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

Deadline for manuscript submissions: 30 April 2025 | Viewed by 12891

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Special Issue Editor

Prof. Dr. Jin Yang

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Key Laboratory of Optoelectronic Technology & Systems, Department of Optoelectronic Engineering, Chongqing University, Chongqing 400044, China
Interests: sensing technology; self-power technology; information acquisition and processing technology
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Special Issue Information

Dear Colleagues,

Electronic Sensors are technologies designed to convert a measurement into electrical signals, which change traditional monitoring methods and revolutionize detection instruments. Displacement or speed in the environment can be obtained through electronic pressure sensors, electronic acoustic sensors hold the capability of capturing abnormal sound signals during operation of the equipment, and flexible wearable sensors can be utilized to track human physiological signals for disease prevention or exercise monitoring. Based on these signals collected by electronic sensors, various instruments have the ability to perceive, control and communicate with the outside world. Applied to various industrial or environmental monitoring, electronic sensors are the key elements used to acquire the original signal accurately and are also critical for the back-end systems that perform various operations or decisions based on the collected signals. The objective of this Special Issue is to gather novel developments in the latest research on electronic sensors for Industrial or Environmental Monitoring Applications. Topics of interest include, but are not limited to, the following keywords:

Electronic sensors;
Novel sensors and transducers;
Novel sensing methods;
Flexible sensing/sensors;
Industrial monitoring applications;
Environmental monitoring applications;
Human physiological signal monitoring

Prof. Dr. Jin Yang
Guest Editor

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Keywords

electronic sensors
novel sensors and transducers
novel sensing methods
flexible sensing/sensors
industrial monitoring applications
environmental monitoring applications
human physiological signal monitoring

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Published Papers (7 papers)

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Research

13 pages, 3986 KiB

Open AccessArticle

An Ionization-Based Aerosol Sensor and Its Performance Study

by Yong Zhang, Chunqi Wang, Liang Xie, Yuqi Peng and Ruizhe Wang

Sensors 2024, 24(17), 5600; https://doi.org/10.3390/s24175600 - 29 Aug 2024

Cited by 1 | Viewed by 994

Abstract

In recent years, with the rapid development of new energy vehicles, the safety issues of lithium-ion batteries have attracted attentions from all sectors of society. Research has found that during the thermal runaway process of lithium-ion batteries, aerosol emissions usually occur earlier than other gases. Accurate and timely measurement of these aerosol concentrations can help to warn the power battery pack fires. However, existing aerosol sensors are unable to meet the requirements of real-time monitoring and high precision. This article proposes an ionization mechanism based aerosol sensor that works at principles of field emission, field charging and gas discharge, and investigates its static and dynamic response characteristics. The sensor is manufactured and assembled using Microelectro Mechanical Systems processing technology. The sensor exhibits superior performances in terms of range, sensitivity, nonlinearity, repeatability, response time, and other aspects. The study provides a new solution for current aerosol detection with great potential for application. Full article

(This article belongs to the Special Issue Electronic Sensors for Industrial or Environmental Monitoring Applications)

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24 pages, 22182 KiB

Open AccessArticle

Multi-Channel Multi-Scale Convolution Attention Variational Autoencoder (MCA-VAE): An Interpretable Anomaly Detection Algorithm Based on Variational Autoencoder

by Jingwen Liu, Yuchen Huang, Dizhi Wu, Yuchen Yang, Yanru Chen, Liangyin Chen and Yuanyuan Zhang

Sensors 2024, 24(16), 5316; https://doi.org/10.3390/s24165316 - 16 Aug 2024

Cited by 2 | Viewed by 2386

Abstract

With the rapid development of industry, the risks factories face are increasing. Therefore, the anomaly detection algorithms deployed in factories need to have high accuracy, and they need to be able to promptly discover and locate the specific equipment causing the anomaly to restore the regular operation of the abnormal equipment. However, the neural network models currently deployed in factories cannot effectively capture both temporal features within dimensions and relationship features between dimensions; some algorithms that consider both types of features lack interpretability. Therefore, we propose a high-precision, interpretable anomaly detection algorithm based on variational autoencoder (VAE). We use a multi-scale local weight-sharing convolutional neural network structure to fully extract the temporal features within each dimension of the multi-dimensional time series. Then, we model the features from various aspects through multiple attention heads, extracting the relationship features between dimensions. We map the attention output results to the latent space distribution of the VAE and propose an optimization method to improve the reconstruction performance of the VAE, detecting anomalies through reconstruction errors. Regarding anomaly interpretability, we utilize the VAE probability distribution characteristics, decompose the obtained joint probability density into conditional probabilities on each dimension, and calculate the anomaly score, which provides helpful value for technicians. Experimental results show that our algorithm performed best in terms of F1 score and AUC value. The AUC value for anomaly detection is 0.982, and the F1 score is 0.905, which is 4% higher than the best-performing baseline algorithm, Transformer with a Discriminator for Anomaly Detection (TDAD). It also provides accurate anomaly interpretation capability. Full article

