Evaluation of the applicability of FRA for inter-turn fault detection in stator windings

In this paper, the applicability of the FRA technique is discussed as a method for detecting inter-turn faults in stator windings. Firstly, this method is tested in an individual medium-voltage stator coil with satisfactory results. Secondly, the tests are extended to a medium-voltage induction motor stator winding, in which inter-turn faults are performed in every coil end of one phase. Results of the frequency response in case of inter-turn faults are evaluated in both cases for different fault resistance values. The experimental setup is also described for each experiment. The results of the application of this technique to the detection of inter-turn faults justify further research in optimizing this technique for preventive maintenance.

Active Optical Sensors for Tree Stem Detection and Classification in Nurseries.

Active optical sensing (LIDAR and light curtain transmission) devices mounted on a mobile platform can correctly detect, localize, and classify trees. To conduct an evaluation and comparison of the different sensors, an optical encoder wheel was used for vehicle odometry and provided a measurement of the linear displacement of the prototype vehicle along a row of tree seedlings as a reference for each recorded sensor measurement. The field trials were conducted in a juvenile tree nursery with one-year-old grafted almond trees at Sierra Gold Nurseries, Yuba City, CA, United States. Through these tests and subsequent data processing, each sensor was individually evaluated to characterize their reliability, as well as their advantages and disadvantages for the proposed task. Test results indicated that 95.7% and 99.48% of the trees were successfully detected with the LIDAR and light curtain sensors, respectively. LIDAR correctly classified, between alive or dead tree states at a 93.75% success rate compared to 94.16% for the light curtain sensor. These results can help system designers select the most reliable sensor for the accurate detection and localization of each tree in a nursery, which might allow labor-intensive tasks, such as weeding, to be automated without damaging crops.

Automatic fault detection on BIPV systems without solar irradiation data

BIPV systems are small PV generation units spread out over the territory, and whose characteristics are very diverse. This makes difficult a cost-effective procedure for monitoring, fault detection, performance analyses, operation and maintenance. As a result, many problems affecting BIPV systems go undetected. In order to carry out effective automatic fault detection procedures, we need a performance indicator that is reliable and that can be applied on many PV systems at a very low cost. The existing approaches for analyzing the performance of PV systems are often based on the Performance Ratio (PR), whose accuracy depends on good solar irradiation data, which in turn can be very difficult to obtain or cost-prohibitive for the BIPV owner. We present an alternative fault detection procedure based on a performance indicator that can be constructed on the sole basis of the energy production data measured at the BIPV systems. This procedure does not require the input of operating conditions data, such as solar irradiation, air temperature, or wind speed. The performance indicator, called Performance to Peers (P2P), is constructed from spatial and temporal correlations between the energy output of neighboring and similar PV systems. This method was developed from the analysis of the energy production data of approximately 10,000 BIPV systems located in Europe. The results of our procedure are illustrated on the hourly, daily and monthly data monitored during one year at one BIPV system located in the South of Belgium. Our results confirm that it is possible to carry out automatic fault detection procedures without solar irradiation data. P2P proves to be more stable than PR most of the time, and thus constitutes a more reliable performance indicator for fault detection procedures. We also discuss the main limitations of this novel methodology, and we suggest several future lines of research that seem promising to improve on these procedures.

Automated detection and classification of brain injury lesions on structural magnetic resonance imaging

