SVM en clasificación de imágenes SAR con características de textura

Las imágenes SAR (Synthetic Aperture Radar) cumplen un rol fundamental en el monitoreo ambiental y observación terrestre debido a que proveen información que las imágenes ópticas no proporcionan. Sin embargo, estas imágenes están contaminadas con un ruido inherente al método de captura, llamado ruido speckle, que dificulta su análisis e interpretación automática. Los modelos avanzados de segmentación de imágenes SAR están dedicados a resolver las dificultades que este ruido provoca. En este sentido, resulta de suma importancia el estudio de parámetros que describan las características estructurales de textura de la imagen en presencia de ruido speckle. En este trabajo, se propone un nuevo modelo de clasificación de imágenes SAR basado en el cálculo de descriptores de textura locales, formando un vector característico, el cual involucra estimaciones de parámetros de una distribución de probabilidad, estimaciones de la dimensión fractal y entropía de Tsallis. Luego, el etiquetado de cada píxel se realiza utilizando el método de clasificación supervisada SVM (Support Vector Machine). Se analizan los resultados de aplicar el algoritmo propuesto en imágenes SAR sintéticas, simples y con valores extremos agregados, los cuales muestran alta eficacia y son prometedores para la aplicación en imágenes SAR reales.Sociedad Argentina de Informática e Investigación Operativ

An automated system for monitoring the use of personal protective equipment in the construction industry

