This work aims to improve the capability of accurate information extraction from high-dimensional data, with a specific neural learning paradigm, the Self-Organizing Map (SOM). The SOM is an unsupervised learning algorithm that can faithfully sense the manifold structure and support supervised learning of relevant information from the data. Yet open problems regarding SOM learning exist. We focus on the following two issues. 1. Evaluation of topology preservation. Topology preservation is essential for SOMs in faithful representation of manifold structure. However, in reality, topology violations are not unusual, especially when the data have complicated structure. Measures capable of accurately quantifying and informatively expressing topology violations are lacking. One contribution of this work is a new measure, the Weighted Differential Topographic Function (WDTF), which differentiates an existing measure, the Topographic Function (TF), and incorporates detailed data distribution as an importance weighting of violations to distinguish severe violations from insignificant ones. Another contribution is an interactive visual tool, TopoView, which facilitates the visual inspection of violations on the SOM lattice. We show the effectiveness of the combined use of the WDTF and TopoView through a simple two-dimensional data set and two hyperspectral images. 2. Learning multiple latent variables from high-dimensional data. We use an existing two-layer SOM-hybrid supervised architecture, which captures the manifold structure in its SOM hidden layer, and then, uses its output layer to perform the supervised learning of latent variables. In the customary way, the output layer only uses the strongest output of the SOM neurons. This severely limits the learning capability. We allow multiple, k, strongest responses of the SOM neurons for the supervised learning. Moreover, the fact that different latent variables can be best learned with different values of k motivates a new neural architecture, the Conjoined Twins, which extends the existing architecture with additional copies of the output layer, for preferential use of different values of k in the learning of different latent variables. We also automate the customization of k for different variables with the statistics derived from the SOM. The Conjoined Twins shows its effectiveness in the inference of two physical parameters from Near-Infrared spectra of planetary ices.

No description available.

No description available.

Neighboring northern Kaohsiung with a dense population of petrochemical and petroleum industrial complexes included China Petroleum Company (CPC) refinery plant, Renwu and Dazher petrochemical industrial plants. In recent years, although many scholars have conducted regional studies, but are still limited by the lack of relevant information evidences (such as odorous matters identification and VOCs fingerprint database), while unable to clearly identify the causes of poor ambient air quality. By sampling and analyzing VOCs, we will be able to understand the major sources of VOCs in northern Kaohsiung and their contribution, and to provide the air quality management and control countermeasures for local environmental protection administration. In this study, we sampled and analyzed the speciation of VOCs and sulfur-containing odorous matters (SOMs) in the CPC refinery plants, Renwu and Dazher petrochemical complexes simultaneously with stack sampling. The sampling of VOCs and SOMs were conducted on January 7th, 14th, and 19th, 2011 (dry season) and May 6th, 13rd, and 23rd, 2011 (wet season). We established the emission source database, investigated the characteristics of VOC fingerprints, and estimate the emission factor of each stack. It helps us understand the temporal and spatial distribution of VOCs and ascertain major sources and their contribution of VOCs. Major VOCs emitted from the stacks of the CPC refinery plant were toluene and acetone. It showed that petroleum refinery processes had similar VOCs characteristics and fingerprints. The fingerprints of stack emissions at Renwu and Dashe industrial complexes varied with their processes. Hydrogen sulfide was the major sulfur-containing odorous matter in all petrochemical plants. Compared to other petrochemical complexes, Renwu industrial complex emitted a variety of SOMs species as well as relatively high concentrations of sulfur-containing odorous matters. The petrochemical industrial complexes in the industrial ambient of VOCs analysis results showed that isobutane, butane, isopentane, pentane, propane of alkanes, propene of alkenes, toluene, ethylbenzene, xylene, styrene of aromatics, 2-Butanone (MEK), acetone, of carbonyls are major species of VOCs. In addition, ethene+acetylene+ethane (C2), 1,2-dichloroethane, chloromethane, dichloromethane, MTBE were also occasionally found. Sulfur-containing odorous matter (SOMs) analytical results showed that major odorous matters included hydrogen sulfide, methanethiol, dimethyl sulfide, and carbon disulfide. The highest hydrogen sulfide concentration went up to 5.5 ppbv. In this study, the species of VOCs were divided into alkanes, alkenes, aromatics, carbonyls, and others. The temporal and spatial distribution of various types of VOCs strongly correlated with near-surface wind direction. The most obvious contaminants were alkanes, aromatics, and carbonyls of the dispersion to the downwind. Generally, the ambient air surrounding the petrochemical industrial complexes was influenced by various pollutants in the case of high wind speeds. It showed that stack emission and fugitive sources had an important contribution to ambient air quality. TSOMs and hydrogen sulfide emitting mainly from local sources resulted in high concentration of TSOMs and hydrogen sulfide surrounding the petrochemical industrial complex. Principal component analysis (PCA) results showed that the surrounding areas of petrochemical industrial complexes, regardless of dry or wet seasons, were mainly influenced by the process emissions and solvent evaporation. The impact of traffic emission sources ranked the second. Chemical mass balance receptor modeling showed that stack emissions from the CPC refinery plants contributed about 48 %, while fugitive emission sources and mobile sources contributed about 30 % and 11%, respectively. The stack emissions from Renwu industrial complex contributed about 75 %, while fugitive emission sources and mobile sources contributed about 17 % and 5 %, respectively. The stack emissions from Dazher industrial complex contributed about 68 %, while fugitive emission sources and mobile sources contributed about 21 % and 2 %, respectively.

