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Artificial Intelligence for Smart Sensing, Test and Measurement
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Artificial Intelligence for Smart Sensing, Test and Measurement

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

Deadline for manuscript submissions: closed (31 December 2022) | Viewed by 18421

Special Issue Editors

Dr. Octavian Postolache

E-Mail Website
Guest Editor
1. Instituto de Telecomunicações, 1049-001 Lisboa, Portugal
2. Escola de Tecnologias e Arquitetura (ISTA), ISCTE-Instituto Universitário de Lisboa, 1600-077 Lisboa, Portugal
3. DCTI-Departamento de Ciências e Tecnologias da Informação, ISCTE-Instituto Universitário de Lisboa, 1600-077 Lisboa, Portugal
Interests: smart sensors; automated measurement systems; artificial intelligence; biomedical sensors; intelligent transportation
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Francisco Javier Ferrero Martín

E-Mail Website
Guest Editor
Department of Electrical and Electronic Engineering, University of Oviedo, 33204 Gijon, Spain
Interests: smart sensors; embedded systems; biomedical signal processing
Special Issues, Collections and Topics in MDPI journals
Dr. José Miguel Costa Dias Pereira

E-Mail Website
Guest Editor
Polytechnic Institute of Setúbal, Setúbal School Of Technology, 2910-761 Setúbal, Portugal
Interests: industrial instrumentation; smart sensing; Industry 4.0; environmental monitoring
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The automated measurement tasks related to the monitoring of biomedical, environmental and industrial processes have become a reality in the context of the latest developments in the field of sensors, real-time processing systems and communication protocols. Nowadays, data acquired in an automated way can be stored in a database and analyzed in an on-line or off-line manner. Not only classical algorithms but also artificial intelligence algorithms can be employed to perform regression, clustering and classification tasks associated with monitored processes. Based on applied computational intelligence and machine learning techniques, useful information can be extracted. In the medical field, this could be information associated with disease diagnosis or the evolution of human health status according to applied treatment. Other examples of applications of computational intelligence and machine learning techniques include the extraction of information of anomalous functioning of an industrial system or the forecasting of the production of a photovoltaic system for a period of time. The acquired data are strongly dependent on the sensor characteristics, which can also be modeled using AI.

This Special Issue aims to highlight recent advances related to artificial intelligence algorithms, including machine learning and computational intelligence algorithms, applied in the field of sensors and automated measurement systems. Thus, we welcome contributions considering different fields of applications of the AMS (e.g., biomedical, environmental, industrial) where the implemented AI can assure higher accuracy, reliability and productivity of the systems.

Keywords

  • Sensor modeling
  • Smart sensors
  • Artificial intelligence
  • Embedded AI
  • Vision measurement systems
  • AI in tests and measurements

Published Papers (5 papers)