El daño cerebral adquirido (DCA) es un problema social y sanitario grave, de magnitud creciente y de una gran complejidad diagnóstica y terapéutica. Su elevada incidencia, junto con el aumento de la supervivencia de los pacientes, una vez superada la fase aguda, lo convierten también en un problema de alta prevalencia. En concreto, según la Organización Mundial de la Salud (OMS) el DCA estará entre las 10 causas más comunes de discapacidad en el año 2020. La neurorrehabilitación permite mejorar el déficit tanto cognitivo como funcional y aumentar la autonomía de las personas con DCA. Con la incorporación de nuevas soluciones tecnológicas al proceso de neurorrehabilitación se pretende alcanzar un nuevo paradigma donde se puedan diseñar tratamientos que sean intensivos, personalizados, monitorizados y basados en la evidencia. Ya que son estas cuatro características las que aseguran que los tratamientos son eficaces. A diferencia de la mayor parte de las disciplinas médicas, no existen asociaciones de síntomas y signos de la alteración cognitiva que faciliten la orientación terapéutica. Actualmente, los tratamientos de neurorrehabilitación se diseñan en base a los resultados obtenidos en una batería de evaluación neuropsicológica que evalúa el nivel de afectación de cada una de las funciones cognitivas (memoria, atención, funciones ejecutivas, etc.). La línea de investigación en la que se enmarca este trabajo de investigación pretende diseñar y desarrollar un perfil cognitivo basado no sólo en el resultado obtenido en esa batería de test, sino también en información teórica que engloba tanto estructuras anatómicas como relaciones funcionales e información anatómica obtenida de los estudios de imagen. De esta forma, el perfil cognitivo utilizado para diseñar los tratamientos integra información personalizada y basada en la evidencia. Las técnicas de neuroimagen representan una herramienta fundamental en la identificación de lesiones para la generación de estos perfiles cognitivos. La aproximación clásica utilizada en la identificación de lesiones consiste en delinear manualmente regiones anatómicas cerebrales. Esta aproximación presenta diversos problemas relacionados con inconsistencias de criterio entre distintos clínicos, reproducibilidad y tiempo. Por tanto, la automatización de este procedimiento es fundamental para asegurar una extracción objetiva de información. La delineación automática de regiones anatómicas se realiza mediante el registro tanto contra atlas como contra otros estudios de imagen de distintos sujetos. Sin embargo, los cambios patológicos asociados al DCA están siempre asociados a anormalidades de intensidad y/o cambios en la localización de las estructuras. Este hecho provoca que los algoritmos de registro tradicionales basados en intensidad no funcionen correctamente y requieran la intervención del clínico para seleccionar ciertos puntos (que en esta tesis hemos denominado puntos singulares). Además estos algoritmos tampoco permiten que se produzcan deformaciones grandes deslocalizadas. Hecho que también puede ocurrir ante la presencia de lesiones provocadas por un accidente cerebrovascular (ACV) o un traumatismo craneoencefálico (TCE). Esta tesis se centra en el diseño, desarrollo e implementación de una metodología para la detección automática de estructuras lesionadas que integra algoritmos cuyo objetivo principal es generar resultados que puedan ser reproducibles y objetivos. Esta metodología se divide en cuatro etapas: pre-procesado, identificación de puntos singulares, registro y detección de lesiones. Los trabajos y resultados alcanzados en esta tesis son los siguientes: Pre-procesado. En esta primera etapa el objetivo es homogeneizar todos los datos de entrada con el objetivo de poder extraer conclusiones válidas de los resultados obtenidos. Esta etapa, por tanto, tiene un gran impacto en los resultados finales. Se compone de tres operaciones: eliminación del cráneo, normalización en intensidad y normalización espacial. Identificación de puntos singulares. El objetivo de esta etapa es automatizar la identificación de puntos anatómicos (puntos singulares). Esta etapa equivale a la identificación manual de puntos anatómicos por parte del clínico, permitiendo: identificar un mayor número de puntos lo que se traduce en mayor información; eliminar el factor asociado a la variabilidad inter-sujeto, por tanto, los resultados son reproducibles y objetivos; y elimina el tiempo invertido en el marcado manual de puntos. Este trabajo de investigación propone un algoritmo de identificación de puntos singulares (descriptor) basado en una solución multi-detector y que contiene información multi-paramétrica: espacial y asociada a la intensidad. Este algoritmo ha sido contrastado con otros algoritmos similares encontrados en el estado del arte. Registro. En esta etapa se pretenden poner en concordancia espacial dos estudios de imagen de sujetos/pacientes distintos. El algoritmo propuesto en este trabajo de investigación está basado en descriptores y su principal objetivo es el cálculo de un campo vectorial que permita introducir deformaciones deslocalizadas en la imagen (en distintas regiones de la imagen) y tan grandes como indique el vector de deformación asociado. El algoritmo propuesto ha sido comparado con otros algoritmos de registro utilizados en aplicaciones de neuroimagen que se utilizan con estudios de sujetos control. Los resultados obtenidos son prometedores y representan un nuevo contexto para la identificación automática de estructuras. Identificación de lesiones. En esta última etapa se identifican aquellas estructuras cuyas características asociadas a la localización espacial y al área o volumen han sido modificadas con respecto a una situación de normalidad. Para ello se realiza un estudio estadístico del atlas que se vaya a utilizar y se establecen los parámetros estadísticos de normalidad asociados a la localización y al área. En función de las estructuras delineadas en el atlas, se podrán identificar más o menos estructuras anatómicas, siendo nuestra