In this study, the effect by Iron with nickel-based catalyst for the combined steam and carbon dioxide reforming of methane was investigated. Fe-promoted and un-promoted Ni–Mg–Ce/γ-Al2O3 catalysts were prepared by co-impregnation and evaluated in a quartz fixed-bed reactor at H2O:CO2:CH4 ratios of 0.9:1:1 and a temperature of 1073 K under atmospheric pressure. The physicochemical properties of the catalysts were investigated by N2 adsorption–desorption, XRD, H2-TPR, CO2-TPD, TGA and FE-SEM. The iron-supported catalysts showed improved resistance to carbon deposition and suppressed sintering of nickel. As a result, NMC-Fe(5) showed the lowest coke and high stability over 70 h among all other catalysts.
Robot which replaces and assumes the role of the human is becoming a common and popular problem. Also, game playing robot has challenges to researchers as well as people being interested in this field. In this paper, we want to introduce a new method to detect and recognize chess pieces of Janggi Chess game. Paper is a uniform approach from input image receiving to chess piece recognition. Besides, some new algorithms are used to get the highest performance of system and can apply for real robot system. The first, Tensor Voting is applied to find four corners of chessboard which can extract the full chessboard from background and noise for both simple and complex cases, which other methods are difficult to overcome. Secondly, Circle Hough Transform can detect the chess pieces' size and position correctly regardless of the effects of light, capture angle, the quality of images, etc. Furthermore, the piece recognition step is implemented using SVM (Support Vector Machine), a popular algorithm for classifying with highest performance. The promising results have confirmed the effectiveness of the proposed method.
Optical music recognition systems are in the general interest recently. These systems achieve accurate symbol recognition at some level. However, chords are not considered in these systems yet they play a role in music. Therefore, we aimed to develop an algorithm that can deal with separation and recognition of chords in music score images. Separation is necessary because the chords can be touched, overlapped or/and broken due to noise and other reasons. By considering these problems, we propose top-down based separation using domain information and characteristics of the chords. To handle recognition, we propose a modified zoning method with k-nearest neighbor classifier. Also, we analyzed several classifiers with different features to see which method is reliable for the chord recognition. Since this topic is not considered with special focus before, there is not a standard benchmark to evaluate performance of the algorithm. Thus, we introduce a new dataset, namely OMR-ChSR6306, which includes a wide range of chords such as single chords, touched chords, and overlapped chords. Experiments on the proposed dataset demonstrate that our algorithm can separate and recognize the chords, with 100% separation and 98.98% recognition accuracy respectively.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.