(This article belongs to the Special Issue Electronic Sensors for Industrial or Environmental Monitoring Applications)

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17 pages, 3182 KiB

Open AccessArticle

E-Nose: Time–Frequency Attention Convolutional Neural Network for Gas Classification and Concentration Prediction

by Minglv Jiang, Na Li, Mingyong Li, Zhou Wang, Yuan Tian, Kaiyan Peng, Haoran Sheng, Haoyu Li and Qiang Li

Sensors 2024, 24(13), 4126; https://doi.org/10.3390/s24134126 - 25 Jun 2024

Cited by 1 | Viewed by 1710

Abstract

In the electronic nose (E-nose) systems, gas type recognition and accurate concentration prediction are some of the most challenging issues. This study introduced an innovative pattern recognition method of time–frequency attention convolutional neural network (TFA-CNN). A time–frequency attention block was designed in the network, aiming to excavate and effectively integrate the temporal and frequency domain information in the E-nose signals to enhance the performance of gas classification and concentration prediction tasks. Additionally, a novel data augmentation strategy was developed, manipulating the feature channels and time dimensions to reduce the interference of sensor drift and redundant information, thereby enhancing the model’s robustness and adaptability. Utilizing two types of metal-oxide-semiconductor gas sensors, this research conducted qualitative and quantitative analysis on five target gases. The evaluation results showed that the classification accuracy could reach 100%, and the coefficient of the determination (R²) score of the regression task was up to 0.99. The Pearson correlation coefficient (r) was 0.99, and the mean absolute error (MAE) was 1.54 ppm. The experimental test results were almost consistent with the system predictions, and the MAE was 1.39 ppm. This study provides a method of network learning that combines time–frequency domain information, exhibiting high performance in gas classification and concentration prediction within the E-nose system. Full article

(This article belongs to the Special Issue Electronic Sensors for Industrial or Environmental Monitoring Applications)

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19 pages, 6475 KiB

Open AccessArticle

Data Clustering Utilization Technologies Using Medians of Current Values for Improving Arc Sensing in Unstructured Environments

by Hee-Jun Kim, Jeong-Ho Kim, Shin-Nyeong Heo, Do-Hyung Jeon and Won-Suk Kim

Sensors 2024, 24(13), 4075; https://doi.org/10.3390/s24134075 - 23 Jun 2024

Viewed by 1176

Abstract

In the shipbuilding industry, welding automation using welding robots often relies on arc-sensing techniques due to spatial limitations. However, the reliability of the feedback current value, core sensing data, is reduced when welding target workpieces have significant curvature or gaps between curved workpieces due to the control of short-circuit transition, leading to seam tracking failure and subsequent damage to the workpieces. To address these problems, this study proposes a new algorithm, MBSC (median-based spatial clustering), based on the DBSCAN (density-based spatial clustering of applications with noise) clustering algorithm. By performing clustering based on the median value of data in each weaving area and considering the characteristics of the feedback current data, the proposed technique utilizes detected outliers to enhance seam tracking accuracy and responsiveness in unstructured and challenging welding environments. The effectiveness of the proposed technique was verified through actual welding experiments in a yard environment. Full article

(This article belongs to the Special Issue Electronic Sensors for Industrial or Environmental Monitoring Applications)

