A multi-objective optimization algorithm based on self-organizing maps applied to wireless power transfer systems

In this work, a new multi-objective population-based optimization algorithm is presented and tested. In this contribution, the concepts of fast non-dominating sorting and density estimation using the crowding distance are used to create a multi-objective optimization algorithm based on previous work, which is a single objective evolutionary optimization algorithm based on self-organizing maps (SOMs). The SOMs paradigm introduces a strong collaboration between neighbors solutions that improves exploitation. Furthermore, the representative power of the SOMs enhances the exploration and diversification. A state of the art benchmark approach is used to evaluate the performance of the proposed algorithm, obtaining positive results. The test problem uses an analytical model of an inductively coupled wireless power transfer system (WPT). The objective is to optimize the WPT model characteristics in order to allow simultaneous data and power transfer between the coils. The WPT design approach uses more degrees of freedom than existing techniques leading to a number of solutions where both the power signals and the data signal can coexist on the same physical channel achieving good figures of merit

An evolutionary algorithm for global optimization based on self-organizing maps

In this article, a new population-based algorithm for real-parameter global optimization is presented, which is denoted as self-organizing centroids optimization (SOC-opt). The proposed method uses a stochastic approach which is based on the sequential learning paradigm for self-organizing maps (SOMs). A modified version of the SOM is proposed where each cell contains an individual, which performs a search for a locally optimal solution and it is affected by the search for a global optimum. The movement of the individuals in the search space is based on a discrete-time dynamic filter, and various choices of this filter are possible to obtain different dynamics of the centroids. In this way, a general framework is defined where well-known algorithms represent a particular case. The proposed algorithm is validated through a set of problems, which include non-separable problems, and compared with state-of-the-art algorithms for global optimization

Clustering techniques applied to a high-speed train’s pantograph–catenary subsystem for electric arc detection and classification

Assessment of the current collection properties of a pantograph–catenary system mounted on a train is of great importance. Excessive electric arcing can lead to wear of the system’s components, and, at the same time, it can be an index of wear status. In this paper we investigate the possibility of detecting arcing events in the pantograph–catenary collection system without the need of additional equipment installed on-board the train. Data that is currently measured and recorded for modern high-speed trains (i.e. voltage and current) are analysed in order to detect and quantify electric arcs and shed light on the current collection quality of the pantograph–catenary system. This work was performed in cooperation with Trenitalia s.p.a. who provided the data it collects on-board high-speed trains in regular passenger service

An Accurate Equivalent Circuit Model of Metasurface-Based Wireless Power Transfer Systems

In this article, we introduce a general analytical procedure to unambiguously characterize a metasurface through its lumped circuital equivalent in resonant inductive Wireless Power Transfer (WPT) applications. The proposed model incorporates the finite extent of the slab, as well as the WPT near field operative regime and the presence of the particular driving/receiving coils arrangement, providing quantitative and easy-to-handle parameters which can be manipulated to achieve WPT performance enhancement. We first develop the theoretical background aimed at the lumped parameters extraction, which reveals, for WPT applications, more accurate and robust with respect to the conventional sub-wavelength homogenization theories based on infinite slab extent and impinging plane wave hypotheses. We provide some general guidelines for the design of metasurfaces for WPT performance enhancement based on the derived circuit model; afterwards, we numerically design a test-case consisting of two resonant coils (driver and receiver, respectively) with an interposed passive metasurface to verify the developed theory. Finally, we show some measurements performed on a fabricated prototype, that present an overall excellent agreement with both the lumped model and the numerical simulations

Design and Experimental Characterization of a Combined WPT - PLC System

In this contribution, the authors perform the design and show the experimental results relative to a prototype of a combined wireless power transfer (WPT)–power line communications (PLC) system, in which the WPT channel is interfaced to a PLC environment to allow data transfer when the cabled connection is no longer available. The main rationale behind this idea stays in the fact that PLC communication is now a popular choice to enable communications, for instance, in smart grids and in home automation, while WPT devices start to be available in the market (i.e. for mobile phones) and soon they will be a reality also for higher power (i.e. vehicle battery charging). In particular, theoretical insights about the requirements of the system are given; a two coils system has been implemented and a measurement campaign, together with simulations, show that the system is of great potentiality and could be used in applications where both wireless power and data transfer are needed (such as vehicles battery charging), achieving maximum power transfer and good data rate in order to transmit high-speed signals

Arc detection in pantograph-catenary systems by the use of support vector machines-based classification

