3D inspection methods for specular or partially specular surfaces

Deflectometric techniques are a powerful tool for the automated quality control of specular or shiny surfaces. These techniques are based on using a camera to observe a reference pattern reflected on the surface under inspection, exploiting the dependence of specular reflections on surface normals to recover shape information from the acquired images. Although deflectometry is already used in industrial environments such as the quality control of lenses or car bodies, there are still some open problems. On the one hand, using quantitative deflectometry, the normal vector field and the 3D shape of a surface can be obtained, but these techniques do not yet take full advantage of their local sensitivity because the achieved global accuracies are affected by calibration errors. On the other hand, qualitative deflectometry is used to detect surface imperfections without absolute measurements, exploiting the local sensitivity of deflectometric recordings with reduced calibration requirements. However, this qualitative approach requires further processing that can involve a considerable engineering effort, particularly for aesthetic defects which are inherently subjective. The first part of this thesis aims to contribute to a better understanding of how deflectometric setups and their calibration errors affect quantitative measurements. Different error sources are considered including the camera calibration uncertainty and several non-ideal characteristics of LCD screens used to generate the light patterns. Experiments performed using real measurements and simulations show that the non-planarity of the LCD screen and the camera calibration are the dominant sources of error. The second part of the thesis investigates the use of deep learning to identify geometrical imperfections and texture defects based on deflectometric data. Two different approaches are explored to extract and combine photometric and geometric information using convolutional neural network architectures: one for automated classification of defective samples, and another one for automated segmentation of defective regions in a sample. The experimental results in a real industrial case study indicate that both architectures are able to learn relevant features from deflectometric data, enabling the classification and segmentation of defects based on a dataset of user-provided examples.Teknika deflektometrikoak tresna baliotsuak dira gainazal espekular edo distiratsuen kalitate kontrol automatikoa gauzatzeko. Teknika hauetan, kamera bat erabiltzen da ikuskatu beharreko gainazalean islatutako erreferentziazko patroi bat behatzeko, eta isladapen espekularrek gainazalen bektore normalengan duten menpekotasuna ustiatzen dute irudietatik informazio geometrikoa berreskuratzeko. Zenbait industria-aplikaziotan deflektometria jada erabiltzen bada ere –adibidez, betaurrekoen edo autoen karrozerien kalitate kontrolean-, oraindik badaude hobetu beharreko hainbat esparru. Batetik, deflektometria kuantitatiboak aukera ematen du gainazal baten bektore-eremu normala eta 3D forma lortzeko, baina gaur egun teknika hauek ez dute beren sentsibilitate lokal guztia aprobetxatzen kalibrazio-akatsek zehaztasun globalean duten eraginagatik. Bestetik, deflektometria kualitatiboa neurketa absoluturik egin gabe gainazal akatsak antzemateko erabili daiteke, kalibrazio-eskakizun murriztuekin sentsibilitate lokala ustiatuz. Hala ere, teknika horiek algoritmoen garapenean esfortzu handia ekar dezakeen prozesamendu bat eskatzen dute, bereziki bere baitan subjektiboak diren akats estetikoetarako. Hala ere, teknika horiek algoritmoen garapenean esfortzu handia ekar dezakeen prozesamendu bat eskatzen dute, bereziki bere baitan subjektiboak diren akats estetikoetarako. Tesi honen lehen zatiaren helburua adkizizio sistema osatzen duten gailuek eta horien kalibrazioek neurketa kuantitatiboei nola eragiten dieten hobeto ulertzen laguntzea da. Hainbat errore-iturri hartzen dira kontuan, besteak beste kameraren kalibrazioaren ziurgabetasuna, eta argi-patroiak sortzeko erabilitako LCD pantailen zenbait ezaugarri ez-ideal. Neurketa errealetan eta simulazioetan egindako esperimentuek erakusten dute LCD pantailaren deformazioak eta kameraren kalibrazioak eragindako erroreak direla neurketen akats eta ziurgabetasun iturri nagusiak. Tesiaren bigarren zatian, datu deflektometrikoetatik abiatuz, inperfekzio geometrikoak eta testura-akatsak identifikatzeko ikaskuntza sakoneko metodoen erabilera ikertzen da. Helburu honekin, irudietatik informazio fotometrikoa eta geometrikoa atera eta konbinatzen duten sare neuronal konboluzionaletan oinarritutako bi arkitektura proposatzen dira: bata, lagin akastunak automatikoki sailkatzeko; eta, bestea, laginetako eremu akastunak automatikoki segmentatzeko. Automobilgintza industriako kasu praktiko baten lortutako emaitzek erakusten dute erabilitako arkitekturek datu deflektometrikoetatik ezaugarri esanguratsuak ikas ditzaketela, erabiltzaileak emandako adibide multzo batean oinarrituta gainazal akatsak sailkatu eta segmentatzea ahalbidetuz.Las técnicas deflectométricas son una herramienta valiosa para automatizar el control de calidad de superficies especulares o reflectantes. Estas técnicas se basan en el uso de una cámara para observar un patrón de referencia reflejado en la superficie bajo inspección, explotando la dependencia de los reflejos especulares en la normal de la superficie para recuperar información geométrica a partir de las imágenes adquiridas. Aunque la deflectometría ya se usa en algunas aplicaciones industriales, tales como el control de calidad de lentes o carrocerías de coches, todavía hay algunos problemas abiertos. Por un lado, la deflectometría cuantitativa permite obtener el campo vectorial normal y la forma 3D de una superficie, pero a día de hoy no es capaz de aprovechar al máximo su sensibilidad local ya que la precisión global se ve afectada por errores de calibración. Por otro lado, la deflectometría cualitativa se utiliza para detectar imperfecciones de la superficie sin mediciones absolutas, explotando la sensibilidad local de la deflectometría con requisitos de calibración reducidos. Sin embargo, estos métodos requieren un procesamiento adicional que puede implicar un esfuerzo considerable en el desarrollo de algoritmos, particularmente para defectos estéticos que son inherentemente subjetivos. La primera parte de esta tesis tiene como objetivo contribuir a una mejor comprensión de cómo el sistema de adquisición y su calibración afectan a las mediciones cuantitativas. Se consideran dife-rentes fuentes de error, incluida la incertidumbre de calibración de la cámara y varias características no ideales de las pantallas LCD utilizadas para generar los patrones de luz. Los experimentos realizados con mediciones reales y simulaciones indican que los errores inducidos por la deformación de la pantalla LCD y la calibración de la cámara son las principales fuentes de error e incertidumbre. La segunda parte de la tesis investiga el uso del aprendizaje profundo para identificar imperfecciones geométricas y defectos de textura a partir de datos deflectométricos. Se adoptan dos enfoques diferentes para extraer y combinar información fotométrica y geométrica utilizando sendas arquitecturas basadas en redes neuronales convolucionales: una para la clasificación automatizada de muestras defectuosas y otra para la segmentación automatizada de regiones defectuosas en una muestra. Los resultados experimentales en un caso de estudio industrial real indican que ambas arquitecturas pueden aprender características relevantes de los datos deflectométricos, permitiendo la clasificación y segmentación de defectos en base a un conjunto de datos de ejemplos proporcionados por el usuario

