Prediction of Trabecular Bone Anisotropy from Quantitative Computed Tomography using Supervised Learning and a Novel Morphometric Feature Descriptor

Patient-specific biomechanical models including local bone mineral density and anisotropy have gained importance for assessing musculoskeletal disorders. However the trabecular bone anisotropy captured by high-resolution imaging is only available at the peripheral skeleton in clinical practice. In this work, we propose a supervised learning approach to predict trabecular bone anisotropy that builds on a novel set of pose invariant feature descriptors. The statistical relationship between trabecular bone anisotropy and feature descriptors were learned from a database of pairs of high resolution QCT and clinical QCT reconstructions. On a set of leave-one-out experiments, we compared the accuracy of the proposed approach to previous ones, and report a mean prediction error of 6% for the tensor norm, 6% for the degree of anisotropy and 19◦ for the principal tensor direction. These findings show the potential of the proposed approach to predict trabecular bone anisotropy from clinically available QCT images.

Comparison of machine learning approaches and statistical methods for estimation of airborne chemical exposures

Accurate quantitative estimation of exposure using retrospective data has been one of the most challenging tasks in the exposure assessment field. To improve these estimates, some models have been developed using published exposure databases with their corresponding exposure determinants. These models are designed to be applied to reported exposure determinants obtained from study subjects or exposure levels assigned by an industrial hygienist, so quantitative exposure estimates can be obtained. ^ In an effort to improve the prediction accuracy and generalizability of these models, and taking into account that the limitations encountered in previous studies might be due to limitations in the applicability of traditional statistical methods and concepts, the use of computer science- derived data analysis methods, predominantly machine learning approaches, were proposed and explored in this study. ^ The goal of this study was to develop a set of models using decision trees/ensemble and neural networks methods to predict occupational outcomes based on literature-derived databases, and compare, using cross-validation and data splitting techniques, the resulting prediction capacity to that of traditional regression models. Two cases were addressed: the categorical case, where the exposure level was measured as an exposure rating following the American Industrial Hygiene Association guidelines and the continuous case, where the result of the exposure is expressed as a concentration value. Previously developed literature-based exposure databases for 1,1,1 trichloroethane, methylene dichloride and, trichloroethylene were used. ^ When compared to regression estimations, results showed better accuracy of decision trees/ensemble techniques for the categorical case while neural networks were better for estimation of continuous exposure values. Overrepresentation of classes and overfitting were the main causes for poor neural network performance and accuracy. Estimations based on literature-based databases using machine learning techniques might provide an advantage when they are applied to other methodologies that combine `expert inputs' with current exposure measurements, like the Bayesian Decision Analysis tool. The use of machine learning techniques to more accurately estimate exposures from literature-based exposure databases might represent the starting point for the independence from the expert judgment.^

