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10 articles for “multi-scale multi-domain model”
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Simulation and Analysis of Battery Pack Using the Multi Scale Multi-Domain Battery Model
Abstract: The creation of sophisticated simulation models has been made necessary by the need for reliable and effective battery packs in energy storage systems and electric vehicles. This study focuses on the simulation and analysis of battery packs using a multi-scale multi-domain battery model. The model enables a thorough knowledge of battery pack behavior across a range of operating situations by integrating the electricity, thermal, and mechanical domains. Multi-scale modeling bridges …
Published in International Journal of Electro-Mechanics and Material Behaviour · Vol. 2, Issue 2, 2024 · pp. 31–46 Read article
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Diffusion-Based Enhancement of Low-SNR Time- Frequency Signals
Abstract: Traditional enhancing techniques are useless in low signal-to-noise ratio (LSNR) situations because noise drastically interferes with communication signals. Based on an enhanced DiffBIR model, this paper suggests a dual-stage signal improvement approach that combines diffusion with deep learning. By combining the Inception module for multi-scale feature extraction with the Pixel Fusion Attention (PFA) module for significant region highlighting, the model improves signal recovery in the time- frequency domain. Experiments show …
Published in Current Trends in Signal Processing · Vol. 17, Issue 2, 2026 Read article
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Automated Machine Learning System for Model Selection and Hyperparameter Optimization
Abstract: The proliferation of machine learning applications in various scientific and industrial domains has given rise to an urgent need for developing principled, automated techniques for optimal architecture selection and hyperparameter tuning for machine learning models without human expert intervention. In this paper, we introduce the Automated Machine Learning System for Model selection and hyperparameter Optimization (AMLSMO)—a state-of-the-art, all-encompassing AutoML system that combines the power of meta-learning-based warm-starting, Bayesian Optimization with …
Published in Recent Trends in Mathematics · Vol. 3, Issue 2, 2026 · pp. 15–23 Read article
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Topology and Geometry in Data Science: Persistent Homology and Beyond
Abstract: In recent years, the interplay between topology, geometry, and data science has gained substantial momentum, offering powerful frameworks to analyze and interpret complex datasets. Traditional statistical and machine learning methods often rely on linear or metric- based assumptions, which may fail to capture the intrinsic structure of high-dimensional or nonlinear data. In contrast, topological and geometric methods provide shape-oriented, scale- invariant tools that focus on the continuity, connectivity, and global …
Published in Recent Trends in Mathematics · Vol. 1, Issue 2, 2024 · pp. 21–27 Read article
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Ai-Driven Healthcare System for Enhanced Diagnosis and Patient Interaction
Abstract: Deep learning techniques are used in an AI-driven healthcare system to improve disease identification and medical picture analysis. Data collection, preprocessing, model training, and evaluation are all part of the system's systematic workflow. Various deep learning architectures, such as ResNet50, VGG-16, and U-Net, are employed for precise classification and segmentation of medical images. The approach incorporates advanced techniques such as optimization, transfer learning, and data augmentation to significantly enhance the …
Published in Research and Reviews: A Journal of Health Professions · Vol. 15, Issue 2, 2025 Read article
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Parallel Privacy-Preserving Adaptive Federated Learning on GPU-Enabled Multi-Core Architectures
Abstract: The increasing deployment of parallel and distributed intelligent systems has intensified the need for privacy-preserving learning frameworks that can exploit multi-core and GPU-based architectures without centralizing sensitive data. This work proposes a parallel Adaptive Federated Learning (AFL) framework that integrates Differential Privacy and Secure Aggregation over heterogeneous multi-core and GPU platforms to enhance both data confidentiality and convergence efficiency. The framework dynamically adjusts client participation, learning rates, and aggregation weights …
Published in Recent Trends in Parallel Computing · Vol. 13, Issue 1, 2026 Read article
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Automation and new mechanical technologies changing the next industrial age
Abstract: The fast growth of automation and new mechanical technologies is changing the way industries work, making manufacturing and production systems more efficient, accurate, and flexible than ever before. This article looks at how sophisticated mechanical design, robotics, and intelligent automation technologies can work together and how they can improve productivity, lower costs, and make operations more sustainable. To show how mechanical engineers are influencing the next industrial era, we look …
Published in International Journal of Manufacturing and Production Engineering · Vol. 3, Issue 2, 2025 · pp. 18–23 Read article
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Change Detection in Aerial Imagery
Abstract: The development of the Multi U-Net engineering marks an essential headway in geospatial question location inside ethereal symbolism investigation. The altered U-Net addresses the complexities of multi-class division in assorted geospatial settings. Leveraging the inalienable growing and contracting pathways inside U-Net plans, the Multi U-Net exceeds expectations in capturing complicated spatial data, in this manner setting up a vigorous establishment for exact division. The extend envelops an advanced picture handling …
Published in Journal of Image Processing & Pattern Recognition Progress · Vol. 11, Issue 2, 2024 · pp. 22–28 Read article
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Bridging Brain-Inspired Learning and Quantum Reasoning for Future AGI Systems
Abstract: This research paper presents a novel neuromorphic–quantum hybrid computing framework envisioned to advance intelligent systems toward artificial general intelligence. The architecture integrates brain-inspired spiking networks for adaptive, energy-efficient learning with quantum processors for non-classical optimization and reasoning. A shared synaptic–quantum memory layer enables dual information representation, while neuromorphic adaptive controllers provide real-time stabilization of noisy quantum circuits. While quantum processors offer features like superposition- enabled exploration and entanglement-based correlations that …
Published in Current Trends in Signal Processing · Vol. 16, Issue 1, 2026 Read article
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The Emerging Golden Age of Astrophysics: Progress, Discoveries, and Future Directions
Abstract: The period between 2024 and 2026 has marked a transformative era in astrophysics, fundamentally reshaping our understanding of the universe and its underlying physical mechanisms. Significant advancements in observational astronomy, computational modeling, and space exploration technologies have enabled scientists to investigate cosmic phenomena with unprecedented precision. Among the most groundbreaking achievements, the James Webb Space Telescope (JWST) has provided high-resolution infrared observations that revealed the large-scale distribution of dark matter, …
Published in International Journal of Universe · Vol. 2, Issue 1, 2026 · pp. 6–9 Read article