A convolutional neural network for impact detection and characterization of complex composite structures

This paper reports on a novel metamodel for impact detection, localization and characterization of complex composite structures based on Convolutional Neural Networks (CNN) and passive sensing. Methods to generate appropriate input datasets and network architectures for impact localization and characterization were proposed, investigated and optimized. The ultrasonic waves generated by external impact events and recorded by piezoelectric sensors are transferred to 2D images which are used for impact detection and characterization. The accuracy of the detection was tested on a composite fuselage panel which was shown to be over 94%. In addition, the scalability of this metamodelling technique has been investigated by training the CNN metamodels with the data from part of the stiffened panel and testing the performance on other sections with similar geometry. Impacts were detected with an accuracy of over 95%. Impact energy levels were also successfully categorized while trained at coupon level and applied to sub-components with greater complexity. These results validated the applicability of the proposed CNN-based metamodel to real-life application such as composite aircraft parts

A Review of Modelling and Simulation Methods for Flashover Prediction in Confined Space Fires

Confined space fires are common emergencies in our society. Enclosure size, ventilation, or type and quantity of fuel involved are factors that determine the fire evolution in these situations. In some cases, favourable conditions may give rise to a flashover phenomenon. However, the difficulty of handling this complicated emergency through fire services can have fatal consequences for their staff. Therefore, there is a huge demand for new methods and technologies to tackle this life-threatening emergency. Modelling and simulation techniques have been adopted to conduct research due to the complexity of obtaining a real cases database related to this phenomenon. In this paper, a review of the literature related to the modelling and simulation of enclosure fires with respect to the flashover phenomenon is carried out. Furthermore, the related literature for comparing images from thermal cameras with computed images is reviewed. Finally, the suitability of artificial intelligence (AI) techniques for flashover prediction in enclosed spaces is also surveyed.This work has been partially funded by the Spanish Government TIN2017-89069-R grant supported with Feder funds. This work was supported in part by the Spanish Ministry of Science, Innovation and Universities through the Project ECLIPSE-UA under Grant RTI2018-094283-B-C32 and the Lucentia AGI Grant

Data mining using intelligent systems : an optimized weighted fuzzy decision tree approach

Data mining can be said to have the aim to analyze the observational datasets to find relationships and to present the data in ways that are both understandable and useful. In this thesis, some existing intelligent systems techniques such as Self-Organizing Map, Fuzzy C-means and decision tree are used to analyze several datasets. The techniques are used to provide flexible information processing capability for handling real-life situations. This thesis is concerned with the design, implementation, testing and application of these techniques to those datasets. The thesis also introduces a hybrid intelligent systems technique: Optimized Weighted Fuzzy Decision Tree (OWFDT) with the aim of improving Fuzzy Decision Trees (FDT) and solving practical problems. This thesis first proposes an optimized weighted fuzzy decision tree, incorporating the introduction of Fuzzy C-Means to fuzzify the input instances but keeping the expected labels crisp. This leads to a different output layer activation function and weight connection in the neural network (NN) structure obtained by mapping the FDT to the NN. A momentum term was also introduced into the learning process to train the weight connections to avoid oscillation or divergence. A new reasoning mechanism has been also proposed to combine the constructed tree with those weights which had been optimized in the learning process. This thesis also makes a comparison between the OWFDT and two benchmark algorithms, Fuzzy ID3 and weighted FDT. SIx datasets ranging from material science to medical and civil engineering were introduced as case study applications. These datasets involve classification of composite material failure mechanism, classification of electrocorticography (ECoG)/Electroencephalogram (EEG) signals, eye bacteria prediction and wave overtopping prediction. Different intelligent systems techniques were used to cluster the patterns and predict the classes although OWFDT was used to design classifiers for all the datasets. In the material dataset, Self-Organizing Map and Fuzzy C-Means were used to cluster the acoustic event signals and classify those events to different failure mechanism, after the classification, OWFDT was introduced to design a classifier in an attempt to classify acoustic event signals. For the eye bacteria dataset, we use the bagging technique to improve the classification accuracy of Multilayer Perceptrons and Decision Trees. Bootstrap aggregating (bagging) to Decision Tree also helped to select those most important sensors (features) so that the dimension of the data could be reduced. Those features which were most important were used to grow the OWFDT and the curse of dimensionality problem could be solved using this approach. The last dataset, which is concerned with wave overtopping, was used to benchmark OWFDT with some other Intelligent Systems techniques, such as Adaptive Neuro-Fuzzy Inference System (ANFIS), Evolving Fuzzy Neural Network (EFuNN), Genetic Neural Mathematical Method (GNMM) and Fuzzy ARTMAP. Through analyzing these datasets using these Intelligent Systems Techniques, it has been shown that patterns and classes can be found or can be classified through combining those techniques together. OWFDT has also demonstrated its efficiency and effectiveness as compared with a conventional fuzzy Decision Tree and weighted fuzzy Decision Tree