Statistical models for short-term wind power ramp forecasting

La predicción de energía eólica ha desempeñado en la última década un papel fundamental en el aprovechamiento de este recurso renovable, ya que permite reducir el impacto que tiene la naturaleza fluctuante del viento en la actividad de diversos agentes implicados en su integración, tales como el operador del sistema o los agentes del mercado eléctrico. Los altos niveles de penetración eólica alcanzados recientemente por algunos países han puesto de manifiesto la necesidad de mejorar las predicciones durante eventos en los que se experimenta una variación importante de la potencia generada por un parque o un conjunto de ellos en un tiempo relativamente corto (del orden de unas pocas horas). Estos eventos, conocidos como rampas, no tienen una única causa, ya que pueden estar motivados por procesos meteorológicos que se dan en muy diferentes escalas espacio-temporales, desde el paso de grandes frentes en la macroescala a procesos convectivos locales como tormentas. Además, el propio proceso de conversión del viento en energía eléctrica juega un papel relevante en la ocurrencia de rampas debido, entre otros factores, a la relación no lineal que impone la curva de potencia del aerogenerador, la desalineación de la máquina con respecto al viento y la interacción aerodinámica entre aerogeneradores. En este trabajo se aborda la aplicación de modelos estadísticos a la predicción de rampas a muy corto plazo. Además, se investiga la relación de este tipo de eventos con procesos atmosféricos en la macroescala. Los modelos se emplean para generar predicciones de punto a partir del modelado estocástico de una serie temporal de potencia generada por un parque eólico. Los horizontes de predicción considerados van de una a seis horas. Como primer paso, se ha elaborado una metodología para caracterizar rampas en series temporales. La denominada función-rampa está basada en la transformada wavelet y proporciona un índice en cada paso temporal. Este índice caracteriza la intensidad de rampa en base a los gradientes de potencia experimentados en un rango determinado de escalas temporales. Se han implementado tres tipos de modelos predictivos de cara a evaluar el papel que juega la complejidad de un modelo en su desempeño: modelos lineales autorregresivos (AR), modelos de coeficientes variables (VCMs) y modelos basado en redes neuronales (ANNs). Los modelos se han entrenado en base a la minimización del error cuadrático medio y la configuración de cada uno de ellos se ha determinado mediante validación cruzada. De cara a analizar la contribución del estado macroescalar de la atmósfera en la predicción de rampas, se ha propuesto una metodología que permite extraer, a partir de las salidas de modelos meteorológicos, información relevante para explicar la ocurrencia de estos eventos. La metodología se basa en el análisis de componentes principales (PCA) para la síntesis de la datos de la atmósfera y en el uso de la información mutua (MI) para estimar la dependencia no lineal entre dos señales. Esta metodología se ha aplicado a datos de reanálisis generados con un modelo de circulación general (GCM) de cara a generar variables exógenas que posteriormente se han introducido en los modelos predictivos. Los casos de estudio considerados corresponden a dos parques eólicos ubicados en España. Los resultados muestran que el modelado de la serie de potencias permitió una mejora notable con respecto al modelo predictivo de referencia (la persistencia) y que al añadir información de la macroescala se obtuvieron mejoras adicionales del mismo orden. Estas mejoras resultaron mayores para el caso de rampas de bajada. Los resultados también indican distintos grados de conexión entre la macroescala y la ocurrencia de rampas en los dos parques considerados. Abstract One of the main drawbacks of wind energy is that it exhibits intermittent generation greatly depending on environmental conditions. Wind power forecasting has proven to be an effective tool for facilitating wind power integration from both the technical and the economical perspective. Indeed, system operators and energy traders benefit from the use of forecasting techniques, because the reduction of the inherent uncertainty of wind power allows them the adoption of optimal decisions. Wind power integration imposes new challenges as higher wind penetration levels are attained. Wind power ramp forecasting is an example of such a recent topic of interest. The term ramp makes reference to a large and rapid variation (1-4 hours) observed in the wind power output of a wind farm or portfolio. Ramp events can be motivated by a broad number of meteorological processes that occur at different time/spatial scales, from the passage of large-scale frontal systems to local processes such as thunderstorms and thermally-driven flows. Ramp events may also be conditioned by features related to the wind-to-power conversion process, such as yaw misalignment, the wind turbine shut-down and the aerodynamic interaction between wind turbines of a wind farm (wake effect). This work is devoted to wind power ramp forecasting, with special focus on the connection between the global scale and ramp events observed at the wind farm level. The framework of this study is the point-forecasting approach. Time series based models were implemented for very short-term prediction, this being characterised by prediction horizons up to six hours ahead. As a first step, a methodology to characterise ramps within a wind power time series was proposed. The so-called ramp function is based on the wavelet transform and it provides a continuous index related to the ramp intensity at each time step. The underlying idea is that ramps are characterised by high power output gradients evaluated under different time scales. A number of state-of-the-art time series based models were considered, namely linear autoregressive (AR) models, varying-coefficient models (VCMs) and artificial neural networks (ANNs). This allowed us to gain insights into how the complexity of the model contributes to the accuracy of the wind power time series modelling. The models were trained in base of a mean squared error criterion and the final set-up of each model was determined through cross-validation techniques. In order to investigate the contribution of the global scale into wind power ramp forecasting, a methodological proposal to identify features in atmospheric raw data that are relevant for explaining wind power ramp events was presented. The proposed methodology is based on two techniques: principal component analysis (PCA) for atmospheric data compression and mutual information (MI) for assessing non-linear dependence between variables. The methodology was applied to reanalysis data generated with a general circulation model (GCM). This allowed for the elaboration of explanatory variables meaningful for ramp forecasting that were utilized as exogenous variables by the forecasting models. The study covered two wind farms located in Spain. All the models outperformed the reference model (the persistence) during both ramp and non-ramp situations. Adding atmospheric information had a noticeable impact on the forecasting performance, specially during ramp-down events. Results also suggested different levels of connection between the ramp occurrence at the wind farm level and the global scale.

