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Engineering Applications of Artificial Intelligence for Sensors

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

Deadline for manuscript submissions: 30 June 2024 | Viewed by 6935

Special Issue Editors


E-Mail Website
Guest Editor
Department of Artificial Intelligence, Institute of Information Technology, Warsaw University of Life Sciences, Nowoursynowska 159, 02-776 Warsaw, Poland
Interests: artificial intelligence; machine learning; deep learning; applications of AI for sensors

E-Mail Website
Guest Editor
Department of Artificial Intelligence, Institute of Information Technology, Warsaw University of Life Sciences, Nowoursynowska 159, 02-776 Warsaw, Poland
Interests: artificial intelligence; machine learning; deep learning; applications; applications of AI for sensors

Special Issue Information

Dear Colleagues,

Artificial Intelligence (AI) is currently one of the most developing techniques in the engineering world and plays an important role worldwide. Thanks to the continuous development of machine learning, deep learning, etc. we constantly observe new applications of these techniques. Artificial intelligence techniques are widely used by engineers to solve a whole range of previously unseen problems.

Artificial intelligence methods require a lot of data, which we often obtain from many types of sensors and sensory technology. The combination of these two areas: data obtained from sensors and artificial intelligence algorithms, creates an extremely interesting, future-proof and promising interdisciplinary research area.

The Special Issue of "Engineering Applications of Artificial Intelligence for Sensors" provides an international space for the publication of papers describing the practical application of AI methods such us machine learning and deep learning in all aspects of engineering for sensors. Artificial intelligence techniques implemented in both open and closed code are acceptable. Artificial intelligence solutions based on cloud computing are particularly expected. This Special Issue aims to report innovative algorithms and applications of Artificial intelligence, machine learning, and deep learning to achieve improvement of life. Submitted articles should show interesting applications of artificial intelligence in engineering world where data for AI derives from sensors.

Potential topics include, but are not limited to:

  • Machine learning application
  • Deep learning applications
  • Internet of things (IoT) and cyber-physical systems
  • Intelligent transportation systems & smart vehicles
  • Big data analytics, understanding complex networks
  • Neural networks, fuzzy systems, neuro-fuzzy systems
  • Deep learning and real-world applications
  • Self-organizing, emerging or bio-inspired system
  • Global optimization, Meta-heuristics and their applications: Evolutionary Algorithms, swarm intelligence, nature and biologically inspired meta-heuristics, etc.
  • Architectures, algorithms and techniques for distributed AI systems, including multi-agent-based control and holonic control
  • Decision-support systems
  • Real-time intelligent automation, and their associated supporting methodologies and techniques, including control theory and industrial informatics
  • Knowledge processing, knowledge elicitation and acquisition, knowledge representation, knowledge compaction, knowledge bases, expert systems
  • Perception, e.g., image processing, pattern recognition, vision systems, tactile systems, speech recognition and synthesis
  • Aspects of software engineering, e.g., intelligent programming environments, verification and validation of AI-based software, software and hardware architectures for the real-time use of AI techniques, safety and reliability
  • Intelligent fault detection, fault analysis, diagnostics and monitoring
  • Industrial experiences in the application of the above techniques, e.g., case studies or benchmarking exercises
  • Robotics

Prof. Dr. Jarosław Kurek
Prof. Dr. Bartosz Świderski 
Guest Editors

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.

Keywords

  • artificial intelligence
  • machine learning
  • deep learning
  • simulation
  • applications of AI for sensors
  • neural network
  • decision-support systems
  • sensors

Published Papers (3 papers)

