Image de couverture de livre

Knowledge-Aware Modeling with Frequency Adaptive Learning for Battery Health Prognostics

Fuente: arXiv

Enregistré dans:

Détails bibliographiques
Auteurs principaux:	Pamshetti, Vijay Babu, Zhang, Wei, Sun, Sumei, Zhang, Jie, Wen, Yonggang, Yan, Qingyu
Format:	Preprint
Publié:	2025
Sujets:	Machine Learning Artificial Intelligence
Accès en ligne:	Acceder al recurso
Tags:	Ajouter un tag Pas de tags, Soyez le premier à ajouter un tag!

Documents similaires

Optimal Signal Decomposition-based Multi-Stage Learning for Battery Health Estimation
par: Pamshetti, Vijay Babu, et autres
Publié: (2025)

GiNet: Integrating Sequential and Context-Aware Learning for Battery Capacity Prediction
par: Sameer, Sara, et autres
Publié: (2025)

Practical Battery Health Monitoring using Uncertainty-Aware Bayesian Neural Network
par: Zhao, Yunyi, et autres
Publié: (2024)

EntroLnn: Entropy-Guided Liquid Neural Networks for Operando Refinement of Battery Capacity Fade Trajectories
par: Li, Wei, et autres
Publié: (2026)

Rethinking Key-Value Cache Compression Techniques for Large Language Model Serving
par: Gao, Wei, et autres
Publié: (2025)

Uncertainty Quantification in Machine Learning for Engineering Design and Health Prognostics: A Tutorial
par: Nemani, Venkat, et autres
Publié: (2023)

Advancing Generative Artificial Intelligence and Large Language Models for Demand Side Management with Internet of Electric Vehicles
par: Zhang, Hanwen, et autres
Publié: (2025)

Deep Reinforcement Learning with Task-Adaptive Retrieval via Hypernetwork
par: Jin, Yonggang, et autres
Publié: (2023)

Network-Aware Bilinear Tokenization for Brain Functional Connectivity Representation Learning
par: Milecki, Leo, et autres
Publié: (2026)

AI-Driven Prognostics for State of Health Prediction in Li-ion Batteries: A Comprehensive Analysis with Validation
par: Ding, Tianqi, et autres
Publié: (2025)

Toward Multiphysics-Informed Machine Learning for Sustainable Data Center Operations: Intelligence Evolution with Deployable Solutions for Computing Infrastructure
par: Wang, Ruihang, et autres
Publié: (2025)

Learning by Doing: An Online Causal Reinforcement Learning Framework with Causal-Aware Policy
par: Cai, Ruichu, et autres
Publié: (2024)

ClimateLLM: Efficient Weather Forecasting via Frequency-Aware Large Language Models
par: Li, Shixuan, et autres
Publié: (2025)

When Smaller Wins: Dual-Stage Distillation and Pareto-Guided Compression of Liquid Neural Networks for Edge Battery Prognostics
par: Kannan, Dhivya Dharshini, et autres
Publié: (2026)

Efficient Mathematical Reasoning Models via Dynamic Pruning and Knowledge Distillation
par: Yu, Fengming, et autres
Publié: (2025)

Neural Probabilistic Logic Learning for Knowledge Graph Reasoning
par: Sun, Fengsong, et autres
Publié: (2024)

Beyond Correctness: Confidence-Aware Reward Modeling for Enhancing Large Language Model Reasoning
par: He, Qianxi, et autres
Publié: (2025)

An Unsupervised Framework for Dynamic Health Indicator Construction and Its Application in Rolling Bearing Prognostics
par: Sun, Tongda, et autres
Publié: (2025)

Exploring Feature-based Knowledge Distillation for Recommender System: A Frequency Perspective
par: Zhu, Zhangchi, et autres
Publié: (2024)

TFWaveFormer: Temporal-Frequency Collaborative Multi-level Wavelet Transformer for Dynamic Link Prediction
par: Feng, Hantong, et autres
Publié: (2026)

CMKL: Modality-Aware Continual Learning for Evolving Biomedical Knowledge Graphs
par: Radwan, Yousef A., et autres
Publié: (2026)

Online Boosting Adaptive Learning under Concept Drift for Multistream Classification
par: Yu, En, et autres
Publié: (2023)

APAR: Modeling Irregular Target Functions in Tabular Regression via Arithmetic-Aware Pre-Training and Adaptive-Regularized Fine-Tuning
par: Wu, Hong-Wei, et autres
Publié: (2024)

Efficient Reinforcement Learning for Large Language Models with Intrinsic Exploration
par: Sun, Yan, et autres
Publié: (2025)

Towards Minimizing Feature Drift in Model Merging: Layer-wise Task Vector Fusion for Adaptive Knowledge Integration
par: Sun, Wenju, et autres
Publié: (2025)

FedRG: Unleashing the Representation Geometry for Federated Learning with Noisy Clients
par: Wen, Tian, et autres
Publié: (2026)

Beyond Frequency: The Role of Redundancy in Large Language Model Memorization
par: Zhang, Jie, et autres
Publié: (2025)

Towards a Probabilistic Fusion Approach for Robust Battery Prognostics
par: Alcibar, Jokin, et autres
Publié: (2024)

Dynamic LLM Routing and Selection based on User Preferences: Balancing Performance, Cost, and Ethics
par: Piskala, Deepak Babu, et autres
Publié: (2025)

Joint Input and Output Coordination for Class-Incremental Learning
par: Wang, Shuai, et autres
Publié: (2024)

TorchGT: A Holistic System for Large-scale Graph Transformer Training
par: Zhang, Meng, et autres
Publié: (2024)

Adaptive Pruning for Large Language Models with Structural Importance Awareness
par: Zheng, Haotian, et autres
Publié: (2024)

StaTS: Spectral Trajectory Schedule Learning for Adaptive Time Series Forecasting with Frequency Guided Denoiser
par: Zhang, Jintao, et autres
Publié: (2026)

Task-Aware Adaptive Modulation: A Replay-Free and Resource-Efficient Approach For Continual Graph Learning
par: Liu, Jingtao, et autres
Publié: (2025)

A Survey on Deep Learning based Time Series Analysis with Frequency Transformation
par: Yi, Kun, et autres
Publié: (2023)

GraphSubDetector: Time Series Subsequence Anomaly Detection via Density-Aware Adaptive Graph Neural Network
par: Chen, Weiqi, et autres
Publié: (2024)

K-Edit: Language Model Editing with Contextual Knowledge Awareness
par: Markowitz, Elan, et autres
Publié: (2025)

Diffusion-Modeled Reinforcement Learning for Carbon and Risk-Aware Microgrid Optimization
par: Zhao, Yunyi, et autres
Publié: (2025)

CAST-CKT: Chaos-Aware Spatio-Temporal and Cross-City Knowledge Transfer for Traffic Flow Prediction
par: Fofanah, Abdul Joseph, et autres
Publié: (2026)

Deep Frequency Derivative Learning for Non-stationary Time Series Forecasting
par: Fan, Wei, et autres
Publié: (2024)