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A Novel Multiobjective Fireworks Algorithm and Its Applications to Imbalanced Distance Minimization Problems

IEEE/CAA Journal of Automatica Sinica 2022年第8期9卷 1476-1489页

作者： Shoufei Han Kun Zhu MengChu Zhou Xiaojing Liu Haoyue Liu Yusuf Al-Turki Abdullah Abusorrah College of Computer Science and Technology Nanjing University of Aeronautics and Astronauticsand also with the Collaborative Innovation Center of Novel Software Technology and IndustrializationNanjing 211106China Department of Electrical and Computer Engineering New Jersey Institute of TechnologyNewark NJ 07102 USA Center of Research Excellence in Renewable Energy and Power Systems Department of Electrical and Computer EngineeringFaculty of Engineeringand also with the K.A.CARE Energy Research and Innovation CenterKing Abdulaziz UniversityJeddah 21589Saudi Arabia

Recently,multimodal multiobjective optimization problems(MMOPs)have received increasing *** goal is to find a Pareto front and as many equivalent Pareto optimal solutions as *** some evolutionary algorithms for them have been proposed,they mainly focus on the convergence rate in the decision space while ignoring solutions *** this paper,we propose a new multiobjective fireworks algorithm for them,which is able to balance exploitation and exploration in the decision *** first extend a latest single-objective fireworks algorithm to handle *** we make improvements by incorporating an adaptive strategy and special archive guidance into it,where special archives are established for each firework,and two strategies(i.e.,explosion and random strategies)are adaptively selected to update the positions of sparks generated by fireworks with the guidance of special ***,we compare the proposed algorithm with eight state-of-the-art multimodal multiobjective algorithms on all 22 MMOPs from CEC2019 and several imbalanced distance minimization *** results show that the proposed algorithm is superior to compared algorithms in solving ***,its runtime is less than its peers'.

关键词： Adaptive strategy fireworks algorithm multimodal multiobjective optimization problems(MMOP)

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Frontiers of Deep Learning: From Novel Application to Real-World Deployment

arXiv

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arXiv 2024年

作者： Xie, Rui Department of Electrical Computer and Systems Engineering Rensselaer Polytechnic Institute United States

Deep learning continues to re-shape numerous fields, from natural language processing and imaging to data analytics and recommendation systems. This report studies two research papers that represent recent progress on deep learning from two largely different aspects: The first paper applied the transformer networks, which are typically used in language models, to improve the quality of synthetic aperture radar image by effectively reducing the speckle noise. The second paper presents an in-storage computing design solution to enable cost-efficient and high-performance implementations of deep learning recommendation systems. In addition to summarizing each paper in terms of motivation, key ideas and techniques, and evaluation results, this report also presents thoughts and discussions about possible future research directions. By carrying out in-depth study on these two representative papers and related references, this doctoral candidate has developed better understanding on the far-reaching impact and efficient implementation of deep learning models. Copyright © 2024, The Authors. All rights reserved.

关键词： Synthetic aperture radar

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Transforming Collaboration: A Vision for Human-Aware Robots to Enhance Worker Safety and Boost Production Efficiency

Transforming Collaboration: A Vision for Human-Aware Robots ...

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Mediterranean Electrotechnical Conference (MELECON)

作者： Mohammad Zarei Andrey Solovov Vishal Gautam Bruno Ferreira Gustavo Assunção Antonio Marin-Hernandez Paulo Menezes Electrical and Computer Engineering Department University of Coimbra Coimbra Portugal Institute of Systems and Robotics University of Coimbra Coimbra Portugal Universidad Veracruzana Xalapa Mexico

ISBN: (数字)9798350387025

ISBN: (纸本)9798350387032

Human-robot interaction is becoming a necessary part of the industry to achieve higher efficiency, productivity, and comfort. However, to ensure human safety, the awareness of human workers has to be under supervision. In this research, we defined a new function to estimate awareness through eye contact between the worker and the robot. This mechanism exploits a proposed robust and real-time eye contact classifier based on SeNet-50, that outperforms previous proposals based on ResNet-18.

关键词： Productivity Industries Service robots Human-robot interaction Collaboration Real-time systems Safety

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A variable structure model of neurons’ application in computer vision

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Multimedia Tools and Applications 2025年 1-11页

作者： Sridi, Alaeddine Attia, Mouna Bouallegue, Kais Electronics and Micro-Electronics Laboratory Faculty of Sciences of Monastir University of Monastir Monastir Tunisia The Higher Institute of Computer Science and Communication Technologies of Hammam Sousse University of Sousse Sousse Tunisia Laboratory of Computer Science Parallelism and Intelligent Communicating Systems Faculty of Sciences of Tunisia University of Tunis ELManar Tunis Tunisia Department of Electrical Engineering Higher Institute of Applied Sciences and Technology of Sousse University of Sousse Sousse Tunisia