Food safety – From pioneering steps to the modern scientific discipline

Food safety is a story that unites all civilizations, cultures, and nations, and it is interlaced with various methods for making food safer. Concern for nutritious and safe food is as ancient as humankind itself, and many of the food safety issues that persist today are not new. Diverse records from the ancient world, as well as the religious writings of the three monotheist religions, actually refer to food, its intake, and prohibitions, as well as pathological diseases that may follow from inappropriate intake. Over time, food safety has evolved into a scientific discipline concerned with the handling, preparation, transport, and distribution of food to avoid the transmission of illnesses. The current state of food safety knowledge is the result of past discoveries, innovations, and laws. In modern times, the right to consume safe food is a fundamental human right. It contributes to and promotes sustainable development while supporting the economy, trade, and tourism. Nevertheless, despite significant improvements, we still know relatively little about food-borne illnesses and how infections affect humans

Anomaly detection and automatic labeling for solar cell quality inspection based on Generative Adversarial Network

Quality inspection applications in industry are required to move towards a zero-defect manufacturing scenario, withnon-destructive inspection and traceability of 100 % of produced parts. Developing robust fault detection and classification modelsfrom the start-up of the lines is challenging due to the difficulty in getting enough representative samples of the faulty patternsand the need to manually label them. This work presents a methodology to develop a robust inspection system, targeting thesepeculiarities, in the context of solar cell manufacturing. The methodology is divided into two phases: In the first phase, an anomalydetection model based on a Generative Adversarial Network (GAN) is employed. This model enables the detection and localizationof anomalous patterns within the solar cells from the beginning, using only non-defective samples for training and without anymanual labeling involved. In a second stage, as defective samples arise, the detected anomalies will be used as automaticallygenerated annotations for the supervised training of a Fully Convolutional Network that is capable of detecting multiple types offaults. The experimental results using 1873 EL images of monocrystalline cells show that (a) the anomaly detection scheme can beused to start detecting features with very little available data, (b) the anomaly detection may serve as automatic labeling in order totrain a supervised model, and (c) segmentation and classification results of supervised models trained with automatic labels arecomparable to the ones obtained from the models trained with manual labels.Comment: 20 pages, 10 figures, 6 tables. This article is part of the special issue "Condition Monitoring, Field Inspection and Fault Diagnostic Methods for Photovoltaic Systems" Published in MDPI - Sensors: see https://www.mdpi.com/journal/sensors/special_issues/Condition_Monitoring_Field_Inspection_and_Fault_Diagnostic_Methods_for_Photovoltaic_System

