Pub Date : 2025-01-07DOI: 10.1109/JSEN.2024.3524388
Liang Ma;Yifei Peng;Kaixiang Peng
Root cause diagnosis is an important part of the fault diagnosis framework, which is often used to locate the root causes and identify the propagation paths. Most of the traditional root cause diagnosis methods consider the time series of industrial processes to be stationary or nearly stationary after faults occur. Since fault information is often propagated according to the causalities between process variables, and the pseudo-regression caused by nonstationary characteristics is not conducive to correct causality analysis, further affects the root cause diagnosis performance. Associated with those trends, in this article, a new causality analysis framework is proposed for root cause diagnosis of faults in nonstationary industrial processes. Specifically, the augmented Dickey-Fuller (ADF) test is first used to determine the stationarity of the time series, and the combination method of cointegration analysis (CA) and higher order difference is used for extracting the stationarity factors from nonstationary time series. Then, an attention minimal gated unit (AMGU)-based nonlinear dynamic causality analysis method is developed for causal topology construction and root cause diagnosis. Finally, industrial verifications on two datasets from actual hot rolling processes (HRPs) show that the proposed scheme is feasible, and is superior to competitive methods in terms of solving the issues of root cause diagnosis of faults in nonstationary industrial processes.
{"title":"An Attention Minimal Gated Unit-Based Causality Analysis Framework for Root Cause Diagnosis of Faults in Nonstationary Industrial Processes","authors":"Liang Ma;Yifei Peng;Kaixiang Peng","doi":"10.1109/JSEN.2024.3524388","DOIUrl":"https://doi.org/10.1109/JSEN.2024.3524388","url":null,"abstract":"Root cause diagnosis is an important part of the fault diagnosis framework, which is often used to locate the root causes and identify the propagation paths. Most of the traditional root cause diagnosis methods consider the time series of industrial processes to be stationary or nearly stationary after faults occur. Since fault information is often propagated according to the causalities between process variables, and the pseudo-regression caused by nonstationary characteristics is not conducive to correct causality analysis, further affects the root cause diagnosis performance. Associated with those trends, in this article, a new causality analysis framework is proposed for root cause diagnosis of faults in nonstationary industrial processes. Specifically, the augmented Dickey-Fuller (ADF) test is first used to determine the stationarity of the time series, and the combination method of cointegration analysis (CA) and higher order difference is used for extracting the stationarity factors from nonstationary time series. Then, an attention minimal gated unit (AMGU)-based nonlinear dynamic causality analysis method is developed for causal topology construction and root cause diagnosis. Finally, industrial verifications on two datasets from actual hot rolling processes (HRPs) show that the proposed scheme is feasible, and is superior to competitive methods in terms of solving the issues of root cause diagnosis of faults in nonstationary industrial processes.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 4","pages":"6952-6966"},"PeriodicalIF":4.3,"publicationDate":"2025-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143446331","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Millimeter-wave (MMW) imaging technology has been widely used in crowded areas such as airports and railway stations for concealed object detection (COD), owing to its characteristics of privacy and safety. However, the low signal-to-noise ratio (SNR) and low resolution in MMW security images lead to indistinct edges of concealed objects and a significant similarity to the human background. These limitations constrain the accurate and rapid detection of concealed objects on individuals. This article proposes a concealed objects detector that uses an enhanced You Only Look Once (YOLO) network, incorporating spatial, edge, and multiscale information to address the issues above. First, an efficient adaptive denoising method is designed to enhance image clarity. Second, considering the lack of prominent edge features in MMW images, an edge-spatial feature fusion (ESFF) module is introduced. This module enhances the network’s ability to learn edge features by combining them with spatial detail information. In addition, this article proposes a hierarchical scale-aware feature fusion (HSAFF) module to address the issue of high similarity between targets and background textures that impairs traditional detection networks, which can effectively reduce classification errors and false detections. Finally, the ESFF and HSAFF modules are integrated into the detection network based on the YOLOv8 framework. The experimental results on the MMW image dataset demonstrate that the proposed model effectively reduces classification and false detection losses, achieving mean average precision (mAP) @0.5 and mAP@ [0.5:0.95] of ${98}.{3}%$ and ${81}.{5}%$ , respectively, while the mAP of the proposed method is ${5}.{1}%$ and ${4}%$ higher than the baseline model, and surpassing other detection models.