The aim of the MAPEC_LIFE (Monitoring Air Pollution Effects on Children for Supporting Public Health Policy) study is to evaluate the associations between the concentrations of air pollutants and early biological effects in children living in five Italian towns (Brescia, Torino, Lecce, Perugia and Pisa) characterised by varying levels of air pollution. This paper presents the results of micronucleus cytome assays performed on the oral mucosa cells of subjects living in Lecce (Puglia, Italy) and their relationship to factors associated with indoor/outdoor exposure and lifestyles. The study was conducted on 6-8-year-old schoolchildren living in Lecce. The micronucleus cytome assay was performed on exfoliated buccal cells collected from the oral mucosa of children using a soft-bristled toothbrush. Micronuclei were evaluated only in normal differentiated cells. Overall, 43.0% of the samples tested were positive, with an average frequency of 0.28 MN/1000 differentiated cells. Data analysis shows positive associations between the frequency of MN in the children’s buccal mucosa cells and obesity, heavy traffic and smoking mothers, while outdoor sports seem to have the opposite effect. These data will be integrated with data from the other cities involved in the MAPEC_LIFE study and could be used to build a model for estimating global genotoxic risk

Fuzzy Integral Based Multi-Sensor Fusion for Arc Detection in the Pantograph-Catenary System

The pantograph-catenary subsystem is a fundamental component of a railway train since it provides the traction electrical power. A bad operating condition or, even worse, a failure can disrupt the railway traffic creating economic damages and, in some cases, serious accidents. Therefore, the correct operation of such subsystems should be ensured in order to have an economically efficient, reliable and safe transportation system. In this study, a new arc detection method was proposed and is based on features from the current and voltage signals collected by the pantograph. A tool named mathematical morphology is applied to voltage and current signals to emphasize the effect of the arc, before applying the fast Fourier transform to obtain the power spectrum. Afterwards, three support vector machine-based classifiers are trained separately to detect the arcs, and a fuzzy integral technique is used to synthesize the results obtained by the individual classifiers, therefore implementing a classifier fusion technique. The experimental results show that the proposed approach is effective for the detection of arcs, and the fusion of classifier has a higher detection accuracy than any individual classifier

Impulsive Noise Characterization in Narrowband Power Line Communication

Currently, narrowband Power line communication (PLC) is considered an attractive communication system in smart grid environments for applications such as advanced metering infrastructure (AMI). In this paper, we will present a comprehensive comparison and analysis in time and frequency domain of noise measured in China and Italy. In addition, impulsive noise in these two countries are mainly analyzed and modeled using two probability based models, Middleton Class A (MCA) model and α stable distribution model. The results prove that noise measured in China is rich in impulsive noise, and can be modeled well by α stable distribution model, while noise measured in Italy has less impulsive noise, and can be better modeled by the MCA model

Indirect monitoring and early detection of faults in trains motors

This paper investigates the ability of temperature sensors installed in the traction core of trains to early detect incipient faults. For instance, the breaking of a bearing is known to be critical as it may cause an increase of the temperature in the motor compartment, that in turn may eventually lead to a winding fault in the induction motor. The technique proposed in this contribution is characterised by extreme generality, since most frequent incipient faults lead to temperature increase that, if properly analyzed, can be a tool for preventive maintenance. In particular, the measured data, provided by the main Italian railway company, are processed by two different methodologies which are characterized by positive, yet different, performances. The results show that preventive maintenance with the proposed approach is feasibl

Enhanced Detection of Expanded Repeat mRNA Foci with Hybridization Chain Reaction

Transcribed nucleotide repeat expansions form detectable RNA foci in patient cells that contribute to disease pathogenesis. The most widely used method for detecting RNA foci, fluorescence in situ hybridization (FISH), is powerful but can suffer from issues related to signal above background. Here we developed a repeat-specific form of hybridization chain reaction (R-HCR) as an alternative method for detection of repeat RNA foci in two neurodegenerative disorders: C9orf72 associated ALS and frontotemporal dementia (C9 ALS/FTD) and Fragile X-associated tremor/ataxia syndrome. R-HCR to both G4C2 and CGG repeats exhibited comparable specificity but \u3e 40 × sensitivity compared to FISH, with better detection of both nuclear and cytoplasmic foci in human C9 ALS/FTD fibroblasts, patient iPSC derived neurons, and patient brain samples. Using R-HCR, we observed that integrated stress response (ISR) activation significantly increased the number of endogenous G4C2 repeat RNA foci and triggered their selective nuclear accumulation without evidence of stress granule co-localization in patient fibroblasts and patient derived neurons. These data suggest that R-HCR can be a useful tool for tracking the behavior of repeat expansion mRNA in C9 ALS/FTD and other repeat expansion disorders