Classi cation of new electricity customers based on surveys and smart metering data

This paper proposes a process for the classifi cation of new residential electricity customers. The current state of the art is extended by using a combination of smart metering and survey data and by using model-based feature selection for the classifi cation task. Firstly, the normalized representative consumption profi les of the population are derived through the clustering of data from households. Secondly, new customers are classifi ed using survey data and a limited amount of smart metering data. Thirdly, regression analysis and model-based feature selection results explain the importance of the variables and which are the drivers of diff erent consumption profi les, enabling the extraction of appropriate models. The results of a case study show that the use of survey data signi ficantly increases accuracy of the classifi cation task (up to 20%). Considering four consumption groups, more than half of the customers are correctly classifi ed with only one week of metering data, with more weeks the accuracy is signifi cantly improved. The use of model-based feature selection resulted in the use of a signifi cantly lower number of features allowing an easy interpretation of the derived models.

A fuzzy clustering approach to a demand response model

This paper proposes a novel demand response model using a fuzzy subtractive cluster approach. The model development provides support to domestic consumer decisions on controllable loads management, considering consumers’ consumption needs and the appropriate load shape or rescheduling in order to achieve possible economic benefits. The model based on fuzzy subtractive clustering method considers clusters of domestic consumption covering an adequate consumption range. Analysis of different scenarios is presented considering available electric power and electric energy prices. Simulation results are presented and conclusions of the proposed demand response model are discussed.

How well can models predict changes in species distributions? A 13-year-old otter model revisited

Species distribution and ecological niche models are increasingly used in biodiversity management and conservation. However, one thing that is important but rarely done is to follow up on the predictive performance of these models over time, to check if their predictions are fulfilled and maintain accuracy, or if they apply only to the set in which they were produced. In 2003, a distribution model of the Eurasian otter (Lutra lutra) in Spain was published, based on the results of a country-wide otter survey published in 1998. This model was built with logistic regression of otter presence-absence in UTM 10 km2 cells on a diverse set of environmental, human and spatial variables, selected according to statistical criteria. Here we evaluate this model against the results of the most recent otter survey, carried out a decade later and after a significant expansion of the otter distribution area in this country. Despite the time elapsed and the evident changes in this species’ distribution, the model maintained a good predictive capacity, considering both discrimination and calibration measures. Otter distribution did not expand randomly or simply towards vicinity areas,m but specifically towards the areas predicted as most favourable by the model based on data from 10 years before. This corroborates the utility of predictive distribution models, at least in the medium term and when they are made with robust methods and relevant predictor variables.

A fuzzy clustering approach to a demand response model

This paper proposes a novel demand response model using a fuzzy subtractive cluster approach. The model development provides support to domestic consumer decisions on controllable loads management, considering consumers’ consumption needs and the appropriate load shape or rescheduling in order to achieve possible economic benefits. The model based on fuzzy subtractive clustering method considers clusters of domestic consumption covering an adequate consumption range. Analysis of different scenarios is presented considering available electric power and electric energy prices. Simulation results are presented and conclusions of the proposed demand response model are discussed.

Development and testing of innovative methodologies for modelling and control of normal and knocking combustion and implementation of novel rapid prototyping solutions

The thesis work deals with topics that led to the development of innovative control-oriented models and control algorithms for modern gasoline engines. Knock in boosted spark ignition engines is the widest topic discussed in this document because it remains one of the most limiting factors for maximizing combustion efficiency in this kind of engine. First chapter is thus focused on knock and a wide literature review is proposed to summarize the preliminary knowledge that even represents the background and the reference for discussed activities. Most relevant results achieved during PhD course in the field of knock modelling and control are then presented, describing every control-oriented model that led to the development of an adaptive model-based combustion control system. The complete controller has been developed in the context of the collaboration with Ferrari GT and it allowed to completely redefine the knock intensity evaluation as well as the combustion phase control. The second chapter is focused on the activity related to a prototyping Port Water Injection system that has been developed and tested on a turbocharged spark ignition engine, within the collaboration with Magneti Marelli. Such system and the effects of injected water on the combustion process were then modeled in a 1-D simulation environment (GT Power). Third chapter shows the development and validation of a control-oriented model for the real-time calculation of exhaust gas temperature that represents another important limitation to the performance increase in modern boosted engines. Indeed, modelling of exhaust gas temperature and thermocouple behavior are themes that play a key role in the optimization of combustion and catalyst efficiency.

