Identyfikatory
Języki publikacji
Abstrakty
The energy consumption of air conditioning systems accounts for more than 50% of building energy consumption. The supercooled and overheated environment provided by intelligent buildings can bring a large amount of energy loss. How to create comfortable spaces with energy-saving goals is currently the focus of research. The aim of this study is to improve the accuracy of human thermal discomfort pose recognition algorithms. This study first extracts human key points on the ground of bone key points, then normalizes the data, and finally constructs a human thermal uncomfortable posture recognition algorithm on the ground of deep learning technology. The experiment showcases that in the training set, when the iteration number is 1500, the accuracy reaches its maximum value, which is 99.98%. In the test set, the accuracy reached its maximum value of 89.85% when the iteration number was 400. After classifying the dataset, the accuracy of the first type dataset reached 99.51%. The accuracy rate of the second type dataset is 98.56%, and the accuracy rate of the third type dataset is 98.95%. In the comparison of the four algorithms, the accuracy of the research algorithm is significantly higher than the other three algorithms, indicating that the research algorithm can accurately recognize the thermal uncomfortable posture of the human body. This research algorithm can timely and effectively identify the uncomfortable posture of the human body, thereby automatically adjusting indoor temperature and achieving energy conservation and emission reduction.
Czasopismo
Rocznik
Tom
Strony
563--578
Opis fizyczny
Bibliogr. 21 poz., il., tab.
Twórcy
autor
- Shandong Jianzhu University, School of Management Engineering, Jinan, China
autor
- Jinan Vocational College, School of Finance and Economics, Jinan, China
Bibliografia
- [1] Z. Wu, Y. Zhao, and N. Zhang, “A literature survey of green and low-carbon economics using natural experiment approaches in top field journal”, Green and Low-Carbon Economy, vol. 1, no. 1, pp. 2-14, 2023, doi: 10.47852/bonviewGLCE3202827.
- [2] A.M. Usman and M.K. Abdullah, “An assessment of building energy consumption characteristics using analytical energy and carbon footprint assessment model”, Green and Low-Carbon Economy, vol. 1, no. 1, pp. 28-40, 2023, doi: 10.47852/bonviewGLCE3202545.
- [3] Y. Fang, B. Luo, T. Zhao, D. He, B.B. Jiang, and Q.L. Liu, “ST-SIGMA: Spatio-temporal semantics and interaction graph aggregation for multi-agent perception and trajectory forecasting”, CAAI Transactions on Intelligence Technology, vol. 7, no. 4, pp. 744-757, 2022, doi: 10.1049/cit2.12145.
- [4] Q. Men, E.S.L. Ho, H.P.H. Shum, and H. Leung, “Focalized contrastive view-invariant learning for self-supervised skeleton-based action recognition”, Neurocomputing, vol. 537, no. 7, pp. 198-209, 2023, doi: 10.1016/j.neucom.2023.03.070.
- [5] Y. Yuan, Z. Lu, Z. Yang, M. Jian, L. Wu, Z. Li, and X. Liu, “Key frame extraction based on global motion statistics for team-sport videos”, Multimedia Systems, vol. 28, no. 2, pp. 387-401, 2022, doi: 10.1007/s00530-021-00777-7.
- [6] W. Zhuang, Y. Chen, J. Su, B. Wang, and C. Gao, “Design of human activity recognition algorithms based on a single wearable IMU sensor”, International Journal of Sensor Networks, vol. 30, no. 3, pp. 193-206, 2019, doi: 10.1504/IJSNET.2019.100218.
- [7] W. Zhu, Y. She, J. Hu, B. Wang, J. Mu, and S. Li, “A hybrid relative navigation algorithm for a largescale free tumbling non-cooperative target”, Acta Astronautica, vol. 194, no. 5, pp. 114-125, 2022, doi: 10.1016/j.actaastro.2022.01.028.
- [8] C. Wang, Z. Zhang, and Z. Xi, “A human body based on sift-neural network algorithm attitude recognition method”, Journal of Medical Imaging and Health Informatics, vol. 10, no. 1, pp. 129-133, 2020, doi: 10.1166/jmihi.2020.2867.
