Pub Date : 2024-10-07DOI: 10.1109/JSEN.2024.3471830
Long Huang;Detao Lu;Wen Zeng;Qu Zhou
As the main monitoring method for the operation status and faults of gas-insulated equipment (GIE) equipment, SF6 decomposition gas analysis technology has been continuously upgraded, and the development of new gas-sensitive materials has always been one of the core breakthroughs. Herin, it is proposed the NiO and Ag2 O metal oxide-doped Janus tin sulfide selenide (SnSSe) (MOS-SnSSe) monolayers as prospective sensing materials for detection of four typical decomposition gases, namely, HF, SO2, SOF2, and SO2F2, to provide an option for upgrading the gas-sensitive units. The results suggest that the NiO and Ag2 O dopants are more inclined to stick to the S-surface of intrinsic SnSSe monolayer. Furthermore, the Mulliken analysis reveals the function of carrier-migration bridge of the NiO and Ag2 O dopants in the gas-sensing reaction that encourages the bidirectional migration of charges between the gas species and the SnSSe surface. The change in energy band confirms the adjusted electronic performances of MOS-SnSSe monolayers upon adsorbing gases, and the density of state redistribution in such four gas systems verifies the chemisorption of HF, SO2, SOF2, and SO2F2 on NiO-SnSSe monolayer, the chemisorption of HF and SO2 on Ag2 O-SnSSe monolayer, and the physisorption of SOF2 and SO2F2 on Ag2 O-SnSSe monolayer. Furthermore, our outcome demonstrates that the NiO-SnSSe monolayer is a feasible candidate for an SO2 resistive sensor operating at 458 K, while Ag2 O-SnSSe monolayer can serve as both a work-function-type sensor for such four gas SPICEs and a scavenger for SO2. These discoveries in this research expose the admirable gas sensitivity potentiality of the SnSSe-based system and the special attribute of the Janus community, which we anticipate could motivate more cutting-edge research in the industry of gas sensing.
{"title":"Favorable Adsorption and Detection Properties of Metal Oxides (NiO and Ag₂O) Modified Janus SnSSe Monolayer Toward SF₆ Decomposition Gases in a Gas-Insulated Equipment","authors":"Long Huang;Detao Lu;Wen Zeng;Qu Zhou","doi":"10.1109/JSEN.2024.3471830","DOIUrl":"https://doi.org/10.1109/JSEN.2024.3471830","url":null,"abstract":"As the main monitoring method for the operation status and faults of gas-insulated equipment (GIE) equipment, SF6 decomposition gas analysis technology has been continuously upgraded, and the development of new gas-sensitive materials has always been one of the core breakthroughs. Herin, it is proposed the NiO and Ag2 O metal oxide-doped Janus tin sulfide selenide (SnSSe) (MOS-SnSSe) monolayers as prospective sensing materials for detection of four typical decomposition gases, namely, HF, SO2, SOF2, and SO2F2, to provide an option for upgrading the gas-sensitive units. The results suggest that the NiO and Ag2 O dopants are more inclined to stick to the S-surface of intrinsic SnSSe monolayer. Furthermore, the Mulliken analysis reveals the function of carrier-migration bridge of the NiO and Ag2 O dopants in the gas-sensing reaction that encourages the bidirectional migration of charges between the gas species and the SnSSe surface. The change in energy band confirms the adjusted electronic performances of MOS-SnSSe monolayers upon adsorbing gases, and the density of state redistribution in such four gas systems verifies the chemisorption of HF, SO2, SOF2, and SO2F2 on NiO-SnSSe monolayer, the chemisorption of HF and SO2 on Ag2 O-SnSSe monolayer, and the physisorption of SOF2 and SO2F2 on Ag2 O-SnSSe monolayer. Furthermore, our outcome demonstrates that the NiO-SnSSe monolayer is a feasible candidate for an SO2 resistive sensor operating at 458 K, while Ag2 O-SnSSe monolayer can serve as both a work-function-type sensor for such four gas SPICEs and a scavenger for SO2. These discoveries in this research expose the admirable gas sensitivity potentiality of the SnSSe-based system and the special attribute of the Janus community, which we anticipate could motivate more cutting-edge research in the industry of gas sensing.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"24 22","pages":"37042-37052"},"PeriodicalIF":4.3,"publicationDate":"2024-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142636514","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 : 2024-10-07DOI: 10.1109/JSEN.2024.3468318
Linh Huynh Thi Thuy;Phu Nguyen Dang;Hung Cao;Anh H. Nguyen;Jung-Chih Chiao;Chun-Ping Jen;Loc Do Quang;Trinh Chu Duc;Tung Thanh Bui