Aeroacoustic noise prediction in high speed trains due to attached and detached flow

This paper aims to set out the influence of the flow field around high speed trains in open field. To achieve this parametric analysis of the sound pressure inside the train was performed. Three vibroacoustic models of a characteristic train section are used to predict the noise inside the train in open field by using finite element method FEM, boundary element method (BEM) and statistical energy analysis (SEA) depending on the frequency range of analysis. The turbulent boundary layer excitation is implemented as the only airborne noise source, in order to focus on the study of the attached and detached flow in the surface of the train. The power spectral densities of the pressure fluctuation in the train surface proposed by [Cockburn and Roberson 1974, Rennison et al. 2009] are applied on the exterior surface of the structural subsystems in the vibroacoustic models. An increase in the sound pressure level up to10 dB can be appreciated due to the detachment of the flow around the train. These results highlight the importance to determine the detached regions prediction, making critical the airborne noise due to turbulent boundary layer.

Experimental and statistical investigation of canonical problems in sloshing

En hidrodinámica, el fenómeno de Sloshing se puede definir como el movimiento de la superficie libre de un fluido dentro de un contenedor sometido a fuerzas y perturbaciones externas. El fluido en cuestión experimenta violentos movimientos con importantes deformaciones de su superficie libre. La dinámica del fluido puede llegar a generar cargas hidrodinámicas considerables las cuales pueden afectar la integridad estructural y/o comprometer la estabilidad del vehículo que transporta dicho contenedor. El fenómeno de Sloshing ha sido extensivamente investigado matemática, numérica y experimentalmente, siendo el enfoque experimental el más usado debido a la complejidad del problema, para el cual los modelos matemáticos y de simulación son aun incapaces de predecir con suficiente rapidez y precisión las cargas debidas a dicho fenómeno. El flujo generado por el Sloshing usualmente se caracteriza por la presencia de un fluido multifase (gas-liquido) y turbulencia. Reducir al máximo posible la complejidad del fenómeno de Sloshing sin perder la esencia del problema es el principal reto de esta tesis doctoral, donde un trabajo experimental enfocado en casos canónicos de Sloshing es presentado y documentado con el objetivo de aumentar la comprensión de dicho fenómeno y por tanto intentar proveer información valiosa para validaciones de códigos numéricos. El fenómeno de Sloshing juega un papel importante en la industria del transporte marítimo de gas licuado (LNG). El mercado de LNG en los últimos años ha reportado un crecimiento hasta tres veces mayor al de los mercados de petróleo y gas convencionales. Ingenieros en laboratorios de investigación e ingenieros adscritos a la industria del LNG trabajan continuamente buscando soluciones económicas y seguras para contener, transferir y transportar grandes volúmenes de LNG. Los buques transportadores de LNG (LNGC) han pasado de ser unos pocos buques con capacidad de 75000 m3 hace unos treinta años, a una amplia flota con una capacidad de 140000 m3 actualmente. En creciente número, hoy día se construyen buques con capacidades que oscilan entre 175000 m3 y 250000 m3. Recientemente un nuevo concepto de buque LNG ha salido al mercado y se le conoce como FLNG. Un FLNG es un buque de gran valor añadido que solventa los problemas de extracción, licuefacción y almacenamiento del LNG, ya que cuenta con equipos de extracción y licuefacción a bordo, eliminando por tanto las tareas de transvase de las estaciones de licuefacción en tierra hacia los buques LNGC. EL LNG por tanto puede ser transferido directamente desde el FLNG hacia los buques LNGC en mar abierto. Niveles de llenado intermedios en combinación con oleaje durante las operaciones de trasvase inducen movimientos en los buques que generan por tanto el fenómeno de Sloshing dentro de los tanques de los FLNG y los LNGC. El trabajo de esta tesis doctoral lidia con algunos de los problemas del Sloshing desde un punto de vista experimental y estadístico, para ello una serie de tareas, descritas a continuación, se han llevado a cabo : 1. Un dispositivo experimental de Sloshing ha sido configurado. Dicho dispositivo ha permitido ensayar secciones rectangulares de tanques LNGC a escala con movimientos angulares de un grado de libertad. El dispositivo experimental ha sido instrumentado para realizar mediciones de movimiento, presiones, vibraciones y temperatura, así como la grabación de imágenes y videos. 