The demand for imported fruit in China has increased dramatically since it first appeared in Chinese markets around 1993. Although imported fruit is much more expensive than domestically produced fruit and people’s income is much lower in China than in developed countries, imported fruit still attracts many willing buyers. Conventional concepts such as meeting basic needs or increasing consumer awareness of the importance of fresh fruit to a healthy lifestyle cannot adequately explain this phenomenon, as there is an abundance of fresh, cheap, local produce available in almost every Chinese market. There must be other factors influencing buying behaviour. To explore these factors and to examine the dynamics of the market for imported fruit, this research adopted a mixed qualitative-quantitative methodology guided by the paradigm of phenomenology. The research examined the characteristics of imported fruit itself, criteria imposed by Chinese buyers on these characteristics, the intended uses of imported fruit and their associated consumption values. To shed light on the possible influence of socio-economic factors, the research also compared buyers from two Chinese cities, Guangzhou and Urumqi. The former is one of the most developed cities in China and the latter is regarded as among the more backward and conservative cities in China. The research identified ten attributes that appeal to Chinese buyers. Six relate to the fruit’s physical attributes: that it has better appearance and packaging, lower chemical residues, better or different taste, and freshness. The remaining four relate to symbolic attributes associated with the fruit: that it represents achievement, wealth, personality and social status. Five intended uses of imported fruit were identified: for gifts, self-consumption, children, aged parents and patients. Four consumption values underlying these intentions were also identified: symbolism, concern for health, meeting basic needs and hedonism. However, the research revealed that no single combination of intended use and consumption value drives the demand for imported fruit in the Chinese market. Most frequently, it is a mix of hedonic and symbolic values behind a range of different intended uses that stimulates demand. Pursuing hedonic and symbolic values also leads to the visual quality of imported fruit generally being the most appealing attribute to Chinese buyers, a pattern common to both Guangzhou and Urumqi. These findings make a significant contribution to empirical knowledge about Chinese consumer behaviour. Results provide valuable insights into the interrelationships among product attributes, intended uses, consumption values and cultural values, and would give essential guidance to the development of strategies to market imported fruit in China. The research also examined limitations of current analytical approaches to the study of consumer behaviour. It demonstrated that approaches based on neural networks and fuzzy logic could be used independently or combined with conventional statistical methods to improve the explanation of consumer behaviour in this case. A comparison was carried out between the most popular form of neural networks (feedforward networks) and multivariate statistical methods in terms of their ability to predict behavioural intention through consumers’ attitudes towards products. Results demonstrated that neural networks were capable of capturing nonlinear aspects of complex relationships and producing better predictions than conventional statistical models. To explore consumer cognitive patterns, the research also compared K-means clustering with a Self-organizing Map (SOM) neural network in terms of the ability to cluster consumers on the basis of perceptions towards imported fruit attributes. Results indicate a superior outcome when K-means is used in conjunction with SOM in clustering analysis: using SOM to determine the ‘natural’ numbers of clusters and using K-means to do clustering. Finally, to quantitatively evaluate the impact of consumption values, this research develops a new approach that combines Means-end Chain theory with fuzzy logic theory. Given the global importance of the Chinese market, the successful application of neural networks and fuzzy logic in this study of the behaviour of Chinese consumers purchasing imported fruit could have wider ramifications. If the approach were proven in other applications, it could significantly improve the ability to understand the demand for consumer goods in China.