Nasal carriage rate and antimicrobial resistance pattern of Staphylococcus aureus among the food handlers in Canton Sarajevo, Bosnia and Herzegovina

Introduction: The nasals and hand carriage of Staphylococcus aureus in food handlers (FHs) represent a significant source of Staphylococcal food contamination and food poisoning. Antimicrobial resistance (AMR) is a microorganism’s ability to resist the action of one or more antimicrobial agents. S. aureus has demonstrated the ability to rapidly respond to each new antimicrobial with the development of a resistance mechanism. The aim of the study was to assess the prevalence of nasal carriage rate and AMR pattern of isolated strains S. aureus among FHs in Canton Sarajevo, Bosnia and Herzegovina. Methods: The retrospective study included laboratory results of 11.139 tested subjects between January 2014 and December 2018. The study was conducted in the laboratory of the Institute of Public Health of the Federation of Bosnia and Herzegovina in Sarajevo. Samples of nasal swabs were collected from FHs, employees in companies located in Canton Sarajevo, during sanitary surveillance prescribed by applicable legal standards. S. aureus isolates were identified according to conventional microbiological methods and antimicrobial susceptibility testing was performed by the agar disk diffusion method according to the European Committee on Antimicrobial Susceptibility Testing; 2013 standard. Results: Among the 11.138 subjects, 792 (7.1%) were carriers of S. aureus. Isolated strains were tested on eight different antibiotics, and the resistance to penicillin, ampicillin, and amoxicillin was 788 (99.5%), 776 (97.9%), and 752 (94.9%), retrospectively. In total, 86.36% of isolated strains were multidrug-resistant. Conclusions: The low percentage of S. aureus carriers indicates that preventive measures of carrier control are being actively implemented within the legally prescribed measures. The emergence of numerous isolated strains with multidrug-resistance characteristics is a significant public health problem and consequently limits the range of antibiotics available for therapeutic purposes. The results of this research indicate that AMR has increased in Sarajevo Canton and it is following the trend of global growth

La influencia de la ONG’d ADEPU en la zona rural de Ghana

Mi trabajo de fin de grado se centra en la eficacia de las ONG (Organización NO Gubernamental) en cuanto al trabajo y las prestaciones que estas realizan, partiendo de la base de la ONG’d ADEPU (Asociación para el Desarrollo de los Pueblos), en la cual pude realizar mis prácticas universitarias, para así conocer el funcionamiento interno de una asociación de esta índole. Lo que quiero demostrar, no es solo el trabajo y el rendimiento que este tipo de Asociaciones posibilita, sino también como repercuten las ONG en la comunidad en la que trabaja, que aportan a sus vecinos y si se lleva a cabo los procesos de integración pertinentes. A lo largo de estos últimos años, da la sensación de que muchas ONG se han lucrado del sufrimiento de las personas, a raíz de ponerse de moda, paquetes estandarizados, llamados “Voluntariados”, sin unos objetivos y sin ningún proyecto, como es el caso de la ONG “Yes We Help” de Yago Zarroca. Por ello es muy importante dar visibilidad al esfuerzo y el trabajo que realizan las Asociaciones mediante un plan de comunicación que integre un programa en el que se explique el funcionamiento de la ONG, ya sea por redes sociales, un blog, mailing a los socios o algo que denote la transparencia del rendimiento realizado por este tipo de Asociaciones, dejando claros sus objetivos y su misión. Mi trabajo se centra en dos temas que a mi parecer precisan de gran importancia a día de hoy. Uno de ellos es la educación, herramienta esencial que nos dota de una serie de valores y conocimientos que hacen posible un crecimiento personal, además, es el pilar fundamental sobre el que se sustenta ADEPU. En Ghana, al igual que en muchos países de África, los niños, tienen difícil acceso a la educación y la calidad de ésta deja mucho que desear. Otro de los temas a tratar es el rol de la mujer en Ghana, sobre todo en el entorno Rural, que se ve desprovista de los derechos y privilegios de los que los hombres gozan, pudiendo poner de ejemplo el de la educación. Estos dos temas vienen dados de la mano ya que con uno se puede dar pie al otro, si conseguimos educar a un pueblo, podremos conseguir un entorno de respeto, igualdad y prosperidad. “La educación es el arma más poderosa que puedes usar para cambiar el mundo”. Nelson Mandela1Grado en Publicidad y Relaciones Pública

