Construction of optimal prediction intervals for load forecasting problems

Short-term load forecasting is fundamental for the reliable and efficient operation of power systems. Despite its importance, accurate prediction of loads is problematic and far remote. Often uncertainties significantly degrade performance of load forecasting models. Besides, there is no index available indicating reliability of predicted values. The objective of this study is to construct prediction intervals for future loads instead of forecasting their exact values. The delta technique is applied for constructing prediction intervals for outcomes of neural network models. Some statistical measures are developed for quantitative and comprehensive evaluation of prediction intervals. According to these measures, a new cost function is designed for shortening length of prediction intervals without compromising their coverage probability. Simulated annealing is used for minimization of this cost function and adjustment of neural network parameters. Demonstrated results clearly show that the proposed methods for constructing prediction interval outperforms the traditional delta technique. Besides, it yields prediction intervals that are practically more reliable and useful than exact point predictions.

Predicting amount of saleable products using neural network metamodels of casthouses

This study aims at developing abstract metamodels for approximating highly nonlinear relationships within a metal casting plant. Metal casting product quality nonlinearly depends on many controllable and uncontrollable factors. For improving the productivity of the system, it is vital for operation planners to predict in advance the amount of high quality products. Neural networks metamodels are developed and applied in this study for predicting the amount of saleable products. Training of metamodels is done using the Levenberg-Marquardt and Bayesian learning methods. Statistical measures are calculated for the developed metamodels over a grid of neural network structures. Demonstrated results indicate that Bayesian-based neural network metamodels outperform the Levenberg-Marquardt-based metamodels in terms of both prediction accuracy and robustness to the metamodel complexity. In contrast, the latter metamodels are computationally less expensive and generate the results more quickly.

Performance score variation between days at Australian national and Olympic women's artistic gymnastics competition

We determined the inter-day variability in elite-standard women's artistic gymnastics competition scores. National (50 gymnasts for up to three days) and Olympic (24 gymnasts for up to five days) competition scores published in the public domain (‘Giant poster pull-out’, 2010; Gymnastics Australia, 2008) were evaluated using three statistical measures. Analyses of the inter-day differences in the mean scores as a percentage (MDiff%), coefficient of variation percentages for the mean score across both days (CV%), and Pearson correlation coefficients for the inter-day score (r) were interpreted using thresholds from trivial to large. National-class gymnasts' two-day performance variation was trivial for vault, small for floor and beam, and moderate for bars. When senior gymnasts competed for a third day the performance variation increased to moderate for vault. Across five days of Olympic competition there were trivial (e.g. CV%: vault = 0.8) to small (e.g. CV%: bars = 2.0) variations in performances between days on all apparatus. Olympians' performance score consistency is superior to senior, national-class competitors. The performance score consistency required for gymnasts who aspire to participate at the Olympics as a top-24 competitor is better than 3%.

Further applications of doppler radar for non-contact respiratory assessment

This paper further investigates the use of Doppler radar for detecting and identifying certain human respiratory characteristics from observed frequency and phase modulations. Specifically, we show how breathing frequencies can be determined from the demodulated signal leading to identifying abnormalities of breathing patterns using signal derivatives, optimal filtering and standard statistical measures. Specifically, we report results on a robust method for distinguishing cessation of the normal breathing cycle. The proposed approach can have potential application in the management of sudden infant death syndrome(SIDS) and sleep apnea.

