Pub Date : 2024-10-09DOI: 10.1109/JSEN.2024.3472065
Tingpei Huang;Haotian Wang;Rongyu Gao;Jianhang Liu;Shibao Li
In gesture recognition based on millimeter-wave radar, generating spectrograms is typically independent of the actual application and designed separately. In this case, the task is simply decoupled, resulting in the generated spectrograms from radar signals not being optimally suited for the recognition task. Additionally, the emergence of gesture categories representing new semantics requires the recollection of a large amount of high-quality labeled data and retraining of the model. To address these problems, we propose a radar-based category-scalable gesture recognition framework, R-CSGR, for gesture spectrogram generation and two-stage gesture recognition. Considering the noise and environmental factors, only gesture-related signals are extracted and aggregated in the Doppler and angle dimensions to form a location-independent, information-dense gesture spectrogram for the two-stage recognition. In the first stage, the reconstruction of spectrogram for the original categories is used as a self-supervised learning task to utilize low-cost unlabeled data. In the second stage, the classification layer based on the cosine nearest-centroid method is used to quickly recognize new gesture categories whereas maintaining the recognition capability of the original categories. The result shows that with the introduction of five new gesture categories and only eight shots per category in the support set, an average recognition accuracy of 96.88% is achieved for all nine gesture categories.
{"title":"A Category-Scalable Framework Using Millimeter-Wave Radar for Spectrogram Generation and Gesture Recognition","authors":"Tingpei Huang;Haotian Wang;Rongyu Gao;Jianhang Liu;Shibao Li","doi":"10.1109/JSEN.2024.3472065","DOIUrl":"https://doi.org/10.1109/JSEN.2024.3472065","url":null,"abstract":"In gesture recognition based on millimeter-wave radar, generating spectrograms is typically independent of the actual application and designed separately. In this case, the task is simply decoupled, resulting in the generated spectrograms from radar signals not being optimally suited for the recognition task. Additionally, the emergence of gesture categories representing new semantics requires the recollection of a large amount of high-quality labeled data and retraining of the model. To address these problems, we propose a radar-based category-scalable gesture recognition framework, R-CSGR, for gesture spectrogram generation and two-stage gesture recognition. Considering the noise and environmental factors, only gesture-related signals are extracted and aggregated in the Doppler and angle dimensions to form a location-independent, information-dense gesture spectrogram for the two-stage recognition. In the first stage, the reconstruction of spectrogram for the original categories is used as a self-supervised learning task to utilize low-cost unlabeled data. In the second stage, the classification layer based on the cosine nearest-centroid method is used to quickly recognize new gesture categories whereas maintaining the recognition capability of the original categories. The result shows that with the introduction of five new gesture categories and only eight shots per category in the support set, an average recognition accuracy of 96.88% is achieved for all nine gesture categories.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"24 22","pages":"38479-38491"},"PeriodicalIF":4.3,"publicationDate":"2024-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142645523","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 : 2024-10-09DOI: 10.1109/JSEN.2024.3472891
Qi Feng;Hongfei Zhang;Cheng Chen;Hui Wang;Jian Wang
Charge-coupled device (CCD) sensors are the primary imaging sensors in scientific astronomical cameras, known for their exceptional channel uniformity. However, the unavoidable baseline drift during alternating current (ac) coupling leads to inconsistencies among pixels in the row direction, especially during high-speed readouts. Standard corrections are insufficient, posing challenges to astronomical observations. The digital correlated double sampling (DCDS) technology enables digital signal processing (DSP) for CCD readout. A real-time digital baseline restoration method was developed and implemented in a field-programmable gate array (FPGA). This method overcomes traditional limitations and can eliminate baseline drift error effectively. Furthermore, the performance of this method in terms of readout noise, channel gain, and nonlinearity was tested, confirming its potential for compensating for baseline drift error in scientific applications.
