Journal of Guangdong University of Technology ›› 2023, Vol. 40 ›› Issue (01): 29-38.doi: 10.12052/gdutxb.220120

Previous Articles     Next Articles

Frost Detection Method of Cold Chain Refrigerating Machine Based on Spiking Neural Network

Chen Jing-yu, Lyu Yi   

  1. School of Computer Science and Technology, Guangdong University of Technology, Guangzhou 510006, China
  • Received:2022-07-16 Online:2023-01-25 Published:2023-01-12

HTML

PDF (PC)

1423

Abstract: The traditional frost detection method based on image processing technology is difficult to flexibly and accurately detect the cold chain refrigeration unit in complex production environment. It is also easy to misjudge due to the influence of environmental factors such as illumination and fog. Therefore, a cold chain refrigerating machine frosting detection method based on spiking neural network is designed. Taking the refrigerator image as the input, the dynamic change of the frosting area of the refrigerator evaporator is automatically detected, the abnormalities caused by interference factors such as illumination and fog are corrected, and the double threshold divided by the cumulative value of pulse emission rate is used as the judgment basis of frosting degree. Experiments are carried out on several cold chain refrigerators put into the production environment. The results show that the designed spiking neural network can adaptively detect the dynamic region of the frosting area of the refrigerator evaporator under the actual production environment and the double threshold which is divided accurately can judge the frosting degree of the evaporator. The detection effect is good and the stability is strong, which can provide a reliable basis for the defrosting time of the defrosting strategy of the cold chain refrigeration unit.

Key words: spiking neural network, frost detection, dynamic region detection, refrigerating machine image

CLC Number: 