Comparativo de metodologias de mensuração de VAR para o mercado financeiro brasileiro

Várias metodologias de mensuração de risco de mercado foram desenvolvidas e aprimoradas ao longo das últimas décadas. Enquanto algumas metodologias usam abordagens não-paramétricas, outras usam paramétricas. Algumas metodologias são mais teóricas, enquanto outras são mais práticas, usando recursos computacionais através de simulações. Enquanto algumas metodologias preservam sua originalidade, outras metodologias têm abordagens híbridas, juntando características de 2 ou mais metodologias. Neste trabalho, fizemos uma comparação de metodologias de mensuração de risco de mercado para o mercado financeiro brasileiro. Avaliamos os resultados das metodologias não-paramétricas e paramétricas de mensuração de VaR aplicados em uma carteira de renda fixa, renda variável e renda mista durante o período de 2000 a 2006. As metodologias não-paramétricas avaliadas foram: Simulação Histórica pesos fixos, Simulação Histórica Antitética pesos fixos, Simulação Histórica exponencial e Análise de Cenário. E as metodologias paramétricas avaliadas foram: VaR Delta-Normal pesos fixos, VaR Delta-Normal exponencial (EWMA), Simulação de Monte Carlo pesos fixos e Simulação de Monte Carlo exponencial. A comparação destas metodologias foi feita com base em medidas estatísticas de conservadorismo, precisão e eficiência.

Métodos estatísticos recursivos aplicados ao problema de estimação de vazão de gás em plantas de Plunger-lift

This work has as main objective to find mathematical models based on linear parametric estimation techniques applied to the problem of calculating the grow of gas in oil wells. In particular we focus on achieving grow models applied to the case of wells that produce by plunger-lift technique on oil rigs, in which case, there are high peaks in the grow values that hinder their direct measurement by instruments. For this, we have developed estimators based on recursive least squares and make an analysis of statistical measures such as autocorrelation, cross-correlation, variogram and the cumulative periodogram, which are calculated recursively as data are obtained in real time from the plant in operation; the values obtained for these measures tell us how accurate the used model is and how it can be changed to better fit the measured values. The models have been tested in a pilot plant which emulates the process gas production in oil wells

Escala Baptista de Depressão (Versão Adulto) - EBADEP-A: validade convergente e estabilidade temporal

Os objetivos principais desta pesquisa foram avaliar evidências de validade convergente entre a Escala Baptista de Depressão (Versão Adulto) - EBADEP-A e o Inventário de Depressão de Beck - BDI-II, além de avaliar a estabilidade temporal da EBADEP-A por intermédio do teste e reteste em um período de um mês. Fizeram parte da pesquisa 173 universitários de uma amostra de conveniência com média de idade de 24,45 (DP=8,45), a maioria mulheres (87,9%). Após um mês, 65 participantes, a maioria mulheres (90,8%), com média de 21 anos (DP=5,48) responderam novamente os instrumentos. Os resultados demonstraram, de acordo com critérios internacionais, excelentes índices de correlação entre ambas as escalas, bem como no teste e reteste, demonstrando adequadas qualidades psicométrica da EBADEP-A também nesses quesitos, comprovando outros estudos já realizados. Esses resultados e as limitações do estudo também são discutidos.

Verificação e análise dos fatos estilizados no mercado de ações brasileiro

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Algorithms for automatic segmentation of bovine embryos produced

In vitro production has been employed in bovine embryos and quantification of lipids is fundamental to understand the metabolism of these embryos. This paper presents a unsupervised segmentation method for histological images of bovine embryos. In this method, the anisotropic filter was used in the differents RGB components. After pre-processing step, the thresholding technique based on maximum entropy was applied to separate lipid droplets in the histological slides in different stages: early cleavage, morula and blastocyst. In the postprocessing step, false positives are removed using the connected components technique that identify regions with excess of dye near pellucid zone. The proposed segmentation method was applied in 30 histological images of bovine embryos. Experiments were performed with the images and statistical measures of sensitivity, specificity and accuracy were calculated based on reference images (gold standard). The value of accuracy of the proposed method was 96% with standard deviation of 3%.

Segmentation of Cell Nuclei Regions in Epithelium of Prostate Glands

This paper proposes a method for segmentation of cell nuclei regions in epithelium of prostate glands. This structure provides information to diagnosis and prognosis of prostate cancer. In the initial step, the contrast stretching technique was applied in image in order to improve the contrast between regions of interest and other regions. After, the global thresholding technique was applied and the value of threshold was defined empirically. Finally, the false positive regions were removed using the connected components technique. The performance of the proposed method was compared with the Otsu technique and statistical measures of accuracy were calculated based on reference images (gold standard). The result of the mean value of accuracy of proposed method was 93% ± 0.07.