Robotic Sensing and Manipulation of Deformable Linear Objects with Learning-based methods

Nowadays robotic applications are widespread and most of the manipulation tasks are efficiently solved. However, Deformable-Objects (DOs) still represent a huge limitation for robots. The main difficulty in DOs manipulation is dealing with the shape and dynamics uncertainties, which prevents the use of model-based approaches (since they are excessively computationally complex) and makes sensory data difficult to interpret. This thesis reports the research activities aimed to address some applications in robotic manipulation and sensing of Deformable-Linear-Objects (DLOs), with particular focus to electric wires. In all the works, a significant effort was made in the study of an effective strategy for analyzing sensory signals with various machine learning algorithms. In the former part of the document, the main focus concerns the wire terminals, i.e. detection, grasping, and insertion. First, a pipeline that integrates vision and tactile sensing is developed, then further improvements are proposed for each module. A novel procedure is proposed to gather and label massive amounts of training images for object detection with minimal human intervention. Together with this strategy, we extend a generic object detector based on Convolutional-Neural-Networks for orientation prediction. The insertion task is also extended by developing a closed-loop control capable to guide the insertion of a longer and curved segment of wire through a hole, where the contact forces are estimated by means of a Recurrent-Neural-Network. In the latter part of the thesis, the interest shifts to the DLO shape. Robotic reshaping of a DLO is addressed by means of a sequence of pick-and-place primitives, while a decision making process driven by visual data learns the optimal grasping locations exploiting Deep Q-learning and finds the best releasing point. The success of the solution leverages on a reliable interpretation of the DLO shape. For this reason, further developments are made on the visual segmentation.

Novel methods of state estimation and parameter identification for high-performance motorcycles

This research activity aims at providing a reliable estimation of particular state variables or parameters concerning the dynamics and performance optimization of a MotoGP-class motorcycle, integrating the classical model-based approach with new methodologies involving artificial intelligence. The first topic of the research focuses on the estimation of the thermal behavior of the MotoGP carbon braking system. Numerical tools are developed to assess the instantaneous surface temperature distribution in the motorcycle's front brake discs. Within this application other important brake parameters are identified using Kalman filters, such as the disc convection coefficient and the power distribution in the disc-pads contact region. Subsequently, a physical model of the brake is built to estimate the instantaneous braking torque. However, the results obtained with this approach are highly limited by the knowledge of the friction coefficient (μ) between the disc rotor and the pads. Since the value of μ is a highly nonlinear function of many variables (namely temperature, pressure and angular velocity of the disc), an analytical model for the friction coefficient estimation appears impractical to establish. To overcome this challenge, an innovative hybrid solution is implemented, combining the benefit of artificial intelligence (AI) with classical model-based approach. Indeed, the disc temperature estimated through the thermal model previously implemented is processed by a machine learning algorithm that outputs the actual value of the friction coefficient thus improving the braking torque computation performed by the physical model of the brake. Finally, the last topic of this research activity regards the development of an AI algorithm to estimate the current sideslip angle of the motorcycle's front tire. While a single-track motorcycle kinematic model and IMU accelerometer signals theoretically enable sideslip calculation, the presence of accelerometer noise leads to a significant drift over time. To address this issue, a long short-term memory (LSTM) network is implemented.

Censored Linear Regression Models For Irregularly Observed Longitudinal Data Using The Multivariate-t Distribution.

