Optimización de armaduras espaciales de acero utilizando algoritmos genéticos auto-adaptados : una primera aproximación.

En las últimas décadas, la optimización estructural mediante metaheurísticas ganó acogida en la comunidad científica; sin embargo, para garantizar buenos resultados se requiere una correcta selección de los parámetros de la metaheurísticas. En este trabajo se propone un algoritmo genético multi-cromosoma auto-adaptado para optimizar armaduras de acero tridimensionales. Las variables de diseño corresponden a las secciones asignadas a cada elemento en la armadura. El objetivo es la minimización del peso de la armadura, considerando desplazamientos y esfuerzos máximos como restricciones. El algoritmo propuesto se aplicó a la optimización de dos armaduras, produciendo diseños que pesan hasta un 35% menos que el mejor diseño inicial y son valores comparables al resultado obtenidos en otros trabajos. No obstante, la adaptación de los parámetros permite mayor robustez cuando se desea optimizar diferentes tipos de armadura y evita las ejecuciones del algoritmo de optimización que son necesarias para la calibración de sus parámetros

Automated Early Detection of Drops in Commercial Egg Production Using Neural Networks

[Abstract] 1. The purpose of this work was to support decision-making in poultry farms by performing automatic early detection of anomalies in egg production. 2. Unprocessed data were collected from a commercial egg farm on a daily basis over 7 years. Records from a total of 24 flocks, each with approximately 20 000 laying hens, were studied. 3. Other similar works have required a prior feature extraction by a poultry expert, and this method is dependent on time and expert knowledge. 4. The present approach reduces the dependency on time and expert knowledge because of the automatic selection of relevant features and the use of artificial neural networks capable of cost-sensitive learning. 5. The optimum configuration of features and parameters in the proposed model was evaluated on unseen test data obtained by a repeated cross-validation technique. 6. The accuracy, sensitivity, specificity and positive predictive value are presented and discussed at 5 forecasting intervals. The accuracy of the proposed model was 0.9896 for the day before a problem occurs.Galicia. Consellería de Cultura, Educación e Ordenación Universitaria; GRC2014/04

Early Warning in Egg Production Curves from Commercial Hens: a SVM Approach

[Abstract] Artificial Intelligence allows the improvement of our daily life, for instance, speech and handwritten text recognition, real time translation and weather forecasting are common used applications. In the livestock sector, machine learning algorithms have the potential for early detection and warning of problems, which represents a significant milestone in the poultry industry. Production problems generate economic loss that could be avoided by acting in a timely manner. In the current study, training and testing of support vector machines are addressed, for an early detection of problems in the production curve of commercial eggs, using farm’s egg production data of 478,919 laying hens grouped in 24 flocks. Experiments using support vector machines with a 5 k-fold cross-validation were performed at different previous time intervals, to alert with up to 5 days of forecasting interval, whether a flock will experience a problem in production curve. Performance metrics such as accuracy, specificity, sensitivity, and positive predictive value were evaluated, reaching 0-day values of 0.9874, 0.9876, 0.9783 and 0.6518 respectively on unseen data (test-set). The optimal forecasting interval was from zero to three days, performance metrics decreases as the forecasting interval is increased. It should be emphasized that this technique was able to issue an alert a day in advance, achieving an accuracy of 0.9854, a specificity of 0.9865, a sensitivity of 0.9333 and a positive predictive value of 0.6135. This novel application embedded in a computer system of poultry management is able to provide significant improvements in early detection and warning of problems related to the production curve

Implementación de herramienta tecnológica para el manejo de casos de tecnología

La empresa Porsche Colombia enfrenta un problema en cuanto al manejo de la información y el procedimiento cuando se reciben incidentes en el departamento de tecnología. Todos los usuarios líderes, presentan reportes de incidentes y estos no se atienden con suficiente prioridad o en forma adecuada, los incidentes inclusive se pierden entre correos o se olvidan y no se solucionan. Todo esto conllevó a que se aprobara un proyecto para organizar, por medio de una herramienta tecnológica, el proceso de recibir y procesar los incidentes relacionados con el área de tecnología