Data mining using intelligent systems : an optimized weighted fuzzy decision tree approach

Data mining can be said to have the aim to analyze the observational datasets to find relationships and to present the data in ways that are both understandable and useful. In this thesis, some existing intelligent systems techniques such as Self-Organizing Map, Fuzzy C-means and decision tree are used to analyze several datasets. The techniques are used to provide flexible information processing capability for handling real-life situations. This thesis is concerned with the design, implementation, testing and application of these techniques to those datasets. The thesis also introduces a hybrid intelligent systems technique: Optimized Weighted Fuzzy Decision Tree (OWFDT) with the aim of improving Fuzzy Decision Trees (FDT) and solving practical problems. This thesis first proposes an optimized weighted fuzzy decision tree, incorporating the introduction of Fuzzy C-Means to fuzzify the input instances but keeping the expected labels crisp. This leads to a different output layer activation function and weight connection in the neural network (NN) structure obtained by mapping the FDT to the NN. A momentum term was also introduced into the learning process to train the weight connections to avoid oscillation or divergence. A new reasoning mechanism has been also proposed to combine the constructed tree with those weights which had been optimized in the learning process. This thesis also makes a comparison between the OWFDT and two benchmark algorithms, Fuzzy ID3 and weighted FDT. SIx datasets ranging from material science to medical and civil engineering were introduced as case study applications. These datasets involve classification of composite material failure mechanism, classification of electrocorticography (ECoG)/Electroencephalogram (EEG) signals, eye bacteria prediction and wave overtopping prediction. Different intelligent systems techniques were used to cluster the patterns and predict the classes although OWFDT was used to design classifiers for all the datasets. In the material dataset, Self-Organizing Map and Fuzzy C-Means were used to cluster the acoustic event signals and classify those events to different failure mechanism, after the classification, OWFDT was introduced to design a classifier in an attempt to classify acoustic event signals. For the eye bacteria dataset, we use the bagging technique to improve the classification accuracy of Multilayer Perceptrons and Decision Trees. Bootstrap aggregating (bagging) to Decision Tree also helped to select those most important sensors (features) so that the dimension of the data could be reduced. Those features which were most important were used to grow the OWFDT and the curse of dimensionality problem could be solved using this approach. The last dataset, which is concerned with wave overtopping, was used to benchmark OWFDT with some other Intelligent Systems techniques, such as Adaptive Neuro-Fuzzy Inference System (ANFIS), Evolving Fuzzy Neural Network (EFuNN), Genetic Neural Mathematical Method (GNMM) and Fuzzy ARTMAP. Through analyzing these datasets using these Intelligent Systems Techniques, it has been shown that patterns and classes can be found or can be classified through combining those techniques together. OWFDT has also demonstrated its efficiency and effectiveness as compared with a conventional fuzzy Decision Tree and weighted fuzzy Decision Tree.EThOS - Electronic Theses Online ServiceUniversity of WarwickOverseas Research Students Awards Scheme (ORSAS)GBUnited Kingdo