Integración de un centro de transformación de servicios auxiliares de turbinas en una central térmica

La ampliación de la capacidad de generación de una central térmica mediante la integración de nuevas turbinas de gas lleva asociada la realización de múltiples proyectos interdisciplinares de menor entidad que el de la propia integración de las turbinas, que dan soporte al proyecto de ampliación de la central. Dentro de estos proyectos de menor entidad, el problema de garantizar el suministro eléctrico de los servicios auxiliares de las turbinas requiere de un estudio específico debido a su especial importancia, pues la indisponibilidad de los servicios auxiliares de una turbina puede provocar consecuencias indeseables tanto a nivel de explotación en la central como a nivel de la propia máquina. En este sentido, la cobertura del suministro de los servicios auxiliares de una turbina se suele garantizar mediante tres vías independientes, de tal forma que se garantice dicho servicio. De las tres vías de alimentación que suelen emplearse, dos de ellas se integran en el propio conjunto turbo-alternador, una de ellas con carácter prioritario de funcionamiento y la otra en condiciones de emergencia excepcionales. La tercera vía de alimentación a los servicios auxiliares de una turbina debe ser proporcionada desde el exterior del conjunto turbo-alternador y comúnmente es denominada “alimentación de respaldo de servicios auxiliares”. Esta alimentación es de especial importancia en condiciones de inicio de la secuencia de arranque de turbina o en ciertos modos de acoplamiento de la turbina a la red eléctrica. El objeto principal de este proyecto consiste en el diseño de un centro de transformación desde el que se proporcionen las diversas alimentaciones de respaldo a los servicios auxiliares de las turbinas existentes en la Central Térmica de Ibiza, así como a las futuras turbinas que van a ser integradas en dicha Central a corto y a medio-largo plazo. En este proyecto se realiza la definición, descripción y el dimensionamiento de la instalación eléctrica que es necesario llevar a cabo para la implantación de dicho centro de transformación de servicios auxiliares de turbinas en la Central Térmica de Ibiza. En la memoria y en sus anexos se recopilan los diversos estudios y cálculos, especificaciones técnicas de equipos y planos necesarios para poder realizar dicha integración y que ésta pueda ser aprobada por la administración.Ingeniería Industria

COVID-19 Pandemic: A Challenge for Healthcare Professionals and Assessment of Anxiety Symptoms

Introduction: Worldwide, COVID-19 pandemic caused millions of infected people and thousands of deaths. Due to enormous pressure on health-care systems and its inadequate preparedness, utter collapse is expected. In the current epidemic response, healthcare workers’ (HCWs) knowledge and practice are crucial, while the impact on their mental health is still unknown.Methods: The cross-sectional study was conducted among HCWs redeployed to COVID points in the Public Institution Health Centre of Sarajevo Canton. According to guidelines and information provided by the World Health Organization and Ministries of Health, a questionnaire was developed. In addition, General Anxiety Disorder-7 as a screening tool for anxiety disorders was used.Results: Of 180 respondents, 26 (14.4%) were in direct contact with the sick patient. In total, 79 (43.9%) respondents consider their personal protective equipment is in accordance with the guidelines of the world health authorities. A total of 72 (39.7%) of respondents used the same mask for several days. In general, the danger from new coronavirus was considered minimal by 59 (32,6%) HCWs. Based on the achieved score for assessing the anxiety disorder, in 63 (35%) subjects, the presence of severe symptoms was detected.Conclusion: This study found that most HCWs do not have enough knowledge about the COVID-19 pandemic. We identified that there are differences in the sources of information and gap in perceptions of the native origin of the virus. Considering the frequency of anxiety symptoms among HCWs, interventions are necessary in order to preserve their mental health

