skip to main content
research-article

Dependable Deep Computation Model for Feature Learning on Big Data in Cyber-Physical Systems

Published: 24 September 2018 Publication History

Abstract

With the ongoing development of sensor devices and network techniques, big data are being generated from the cyber-physical systems. Because of sensor equipment occasional failure and network transmission unreliability, a large number of low-quality data, such as noisy data and incomplete data, is collected from the cyber-physical systems. Low-quality data pose a remarkable challenge on deep learning models for big data feature learning. As a novel deep learning model, the deep computation model achieves superior performance for big data feature learning. However, it is difficult for the deep computation model to learn dependable features for low-quality data, since it uses the nonlinear function as the encoder. In this article, a dependable deep computation model is proposed for feature learning on low-quality big data in cyber-physical systems. Specially, a regularity is added into the objective function of the deep computation model to obtain reliable features in the intermediate-level representation space. Furthermore, a learning algorithm based on the back-propagation strategy is devised to train the parameters of the proposed model. Finally, experiments are conducted on three representative datasets and a real dataset to evaluate the effectiveness of the dependable deep computation model for low-quality big data feature learning. Results show that the proposed model achieves a remarkable result for the tasks of classification, restoration, and prediction, proving the potential of this work for practical applications in cyber-physical systems.

References

[1]
Adam Coates, Honglak Lee, and Andrew Y. Ng. 2011. An analysis of single-layer networks in unsupervised feature learning. In Proceedings of the International Conference on Artificial Intelligence and Statistics. JMLR, 215--223.
[2]
Roberto Calandra, Tapani Raiko, Marc P. Deisenroth, and Federico M. Pouzols. 2012. Learning deep belief networks from non-stationary streams. In Proceedings of the International Conference on Artificial Neural Networks and Machine Learning. Springer, 379--386.
[3]
Wenli Chen, James T. Wilson, Stephen Tyree, Kilian Q. Weinberger, and Yixin Chen. 2015. Compressing neural networks with the hashing trick. In Proceedings of the International Conference on Machine Learning. ACM, 2285--2294.
[4]
David D. Cox and Robert L. Savoy. 2003. Functional magnetic resonance imaging (fMRI) ‘brain reading’: Detecting and classifying distributed patterns of fMRI activity in human visual cortex. Neuroimage. 19, 2 (2003), 261--270.
[5]
Li Deng, Dong Yu, and John Platt. 2012. Scalable stacking and learning for building deep architectures. In Proceedings of the International Conference on Acoustics, Speech and Signal Processing. IEEE, 2133--2136.
[6]
Robert Harrison, Daniel Vera, and Bilal Ahmad. 2016. Engineering methods and tools for cyber-physical automation systems. Proc. IEEE 104, 5 (2016), 973--985.
[7]
Liwei Kuang, Fei Hao, Laurence T. Yang, Man Lin, Changqing Luo, and Ggeyong Min. 2014. A tensor-based approach for big data representation and dimensionality reduction. IEEE Trans. Emerg. Top. Comput. 2, 3 (2014), 280--291.
[8]
Stamatios Lefkimmiatis, Aurelien Bourquard, and Michael Unser. 2012. Hessian-based norm regularization for image restoration with biomedical applications. IEEE Trans. Image Process. 21, 3 (2012), 983--995.
[9]
Yang Liu, Yan Liu, and Keith C. C. Chan. 2010. Tensor distance based multilinear locality-preserved maximum information embedding. IEEE Trans. Neur. Netw. 21, 11 (2010), 1848--1854.
[10]
Yang Liu, Yu Peng, Bailing Wang, Sirui Yao, and Zhihe Liu. 2017. Review on cyber-physical systems. IEEE/CAA J. Automact. Sin. 4, 1 (2017), 27--40.
[11]
Jiquan Ngiam, Aditya Khosla, Mingyu Kim, Honglak Lee, and Andrew Y. Ng. 2011. Multimodal deep learning. In Proceedings of the 28th International Conference on Machine Learning. ACM, 689--696.
[12]
Alexander Novikov, Dmitrii Podoprikhin, Anton Osokin, and Dmitry P. Vetrov. 2015. Tensorizing neural netwroks. In Advances in Neural Information Processing Systems. MIT, 442--450.
[13]
Wanli Ouyang, Xiao Chu, and Xiaogang Wang. 2014. Multi-source deep learning for human pose estimation. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. IEEE, 2337--2344.
[14]
Eric Patterson, Ozgur Gurbuz, Zeynep Tufekci, and John N. Gowdy. 2002. CUAVE: A new audio-visual database for multimodal human-computer interface research. In Proceedings of the 2002 IEEE International Conference on Acoustics, Speech, and Signal Processing. IEEE, 2017--2020.
[15]
Rajat Raina, Anand Madhavan, and Andrew Y. Ng. 2009. Large-scale deep unsupervised learning using graphics processors. In Proceedings of the 26th International Conference on Machine Learning. ACM, 873--880.
[16]
Tara N. Sainath, Brian Kingsbury, Vikas Sindhwani, Ebru Arisoy, and Bhuvana Ramabhadran. 2013. Low-rank matrix factorization for deep neural network training with high-dimensional output targets. In Proceedings of IEEE International Conference of Acoustics, Speech, and Signal Processing. IEEE, 6655--6659.
[17]
Jurgen Schmidhuber. 2015. Deep learning in neural networks: An overview. Neur. Netw. 61 (2015), 85--117.
[18]
Nitish Srivastava and Ruslan R. Salakhutdinov. 2012. Multimodal learning with deep boltzmann Machines. In Advances in Neural Information Processing Systems. MIT, 2222--2230.
[19]
Pascal Vincent, Hugo Larochelle, Isabelle Lajoie, Yoshua Bengio, and Pierre-Antoine Manzagol. 2010. Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion. J. Mach. Learn. Res. 11 (2010), 3371--3408.
[20]
Runxin Wang and Dacheng Tao. 2016. Non-local auto-encoder with collaborative stabilization for image restoration. IEEE Trans. Image Process. 25, 5 (2016), 2117--2129.
[21]
Xindong Wu, Xingquan Zhu, Gong-Qing Wu, and Wei Ding. 2014. Data mining with big data. IEEE Trans. Knowl. Data Eng. 26, 1 (2014), 97--107.
[22]
Qingchen Zhang, Laurence T. Yang, and Zhikui Chen. 2016. Deep computation model for unsupervised feature learning on big data. IEEE Trans. Serv. Comput. 9, 1, 161--171.
[23]
Qingchen Zhang, Laurence T. Yang, Zhikui Chen, and Peng Li. 2018. High-order Possibilistic c-means algorithms based on tensor decompositions for big data in IoT. Information Fusion 39 (2018), 72--80.
[24]
Qingchen Zhang, Laurence T. Yang, Zhikui Chen, and Peng Li. 2018. PPHOPCM: Privacy-preserving high-order possibilistic c-means algorithm for heterogeneous data fuzzy clustering. IEEE Transactions on Big Data. 2017. https://ieeexplore.ieee.org/abstract/document/7920374/.
[25]
Guanyu Zhou, Kihyuk Sohn, and Honglak Lee. 2012. Online incremental feature learning with denoising autoencoders. In Proceedings of International Conference on Artificial Intelligence and Statistics. JMLR, 1453--1461.

