This article presents an ingestion procedure towards an interoperable repository called ALPACS (Anonymized Local Picture Archiving and Communication System). ALPACS provides services to clinical and hospital users, who can access the repository data through an Artificial Intelligence (AI) application called PROXIMITY. This article shows the automated procedure for data ingestion from the medical imaging provider to the ALPACS repository. The data ingestion procedure was successfully applied by the data provider (Hospital Clínico de la Universidad de Chile, HCUCH) using a pseudo-anonymization algorithm at the source, thereby ensuring that the privacy of patients’ sensitive data is respected. Data transfer was carried out using international communication standards for health systems, which allows for replication of the procedure by other institutions that provide medical images. Objectives: This article aims to create a repository of 33,000 medical CT images and 33,000 diagnostic reports with international standards (HL7 HAPI FHIR, DICOM, SNOMED). This goal requires devising a data ingestion procedure that can be replicated by other provider institutions, guaranteeing data privacy by implementing a pseudo-anonymization algorithm at the source, and generating labels from annotations via NLP. Methodology: Our approach involves hybrid on-premise/cloud deployment of PACS and FHIR services, including transfer services for anonymized data to populate the repository through a structured ingestion procedure. We used NLP over the diagnostic reports to generate annotations, which were then used to train ML algorithms for content-based similar exam recovery. Outcomes: We successfully implemented ALPACS and PROXIMITY 2.0, ingesting almost 19,000 thorax CT exams to date along with their corresponding reports.

This article studies robust output tracking and disturbance rejection for boundary-controlled infinite-dimensional Port– Hamiltonian systems including second-order models such as the Euler–Bernoulli beam equation. The control design is achieved using the internal model principle and the stability analysis using a Lyapunov approach. Contrary to existing works on the same topic, no assumption is made on the external well-posedness of the considered class of PDEs. The results are applied to robust tracking of a piezo actuated tube used in atomic force imaging.

In this paper we consider the analysis of multiple-input multiple-output systems using the Participation Matrix, which provides a tool both for interaction measure and controller structure selection. For this matrix we present novel interpretations in the time and in the frequency domain, based on definition of Hilbert-Schmidt-Hankel norm. Moreover, the time domain interpretation is exploited to obtain an empirical estimate of the participation matrix directly from input-output data of the multivariable system.

The behavior of a fluid in pipes with irregular geometries is studied. Departing from the partial differential equations that describe mass and momentum balances a scalable lumped-parameter model is proposed. To this end the framework of port-Hamiltonian systems is instrumental to derive a modular system which upon interconnection describes segments with different cross sections and dissipation effects. In order to perform the interconnection between different segments the incompressibility hypothesis is relaxed in some infinitesimal section to admit density variations and energy transference between segments. Numerical simulations are performed in order to illustrate the model.

An irreversible port-Hamiltonian system formulation of a class of piezoelectric actuator with non-negligible entropy increase is proposed. The proposed model encompasses the hysteresis and the irreversible thermodynamic changes due to mechanical friction, electrical resistance and heat exchange between the actuator and the environment. The electromechanical dynamic of the actuator is modeled using a non-linear resistive-capacitive-inductor circuit coupled with a mass-spring-damper system, while the non-linear hysteresis is characterized using hysterons. The thermodynamic behavior of the model is constructed by making the electromechanical coupling temperature dependent, and by characterizing the entropy produced by the irreversible phenomena. By means of numerical simulations it is shown that the proposed model is capable of reproducing the expected behaviors and is in line with reported experimental results.

We consider the discretization of continuous-time nonlinear systems described by normal forms. In particular, we consider the case when the input to the system is generated by a B-spline hold device to obtain an approximate discrete-time model. It is shown that the corresponding sampled-data model and its accuracy (measured in terms of the local truncation error) depend on the smoothness of the input and on the applied integration strategy, namely, the truncated Taylor series expansion. Moreover, the sampling zero dynamics of the discrete-time model are asymptotically characterized as the sampling period goes to zero, and it is shown that these zero dynamics converge to the asymptotic sampling zeros of the linear case.

The control law of electronically-interfaced distributed energy resources (DERs) must be able to maintain the stability and voltage regulation of the host microgrid in the two modes of operation. Ideally, this should be achieved by a decentralized primary control strategy that is independent of any communication infrastructure in order to increase the resilience of the microgrid. This is challenging as the primary control objectives in islanded and grid-connected modes of operation are conflicting. This paper proposes a decentralized control law for DER units based on state feedback and disturbance rejection. The controller provides an integral action which enables output current reference tracking. An ad-hoc partial input saturation technique is also proposed in order to prevent the integral action from having an adverse impact on the voltage amplitude and frequency regulation in the islanded mode of operation. The effectiveness of the proposed control strategy is demonstrated via a time-domain simulation of a medium-voltage distribution network with three embedded DER units, as well as through an experimental three-bus microgrid with two DER units. The results demonstrate the robustness of the proposed control strategy to transitions between the modes of operation and other network topological changes.

