Diamantino Freitas

Combined Sizing and Energy Management in EVs With Batteries and Supercapacitors

This paper is concerned with the study of combined sizing and energy management algorithms for electric vehicles (EVs) endowed with batteries and supercapacitors (SCs). The main goal is to find the number of cells of each source that minimizes the installation and running costs of the EV, taking into account the performance requirements specified for the vehicle and the technical constraints of the energy sources. To tackle this problem, two methodologies will be investigated. The first considers a filter-based approach to perform the power split among the sources; it will be shown that, under some practical assumptions, the resultant sizing problem can be posed as a linear programming problem and solved using efficient numerical techniques. The second methodology employs an optimal noncausal energy management, which, when integrated with the sizing problem, yields a nonlinear optimization problem. These two methodologies will be then applied to size the storage unit of a small EV. The results indicate that the filter-based approach, although simple and numerically efficient, generally requires an oversized storage unit. Furthermore, it was also concluded that, if the range requirements of the EV are not very high (below 50 km, in our case study), the use of SCs enables energy savings of up to 7.8%.

Full and reduced order extended kalman filter for speed estimation in induction motor drives: a comparative study

This work presents a comparative study between a new approach for robust speed estimation in induction motor sensorless control, using a reduced order extended Kalman filter (EKF), and the one performed by the full order EKF. The new EKF algorithm uses a reduced order state-space model that is discretized in a particular and innovative way. In this case only the rotor flux components are estimated, besides the rotor speed, while the full order EKF also estimates stator current components. This new approach strongly reduces the execution time and simplifies the tuning of covariance matrices. The performance of speed estimation using both EKF techniques is compared with respect to computation effort, tuning of the algorithms, speed range including low speeds, load torque conditions and robustness relatively to motor parameter sensitivity.

Toward Dynamic Magnetic Resonance Imaging of the Vocal Tract During Speech Production

OBJECTIVES/HYPOTHESIS The most recent and significant magnetic resonance imaging (MRI) improvements allow for the visualization of the vocal tract during speech production, which has been revealed to be a powerful tool in dynamic speech research. However, a synchronization technique with enhanced temporal resolution is still required. STUDY DESIGN AND METHODS The study design was transversal in nature. Throughout this work, a technique for the dynamic study of the vocal tract with MRI by using the hearts signal to synchronize and trigger the imaging-acquisition process is presented and described. The technique in question is then used in the measurement of four speech articulatory parameters to assess three different syllables (articulatory gestures) of European Portuguese Language. RESULTS The acquired MR images are automatically reconstructed so as to result in a variable sequence of images (slices) of different vocal tract shapes in articulatory positions associated with Portuguese speech sounds. CONCLUSIONS The knowledge obtained as a result of the proposed technique represents a direct contribution to the improvement of speech synthesis algorithms, thereby allowing for novel perceptions in coarticulation studies, in addition to providing further efficient clinical guidelines in the pursuit of more proficient speech rehabilitation processes.

A new approach for speed estimation in induction motor drives based on a reduced-order extended Kalman filter

This paper presents and proposes a new approach to achieve robust speed estimation in induction motor sensorless control. The estimation method is based on a reduced-order extended Kalman filter (EKF), instead of a full-order EKF. The EKF algorithm uses a reduced-order state-space model structure that is discretized in a particular and innovative way proposed in this paper. With this model structure, only the rotor flux components are estimated, besides the rotor speed itself. Important practical aspects and new improvements are introduced that enable us to reduce the execution time of the algorithm without difficulties related to the tuning of covariance matrices, since the number of elements to be adjusted is reduced.

Morphologic Differences in the Vocal Tract Resonance Cavities of Voice Professionals: An MRI-Based Study

UNLABELLED The processes that take place during singing and acting are complex. However, morphologic and dynamic studies of the vocal tracts during speech have been gaining greater attention, mainly because of the rapid technical advances being made with magnetic resonance imaging (MRI) and image analysis and processing techniques. OBJECTIVES/HYPOTHESIS AND METHODS: Our aim was to describe the morphologic differences in the vocal tract resonance cavities among voice professionals using MRI, and with the three-dimensional models built from the MRI data, compare the volumes calculated for the whole vocal tract and its major resonance cavities. STUDY DESIGN Prospective study. RESULTS The images acquired during the spoken and singing tasks provided morphologic information about the whole vocal tract cavity and also its two major resonance cavities: the oral and the pharynx cavities; differences were observed in the volumes measured during the production of some vowels. CONCLUSIONS The morphologic differences found in the singing voices indicated an increase in the oral cavity volume, resulting in an increase in the overall volumes measured. In the acting voices, both the resonance cavities of the vocal tract contributed to the differences measured.

