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Special Issue "Sensor Data Fusion and Analysis for Automation Systems"

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Physical Sensors".

Deadline for manuscript submissions: 15 August 2021.

Special Issue Editor

Prof. Dr. Simon X. Yang
Website
Guest Editor
Advanced Robotics & Intelligent Systems (ARIS) Lab, School of Engineering, University of Guelph, Guelph, Ontario, N1G 2W1, Canada
Interests: electronic noses; smart sensors; sensor signal process; multi-sensor fusion; sensor networks; robotics; intelligent systems; control systems; systems modeling and analysis
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

Research on sensor data fusion and data analysis for various automation systems has made significant progress in both theoretical investigation and practical applications in many fields, such as sensing, path planning, tracking, and control of various autonomous robotic systems; health monitoring, damage identification, multi-sensor fusion, sensing and signal processing for bridges and roads; and monitoring, information analysis, and decision making of various environmental, structural, agricultural, and manufacturing systems. Various advanced intelligent algorithms and technologies have been developed for accurate information acquisition, effective monitoring, accurate prediction, optimal decision making, and efficient operation for diversified automation systems.

This Special Issue is devoted to new advances and research results on sensor data fusion and analysis for various automation systems in transportation, robotics, agriculture, and industry. It will publish work exploring frontier technology and applications in related fields. The topics of interest for this issue include, but are not limited to the following:

  • Multi-sensor fusion and feature representation
  • Information acquisition and analysis for automation
  • Sensor signal processing and data analysis
  • Big data mining for automation
  • Data fusion based on monitoring for automation
  • Artificial intelligence for automation systems
  • Intelligent robotics and machine vision
  • Machine learning based on prediction and decision making
  • Intelligent control for automation systems

Prof. Dr. Simon X. Yang
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2000 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Artificial intelligence
  • Sensor signal processing and data analysis
  • Multi-sensor fusion
  • Big data and data mining
  • Modeling, prediction and decision making
  • Robotics and machine vision

Published Papers (3 papers)

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Open AccessArticle
Convolutional Neural Networks-Based Object Detection Algorithm by Jointing Semantic Segmentation for Images
Sensors 2020, 20(18), 5080; https://doi.org/10.3390/s20185080 - 07 Sep 2020
Abstract
In recent years, increasing image data comes from various sensors, and object detection plays a vital role in image understanding. For object detection in complex scenes, more detailed information in the image should be obtained to improve the accuracy of detection task. In [...] Read more.
In recent years, increasing image data comes from various sensors, and object detection plays a vital role in image understanding. For object detection in complex scenes, more detailed information in the image should be obtained to improve the accuracy of detection task. In this paper, we propose an object detection algorithm by jointing semantic segmentation (SSOD) for images. First, we construct a feature extraction network that integrates the hourglass structure network with the attention mechanism layer to extract and fuse multi-scale features to generate high-level features with rich semantic information. Second, the semantic segmentation task is used as an auxiliary task to allow the algorithm to perform multi-task learning. Finally, multi-scale features are used to predict the location and category of the object. The experimental results show that our algorithm substantially enhances object detection performance and consistently outperforms other three comparison algorithms, and the detection speed can reach real-time, which can be used for real-time detection. Full article
(This article belongs to the Special Issue Sensor Data Fusion and Analysis for Automation Systems)
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Open AccessArticle
Modeling of Stochastic Wind Based on Operational Flight Data Using Karhunen–Loève Expansion Method
Sensors 2020, 20(16), 4634; https://doi.org/10.3390/s20164634 - 18 Aug 2020
Abstract
Wind has a significant influence on the operational flight safety. To quantify the influence of the wind characteristics, a wind series generator is required in simulations. This paper presents a method to model the stochastic wind based on operational flight data using the [...] Read more.
Wind has a significant influence on the operational flight safety. To quantify the influence of the wind characteristics, a wind series generator is required in simulations. This paper presents a method to model the stochastic wind based on operational flight data using the Karhunen–Loève expansion. The proposed wind model allows us to generate new realizations of wind series, which follow the original statistical characteristics. To improve the accuracy of this wind model, a vine copula is used in this paper to capture the high dimensional dependence among the random variables in the expansions. Besides, the proposed stochastic model based on the Karhunen–Loève expansion is compared with the well-known von Karman turbulence model based on the spectral representation in this paper. Modeling results of turbulence data validate that the Karhunen–Loève expansion and the spectral representation coincide in the stationary process. Furthermore, construction results of the non-stationary wind process from operational flights show that the generated wind series have a good match in the statistical characteristics with the raw data. The proposed stochastic wind model allows us to integrate the new wind series into the Monte Carlo Simulation for quantitative assessments. Full article
(This article belongs to the Special Issue Sensor Data Fusion and Analysis for Automation Systems)
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Open AccessLetter
Active Learning Plus Deep Learning Can Establish Cost-Effective and Robust Model for Multichannel Image: A Case on Hyperspectral Image Classification
Sensors 2020, 20(17), 4975; https://doi.org/10.3390/s20174975 - 02 Sep 2020
Abstract
Relying on large scale labeled datasets, deep learning has achieved good performance in image classification tasks. In agricultural and biological engineering, image annotation is time-consuming and expensive. It also requires annotators to have technical skills in specific areas. Obtaining the ground truth is [...] Read more.
Relying on large scale labeled datasets, deep learning has achieved good performance in image classification tasks. In agricultural and biological engineering, image annotation is time-consuming and expensive. It also requires annotators to have technical skills in specific areas. Obtaining the ground truth is difficult because natural images are expensive. In addition, images in these areas are usually stored as multichannel images, such as computed tomography (CT) images, magnetic resonance images (MRI), and hyperspectral images (HSI). In this paper, we present a framework using active learning and deep learning for multichannel image classification. We use three active learning algorithms, including least confidence, margin sampling, and entropy, as the selection criteria. Based on this framework, we further introduce an “image pool” to make full advantage of images generated by data augmentation. To prove the availability of the proposed framework, we present a case study on agricultural hyperspectral image classification. The results show that the proposed framework achieves better performance compared with the deep learning model. Manual annotation of all the training sets achieves an encouraging accuracy. In comparison, using active learning algorithm of entropy and image pool achieves a similar accuracy with only part of the whole training set manually annotated. In practical application, the proposed framework can remarkably reduce labeling effort during the model development and upadting processes, and can be applied to multichannel image classification in agricultural and biological engineering. Full article
(This article belongs to the Special Issue Sensor Data Fusion and Analysis for Automation Systems)
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