Results 51 to 60 of about 33,535 (242)

Forecasting renewable energy for microgrids using machine learning

Discover Applied Sciences
Microgrids, comprised of interconnected loads and distributed energy resources, function as single controllable entities with respect to the main grid. However, the inherent variability of distributed wind and solar generation within microgrids presents ...
Piyumi Sudasinghe, Damayanthi Herath, Isiwara Karunarathne, Hansani Weeratunge, Lahiru Jayasuriya   +4 more
doaj   +1 more source

High‐Throughput Screening and Interpretable Machine Learning for Rational Design of Bimetallic Catalysts for Methane Activation

Advanced Science, EarlyView.
ABSTRACT Methane's efficient catalytic removal is vital for sustainable development. Bimetallic catalysts, though promising for methane activation, pose a design challenge due to their complex compositional space. This work introduces an integrated framework that combines high‐throughput density functional theory (DFT) and interpretable machine ...
Mingzhang Pan, Tian Zhang, Jiawei Dong, Yubao Xie, Kaifeng Zhong, Wei Guan, Haiqiao Wei, Changcheng Fu, Yaqiong Su   +8 more
wiley   +1 more source

Mean Squared Error (MSE) dan Penggunaannya

Jurnal Pemanfaatan Teknologi untuk Masyarakat: Jurnal Pengabdian Masyarakat
Mean Squared Error (MSE) adalah metrik evaluasi yang umum digunakan dalam statistik dan machine learning untuk mengukur seberapa akurat sebuah model regresi dalam memprediksi nilai numerik. MSE menghitung selisih antara nilai prediksi model dan nilai sebenarnya dari data, kemudian mengkuadratkan selisih tersebut agar tidak ada selisih yang bernilai ...
openaire   +1 more source

Machine Learning‐Assisted KCl‐CaCl2‐LiCl Electrolyte Design for Low‐Temperature, High‐Performance Calcium‐Based Liquid Metal Batteries

Advanced Science, EarlyView.
A machine learning‐assisted framework optimizes the KCl‐CaCl2‐LiCl ternary electrolyte. The optimized 13:35:52 mol% composition enables Ca‐based liquid metal batteries to operate stably at 480 °C, with >99.5% coulombic efficiency, ultralow self‐discharge, and excellent cycling stability, advancing low‐temperature large‐scale energy storage.
Xinglin Zhou, Lei Huang, Yan Zhou, Xiaohui Ning   +3 more
wiley   +1 more source

Native AI-based hybrid deep learning for wireless link quality prediction in NTN waterside scenarios

ICT Express
Predicting link quality before establishing communication between transmitter and receiver enhances channel selection. With the advancements in artificial intelligence, prediction is now possible for complex environments such as riverside, maritime and ...
Shrutika Sinha, G. Pradeep Reddy, Sea-Moon Kim, Soo-Hyun Park   +3 more
doaj   +1 more source

Learning Moisture‐Induced Damage From Vision: Diffusion Models for Real‐Time Monitoring of Additive Manufacturing Processes

Advanced Science, EarlyView.
We introduce a vision‐based real‐time monitoring system for additive manufacturing that detects subtle moisture‐induced degradation via a diffusion model‐based framework. The approach enables nondestructive assessment of moisture‐induced damage level and mechanical performance and establishes a practical route toward more intelligent, reliable, and ...
Jiyoung Jung, Yuna Yoo, Dharneedar Ravichandran, Dahyun Daniel Lim, Grace X. Gu   +4 more
wiley   +1 more source

Dual‐Module Near‐Infrared Fluorophores Discovery System via Knowledge Transfer

Advanced Science, EarlyView.
This study presents a dual‐module deep learning system for the design of near‐infrared (NIR) fluorophores. A large molecular library is generated and analyzed, leading to the suggestions of promising candidates. The effectiveness of the system is further validated through the synthesis, characterization, and in vivo imaging, demonstrating its potential
Yixin Zhu, Xia Ling, Xianhe Zhang, Chuanjiang Jian, Leilei Shi, Wentao Song, Xiaonan Wang, Bin Liu   +7 more
wiley   +1 more source

Innovative transformer neural network for wind density function estimation at different hub heights of turbine

Scientific Reports
Accurate estimation of wind power potential is important for resource assessment to install wind turbine. Weibull distribution functions (WDF) have been widely used and it is a function of wind speed (WS).
Amit Kumar Yadav, Vibha Yadav, Walle Tilahun   +2 more
doaj   +1 more source

StackingNet: Collective Inference Across Independent AI Foundation Models

Advanced Science, EarlyView.
ABSTRACT Artificial intelligence (AI) built on large foundation models has transformed language understanding, computer vision, and reasoning, yet these systems remain isolated and cannot readily share their capabilities. Coordinating the complementary strengths of independently developed, black‐box foundation models is essential for trustworthy ...
Siyang Li, Chenhao Liu, Dongrui Wu, Zhigang Zeng, Lieyun Ding   +4 more
wiley   +1 more source

Using artificial intelligence for wind speed prediction [PDF]

E3S Web of Conferences
Accurate wind speed prediction is critical for renewable energy management, agriculture, and weather forecasting. This study investigates the use of machine learning techniques for predicting daily average wind speed using meteorological features ...
Omari Asem, Alazzam Azmi, Badreddine Said, Abu Sharkh Hani, Alazzam Yasmeen, Bani Mustafa Ahmed, Al Alawi Roudha   +6 more
doaj   +1 more source