Made available in DSpace on 2014-06-12T16:00:25Z (GMT). No. of bitstreams: 2 arquivo6557_1.pdf: 4867823 bytes, checksum: 64578a5cde42f460f0745045ec1bb555 (MD5) license.txt: 1748 bytes, checksum: 8a4605be74aa9ea9d79846c1fba20a33 (MD5) Previous issue date: 2007 / Conselho Nacional de Desenvolvimento Científico e Tecnológico / Neste trabalho apresentamos um novo modelo neural para segmentação de imagens, baseado nos Mapas Auto-organizáveis SOM (Mapa Auto-organizável - Self-organizing Map) e GWR (Crescer Quando Requerido - Grow When Required) chamado de LARFSOM (Mapa Auto-organizável com Campo Receptivo Adaptativo Local - Local Adaptive Receptive Field Self-organizing Map). As características principais do modelo são: número adaptativo de nodos, topologia variável, inserção de novos nodos baseada em uma medida de similaridade dos protótipos existentes em relação ao padrão de entrada aferida por meio de campo receptivo, remoção de nodos com informações não significativas ao final do treinamento, rápida convergência e baixo custo de processamento para o treinamento. A rede LARFSOM é capaz de segmentar imagens por cor ou por borda: a primeira, é feita através do agrupamento de informações ocorrido no treinamento da rede LAFRSOM seguido de um processo de quantização de cores; já a segunda, ocorre pelo acréscimo de dois nodos RBF (Função de Base Radial - Radial Basis Function) à rede LARFSOM, criando um modelo de dois estágios chamado LARFSOM-RBF. Adicionalmente, o modelo é capaz de salvar em um formato variante do BMP indexado tanto a rede treinada como as informações espaciais dos pixels da imagem. Acrescido de compactação tipo ZIP o arquivo a ser salvo torna-se bem reduzido. Comparações com outros modelos neurais como o SOM, FS-SOM (Mapa Auto-organizável Sensível à Freqüência - Frequency Sensitive Self-organizing Map) e GNG (Gás Neural Crescente - Growing Neural Gas) são feitas mediante segmentação de imagens do mundo real com diferentes níveis de complexidade. Técnicas de processamento de imagens e o formato JPEG são usados para fins de comparação. Os resultados mostram que a rede LARFSOM atinge maior variação de cores da paleta e melhor distribuição espacial 3D RGB das cores selecionadas que os demais modelos. A qualidade das imagens geradas também figura entre os melhores resultados obtidos

No description available.

No description available.