Experimental Study Employing EMI-based Piezoelectric Diaphragms to Detect Water Leakages in PVC Pipes

Considering the increased demand for using natural resources sustainably, water is an ultra-valuable resource to be preserved. Notwithstanding, Polyvinyl Chloride (PVC) pipes, which are mainly used in complex building water distribution systems, are still in use and present a considerable rate of water loss. Unfortunately, the recently developed Structural Health Monitoring (SHM) has not deeply covered leaks in those pipes. To fill this gap, this approach proposes an experimental study to assess the feasibility of using Lead Zirconate Titanate (PZT) diaphragms to detect water leaks in these structures. Experimental tests were conducted by attaching one PZT diaphragm to a mini-scale water distribution system, where the leakage scenarios were simulated by opening taps. To attest to the effectiveness of the methodology, it calculated statistical metrics between the baseline (without leaks) and unknown conditions. The experimental results are promising and may be extrapolated to detect water losses in a full-scale water-distributing system

VLSI Design

This book provides some recent advances in design nanometer VLSI chips. The selected topics try to present some open problems and challenges with important topics ranging from design tools, new post-silicon devices, GPU-based parallel computing, emerging 3D integration, and antenna design. The book consists of two parts, with chapters such as: VLSI design for multi-sensor smart systems on a chip, Three-dimensional integrated circuits design for thousand-core processors, Parallel symbolic analysis of large analog circuits on GPU platforms, Algorithms for CAD tools VLSI design, A multilevel memetic algorithm for large SAT-encoded problems, etc

DESARROLLOS TECNOLÓGICOS PARA LA MEJORA Y CONTROL DEL PROCESO DE OBTENCIÓN DE BIOETANOL A PARTIR DE RESIDUOS AGROALIMENTARIOS

