Estudio de métodos de construcción de ensembles de clasificadores y aplicaciones

La inteligencia artificial se dedica a la creación de sistemas informáticos con un comportamiento inteligente. Dentro de este área el aprendizaje computacional estudia la creación de sistemas que aprenden por sí mismos. Un tipo de aprendizaje computacional es el aprendizaje supervisado, en el cual, se le proporcionan al sistema tanto las entradas como la salida esperada y el sistema aprende a partir de estos datos. Un sistema de este tipo se denomina clasificador. En ocasiones ocurre, que en el conjunto de ejemplos que utiliza el sistema para aprender, el número de ejemplos de un tipo es mucho mayor que el número de ejemplos de otro tipo. Cuando esto ocurre se habla de conjuntos desequilibrados. La combinación de varios clasificadores es lo que se denomina "ensemble", y a menudo ofrece mejores resultados que cualquiera de los miembros que lo forman. Una de las claves para el buen funcionamiento de los ensembles es la diversidad. Esta tesis, se centra en el desarrollo de nuevos algoritmos de construcción de ensembles, centrados en técnicas de incremento de la diversidad y en los problemas desequilibrados. Adicionalmente, se aplican estas técnicas a la solución de varias problemas industriales.Ministerio de Economía y Competitividad, proyecto TIN-2011-2404

Data-mining modeling for the prediction of wear on forming-taps in the threading of steel components

An experimental approach is presented for the measurement of wear that is common in the threading of cold-forged steel. In this work, the first objective is to measure wear on various types of roll taps manufactured to tapping holes in microalloyed HR45 steel. Different geometries and levels of wear are tested and measured. Taking their geometry as the critical factor, the types of forming tap with the least wear and the best performance are identified. Abrasive wear was observed on the forming lobes. A higher number of lobes in the chamber zone and around the nominal diameter meant a more uniform load distribution and a more gradual forming process. A second objective is to identify the most accurate data-mining technique for the prediction of form-tap wear. Different data-mining techniques are tested to select the most accurate one: from standard versions such as Multilayer Perceptrons, Support Vector Machines and Regression Trees to the most recent ones such as Rotation Forest ensembles and Iterated Bagging ensembles. The best results were obtained with ensembles of Rotation Forest with unpruned Regression Trees as base regressors that reduced the RMS error of the best-tested baseline technique for the lower length output by 33%, and Additive Regression with unpruned M5P as base regressors that reduced the RMS errors of the linear fit for the upper and total lengths by 25% and 39%, respectively. However, the lower length was statistically more difficult to model in Additive Regression than in Rotation Forest. Rotation Forest with unpruned Regression Trees as base regressors therefore appeared to be the most suitable regressor for the modeling of this industrial problem.ThisinvestigationwaspartiallysupportedbyProjects TIN2011-24046,IPT-2011-1265-020000andDPI2009- 06124-E/DPIoftheSpanishMinistryofEconomyand Competitiveness.WethanktheUFIinMechanicalEngineer- ing oftheUPV/EHU(UFIMECA-1.0.2016(ext))forits support

Modelling laser milling of microcavities for the manufacturing of DES with ensembles

A set of designed experiments, involving the use of a pulsed Nd:YAG laser system milling 316L Stainless Steel, serve to study the laser-milling process of microcavities in the manufacture of drug-eluting stents (DES). Diameter, depth, and volume error are considered to be optimized as functions of the process parameters, which include laser intensity, pulse frequency, and scanning speed. Two different DES shapes are studied that combine semispheres and cylinders. Process inputs and outputs are defined by considering the process parameters that can be changed under industrial conditions and the industrial requirements of this manufacturing process. In total, 162 different conditions are tested in a process that is modeled with the following state-of-the-art data-mining regression techniques: Support Vector Regression, Ensembles, Artificial Neural Networks, Linear Regression, and Nearest Neighbor Regression. Ensemble regression emerged as the most suitable technique for studying this industrial problem. Specifically, Iterated Bagging ensembles with unpruned model trees outperformed the other methods in the tests. This method can predict the geometrical dimensions of the machined microcavities with relative errors related to the main average value in the range of 3 to 23%, which are considered very accurate predictions, in view of the characteristics of this innovative industrial task.This work was partially funded through Grants fromthe IREBID Project (FP7-PEOPLE-2009-IRSES- 247476) of the European Commission and Projects TIN2011- 24046 and TECNIPLAD (DPI2009-09852) of the Spanish Ministry of Economy and Competitivenes

