Recurrent neural network based approach for estimating the dynamic evolution of grinding process variables

170 p.El proceso de rectificado es ampliamente utilizado para la fabricación de componentes de precisión por arranque de viruta por sus buenos acabados y excelentes tolerancias. Así, el modelado y el control del proceso de rectificado es altamente importante para alcanzar los requisitos económicos y de precisión de los clientes. Sin embargo, los modelos analíticos desarrollados hasta ahora están lejos de poder ser implementados en la industria. Es por ello que varias investigaciones han propuesto la utilización de técnicas inteligentes para el modelado del proceso de rectificado. Sin embargo, estas propuestas a) no generalizan para nuevas muelas y b) no tienen en cuenta el desgaste de la muela, efecto esencial para un buen modelo del proceso de rectificado. Es por ello que se propone la utilización de las redes neuronales recurrentes para estimar variables del proceso de rectificado que a) sean capaces de generalizar para muelas nuevas y b) que tenga en cuenta el desgaste de la muela, es decir, que sea capaz de estimar variables del proceso de rectificado mientras la muela se va desgastando. Así, tomando como base la metodología general, se han desarrollado sensores virtuales para la medida del desgaste de la muela y la rugosidad de la pieza, dos variables esenciales del proceso de rectificado. Por otro lado, también se plantea la utilización la metodología general para estimar fuera de máquina la energía específica de rectificado que puede ayudar a seleccionar la muela y los parámetros de rectificado por adelantado. Sin embargo, una única red no es suficiente para abarcar todas las muelas y condiciones de rectificado existentes. Así, también se propone una metodología para generar redes ad-hoc seleccionando unos datos específicos de toda la base de datos. Para ello, se ha hecho uso de los algoritmos Fuzzy c-Means. Finalmente, hay que decir que los resultados obtenidos mejoran los existentes hasta ahora. Sin embargo, estos resultados no son suficientemente buenos para poder controlar el proceso. Así, se propone la utilización de las redes neuronales de impulsos. Al trabajar con impulsos, estas redes tienen inherentemente la capacidad de trabajar con datos temporales, lo que las hace adecuados para estimar valores que evolucionan con el tiempo. Sin embargo, estas redes solamente se usan para clasificación y no predicción de evoluciones temporales por la falta de métodos de codificación/decodificación de datos temporales. Así, en este trabajo se plantea una metodología para poder codificar en trenes de impulsos señales secuenciales y poder reconstruir señales secuenciales a partir de trenes de impulsos. Esto puede llevar a en un futuro poder utilizar las redes neuronales de impulsos para la predicción de secuenciales y/o temporales

[18F]FET-PET brain image segmentation using k-means: Evaluation of five cluster validity indices

Purpose: Dynamic [18F]fluoro-ethyl-L-tyrosine positron emission tomography ([18F]FET-PET) is used to identify tumor lesions for radiotherapy treatment planning, to differentiate glioma recurrence from radiation necrosis and to classify gliomas grading. To segment different regions in the brain k-means cluster analysis can be used. The main disadvantage of k-means is that the number of clusters must be pre-defined. In this study, we therefore compared different cluster validity indices for automated and reproducible determination of the optimal number of clusters based on the dynamic PET data. Methods: The k-means algorithm was applied to dynamic [18F]FET-PET images of 8 patients. Akaike information criterion (AIC), WB, I, modified Dunn’s and Silhouette indices were compared on their ability to determine the optimal number of clusters based on requirements for an adequate cluster validity index. To check the reproducibility of k-means, the coefficients of variation CVs of the objective function values OFVs (sum of squared Euclidean distances within each cluster) were calculated using 100 random centroid initialization replications RCI100 for 2 to 50 clusters. k-means was performed independently on 3 neighboring slices containing tumor for each patient to investigate the stability of the optimal number of clusters within them. To check the independence of the validity indices on the number of voxels, cluster analysis was applied after duplication of a slice selected from each patient. CVs of index values were calculated at the optimal number of clusters using RCI100 to investigate the reproducibility of the validity indices. To check if the indices have a single extremum, visual inspection was performed on the replication with minimum OFV from RCI100. Results: The maximum CV of OFVs was 2.7×10-2 from all patients. The optimal number of clusters given by modified Dunn’s and Silhouette indices was 2 or 3 leading to a very poor segmentation. WB and I indices suggested in median 5, [range 4-6] and 4, [range 3-6] clusters, respectively. For WB, I, modified Dunn’s and Silhouette validity indices the suggested optimal number of clusters was not affected by the number of the voxels. The maximum coefficient of variation of WB, I, modified Dunn’s, and Silhouette validity indices were 3×10-2, 1, 2×10-1 and 3×10-3 respectively. WB-index showed a single global maximum, whereas the other indices showed also local extrema. Conclusion: From the investigated cluster validity indices, the WB-index is best suited for automated determination of the optimal number of clusters for [18F]FET-PET brain images for the investigated image reconstruction algorithm and the used scanner: it yields meaningful results allowing better differentiation of tissues with higher number of clusters, it is simple, reproducible and has an unique global minimum

