CONFIGR: A Vision-Based Model for Long-Range Figure Completion

CONFIGR (CONtour FIgure GRound) is a computational model based on principles of biological vision that completes sparse and noisy image figures. Within an integrated vision/recognition system, CONFIGR posits an initial recognition stage which identifies figure pixels from spatially local input information. The resulting, and typically incomplete, figure is fed back to the “early vision” stage for long-range completion via filling-in. The reconstructed image is then re-presented to the recognition system for global functions such as object recognition. In the CONFIGR algorithm, the smallest independent image unit is the visible pixel, whose size defines a computational spatial scale. Once pixel size is fixed, the entire algorithm is fully determined, with no additional parameter choices. Multi-scale simulations illustrate the vision/recognition system. Open-source CONFIGR code is available online, but all examples can be derived analytically, and the design principles applied at each step are transparent. The model balances filling-in as figure against complementary filling-in as ground, which blocks spurious figure completions. Lobe computations occur on a subpixel spatial scale. Originally designed to fill-in missing contours in an incomplete image such as a dashed line, the same CONFIGR system connects and segments sparse dots, and unifies occluded objects from pieces locally identified as figure in the initial recognition stage. The model self-scales its completion distances, filling-in across gaps of any length, where unimpeded, while limiting connections among dense image-figure pixel groups that already have intrinsic form. Long-range image completion promises to play an important role in adaptive processors that reconstruct images from highly compressed video and still camera images.

Plant-wide predictive control for a thermal power plant based on a physical plant model

A constrained non-linear, physical model-based, predictive control (NPMPC) strategy is developed for improved plant-wide control of a thermal power plant. The strategy makes use of successive linearisation and recursive state estimation using extended Kalman filtering to obtain a linear state-space model. The linear model and a quadratic programming routine are used to design a constrained long-range predictive controller One special feature is the careful selection of a specific set of plant model parameters for online estimation, to account for time-varying system characteristics resulting from major system disturbances and ageing. These parameters act as nonstationary stochastic states and help to provide sufficient degrees-of-freedom to obtain unbiased estimates of controlled outputs. A 14th order non-linear plant model, simulating the dominant characteristics of a 200 MW oil-fired pou er plant has been used to test the NPMPC algorithm. The control strategy gives impressive simulation results, during large system disturbances and extremely high rate of load changes, right across the operating range. These results compare favourably to those obtained with the state-space GPC method designed under similar conditions.

Short, medium and long term load forecasting model and virtual load forecaster based on radial basis neural networks

Artificial neural networks (ANNs) can be easily applied to short-term load forecasting (STLF) models for electric power distribution applications. However, they are not typically used in medium and long term load forecasting (MLTLF) electric power models because of the difficulties associated with collecting and processing the necessary data. Virtual instrument (VI) techniques can be applied to electric power load forecasting but this is rarely reported in the literature. In this paper, we investigate the modelling and design of a VI for short, medium and long term load forecasting using ANNs. Three ANN models were built for STLF of electric power. These networks were trained using historical load data and also considering weather data which is known to have a significant affect of the use of electric power (such as wind speed, precipitation, atmospheric pressure, temperature and humidity). In order to do this a V-shape temperature processing model is proposed. With regards MLTLF, a model was developed using radial basis function neural networks (RBFNN). Results indicate that the forecasting model based on the RBFNN has a high accuracy and stability. Finally, a virtual load forecaster which integrates the VI and the RBFNN is presented.

Fast automatic two-stage nonlinear model identification based on the extreme learning machine

It is convenient and effective to solve nonlinear problems with a model that has a linear-in-the-parameters (LITP) structure. However, the nonlinear parameters (e.g. the width of Gaussian function) of each model term needs to be pre-determined either from expert experience or through exhaustive search. An alternative approach is to optimize them by a gradient-based technique (e.g. Newton’s method). Unfortunately, all of these methods still need a lot of computations. Recently, the extreme learning machine (ELM) has shown its advantages in terms of fast learning from data, but the sparsity of the constructed model cannot be guaranteed. This paper proposes a novel algorithm for automatic construction of a nonlinear system model based on the extreme learning machine. This is achieved by effectively integrating the ELM and leave-one-out (LOO) cross validation with our two-stage stepwise construction procedure [1]. The main objective is to improve the compactness and generalization capability of the model constructed by the ELM method. Numerical analysis shows that the proposed algorithm only involves about half of the computation of orthogonal least squares (OLS) based method. Simulation examples are included to confirm the efficacy and superiority of the proposed technique.

