Individual and ensemble functional link neural networks for data classification

This study investigated the Functional Link Neural Network (FLNN) for solving data classification problems. FLNN based models were developed using evolutionary methods as well as ensemble methods. The outcomes of the experiments covering benchmark classification problems, positively demonstrated the efficacy of the proposed models for undertaking data classification problems

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

Exploring Locational Criteria to Optimise Biofuel Production Potential in Nigeria

Energy is one of the important building blocks of any economy and the sustainability of its supply is crucial. Renewable energy sources are being explored with the objective of harnessing their potential to address demand shortages and provide sustainable clean energy. Biofuels, as one of these renewables, continue to expand and their share in global energy consumption continues to increase. Apart from lower net carbon emissions compared to fossil fuels and their role as transitional fuel sources in global shift towards renewable energy, biofuels offer other benefits such as increasing the volume of liquid fuels, improving air quality, expanding trade, import substitution and energy diversification. Therefore, there are strong environmental and economic arguments for the Nigerian Government to embark on deployment of renewable energy, including biofuels. Despite abundant biomass resources, biofuel programmes have not been fully operationalised in the country, partly because biofuels vary in their favourability profiles which depend on local conditions and practices, as well as spatial conflicts between land designed for energy production and other land uses such as agriculture or nature reserves. Consequently, there is a need for robust and detailed approaches to this location-related problem. Although Spatial Multi-criteria Analysis (SMCA) as a support tool has been applied to biofuel production analysis, accounting for multiple stakeholder opinions has been one of the major challenges. In Nigeria, there have been few attempts to apply spatial analysis to locational problems related to biofuel production. In addition, these studies are limited in terms of scope, were based on feedstock other than energy crops, and provided superficial analysis of suitability of the identified sites. The goal of this thesis was to show how to improve the robustness and transparency of spatial analysis in Nigeria through answering some spatial questions about biofuel production, which extends our knowledge of GIS and is relevant to practice. Robustness implies detailed exploration of the required environmental criteria and incorporation of the expert decisions on the criteria preferences. This work transparently demonstrates detailed application of the combined geospatial and multi-criteria methods to make the academic contribution transferable. The technical goal of the work was to conduct spatial optimisation for biofuel production in the country through detailed assessment of environmental criteria, modelling land suitability for cultivating sweet sorghum, sugarcane, cassava, oil palm and jatropha as biofuel crops in Nigeria and modelling optimal sites for biofuel processing and/or blending. This will provide support for spatial decisions regarding establishing biofuel processing plants or expanding the existing ones. Analytical Hierarchy Process (pairwise comparison) was adopted as the multi-criteria analysis method due to its robustness regarding stakeholder inclusion. Weighted overlay was adopted as method of land suitability modelling and supply area modelling was adopted as the method of site optimisation. The analysis showed that northcentral geo-political zone of Nigeria has the largest areas of land that is very suitable for cultivating sugarcane, cassava, oil palm and jatropha, while northeast has the largest areas of land that is very suitable for cultivating sweet sorghum. Based on these, three sizes of service area were considered assuming worst, average and highest crop yields scenarios to optimise processing/blending sites. Existing petroleum depots were considered as the candidate sites. Ilorin petroleum depot was found to be the most optimal location for processing/blending biofuel in Nigeria based on all the crop yields scenarios, within 300 km service area. However, assuming worst case yields scenario within 100 km service area, Maiduguri depot was found to be the best location for sweet sorghum and sugarcane biofuel processing/blending, but Yola depot was suggested as replacement for sugarcane. Ibadan was found to be the best for oil palm and jatropha, but Ikot Abasi depot was suggested as replacement for oil palm. Aba was found to be the best for cassava, but Makurdi was suggested as replacement. This work had demonstrated how robust integration of GIS tools with MCDM techniques could improve the effectiveness of spatial decision-making process regarding positioning biofuel production in developing countries like Nigeria. It is therefore concluded that this work will serve as a point of reference for state-of-the-art application of spatial multi-criteria evaluation analysis, not only for the biofuel industry, but also for other sectors of environmental management such as river basin management, land use or settlement planning. The tendency of a biofuel programme in Nigeria to succeed would greatly be enhanced by adopting sustainability strategies along its value chain through climate smart agriculture, designing and/or adopting a suitable feedstock supply model, effective land use management, realigning policy objectives, enforcing policy directives and balancing between strong and weak sustainability strategies. This will create a conducive environment for stimulating biofuel programme, delivering energy source diversification, economic growth and sustainable development for Nigeria

Exploring Locational Criteria to Optimise Biofuel Production Potential in Nigeria

