TY  - JOUR
AU  - Ren, Hanchi
AU  - Wang, Yeqing
AU  - Ma, Huijun
PY  - 2024
DA  - 2024/05/25
TI  - Deep Prediction Network Based on Covariance Intersection Fusion for Sensor Data
JO  - ICCK Transactions on Intelligent Systematics
T2  - ICCK Transactions on Intelligent Systematics
JF  - ICCK Transactions on Intelligent Systematics
VL  - 1
IS  - 1
SP  - 10
EP  - 18
DO  - 10.62762/TIS.2024.136898
UR  - https://www.icck.org/article/abs/TIS.2024.136898
KW  - deep prediction network
KW  - covariance intersection (CI) fusion
KW  - sensor data analytics
AB  - To predict future trends based on the data from sensors is an important technology for many applications, such as the Internet of Things, smart cities, etc. Based on the predicted results, further decisions and system controls can be made. Raw sensor data sets are often complex non-linear data with noise, which results in the difficulty of accurate prediction. This paper proposes a distributed deep prediction network based on a covariance intersection (CI) fusion algorithm in which the deep learning networks, such as long short-term memory networks (LSTM) and gated recurrent unit networks (GRU) are fused by CI fusion algorithm to effectively improve the performance of prediction. Moreover, the variance is obtained to evaluate the prediction results. The model is validated on the real weather dataset in Beijing. The experiments show that LSTM and GRU have their pros and cons for different data, CI fusion can improve the accuracy of the final predictions, and the entire framework has robust prediction results with a reasonable estimated variance.
SN  - 3068-5079
PB  - Institute of Central Computation and Knowledge
LA  - English
ER  - 

@article{Ren2024Deep,
  author = {Hanchi Ren and Yeqing Wang and Huijun Ma},
  title = {Deep Prediction Network Based on Covariance Intersection Fusion for Sensor Data},
  journal = {ICCK Transactions on Intelligent Systematics},
  year = {2024},
  volume = {1},
  number = {1},
  pages = {10-18},
  doi = {10.62762/TIS.2024.136898},
  url = {https://www.icck.org/article/abs/TIS.2024.136898},
  abstract = {To predict future trends based on the data from sensors is an important technology for many applications, such as the Internet of Things, smart cities, etc. Based on the predicted results, further decisions and system controls can be made. Raw sensor data sets are often complex non-linear data with noise, which results in the difficulty of accurate prediction. This paper proposes a distributed deep prediction network based on a covariance intersection (CI) fusion algorithm in which the deep learning networks, such as long short-term memory networks (LSTM) and gated recurrent unit networks (GRU) are fused by CI fusion algorithm to effectively improve the performance of prediction. Moreover, the variance is obtained to evaluate the prediction results. The model is validated on the real weather dataset in Beijing. The experiments show that LSTM and GRU have their pros and cons for different data, CI fusion can improve the accuracy of the final predictions, and the entire framework has robust prediction results with a reasonable estimated variance.},
  keywords = {deep prediction network, covariance intersection (CI) fusion, sensor data analytics},
  issn = {3068-5079},
  publisher = {Institute of Central Computation and Knowledge}
}