Detecting broken receiver tubes in CSP plants using intelligent sampling and dual loss
Published 2023 View Full Article
- Home
- Publications
- Publication Search
- Publication Details
Title
Detecting broken receiver tubes in CSP plants using intelligent sampling and dual loss
Authors
Keywords
-
Journal
APPLIED INTELLIGENCE
Volume -, Issue -, Pages -
Publisher
Springer Science and Business Media LLC
Online
2023-11-04
DOI
10.1007/s10489-023-05093-3
References
Ask authors/readers for more resources
Related references
Note: Only part of the references are listed.- Updating digital twins: Methodology for data accuracy quality control using machine learning techniques
- (2023) Fabio Rodríguez et al. COMPUTERS IN INDUSTRY
- A cascade neural network methodology for fault detection and diagnosis in solar thermal plants
- (2023) Sara Ruiz-Moreno et al. Renewable Energy
- Machine learning techniques applied to mechanical fault diagnosis and fault prognosis in the context of real industrial manufacturing use-cases: a systematic literature review
- (2022) Marta Fernandes et al. APPLIED INTELLIGENCE
- Ornithopter Trajectory Optimization with Neural Networks and Random Forest
- (2022) M. A. Pérez-Cutiño et al. JOURNAL OF INTELLIGENT & ROBOTIC SYSTEMS
- A Combined Computer Vision and Deep Learning Approach for Rapid Drone-Based Optical Characterization of Parabolic Troughs
- (2022) Devon Kesseli et al. JOURNAL OF SOLAR ENERGY ENGINEERING-TRANSACTIONS OF THE ASME
- tofee-tree: automatic feature engineering framework for modeling trend-cycle in time series forecasting
- (2021) Santhosh Kumar Selvam et al. NEURAL COMPUTING & APPLICATIONS
- RUF: Effective Sea Ice Floe Segmentation Using End-To-End RES-UNET-CRF with Dual Loss
- (2021) Anmol Sharan Nagi et al. Remote Sensing
- Analyzing the performance of photovoltaic systems using support vector machine classifier
- (2021) Hichem Hafdaoui et al. Sustainable Energy Grids & Networks
- Performance assessment of selective machine learning techniques for improved PV array fault diagnosis
- (2021) Dhritiman Adhya et al. Sustainable Energy Grids & Networks
- Deep Learning for Multilabel Remote Sensing Image Annotation With Dual-Level Semantic Concepts
- (2020) Panpan Zhu et al. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
- HOBA: A novel feature engineering methodology for credit card fraud detection with a deep learning architecture
- (2019) Xinwei Zhang et al. INFORMATION SCIENCES
- Development of Photovoltaic abnormal condition detection system using combined regression and Support Vector Machine
- (2019) Fauzan Hanif Jufri et al. ENERGY
- Automated Detection of Radiology Reports that Require Follow-up Imaging Using Natural Language Processing Feature Engineering and Machine Learning Classification
- (2019) Robert Lou et al. JOURNAL OF DIGITAL IMAGING
- Hotspot diagnosis for solar photovoltaic modules using a Naive Bayes classifier
- (2019) Kamran Ali Khan Niazi et al. SOLAR ENERGY
- Deep residual network based fault detection and diagnosis of photovoltaic arrays using current-voltage curves and ambient conditions
- (2019) Zhicong Chen et al. ENERGY CONVERSION AND MANAGEMENT
- Metaheuristic optimization based fault diagnosis strategy for solar photovoltaic systems under non-uniform irradiance
- (2018) Saborni Das et al. RENEWABLE ENERGY
- Fault detection and diagnosis based on C4.5 decision tree algorithm for grid connected PV system
- (2018) Rabah Benkercha et al. SOLAR ENERGY
- Random forest based intelligent fault diagnosis for PV arrays using array voltage and string currents
- (2018) Zhicong Chen et al. ENERGY CONVERSION AND MANAGEMENT
- Cracks and welds detection approach in solar receiver tubes employing electromagnetic acoustic transducers
- (2017) Carlos Quiterio Gómez Muñoz et al. STRUCTURAL HEALTH MONITORING-AN INTERNATIONAL JOURNAL
- Vacuum evaluation of parabolic trough receiver tubes in a 50 MW concentrated solar power plant
- (2016) Guillermo Espinosa-Rueda et al. SOLAR ENERGY
- Effects of glass cover on heat flux distribution for tube receiver with parabolic trough collector system
- (2015) Wang Fuqiang et al. ENERGY CONVERSION AND MANAGEMENT
Add your recorded webinar
Do you already have a recorded webinar? Grow your audience and get more views by easily listing your recording on Peeref.
Upload NowBecome a Peeref-certified reviewer
The Peeref Institute provides free reviewer training that teaches the core competencies of the academic peer review process.
Get Started