Applied Mathematics and Algorithms for Cloud Computing and IoT

Mao, Yuxin; Bhuse, Vijay; Zhou, Zhongmei; Pichappan, Pit; Abdel‐Aty, Mahmoud; Hayafuji, Yoshinori

doi:10.1155/2014/946860

Cited by 8 publications

(8 citation statements)

References 6 publications

(5 reference statements)

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“…Researchers stress on the importance of showing practical opportunities which mathematical methods give to future engineers and the fact that teachers lack in such knowledge. Mao et al [19] confirm that at the beginning of their career path, Mathematics teachers encounter problems, selecting the means for visualizing mathematical notions and do not focus the students' attention on building mathematical models of real processes.…”

Section: Analysis Of the Recent Research And Publicationsmentioning

confidence: 99%

“…Mell and Grance [23], where they describe the relevance of using cloud computing in order to analyse and interpret the results of the calculated mathematical models. Mao et al [19] suggest using MatLab for supporting engineering work. Lima et al [24] studied the problem of the efficient management of interdisciplinary projects in mathematical education of engineers.…”

Section: Analysis Of the Recent Research And Publicationsmentioning

confidence: 99%

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Model of the On-Line Course for Training Master Students Majoring in Mathematics for Teaching at University

Lovianova¹,

Vlasenko²,

Sitak³

et al. 2020

ujer

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This article considers the issue of on-line training of Master students, majoring in Mathematics (Speciality code in Ukrainian educational system "014.04 Secondary Education. Mathematics") and preparing them for teaching at University. The research papers and resources, focusing on the vocational bent of on-line training of undergraduate students majoring in Mathematics have been analyzed in this paper. The present paper describes designing the structure and components for the on-line course "Methods for Teaching Mathematics to Students at Technical Universities", which is in open access on the educational platform "Higher School Mathematics Teacher". This article presents the analysis of the survey results, in which 107 students in their first and second year of studying took part, which allowed defining the content of the course. The model is presented as a polyhedron, vertices, faces and edges of which show structural elements of the model and relations between them. The paper also introduces the analysis of the internship syllabi at Ukrainian universities and aims at defining the criteria for assessing learning performance of the on-line course users. It also considers the impact of the course on the efficiency of training 112 Master students in their second year of study, when they do their assistant internship at technical universities. The qualitative and quantitative analysis of the results of the experiment proved the efficiency of introducing the course into the process of practical training of Master students. The interns who joined the course, showed a better understanding of how to motivate students when teaching Mathematics to them. They also showed better results in applying active learning tools and techniques for teaching Mathematics, as well as in managing the audience through information technologies and communication strategies.

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Section: Analysis Of the Recent Research And Publicationsmentioning

confidence: 99%

Section: Analysis Of the Recent Research And Publicationsmentioning

confidence: 99%

Model of the On-Line Course for Training Master Students Majoring in Mathematics for Teaching at University

Lovianova¹,

Vlasenko²,

Sitak³

et al. 2020

ujer

View full text Add to dashboard Cite

show abstract

“…It is inspired by the deep learning algorithms, and built in a multilayer manner. HELM has been frequently used in many fields, such as image-quality assessment (Mao et al 2014), human action recognition (Minhas et al 2010) and hyper-spectral image classification . Using the HELM algorithm in Paper I, we obtained an accuracy and efficiency of classifying single-lined hot subdwarf stars in LAMOST spectra up to 92% and 96% respectively, which demonstrated the reliability of the method to search for hot subdwarf stars in the LAMOST survey spectral database.…”

Section: Introductionmentioning

confidence: 99%

Searching for hot subdwarf stars from the LAMOST Spectra. II. Pure spectroscopic identification method for hot subdwarfs

Lei

Zhao

et al. 2019

Publications of the Astronomical Society of Japan

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Employing a new machine learning method, named hierarchical extreme learning machine (HELM) algorithm, we identified 56 hot subdwarf stars in the first data release (DR1) of the Large Sky Area Multi-Object Fibre Spectroscopic Telescope (LAMOST) survey. The atmospheric parameters of the stars are obtained by fitting the profiles of hydrogen (H) Balmer lines and helium (He) lines with synthetic spectra calculated from non-Local Thermodynamic Equilibrium (NLTE) model atmospheres. Five He-rich hot subdwarf stars were found in our sample with their log(nHe/nH) > −1, while 51 stars are He-poor sdB, sdO and sdOB stars. We also confirmed the two He sequences of hot subdwarf stars found by Edelmann et al. (2003) in T eff -log(nHe/nH) diagram. The HELM algorithm works directly on the observed spectroscopy and is able to filter out spectral properties without supplementary photometric data. The results presented in this study demonstrate that the HELM algorithm is a reliable method to search for hot subdwarf stars after a suitable training is performed, and it is also suitable to search for other objects which have obvious features in their spectra or images.

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“…Recently, with the rapid development of key technology of the Internet of Things, most platforms and systems, based on the Internet of Things, are widely applied to various fields and industries, such as intelligent transportation, smart buildings, healthcare field, positioning and navigation field, and logistics field [1][2][3]. Meanwhile, it generates a lot of urgent problems to be solved, and one of these problems is missing value imputation for the Internet of Things [4].…”

Section: Introductionmentioning

confidence: 99%

Missing Value Imputation Based on Gaussian Mixture Model for the Internet of Things

Yan

Xiong

et al. 2015

Mathematical Problems in Engineering

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This paper addresses missing value imputation for the Internet of Things (IoT). Nowadays, the IoT has been used widely and commonly by a variety of domains, such as transportation and logistics domain and healthcare domain. However, missing values are very common in the IoT for a variety of reasons, which results in the fact that the experimental data are incomplete. As a result of this, some work, which is related to the data of the IoT, can’t be carried out normally. And it leads to the reduction in the accuracy and reliability of the data analysis results. This paper, for the characteristics of the data itself and the features of missing data in IoT, divides the missing data into three types and defines three corresponding missing value imputation problems. Then, we propose three new models to solve the corresponding problems, and they are model of missing value imputation based on context and linear mean (MCL), model of missing value imputation based on binary search (MBS), and model of missing value imputation based on Gaussian mixture model (MGI). Experimental results showed that the three models can improve the accuracy, reliability, and stability of missing value imputation greatly and effectively.

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Applied Mathematics and Algorithms for Cloud Computing and IoT

Cited by 8 publications

References 6 publications

Model of the On-Line Course for Training Master Students Majoring in Mathematics for Teaching at University

Model of the On-Line Course for Training Master Students Majoring in Mathematics for Teaching at University

Searching for hot subdwarf stars from the LAMOST Spectra. II. Pure spectroscopic identification method for hot subdwarfs

Missing Value Imputation Based on Gaussian Mixture Model for the Internet of Things

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