Towards the Automatic Study of the Vocal Tract From Magnetic Resonance Images

Over the last few decades, researchers have been investigating the mechanisms involved in speech production. Image analysis can be a valuable aid in the understanding of the morphology of the vocal tract. The application of magnetic resonance imaging to study these mechanisms has been proven to be reliable and safe. We have applied deformable models in magnetic resonance images to conduct an automatic study of the vocal tract; mainly, to evaluate the shape of the vocal tract in the articulation of some European Portuguese sounds, and then to successfully automatically segment the vocal tracts shape in new images. Thus, a point distribution model has been built from a set of magnetic resonance images acquired during artificially sustained articulations of 21 sounds, which successfully extracts the main characteristics of the movements of the vocal tract. The combination of that statistical shape model with the gray levels of its points is subsequently used to build active shape models and active appearance models. Those models have then been used to segment the modeled vocal tract into new images in a successful and automatic manner. The computational models have thus been revealed to be useful for the specific area of speech simulation and rehabilitation, namely to simulate and recognize the compensatory movements of the articulators during speech production.

Flux and parameters identification of vector-controlled induction motor in the rotor reference frame

This paper presents a new approach for the simultaneous identification of rotor flux components in the rotor reference frame and electrical parameters of a vector controlled induction motor, for real-time implementations, using an extended Kalman filter (EKF) and a reduced order model structure for lower computational effort. The proposed new method requires the measurement of motor speed, stator voltages and currents signals. Using a motor model structure with four electrical parameters, the estimation of flux space phasor and rotor parameters is presented The estimation is subsequently further extended to include the motor stator parameters and the results are analyzed as well as robustness. Simulated and experimental studies highlight the improvements brought by this new approach, mainly, a simple and reduced state equation, the introduced scalar output equation and lower computational cadency by using lower sampling frequencies in the proposed rotor reference frame.

Chronic exposure of rats to cotton-mill-room noise changes the cell composition of the tracheal epithelium.

The work environment of cotton mill rooms of modern textile plants is characterized by noise pollution. We have taped and reproduced this noisy environment to study its effects on experimentally exposed rats. Because we have previously documented that chronic noise causes alterations in the respiratory epithelium, we have focused our investigation on the morphology of the tracheal lining. Wistar rats were exposed to the textile-type noise from 1 up to 7 months, with an average 40 hours weekly exposure of the animals. The rats were sacrificed monthly and the tracheas were studied by scanning electron microscopy (SEM) to quantify the areas of the airway lining that were covered by ciliated, serous or other cells of the epithelium. We found that noise exposure of the rats caused a significant loss of tracheal ciliated cells; an increased density of serous cells on the epithelium balanced this change. This modification of the rat trachea was already established after 1 month of noise treatment of the animals; it did not change significantly throughout the 7-month course of the herein investigation. Loss of ciliated cells was more intense in areas of the tracheal epithelium located between the regions of cartilage rings. We conclude that the ciliated cell is an elective target for damage caused on the respiratory epithelium by the workplace noise occurring in cotton mill rooms. This modification of the respiratory epithelium is likely to impair clearance of the airways since this function depends on the activity of ciliated cells.

The development of vector control signal processing blockset for Simulink: philosophy and implementation

Simulation is a useful tool which can significantly reduce resources invested during product development. The paper describes the development of the vector control signal processing (VCSP) blockset for use with MATLAB/Simulink(R). The originality of this blockset consists on the extension of Simulink for design, simulation and prototyping of signal processing algorithms in power conversion systems. The VCSP blockset, consists of a number of newly developed blocks. Together with Real-Time Workshop/sup TM/ and a number of other generally available blocks, it takes advantage of the inherent visual programming techniques of Simulink to reach the above goals. Due to its open and flexible nature, this approach is also very useful as a tool for teaching. The work is focused on modelling and simulation for power conversion systems use in the drive of rotating AC machines with vector control methods. By concept, a simulation environment can be examined at different abstraction or hierarchy levels. The approach in this paper is the formulation of a simulation task at different levels: component level, electric circuit level, functional description and implementation description. The basic blockset functions are presented and some examples of modelling techniques for both simple and complex drive structures are included. Simulation results are presented and discussed as well.

A new linear parametrization for peak friction coefficient estimation in real time

The correct estimation of the friction coefficient in automotive applications is of paramount importance in the design of effective vehicle safety systems. In this article a new parametrization for estimating the peak friction coefficient, in the tire-road interface, is presented. The proposed parametrization is based on a feedforward neural network (FFNN), trained by the Extreme Learning Machine (ELM) method. Unlike traditional learning techniques for FFNN, typically based on backpropagation and inappropriate for real time implementation, the ELM provides a learning process based on random assignment in the weights between input and the hidden layer. With this approach, the network training becomes much faster, and the unknown parameters can be identified through simple and robust regression methods, such as the Recursive Least Squares. Simulation results, obtained with the CarSim program, demonstrate a good performance of the proposed parametrization; compared with previous methods described in the literature, the proposed method reduces the estimation errors using a model with a lower number of parameters.