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81.
Abdelmoty M. Ahmed Reda Abo Alez Gamal Tharwat Muhammad Taha B. Belgacem Ahmad M. J. Al Moustafa 《成像科学杂志》2020,68(1):11-23
ABSTRACTArabic sign language (ArSL) is method of communication between deaf communities in Arab countries; therefore, the development of systemsthat can recognize the gestures provides a means for the Deaf to easily integrate into society. In this research we implemented a computational structurefor an intelligent interpreter that automatically recognizes the isolated dynamic gestures. The proposed system recognizes and translates gesturesperformed with one or both hands. It comprises five subsystems, building dataset, video processing, feature extraction, mapping between ArSL and Arabictext, and text generation. To apply the system, 100-signs of ArSL was used, which was applied on 1500 video files. It's were divided into five classes:alphabet, numbers, "prepositions, pronouns and question words", Arabic life expressions, and "nouns and verbs". The evaluation indicated that thesystem automatically recognizes and translates isolated dynamic ArSL gestures by highly accurate manner. The results showed that the system accuracy is 95.8%. 相似文献
82.
Krishnan Balasubramanian 《Fullerenes, Nanotubes and Carbon Nanostructures》2020,28(9):687-696
AbstractWe have developed combinatorial generation function methods that combine M?bius inversion and character cycle indices for the enumeration of stereo, position and chiral isomers of icosahedral giant fullerenes C180 and C240. Techniques are also developed for the machine perception of symmetries of especially giant fullerenes. The techniques yield, symmetries, position, stereo and chiral isomers of giant fullerenes which we illustrate with applications to icosahedral C180(Ih), and C240(Ih). We have obtained combinatorial tables for the isomers of C180Xk and C240Xk. Our results point to errors in previous computations on C240 permutations. We have also outlined applications to NMR and ESR spectroscopy. 相似文献
83.
Due to its outstanding ability in processing large quantity and high-dimensional
data, machine learning models have been used in many cases, such as pattern recognition,
classification, spam filtering, data mining and forecasting. As an outstanding machine
learning algorithm, K-Nearest Neighbor (KNN) has been widely used in different situations,
yet in selecting qualified applicants for winning a funding is almost new. The major problem
lies in how to accurately determine the importance of attributes. In this paper, we propose a
Feature-weighted Gradient Decent K-Nearest Neighbor (FGDKNN) method to classify
funding applicants in to two types: approved ones or not approved ones. The FGDKNN is
based on a gradient decent learning algorithm to update weight. It updatesthe weight of labels
by minimizing error ratio iteratively, so that the importance of attributes can be described
better. We investigate the performance of FGDKNN with Beijing Innofund. The results show
that FGDKNN performs about 23%, 20%, 18%, 15% better than KNN, SVM, DT and ANN,
respectively. Moreover, the FGDKNN has fast convergence time under different training
scales, and has good performance under different settings. 相似文献
84.
Neural Machine Translation (NMT) is an end-to-end learning approach for
automated translation, overcoming the weaknesses of conventional phrase-based translation
systems. Although NMT based systems have gained their popularity in commercial
translation applications, there is still plenty of room for improvement. Being the most
popular search algorithm in NMT, beam search is vital to the translation result. However,
traditional beam search can produce duplicate or missing translation due to its target
sequence selection strategy. Aiming to alleviate this problem, this paper proposed neural
machine translation improvements based on a novel beam search evaluation function. And
we use reinforcement learning to train a translation evaluation system to select better
candidate words for generating translations. In the experiments, we conducted extensive
experiments to evaluate our methods. CASIA corpus and the 1,000,000 pairs of bilingual
corpora of NiuTrans are used in our experiments. The experiment results prove that the
proposed methods can effectively improve the English to Chinese translation quality. 相似文献
85.
Dah-Jing Jwo 《计算机、材料和连续体(英文)》2020,65(2):993-1014
The Global Positioning System (GPS) offers the interferometer for attitude
determination by processing the carrier phase observables. By using carrier phase
observables, the relative positioning is obtained in centimeter level. GPS interferometry
has been firstly used in precise static relative positioning, and thereafter in kinematic
positioning. The carrier phase differential GPS based on interferometer principles can
solve for the antenna baseline vector, defined as the vector between the antenna
designated master and one of the slave antennas, connected to a rigid body. Determining
the unknown baseline vectors between the antennas sits at the heart of GPS-based attitude
determination. The conventional solution of the baseline vectors based on least-squares
approach is inherently noisy, which results in the noisy attitude solutions. In this article,
the complementary Kalman filter (CKF) is employed for solving the baseline vector in
the attitude determination mechanism to improve the performance, where the receiversatellite double differenced observable was utilized as the measurement. By using the
carrier phase observables, the relative positioning is obtained in centimeter level.
