsieh Dept. of Electrical and Computer Engineering University of Southern California Los Angeles USA

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Spiking Neural Networks (SNNs) have emerged as a popular spatio-temporal computing paradigm for complex vision tasks. Recently proposed SNN training algorithms have significantly reduced the number of time steps (down to 1) for improved latency and energy efficiency, however, they target only convolutional neural networks (CNN). These algorithms, when applied to the recently spotlighted vision transformers (ViT), either require a large number of time steps or fail to converge. Based on the analysis of the histograms of the ANN and SNN activation maps, we hypothesize that each ViT block has a different sensitivity to the number of time steps. We propose a novel training framework that dynamically allocates the number of time steps to each ViT module depending on a trainable score assigned to each timestep. In particular, we generate a scalar binary time step mask that filters spikes emitted by each neuron in a leaky-integrate-and-fire (LIF) layer. The resulting SNNs have high activation sparsity and require only accumulate operations (AC), except for the input embedding layer, in contrast to expensive multiply-and-accumulates (MAC) needed in traditional ViTs. This yields significant improvements in energy efficiency. We evaluate our training framework and resulting SNNs on image recognition tasks including CIFAR10, CIFAR100, and ImageNet with different ViT architectures. We obtain a test accuracy of 95.97% with 4.97 time steps with direct encoding on CIFAR10.

关键词： Training computer vision Sensitivity Neurons Signal processing algorithms Spiking neural networks Transformers Energy efficiency Convolutional neural networks Speech processing

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Improving the Efficiency of a Multistage Evaporator Station for Sugar Production Using Neural Networks 4

Improving the Efficiency of a Multistage Evaporator Station ...

引用

4th IEEE International Conference on Modern Electrical and Energy System, MEES 2022

作者： Zaiets, Nataliia Lutska, Nataliia Vlasenko, Lidiia National University of Life and Environmental Sciences of Ukraine Det. of Automation and Robotic Systems Kyiv Ukraine National University of Food Technologies Dept. of Automation and Computer Technologies of Control Systems Kyiv Ukraine State University of Trade and Economics Dept. of Software Engineering and Cyber Security Kyiv Ukraine

ISBN: (纸本)9798350346831

The paper shows the process of developing a model of multistage evaporator station of a sugar factory based on a neural network. The neural network predicts the main performance indicators of the multistage evaporator station, such as the concentration of syrup at the output of the evaporator and the amount of evaporated water at the station. Unlike existing solutions, forecasting is performed in real time based on a set of indicators of the automation system (temperatures, flow rates, levels, pressure, density in the apparatus) and industrial laboratory (concentration of solids at the inlet to the evaporation station). The proposed neural network model has an MLP structure with 27 inputs, 2 outputs and one hidden layer with 20 neurons and predicts these indicators with an error of less than 5%. This allows to increase the informative support of the operator-technologist on the quality indicators of the enterprise without additional load on the industrial laboratory and without additional cost automatic quality devices, as well as to perform timely adjustment of the technological mode of the department. © 2022 IEEE.

关键词： Evaporation

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Augmented Karuta: Interactive Playing Cards on the Floor for Learning Local Culture

Augmented Karuta: Interactive Playing Cards on the Floor for...

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IEEE International Conference on Artificial Intelligence and Virtual Reality (AIVR)

作者： Nobuyuki Umezu Hideki Bando Dept. of Computer and Information Sciences Ibaraki University Hitachi Japan Dept. of Mechanical Systems Engineering Ibaraki University Hitachi Japan

ISBN: (数字)9798331521578

ISBN: (纸本)9798331521585

This paper introduces Augmented Karuta, an interactive floor projection experience inspired by traditional Japanese playing cards, known as ‘Karuta’. Designed to engage users and stimulate interest in local cultures, history, and traditions, the system utilizes three projectors. Two projectors project 46 “grabbing” cards onto the floor screen of 4.1 x 3.7 (m), while the third displays a corresponding “reading” card on a wall screen. Each matching pair of cards shares a poem or sentence that begins with one of the Japanese phonograms. During a session, users actively engage by stepping onto the projected ‘grabbing’ card that matches the ‘reading’ card displayed on the wall, effectively blending physical movement with cognitive skills to find the correct match. By integrating a two-dimensional LiDAR sensor, the system tracks the user's position on the interactive floor. This tracking is vital for the proposed system to immediately respond to the user's actions, ensuring an immersive and seamless interaction with the digital karutas on the floor. We conducted evaluation experiments at two museums, gathering positive feedback from 57 participants. Future work will focus on quantitatively evaluating the system's impact on learning effectiveness.

