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In the present magnetically confined plasmas, the prediction of particle loading on material surfaces is a primary concern in view of the protection of plasma facing components for next step devices. Thus, an understanding of filament dynamics is needed. In this context, this work aims to develop an automatic detector for filaments arising in the MAST-U plasma. The identification of the filaments has been done starting from 2D images acquired with a fast visible camera. Therefore, it can be faced as an image object recognition problem. Currently, the object recognition is a key output of deep learning and machine learning algorithms. In this paper, a database of several thousands of images generated by a synthetic diagnostic, which reproduces the statistical properties of experimental filaments in terms of position, size and intensity has been used. The synthetic images are pre-processed by mapping them onto the toroidal midplane of the machine. Then a Faster R-CNN is customized to the problem of identifying the filaments. In particular, in order to enhance the performance of the detector, a suitable definition of the target-boxes defining the filament positions and sizes is adopted with good results.

In tokamak nuclear fusion reactors, one of the main issues is to know the total emission of radiation, which is mandatory to understand the plasma physics and is very useful to monitor and control the plasma evolution. This radiation can be measured by means of a bolometer system that consists in a certain number of elements sensitive to the integral of the radiation along straight lines crossing the plasma. By placing the sensors in such a way to have families of crossing lines, sophisticated tomographic inversion algorithms allow to reconstruct the radiation tomography in the 2D poloidal cross-section of the plasma. In tokamaks, the number of projection cameras is often quite limited resulting in an inversion mathematic problem very ill conditioned so that, usually, it is solved by means of a grid-based, iterative constrained optimization procedure, whose convergence time is not suitable for the real time requirements. In this paper, to illustrate the method, an assumption not valid in general is made on the correlation among the grid elements, based on the statistical distribution of the radiation emissivity over a set of tomographic reconstructions, performed off-line. Then, a regularization procedure is carried out, which merge highly correlated grid elements providing a squared coefficients matrix with an enough low condition number. This matrix, which is inverted offline once for all, can be multiplied by the actual bolometer measures returning the tomographic reconstruction, with calculations suitable for real time application. The proposed algorithm is applied, in this paper, to a synthetic case study.

Non-intrusive load monitoring (NILM) is a process of determining the operating states and the energy consumption of single electric devices using a single energy meter providing aggregate load measurements. Due to the large spread of power electronic-based and nonlinear devices connected to the network, the time signals of both voltage and current are typically non-sinusoidal. The effectiveness of a NILM algorithm strongly depends on determining a set of discriminative features. In this paper, voltage and current signals were combined to define, according to the definitions provided in Standard IEEE 1459, different power quantities, that can be used to distinguish different types of appliance. Multi-layer perceptron (MLP) classifiers were trained to solve the appliance detection problem as a multi-class event classification problem, varying the electric features in input. This allowed to select an optimal set of features guarantying good classification performance in identifying typical electric loads.

Nowadays, the demand for communication multi-carriers’ channels, where the sub-channels are made mutually independent by using orthogonal frequency division multiplexing (OFDM), is widespread both for wireless and wired communication systems. Even if OFDM is a spectrally efficient modulation scheme, due to the allowed number of subcarriers, high data rate, and good coverage, the transmitted signal can present high peak values in the time domain, due to inverse fast Fourier transform operations. This gives rise to high peak-to-average power ratio (PAPR) with respect to single carrier systems. These peaks can saturate the transmitting amplifiers, modifying the shape of the OFDM symbol and affecting its information content, and they give rise to electromagnetic compatibility issues for the surrounding electric devices. In this paper, a closed form PAPR reduction algorithm is proposed, which belongs to selected mapping (SLM) methods. These methods consist in shifting the phases of the components to minimize the amplitude of the peaks. The determination of the optimal set of phase shifts is a very complex problem; therefore, the SLM approaches proposed in literature all resort to iterative algorithms. Moreover, as this calculation must be performed online, both the computational cost and the effect on the bit rate (BR) cannot be established a priori. The proposed analytic algorithm finds the optimal phase shifts of an approximated formulation of the PAPR. Simulation results outperform unprocessed conventional OFDM transmission by several dBs. Moreover, the complementary cumulative distribution function (CCDF) shows that, in most of the packets, the proposed algorithm reduces the PAPR if compared with randomly selected phase shifts. For example, with a number of shifted phases U=8, the CCFD curves corresponding to the analytical and random methods intersect at a probability value equal to 10−2, which means that in 99% of cases the former method reduces the PAPR more than the latter one. This is also confirmed by the value of the gain, which, at the same number of shifted phases and at the probability value equal to 10−1, changes from 2.09 dB for the analytical to 1.68 dB for the random SLM.

