Beyond Simulation: Computer Aided Control System Design Using Equation-Based Object Oriented Modelling for the Next Decade

After 20 years since their birth, equation-oriented and object-oriented modelling techniques and tools are now mature, as far as solving simulation problems is concerned. Conversely, there is still much to be done in order to provide more direct support for the design of advanced, model-based control systems, starting from object-oriented plant models. Following a brief review of the current state of the art in this field, the paper presents some proposals for future developments: open model exchange formats, automatic model-order reduction techniques, automatic derivation of simplified transfer functions, automatic derivation of LFT models, automatic generation of inverse models for robotic systems, and support for nonlinear model predictive control

An XML Representation of DAE Systems Obtained from Modelica Models

This contribution presents an XML format for representation of flat Modelica models. The purpose is to offer a standardized model exchange format which is based on the DAE formalism and wich is neutral with respect to model usage. Many usages of models goes beyond what can be obtained from an execution interface offering evaluation of the model equations. Several such usages arises in the area of control engineering, where LFT transformations, derivation of robotic controllers, optimization, and real time code generation are some examples. The choice of XML is motivated by its defacto standard status and the availability of free and efficient tools. Also, the XSLT language enables specification of transformation of the XML model representation into other formats

Overdetermined Steady-State Initialization Problems in Object-Oriented Fluid System Models

The formulation of steady-state initialization problems for fluid systems is a non-trivial task. If steady-state equations are specified at the component level, the corresponding system of initial equations at the system level might be overdetermined, if index reduction eliminates some states. On the other hand, steady-state equations are not sufficient to uniquely identify one equilibrium state in the case of closed systems, so additional equations are required. The paper shows how these problems might be solved in an elegant way by formulating overdetermined initialization problems, which have more equations than unknowns and a unique solution, then solving them using a least-squares minimization algorithm. The concept is tested on a representative test case using the OpenModelica compiler

Towards the prediction of renewable energy unbalance in smart grids

The production of renewable energy is increasing worldwide. To integrate renewable sources in electrical smart grids able to adapt to changes in power usage in heterogeneous local zones, it is necessary to accurately predict the power production that can be achieved from renewable energy sources. By using such predictions, it is possible to plan the power production from non-renewable energy plants to properly allocate the produced power and compensate possible unbalances. In particular, it is important to predict the unbalance between the power produced and the actual power intake at a local level (zones). In this paper, we propose a novel method for predicting the sign of the unbalance between the power produced by renewable sources and the power intake at the local level, considering zones composed of multiple power plants and with heterogeneous characteristics. The method uses a set of historical features and is based on Computational Intelligence techniques able to learn the relationship between historical data and the power unbalance in heterogeneous geographical regions. As a case study, we evaluated the proposed method using data collected by a player in the energy market over a period of seven months. In this preliminary study, we evaluated different configurations of the proposed method, achieving results considered as satisfactory by a player in the energy market

Noisy iris segmentation with boundary regularization and reflections removal

The paper presents an innovative algorithm for the segmentation of the iris in noisy images, with boundaries regularization and the removal of the possible existing reflections. In particular, the method aims to extract the iris pattern from the eye image acquired at the visible wavelength, in an uncontrolled environment where reflections and occlusions can also be present, on-the-move and at variable distance. The method achieves the iris segmentation by the following three main steps. The first step locates the centers of the pupil and the iris in the input image. Then two image strips containing the iris boundaries are extracted and linearizated. The last step locates the iris boundary points in the strips and it performs a regularization operation by achieving the exclusion of the outliers and the interpolation of missing points. The obtained curves are then converted into the original image space in order to produce a first segmentation output. Occlusions such as reflections and eyelashes are then identified and removed from the final area of the segmentation. Results indicate that the presented approach is effective and suitable to deal with the iris acquisition in noisy environments. The proposed algorithm ranked seventh in the international Noisy Iris Challenge Evaluation (NICE.I)