关键词： Laser radar Tracking Virtual reality Learning (artificial intelligence) Museums History Floors

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Dual Auction Peer-to-Peer Control Scheme for Empowering Prosumers Participation in the Local Energy Markets

Dual Auction Peer-to-Peer Control Scheme for Empowering Pros...

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International Conference on Computing, Mathematics and engineering Technologies (iCoMET)

作者： Muhammad Ikram Salman Ahmed Safdar Nawaz Khan Marwat Dept. of Computer Systems Engineering University of Engineering and Technology Peshawar Pakistan

The emerging trend of distributed and renewable energy sources encourages energy trading in the electricity market. Various forms of energy markets are evolving for successful energy trading. The local energy market is one of them in which various participants are actively coordinating for optimal utilization of power. Such type of local energy markets provides individual prosumers to trade their surplus energy in the neighbourhood in peer-to-peer topology. The complexity of such type of local energy market is increasing in terms of structure, control, coordination, and cost estimation. For the cooperative behaviour of the local energy markets, a dual auction coordination control scheme is important to encourage more prosumers for energy trading. We proposed a novel scheme that performs dual auctions for prosumers in a decentralized fashion. We assessed dual auction matching by coordinating prosumers and consumers to sell and purchase energy in the local energy market. The aggregator performed balancing in the proposed dual auction scheme as an autonomous entity. The results were simulated in MATLAB and JADE for the complex prosumer peer-to-peer network. Our proposed scheme shows each peer dual auction matching improves from 0.95 to 2.03 for optimal hierarchal energy trading in energy markets. For peer participation, the participation ratio increased from 0.684 to 1.74 and optimal aggregator coordination from 9.18% to 19% in the local energy markets.

关键词： Renewable energy sources Costs Estimation Consensus control Market research Electricity supply industry Peer-to-peer computing

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LightNN: An Optimized Deep Learning Framework for Enhanced Fruit Classification

LightNN: An Optimized Deep Learning Framework for Enhanced F...

引用

Computing, Applications and systems (COMPAS), IEEE International Conference on

作者： Maharin Afroj Israt Jahan Md Polash Islam Md. Saifur Rahman Dept. of Computer Science and Engineering Bangladesh University of Business and Technology Dhaka Bangladesh Dept. of Computer and Systems Sciences Stockholm University Stockholm Sweden

ISBN: (数字)9798331529765

ISBN: (纸本)9798331529772

The crucial field of fruit classification in computer vision and deep learning brings about breakthroughs that have broad applications in the retail and agricultural industries. By automating the identification and sorting of various fruit types, these technologies enhance efficiency, accuracy, and scalability, benefiting processes from farm production to market distribution. The integration of sophisticated algorithms and neural networks not only improves the speed and reliability of fruit classification but also offers the potential for real-time quality control and inventory management, revolutionizing traditional methods and paving the way for more innovative, data-driven approaches in these sectors. This study introduces LightNN, a novel and effective paradigm with a lightweight architecture that tackles the complex problems related to computational efficiency. Combining a small classifier with a simplified feature extractor, LightNN achieves the ideal balance between simplicity and accuracy. The feature extractor is capable of extracting hierarchical features from RGB images by using convolutional layers with ReLU activations and max-pooling procedures. The flattened output is then processed by the classifier, which consists of fully connected layers with ReLU activations and dropout regularisation. Remarkably, LightNN, with just 298,835 parameters, manages to get a respectable 96.37% test accuracy in fruit categorization tasks. The results highlight LightNN’s promise as an effective and efficient fruit classifying method, particularly in situations with constrained computational resources.

关键词： Deep learning Training Visualization Accuracy Computational modeling Scalability computer architecture Feature extraction Computational efficiency Sorting

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Improving ASTRO's Perception Capability for a more Natural Follow-Me Task 6

Improving ASTRO's Perception Capability for a more Natural F...