Lacking effective treatment, white spot syndrome virus (WSSV) causes mass mortality in farmed shrimp, significantly damaging the shrimp industry worldwide. To enable early detection and diagnosis of this disease, we have demonstrated an electrochemical immunosensor to detect the VP28 antigen, a key envelope protein of this virus. First, gold nanoparticles (AuNPs) were electrodeposited on the sensor’s carbon surface to enhance both its sensitivity and its capability to form a self-assembled monolayer (SAM). Subsequently, an SAM using 11-Mercaptoundecanoic acid was coated as an intermediary layer for immobilizing VP28 antibodies. The specific interaction of VP28 antigen-antibody, confirmed by Western Blot, impeded electrons transfer, leading to an increase in charge transfer resistance. Cyclic voltammetry (CV) was utilized to deposit AuNPs and combined with electrochemical impedance spectroscopy (EIS) to characterize the modification of the sensor’s electrode surface through changes in electric current and impedance. The scanning electron microscope (SEM) results showed that nanosized gold particles were uniformly dispersed on the carbon electrode surface. Electric measurement results showed that the change in charge transfer resistance ( $Delta {R}_{text {CT}}$ ) correlates linearly with the VP28 protein concentration range from 0 to 60 ng/mL, with an ${R}^{,{2}}$ coefficient of 0.98945 and a low limit of detection (LOD) of 2.38 ng/mL. Additionally, the anode peak current decreased from 15.150 to $7.725~mu $ A with increasing antigen concentration. Control experiments with various proteins demonstrated the specificity of the proposed immunosensor for detecting VP28. This sensor offers an accessible electrochemical sensing tool, enabling convenient, direct, and immediate diagnosis of WSSV on shrimp farms.
{"title":"Electrodeposition of Gold Nanoparticles for a Highly Sensitive Immunosensor for White Spot Syndrome Virus (WSSV) Envelope Protein VP28 Detection Based on Electrochemical Impedance Spectroscopy Technique","authors":"Linh Huynh Thi Thuy;Phu Nguyen Dang;Hung Cao;Anh H. Nguyen;Jung-Chih Chiao;Chun-Ping Jen;Loc Do Quang;Trinh Chu Duc;Tung Thanh Bui","doi":"10.1109/JSEN.2024.3468318","DOIUrl":"https://doi.org/10.1109/JSEN.2024.3468318","url":null,"abstract":"Lacking effective treatment, white spot syndrome virus (WSSV) causes mass mortality in farmed shrimp, significantly damaging the shrimp industry worldwide. To enable early detection and diagnosis of this disease, we have demonstrated an electrochemical immunosensor to detect the VP28 antigen, a key envelope protein of this virus. First, gold nanoparticles (AuNPs) were electrodeposited on the sensor’s carbon surface to enhance both its sensitivity and its capability to form a self-assembled monolayer (SAM). Subsequently, an SAM using 11-Mercaptoundecanoic acid was coated as an intermediary layer for immobilizing VP28 antibodies. The specific interaction of VP28 antigen-antibody, confirmed by Western Blot, impeded electrons transfer, leading to an increase in charge transfer resistance. Cyclic voltammetry (CV) was utilized to deposit AuNPs and combined with electrochemical impedance spectroscopy (EIS) to characterize the modification of the sensor’s electrode surface through changes in electric current and impedance. The scanning electron microscope (SEM) results showed that nanosized gold particles were uniformly dispersed on the carbon electrode surface. Electric measurement results showed that the change in charge transfer resistance (\u0000<inline-formula> <tex-math>$Delta {R}_{text {CT}}$ </tex-math></inline-formula>\u0000) correlates linearly with the VP28 protein concentration range from 0 to 60 ng/mL, with an \u0000<inline-formula> <tex-math>${R}^{,{2}}$ </tex-math></inline-formula>\u0000 coefficient of 0.98945 and a low limit of detection (LOD) of 2.38 ng/mL. Additionally, the anode peak current decreased from 15.150 to \u0000<inline-formula> <tex-math>$7.725~mu $ </tex-math></inline-formula>\u0000A with increasing antigen concentration. Control experiments with various proteins demonstrated the specificity of the proposed immunosensor for detecting VP28. This sensor offers an accessible electrochemical sensing tool, enabling convenient, direct, and immediate diagnosis of WSSV on shrimp farms.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"24 22","pages":"36350-36357"},"PeriodicalIF":4.3,"publicationDate":"2024-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142636550","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 : 2024-10-07DOI: 10.1109/JSEN.2024.3471675
Gaige Chen;Xiaoyu Hao;Jun Huang;Hongbo Ma;Xianzhi Wang;Xianguang Kong
