Impact of the background region of interest in the relative renal function

Renal scintigraphy with 99mTc-dimercaptosuccinic acid (99mTc-DMSA) is performed with the aim of detect cortical abnormalities related to urinary tract infection and accurately quantify relative renal function (RRF). For this quantitative assessment Nuclear Medicine Technologist should draw regions of interest (ROI) around each kidney (KROI) and peri-renal background (BKG) ROI, although, controversy still exists about BKG-ROI. The aim of this work was to evaluate the effect of the normalization procedure, number and location of BKG-ROI on the RRF in 99mTc-DMSA scintigraphy.

Fuzzy linguistic model for evaluating the risk of neonatal death

OBJECTIVE: To introduce a fuzzy linguistic model for evaluating the risk of neonatal death. METHODS: The study is based on the fuzziness of the variables newborn birth weight and gestational age at delivery. The inference used was Mamdani's method. Neonatologists were interviewed to estimate the risk of neonatal death under certain conditions and to allow comparing their opinions and the model values. RESULTS: The results were compared with experts' opinions and the Fuzzy model was able to capture the expert knowledge with a strong correlation (r=0.96). CONCLUSIONS: The linguistic model was able to estimate the risk of neonatal death when compared to experts' performance.

Application of adaptive neuro-fuzzy inference for wind power short-term forecasting

The increased integration of wind power into the electric grid, as nowadays occurs in Portugal, poses new challenges due to its intermittency and volatility. Hence, good forecasting tools play a key role in tackling these challenges. In this paper, an adaptive neuro-fuzzy inference approach is proposed for short-term wind power forecasting. Results from a real-world case study are presented. A thorough comparison is carried out, taking into account the results obtained with other approaches. Numerical results are presented and conclusions are duly drawn. (C) 2011 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

Investment optimization in distribution network based on fuzzy outage parameters

This paper presents a methodology that aims to increase the probability of delivering power to any load point of the electrical distribution system by identifying new investments in distribution components. The methodology is based on statistical failure and repair data of the distribution power system components and it uses fuzzy-probabilistic modelling for system component outage parameters. Fuzzy membership functions of system component outage parameters are obtained by statistical records. A mixed integer non-linear optimization technique is developed to identify adequate investments in distribution networks components that allow increasing the availability level for any customer in the distribution system at minimum cost for the system operator. To illustrate the application of the proposed methodology, the paper includes a case study that considers a real distribution network.

Increase of the delivered energy probability in DES using a fuzzy probabilistic modeling

The paper proposes a methodology to increase the probability of delivering power to any load point by identifying new investments in distribution energy systems. The proposed methodology is based on statistical failure and repair data of distribution components and it uses a fuzzy-probabilistic modeling for the components outage parameters. The fuzzy membership functions of the outage parameters of each component are based on statistical records. A mixed integer nonlinear programming optimization model is developed in order to identify the adequate investments in distribution energy system components which allow increasing the probability of delivering power to any customer in the distribution system at the minimum possible cost for the system operator. To illustrate the application of the proposed methodology, the paper includes a case study that considers a 180 bus distribution network.

Hybrid fuzzy Monte Carlo technique for reliability assessment in transmission power systems

This paper proposes a new methodology to reduce the probability of occurring states that cause load curtailment, while minimizing the involved costs to achieve that reduction. The methodology is supported by a hybrid method based on Fuzzy Set and Monte Carlo Simulation to catch both randomness and fuzziness of component outage parameters of transmission power system. The novelty of this research work consists in proposing two fundamentals approaches: 1) a global steady approach which deals with building the model of a faulted transmission power system aiming at minimizing the unavailability corresponding to each faulted component in transmission power system. This, results in the minimal global cost investment for the faulted components in a system states sample of the transmission network; 2) a dynamic iterative approach that checks individually the investment’s effect on the transmission network. A case study using the Reliability Test System (RTS) 1996 IEEE 24 Buses is presented to illustrate in detail the application of the proposed methodology.

