Construct stochastic processes model to solve graphic and math problems

Research Article
Open access

Construct stochastic processes model to solve graphic and math problems

Shuyang Ding 1*
  • 1 College of Computer Science and Technology, Harbin Institute of Technology , Weihai, China    
  • *corresponding author mofangmf@alumni.tongji.edu.cn
Published on 14 June 2023 | https://doi.org/10.54254/2755-2721/6/20230815
ACE Vol.6
ISSN (Print): 2755-273X
ISSN (Online): 2755-2721
ISBN (Print): 978-1-915371-59-1
ISBN (Online): 978-1-915371-60-7

Abstract

The use of probabilistic reasoning has been used to a variety of applications, such as image identification, computer diagnostics, stock price prediction, movie recommendation, and cyber intrusion detection. However, the range of probabilistic programming was limited until recently (partly due to the low computing capacity), and the bulk of inference techniques needed to be developed manually for each task. However, in 2015, using a 50-line probabilistic computer vision program, 3D representations of human faces were produced from 2D images of those faces.Use a computer to model various complex stochastic phenomena. All projects are programmed in java language. Find the actual number of 1s in a binary number. Formulate and analyze mathematical models to real life phenomena like Data Matrix and QR code. Those code mentioned above can provide a link to the real-life website or some certain message needed to be shown. using some random numbers to model uncertainty. To model some fractals. The first fractal maybe not self-avoiding. But the second one will never meet the same path for twice. To apply the concept stochastic processes. More java features and some random fractals and regression. Multiple factor analysis.

Keywords:

Stochasitic Process, Data Matrix and QR Code, Sierpinski Trangle, Fractals, Find 1s in a Binary Number

Ding,S. (2023). Construct stochastic processes model to solve graphic and math problems. Applied and Computational Engineering,6,299-306.
Export citation

References

[1]. Avi Pfeffer. Practical Probabilistic Programming.

[2]. Rick Durrett. Probability Theory and Examples. Fourth Edition.

[3]. ZouYang, WuHecheng, Zhao Yingding, Jiang Yunzhi. Random walk recommendation algorithm based on multi-weight similarity [J]. Computer Application Research, 2020,37(11):3267-3270+3296.DOI:10.19734/j.issn. 1001-3695.2019.08.0275.

[4]. Zou [1] Liao Yongxin. Research on the Joint Classification Algorithm of Heterogeneous Label Sets Based on Random Walk and Dynamic Label Propagation [D]. South China University of Technology, 2017.

[5]. Lu Yuke. Research on Complex Code Recognition Technology [D]. University of Electronic Science and Technology of China, 2022. DOI: 10.27005/d.cnki.gdzku.2022.003580.

[6]. Song Bin, Liu Lili, Zhang Lei, Wang Lei, Du Yuxin, Zhang Ning. Information Management of Coal Mine Equipment Based on Data Matrix Code [J]. Industrial and Mining Automation, 2020, 46(11): 83-86+94. DOI: 10.13272/ j.issn.1671-251x.2020050059.

[7]. Yuan Tailing, Xu Kun. Length minimization algorithm of two-dimensional code bit stream [J]. Chinese Journal of Image and Graphics, 2022, 27(08): 2356-2367.

[8]. GB/T 41208-2021, Data Matrix Code [S].

[9]. Deng Huipeng, Wang Yi, Dong Xiaowen. Comparison of Hanxin code with PDF417, data matrix code (DM code), and QR code [J]. China Automatic Identification Technology, 2021(05):50-53.

[10]. Guo Lijie. Random walk and its application in leaf image segmentation [D]. Lanzhou University, 2018.

[11]. Karatzas, I. and S. E. Shreve (1991). Brownian Motion and Stochastic Calculus. SpringerVerlag, New York

[12]. Lukacs, E. (1970). Characteristic Functions, 2nd Ed. Griffin.

[13]. Meyn, S. P. and Tweedie, R. L. (1993). Markov Chains and Stochastic Stability. SpringerVerlag, New York.

[14]. Petrov, V. V. (1995). Limit Theorems of Probability Theory. Oxford University Press, Oxford.

[15]. Samorodnitsky, G. and M. S. Taqqu. (1994). Stable Non-Gaussian Random Processes. Chapman-Hall/CRC,

[16]. Shorack, G. R. (2000). Probability for Statisticians. Springer-Verlag, New York.