An insulated-gate bipolar transistor (IGBT) has multiple degradation mechanisms; it is a challenge to accurately integrating multiple signals to capture the device’s degradation patterns and health state. Therefore, comprehensively characterizing the health state of IGBT and predicting its remaining useful life (RUL) using multiple signals poses a significant challenge. To address this challenge, a RUL prediction method for IGBT based on the deep fusion of nonlinear features from multisource data is proposed. First, the time-domain multifeatures of IGBT degradation data are constructed, and key features are selectively selected; then, dimensionality reduction is performed and these features are fused into health indicators (HIs) to characterize the health level. Second, the health of IGBT is effectively evaluated by unsupervised clustering without data labeling. Third, end-condition monitoring is refined to enable the identification of near-failure state. Finally, deep learning is utilized to provide the accurate and reliable prediction of the RUL of IGBT devices [ ${R}^{{2}}$ are all greater than 0.98, the mean absolute error (MAE) all less than 2.3, and the root mean square error (RMSE) all less than 5.5.]. The results demonstrate that the method effectively integrates multisource information, characterizes the health state of the device, and can more accurately and reliably predict the RUL of IGBT. The proposed method can enhance the scientific basis for the health management of new energy systems such as wind power and photovoltaic systems.
{"title":"A Remaining Useful Life Prediction Method for Insulated-Gate Bipolar Transistor Based on Deep Fusion of Nonlinear Features From Multisource Data","authors":"Gaige Chen;Xiaoyu Hao;Jun Huang;Hongbo Ma;Xianzhi Wang;Xianguang Kong","doi":"10.1109/JSEN.2024.3471675","DOIUrl":"https://doi.org/10.1109/JSEN.2024.3471675","url":null,"abstract":"An insulated-gate bipolar transistor (IGBT) has multiple degradation mechanisms; it is a challenge to accurately integrating multiple signals to capture the device’s degradation patterns and health state. Therefore, comprehensively characterizing the health state of IGBT and predicting its remaining useful life (RUL) using multiple signals poses a significant challenge. To address this challenge, a RUL prediction method for IGBT based on the deep fusion of nonlinear features from multisource data is proposed. First, the time-domain multifeatures of IGBT degradation data are constructed, and key features are selectively selected; then, dimensionality reduction is performed and these features are fused into health indicators (HIs) to characterize the health level. Second, the health of IGBT is effectively evaluated by unsupervised clustering without data labeling. Third, end-condition monitoring is refined to enable the identification of near-failure state. Finally, deep learning is utilized to provide the accurate and reliable prediction of the RUL of IGBT devices [\u0000<inline-formula> <tex-math>${R}^{{2}}$ </tex-math></inline-formula>\u0000 are all greater than 0.98, the mean absolute error (MAE) all less than 2.3, and the root mean square error (RMSE) all less than 5.5.]. The results demonstrate that the method effectively integrates multisource information, characterizes the health state of the device, and can more accurately and reliably predict the RUL of IGBT. The proposed method can enhance the scientific basis for the health management of new energy systems such as wind power and photovoltaic systems.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"24 22","pages":"37531-37543"},"PeriodicalIF":4.3,"publicationDate":"2024-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142645498","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}
This study introduces the design and implementation of an innovative imaging polarized skylight compass (PSC) based on mechanical rotating components. The device employs a stepper motor to drive the polarizer, facilitating rapid and uniform rotation in front of the camera lens for efficient polarization imaging. The mechanical rotating components are fabricated using 3-D printing technology, which enhances its engineering application capabilities. To mitigate angle errors arising from the rotating mechanism, we propose an optimal robust measurement method based on Newton–Gauss iterations. The method utilizes the intensity images from four distinct polarization directions in consecutive compass output frames to estimate the rotation angle and skylight polarization information. Even in the presence of notable errors in the detected rotation angle, the calculated values of rotation angle, polarization angle, and polarization degree reliably converge to their actual values. To assess the performance of the compass, measurements of actual skylight polarization patterns were conducted and compared with polarizing camera measurements and the predictions based on single-scattering Rayleigh theory. The results affirm the compass’s high sensitivity to detecting polarized skylight. Shipborne experiments under optimal weather conditions illustrate that the compass attains a root mean square error (RMSE) of 3.0269° while independently orienting the dynamic heading angle, with an output frequency of 2 Hz. In practical applications, the compass proves capable of providing relatively accurate heading angle information, thus contributing to the advancement of mechanical rotating polarized imaging sensors in the field of polarized navigation.