In this paper, an innovative approach based on an artificial neural network (ANN) load forecasting model to improve the distribution system state estimation accuracy is proposed. High-quality pseudomeasurements are produced by a neural model fed with both exogenous and historical load information and applied in a realistic measurement scenario. Aggregated active and reactive powers of small or medium enterprises and residential loads are simultaneously predicted by a one-step ahead forecast. The correlation between the forecasted real and reactive power errors is duly kept into account in the definition of the estimator together with the uncertainty of the overall measurement chain. The beneficial effects of the ANN-based pseudomeasurements on the quality of the state estimation are demonstrated by simulations carried out on a small medium-voltage distribution grid.

This paper presents the first results of a study, carried out in the framework of an industrial collaboration project, whose aim was the development of a Non-Intrusive Load Monitoring system of electrical device activities to be implemented in an IoT platform for Smart Grid. A load disaggregation algorithm has been developed which implements original strategies to deal with some limits of the methods proposed in the literature.

In this paper, a procedure is proposed to design a power line communication (PLC) system to perform the digital transmission in a distributed energy storage system consisting of fleets of electric cars. PLC uses existing power cables or wires as data communication multicarrier channels. For each vehicle, the information to be transmitted can be, for example: the models of the batteries, the level of the charge state, and the schedule of charging/discharging. Orthogonal frequency division multiplexing modulation (OFDM) is used for the bit loading, whose parameters are optimized to find the best compromise between the communication conflicting objectives of minimizing the signal power, maximizing the bit rate, and minimizing the bit error rate. The off-line design is modeled as a multi-objective optimization problem, whose solution supplies a set of Pareto optimal solutions. At the same time, as many charging stations share part of the transmission line, the optimization problem includes also the assignment of the sub-carriers to the single charging stations. Each connection between the control node and a charging station has its own frequency response and is affected by a noise spectrum. In this paper, a procedure is presented, called Chimera, which allows one to solve the multi-objective optimization problem with respect to a unique frequency response, representing the whole set of lines connecting each charging station with the central node. Among the provided Pareto solutions, the designer will make the final decision based on the control system requirements and/or the hardware constraints.

Nuclear fusion is one of the best options to achieve a virtually limitless energy source in the future. However, sustaining burning plasma reactions is very challenging because disruptive events cause the loss of plasma confinement and damages to the tokamak machine. Thus, reproducing a nuclear fusion reaction on Earth represents an actual scientific and technical challenge. Next generation machines must be able to predict, mitigate or avoid the disruptions but the present understanding of the phenomenon has not yet gone so far as to provide a physical model describing their root cause. On the contrary, a large quantity of experimental disruption data exists. For these reason, in the last decades, machine learning techniques have been applied to design alarm systems both for mitigating or to actively avoid approaching disruptions. More recently, the disruption predictor concept has been evolving toward a more complex system, able to detect the proximity of the plasma state to the boundaries of the operational space free from disruptions in order to schedule avoidance strategies rather than mitigation actions. This system should also be able to determine which type of disruption is about to occur in order to efficiently take suitable disruption avoidance actions. This paper reports an overview of machine learning algorithms as tools for disruption prediction and classification at JET, the world's largest operational plasma physics experiment, located at Culham Centre for Fusion Energy in Oxfordshire, UK. Both traditional neural network models for disruption prediction and manifold learning approaches as a tool for plasma operational space mapping and visualization, disruption prediction and classification, are described.