Tesis por compendioAs stated by the United Nations Department of Economic and Social Affairs, the first half of the present century will experience a significant increase in global energy demand due to the expected growth of world population and global economic development. On the other hand, recent reports from the Intergovernmental Panel on Climate Change definitely evidence the link between the continued use of fossil fuels and the increasing concentration of greenhouse gases into the atmosphere being responsible for climate change. In this context, a global commitment is needed in the search for cleaner, environmentally friendly and sustainable energy sources, such as second¿generation bioethanol from agro¿industrial waste. Thus, this PhD Thesis aims to advance in the agro¿industrial waste recovery of fruits such as pineapple and persimmon. Specifically, different strategies for enhancing the bioethanol production process were evaluated. Additionally on¿line monitoring of the saccharification step and final alcohol content in the studied wastes were taken into consideration. Thus, different technologies were studied to improve the enzymatic hydrolysis performance in pineapple waste. First, hydrolytic performances of commercial enzymes produced by the filamentous fungi Aspergillus niger and Trichoderma reesei were compared. Next, the use of microwave pretreatments, alone or combined with an alkali treatment, was evaluated to improve the saccharification performance. On the other hand, "Rojo Brillante" persimmon waste wasstudied as a potentialsource of high added value products. Finally, electrochemical impedance spectroscopy based techniques were evaluated for monitoring saccharification and quantifying ethanol in pineapple waste. Results showed that A. niger cellulase is an effective alternative to that obtained from T. reesei for the saccharification of industrial pineapple waste, especially when combined with hemicellulase. On the other hand, microwave pretreatments at appropriate power and exposure times significantly improved the enzymatic hydrolysis performance. This improvement was particularly remarkable when microwaves were combined with an alkali treatment. On the other hand, industrial persimmon waste was shown to be a low¿cost source of bioethanol and antioxidant compounds, mainly carotenoids. Finally, electrochemical impedance spectroscopy was validated as an easy, fast, non¿destructive, inexpensive and alternative methodology to the traditional laboratory ones for monitoring saccharification and fermentation processes. This validation was achieved by combining impedance spectroscopy with mathematical models based on artificial neural networks, being robust, reliable, adaptive and easily implementable in electronic systems.   To conclude, the present PhD Thesis has provided substantial progress towards agro¿industrial waste recovery processes. In fact, several technological developments have been implemented in order to increase the saccharification yield in pineapple waste. Moreover, high added value products have been obtained from persimmon residue. Likewise, these processes can be accurately controlled on¿line by electrochemical impedance spectroscopy based techniques combined with specific mathematical models,  representing a significant advance in this field.Durante la primera mitad de este siglo, se espera que la demanda mundial de energía aumente significativamente debido al previsible incremento de la población mundial y al desarrollo económico global, tal como afirman recientes estudios de la Departamento de Asuntos Económicos y Sociales de las Naciones Unidas. Por otro lado, los datos del Panel Intergubernamental del Cambio Climático vinculan el uso continuado de los combustibles fósiles con el aumento de la concentración de CO2 y partículas contaminantes a la atmósfera causantes, entre otros, del cambio climático. En este contexto, es necesario seguir avanzando en la búsqueda de alternativas energéticas más limpias y medioambientalmente sostenibles, como es el caso del bioetanol de segunda generación obtenido a partir de residuos agroindustriales. Así, la presente Tesis Doctoral plantea como objetivo profundizar en la revalorización de los residuos agroindustriales de frutas como la piña y el caqui. En concreto, se evaluaron diferentes estrategias para la mejora del proceso de obtención de bioetanol y el control en línea de la sacarificación y contenido en alcohol en los residuos. Para ello, se estudiaron diferentes tecnologías para la mejora del rendimiento de la hidrólisis enzimática de los residuos de piña. En primer lugar se comparó la acción hidrolítica de las enzimas comerciales producidas por los hongos filamentosos Aspergillus niger y Trichoderma reesei. A continuación, se evaluó la aplicación de pretratamientos con microondas, solos o combinados con un medio alcalino, para la mejora del rendimiento de la sacarificación. Por otro lado, se analizó el potencial de revalorización del residuo industrial de caqui de la variedad "Rojo Brillante". Finalmente se estudió la aplicación de técnicas basadas en la espectroscopía de impedancias para la monitorización de la sacarificación y la cuantificación de bioetanol en piña. Los resultados obtenidos demostraron que la celulasa de A. niger es una alternativa eficaz a la obtenida a partir de T. reesei para la sacarificación de los residuos industriales de piña, especialmente cuando se combina con hemicelulasa. Por otra