{"title":"Enhanced Concealed Object Detection Method for MMW Security Images Based on YOLOv8 Framework With ESFF and HSAFF","authors":"Shuliang Gui;Haitao Tian;Yizhe Wang;Sihang Dang;Ze Li;Kaikai Liu;Zengshan Tian","doi":"10.1109/JSEN.2024.3524441","DOIUrl":"https://doi.org/10.1109/JSEN.2024.3524441","url":null,"abstract":"Millimeter-wave (MMW) imaging technology has been widely used in crowded areas such as airports and railway stations for concealed object detection (COD), owing to its characteristics of privacy and safety. However, the low signal-to-noise ratio (SNR) and low resolution in MMW security images lead to indistinct edges of concealed objects and a significant similarity to the human background. These limitations constrain the accurate and rapid detection of concealed objects on individuals. This article proposes a concealed objects detector that uses an enhanced You Only Look Once (YOLO) network, incorporating spatial, edge, and multiscale information to address the issues above. First, an efficient adaptive denoising method is designed to enhance image clarity. Second, considering the lack of prominent edge features in MMW images, an edge-spatial feature fusion (ESFF) module is introduced. This module enhances the network’s ability to learn edge features by combining them with spatial detail information. In addition, this article proposes a hierarchical scale-aware feature fusion (HSAFF) module to address the issue of high similarity between targets and background textures that impairs traditional detection networks, which can effectively reduce classification errors and false detections. Finally, the ESFF and HSAFF modules are integrated into the detection network based on the YOLOv8 framework. The experimental results on the MMW image dataset demonstrate that the proposed model effectively reduces classification and false detection losses, achieving mean average precision (mAP) @0.5 and mAP@ [0.5:0.95] of <inline-formula> <tex-math>${98}.{3}%$ </tex-math></inline-formula> and <inline-formula> <tex-math>${81}.{5}%$ </tex-math></inline-formula>, respectively, while the mAP of the proposed method is <inline-formula> <tex-math>${5}.{1}%$ </tex-math></inline-formula> and <inline-formula> <tex-math>${4}%$ </tex-math></inline-formula> higher than the baseline model, and surpassing other detection models.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 4","pages":"7630-7641"},"PeriodicalIF":4.3,"publicationDate":"2025-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143438355","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-07DOI: 10.1109/JSEN.2024.3524094
Yiru Xiao;Bo Feng;Jikai Zhang;Kun Mao;Kai Wang;Yini Song;Yihua Kang
The method of motion-induced eddy current (MIEC) is significant not only for defect detection but also for speed sensing. This article investigates the effect of excitation frequency on the distribution of MIEC and the speed sensing results. First, a 2-D analytical model of MIEC with ac excitation is proposed, based on Maxwell’s equations. The speed sensor in the model is composed of an excitation coil and a Hall element. The density of the eddy currents within the conductor and the magnetic field strength in the air are calculated. Subsequently, finite element analyses are used to verify the accuracy of the theoretical model. The distribution of eddy current and speed sensing results with dc and ac excitation are analyzed, along with the effect of excitation frequency and conductor moving speed on the eddy current in the specimen and the magnetic field in the air. It was found that the speed sensor based on dc excitation exhibits higher sensitivity in the speed range of 0–30 m/s, while the speed measurement results under 0–120 Hz ac excitation demonstrate improved linearity. In addition, the interference of MIECs on induced eddy currents decreases as the excitation frequency increases. Speed sensing experiments are conducted on a rotating disk, and the experimental results are in good agreement with the theoretical calculations.