A Data Science Approach to Extracting Insights About Cities and Zones Using Open Government Data

VitaIncludes bibliographical references (pages 50-52)Thesis (M.S.)--School of Computing and Engineering. University of Missouri--Kansas City, 2017Thesis advisor: Praveen R. RaoTitle from PDF of title page viewed October 30, 2017In this research, we introduce a system that utilizes open government data and machine learning algorithms to extract meaningful insights about cities and zones in the United States. It is estimated that 4% of the world’s population occupies the United States of America. Remarkably, the US is considered the largest country to host prominent websites on the internet [16]. It is estimated that 43% of the top one million websites in the world are hosted in the United States (see Figure 1); promoting it as the largest influential country in producing data on the web (followed by Germany hosting only 8%) [16]. Although most data content on the web is unstructured, the US government adopted the initiative to release structured data related to different fields such as health, education, safety, development and finance. Such datasets are referred to as Open Government Data (OGD) and are aimed at increasing the transparency and accountability of the US government. Our aim is to provide a well-defined procedure to process raw OGD information and produce expressive insights regarding different zones within a city, differences between cities, or differences among zones located in different cities.Introduction -- Approach and method -- Evaluation and results -- Conclusion and future wor

Recurrent neural network based approach for estimating the dynamic evolution of grinding process variables

170 p.El proceso de rectificado es ampliamente utilizado para la fabricación de componentes de precisión por arranque de viruta por sus buenos acabados y excelentes tolerancias. Así, el modelado y el control del proceso de rectificado es altamente importante para alcanzar los requisitos económicos y de precisión de los clientes. Sin embargo, los modelos analíticos desarrollados hasta ahora están lejos de poder ser implementados en la industria. Es por ello que varias investigaciones han propuesto la utilización de técnicas inteligentes para el modelado del proceso de rectificado. Sin embargo, estas propuestas a) no generalizan para nuevas muelas y b) no tienen en cuenta el desgaste de la muela, efecto esencial para un buen modelo del proceso de rectificado. Es por ello que se propone la utilización de las redes neuronales recurrentes para estimar variables del proceso de rectificado que a) sean capaces de generalizar para muelas nuevas y b) que tenga en cuenta el desgaste de la muela, es decir, que sea capaz de estimar variables del proceso de rectificado mientras la muela se va desgastando. Así, tomando como base la metodología general, se han desarrollado sensores virtuales para la medida del desgaste de la muela y la rugosidad de la pieza, dos variables esenciales del proceso de rectificado. Por otro lado, también se plantea la utilización la metodología general para estimar fuera de máquina la energía específica de rectificado que puede ayudar a seleccionar la muela y los parámetros de rectificado por adelantado. Sin embargo, una única red no es suficiente para abarcar todas las muelas y condiciones de rectificado existentes. Así, también se propone una metodología para generar redes ad-hoc seleccionando unos datos específicos de toda la base de datos. Para ello, se ha hecho uso de los algoritmos Fuzzy c-Means. Finalmente, hay que decir que los resultados obtenidos mejoran los existentes hasta ahora. Sin embargo, estos resultados no son suficientemente buenos para poder controlar el proceso. Así, se propone la utilización de las redes neuronales de impulsos. Al trabajar con impulsos, estas redes tienen inherentemente la capacidad de trabajar con datos temporales, lo que las hace adecuados para estimar valores que evolucionan con el tiempo. Sin embargo, estas redes solamente se usan para clasificación y no predicción de evoluciones temporales por la falta de métodos de codificación/decodificación de datos temporales. Así, en este trabajo se plantea una metodología para poder codificar en trenes de impulsos señales secuenciales y poder reconstruir señales secuenciales a partir de trenes de impulsos. Esto puede llevar a en un futuro poder utilizar las redes neuronales de impulsos para la predicción de secuenciales y/o temporales

