research-article

DNTC: : An unsupervised Deep Networks for Temperature Compensation in non-stationary data

Authors:

Yuanhai ShaoAuthors Info & Claims

Volume 127, Issue PB

https://doi.org/10.1016/j.engappai.2023.107319

Published: 01 February 2024 Publication History

Abstract

Temperature changes usually result in abnormal signals and hence could significantly decrease the performance of sensors. To reduce the system errors caused by temperature changes, constructing an accurate temperature compensation model from the data is a promising issue. However, there are few effective methods to address the challenges when faced with practical difficulties, including large amounts of data, high volatility, strong non-stationarity, and high training cost. In this paper, we propose an unsupervised Deep Networks for Temperature Compensation (DNTC) to compensate temperature for non-stationary data. DNTC consists of three submodules: pooling, generation, and reconstruction modules. The pooling module employs a convolutional network to effectively compress features, the generation module uses a De-stationary Attention-based encoder–decoder structure to extract non-stationary information from the sequence, and the reconstruction module solves the statistical quantity deviation problem of the output sequence to achieve temperature compensation. DNTC employs an end-to-end architecture, avoiding the bias accumulation phenomenon caused by multi-stage operations in traditional methods; and it breaks free from the awkwardness of relying on labeled learning, thus expanding its applicability. Furthermore, it effectively handles large-scale sequences while considering their non-stationary characteristics. Extensive experiments on 9 datasets demonstrate that DNTC exhibits excellent compensation effects and is expected to be widely used in the engineering field. Code and data are available at: https://github.com/tietoucun/Temperature-Compensation.

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Index Terms

DNTC: An unsupervised Deep Networks for Temperature Compensation in non-stationary data
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
  2. Machine learning
    1. Machine learning approaches
      1. Neural networks
2. Theory of computation
  1. Theory and algorithms for application domains
    1. Machine learning theory

Index terms have been assigned to the content through auto-classification.

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cover image Engineering Applications of Artificial Intelligence

Engineering Applications of Artificial Intelligence Volume 127, Issue PB

Jan 2024

1549 pages

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Elsevier Ltd.

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Pergamon Press, Inc.

United States

Publication History

Published: 01 February 2024

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