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Several warm winter events have occurred across the Southeast in the past decade, including 2 major events in 2017 and 2018 in Georgia and South Carolina. Plants will begin their spring growth sooner than climatology would suggest and then be damaged by early spring frosts in what is commonly known as a "false spring" event. Some species of plants, like peaches and blueberries, which produce buds early in the season, are just an example of some of the agricultural products more at risk than others. As an important measure of dormancy time in plants, chill hours present a measurement capable of tracking phenological shifts in plants. While a lack of required chill hours can delay spring emergence, intense warm periods can override the chilling hour requirement and induce spring emergence. This project involves training self-organizing maps (SOMs) to identify periods of anomalous winter warming based on a reduced number of chill hours within a 5-day temporal period compared to the period's climatological average. A second SOM is nested in the node that produced the most anomalous events to identify the range of warming that occurs in the most anomalous events, the synoptic setups of these events, and when these occurred. Hourly 2-meter temperature from ERA5 is used to conduct this analysis over a domain centered primarily over South Carolina and Georgia with a temporal period of 1980-2020. Climatological examination of chill hour accumulations in the past 4 decades show an overall decrease in chill hour accumulation across the past decade (2011-2020) Results indicated that periods of higher-than-average temperatures are increasing with time while periods of average or lower than average temperatures are decreasing with time. Both results were statistically significant by Mann-Kendall test. The results of the nested SOMs suggest that an increase in patterns of southerly flow (a common pattern for warmer temperatures) is occurring through time. A third SOM investigating early spring hard freezes was inconclusive but illustrated that some years had more early spring frosts than others independent of how many warmer than average periods occurred in the main winter. The use of SOMs for investigating climatological and synoptic changes in winter and early spring proved successful and effective. Future modifications to these SOMs could be used to identify more trends that exist within these seasons. / Master of Science / Several warm winter events have occurred across the Southeast in the past decade, including 2 major events in 2017 and 2018 in Georgia and South Carolina. Plants will begin their spring growth sooner than climatology would suggest and then be damaged by early spring frosts in what is commonly known as a "false spring" event. Some species of plants, like peaches and blueberries, which produce buds early in the season, are just an example of some of the agricultural products more at risk than others. As an important measure of dormancy time in plants, chill hours present a measurement capable of tracking shifts from normal winter to spring transition in plants. While a lack of required chill hours can delay leaf emergence and spring blooms, intense warm periods can override the chilling hour requirement and induce this spring emergence. This project involves training self-organizing maps (SOMs), a machine learning model, to identify periods of anomalous winter warming based on a reduced number of chill hours within a 5-day temporal period compared to the period's climatological average. A second SOM is nested in the node that produced the most anomalously warm events to identify the range of warming that occurs in the most anomalous events, the large-scale meteorological setups of these events, and when these occurred. Hourly 2-meter temperature from ERA5, a climatological dataset, is used to conduct this analysis over a domain centered primarily over South Carolina and Georgia with a temporal period of 1980-2020. Climatological examination of chill hour accumulations in the past 4 decades show an overall decrease in chill hour accumulation across the past decade (2011-2020) Results indicated that periods of higher-than-average temperatures are increasing with time while periods of average or lower than average temperatures are decreasing with time. Both of these trend findings were statistically significant by Mann-Kendall test. The results of the nested SOMs suggest that an increase in patterns of southerly flow (a common pattern for warmer temperatures) is occurring through time. A third SOM investigating early spring hard freezes (temperatures low enough to cause damage to plant cellular structures) was inconclusive but illustrated that some years had more early spring frosts than others independent of how many warmer than average periods occurred in the main winter. The use of SOMs for investigating climatological and synoptic changes in winter and early spring proved successful and effective. Future modifications to these SOMs could be used to identify more trends that exist within these seasons.

Yes / The emerging information revolution makes it necessary to manage vast amounts of unstructured data rapidly. As the world is increasingly populated by IoT devices and sensors that can sense their surroundings and communicate with each other, a digital environment has been created with vast volumes of volatile and diverse data. Traditional AI and machine learning techniques designed for deterministic situations are not suitable for such environments. With a large number of parameters required by each device in this digital environment, it is desirable that the AI is able to be adaptive and self-build (i.e. self-structure, self-configure, self-learn), rather than be structurally and parameter-wise pre-defined. This study explores the benefits of self-building AI and machine learning with unsupervised learning for empowering big data analytics for smart city environments. By using the growing self-organizing map, a new suite of self-building AI is proposed. The self-building AI overcomes the limitations of traditional AI and enables data processing in dynamic smart city environments. With cloud computing platforms, the selfbuilding AI can integrate the data analytics applications that currently work in silos. The new paradigm of the self-building AI and its value are demonstrated using the IoT, video surveillance, and action recognition applications. / Supported by the Data to Decisions Cooperative Research Centre (D2D CRC) as part of their analytics and decision support program and a La Trobe University Postgraduate Research Scholarship.

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.