Partial least squares model for dynamic rating of overhead line in wind intensive areas based on field measurements

This paper presents a statistical model for the thermal behaviour of the line model based on lab tests and field measurements. This model is based on Partial Least Squares (PLS) multi regression and is used for the Dynamic Line Rating (DLR) in a wind intensive area. DLR provides extra capacity to the line, over the traditional seasonal static rating, which makes it possible to defer the need for reinforcement the existing network or building new lines. The proposed PLS model has a number of appealing features; the model is linear, so it is straightforward to use for predicting the line rating for future periods using the available weather forecast. Unlike the available physical models, the proposed model does not require any physical parameters of the line, which avoids the inaccuracies resulting from the errors and/or variations in these parameters. The developed model is compared with physical model, the Cigre model, and has shown very good accuracy in predicting the conductor temperature as well as in determining the line rating for future time periods. 

A Tree Augmented Classifier Based on Extreme Imprecise Dirichlet Model

In this paper we present TANC, i.e., a tree-augmented naive credal classifier based on imprecise probabilities; it models prior near-ignorance via the Extreme Imprecise Dirichlet Model (EDM) (Cano et al., 2007) and deals conservatively with missing data in the training set, without assuming them to be missing-at-random. The EDM is an approximation of the global Imprecise Dirichlet Model (IDM), which considerably simplifies the computation of upper and lower probabilities; yet, having been only recently introduced, the quality of the provided approximation needs still to be verified. As first contribution, we extensively compare the output of the naive credal classifier (one of the few cases in which the global IDM can be exactly implemented) when learned with the EDM and the global IDM; the output of the classifier appears to be identical in the vast majority of cases, thus supporting the adoption of the EDM in real classification problems. Then, by experiments we show that TANC is more reliable than the precise TAN (learned with uniform prior), and also that it provides better performance compared to a previous (Zaffalon, 2003) TAN model based on imprecise probabilities. TANC treats missing data by considering all possible completions of the training set, but avoiding an exponential increase of the computational times; eventually, we present some preliminary results with missing data.

Patient-specific anisotropic model of human trunk based on MR data

There are many ways to generate geometrical models for numerical simulation, and most of them start with a segmentation step to extract the boundaries of the regions of interest. This paper presents an algorithm to generate a patient-specific three-dimensional geometric model, based on a tetrahedral mesh, without an initial extraction of contours from the volumetric data. Using the information directly available in the data, such as gray levels, we built a metric to drive a mesh adaptation process. The metric is used to specify the size and orientation of the tetrahedral elements everywhere in the mesh. Our method, which produces anisotropic meshes, gives good results with synthetic and real MRI data. The resulting model quality has been evaluated qualitatively and quantitatively by comparing it with an analytical solution and with a segmentation made by an expert. Results show that our method gives, in 90% of the cases, as good or better meshes as a similar isotropic method, based on the accuracy of the volume reconstruction for a given mesh size. Moreover, a comparison of the Hausdorff distances between adapted meshes of both methods and ground-truth volumes shows that our method decreases reconstruction errors faster. Copyright © 2015 John Wiley & Sons, Ltd.

Comparison of energy evaluation systems and a mechanistic model for milk production by dairy cattle offered fresh grass-based diets

Grass-based diets are of increasing social-economic importance in dairy cattle farming, but their low supply of glucogenic nutrients may limit the production of milk. Current evaluation systems that assess the energy supply and requirements are based on metabolisable energy (ME) or net energy (NE). These systems do not consider the characteristics of the energy delivering nutrients. In contrast, mechanistic models take into account the site of digestion, the type of nutrient absorbed and the type of nutrient required for production of milk constituents, and may therefore give a better prediction of supply and requirement of nutrients. The objective of the present study is to compare the ability of three energy evaluation systems, viz. the Dutch NE system, the agricultural and food research council (AFRC) ME system, and the feed into milk (FIM) ME system, and of a mechanistic model based on Dijkstra et al. [Simulation of digestion in cattle fed sugar cane: prediction of nutrient supply for milk production with locally available supplements. J. Agric. Sci., Cambridge 127, 247-60] and Mills et al. [A mechanistic model of whole-tract digestion and methanogenesis in the lactating dairy cow: model development, evaluation and application. J. Anim. Sci. 79, 1584-97] to predict the feed value of grass-based diets for milk production. The dataset for evaluation consists of 41 treatments of grass-based diets (at least 0.75 g ryegrass/g diet on DM basis). For each model, the predicted energy or nutrient supply, based on observed intake, was compared with predicted requirement based on observed performance. Assessment of the error of energy or nutrient supply relative to requirement is made by calculation of mean square prediction error (MSPE) and by concordance correlation coefficient (CCC). All energy evaluation systems predicted energy requirement to be lower (6-11%) than energy supply. The root MSPE (expressed as a proportion of the supply) was lowest for the mechanistic model (0.061), followed by the Dutch NE system (0.082), FIM ME system (0.097) and AFRCME system(0.118). For the energy evaluation systems, the error due to overall bias of prediction dominated the MSPE, whereas for the mechanistic model, proportionally 0.76 of MSPE was due to random variation. CCC analysis confirmed the higher accuracy and precision of the mechanistic model compared with energy evaluation systems. The error of prediction was positively related to grass protein content for the Dutch NE system, and was also positively related to grass DMI level for all models. In conclusion, current energy evaluation systems overestimate energy supply relative to energy requirement on grass-based diets for dairy cattle. The mechanistic model predicted glucogenic nutrients to limit performance of dairy cattle on grass-based diets, and proved to be more accurate and precise than the energy systems. The mechanistic model could be improved by allowing glucose maintenance and utilization requirements parameters to be variable. (C) 2007 Elsevier B.V. All rights reserved.

