To reduce the resource and energy waste of colleges and universities more accurately and efficiently, this paper has developed a smart classroom data analysis system based on the Internet of Things, which realizes a variety of sensor information (temperature, humidity, smoke). Environmental parameters such as carbon dioxide concentration and light intensity), remote collection of equipment information, data storage and data analysis functions, and intelligent control of smart classrooms. Data analysis uses an improved LSTM model to predict energy consumption. The model uses LSTM and bidirectional LSTM and uses the ELU activation function instead of the sigmoid and tanh activation functions of the LSTM. Compared with the standard LSTM model and the LSTM model without the ELU activation function, the model improves the prediction accuracy, better avoid the gradient disappearance, and reduces the over-fitting. The system implementation results show that the system can effectively reduce school energy waste.
A Low-Overhead, Confidentiality-Assured, and Authenticated Data Acquisition Framework for IoT, Yushu Zhang; Qi He; Guo Chen; Xinpeng Zhang; Yong Xiang, IEEE Transactions on Industrial Informatics, Publication Year: 2019, pp. 1–1
2.
LvZ., LiX., WangW., ZhangB., HuJ. and FengS., Government Affairs Service Platform for Smart City, Future Generation Computer Systems81 (2018), 443–451.
3.
JuanX., FanW., WeijianC., HuiL. and XingqiaoL., Development of Water Quality Monitoring System for Aquaculture Pond Based on Narrowband Internet of Things, Transactions of the Chinese Society of Agricultural Engineering35(08) (2019), 252–261.
4.
HuiW., HongyanX., HongjunW., GangL., YanY., PeiY. and XuefeiD., Farmland Information Collection System Based on Internet of Things Mode, Electronic Devices42(04) (2019), 1056–1062.
5.
LeL., Design of Solar Power Generation Monitoring System Based on the Internet of Things, Instrumentation Technology and Sensors (11) (2018), 104–107.
6.
DongW., WeiY. and DiW., Library Environment Monitoring System Based on WiFi Internet of Things, Computer Science45(S2) (2018), 532–534 + 557.
7.
ChaoL., ShengliY. and YuqiangC., Design of Intelligent Energy Saving System in the Classroom Based on WSNs and Power Carrier, Measurement & Control Technology36(07) (2017), 92–96 + 100.
8.
Yu-FengY., HeW., Su-PingB. and XinY., Beam Compressed System in Front of Wavefront Sensor, Acta Microscopica28(5) (2019).
9.
TanH.R., Global Financial Crisis and the Impact of the Food Crisis on the Great Depression Based on Agricultural Technology Iteration: A 15-year Population-Based Time-Series Study, Revista de la Facultad de Agronomia de la Universidad del Zulia36(2), 2019.
10.
LiD. and QianJ., Text sentiment analysis based on long short- term memory. Proceedings of the 2016 IEEE First International Conference on Computer Communication and the Internet. Piscataway: IEEE (2016), 471–475.
11.
SahooS., JhaS. and PrasharD., A novel approach for Spam Email Filtering Using Machine Learning, Journal of Cybersecurity and Information Management2(2) (2020), 44–57.
12.
MohamedM., A comparative study on Internet of Things (IoT): Frameworks, Tools, Applications and Future directions, Journal of Intelligent Systems and Internet of Things1(1) (2020), 13–39.
13.
ChristiantoV., BoydR.N. and SmarandacheF., Three Possible Applications of Neutrosophic Logic in Fundamental and Applied Sciences, International Journal of Neutrosophic Science1(2) (2020), 90–95.
14.
XuH., HongsuW., SanqianZ., QunchaoF. and JunL., Sentence segmentation for classical Chinese based on LSTM with radical embed-ding, The Journal of China Universities of Posts and Telecommunications26(02) (2019), 1–8.
15.
LiuY., DongS., LuM. and WangJ., LSTM Based Reserve Prediction for Bank Outlets, Tsinghua Science and Technology24(01) (2019), 77–85.
16.
GaofengC., XinL. and YonghongY., Using highway connection to realize deep and small LSTM-RNN speech recognition, Acta Electronica Sinica28(01) (2019), 107–112.
17.
ChoK., van MerrienboerB., GulcehreC., et al., Learning phrase representations using RNN en-coder-decoder for statistical machine translation. Proceedings of the 19th International Conference on Empirical Methods in Natural Language Pro-cessing. Stroudsburg: Association for Computational Linguistics (2014), 1724–1735.
18.
LiC., SunF., CioffiJ.M. and YangL., Energy Efficient MIMO Relay Transmissions via Joint Power Allocations, IEEE Transactions on Circuits and Systems II: Express Briefs61(7) (2014), 531–535.
19.
LuoY., Graphical Design of Deformed Koch Curve Fractal Animal Based on Wavelet Function, Revista Cientifica-Facultad de Ciencias Veterinarias29(5) (2019).
20.
SchusterM. and PaliwalK.K., Bidirectional recurrent neural networks, IEEE Transactions on Signal Processing45(11) (1997), 2673–2681.
21.
ShanP., LaiX. and LiuX., Correlational Analytical Characterization of Energy Dissipation-Liberation and Acoustic Emission during Coal and Rock Fracture Inducing by Underground Coal Excavation, Energies12(12) (2019), 2382.
22.
ZhengbinG. and YangsenZ., Bidirectional LSTM word segmentation optimization method based on fixed-length sequences, Journal of Zhengzhou University (Science Edition)50(02) (2018), 97–101.
23.
ZhaorongH., TingleiH., WenjuanR. and GuanghuanX., Trajectory outlier detection algorithm based on Bi-LSTM model, Acta Radar8(01) (2019), 36–43.
24.
YongjunM. and HaishanC., Research on Food Safety Network Public Opinion Early Warning Based on LSTM Model, Computer Engineering and Science41(09) (2019), 1603–1611.
25.
ClevertD.A., UnterthinerT. and HochreiterS., Fast and accurate deep network learning by exponential linear units (elus), ICLR, 2015.
26.
ChengL., ZhaoX.G. and NiuW.C., ApplicationTechnology of New Nanomaterials Electrochemi (2019).
