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International Conference on Artificial Intelligence in Renewable Energetic Systems, IC-AIRES 2024

作者： Khellaf, L. Djellal, A. Mayache, H. Sheta, A. Department of E.E.A National Higher School of Technology and Engineering Annaba23005 Algeria Computer Science Department Southern Connecticut State University New HavenCT06515 United States

ISBN: (纸本)9783031803000

Photovoltaic systems have proven to be one of the most widely used renewable energies and the best replacement for conventional energy. Yet, their non-linear nature remains a challenge when it comes to extracting maximum power from photovoltaic modules. Therefore, in this work, a nonlinear PID controller has been used to meet the requirements of the photovoltaic system. In addition, to improve system performance and response, metaheuristic search algorithms were introduced into the tuning process of both the NPID controller and conventional PID controller parameters in order to compare them. The use of Artificial Intelligence to fine-tune the controller parameters will enable the optimum values of proportional, integral, derivative and nonlinear gains to be determined as system condition change. Finally, a comparison between the algorithms applied is conducted in terms of efficiency, rise time, settling time and overshoot as well as the overall system stability. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Three term control systems

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Exploring time series analysis in frequency domain with complex-valued spectral attention and bidirectional variable mamba

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The Journal of Supercomputing 2025年第8期81卷 1-26页

作者： Zhao, Qinghe Lai, Fang Mo, Xingye Faculty of Environment Science and Economy University of Exeter Exeter Devon UK Department of Computer Science University of Denver Denver USA Department of Electrical and Computer Engineering New York University New York USA

Recently, deep learning has made significant strides in multivariate time series forecasting. While frequency-domain-based methods have shown promising results, existing models often struggle with frequency misalignment when handling diverse frequency combinations, leading to reduced forecasting accuracy. To address these issues, we propose the Spectral Attention Module (SAM), which integrates temporal and frequency-domain information to effectively capture both local and global dependencies in time series data. Within the frequency-domain module, we introduce an Extended Discrete Fourier Transform to overcome frequency misalignment challenges and design a Complex-Valued Spectral Attention Mechanism (CV-SAM) to identify and exploit complex relationships among different frequency combinations. To further capture inter-variable correlations, we propose the Bidirectional Variable Mamba. It uses linear layers to encode timestamps for each variable and employs the Mamba layer to extract inter-variable correlations, supported by a feedforward network to learn temporal dependencies. By combining the SAM and the BV-Mamba, we construct the SpectroMamba, which demonstrates superior performance over state-of-the-art methods in long-term time series forecasting across multiple real-world datasets.

关键词： State space methods

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Wavelength-multiplexed multiplane quantitative phase imaging using a diffractive optical processor 11

Wavelength-multiplexed multiplane quantitative phase imaging...

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Quantitative Phase Imaging XI 2025

作者： Shen, Che-Yung Li, Jingxi Gan, Tianyi Li, Yuhang Bai, Langxing Jarrahi, Mona Ozcan, Aydogan Electrical and Computer Engineering Department University of California Los AngelesCA90095 United States Bioengineering Department University of California Los AngelesCA90095 United States University of California Los AngelesCA90095 United States Department of Computer Science University of California Los AngelesCA90095 United States

ISBN: (纸本)9781510684065

We present deep learning-designed all-optical processors that can perform multiplane quantitative phase imaging (QPI). By leveraging diffractive processing and wavelength multiplexing, our approach allows the direct capture of quantitative phase distributions of input objects located at different axial planes using an intensity-only image sensor, eliminating the need for digital phase recovery algorithms. A proof-of-concept experiment validated the approach, showcasing successful imaging of distinct phase objects at different axial positions in the terahertz spectrum. By providing a faster, more efficient method for QPI, this technology can be applied to medical diagnosis, material science, and environmental monitoring, among other applications. © 2025 SPIE.

关键词： Phase imaging Biological imaging Image processing Biomedical optics Imaging devices Multiplexing Stereoscopy

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Collaborative and Integrated Platform to Support Distributed Manufacturing System Using a Service-Oriented Approach Based on Cloud Computing Paradigm

Collaborative and Integrated Platform to Support Distributed...

