Browsing by Keyword "Classification"
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Item Deep Learning-Based Method for Accurate Real-Time Seed Detection in Glass Bottle Manufacturing(2022-11-04) Bereciartua-Perez, Arantza; Duro, Gorka; Echazarra, Jone; González, Francico Javier; Serrano, Alberto; Irizar, Liher; COMPUTER_VISIONGlass bottle-manufacturing companies produce bottles of different colors, shapes and sizes. One identified problem is that seeds appear in the bottle mainly due to the temperature and parameters of the oven. This paper presents a new system capable of detecting seeds of 0.1 mm2 in size in glass bottles as they are being manufactured, 24 h per day and 7 days per week. The bottles move along the conveyor belt at 50 m/min, at a production rate of 250 bottles/min. This new proposed method includes deep learning-based artificial intelligence techniques and classical image processing on images acquired with a high-speed line camera. The algorithm comprises three stages. First, the bottle is identified in the input image. Next, an algorithm based in thresholding and morphological operations is applied on this bottle region to locate potential candidates for seeds. Finally, a deep learning-based model can classify whether the proposed candidates are real seeds or not. This method manages to filter out most of false positives due to stains in the glass surface, while no real seeds are lost. The F1 achieved is 0.97. This method reveals the advantages of deep learning techniques for problems where classical image processing algorithms are not sufficient.Item Initiating the Human-robot Collaboration During the WEEE Management(Fraunhofer, 2020) Arnaiz, Sixto; Cacho, Iñigo; Uria, Iratxe; Guarde, Dorleta; Arieta-araunabeña, Maider; Stergiou, Athanasios; Karamoutsos, Spyridon-Dionysios; Antunes, Ana-Catarina; Oliveira, Elisabete; Sillaurren, Sara; Bastida, LeireThe amount and variety of WEEE (waste electrical and electronic equipment) that is generated, in Europe and globally, has steeply increased during the last years.Item Medical needs related to the endoscopic technology and colonoscopy for colorectal cancer diagnosis(2021-12) Ortega-Morán, Juan Francisco; Azpeitia, Águeda; Sánchez-Peralta, Luisa F.; Bote-Curiel, Luis; Pagador, Blas; Cabezón, Virginia; Saratxaga, Cristina L.; Sánchez-Margallo, Francisco M.; VISUALBackground. The high incidence and mortality rate of colorectal cancer require new technologies to improve its early diagnosis. This study aims at extracting the medical needs related to the endoscopic technology and the colonoscopy procedure currently used for colorectal cancer diagnosis, essential for designing these demanded technologies. Methods. Semi-structured interviews and an online survey were used. Results. Six endoscopists were interviewed and 103 were surveyed, obtaining the demanded needs that can be divided into: a) clinical needs, for better polyp detection and classification (especially flat polyps), location, size, margins and penetration depth; b) computer-aided diagnosis (CAD) system needs, for additional visual information supporting polyp characterization and diagnosis; and c) operational/physical needs, related to limitations of image quality, colon lighting, flexibility of the endoscope tip, and even poor bowel preparation.Item MRI Deep Learning-Based Solution for Alzheimer’s Disease Prediction(2021-09-09) Saratxaga, Cristina L.; Moya, Iratxe; Picón, Artzai; Acosta, Marina; Moreno-Fernandez-de-Leceta, Aitor; Garrote, Estibaliz; Bereciartua-Perez, Arantza; VISUAL; COMPUTER_VISION; QuantumBackground: Alzheimer’s is a degenerative dementing disorder that starts with a mild memory impairment and progresses to a total loss of mental and physical faculties. The sooner the diagnosis is made, the better for the patient, as preventive actions and treatment can be started. Al though tests such as the Mini-Mental State Tests Examination are usually used for early identification, diagnosis relies on magnetic resonance imaging (MRI) brain analysis. Methods: Public initiatives such as the OASIS (Open Access Series of Imaging Studies) collection provide neuroimaging datasets openly available for research purposes. In this work, a new method based on deep learning and image processing techniques for MRI-based Alzheimer’s diagnosis is proposed and compared with previous literature works. Results: Our method achieves a balance accuracy (BAC) up to 0.93 for image-based automated diagnosis of the disease, and a BAC of 0.88 for the establishment of the disease stage (healthy tissue, very mild and severe stage). Conclusions: Results obtained surpassed the state-of-the-art proposals using the OASIS collection. This demonstrates that deep learning-based strategies are an effective tool for building a robust solution for Alzheimer’s-assisted diagnosis based on MRI data.Item Regulating Grip Forces through EMG-Controlled Protheses for Transradial Amputees(2021-11-25) Rasines, Irati; Prada, Miguel; Bobrov, Viacheslav; Agrawal, Dhruv; Martinez, Leire; Iriondo, Pedro; Remazeilles, Anthony; McIntyre, Joseph; Tecnalia Research & Innovation; Robótica MédicaThis study aims to evaluate different combinations of features and algorithms to be used in the control of a prosthetic hand wherein both the configuration of the fingers and the gripping forces can be controlled. This requires identifying machine learning algorithms and feature sets to detect both intended force variation and hand gestures in EMG signals recorded from upper-limb amputees. However, despite the decades of research into pattern recognition techniques, each new problem requires researchers to find a suitable classification algorithm, as there is no such thing as a universal ’best’ solution. Consideration of different techniques and data representation represents a fundamental practice in order to achieve maximally effective results. To this end, we employ a publicly-available database recorded from amputees to evaluate different combinations of features and classifiers. Analysis of data from 9 different individuals shows that both for classic features and for time-dependent power spectrum descriptors (TD-PSD) the proposed logarithmically scaled version of the current window plus previous window achieves the highest classification accuracy. Using linear discriminant analysis (LDA) as a classifier and applying a majority-voting strategy to stabilize the individual window classification, we obtain 88% accuracy with classic features and 89% with TD-PSD features.Item Soft-Sensor for Class Prediction of the Percentage of Pentanes in Butane at a Debutanizer Column(2021-06-09) Niño-Adan, Iratxe; Landa-Torres, Itziar; Manjarres, Diana; Portillo, Eva; Orbe, Lucía; Tecnalia Research & Innovation; IARefineries are complex industrial systems that transform crude oil into more valuable subproducts. Due to the advances in sensors, easily measurable variables are continuously monitored and several data-driven soft-sensors are proposed to control the distillation process and the quality of the resultant subproducts. However, data preprocessing and soft-sensor modelling are still complex and time-consuming tasks that are expected to be automatised in the context of Industry 4.0. Although recently several automated learning (autoML) approaches have been proposed, these rely on model configuration and hyper-parameters optimisation. This paper advances the state-ofthe- art by proposing an autoML approach that selects, among different normalisation and feature weighting preprocessing techniques and various well-known Machine Learning (ML) algorithms, the best configuration to create a reliable soft-sensor for the problem at hand. As proven in this research, each normalisation method transforms a given dataset differently, which ultimately affects the ML algorithm performance. The presented autoML approach considers the features preprocessing importance, including it, and the algorithm selection and configuration, as a fundamental stage of the methodology. The proposed autoML approach is applied to real data from a refinery in the Basque Country to create a soft-sensor in order to complement the operators’ decision-making that, based on the operational variables of a distillation process, detects 400 min in advance with 98.925% precision if the resultant product does not reach the quality standards.