Σκοπός της παρούσας διδακτορικής διατριβής είναι η ανάπτυξη αλγορίθμων επεξεργασίας ιατρικής εικόνας για την ενσωμάτωση τους σε ιατρικές εφαρμογές ακτινοθεραπευτικού ενδιαφέροντος. Οι αλγόριθμοι αυτοί στηρίζονται στην αρχή λειτουργίας των χαρτών αυτο-οργάνωσης Kohonen και αξιοποιούν την πληροφορία που περιέχεται σε περιοχές των εικόνων γύρω από σημεία ενδιαφέροντος, ώστε να εντοπίσουν αυτόματα, με ακρίβεια και αξιοπιστία, αντιστοιχίες μεταξύ των εικόνων. Πιο συγκεκριμένα, ένας επαναληπτικός αλγόριθμος προτείνεται για την αυτόματη εύρεση αντίστοιχων σημείων σε ιατρικές εικόνες δύο διαστάσεων. Ο προτεινόμενος αλγόριθμος προϋποθέτει την εύρεση σημείων ενδιαφέροντος μόνο στη μια από τις δύο εικόνες και εντοπίζει τα αντίστοιχα σημεία στη δεύτερη εικόνα μέσα από μια επαναληπτική διαδικασία, η οποία προσομοιάζει τη φάση εκπαίδευσης του νευρωνικού δικτύου. Με βάση τα ζεύγη των αντίστοιχων σημείων, υπολογίζονται στη συνέχεια οι παράμετροι ενός μετασχηματισμού, κατάλληλου για να περιγράψει τη σχέση μεταξύ των δεδομένων εικόνων. Ο αλγόριθμος ευθυγράμμισης εφαρμόζεται σε δεδομένες εικόνες ηλεκτρονικής πυλαίας απεικόνισης (Electronic Portal Images), που λαμβάνονται πριν από κάθε συνεδρία της ακτινοθεραπείας, για τον υπολογισμό του σφάλματος τοποθέτησης του ασθενούς. Το ζήτημα της επαλήθευσης της θέσης του ασθενούς στην ακτινοθεραπεία αντιμετωπίζεται επίσης με τη βοήθεια μιας αυτόματης μεθόδου εύρεσης αντίστοιχων σημείων σε τρισδιάστατα δεδομένα, η οποία εφαρμόζεται για την ευθυγράμμιση της αξονικής τομογραφίας του σχεδιασμού της ακτινοθεραπείας και μιας αξονικής τομογραφίας επαλήθευσης, που λαμβάνεται πριν την πρώτη συνεδρία της ακτινοθεραπείας. Ο προτεινόμενος αλγόριθμος εντοπίζει αντίστοιχα σημεία ενδιαφέροντος στις δεδομένες τομογραφικές εικόνες και υπολογίζει τις παραμέτρους ενός μη γραμμικού μετασχηματισμού ευθυγράμμισης. Μετά την ευθυγράμμιση των δύο τομογραφιών, υπολογίζεται η μετατόπιση του ισοκέντρου στην τομογραφία επαλήθευσης σε σχέση με τη θέση του ισοκέντρου που προβλέπεται στην αρχική τομογραφία του σχεδιασμού. Με την ενσωμάτωση αυτής της μεθόδου ευθυγράμμισης στη διαδικασία της ακτινοθεραπείας, ικανοποιούνται δύο ανάγκες της κλινικής πρακτικής. Αφενός, η μετατόπιση του ισοκέντρου, όπως υπολογίζεται από την προτεινόμενη μέθοδο, παρέχει μια αξιόπιστη ένδειξη για τη μετατόπιση του ασθενούς που απαιτείται πριν τη χορήγηση της ακτινοβολίας. Αφετέρου, επιχειρείται η καλύτερη αξιοποίηση των πόρων του τμήματος της ακτινοθεραπείας με τη διαδικασία της εύρεσης του ισοκέντρου της ακτινοθεραπείας να λαμβάνει χώρα στην αίθουσα του αξονικού τομογράφου και να μειώνεται συνεπώς ο χρόνος που απαιτείται για την προετοιμασία του ασθενούς στον γραμμικό επιταχυντή κατά την πρώτη συνεδρία της ακτινοθεραπείας. / Aim of the present thesis is the development of image processing algorithms for radiotherapy applications. These algorithms are based on the principles of Kohonen Self Organizing Maps and exploit the information contained in image regions around distinctive points of interest, in order to determine image correspondences in an automatic, accurate and robust way. In particular, an iterative algorithm is proposed for automatic detection of point correspondences in two-dimensional medical images. The proposed algorithm requires the extraction of interest points only in one image and detects the homologous points in the second image through an iterative procedure, respective to the training phase of a neural network. Subsequently, the parameters of an appropriate registration transformation are computed to describe the mapping between the two images. The computation is based on the detected point correspondence. The proposed registration algorithm is applied to Electronic Portal Images, acquired prior to the radiotherapy treatment delivery, in order to estimate the setup error of the patient. The issue of patient position verification in radiotherapy is also addressed in the present thesis by developing an algorithm for automatic detection of point correspondences in three-dimensional medical data. The algorithm is used to register the CT data of radiotherapy planning to an additional verification CT, acquired prior to the first treatment fraction. The proposed algorithm detects corresponding points in the two CT images and computes the parameters of a non-rigid registration transformation. After the registration of the two CT images, the isocenter displacement of the verification CT is calculated with respect to the ideal isocenter position, defined in the planning CT. By integrating the proposed registration procedure in the clinical practice, two needs are met. Firstly, the isocenter displacement, calculated by the proposed method, provides a reliable indication of the patient shift, needed before the treatment delivery, for optimization of the dose delivery. Secondly, an improvement of the radiotherapy department efficiency is attempted by performing the procedure of isocenter marking in the CT scanner room and, consequently, reducing the time expenditure of the patient in the LINAC during the first radiotherapy fraction.