2. Los impactos de olas generadas dentro de una sección rectangular de un LNGC sujeto a movimientos regulares forzados han sido estudiados mediante la caracterización del fenómeno desde un punto de vista estadístico enfocado en la repetitividad y la ergodicidad del problema. 3. El estudio de los impactos provocados por movimientos regulares ha sido extendido a un escenario más realístico mediante el uso de movimientos irregulares forzados. 4. El acoplamiento del Sloshing generado por el fluido en movimiento dentro del tanque LNGC y la disipación de la energía mecánica de un sistema no forzado de un grado de libertad (movimiento angular) sujeto a una excitación externa ha sido investigado. 5. En la última sección de esta tesis doctoral, la interacción entre el Sloshing generado dentro en una sección rectangular de un tanque LNGC sujeto a una excitación regular y un cuerpo elástico solidario al tanque ha sido estudiado. Dicho estudio corresponde a un problema de interacción fluido-estructura. Abstract In hydrodynamics, we refer to sloshing as the motion of liquids in containers subjected to external forces with large free-surface deformations. The liquid motion dynamics can generate loads which may affect the structural integrity of the container and the stability of the vehicle that carries such container. The prediction of these dynamic loads is a major challenge for engineers around the world working on the design of both the container and the vehicle. The sloshing phenomenon has been extensively investigated mathematically, numerically and experimentally. The latter has been the most fruitful so far, due to the complexity of the problem, for which the numerical and mathematical models are still incapable of accurately predicting the sloshing loads. The sloshing flows are usually characterised by the presence of multiphase interaction and turbulence. Reducing as much as possible the complexity of the sloshing problem without losing its essence is the main challenge of this phd thesis, where experimental work on selected canonical cases are presented and documented in order to better understand the phenomenon and to serve, in some cases, as an useful information for numerical validations. Liquid sloshing plays a key roll in the liquified natural gas (LNG) maritime transportation. The LNG market growth is more than three times the rated growth of the oil and traditional gas markets. Engineers working in research laboratories and companies are continuously looking for efficient and safe ways for containing, transferring and transporting the liquified gas. LNG carrying vessels (LNGC) have evolved from a few 75000 m3 vessels thirty years ago to a huge fleet of ships with a capacity of 140000 m3 nowadays and increasing number of 175000 m3 and 250000 m3 units. The concept of FLNG (Floating Liquified Natural Gas) has appeared recently. A FLNG unit is a high value-added vessel which can solve the problems of production, treatment, liquefaction and storage of the LNG because the vessel is equipped with a extraction and liquefaction facility. The LNG is transferred from the FLNG to the LNGC in open sea. The combination of partial fillings and wave induced motions may generate sloshing flows inside both the LNGC and the FLNG tanks. This work has dealt with sloshing problems from a experimental and statistical point of view. A series of tasks have been carried out: 1. A sloshing rig has been set up. It allows for testing tanks with one degree of freedom angular motion. The rig has been instrumented to measure motions, pressure and conduct video and image recording. 2. Regular motion impacts inside a rectangular section LNGC tank model have been studied, with forced motion tests, in order to characterise the phenomenon from a statistical point of view by assessing the repeatability and practical ergodicity of the problem. 3. The regular motion analysis has been extended to an irregular motion framework in order to reproduce more realistic scenarios. 4. The coupled motion of a single degree of freedom angular motion system excited by an external moment and affected by the fluid moment and the mechanical energy dissipation induced by sloshing inside the tank has been investigated. 5. The last task of the thesis has been to conduct an experimental investigation focused on the strong interaction between a sloshing flow in a rectangular section of a LNGC tank subjected to regular excitation and an elastic body clamped to the tank. It is thus a fluid structure interaction problem.