parte, la aplicación de pretratamientos con microondas a potencias y tiempos de exposición apropiados mejoraron significativamente el rendimiento de la hidrólisis enzimática. Esta mejora demostró ser particularmente destacable cuando se combinaron las microondas con un medio alcalino. Por otra parte, se demostró que los residuos industriales de caqui son una fuente de obtención de compuestos de alto valor añadido tales como: bioetanol y compuestos antioxidantes, principalmente carotenoides. Por último, se validó la espectroscopía de impedancias electroquímica como una metodología fácil, rápida, no destructiva, económica y alternativa a las técnicas de laboratorio tradicionales para el control de la sacarificación y fermentación. Esto se consiguió combinando la espectroscopía de impedancias con el desarrollo de modelos matemáticos basados en redes neuronales artificiales que se caracterizan por ser robustos, fiables, adaptativos y fácilmente implementables en sistemas electrónicos. A modo de conclusión, la presente Tesis Doctoral ha permitido avanzar en el conocimiento sobre el proceso de revalorización de los residuos industriales de frutas mediante la implementación de desarrollos tecnológicos para el aumento del rendimiento de la hidrólisis enzimática del residuo de piña y la obtención de compuestos de alto valor añadido a partir de caqui. Asimismo, se ha demostrado que es posible aplicar técnicas basadas en la espectroscopía de impedancias y modelos matemáticos específicos para mejorar la monitorización y control en línea de estos procesos, lo que supone un avance significativo en este campo.Durant la primera meitat d'aquest segle, s'espera que la demanda mundial d'energia augmente significativament a causa del previsible increment de la població mundial i al desenvolupament econòmic global, tal com afirmen recents estudis del Departament d'Assumptes Econòmics i Socials de les Nacions Unides. D'altra banda, les dades del Panell Intergovernamental del Canvi Climàtic vinculen l'ús continuat dels combustibles fòssils amb l'augment de la concentració de CO2 i partícules contaminants a l'atmosfera causants, entre uns altres, del canvi climàtic. En aquest context, és necessari seguir avançant en la cerca d'alternatives energètiques més netes i mediambientalment sostenibles, com és el cas del bioetanol de segona generació obtingut a partir de residus agroindustrials. Així, la present Tesi Doctoral planteja com a objectiu aprofundir en la revalorització dels residus agroindustrials de fruites com la pinya i el caqui. En concret, es van avaluar diferents estratègies per a la millora del procés d'obtenció de bioetanol i el control en línia de la sacarificació i contingut en alcohol en els residus. Per a això, es van estudiar diferents tecnologies per a la millora del rendiment de la hidròlisi enzimàtica dels residus de pinya. En primer lloc es va comparar l'acció hidrolítica dels enzims comercials produïts pels fongs filamentosos Aspergillus niger i Trichoderma reesei. A continuació, es va avaluar l'aplicació de pretractaments amb microones, sols o combinats amb un medi alcalí, per a la millora del rendiment de la sacarificació. D'altra banda, es va analitzar el potencial de revaloració del residu industrial de caqui de la varietat "Rojo Brillante". Finalment es va estudiar l'aplicació de tècniques basades en l'espectroscòpia d'impedàncies per al monitoratge de la sacarificació i la quantificació de bioetanol en pinya. Els resultats obtinguts van demostrar que la cel¿lulasa d'A. niger és una alternativa eficaç a l'obtinguda a partir de T. reesei per a la sacarificació dels residus industrials de pinya, especialment quan es combina amb hemicel¿lulasa. D'altra banda, l'aplicació de pretractaments amb microones a potències i temps d'exposició apropiats van millorar significativament el rendiment de la hidròlisi enzimàtica. Aquesta millora va demostrar ser particularment destacable quan es van combinar les microones amb un medi alcalí. D'altra banda, es va demostrar que els residus industrials de caqui són una font d'obtenció de compostos d'alt valor afegit tals com: bioetanol i compostos antioxidants, principalment carotenoides. Finalment, es va validar l'espectroscòpia d'impedàncies com una metodologia fàcil, ràpida, no destructiva, econòmica i alternativa a les tècniques de laboratori tradicionals per al control de la sacarificació i fermentació. Això es va conseguir combinant l'espectroscòpia d'impedàncies amb el desenvolupament de models matemàtics basats en xarxes neuronals artificials que es caracteritzen per ser robustos, fiables, adaptatius i fàcilment implementables en sistemes electrònics. A manera de conclusió, la present Tesi Doctoral ha permès avançar en el coneixement del procés de revalorització dels residus industrials de fruites mitjançant la implementació de desenvolupaments tecnològics per a l'augment del rendiment de la hidròlisi enzimàtica del residu de pinya i l'obtenció de compostos d'alt valor afegit a partir de caqui. Així mateix, s'ha demostrat que és possible aplicar tècniques basades en l'espectroscòpia d'impedàncies i models matemàtics específics per a millorar el monitoratge i control d'aquests processos, fet que suposa un avanç significatiu en aquest camp.Conesa Domínguez, C. (2017). DESARROLLOS TECNOLÓGICOS PARA LA MEJORA Y CONTROL DEL PROCESO DE OBTENCIÓN DE BIOETANOL A PARTIR DE RESIDUOS AGROALIMENTARIOS [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/86143TESISCompendi