The paper proposes a region-based deep learning convolutional neural network to detect objects within images able to identify the filamentary plasma structures that arise in the boundary region of the plasma in toroidal nuclear fusion reactors. The images required to train and test the neural model have been synthetically generated from statistical distributions, which reproduce the statistical properties in terms of position and intensity of experimental filaments. The recently proposed Faster Region-based Convolutional Network algorithm has been customized to the problem of identifying the filaments both in location and size with the associated score. The results demonstrate the suitability of the deep learning approach for the filaments detection.

Reliable algorithms for disruption avoidance and prediction are foreseen to play a fundamental role in the JET control system for the successful operation of the machine in the upcoming deuterium-Tritium campaigns. The integration of such algorithms is expected to be a key part also in the implementation of the ITER plasma control system. So far, most of the effort has been devoted to the prediction of disruptions, which is required to mitigate the effects of these transient events, protecting the integrity of in-vessel components. Nevertheless, in order to put in place recover strategies or to have the possibility of a soft landing for the plasma current, the paradigm must be shifted to avoiding disruptions. In this paper, plasma profile-based indicators will be statistically analyzed showing their potential in such a perspective, where warning times and reliability of detection are crucial.

A Tabu Search metaheuristic procedure has been developed to solve the Moving Vector Hysteron Model identification problem. An analytic modified Preisach model is used to describe the hysteretic behavior of a magnetic material in case of scalar magnetization. Using the Tabu Search optimization algorithm it is possible to explore the model parameters space in a smart way identifying the corresponding values that minimize an error function while limiting the computation effort. The search has been performed for different magnetization values and the best fitting functions have been derived. Model validation is performed with experimental data.

In this paper a procedure for the optimization of the bit loading in a PLC system implementing OFDM modulation is presented. The optimization strategy aims to find the best compromise between the conflicting objectives of minimal Signal Power, maximal Bit Rate, and minimal Bit Error Rate. The optimization is performed off line by means of a Multi-Objective approach. A set of Pareto solutions is determined, among which the designer has to take the final choice on the basis of the custom requirements. The approach is used to optimize the communication within a distributed energy storage system, which exploits an available electrical car fleet. The power lines supplying the charging points are used for the communication, while the information concerns, for each car, the identification code, the charge state, the schedule of charging/discharging. A unique OFDM modulation is used to dialogue with the whole cars fleet, so that it has to be optimized taking into account the frequency response of any involved power lines.

In this paper, an innovative Artificial Neural Network (ANN)-based approach to solve the bi-dimensional Electric Capacitance Tomography inverse problem is proposed in order to assess the health state of standing trees. Using a set of examples, an ANN is trained to solve the direct problem, ie, to associate the properties of the tree trunk affected by defects to a set of capacitance values. Then, the trained ANN is inverted in order to obtain the defect position and dimensions on the basis of measured capacitance values. The potentiality of the method is presented making reference to several target examples.

A neural network inverse procedure has been proposed to solve the Moving Vector Hysteron Model identification. Using the analytic modified Preisach model, a multilayer perceptron neural network is firstly trained to associate the direct relationship among the model parameters and the hysteretic behavior of the material to be modeled both in case of scalar and rotational magnetization. The neural model is then inverted using as input the magnetization identifying the corresponding model parameters values. Model validations with experimental tests and simulations will be performed.

An Inverse Neural Network approach is proposed to solve inverse problems in the field of material characterization in different electrical engineering applications. The flexibility and robustness of the inverse approach is demonstrated making reference to two different problems: the model identification of a magnetic material and the electric capacitance tomography of a polymeric material.