Ensemble prediction distribution maps of macroalgae for current conditions and four climate change scenarios and high resultion bathymetry for Potter Cove, WAP, Antarctica

Species distribution models (SDM) predict species occurrence based on statistical relationships with environmental conditions. The R-package biomod2 which includes 10 different SDM techniques and 10 different evaluation methods was used in this study. Macroalgae are the main biomass producers in Potter Cove, King George Island (Isla 25 de Mayo), Antarctica, and they are sensitive to climate change factors such as suspended particulate matter (SPM). Macroalgae presence and absence data were used to test SDMs suitability and, simultaneously, to assess the environmental response of macroalgae as well as to model four scenarios of distribution shifts by varying SPM conditions due to climate change. According to the averaged evaluation scores of Relative Operating Characteristics (ROC) and True scale statistics (TSS) by models, those methods based on a multitude of decision trees such as Random Forest and Classification Tree Analysis, reached the highest predictive power followed by generalized boosted models (GBM) and maximum-entropy approaches (Maxent). The final ensemble model used 135 of 200 calculated models (TSS > 0.7) and identified hard substrate and SPM as the most influencing parameters followed by distance to glacier, total organic carbon (TOC), bathymetry and slope. The climate change scenarios show an invasive reaction of the macroalgae in case of less SPM and a retreat of the macroalgae in case of higher assumed SPM values.

Statistical models of accidents on interchange ramps and speed-change lanes.

Federal Highway Administration, Office of Safety and Traffic Operations, Washington, D.C.

Statistical analysis of crash conditions and their relationship to injuries. Final report.

National Highway Traffic Safety Administration, Washington, D.C.

Rear-end crashes: problem size assessment and statistical description.

Mode of access: Internet.

Understanding the limits of the seasonal prediction of sea ice in the Arctic

Thesis (Master's)--University of Washington, 2016-06

Spatio-temporal scanning and statistical test of the accelerating moment release (AMR) model using Australian earthquake data

The Accelerating Moment Release (AMR) preceding earthquakes with magnitude above 5 in Australia that occurred during the last 20 years was analyzed to test the Critical Point Hypothesis. Twelve earthquakes in the catalog were chosen based on a criterion for the number of nearby events. Results show that seven sequences with numerous events recorded leading up to the main earthquake exhibited accelerating moment release. Two occurred near in time and space to other earthquakes preceded by AM R. The remaining three sequences had very few events in the catalog so the lack of AMR detected in the analysis may be related to catalog incompleteness. Spatio-temporal scanning of AMR parameters shows that 80% of the areas in which AMR occurred experienced large events. In areas of similar background seismicity with no large events, 10 out of 12 cases exhibit no AMR, and two others are false alarms where AMR was observed but no large event followed. The relationship between AMR and Load-Unload Response Ratio (LURR) was studied. Both methods predict similar critical region sizes, however, the critical point time using AMR is slightly earlier than the time of the critical point LURR anomaly.