Fuzzy Logic

The capability of Fuzzy Logic in the development of emerging technologies is introduced in this book. The book consists of sixteen chapters showing various applications in the field of Bioinformatics, Health, Security, Communications, Transportations, Financial Management, Energy and Environment Systems. This book is a major reference source for all those concerned with applied intelligent systems. The intended readers are researchers, engineers, medical practitioners, and graduate students interested in fuzzy logic systems

Household Management of Endoparasitic Infection in a Border Community in Tamaulipas, Mexico

Fecal samples from 438 children in 217 families were examined for helminth eggs/larve and protozoan cysts to study the occurrence of parasitic infection and household knowledge of cholera preventive measures in a border community in Tamaulipas, Mexico. The age of the children ranged from 1 month to 16 years. Parasitic infections occurred in 30% of children residing in 79 of 217 households. Giardia lamblia accounted for 12.5% of all infections. Other endoparasitic species found in the children were: Hymenolepis nana, (28/438), Ascaris lumbricoides (16/438), Trichuris trichiura (6/438), Enterobius vermicularis (6/438), Ancylostoma-Necator (1/438),Strongyloides sercoralis (1/438), Entamoeba coli (27/438), Ent. hartmanni (24/438), Ent. histolytica (1/438), Endolimax nana (23/438), and Iodamoeba buetschlii (10/438). Infected children were older (mean age 6.32 years, p = .05) and often had infected sibling (odds ratio = 2.88, p \u3c .01). Households with ≥ 3 children were also more likely to have an infected child (odds ratio = 4.76, p \u3c .01). Household knowledge and attitudes about infection and prevention of endoparasites and cholera was also collected with a structured interview schedule. Knowledge of cholera preventive measures was found in 72% of parasite infected households, and 81% of uninfected households. Parasite infected households were distinguished by their inability to list ≥ 3 cholera prevention measures (odds ratio = 2.19, p \u3c .01). Informants from these households were more inclined to accept parasitic infections as a consequence of everyday life and recognize that the adverse effects of parasitism can be controlled with highly effective medicines. In contrast, informants from uninfected households shared a model which regarded parasitic infection in children as a preventable health effect. Public health interventions that emphasize the economic benefits of preventing rather than curing parasitic infections and other hygiene-related diseases can be an important adjunct to existing community health programs aimed at reducing childhood morbidity and mortality. The relatively low frequency of endoparasitism in communities such as the one investigated in this research is encouragement that higiene es salud (hygiene is health) and that achievement of community-wide reductions in parasite-related morbidity is a public health problem for which there is a solution

Studies on the Invasion Biology of Social Insects

Social insects (e.g., ants, termites) are among the most prolific group of invasive organisms worldwide. The rapid expansion of both their ecological (i.e., habitats) and global (i.e., countries/continents) distributions has likely been facilitated by the world’s most successful invader – humankind. Therefore, for my PhD research, I performed several investigations into the invasions of social insects to gain further insight into how their invasions have been and are currently being shaped in the Anthropocene. For my first study, I compiled a comprehensive dataset of termite interceptions at US ports of entry spanning the years 1923 to 2017 to elucidate broad patterns in the spread invasive termites to the US. My main findings included a strong regional bias in both the origin (i.e., country/continent) and destination (i.e., port of entry/US region) of interceptions and convincing evidence that invasive termites utilize bridgeheads (i.e., previously invaded locations) to expand their global range. In my next two studies, I reconstructed the invasion histories of two prominent invasive termites – Coptotermes formosanus (native to East Asia) and Reticulitermes flavipes (native to North America). By leveraging existing sample sets previously collected from a large geographic range (i.e., both native and invasive ranges), robust genetic datasets, and approximate Bayesian computation, I inferred a complex invasion history for both species, with multiple invasions from their respective native ranges occurring in conjunction with bridgehead invasions (i.e., invasions originating from a non-native locality). For my final study, I examined Tapinoma sessile’s (odorous house ant) invasion of the urban environment (i.e., cities) from its native natural environments (e.g., forests) across the US. By integrating genetic, chemical, and behavioral data, I discovered strong differentiation between urban and natural populations of the ant in each locality, suggesting cities may be restricting gene flow between habitats and exerting intense selection pressure. Overall, the findings from each of my studies highlight humankind’s powerful and ever-growing influence on the ecological and global distribution of species