Hybrid fuzzy Monte Carlo and logic programming model for distribution network reconfiguration in the presence of outages

This paper presents a methodology for distribution networks reconfiguration in outage presence in order to choose the reconfiguration that presents the lower power losses. The methodology is based on statistical failure and repair data of the distribution power system components and uses fuzzy-probabilistic modelling for system component outage parameters. Fuzzy membership functions of system component outage parameters are obtained by statistical records. A hybrid method of fuzzy set and Monte Carlo simulation based on the fuzzy-probabilistic models allows catching both randomness and fuzziness of component outage parameters. Once obtained the system states by Monte Carlo simulation, a logical programming algorithm is applied to get all possible reconfigurations for every system state. In order to evaluate the line flows and bus voltages and to identify if there is any overloading, and/or voltage violation a distribution power flow has been applied to select the feasible reconfiguration with lower power losses. To illustrate the application of the proposed methodology to a practical case, the paper includes a case study that considers a real distribution network.

Fuzzy Monte Carlo mathematical model for load curtailment minimization in transmission power systems

This paper presents a methodology which is based on statistical failure and repair data of the transmission power system components and uses fuzzyprobabilistic modeling for system component outage parameters. Using statistical records allows developing the fuzzy membership functions of system component outage parameters. The proposed hybrid method of fuzzy set and Monte Carlo simulation based on the fuzzy-probabilistic models allows catching both randomness and fuzziness of component outage parameters. A network contingency analysis to identify any overloading or voltage violation in the network is performed once obtained the system states by Monte Carlo simulation. This is followed by a remedial action algorithm, based on optimal power flow, to reschedule generations and alleviate constraint violations and, at the same time, to avoid any load curtailment, if possible, or, otherwise, to minimize the total load curtailment, for the states identified by the contingency analysis. In order to illustrate the application of the proposed methodology to a practical case, the paper will include a case study for the Reliability Test System (RTS) 1996 IEEE 24 BUS.

Logic programming and fuzzy Monte Carlo for distribution network reconfiguration

This paper present a methodology to choose the distribution networks reconfiguration that presents the lower power losses. The proposed methodology is based on statistical failure and repair data of the distribution power system components and uses fuzzy-probabilistic modeling for system component outage parameters. The proposed hybrid method using fuzzy sets and Monte Carlo simulation based on the fuzzyprobabilistic models allows catching both randomness and fuzziness of component outage parameters. A logic programming algorithm is applied, once obtained the system states by Monte Carlo Simulation, to get all possible reconfigurations for each system state. To evaluate the line flows and bus voltages and to identify if there is any overloading, and/or voltage violation an AC load flow has been applied to select the feasible reconfiguration with lower power losses. To illustrate the application of the proposed methodology, the paper includes a case study that considers a 115 buses distribution network.

Power systems reliability calculation based on fuzzy data mining

This paper presents a methodology supported on the data base knowledge discovery process (KDD), in order to find out the failure probability of electrical equipments’, which belong to a real electrical high voltage network. Data Mining (DM) techniques are used to discover a set of outcome failure probability and, therefore, to extract knowledge concerning to the unavailability of the electrical equipments such us power transformers and high-voltages power lines. The framework includes several steps, following the analysis of the real data base, the pre-processing data, the application of DM algorithms, and finally, the interpretation of the discovered knowledge. To validate the proposed methodology, a case study which includes real databases is used. This data have a heavy uncertainty due to climate conditions for this reason it was used fuzzy logic to determine the set of the electrical components failure probabilities in order to reestablish the service. The results reflect an interesting potential of this approach and encourage further research on the topic.

Impact of the background Region of Interest in the relative renal function

Renal scintigraphy with 99mTc-dimercaptosuccinic acid (99mTc-DMSA) is performed with the aim of detect cortical abnormalities related to urinary tract infection and accurately quantify relative renal function (RRF). For this quantitative assessment Nuclear Medicine Technologist should draw regions of interest (ROI) around each kidney (KROI) and peri-renal background (BKG) ROI although controversy still exists about BKG-ROI. The aim of this work was to evaluate the effect of the normalization procedure, number and location of BKG-ROI on the RRF in 99mTc-DMSA scintigraphy.