{"title":"Effect of Excitation Frequency on Motion-Induced Eddy Current and Speed Measurement","authors":"Yiru Xiao;Bo Feng;Jikai Zhang;Kun Mao;Kai Wang;Yini Song;Yihua Kang","doi":"10.1109/JSEN.2024.3524094","DOIUrl":"https://doi.org/10.1109/JSEN.2024.3524094","url":null,"abstract":"The method of motion-induced eddy current (MIEC) is significant not only for defect detection but also for speed sensing. This article investigates the effect of excitation frequency on the distribution of MIEC and the speed sensing results. First, a 2-D analytical model of MIEC with ac excitation is proposed, based on Maxwell’s equations. The speed sensor in the model is composed of an excitation coil and a Hall element. The density of the eddy currents within the conductor and the magnetic field strength in the air are calculated. Subsequently, finite element analyses are used to verify the accuracy of the theoretical model. The distribution of eddy current and speed sensing results with dc and ac excitation are analyzed, along with the effect of excitation frequency and conductor moving speed on the eddy current in the specimen and the magnetic field in the air. It was found that the speed sensor based on dc excitation exhibits higher sensitivity in the speed range of 0–30 m/s, while the speed measurement results under 0–120 Hz ac excitation demonstrate improved linearity. In addition, the interference of MIECs on induced eddy currents decreases as the excitation frequency increases. Speed sensing experiments are conducted on a rotating disk, and the experimental results are in good agreement with the theoretical calculations.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 4","pages":"6151-6159"},"PeriodicalIF":4.3,"publicationDate":"2025-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143430474","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-07DOI: 10.1109/JSEN.2024.3523963
Jie Yin;Xiangyi Liu;Yinuo Zhang;Xufeng Liao;Lianxi Liu
This article proposes a low-channel-mismatch ExG analog front end (AFE) for biosensors. An orthogonal nested-chopper technique is proposed to suppress the mismatch in interchannel input impedance. The first stage of the orthogonal nested chopper has the same frequency to ensure that each channel has the same input impedance. The second-stage chopper is controlled by Walsh-Hadamard codes, which can suppress the signal crosstalk among channels and lower the system’s modulation frequency. In addition, an input capacitance calibration technique based on successive-approximation (SA) logic is proposed, effectively suppressing the gain mismatch by gradually calibrating the input capacitance of each channel. The proposed AFE was fabricated in a 65-nm CMOS process, and the core area is $0.95times 0.9$ mm2. The measured results show that the proposed AFE consumes an average of $2.6~mu $ W per channel at a 1.2-V supply voltage. The input impedance is greater than 1.96 G$Omega $ , and the mismatch in input impedance among channels is 0.21%. The gain range is 26–46 dB, and the gain mismatch among channels is only 0.11%. The total common-mode rejection ratio (CMRR) is increased to 91 dB. The proposed AFE can clearly acquire electrocardiography (ECG), electromyography (EMG), and electroencephalography (EEG).