Optimización de procesos industriales con técnicas de Minería de Datos: mantenimiento de aerogeneradores y fabricación con tecnologías láser

En este trabajo se emplean técnicas de Minería de Datos para mejorar la eficiencia de dos procesos industriales: el diagnóstico de fallos en aerogeneradores y la fabricación de piezas metálicas de geometría compleja mediante tecnologías láser. Se mejora la validación experimental de estudios anteriores, en los que no se usó validación cruzada ni se tuvieron en cuenta algunas particularidades de los problemas analizados. Para el diagnóstico de fallos en aerogeneradores, se identifica la técnica de clasificación más adecuada para relacionar medidas de vibraciones con el tipo de fallo. Además, se define la métrica más adecuada para evaluar su precisión. Para la fabricación de piezas metálicas de geometría compleja, se estima la técnica de clasificación más adecuada para predecir la calidad superficial obtenida con pulido superficial láser, así como la técnica de regresión para predecir los errores en los distintos requerimientos geométricos de piezas obtenidas mediante microfresado 3D láser.Ministerio de Economía y Competitividad, proyecto TIN-2011-24046

Environmental and Economic Assessment of Carbon Dioxide Recovery and Mitigation in the Industrial and Energy Sectors.

Anthropogenic carbon dioxide (CO₂) is a global pollutant that needs urgent control to prevent large-scale vitiation of ecosystems. Generally speaking, anthropogenic CO2 emissions can be reduced through (1) CO2 capture for long-term sequestration or use in other applications, (2) renewable and low-carbon energy sources and technologies, and (3) demand reduction of carbon-intensive services and products through reduced consumption and efficiency improvements. The first two approaches constitute the “supply-side” of carbon abatement measures, and are the focus of this dissertation in which I examine the environmental and economic attributes of CO2 recovery and mitigation technologies in the U.S. industrial and energy sectors. Starting by developing a comprehensive picture of the recovered CO2 supply chain, this dissertation provides process-based emissions inventories for recovering and purifying CO2 from combustion flue gases and higher purity point sources for sequestration and use in industrial applications. The strong influence of CO2 quality on the emissions, energy consumption, and costs of carbon capture found through this analysis warrants deeper scientific and economic analyses of carbon capture and sequestration as a carbon abatement option. To estimate the marginal emissions from use of recovered CO2 in industrial applications, a market-based allocation methodology is developed in a consequential life cycle assessment framework, along with new greenhouse gas accounting procedures that incorporate reuse and sequestration as fates for CO2. This methodology is presented with results from experimental studies on recovered CO2-based metalworking fluids, and motivates further exploration of applications employing the thermal and chemical properties of CO2 for pollution prevention and carbon abatement. The dissertation concludes with an examination of carbon mitigation strategies from the standpoint of CO2 prevention in the U.S. electric and automotive sectors. By creating a stock-and-flow model of the U.S. automotive and power generation fleets, and considering the evolution of all major technologies in both sectors in an optimization framework, cost-minimizing technology trajectories are identified, which collectively cut about 55 gigatons of CO2 emissions by 2050. The analysis reveals that despite anticipated advancements and cost reductions in carbon abatement technologies with time, the technological costs of carbon abatement are likely to increase markedly with delay in climate-action.PhDMechanical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/111423/1/supekar_1.pd

Statistical investigation on effect of Electroless coating Parameter on Coating Morphology of Short Basalt Fiber.

