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Author Alvarenga, T.C.; De Lima, R.R.; Simao, S.D.; Junior, L.C.B.; Bueno, J.S.D.; Alvarenga, R.R.; Rodrigues, P.B.; Furtado Leite, D. doi  openurl
  Title Ensemble of hybrid Bayesian networks for predicting the AMEn of broiler feedstuffs Type
  Year 2022 Publication Computers and Electronics in Agriculture Abbreviated Journal Comput. Electron. Agric.  
  Volume 198 Issue Pages 107067  
  Keywords Bnlearn package; Empirical distribution; Ensemble learning; Metabolizable energy  
  Abstract To adequately meet the nutritional needs of broilers, it is necessary to know the values of apparent metabolizable energy corrected by the nitrogen balance (AMEn) of the feedstuffs. To determine AMEn values, biological assays, feedstuff composition tables, or prediction equations are used as a function of the chemical composition of feedstuffs, the latter using statistical methodologies such as multiple linear regression, neural networks, and Bayesian networks (BN). BN is a statistical and computational methodology that consists of graphical (graph) and probabilistic models of quantitative and/or qualitative variables. Ensembles of BN in the area of broiler nutrition are expected, as there is no research showing their AMEn prediction performance. The purpose of this article is to propose and use ensembles of hybrid Bayesian networks (EHBNs) and obtain prediction equations for the AMEn of feedstuffs used in broiler nutrition from their chemical compositions. We trained 100, 1,000, and 10,000 EHBN, and in this way, empirical distributions were found for the coefficients of the covariates (crude protein, ether extract, mineral matter, and crude fiber). Thus, the mean, median, and mode of these distributions were calculated to build prediction equations for AMEn. It is observed that the method for obtaining the coefficients of the covariates discussed in this article is an unprecedented proposal in the field of broiler nutrition. The data used to obtain the equations were obtained by meta-analysis, and the data for the validation of the equations were obtained from metabolic tests. The proposed equations were evaluated by precision measures such as the mean square error (MSE), mean absolute deviation (MAD), and mean absolute percentage error (MAPE). The best equations for predicting AMEn were derived from the mean or mode coefficients for the 10,000 EHBN results. In conclusion, the methodology used is a good tool to obtain prediction equations for AMEn as a function of the chemical composition of their feedstuffs. The coefficients were found to differ from those found by other methodologies, such as the usual neural network or multiple linear regressions. The field of broiler nutrition contributed with new equations and with a never-applied methodology and differentiated in obtaining its coefficients by empirical distributions.  
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  ISSN 0168-1699 ISBN Medium  
  Area Expedition Conference  
  Notes WOS:000809797700002 Approved  
  Call Number UAI @ alexi.delcanto @ Serial 1601  
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Author Carrasco, M.; Alvarez, F.; Velazquez, R.; Concha, J.; Perez-Cotapos, F. doi  openurl
  Title Brush-Holder Integrated Load Sensor Prototype for SAG Grinding Mill Motor Type
  Year 2019 Publication Electronics Abbreviated Journal Electronics  
  Volume 8 Issue 11 Pages 14 pp  
  Keywords electrical motor sensor; SAG grinding mill motor; inspection protocol; mining industry  
  Abstract One of the most widely used electro-mechanical systems in large-scale mining is the electric motor. This device is employed in practically every phase of production. For this reason, it needs to be inspected regularly to maintain maximum operability, thus avoiding unplanned stoppages. In order to identify potential faults, regular check-ups are performed to measure the internal parameters of the components, especially the brushes and brush-holders. Both components must be properly aligned and calibrated to avoid electric arcs to the internal insulation of the motor. Although there is an increasing effort to improve inspection tasks, most inspection procedures are manual, leading to unnecessary costs in inspection time, errors in data entry, and, in extreme cases, measurement errors. This research presents the design, development, and assessment of an integrated measurement prototype for measuring spring tension and other key parameters in brush-holders used in electric motors. It aims to provide the mining industry with a new, fully automatic inspection system that will facilitate maintenance and checking. Our development research was carried out specifically on the brush system of a SAG grinding mill motor. These machines commonly use SIEMENS motors; however, the instrument can be easily adapted to any motor by simply changing the physical dimensions of the prototype.  