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Research

28 pages, 9008 KiB  
Article
Multi-Sensor Data Fusion and CNN-LSTM Model for Human Activity Recognition System
by Haiyang Zhou, Yixin Zhao, Yanzhong Liu, Sichao Lu, Xiang An and Qiang Liu
Sensors 2023, 23(10), 4750; https://doi.org/10.3390/s23104750 - 14 May 2023
Cited by 3 | Viewed by 2426
Abstract
Human activity recognition (HAR) is becoming increasingly important, especially with the growing number of elderly people living at home. However, most sensors, such as cameras, do not perform well in low-light environments. To address this issue, we designed a HAR system that combines [...] Read more.
Human activity recognition (HAR) is becoming increasingly important, especially with the growing number of elderly people living at home. However, most sensors, such as cameras, do not perform well in low-light environments. To address this issue, we designed a HAR system that combines a camera and a millimeter wave radar, taking advantage of each sensor and a fusion algorithm to distinguish between confusing human activities and to improve accuracy in low-light settings. To extract the spatial and temporal features contained in the multisensor fusion data, we designed an improved CNN-LSTM model. In addition, three data fusion algorithms were studied and investigated. Compared to camera data in low-light environments, the fusion data significantly improved the HAR accuracy by at least 26.68%, 19.87%, and 21.92% under the data level fusion algorithm, feature level fusion algorithm, and decision level fusion algorithm, respectively. Moreover, the data level fusion algorithm also resulted in a reduction of the best misclassification rate to 2%~6%. These findings suggest that the proposed system has the potential to enhance the accuracy of HAR in low-light environments and to decrease human activity misclassification rates. Full article
(This article belongs to the Special Issue Engineering Applications of Artificial Intelligence for Sensors)
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15 pages, 4253 KiB  
Article
Vehicle Detection and Recognition Approach in Multi-Scale Traffic Monitoring System via Graph-Based Data Optimization
by Grzegorz Wieczorek, Sheikh Badar ud din Tahir, Israr Akhter and Jaroslaw Kurek
Sensors 2023, 23(3), 1731; https://doi.org/10.3390/s23031731 - 03 Feb 2023
Cited by 3 | Viewed by 2054
Abstract
Over the past few years, significant investments in smart traffic monitoring systems have been made. The most important step in machine learning is detecting and recognizing objects relative to vehicles. Due to variations in vision and different lighting conditions, the recognition and tracking [...] Read more.
Over the past few years, significant investments in smart traffic monitoring systems have been made. The most important step in machine learning is detecting and recognizing objects relative to vehicles. Due to variations in vision and different lighting conditions, the recognition and tracking of vehicles under varying extreme conditions has become one of the most challenging tasks. To deal with this, our proposed system presents an adaptive method for robustly recognizing several existing automobiles in dense traffic settings. Additionally, this research presents a broad framework for effective on-road vehicle recognition and detection. Furthermore, the proposed system focuses on challenges typically noticed in analyzing traffic scenes captured by in-vehicle cameras, such as consistent extraction of features. First, we performed frame conversion, background subtraction, and object shape optimization as preprocessing steps. Next, two important features (energy and deep optical flow) were extracted. The incorporation of energy and dense optical flow features in distance-adaptive window areas and subsequent processing over the fused features resulted in a greater capacity for discrimination. Next, a graph-mining-based approach was applied to select optimal features. Finally, the artificial neural network was adopted for detection and classification. The experimental results show significant performance in two benchmark datasets, including the LISA and KITTI 7 databases. The LISA dataset achieved a mean recognition rate of 93.75% on the LDB1 and LDB2 databases, whereas KITTI attained 82.85% accuracy on separate training of ANN. Full article
(This article belongs to the Special Issue Engineering Applications of Artificial Intelligence for Sensors)
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20 pages, 1386 KiB  
Article
Improved Drill State Recognition during Milling Process Using Artificial Intelligence
by Jarosław Kurek, Artur Krupa, Izabella Antoniuk, Arlan Akhmet, Ulan Abdiomar, Michał Bukowski and Karol Szymanowski
Sensors 2023, 23(1), 448; https://doi.org/10.3390/s23010448 - 01 Jan 2023
Cited by 1 | Viewed by 1409
Abstract
In this article, an automated method for tool condition monitoring is presented. When producing items in large quantities, pointing out the exact time when the element needs to be exchanged is crucial. If performed too early, the operator gets rid of a good [...] Read more.
In this article, an automated method for tool condition monitoring is presented. When producing items in large quantities, pointing out the exact time when the element needs to be exchanged is crucial. If performed too early, the operator gets rid of a good drill, also resulting in production downtime increase if this operation is repeated too often. On the other hand, continuing production with a worn tool might result in a poor-quality product and financial loss for the manufacturer. In the presented approach, drill wear is classified using three states representing decreasing quality: green, yellow and red. A series of signals were collected as training data for the classification algorithms. Measurements were saved in separate data sets with corresponding time windows. A total of ten methods were evaluated in terms of overall accuracy and the number of misclassification errors. Three solutions obtained an acceptable accuracy rate above 85%. Algorithms were able to assign states without the most undesirable red-green and green-red errors. The best results were achieved by the Extreme Gradient Boosting algorithm. This approach achieved an overall accuracy of 93.33%, and the only misclassification was the yellow sample assigned as green. The presented solution achieves good results and can be applied in industry applications related to tool condition monitoring. Full article
(This article belongs to the Special Issue Engineering Applications of Artificial Intelligence for Sensors)
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