Magíster en Ciencias de la Ingeniería, Mención Eléctrica / Las micro-redes son soluciones sustentables para la electrificación de zonas rurales que pueden hacer uso de sus recursos renovables. En este estudio, se propone un nuevo método para la planificación de micro-redes que incluye explícitamente el efecto de la incertidumbre de variables críticas que definen el diseño del sistema, como lo son las fuentes renovables y la demanda eléctrica. Se propone la utilización modelos difusos de T&S y modelos de intervalos basados en el método de la covarianza, ya que los primeros permiten caracterizar las no-linealidades del fenómeno a modelar, mientras que los segundos pueden representar sistemáticamente las incertidumbres asociadas a dichas variables críticas con un cierto nivel de confianza. Para el diseño de cualquier sistema eléctrico se requiere identificar la dimensión y comportamiento de carga a la cual se desea abastecer a través de su registro continuo y prolongado, lo cual no siempre es posible, especialmente si se trata de localidades aisladas o de difícil acceso. Para solucionar esto, se implementa un simulador de carga basado en Cadenas de Markov, obtenido a partir de la agrupación previa de hogares en función de su información socio-demográfica empleando el algoritmo de redes neuronales Self-Organizing Map . Basado en modelos de intervalos, se obtienen un conjunto de escenarios posibles, en donde se resuelve el problema de optimización de planificación de la micro-red, obteniendo la topología del sistema y el dimensionamiento de cada una de sus unidades. Los resultados obtenidos en este proceso son la base para el estudio de factibilidad y de diseño de un proyecto de micro-red. La metodología propuesta es aplicada para la planificación de una micro-red conectada a la red principal, basada en fuentes solar y eólica, en la comunidad rural Mapuche de José Painecura Hueñalihuen, IX Región de la Araucanía, Chile. En este caso de estudio, se determina que la modelación lineal es suficiente para caracterizar el comportamiento de la velocidad del viento y de la radiación solar, mientras que se requiere de la identificación de un modelo difuso para representar la no-linealidad del comportamiento de la demanda eléctrica. Por otro lado, a través de la identificación de modelos de intervalos, se obtiene que la velocidad del viento presenta una mayor incertidumbre que la radiación solar y la demanda eléctrica. Por lo tanto, el recurso eólico es la variable que mayor influencia tiene en la diferenciación de los distintos escenarios posibles. En función de las características locales y técnicas de cada unidad considerada, junto con los costos estimados y considerando un escenario conversador, se obtiene que el diseño final la micro-red es principalmente compuesta por tecnología fotovoltaica.

Unmanned Aerial Vehicles (UAVs) may develop cracks, erosion, delamination or other damages due to aging, fatigue or extreme loads. Identifying these damages is critical for the safe and reliable operation of the systems. Structural Health Monitoring (SHM) is capable of determining the conditions of systems automatically and continually through processing and interpreting the data collected from a network of sensors embedded into the systems. With the desired awareness of the systems’ health conditions, SHM can greatly reduce operational cost and speed up maintenance processes. The purpose of this study is to develop an effective, low-cost, flexible and fault tolerant structural health monitoring system. The proposed Index Based Reasoning (IBR) system started as a simple look-up-table based diagnostic system. Later, Fast Fourier Transformation analysis and neural network diagnosis with self-learning capabilities were added. The current version is capable of classifying different health conditions with the learned characteristic patterns, after training with the sensory data acquired from the operating system under different status. The proposed IBR systems are hierarchy and distributed networks deployed into systems to monitor their health conditions. Each IBR node processes the sensory data to extract the features of the signal. Classifying tools are then used to evaluate the local conditions with health index (HI) values. The HI values will be carried to other IBR nodes in the next level of the structured network. The overall health condition of the system can be obtained by evaluating all the local health conditions. The performance of IBR systems has been evaluated by both simulation and experimental studies. The IBR system has been proven successful on simulated cases of a turbojet engine, a high displacement actuator, and a quad rotor helicopter. For its application on experimental data of a four rotor helicopter, IBR also performed acceptably accurate. The proposed IBR system is a perfect fit for the low-cost UAVs to be the onboard structural health management system. It can also be a backup system for aircraft and advanced Space Utility Vehicles.

Im Bereich der medizinischen Geräte haben eingebettete Systeme verstärkt Einzug gehalten. Nicht nur in Operationssälen oder Intensivstationen, auch im Bereich der Prothesensteuerung spielt moderne Computertechnik in zunehmendem Maße eine Rolle, auch und insbesondere im Bereich der Prothesensteuerung durch elektronisch vorverarbeitete Nervensignale. Die zur Signalverarbeitung eingesetzte, vortrainierte selbst-organisierende Karte stößt jedoch auf das Problem, sich den verändernden Gegebenheiten in den Nervensignalen des Patienten nicht anpassen zu können. In dieser Arbeit wird die Möglichkeit untersucht, die Steuerung der Handprothese mit einer Nachlernfunktion auszustatten, um während des Einsatzes der Prothese auf die Veränderungen der Nervensignale des Patienten reagieren zu können. Da diese Veränderungen höchst individuell verlaufen, werden Parameter eingeführt, mit denen das Nachlernverfahren an die Gegebenheiten des Patienten angepasst werden kann. Verschiedene denkbare Lernstrategien werden untersucht und hinsichtlich ihrer Effizienz und ihrer Aktualität bewertet. Um die Verwendbarkeit der Implementierung sicherzustellen, muss darauf geachtet werden, dass der entstehende SystemC-Code keine Elemente des nicht synthetisierbaren Subsets enthält. Zusätzlich wird die Synthetisierbarkeit mit dem Agility-Compiler untersucht.