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Research

14 pages, 4890 KiB  
Article
An Optimized DNN Model for Real-Time Inferencing on an Embedded Device
by Jungme Park, Pawan Aryal, Sai Rithvick Mandumula and Ritwik Prasad Asolkar
Sensors 2023, 23(8), 3992; https://doi.org/10.3390/s23083992 - 14 Apr 2023
Cited by 3 | Viewed by 1852
Abstract
For many automotive functionalities in Advanced Driver Assist Systems (ADAS) and Autonomous Driving (AD), target objects are detected using state-of-the-art Deep Neural Network (DNN) technologies. However, the main challenge of recent DNN-based object detection is that it requires high computational costs. This requirement [...] Read more.
For many automotive functionalities in Advanced Driver Assist Systems (ADAS) and Autonomous Driving (AD), target objects are detected using state-of-the-art Deep Neural Network (DNN) technologies. However, the main challenge of recent DNN-based object detection is that it requires high computational costs. This requirement makes it challenging to deploy the DNN-based system on a vehicle for real-time inferencing. The low response time and high accuracy of automotive applications are critical factors when the system is deployed in real time. In this paper, the authors focus on deploying the computer-vision-based object detection system on the real-time service for automotive applications. First, five different vehicle detection systems are developed using transfer learning technology, which utilizes the pre-trained DNN model. The best performing DNN model showed improvements of 7.1% in Precision, 10.8% in Recall, and 8.93% in F1 score compared to the original YOLOv3 model. The developed DNN model was optimized by fusing layers horizontally and vertically to deploy it in the in-vehicle computing device. Finally, the optimized DNN model is deployed on the embedded in-vehicle computing device to run the program in real-time. Through optimization, the optimized DNN model can run 35.082 fps (frames per second) on the NVIDIA Jetson AGA, 19.385 times faster than the unoptimized DNN model. The experimental results demonstrate that the optimized transferred DNN model achieved higher accuracy and faster processing time for vehicle detection, which is vital for deploying the ADAS system. Full article
(This article belongs to the Special Issue Artificial Intelligence for Smart Sensing, Test and Measurement)
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22 pages, 5813 KiB  
Article
Towards Reliable Parameter Extraction in MEMS Final Module Testing Using Bayesian Inference
by Monika E. Heringhaus, Yi Zhang, André Zimmermann and Lars Mikelsons
Sensors 2022, 22(14), 5408; https://doi.org/10.3390/s22145408 - 20 Jul 2022
Cited by 3 | Viewed by 1469
Abstract
In micro-electro-mechanical systems (MEMS) testing high overall precision and reliability are essential. Due to the additional requirement of runtime efficiency, machine learning methods have been investigated in recent years. However, these methods are often associated with inherent challenges concerning uncertainty quantification and guarantees [...] Read more.
In micro-electro-mechanical systems (MEMS) testing high overall precision and reliability are essential. Due to the additional requirement of runtime efficiency, machine learning methods have been investigated in recent years. However, these methods are often associated with inherent challenges concerning uncertainty quantification and guarantees of reliability. The goal of this paper is therefore to present a new machine learning approach in MEMS testing based on Bayesian inference to determine whether the estimation is trustworthy. The overall predictive performance as well as the uncertainty quantification are evaluated with four methods: Bayesian neural network, mixture density network, probabilistic Bayesian neural network and BayesFlow. They are investigated under the variation in training set size, different additive noise levels, and an out-of-distribution condition, namely the variation in the damping factor of the MEMS device. Furthermore, epistemic and aleatoric uncertainties are evaluated and discussed to encourage thorough inspection of models before deployment striving for reliable and efficient parameter estimation during final module testing of MEMS devices. BayesFlow consistently outperformed the other methods in terms of the predictive performance. As the probabilistic Bayesian neural network enables the distinction between epistemic and aleatoric uncertainty, their share of the total uncertainty has been intensively studied. Full article
(This article belongs to the Special Issue Artificial Intelligence for Smart Sensing, Test and Measurement)
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23 pages, 3894 KiB  
Article
Neural Architecture Search for 1D CNNs—Different Approaches Tests and Measurements
by João Rala Cordeiro, António Raimundo, Octavian Postolache and Pedro Sebastião
Sensors 2021, 21(23), 7990; https://doi.org/10.3390/s21237990 - 30 Nov 2021
Cited by 26 | Viewed by 2768
Abstract
In the field of sensors, in areas such as industrial, clinical, or environment, it is common to find one dimensional (1D) formatted data (e.g., electrocardiogram, temperature, power consumption). A very promising technique for modelling this information is the use of One Dimensional Convolutional [...] Read more.