{"title":"A Low-Channel-Mismatch ExG AFE Based on Orthogonal Nested-Chopper and Successive-Approximation Input Capacitance Calibration","authors":"Jie Yin;Xiangyi Liu;Yinuo Zhang;Xufeng Liao;Lianxi Liu","doi":"10.1109/JSEN.2024.3523963","DOIUrl":"https://doi.org/10.1109/JSEN.2024.3523963","url":null,"abstract":"This article proposes a low-channel-mismatch ExG analog front end (AFE) for biosensors. An orthogonal nested-chopper technique is proposed to suppress the mismatch in interchannel input impedance. The first stage of the orthogonal nested chopper has the same frequency to ensure that each channel has the same input impedance. The second-stage chopper is controlled by Walsh-Hadamard codes, which can suppress the signal crosstalk among channels and lower the system’s modulation frequency. In addition, an input capacitance calibration technique based on successive-approximation (SA) logic is proposed, effectively suppressing the gain mismatch by gradually calibrating the input capacitance of each channel. The proposed AFE was fabricated in a 65-nm CMOS process, and the core area is <inline-formula> <tex-math>$0.95times 0.9$ </tex-math></inline-formula> mm2. The measured results show that the proposed AFE consumes an average of <inline-formula> <tex-math>$2.6~mu $ </tex-math></inline-formula>W per channel at a 1.2-V supply voltage. The input impedance is greater than 1.96 G<inline-formula> <tex-math>$Omega $ </tex-math></inline-formula>, and the mismatch in input impedance among channels is 0.21%. The gain range is 26–46 dB, and the gain mismatch among channels is only 0.11%. The total common-mode rejection ratio (CMRR) is increased to 91 dB. The proposed AFE can clearly acquire electrocardiography (ECG), electromyography (EMG), and electroencephalography (EEG).","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 4","pages":"6582-6592"},"PeriodicalIF":4.3,"publicationDate":"2025-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143446267","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-07DOI: 10.1109/JSEN.2024.3524456
Juan José López-Escobar;Pablo Fondo-Ferreiro;Francisco Javier González-Castaño;Felipe Gil-Castiñeira;Vicente Piorno-González;Ignacio Munilla-Rumbao;Alberto Gil-Carrrera
The Internet of Things (IoT), together with low power wide area network (LPWAN) technologies, have revolutionized wildlife monitoring and tracking systems. The research in this article has been motivated by the need of an adequate tracking solution based on LoRaWAN technology to study the population of the yellow-legged gull at Sálvora Island, Atlantic Islands of Galicia National Park. The main contribution is an intelligent approach that estimates the positions from LoRa signal features [received signal strength indicator (RSSI) and signal-to-noise ratio (SNR)] and trajectory information from previous positions, combined with as less frequent GNSS information as possible. By doing so, we achieve a good compromise between energy consumption, sampling rate, and application-level estimation accuracy. The results show that the approach achieves satisfactory performance for sampling frequencies according to the biological problems of interest, minimizing recharging cycles and, thus maximizing the duration of monitoring sessions. Specifically, the combination of previous GNSS positions and LoRa radio indicators within an intelligent framework can improve energy efficiency for extended periods with sporadic power-intensive GNSS position updates.
{"title":"Intelligent Energy-Efficient GNSS-Assisted and LoRa-Based Positioning for Wildlife Tracking","authors":"Juan José López-Escobar;Pablo Fondo-Ferreiro;Francisco Javier González-Castaño;Felipe Gil-Castiñeira;Vicente Piorno-González;Ignacio Munilla-Rumbao;Alberto Gil-Carrrera","doi":"10.1109/JSEN.2024.3524456","DOIUrl":"https://doi.org/10.1109/JSEN.2024.3524456","url":null,"abstract":"The Internet of Things (IoT), together with low power wide area network (LPWAN) technologies, have revolutionized wildlife monitoring and tracking systems. The research in this article has been motivated by the need of an adequate tracking solution based on LoRaWAN technology to study the population of the yellow-legged gull at Sálvora Island, Atlantic Islands of Galicia National Park. The main contribution is an intelligent approach that estimates the positions from LoRa signal features [received signal strength indicator (RSSI) and signal-to-noise ratio (SNR)] and trajectory information from previous positions, combined with as less frequent GNSS information as possible. By doing so, we achieve a good compromise between energy consumption, sampling rate, and application-level estimation accuracy. The results show that the approach achieves satisfactory performance for sampling frequencies according to the biological problems of interest, minimizing recharging cycles and, thus maximizing the duration of monitoring sessions. Specifically, the combination of previous GNSS positions and LoRa radio indicators within an intelligent framework can improve energy efficiency for extended periods with sporadic power-intensive GNSS position updates.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 4","pages":"7291-7300"},"PeriodicalIF":4.3,"publicationDate":"2025-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10832495","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143446348","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-07DOI: 10.1109/JSEN.2024.3524319
Muhammad Zeeshan Arshad;Aliaa Gouda;Jan Andrysek
This article addresses the challenge of improving locomotion mode recognition (LMR) for lower limb prosthetic users (LLPUs) by developing more generalizable machine learning (ML) models. Current models are limited to subject-specific models mostly as subject-independent models are hindered by the high variability within the LLPU population and the limited availability of LLPU data. This article investigates leveraging non-disabled (ND) datasets to enhance model generalizability by first identifying more appropriate sensor locations. Different methods are tested that use the ND and LLPU datasets in different ways for feature selection and model training to optimize the performance of subject-independent ML models. It is shown that using vertical sensor combination on the intact side of LLPUs, feature selection with only LLPU and then training with both the datasets combined, can greatly enhance LMR accuracy, achieving a 91.8% accuracy with a linear discriminant analysis (LDA) model. This approach aims to reduce the need for extensive training sessions for new users while maintaining high accuracy.