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Classification tasks are an integral part of science, industry, medicine, and business; being such a pervasive technique, its smallest improvement is valuable. Artificial Neural Network (ANN) is one of the strongest techniques used in many disciplines for classification. The ANN technique suffers from drawbacks such as intransparency in spite of its high prediction power. In this dissertation, motivated by learning styles in human brains, ANN’s shortcomings are assuaged and its learning power is improved. Self-Organizing Map (SOM), an ANN variation which has strong unsupervised power, and Feedforward ANN, traditionally used for classification tasks, are hybridized to solidify their benefits and help remove their limitations. These benefits are in two directions: enhancing ANN’s learning power, and improving decision-making. First, the proposed method, named Self-Organizing Error-Driven (SOED) Artificial Neural Network (ANN), shows significant improvements in comparison with usual ANNs. We show SOED is a more accurate, more reliable, and more transparent technique through experimentation with five famous benchmark datasets. Second, the hybridization creates space for inclusion of decision-making goals at the level of ANN’s learning. This gives the classifier the opportunity to handle the inconclusiveness of the data smarter and in the direction of decision-making goals. Through three case studies, naming 1) churn decision analytics, 2) breast cancer diagnosis, and 3) quality control decision making through thermal monitoring of additive manufacturing processes, this novel and cost-sensitive aspect of SOED has been explored and lead to much quantified improvement in decision-making.

The Fennoscandian Shield as a part of a large Precambrian basement area is located in northern Europe and hosts economically important mineral deposits including base metals and precious metals. Regional geophysical data such as potential field and magnetotelluric data in combination with other geoscientific data contain information of importance for an understanding of the crustal and upper mantle structure. Knowledge about regional-scale structures is important for an optimized search for mineralisation. In order to investigate in more detail the spatial distribution of regional electrically conductive structures and near-surface mineral deposits, complementary magnetotelluric measurements have been done within the Precambrian Shield in the north-eastern part of the Norrbotten ore province. The potential field data provided by the Geological Survey of Sweden have been included in the current study. Processing of magnetotelluric data was performed using a robust multi-remote reference technique. The dimensionality analysis of the phase tensors indicates complex 3D structures in the area. A 3D crustal model of the electrical conductivity structure was derived based on 3D inversion of the data using the ModEM code. The final inversion 3D resistivity model revealed the presence of strong crustal conductors with the conductance of more than 3000 S at depth of tens of kilometres within a generally resistive crust. A significant part of the middle crust conductors is elongated in directions that coincide with major ductile deformation zones that have been mapped from airborne magnetic data and geological fieldwork. Some of these conductors have near-surface expression where they spatially correlate with the locations of known mineralisation. Processing and 3D inversion of the regional magnetic and gravity field data were performed, and the structural information derived from these data by using an open-source object-oriented package code written in Python called SimPEG. In this study, a new approach is proposed to extract and analyse the correlation between the modelled physical properties and for domain classification. For this, a neural net Self-Organizing Map procedure (SOM) was used for data reduction and simplification. The input data to the SOM analysis contain resistivity, magnetic susceptibility, and density model values for some selected depth levels. The domain classification is discussed with respect to geological boundaries and composition. The classification is furthermore applied for prediction of favourable areas for mineralisation. Based on visual inspection of processed regional gravity and magnetic field data and a SOM analysis performed on higher-order derivatives of the magnetic data, an interpretation of a sinistral fault with 52 km offset is proposed. The fault is oriented N10E and can be traced 250 km from Karesuando at the Swedish-Finish border southwards to the Archaean-Proterozoic boundary marked by the Luleå-Jokkmokk Zone.

In this paper, the problem of analyzing hyperspectral data is presented. The complexity of multi-dimensional data leads to the need for computer assisted data compression and labeling of important features. A brief overview of Self-Organizing Maps and their variants is given and then two possible methods of data analysis are examined. These methods are incorporated into a program derived from som_toolbox2. In this program, ASD data (data collected by an Analytical Spectral Device sensor) is read into a variable, relevant bands for discrimination between classes are extracted, and several different methods of analyzing the results are employed. A GUI was developed for easy implementation of these three stages.

