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Intelligent Sensor Signal in Machine Learning II
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Intelligent Sensor Signal in Machine Learning II

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

Deadline for manuscript submissions: closed (30 November 2023) | Viewed by 3819

Special Issue Editor

Prof. Dr. ByoungChul Ko

E-Mail Website
Guest Editor
Department of Computer Engineering, Keimyung University, Shindang-Dong, Dalseo-Gu, Daegu 704-701,Republic of Korea
Interests: computer vision; pattern recognition; object detection tracking; deep learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

With the advancement of sensor technology, research has been actively carried out to fuse sensor signals and to extract useful information for various recognition problems based on machine learning. Recently, we have been obtaining signals from various sensors, such as wearable sensors, mobile sensors, cameras, heart rate monitoring devices, EEG head-caps and headbands, ECG sensors, breathing monitors, EMG sensors, and temperature sensors. However, as the sensor signal itself has no meaning, the machine learning algorithm must be combined in order to process the signals and make various decisions. Therefore, the use of machine learning, including deep learning, is appropriate for these challenging tasks.

The purpose of this Special Issue is to take the opportunity to introduce the current developments of intelligent sensor applications and innovative sensor fusion techniques combined with machine learning, including computer vision, pattern recognition, expert systems, deep learning, and so on. In this Special Issue, you are invited to submit contributions of original research, advancement, developments, and experiments pertaining to machine learning combined with sensors. Therefore, this Special Issue welcomes newly developed methods and ideas combining the data obtained from various sensors in the following fields (but not limited to these fields):

  • Sensor fusion techniques based on machine learning;
  • Sensors and big data analysis with machine learning;
  • Autonomous vehicle technologies combining sensors and machine learning;
  • Wireless sensor networks and communication based on machine learning;
  • Deep network structure/learning algorithm for intelligent sensing;
  • Autonomous robotics with intelligent sensors and machine learning;
  • Multi-modal/task learning for decision-making and control;
  • Decision algorithms for autonomous driving;
  • Machine learning and artificial intelligence for traffic/quality of experience management in IoT;
  • Fuzzy fusion of sensors, data, and information;
  • Machine learning for IoT and sensor research challenges;
  • Advanced driver assistant systems (ADAS) based on machine learning;
  • State-of-practice, research overview, experience reports, industrial experiments, and case studies in the intelligent sensors or IoT.

Prof. Dr. ByoungChul Ko
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 submissions that pass pre-check are 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 2600 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.

Published Papers (2 papers)

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Research

13 pages, 4941 KiB  
Article
Label-Free Anomaly Detection Using Distributed Optical Fiber Acoustic Sensing
by Yuyuan Xie, Maoning Wang, Yuzhong Zhong, Lin Deng and Jianwei Zhang
Sensors 2023, 23(8), 4094; https://doi.org/10.3390/s23084094 - 19 Apr 2023
Cited by 6 | Viewed by 1726
Abstract
Deep learning anomaly detection is important in distributed optical fiber acoustic sensing (DAS). However, anomaly detection is more challenging than traditional learning tasks, due to the scarcity of true-positive data and the vast imbalance and irregularity within datasets. Furthermore, it is impossible to [...] Read more.
Deep learning anomaly detection is important in distributed optical fiber acoustic sensing (DAS). However, anomaly detection is more challenging than traditional learning tasks, due to the scarcity of true-positive data and the vast imbalance and irregularity within datasets. Furthermore, it is impossible to catalog all types of anomalies, therefore, the direct application of supervised learning is deficient. To overcome these problems, an unsupervised deep learning method that only learns the normal data features from ordinary events is proposed. First, a convolutional autoencoder is used to extract DAS signal features. A clustering algorithm then locates the feature center of the normal data, and the distance to the new signal is used to determine whether it is an anomaly. The efficacy of the proposed method was evaluated in a real high-speed rail intrusion scenario, and considered all behaviors that may threaten the normal operation of high-speed trains as abnormal. The results show that the threat detection rate of this method reaches 91.5%, which is 5.9% higher than that of the state-of-the-art supervised network and, at 7.2%, the false alarm rate is 0.8% lower than the supervised network. Moreover, using a shallow autoencoder reduces the parameters to 1.34 K, which is significantly lower than the 79.55 K of the state-of-the-art supervised network. Full article
(This article belongs to the Special Issue Intelligent Sensor Signal in Machine Learning II)
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13 pages, 1548 KiB  
Article
Lightweight Semantic-Guided Neural Networks Based on Single Head Attention for Action Recognition
by Seon-Bin Kim, Chanhyuk Jung, Byeong-Il Kim and Byoung Chul Ko
Sensors 2022, 22(23), 9249; https://doi.org/10.3390/s22239249 - 28 Nov 2022
Cited by 2 | Viewed by 1618
Abstract
Skeleton-based action recognition can achieve a relatively high performance by transforming the human skeleton structure in an image into a graph and applying action recognition based on structural changes in the body. Among the many graph convolutional network (GCN) approaches used in skeleton-based [...] Read more.
Skeleton-based action recognition can achieve a relatively high performance by transforming the human skeleton structure in an image into a graph and applying action recognition based on structural changes in the body. Among the many graph convolutional network (GCN) approaches used in skeleton-based action recognition, semantic-guided neural networks (SGNs) are fast action recognition algorithms that hierarchically learn spatial and temporal features by applying a GCN. However, because an SGN focuses on global feature learning rather than local feature learning owing to the structural characteristics, there is a limit to an action recognition in which the dependency between neighbouring nodes is important. To solve these problems and simultaneously achieve a real-time action recognition in low-end devices, in this study, a single head attention (SHA) that can overcome the limitations of an SGN is proposed, and a new SGN-SHA model that combines SHA with an SGN is presented. In experiments on various action recognition benchmark datasets, the proposed SGN-SHA model significantly reduced the computational complexity while exhibiting a performance similar to that of an existing SGN and other state-of-the-art methods. Full article
(This article belongs to the Special Issue Intelligent Sensor Signal in Machine Learning II)
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