{"title":"Real-Time Digital Baseline Restoration for CCD Sensors With Implementation in FPGA","authors":"Qi Feng;Hongfei Zhang;Cheng Chen;Hui Wang;Jian Wang","doi":"10.1109/JSEN.2024.3472891","DOIUrl":"https://doi.org/10.1109/JSEN.2024.3472891","url":null,"abstract":"Charge-coupled device (CCD) sensors are the primary imaging sensors in scientific astronomical cameras, known for their exceptional channel uniformity. However, the unavoidable baseline drift during alternating current (ac) coupling leads to inconsistencies among pixels in the row direction, especially during high-speed readouts. Standard corrections are insufficient, posing challenges to astronomical observations. The digital correlated double sampling (DCDS) technology enables digital signal processing (DSP) for CCD readout. A real-time digital baseline restoration method was developed and implemented in a field-programmable gate array (FPGA). This method overcomes traditional limitations and can eliminate baseline drift error effectively. Furthermore, the performance of this method in terms of readout noise, channel gain, and nonlinearity was tested, confirming its potential for compensating for baseline drift error in scientific applications.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"24 22","pages":"37104-37113"},"PeriodicalIF":4.3,"publicationDate":"2024-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142636401","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}
Reliable task execution of wheeled platform requires high perceptive ability in terrains. Currently, vision perception is susceptible to external factors such as lighting conditions and air particles, and vibration perception reflects no surface features of terrains. In this article, we propose a novel system geared toward terrain classification based on tactile perception, well addressing those shortcomings. We develop a type of capacitive flexible tactile sensors array for 3-D forces with a wide measuring range, high sensitivity, considerable adaptability, and strong durability. To fully exploit the terrain features of the collected data, we propose a characterization method that encodes tactile information as image flow encompassing spatiotemporal information and establish a novel tactile-based terrain classification dataset. We construct the image flow as special tokens and feed them to a multihead spatiotemporal attention network, with spatial and temporal heads evenly constructed, to ultimately realize terrain classification. Our network achieves an accuracy of 91.9%, demonstrating the superiority over existing algorithms. Accuracies achieved are 81.3% and 76.3%, respectively, with 8-kg burden and at triple speed. Moreover, the performance degradation caused by increasing speed can be alleviated by decreasing time steps.
{"title":"Terrain Classification Based on Spatiotemporal Multihead Attention With Flexible Tactile Sensors Array","authors":"Tong Li;Chengshun Yu;Yuhang Yan;Xudong Zheng;Minghui Yin;Gang Chen;Yifan Wang;Jing An;Qizheng Feng;Ning Xue","doi":"10.1109/JSEN.2024.3472789","DOIUrl":"https://doi.org/10.1109/JSEN.2024.3472789","url":null,"abstract":"Reliable task execution of wheeled platform requires high perceptive ability in terrains. Currently, vision perception is susceptible to external factors such as lighting conditions and air particles, and vibration perception reflects no surface features of terrains. In this article, we propose a novel system geared toward terrain classification based on tactile perception, well addressing those shortcomings. We develop a type of capacitive flexible tactile sensors array for 3-D forces with a wide measuring range, high sensitivity, considerable adaptability, and strong durability. To fully exploit the terrain features of the collected data, we propose a characterization method that encodes tactile information as image flow encompassing spatiotemporal information and establish a novel tactile-based terrain classification dataset. We construct the image flow as special tokens and feed them to a multihead spatiotemporal attention network, with spatial and temporal heads evenly constructed, to ultimately realize terrain classification. Our network achieves an accuracy of 91.9%, demonstrating the superiority over existing algorithms. Accuracies achieved are 81.3% and 76.3%, respectively, with 8-kg burden and at triple speed. Moreover, the performance degradation caused by increasing speed can be alleviated by decreasing time steps.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"24 22","pages":"38507-38517"},"PeriodicalIF":4.3,"publicationDate":"2024-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142645432","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 : 2024-10-08DOI: 10.1109/JSEN.2024.3472281
S. Maity;S. K. Paul;S. Maur;B. Chakraborty;A. K. Pradhan;S. Dalai;B. Chatterjee;S. Chatterjee
This article proposes a methodology to estimate the aging state of the Nomex-based dry-type nanocomposite (NDNC) insulation. For this purpose, seven types of NDNC insulation samples are prepared in the laboratory for experimental investigation. The test samples are undergone through accelerated thermal aging at 145 °C for 600 h in steps of 100 h. The dielectric spectroscopy measurements are conducted on the NDNC-type insulation samples at each aging stages. Three aging sensitive parameters [i.e., transfer function pole (TFP), detrapped charge, and hopping conductivity] are extracted from the experimental results obtained by dielectric spectroscopy [i.e., polarization and depolarization current (PDC) and complex permittivity]. From the experimental results, it is observed that these three parameters are altered with the duration of the thermal aging. This fact signifies that these parameters can be utilized as aging sensitive markers. Therefore, using these three aging sensitive parameters, three empirical relationships are derived to correlate the thermal aging status of the NDNC insulation. Finally, the derived relationships are validated through different sets of test samples having known aging status. Hence, this experimental investigation reveals that the proposed technique can suitably assess the thermal aging state of the NDNC insulation.
