Apriori Research Articles

Low-complexity Behavioral Model for Predictive Maintenance of Railway Turnouts

Maintenance of railway infrastructures represents a major cost driver for any infrastructure manager since reliability and dependability must be guaranteed at all times. Implementation of predictive maintenance policies relies on the availability of condition monitoring systems able to assess the infrastructure health state. The core of any condition monitoring system is the a-priori knowledge about the process to be monitored, in the form of either mathematical models of different complexity or signal features characterizing the healthy/faulty behavior. This study investigates the identification of a low-complexity behavioral model of a railway turnout capable of capturing the dominant dynamics due to the ballast and railpad components. Measured rail accelerations, acquired through a receptance test carried out on the switch panel of a turnout of the Danish railway network, have been utilized together with the Eigensystem Realization Algorithm – a type of subspace identification – to identify a fourth order model of the infrastructure. The robustness and predictive capability of the low-complexity behavioral model to reproduce track responses under different types of train excitations have been successfully validated. It is anticipated that the identified model will be instrumental for the development of methods for diagnosis and prognosis of faults and degradation process in switches and crossings.

Passive actuator fault tolerant control for a class of MIMO nonlinear systems with uncertainties

ABSTRACTFault tolerant control of affine class of multi-input multi-output (MIMO) nonlinear systems has not received considerable attention of researchers compared to other class of nonlinear systems. Therefore, this paper proposes an adaptive passive fault tolerant control method for actuator faults of affine class of MIMO nonlinear systems with uncertainties using sliding mode control . The actuator fault is represented by a multiplicative factor of the control signal which reflects the loss of actuator effectiveness. The design of the controller is based on the assumption that the maximum loss level of the actuator effectiveness is known. Furthermore, since the proposed controller is adaptive, it does not require any a-priori knowledge of the uncertainty bounds. The closed-loop stability conditions of the controller are derived based on Lyapunov theory. The effectiveness of the proposed controller is demonstrated considering two examples: a two degree of freedom helicopter and a two-link robot manipulator and has been found to be satisfactory.

Policy learning in continuous-time Markov decision processes using Gaussian Processes

Continuous-time Markov decision processes provide a very powerful mathematical framework to solve policy-making problems in a wide range of applications, ranging from the control of populations to cyber–physical systems. The key problem to solve for these models is to efficiently compute an optimal policy to control the system in order to maximise the probability of satisfying a set of temporal logic specifications. Here we introduce a novel method based on statistical model checking and an unbiased estimation of a functional gradient in the space of possible policies. Our approach presents several advantages over the classical methods based on discretisation techniques, as it does not assume the a-priori knowledge of a model that can be replaced by a black-box, and does not suffer from state-space explosion. The use of a stochastic moment-based gradient ascent algorithm to guide our search considerably improves the efficiency of learning policies and accelerates the convergence using the momentum term. We demonstrate the strong performance of our approach on two examples of non-linear population models: an epidemiology model with no permanent recovery and a queuing system with non-deterministic choice.

Living with Others. Living like Others. An ethical basis for intercultural education

The Other, both guest and stranger, is what undermines the granitic concept of our reassuring identity, questions it, upsets its order and infects it by putting it on its own edge. Exactly like the encounter with the stranger, education open up the possibility for us to be or not to be an Other, it is the process by which we recognise ourselves as identity stateless individuals. Through the phenomenological analysis of Max Scheler, who, in contrast to every theoreticism of a solitary ego, sets the origin of the Other’s existence in the interpersonal sphere of the we (egoita-tuita), leading to an ethical re-foundation capable of exalting the emotional dimension in the definition of value (emotional apriorism), an attempt will be made to identify a possible ethical basis for intercultural education.