In this paper we consider general port-Hamiltonian formulations of multidimensional Maxwell’s viscoelastic fluids. Two different cases are considered to describe the energy fluxes in isentropic compressible and incompressible fluids. In the compressible case, the viscoelastic effects of shear and dilatational strains on the stress tensor are described individually through the corresponding constitutive equations. In the incompressible case, an approach based on the bulk modulus definition is proposed in order to obtain an appropriate characterization, from the port-Hamiltonian point of view, of the pressure and nonlinear terms in the momentum equation, associated with both dynamic pressure and vorticity of the flow.

In this paper we develop a novel algorithm to identify an auto-regressive with exogenous signal system utilizing quantized output data. We use the Expectation-Maximization algorithm to obtain the Maximum Likelihood estimate.

In this paper, we explore the identification of sparse FIR systems having quantized output data. Our approach is based on the use of regularization. We explore several aspects concerning the implementation of the Expectation-Maximization (EM) algorithm, including: i) a general framework, based on mean-variance Gaussian mixtures, for incorporating a regularization term that forces sparsity, ii) utilization of Markov Chain Monte Carlo techniques (namely a Gibbs sampler) and scenarios to implement the EM algorithm for multiple input multiple output systems. We show that for single input single output systems, it is possible to obtain closed form expressions for solving the EM algorithm.

In this paper we address the joint estimation of the channel impulse response in orthogonal frequency division multiplexing systems with phase distortion, namely phase noise and carrier frequency offset, phase noise bandwidth and the additive noise variance. The estimation algorithm is based on an implementation of the Extended Kalman Filter within the general framework of the Expectation-Maximization algorithm. We focus on the partial training case, where the transmitted signal is not fully known. To tackle this problem, we utilize a Rao-Blackwellized Extended Kalman Filter. We also compare our results with another nonlinear filtering technique, namely Rao-Blackwellized Particle Filtering, applied to this joint estimation problem. The performance of the two filtering techniques considered in this paper is evaluated in terms of the mean square error of the channel estimates and the numerical complexity introduced by each of these techniques.

Irreversible port-Hamiltonian systems (IPHS) are an extension of port-Hamiltonian systems (PHS) for irreversible thermodynamics which encompass a large class of thermodynamic systems that may contain reversible and irreversible phenomena. Energy shaping and damping injection are standard structure preserving passivity based control approaches which have proven to be very successful for the stabilization of PHS. However, in the case of irreversible thermodynamics, the non-linear nature of the systems make it non-trivial to apply these approaches for stabilization. In this paper we propose a systematic procedure to perform, in a first control loop, energy shaping by state modulated interconnection with a controller in IPHS form. Then, a second control loop guarantees asymptotic stability by the feedback of a new closed-loop passive output. The approach allows to stabilize IPHS while preserving the IPHS structure in closed-loop, allowing to interpret the closed-loop system as a desired thermodynamic system. The example of the continuous stirred tank reactor is used to illustrate the approach.

The control of a flexible beam using ionic polymer metal composites (IPMCs) is investigated in this paper. The mechanical flexible dynamics are modelled as a Timoshenko beam. The electric dynamics of the IPMCs are considered in the model. The port-Hamiltonian framework is used to propose an interconnected control model of the mechanical flexible beam and IPMC actuator. Furthermore, a passive and Hamiltonian structure-preserving linear quadratic Gaussian (LQG) controller is used to achieve the desired configuration of the system, and the asymptotic stability of the closed-loop system is shown using damping injection. An experimental setup is built using a flexible beam actuated by two IPMC patches to validate the proposed model and show the performance of the proposed control law.

Multilevel converters are widely considered to be the most suitable configurations for renewable energy sources. Their high-power quality, efficiency and performance make them interesting for PV applications. In low-power applications such as rooftop grid-connected PV systems, power converters with high efficiency and reliability are required. For this reason, multilevel converters based on parallel and cascaded configurations have been proposed and commercialized in the industry. Motivated by the features of multilevel converters based on cascaded configurations, this work presents the modulation and control of a rooftop single-phase grid-connected photovoltaic multilevel system. The configuration has a symmetrical cascade connection of two three-level T-type neutral point clamped power legs, which creates a five-level converter with two independent string connections. The proposed topology merges the benefits of multi-string PV and symmetrical cascade multilevel inverters. The switching operation principle, modulation technique and control scheme under an unbalanced power operation among the cell are addressed. Simulation and experimental validation results in a reduced-scale power single-phase converter prototype under variable conditions at different set points for both PV strings are presented. Finally, a comparative numerical analysis between other T-type configurations to highlight the advantages of the studied configuration is included.

