Using topology for feature analysis in flow fields faces several problems. First of all, not all features can be detected using topology based methods. Second, while in flow feature analysis the user is interested in a quantification of feature parameters like position, size, shape, radial velocity and other parameters of feature models, many of these parameters can not be determined using topology based methods alone. Additionally, in some applications it is advantageous to regard the vector field as a superposition of several, possibly simple, features. As topology based methods are quite sensitive to superposition effects, their precision and usability is limited in these cases. In this paper, topology based analysis and visualization of flow fields is estimated and compared to other feature based approaches demonstrating these problems.

Svenska Kraftnät (Svk) is responsible for ensuring that Sweden has a safe, sustainable, and cost-effective transmission system for electricity. In an effort to reduce costs, Svk has participated in a study where it has been determined that there are mostly costs for common facilities that stand out cost-wise. The goal of this master thesis is to identify and assess what factors influence maintenance costs for the substations in the Swedish national grid for electricity. Response feature modeling was applied on longitudinal data for the substations (N=53) for years 2017-2020 to obtain individual intercepts with a common slope for further analysis. The factors included in the global model were based on Pearson correlation analysis and consultation with experts. Further attempts to improve upon the global model were made based on a stepwise variable selection made by comparing AIC, a log-transformation of the response, and by applying expert knowledge to attain a subset of predictors. all the resulting models were significant (P<0.001) with the best model having an R2 of 0.8376. Predictions for a proposed substation was made for the first fifty years of lifespan. Predictors that were found significant in multiple models includes variables regarding substation size and age. Factors that were not significant in any model related to substation fence length and location amongst others. / Svenska kraftnät (Svk) är ansvariga för att försäkra att Sverige har ett säkert, hållbart och kostnadseffektivt transmissionsnät för elektricitet. I ett försök att minska kostnader deltog Svk i en studie där det fastslogs att det var främst kostnader för deras stamnnätsstationer som sticker ut kostnadsmässigt. Målet i den här uppsatsen är att identifiera och värdera faktorer som kan influera underhållskostnader för stamnätsstationerna i det Svenska stamnätet. Response feature modeling applicerades på longitudinell data (N=53) för åren 2017-2020. Individuella intercept skattades med en gemensam lutning för att användas vid vidare analys. Faktorerna som initialt inkluderades i den globala modellen var antingen inkluderade eller exkluderade genom en analys av Pearson korrelationer samt i rådfrågan med experter med sakkunskap. Vidare försök att förbättra den globala modellen gjordes genom att applicera en stegvis variable selection baserad på AIC. Samma process genomfördes efter en log-transformation av responsvariabeln. Slutligen skapades en ytterligare modell med en delmängd faktorer skapad av experter på Svk. Samtliga regressionsmodeller var signifikanta (P<0.001) där den bästa modellen med avseende på R2 hade värdet 0.8376. Skattningar av framtida kostnader för en föreslagen stations första femtio år av dess livsspann gjordes. Faktorer som var signifikanta (P<0.05) inkluderade variabler som beskrev stationsstorlek, ställverkstyp och stationsålder. Faktorer som inte var signifikanta i någon modell var bland annat faktorer med anknytning till geografiskt läge, staketlängd bland andra.

Fault feature analysis of gear tooth spall plays a vital role in gear fault diagnosis. Knowing the characteristic of fault features and their evolution as a gear tooth fault progresses is key to fault severity assessment. This thesis provides a comprehensive (both theoretical and experimental) analysis of the fault vibration features of a gear transmission with progressive localized gear tooth pitting and spalling. A dynamic model of a one-stage spur gear transmission is proposed to analyze the vibration behavior of a gear transmission with tooth fault. The proposed dynamic model considers the effects of Time Varying Mesh Stiffness (TVMS), tooth surface roughness changes and geometric deviations due to pitting and spalling, and also incorporates a time-varying load sharing ratio, as well as dynamic tooth contact friction forces, friction moments and dynamic mesh damping ratios. The gear dynamical model is validated by comparison with responses obtained from an experimental test rig under different load and fault conditions. In addition, several methods are proposed for the evaluation of the TVMS of a gear pair with tooth spall(s) with curved bottom and irregular shapes, which fills the current research gap on modelling tooth spalls with irregular shapes and randomly distribution conditions. Experiments are conducted and the fault vibration features and their evolution as the tooth fault progresses are analyzed. Based on feature analysis, a new health indicator is proposed to detect progressive localized tooth spall.