PurposeThe purpose of this paper is to present a constructive algorithm to design multilayer perceptron neural networks used as approximation models of electromagnetic devices.Design/methodology/approachThe proposed procedure allows automatic determination of both the number of neurons and the synaptic weights of networks with a single hidden layer. The approximation model is used in design optimization problems. The inputs of the neural network correspond to the design parameters whereas the output corresponds to the objective function of the optimization problem. The neural model is then inverted in order to determine which input is associated to a prefixed output.FindingsThe performance of the algorithm has been tested on analytical function and on the TEAM workshop problem 25.Originality/valueAs the reliability of the optimum solution is strongly affected by the accuracy of the neural approximation model, the approximation error is kept as low as possible, especially in the maximum/minimum points.

PurposeThe aim of the paper is to compare two different approaches to multi‐objective optimisation in magnetostatics; in this way, the case study is investigated as a possible benchmark.Design/methodology/approachA Tabu search method modified with ε‐constraint algorithm is compared with a multi‐objective multi‐individual evolution strategy. The case study is the automated shape design of a magnetic pole. In order to reduce the computational cost of solving the direct problem, which requires repeated analyses of the magnetic field, a neural network has been used to approximate the objective functions that depend on the design variables.FindingsAn approximation of the Pareto front for each method is obtained. A twofold comparison between the two methods is made, based on both the result accuracy and the computational cost.Originality/valueTwo different methods were already tested on a case study proposed as a benchmark for multi‐objective optimization in magnetostatics. The paper represents a contribution to bridge the gap between analytical and numerical benchmarks in electromagnetism.

Wave equations are hyperbolic partial differential equations (PDEs) which describe the propagation of various types of waves, such as acoustic, elastic, and electromagnetic waves. In order to solve PDEs, the finite element method (FEM) can be used. After a brief introduction to the mathematical method used by FEM to evaluate the solution in nodes, where the polynomial curve that interpolates the differential equation has to be solved, we will describe the methodology used to solve the wave propagation problem described by the Helmholtz’s equation. The wave propagation problem is analyzed by following specific steps: construction of the geometry to study, application of the boundary conditions, and meshing of the domain to be solved. The same procedure is used to simulate the behavior of piezoelectric transducers and the problem of wave propagation in medium with defects. Finally, the procedure followed for the simulation of acoustic problems using a specific software, i.e., COMSOL Multiphysics, is illustrated.

The ultrasonic techniques are today one of the most used methods for nondestructive testing (NDT) and for the characterization of the materials. Often it is necessary to simulate the system under test before to apply this technique to the real case. In fact, during the test in general a great number of parameters have to be calibrated in order to maximize the information captured by means of the measurements. In this chapter we will give some examples of ultrasonic NDT applications. The first one is a tutorial example and it considers the simulation of ultrasonic waves within a fish tank. The next two examples are concerned with the application of ultrasonic testing to masonry and concrete structures, respectively. In particular the problems of detecting a void in a trachite stone wall and in a concrete pillar are considered. Finally, the last example presented here is concerned with the detection of a hole in a metallic specimen. In fact, the ultrasonic inspection technique is widely employed in the steel industry due to favorable propagation conditions that such materials ensure. All the simulations have been done using COMSOL as finite element analysis, solver, and simulation software.

The aim of this chapter is to show the potentiality of a novel approach to ultrasonic investigation of masonry structures. In order to obtain useful, rapid and relatively low cost informations, the ultrasonic “Direct Transmission Technique” is applied on the structure under exam. This technique uses a beam of ultrasonic waves emitted by a transducer and received by another one placed on the opposite surface of the structure. The waves pass through the (ample under test) and are reflected and refracted by incidental discontinuities encountered along the path. The characteristics of the ultrasonic vibration are then investigated in order to obtain interesting information on the conditions of the materials. In particular, the attention has been focused on the correlation between the average density of the ray paths and the characteristics of acquired signals. This feature has been extracted from signals obtained by direct measurements on a real stone masonry with a known internal cavity. Then measurements have been related to the known geometry to verify the actual correlation with the characteristics of the traversed paths.