Software evolution: a graph based model

A model based on graph isomorphisms is used to formalize software evolution. Step by step we narrow the search space by an informed selection of the attributes based on the current state-of-the-art in software engineering and generate a seed solution. We then traverse the resulting space using graph isomorphisms and other set operations over the vertex sets. The new solutions will preserve the desired attributes. The goal of defining an isomorphism based search mechanism is to construct predictors of evolution that can facilitate the automation of ’software factory’ paradigm. The model allows for automation via software tools implementing the concepts.

Software evolution: a graph based model

A model based on graph isomorphisms is used to formalize software evolution. Step by step we narrow the search space by an informed selection of the attributes based on the current state-of-the-art in software engineering and generate a seed solution. We then traverse the resulting space using graph isomorphisms and other set operations over the vertex sets. The new solutions will preserve the desired attributes. The goal of defining an isomorphism based search mechanism is to construct predictors of evolution that can facilitate the automation of ’software factory’ paradigm. The model allows for automation via software tools implementing the concepts.

Non-linear dynamics of a tower orchard sprayer based on an inverted pendulum model

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

A new transmission model in cognitive radio based on cyclic generalized polynomial codes for bandwidth reduction

The frequency spectrums are inefficiently utilized and cognitive radio has been proposed for full utilization of these spectrums. The central idea of cognitive radio is to allow the secondary user to use the spectrum concurrently with the primary user with the compulsion of minimum interference. However, designing a model with minimum interference is a challenging task. In this paper, a transmission model based on cyclic generalized polynomial codes discussed in [2] and [15], is proposed for the improvement in utilization of spectrum. The proposed model assures a non interference data transmission of the primary and secondary users. Furthermore, analytical results are presented to show that the proposed model utilizes spectrum more efficiently as compared to traditional models.

Single fluoroscopic image based 2D/3D reconstruction of a scaled, patient-specific vertebral model

Purpose Accurate three-dimensional (3D) models of lumbar vertebrae can enable image-based 3D kinematic analysis. The common approach to derive 3D models is by direct segmentation of CT or MRI datasets. However, these have the disadvantages that they are expensive, timeconsuming and/or induce high-radiation doses to the patient. In this study, we present a technique to automatically reconstruct a scaled 3D lumbar vertebral model from a single two-dimensional (2D) lateral fluoroscopic image. Methods Our technique is based on a hybrid 2D/3D deformable registration strategy combining a landmark-to-ray registration with a statistical shape model-based 2D/3D reconstruction scheme. Fig. 1 shows different stages of the reconstruction process. Four cadaveric lumbar spine segments (total twelve lumbar vertebrae) were used to validate the technique. To evaluate the reconstruction accuracy, the surface models reconstructed from the lateral fluoroscopic images were compared to the associated ground truth data derived from a 3D CT-scan reconstruction technique. For each case, a surface-based matching was first used to recover the scale and the rigid transformation between the reconstructed surface model Results Our technique could successfully reconstruct 3D surface models of all twelve vertebrae. After recovering the scale and the rigid transformation between the reconstructed surface models and the ground truth models, the average error of the 2D/3D surface model reconstruction over the twelve lumbar vertebrae was found to be 1.0 mm. The errors of reconstructing surface models of all twelve vertebrae are shown in Fig. 2. It was found that the mean errors of the reconstructed surface models in comparison to their associated ground truths after iterative scaled rigid registrations ranged from 0.7 mm to 1.3 mm and the rootmean squared (RMS) errors ranged from 1.0 mm to 1.7 mm. The average mean reconstruction error was found to be 1.0 mm. Conclusion An accurate, scaled 3D reconstruction of the lumbar vertebra can be obtained from a single lateral fluoroscopic image using a statistical shape model based 2D/3D reconstruction technique. Future work will focus on applying the reconstructed model for 3D kinematic analysis of lumbar vertebrae, an extension of our previously-reported imagebased kinematic analysis. The developed method also has potential applications in surgical planning and navigation.

