Nonparametric Evaluation of Dynamic Disease Risk: A Spatio-Temporal Kernel Approach

Quantifying the distributions of disease risk in space and time jointly is a key element for understanding spatio-temporal phenomena while also having the potential to enhance our understanding of epidemiologic trajectories. However, most studies to date have neglected time dimension and focus instead on the “average” spatial pattern of disease risk, thereby masking time trajectories of disease risk. In this study we propose a new idea titled “spatio-temporal kernel density estimation (stKDE)” that employs hybrid kernel (i.e., weight) functions to evaluate the spatio-temporal disease risks. This approach not only can make full use of sample data but also “borrows” information in a particular manner from neighboring points both in space and time via appropriate choice of kernel functions. Monte Carlo simulations show that the proposed method performs substantially better than the traditional (i.e., frequency-based) kernel density estimation (trKDE) which has been used in applied settings while two illustrative examples demonstrate that the proposed approach can yield superior results compared to the popular trKDE approach. In addition, there exist various possibilities for improving and extending this method

Contribution to the characterization and modeling of photovoltaic generators

A crucial aspect of evaluating and maintaining a photovoltaic (PV) installation connected to the grid is the availability of models that describe its operation reliably in real operating conditions. The nominal power of the PV generator (P*M) is considered an essential input parameter, and several models have been proposed to estimate P*M for characterizing the PV system. In the case of PV generators in outdoor conditions, the American Society for Testing and Materials, the International Electrotechnical Commission, and others have proposed procedures to determine the P*M of the generator. As part of these procedures, monitoring days with ideal conditions is mandatory, notably days with a clear sky, high irradiance values, and low wind speeds. Such restrictions can limit the number of suitable monitoring days, especially in places where clouds frequently form. This thesis proposes a new approach that allows estimating the P*M with data even from non-ideal, partially cloudy days. Based on non-parametric statistics, this procedure identifies and filters out noise as well as deviations from ideal conditions of irradiance, allowing for an estimation of P*M with similar accuracy as for a clear-sky day. This new procedure enables the characterization of a PV generator on a daily basis without the requirement to meet ideal conditions, thus, considerably enhancing the number of suitable monitoring days. To overcome the limitation in the P*M estimation and considerably extend the number of monitoring days, the new procedure can be applied to ideal and non-ideal conditions, such as partially cloudy days. This procedure determines the most probable nominal power value within one monitoring day using non-parametric statistics. In order to test the new procedure, a 109.44 kW photovoltaic plant in Granada, Spain, was monitored for six months. A referential procedure reported in the literature for large PV plants under ideal climatic conditions is first applied to estimate its nominal power. The results indicate that the nominal power can be estimated reliably in non-ideal conditions, maintaining the same precision as in ideal conditions. Then validating the procedure for a smaller PV generator and under different conditions, two small grid-connected 1.5 kW PV arrays were used. The PV systems in question are located in two different cities in Peru: Chachapoyas (tropical highland) and Lima (coastal desert). The objective of this study in Chachapoyas was to validate the methodology in a tropical climate with a high presence of clouds but at the same time with high irradiance values above 800 W/m2. According to the results obtained, under these conditions, the nominal power of the system can be calculated with reasonable certainty. As a precaution, monitoring for more than one day is recommended to obtain more data (at least 3 hours with high irradiance) to reduce uncertainties. Lima, Peru's second location under study, has a particular climate. Since the capital is located in a desert with high relative humidity values, dust deposition increases and power output decreases due to these conditions. For this purpose, the nominal power was used as a parameter to determine the maintenance schedule. Since keeping the system in optimal performance, considering this in future installations for operation and maintenance costs, is essential. The new procedure developed in this work can be applied