As the focus in southeastern archaeology shifts away from large scale hierarchical analyses in favor of agency based approaches, our understanding of Mississippian settlements has changed. This research is an attempt to fill the “fuzzy gap” in Mississippian archaeological literature left by decades of research premised on Neo-evolutionary models and theories. In this thesis, I present my case study on Riverfront Village, a small Mississippian “hamlet” located in the Savannah River Valley. Through an analysis of subterranean pit features, I present a new feature classification scheme open to variability, and address how variations within the practice of subterranean storage relate to social complexity.

The Prostate is a male gland that is located around the urethra. Prostate Cancer is the second most diagnosed malignancy in men over the age of fifty. Typically, prostate cancer is diagnosed from clinical data, medical images, and biopsy. <br /><br /> Computer Aided Diagnosis (CAD) was introduced to help in the diagnosis in order to assist in the biopsy operations. Usually, CAD is carried out utilizing either the clinical data, using data mining techniques, or using features extracted from either TransRectal UltraSound (TRUS) images or the Radio Frequency (RF) signals. <br /><br /> The challenge is that TRUS images' quality is usually poor compared to either Magnetic Resonance Imaging (MRI) or the Computed Tomography (CT). On the other hand, ultrasound imaging is more convenient because of its simple instrumentation and mobility capability compared to either CT or MRI. Moreover, TRUS is far less expensive and does not need certain settings compared to either MRI or CT. Accordingly; the main motivation of this research is to enhance the outcome of TRUS images by extracting as much information as possible from it. The main objective of this research is to implement a powerful noninvasive CAD tool that integrates all the possible information gathered from the TRUS images in order to mimic the expert radiologist opinion and even go beyond his visual system capabilities, a process that will in turn assist the biopsy operation. In this sense, looking deep in the TRUS images by getting some mathematical measures that characterize the image and are not visible by the radiologist is required to achieve the task of cancer recognition. <br /><br /> This thesis presents several comprehensive algorithms for integrated feature analysis systems for the purpose of prostate tissue classification. The proposed algorithm is composed of several stages, which are: First, the regions that are highly suspicious are selected using the proposed Gabor filter based ROI identification algorithm. <br /><br /> Second, the selected regions are further examined by constructing different novel as well as typical feature sets. The novel constructed feature sets are composed of statistical feature sets, spectral feature sets and model based feature sets. <br /><br /> Next, the constructed features were further analyzed by selecting the best feature subset that identifies the cancereus regions. This task is achieved by proposing different dimensionality reduction methods which can be categorized into: Classifier dependent feature selection (Mutual Information based feature selection), classifier independent feature selection, which is based mainly on tailoring the Artificial life optimization techniques to fit the feature selection problem and Feature Extraction, which transforms the data to a new lower dimension space without any degradation in the information and with no correlation among the transformed lower dimensional features. <br /><br /> Finally, the last proposed fragment in this thesis is the Spectral Clustering algorithm, which is applied to the TRUS images. Spectral Clustering is a novel fast algorithm that can be used in order to obtain a fast initial estimate of the cancer regions. Moreover, it can be used to support the decision obtained by the proposed cancer recognition algorithm. This decision support process is crucial at this stage as the gold standards used in obtaining the results shown in this thesis is mainly the radiologist's markings on the TRUS images. This gold standards is not considered as credible since the radiologist's best accuracy is approximately 65 %. <br /><br /> In conclusion, this thesis introduces different novel complete algorithms for automatic cancerous regions detection in the prostate gland utilizing TRUS images. These proposed algorithms complement each other in which the results obtained using either of the proposed algorithms support each other by resulting in the same classification accuracy, sensitivity and specificity. This result proves the remarkable quality of the constructed features as well as the superiority of the introduced feature selection and feature extraction methods to detect cancerous regions in the prostate gland.