- [9] X. Gao, Y. Yang, Y. Zhang, M. Li, J. Yu, and S. Du, “Efficient Spatio-Temporal Contrastive Learning for Skeleton-Based 3-D Action Recognition”, IEEE Transactions on Multimedia, vol. 25, no. 1, pp. 405-417, 2023, doi: 10.1109/TMM.2021.3127040.
- [10] C.L. Quan, L. You, F. Shen, et al., “Pose recognition in sports scenes based on deep learning skeleton sequence model”, Journal of Intelligent and Fuzzy Systems, vol. 53, no. 3, pp. 1-10, 2021, doi: 10.3233/JIFS-189834.
- [11] C. Pan, H. Cao, W. Zhang, X. Song, and M. Li, “Driver activity recognition using spatial-temporal graph convolutional LSTM networks with attention mechanism”, IET Intelligent Transport Systems, vol. 15, no. 2, pp. 297-307, 2021, doi: 10.1049/itr2.12025.
- [12] X. Wen, “Energy consumption monitoring model of green energy-saving building based on fuzzy neural network”, International Journal of Global Energy Issues, vol. 44, no. 5-6, pp. 396-412, 2022, doi: 10.1504/IJGEI.2022.125405.
- [13] Q. Wang, Y.J. Hu, J. Hao, N. Lv, T. Li, and B. Tang, “Exploring the influences of green industrial building on the energy consumption of industrial enterprises: A case study of Chinese cigarette manufactures”, Journal of Cleaner Production, vol. 231, no. 10, pp. 370-385, 2019, doi: 10.1016/j.jclepro.2019.05.136.
- [14] Y. Wang, M. Lin, K. Xu, S. Zhang, and H. Ma, “Energy consumption analysis of glass house using electrochromic window in the subtropical region”, Journal of Engineering Design and Technology, vol. 19, no. 1, pp. 203-218, 2021, doi: 10.1108/JEDT-12-2019-0348.
- [15] J. Xu, “Research on energy consumption control method of green building based on BIM technology”, International Journal of Industrial and Systems Engineering, vol. 40, no. 3, pp. 399-414, 2022, doi: 10.1504/IJISE.2022.122248.
- [16] Y. Singh and L. Kaur, “Effective key-frame extraction approach using TSTBTC-BBA”, IET Image Processing, vol. 14, no. 4, pp. 638-647, 2020, doi: 10.1049/iet-ipr.2018.6361.
- [17] S. Xu, S. Sun, Z. Zhang, F. Xu, and J.H. Liu, “BERT gated multi-window attention network for relation extraction”, Neurocomputing, vol. 492, no. 1, pp. 516-529, 2022, doi: 10.1016/j.neucom.2021.12.044.
- [18] X. Gu, L. Lu, S. J. Qiu, Q. Y. Zou, and Z.Y. Yang, “Sentiment key frame extraction in user-generated micro-videos via low-rank and sparse representation”, Neurocomputing, vol. 410, no. 14, pp. 441-453, 2020, doi: 10.1016/j.neucom.2020.05.026.
- [19] H. Xie, Y. Zhong, Z. Yu, A. Hussain, and G. Chen, “Automatic 3D human body landmarks extraction and measurement based on mean curvature skeleton for tailoring”, The Journal of the Textile Institute, vol. 113, no. 8, pp. 1677-1687, 2022, doi: 10.1080/00405000.2021.1944513.
- [20] R. Liao, Y. Zhang, Y. Wang, and D. Dai, “Multi-view face pose recognition model construction based on a typical correlation analysis algorithm”, International Journal of Biometrics, vol. 13, no. 2-3, pp. 289-304, 2021, doi: 10.1504/IJBM.2021.114654.
- [21] A.Z.O. Al-Hijazeen, M. Fawad, M. Gerges, K. Koris, and M. Salamak, “Implementation of digital twin and support vectormachine in structural health monitoring of bridges”, Archives of Civil Engineering, vol. 69, no. 3, pp. 31-47, 2023, doi: 10.24425/ace.2023.146065.
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