{"title":"Enhancing ML Model Generalizability for Locomotion Mode Recognition in Prosthetic Gait","authors":"Muhammad Zeeshan Arshad;Aliaa Gouda;Jan Andrysek","doi":"10.1109/JSEN.2024.3524319","DOIUrl":"https://doi.org/10.1109/JSEN.2024.3524319","url":null,"abstract":"This article addresses the challenge of improving locomotion mode recognition (LMR) for lower limb prosthetic users (LLPUs) by developing more generalizable machine learning (ML) models. Current models are limited to subject-specific models mostly as subject-independent models are hindered by the high variability within the LLPU population and the limited availability of LLPU data. This article investigates leveraging non-disabled (ND) datasets to enhance model generalizability by first identifying more appropriate sensor locations. Different methods are tested that use the ND and LLPU datasets in different ways for feature selection and model training to optimize the performance of subject-independent ML models. It is shown that using vertical sensor combination on the intact side of LLPUs, feature selection with only LLPU and then training with both the datasets combined, can greatly enhance LMR accuracy, achieving a 91.8% accuracy with a linear discriminant analysis (LDA) model. This approach aims to reduce the need for extensive training sessions for new users while maintaining high accuracy.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 4","pages":"7620-7629"},"PeriodicalIF":4.3,"publicationDate":"2025-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10832470","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143438358","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
We propose and experimentally demonstrate a noncontact optical thermometric study based on fluorescence intensity ratio (FIR) of nonthermally coupled levels (NTCLs) for real-time temperature detection of lithium batteries. The no-core Er3+/Yb3+ co-doped TeO2–Al2 O3–WO3–BaF2 + Er2O3 + Yb2O3 (TWA) optical fiber was prepared using the traditional melt-quenching method, and influence of the Al2O3 concentration on the fiber luminescence was investigated in detail. Meanwhile, temperature demodulation by means of thermally coupled level (TCL)-based FIR was juxtaposed for comparison. It was found that the sensor’s sensitivity was enhanced at least ten times utilizing the NTCL-based FIR method, and at an Al2O3 concentration of 4 mol%, the sensor demonstrated the best sensing performance, which was $434.3times 10^{-{4}}$ K−1 in the temperature range of 258–371 K. In addition, this sensor had high-temperature resolution and good repeatability, and its maximum measurement error during thermal monitoring of the charging/ discharging process of an 18650-type lithium battery was only 0.7 K. To the best of our knowledge, this was the first report to use a temperature sensor based on FIR for real-time monitoring of 18650-type lithium batteries. The proposed sensor demonstrated excellent stability and repeatability, resistance to electromagnetic interference and corrosion, and the capability to avoid cross-sensitivity. It provides an effective and reliable measurement solution for long-distance and real-time temperature monitoring in new energy battery applications.