{"title":"Sensing the Aging State of Nomex-Based Nanocomposite Insulation by Dielectric Spectroscopy","authors":"S. Maity;S. K. Paul;S. Maur;B. Chakraborty;A. K. Pradhan;S. Dalai;B. Chatterjee;S. Chatterjee","doi":"10.1109/JSEN.2024.3472281","DOIUrl":"https://doi.org/10.1109/JSEN.2024.3472281","url":null,"abstract":"This article proposes a methodology to estimate the aging state of the Nomex-based dry-type nanocomposite (NDNC) insulation. For this purpose, seven types of NDNC insulation samples are prepared in the laboratory for experimental investigation. The test samples are undergone through accelerated thermal aging at 145 °C for 600 h in steps of 100 h. The dielectric spectroscopy measurements are conducted on the NDNC-type insulation samples at each aging stages. Three aging sensitive parameters [i.e., transfer function pole (TFP), detrapped charge, and hopping conductivity] are extracted from the experimental results obtained by dielectric spectroscopy [i.e., polarization and depolarization current (PDC) and complex permittivity]. From the experimental results, it is observed that these three parameters are altered with the duration of the thermal aging. This fact signifies that these parameters can be utilized as aging sensitive markers. Therefore, using these three aging sensitive parameters, three empirical relationships are derived to correlate the thermal aging status of the NDNC insulation. Finally, the derived relationships are validated through different sets of test samples having known aging status. Hence, this experimental investigation reveals that the proposed technique can suitably assess the thermal aging state of the NDNC insulation.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"24 22","pages":"37586-37594"},"PeriodicalIF":4.3,"publicationDate":"2024-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142645463","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 : 2024-10-08DOI: 10.1109/JSEN.2024.3472032
Chenxing Zhao;Yang Li;Shihao Wu;Wenyi Tan;Shuangju Zhou;Quan Pan
Adversarial attacks against monocular depth estimation (MDE) systems, which serve as critical visual sensors in autonomous driving and various safety-critical applications, pose significant challenges. These depth cameras provide essential distance information, enabling accurate perception and decision-making. Existing patch-based adversarial attacks for MDE are confined to the vicinity of the patch, limiting their impact on the entire target. To address this limitation, we propose a physics-based adversarial attack on MDE using a framework called an attack with shape-varying patches (ASP). This framework optimizes the content, shape, and position of patches to maximize its disruptive effectiveness on the sensor’s output. We introduce various mask shapes, including quadrilateral, rectangular, and circular masks, to enhance the flexibility and efficiency of the attack. In addition, we propose a new loss function to extend the influence of patches beyond the overlapping regions. Experimental results demonstrate that our attack method generates an average depth error of 18 m on the target car with a patch area of 1/9, impacting over 98% of the target area. This work underscores the vulnerability of visual sensors, such as depth cameras, to adversarial attacks and highlights the imperative for enhanced security measures in sensor technology to ensure reliable and safe operation.
