Smart enterprise for pulp & paper mills: Data processing and reconciliation

An ad-hoc data reconciliation procedure developed for the recausticizing section of a new pulp and paper industry is presented in this work. A comprehensive model was formulated to take into account different unit operation modes. It was also extended to incorporate specific knowledge of some pieces of equipment to increase redundancy, and consequently enhance estimate precision and gross error detectability. © 2002 Elsevier B.V. All rights reserved.Fil: Sanchez, Mabel Cristina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Planta Piloto de Ingeniería Química. Universidad Nacional del Sur. Planta Piloto de Ingeniería Química; ArgentinaFil: Leung David. Visy Pulp And Paper Pty Ltd; AustraliaFil: Konecsny Helmut. Visy Pulp And Paper Pty Ltd; AustraliaFil: Bigaran Carlo. Visy Pulp And Paper Pty Ltd; AustraliaFil: Romagnoli José. University Of Sydney; Australi

Sparse Representation of Astronomical Images

Sparse representation of astronomical images is discussed. It is shown that a significant gain in sparsity is achieved when particular mixed dictionaries are used for approximating these types of images with greedy selection strategies. Experiments are conducted to confirm: i)Effectiveness at producing sparse representations. ii)Competitiveness, with respect to the time required to process large images.The latter is a consequence of the suitability of the proposed dictionaries for approximating images in partitions of small blocks.This feature makes it possible to apply the effective greedy selection technique Orthogonal Matching Pursuit, up to some block size. For blocks exceeding that size a refinement of the original Matching Pursuit approach is considered. The resulting method is termed Self Projected Matching Pursuit, because is shown to be effective for implementing, via Matching Pursuit itself, the optional back-projection intermediate steps in that approach.Comment: Software to implement the approach is available on http://www.nonlinear-approx.info/examples/node1.htm

Pupil remapping for high contrast astronomy: results from an optical testbed

The direct imaging and characterization of Earth-like planets is among the most sought-after prizes in contemporary astrophysics, however current optical instrumentation delivers insufficient dynamic range to overcome the vast contrast differential between the planet and its host star. New opportunities are offered by coherent single mode fibers, whose technological development has been motivated by the needs of the telecom industry in the near infrared. This paper presents a new vision for an instrument using coherent waveguides to remap the pupil geometry of the telescope. It would (i) inject the full pupil of the telescope into an array of single mode fibers, (ii) rearrange the pupil so fringes can be accurately measured, and (iii) permit image reconstruction so that atmospheric blurring can be totally removed. Here we present a laboratory experiment whose goal was to validate the theoretical concepts underpinning our proposed method. We successfully confirmed that we can retrieve the image of a simulated astrophysical object (in this case a binary star) though a pupil remapping instrument using single mode fibers.Comment: Accepted in Optics Expres

MYSTIC: Michigan Young STar Imager at CHARA

We present the design for MYSTIC, the Michigan Young STar Imager at CHARA. MYSTIC will be a K-band, cryogenic, 6-beam combiner for the Georgia State University CHARA telescope array. The design follows the image-plane combination scheme of the MIRC instrument where single-mode fibers bring starlight into a non-redundant fringe pattern to feed a spectrograph. Beams will be injected in polarization-maintaining fibers outside the cryogenic dewar and then be transported through a vacuum feedthrough into the ~220K cold volume where combination is achieved and the light is dispersed. We will use a C-RED One camera (First Light Imaging) based on the eAPD SAPHIRA detector to allow for near-photon-counting performance. We also intend to support a 4-telescope mode using a leftover integrated optics component designed for the VLTI-GRAVITY experiment, allowing better sensitivity for the faintest targets. Our primary science driver motivation is to image disks around young stars in order to better understand planet formation and how forming planets might influence disk structures.Comment: Presented at the 2018 SPIE Astronomical Telescopes + Instrumentation, Austin, Texas, US

Dimensionality Reduction and Classification feature using Mutual Information applied to Hyperspectral Images : A Filter strategy based algorithm

Hyperspectral images (HIS) classification is a high technical remote sensing tool. The goal is to reproduce a thematic map that will be compared with a reference ground truth map (GT), constructed by expecting the region. The HIS contains more than a hundred bidirectional measures, called bands (or simply images), of the same region. They are taken at juxtaposed frequencies. Unfortunately, some bands contain redundant information, others are affected by the noise, and the high dimensionality of features made the accuracy of classification lower. The problematic is how to find the good bands to classify the pixels of regions. Some methods use Mutual Information (MI) and threshold, to select relevant bands, without treatment of redundancy. Others control and eliminate redundancy by selecting the band top ranking the MI, and if its neighbors have sensibly the same MI with the GT, they will be considered redundant and so discarded. This is the most inconvenient of this method, because this avoids the advantage of hyperspectral images: some precious information can be discarded. In this paper we'll accept the useful redundancy. A band contains useful redundancy if it contributes to produce an estimated reference map that has higher MI with the GT.nTo control redundancy, we introduce a complementary threshold added to last value of MI. This process is a Filter strategy; it gets a better performance of classification accuracy and not expensive, but less preferment than Wrapper strategy.Comment: 11 pages, 5 figures, journal pape