to facilitate technical due diligence and quality control processes for PV generators of different sizes and under different operating conditions that are being re-purchased or have been recently installed. The possibility of daily monitoring of the P*M also enables long-term monitoring of a PV generator to ensure the correct operation or identify possible degradation effectsUn aspecto crucial a la hora de evaluar y mantener una instalacion fotovoltaica (FV) conectada a la red es la disponibilidad de modelos que describan su funcionamiento de forma fiable en condiciones reales de funcionamiento. La potencia nominal del generador fotovoltaico (P*M ) se considera un parametro de entrada esencial y se han propuesto varios modelos para estimar P*M para caracterizar el sistema fotovoltaico. En el caso de generadores fotovoltaicos en condiciones exteriores, la Sociedad Estadounidense de Pruebas y Materiales (abreviatura del ingles ASTM), la Comision Electrotecnica Internacional (abreviatura del ingles IEC) y otros han propuesto procedimientos para determinar la P*M del generador. Como parte de estos procedimientos, es obligatorio monitorear los dias con condiciones ideales, en particular los dias con cielo despejado, valores de irradiancia altos y velocidades de viento bajas. Tales restricciones pueden limitar la cantidad de dias de monitoreo adecuados, especialmente en lugares donde se forman nubes con frecuencia. Esta tesis propone un nuevo enfoque que permite estimar la P*M con datos incluso de dias parcialmente nublados no ideales. Basado en estadistica no parametricas, este procedimiento identifica y filtra el ruido, asi como las desviaciones de las condiciones ideales de irradiancia, lo que permite una estimacion de P*M con una precision similar a la de un dia de cielo despejado. Este nuevo procedimiento permite la caracterizacion diaria de un generador fotovoltaico sin el requisito de cumplir con las condiciones ideales, lo que aumenta considerablemente el numero de dias de monitoreo adecuados. Para superar la limitacion en la estimacion de P*M y extender considerablemente el numero de dias de monitoreo, el nuevo procedimiento se puede aplicar a condiciones ideales y no ideales, como dias parcialmente nublados. Este procedimiento determina el valor de potencia nominal mas probable dentro de un dia de monitoreo utilizando estadisticas no parametricas. Para probar el nuevo procedimiento, se monitorizo durante seis meses una planta fotovoltaica de 109,44 kW en Granada, Espana. Primero se aplica un procedimiento referencial reportado en la literatura para grandes plantas fotovoltaicas en condiciones climaticas ideales para estimar su potencia nominal. Los resultados indican que la potencia nominal se puede estimar de forma fiable en condiciones no ideales, manteniendo la misma precision que en condiciones ideales. Luego, para validar el procedimiento para un generador fotovoltaico mas pequeno y en diferentes condiciones, se utilizaron dos pequenos generador fotovoltaicos de 1,5 kW conectados a la red. Los sistemas fotovoltaicos en cuestion estan ubicados en dos ciudades diferentes de Peru: Chachapoyas (altiplano tropical) y Lima (desierto costero). El objetivo de este estudio en Chachapoyas fue validar la metodologia en un clima tropical con alta presencia de nubes pero al mismo tiempo con altos valores de irradiancia por encima de 800 W/m2. De acuerdo con los resultados obtenidos, en estas condiciones se puede calcular con razonable certeza la potencia nominal del sistema. Como precaucion, se recomienda monitorear durante mas de un dia para obtener mas datos (al menos 3 horas con alta irradiacion) para reducir las incertidumbres. Lima, la segunda localidad del Peru bajo estudio, tiene un clima particular. Dado que la capital esta ubicada en un desierto con altos valores de humedad relativa, la deposicion de polvo aumenta y la produccion de energia disminuye debido a estas condiciones. Para ello, se utilizo la potencia nominal como parametro para determinar el programa de mantenimiento. Ya que mantener el VI sistema en un desempeno optimo, considerando esto en futuras instalaciones para costos de operacion y mantenimiento, es fundamental. El nuevo procedimiento desarrollado en este trabajo se puede aplicar para facilitar los procesos de diligencia debida tecnica y control de calidad para generadores fotovoltaicos de diferentes tamaños y en diferentes condiciones de funcionamiento que se están recomprando o que se han instalado recientemente. La posibilidad de monitorear diariamente la P*M también permite monitorear a largo plazo un generador fotovoltaico para asegurar el correcto funcionamiento o identificar posibles efectos de degradación

Analysis of Vehicle Use Patterns during Military Field Exercises to Identify Potential Roads