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13 pages, 6143 KiB

Open AccessArticle

Design of an Electromagnetic Micro Mirror Driving System for LiDAR

by Jie Chen, Haiqiang Zhang, Zhongjin Zhang and Wenjie Yan

Sensors 2024, 24(12), 3969; https://doi.org/10.3390/s24123969 - 19 Jun 2024

Cited by 1 | Viewed by 1242

Abstract

Electromagnetic micro mirrors are in great demand for light detection and ranging (LiDAR) applications due to their light weight and low power consumption. The driven frequency of electromagnetic micro mirrors is very important to their performance and consumption. An electromagnetic micro mirror system is proposed in this paper. The model of the system was composed of a micro mirror, an integrated piezoresistive (PR) sensor, and a driving circuit was developed. The twisting angle of the mirror edge was monitored by an integrated PR sensor, which provides frequency feedback signals, and the PR sensor has good sensitivity and linearity in testing, with a maximum of 24.45 mV/deg. Stable sinusoidal voltage excitation and frequency tracking was realized via a phase-locked loop (PLL) in the driving circuit, with a frequency error within 10 Hz. Compared with other high-cost solutions using PLL circuits, it has greater advantages in power consumption, cost, and occupied area. The mechanical and piezoresistive properties of micro mirrors were performed in ANSYS 19.2 software. The behavior-level models of devices, circuits, and systems were validated by MATLAB R2023a Simulink, which contributes to the research on the large-angle deflection and low-power-consumption drive of the electromagnetic micro mirror. The maximum optical scan angle reached 37.6° at 4 kHz in the behavior-level model of the micro mirror. Full article

(This article belongs to the Special Issue Electronic Sensors for Industrial or Environmental Monitoring Applications)

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13 pages, 1959 KiB

Open AccessArticle

Dual-Frequency Soil Moisture Meter Method for Simultaneous Estimation of Soil Moisture and Conductivity

by Jerzy S. Witkowski and Andrzej F. Grobelny

Sensors 2024, 24(10), 2969; https://doi.org/10.3390/s24102969 - 7 May 2024

Viewed by 3469

Abstract

The measurement of soil water content is a very important factor in plant cultivation, both from an economic and ecological point of view. Proper estimation of moisture content not only allows for proper yields but can also contribute to ecologically appropriate use of fresh water, of which the world’s resources are limited. It is important, for example, that the moisture content in the root area of plants is optimal for their growth, while over-watering can result in losses in the form of water, which seeps below the root layer and is lost. The novel, inexpensive electronic meter for measuring soil moisture is presented in the article. The meter, based on a capacitive method, uses an optimization algorithm to calculate soil electrical permeability and a simplified new formula between soil electrical permeability and volumetric moisture content. Moreover, by using two high-frequency signals for measurements, it is possible not only to estimate moisture content but also soil conductivity. Both readings obtained from the meter not only allow for rational management of crop optimization for economic reasons but are also important for environmental protection. In addition, the inexpensive meter, based on the principle of operation presented, can be made as an IoT module, which allows for its wide application. Full article

(This article belongs to the Special Issue Electronic Sensors for Industrial or Environmental Monitoring Applications)

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12 pages, 13655 KiB

Open AccessArticle

Characteristics of Micro-Seismic Events Induced by Ground Collapse—A Case Study in the Rongxing Gypsum Mine in Hubei Province, China

by Hongzhu Wang, Taiyi Chen and Guangli Xu

Sensors 2024, 24(4), 1309; https://doi.org/10.3390/s24041309 - 18 Feb 2024

Viewed by 1143

Abstract

Mining activities can damage rock masses and easily induce ground collapse, which seriously threatens safe production in mining areas. Micro-seismic systems can monitor rock mass deformation signals in real time and provide more accurate data for rock mass deformation analysis. Therefore, in this study, the waveform characteristics of micro-seismic events induced by ground collapse in the Rongxing gypsum mine were analyzed; the occurrence of these events was introduced on the basis of Fast Fourier Transform, an established Frequency–Time–Amplitude model, in order to put forward the index of energy proportion of the main band. The results showed the following. (1) The seismic sequence type of ground collapse was foreshock–mainshock–aftershocks. The interval between the foreshock and mainshock was longer than that between the mainshock and aftershocks. (2) The deformation corresponding to the foreshock micro-seismic events was mainly that of a small-scale crack. The deformation corresponding to the micro-seismic events during the mainshock was characterized by the gradual development of small-scale cracks, and the development of large-scale cracks accelerated, accompanied by slight rock collapse. The deformation corresponding to the micro-seismic events during the aftershocks showed that almost no small-scale cracks developed, and the large-scale crack development was intense, and accompanied by numerous rock and soil mass collapses. (3) The observed decreasing frequency distribution and energy dispersion can be used as possible precursors of ground collapse. Full article

(This article belongs to the Special Issue Electronic Sensors for Industrial or Environmental Monitoring Applications)

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