{"title":"Design of an Imaging Polarized Skylight Compass Using Mechanical Rotation","authors":"Fang Kong;Xiaojing Fan;Xiaohan Guo;Xiaohui Deng;Xiaolong Chen;Yinjing Guo","doi":"10.1109/JSEN.2024.3468020","DOIUrl":"https://doi.org/10.1109/JSEN.2024.3468020","url":null,"abstract":"This study introduces the design and implementation of an innovative imaging polarized skylight compass (PSC) based on mechanical rotating components. The device employs a stepper motor to drive the polarizer, facilitating rapid and uniform rotation in front of the camera lens for efficient polarization imaging. The mechanical rotating components are fabricated using 3-D printing technology, which enhances its engineering application capabilities. To mitigate angle errors arising from the rotating mechanism, we propose an optimal robust measurement method based on Newton–Gauss iterations. The method utilizes the intensity images from four distinct polarization directions in consecutive compass output frames to estimate the rotation angle and skylight polarization information. Even in the presence of notable errors in the detected rotation angle, the calculated values of rotation angle, polarization angle, and polarization degree reliably converge to their actual values. To assess the performance of the compass, measurements of actual skylight polarization patterns were conducted and compared with polarizing camera measurements and the predictions based on single-scattering Rayleigh theory. The results affirm the compass’s high sensitivity to detecting polarized skylight. Shipborne experiments under optimal weather conditions illustrate that the compass attains a root mean square error (RMSE) of 3.0269° while independently orienting the dynamic heading angle, with an output frequency of 2 Hz. In practical applications, the compass proves capable of providing relatively accurate heading angle information, thus contributing to the advancement of mechanical rotating polarized imaging sensors in the field of polarized navigation.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"24 22","pages":"37809-37821"},"PeriodicalIF":4.3,"publicationDate":"2024-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142636319","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 : 2024-10-07DOI: 10.1109/JSEN.2024.3470995
Yuan Yuan;Tianyao Zhang;Zhaohui Zhang;Xiaoyan Zhao;Xianhao Wu;Shaowen Zheng;Liang Liang;Can Cao
By enhancing light-matter interaction, terahertz (THz) metasurface can significantly improve the performance of THz spectroscopic sensing. Despite their theoretical promise, a robust and practical characterization method for THz metasurface remains urgently needed. This article presents a novel characterization approach for THz metasurface that is resilient to environmental water vapor, enabling ultrahigh frequency selectivity and polarization sensitivity. The performance of the proposed method is demonstrated using a series of lithography-fabricated split-ring metasurface, theoretically designed to be evenly separated over the 0.6–1.0 THz range. A continuous wave THz frequency-domain spectroscopy system was employed for experimental characterization. Following sophisticated raw photocurrent data processing, ultrahigh frequency resolution (0.05 GHz) spectral characterization was achieved within the frequency range of 0.05 to 1.4 THz. The measured data exhibit linear correlation with the theoretical simulation results, and deviations of the resonance frequencies are less than 0.02 THz. By presenting the characterization results with and without water vapor exhibited in the THz pathway, we demonstrate the robustness of the proposed method in the ambient environment. Furthermore, we incorporated a sample rotating frame into the THz optical path to achieve polarization-sensitive measurements. As the era of 6G integrated sensing and communication approaches, our research significantly advances the practicality of metasurface enhanced THz sensing.
