Skip to main content
SpringerLink
Log in

A relation based algorithm for solving direct current circuit problems

  • Published:
Applied Intelligence Aims and scope Submit manuscript

Abstract

This paper addresses the challenging problem of developing the automatic algorithm for solving direct current circuit problem. Leveraging on the innovated methods it proposes a high-performance relation based algorithm, called RaDCC. The challenges of the problem lie in relation acquisition and relation inference presentation after adopting the newly-established relation principle of solving problems. A high-performance procedure is developed for the challenging task of relation acquisition by leveraging on three innovated methods. Three methods are an enhanced schematics understanding method that can understand complicated structures of schematics, an extended syntax-semantics model method and a unit-theorem inference method to acquire schematic relations, explicit text relations and implicit text relations, respectively. To address another challenging problem of readable solution generation an action-schema presentation method is proposed to convert relation inference actions into relation inference presentations. The experimental results show that the proposed algorithm is high-performance since it achieves an accuracy of over 83.2% for solving problems from textbooks and 70.6% for solving problems from examination papers on a dataset that contains 1012 direct current circuit problems collected from the authority sources, much higher than the performance of the baseline algorithm.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

References

  1. Abualigah LM (2019) Feature selection and enhanced krill herd algorithm for text document clustering, Studies in Computational Intelligence, vol 816. Springer International Publishing, Cham

  2. Abualigah LM, Khader AT, Hanandeh ES (2018) Hybrid clustering analysis using improved krill herd algorithm. Appl Intell 48(11):4047–4071

    Article  Google Scholar 

  3. An W, Chen X, Wang D (2016) Searching for geometric theorems using features retrieved from diagrams. In: Kotsireas IS, Rump SM, Yap CK (eds) Mathematical aspects of computer and information sciences. Springer International Publishing, pp 383–397

  4. Bai L, Zhu L, Jia W (2017) Determining topological relations of uncertain spatiotemporal data based on counter-clock-wisely directed triangle. Appl Intell 48(9):2527–2545

    Article  Google Scholar 

  5. De P, Mandal S, Bhowmick P (2017) Hierarchical vectorization of electrical drawings in document images by connectivity analysis of symbols and super-components. Pattern Recognition and Image Analysis 27(2):309–325

    Article  Google Scholar 

  6. Gan W, Yu X, Zhang T, Wang M (2019a) Automatically proving plane geometry theorems stated by text and diagram. Int J Pattern Recognit Artif Intell 33(7):19400032

    Article  Google Scholar 

  7. Gan W, Yu X, Wang M (2019b) Automatic understanding and formalization of plane geometry proving problems in natural language: a supervised approach. International Journal on Artificial Intelligence Tools 28(4):1940003

    Article  Google Scholar 

  8. Ganesalingam M, Gowers WT (2017) A fully automatic theorem prover with human-style output. J Autom Reason 58(2):253–291

    Article  MathSciNet  Google Scholar 

  9. Huang D, Shi S, Lin CY, Yin J (2017) Learning fine-grained expressions to solve math word problems. In: Proceedings of the 2017 conference on empirical methods in natural language processing. Association for Computational Linguistics, Copenhagen, Denmark, pp 805–814

  10. Jian P, Sun C, Yu X, He B, Xia M (2019) An end-to-end algorithm for solving circuit problems. Int J Pattern Recognit Artif Intell 33(7):1940004

    Article  Google Scholar 

  11. Jiang J, Zhang J (2012) A review and prospect of readable machine proofs for geometry theorems. J Syst Sci Complex 25(4):802–820

    Article  MathSciNet  Google Scholar 

  12. Kalita S, Karmakar A, Hazarika SM (2017) Efficient extraction of spatial relations for extended objects vis-ã -vis human activity recognition in video. Appl Intell 48(1):204–219

    Article  Google Scholar 

  13. Kushman N, Artzi Y, Zettlemoyer L, Barzilay R (2014) Learning to automatically solve algebra word problems. In: Proceedings of the 52nd annual meeting of the association for computational linguistics vol 1, pp 271–281. Association for Computational Linguistics, Baltimore, Maryland