Lógica fuzzy e regressão logística na decisão para prática de cintilografia das paratiróides

OBJETIVO: Desenvolver e comparar dois modelos matemáticos, um deles baseado em regressão logística e o outro em teoria de conjuntos fuzzy, para definir a indicação para a realização do exame cintilográfico a partir de resultados dos exames laboratoriais. MÉTODOS: Foram identificados 194 pacientes que tiveram cálcio e paratormônio séricos medidos a partir da base de registros de cintilografia de paratiróides realizadas em laboratório de diagnóstico de São Paulo, no período de janeiro de 2000 a dezembro de 2004. O modelo de regressão logística foi desenvolvido utilizando-se o software SPSS e o modelo fuzzy, o Matlab. A performance dos modelos foi comparada utilizando-se curvas ROC. RESULTADOS: Os modelos apresentaram diferenças estatisticamente significantes (p=0,026) nos seus desempenhos. A área sob a curva ROC do modelo de regressão logística foi de 0,862 (IC 95%: 0,811-0,913) e do modelo de lógica fuzzy foi 0,887 (IC 95%: 0,840-0,933). Este último destacou-se como particularmente útil porque, ao contrário do modelo logístico, mostrou capacidade de utilizar informações de paratormônio em intervalo em que os valores de cálcio mostraram-se pouco discriminantes. CONCLUSÕES: O modelo matemático baseado em teoria de conjuntos fuzzy pareceu ser mais adequado do que o baseado em regressão logística como método para decisão da realização de cintilografia das paratiróides. Todavia, sendo resultado de um exercício metodológico, inferências sobre o comportamento do objeto podem ser impróprias, dada a não representatividade populacional dos dados.

Comparative study between two distinct methods for attenuation correction and the use of no-attenuation correction for the assessment of relative uptake of 99mTc-DMSA on pediatric patients

Introduction: Although relative uptake values aren’t the most important objective of a 99mTc-DMSA scan, they are important quantitative information. In most of the dynamic renal scintigraphies attenuation correction is essential if one wants to obtain a reliable result of the quantification process. Although in DMSA scans the absent of significant background and the lesser attenuation in pediatric patients, makes that this attenuation correction techniques are actually not applied. The geometric mean is the most common method, but that includes the acquisition of an anterior (extra) projection, which it is not acquired by a large number of NM departments. This method and the attenuation factors proposed by Tonnesen will be correlated with the absence of attenuation correction procedures. Material and Methods: Images from 20 individuals (aged 3 years +/- 2) were used and the two attenuation correction methods applied. The mean time of acquisition (time post DMSA administration) was 3.5 hours +/- 0.8h. Results: The absence of attenuation correction showed a good correlation with both attenuation methods (r=0.73 +/- 0.11) and the mean difference verified on the uptake values between the different methods were 4 +/- 3. The correlation was higher when the age was lower. The attenuation correction methods correlation was higher between them two than with the “no attenuation correction” method (r=0.82 +/- 0.8), and the mean differences of the uptake values were 2 +/- 2. Conclusion: The decision of not doing any kind of attenuation correction method can be justified by the minor differences verified on the relative kidney uptake values. Nevertheless, if it is recognized that there is a need for an accurate value of the relative kidney uptake, then an attenuation correction method should be used. Attenuation correction factors proposed by Tonnesen can be easily implemented and so become a practical and easy to implement alternative, namely when the anterior projection - needed for the geometric mean methodology – is not acquired.

Fuzzy Monte Carlo model for transmission power systems reliability based decision making

This thesis presents the Fuzzy Monte Carlo Model for Transmission Power Systems Reliability based studies (FMC-TRel) methodology, which is based on statistical failure and repair data of the transmission power system components and uses fuzzyprobabilistic modeling for system component outage parameters. Using statistical records allows developing the fuzzy membership functions of system component outage parameters. The proposed hybrid method of fuzzy set and Monte Carlo simulation based on the fuzzy-probabilistic models allows catching both randomness and fuzziness of component outage parameters. A network contingency analysis to identify any overloading or voltage violation in the network is performed once obtained the system states. This is followed by a remedial action algorithm, based on Optimal Power Flow, to reschedule generations and alleviate constraint violations and, at the same time, to avoid any load curtailment, if possible, or, otherwise, to minimize the total load curtailment, for the states identified by the contingency analysis. For the system states that cause load curtailment, an optimization approach is applied to reduce the probability of occurrence of these states while minimizing the costs to achieve that reduction. This methodology is of most importance for supporting the transmission system operator decision making, namely in the identification of critical components and in the planning of future investments in the transmission power system. A case study based on Reliability Test System (RTS) 1996 IEEE 24 Bus is presented to illustrate with detail the application of the proposed methodology.

Controladores linguísticos Fuzzy

Trabalho Final de Mestrado para obtenção do grau de Mestre em Engenharia Mecânica