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2025 IEEE International Conference on Computational, Communication and Information Technology, ICCCIT 2025

作者： Nainwal, Ankita Sharma, Vishal Varshney, Neeraj Shirish Boob, Nandini Reddy, N.V. Uma Reddy, K. Jyothsna Graphic Era Deemed to Be University Department of Computer Science & Engineering Dehradun India Lovely Professional University Phagwara India GLA University Department of Computer Engineering and Applications Mathura India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Tamilnadu Chennai India New Horizon College of Engineering Department of Artificial Intelligence & Machine Learning Bangalore India MLR Institute of Technology Department of CSE-AI&ML Telangana Hyderabad India

ISBN: (纸本)9798331512965

Increasing interest in distributed manufacturing systems is a result of the dynamic nature of today's business environment and with that comes the popularity of their use. This has given rise in the interest in distributed manufacturing systems. The indirect consequence of this has led to much higher use of distributed manufacturing systems. A large number of production processes, dispersed across a multitude of locations geographically separate, must be controlled and integrated in order to achieve production agility, flexibility and efficiency of costs. In our most recent project, we developed an integrated, collaborative manufacturing platform, the centerpiece of which was LAYMOD. To address the issues posed by such systems, this research presents a collateral and integrated platform based on a service oriented approach that can operate within the cloud computing paradigm. These systems present problems which the study is conducted in order to solve. Utilising the features of cloud computing, it aims to serve as a platform in solving process coordination and hence improving efficiency and scalability of distant manufacturing processes and potential real time communications among various stake holders. Furthermore, statistical research on the rates of platform module adoption in different settings provides specifics of (a) use patterns of some platform modules and (b) what capabilities are provided by some platform modules. When extrapolated to a wider size, our work is applied towards the creation of collaborative and integrated platforms for a number of the various manufacturing processes which are currently in use. © 2025 IEEE.

关键词： Cloud platforms

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CiGeN 1.0: A Generative AI Enabled Medical Image Augmentation Pipeline for Deep Network Models

CiGeN 1.0: A Generative AI Enabled Medical Image Augmentatio...

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Ambient Intelligence in Health Care (ICAIHC), International Conference on

作者： Archana Dash Debasish Swapnesh Kumar Nayak Tripti Swarnkar Department of Computer Science and Engineering Siksha ‘O’ Anusandhan Deemed to be University Bhubaneswar India Department of Computer Science and Engineering Centurion University of Technology and Management Bhubaneswar India Department of Computer Application National Institute of Technology Raipur India

ISBN: (数字)9798331530259

ISBN: (纸本)9798331530266

Deep Learning methods have gained a significant momentum in medical image classification tasks, including many ranges of datasets. However, these datasets often suffer from class imbalance, where the minority class (e.g., diseased cases) is underrepresented. This imbalance leads to biased predictions, affecting the model's performance. Generative Adversarial Networks (GANs) offer a potential solution by generating synthetic images for the minority class, helping balance the dataset. Methodology: In this study we further evaluated our Deep Learning Model CoVaD-GAN with separate set of datasets to test the dynamics behind it. We applied a Deep Convolutional GAN (DCGAN) to generate synthetic images for underrepresented classes in the chest X-ray (CXR) and Break His histopathological dataset. Augmented synthetic images were made a part of training set, and the model's performance was evaluated before and after augmentation using VGG-16, ResNet-50 with necessary metric measures as accuracy, sensitivity, and specificity were compared pre- and post-GAN augmentation. We also evaluated the quality of GAN-generated images using Fréchet Inception Distance (FID) and manual expert reviews. The results demonstrate significant improvements in model accuracy, sensitivity, and F1-scores after applying GAN-based augmentation. The use of GANs for data augmentation enhances the ability of deep learning models to generalize, particularly for minority classes. GAN-generated images were of high quality and contributed positively to model performance. The Proposed model (CiGeN 1.0) dynamically generalizes the other medical dataset introduced to it and derived a fair resulting diagnosis.

