147,467 research outputs found

    Automated robotic inspection system for electronic manufacturing

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    An automated robotic inspection system for electronic manufacturing has been developed to identify pin defects of IC packages mounted on printed circuit boards using surface mount technology. The automated robotic inspection system consists of two robots, a computer, a CCD camera with frame gabber for image acquisition, and a customized windows program using neural network for on-line defect identification. Gray scale images of the pins on IC packages are acquired using ambient light. The images are filtered and formatted to appropriate size, so that Matlab neural network tool could be used. The images are used to train neural networks using Matlab\u27s Bayesian Regularization module. Optimal network was found to be a single-layer network with three outputs for each IC investigated. The weights and biases of each of the ICs investigated and the matrices of gray scale values for the IC images are saved as text files. A customized windows program uses these text files for on-line defect identification. The defect identification for the networks was found to be 100 percent for the two ICs investigated. The analysis and integration of an automated robotic inspection system for on-line monitoring of electronic manufacturing using neural networks is presented in this work

    Hybrid Signal Processing and Machine Learning Algorithm for Adaptive Fault Classification of Wind Farm Integrated Transmission Line Protection

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    The technological advancement in integration of Renewable Green Energy Sources (RGES) like Wind Farm Generators (WFG), and Photovoltaic (PV) system into conventional power system as a future solution to meet the increase in global energy demands in order to reduce the cost of power generation, and improve on the climate change impact. This innovation also introduces challenges in the power system protection by it being compromised due to injected fault current infeeds on existing facilities. These infeed lead to the undesired trip of a healthy section of the line, and protection system failure. This paper presents a soft computational approach to adaptive fault classification model on High Voltage Transmission Line (HVTL) with and without RGES-WFG integration topologies, using extracted one-cycle fault signature of voltage and current signals with wavelet statistical approach in Matlab. The results are unique signatures across all fault types and fault distances with distinct entropy energy values on proposed network architecture. The supervised machine learning algorithm from Bayesian network classified 99.15 % faults correctly with the operation time of 0.01 s to produced best-generalized model with an RMS error value of 0.05 for single line-to-ground (SLG) fault identification and classification. Best suitable for adaptive unit protection scheme integration

    Hybrid signal processing and machine learning algorithm for adaptive fault classification of wind farm integrated transmision line protection

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    The technological advancement in integration of Renewable Green Energy Sources (RGES) like Wind Farm Generators (WFG), and Photovoltaic (PV) system into conventional power system as a future solution to meet the increase in global energy demands in order to reduce the cost of power generation, and improve on the climate change impact. This innovation also introduces challenges in the power system protection by it being compromised due to injected fault current infeeds on existing facilities. These infeed leadto the undesired trip of a healthy section of the line, and protection system failure. This paper presents a soft computational approach to adaptive fault classification model on High VoltageTransmission Line (HVTL) with and without RGES-WFG integration topologies, using extracted one-cycle fault signature of voltage and current signals with wavelet statistical approach in Matlab. The results are unique signatures across all fault types and fault distances with distinct entropy energy values on proposed network architecture. The supervised machine learning algorithm from Bayesian network classified 99.15 % faults correctly with the operation time of 0.01 s to produced best-generalized model with an RMS error value of 0.05 for single line-to-ground (SLG) fault identification and classification. Best suitable for adaptive unit protection scheme integration

    Maximum Entropy Vector Kernels for MIMO system identification

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    Recent contributions have framed linear system identification as a nonparametric regularized inverse problem. Relying on 2\ell_2-type regularization which accounts for the stability and smoothness of the impulse response to be estimated, these approaches have been shown to be competitive w.r.t classical parametric methods. In this paper, adopting Maximum Entropy arguments, we derive a new 2\ell_2 penalty deriving from a vector-valued kernel; to do so we exploit the structure of the Hankel matrix, thus controlling at the same time complexity, measured by the McMillan degree, stability and smoothness of the identified models. As a special case we recover the nuclear norm penalty on the squared block Hankel matrix. In contrast with previous literature on reweighted nuclear norm penalties, our kernel is described by a small number of hyper-parameters, which are iteratively updated through marginal likelihood maximization; constraining the structure of the kernel acts as a (hyper)regularizer which helps controlling the effective degrees of freedom of our estimator. To optimize the marginal likelihood we adapt a Scaled Gradient Projection (SGP) algorithm which is proved to be significantly computationally cheaper than other first and second order off-the-shelf optimization methods. The paper also contains an extensive comparison with many state-of-the-art methods on several Monte-Carlo studies, which confirms the effectiveness of our procedure

    A Sub-Saturn Mass Planet, MOA-2009-BLG-319Lb

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    We report the gravitational microlensing discovery of a sub-Saturn mass planet, MOA-2009-BLG-319Lb, orbiting a K- or M-dwarf star in the inner Galactic disk or Galactic bulge. The high-cadence observations of the MOA-II survey discovered this microlensing event and enabled its identification as a high-magnification event approximately 24 hr prior to peak magnification. As a result, the planetary signal at the peak of this light curve was observed by 20 different telescopes, which is the largest number of telescopes to contribute to a planetary discovery to date. The microlensing model for this event indicates a planet-star mass ratio of q = (3.95 ± 0.02) × 10^(–4) and a separation of d = 0.97537 ± 0.00007 in units of the Einstein radius. A Bayesian analysis based on the measured Einstein radius crossing time, t_E, and angular Einstein radius, θ_E, along with a standard Galactic model indicates a host star mass of M_L = 0.38^(+0.34)_(–0.18) M_☉ and a planet mass of M_p = 50^(+44)_(–24) M_⊕, which is half the mass of Saturn. This analysis also yields a planet-star three-dimensional separation of a = 2.4^(+1.2)_(–0.6) AU and a distance to the planetary system of D_L = 6.1^(+1.1)_(–1.2) kpc. This separation is ~2 times the distance of the snow line, a separation similar to most of the other planets discovered by microlensing

    Total Mass TCI driven by Parametric Estimation

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    This paper presents the Total Mass Target Controlled Infusion algorithm. The system comprises an On Line tuned Algorithm for Recovery Detection (OLARD) after an initial bolus administration and a Bayesian identification method for parametric estimation based on sparse measurements of the accessible signal. To design the drug dosage profile, two algorithms are here proposed. During the transient phase, an Input Variance Control (IVC) algorithm is used. It is based on the concept of TCI and aims to steer the drug effect to a predefined target value within an a priori fixed interval of time. After the steady state phase is reached the drug dose regimen is controlled by a Total Mass Control (TMC) algorithm. The mass control law for compartmental systems is robust even in the presence of parameter uncertainties. The whole system feasibility has been evaluated for the case of Neuromuscular Blockade (NMB) level and was tested both in simulation and in real cases

    Regularized linear system identification using atomic, nuclear and kernel-based norms: the role of the stability constraint

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    Inspired by ideas taken from the machine learning literature, new regularization techniques have been recently introduced in linear system identification. In particular, all the adopted estimators solve a regularized least squares problem, differing in the nature of the penalty term assigned to the impulse response. Popular choices include atomic and nuclear norms (applied to Hankel matrices) as well as norms induced by the so called stable spline kernels. In this paper, a comparative study of estimators based on these different types of regularizers is reported. Our findings reveal that stable spline kernels outperform approaches based on atomic and nuclear norms since they suitably embed information on impulse response stability and smoothness. This point is illustrated using the Bayesian interpretation of regularization. We also design a new class of regularizers defined by "integral" versions of stable spline/TC kernels. Under quite realistic experimental conditions, the new estimators outperform classical prediction error methods also when the latter are equipped with an oracle for model order selection
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