The Prostate is a male gland that is located around the urethra. Prostate Cancer is the second most diagnosed malignancy in men over the age of fifty. Typically, prostate cancer is diagnosed from clinical data, medical images, and biopsy. <br /><br /> Computer Aided Diagnosis (CAD) was introduced to help in the diagnosis in order to assist in the biopsy operations. Usually, CAD is carried out utilizing either the clinical data, using data mining techniques, or using features extracted from either TransRectal UltraSound (TRUS) images or the Radio Frequency (RF) signals. <br /><br /> The challenge is that TRUS images' quality is usually poor compared to either Magnetic Resonance Imaging (MRI) or the Computed Tomography (CT). On the other hand, ultrasound imaging is more convenient because of its simple instrumentation and mobility capability compared to either CT or MRI. Moreover, TRUS is far less expensive and does not need certain settings compared to either MRI or CT. Accordingly; the main motivation of this research is to enhance the outcome of TRUS images by extracting as much information as possible from it. The main objective of this research is to implement a powerful noninvasive CAD tool that integrates all the possible information gathered from the TRUS images in order to mimic the expert radiologist opinion and even go beyond his visual system capabilities, a process that will in turn assist the biopsy operation. In this sense, looking deep in the TRUS images by getting some mathematical measures that characterize the image and are not visible by the radiologist is required to achieve the task of cancer recognition. <br /><br /> This thesis presents several comprehensive algorithms for integrated feature analysis systems for the purpose of prostate tissue classification. The proposed algorithm is composed of several stages, which are: First, the regions that are highly suspicious are selected using the proposed Gabor filter based ROI identification algorithm. <br /><br /> Second, the selected regions are further examined by constructing different novel as well as typical feature sets. The novel constructed feature sets are composed of statistical feature sets, spectral feature sets and model based feature sets. <br /><br /> Next, the constructed features were further analyzed by selecting the best feature subset that identifies the cancereus regions. This task is achieved by proposing different dimensionality reduction methods which can be categorized into: Classifier dependent feature selection (Mutual Information based feature selection), classifier independent feature selection, which is based mainly on tailoring the Artificial life optimization techniques to fit the feature selection problem and Feature Extraction, which transforms the data to a new lower dimension space without any degradation in the information and with no correlation among the transformed lower dimensional features. <br /><br /> Finally, the last proposed fragment in this thesis is the Spectral Clustering algorithm, which is applied to the TRUS images. Spectral Clustering is a novel fast algorithm that can be used in order to obtain a fast initial estimate of the cancer regions. Moreover, it can be used to support the decision obtained by the proposed cancer recognition algorithm. This decision support process is crucial at this stage as the gold standards used in obtaining the results shown in this thesis is mainly the radiologist's markings on the TRUS images. This gold standards is not considered as credible since the radiologist's best accuracy is approximately 65 %. <br /><br /> In conclusion, this thesis introduces different novel complete algorithms for automatic cancerous regions detection in the prostate gland utilizing TRUS images. These proposed algorithms complement each other in which the results obtained using either of the proposed algorithms support each other by resulting in the same classification accuracy, sensitivity and specificity. This result proves the remarkable quality of the constructed features as well as the superiority of the introduced feature selection and feature extraction methods to detect cancerous regions in the prostate gland.

An analysis that is based on the monitoring of dynamic features from in-situ sensors of an industrial PECVD tool is presented. Linear Discriminant Analysis is used to determine which features are the most sensitive to various changes in the tool condition. The concept of Confidence Values (CVs) is used to quantify statistical changes of these dynamic features as the condition of the tool changed. Two data sets were collected from a PECVD tool in the facilities of a well-known equipment supplier. Dynamic features coming from the RF plasma power and matching capacitors’ sensors are shown to be sensitive to various changes in the cleaning cycles for Si-N, Si-O₂, and TEOS depositions. Quantifying the statistical distributions of the sensitive sensor features during tool condition changes is important for determining which sensor features are necessary to monitor in order to predict the tool chamber health. Results show that these RF plasma sensors could be used to track changes inside the tool chamber. / text