我们提出并通过实验证明了一种基于非热耦合电平（NTCL）荧光强度比（FIR）的非接触式光学测温研究，可用于锂电池的实时温度检测。采用传统的熔淬法制备了无芯 Er3+/Yb3+ 共掺杂 TeO2-Al2 O3-WO3-BaF2 + Er2O3 + Yb2O3 (TWA) 光纤，并详细研究了 Al2O3 浓度对光纤发光的影响。同时，比较了基于热耦合电平（TCL）的 FIR 温度解调方法。结果发现，利用基于 NTCL 的 FIR 方法，传感器的灵敏度至少提高了十倍，在 Al2O3 浓度为 4 mol% 时，传感器的传感性能最佳，在 258-371 K 的温度范围内，传感器的传感性能为 10^{-{4}}$ K-1 的 434.3 倍。此外，该传感器还具有较高的温度分辨率和良好的重复性，在对 18650 型锂电池充放电过程进行热监测时，其最大测量误差仅为 0.7 K。据我们所知，这是第一份使用基于 FIR 的温度传感器对 18650 型锂电池进行实时监测的报告。所提出的传感器具有出色的稳定性和可重复性、抗电磁干扰和抗腐蚀能力，以及避免交叉敏感的能力。它为新能源电池应用中的远距离实时温度监测提供了有效可靠的测量解决方案。
{"title":"An Er³+/Yb³+ Co-Doped Tellurite Fiber Temperature Sensor for Real-Time Thermal Monitoring for Lithium Battery","authors":"Huifang Wang;Ning Yang;Xinghui Li;Xiaopeng Chen;Xiaoyu Chen;Qi Zhang;Zhiyuan Yin;Xue Zhou;Xin Yan;Tonglei Cheng","doi":"10.1109/JSEN.2024.3523955","DOIUrl":"https://doi.org/10.1109/JSEN.2024.3523955","url":null,"abstract":"We propose and experimentally demonstrate a noncontact optical thermometric study based on fluorescence intensity ratio (FIR) of nonthermally coupled levels (NTCLs) for real-time temperature detection of lithium batteries. The no-core Er3+/Yb3+ co-doped TeO2–Al2 O3–WO3–BaF2 + Er2O3 + Yb2O3 (TWA) optical fiber was prepared using the traditional melt-quenching method, and influence of the Al2O3 concentration on the fiber luminescence was investigated in detail. Meanwhile, temperature demodulation by means of thermally coupled level (TCL)-based FIR was juxtaposed for comparison. It was found that the sensor’s sensitivity was enhanced at least ten times utilizing the NTCL-based FIR method, and at an Al2O3 concentration of 4 mol%, the sensor demonstrated the best sensing performance, which was <inline-formula> <tex-math>$434.3times 10^{-{4}}$ </tex-math></inline-formula> K−1 in the temperature range of 258–371 K. In addition, this sensor had high-temperature resolution and good repeatability, and its maximum measurement error during thermal monitoring of the charging/ discharging process of an 18650-type lithium battery was only 0.7 K. To the best of our knowledge, this was the first report to use a temperature sensor based on FIR for real-time monitoring of 18650-type lithium batteries. The proposed sensor demonstrated excellent stability and repeatability, resistance to electromagnetic interference and corrosion, and the capability to avoid cross-sensitivity. It provides an effective and reliable measurement solution for long-distance and real-time temperature monitoring in new energy battery applications.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 4","pages":"6380-6387"},"PeriodicalIF":4.3,"publicationDate":"2025-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143446223","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Flexible, screen-printed electrode (SPE) systems offer significant advantages in biomedical applications due to their rapid fabrication capability and adaptability to irregular surfaces. Despite this, no flexible, screen-printed biosensors for pH measurement are readily available on the market. This article introduces an innovative fabrication technique for integrating flexible SPE systems with multiwall carbon nanotubes (MWCNTs). These SPE systems feature flexible screen-printed working electrodes (WEs) functionalized with MWCNTs (f-MWCNTs) for pH sensing, achieving a fabrication success rate of 78.57%. Furthermore, these SPE systems have been optimized to improve voltage changes and thoroughly characterized for performance using pH buffer solutions. This was done utilizing open-circuit potentiometry. The results demonstrate that the SPE systems can detect pH ranges from 4.0 to 8.9 with an average sensitivity of 33.66 mV/pH, an output range of 202.62 mV, a settling time of 19.71 s, and stability for up to 72 h. Additionally, the SPE systems have been tested with artificial saliva in the pH range of 5.0–8.3, achieving a sensitivity of 44.86 mV/pH and an output range of 192.90 mV. These findings pave the way for further exploration in pH sensing research, particularly utilizing SPE systems coated with MWCNTs.