In acquired immunodeficiency syndrome (AIDS) studies it is quite common to observe viral load measurements collected irregularly over time. Moreover, these measurements can be subjected to some upper and/or lower detection limits depending on the quantification assays. A complication arises when these continuous repeated measures have a heavy-tailed behavior. For such data structures, we propose a robust structure for a censored linear model based on the multivariate Student's t-distribution. To compensate for the autocorrelation existing among irregularly observed measures, a damped exponential correlation structure is employed. An efficient expectation maximization type algorithm is developed for computing the maximum likelihood estimates, obtaining as a by-product the standard errors of the fixed effects and the log-likelihood function. The proposed algorithm uses closed-form expressions at the E-step that rely on formulas for the mean and variance of a truncated multivariate Student's t-distribution. The methodology is illustrated through an application to an Human Immunodeficiency Virus-AIDS (HIV-AIDS) study and several simulation studies.

Supercritical Carbon Dioxide Extraction Of Capsaicinoids From Malagueta Pepper (capsicum Frutescens L.) Assisted By Ultrasound.

Extracts from malagueta pepper (Capsicum frutescens L.) were obtained using supercritical fluid extraction (SFE) assisted by ultrasound, with carbon dioxide as solvent at 15MPa and 40°C. The SFE global yield increased up to 77% when ultrasound waves were applied, and the best condition of ultrasound-assisted extraction was ultrasound power of 360W applied during 60min. Four capsaicinoids were identified in the extracts and quantified by high performance liquid chromatography. The use of ultrasonic waves did not influence significantly the capsaicinoid profiles and the phenolic content of the extracts. However, ultrasound has enhanced the SFE rate. A model based on the broken and intact cell concept was adequate to represent the extraction kinetics and estimate the mass transfer coefficients, which were increased with ultrasound. Images obtained by field emission scanning electron microscopy showed that the action of ultrasonic waves did not cause cracks on the cell wall surface. On the other hand, ultrasound promoted disturbances in the vegetable matrix, leading to the release of extractable material on the solid surface. The effects of ultrasound were more significant on SFE from larger solid particles.

Modeling The Effects Of Temperature And Ph On The Resistance Of Alicyclobacillus Acidoterrestris In Conventional Heat-treated Fruit Beverages Through A Meta-analysis Approach.

In this work, all publicly-accessible published findings on Alicyclobacillus acidoterrestris heat resistance in fruit beverages as affected by temperature and pH were compiled. Then, study characteristics (protocols, fruit and variety, °Brix, pH, temperature, heating medium, culture medium, inactivation method, strains, etc.) were extracted from the primary studies, and some of them incorporated to a meta-analysis mixed-effects linear model based on the basic Bigelow equation describing the heat resistance parameters of this bacterium. The model estimated mean D* values (time needed for one log reduction at a temperature of 95 °C and a pH of 3.5) of Alicyclobacillus in beverages of different fruits, two different concentration types, with and without bacteriocins, and with and without clarification. The zT (temperature change needed to cause one log reduction in D-values) estimated by the meta-analysis model were compared to those ('observed' zT values) reported in the primary studies, and in all cases they were within the confidence intervals of the model. The model was capable of predicting the heat resistance parameters of Alicyclobacillus in fruit beverages beyond the types available in the meta-analytical data. It is expected that the compilation of the thermal resistance of Alicyclobacillus in fruit beverages, carried out in this study, will be of utility to food quality managers in the determination or validation of the lethality of their current heat treatment processes.

Indução assimétrica 1,5-Anti na adição de enolatos de boro de metilcetonas beta-oxigenadas a aldeídos

High levels of substrate-based 1,5-stereoinduction are obtained in the boron-mediated aldol reactions of beta-oxygenated methyl ketones with achiral and chiral aldehydes. Remote induction from the boron enolates gives the 1,5-anti adducts, with the enolate pi-facial selectivity critically dependent upon the nature of the beta-alkoxy protecting group. This 1,5-anti aldol methodology has been strategically employed in the total synthesis of several natural products. At present, the origin of the high level of 1,5-anti induction obtained with the boron enolates is unclear, although a model based on a hydrogen bonding between the alkoxy oxygen and the formyl hydrogen has been recently proposed.