In this work a new formalization of selecting the optimal set of path-rays problem in linear tomography is presented. In particular the problem of selecting the optimal set of path-rays is formalized as a problem of selecting a sub-matrix of a matrix with certain spectral properties, which is known to be an NP-hard problem. New criteria of optimality of the set of path-rays are introduced, and an optimization algorithm is proposed to deal with the combinatory search problem. The obtained results are compared to those of existing approximation algorithms. Numerical results show that the optimal solutions yielded by the proposed optimization algorithm, outperform existing algorithms in terms of conditioning of the tomography linear equations system.

Following the great progress made in civil engineering and building materials, the 2014 4th International Conference on Civil Engineering and Building Materials (CEBM 2014) aimed at providing a forum for presentation and discussion of state-of-the-art development in Structural Engineering, Road & Bridge Engineering, Geotechnical Engineering, Architecture & Urban Planning, Transportation Engineering, Hydraulic Engineering, Engineering Management, Computational Mechanics, Construction Technology, Building Materials, Environmental Engineering, Computer Simulation&CAD/CAE. Emphasiswas given to basic methodologies, scientific development and engineering applications. This conference is co-sponsored by Asia Civil Engineering Association, the International Association for Scientific and High Technology and International Science and Engineering Research Center. The purpose of CEBM 2014 is to bring together researchers and practitioners from academia, industry, and government to exchange their research ideas and results in the areas of the conference. In addition, the participants of the conference will have a chance to hear from renowned keynote speakers Prof. XIAO-YAN LI from University of Hong Kong.

In this paper an inductive MHD generator is proposed, which aims to overcome the typical drawbacks of the conventional MHD generators, such as the need to operate at high temperatures and to have superconducting coils for the generation of the external magnetic field. The conceptual idea is described and the energy conversion process is illustrated by means of a finite element method model, which describes the fluid dynamic aspects, joined to a Simulink® model, which describes the electromagnetic phenomena involved in the process. Some results of analysis are reported, putting in evidence the influence of design parameters of the machine.

Renewable energy exploitation in the urbanized areas has been receiving an increased attention over recent years because the proximity to electricity users makes it possible to obtain better global efficiency and to save territory. In this work a new wind energy system that uses the roof of the building in combination with a centripetal turbine is presented. This — could be considered as an amplifier of the wind speed that should allow the wind turbine to work under both lower and higher velocities than the conventional aerogenerators and therefore to increase its delivered power output. Considering the local wind climate of the area of interest, in this work, firstly, a Computational Fluid Dynamics (CFD) model is used to simulate the flow into the eolic system. Then, a laboratory-scale prototype has been made in order to test the potentialities of the proposed system for fulfillment of energy demands.

In this work a diagnostic approach for nonlinear systems is presented. The diagnosis is performed resorting to a neural predictor of the output of the system, and by using the error prediction as a feature for the diagnosis. A locally recurrent neural network is used as predictor, after it has been trained on a reference behavior of the system. In order to model the system under test a novel training algorithm that uses an explicit calculation of the cost function gradient is proposed. The residuals of the prediction are affected by the deviation of the parameters from their nominal values. In this way, by a simple statistical analysis of the residuals, we can perform a diagnosis of the system. The Rossler hyperchaotic system is used as benchmark problem in order to validate the diagnostic neural approach proposed.

In the last years, several data communication over the emerging smart grid system has been implemented on the existing low voltage power lines, which have high performance and limited costs. The most common Power Line Communication (PLC) technologies adopt OFDM as modulation scheme. However the Peak-to-Average Power Ratio (PAPR) of OFDM is an undesirable problem that has to be reduced. For this purpose, in this work, a new semi-analytical method is proposed, which allows one to solve the problem at low computational cost and at the same time to preserve the information content on the signal. The suitability of the proposed method has been validated on a case study represented by a real case problem. (5 pages)