metodología independiente del atlas seleccionado. En general, esta tesis doctoral corrobora las hipótesis de investigación postuladas relativas a la identificación automática de lesiones utilizando estudios de imagen médica estructural, concretamente estudios de resonancia magnética. Basándose en estos cimientos, se han abrir nuevos campos de investigación que contribuyan a la mejora en la detección de lesiones. ABSTRACT Brain injury constitutes a serious social and health problem of increasing magnitude and of great diagnostic and therapeutic complexity. Its high incidence and survival rate, after the initial critical phases, makes it a prevalent problem that needs to be addressed. In particular, according to the World Health Organization (WHO), brain injury will be among the 10 most common causes of disability by 2020. Neurorehabilitation improves both cognitive and functional deficits and increases the autonomy of brain injury patients. The incorporation of new technologies to the neurorehabilitation tries to reach a new paradigm focused on designing intensive, personalized, monitored and evidence-based treatments. Since these four characteristics ensure the effectivity of treatments. Contrary to most medical disciplines, it is not possible to link symptoms and cognitive disorder syndromes, to assist the therapist. Currently, neurorehabilitation treatments are planned considering the results obtained from a neuropsychological assessment battery, which evaluates the functional impairment of each cognitive function (memory, attention, executive functions, etc.). The research line, on which this PhD falls under, aims to design and develop a cognitive profile based not only on the results obtained in the assessment battery, but also on theoretical information that includes both anatomical structures and functional relationships and anatomical information obtained from medical imaging studies, such as magnetic resonance. Therefore, the cognitive profile used to design these treatments integrates information personalized and evidence-based. Neuroimaging techniques represent an essential tool to identify lesions and generate this type of cognitive dysfunctional profiles. Manual delineation of brain anatomical regions is the classical approach to identify brain anatomical regions. Manual approaches present several problems related to inconsistencies across different clinicians, time and repeatability. Automated delineation is done by registering brains to one another or to a template. However, when imaging studies contain lesions, there are several intensity abnormalities and location alterations that reduce the performance of most of the registration algorithms based on intensity parameters. Thus, specialists may have to manually interact with imaging studies to select landmarks (called singular points in this PhD) or identify regions of interest. These two solutions have the same inconvenient than manual approaches, mentioned before. Moreover, these registration algorithms do not allow large and distributed deformations. This type of deformations may also appear when a stroke or a traumatic brain injury (TBI) occur. This PhD is focused on the design, development and implementation of a new methodology to automatically identify lesions in anatomical structures. This methodology integrates algorithms whose main objective is to generate objective and reproducible results. It is divided into four stages: pre-processing, singular points identification, registration and lesion detection. Pre-processing stage. In this first stage, the aim is to standardize all input data in order to be able to draw valid conclusions from the results. Therefore, this stage has a direct impact on the final results. It consists of three steps: skull-stripping, spatial and intensity normalization. Singular points identification. This stage aims to automatize the identification of anatomical points (singular points). It involves the manual identification of anatomical points by the clinician. This automatic identification allows to identify a greater number of points which results in more information; to remove the factor associated to inter-subject variability and thus, the results are reproducible and objective; and to eliminate the time spent on manual marking. This PhD proposed an algorithm to automatically identify singular points (descriptor) based on a multi-detector approach. This algorithm contains multi-parametric (spatial and intensity) information. This algorithm has been compared with other similar algorithms found on the state of the art. Registration. The goal of this stage is to put in spatial correspondence two imaging studies of different subjects/patients. The algorithm proposed in this PhD is based on descriptors. Its main objective is to compute a vector field to introduce distributed deformations (changes in different imaging regions), as large as the deformation vector indicates. The proposed algorithm has been compared with other registration algorithms used on different neuroimaging applications which are used with control subjects. The obtained results are promising and they represent a new context for the automatic identification of anatomical structures. Lesion identification. This final stage aims to identify those anatomical structures whose characteristics associated to spatial location and area or volume has been modified with respect to a normal state. A statistical study of the atlas to be used is performed to establish which are the statistical parameters associated to the normal state. The anatomical structures that may be identified depend on the selected anatomical structures identified on the atlas. The proposed methodology is independent from the selected atlas. Overall, this PhD corroborates the investigated research hypotheses regarding the automatic identification of lesions based on structural medical imaging studies (resonance magnetic studies). Based on these foundations, new research fields to improve the automatic identification of lesions in brain injury can be proposed.

