1,721,003 research outputs found
Optimizing energy production of an Inertial Sea Wave Energy Converter via Model Predictive Control
No full textWe introduce an original Model Predictive Control (MPC) approach, aimed at optimizing the energy production of an Inertial Sea Wave Energy Converter (ISWEC). The aim of the proposed method is to handle the performance tradeoff among different control objectives such as energy production, control effort and speed limitation through a suitable MPC formulation. Mechanical and electrical physical constraints are taken into account to ensure proper working conditions to the ISWEC system. We employ a linear model of the ISWEC device to cast the underlying MPC optimization problem as a quadratic program, to allow a fast online implementation of the controller by means of an efficient solver. We introduce extensive simulation tests performed on a detailed nonlinear ISWEC model to show the effectiveness of the presented approach
Fast sparse optimization via adaptive shrinkage
Full text linkThe need for fast sparse optimization is emerging, e.g., to deal with large-dimensional data-driven problems and to track time-varying systems. In the framework of linear sparse optimization, the iterative shrinkage-thresholding algorithm is a valuable method to solve Lasso, which is particularly appreciated for its ease of implementation. Nevertheless, it converges slowly. In this paper, we develop a proximal method, based on logarithmic regularization, which turns out to be an iterative shrinkage-thresholding algorithm with adaptive shrinkage hyperparameter. This adaptivity substantially enhances the trajectory of the algorithm, in a way that yields faster convergence, while keeping the simplicity of the original method. Our contribution is twofold: on the one hand, we derive and analyze the proposed algorithm; on the other hand, we validate its fast convergence via numerical experiments and we discuss the performance with respect to state-of-the-art algorithms
One-shot set-membership identification of Generalized Hammerstein–Wiener systems
No full textIn this paper we consider the identification of Generalized Hammerstein–Wiener systems in the presence of bounded noise affecting both the input and the output sequences. The considered class of systems is characterized by the cascade connection of Hammerstein building blocks, where the nonlinear static input map is a polynomial, which is not assumed to be invertible. The problem of computing the parameter uncertainty intervals is formulated in terms of a set of suitable semialgebraic optimization problems, where both the parameter to be estimated and the inner unmeasurable signals connecting all the subsystems are considered as decision variables. The effectiveness of the proposed algorithm is shown by means of a numerical simulation example. The presented approach is also applied to the problem of identifying the mathematical model of a JFET amplifier from a set of experimentally collected data
A kernel-based approach to errors-in-variables identification of stable multivariable linear systems
Full text linkIn this paper, we present a kernel-based non-parametric approach to identifying stable multi-input multi-output linear systems in the presence of bounded noise affecting both the input and the output measurements. Firstly, we formulate the considered problem in terms of robust optimization techniques. Then, we show that the formulated robust optimization problem can be solved using semidefinite optimization. Since the involved optimization problem is computationally demanding, we also provide a result that allows the user to compute a bound on the approximation error introduced by considering reduced complexity models. We present some simulation examples to show the effectiveness of the proposed approach. Finally, we apply the proposed identification method to the dataset experimentally collected on a linear electronic filter
A non-convex adaptive regularization approach to binary optimization
Full text linkBinary optimization is a long-time problem ubiquitous in many engineering applications, e.g., automatic control, cyber-physical systems and machine learning. From a mathematical viewpoint, binary optimization is an NP-hard problem, to solve which one can find some suboptimal strategies in the literature. Among the most popular approaches, semidefinite relaxation has attracted much attention in the last years. In contrast, this work proposes and analyzes a non-convex regularization approach, through which we obtain a relaxed problem whose global minimum corresponds to the true binary solution of the original problem. Moreover, because the problem is non-convex, we propose an adaptive regularization that promotes the descent towards the global minimum. We provide both theoretical results that characterize the proposed model and numerical experiments that prove its effectiveness with respect to state-of-the-art methods
A Pitch Wave Force Prediction Algorithm for the Inertial Sea Wave Energy Converter
No full textIn this paper we introduce a pitch wave force predictor for a wave energy converter. The designed predictor is composed of two subsystems. First, we design a virtual sensor in order to provide a real-time estimate of the pitch wave force acting on the converter, from measurements provided by a set of on-board sensors. Then, we build an autoregressive predictor on such a virtual measurement to predict the future value of the wave force over a finite prediction horizon length. Simulation results, performed by applying the proposed algorithm on a first-principles based Simulink model of the considered sea wave energy converter, demonstrate the effectiveness of the proposed approach
The platform switching approach to optimize split crest technique.
No full textThe split crest technique is a reliable procedure used simultaneously in the implant positioning. In the literature some authors describe a secondary bone resorption as postoperative complication. The authors show how platform switching can be able to avoid secondary resorption as complication of split crest technique
Lasso-based state estimation for cyber-physical systems under sensor attacks
Full text linkThe development of algorithms for secure state estimation in vulnerable cyber-physical systems has been gaining attention in the last years. A consolidated assumption is that an adversary can tamper a relatively small number of sensors. In the literature, block-sparsity methods exploit this prior information to recover the attack locations and the state of the system. In this paper, we propose an alternative, Lasso-based approach and we analyse its effectiveness. In particular, we theoretically derive conditions that guarantee successful attack/state recovery, independently of established time sparsity patterns. Furthermore, we develop a sparse state observer, by starting from the iterative soft thresholding algorithm for Lasso, to perform online estimation. Through several numerical experiments, we compare the proposed methods to the state-of-the-art algorithms
Multidisciplinary Approach to Fused Maxillary central Incisors: a Case Report
No full textIntroduction: The fusion of permanent teeth is a development anomaly of dental hard tissue. It may require a hard
multidisciplinary approach with orthodontics, endodontics, surgery and prosthetics to solve aesthetic and functional
problems.
Case presentation: A 20-year-old Caucasian man presented to our Department to solve a dental anomaly of his
upper central incisors. An oral investigation revealed the fusion of his maxillary central incisors and dyschromia of
right central incisor. Vitality pulp tests were negative for lateral upper incisors and left central incisor. Radiographic
examinations showed a fused tooth with two separate pulp chambers, two distinct roots and two separate root
canals. There were also periapical lesions of central incisors and right lateral incisor, so he underwent endodontic
treatment. Six months later, OPT examination revealed persistence of the periapical radiolucency, so endodontic
surgery was performed, which included exeresis of the lesion, an apicoectomy and retrograde obturation with a
reinforced zinc oxide-eugenol cement (SuperEBA) Complete healing of the lesion was obtained six months
postoperatively. Fused teeth crowns were separated and orthodontic appliances were put in place. When correct
teeth position was achieved (after nine months), the anterior teeth were prosthetically rehabilitated.
Conclusion: Many treatment options have been proposed in the literature to solve cases of dental fusion. The best
treatment plan depends on the nature of the anomaly, its location, the morphology of the pulp chamber
and root canal system, the subgingival extent of the separation line, and the patient compliance. Following
an analysis of radiographical and clinical data, it was possible to solve our patient’s dental anomaly with a
multidisciplinary approach
- …
