Results 201 to 210 of about 7,478 (267)

Overcoming the Nyquist Limit in Molecular Hyperspectral Imaging by Reinforcement Learning

Advanced Intelligent Discovery, EarlyView.
Explorative spectral acquisition guide automatically selects informative spectral bands to optimize downstream tasks, outperforming full‐spectrum acquisition. The selected hyperspectral data are used for tasks such as unmixing and segmentation. BandOptiNet encodes selection states and outputs optimal bands to guide spectral acquisition. Recent advances
Xiaobin Tang, Yongqing Zhang, Xinyu Deng, Hyeon Jeong Lee, Delong Zhang   +4 more
wiley   +1 more source

A Robust Deep Temporal Causal Discovery Platform for Single‐Cell Gene Regulatory Network Reconstruction

Advanced Intelligent Discovery, EarlyView.
scTIGER2.0 is a deep‐learning framework that infers gene regulatory networks from single‐cell RNA sequencing data. By integrating correlation, pseudotime ordering, deep learning and bootstrap‐based significance testing, it reduces false positives and reveals directional gene interactions.
Nishi Gupta, Madison Dautle, Shaoqiang Zhang, Yong Chen   +3 more
wiley   +1 more source

AI‐Driven Cancer Multi‐Omics: A Review From the Data Pipeline Perspective

Advanced Intelligent Discovery, EarlyView.
The exponential growth of cancer multi‐omics data brings opportunities and challenges for precision oncology. This review systematically examines AI's role in addressing these challenges, covering generative models, integration architectures, Explainable AI for clinical trust, clinical applications, and key directions for clinical translation.
Shilong Liu, Shunxiang Li, Kun Qian
wiley   +1 more source

Machine‐Learning‐Assisted Onset‐Time Determination in Transient Luminescence Thermometry

Advanced Intelligent Discovery, EarlyView.
Artificial neural networks enable autonomous extraction of onset times from transient heating curves in luminescence thermometry. Using Ln3+‐doped upconverting nanoparticles as luminescent thermometers, we combine experimental transients with physically motivated synthetic curves to enhance data diversity and improve generalization.
David J. Sousa, Erving Ximendes, Luís D. Carlos, Carlos D. S. Brites   +3 more
wiley   +1 more source

Fluorescent Hydrogel‐Based Strain Sensor With Machine Learning‐Augmented Performance

Advanced Intelligent Discovery, EarlyView.
Fluorescent hydrogel strain sensor based on carbon quantum dots enabling optical readout of deformation through strain‐dependent emission changes, coupled with Random Forest analysis to capture nonlinear fluorescence‐concentration relationships and identify optimal sensing conditions. Hydrogels are ideal matrices for bio‐integrated wearable sensors due
Tailai Chen, Tianlong Liu, Chao Lu, Rimsha Malik, Jin Zhang   +4 more
wiley   +1 more source

Solving Data Overlapping Problem Using A Class‐Separable Extreme Learning Machine Auto‐Encoder

Advanced Intelligent Systems, Volume 7, Issue 3, March 2025.
The overlapping and imbalanced data in classification present key challenges. Class‐separable extreme learning machine auto‐encoding (CS‐ELM‐AE) is proposed, which is an enhancement of ELM‐AE that better handles overlapping data by clustering points from the same class together. Applying oversampling addresses imbalanced data.
Ekkarat Boonchieng, Wanchaloem Nadda
wiley   +1 more source

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Intelligent Fault Diagnosis with Deep Architecture

2020 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC), 2020
The status judgment of converter valve equipment is an important part of ultra high voltage maintenance. However, accurate judgment of possible failures remains challenging. This paper proposes a novel multi-region deep architecture to improve abnormal judgement by considering long-range context information in increasingly finer spatial regions ...
Jinrui Gan, Peng Wu, Guoliang Zhang, Shiwen Dong, Zhaogang Han, Weiwei Liu   +5 more
openaire   +1 more source

Intelligent fault diagnosis of synchronous generators

Expert Systems with Applications, 2016
A 3?kVA generator fault model is used to diagnose faults in a 5 kVA generator.The model is trained using 3 kVA generator data and 5 kVA generator (no-fault data).System-dependent dimensions are removed using nuisance attribute projection (NAP).Classification and regression tree (CART) is used as a back-end classifier with NAP.NAP improves the ...
R. Gopinath, C. Santhosh Kumar, K. I. Ramachandran, V. Upendranath, P. V. R. Sai Kiran   +4 more
openaire   +1 more source

Intelligent fault diagnosis for analog circuits

Proceedings of 2011 International Conference on Electronic & Mechanical Engineering and Information Technology, 2011
This paper describes the design of fault diagnosis system based on neural networks and expert system for analog circuit. This system avoids wrong output from neural networks, the other hand reduces the missed diagnosis from expert system, and simulates for analog circuit with Matlab.
Rui Chen, Zhenchao Zhou
openaire   +1 more source

Intelligent Fault Diagnosis in Nonlinear Systems

Intelligent Automation & Soft Computing, 2013
Fault diagnosis in nonlinear systems is a challenging and very active research area. One of the difficulties to detect and isolate faults in nonlinear systems via observer-based methods is the design of a residual generator. In this work an integrated procedure combining conventional decoupling methods and Fuzzy Takagi-Sugeno observers for fault ...
Efraín Alcorta-García, Salvador Saucedo-Flores, David A. Diaz-Romero   +2 more
openaire   +1 more source