Energy is one of the important building blocks of any economy and the sustainability of its supply is crucial. Renewable energy sources are being explored with the objective of harnessing their potential to address demand shortages and provide sustainable clean energy. Biofuels, as one of these renewables, continue to expand and their share in global energy consumption continues to increase. Apart from lower net carbon emissions compared to fossil fuels and their role as transitional fuel sources in global shift towards renewable energy, biofuels offer other benefits such as increasing the volume of liquid fuels, improving air quality, expanding trade, import substitution and energy diversification. Therefore, there are strong environmental and economic arguments for the Nigerian Government to embark on deployment of renewable energy, including biofuels. Despite abundant biomass resources, biofuel programmes have not been fully operationalised in the country, partly because biofuels vary in their favourability profiles which depend on local conditions and practices, as well as spatial conflicts between land designed for energy production and other land uses such as agriculture or nature reserves. Consequently, there is a need for robust and detailed approaches to this location-related problem. Although Spatial Multi-criteria Analysis (SMCA) as a support tool has been applied to biofuel production analysis, accounting for multiple stakeholder opinions has been one of the major challenges. In Nigeria, there have been few attempts to apply spatial analysis to locational problems related to biofuel production. In addition, these studies are limited in terms of scope, were based on feedstock other than energy crops, and provided superficial analysis of suitability of the identified sites. The goal of this thesis was to show how to improve the robustness and transparency of spatial analysis in Nigeria through answering some spatial questions about biofuel production, which extends our knowledge of GIS and is relevant to practice. Robustness implies detailed exploration of the required environmental criteria and incorporation of the expert decisions on the criteria preferences. This work transparently demonstrates detailed application of the combined geospatial and multi-criteria methods to make the academic contribution transferable. The technical goal of the work was to conduct spatial optimisation for biofuel production in the country through detailed assessment of environmental criteria, modelling land suitability for cultivating sweet sorghum, sugarcane, cassava, oil palm and jatropha as biofuel crops in Nigeria and modelling optimal sites for biofuel processing and/or blending. This will provide support for spatial decisions regarding establishing biofuel processing plants or expanding the existing ones. Analytical Hierarchy Process (pairwise comparison) was adopted as the multi-criteria analysis method due to its robustness regarding stakeholder inclusion. Weighted overlay was adopted as method of land suitability modelling and supply area modelling was adopted as the method of site optimisation. The analysis showed that northcentral geo-political zone of Nigeria has the largest areas of land that is very suitable for cultivating sugarcane, cassava, oil palm and jatropha, while northeast has the largest areas of land that is very suitable for cultivating sweet sorghum. Based on these, three sizes of service area were considered assuming worst, average and highest crop yields scenarios to optimise processing/blending sites. Existing petroleum depots were considered as the candidate sites. Ilorin petroleum depot was found to be the most optimal location for processing/blending biofuel in Nigeria based on all the crop yields scenarios, within 300 km service area. However, assuming worst case yields scenario within 100 km service area, Maiduguri depot was found to be the best location for sweet sorghum and sugarcane biofuel processing/blending, but Yola depot was suggested as replacement for sugarcane. Ibadan was found to be the best for oil palm and jatropha, but Ikot Abasi depot was suggested as replacement for oil palm. Aba was found to be the best for cassava, but Makurdi was suggested as replacement. This work had demonstrated how robust integration of GIS tools with MCDM techniques could improve the effectiveness of spatial decision-making process regarding positioning biofuel production in developing countries like Nigeria. It is therefore concluded that this work will serve as a point of reference for state-of-the-art application of spatial multi-criteria evaluation analysis, not only for the biofuel industry, but also for other sectors of environmental management such as river basin management, land use or settlement planning. The tendency of a biofuel programme in Nigeria to succeed would greatly be enhanced by adopting sustainability strategies along its value chain through climate smart agriculture, designing and/or adopting a suitable feedstock supply model, effective land use management, realigning policy objectives, enforcing policy directives and balancing between strong and weak sustainability strategies. This will create a conducive environment for stimulating biofuel programme, delivering energy source diversification, economic growth and sustainable development for Nigeria

Knowledge Modelling and Learning through Cognitive Networks

One of the most promising developments in modelling knowledge is cognitive network science, which aims to investigate cognitive phenomena driven by the networked, associative organization of knowledge. For example, investigating the structure of semantic memory via semantic networks has illuminated how memory recall patterns influence phenomena such as creativity, memory search, learning, and more generally, knowledge acquisition, exploration, and exploitation. In parallel, neural network models for artificial intelligence (AI) are also becoming more widespread as inferential models for understanding which features drive language-related phenomena such as meaning reconstruction, stance detection, and emotional profiling. Whereas cognitive networks map explicitly which entities engage in associative relationships, neural networks perform an implicit mapping of correlations in cognitive data as weights, obtained after training over labelled data and whose interpretation is not immediately evident to the experimenter. This book aims to bring together quantitative, innovative research that focuses on modelling knowledge through cognitive and neural networks to gain insight into mechanisms driving cognitive processes related to knowledge structuring, exploration, and learning. The book comprises a variety of publication types, including reviews and theoretical papers, empirical research, computational modelling, and big data analysis. All papers here share a commonality: they demonstrate how the application of network science and AI can extend and broaden cognitive science in ways that traditional approaches cannot