Proceedings of the 2004 first annual DG ECFIN research conference on “Business Cycles and Growth in Europeâ€

In October 2004, the Directorate General for Economic and Financial Affairs (ECFIN) held its first annual research conference. Its theme was “Business Cycles and Growth in Europeâ€. The thirteen papers presented are collected here in revised form in two volumes as Economic Paper number 227, 1/2 and 2/2. Some of the contributions are followed by comments made by the discussants. The conference was subdivided into four main sessions:Differences and commonalities in business cycles and growth: evidence from the EU and US; International transmission of business cycles; Business cycles in Europe; Business cycles and growth: theory and evidence from old and new Member States. The papers presented here are printed in the order of the programme.business cycle, growth, conference, dg ecfin, Lars Jonung, research conference 2004,

Trends in Opioid Prescribing for Non-Cancer pain and Associated Resource Utilisation in Wales

Background:Opioid prescribing in the UK has increased significantly since the start of the millennium and has been associated with a rise in chronic pain reporting. In Wales, despite concern about rising rates of opioid analgesic prescribing, no detailed examination of the data had been undertaken to assess the changes in prescribing and its consequent impact on the population. Methods:In this study, anonymised, individual level data of people diagnosed with non-cancer pain in Wales was extracted from the Secure Anonymised Information Linkage (SAIL) Databank and used to scrutinise opioid analgesic prescribing trends in people aged 18 years and over, establish whether legislation or clinical guidance impacted on those trends and examine associations with increased healthcare use. The study was conducted in two phases. Phase 1 included a retrospective, repeated cross-sectional analysis of opioid analgesics issued from Primary Care, stratified by gender, age and socioeconomic status. Phase 2 of the study evaluated differences in healthcare service use and costs between individuals receiving opioids for defined non-cancer pain-related diagnoses and matched patients not receiving opioids. Results:Total opioid prescribing increased by 43.6% and strong opioids by 306.2% between 2005 and 2015. Women received 1.5 times more prescriptions than men. Increasing age was associated with higher prescribing rates. People in the most deprived areas received 2.4 times more prescriptions than in least deprived. People receiving opioid prescriptions accessed primary care four times more frequently than controls and had twice the number of hospital admissions. Opioid prescription was associated with 41% higher healthcare costs than noted in controls. Conclusion:This research highlights the need to develop a national strategy to address pain management and opioid stewardship in Wales. We must consider how to address the wide variability observed, particularly between areas of differing socioeconomic status. Further research should investigate what underlies continued opioid prescribing and how alternative strategies can be implemented in practice to reduce population harm and optimise the use of limited healthcare resources

A Quantitative Methodology for Vetting Dark Network Intelligence Sources for Social Network Analysis

Social network analysis (SNA) is used by the DoD to describe and analyze social networks, leading to recommendations for operational decisions. However, social network models are constructed from various information sources of indeterminate reliability. Inclusion of unreliable information can lead to incorrect models resulting in flawed analysis and decisions. This research develops a methodology to assist the analyst by quantitatively identifying and categorizing information sources so that determinations on including or excluding provided data can be made. This research pursued three main thrusts. It consolidated binary similarity measures to determine social network information sources\u27 concordance and developed a methodology to select suitable measures dependent upon application considerations. A methodology was developed to assess the validity of individual sources of social network data. This methodology utilized source pairwise comparisons to measure information sources\u27 concordance and a weighting schema to account for sources\u27 unique perspectives of the underlying social network. Finally, the developed methodology was tested over a variety of generated networks with varying parameters in a design of experiments paradigm (DOE). Various factors relevant to conditions faced by SNA analysts potentially employing this methodology were examined. The DOE was comprised of a 24 full factorial design augmented with a nearly orthogonal Latin hypercube. A linear model was constructed using quantile regression to mitigate the non-normality of the error terms