Prediction of vapor-moisture equilibriums for a wood-moisture system using a modified UNIQUAC model

A modified UNIQUAC model has been extended to describe and predict the equilibrium relative humidity and moisture content for wood. The method is validated over a range of moisture content from oven-dried state to fiber saturation point, and over a temperature range of 20-70 degrees C. Adjustable parameters and binary interaction parameters of the UNIQUAC model were estimated from experimental data for Caribbean pine and Hoop pine as well as data available in the literature. The two group-interaction parameters for the wood-moisture system were consistent with using function group contributions for H2O, -OH and -CHO. The result reconfirms that the main contributors to water adsorption in cell walls are the hydroxyl groups of the carbohydrates in cellulose and hemicelluloses. This provides some physical insight into the intermolecular force and energy between bound water and the wood material. (c) 2006 Elsevier Ltd. All rights reserved.

Modelling pre-clearing vegetation distribution using GIS-integrated statistical, ecological and data models: A case study from the wet tropics of Northeastern Australia

Traditional vegetation mapping methods use high cost, labour-intensive aerial photography interpretation. This approach can be subjective and is limited by factors such as the extent of remnant vegetation, and the differing scale and quality of aerial photography over time. An alternative approach is proposed which integrates a data model, a statistical model and an ecological model using sophisticated Geographic Information Systems (GIS) techniques and rule-based systems to support fine-scale vegetation community modelling. This approach is based on a more realistic representation of vegetation patterns with transitional gradients from one vegetation community to another. Arbitrary, though often unrealistic, sharp boundaries can be imposed on the model by the application of statistical methods. This GIS-integrated multivariate approach is applied to the problem of vegetation mapping in the complex vegetation communities of the Innisfail Lowlands in the Wet Tropics bioregion of Northeastern Australia. The paper presents the full cycle of this vegetation modelling approach including sampling sites, variable selection, model selection, model implementation, internal model assessment, model prediction assessments, models integration of discrete vegetation community models to generate a composite pre-clearing vegetation map, independent data set model validation and model prediction's scale assessments. An accurate pre-clearing vegetation map of the Innisfail Lowlands was generated (0.83r(2)) through GIS integration of 28 separate statistical models. This modelling approach has good potential for wider application, including provision of. vital information for conservation planning and management; a scientific basis for rehabilitation of disturbed and cleared areas; a viable method for the production of adequate vegetation maps for conservation and forestry planning of poorly-studied areas. (c) 2006 Elsevier B.V. All rights reserved.

Customer information system data pre-processing with feature selection techniques for non-technical losses prediction in an electricity market

Non-technical losses (NTL) identification and prediction are important tasks for many utilities. Data from customer information system (CIS) can be used for NTL analysis. However, in order to accurately and efficiently perform NTL analysis, the original data from CIS need to be pre-processed before any detailed NTL analysis can be carried out. In this paper, we propose a feature selection based method for CIS data pre-processing in order to extract the most relevant information for further analysis such as clustering and classifications. By removing irrelevant and redundant features, feature selection is an essential step in data mining process in finding optimal subset of features to improve the quality of result by giving faster time processing, higher accuracy and simpler results with fewer features. Detailed feature selection analysis is presented in the paper. Both time-domain and load shape data are compared based on the accuracy, consistency and statistical dependencies between features.

Leading edge forecasting techniques for exchange rate prediction

This paper describes how modern machine learning techniques can be used in conjunction with statistical methods to forecast short term movements in exchange rates, producing models suitable for use in trading. It compares the results achieved by two different techniques, and shows how they can be used in a complementary fashion. The paper draws on experience of both inter- and intra-day forecasting taken from earlier studies conducted by Logica and Chemical Bank Quantitative Research and Trading (QRT) group's experience in developing trading models.