{"title":"Physical Adversarial Attack on Monocular Depth Estimation via Shape-Varying Patches","authors":"Chenxing Zhao;Yang Li;Shihao Wu;Wenyi Tan;Shuangju Zhou;Quan Pan","doi":"10.1109/JSEN.2024.3472032","DOIUrl":"https://doi.org/10.1109/JSEN.2024.3472032","url":null,"abstract":"Adversarial attacks against monocular depth estimation (MDE) systems, which serve as critical visual sensors in autonomous driving and various safety-critical applications, pose significant challenges. These depth cameras provide essential distance information, enabling accurate perception and decision-making. Existing patch-based adversarial attacks for MDE are confined to the vicinity of the patch, limiting their impact on the entire target. To address this limitation, we propose a physics-based adversarial attack on MDE using a framework called an attack with shape-varying patches (ASP). This framework optimizes the content, shape, and position of patches to maximize its disruptive effectiveness on the sensor’s output. We introduce various mask shapes, including quadrilateral, rectangular, and circular masks, to enhance the flexibility and efficiency of the attack. In addition, we propose a new loss function to extend the influence of patches beyond the overlapping regions. Experimental results demonstrate that our attack method generates an average depth error of 18 m on the target car with a patch area of 1/9, impacting over 98% of the target area. This work underscores the vulnerability of visual sensors, such as depth cameras, to adversarial attacks and highlights the imperative for enhanced security measures in sensor technology to ensure reliable and safe operation.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"24 22","pages":"38440-38452"},"PeriodicalIF":4.3,"publicationDate":"2024-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142645528","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 article presents the development and experimental verification of a temperature and humidity sensor featuring a stable structure and high sensitivity. The sensor utilizes a Mach-Zehnder interferometer (MZI) formed by coating a layer of Fe2O3 nanorods onto the surface of a tapered coreless fiber (NCF) via water bath method. The nanostructures formed on the NCF silver film exhibit remarkable stability and strength. Variations in external temperature and humidity alter the permeability of the Fe2O3 nanorods, leading to changes in their refractive index (RI) and a linear shift in the MZI’s resonance wavelength. Experimental findings reveal a temperature sensitivity of 0.454 nm/°C within the range of 25 °C–60 °C and a humidity sensitivity of 0.3332 nm/%RH within the range of 40%RH–70%RH. To enhance measurement sensitivity and accuracy, the MZI sensor is cascaded with a fiber Bragg grating (FBG) to mitigate cross-sensitivity between temperature and humidity.
{"title":"Enhancing the Sensitivity of a Temperature and Relative Humidity Sensor Utilizing Fe₂O₃-Coated Tapered Optical Fiber","authors":"Qichang Jiang;Su Sheng;Fulin Chen;Zinan Tu;Jian Wen;Chao Jiang","doi":"10.1109/JSEN.2024.3472070","DOIUrl":"https://doi.org/10.1109/JSEN.2024.3472070","url":null,"abstract":"This article presents the development and experimental verification of a temperature and humidity sensor featuring a stable structure and high sensitivity. The sensor utilizes a Mach-Zehnder interferometer (MZI) formed by coating a layer of Fe2O3 nanorods onto the surface of a tapered coreless fiber (NCF) via water bath method. The nanostructures formed on the NCF silver film exhibit remarkable stability and strength. Variations in external temperature and humidity alter the permeability of the Fe2O3 nanorods, leading to changes in their refractive index (RI) and a linear shift in the MZI’s resonance wavelength. Experimental findings reveal a temperature sensitivity of 0.454 nm/°C within the range of 25 °C–60 °C and a humidity sensitivity of 0.3332 nm/%RH within the range of 40%RH–70%RH. To enhance measurement sensitivity and accuracy, the MZI sensor is cascaded with a fiber Bragg grating (FBG) to mitigate cross-sensitivity between temperature and humidity.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"24 22","pages":"36916-36922"},"PeriodicalIF":4.3,"publicationDate":"2024-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142636516","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 : 2024-10-08DOI: 10.1109/JSEN.2024.3472024
Qizhi Wan;Jiajun Chen;Weifang Chen;Rupeng Zhu
Wide-faced helical gears are commonly used in ships and industrial applications, where high torque transmission is required. These gears are highly sensitive to shaft misalignment, which can alter the load distribution across a gear pair, leading to increased contact stress and tooth root stress (TRS). In this study, the finite element method is employed to analyze the relationship between load distribution on the tooth face and TRS distribution at various positions under different misalignment errors (MEs). It was ultimately determined that the TRS distribution through the tooth slot center reflects the contact state of the tooth face, and through quantitative analysis, reveals the relationship between ME and the degree of load distribution unevenness, establishing a method to identify the degree of load distribution unevenness on the tooth face by the TRS distribution through the center of the tooth slot. Finally, a new strain gauge arrangement method is proposed and experimentally validated. This method effectively captures the TRS of wide-faced helical gears with misalignment and pitch errors, thereby obtaining a more accurate TRS distribution at the center of the tooth slot.