  • TP391
[1] 辛修瑞, 牛继开, 白鑫源, 等. 冷库发展现状以及节能与环保[J]. 家电科技, 2018(3): 22-23.
[2] 慕飞鸿. 从节能高效简述土建冷库的建筑优化设计[J]. 绿色环保建材, 2019(8): 76-77.
[3] WANG W, FENG Y C, ZHU J H, et al. Performances of air source heat pump system for a kind of mal-defrost phenomenon appearing in moderate climate conditions [J]. Applied Energy, 2013, 112(C): 1138-1145.
[4] JIANG Y Q, DONG J K, QU M L, et al. A novel defrosting control method based on the degree of refrigerant superheat for air source heat pumps [J]. International Journal of Refrigeration, 2013, 36(8): 2278-2288.
[5] BUICK T R, MCMULLAN J T, MORGAN R, et al. Ice detection in heat pumps and coolers [J]. International Journal of Energy Research, 1978, 2(1): 85-98.
[6] GE Y J, SUN Y Y, WANG W, et al. Field test study of a novel defrosting control method for air-source heat pumps by applying tube encircled photoelectric sensors [J]. International Journal of Refrigeration, 2016, 66: 133-144.
[7] ZHU J, SUN Y, WEI W, et al. A novel Temperature-Humidity-Time defrosting control method based on a frosting map for air-source heat pumps [J]. International Journal of Refrigeration, 2015, 54: 45-54.
[8] MATSUMOTO K, KOSHIZUKA M, HONDA M, et al. Measurement on nano scale by scanning probe microscope for obtaining real ice adhesion force [J]. International Journal of Refrigeration, 2014, 41: 181-189.
[9] WANG F, LIANG C, YANG M, et al. Effects of surface characteristic on frosting and defrosting behaviors of fin-tube heat exchangers [J]. Applied Thermal Engineering, 2015, 75(C): 1126-1132.
[10] 侯普秀, 蔡亮, 虞维平. 霜层生长初期冰晶体结构实验研究及分形分析[J]. 应用科学学报, 2007(2): 193-197.
HOU P X, CAI L, YU W P. Experimental study and fractal analysis of ice crystal structure at initial period of frost formation [J]. Journal of Applied Sciences, 2007(2): 193-197.
[11] CAI L, WANG R, HOU P, et al. Study on restraining frost growth at initial stage by hydrophobic coating and hygroscopic coating [J]. Energy and Buildings, 2011, 43(5): 1159-1163.
[12] LIU Y M, LIU Z L, HUANG L Y. Experimental and theoretical investigations of the fractal characteristics of frost crystals during frost formation process [J]. Experimental Thermal and Fluid Science, 2011, 36(1): 217-223.
[13] ZHENG X J, SHI R, YOU S J, et al. Experimental study of defrosting control method based on image processing technology for air source heat pumps [J]. Sustainable Cities and Society, 2019, 51(C): 101667.
[14] LI Z Y, WANG W, SUN Y Y, et al. Applying image recognition to frost built-up detection in air source heat pumps [J]. Energy, 2021(5): 121004.
[15] YU Q, TANG H J, TAN K C, et al. Rapid feedforward computation by temporal encoding and learning with spiking neurons [J]. IEEE Transactions on Neural Networks and Learning Systems, 2013, 24(10): 1539-1552.
[16] BOHTE S M, KOK J N, POUTRÉ H L. Error-backpropagation in temporally encoded networks of spiking neurons [J]. Neurocomputing, 2002, 48(1): 17-37.
[17] MASQUELIER T, THORPE S J. Unsupervised learning of visual features through spike timing dependent plasticity [J]. PLoS Computational Biology, 2007, 3(2): e31.
[18] ORCHARD G, MEYER C, ETIENNE-CUMMINGS R, et al. HFirst: a temporal approach to object recognition [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2015, 37(10): 2028-40.
[19] CAI R T, WU Q, PING W, et al. Moving target detection and classification using spiking neural networks[C]// Proceedings of the Second Sino-foreign-interchange conference on Intelligent Science and Intelligent Data Engineering: Springer, Berlin, Heidelberg, 2011: 210-217.
[20] 蔺想红, 王向文, 张宁, 等. 脉冲神经网络的监督学习算法研究综述[J]. 电子学报, 2015, 43(3): 577-586.
LIN X H, WANG X W, ZHANG N, et al. Supervised learning algorithms for spiking neural networks: a review [J]. Acta Electronica Sinica, 2015, 43(3): 577-586.
[21] FENG J, BROWN D. Integrate-and-fire models with nonlinear leakage [J]. Bulletin of Mathematical Biology, 2000, 62(3): 467-81.
[22] MOON J, WU Y T, ZHU X J, et al. Neural connectivity inference with spike-timing dependent plasticity network [J]. Science China Information Sciences, 2021, 64(6): 10.
[23] 徐彦, 杨静. 脉冲神经元序列学习方法的影响因素研究[J]. 计算机工程, 2015, 41(11): 194-201.
XU Y, YANG J. Research on affect factors of spiking neuron sequence learning method [J]. Computer Engineering, 2015, 41(11): 194-201.
[24] GISON M, MASQUELE R T, HUGUES E. STDP allows fast rate-modulated coding with poisson-like spike trains [J]. PLoS Computational Biology, 2011, 7(10): 100-111.
[25] DIEHL P U, COOK M. Unsupervised learning of digit recognition using spike-timing-dependent plasticity [J]. Frontiers in Computational Neuroscience, 2015, 9: 99.
[26] 张洋念. 霜层生长过程实验研究与分形模拟[D]. 南京: 东南大学, 2005.
[1] Xie Guo-bo, Lin Li, Lin Zhi-yi, He Di-xuan, Wen Gang. An Insulator Burst Defect Detection Method Based on YOLOv4-MP [J]. Journal of Guangdong University of Technology, 2023, 40(02): 15-21.
[2] Ye Wen-quan, Li Si, Ling Jie. Sparse-view SPECT Image Reconstruction Based on Multilevel-residual U-Net [J]. Journal of Guangdong University of Technology, 2023, 40(01): 61-67.
[3] Zou Heng, Gao Jun-li, Zhang Shu-wen, Song Hai-tao. Design and Implementation of a Dropping Guidance Device for Go Robot [J]. Journal of Guangdong University of Technology, 2023, 40(01): 77-82,91.
[4] Xie Guang-qiang, Xu Hao-ran, Li Yang, Chen Guang-fu. Consensus Opinion Enhancement in Social Network with Multi-agent Reinforcement Learning [J]. Journal of Guangdong University of Technology, 2022, 39(06): 36-43.
[5] Liu Xin-hong, Su Cheng-yue, Chen Jing, Xu Sheng, Luo Wen-jun, Li Yi-hong, Liu Ba. Real Time Detection of High Resolution Bridge Crack Image [J]. Journal of Guangdong University of Technology, 2022, 39(06): 73-79.
[6] Xiong Wu, Liu Yi. Application of Particle Filter Algorithm in Static Deformation Monitoring of BDS High-Speed Rail [J]. Journal of Guangdong University of Technology, 2022, 39(04): 66-72.
[7] Yi Min-qi, Liu Hong-wei, Gao Hong-ming. Research on the Factors Influencing the Co-purchase Network of Products on E-commerce Platforms [J]. Journal of Guangdong University of Technology, 2022, 39(03): 16-24.
[8] Qiu Zhan-chun, Fei Lun-ke, Teng Shao-hua, Zhang Wei. Palmprint Recognition Based on Cosine Similarity [J]. Journal of Guangdong University of Technology, 2022, 39(03): 55-62.
[9] Zheng Jia-bi, Yang Zhen-guo, Liu Wen-yin. Marketing-Effect Estimation Based on Fine-grained Confounder Balancing [J]. Journal of Guangdong University of Technology, 2022, 39(02): 55-61.
[10] Gary Yen, Li Bo, Xie Sheng-li. An Evolutionary Optimization of LSTM for Model Recovery of Geophysical Fluid Dynamics [J]. Journal of Guangdong University of Technology, 2021, 38(06): 1-8.
[11] Li Guang-cheng, Zhao Qing-lin, Xie Kan. A Design of Decentralized Data Processing Scheme [J]. Journal of Guangdong University of Technology, 2021, 38(06): 77-83.
[12] Xie Guang-qiang, Zhao Jun-wei, Li Yang, Xu Hao-ran. Cooperative Lane-changing Based on Multi-cluster System [J]. Journal of Guangdong University of Technology, 2021, 38(05): 1-9.
[13] Zhang Wei, Zhang Zhen-bin. Joint Graph Embedding and Feature Weighting for Unsupervised Feature Selection [J]. Journal of Guangdong University of Technology, 2021, 38(05): 16-23.
[14] Deng Jie-hang, Yuan Zhong-ming, Lin Hao-run, Gu Guo-sheng. Superpixel and Visual Saliency Synergetic Image Quality Assessment [J]. Journal of Guangdong University of Technology, 2021, 38(05): 33-39.
[15] Zhang Guo-sheng, Feng Guang, Li Dong. Pose-based Oriented Object Detection Network for Aerial Images [J]. Journal of Guangdong University of Technology, 2021, 38(05): 40-47.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
No Suggested Reading articles found!
Copyright © 2017 Journal of Guangdong University of Technology
Add：729 East Dongfeng RD., Guangzhou PRC. Postcode: 510090
Tel：020-37626653,020-37626139,020-37626396
Email：xbzrb@gdut.edu.cn
Supported:Beijing Magtech