{"title":"Robust Characterization of Terahertz Metasurface Sensor With Ultrahigh Frequency Selectivity and Polarization Sensitivity","authors":"Yuan Yuan;Tianyao Zhang;Zhaohui Zhang;Xiaoyan Zhao;Xianhao Wu;Shaowen Zheng;Liang Liang;Can Cao","doi":"10.1109/JSEN.2024.3470995","DOIUrl":"https://doi.org/10.1109/JSEN.2024.3470995","url":null,"abstract":"By enhancing light-matter interaction, terahertz (THz) metasurface can significantly improve the performance of THz spectroscopic sensing. Despite their theoretical promise, a robust and practical characterization method for THz metasurface remains urgently needed. This article presents a novel characterization approach for THz metasurface that is resilient to environmental water vapor, enabling ultrahigh frequency selectivity and polarization sensitivity. The performance of the proposed method is demonstrated using a series of lithography-fabricated split-ring metasurface, theoretically designed to be evenly separated over the 0.6–1.0 THz range. A continuous wave THz frequency-domain spectroscopy system was employed for experimental characterization. Following sophisticated raw photocurrent data processing, ultrahigh frequency resolution (0.05 GHz) spectral characterization was achieved within the frequency range of 0.05 to 1.4 THz. The measured data exhibit linear correlation with the theoretical simulation results, and deviations of the resonance frequencies are less than 0.02 THz. By presenting the characterization results with and without water vapor exhibited in the THz pathway, we demonstrate the robustness of the proposed method in the ambient environment. Furthermore, we incorporated a sample rotating frame into the THz optical path to achieve polarization-sensitive measurements. As the era of 6G integrated sensing and communication approaches, our research significantly advances the practicality of metasurface enhanced THz sensing.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"24 22","pages":"36706-36713"},"PeriodicalIF":4.3,"publicationDate":"2024-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142636302","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 : 2024-10-07DOI: 10.1109/JSEN.2024.3452114
Chu Wu;Xu Li;Dong Kong;Yue Hu;Peizhou Ni
Due to the homogeneity of the ground in outdoor scenes, i.e., self-similar textures, it is prone to cause inaccurate or even incorrect match of ground features. This mismatch inevitably introduces additional errors when calculating reprojection function, which in turn degrades the accuracy of simultaneous localization and mapping (SLAM). In this article, we propose a ground-aware decoupled optimized SLAM, called GDO-SLAM, which is essentially a pruning semantics-guided SLAM where a custom ground decoupling optimization module is introduced in the tracking and local mapping threads based on ORB-SLAM2. Essentially, the optimization module is a decoupling constraint that adds the weights of vertical observations of ground features and reduces the weights of horizontal observations in the reprojection error function. Specifically, we design a novel ground segmentation network that achieves an optimal balance between accuracy and real-time performance, and verify its ground category IoU of 98.6% on the urban landscape dataset. Extensive experiments on both the public KITTI dataset and our self-collected dataset demonstrate that our proposed ground-aware decoupling optimized SLAM (GDO-SLAM) outperforms the representative baseline ORB-SLAM2 in terms of translation and rotation accuracy by 7.5% and 8.3%, respectively.
{"title":"GDO-SLAM: Visual-Based Ground-Aware Decoupling Optimized SLAM for UGV in Outdoor Environments","authors":"Chu Wu;Xu Li;Dong Kong;Yue Hu;Peizhou Ni","doi":"10.1109/JSEN.2024.3452114","DOIUrl":"https://doi.org/10.1109/JSEN.2024.3452114","url":null,"abstract":"Due to the homogeneity of the ground in outdoor scenes, i.e., self-similar textures, it is prone to cause inaccurate or even incorrect match of ground features. This mismatch inevitably introduces additional errors when calculating reprojection function, which in turn degrades the accuracy of simultaneous localization and mapping (SLAM). In this article, we propose a ground-aware decoupled optimized SLAM, called GDO-SLAM, which is essentially a pruning semantics-guided SLAM where a custom ground decoupling optimization module is introduced in the tracking and local mapping threads based on ORB-SLAM2. Essentially, the optimization module is a decoupling constraint that adds the weights of vertical observations of ground features and reduces the weights of horizontal observations in the reprojection error function. Specifically, we design a novel ground segmentation network that achieves an optimal balance between accuracy and real-time performance, and verify its ground category IoU of 98.6% on the urban landscape dataset. Extensive experiments on both the public KITTI dataset and our self-collected dataset demonstrate that our proposed ground-aware decoupling optimized SLAM (GDO-SLAM) outperforms the representative baseline ORB-SLAM2 in terms of translation and rotation accuracy by 7.5% and 8.3%, respectively.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"24 22","pages":"37218-37228"},"PeriodicalIF":4.3,"publicationDate":"2024-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142636229","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}
To improve the driving performance of vehicles, it is of great significance to study the changes in the driver’s brain cognition during driving and to establish an intelligent driving computational framework based on the cognitive process. Electroencephalogram (EEG) is an effective means to study driver cognition because of its low cost, high temporal resolution, and different cognitive state information. The application of brain-computer interface (BCI) technology based on EEG signals to driver assistance systems has the potential to transform the way humans interact with vehicles. It can also help intelligent vehicles to understand and predict driver’s behavior and to enhance the cognitive ability of vehicles. This article reviews the research on theorizing and modeling driver cognitive processes based on cognitive architectures (e.g., adaptive control for thoughtful rationality (ACT-R), queuing network (QN), and Soar) and proposes an EEG-based driver cognitive architecture. Then, according to the relationship between the modules of this proposed driver cognitive architecture, the driver’s perception of stationary and hazardous scenarios in the driving environment, the understanding of the driver’s intention to control the longitudinal and lateral movements of the vehicle, and the influence of driver’s working memory as well as human factors, such as fatigue, distraction, and emotion on driving performance based on EEG signals, are reviewed. The integration of EEG signals with cognitive modeling has the potential to improve the accuracy of driver perception, intention, and cognitive state prediction, thereby enhancing vehicle safety.