{"title":"An Effective Method for Identifying Uneven Load Distribution on the Tooth Faces of Misaligned Wide-Faced Helical Gear Pairs","authors":"Qizhi Wan;Jiajun Chen;Weifang Chen;Rupeng Zhu","doi":"10.1109/JSEN.2024.3472024","DOIUrl":"https://doi.org/10.1109/JSEN.2024.3472024","url":null,"abstract":"Wide-faced helical gears are commonly used in ships and industrial applications, where high torque transmission is required. These gears are highly sensitive to shaft misalignment, which can alter the load distribution across a gear pair, leading to increased contact stress and tooth root stress (TRS). In this study, the finite element method is employed to analyze the relationship between load distribution on the tooth face and TRS distribution at various positions under different misalignment errors (MEs). It was ultimately determined that the TRS distribution through the tooth slot center reflects the contact state of the tooth face, and through quantitative analysis, reveals the relationship between ME and the degree of load distribution unevenness, establishing a method to identify the degree of load distribution unevenness on the tooth face by the TRS distribution through the center of the tooth slot. Finally, a new strain gauge arrangement method is proposed and experimentally validated. This method effectively captures the TRS of wide-faced helical gears with misalignment and pitch errors, thereby obtaining a more accurate TRS distribution at the center of the tooth slot.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"24 22","pages":"36569-36578"},"PeriodicalIF":4.3,"publicationDate":"2024-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142636507","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}
Accurate localization is essential for robot autonomous navigation. The localization methods that rely overly on the global navigation satellite system (GNSS) are not reliable in urban environments where GNSS signals are vulnerable to occlusion. In this work, we fuse data from IMU, LiDAR, and GNSS with a particle filter, presenting a novel method based on a two-step particle adjustment strategy. Our algorithm first uses GNSS data to evaluate the current particles and adjust their distribution if necessary. Subsequently, we use laser measurements to evaluate old particles and the reliability of the GNSS data, adjusting the particle distribution for correction. In addition, we use statistical features of point clouds for laser measurements, which transform the global map into a series of normal distribution models, and use these models to match with 3-D laser scans for particle state evaluation. Our method improves the processing efficiency of 3-D point cloud data and fully utilizes its 3-D features during localization. Experimental results demonstrate that our algorithm achieves higher localization accuracy on the publicly available KITTI dataset and in real campus environments. In addition, our algorithm consistently delivers precise localization in both open areas and GNSS-unavailable scenarios, showcasing superior reliability.
{"title":"A Reliable Robot Localization Method Using LiDAR and GNSS Fusion Based on a Two-Step Particle Adjustment Strategy","authors":"Wei Tang;Anmin Huang;Enbo Liu;Jiale Wu;Renyuan Zhang","doi":"10.1109/JSEN.2024.3472470","DOIUrl":"https://doi.org/10.1109/JSEN.2024.3472470","url":null,"abstract":"Accurate localization is essential for robot autonomous navigation. The localization methods that rely overly on the global navigation satellite system (GNSS) are not reliable in urban environments where GNSS signals are vulnerable to occlusion. In this work, we fuse data from IMU, LiDAR, and GNSS with a particle filter, presenting a novel method based on a two-step particle adjustment strategy. Our algorithm first uses GNSS data to evaluate the current particles and adjust their distribution if necessary. Subsequently, we use laser measurements to evaluate old particles and the reliability of the GNSS data, adjusting the particle distribution for correction. In addition, we use statistical features of point clouds for laser measurements, which transform the global map into a series of normal distribution models, and use these models to match with 3-D laser scans for particle state evaluation. Our method improves the processing efficiency of 3-D point cloud data and fully utilizes its 3-D features during localization. Experimental results demonstrate that our algorithm achieves higher localization accuracy on the publicly available KITTI dataset and in real campus environments. In addition, our algorithm consistently delivers precise localization in both open areas and GNSS-unavailable scenarios, showcasing superior reliability.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"24 22","pages":"37846-37858"},"PeriodicalIF":4.3,"publicationDate":"2024-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142636545","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 : 2024-10-08DOI: 10.1109/JSEN.2024.3472025
Yufei Zhang;Zhong Wu;Tong Wei
As the main factor affecting the safety of quadrotor unmanned aerial vehicles (UAVs) on moving platforms, aerodynamic disturbances are not easy to directly measure but can be effectively estimated from control system information by extended disturbance observers (EDOs). To guarantee estimation accuracy for aerodynamic disturbances with fast dynamics induced by increased speed of landing platforms, high bandwidth is necessary for EDOs. However, high bandwidth of EDOs will result in high gain problems which may amplify measurement noises in the control system. To suppress the effects of measurement noises on estimation accuracy, a pair of noise reduction EDOs (NREDOs) are proposed to estimate aerodynamic disturbances for quadrotor UAVs landing on moving platforms. The pair observers are designed to estimate force and torque disturbances for translational and rotational subsystems, respectively. Different from EDOs, each NREDO takes the integral of the lumped disturbance as an augmented state and virtual measurement in the state-space disturbance model. The prediction error of the virtual measurement is taken as an innovation to update the observer. Moreover, a tuning rule of observer gains is proposed to further improve estimation accuracy. Theoretical analysis indicates that the integrals provide NREDOs with superior performance in noise suppression than EDOs. Landing experiments on a platform of 25 km/h demonstrate the effectiveness of the proposed scheme.
