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Advances in Artificial Intelligence and Sensors Technology for Prognostics Health Management

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Dear Colleagues,

In recent decades, science and technology has developed rapidly. In order to keep up with new developments, various fields of equipment need to improve their complexity, accuracy, and efficiency. However, the working status of various pieces of equipment directly determines their health condition. Artificial intelligence and Sensor Technology are new technological sciences that study and develop theories, methods, technologies, and application systems for simulating, extending, and expanding human intelligence. This not only improves their efficiency and makes them less error-prone, but also has the advantages of imitating human decision-making, self-learning, and self transformation, and avoiding repeated errors. Prognostics health management is a key technology to ensure the safe and reliable operation of various equipment. Therefore, prognostics health management method and technology are crucial for ensuring the safe and reliable operation of various pieces of equipment, and avoiding serious accidents. This Special Issue welcomes any original and high-quality papers that address, but are not limited to, the following:

Machine learning;
Artificial neural networks;
Deep learning;
Transfer learning;
Multi-task learning;
Sensor networks;
Remote sensing;
Data sharing and privacy;
Data mining and fusion;
Signal processing;
Feature extraction;
Fault detection and diagnosis;
Health monitoring and assessment;
Residual life prediction;
Intelligent optimization algorithm and application.

Prof. Dr. Huimin Zhao
Dr. Huayue Chen
Prof. Dr. Wu Deng
Guest Editors

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Research

16 pages, 7608 KiB

Open AccessArticle

Research on Student’s T-Distribution Point Cloud Registration Algorithm Based on Local Features

by Houpeng Sun, Yingchun Li, Huichao Guo, Chenglong Luan, Laixian Zhang, Haijing Zheng and Youchen Fan

Sensors 2024, 24(15), 4972; https://doi.org/10.3390/s24154972 - 31 Jul 2024

Viewed by 983

Abstract

LiDAR offers a wide range of uses in autonomous driving, remote sensing, urban planning, and other areas. The laser 3D point cloud acquired by LiDAR typically encounters issues during registration, including laser speckle noise, Gaussian noise, data loss, and data disorder. This work suggests a novel Student’s t-distribution point cloud registration algorithm based on the local features of point clouds to address these issues. The approach uses Student’s t-distribution mixture model (SMM) to generate the probability distribution of point cloud registration, which can accurately describe the data distribution, in order to tackle the problem of the missing laser 3D point cloud data and data disorder. Owing to the disparity in the point cloud registration task, a full-rank covariance matrix is built based on the local features of the point cloud during the objective function design process. The combined penalty of point-to-point and point-to-plane distance is then added to the objective function adaptively. Simultaneously, by analyzing the imaging characteristics of LiDAR, according to the influence of the laser waveform and detector on the LiDAR imaging, the composite weight coefficient is added to improve the pertinence of the algorithm. Based on the public dataset and the laser 3D point cloud dataset acquired in the laboratory, the experimental findings demonstrate that the proposed algorithm has high practicability and dependability and outperforms the five comparison algorithms in terms of accuracy and robustness. Full article

(This article belongs to the Special Issue Advances in Artificial Intelligence and Sensors Technology for Prognostics Health Management)

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20 pages, 6290 KiB

Open AccessArticle

Broad Learning System under Label Noise: A Novel Reweighting Framework with Logarithm Kernel and Mixture Autoencoder

by Jiuru Shen, Huimin Zhao and Wu Deng

Sensors 2024, 24(13), 4268; https://doi.org/10.3390/s24134268 - 30 Jun 2024

Cited by 1 | Viewed by 1437

Abstract

The Broad Learning System (BLS) has demonstrated strong performance across a variety of problems. However, BLS based on the Minimum Mean Square Error (MMSE) criterion is highly sensitive to label noise. To enhance the robustness of BLS in environments with label noise, a function called Logarithm Kernel (LK) is designed to reweight the samples for outputting weights during the training of BLS in order to construct a Logarithm Kernel-based BLS (L-BLS) in this paper. Additionally, for image databases with numerous features, a Mixture Autoencoder (MAE) is designed to construct more representative feature nodes of BLS in complex label noise environments. For the MAE, two corresponding versions of BLS, MAEBLS, and L-MAEBLS were also developed. The extensive experiments validate the robustness and effectiveness of the proposed L-BLS, and MAE can provide more representative feature nodes for the corresponding version of BLS. Full article

(This article belongs to the Special Issue Advances in Artificial Intelligence and Sensors Technology for Prognostics Health Management)

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