{"title":"Driver Cognitive Architecture Based on EEG Signals: A Review","authors":"Peiwen Mi;Lirong Yan;Yu Cheng;Yan Liu;Jun Wang;Muhammad Usman Shoukat;Fuwu Yan;Guofeng Qin;Peng Han;Yikang Zhai","doi":"10.1109/JSEN.2024.3471699","DOIUrl":"https://doi.org/10.1109/JSEN.2024.3471699","url":null,"abstract":"To improve the driving performance of vehicles, it is of great significance to study the changes in the driver’s brain cognition during driving and to establish an intelligent driving computational framework based on the cognitive process. Electroencephalogram (EEG) is an effective means to study driver cognition because of its low cost, high temporal resolution, and different cognitive state information. The application of brain-computer interface (BCI) technology based on EEG signals to driver assistance systems has the potential to transform the way humans interact with vehicles. It can also help intelligent vehicles to understand and predict driver’s behavior and to enhance the cognitive ability of vehicles. This article reviews the research on theorizing and modeling driver cognitive processes based on cognitive architectures (e.g., adaptive control for thoughtful rationality (ACT-R), queuing network (QN), and Soar) and proposes an EEG-based driver cognitive architecture. Then, according to the relationship between the modules of this proposed driver cognitive architecture, the driver’s perception of stationary and hazardous scenarios in the driving environment, the understanding of the driver’s intention to control the longitudinal and lateral movements of the vehicle, and the influence of driver’s working memory as well as human factors, such as fatigue, distraction, and emotion on driving performance based on EEG signals, are reviewed. The integration of EEG signals with cognitive modeling has the potential to improve the accuracy of driver perception, intention, and cognitive state prediction, thereby enhancing vehicle safety.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"24 22","pages":"36261-36286"},"PeriodicalIF":4.3,"publicationDate":"2024-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142636286","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}
A successful automatic control was developed using Arduino and ESP32 microcontrollers to operate a low-cost prototype bioreactor, which resulted in the effective production of biobutanol. The system maintained a temperature of 30.70 °C, an average pH of 6.61, and effectively reduced oxygen levels to 0% v/v within 130 s. The highest cell concentration was $1.26 times 10^{{8}}$ CFU/mL, and GC-FID analysis showed acetone, butanol, and ethanol (ABE) concentrations of 0.3925, 0.05304, and 1.1184 g/L, respectively. In addition, a sample collector was designed to collect samples with a precision of $10 ; pm ; 1$ mL and a time deviation of $2 ; pm ; 13$ s. In terms of the cloud system, NETPIE was used for device management. It can effectively display real-time conditions and control equipment as required. Google Sheets collected 43 200 data points for each parameter for data analysis and observation. Moreover, the Line application was applied for message notification when a sample was collected. The cost of the automated prototype bioreactor was U.S. ${$}$ 1093.61 for a 10-L production volume, and the automated sample was U.S. ${$}$ 210.99 for 24 tubes, with a maximum volume capacity of 15 mL. Therefore, both the automated bioreactor and the sample collector system were effective for control and monitoring, contributing to improve the biobutanol production process.