Smart process monitoring of machining operations

The following thesis explores the possibilities to applying artificial intelligence techniques in the field of sensory monitoring in the manufacturing sector. There are several case studies considered in the research activity. The first case studies see the implementation of supervised and unsupervised neural networks to monitoring the condition of a grinding wheel. The monitoring systems have acoustic emission sensors and a piezoelectric sensor capable to measuring electromechanical impedance. The other case study is the use of the bees' algorithm to determine the wear of a tool during the cutting operations of a steel cylinder. A script permits this operation. The script converts the images into a numerical matrix and allows the bees to correctly detect tool wear

Novel control of a high performance rotary wood planing machine

Rotary planing, and moulding, machining operations have been employed within the woodworking industry for a number of years. Due to the rotational nature of the machining process, cuttermarks, in the form of waves, are created on the machined timber surface. It is the nature of these cuttermarks that determine the surface quality of the machined timber. It has been established that cutting tool inaccuracies and vibrations are a prime factor in the form of the cuttermarks on the timber surface. A principal aim of this thesis is to create a control architecture that is suitable for the adaptive operation of a wood planing machine in order to improve the surface quality of the machined timber. In order to improve the surface quality, a thorough understanding of the principals of wood planing is required. These principals are stated within this thesis and the ability to manipulate the rotary wood planing process, in order to achieve a higher surface quality, is shown. An existing test rig facility is utilised within this thesis, however upgrades to facilitate higher cutting and feed speeds, as well as possible future implementations such as extended cutting regimes, the test rig has been modified and enlarged. This test rig allows for the dynamic positioning of the centre of rotation of the cutterhead during a cutting operation through the use of piezo electric actuators, with a displacement range of ±15μm. A new controller for the system has been generated. Within this controller are a number of tuneable parameters. It was found that these parameters were dependant on a high number external factors, such as operating speeds and run‐out of the cutting knives. A novel approach to the generation of these parameters has been developed and implemented within the overall system. Both cutterhead inaccuracies and vibrations can be overcome, to some degree, by the vertical displacement of the cutterhead. However a crucial information element is not known, the particular displacement profile. Therefore a novel approach, consisting of a subtle change to the displacement profile and then a pattern matching approach, has been implemented onto the test rig. Within the pattern matching approach the surface profiles are simplified to a basic form. This basic form allows for a much simplified approach to the pattern matching whilst producing a result suitable for the subtle change approach. In order to compress the data levels a Principal Component Analysis was performed on the measured surface data. Patterns were found to be present in the resultant data matrix and so investigations into defect classification techniques have been carried out using both K‐Nearest Neighbour techniques and Neural Networks. The application of these novel approaches has yielded a higher system performance, for no additional cost to the mechanical components of the wood planing machine, both in terms of wood throughput and machined timber surface quality

A Review of Findings from Neuroscience and Cognitive Psychology as Possible Inspiration for the Path to Artificial General Intelligence

This review aims to contribute to the quest for artificial general intelligence by examining neuroscience and cognitive psychology methods for potential inspiration. Despite the impressive advancements achieved by deep learning models in various domains, they still have shortcomings in abstract reasoning and causal understanding. Such capabilities should be ultimately integrated into artificial intelligence systems in order to surpass data-driven limitations and support decision making in a way more similar to human intelligence. This work is a vertical review that attempts a wide-ranging exploration of brain function, spanning from lower-level biological neurons, spiking neural networks, and neuronal ensembles to higher-level concepts such as brain anatomy, vector symbolic architectures, cognitive and categorization models, and cognitive architectures. The hope is that these concepts may offer insights for solutions in artificial general intelligence.Comment: 143 pages, 49 figures, 244 reference