Neural-based quality measurement of fingerprint images in contactless biometric systems

Traditional fingerprint biometric systems capture the user fingerprint images by a contact-based sensor. Differently, contactless systems aim to capture the fingerprint images by an approach based on a vision system without the need of any contact of the user with the sensor. The user finger is placed in front of a special CCD-based system that captures the pattern of ridges and valleys of the fingertips. This approach is less constrained by the point of view of the user, but it requires much more capability of the system to deal with the focus of the moving target, the illumination problems and the complexity of the background in the captured image. During the acquisition procedure, the quality of each frame must be carefully evaluated in order to extract only the correct frames with valuable biometric information from the sequence. In this paper, we present a neural-based approach for the quality estimation of the contactless fingertips images. The application of the neural classification models allowed for a relevant reduction of the computational complexity permitting the application in realtime. Experimental results show that the proposed method has an adequate accuracy, and it can capture fingerprints at a distance up to 0.2 meters

ALL-IDB : the acute lymphoblastic leukemia image database for image processing

The visual analysis of peripheral blood samples is an important test in the procedures for the diagnosis of leukemia. Automated systems based on artificial vision methods can speed up this operation and increase the accuracy and homogeneity of the response also in telemedicine applications. Unfortunately, there are not available public image datasets to test and compare such algorithms. In this paper, we propose a new public dataset of blood samples, specifically designed for the evaluation and the comparison of algorithms for segmentation and classification. For each image in the dataset, the classification of the cells is given, as well as a specific set of figures of merits to fairly compare the performances of different algorithms. This initiative aims to offer a new test tool to the image processing and pattern matching communities, direct to stimulating new studies in this important field of research

Neural-based iterative approach for iris detection in iris recognition systems

The detection of the iris boundaries is considered in the literature as one of the most critical steps in the identification task of the iris recognition systems. In this paper we present an iterative approach to the detection of the iris center and boundaries by using neural networks. The proposed algorithm starts by an initial random point in the input image, then it processes a set of local image properties in a circular region of interest searching for the peculiar transition patterns of the iris boundaries. A trained neural network processes the parameters associated to the extracted boundaries and it estimates the offsets in the vertical and horizontal axis with respect to the estimated center. The coordinates of the starting point are then updated with the processed offsets. The steps are then iterated for a fixed number of epochs, producing an iterative refinements of the coordinates of the pupils center and its boundaries. Experiments showed that the method is feasible and it can be exploited even in non-ideal operative condition of iris recognition biometric systems

Automated border control systems: biometric challenges and research trends

Automated Border Control (ABC) systems automatically verify the travelers\u2019 identity using their biometric information, without the need of a manual check, by comparing the data stored in the electronic document (e.g., the e-Passport) with a live sample captured during the crossing of the border. In this paper, the hardware and software components of the biometric systems used in ABC systems are described, along with the latest challenges and research trends

ECG biometric recognition : permanence analysis of QRS signals for 24 hours continuous authentication

Recent studies regard the use of ECG signals for biometric recognition exploiting the possibility of these signals to be frequently recorded for long time periods without any explicit actions performed by the users during the acquisitions. This aspect makes ECG signals particularly suitable for continuous authentication applications. In this context, researches have proved that the QRS complex is the most stable component of the ECG signal. In this paper, we perform a preliminary study on the persistency of QRS signals for continuous authentication systems. A recognition method based on multiple leads is proposed, and used to evaluate the persistency of the QRS complex in 24 hours Holter signals. This time interval can be considered as adequate for many possible applications in continuous authentication scenarios. The analysis is performed on a significantly large public Holter dataset and aims to search accurate matching and enrollment strategies for continuous authentication systems. At the best our knowledge, the results presented in this paper are based on the biggest set of ECG signals used to design continuous authentication applications in the literature. Results suggest that the QRS complex is stable only for a relatively small time period, and the performance of the proposed recognition method starts decreasing after two hours