{"title":"Effective IoT-Based Arduino Design for Automated Bioreactor Control and Sample Collection in Biobutanol Production","authors":"Eakkachai Klaithin;Vissavakawn Matimapa-Kay;Wachira Daosud;Yanisa Laoonguthai","doi":"10.1109/JSEN.2024.3469273","DOIUrl":"https://doi.org/10.1109/JSEN.2024.3469273","url":null,"abstract":"A successful automatic control was developed using Arduino and ESP32 microcontrollers to operate a low-cost prototype bioreactor, which resulted in the effective production of biobutanol. The system maintained a temperature of 30.70 °C, an average pH of 6.61, and effectively reduced oxygen levels to 0% v/v within 130 s. The highest cell concentration was \u0000<inline-formula> <tex-math>$1.26 times 10^{{8}}$ </tex-math></inline-formula>\u0000 CFU/mL, and GC-FID analysis showed acetone, butanol, and ethanol (ABE) concentrations of 0.3925, 0.05304, and 1.1184 g/L, respectively. In addition, a sample collector was designed to collect samples with a precision of \u0000<inline-formula> <tex-math>$10 ; pm ; 1$ </tex-math></inline-formula>\u0000 mL and a time deviation of \u0000<inline-formula> <tex-math>$2 ; pm ; 13$ </tex-math></inline-formula>\u0000 s. In terms of the cloud system, NETPIE was used for device management. It can effectively display real-time conditions and control equipment as required. Google Sheets collected 43 200 data points for each parameter for data analysis and observation. Moreover, the Line application was applied for message notification when a sample was collected. The cost of the automated prototype bioreactor was U.S. \u0000<inline-formula> <tex-math>${$}$ </tex-math></inline-formula>\u00001093.61 for a 10-L production volume, and the automated sample was U.S. \u0000<inline-formula> <tex-math>${$}$ </tex-math></inline-formula>\u0000210.99 for 24 tubes, with a maximum volume capacity of 15 mL. Therefore, both the automated bioreactor and the sample collector system were effective for control and monitoring, contributing to improve the biobutanol production process.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"24 22","pages":"37997-38004"},"PeriodicalIF":4.3,"publicationDate":"2024-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142636462","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}
Three-dimensional millimeter-wave (MMW) low-resolution (LR) images commonly exhibit issues including noise, nonuniform sampling, and blocky sparsity. These characteristics lead to significant source domain inadaptation, making the straightforward adaptation of upsampling networks for 3-D MMW high-resolution (HR) reconstruction less effectiveness. To circumvent this issue, a preprocessing method based on image segmentation is proposed designed for MMW images, which consists of two stages: image expansion and image segmentation. Initially, a conventional clustering algorithm separates the primary foreground image from the raw data. Following this, the first stage involves a 3-D expansion of the foreground image to preserve the structural integrity of the object under the test to the highest degree feasible, thereby generating an initial LR image. Nevertheless, these LR images do not yet meet the prerequisites for the upsampling network. Therefore, a uniform sparse resampling is conducted in the subsequent image-segmentation stage. In this second stage, standard MMW LR images are constructed from their standard MMW HR dataset using the farthest point sampling technique. The similarity between the initial LR images and these standard LR images is computed to create a ground-truth foreground image. PointNet++ is utilized to learn the structural characteristics of the human foreground and further refines the preprocessed LR images through resampling and background separation. These segmented LR images are then fed into the upsampling network, culminating in the reconstruction of 3-D MMW HR images. In the experimental comparison, PUGeo-Net serves as the baseline model. Compared against cluster-based MSGD K-Means and FCM, the proposed method shows improvements of 63%, 59%, and 60% in reconstruction metrics of Chamfer distance (CD), Hausdorff distance (HD), and Jensen-Shannon divergence (JSD), respectively, thus confirming its efficacy.
三维毫米波（MMW）低分辨率（LR）图像通常表现出噪音、不均匀采样和块状稀疏等问题。这些特征会导致严重的源域不适应，使直接适应三维毫米波高分辨率（HR）重建的上采样网络变得不那么有效。为避免这一问题，我们提出了一种基于图像分割的预处理方法，该方法专为 MMW 图像设计，包括两个阶段：图像扩展和图像分割。首先，采用传统的聚类算法从原始数据中分离出主要前景图像。随后，第一阶段是对前景图像进行三维扩展，以最大限度地保持被测物体的结构完整性，从而生成初始 LR 图像。然而，这些 LR 图像还不符合上采样网络的先决条件。因此，在随后的图像分割阶段要进行均匀的稀疏重采样。在第二阶段，使用最远点采样技术从标准 MMW HR 数据集构建标准 MMW LR 图像。计算初始 LR 图像与这些标准 LR 图像之间的相似度，以创建地面实况前景图像。利用 PointNet++ 学习人类前景的结构特征，并通过重采样和背景分离进一步完善预处理的 LR 图像。然后将这些分割后的 LR 图像输入上采样网络，最终重建出三维 MMW HR 图像。在实验比较中，PUGeo-Net 作为基准模型。与基于聚类的 MSGD K-Means 和 FCM 相比，所提出的方法在重建指标 Chamfer distance (CD)、Hausdorff distance (HD) 和 Jensen-Shannon divergence (JSD) 上分别提高了 63%、59% 和 60%，从而证实了其有效性。