Employing the CKF provides several advantages, such as accuracy improvement,
reliability enhancement, and real-time assurance. Simulation results based on the
conventional method where the least-squares approach is involved, and the proposed
method where the CKF is involved are compared and discussed. 相似文献
86.
Effect of Supports and Promoters on the Performance of Ni-Based Catalysts in Ethanol Steam Reforming
Thanh Khoa Phung Thong Le Minh Pham Anh-Nga T. Nguyen Khanh B. Vu Ha Ngoc Giang Tuan-Anh Nguyen Thanh Cong Huynh Hong Duc Pham 《化学工程与技术》2020,43(4):672-688
Ethanol steam reforming (ESR) is one of the potential processes to convert ethanol into valuable products. Hydrogen produced from ESR is considered as green energy for the future and can be an excellent alternative to fossil fuels with the aim of mitigating the greenhouse gas effect. The ESR process has been well studied, using transition metals as catalysts coupled with both acidic and basic oxides as supports. Among various reported transition metals, Ni is an inexpensive material with activity comparable to that of noble metals, showing promising ethanol conversion and hydrogen yields. Additionally, different promoters and supports were utilized to enhance the hydrogen yield and the catalyst stability. This review summarizes and discusses the influences of the supports and promoters of Ni-based catalysts on the ESR process. 相似文献
87.
This article introduces a new class of functional-coefficient predictive regression models, where the regressors consist of auto-regressors and latent factor regressors, and the coefficients vary with certain index variable. The unobservable factor regressors are estimated through imposing an approximate factor model on high dimensional exogenous variables and subsequently implementing the classical principal component analysis. With the estimated factor regressors, a local linear smoothing method is used to estimate the coefficient functions (with appropriate rotation) and obtain a one-step ahead nonlinear forecast of the response variable, and then a wild bootstrap procedure is introduced to construct the prediction interval. Under regularity conditions, the asymptotic properties of the proposed methods are derived, showing that the local linear estimator and the nonlinear forecast using the estimated factor regressors are asymptotically equivalent to those using the true latent factor regressors. The developed model and methodology are further generalized to the factor-augmented vector predictive regression with functional coefficients. Finally, some extensive simulation studies and an empirical application to forecast the UK inflation are given to examine the finite-sample performance of the proposed model and methodology. 相似文献
88.
89.
90.
Chafic Saide Régis Lengelle Paul Honeine Cédric Richard Roger Achkar 《International Journal of Adaptive Control and Signal Processing》2015,29(11):1391-1410
Nonlinear adaptive filtering has been extensively studied in the literature, using, for example, Volterra filters or neural networks. Recently, kernel methods have been offering an interesting alternative because they provide a simple extension of linear algorithms to the nonlinear case. The main drawback of online system identification with kernel methods is that the filter complexity increases with time, a limitation resulting from the representer theorem, which states that all past input vectors are required. To overcome this drawback, a particular subset of these input vectors (called dictionary) must be selected to ensure complexity control and good performance. Up to now, all authors considered that, after being introduced into the dictionary, elements stay unchanged even if, because of nonstationarity, they become useless to predict the system output. The objective of this paper is to present an adaptation scheme of dictionary elements, which are considered here as adjustable model parameters, by deriving a gradient‐based method under collinearity constraints. The main interest is to ensure a better tracking performance. To evaluate our approach, dictionary adaptation is introduced into three well‐known kernel‐based adaptive algorithms: kernel recursive least squares, kernel normalized least mean squares, and kernel affine projection. The performance is evaluated on nonlinear adaptive filtering of simulated and real data sets. As confirmed by experiments, our dictionary adaptation scheme allows either complexity reduction or a decrease of the instantaneous quadratic error, or both simultaneously. Copyright © 2015 John Wiley & Sons, Ltd. 相似文献