Submitted by Fabio Sobreira Campos da Costa (fabio.sobreira@ufpe.br) on 2017-04-24T15:04:03Z No. of bitstreams: 2 license_rdf: 1232 bytes, checksum: 66e71c371cc565284e70f40736c94386 (MD5) mapas-auto-organizaveis2.pdf: 2835656 bytes, checksum: 8836a86bd2cced9353cb25b53383b305 (MD5) / Made available in DSpace on 2017-04-24T15:04:03Z (GMT). No. of bitstreams: 2 license_rdf: 1232 bytes, checksum: 66e71c371cc565284e70f40736c94386 (MD5) mapas-auto-organizaveis2.pdf: 2835656 bytes, checksum: 8836a86bd2cced9353cb25b53383b305 (MD5) Previous issue date: 2016-08-16 / Métodos e algoritmos em aprendizado de máquina não supervisionado têm sido empregados em diversos problemas significativos. Uma explosão na disponibilidade de dados de várias fontes e modalidades está correlacionada com os avanços na obtenção, compressão, armazenamento, transferência e processamento de grandes quantidades de dados complexos com alta dimensionalidade, como imagens digitais, vídeos de vigilância e microarranjos de DNA. O agrupamento se torna difícil devido à crescente dispersão desses dados, bem como a dificuldade crescente em discriminar distâncias entre os pontos de dados. Este trabalho apresenta um algoritmo de agrupamento suave em subespaços baseado em um mapa auto-organizável (SOM) com estrutura variante no tempo, o que significa que o agrupamento dos dados pode ser alcançado sem qualquer conhecimento prévio, tais como o número de categorias ou a topologia dos padrões de entrada, nos quais ambos são determinados durante o processo de treinamento. O modelo também atribui diferentes pesos a diferentes dimensões, o que implica que cada dimensão contribui para o descobrimento dos aglomerados de dados. Para validar o modelo, diversos conjuntos de dados reais foram utilizados, considerando uma diversificada gama de contextos, tais como mineração de dados, expressão genética, agrupamento multivista e problemas de visão computacional. Os resultados são promissores e conseguem lidar com dados reais caracterizados pela alta dimensionalidade. / Unsupervised learning methods have been employed on many significant problems. A blast in the availability of data from multiple sources and modalities is correlated with advancements in how to obtain, compress, store, transfer, and process large amounts of complex high-dimensional data, such as digital images, surveillance videos, and DNA microarrays. Clustering becomes challenging due to the increasing sparsity of such data, as well as the increasing difficulty in discriminating distances between data points. This work presents a soft subspace clustering algorithm based on a self-organizing map (SOM) with time-variant structure, meaning that clustering data can be achieved without any prior knowledge such as the number of categories or input data topology, in which both are determined during the training process. The model also assigns different weights to different dimensions, this implies that every dimension contributes to uncover clusters. To validate the model, we used a number of real-world data sets, considering a diverse range of contexts such as data mining, gene expression, multi-view and computer vision problems. The promising results can handle real-world data characterized by high dimensionality.

The Internet of Things (IoT) is a concept that is getting a lot of attention. IoT devices are growing in popularity and so is the need to protect these devices from attacks and vulnerabilities. Future developers and users of IoT devices need to know what type of devices need extra care and which are more likely to be vulnerable. Therefore this study has researched the correlations between combinations of protocols and software vulnerabilities. Fifteen protocols used by common services over the internet were selected to base the study around. Then an artificial neural network was used to group the devices into 4 groups based on which of these fifteen protocols were running. Publicly disclosed vulnerabilities were then enumerated for all devices in each group. It was found that the percentage of vulnerable devices in each group differed meaning there is some correlation between running combinations of protocols and how likely a device is vulnerable. The severity of the vulnerabilities in the vulnerable devices were also analyzed but no correlation was found between the groups. / Sakernas internet eller Internet of things (IoT) är ett koncept som fått mycket uppmärksamhet. IoT enheter växer drastisk i popularitet, därför är det mer nödvändigt att skydda dessa enheter från attacker och säkerhetsbrister. Framtida utvecklare och användare av IoT system behöver då veta vilka enheter som är mer troliga att ha säkerhetsbrister. Denna studie har utforskat om det finns något samband mellan kombinationer av aktiva protokoll i enheter och säkerhetsbrister. Femton vanligt använda protokoll valdes som bas för studien, ett artificiellt neuralt nätverk användes sedan för att gruppera enheter baserat på dessa protokoll. Kända sårbarheter i enheterna räknades upp för varje grupp. En korrelation mellan kombinationer av protokoll och trolighet för sårbarheter hittades. Allvarlighetsgraden av säkerhetsbristerna i sårbara enheter analyserades också, men ingen korrelation hittades mellan grupperna.