Browsing by Keyword "Convolutional neural network (CNN)"
Now showing 1 - 2 of 2
Results Per Page
Sort Options
Item Condition monitoring of mooring systems for Floating Offshore Wind Turbines using Convolutional Neural Network framework coupled with Autoregressive coefficients(2024-06-15) Sharma, Smriti; Nava, Vincenzo; RENOVABLES OFFSHOREThis research presents a novel approach proposed for the monitoring of mooring systems in Floating Offshore Wind Turbines (FOWTs), employing a combination of Convolutional Neural Networks (CNNs) and Auto-Regressive (AR) models. CNN finds broad application in monitoring intricate structures, as they adeptly handle noisy response data without necessitating profound domain expertise. The precision of CNNs relies on the extraction of meaningful features from input data, necessitating meticulous data curation and labeling for optimal computational efficiency and accurate estimation. Emphasis is placed on the preference for feature-rich small datasets over voluminous yet sparse datasets, aiming to enable CNNs to discern crucial patterns more effectively and mitigate issues such as overfitting and extensive preprocessing. The novelty of the proposed approach lies in the integration of AR models, which serve to compress data and enhance damage-sensitive characteristics in the input for CNNs. This integration involves deploying regression models fitted to historical responses, parameterized with AR coefficients sensitive to damage, and further classifying severity using CNNs. The sequential nature of this approach addresses challenges such as vanishing/exploding gradients, particularly for extended historical data, while also attenuating the impact of noise and irrelevant information through data compression. The study explores the effectiveness of the coupled AR-CNN method in monitoring FOWT mooring lines, with a specific focus on two levels of damage identification: detection with classification and damage severity across diverse damage and operational scenarios. The modified methodology exhibits superior outcomes by conducting a performance analysis against traditional CNNs and other machine-learning methods, highlighting the potential of the AR-CNN strategy to improve the precision of FOWT mooring line condition monitoring. These findings underscore the AR-CNN strategy's potential to enhance the accuracy of FOWT mooring line condition monitoring.Item Damage assessment of soybean and redroot amaranth plants in greenhouse through biomass estimation and deep learning-based symptom classification(2023-10) Gómez-Zamanillo, Laura; Bereciartua-Pérez, Arantza; Picón, Artzai; Parra, Liliana; Oldenbuerger, Marian; Navarra-Mestre, Ramón; Klukas, Christian; Eggers, Till; Echazarra, Jone; COMPUTER_VISIONGreenhouse plant assessment is key part in the process of developing and testing new herbicides as it serves to analyze the response of the species to those different products and doses in a controlled way. With that purpose, trials are carried out in greenhouse where the damage in the treated plants is daily assessed. This assessment of every pot is often performed in comparison with an untreated reference pot, also named as control pot. This assessment is currently done pot by pot through a time-consuming process which consists of visual assessments done by experts in the field. Digital tools to reduce time and to endow the experts with more objective and repetitive methods for establishing the damage in the plants are required. A novel solution based on image processing and deep learning techniques is proposed to estimate the damage in the plants in different growing stages in the greenhouse. Different damage types and in different stages are produced in plants and images of them are acquired to create a dataset. The available annotation is the damage estimation value provided by the experts. The proposed methodology tries to emulate the way the experts estimate the damage over the plants through a two-step procedure. First, the biomass reduction of the assessed plant compared to the corresponding control plant is calculated, and secondly, the possible disease symptoms in the plant are detected. The first part is done using classical image processing techniques and the second part relies on a deep learning based multi-label classification model for symptom classification. The algorithm has been tested over two species: Glycine max (soybean) and Amaranthus retroflexus (redroot amaranth). An R2 of 0.87 and 0.89 respectively is obtained for the damage estimation. The method improves the performance of the current manual process in terms of efficiency and objectivity.