Classical Music Prediction and Composition by Means of Variational Autoencoders

[Abstract] This paper proposes a new model for music prediction based on Variational Autoencoders (VAEs). In this work, VAEs are used in a novel way to address two different issues: music representation into the latent space, and using this representation to make predictions of the future note events of the musical piece. This approach was trained with different songs of Handel. As a result, the system can represent the music in the latent space, and make accurate predictions. Therefore, the system can be used to compose new music either from an existing piece or from a random starting point. An additional feature of this system is that a small dataset was used for training. However, results show that the system is able to return accurate representations and predictions on unseen dataThis work is supported by Instituto de Salud Carlos III, grant number PI17/01826 (Collaborative Project in Genomic Data Integration CICLOGEN) funded by the Instituto de Salud Carlos III from the Spanish National plan for Scientific and Technical Research and Innovation 2013-2016 and the European Regional Development Funds (FEDER). This project was also supported by the General Directorate of Culture, Education and University Management of Xunta de Galicia ED431D 2017/16 and Drug Discovery Galician Network Ref. ED431G/01 and the Galician Network for Colorectal Cancer Research (Ref. ED431D 2017/23), and by the Spanish Ministry of Economy and Competitiveness through the funding of the unique installation BIOCAI (UNLC08-1E-002, UNLC13-13-3503) and the European Regional Development Funds (FEDER) by the European Union. This work was also funded by the grant for the consolidation and structuring of competitive research units (ED431C 2018/49) from the General Directorate of Culture, Education and University Management of Xunta de Galicia, and the CYTED network (PCI2018_093284) funded by the Spanish Ministry of Ministry of Innovation and Science. The experiments described in this section were carried out using the equipment of the Galician Supercomputing Center (CESGA)Xunta de Galicia; ED431D 2017/16Xunta de Galicia; ED431G/01Xunta de Galicia; ED431D 2017/23Xunta de Galicia; ED431C 2018/4

Determination of Egg Storage Time at Room Temperature Using a Low-Cost NIR Spectrometer and Machine Learning Techniques

[Abstract] Currently, consumers are more concerned about freshness and quality of food. Poultry egg storage time is a freshness and quality indicator in industrial and consumer applications, even though egg marking is not always required outside the European Union. Other authors have already published works using expensive laboratory equipment in order to determine the storage time and freshness of eggs. This paper presents a novel alternative method based on low-cost devices for the rapid and non-destructive prediction of egg storage time at room temperature (23 ± 1 °C). H&N brown flock with 49-week-old hens were used as a source for the sampled eggs. Samples were scanned for a period of 22 days beginning from the time the egg was laid. The spectral acquisition was performed using a low-cost near-infrared reflectance (NIR) spectrometer which has a wavelength range between 740 nm and 1070 nm. The resulting dataset of 660 samples was randomly split according to a 10-fold cross-validation in order to be used in a contrast and optimization process of two machine learning algorithms. During the optimization, several models were tested to develop a robust calibration model. The best model used a Savitzky Golay pre-processing technique with a third derivative order and an artificial neural network with ten neurons in one hidden layer. Regressing the storage time of the eggs, tests achieved a coefficient of determination (R-squared) of 0.8319 ± 0.0377 and a root mean squared error in cross-validation test set (RMSECV) of 1.97 days. Although further work is needed, this technique shows industrial potential and consumer utility to determine an egg's freshness using a low-cost spectrometer connected to a smartphone

Detection of Bovine Mastitis in Raw Milk, Using a Low-Cost NIR Spectrometer and k-NN Algorithm