Une nouvelle approche du traitement de l'image, appelée SOM-QE, qui exploite quantization error (QE) des self-organizing maps (SOM) est proposée dans cette thèse. Les SOM produisent des représentations discrètes de faible dimension des données d'entrée de haute dimension. QE est déterminée à partir des résultats du processus d'apprentissage non supervisé du SOM et des données d'entrée. SOM-QE d'une série chronologique d'images peut être utilisé comme indicateur de changements dans la série chronologique. Pour configurer SOM, on détermine la taille de la carte, la distance du voisinage, le rythme d'apprentissage et le nombre d'itérations dans le processus d'apprentissage. La combinaison de ces paramètres, qui donne la valeur la plus faible de QE, est considérée comme le jeu de paramètres optimal et est utilisée pour transformer l'ensemble de données. C'est l'utilisation de l'assouplissement quantitatif. La nouveauté de la technique SOM-QE est quadruple : d'abord dans l'usage. SOM-QE utilise un SOM pour déterminer la QE de différentes images - typiquement, dans un ensemble de données de séries temporelles - contrairement à l'utilisation traditionnelle où différents SOMs sont appliqués sur un ensemble de données. Deuxièmement, la valeur SOM-QE est introduite pour mesurer l'uniformité de l'image. Troisièmement, la valeur SOM-QE devient une étiquette spéciale et unique pour l'image dans l'ensemble de données et quatrièmement, cette étiquette est utilisée pour suivre les changements qui se produisent dans les images suivantes de la même scène. Ainsi, SOM-QE fournit une mesure des variations à l'intérieur de l'image à une instance dans le temps, et lorsqu'il est comparé aux valeurs des images subséquentes de la même scène, il révèle une visualisation transitoire des changements dans la scène à l'étude. Dans cette recherche, l'approche a été appliquée à l'imagerie artificielle, médicale et géographique pour démontrer sa performance. Les scientifiques et les ingénieurs s'intéressent aux changements qui se produisent dans les scènes géographiques d'intérêt, comme la construction de nouveaux bâtiments dans une ville ou le recul des lésions dans les images médicales. La technique SOM-QE offre un nouveau moyen de détection automatique de la croissance dans les espaces urbains ou de la progression des maladies, fournissant des informations opportunes pour une planification ou un traitement approprié. Dans ce travail, il est démontré que SOM-QE peut capturer de très petits changements dans les images. Les résultats confirment également qu'il est rapide et moins coûteux de faire la distinction entre le contenu modifié et le contenu inchangé dans les grands ensembles de données d'images. La corrélation de Pearson a confirmé qu'il y avait des corrélations statistiquement significatives entre les valeurs SOM-QE et les données réelles de vérité de terrain. Sur le plan de l'évaluation, cette technique a donné de meilleurs résultats que les autres approches existantes. Ce travail est important car il introduit une nouvelle façon d'envisager la détection rapide et automatique des changements, même lorsqu'il s'agit de petits changements locaux dans les images. Il introduit également une nouvelle méthode de détermination de QE, et les données qu'il génère peuvent être utilisées pour prédire les changements dans un ensemble de données de séries chronologiques. / A new approach for image processing, dubbed SOM-QE, that exploits the quantization error (QE) from self-organizing maps (SOM) is proposed in this thesis. SOM produce low-dimensional discrete representations of high-dimensional input data. QE is determined from the results of the unsupervised learning process of SOM and the input data. SOM-QE from a time-series of images can be used as an indicator of changes in the time series. To set-up SOM, a map size, the neighbourhood distance, the learning rate and the number of iterations in the learning process are determined. The combination of these parameters that gives the lowest value of QE, is taken to be the optimal parameter set and it is used to transform the dataset. This has been the use of QE. The novelty in SOM-QE technique is fourfold: first, in the usage. SOM-QE employs a SOM to determine QE for different images - typically, in a time series dataset - unlike the traditional usage where different SOMs are applied on one dataset. Secondly, the SOM-QE value is introduced as a measure of uniformity within the image. Thirdly, the SOM-QE value becomes a special, unique label for the image within the dataset and fourthly, this label is used to track changes that occur in subsequent images of the same scene. Thus, SOM-QE provides a measure of variations within the image at an instance in time, and when compared with the values from subsequent images of the same scene, it reveals a transient visualization of changes in the scene of study. In this research the approach was applied to artificial, medical and geographic imagery to demonstrate its performance. Changes that occur in geographic scenes of interest, such as new buildings being put up in a city or lesions receding in medical images are of interest to scientists and engineers. The SOM-QE technique provides a new way for automatic detection of growth in urban spaces or the progressions of diseases, giving timely information for appropriate planning or treatment. In this work, it is demonstrated that SOM-QE can capture very small changes in images. Results also confirm it to be fast and less computationally expensive in discriminating between changed and unchanged contents in large image datasets. Pearson's correlation confirmed that there was statistically significant correlations between SOM-QE values and the actual ground truth data. On evaluation, this technique performed better compared to other existing approaches. This work is important as it introduces a new way of looking at fast, automatic change detection even when dealing with small local changes within images. It also introduces a new method of determining QE, and the data it generates can be used to predict changes in a time series dataset.

