Journal of Convergence for Information Technology (융합정보논문지)

Convergence Society for SMB (중소기업융합학회)
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A Study on the Air Pollution Monitoring Network Algorithm Using Deep Learning

심층신경망 모델을 이용한 대기오염망 자료확정 알고리즘 연구

  • 이선우 (인하대학교 전기컴퓨터공학과) ;
  • 양호준 (인하대학교 전기컴퓨터공학과) ;
  • 이문형 (인하대학교 컴퓨터공학과) ;
  • 최정무 (인하대학교 컴퓨터공학과) ;
  • 윤세환 (인하대학교 컴퓨터공학과) ;
  • 권장우 (인하대학교 컴퓨터공학과) ;
  • 박지훈 (국립환경과학원 대기환경연구과) ;
  • 정동희 (국립환경과학원 대기환경연구과) ;
  • 신혜정 (국립환경과학원 대기환경연구과)
  • Received : 2021.09.03
  • Accepted : 2021.11.20
  • Published : 2021.11.28
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Abstract

We propose a novel method to detect abnormal data of specific symptoms using deep learning in air pollution measurement system. Existing methods generally detect abnomal data by classifying data showing unusual patterns different from the existing time series data. However, these approaches have limitations in detecting specific symptoms. In this paper, we use DeepLab V3+ model mainly used for foreground segmentation of images, whose structure has been changed to handle one-dimensional data. Instead of images, the model receives time-series data from multiple sensors and can detect data showing specific symptoms. In addition, we improve model's performance by reducing the complexity of noisy form time series data by using 'piecewise aggregation approximation'. Through the experimental results, it can be confirmed that anomaly data detection can be performed successfully.

본 논문은 딥 러닝(Deep Learning)을 이용하여 대기오염측정망 데이터 중 특정 증상이 나타나는 이상 데이터를 탐지하는 방법을 제시한다. 기존 방법들은 일반적으로 시계열 데이터 내에서 기존과는 다른 특이한 패턴이 나타나는 데이터를 탐지하여 이상치로 분류하며, 이는 특정 증상만을 탐지하기에는 적합하지 않다. 본 논문에서는 주로 이미지의 전경 분리(Sementic Segmentation)에 사용되는 DeepLab V3+ 모델의 2차원 합성곱 신경망 구조를 1차원 구조로 변형하여 이미지 대신 여러 센서의 시계열 측정값을 입력받고 특정 증상이 나타나는 데이터를 탐지하도록 하는 방법을 제시한다. 또한, 데이터에 '조각별 집계 근사법(Piecewise Aggregate Approximation)'을 적용하여 잡음이 많은 대기오염측정망 데이터의 복잡도를 줄임으로써 성능을 높인다. 실험 결과를 통해 준수한 성능으로 이상치 탐지를 수행할 수 있음을 확인할 수 있다.

Keywords

Acknowledgement

This work was supported by a grant from the National Institute of Environmental Research (NIER), funded by the Ministry of Environment (MOE) of the Republic of Korea (NIER-RP-2020-04-02-118).

References

  1. L. C. Chen, G. Papandreou, F. Schroff & H. Adam (2017). Rethinking atrous convolution for semantic image segmentation. arXiv preprint arXiv:1706.05587.
  2. L. C. Chen, G. Papandreou, I. Kokkinos, K. Murphy & A. L. Yuille (2017). Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs. IEEE transactions on pattern analysis and machine intelligence, 40(4), 834-848. DOI : 10.1109/TPAMI.2017.2699184
  3. K. He, X. Zhang, S. Ren & J. Sun. (2016). Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 770-778). DOI : 10.1109/CVPR.2016.90
  4. H. I. Fawaz et al. (2020). Inceptiontime: Finding alexnet for time series classification. Data Mining and Knowledge Discovery, 34(6), 1936-1962. DOI : 10.1007/s10618-020-00710-y
  5. W. Tang, G. Long et al. (2021). Rethinking 1D-CNN for Time Series Classification: A stronger baseline. arXiv preprint arXiv:2002.10061.
  6. N. A. Zainuri, A. A. Jemain & N. Muda. (2015). A comparison of various imputation methods for missing values in air quality data. Sains Malaysiana, 44(3), 449-456. https://doi.org/10.17576/jsm-2015-4403-17
  7. J. Faouzi & H. Janati, (n.d.). pyts: A Python Package for Time Series Classification. 6.
  8. Y. Kim & H. Park. (2019). Comparision of Missing Imputaion Methods In fine dust data. The Journal of Bigdata, 4(2), 105-114. https://doi.org/10.36498/kbigdt.2019.4.2.105
  9. H. Ren et al. (2019). Time-series anomaly detection service at microsoft. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (pp. 3009-3017).
  10. B. Zhou, S. Liu, B. Hooi, X. Cheng & J. Ye. (2019, August). BeatGAN: Anomalous Rhythm Detection using Adversarially Generated Time Series. In IJCAI (pp. 4433-4439).
  11. N. Tatbul, T. J. Lee, S. Zdonik, M. Alam & J. Gottschlich. (2019). Precision and Recall for Time Series. arXiv preprint arXiv:1803.03639.
  12. H. Ren. (2019). Time-Series Anomaly Detection Service at Microsoft. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (pp. 3009-3017). DOI : 10.1145/3292500.3330680
  13. D. Bank, N. Koenigstein & R. Giryes. (2020). Autoencoders. arXiv preprint arXiv:2003.05991.
  14. I. J. Goodfellow et al. (2014). Generative Adversarial Networks. ArXiv:1406.2661. http://arxiv.org/abs/1406.2661