In the field of sensors, in areas such as industrial, clinical, or environment, it is common to find one dimensional (1D) formatted data (e.g., electrocardiogram, temperature, power consumption). A very promising technique for modelling this information is the use of One Dimensional Convolutional Neural Networks (1D CNN), which introduces a new challenge, namely how to define the best architecture for a 1D CNN. This manuscript addresses the concept of One Dimensional Neural Architecture Search (1D NAS), an approach that automates the search for the best combination of Neuronal Networks hyperparameters (model architecture), including both structural and training hyperparameters, for optimising 1D CNNs. This work includes the implementation of search processes for 1D CNN architectures based on five strategies: greedy, random, Bayesian, hyperband, and genetic approaches to perform, collect, and analyse the results obtained by each strategy scenario. For the analysis, we conducted 125 experiments, followed by a thorough evaluation from multiple perspectives, including the best-performing model in terms of accuracy, consistency, variability, total running time, and computational resource consumption. Finally, by presenting the optimised 1D CNN architecture, the results for the manuscript’s research question (a real-life clinical case) were provided. Full article
(This article belongs to the Special Issue Artificial Intelligence for Smart Sensing, Test and Measurement)
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13 pages, 2524 KiB  
Article
Real-Time High-Performance Laser Welding Defect Detection by Combining ACGAN-Based Data Enhancement and Multi-Model Fusion
by Kui Fan, Peng Peng, Hongping Zhou, Lulu Wang and Zhongyi Guo
Sensors 2021, 21(21), 7304; https://doi.org/10.3390/s21217304 - 02 Nov 2021
Cited by 9 | Viewed by 2918
Abstract
Most of the existing laser welding process monitoring technologies focus on the detection of post-engineering defects, but in the mass production of electronic equipment, such as laser welding metal plates, the real-time identification of defect detection has more important practical significance. The data [...] Read more.
Most of the existing laser welding process monitoring technologies focus on the detection of post-engineering defects, but in the mass production of electronic equipment, such as laser welding metal plates, the real-time identification of defect detection has more important practical significance. The data set of laser welding process is often difficult to build and there is not enough experimental data, which hinder the applications of the data-driven laser welding defect detection method. In this paper, an intelligent welding defect diagnosis method based on auxiliary classifier generative adversarial networks (ACGAN) has been proposed. Firstly, a ten-class dataset consisting of 6467 samples, was constructed, which originate from the optical and thermal sensory parameters in the welding process. A new structured ACGAN network model is proposed to generate fake data similar to the true defect feature distributions. In addition, in order to make the difference between different defects categories more obvious after data expansion, a data filtering and data purification scheme was proposed based on ensemble learning and an SVM (support vector machine), which is used to filter the bad generated data. In the experiments, the classification accuracy can reach 96.83% and 85.13%, for the CNN (convolutional neural network) algorithm model and ACGAN model, respectively. However, the accuracy can further improve to 97.86% and 98.37% for the fusion models of ACGAN-CNN and ACGAN-SVM-CNN models, respectively. The results show that ACGAN can not only be used as an algorithm model for classification, but also be used to achieve superior real-time classification and recognition through data enhancement and multi-model fusion. Full article
(This article belongs to the Special Issue Artificial Intelligence for Smart Sensing, Test and Measurement)
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18 pages, 6365 KiB  
Article
An Improvement of the Fire Detection and Classification Method Using YOLOv3 for Surveillance Systems
by Akmalbek Abdusalomov, Nodirbek Baratov, Alpamis Kutlimuratov and Taeg Keun Whangbo
Sensors 2021, 21(19), 6519; https://doi.org/10.3390/s21196519 - 29 Sep 2021
Cited by 53 | Viewed by 7862
Abstract
Currently, sensor-based systems for fire detection are widely used worldwide. Further research has shown that camera-based fire detection systems achieve much better results than sensor-based methods. In this study, we present a method for real-time high-speed fire detection using deep learning. A new [...] Read more.
Currently, sensor-based systems for fire detection are widely used worldwide. Further research has shown that camera-based fire detection systems achieve much better results than sensor-based methods. In this study, we present a method for real-time high-speed fire detection using deep learning. A new special convolutional neural network was developed to detect fire regions using the existing YOLOv3 algorithm. Due to the fact that our real-time fire detector cameras were built on a Banana Pi M3 board, we adapted the YOLOv3 network to the board level. Firstly, we tested the latest versions of YOLO algorithms to select the appropriate algorithm and used it in our study for fire detection. The default versions of the YOLO approach have very low accuracy after training and testing in fire detection cases. We selected the YOLOv3 network to improve and use it for the successful detection and warning of fire disasters. By modifying the algorithm, we recorded the results of a rapid and high-precision detection of fire, during both day and night, irrespective of the shape and size. Another advantage is that the algorithm is capable of detecting fires that are 1 m long and 0.3 m wide at a distance of 50 m. Experimental results showed that the proposed method successfully detected fire candidate areas and achieved a seamless classification performance compared to other conventional fire detection frameworks. Full article
(This article belongs to the Special Issue Artificial Intelligence for Smart Sensing, Test and Measurement)
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