Military training is an intensive land use and can cause negative environmental effects. Many studies conducted under Integrated Training Area Management (ITAM) for quantifying the impact resulted from the military training exercise found that off-road vehicular activities during training exercises cause the major impact to the training land. Vehicle land use patterns at a certain location affect the impact severity: concentrated and repeated traffic create more serious damage to the land compared to the dispersed offroad vehicle movements. Those areas heavily disturbed by off-road traffic may require a longer period of time or special treatments for the land to return to its pre-disturbed status. Based on the impact severity and the shape of the disturbed area, some areas can be considered as potential roads, defined as the roads newly formed by concentrated offroad traffic during the military training exercises, or the roads currently exist but have not been mapped. Potential roads need to be rehabilitated, have traffic dispersed to return the land to its natural status, or to be included in the established road construction and maintenance programs. As Global Positioning System (GPS) has been used for monitoring vehicles\u27 activities during military training exercises; it enables the analysis of vehicle movement patterns. The vehicle movement patterns are characterized as the percentage of vehicle travel every day, vehicles\u27 on and off road travel, the frequencies of vehicle\u27s off-road velocity and turning radius. GPS vehicle tracking data collected during an eight-day reconnaissance training exercises in Yakima Training Center (YTC) in October 2001 were analyzed for vehicle movement patterns. Comparison of the on-road and off-road movement patterns indicates that potential roads may exist on the locations where the concentrated traffic or a high speed movement occurred. Based on the analysis of the movement patterns, factors were extracted to characterize the special movement patterns that indicate the vehicles moved on a potential road. The YTC was divided into small study units, and a multicriteria method was developed to determine if a study unit is a portion of a potential road. The multicriteria method was evaluated by comparing the predictions to the site visit results on 34 selected road segments that met different criteria levels. Results show that locations met higher criteria levels have higher possibilities to be roads: the location met all five criteria has an approximately 91% possibility for road existence; those met four criteria has an approximately 55% possibility; and for those met criteria level two or three, there is an approximately 14% probability for road existence. The analysis of updated off-road shows the percentage of vehicle off-road movement drops from 20.0% to 15.8% after excluding the potential road moving data. As an alternative method, a neural network approach for identifying the potential roads was introduced and compared to the multicriteria method. The neural network method obtained an approximately 85% accuracy when tested by on-road grids, successfully identified the high-way segment as road, and predicted approximately 31% off-road grids as potential road grids. Results show that the neural network method, although emphasized in factors different from the multicriteria method, has approximately 78% accuracy for identifying the potential road locations. The prediction from the neural network method was found highly correlated to the one of the criterion: vehicles travel in different directions. Simplified methods were also developed to identify potential roads by investigating the GPS point density, vehicle velocity, and the number of passes within a study unit. A simple linear relationship was found between the number of passes and the possibility for road existence. Although using vehicle velocity for identifying the potential roads may not be the best choose, velocity is still considered as one of the most important features to characterize vehicle movements and to locate special movement patterns. Considering the discrete situation in the predicted potential road areas, a kernel smoothing technique was introduced and applied to smooth the results to improve the continuity of the potential roads. The application found the kernel smoothing technique was able to obtain continuous potential road grids by selecting reasonable bandwidth

A bandwidth selection for kernel density estimation of functions of random variables

In this investigation, the problem of estimating the probability density function of a function of m independent identically distributed random variables, g(X-1,X-2,...,X-m) is considered. The choice of the bandwidth in the kernel density estimation is very important. Several approaches are known for the choice of bandwidth in the kernel smoothing methods for the case m = 1 and g is the identity. In this study we will derive the bandwidth using the least square cross validation and the contrast methods. We will compare between the two methods using Monte Carlo simulation and using an example from the real life. (C) 2003 Elsevier B.V. All rights reserved

A Bandwidth Selection For Kernel Density Estimation Of Functions Of Random Variables

In this investigation, the problem of estimating the probability density function of a function of m independent identically distributed random variables, g(X1,X2,...,Xm) is considered. The choice of the bandwidth in the kernel density estimation is very important. Several approaches are known for the choice of bandwidth in the kernel smoothing methods for the case m=1 and g is the identity. In this study we will derive the bandwidth using the least square cross validation and the contrast methods. We will compare between the two methods using Monte Carlo simulation and using an example from the real life. © 2003 Elsevier B.V. All rights reserved

HUMAN-ROBOT COLLABORATION IN ROBOTIC-ASSISTED SURGICAL TRAINING

Ph.DDOCTOR OF PHILOSOPH