作为影响移动平台上四旋翼无人飞行器（UAV）安全的主要因素，气动扰动不易直接测量，但可通过扩展扰动观测器（EDOs）从控制系统信息中进行有效估计。为了保证对着陆平台速度增加引起的快速动态气动扰动的估计精度，EDOs 必须具有高带宽。然而，EDO 的高带宽会导致高增益问题，从而可能放大控制系统中的测量噪声。为了抑制测量噪声对估计精度的影响，我们提出了一对降噪 EDO（NREDO），用于估计在移动平台上着陆的四旋翼无人机的气动干扰。这对观测器分别用于估计平移子系统和旋转子系统的力和扭矩干扰。与 EDO 不同的是，每个 NREDO 都将叠加干扰的积分作为状态空间干扰模型中的增强状态和虚拟测量。虚拟测量的预测误差作为更新观测器的创新。此外，还提出了观测器增益的调整规则，以进一步提高估计精度。理论分析表明，积分为 NREDO 提供了比 EDO 更优越的噪声抑制性能。在时速 25 公里的平台上进行的着陆实验证明了所提方案的有效性。
{"title":"Aerodynamic Disturbance Estimation in Quadrotor Landing on Moving Platform via Noise Reduction Extended Disturbance Observer","authors":"Yufei Zhang;Zhong Wu;Tong Wei","doi":"10.1109/JSEN.2024.3472025","DOIUrl":"https://doi.org/10.1109/JSEN.2024.3472025","url":null,"abstract":"As the main factor affecting the safety of quadrotor unmanned aerial vehicles (UAVs) on moving platforms, aerodynamic disturbances are not easy to directly measure but can be effectively estimated from control system information by extended disturbance observers (EDOs). To guarantee estimation accuracy for aerodynamic disturbances with fast dynamics induced by increased speed of landing platforms, high bandwidth is necessary for EDOs. However, high bandwidth of EDOs will result in high gain problems which may amplify measurement noises in the control system. To suppress the effects of measurement noises on estimation accuracy, a pair of noise reduction EDOs (NREDOs) are proposed to estimate aerodynamic disturbances for quadrotor UAVs landing on moving platforms. The pair observers are designed to estimate force and torque disturbances for translational and rotational subsystems, respectively. Different from EDOs, each NREDO takes the integral of the lumped disturbance as an augmented state and virtual measurement in the state-space disturbance model. The prediction error of the virtual measurement is taken as an innovation to update the observer. Moreover, a tuning rule of observer gains is proposed to further improve estimation accuracy. Theoretical analysis indicates that the integrals provide NREDOs with superior performance in noise suppression than EDOs. Landing experiments on a platform of 25 km/h demonstrate the effectiveness of the proposed scheme.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"24 22","pages":"37566-37574"},"PeriodicalIF":4.3,"publicationDate":"2024-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142645426","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 : 2024-10-08DOI: 10.1109/JSEN.2024.3472648
Liangliang Wei;Yiwen Sun;Qi Diao;Hongzhang Xu;Xiaojun Tan;Yuqian Fan
It is critical to accurately estimate the state of health (SOH) to ensure the safe and efficient operation of lithium-ion batteries. To reduce the training amounts of existing data-driven methods, the transfer learning (TL) method has attracted more attention. However, most previous studies lack validation with different battery types and working conditions. Furthermore, the shared knowledge just relies on raw current and voltage data, resulting in insufficient accuracy. This article proposes a stacked-long short-term memory (LSTM) TL method based on Bayesian optimization (BO-Stacked-LSTM), which integrates multiple features to estimate SOH. By improving the structure of the BO-Stacked-LSTM networks and the fine-tuning strategy of TL, as well as employing a Bayesian optimization (BO) algorithm to optimize hyperparameters, the proposed method can achieve accurate SOH estimation. Experimental results demonstrate that it just requires a small quantity of target dataset to accurately estimate SOH on the target dataset. Furthermore, experiments were performed on three different lithium-ion battery datasets, to validate the effectiveness.
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