  14. Mandal S, Naskar SK (2019) Solving arithmetic mathematical word problems: a review and recent advancements. In: Chandra P, Giri D, Li F, Kar S, Jana DK (eds) Information technology and applied mathematics, advances in intelligent systems and computing. Springer, Singapore, pp 95–114

    Chapter  Google Scholar 

  15. Ortega-Alvarez JD, Sanchez W, Magana AJ (2018) Exploring undergraduate students’ computational modeling abilities and conceptual understanding of electric circuits. IEEE Trans Educ 61(3):204–213

    Article  Google Scholar 

  16. Reisslein J, Johnson AM, Reisslein M (2015) Color coding of circuit quantities in introductory circuit analysis instruction. IEEE Trans Educ 58(1):7–14

    Article  Google Scholar 

  17. Roy S, Roth D (2015) Solving general arithmetic word problems. In: Proceedings of the conference on empirical methods in natural language processing. Association for Computational Linguistics, Lisbon, Portugal, pp 1743–1752

  18. Wang Y, Liu X, Shi S (2017) Deep neural solver for math word problems. In: Proceedings of the conference on empirical methods in natural language processing. Association for Computational Linguistics, Copenhagen, Denmark, pp 845–854

  19. Wang L, Wang Y, Cai D, Zhang D, Liu X (2018a) Translating a math word problem to a expression tree. In: Proceedings of the 2018 conference on empirical methods in natural language processing. Association for Computational Linguistics, Brussels, Belgium, pp 1064–1069

  20. Wang L, Zhang D, Gao L, Song J, Guo L, Shen HT (2018b) MathDQN: Solving arithmetic word problems via deep reinforcement learning. In: Proceedings of the thirty-second AAAI conference on artificial intelligence, pp 5545–5552

  21. Wang L, Zhang D, Zhang J, Xu X, Gao L, Dai B, Shen H (2019) Template-based math word problem solvers with recursive neural networks. Proceedings of the AAAI Conference on Artificial Intelligence 33:7144–7151

    Article  Google Scholar 

  22. Whitlatch CD, Wang Q, Skromme BJ (2012) Automated problem and solution generation software for computer-aided instruction in elementary linear circuit analysis. In: 119th ASEE annual conference and exposition

  23. Wu WT (1978) On the decision problem and the mechanization of theorem-proving in elementary geometry. Sci Sinica 21:159–172

    MathSciNet  MATH  Google Scholar 

  24. Yu X, Jian P, Wang M, Wu S (2016) Extraction of implicit quantity relations for arithmetic word problems in Chinese. In: International conference on educational innovation through technology, pp 242–245

  25. Yu X, Wang M, Gan W, He B, Ye N (2019) A framework for solving explicit arithmetic word problems and proving plane geometry theorems. Int J Pattern Recognit Artif Intell 33(7):1940005

    Article  Google Scholar 

  26. Zhang JZ (2000) Points elimination methods for geometric problem solving. Mathematics Mechanization & Applications, pp 175–204

  27. Zhang HP, Liu Q, Cheng XQ, Zhang H, Yu HK (2003) Chinese lexical analysis using hierarchical hidden markov model. In: Proceedings of the second SIGHAN workshop on Chinese language processing vol 17, pp 63–70. Association for Computational Linguistics, Sapporo, Japan

  28. Zhang D, Wang L, Zhang L, Dai BT, Shen HT (2019) The gap of semantic parsing: a survey on automatic math word problem solvers. IEEE Trans Pattern Anal Mach Intell 1–1

Download references

Acknowledgements

This work is partially supported by the National Natural Science Foundation of China (No. 61977029), China Postdoctoral Science Foundation (No. 2019 M652678) and the Fundamental Research Funds for the Central Universities (No. CCNU19QN036).

Author information

Authors and Affiliations

  1. National Engineering Research Center for E-Learning, Central China Normal University, Wuhan, 430079, People’s Republic of China

    Bin He, Xinguo Yu, Pengpeng Jian & Ting Zhang

Authors
  1. Bin He
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Xinguo Yu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Pengpeng Jian
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Ting Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ting Zhang.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

He, B., Yu, X., Jian, P. et al. A relation based algorithm for solving direct current circuit problems. Appl Intell 50, 2293–2309 (2020). https://doi.org/10.1007/s10489-020-01667-7

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10489-020-01667-7

Keywords

Search

Navigation