The neuron doctrine defines the neuron as the basic unit of the nervous system, which drives the dynamic behavior of our organs. This has led to neurons becoming the focus of modern neuroscience research and to the rise of neurocomputing, in which we try to digitally simulate human behavior. The goal being to better understand the human nervous system and gain more control over neuron-related diseases, helping doctors establish faster and more reliable medical diagnoses. In this context, we aim to simulate eye diseases and vision problems by convolving input images using a new class of neural networks based on a variable structure model of neurons presented in [1]. In this work, we focus on recreating the common convolution filters as a first step. This is the first time in neurocomputing literature that dendritic neurons have been used for dynamic convolution kernels generation and eye diseases simulation. We start with a presentation of our upgrades to the model’s flexibility and accuracy. Then, we propose a novel kernel generator algorithm to convolve the input images. Finally, we show its promising results in creating the outline, pixelization, and night convolution kernels, as well as control some metrics, including brightness and data retention. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Neurons

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A Photonic Front End for High Resolution Blue Spectral Sensor in 45 nm SOI SiPh Process

A Photonic Front End for High Resolution Blue Spectral Senso...

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Annual IEEE Northeast Workshop on Circuits and systems (NEWCAS)

作者： Md Nabil Shehtab Dhrubo Jason Case Agung Julius Robert Karlicek Mona Hella Department of Electrical Computer and Systems Engineering Rensselaer Polytechnic Institute Troy NY

ISBN: (数字)9798350361759

ISBN: (纸本)9798350361766

A photonic front end for a blue spectral sensor in a standard silicon photonic process is presented. The detector is capable of identifying multiple spectral peaks within the blue wavelength range (410 nm ~ 500 nm), The front end consists of grating structures with different grating pitches, followed by tapered waveguides. From the waveguide, the optical power is evanescently coupled to a silicon photodetector. To further improve the overall optical collection for the photo detector, an optical power combining network is also designed and optimized. The designs are fabricated using GlobalFoundries' 45SPCLO technology. The measurement results demonstrate the effect of varying the grating pitch in detecting different spectral peaks within the blue region, with peaks demonstrating responsivities ranging between 32~135 mAIW, depending on the grating pitch and the reverse bias.

关键词： Optical filters Biomedical optical imaging Optical device fabrication Optical detectors Silicon photonics Optical coupling Gratings

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Joint Deployment and Resource Allocation for Multi-AeBS Networks: A Two-Timescale Optimization Framework Using MADRL

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IEEE Transactions on Communications 2024年第6期73卷 4272-4289页

作者： Zhao, Yikun Zhou, Fanqin Feng, Lei Sun, Yao Li, Wenjing Lim, Wei Yang Bryan Xiong, Zehui Mao, Shiwen Han, Zhu State Key Laboratory of Networking and Switching Technology Beijing100876 China University of Glasgow James Watt School of Engineering GlasgowG12 8QQ United Kingdom Nanyang Technological University Alibaba Group and Alibaba-NTU Joint Research Institute Singapore Singapore University of Technology and Design Information Systems Technology and Design Pillar Singapore487372 Singapore Auburn University Department of Electrical and Computer Engineering AuburnAL36849 United States University of Houston Department of Electrical and Computer Engineering HoustonTX77004 United States Kyung Hee University Department of Computer Science and Engineering Seoul446-701 Korea Republic of

As an important component of the space-air-ground integrated network, aerial base station (AeBS) systems have gained significant attention for their flexibility in mobility and cost-effective construction. Nevertheless, the scarce spectrum resources and difficulty in accessing global information bring necessity and challenges to the deployment and resource allocation of AeBSs. In this paper, we propose a practical two-timescale framework to solve the resource allocation and deployment optimization problem in multi-AeBS networks. Specifically, the subcarrier allocation problem is first transformed into a many-to-one matching game coupled with power allocation and solved in a small timescale. Then, in a large timescale, the AeBS deployment subproblem is transformed into a distributed partially observable Markov decision process (Dec-POMDP), and then a novel multi-agent hypergraph convolutional deep reinforcement learning (MAHGCDRL) is proposed to solve this problem. The proposed MAHGCDRL extracts features of neighboring AeBSs through hypergraph convolutional networks, enabling AeBS agents to achieve better coordination in a distributed manner. Simulation results show that our proposed approach can attain a higher sum rate, and the proposed MAHGCDRL algorithm achieves better learning performance compared to the existing benchmarks in the literature. © 1972-2012 IEEE.

关键词： Deep reinforcement learning

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Domain Adaptation for Offline Reinforcement Learning with Limited Samples

arXiv

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arXiv 2024年

作者： Chen, Weiqin Mishra, Sandipan Paternain, Santiago Department of Electrical Computer and Systems Engineering Rensselaer Polytechnic Institute United States

Offline reinforcement learning (RL) learns effective policies from a static target dataset. Despite state-of-the-art (SOTA) offline RL algorithms being promising, they highly rely on the quality of the target dataset. The performance of SOTA algorithms can degrade in scenarios with limited samples in the target dataset, which is often the case in real-world applications. To address this issue, domain adaptation that leverages auxiliary samples from related source datasets (such as simulators) can be beneficial. In this context, determining the optimal way to trade off the source and target datasets remains a critical challenge in offline RL. To the best of our knowledge, this paper proposes the first framework that theoretically and experimentally explores how the weight assigned to each dataset affects the performance of offline RL. We establish the performance bounds and convergence neighborhood of our framework, both of which depend on the selection of the weight. Furthermore, we identify the existence of an optimal weight for balancing the two datasets. All theoretical guarantees and optimal weight depend on the quality of the source dataset and the size of the target dataset. Our empirical results on the well-known Procgen Benchmark substantiate our theoretical contributions. Copyright © 2024, The Authors. All rights reserved.