The Creation of a Legal Ordinance and Program Manual for the Purpose of Illicit Discharge Detection and Elimination

Illegal dumping and improper disposal of pollutants in urban areas can contribute significant pollutant loads to the municipal separate storm sewer system (MS4) and natural environments. Illicit discharges to the MS4 can pose a significant risk to human and environmental health. The Clean Water Act requires that municipalities implement a legal mechanism and plan to detect and eliminate illicit discharges to the MS4. The methodology for program creation included the analysis of other municipal illicit discharge programs, review of state and federal guidance publications, and the review of illicit discharge case-studies. This paper describes a systematic approach applied to the creation and implementation of a legal ordinance and program manual designed for the purpose of illicit discharge detection and elimination (IDDE).

Conversion of decision tables into decision trees.

Originally presented as the author's thesis, University of Illinois at Urbana-Champaign.

A test mode approach to fault detection in sequential circuits,

"C00-1469-0117."

Heuristic algorithms for constructing near-optimal decision trees /

Thesis (M.S.)--University of Illinois.

Application of surface geophysics to detection and mapping of mine subsidence fractures in drift and bedrock /

"Illinois Mine Subsidence Research Program."

Simultaneous detection and differentiation of human Polyomaviruses JC and BK by a rapid and sensitive PCR-ELAHA assay and a survey of the JCV subtypes within an Australian population

Human polyomaviruses JCV and BKV can cause several clinical manifestations in immunocompromised hosts, including progressive multifocal leukoencephalopathy (PML) and haemorrhagic cystitis. Molecular detection by polymerase chain reaction (PCR) is recognised as a sensitive and specific method for detecting human polyomaviruses in clinical samples. In this study, we developed a PCR assay using a single primer pair to amplify a segment of the VP1 gene of JCV and BKV. An enzyme linked amplicon hybridisation assay (ELAHA) using species-specific biotinylated oligonucleotide probes was used to differentiate between JCV and BKV. This assay (VP1-PCR-ELAHA) was evaluated and compared to a PCR assay targeting the human polyomavirus T antigen gene (pol-PCR). DNA sequencing was used to confirm the polyomavirus species identified by the VP1-PCR-ELAHA and to determine the subtype of each JCV isolate. A total of 297 urine specimens were tested and human polyomavirus was detected in 105 specimens (35.4%) by both PCR assays. The differentiation of JCV and BKV by the VP1-PCR-ELAHA showed good agreement with the results of DNA sequencing. Further, DNA sequencing of the JCV positive specimens showed the most prevalent JCV subtype in our cohort was 2a (27%) followed by 1b (20%), 1a (15%), 2c (14%), 4 (14%) and 2b (10%). The results of this study show that the VP1-PCR-ELAHA is a sensitive, specific and rapid method for detecting and differentiating human polyomaviruses JC and BK and is highly suitable for routine use in the clinical laboratory. (C) 2004 Wiley-Liss, Inc.

