Non-RF to RF test correlation using learning machines: a case-study

International audienceThe authors present a case study that employs production test data from an RF device to assess the effectiveness of four different methods in predicting the pass/fail labels of fabricated devices based on a subset of performances and, thereby, in decreasing test cost. The device employed is a zero-IF down-converter for cell-phone applications and the four methods range from a sample maximum-cover algorithm to an advanced ontogenic neural network. The results indicate that a subset of non-RF performances suffice to predict correctly the pass/fail label for the vast majority of the devices and that the addition of a few select RF performances holds great potential for reducing misprediction to industrially acceptable levels. Based on these results, the authors then discuss enhancements and experiments that will further corroborate the utility of these methods within the cost realities of analog/RF production testing

RF Specification Test Compaction Using Learning Machines

International audienceWe present a machine learning approach to the problem of RF specification test compaction. The proposed compaction flow relies on a multi-objective genetic algorithm, which searches in the power-set of specification tests to select appropriate subsets, and a classifier, which makes pass/fail decisions based solely on these subsets. The method is demonstrated on production test data from an RF device fabricated by IBM. The results indicate that machine learning can identify intricate correlations between specification tests, which allows us to infer the outcome of all tests from a subset of tests. Thereby, the number of tests that need to be explicitly carried out and the corresponding cost are reduced significantly without adversely impacting test accuracy

Four-phase interleaved DC/DC boost converter interfaces for super-capacitors in electric vehicle application based onadvanced sliding mode control design

International audienceElectricvehicles(EVs)hasreceivedanincreasinginterestfromthepowercommunityandhavebecomeincreasinglypopularduetotheirimportanceinfightingclimatechange.Anadvancedpowerelectronicconverterremainsthemainandcommontopicforresearchinthisarea.Inthispaper,anadvancedslidingmodecontrol(ASMC)isdesignedforFourPhaseInterleavedBoostConverter(FP-IBC).Thenoveltyoftheproposedcontrollerreliesontheuseofanadaptivedelay-timeblockwiththeconventionalslidingmodecontrolschemewhichisdesignedbasedonchatteringeliminationmethod.Thenewschemesucceedtoreducetheripplecontentsintheconverter’soutputvoltageandinputscurrents,andachievefastconvergenceandtransientresponse.AnexperimentalcomparisonstudyhasbeeninvestigatedbetweentheproposedcontrollerandSelf-tuningLeadLagCompensator(SLLC).ThecomparisonstudyisperformedatdifferentloadingconditionsusingSuper-Capacitor(SC)module.TheexperimentalresultsshowthatthedynamicresponseofFP-IBCissignificantlyimprovedbasedontheproposedcontroldesig

Optimization of tidal turbine positioning in large tidal farms

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Modeling, Control and Optimization of a Small Scale CHP System in Island operating Mode based on Fuzzy logic controller

International audienceThis paper presents the modeling, controlscheme and optimization of a small scale combined heat andpower (SSCHP) in island operating mode. A thermo dynamicmodel is carried out for SSCHP engine using Carnot machinemethod, while an optimization technique is formulated toidentify CHP engine parameters. In addition, a dynamic model isbuilt based on a system identification toolbox for SSCHP engineto study the dynamic behavior of the SSCHP during operatingmode. In island mode, the Fuzzy logic controller (FLC) is usedto regulate the inputs of SSCHP, and to match electric loaddemand. The complete system was represented and simulated inMatlab/Simulink

Analysis, Modeling, and Control of an AC Microgrid System Based on Green Energy

International audienceThe integration of distributed generators (DGs), electricstorage system (ESS), distributed electric loads and the utilitygrid through the point of common coupling is called microgrid(MG) .The coordinated operation of MG and the main utilitygrid with variable load demand is controlled by using microgridcontrol center (MGCC). MGCC methodology is introduced toregulate power flow for each source depends on the outputscommands from MG energy management center (MGEMC).This paper is focused only on developing an efficient and fastMGCC based on power control mode taking into account theoutputs commands from MGEMC without considering itsstrategy. The MG in this paper is assumed to be interconnectedto the main utility gird, and can purchase some power fromutility grid at off-peak hours or when the production of the MGis insufficient to meet the load demand. On the other hand, thereis a daily income to the MG when the generated power exceedsthe load demand during on-peak hours. The proposed MGCCand electrical power system is simulated using Matlab/Simulink

Machine learning and data mining methods in testing and diagnostics of analog and mixed-signal integrated circuits: Case study

© 2019, Springer Nature Singapore Pte Ltd. Artificial intelligence methods are widely used in different interdisciplinary areas. The paper is devoted to application the method of machine learning and data mining to construction a neuromorphic fault dictionary (NFD) for testing and fault diagnostics in analog/mixed-signal integrated circuits. The main issues of constructing a NFD from the big data point of view are considered. The method of reducing a set of essential characteristics based on the principal component analysis and approach to a cut down the training set using entropy estimation are proposed. The metrics used for estimating the classification quality are specified based on the confusion matrix. The case study results for analog filters are demonstrated and discussed. Experimental results for both cases demonstrate the essential reduction of initial training set and saving of time on the NFD training with high fault coverage up to 100%. The proposed method and approach can be used according to the design-for-testability flow for analog/mixed-signal integrated circuits

Structural, Mechanical and Thermodynamic Properties Under Pressure Effect of Rubidium Telluride: First Principle Calculations

First-principles density functional theory calculations have been performed to investigate the structural, elastic and thermodynamic properties of rubidium telluride in cubic anti-fluorite (anti-CaF2-type) structure. The calculated ground-state properties of Rb2Te compound such as equilibrium lattice parameter and bulk moduli are investigated by generalized gradient approximation (GGA-PBE) that are based on the optimization of total energy. The elastic constants, Young’s and shear modulus, Poisson ratio, have also been calculated. Our results are in reasonable agreement with the available theoretical and experimental data. The pressure dependence of elastic constant and thermodynamic quantities under high pressure are also calculated and discussed

A Nonlinear State Feedback for DC/DC Boost Converters

International audienceThis paper investigates the control problem for static boost type converters using a high gain state feedback robust controller incorporating an integral action. The robust feature allows to achieve the required performance in the presence of parametric uncertainties, while the integral action provides an offset free performance with respect to the desired levels of voltage. The adopted high gain approach is motivated by both fundamental as well as practical considerations, namely the underlying fundamental potential and the design parameter specification simplicity. The stability and convergence analysis has been carried out using an adequate Lyapunov approach, and the control system calibration is achieved throughout a few design parameters which are closely related to the desired dynamical performances. The effectiveness of the proposed control approach has been corroborated by numerical simulations and probing experimental results

Detection and tracking of multiple metallic objects in millimetre-wave images

In this paper we present a system for the automatic detection and tracking of metallic objects concealed on moving people in sequences of millimetre-wave (MMW) images. The millimetre-wave sensor employed has been demonstrated for use in covert detection because of its ability to see through clothing, plastics and fabrics.The system employs two distinct stages: detection and tracking. In this paper a single detector, for metallic objects, is presented which utilises a statistical model also developed in this paper. The second stage tracks the target locations of the objects using a Probability Hypothesis Density filter. The advantage of this filter is that it has the ability to track a variable number of targets, estimating both the number of targets and their locations. This avoids the need for data association techniques as the identities of the individual targets are not required. Results are presented for both simulations and real millimetre-wave image test sequences demonstrating the benefits of our system for the automatic detection and tracking of metallic objects