关键词： Deep learning Training Sensitivity Accuracy Generative adversarial networks Data augmentation Stability analysis Convolutional neural networks Medical diagnostic imaging X-ray imaging

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Borehole Inner Surface Visualization System with Vibration Cancellation and Trajectory Smoothing Based on Optical Monocular Video Camera

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SN computer science 2025年第5期6卷 1-17页

作者： Zong, Nan Al-Nuaimy, Waleed Worthington, Paul School of Electrical Engineering Electronics and Computer Science University of Liverpool Brownlow Hill Liverpool L38HA United Kingdom Department of Engineering and Development Robertson Geologging Ltd. York Road Deganwy 610101 United Kingdom

The rapid digitization and modeling of the planet brings with it increased demand for the tools necessary to process and visualize disparate streams of multivariate and often highly complex geophysical data. However, more complicated the detection system is, less reliability can it guarantees. In order to addresses the challenge of visual reconstruction of the geometry of the inner surface of a borehole from video data collected via a simple monocular optical camera detector, We introduce a novel system of algorithms to unwrap the cylindrical borehole inner surface data, to compensate for the offsets and errors arising during data acquisition and finally to remap physical depth of pixels with time based video data. For unwrapping and compensation, three modules are designed: unwrapping module to generate visualization results of borehole inner surfaces;Vibration cancellation module that compensates for rotation and drift errors meanwhile balancing computational cost and performance;Trajectory smoothing based on image convolution signal processing methods to filter out anomalies and interruptions that arise as a result of the other processing stages. For physical depth estimation of pixels, two works are implemented: General time to depth matching using video frame time index and distance of released cable recorded by winch rotary encoder;Estimation of distances between pixels and detector on a single frame modeled by the relationship between radius of boreholes and the visual deformation on camera image of the points positions on the borehole surface. The proposed system integrates these modules to generate planar side-view images with a high level of spatial accuracy. It also contributes to establish a novel and easy-to-access visualization tool of boreholes with simplified detectors that only consists of a monocular camera and a fixed circular LED band. Results has demonstrated the system is capable of resisting high frequency drift and the effects of rotation and

关键词： Data visualization Geological data logging Image processing Monocular video

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Diamagnetically Levitated and Trapped Graphite Mechanical Resonators

Diamagnetically Levitated and Trapped Graphite Mechanical Re...

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IEEE International Conference on Micro Electro Mechanical Systems

作者： Yunong Wang Alexander Gage Jaesung Lee Philip X.-L. Feng Department of Electrical and Computer Engineering Herbert Wertheim College of Engineering University of Florida Gainesville FL USA Department of Electrical and Computer Engineering College of Engineering & Computer Science University of Central Florida Orlando FL USA

ISBN: (数字)9798331508890

ISBN: (纸本)9798331508906

We report on studying diamagnetic levitation and rigid body resonances of millimeter- to centimeter-scale trapped graphite mechanical resonators, by combining theoretical analysis with experimental demonstrations. Harnessing the strong diamagnetic susceptibility of pyrolytic graphite, we demonstrate stable levitation of square graphite thin plates with varying dimensions and masses above permanent magnets, without the need of active control. The resonance motions are excited by dielectric gradient forces and detected via an ultrasensitive optical interferometry system at room temperature. We observe rigid body resonance modes with frequencies between 25 Hz and 50 Hz and with their quality (Q) factors from 30 to 70. The Q factors are primarily constrained by air damping and eddy current damping. This work represents an initial step toward developing high-stability, anchor-less levitating resonant systems with large masses, paving the way for high-performance resonant sensors with minimal energy dissipation and exceptional thermal and mechanical isolation with very small power consumption.

