Journal of Integrative Natural Science (통합자연과학논문집)

The Basic Science Institute Chosun University (조선대학교 기초과학연구원)
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Smart Control System Using Fuzzy and Neural Network Prediction System

  • Kim, Tae Yeun (SW Convergence Education Institute, Chosun University) ;
  • Bae, Sang Hyun (Department of Computer Science & Statistics, Chosun University)
  • Received : 2019.11.29
  • Accepted : 2019.12.17
  • Published : 2019.12.30
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Abstract

In this paper, a prediction system is proposed to control the brightness of smart street lamps by predicting the moving path through the reduction of consumption power and information of pedestrian's past moving direction while meeting the function of existing smart street lamps. The brightness of smart street lamps is adjusted by utilizing the walk tracking vector and soft hand-off characteristics obtained through the motion sensing sensor of smart street lamps. In addition, the motion vector is used to analyze and predict the pedestrian path, and the GPU is used for high-speed computation. Pedestrians were detected using adaptive Gaussian mixing, weighted difference imaging, and motion vectors, and motions of pedestrians were analyzed using the extracted motion vectors. The preprocessing process using linear interpolation is performed to improve the performance of the proposed prediction system. Fuzzy prediction system and neural network prediction system are designed in parallel to improve efficiency and rough set is used for error correction.

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References

  1. S.-K. Jeon, " Research on Smart-LED Street Lighting System with Micro ESS for Demand side Energy Management," Master Thesis, Chung-Ang Universtiy, 2018.
  2. J.-S. Kim, and K.-S. Kim, "An Analysis on the Policy Trends of LED-Lighting Policy in Major Countries," Electronics and Telecommunications Trends, Vol. 28, No. 6, pp. 192-205, 2013. https://doi.org/10.22648/ETRI.2013.J.280619
  3. T.-Y. Kim, and N.-H. Park, “Design of Path Prediction Smart Street Lighting System on the Internet of Things,” Journal of the Chosun Natural Science, Vol. 12, No. 1, pp. 14-19, 2019. https://doi.org/10.13160/ricns.2019.12.1.14
  4. Z. Kaleem, T.-M. Yoon, and C. Lee, “Energy efficient outdoor light monitoring and control architecture using embedded system,” IEEE Embedded Systems Letters, Vol. 8, No. 1, pp. 18-21, 2016. https://doi.org/10.1109/LES.2015.2494598
  5. F. Leccese, “Remote-control system of high efficiency and intelligent street lighting using a zigbee network of devices and sensors,” IEEE Transaction on Power Delivery, Vol. 28, No. 1, pp. 21-28, 2013. https://doi.org/10.1109/TPWRD.2012.2212215
  6. J.-S. Jeon, “A study on the street security light management system using Zigbee network,” Journal of the Korean Society of Marine Engineering, Vol. 38, No. 4, pp. 430-436, 2014. https://doi.org/10.5916/jkosme.2014.38.4.430
  7. S.-H. Park, J.-Y. Hong, M.-S. Seok, J.-Y. Um, and J.-S. Ahn, "An Autonomous Street Light Switch Based on Motion Vector," in Proc. of Korea Information Processing Society, Vol. 23, No. 2, pp. 810-813. 2016.
  8. C.-S. Chung, I.-S. Shin, J.-S. Seo, S.-D. Eun, and K. In, “The Analysis of a Gait Pattern and the Mechanical Efficiency on Ages and Speed Conditions,” Journal of Korean Society of Sport Biomechanics, Vol. 10, No. 2, pp. 205-219, 2001.
  9. J. Kim, and S.-W. Park, “Performance Analysis of Uplink Transmit Power Control during Soft Handoff,” The Journal of Korean Institute of Communications and Information Sciences, Vol. 37A, No. 8, pp. 632-638, 2012.
  10. S.-W. Kim, and Y.-S. Choi, “Analysis of Activity Using Motion Vector and GPU,” The Journal of the Korea Institute of Electronic Communication Sciences, Vol. 9, No. 10, pp. 1095-1102, 2014. https://doi.org/10.13067/JKIECS.2014.9.10.1095
  11. H.-T. Kim, G.-H. Lee, J.-S. Park, and Y.-S. Yu, “Vehicle Detection in Tunnel using Gaussian Mixture Model and Mathematical Morphological Processing,” The Journal of Korean Institute of Communications and Information Sciences, Vol. 7, No. 5, pp. 967-974, 2012.
  12. Y. Ismail, J. B. McNeely, M. Shaaban, H. Mahmoud, and M. A. Bayoumi, “Fast Motion Estimation System Using Dynamic Models for H.264/AVC Video Coding,” IEEE Transactions on Circuits and Systems for Video Technology, Vol. 22, No, 1, pp. 28-42, 2012. https://doi.org/10.1109/TCSVT.2011.2148450
  13. J. Snaders, and E. Kandrot, CUDA by Example : An Introdution to General Purpose GPU Programing, Boston : Addison Wesley, 2010.
  14. J.-H. Kim, "The design of the system forecasting short-term peak load using neural network and fuzzy predictor," Master Thesis, Kangwon National University, 2016.
  15. Y.-K. Bang, and C.-H. Lee, “Design of Multiple Model Fuzzy Predictors using Data Preprocessing and its Application,” The Transactions of The Korean Institute of Electrical Engineers, Vol. 58, No. 1, pp. 173-180, 2009.
  16. R. Zhang, B. Ashuri, and Y. Deng, “A novel method for forecasting time series based on fuzzy logic and visibility graph,” Advances in Data Analysis and Classification, Vol. 11, No. 4, pp. 759-783, 2017. https://doi.org/10.1007/s11634-017-0300-3
  17. Z. Xiao, S. J. Ye, B. Zhong, and C. X. Sun, “BP neural network with rough set f or short term load forecasting,” Expert Systems with Applications, Vol. 36, No. 1, pp. 273-279, 2009. https://doi.org/10.1016/j.eswa.2007.09.031
  18. H. Quan, D. Srinivasan, and A. Khosravi, “Short- Term Load and Wind Power Forecasting Using Neural Network-Based Prediction Intervals,” IEEE Transactions on Neural Networks and Learning Systems, Vol. 25, No. 2, pp. 303-315, 2014. https://doi.org/10.1109/TNNLS.2013.2276053
  19. W. Zhang, L. Ren, and L. Wang, "Method of Deep Belief Network Image Classification Based on Probability Measure Rough Set Theory," International Journal of Pattern Recognition and Artificial Intelligence, Vol. 32, No. 11, 2018.