  Address [Carrasco, Miguel; Concha, Javier; Perez-Cotapos, Francisco] Univ Adolfo Ibanez, Fac Ingn & Ciencias, Av Diagonal Las Torres 20700, Santiago 7941169, Chile, Email: miguel.carrasco@uai.cl;  
  Corporate Author Thesis  
  Publisher Mdpi Place of Publication Editor  
  Language English Summary Language Original Title  
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  Series Volume Series Issue Edition  
  ISSN 2079-9292 ISBN Medium  
  Area Expedition Conference  
  Notes WOS:000502269500023 Approved  
  Call Number UAI @ eduardo.moreno @ Serial 1066  
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Author Carrasco, M.; Mery, D.; Concha, A.; Velazquez, R.; De Fazio, R.; Visconti, P. doi  openurl
  Title An Efficient Point-Matching Method Based on Multiple Geometrical Hypotheses Type
  Year 2021 Publication Electronics Abbreviated Journal Electronics  
  Volume 10 Issue 3 Pages 246  
  Keywords computer vision; correspondence problem; fundamental matrix; multiple view geometry; point matching; trifocal tensor  
  Abstract Point matching in multiple images is an open problem in computer vision because of the numerous geometric transformations and photometric conditions that a pixel or point might exhibit in the set of images. Over the last two decades, different techniques have been proposed to address this problem. The most relevant are those that explore the analysis of invariant features. Nonetheless, their main limitation is that invariant analysis all alone cannot reduce false alarms. This paper introduces an efficient point-matching method for two and three views, based on the combined use of two techniques: (1) the correspondence analysis extracted from the similarity of invariant features and (2) the integration of multiple partial solutions obtained from 2D and 3D geometry. The main strength and novelty of this method is the determination of the point-to-point geometric correspondence through the intersection of multiple geometrical hypotheses weighted by the maximum likelihood estimation sample consensus (MLESAC) algorithm. The proposal not only extends the methods based on invariant descriptors but also generalizes the correspondence problem to a perspective projection model in multiple views. The developed method has been evaluated on three types of image sequences: outdoor, indoor, and industrial. Our developed strategy discards most of the wrong matches and achieves remarkable F-scores of 97%, 87%, and 97% for the outdoor, indoor, and industrial sequences, respectively.  
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  ISSN 2079-9292 ISBN Medium  
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  Notes WOS:000614991900001 Approved  
  Call Number UAI @ alexi.delcanto @ Serial 1341  
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Author Rozas Andaur, J.M.; Ruz, G.A.; Goycoolea, M. doi  openurl
  Title Predicting Out-of-Stock Using Machine Learning: An Application in a Retail Packaged Foods Manufacturing Company Type
  Year 2021 Publication Electronics Abbreviated Journal Electronics  
  Volume 10 Issue 22 Pages 2787  
  Keywords out of stock; machine learning; classification algorithms; imbalance data; supply chain management; decision support; retail industry application  
  Abstract For decades, Out-of-Stock (OOS) events have been a problem for retailers and manufacturers. In grocery retailing, an OOS event is used to characterize the condition in which customers do not find a certain commodity while attempting to buy it. This paper focuses on addressing this problem from a manufacturer’s perspective, conducting a case study in a retail packaged foods manufacturing company located in Latin America. We developed two machine learning based systems to detect OOS events automatically. The first is based on a single Random Forest classifier with balanced data, and the second is an ensemble of six different classification algorithms. We used transactional data from the manufacturer information system and physical audits. The novelty of this work is our use of new predictor variables of OOS events. The system was successfully implemented and tested in a retail packaged foods manufacturer company. By incorporating the new predictive variables in our Random Forest and Ensemble classifier, we were able to improve their system’s predictive power. In particular, the Random Forest classifier presented the best performance in a real-world setting, achieving a detection precision of 72% and identifying 68% of the total OOS events. Finally, the incorporation of our new predictor variables allowed us to improve the performance of the Random Forest by 0.24 points in the F-measure.  
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  ISSN 2079-9292 ISBN Medium  
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  Notes Approved  
  Call Number UAI @ alexi.delcanto @ Serial 1487  
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Author Vahabi, M.; Bahar, A.N.; Otsuki, A.; Wahid, K.A. doi  openurl
  Title Ultra-Low-Cost Design of Ripple Carry Adder to Design Nanoelectronics in QCA Nanotechnology Type
  Year 2022 Publication Electronics Abbreviated Journal Electronics  
  Volume 11 Issue 15 Pages 2320  
  Keywords full adder; ripple carry adder; coplanar; cost function; quantum-dot cellular automata; energy dissipation  
  Abstract Due to the development of integrated circuits and the lack of responsiveness to existing technology, researchers are looking for an alternative technology. Quantum-dot cellular automata (QCA) technology is one of the promising alternatives due to its higher switch speed, lower power dissipation, and higher device density. One of the most important and widely used circuits in digital logic calculations is the full adder (FA) circuit, which actually creates the problem of finding its optimal design and increasing performance. In this paper, we designed and implemented two new FA circuits in QCA technology and then implemented ripple carry adder (RCA) circuits. The proposed FAs and RCAs showed excellent performance in terms of QCA evaluation parameters, especially in cost and cost function, compared to the other reported designs. The proposed adders' approach was 46.43% more efficient than the best-known design, and the reason for this superiority was due to the coplanar form, without crossovers and inverter gates in the designs.  
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  ISSN 2079-9292 ISBN Medium  
  Area Expedition Conference  
  Notes WOS:000840152800001 Approved  
  Call Number UAI @ alexi.delcanto @ Serial 1633  
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