The Physical Parameters of the Retired A Star HD185351

We report here an analysis of the physical stellar parameters of the giant star HD185351 using Kepler short-cadence photometry, optical and near infrared interferometry from CHARA, and high-resolution spectroscopy. Asteroseismic oscillations detected in the Kepler short-cadence photometry combined with an effective temperature calculated from the interferometric angular diameter and bolometric flux yield a mean density, rho_star = 0.0130 +- 0.0003 rho_sun and surface gravity, logg = 3.280 +- 0.011. Combining the gravity and density we find Rstar = 5.35 +- 0.20 Rsun and Mstar = 1.99 +- 0.23 Msun. The trigonometric parallax and CHARA angular diameter give a radius Rstar = 4.97 +- 0.07 Rsun. This smaller radius,when combined with the mean stellar density, corresponds to a stellar mass Mstar = 1.60 +- 0.08 Msun, which is smaller than the asteroseismic mass by 1.6-sigma. We find that a larger mass is supported by the observation of mixed modes in our high-precision photometry, the spacing of which is consistent only for Mstar =~ 1.8 Msun. Our various and independent mass measurements can be compared to the mass measured from interpolating the spectroscopic parameters onto stellar evolution models, which yields a model-based mass M_star = 1.87 +- 0.07 Msun. This mass agrees well with the asteroseismic value,but is 2.6-sigma higher than the mass from the combination of asteroseismology and interferometry. The discrepancy motivates future studies with a larger sample of giant stars. However, all of our mass measurements are consistent with HD185351 having a mass in excess of 1.5 Msun.Comment: ApJ accepte

Quantification of some additives in energy drinks using high-performance liquid chromatography

Introduction: Energy drinks (EDs) are products in the form of a beverage or concentrated liquid designed to increase both mental and physical stimulations. Their popularity has grown tremendously, especially among children and adolescents, regardless of the growing number of undesirable health consequences associated with their consumption. This study aimed to evaluate the content of additives in EDs available in the Bosnian and Herzegovinian (B&H) markets. Methods: Twenty-two EDs from 15 brands were analyzed. The contents of quinine (QUIN), caffeine (CAF), benzoic acid (BZA), and sorbic acid (SA) were determined by high-performance liquid chromatography. Results: The median value of QUIN, CAF, SA, and BZA was 0.15 ppm, 309.05 ppm, 75.35 ppm, and 90.80 ppm, respectively. The highest CAF content variation was found in EDs of brand 4, and the lowest was in brand 6. A statistically significant difference was found between the obtained values in relation to the recommended daily intake of CAF for adolescents by the Centers for Disease Control and Prevention and the American Academy of Pediatrics (p < 0.001). Conclusion: The CAF content in EDs deviates by 10% from the content stated in the product declaration. All EDs on the B&H market should carry a clear warning: “High CAF content must not be mixed with alcohol and is not recommended for children, pregnant and/or lactating women, and CAF-sensitive individuals.” Given the behavioral trends associated with the potential risks of excessive CAF consumption, particularly among youth, national agencies in B&H should recognize areas of intervention such as responsible marketing and advertising, and education and awareness-raising. Further research and monitoring would be needed to determine the effectiveness of the various aspects of the proposed risk management approach

Depth Data Denoising in Optical Laser Based Sensors for Metal Sheet Flatness Measurement: A Deep Learning Approach

Surface flatness assessment is necessary for quality control of metal sheets manufactured from steel coils by roll leveling and cutting. Mechanical-contact-based flatness sensors are being replaced by modern laser-based optical sensors that deliver accurate and dense reconstruction of metal sheet surfaces for flatness index computation. However, the surface range images captured by these optical sensors are corrupted by very specific kinds of noise due to vibrations caused by mechanical processes like degreasing, cleaning, polishing, shearing, and transporting roll systems. Therefore, high-quality flatness optical measurement systems strongly depend on the quality of image denoising methods applied to extract the true surface height image. This paper presents a deep learning architecture for removing these specific kinds of noise from the range images obtained by a laser based range sensor installed in a rolling and shearing line, in order to allow accurate flatness measurements from the clean range images. The proposed convolutional blind residual denoising network (CBRDNet) is composed of a noise estimation module and a noise removal module implemented by specific adaptation of semantic convolutional neural networks. The CBRDNet is validated on both synthetic and real noisy range image data that exhibit the most critical kinds of noise that arise throughout the metal sheet production process. Real data were obtained from a single laser line triangulation flatness sensor installed in a roll leveling and cut to length line. Computational experiments over both synthetic and real datasets clearly demonstrate that CBRDNet achieves superior performance in comparison to traditional 1D and 2D filtering methods, and state-of-the-art CNN-based denoising techniques. The experimental validation results show a reduction in error than can be up to 15% relative to solutions based on traditional 1D and 2D filtering methods and between 10% and 3% relative to the other deep learning denoising architectures recently reported in the literature.This work was partially supported by by FEDER funds through MINECO project TIN2017-85827-P, and ELKARTEK funded projects ENSOL2 and CODISAVA2 (KK-202000077 and KK-202000044) supported by the Basque Governmen