关键词： Q-factor Temperature measurement Optical interferometry Temperature Power demand Resonant frequency Graphite Levitation Sensor systems Resonators

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Linear Convergent Distributed Nash Equilibrium Seeking With Compression

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IEEE Transactions on Automatic Control 2025年第5期70卷 3316-3323页

作者： Chen, Xiaomeng Wu, Yuchi Yi, Xinlei Huang, Minyi Shi, Ling Hong Kong University of Science and Technology Department of Electronic and Computer Engineering Hong Kong Shanghai University School of Mechatronic Engineering and Automation Shanghai200444 China College of Electronics and Information Engineering Tongji University Department of Control Science and Engineering Shanghai201804 China Massachusetts Institute of Technology Lab for Information & Decision Systems CambridgeMA02139 United States Carleton University School of Mathematics and Statistics OttawaONK1S 5B6 Canada

Information compression techniques are majorly employed to reduce communication cost over peer-to-peer links. In this article, we investigate distributed Nash equilibrium (NE) seeking problems in a class of noncooperative games over directed graphs with information compression. To improve communication efficiency, a compressed distributed NE seeking (C-DNES) algorithm is proposed to obtain an NE for games, where the differences between decision vectors and their estimates are compressed. The proposed algorithm is compatible with a general class of compressors, including both unbiased and biased compressors. Moreover, our approach only requires the adjacency matrix of the directed graph to be row-stochastic, in contrast to past works that relied on balancedness or specific global network parameters. It is shown that C-DNES not only inherits the advantages of conventional distributed NE algorithms, achieving linear convergence rate for games with restricted strongly monotone mappings, but also saves communication costs in terms of transmitted bits. Finally, numerical simulations illustrate the advantages of C-DNES in saving communication cost by an order of magnitude under different compressors. © 1963-2012 IEEE.

关键词： Stochastic systems

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FedQP: Large-Scale Private and Flexible Federated Query Processing

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IEEE Access 2025年 13卷 92019-92035页

作者： Almohri, Hussain M. J. Watson, Layne T. Kuwait University Department of Computer Science Kuwait City Kuwait Virginia Tech Departments of Computer Science Mathematics Aerospace and Ocean Engineering BlacksburgVA United States

State-of-the-art federated learning methods orchestrate iterations of the stochastic gradient descent algorithm among a network of clients to refine a unified set of model parameters, all while safeguarding individual client datasets. Although promising, the success of existing approaches hinges on using a uniform data model with the same optimization and learning algorithms by the participants. In addition, existing methods can be subject to privacy attacks, such as model-inversion attacks. A pivotal challenge lies in crafting an algorithm that can efficiently aggregate client inputs, ensuring minimal client dataset exposure and maximum flexibility in model adaptation. We propose a new scheme for aggregating local, diverse, and independent data models, addressing data inconsistency and model-inversion attacks. The proposed scheme, federated query processing FedQP, enables clients to build local models independently, without coordinating with the server or other clients. The server communicates with selected clients only to predict future values. Our scheme only requires one communication round between each client and the server for model initialization without the need for a training phase. Clients do not share data or model parameters. We evaluated FedQP by assessing the impact of our new aggregation mechanism on model accuracy and the communication efficacy of our architecture. We show that the caching algorithm in FedQP reduces redundant client communications during query processing. In addition, we present a privacy analysis that shows that our method outperforms prominent gradient-based approaches to federated learning. Our experiments show that FedQP consistently achieves higher classification accuracy in non-IID settings and demonstrates stronger resilience to reconstruction attacks compared to gradient-based methods such as federated averaging (FedAvg). © 2013 IEEE.

关键词： Federated learning

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Some Results on Ricci-Yamabe Soliton and Its Cosmological Models

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IAENG International Journal of Applied Mathematics 2025年第4期55卷 979-983页

作者： Pandey, Pankaj Yadav, Santosh Kumar Sharma, Kamakshi Chaturvedi, Braj Bhushan Department of Mathematics School of Chemical Engineering and Physical Sciences Lovely Professional University Punjab Phagwara144411 India Department of Mathematics School of Computer Science & Artificial Intelligence SR University Telangana Warangal506371 India Department of Pure & Applied Mathematics Guru Ghasidas Vishwavidyalaya Chhattisgarh Bilaspur495009 India

This article aims to investigate the characteristics of Ricci-Yamabe Soliton (briefly: (RYS)n). We study the cosmological models on (RYS)4 under Lorentzian para Sasakian (LPS)4 spacetime. Parallel Ricci tensor, Poisson structure and gradient property also reveal on (RYS)n. Example of (RYS)4 constructed to verify the results. © (2025), (International Association of Engineers). All rights reserved.

关键词： Solitons

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