The full Bayesian significance test for mixture models: results in gene expression clustering

Gene clustering is a useful exploratory technique to group together genes with similar expression levels under distinct cell cycle phases or distinct conditions. It helps the biologist to identify potentially meaningful relationships between genes. In this study, we propose a clustering method based on multivariate normal mixture models, where the number of clusters is predicted via sequential hypothesis tests: at each step, the method considers a mixture model of m components (m = 2 in the first step) and tests if in fact it should be m - 1. If the hypothesis is rejected, m is increased and a new test is carried out. The method continues (increasing m) until the hypothesis is accepted. The theoretical core of the method is the full Bayesian significance test, an intuitive Bayesian approach, which needs no model complexity penalization nor positive probabilities for sharp hypotheses. Numerical experiments were based on a cDNA microarray dataset consisting of expression levels of 205 genes belonging to four functional categories, for 10 distinct strains of Saccharomyces cerevisiae. To analyze the method's sensitivity to data dimension, we performed principal components analysis on the original dataset and predicted the number of classes using 2 to 10 principal components. Compared to Mclust (model-based clustering), our method shows more consistent results.

Free expanding cloud of cold atoms as an atomic standard: Ramsey fringes contrast

A compact frequency standard based on an expanding cold (133)CS cloud is under development in our laboratory. In a first experiment, Cs cold atoms were prepared by a magneto-optical trap in a vapor cell, and a microwave antenna was used to transmit the radiation for the clock transition. The signal obtained from fluorescence of the expanding cold atoms cloud is used to lock a microwave chain. In this way the overall system stability is evaluated. A theoretical model based on a two-level system interacting with the two microwave pulses enables interpretation for the observed features, especially the poor Ramsey fringes contrast. (C) 2008 Optical Society of America.

The Effect of Spatial Scale on Predicting Time Series: A Study on Epidemiological System Identification

A susceptible-infective-recovered (SIR) epidemiological model based on probabilistic cellular automaton (PCA) is employed for simulating the temporal evolution of the registered cases of chickenpox in Arizona, USA, between 1994 and 2004. At each time step, every individual is in one of the states S, I, or R. The parameters of this model are the probabilities of each individual (each cell forming the PCA lattice ) passing from a state to another state. Here, the values of these probabilities are identified by using a genetic algorithm. If nonrealistic values are allowed to the parameters, the predictions present better agreement with the historical series than if they are forced to present realistic values. A discussion about how the size of the PCA lattice affects the quality of the model predictions is presented. Copyright (C) 2009 L. H. A. Monteiro et al.

Photolysis of ferric ions in the presence of sulfate or chloride ions: implications for the photo-Fenton process

The photo-Fenton process (Fe(2+)/Fe(3+), H(2)O(2), UV light) is one of the most efficient and advanced oxidation processes for the mineralization of the organic pollutants of industrial effluents and wastewater. The overall rate of the photo-Fenton process is controlled by the rate of the photolytic step that converts Fe(3+) back to Fe(2+). In this paper, the effect of sulfate or chloride ions on the net yield of Fe(2+) during the photolysis of Fe(3+) has been investigated in aqueous solution at pH 3.0 and 1.0 in the absence of hydrogen peroxide. A kinetic model based on the principal reactions that occur in the system fits the data for formation of Fe(2+) satisfactorily. Both experimental data and model prediction show that the availability of Fe(2+) produced by photolysis of Fe(3+) is inhibited much more in the presence of sulfate ion than in the presence of chloride ion as a function of the irradiation time at pH 3.0.