The Objective of the present paper is to investigate the effect of electroless coating parameters, such as Sensitization time (A), Activation time (B) and Metallization time (C), on the coating morphology of the basalt short fiber and the optimization of the coating process parameters based on L27 Taguchi orthogonal design. Coated and non-coated basalt short fiber, typically used with 7075 Aluminium alloy as einforcement, is studied. The effect of coating the short basalt fiber with copper has proved beneficial to interfacial bonding (wettability) between the reinforcement and the matrix. The interface between the matrix and the reinforcement plays a crucial role in determining the properties of metal matrix composites (MMCs). An L27 array was used to accommodate the three levels of factors as well as their interaction effects. From the Taguchi methodology, the optimal combinations for coating parameters were found to be A1B3C3 (i.e., 5 min. sensitization time, 15 min. activation time and 3 min. for metallization time). In addition, the interaction between pH value and the coating time and that between the coating time and the temperature, influence the coating parameters significantly. Furthermore, a statistical analysis of variance reveals that the metallization time has the highest influence followed by the activation time and the sensitization time. Finally, confirmation tests were carried out to verify the experimental results, Scanning Electron Microscopic (SEM) & Energy Dispersive Spectroscope (EDS) studies were carried out on basalt fiber

Concept design of a fast sail assisted feeder container ship

A fast sail assisted feeder container ship concept has been developed for the 2020 container market in the South East Asian and Caribbean regions.The design presented has met the requirements of an initial economic study, with a cargo capacity of 1270 twenty-foot equivalent unit containers, meeting the predictions of container throughput derived from historical data. In determining suitable vessel dimensions, account has also been taken for port and berthing restrictions, and considering hydrodynamic performance. The vessel has been designed for a maximum speed of 25 knots, allowing it to meet the demand for trade whilst reducing the number of ships operating on the routes considered.The design development of the fast feeder concept has involved rigorous analyses in a number of areas to improve the robustness of the final design. Model testing has been key to the development of the concept, by increasing confidence in the final result. This is due to the fact that other analysis techniques are not always appropriate or accurate. Two hull forms have been developed to meet requirements whilst utilising different propulsor combinations. This has enabled evaluation of efficiency gains resulting from different hydrodynamic phenomena for each design. This includes an evaluation of the hydrodynamic performance when utilising the sail system. This has been done using a combination of model test results and data from regression analysis. The final propulsor chosen is a contra-rotating podded drive arrangement. Wind tunnel testing has been used to maximise the performance of a Multi-wing sail system by investigating the effects of wing spacing, stagger and sail-container interactions. This has led to an increase in lift coefficient of 32% from initial predictions. The savings in power requirement due to the sail system are lower than initially predicted. However, another benefit of their installation, motion damping, has been identified. Whilst this has not been fully investigated, additional fuel savings are possible as well as improved seakeeping performance.The design is shown to be environmentally sustainable when compared to existing vessels operating on the proposed routes. This is largely due to the use of low-carbon and zero-sulphur fuel (liquefied natural gas) and improvements in efficiency regarding operation. This especially relates to cargo handling and scheduling. Green house gas emissions have been predicted to fall by 42% and 40% in the two regions should the design be adopted. These savings are also due to the use of the Multi-wing sail system, which contributes to reductions in power requirement of up to 6% when the vessel operates at its lower speed of 15 knots. It is demonstrated that the fast feeder is also economically feasible, with predicted daily cost savings of 27% and 33% in the South East Asian and Caribbean regions respectively. Thus the fast feeder container ship concept is a viable solution for the future of container transhipment. <br/