[Abstract] Among the bovine diseases, mastitis causes high economic losses in the dairy production system. Nowadays, detection under field conditions is mainly performed by the California Mastitis Test, which is considered the de facto standard. However, this method presents with problems of slowness and the expensiveness of the chemical-reactive process, which is deeply dependent on an expert’s trained eye and, consequently, is highly imprecise. The aim of this work is to propose a new method for bovine mastitis detection under field conditions. The proposed method uses a low-cost, smartphone-connected NIR spectrometer which solves the aforementioned problems of slowness, expert dependency and disposability of the chemical methods. This method uses spectra in combination with two k-Nearest Neighbors models. The first model is used to detect the presence of mastitis while the second model classifies the positive cases into weak and strong. The resulting method was validated by using a leave-one-out technique where the ground truth was obtained by the California Mastitis Test. The detection model achieved an accuracy of 92.4%, while the one classifying the severity showed an accuracy of 95%.This work is part of DINTA-UTMACH and RNASA-UDC research groups. This work is partially supported by Instituto de Salud Carlos III, grant number PI17/01826. It was also partially supported by different grants and projects from the Xunta de Galicia [ED431D 2017/23; ED431D 2017/16; ED431G/01; ED431C 2018/49; IN845D-2020/03]. Another source of support was the CYTED network (PCI2018\_093284) funded by the Spanish Ministry of Innovation and ScienceXunta de Galicia; ED431D 2017/23Xunta de Galicia; ED431D 2017/16Xunta de Galicia; ED431G/01Xunta de Galicia; ED431C 2018/49Xunta de Galicia; IN845D-2020/0

Estudio de prefactibilidad para el sistema de conducción y tratamiento de agua cruda en la Comuna Quinticusig – cantón Sigchos

El proyecto técnico pretende realizar el estudio de prefactibilidad para el sistema de conducción y tratamiento de agua cruda que permitan cubrir la demanda de agua potable de la comuna Quinticusig, parroquia Sigchos, cantón Sigchos en cualquier época del año durante 24 horas diarias. La comuna levantó un sistema para transportar agua cruda hace 35 años aproximadamente, no contó con asistencia técnica; la población sufre del desabastecimiento de agua por periodos superiores a los 30 días, en época de verano, mientras que en época de invierno se presenta el desperdicio del recurso hídrico. Se utilizó la investigación científica, la cual utiliza herramientas teóricas y prácticas permitiendo visualizar mejor el entorno e identificar los problemas presentes con el fin de determinar una solución. El sistema de conducción propuesto cuenta con: captación, línea de aducción, desarenador horizontal semienterrado y tanque de almacenamiento enterrado hasta la corona de los muros, las estructuras complementarias del sistema son: un cruce aéreo y cinco cámaras rompe presión. El tratamiento empleado en el agua tiene una fase física que se da a través de la unidad desarenadora, una fase química que consiste en la desinfección por cloro, realizada en el tanque de almacenamiento. El análisis técnico-económico contiene el presupuesto detallado y los indicadores económicos (VAN, TIR, B/C), los resultados obtenidos revelan que el proyecto es viable. Finalmente, en el estudio del impacto ambiental se concluyó que el proyecto no tiene efectos negativos significativos durante las fases de ejecución, operación y mantenimiento.The technical project intends to carry out a pre-feasibility study for the raw water supply and treatment system to meet the demand for drinking water in the Quinticusig commune, Sigchos parish, Sigchos canton at any time of the year for 24 hours a day. The commune built a system to transport raw water approximately 35 years ago, it did not have technical assistance; the population suffers from a shortage of water for periods of more than 30 days, in summer, while in winter there is a waste of water resources. Scientific research was used, which uses theoretical and practical tools allowing a better visualization of the environment and identifying the current problems in order to determine a solution. The proposed conduction system has: catchment, adduction line, semi-buried horizontal sand trap and buried storage tank up to the crown of the walls, the complementary structures of the system are: an overhead crossing and five pressure break chambers. The treatment used in the water has a physical phase that takes place through the sandblasting unit, a chemical phase that consists of chlorine disinfection, carried out in the storage tank. The technical-economic analysis contains the detailed budget and the economic indicators (NPV, IRR, B / C), the results obtained reveal that the project is viable. Finally, in the environmental impact study it was concluded that the project does not have significant negative effects during the execution, operation and maintenance phases

Boletín NUESTRA AMÉRICA XXI - Desafíos y alternativas - num. 8, junio 2017

Una excelente iniciativa del Grupo de Trabajo Crisis y economía mundial, coordinado por María Josefina Morales y Gabriela Roffinelli