Detection and differentiation of Plasmodium species by polymerase chain reaction and colorimetric detection in blood samples of patients with suspected malaria

Polymerase chain reaction (PCR) is now recognized as a sensitive and specific method for detecting Plasmodium species in blood. In this Study. we tested 279 blood samples, from patients with Suspected malaria, by a PCR assay utilizing species-specific colorimetric detection. and compared the results to light microscopy. Overall, both assays were in agreement for 270 of the 279 specimens. P. vivax was detected in 131 (47.0%) specimens. P. falciparum in 64 (22.9%) specimens, P. ovale in 6 (2.1%) specimens, and P. malariae in 5 (1.8%) specimens. Both P. falciparum and P. vivax were detected in a further 10 (3.6%) specimens, and 54 (19.3%) specimens were negative by both assays. In the remaining nine specimens, microscopy either failed to detect the parasite or incorrectly identified the species present. In summary, the sensitivity, specificity and simplicity of the PCR assay makes it particularly suitable for use in a diagnostic laboratory. (C) 2004 Elsevier Inc. All rights reserved.

Identification, detection, and spatial resolution of Clostridium populations responsible for cellulose degradation in a methanogenic landfill leachate bioreactor

An anaerobic landfill leachate bioreactor was operated with crystalline cellulose and sterile landfill leacbate until a steady state was reached. Cellulose hydrolysis, acidogenesis, and methanogenesis were measured. Microorganisms attached to the cellulose surfaces were hypothesized to be the cellulose hydrolyzers. 16S rRNA gene clone libraries were prepared from this attached fraction and also from the mixed fraction (biomass associated with cellulose particles and in the planktonic phase). Both clone libraries were dominated by Firmicutes phylum sequences (100% of the attached library and 90% of the mixed library), and the majority fell into one of five lineages of the clostridia. Clone group 1 (most closely related to Clostridium stercorarium), clone group 2 (most closely related to Clostridium thermocellum), and clone group 5 (most closely related to Bacteroides cellulosolvens) comprised sequences in Clostridium group III. Clone group 3 sequences were in Clostridium group XIVa (most closely related to Clostridium sp. strain XB90). Clone group 4 sequences were affiliated with a deeply branching clostridial lineage peripherally associated with Clostridium group VI. This monophyletic group comprises a new Clostridium cluster, designated cluster VIa. Specific fluorescence in situ hybridization (FISH) probes for the five groups were designed and synthesized, and it was demonstrated in FISH experiments that bacteria targeted by the probes for clone groups 1, 2, 4, and 5 were very abundant on the surfaces of the cellulose particles and likely the key cellulolytic microorganisms in the landfill bioreactor. The FISH probe for clone group 3 targeted cells in the planktonic phase, and these organisms were hypothesized to be glucose fermenters.

Guidelines for the prevention, detection and management of people with chronic heart failure in Australia 2006

* Chronic heart failure (CHF) is found in 1.5%–2.0% of Australians. Considered rare in people aged less than 45 years, its prevalence increases to over 10% in people aged ≥ 65 years. * CHF is one of the most common reasons for hospital admission and general practitioner consultation in the elderly (≥ 70 years). * Common causes of CHF are ischaemic heart disease (present in > 50% of new cases), hypertension (about two-thirds of cases) and idiopathic dilated cardiomyopathy (around 5%–10% of cases). * Diagnosis is based on clinical features, chest x-ray and objective measurement of ventricular function (eg, echocardiography). Plasma levels of B-type natriuretic peptide (BNP) may have a role in diagnosis, primarily as a test for exclusion. Diagnosis may be strengthened by a beneficial clinical response to treatment(s) directed towards amelioration of symptoms. * Management involves prevention, early detection, amelioration of disease progression, relief of symptoms, minimisation of exacerbations, and prolongation of survival.