Cited By

View all
  • (2022)Spatial Data Quality in the IoT Era: Management and ExploitationProceedings of the 2022 International Conference on Management of Data10.1145/3514221.3522568(2474-2482)Online publication date: 10-Jun-2022
  • (2021)Deep-Learning-Enabled Security Issues in the Internet of ThingsIEEE Internet of Things Journal10.1109/JIOT.2020.30071308:12(9531-9538)Online publication date: 15-Jun-2021
  • (2021)Industrial Security Solution for Virtual RealityIEEE Internet of Things Journal10.1109/JIOT.2020.30044698:8(6273-6281)Online publication date: 15-Apr-2021
  • Show More Cited By

Index Terms

  1. Dependable Deep Computation Model for Feature Learning on Big Data in Cyber-Physical Systems

      Recommendations

      Comments

      Information & Contributors

      Information

      Published In

      cover image ACM Transactions on Cyber-Physical Systems
      ACM Transactions on Cyber-Physical Systems  Volume 3, Issue 1
      Special Issue on Dependability in CPS
      January 2019
      256 pages
      ISSN:2378-962X
      EISSN:2378-9638
      DOI:10.1145/3274532
      • Editor:
      • Tei-Wei Kuo
      Issue’s Table of Contents
      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

      Publisher

      Association for Computing Machinery

      New York, NY, United States

      Journal Family

      Publication History

      Published: 24 September 2018
      Accepted: 01 June 2017
      Revised: 01 May 2017
      Received: 01 March 2017
      Published in TCPS Volume 3, Issue 1

      Permissions

      Request permissions for this article.

      Check for updates

      Author Tags

      1. Cyber-physical systems
      2. back-propagation algorithm
      3. big data
      4. dependable deep computation model
      5. feature learning

      Qualifiers

      • Research-article
      • Research
      • Refereed

      Contributors

      Other Metrics

      Bibliometrics & Citations

      Bibliometrics

      Article Metrics

      • Downloads (Last 12 months)6
      • Downloads (Last 6 weeks)1
      Reflects downloads up to 30 Jan 2025

      Other Metrics

      Citations

      Cited By

      View all
      • (2022)Spatial Data Quality in the IoT Era: Management and ExploitationProceedings of the 2022 International Conference on Management of Data10.1145/3514221.3522568(2474-2482)Online publication date: 10-Jun-2022
      • (2021)Deep-Learning-Enabled Security Issues in the Internet of ThingsIEEE Internet of Things Journal10.1109/JIOT.2020.30071308:12(9531-9538)Online publication date: 15-Jun-2021
      • (2021)Industrial Security Solution for Virtual RealityIEEE Internet of Things Journal10.1109/JIOT.2020.30044698:8(6273-6281)Online publication date: 15-Apr-2021
      • (2020)Mobile Cyber-Physical Systems for Smart CitiesCompanion Proceedings of the Web Conference 202010.1145/3366424.3382121(546-548)Online publication date: 20-Apr-2020
      • (2020)Active Balancing Mechanism for Imbalanced Medical Data in Deep Learning–Based Classification ModelsACM Transactions on Multimedia Computing, Communications, and Applications10.1145/335725316:1s(1-15)Online publication date: 12-Mar-2020
      • (2019)Tensor-Train Fuzzy Deep Computation Model for Citywide Traffic Flow PredictionIEEE Access10.1109/ACCESS.2019.29204307(120581-120593)Online publication date: 2019
      • (2019)A smart agriculture IoT system based on deep reinforcement learningFuture Generation Computer Systems10.1016/j.future.2019.04.041Online publication date: May-2019

      View Options

      Login options

      Full Access

      View options

      PDF

      View or Download as a PDF file.

      PDF

      eReader

      View online with eReader.

      eReader

      HTML Format

      View this article in HTML Format.

      HTML Format

      Figures

      Tables

      Media

      Share

      Share

      Share this Publication link

      Share on social media