During the last years, several optimization algorithms have been presented and widely investigated in literature, most of which based on deterministic or stochastic methods, in order to solve optimization problems with multiple objectives that conflict with each other. Some multi-objective stochastic optimizers have been developed, based on local or global search methods, in order to solve optimal design problems. Despite the significant progress obtained in this field, there are still many open issues. In fact, both the deterministic and stochastic approaches present hard limits. In the first case, although the number of function evaluations needed to reach the optimal solution is generally small, the risk to be trapped in local minima is very high, whereas in the second case, the probability to reach the optimal solution is higher but the computational cost could become prohibitive. In particular, this is the case of the electromagnetic problems. Electromagnetic devices are fundamental in the modern society. They are used for storing and converting energy (Magele, 1996), manufacturing processes (Takahashi et al., 1996), magnetic resonance imaging (Gottvald et al., 1992), telecommunications, etc. The design optimization of the electromagnetic devices is one key to enhance product quality and manufacturing efficiency. Definition of geometric boundaries to achieve specific design goals together with nonlinear behaviour of ferromagnetic materials often give rise to multimodal, non-linear, and non-derivable objective functions. For this reason, resorting to numerical approaches, such as the Finite Element Method (FEM), to evaluate objective functions in many cases is compulsory. When the number of design parameters to be optimized is considerable, the number of objectives evaluations to be performed could be of the order of thousand and the use of numerical solution during the optimization process can be unfeasible. Approximating techniques have been proposed as a way to overcome the time consuming numerical procedure (Alotto et al., 2001, Canova et al., 2003, Wang & Lowther, 2006). One of the most effective approximation approaches is based on Artificial Neural Networks. In fact, an alternative method to numerical evaluation consists of applying the optimization procedure to the approximation of the objective function, rather than to its numerical model (Abbass, 2003, Fieldsend & Singh, 2005, Carcangiu et al., 2008). On the other hand, the quality of the solution of the optimization problem depends on the error introduced by the approximation

In this work a procedure for the optimization of the bit loading in a PLC system implementing OFDM modulation is presented. The optimization strategy aims to find the best compromise between Signal Power, Bit Rate (BR), Bit Error Rate (BER) and Peak-to-Average Power Ratio (PAPR). The problem is approached from two points of view. First, the conflicting objectives of Signal Power, Bit Rate and Bit Error Rate are optimized off line by means of a Multi-Objective approach. A Tabu Search-based search engine has been adapted to the Multi Objective Optimization in order to find a set of Pareto solutions, among which the designer has to take the final choice on the basis of the custom requirements. Secondly, the Selected Mapping method is used on line to reduce the PAPR, finding the best compromise between maximum Bit Rate and minimum PAPR. A semi analytical procedure, described in the paper, is used to determine the optimal phase factor vector. The suitability of the approach has been evaluated referring to the real case of a PLC system implemented in a mega yacht.

In this paper a Tabu Search based procedure for Peak to Average Power Ratio (PAPR) reduction in Power Line Communication (PLC) channels is presented. The Orthogonal Frequency Division Multiplexing has been assumed as communication technique. The Selected Mapping approach is applied in order to investigate the possibility to define with a limited computational cost a phase factors vector that represents a sub-optimal solution. The study has been developed referring to the real case of a PLC system implemented in a mega yacht.

Acoustic methods seem to be very suitable for evaluating a building condition because they give information with immediacy, rapidity and relatively low cost. They are based on measurements of the velocity of acoustic waves propagating through the material. Those methods are often carried out applying the Through Transmission Technique, in which the wave is transmitted by a transducer through the test object and received by a second transducer on the opposite side. In this paper, the sonic tomography imaging based on this technique has been used for non destructive testing of a stone masonry. The tomography implies that a ill-posed linear equations system has to be solved, in order to determine the velocities distribution inside the tested structure, thus highlighting the presence of anomalies. Different inversion algorithms, chosen between the most commonly used, have been implemented for determining the distribution of waves velocity in selected sections of the tested masonry.

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