{"title":"Flexible Screen-Printed Carbon-Based Electrode Functionalized With Multiwall Carbon Nanotubes for Portable Point-of-Care pH Sensing","authors":"Lazar Milić;Nor Syafirah Zambry;Fatimah Ibrahim;Bojan Petrović;Sanja Kojić;Karolina Laszczyk;Nurul Fauzani Jamaluddin;Md. Shalauddin;Wan Jefrey Basirun;Goran M. Stojanović","doi":"10.1109/JSEN.2024.3522569","DOIUrl":"https://doi.org/10.1109/JSEN.2024.3522569","url":null,"abstract":"Flexible, screen-printed electrode (SPE) systems offer significant advantages in biomedical applications due to their rapid fabrication capability and adaptability to irregular surfaces. Despite this, no flexible, screen-printed biosensors for pH measurement are readily available on the market. This article introduces an innovative fabrication technique for integrating flexible SPE systems with multiwall carbon nanotubes (MWCNTs). These SPE systems feature flexible screen-printed working electrodes (WEs) functionalized with MWCNTs (f-MWCNTs) for pH sensing, achieving a fabrication success rate of 78.57%. Furthermore, these SPE systems have been optimized to improve voltage changes and thoroughly characterized for performance using pH buffer solutions. This was done utilizing open-circuit potentiometry. The results demonstrate that the SPE systems can detect pH ranges from 4.0 to 8.9 with an average sensitivity of 33.66 mV/pH, an output range of 202.62 mV, a settling time of 19.71 s, and stability for up to 72 h. Additionally, the SPE systems have been tested with artificial saliva in the pH range of 5.0–8.3, achieving a sensitivity of 44.86 mV/pH and an output range of 192.90 mV. These findings pave the way for further exploration in pH sensing research, particularly utilizing SPE systems coated with MWCNTs.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 4","pages":"6025-6034"},"PeriodicalIF":4.3,"publicationDate":"2025-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10832500","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143422791","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-07DOI: 10.1109/JSEN.2024.3519903
Ru Hong;Zeyu Cai;Jiming Yang;Feipeng Da
Multiobject tracking (MOT) aims to associate objects of the same identity across video frames, with robust similarity measurement being crucial for maintaining tracking performance. However, the current inefficient integration of motion and appearance cues often leads to tracking failures in challenging scenarios, such as occlusions and missed detections. In this article, we introduce LV2DMOT, a tracker that employs a novel paradigm for integrating motion and appearance cues through language and visual multimodal feature learning, thereby generating more distinctive data association similarities. We propose three key techniques: 1) a text-matching task between tracking trajectories and candidate detections. This method uses text encoding of detection geometric information combined with a temporal model, Mamba, to extract temporal motion features of trajectories, enabling more accurate motion similarity calculations; 2) a multimodal, multilevel feature fusion model that integrates motion and appearance features via a cross-modal learning mechanism, resulting in more robust fused similarities; and 3) a learnable temporal attention model for trajectory appearance feature updates, which effectively aggregates historical visual features to improve the representational ability of trajectory appearance features, employing k-medoids for feature selection. Extensive experiments on the MOT17 and MOT20 datasets demonstrate that our method achieves state-of-the-art (SOTA) tracking performance.