[EN] We present a novel computer vision system which generates automated indicators of proper use of personal protective equipment(PPE) of great importance in the construction industry, specifically the use of safety helmet and high visibility vest. The system is built on a neural network architecture that works on digital images. First, the OpenPose network is used for the detection of anthropometric points of the visualized workers. These points are used next to automatically segment regions of interest (ROI) located about a worker’s head and trunk. On these ROIs, a neuronal classifier estimates the presence or absence of each PPE of interest. Obtained results in moving videos from drones or artphones show that our system is fully capable of carrying out a complete evaluation of usage indicators of these two PPEs without human intervention, with the main purpose of preventing potentially dangerous incidents in the workplace.[ES] Este artı́culo presenta un novedoso sistema de visión por computador que genera indicadores automatizados de uso adecuado de equipos de protección personal (EPP) de gran importancia en la industria de la construcción,  concretamente el uso de casco de seguridad y chaleco de alta visibilidad. El sistema se construye sobre una arquitectura de redes neuronales que trabaja sobre imágenes digitales. Primero se utiliza la red neuronal OpenPose para la detección de puntos antropométricos de los trabajadores visualizados, los cuales se utilizan para segmentar automáticamente regiones de interés (ROI) ubicadas en la cabeza y el tronco. Sobre estas ROI, un clasificador neuronal estima la presencia o ausencia de los dos EPP de interés. Los resultados obtenidos en vı́deos tomados en movimiento por drones o smartphones muestran que nuestro sistema es plenamente capaz de realizar una evaluación completa de indicadores de utilización de estos dos EPP sin asistencia, con el propósito principal de prevenir incidentes potencialmente peligrosos en el lugar de trabajo.Este trabajo ha sido realizado parcialmente gracias al apoyo del Consejo Nacional de Investigaciones Científicas y Técnicas de Argentina (CONICET), la Junta de Extremadura (España) a través del Fondo Europeo de Desarrollo Regional (código GR18135) y la Universidad Nacional del Sur (código 24 /K083).Massiris, M.; Fernández, JA.; Bajo, J.; Delrieux, C. (2020). Sistema automatizado para monitorear el uso de equipos de protección personal en la industria de la construcción. Revista Iberoamericana de Automática e Informática industrial. 18(1):68-74. https://doi.org/10.4995/riai.2020.13243OJS6874181Ankrum, D. R., Nemeth, K. J., 2000. Head and neck posture at computer workstations - what is neutral? In: Proc Human Factors and Ergonomics Soc Annual Meeting. Vol. 44. pp. 565-568. https://doi.org/10.1177/154193120004403046Arias Gallegos, W. L., 2011. Uso y desuso de los equipos de protección personal en trabajadores de construccion. Ciencia & Trabajo 40, 119-124.Brilakis, I., Park, M. W., Jog, G., 2011. Automated vision tracking of project related entities. Advanced Engineering Informatics 25 (4), 713-724. https://doi.org/10.1016/j.aei.2011.01.003Cao, Z., Simon, T., Wei, S.-E., Sheikh, Y., 2017. Realtime multi-person 2d pose estimation using part affinity fields. In: Proc IEEE Conf CVPR. pp. 7291- 7299. URL: https://arxiv.org/abs/1611.08050v2 https://doi.org/10.1109/CVPR.2017.143Fang, Q., Li, H., Luo, X., Ding, L., Luo, H., Rose, T. M., An, W., 2018. Detecting non-hardhat-use by a deep learning method from far-field surveillance videos. Automation in Construction 85 (1), 1-9. https://doi.org/10.1016/j.autcon.2017.09.018He, K., Zhang, X., Ren, S., Sun, J., 2016. Deep residual learning for image recognition. Proc IEEE Conf CVPR, 770-778. https://doi.org/10.1109/CVPR.2016.90ILO, 2002. ILO-OSH 2001. Directrices relativas a los sistemas de gestión de la seguridad y la salud en el trabajo. Oficina Internacional del Trabajo, Ginebra (Suiza).Konstantinou, E., Lasenby, J., Brilakis, I., 2019. Adaptive computer vision based 2d tracking of workers in complex environments. Automation in Construction 103, 168 - 184. https://doi.org/10.1016/j.autcon.2019.01.018Massiris Fernandez, M., Delrieux, C., Fernández Muñoz, J. A., 2018. Detección de equipos de protección personal mediante la red neuronal convolucional ' Yolo. In: Actas de las XXXIX Jornadas de Automatica. pp. 1022-1029. URL: http://hdl.handle.net/10662/8846Memarzadeh, M., Golparvar-Fard, M., Niebles, J. C., jul 2013. Automated 2D detection of construction equipment and workers from site video streams using histograms of oriented gradients and colors. Automation in Construction 32, 24-37. https://doi.org/10.1016/j.autcon.2012.12.002Mneymneh, B. E., Abbas, M., Khoury, H., 2017. Automated hardhat detection for construction safety applications. Procedia Engineering 196, 895 - 902. https://doi.org/10.1016/j.proeng.2017.08.022Mosberger, R., Andreasson, H., Lilienthal, A. J., 2014. A customized vision system for tracking humans wearing reflective safety clothing from industrial vehicles and machinery. Sensors (Switzerland) 14 (10), 17952-17980. https://doi.org/10.3390/s141017952Park, J., Yang, X., Cho, Y. K., Seo, J., 2017. Improving dynamic proximity sensing and processing for smart work-zone safety. Automation in Construction 84, 111 - 120. https://doi.org/10.1016/j.autcon.2017.08.025Park, M. W., Brilakis, I., 2012. Construction worker detection in video frames for initializing vision trackers. Automation in Construction 28, 15-25. https://doi.org/10.1016/j.autcon.2012.06.001Park, M.-W., Brilakis, I., 2016. Continuous localization of construction workers via integration of detection and tracking. Automation in Construction 72, 129 - 142. https://doi.org/10.1016/j.autcon.2016.08.039Park, M.-W., Elsafty, N., Zhu, Z., 2015. Hardhat-wearing detection for enhancing on-site safety of construction workers. Journal of Construction Engineering and Management 141 (9), 4015024. https://doi.org/10.1061/(ASCE)CO.1943-7862.0000974Seong, H., Choi, H., Cho, H., Lee, S., Son, H., Kim, C., 2017. Vision-based safety vest detection in a construction scene. In: ISARC 2017 - Proceedings of the 34th International Symposium on Automation and Robotics in Construction. Isarc. pp. 288-293. https://doi.org/10.22260/ISARC2017/0039Shrestha, K., Shrestha, P. P., Bajracharya, D., Yfantis, E. A., 2015. Hard-hat detection for construction safety visualization. Journal of Construction Engineering 2015. https://doi.org/10.1155/2015/721380Son, H., Choi, H., Seong, H., Kim, C., 2019. Detection of construction workers under varying poses and changing background in image sequences via very deep residual networks. Automation in Construction 99, 27-38. https://doi.org/10.1016/j.autcon.2018.11.033Union Europea, P. y. C. d. l. U., 1989. Directiva 89/656/cee relativa a las disposiciones mínimas de seguridad y de salud para la utilización por los trabajadores en el trabajo de equipos de protección individual.Xie, Z., Liu, H., Li, Z., He, Y., 2018. A convolutional neural network based approach towards real-time hard hat detection. Proceedings of the 2018 IEEE International Conference on Progress in Informatics and Computing, PIC 2018, 430-434. https://doi.org/10.1109/PIC.2018.870626