Knowledge-based adaptive detection of radar targets in generalized Pareto clutter

This paper studies adaptive detection of radar targets embedded in generalized Pareto clutter on the condition with the limited secondary data. In order to alleviate the effects of the non-Gaussian characteristic of the clutter, a-priori knowledge of the non-Gaussian clutter is considered in the designed detector. More precisely, we consider that the texture of clutter obeys the inverse gamma distribution and the inverse covariance matrix of speckle is a combination of multiple a-priori spectral models. Within these considerations, we obtain an adaptive detector based on the generalized likelihood ratio test. Finally, the performance of the proposed detector is evaluated via the Monte-Carlo technique. The experiments results indicate that the proposed detector outperforms the 1S-GLRT detector in limited secondary data scenarios.

Automotive magnetorheological dampers: modelling and parameter identification using contrast-based fruit fly optimisation

The present study discusses the mechanical behaviour and modelling of a prototype automotive magnetorheological (MR) damper, which presents different viscous damping coefficients in jounce and rebound. The force generated by the MR damper is measured at different velocities and electrical currents, and a modified damper model is proposed to improve fitting of the experimental data. The model is calibrated by means of parameter identification, and for this purpose a new swarm intelligence algorithm is proposed, that we call the contrast-based Fruit Fly Optimisation Algorithm (c-FOA). The performance of c-FOA is compared with that of Genetic Algorithms, Particle Swarm Optimisation, Differential Evolution and Artificial Bee Colony. The comparison is made on the basis of no a-priori knowledge of the damper model parameters range. The results confirm the good performance of c-FOA under parametric range uncertainty. A sensitivity analysis discusses c-FOA’s performance with respect to its tuning parameters. Finally, a ride comfort simulation study quantifies the discrepancies in the results, for different identified damper model sets. The discrepancies underline the importance of accurately describing MR damper nonlinear behaviour, considering that virtual sign-off processes are increasingly gaining momentum in the automotive industry.

Genes and pathways underlying susceptibility to impaired lung function in the context of environmental tobacco smoke exposure

BackgroundStudies aiming to assess genetic susceptibility for impaired lung function levels upon exposure to environmental tobacco smoke (ETS) have thus far focused on candidate-genes selected based on a-priori knowledge of potentially relevant biological pathways, such as glutathione S-transferases and ADAM33. By using a hypothesis-free approach, we aimed to identify novel susceptibility loci, and additionally explored biological pathways potentially underlying this susceptibility to impaired lung function in the context of ETS exposure.MethodsGenome-wide interactions of single nucleotide polymorphism (SNP) by ETS exposure (0 versus ≥1 h/day) in relation to the level of forced expiratory volume in one second (FEV1) were investigated in 10,817 subjects from the Dutch LifeLines cohort study, and verified in subjects from the Swiss SAPALDIA study (n = 1276) and the Dutch Rotterdam Study (n = 1156). SNP-by-ETS exposure p-values obtained from the identification analysis were used to perform a pathway analysis.ResultsFourty Five SNP-by-ETS exposure interactions with p-values <10−4 were identified in the LifeLines study, two being replicated with nominally significant p-values (<0.05) in at least one of the replication cohorts. Three pathways were enriched in the pathway-level analysis performed in the identification cohort LifeLines, i.E. the apoptosis, p38 MAPK and TNF pathways.ConclusionThis unique, first genome-wide gene-by-ETS interaction study on the level of FEV1 showed that pathways previously implicated in chronic obstructive pulmonary disease (COPD), a disease characterized by airflow obstruction, may also underlie susceptibility to impaired lung function in the context of ETS exposure.

Closed-loop Identification of an Industrial Extrusion Process

This paper deals with the challenging problem of closed-loop identification for multivariable chemical processes and particularly the estimation of an open-loop plant model for a lab-scale industrial twin-screw extruder used in a powder coatings manufacturing line. The aim is to produce a low order efficient model in order to assist the scaling-up and the model-based control design of the manufacturing process. To achieve this goal, a two-stage indirect approach has been deployed which relies on the a-priori knowledge of the controller parameters in order to extract good estimates of the open-loop dynamics of the underlying process. As input excitation signals we have used multiple single variable step tests at various operating conditions (current industrial practice) carried out manually in order to generate the data-set which captures the dynamics of the extrusion process. In order to increase the efforts for obtaining a suitable plant model, we have employed various identification techniques, such as Prediction Error Methods (PEM) and Subspace Identification Methods (SIM) in order to generate candidate closed-loop models that fit to the original input-output process data. Then, a comparison of the estimated models was performed by means of the mean square error and data fitting criteria in order to select the model that best describes the dynamic behaviour of the extrusion process. Model validation based on closed-loop step responses also used as verification of the results.