{"title":"Segmentation-Aided Upsampling for High-Resolution Reconstruction of 3-D MMW Images","authors":"Qingtao Wang;Tao Wang;Zhaohui Bu;Mengting Cui;Haipo Cui;Li Ding","doi":"10.1109/JSEN.2024.3471650","DOIUrl":"https://doi.org/10.1109/JSEN.2024.3471650","url":null,"abstract":"Three-dimensional millimeter-wave (MMW) low-resolution (LR) images commonly exhibit issues including noise, nonuniform sampling, and blocky sparsity. These characteristics lead to significant source domain inadaptation, making the straightforward adaptation of upsampling networks for 3-D MMW high-resolution (HR) reconstruction less effectiveness. To circumvent this issue, a preprocessing method based on image segmentation is proposed designed for MMW images, which consists of two stages: image expansion and image segmentation. Initially, a conventional clustering algorithm separates the primary foreground image from the raw data. Following this, the first stage involves a 3-D expansion of the foreground image to preserve the structural integrity of the object under the test to the highest degree feasible, thereby generating an initial LR image. Nevertheless, these LR images do not yet meet the prerequisites for the upsampling network. Therefore, a uniform sparse resampling is conducted in the subsequent image-segmentation stage. In this second stage, standard MMW LR images are constructed from their standard MMW HR dataset using the farthest point sampling technique. The similarity between the initial LR images and these standard LR images is computed to create a ground-truth foreground image. PointNet++ is utilized to learn the structural characteristics of the human foreground and further refines the preprocessed LR images through resampling and background separation. These segmented LR images are then fed into the upsampling network, culminating in the reconstruction of 3-D MMW HR images. In the experimental comparison, PUGeo-Net serves as the baseline model. Compared against cluster-based MSGD K-Means and FCM, the proposed method shows improvements of 63%, 59%, and 60% in reconstruction metrics of Chamfer distance (CD), Hausdorff distance (HD), and Jensen-Shannon divergence (JSD), respectively, thus confirming its efficacy.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"24 22","pages":"37524-37530"},"PeriodicalIF":4.3,"publicationDate":"2024-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142645446","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 : 2024-10-07DOI: 10.1109/JSEN.2024.3471894
Guoqian Jiang;Kunyu Wang;Qun He;Ping Xie
In light of the growing population of individuals with limb disorders, there is an increasing need to address the challenges they face in their daily lives. Existing rehabilitation technologies, often relying on single physiological signals and plagued by poor signal quality, have limitations in their effectiveness. To overcome these constraints, we present E2FNet, a multimodal physiological information fusion network designed for motor intent recognition in individuals with limb disorders. This study involved eight healthy participants who recorded electromyography (EMG) and electroencephalography (EEG) signals during various hand movements. E2FNet utilizes a multiscale convolutional neural network to extract features from EEG and EMG data, focusing on information fusion across different scales. We also introduce a cross-attention mechanism to capture cross-modal information interactions, enhancing EEG and EMG information fusion. Through extensive experiments, E2FNet achieved an impressive 92.08% classification accuracy, and the effectiveness of each module has been verified. Multiscale separable convolution and cross-attention significantly improved EEG and EMG signal fusion, enhancing accuracy and robustness in motion intent recognition. This research promises to enhance the quality of life and independence of individuals with movement disorders, while also advancing the field of rehabilitation robotics and assistive technology.
{"title":"E2FNet: An EEG- and EMG-Based Fusion Network for Hand Motion Intention Recognition","authors":"Guoqian Jiang;Kunyu Wang;Qun He;Ping Xie","doi":"10.1109/JSEN.2024.3471894","DOIUrl":"https://doi.org/10.1109/JSEN.2024.3471894","url":null,"abstract":"In light of the growing population of individuals with limb disorders, there is an increasing need to address the challenges they face in their daily lives. Existing rehabilitation technologies, often relying on single physiological signals and plagued by poor signal quality, have limitations in their effectiveness. To overcome these constraints, we present E2FNet, a multimodal physiological information fusion network designed for motor intent recognition in individuals with limb disorders. This study involved eight healthy participants who recorded electromyography (EMG) and electroencephalography (EEG) signals during various hand movements. E2FNet utilizes a multiscale convolutional neural network to extract features from EEG and EMG data, focusing on information fusion across different scales. We also introduce a cross-attention mechanism to capture cross-modal information interactions, enhancing EEG and EMG information fusion. Through extensive experiments, E2FNet achieved an impressive 92.08% classification accuracy, and the effectiveness of each module has been verified. Multiscale separable convolution and cross-attention significantly improved EEG and EMG signal fusion, enhancing accuracy and robustness in motion intent recognition. This research promises to enhance the quality of life and independence of individuals with movement disorders, while also advancing the field of rehabilitation robotics and assistive technology.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"24 22","pages":"38417-38428"},"PeriodicalIF":4.3,"publicationDate":"2024-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142645460","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}
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