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6th Experiment at International Conference, exp.at 2023

作者： Ferreira, Bruno Sorrentino, Alessandra Menezes, Paulo Batista, Jorge Dias, Jorge Fiorini, Laura Cavallo, Filipo University of Coimbra Institute of Systems and Robotics Dept. of Electrical and Computer Engineering Coimbra Portugal Khalifa University of Science and Technology Khalifa University Center for Autonomous Robotic Systems Abu Dhabi United Arab Emirates University of Florence Dept. of Industrial Engineering Florence Italy BioRobotics Institute Scuola Superiore Sant'Anna Pisa Italy

ISBN: (纸本)9798350314403

Social assistive robots usually encompass a great compromise between the advanced perception models that one can use and their computing capabilities. The ideal approaches are always oriented towards low power consumption while maintaining a higher order of responsiveness to the surroundings. Therefore, we present in this paper, an improvement of the follow-me system on ASTRO. In detail, we propose the use of Mediapipe SDK for human detection and tracking, when ASTRO is meant to accompany someone that is walking. A study on the new velocity and distance profiles that the robot keeps from the individuals is presented and we have also evaluated how it affects their perception of being safe. The presented results show that our new approach allows the system to achieve real-time performance by becoming ≈ 9.1× faster, smoothing, and keeping a more natural distance from the user. © 2023 IEEE.

关键词： Human robot interaction

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Enabling A Network AI Gym for Autonomous Cyber Agents

Enabling A Network AI Gym for Autonomous Cyber Agents

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2022 International Conference on Computational Science and Computational Intelligence, CSCI 2022

作者： Li, Li El Rami, Jean-Pierre S. Taylor, Adrian Rao, James Hailing Kunz, Thomas Defence Research & Development Canada Ottawa Canada Queen's University Dept of Electrical and Computer Engineering Kingston Canada Carleton University Dept. of Systems & Computer Engineering Ottawa Canada

ISBN: (纸本)9798350320282

This work aims to enable autonomous agents for network cyber operations (CyOps) by applying reinforcement and deep reinforcement learning (RL/DRL). The required RL training environment is particularly challenging, as it must balance the need for high fidelity, best achieved through real network emulation, with the need for running large numbers of training episodes, best achieved using simulation. A unified training environment, namely the Cyber Gym for Intelligent Learning (CyGIL), is developed where an emulated CyGIL-E automatically generates a simulated CyGIL-S. From preliminary experimental results, CyGIL-S can train agents in minutes compared with the days required in CyGIL-E. The agents trained in CyGIL-S are transferrable directly to CyGIL-E, showing full decision proficiency in the emulated 'real' network. Enabling offline RL, the CyGIL solution presents a promising direction towards sim-to-real in leveraging RL agents in real-world cyber networks. © 2022 IEEE.

关键词： Reinforcement learning

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Deep Learning-Based Energy Efficiency Maximization in Multi-STAR-RIS-Assisted Massive MIMO-NOMA Networks

Deep Learning-Based Energy Efficiency Maximization in Multi-...

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International Conference on Artificial Intelligence in Information and Communication (ICAIIC)

作者： Ridho Hendra Yoga Perdana Toan-Van Nguyen Yushintia Pramitarini Duy H. N. Nguyen Beongku An Dept. of Software and Communications Engineering in Graduate School Hongik University Republic of Korea Department of Electrical and Computer Engineering San Diego State University San Diego CA USA Dept. of Software and Communications Engineering Hongik University Republic of Korea

ISBN: (数字)9798331507022

ISBN: (纸本)9798331507039

This paper studies deep learning (DL)-based energy efficiency maximization (EEM) in multi-simultaneous transmission and reflection-reconfigurable intelligent surfaces (STAR-RISs) assisted massive multiple-input multiple-output (mMIMO)-non-orthogonal multiple access (NOMA) networks. We formulate the EEM problem to jointly optimize the precoding matrix and STAR-RIS phase shifts subject to the power budget at the base station, STAR-RIS phase shift constraints, and minimum quality-of-service (QoS) requirements. The formulated EEM problem belongs to the mixed-integer programming class, which is difficult to solve optimally. Thus, we develop an alternative optimization approach by dividing the original EEM problem into two sub-problems such as phase shift and beamforming optimization, and solve them alternatively. A bisection search algorithm is proposed to solve the phase shift optimization, while the inner approximation method is employed to address the non-convex beamforming problem through our newly tractable transformations. To enable real-time optimization, we design a DL framework that predicts optimal phase shifts and precoding matrices under various parameter settings. Simulation results demonstrate that the DL-based approach accurately predicts the optimal solutions and is significantly faster than conventional methods. We also evaluate the impact of the essential parameters on the system's performance.

关键词： Array signal processing Precoding Simulation Quality of service Programming Search problems Prediction algorithms Energy efficiency Real-time systems Optimization

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