Results 71 to 80 of about 27,765 (261)

Data‐Efficient Cycle‐Level Capacity Prediction Using 1D Deep Convolutional Network

Advanced Intelligent Discovery, EarlyView.
We introduce DeepBat, a deep learning framework featuring a 1D convolutional backbone designed to extract latent degradation patterns from a microstructurally diverse electrode dataset. By learning complex formulation–performance relationships, the model accurately predicts long‐term specific discharge capacity using limited early‐cycle data, providing
Tao Huang, Bing Sun, Zhigang Chen, Chenyu Zhao, Eugene Bakker, Yaojie Lei, Lingfei Zhao, Shijian Wang, Chao Liu, Virginia Xiaojun He, Bahram Shirzadi, Hao Liu, Xin Xi, Ruiliu Liu, Dongqing Wu, Guodong Long, Guoxiu Wang   +16 more
wiley   +1 more source

A NEAT Approach to Evolving Neural‐Network‐Based Optimization of Chiral Photonic Metasurfaces: Application of a NeuroEvolution‐of‐Augmenting‐Topologies Pipeline

Advanced Intelligent Systems, EarlyView.
Neuro‐evolution can boost machine‐learning optimization of chiral metasurfaces. By integrating the NEAT algorithm into a deep‐learning framework, we enable the efficient design of visible‐spectrum chiroptical responses. NEAT autonomously evolves neural‐network architectures and weights, reducing manual tuning.
Davide Filippozzi, Arash Rahimi‐Iman
wiley   +1 more source

A double‐layer neural network wind speed prediction framework based on training set segmentation and error correction

IET Renewable Power Generation
Highly accurate wind speed prediction is one of the effective ways to improve wind energy utilization. Therefore, this study proposes a novel double‐layer neural network (D‐NN) framework based on feedforward neural networks.
Mingde Liu, Mingbo Liu, Siyi Zhang, Zhenxing Lei   +3 more
doaj   +1 more source

Relativistic Electron Flux Prediction at Geosynchronous Orbit Based on the Neural Network and the Quantile Regression Method

Space Weather, 2020
Geosynchronous satellites are exposed to the relativistic electrons, which may cause irreparable damage to the satellites. The prediction of the relativistic electron flux is therefore important for the safety of the satellites.
Hui Zhang, Suiyan Fu, Lun Xie, Duo Zhao, Chao Yue, Zuyin Pu, Ying Xiong, Tong Wu, Shaojie Zhao, Yixin Sun, Bo Cui, Zhekai Luo   +11 more
doaj   +1 more source

Interpretable Short‐Term Electric Load Forecasting

Advanced Intelligent Systems, EarlyView.
A temporal fusion transformer is implemented to generate day‐ahead forecasts of the hourly electrical load of a departmentbuilding at an Italian university. A forecasting performance improvement of more than 25% compared with established benchmark models and a provision of inherent robust interpretability insights reveal the potential of this model for
Alessandro Nicola, Giorgia Ghione, Vincenzo Randazzo, Vincenzo Franzitta, Marco Badami, Eros Pasero, Giansalvo Cirrincione   +6 more
wiley   +1 more source

A Language‐Guided Multimodal Foundation Model for Zero‐Shot and Multi‐Task Brain Signal Analysis

Advanced Intelligent Systems, EarlyView.
METIS aligns brain signals with natural‐language instructions to enable zero‐shot and multi‐task brain signal analysis. Pretrained on over 70 000 h of EEG and iEEG recordings, it generalizes across sleep stage classification, epilepsy detection, and neurological disorder diagnosis, providing a scalable foundation model for clinically meaningful brain ...
Mingzhi Chen, Yiyu Gui, Guibo Luo, Yuchao Yang   +3 more
wiley   +1 more source

Loan classification using a feed-forward neural network

Informatika
Objectives. The purpose of the study is to construct and study the use of a feed-forward neural network to solve the problem of loan classification, as well as to conduct a comparative analysis of the neural networkbased approach with the existing ...
U. I. Behunkou
doaj   +1 more source

Ecoefficiency Analysis and Regression in Data Conversion for Spiking Neural Network Training

Advanced Intelligent Systems, EarlyView.
The environmental footprint of spiking neural networks is quantified during dataset encoding and training for autonomous driving regression across three benchmarks. Temporal depth emerges as the dominant driver of energy consumption and CO2 emissions, while the accuracy–energy trade‐off proves dataset‐dependent. On conventional hardware, spiking models
Fernando S. Martínez, Raúl Parada, Jordi Casas‐Roma, Laia Subirats   +3 more
wiley   +1 more source

BatTS: a hybrid method for optimizing deep feedforward neural network. [PDF]

PeerJ Comput Sci, 2023
Pan S, Gupta TK, Raza K.
europepmc   +1 more source

Benchmarking Data‐Driven Control of Octopus‐Inspired Soft Arms in Underwater Environment

Advanced Intelligent Systems, EarlyView.
Underwater soft robots present safe, compliant interaction, yet reproducible control remains scarce. This work presents an open benchmark for octopus‐inspired arms: a smooth, velocity‐diverse data‐collection scheme produces a compact dataset to train a vanilla policy.
Muhammad Sunny Nazeer, Yasmin Tauqeer Ansari, Emanuele Solfiti, Baljinder Singh Bal, Matteo Lo Preti, Barbara Mazzolai, Cecilia Laschi   +6 more
wiley   +1 more source