Avaliação do estresse oxidativo, estado nutricional e vitaminas lipossolúveis em crianças atendidas no ambulatório de puericultura. Recife (PE) Brasil

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

The exponential COM-Poisson distribution

The Conway-Maxwell Poisson (COMP) distribution as an extension of the Poisson distribution is a popular model for analyzing counting data. For the first time, we introduce a new three parameter distribution, so-called the exponential-Conway-Maxwell Poisson (ECOMP) distribution, that contains as sub-models the exponential-geometric and exponential-Poisson distributions proposed by Adamidis and Loukas (Stat Probab Lett 39:35-42, 1998) and KuAY (Comput Stat Data Anal 51:4497-4509, 2007), respectively. The new density function can be expressed as a mixture of exponential density functions. Expansions for moments, moment generating function and some statistical measures are provided. The density function of the order statistics can also be expressed as a mixture of exponential densities. We derive two formulae for the moments of order statistics. The elements of the observed information matrix are provided. Two applications illustrate the usefulness of the new distribution to analyze positive data.

A Phase Space Box-counting based Method for Arrhythmia Prediction from Electrocardiogram Time Series

Arrhythmia is one kind of cardiovascular diseases that give rise to the number of deaths and potentially yields immedicable danger. Arrhythmia is a life threatening condition originating from disorganized propagation of electrical signals in heart resulting in desynchronization among different chambers of the heart. Fundamentally, the synchronization process means that the phase relationship of electrical activities between the chambers remains coherent, maintaining a constant phase difference over time. If desynchronization occurs due to arrhythmia, the coherent phase relationship breaks down resulting in chaotic rhythm affecting the regular pumping mechanism of heart. This phenomenon was explored by using the phase space reconstruction technique which is a standard analysis technique of time series data generated from nonlinear dynamical system. In this project a novel index is presented for predicting the onset of ventricular arrhythmias. Analysis of continuously captured long-term ECG data recordings was conducted up to the onset of arrhythmia by the phase space reconstruction method, obtaining 2-dimensional images, analysed by the box counting method. The method was tested using the ECG data set of three different kinds including normal (NR), Ventricular Tachycardia (VT), Ventricular Fibrillation (VF), extracted from the Physionet ECG database. Statistical measures like mean (μ), standard deviation (σ) and coefficient of variation (σ/μ) for the box-counting in phase space diagrams are derived for a sliding window of 10 beats of ECG signal. From the results of these statistical analyses, a threshold was derived as an upper bound of Coefficient of Variation (CV) for box-counting of ECG phase portraits which is capable of reliably predicting the impeding arrhythmia long before its actual occurrence. As future work of research, it was planned to validate this prediction tool over a wider population of patients affected by different kind of arrhythmia, like atrial fibrillation, bundle and brunch block, and set different thresholds for them, in order to confirm its clinical applicability.

Decomposition driven interface evolution for layers of binary mixtures: I. Model dirivation and base states

Algebraic topology (homology) is used to analyze the state of spiral defect chaos in both laboratory experiments and numerical simulations of Rayleigh-Bénard convection. The analysis reveals topological asymmetries that arise when non-Boussinesq effects are present. The asymmetries are found in different flow fields in the simulations and are robust to substantial alterations to flow visualization conditions in the experiment. However, the asymmetries are not observable using conventional statistical measures. These results suggest homology may provide a new and general approach for connecting spatiotemporal observations of chaotic or turbulent patterns to theoretical models.

Inference of binding affinity from neuronal receptors in humans

Tese de mestrado, Bioinformática e Biologia Computacional (Bioinformática), Universidade de Lisboa, Faculdade de Ciências, 2016