An important requirement of the power grid with high penetration of renewable energy sources is the mitigation of potential harmonic interactions between different distributed large grid-tie inverters and the mains. This work presents the harmonic interaction between multiple multilevel photovoltaic (PV) inverters based on the well-known T-type neutral-point-clamped inverter (3L-TNPC). The multiple 3L-TNPC is connected in parallel to a common ac bus by using distribution voltage feeders. The analysis is performed by using the Norton equivalence model of each power circuit, its admittance matrix modeling, and the potential overall impedance resonances with the ac grid. The main contribution of this work is the development of a current harmonic injection model of the system operating under a polluted voltage grid for harmonic analysis, while overall filtering design restrictions due to impedance limits based on current and voltage standards are considered. The proposed impedance Norton model is compared with the electromagnetic transient model (EMT model) by using comprehensive simulations, showing good match between both models.

The identification and characterization of independent entities within molecular clouds is a key challenge for astronomical data analysis. The ever-increasing volume, resolution and sensitivity of observations demand automatic routines to identify and deblend candidate entities to be analysed. Additionally, the intrinsically hierarchical nature of molecular gas distributions demands an automatic identification of the nesting relations between these entities. We propose a novel approach for decomposing molecular clouds in two steps: first we fit the data to a Gaussian mixture with many components, then reconstruct the cloud using a hierarchical model using a Gaussian-mixture reduction algorithm. We use a continuous-space representation, because it is well suited for disentangling coupled entities of emission compared with pixel-based ones, and build a tree structure to represent the hierarchical connections between mixture components. This allows us to select different groups of components in the tree without additional computational effort, including overlapping substructures. We assess our proposal quantitatively and qualitatively using data from the Atacama Large Millimeter Array (ALMA) science verification archive, as well as synthetic data. We also compare the results from some state-of-the-art clump identification algorithms. The experiments and comparisons show that our approach is an effective way to inspect and represent the hierarchical structure of molecular clouds.

Electrochemical processes have been developed for a wide range of applications such as, mineral refining, water purification, energy storage and generation. The development of models to describe these processes is very important for their analysis, optimization and operation. The framework of irreversible port-Hamiltonian systems has proven to be an important tool to analyze and integrate thermal models with models of different domains. This work discusses the modeling of non-isothermal electrochemical processes as irreversible port-Hamiltonian systems. An irreversible port-Hamiltonian model based on the internal energy function is derived for a simple but general example. The irreversible model is obtained from the molar and charge balance equations combined with the entropy balance equation. The resulting model can be interpreted as a thermodynamic system and aspects such as entropy production, thermodynamic driving forces and intensive/extensive variables are encoded in the representation. An electrochemical process with two simultaneous reactions is considered to illustrate the approach. The interconnection with a resistive load is also considered to illustrate the benefit of the port-based formulation of the model.

In this paper, a novel expression for the convergence of an iterative learning control algorithm for sampled linear multivariable systems is stated. The convergence analysis shows that, applying this algorithm, the input sequence converges to the system output inverse sequence, specified as a finite-time output trajectory, with zero tracking error on all the sampled points. Also, it gives insight on the learning gain matrix selection to act on the convergence speed or the decoupling of inputs, allowing for an easy tuning using methods from modern control theory. The results are illustrated by some examples, showing a number of options to be investigated.

This paper proposes a model-based predictive control strategy based on mixed-integer linear programming for a photovoltaic power plant with battery energy storage. The control objective is to maximize the revenues from energy delivered from both photovoltaic panels and batteries to the grid in a deregulated electricity market. For each control interval, the proposed algorithm incorporates information on solar radiation, market prices, and the state of charge of the batteries to determine the intervals of energy injection into the grid to maximize the economic benefits. The proposed strategy considers the rate-based variable efficiency in the battery model and time-varying energies prices, thus providing a more general implementation than previous schemes proposed in the literature for the same purpose. Simulations considering the operational procedures of the Spanish market as a case study show that, by integrating the battery efficiency in the model, the proposed control strategy increments the economic benefits in 21% compared to previous results reported in the literature for the same operational conditions. Additionally, the proposed approach reduces the number of charge and discharge cycles, potentially extending the lifespan of batteries.

This paper aims to propose a finite-dimensional observer-based state feedback controller to stabilize a class of boundary controlled system. To this end, we propose to use an early-lumping approach, where the infinite-dimensional port-Hamiltonian system is first discretized using a structure-preserving method. Then, we build a passive observed-based controller using a Linear Matrix Inequality (LMI) and finally, the controller is interconnected with the infinite-dimensional system in a passive way. Due to its passivity and Hamiltonian structure, this observer-based controller can stabilize not only the discretized lumped parameter system but also the original distributed parameter system. This approach avoids the intrinsic drawback of early lumping approach and spillover effects. Finally, the boundary controlled undamped wave equation is used to illustrate the effectiveness of the proposed controller.