Made available in DSpace on 2015-04-11T14:03:18Z (GMT). No. of bitstreams: 1 CLAYTON_SANTOS.pdf: 1942330 bytes, checksum: 433f37e09e23e95f9322abb48fc95a5d (MD5) Previous issue date: 2012-03-30 / CAPES - Coordenação de Aperfeiçoamento de Pessoal de Nível Superior / The popularization of Internet access has lead institutions and parents to face serious problems on preventing employees, as well as children, to have access to inappropriate content, such as pornographic pages. This kind of content is available in different forms, including videos, sounds, text, and especially images, on the Web. Since most of this inappropriate content is provided as images, it is necessary to employ strategies which allow the analysis of image content in order to control access to inappropriate content. In this context, nudity detection in images plays an important role. Several approaches apply skin detection as a key step toward nudity detection. Skin detection is a difficult task due to the fact that it is necessary to use skin filters robust to shade variations caused by light. In addition, these methods employ a combination of features based on color, texture and shape, which may increase the complexity and time processing of detection algorithms. In despite of this drawback, feature analysis, or selection, is not carried out in most of the work available in the literature, The objective of this work is to investigate the features most frequently used in the literature for the description of nude images, as well as to select the most relevant subset of features taking into account classification accuracy. The feature analysis is carried out through three series of experiments focusing on investigating different scenarios of comparison. In the first series, we compare features extracted without applying skin filter, called global evidence in this work. In the second series, features extracted after skin filter are also compared. These features are called local evidence. Finally, in the third series of experiments, a zoning algorithm is used in order to allow us to analyze the impact of both local and global features in each area of the image. In all series of experiments, each feature is analyzed individually and all subsets of features are tested so as to determinate the best tradeoff between feature set and classification accuracy. In addition, an architecture called ANDImage (Architecture for Nude Detection in Image) is proposed. ANDImage allows that different modules may be used in order to provide the possibility of dealing with different scenarios of features comparison. / Com a popularização do acesso a Internet, instituições e pais têm encontrado sérios problemas para evitar o acesso de funcionários e crianças a conteúdos impróprios como páginas pornográficas. Na Web, este tipo de conteúdo pode estar disponibilizado em forma de vídeos, sons, texto e, principalmente em forma de imagens. Como a maior parte do conteúdo impróprio está disponível através de imagens, faz-se necessária a utilização de mecanismos que possibilitem analisar o conteúdo da imagem para combater este tipo de abuso. Nesse contexto, a detecção de nudez em imagens é normalmente uma etapa importante. Diversas abordagens aplicam detecção de pele como passo fundamental para a detecção de nudez. Esta tarefa não é trivial, uma vez que há a necessidade de uso de filtros de pele robustos a variações de tonalidades ocasionadas por luminosidade. Adicionalmente, é utilizada uma combinação de características baseadas em cor, textura e formas, que podem ocasionar um aumento indesejado na complexidade e no tempo de processamento dos algoritmos de detecção. Apesar dessa desvantagem, em muitos trabalhos disponíveis na literatura, não é realizada uma análise da relevância das características envolvidas no processo de classificação das imagens. O objetivo desta dissertação é investigar as principais características para descrição de imagens de nudez e selecionar as que obtiverem maior relevância em função da precisão do classificador. A análise das características é feita através de um conjunto de séries de experimentos que representam diferentes cenários de comparação. Primeiramente, são feitas comparações entre características extraídas sem aplicação de filtro de pele, denominadas propriedades globais. Em seguida, características extraídas a partir da aplicação do filtro de pele são também comparadas. Essas características são denominadas propriedades locais. Em uma terceira série de experimentos, um algoritmo de zoneamento é utilizado para que seja analisado o impacto das características, tanto locais como globais, em cada zona da imagem. Em todas as séries de experimentos, cada característica é analisada tanto de forma isolada, quanto em subconjuntos, para que seja determinado o melhor compromisso entre o conjunto de características e o desempenho do classificador. Para isso, é utilizada uma arquitetura denominada ANDImage (Architecture for Nude Detection in Image), que permite a inclusão e exclusão desses diferentes módulos.

The aims of this study were to determine if various treatment, item, and patient-level factors could be used to predict probe naming accuracy in a bilingual Spanish-English SFA treatment study. At the treatment-level, variables included phase (baseline vs. treatment), training condition (trained set 1 items vs. translations), and time (session). At the item-level, psycholinguistic variables were investigated including lexical frequency, phonological length in phones, and phonological neighborhood density. Finally, at the patient-level, impairment measures were used including aphasia severity (as measured by WAB AQ) naming impairment (represented by a composite naming score from pre-treatment assessments). Mixed-effects logistic regression methods were used to fit the data with fixed effects for the variables of interest as well as random effects for subject and item. The regression analyses revealed significant main effects of phase, time, and interactions with training condition such that naming accuracy on probes was higher for the treatment language during the treatment phase and over time in general. Significant effects were also noted for each of the psycholinguistic variables such that increased frequency, shorter length, and a larger neighborhood increased the likelihood of correct naming responses. Finally, overall aphasia severity and naming impairment both correlated with naming outcomes.

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