{"title":"LV2DMOT: Language and Visual Multimodal Feature Learning for Multiobject Tracking","authors":"Ru Hong;Zeyu Cai;Jiming Yang;Feipeng Da","doi":"10.1109/JSEN.2024.3519903","DOIUrl":"https://doi.org/10.1109/JSEN.2024.3519903","url":null,"abstract":"Multiobject tracking (MOT) aims to associate objects of the same identity across video frames, with robust similarity measurement being crucial for maintaining tracking performance. However, the current inefficient integration of motion and appearance cues often leads to tracking failures in challenging scenarios, such as occlusions and missed detections. In this article, we introduce LV2DMOT, a tracker that employs a novel paradigm for integrating motion and appearance cues through language and visual multimodal feature learning, thereby generating more distinctive data association similarities. We propose three key techniques: 1) a text-matching task between tracking trajectories and candidate detections. This method uses text encoding of detection geometric information combined with a temporal model, Mamba, to extract temporal motion features of trajectories, enabling more accurate motion similarity calculations; 2) a multimodal, multilevel feature fusion model that integrates motion and appearance features via a cross-modal learning mechanism, resulting in more robust fused similarities; and 3) a learnable temporal attention model for trajectory appearance feature updates, which effectively aggregates historical visual features to improve the representational ability of trajectory appearance features, employing k-medoids for feature selection. Extensive experiments on the MOT17 and MOT20 datasets demonstrate that our method achieves state-of-the-art (SOTA) tracking performance.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 4","pages":"7482-7495"},"PeriodicalIF":4.3,"publicationDate":"2025-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143446281","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-07DOI: 10.1109/JSEN.2024.3521436
Peng Li;Cheng Chen;Youpeng Sun;Wenhui Wang
The Poisson multi-Bernoulli mixture (PMBM) filter has been proved to be effective in tracking missed objects and an unknown number of objects with clutter in complex scenarios. However, when the objects are closely spaced, spawning or maneuvering will affect the performance of traditional object prior-based partitioning methods. To solve this problem, this article proposes an expectation-maximization (EM) partitioning method for PMBM filtering of extended target tracking based on the improved Kolmogorov-Smirnov (KS) test. The initial partitioning and correlation of measurements are performed from the perspective of measurement distribution. Data association is further optimized by combining with the prior to obtain more reasonable associations and guide the generation of the spawning object Poisson point process (PPP). In addition, a method is proposed for dynamically generating the spawning object PPP to achieve accurate tracking of spawning objects. Simulation results show that the proposed method has good robustness in scenarios involving intersection, closely spaced objects, and derivation compared with the Gamma-Gaussian inverse Wishart (GGIW)-PMBM filter.
{"title":"Poisson Multi-Bernoulli Mixture Filter for Multiple Extended Object Tracking Using Kolmogorov–Smirnov Test","authors":"Peng Li;Cheng Chen;Youpeng Sun;Wenhui Wang","doi":"10.1109/JSEN.2024.3521436","DOIUrl":"https://doi.org/10.1109/JSEN.2024.3521436","url":null,"abstract":"The Poisson multi-Bernoulli mixture (PMBM) filter has been proved to be effective in tracking missed objects and an unknown number of objects with clutter in complex scenarios. However, when the objects are closely spaced, spawning or maneuvering will affect the performance of traditional object prior-based partitioning methods. To solve this problem, this article proposes an expectation-maximization (EM) partitioning method for PMBM filtering of extended target tracking based on the improved Kolmogorov-Smirnov (KS) test. The initial partitioning and correlation of measurements are performed from the perspective of measurement distribution. Data association is further optimized by combining with the prior to obtain more reasonable associations and guide the generation of the spawning object Poisson point process (PPP). In addition, a method is proposed for dynamically generating the spawning object PPP to achieve accurate tracking of spawning objects. Simulation results show that the proposed method has good robustness in scenarios involving intersection, closely spaced objects, and derivation compared with the Gamma-Gaussian inverse Wishart (GGIW)-PMBM filter.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 4","pages":"6541-6555"},"PeriodicalIF":4.3,"publicationDate":"2025-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143446217","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：