Made available in DSpace on 2016-03-15T19:37:44Z (GMT). No. of bitstreams: 1 Raphael Leal Mendonca.pdf: 10106411 bytes, checksum: 1b61d6ad0eeb168109bf2774dd8b54fc (MD5) Previous issue date: 2012-08-14 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior / Digital games have been highlighted in recent years because of its high growth in the international market. On this basis, some elements forming the digital games can be separated for study, and thus contribute to the development of the entertainment area. One of this elements is the concept of immersion. When this is analyzed, it is realized that there is a range of different variables that may influence the relationship with the link between the game and the player. From this, this dissertation aimed to the development of an expert system that is capable of, based on data obtained from responses to a set of statements online, provide a preview of the degree of immersion of a digital game. To do this, it was done a characterization the relevant variables and using techniques of artificial intelligence, such as expert systems. In this scope, it was also considered how these variables influence the player s emotion about the game. From this results in the degree of immersion, it was analyzed with the aid of other Artificial Intelligence techniques, such as expert systems, fuzzy logic and Self-Organizing Maps, to maximize the effectiveness of the process. The study s definition of these parameters involves the exploitation of basic theoretical concepts of digital games and the variables already found in the previous research (narrative, audio, video, social relations, artificial intelligence and gameplay). With this, it was found as the immersion and the elements of the game can influence the emotions of the player. To conduct the survey, a questionnaire was designed for online data capture, which was filled by a group of 96 players, resulting in 133 completed questionnaires, with 672 sets of defined variables and 3406 completed statements. These data were then transferred to the applications of Fuzzy Logic and Self-Organizing Maps, which analyzed the relationship between emotion and immersion. From the data collected, analyzes of the influence of the elements of digital games Action/Adventure style were made, setting how immersion and emotion behave between digital games of the same series and style, of the same platform and the same game released on different platforms. The potential for a type of emotion to show up was set for each game and emotion groups were found for the Action/Adventure games, with the highlights of groups where emotions like Love, Hate, Pride and Shame are more likely to affect the players. With this results of the research, it is expected to enable new researches and the establishment of a possible referral to the definition of guidelines to cooperate with the activities of professional game developers, as well as establish new intersections between the area of digital games and psychology. / Jogos digitais tęm se destacado nos últimos anos por conta do seu elevado crescimento no mercado internacional. Tendo isto como base, alguns elementos que formam os jogos digitais podem ser separados para serem estudados e, assim, contribuírem para o desenvolvimento desta área de entretenimento. Um deles é o conceito de imersăo. Ao analisá-lo, percebeu-se que existe uma gama diferenciada de variáveis que podem influenciar a relaçăo deste com o enlace entre jogo e jogador. A partir disso, esta dissertaçăo teve como objetivo a elaboraçăo de um sistema especialista que é capaz de, com base em dados obtidos pelas respostas dadas a um conjunto de afirmaçőes on-line, apresentar uma visualizaçăo do grau de imersăo de um jogo digital. Para tal, foi feita a caracterizaçăo das variáveis relevantes e utilizaçăo de técnicas inteligęncia artificial, tal como sistemas especialistas. Neste escopo também foram consider adas como essas variáveis influenciam a emoçăo do jogador em relaçăo ao jogo. A partir disto os resultados em relaçăo ao grau de imersăo foram analisados com o auxílio de outras técnicas de Inteligęncia Artificial, como sistema especialista, lógica fuzzy e Self-Organizing Maps, para maximizar a eficácia do processo. O estudo da definiçăo destes parâmetros envolve a exploraçăo de conceitos teóricos básicos de jogos digitais e das variáveis já encontradas em uma pesquisa anterior (narrativa, áudio, vídeo, aspectos sociais, inteligęncia artificial e jogabilidade). Com isto, encontrou-se como a imersăo e os elementos do jogo podem exercer influęncia sobre os sentimentos do jogador. Para a realizaçăo da pesquisa, foi elaborado um questionário on-line para a captaçăo de dados, que foi preenchido por um grupo de 96 jogadores, resultando em 133 questionários completos, com 672 grupos de variáveis preenchidas e 3406 afirmaçőes definidas. Estes dados entăo foram repassados para os apl icativos com Sistema Especialista, Lógica Fuzzy e Self-Organizing Maps, que analisaram a relaçăo da imersăo com a emoçăo. A partir dos dados coletados, análises sobre a influęncia dos elementos dos jogos digitais do estilo Açăo/Aventura foram feitas, definindo como se comporta a imersăo e a emoçăo entre jogos digitais de uma série e estilo, de jogos de uma mesma plataforma e um mesmo jogo lançado em plataformas diferentes. O potencial de um tipo de emoçăo aparecer foi definido para cada jogo e grupos de emoçőes foram encontrados para os jogos de Açăo/Aventura, destacando grupos onde emoçőes como Amor, Ódio, Orgulho e Vergonha săo mais propensos a afetarem os jogadores. Com estes resultados, espera-se possibilitar novas investigaçőes e o estabelecimento de um possível encaminhamento para a definiçăo de diretrizes que colaborem com as atividades profissionais dos desenvolvedores de jogos, bem como estabelecer novas intersecçőes entre a área de jogos digitais e a de psicologia.