Multiple Fiber Fault Location With Low-Frequency Sub-Carrier Tone Sweep

We present a monitoring technique that can be directly integrated in the sub-carrier multiplexing (SCM) transceiver for PtP and wavelength division multiplexing and sub-carrier multiplexing passive optical networks applications. The technique is based on the detection of the backscattered signal from a baseband tone (100 Hz to 100 kHz) and interpretation with the least absolute shrinkage and selection operator (LASSO) operator, a signal processing technique which identifies the fault positions based on the mathematical model of the received signal. The proposed single-ended fiber probing method enables the location of multiple faults with no a-priori knowledge of the fiber, and offers low-cost in-service operation with negligible impact on data transmission, few meters spatial resolution, and limited dynamic range. Due to all its advantages, but also considering its reach limitation, the SincLASSO method features as an ideal monitoring candidate for short reach sub-carrier multiplexed optical fiber networks, such as the ones intended for analog mobile Fronthaul based on fiber and fiber-extended copper lines.

A DISTRIBUTED GENETIC ALGORITM AND A-PRIORI ALGORITHM FOR THE HUB AND FACILITY LOCATION PROBLEMS

A-priori is an influential data mining algorithm employed in market basket analysis to understand the purchase behavior of buyers. It has many other applications. In this study, we combine a-priori with a genetic algorithm (GA) to solve two classical NP-hard location problems namely the Un-capacitated Single Allocation Problem (USAHLP) and Un-capacitated Facility Location Problem (UFLP). A distributed model of the proposed algorithm has been implemented. The performance of the algorithm has been evaluated with standard benchmark problems for USAHLP and UFLP. Results have been found encouraging.

A Sequential Optimization Calibration Algorithm for Near-Field Source Localization.

This paper considers the near-field source location problem for a nonuniform linear array (non-ULA) in the presence of sensor gain and phase errors. A sequential optimization calibration method is proposed to simultaneously estimate the gain and phase errors as well as the locations of calibration sources involving the ranges and the azimuths by exploiting some imprecise a-priori knowledge of calibration sources. At each iteration of the proposed method, the source locations, and the gain and phase errors are obtained iteratively. Finally, at the analysis stage, we evaluate the effectiveness of the proposed technique using some numerical simulations. Results show that the proposed algorithm shares the capability to jointly estimate the source locations and the errors.

Evolutionary Programming Based Approach for SOFC Cathode Characterization: A Case Study on Co-Free Mixed Conducting Perovskites

Cobalt-free perovskites were investigated in this work, in order to find more economical and chemically stable materials to serve as cathodes for intermediate temperature solid oxide fuel cells. The electrochemical performance of Ba0.5Sr0.5Fe0.91Al0.09O3-δ and Ba0.5Sr0.5Fe0.8Cu0.2O3-δ with oxide ion conducting La0.8Sr0.2Ga0.8Mg0.2O3-δ and proton conducting Ba0.5Sr0.5Ce0.6Zr0.2Gd0.1Y0.1O3-δ electrolytes was studied using non-destructive electrochemical impedance spectroscopy. The measured data was analyzed by impedance spectroscopy genetic programming (ISGP), which results in a unique model that requires no a-priori knowledge of the tested system. In-depth cathode operation mechanism is shown by finding the distribution function of relaxation times (DFRT) using ISGP. By monitoring the changes in the DFRT models at different temperatures, we are able to de-convolute the oxygen reduction reaction to its several polarization processes. Using our analysis approach it is possible to gain additional information, which may be convoluted and therefore undetected in conventional impedance analysis.

