Continual learning for energy management systems: A review of methods and applications, and a case study

Applied Energy, Год журнала: 2025, Номер 384, С. 125458 - 125458

Опубликована: Фев. 10, 2025

Язык: Английский

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DHR-BLS: A Huber-type robust broad learning system with its distributed version

Knowledge-Based Systems, Год журнала: 2025, Номер unknown, С. 113184 - 113184

Опубликована: Фев. 1, 2025

Язык: Английский

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Artificial intelligence-driven distributed acoustic sensing technology and engineering application

PhotoniX, Год журнала: 2025, Номер 6(1)

Опубликована: Фев. 24, 2025

Abstract Distributed acoustic sensing (DAS) technology is a fiber-optic based distributed technology. It achieves real-time monitoring of signals by detecting weak disturbances along the fiber. has advantages such as long measurement distance, high spatial resolution and large dynamic range. Artificial intelligence (AI) great application potential in DAS technology, including data augmentation, preprocessing classification recognition events. By introducing AI algorithms, system can process massive more automatically intelligently. Through analysis prediction, AI-enabled wide applications fields transportation, energy security due to its accuracy reliability intelligent decision-making. In future, continuous advancement will bring greater breakthroughs innovations for engineering play important role various fields, promote innovation development industry.

Язык: Английский

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Fusion Network Model Based on Broad Learning System for Multidimensional Time‐Series Forecasting

International Journal of Intelligent Systems, Год журнала: 2025, Номер 2025(1)

Опубликована: Янв. 1, 2025

Multidimensional time‐series prediction is significant in various fields, such as human production and life, weather forecasting, artificial intelligence. However, a single model can only focus on specific features of data, making it unable to consider both linear nonlinear components simultaneously. In this study, we propose fusion network that combines the advantages deep broad networks for multidimensional tasks. The complex data are divided into data. Restricted Boltzmann machine mapping functions used feature learning generating nodes at layer. echo state gate recurrent unit applied enhancement proposed has been validated PM2.5 wind turbine power datasets, proving superior performance multistep tasks compared baseline models.

Язык: Английский

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Cable security state detection based on multi-scale information fusion and distributed fiber optic sensing

Journal of Physics Conference Series, Год журнала: 2025, Номер 2990(1), С. 012007 - 012007

Опубликована: Апрель 1, 2025

Abstract Mechanical operations in proximity to cables present considerable risks the stability of power transmission and reliability energy supply systems. As an advanced intelligent sensing technique, Distributed Fiber Optic Sensing (DFOS) has capability detect identify potential external hazards affecting cables. However, due massive volume data, existing studies primarily rely on classification algorithms distinguish threat events, lacking efficient end-to-end detection for differentiation localization. This constraint results inefficient information usage limited real-time responsiveness. To overcome these challenges, this study introduces a high-performance algorithm that integrates multi-scale fusion. First, attention mechanism called MRFA is proposed achieve effective feature extraction, which characterized by its flexibility multi-receptive fields. Second, innovative Information Dialysis Module (DM) enhance efficiency inter-layer filtering models. Finally, methods are integrated into improved YOLOv8 framework. Experimental comparisons across multiple datasets validate method’s effectiveness efficiency, demonstrating surveillance smart recognition cable security applications.

Язык: Английский

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Cross-PIC: A cross-scale in-context learning network for 3D multibeam point cloud segmentation of submarine pipelines

Ocean Engineering, Год журнала: 2024, Номер 315, С. 119778 - 119778

Опубликована: Ноя. 25, 2024

Язык: Английский

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