Submitted by Cássia Santos (cassia.bcufg@gmail.com) on 2017-07-05T12:29:15Z No. of bitstreams: 2 Dissertação - Ricardo Henrique Fonseca Alves - 2017.pdf: 7774118 bytes, checksum: a675bf30443ae9eb20be6a3e5623bb4a (MD5) license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) / Approved for entry into archive by Luciana Ferreira (lucgeral@gmail.com) on 2017-07-10T14:30:07Z (GMT) No. of bitstreams: 2 Dissertação - Ricardo Henrique Fonseca Alves - 2017.pdf: 7774118 bytes, checksum: a675bf30443ae9eb20be6a3e5623bb4a (MD5) license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) / Made available in DSpace on 2017-07-10T14:30:07Z (GMT). No. of bitstreams: 2 Dissertação - Ricardo Henrique Fonseca Alves - 2017.pdf: 7774118 bytes, checksum: a675bf30443ae9eb20be6a3e5623bb4a (MD5) license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) Previous issue date: 2017-03-09 / Conselho Nacional de Pesquisa e Desenvolvimento Científico e Tecnológico - CNPq / The main goal of this work is to propose a methodology for the selection of 51 consumers in Nova Veneza-GO connected to two transformers in the pre-Smart Grid network. The methodology consists of ten stages ranging from the grouping of consumers with the same power consumption profile using a neural network, that is, a Non Parametric Self-Organizing Map (PSOM), until the complete and optimal allocation of financial resources through of an Integer Linear Programming. We obtained 12 different groups (clusters) of consumers of the two transformers with the same power consumption profile using the network PSOM algorithm. This grouping (clustering) was considered in the dimensioning and design of Photovoltaic Systems Connected to the Grid (Grid-Tie Systems) using three different computational tools, among them, an approach based on the PVSyst software, trial version V6.39. In addition, a study of Economic Engineering was carried out to expand the R&D pilot project aiming at the implementation of Grid Tie Systems for all the consumers of Nova Veneza-GO and Goiânia-GO, considering consumption data available by Celg-D and also considering two different scenarios based on the implementation of photovoltaic systems with and without government incentive. An Economic Engineering analysis was performed considering that 1%, 5%, 10%, 20%, 30% and 100% of Consumer Units (UC) adhere to the implantation of solar systems in Goiânia-GO. Environmental results were found for the city of Nova Veneza-GO and Goiânia-GO, evidencing an expressive reduction in CO2 emissions and a great saving of water. / O principal objetivo deste trabalho é propor uma metodologia para a escolha de 51 Unidades Consumidoras (UC) em Nova Veneza-GO ligados a dois transformadores pertencentes a uma rede pré Smart Grid localizada na cidade. A metodologia consiste de dez etapas que vão desde o agrupamento de consumidores com mesmo perfil de consumo de energia elétrica utilizando uma rede neural PSOM (do inglês: Non Parametric Self-Organizing Map), incluindo a realização de alocação de recursos financeiros de forma otimizada por meio de Programação Linear Inteira. Utilizando a rede PSOM, foi possível agrupar os consumidores dos dois transformadores em 12 grupos distintos com mesmo “perfil de consumo”. Esse agrupamento foi importante para o dimensionamento de Sistemas Fotovoltaicos Conectados à Rede Elétrica (Sistemas Grid-Tie) utilizando diferentes ferramentas computacionais, dentre elas, o software PVSyst na versão trial V6.39. Adicionalmente, foi feito um estudo de Engenharia Econômica visando a implantação de Sistemas Fotovoltaicos Conectados à Rede Elétrica para todos os consumidores de Nova Veneza-GO e de Goiânia-GO, considerando dados de consumo disponibilizados pela concessionária local e também considerando dois diferentes cenários: implantação de sistemas fotovoltaicos com e sem incentivo do governo. Foi realizada ainda uma análise de Engenharia Econômica considerando uma adesão em Goiânia-GO de 1%, 5%, 10%, 20%, 30% e 100% das Unidades Consumidoras (UC). Resultados ambientais foram encontrados para a cidade de Nova Veneza-GO e Goiânia-GO, evidenciando uma redução expressiva na emissão de CO2 e uma grande economia de água.