Adapted numerical methods for advection–reaction–diffusion problems generating periodic wavefronts

The paper presents an adapted numerical integration for advection–reaction–diffusion problems. The numerical scheme, exploiting the a-priori knowledge of the qualitative behaviour of the solution, gains advantages in terms of efficiency and accuracy with respect to classic schemes already known in literature. The adaptation is here carried out through the so-called trigonometrical fitting technique for the discretization in space, giving rise to a system of ODEs whose vector field contains both stiff and non-stiff terms. Due to this mixed nature of the vector field, an Implicit–Explicit (IMEX) method is here employed for the integration in time, based on the first order forward–backward Euler method. The coefficients of the method here introduced rely on unknown parameters which have to be properly estimated. In this work, such an estimate is performed by minimizing the leading term of the local truncation error. The effectiveness of this problem-oriented approach is shown through a rigorous theoretical analysis and some numerical experiments.

TFS: Towards Fuzzy Rule-Based Feedback Scheduling in Networked Control Systems

With the pervasive applications of networked control systems (NCSs), it is increasingly important to design the network scheduling algorithm to ensure good overall system performance. Traditional static scheduling algorithms may work, but they require a-priori knowledge of the workload for processors and the network. As a result, using static scheduling may degrade the quality of control (QoC) of the control systems in the NCSs with limited bandwidth and dynamic workload. To achieve the desired overall performance of NCSs even when the available network resource for the control applications is scarce and unpredictable, we propose a dynamic time division multiple access (TDMA)-based fuzzy-controlled scheduling (TFS) scheme in this paper. Our approach uses the QoC of the control loops as continuous feedback for dynamic resource allocation. As a novel approach for network scheduling and control application co-design, the proposed scheme uses a condition-based cyclic state-switching framework to balance the network resource allocation among all the control loops. The scheme uses a fuzzy controller to provide the QoC guarantees by maintaining the overall system performance at a desired level. We investigate a simplified servo target tracking system as our case study to show the effectiveness of the proposed TFS scheme for achieving a desired level of the system performance. The simulation results confirm the advantages of TFS in comparison with the typical static and feedback scheduling algorithms.

Improved Rollout Search for the Vehicle Routing Problem with Stochastic Demands

We propose two variants of a known rollout algorithm for the single vehicle routing problem with stochastic demands (VRPSD) under both the a priori (AP) and restocking (RS) strategies by expanding the search space of this algorithm. To reduce the increased computational effort, we introduce a novel approach for computing the expected cost of a route when executing a rollout algorithm, which is, however, approximate in the RS case. With a sufficiently large number of customers the theoretical speed-up factor of this approach is of big-o order 1/3. On a set of instances from the literature, our rollout algorithm variants yield better routes than the known rollout algorithm, with average expected cost improvements between 1.25% and 1.89% in the AP case and 0.64% and 0.96% in the RS case. In the AP case our second variant dominates our first variant by 0.43-0.46% on average, an observation for which we provide some theoretical support. In the RS case we do not observe any such dominance. Moreover, across all the considered rollout algorithms, our expected cost evaluation approach achieves speed-up factors that range from 0.16 to 0.38 in the AP case and 0.26 and 0.34 in the RS case, with the quality of the resulting RS routes only marginally degraded. Other VRPSD heuristics can potentially benefit from the use of this approach.

Lithographic source optimization based on adaptive projection compressive sensing.

This paper proposes to use the a-priori knowledge of the target layout patterns to design data-adaptive compressive sensing (CS) methods for efficient source optimization (SO) in lithography systems. A set of monitoring pixels are selected from the target layout based on blue noise random patterns. The SO is then formulated as an under-determined linear problem to improve image fidelity according to the monitoring pixels. Adaptive projections are then designed, based on the a-priori knowledge of the target layout, in order to further reduce the dimension of the optimization problem, while trying to retain the SO performance. Different from traditional CS methods, adaptive projections are constructed directly from the target layout data via a nonlinear thresholding operation. Adaptive projections are proved to achieve superior SO performance over the random projections. This paper also studies and compares the impact of different sparse representation bases on the SO performance. It is shown that the discrete cosine transform (DCT), spatial and Haar wavelet bases are good choices for source representation.