An ICT-based closed-loop model to improve care for heart failure patients

Durante las últimas décadas se ha producido un fenómeno global de envejecimiento en la población. Esta tendencia se puede observar prácticamente en todos los países del mundo y se debe principalmente a los avances en la medicina, y a los descensos en las tasas de fertilidad y mortalidad. El envejecimiento de la población tiene un gran impacto en la salud de los ciudadanos, y a menudo es la causa de aparición de enfermedades crónicas. Este tipo de enfermedades supone una amenaza y una carga importantes para la sociedad, especialmente en aspectos como la mortalidad o los gastos en los sistemas sanitarios. Entre las enfermedades cardiovasculares, la insuficiencia cardíaca es probablemente la condición con mayor prevalencia y afecta a 23-26 millones de personas en todo el mundo. Normalmente, la insuficiencia cardíaca presenta un mal pronóstico y una tasa de supervivencia bajas, en algunos casos peores que algún tipo de cáncer. Además, suele ser la causa de hospitalizaciones frecuentes y es una de las enfermedades más costosas para los sistemas sanitarios. La tendencia al envejecimiento de la población y la creciente incidencia de las enfermedades crónicas están llevando a una situación en la que los sistemas de salud no son capaces de hacer frente a la demanda de la sociedad. Los servicios de salud existentes tendrán que adaptarse para ser efectivos y sostenibles en el futuro. Es necesario identificar nuevos paradigmas de cuidado de pacientes, así como mecanismos para la provisión de servicios que ayuden a transformar estos sistemas sanitarios. En este contexto, esta tesis se plantea la búsqueda de soluciones, basadas en las Tecnologías de la Información y la Comunicación (TIC), que contribuyan a realizar la transformación en los sistemas sanitarios. En concreto, la tesis se centra en abordar los problemas de una de las enfermedades con mayor impacto en estos sistemas: la insuficiencia cardíaca. Las siguientes hipótesis constituyen la base para la realización de este trabajo de investigación: 1. Es posible definir un modelo basado en el paradigma de lazo cerrado y herramientas TIC que formalice el diseño de mejores servicios para pacientes con insuficiencia cardíaca. 2. El modelo de lazo cerrado definido se puede utilizar para definir un servicio real que ayude a gestionar la insuficiencia cardíaca crónica. 3. La introducción, la adopción y el uso de un servicio basado en el modelo definido se traducirá en mejoras en el estado de salud de los pacientes que sufren insuficiencia cardíaca. a. La utilización de un sistema basado en el modelo de lazo cerrado definido mejorará la experiencia del usuario de los pacientes. La definición del modelo planteado se ha basado en el estándar ISO / EN 13940- Sistema de conceptos para dar soporte a la continuidad de la asistencia. Comprende un conjunto de conceptos, procesos, flujos de trabajo, y servicios como componentes principales, y representa una formalización de los servicios para los pacientes con insuficiencia cardíaca. Para evaluar el modelo definido se ha definido un servicio real basado en el mismo, además de la implementación de un sistema de apoyo a dicho servicio. El diseño e implementación de dicho sistema se realizó siguiendo la metodología de Diseño Orientado a Objetivos. El objetivo de la evaluación consistía en investigar el efecto que tiene un servicio basado en el modelo de lazo cerrado sobre el estado de salud de los pacientes con insuficiencia cardíaca. La evaluación se realizó en el marco de un estudio clínico observacional. El análisis de los resultados ha comprendido métodos de análisis cuantitativos y cualitativos. El análisis cuantitativo se ha centrado en determinar el estado de salud de los pacientes en base a datos objetivos (obtenidos en pruebas de laboratorio o exámenes médicos). Para realizar este análisis se definieron dos índices específicos: el índice de estabilidad y el índice de la evolución del estado de salud. El análisis cualitativo ha evaluado la autopercepción del estado de salud de los pacientes en términos de calidad de vida, auto-cuidado, el conocimiento, la ansiedad y la depresión, así como niveles de conocimiento. Se ha basado en los datos recogidos mediante varios cuestionarios o instrumentos estándar (i.e. EQ-5D, la Escala de Ansiedad y Depresión (HADS), el Cuestionario de Cardiomiopatía de Kansas City (KCCQ), la Escala Holandesa de Conocimiento de Insuficiencia Cardíaca (DHFKS), y la Escala Europea de Autocuidado en Insuficiencia Cardíaca (EHFScBS), así como cuestionarios dedicados no estandarizados de experiencia de usuario. Los resultados obtenidos en ambos análisis, cuantitativo y cualitativo, se compararon con el fin de evaluar la correlación entre el estado de salud objetivo y subjetivo de los pacientes. Los resultados de la validación demostraron que el modelo propuesto tiene efectos positivos en el cuidado de los pacientes con insuficiencia cardíaca y contribuye a mejorar su estado de salud. Asimismo, ratificaron al modelo como instrumento válido para la definición de servicios mejorados para la gestión de esta enfermedad. ABSTRACT During the last decades we have witnessed a global aging phenomenon in