关键词： Adversarial machine learning

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Automated analysis of fundus images for the diagnosis of retinal diseases: a review

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Research on Biomedical engineering 2024年第1期40卷 225-251页

作者： Berto, Alexa Scarpa, Fabio Tsiknakis, Nikos Manikis, Georgios Fotiadis, Dimitrios I. Marias, Kostas Scarpa, Alberto D-Eye s.r.l. Padua Italy Department of Information Engineering University of Padova Padua35131 Italy Heraklion70013 Greece Department of Biomedical Research Institute of Molecular Biology and Biotechnology FORTH Ioannina45115 Greece Department of Materials Science and Engineering Unit of Medical Technology and Intelligent Information Systems University of Ioannina Ioannina45110 Greece Department of Electrical and Computer Engineering Hellenic Mediterranean University Heraklion71004 Greece

Purpose: Colour fundus images are widely used in diagnosis treatment decision of several retinal diseases such as diabetic retinopathy (DR), glaucoma and age-related macular degeneration (AMD). These very common conditions must be detected early and monitored to prevent progression and avoid permanent damage. Fundus images revealed to be useful also for the diagnosis of cataracts. In recent years, various automatic diagnostic support methods have been proposed in the literature, with the aim of facilitating widespread screening and obtaining quantitative, objective, and reproducible information. Methods: In this review paper, an overview of traditional, machine learning and modern deep learning techniques for ophthalmic disease diagnosis (i.e. glaucoma, DR, AMD, and cataract) using retinal fundus images is presented. In addition, various publicly available image datasets used for such purposes are described. Results: The current main challenges and findings are identified, as well as common aspects and discrepancies between the various methods developed for the various diseases. Conclusion: The overview of what has been done about all pathologies, rather than only on a specific one, could favour a migration of the best solutions and, hopefully, the development of a more precise and clinically useful automatic analysis of all pathologies. Important critical insights and research trends are also discussed. Graphical abstract: (Figure presented.) © The Author(s), under exclusive licence to The Brazilian Society of Biomedical engineering 2023.

关键词： Pathology

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Multi-Sensor Aircraft Classification

Multi-Sensor Aircraft Classification

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2023 Congress in computer Science, computer engineering, and Applied Computing, CSCE 2023

作者： Bolton, Sarah Dill, Richard Grimaila, Michael R. Hodson, Douglas D. Air Force Institute of Technology Department of Electrical and Computer Engineering Wright-Patterson AFBOH United States Air Force Institute of Technology Department of Systems Engineering and Management Wright-Patterson AFBOH United States

ISBN: (纸本)9798350327595

Automatic Dependent Surveillance-Broadcast (ADS-B) is a useful tool to for air traffic controllers, military and other sources that are invested in understanding a national or global air picture. While it is highly available, it can sometimes lack integrity due to hacking, spoofing or, even, unintentional inaccuracies in the broadcast. Unlike primary radar, ADS-B's lack of trustworthiness makes it not feasible to rely on it alone. Fusing other data sources with ADS-B can help confirm the accuracy of the broadcasts or allow ADS-B to act as a surrogate for primary radar and bolster the information that primary radar can provide. This paper presents an effective method of using ADS-B data as a surrogate for primary 3D radar by combining the kinematic information that ADS-B data provides with weather and aircraft images to make predictions about aircraft characteristics. © 2023 IEEE.

关键词： Automatic Dependent Surveillance-Broadcast classification machine learning Multivariate Long Short-Term Memory - Fully Convolutional Network open-source data sensor fusion

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Computationally efficient driving cycle based design and optimisation for variable air gap axial flux machines

Computationally efficient driving cycle based design and opt...

引用

2023 IEEE Energy Conversion Congress and Exposition, ECCE 2023

作者： Weissitsch, Gabriel Klink, David Bramerdorfer, Gerd Heins, Greg Johannes Kepler University Linz Institute of Electric Drives and Power Electronics Linz Austria Monash University Department of Electrical and Computer Systems Engineering Melbourne Australia

ISBN: (纸本)9798350316445

Axial flux permanent magnet machines provide a unique set of possibilities and challenges to electric machine designers when compared to their radial flux counterparts. By varying the air gap width between the rotor and stator a mechanical rotor flux linkage control is made possible. This paper introduces a computationally efficient method of evaluating axial flux machines with air gap variation. The investigated machine is designed as a traction drive for a student racing series. Two different design scenarios are presented, comparing optimisation results from a single operating point and static air gap model to a variable air gap, driving cycle based design. A validation design is chosen for an in-depth investigation and a prototype motor is built and tested at a range of air gaps. This experimental data is compared to FEA results to validate the model. © 2023 IEEE.

关键词： air gap variation axial flux permanent magnet machine finite element (FE) multiobjective optimisation

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