An adaptive distribution-matched recurrent network for wind power prediction using time-series distribution period division

Energy, Год журнала: 2024, Номер 299, С. 131383 - 131383

Опубликована: Апрель 25, 2024

Язык: Английский

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Multi-sentiment fusion for stock price crash risk prediction using an interpretable ensemble learning method

Engineering Applications of Artificial Intelligence, Год журнала: 2024, Номер 135, С. 108842 - 108842

Опубликована: Июль 4, 2024

Язык: Английский

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Machine learning-based prediction of indoor thermal comfort in traditional Chinese dwellings: A case study of Hankou Lifen

Case Studies in Thermal Engineering, Год журнала: 2024, Номер 61, С. 105048 - 105048

Опубликована: Авг. 30, 2024

Язык: Английский

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A novel hybrid BWO-BiLSTM-ATT framework for accurate offshore wind power prediction

Ocean Engineering, Год журнала: 2024, Номер 312, С. 119227 - 119227

Опубликована: Сен. 12, 2024

Язык: Английский

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A hydrogen concentration evolution prediction method for hydrogen refueling station leakage based on the Informer model

International Journal of Hydrogen Energy, Год журнала: 2025, Номер unknown

Опубликована: Янв. 1, 2025

Язык: Английский

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Application of deep learning for high-throughput phenotyping of seed: a review

Artificial Intelligence Review, Год журнала: 2025, Номер 58(3)

Опубликована: Янв. 6, 2025

Abstract Seed quality is of great importance for agricultural cultivation. High-throughput phenotyping techniques can collect magnificent seed information in a rapid and non-destructive manner. Emerging deep learning technology brings new opportunities effectively processing massive diverse data from seeds evaluating their quality. This article comprehensively reviews the principle several high-throughput non-destructively collection information. In addition, recent research studies on application learning-based approaches inspection are reviewed summarized, including variety classification grading, damage detection, components prediction, cleanliness, vitality assessment, etc. review illustrates that combination be promising tool various phenotype seeds, which used effective evaluation industrial practical applications, such as breeding, management, selection food source.

Язык: Английский

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SVM-Based Approach Fault Detection for PMSG-Wind Energy Conversion System

Journal of Engineering Research, Год журнала: 2025, Номер unknown

Опубликована: Янв. 1, 2025

Язык: Английский

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Optimization and Interpretability Analysis of Machine Learning Methods for ZnO Colloid Particle Size Prediction

ACS Applied Nano Materials, Год журнала: 2025, Номер unknown

Опубликована: Фев. 2, 2025

Язык: Английский

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Multi-performance coupled optimization drives low-carbon retrofitting of site museums

Building and Environment, Год журнала: 2025, Номер unknown, С. 112689 - 112689

Опубликована: Фев. 1, 2025

Язык: Английский

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Deep Learning Utilization for In-Line Monitoring of an Additive Co-Extrusion Process Based on Evaluation of Laser Profiler Data

Applied Sciences, Год журнала: 2025, Номер 15(4), С. 1727 - 1727

Опубликована: Фев. 8, 2025

Additive manufacturing is gaining importance in a number of application areas, and there an increased demand for mechanically resilient components. A way to improve the mechanical properties parts made thermoplastics by using reinforcing material. The study demonstrates development monitoring procedure fused filament fabrication-based co-extrusion process wire-reinforced thermoplastic Test components two variants are produced, data acquisition carried out with laser line scanner. collected employed train deep neural networks classify printed layers, aiming be able four different classes identify layers insufficient quality. dedicated convolutional network designed taking into account various factors such as layer architecture, pre-processing optimization methods. Several architectures, including transfer learning (based on VGG16 ResNet50), without fine-tuning, compared terms their performance based F1 score. Both model ResNet50 fine-tuning achieve score 84% 83%, respectively, decisive class ‘wire bad’ classifying inadequate reinforcement.

Язык: Английский

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