the population. This can be observed in practically every country in the world, and it is mainly caused by the advances in medicine, and the decrease of mortality and fertility rates. Population aging has an important impact on citizens’ health and it is often the cause for chronic diseases, which constitute global burden and threat to the society in terms of mortality and healthcare expenditure. Among chronic diseases, Chronic Heart Failure (CHF) or Heart Failure (HF) is probably the one with highest prevalence, affecting between 23 and 26 million people worldwide. Heart failure is a chronic, long-term and serious condition with very poor prognosis and worse survival rates than some type of cancers. Additionally, it is often the cause of frequent hospitalizations and one of the most expensive conditions for the healthcare systems. The aging trends in the population and the increasing incidence of chronic diseases are leading to a situation where healthcare systems are not able to cope with the society demand. Current healthcare services will have to be adapted and redefined in order to be effective and sustainable in the future. There is a need to find new paradigms for patients’ care, and to identify new mechanisms for services’ provision that help to transform the healthcare systems. In this context, this thesis aims to explore new solutions, based on ICT, that contribute to achieve the needed transformation within the healthcare systems. In particular, it focuses on addressing the problems of one of the diseases with higher impact within these systems: Heart Failure. The following hypotheses represent the basis to the elaboration of this research: 1. It is possible to define a model based on a closed-loop paradigm and ICT tools that formalises the design of enhanced healthcare services for chronic heart failure patients. 2. The described closed-loop model can be exemplified in a real service that supports the management of chronic heart failure disease. 3. The introduction, adoption and use of a service based on the outlined model will result in improvements in the health status of patients suffering heart failure. 4. The user experience of patients when utilizing a system based on the defined closed-loop model will be enhanced. The definition of the closed-loop model for health care support of heart failure patients have been based on the standard ISO/EN 13940 System of concepts to support continuity of care. It includes a set of concept, processes and workflows, and services as main components, and it represent a formalization of services for heart failure patients. In order to be validated, the proposed closed-loop model has been instantiated into a real service and a supporting IT system. The design and implementation of the system followed the user centred design methodology Goal Oriented Design. The validation, that included an observational clinical study, aimed to investigate the effect that a service based on the closed-loop model had on heart failure patients’ health status. The analysis of results comprised quantitative and qualitative analysis methods. The quantitative analysis was focused on determining the health status of patients based on objective data (obtained in lab tests or physical examinations). Two specific indexes where defined and considered in this analysis: the stability index and the health status evolution index. The qualitative analysis assessed the self-perception of patients’ health status in terms of quality of life, self-care, knowledge, anxiety and depression, as well as knowledge levels. It was based on the data gathered through several standard instruments (i.e. EQ-5D, the Hospital Anxiety and Depression Scale, the Kansas City Cardiomyopathy Questionnaire, the Dutch Heart Failure Knowledge Scale, and the European Heart Failure Self-care Behaviour Scale) as well as dedicated non-standardized user experience questionnaires. The results obtained in both analyses, quantitative and qualitative, were compared in order to assess the correlation between the objective and subjective health status of patients. The results of the validation showed that the proposed model contributed to improve the health status of the patients and had a positive effect on the patients’ care. It also proved that the model is a valid instrument for designing enhanced healthcare services for heart failure patients.

Development of a Strengths-Based Model for Insurance Automobile, Property, and Casualty Claims Operations

ABSTRACT \ Employers know that to have a successful organization, they must have the right people in the right jobs. But how will they know whom to place where? The development of a model based upon an individual's personality traits and strengths, and how to best use them, is a good place to start. Employees working in positions in which their traits and strengths are maximized enjoy work more, are more efficient, and are less apt to be absent or to look for work elsewhere. It is a mutually beneficial process of selection for both employers and employees. This model illustrates the process in an automobile and property insurance claims operation through utilization of the Myers-Briggs Type Indicators and the StrengthsFinder Profiles.