Mid-infrared ellipsometry, Raman and X-ray diffraction studies of AlxGa1−xN/AlN/Si structures

We report an investigation of the optical and structural properties of wurtzite phase AlxGa1−xN/AlN structure grown on Si(111) within the compositional range of 0≤x≤1. The study focuses on providing essential physical quantities for the fabrication process control, namely the composition dependence of phonon mode energy and refractive index. Three complementary techniques, infrared ellipsometry, Raman spectroscopy and X-ray diffraction, have been used to minimize uncertainties in our analysis. Based on the high quality and nearly strain-free AlxGa1−xN/AlN double layer samples, we determined the calibration curve for the A1(LO) phonon mode. We have also constructed the ellipsometry model which uses a-priori knowledge of experimentally measured A1(TO) phonon mode frequencies. From the best model fit to the collected ellipsometry spectra of the entire sample series, we obtained the anisotropic refractive indices of the AlxGa1−xN alloys with a very satisfactory accuracy.

Distribution of Relaxation Times Analysis of High-Temperature PEM Fuel Cell Impedance Spectra

In this study, Distribution of Relaxation Times (DRT) was successfully demonstrated in the analysis of the impedance spectra of High-Temperature Polymer Electrolyte Membrane Fuel Cells (HT-PEMFC) doped with phosphoric acid. Electrochemical impedance spectroscopy (EIS) was performed and the quality of the recorded spectra was verified by Kramers-Kronig relations. DRT was then applied to the measured spectra and polarization losses were separated on the basis of their typical time constants. The main features of the distribution function were assigned to the cell’s polarization processes by selecting appropriate experimental conditions. DRT can be used to identify individual internal HT-PEMFC fuel cell phenomena without any a-priori knowledge about the physics of the system. This method has the potential to further improve EIS spectra interpretation with either equivalent circuits or physical models.

A Method for Neutron Scattering Quantification and Correction Applied to Neutron Imaging

Either the composition, the thickness or the density of a sample can be derived, given the other two are known, by measuring the attenuation of a given incident radiation intensity through the sample. However, in the case of neutron imaging, the separation of the transmitted intensity from the scattered intensity using scintillator-based detection systems is yet to be solved. Several methods have been proposed for the correction of disturbing neutron scattering, but they are only applicable to specific materials or require some a-priori knowledge of the sample. Here we present a method for white beam neutron imaging which compares transmitted neutron images at different distances from the scintillator to improve the quantification capabilities of neutron imaging.

Optimal design of sensor networks for damage detection

The structural integrity of buildings and infrastructures can be affected by either environmental conditions or unforeseen external actions. In order to efficiently detect damage, intended as an irreversible degradation of the structural stiffness, many identification algorithms have been proposed in the literature. Nevertheless, a crucial aspect to accurately estimate and locate such damage pertains to the configuration of the deployed structural health monitoring (SHM) system. In addressing this goal, a framework is here proposed for the optimal design of sensor networks, in terms of number, type and spatial deployment of the sensors. The rationale of the method is to simultaneously maximize the information associated with the measurements, and minimize the total cost of the experimental setup; the overarching goal thus lies in the maximization of the information per unit cost, for the efficient allocation of resources. The value of the SHM system is quantified through the Shannon information gain between the a-priori knowledge of the mechanical properties and the values estimated, on the basis of measurements. The types of sensors contained into the overall SHM mix largely affects the estimation accuracy since, as a rule of thumb, the higher the sensor cost, the higher the signal-to-noise ratio and, therefore, the better the attainable estimation. In order to tackle the aforementioned multi objective optimization problem and to derive the associated Pareto front, a-posteriori solution methods relying on evolutionary algorithms are adopted. The proposed method is applied to a shear-type structure, namely the Pirelli tower in Milan, and the relevant multi-criteria optimization solutions are presented.