Reinforcement Learning Approach for Autonomous UAV Navigation in 3D Space

In the last two decades, the rapid development of unmanned aerial vehicles (UAVs) resulted in their usage for a wide range of applications. Miniaturization and cost reduction of electrical components have led to their commercialization, and today they can be utilized for various tasks in an unknown environment. Finding the optimal path based on the start and target pose information is one of the most complex demands for any intelligent UAV system. As this problem requires a high level of adaptability and learning capability of the UAV, the framework based on reinforcement learning is proposed for the localization and navigation tasks. In this paper, Q-learning algorithm for the autonomous navigation of the UAV in 3D space is implemented. To test the proposed methodology for UAV intelligent control, the simulation is conducted in ROS-Gazebo environment. The obtained simulation results have shown that the UAV can reach the target pose autonomously in an efficient way

Reinforcement Learning Approach for Autonomous UAV Navigation in 3D Space

In the last two decades, the rapid development of unmanned aerial vehicles (UAVs) resulted in their usage for a wide range of applications. Miniaturization and cost reduction of electrical components have led to their commercialization, and today they can be utilized for various tasks in an unknown environment. Finding the optimal path based on the start and target pose information is one of the most complex demands for any intelligent UAV system. As this problem requires a high level of adaptability and learning capability of the UAV, the framework based on reinforcement learning is proposed for the localization and navigation tasks. In this paper, Q-learning algorithm for the autonomous navigation of the UAV in 3D space is implemented. To test the proposed methodology for UAV intelligent control, the simulation is conducted in ROS-Gazebo environment. The obtained simulation results have shown that the UAV can reach the target pose autonomously in an efficient way

Experimental and Theoretical Investigations into the Development of an Efficient Wind Turbine

The small-scale wind turbine is considered as one of the most effective renewable technologies due to their potential to provide useful amount of electricity, particularly in ‘‘off-grid’’ settings as well as promising future prospects to decarbonise the power sector and ultimately stabilise energy security. Due to the huge potential of the wind resources and financial incentives, the UK is a promising region for small-scale wind energy development but there has been lack of comprehensive assessment of the wind resource for relevant locations. Thus efficient and low cost techniques are urgently needed to assess the resource potential since the long-term measurement techniques usually employed in the large-scale industry are very expensive and often not feasible for small-scale development. The research developed during this thesis focuses on cost effective techniques for predicting the wind resource using two main approaches, namely the boundary layer meteorology and measure-correlate-predict (MCP). These approaches were evaluated using a long-term dataset from the Modern Era Retrospective-Analysis and short-term onsite dataset from meteorological measurement station. To begin with, the performance of a modified methodology based on the boundary layer meteorology was evaluated at four UK sites, and the results were validated using traditional error metrics. Averaged across all sites, the percentage error in the predicted wind power density was found to be about 25% due to the uncertainties associated with the choice of the input parameters. Although the result is very encouraging, it was concluded that such a method is better applied in a ‘‘preliminary’’ analysis to identify viable sites worthy of further investigation. To reduce these uncertainties, an MCP technique was utilised along with onsite measurements over a period of 12 months at a subset of 1 of the 4 UK sites, and the results show a significant improvement on the predicted wind speed and power density. Comparison of both approaches show that the best performing MCP approaches resulted in percentage error in the predicted mean wind speed and power density of 7.2 % and 12.9 % in contrast to the 18.9 % and 17.0% obtained using the boundary layer approach. Seasonal trends, direction behaviours and frequency distribution were analysed and their characteristics reflected the general wind conditions across most UK sites. Based on the output of the wind resource assessment, the potential of a small-scale vertical axis wind turbine (VAWT) was assessed using the double multiple streamtube model. VAWTs based on the Darrieus concept are potentially more efficient and economical, but those with fixed pitch blades are inherently non self-starting and are unsuitable for decentralised application. It is shown that the self-starting problem can be alleviated by a combination of a suitable aerofoil sections, solidity and pitch angles. The thesis provides a technique for inexpensive wind resource assessment where direct long-term measurements are not feasible. In addition, it provides a suitable solution strategy to the problem of self-starting in small-scale fixed pitch wind turbines.