[17]. Durrett, R. (2019). Probability: theory and examples (Vol. 49). Cambridge university press.

[18]. Chung, K. L. (1967). Markov Chains with Stationary Transition Probabilities, 2nd Ed. SpringerVerlag.

Cite this article

Ding,S. (2023). Construct stochastic processes model to solve graphic and math problems. Applied and Computational Engineering,6,299-306.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

Disclaimer/Publisher's Note

The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of EWA Publishing and/or the editor(s). EWA Publishing and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

About volume

Volume title: Proceedings of the 3rd International Conference on Signal Processing and Machine Learning

ISBN：978-1-915371-59-1(Print) / 978-1-915371-60-7(Online)
Editor：Omer Burak Istanbullu
Conference website: http://www.confspml.org
Conference date: 25 February 2023
Series: Applied and Computational Engineering
Volume number: Vol.6
ISSN：2755-2721(Print) / 2755-273X(Online)

© 2024 by the author(s). Licensee EWA Publishing, Oxford, UK. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license. Authors who publish this series agree to the following terms:
1. Authors retain copyright and grant the series right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this series.
2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the series's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this series.
3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See Open access policy for details).

References

[1]. Avi Pfeffer. Practical Probabilistic Programming.

[2]. Rick Durrett. Probability Theory and Examples. Fourth Edition.

[3]. ZouYang, WuHecheng, Zhao Yingding, Jiang Yunzhi. Random walk recommendation algorithm based on multi-weight similarity [J]. Computer Application Research, 2020,37(11):3267-3270+3296.DOI:10.19734/j.issn. 1001-3695.2019.08.0275.

[4]. Zou [1] Liao Yongxin. Research on the Joint Classification Algorithm of Heterogeneous Label Sets Based on Random Walk and Dynamic Label Propagation [D]. South China University of Technology, 2017.

[5]. Lu Yuke. Research on Complex Code Recognition Technology [D]. University of Electronic Science and Technology of China, 2022. DOI: 10.27005/d.cnki.gdzku.2022.003580.

[6]. Song Bin, Liu Lili, Zhang Lei, Wang Lei, Du Yuxin, Zhang Ning. Information Management of Coal Mine Equipment Based on Data Matrix Code [J]. Industrial and Mining Automation, 2020, 46(11): 83-86+94. DOI: 10.13272/ j.issn.1671-251x.2020050059.

[7]. Yuan Tailing, Xu Kun. Length minimization algorithm of two-dimensional code bit stream [J]. Chinese Journal of Image and Graphics, 2022, 27(08): 2356-2367.

[8]. GB/T 41208-2021, Data Matrix Code [S].

[9]. Deng Huipeng, Wang Yi, Dong Xiaowen. Comparison of Hanxin code with PDF417, data matrix code (DM code), and QR code [J]. China Automatic Identification Technology, 2021(05):50-53.

[10]. Guo Lijie. Random walk and its application in leaf image segmentation [D]. Lanzhou University, 2018.

[11]. Karatzas, I. and S. E. Shreve (1991). Brownian Motion and Stochastic Calculus. SpringerVerlag, New York

[12]. Lukacs, E. (1970). Characteristic Functions, 2nd Ed. Griffin.

[13]. Meyn, S. P. and Tweedie, R. L. (1993). Markov Chains and Stochastic Stability. SpringerVerlag, New York.

[14]. Petrov, V. V. (1995). Limit Theorems of Probability Theory. Oxford University Press, Oxford.

[15]. Samorodnitsky, G. and M. S. Taqqu. (1994). Stable Non-Gaussian Random Processes. Chapman-Hall/CRC,

[16]. Shorack, G. R. (2000). Probability for Statisticians. Springer-Verlag, New York.

[17]. Durrett, R. (2019). Probability: theory and examples (Vol. 49). Cambridge university press.

[18]. Chung, K. L. (1967). Markov Chains with Stationary Transition Probabilities, 2nd Ed. SpringerVerlag.

For authors
Guide for authors Policies for authors Aims and scope Subscription Open access policy
For editors
Policies for editors Editorial board
For reviewers
Policies for reviewers Privacy policy

Cookies are used by this site.
Copyright © 2024 EWA Publishing, its licensors, and contributors. All rights reserved, including text and data mining, AI training, and similar technologies.