Smart Twisting Active Rotor (STAR) - Pre-Test Predictions

A Mach-scaled model rotor with active twist capability is in preparation for a wind tunnel test in the large low-speed facility (LLF) of the German-Dutch wind tunnel (DNW) with international participation by DLR, US Ar-my, NASA, ONERA, KARI, Konkuk University, JAXA, Glasgow University and DNW. To get the maximum benefit from the test and the most valuable data within the available test time, the tentative test matrix was covered by predictions of the partners, active twist benefits were evaluated, and support was provided to the test team to focus on the key operational conditions

Associations between depressive symptoms and disease progression in older patients with chronic kidney disease: results of the EQUAL study

Background Depressive symptoms are associated with adverse clinical outcomes in patients with end-stage kidney disease; however, few small studies have examined this association in patients with earlier phases of chronic kidney disease (CKD). We studied associations between baseline depressive symptoms and clinical outcomes in older patients with advanced CKD and examined whether these associations differed depending on sex. Methods CKD patients (>= 65 years; estimated glomerular filtration rate <= 20 mL/min/1.73 m(2)) were included from a European multicentre prospective cohort between 2012 and 2019. Depressive symptoms were measured by the five-item Mental Health Inventory (cut-off <= 70; 0-100 scale). Cox proportional hazard analysis was used to study associations between depressive symptoms and time to dialysis initiation, all-cause mortality and these outcomes combined. A joint model was used to study the association between depressive symptoms and kidney function over time. Analyses were adjusted for potential baseline confounders. Results Overall kidney function decline in 1326 patients was -0.12 mL/min/1.73 m(2)/month. A total of 515 patients showed depressive symptoms. No significant association was found between depressive symptoms and kidney function over time (P = 0.08). Unlike women, men with depressive symptoms had an increased mortality rate compared with those without symptoms [adjusted hazard ratio 1.41 (95% confidence interval 1.03-1.93)]. Depressive symptoms were not significantly associated with a higher hazard of dialysis initiation, or with the combined outcome (i.e. dialysis initiation and all-cause mortality). Conclusions There was no significant association between depressive symptoms at baseline and decline in kidney function over time in older patients with advanced CKD. Depressive symptoms at baseline were associated with a higher mortality rate in men

SAR image segmentation through B-spline deformable contours and fractal dimension

Synthetic Aperture Radar (SAR) images are usually corrupted by a signal-dependent non-additive noise called speckle. This makes difficult the segmentation, object identification, and feature extraction within this kind of images. In this work we propose the combination of local fractal estimation and B-Spline based active contours as a solution for the boundary extraction problem in SAR images. After a supervised initialization (the specification of a an initial curve laying completely within the region of interest), the algorithm searches the control points (vertices) of a B-Spline curve that fits the boundary of the region to be segmented. The vertices of the curve are found by a local estimation of the fractal dimension in the surrounding. Fractal dimension provides a good local roughens and statistical correlation estimation. Box-counting measurement of the fractal dimension is widely acknowledged to be the most adequate in terms of robustness and computational requirements. Box counting algorithms are based on a statistical analysis of the brightness distribution of the pixels in a surrounding of varying sizes. A power law can be established between the surrounding size and the amount of pixels with a given brightness profile, and then an adequate assessment of the local fractal dimension can be performed. The proposed algorithm is systematically tested on synthetic and real SAR images, and both the accuracy and the performance of our proposal are assessed.

CCDTL conditioning report: Module 2 and Module 3

Two CCDTL modules for Linac4 have been conditioned at SM18. The modules were tuned for resonance at 352.2 MHz, and stable operation has been achieved with 800 μs RF pulses with a repetition rate of 2 Hz. Maximum power of 700 kW loaded in cells has been achieved with a nominal Linac4 klystron and modulator setup. Since those were the first CCDTL modules to be conditioned, both hardware and software have been developed along with the conditionings

Archéologie des hautes Chaumes du Forez. Rapport 2016, Prospection thématique

Autorisation n° 2016/477, code opération, 2212112 : 90 p. + annexes

Archéologie des hautes Chaumes du Forez. Rapport 2016, Prospection thématique

Autorisation n° 2016/477, code opération, 2212112 : 90 p. + annexes

Archéologie des hautes Chaumes du Forez. Rapport 2016, Prospection thématique

Autorisation n° 2016/477, code opération, 2212112 : 90 p. + annexes

Archéologie des hautes Chaumes du Forez. Rapport intermédiaire 2018, Prospection thématique.

Autorisation n° 2017/431, code opération, 2212542 : 42 p. + annexe